From 0e20bce8f23562deee9194d5054213b190a6c905 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 13:19:36 +0100 Subject: [PATCH 01/47] refactor: migrate dependency management from Conda to Pixi by adding `pixi.toml` and removing `environment.yml`. --- environment.yml | 29 -------------------------- pixi.toml | 47 ++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 54 +++++++++---------------------------------------- 3 files changed, 56 insertions(+), 74 deletions(-) delete mode 100644 environment.yml create mode 100644 pixi.toml diff --git a/environment.yml b/environment.yml deleted file mode 100644 index ef68a6d..0000000 --- a/environment.yml +++ /dev/null @@ -1,29 +0,0 @@ -channels: -- conda-forge -- nodefaults -dependencies: -- make -- mypy -- myst-parser -- pre-commit -# - pydata-sphinx-theme -- sphinx-rtd-theme -- pytest -- pytest-cov -- pytest-mock -- sphinx -- sphinx-autoapi -- nbsphinx -# EXAMPLE: -# dependencies: -# - package1 -# - package2 -# DO NOT EDIT ABOVE THIS LINE, ADD DEPENDENCIES BELOW AS SHOWN IN THE EXAMPLE -- nbsphinx -- numpy -- xarray -- pip -- geopandas -- earthkit-data -- xclim -- xsdba diff --git a/pixi.toml b/pixi.toml new file mode 100644 index 0000000..a9728d9 --- /dev/null +++ b/pixi.toml @@ -0,0 +1,47 @@ +[workspace] +name = "earthkit-climate" +channels = ["conda-forge"] +platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] + +[tasks] +qa = "pre-commit run --all-files" +template-update = "pre-commit run --all-files cruft -c .pre-commit-config-cruft.yaml" +type-check = "python -m mypy . --no-namespace-packages" +unit-tests = "python -m pytest -vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" + +[dependencies] +earthkit-data = ">=0.17.0" +numpy = ">=1.22" +xarray = ">=2023.1" +xclim = ">=0.59.1" +xdba = ">=0.5,<0.6" +python = "3.12.*" + +[pypi-dependencies] +earthkit-plots = ">=0.5.0" +earthkit-climate = { path = ".", editable = true } + +[environments] +dev = ["dev"] +docs = ["docs"] + +[feature.dev.dependencies] +black = "*" +ruff = "*" +mypy = "*" +pre-commit = "*" +ipython = "*" +pytest = "*" +pytest-cov = "*" +pytest-mock = "*" +ipykernel = "*" + +[feature.docs.dependencies] +nbsphinx = ">=0.9.8,<0.10" +pandoc = "*" +sphinx = "*" +sphinx-autoapi = ">=3.6.1,<4" +sphinx-rtd-theme = ">=3.0.2,<4" + +[feature.docs.tasks] +docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_build" diff --git a/pyproject.toml b/pyproject.toml index 3fee9ee..a83c816 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,16 +33,18 @@ name = "earthkit-climate" requires-python = ">=3.10" [project.optional-dependencies] -dev = [ +tests = [ +  "pytest", +  "pytest-cov", +  "pytest-mock", +  "ipython", +  "ipykernel" +] +all = [ "black", "ruff", "mypy", - "pre-commit", - "ipython", - "pytest", - "pytest-cov", - "pytest-mock", - "ipykernel" + "pre-commit" ] docs = [ "nbsphinx", @@ -55,44 +57,6 @@ docs = [ [tool.coverage.run] branch = true -[tool.pixi.dependencies] -ipykernel = ">=7.1.0,<8" -pre-commit = "*" -pytest = "*" -pytest-cov = "*" -pytest-mock = ">=3.15.1,<4" -python = "3.12.*" - -# ---- Pixi environments ---- -[tool.pixi.environments] -dev = {features = ["dev"]} -docs = {features = ["docs"]} - -[tool.pixi.feature.docs.dependencies] -nbsphinx = ">=0.9.8,<0.10" -pandoc = "*" -sphinx = "*" -sphinx-autoapi = ">=3.6.1,<4" -sphinx-rtd-theme = ">=3.0.2,<4" - -[tool.pixi.feature.docs.tasks] -docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_build" - -[tool.pixi.pypi-dependencies] -earthkit-plots = ">=0.5.0" - -[tool.pixi.tasks] -docker-build = "docker build -t earthkit-climate ." -docker-run = "docker run --rm -ti -v $(pwd):/srv earthkit-climate" -qa = "pre-commit run --all-files" -template-update = "pre-commit run --all-files cruft -c .pre-commit-config-cruft.yaml" -type-check = "python -m mypy . --no-namespace-packages" -unit-tests = "python -m pytest -vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" - -# ---- Pixi CONFIGURATION ---- -[tool.pixi.workspace] -channels = ["conda-forge"] -platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] [tool.pytest.ini_options] addopts = "-vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" From ee72a6fd67c1040be1f5ce17b7400ca8b3fa5dbb Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 13:26:21 +0100 Subject: [PATCH 02/47] fix: toml parse error in pyproject --- pyproject.toml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index a83c816..eca5f18 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,11 +34,11 @@ requires-python = ">=3.10" [project.optional-dependencies] tests = [ -  "pytest", -  "pytest-cov", -  "pytest-mock", -  "ipython", -  "ipykernel" + "pytest", + "pytest-cov", + "pytest-mock", + "ipython", + "ipykernel" ] all = [ "black", From bd898ac8bc279eae06437a1fc1d11306b18b7f3b Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 13:36:38 +0100 Subject: [PATCH 03/47] Update project dependencies and configuration in `pyproject.toml`. --- pyproject.toml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index eca5f18..d511695 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,14 +33,12 @@ name = "earthkit-climate" requires-python = ">=3.10" [project.optional-dependencies] -tests = [ +dev = [ "pytest", "pytest-cov", "pytest-mock", "ipython", "ipykernel" -] -all = [ "black", "ruff", "mypy", From 9ac2e993710d18f2c7c5ae963746d2bd8cccade5 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 13:38:37 +0100 Subject: [PATCH 04/47] fix: TOML parse error --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d511695..aabe888 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,7 @@ dev = [ "pytest-cov", "pytest-mock", "ipython", - "ipykernel" + "ipykernel", "black", "ruff", "mypy", From 438f2dc1b195a7e4d9dc18f33e4ce90013bebf80 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 13:52:50 +0100 Subject: [PATCH 05/47] chore: Update project dependencies and lock file. --- pixi.lock | 16560 ++++++++++++++++++++++++++++++++--------------- pixi.toml | 40 +- pyproject.toml | 1 - 3 files changed, 11203 insertions(+), 5398 deletions(-) diff --git a/pixi.lock b/pixi.lock index 79775c7..3f769e3 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,655 +9,916 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.13.0-pyhcf101f3_0.conda + - 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[dependencies] earthkit-data = ">=0.17.0" numpy = ">=1.22" +python = "3.12.*" xarray = ">=2023.1" xclim = ">=0.59.1" -xdba = ">=0.5,<0.6" -python = "3.12.*" - -[pypi-dependencies] -earthkit-plots = ">=0.5.0" -earthkit-climate = { path = ".", editable = true } +xsdba = ">=0.5,<0.6" [environments] dev = ["dev"] @@ -27,14 +12,14 @@ docs = ["docs"] [feature.dev.dependencies] black = "*" -ruff = "*" +ipykernel = "*" +ipython = "*" mypy = "*" pre-commit = "*" -ipython = "*" pytest = "*" pytest-cov = "*" pytest-mock = "*" -ipykernel = "*" +ruff = "*" [feature.docs.dependencies] nbsphinx = ">=0.9.8,<0.10" @@ -45,3 +30,18 @@ sphinx-rtd-theme = ">=3.0.2,<4" [feature.docs.tasks] docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_build" + +[pypi-dependencies] +earthkit-climate = {path = ".", editable = true} +earthkit-plots = ">=0.5.0" + +[tasks] +qa = "pre-commit run --all-files" +template-update = "pre-commit run --all-files cruft -c .pre-commit-config-cruft.yaml" +type-check = "python -m mypy . --no-namespace-packages" +unit-tests = "python -m pytest -vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" + +[workspace] +channels = ["conda-forge"] +name = "earthkit-climate" +platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] diff --git a/pyproject.toml b/pyproject.toml index aabe888..d979c49 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,6 @@ docs = [ [tool.coverage.run] branch = true - [tool.pytest.ini_options] addopts = "-vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" From c329bc4bb20b31ccb8ecb7464c38545d8ac2c92f Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 16:48:37 +0100 Subject: [PATCH 06/47] feat: Add new temperature, synoptic, and wind indicators, leveraging a new xclim wrapper generation tool. --- .../climate/indicators/precipitation.py | 1434 ++++++++- src/earthkit/climate/indicators/synoptic.py | 51 + .../climate/indicators/temperature.py | 2600 ++++++++++++++++- src/earthkit/climate/indicators/wind.py | 251 ++ tools/__init__.py | 9 + tools/xclim_wrappers_generator.py | 235 ++ 6 files changed, 4537 insertions(+), 43 deletions(-) create mode 100644 src/earthkit/climate/indicators/synoptic.py create mode 100644 src/earthkit/climate/indicators/wind.py create mode 100644 tools/__init__.py create mode 100644 tools/xclim_wrappers_generator.py diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index ef788c4..1ace9ed 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -6,7 +6,7 @@ # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. -"""Precipitation-based climate indices.""" +"""Precipitation indices.""" from typing import Any @@ -17,51 +17,1459 @@ from earthkit.climate.api.wrapper import wrap_xclim_indicator -def daily_precipitation_intensity( +def antecedent_precipitation_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Compute the Daily Precipitation Intensity (SDII) using the xclim indices module. + Antecedent Precipitation Index. + + Calculate the running weighted sum of daily precipitation values given a window and + weighting exponent. This index serves as an indicator for soil moisture. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.api`. Parameters ---------- ds : conversions.EarthkitData | xarray.Dataset - Daily precipitation flux. + Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indices.daily_pr_intensity`. + :func:`xclim.indicators.atmos.api`. Returns ------- conversions.EarthkitData - The computed Daily Precipitation Intensity as an Earthkit-compatible field. + The computed index as an Earthkit-compatible field. """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.antecedent_precipitation_index) + return wrapper(ds, **kwargs) + +def maximum_consecutive_dry_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum consecutive dry days. + + The longest number of consecutive days where daily precipitation below a given + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cdd`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cdd`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_dry_days) + return wrapper(ds, **kwargs) + +def cffwis_indices( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Canadian Fire Weather Index System indices. + + Computes the six (6) fire weather indexes, as defined by the Canadian Forest Service: - + The Drought Code - The Duff-Moisture Code - The Fine Fuel Moisture Code - The Initial + Spread Index - The Build Up Index - The Fire Weather Index. + + **Units:** ['', '', '', '', '', ''] + + This function wraps :func:`xclim.indicators.atmos.cffwis`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cffwis`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cffwis_indices) + return wrapper(ds, **kwargs) + +def cold_and_dry_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold and dry days. + + Number of days with temperature below a given percentile and precipitation below a given + percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_and_dry_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_and_dry_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_dry_days) return wrapper(ds, **kwargs) +def cold_and_wet_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold and wet days. + + Number of days with temperature below a given percentile and precipitation above a given + percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_and_wet_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_and_wet_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_wet_days) + return wrapper(ds, **kwargs) def maximum_consecutive_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Compute the Maximum Consecutive Wet Days (CWD) using the xclim indices module. + Maximum consecutive wet days. + + The longest number of consecutive days where daily precipitation is at or above a given + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cwd`. Parameters ---------- ds : conversions.EarthkitData | xarray.Dataset - Daily precipitation flux. + Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indices.maximum_consecutive_wet_days`. + :func:`xclim.indicators.atmos.cwd`. Returns ------- conversions.EarthkitData - The computed Maximum Consecutive Wet Days as an Earthkit-compatible field. + The computed index as an Earthkit-compatible field. """ - # Create wrapper inside the function wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_wet_days) return wrapper(ds, **kwargs) + +def days_over_precip_doy_thresh( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with precipitation above a given daily percentile. + + Number of days in a period where precipitation is above a given daily percentile and a + fixed threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.days_over_precip_doy_thresh`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.days_over_precip_doy_thresh`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_doy_thresh) + return wrapper(ds, **kwargs) + +def days_over_precip_thresh( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with precipitation above a given percentile. + + Number of days in a period where precipitation is above a given percentile, calculated + over a given period and a fixed threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.days_over_precip_thresh`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.days_over_precip_thresh`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_thresh) + return wrapper(ds, **kwargs) + +def days_with_snow( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with snowfall. + + Number of days with snow between a lower and upper limit. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.days_with_snow`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.days_with_snow`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_with_snow) + return wrapper(ds, **kwargs) + +def drought_code( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Daily drought code. + + The Drought Index is part of the Canadian Forest-Weather Index system. It is a numerical + code that estimates the average moisture content of organic layers. + + This function wraps :func:`xclim.indicators.atmos.dc`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dc`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.drought_code) + return wrapper(ds, **kwargs) + +def griffiths_drought_factor( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Griffiths drought factor based on the soil moisture deficit. + + The drought factor is a numeric indicator of the forest fire fuel availability in the + deep litter bed. It is often used in the calculation of the McArthur Forest Fire Danger + Index. The method implemented here follows :cite:t:`ffdi-finkele_2006`. + + This function wraps :func:`xclim.indicators.atmos.df`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.df`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.griffiths_drought_factor) + return wrapper(ds, **kwargs) + +def duff_moisture_code( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Duff moisture code (FWI component). + + The duff moisture code is part of the Canadian Forest Fire Weather Index System. It is a + numeric rating of the average moisture content of loosely compacted organic layers of + moderate depth. + + This function wraps :func:`xclim.indicators.atmos.dmc`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dmc`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.duff_moisture_code) + return wrapper(ds, **kwargs) + +def dry_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of dry days. + + The number of days with daily precipitation under a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.dry_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dry_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_days) + return wrapper(ds, **kwargs) + +def dry_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Dry spell frequency. + + The frequency of dry periods of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is below a given threshold. + + This function wraps :func:`xclim.indicators.atmos.dry_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dry_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_frequency) + return wrapper(ds, **kwargs) + +def dry_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Dry spell maximum length. + + The maximum length of a dry period of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.dry_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dry_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_max_length) + return wrapper(ds, **kwargs) + +def dry_spell_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Dry spell total length. + + The total length of dry periods of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.dry_spell_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dry_spell_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_total_length) + return wrapper(ds, **kwargs) + +def dryness_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Dryness index. + + The dryness index is a characterization of the water component in winegrowing regions + which considers the precipitation and evapotranspiration factors without deduction for + surface runoff or drainage. Metric originally published in Riou et al. (1994). + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.dryness_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dryness_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dryness_index) + return wrapper(ds, **kwargs) + +def mcarthur_forest_fire_danger_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + McArthur forest fire danger index (FFDI) Mark 5. + + The FFDI is a numeric indicator of the potential danger of a forest fire. + + This function wraps :func:`xclim.indicators.atmos.ffdi`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.ffdi`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) + return wrapper(ds, **kwargs) + +def first_snowfall( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day where snowfall exceeded a given threshold. + + The first day where snowfall exceeded a given threshold during a time period (the + threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). + + This function wraps :func:`xclim.indicators.atmos.first_snowfall`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_snowfall`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_snowfall) + return wrapper(ds, **kwargs) + +def fraction_over_precip_doy_thresh( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Fraction of precipitation due to wet days with daily precipitation over a given daily percentile. + + The percentage of the total precipitation over a period occurring for days when the + precipitation is above a threshold defining wet days and above a given percentile for + that day. + + This function wraps :func:`xclim.indicators.atmos.fraction_over_precip_doy_thresh`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.fraction_over_precip_doy_thresh`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_doy_thresh) + return wrapper(ds, **kwargs) + +def fraction_over_precip_thresh( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Fraction of precipitation due to wet days with daily precipitation over a given percentile. + + The percentage of the total precipitation over a period occurring for days when the + precipitation is above a threshold defining wet days and above a given percentile for + that day. + + This function wraps :func:`xclim.indicators.atmos.fraction_over_precip_thresh`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.fraction_over_precip_thresh`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_thresh) + return wrapper(ds, **kwargs) + +def high_precip_low_temp( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with precipitation and cold temperature. + + Number of days with precipitation above a given threshold and temperature below a given + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.high_precip_low_temp`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.high_precip_low_temp`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.high_precip_low_temp) + return wrapper(ds, **kwargs) + +def keetch_byram_drought_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Keetch-Byram drought index (KBDI) for soil moisture deficit. + + The KBDI indicates the amount of water necessary to bring the soil moisture content back + to field capacity. It is often used in the calculation of the McArthur Forest Fire + Danger Index. The method implemented here follows :cite:t:`ffdi-finkele_2006` but limits + the maximum KBDI to 203.2 mm, rather than 200 mm, in order to align best with the + majority of the literature. + + **Units:** mm/day + + This function wraps :func:`xclim.indicators.atmos.kbdi`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.kbdi`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.keetch_byram_drought_index) + return wrapper(ds, **kwargs) + +def last_snowfall( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Last day where snowfall exceeded a given threshold. + + The last day where snowfall exceeded a given threshold during a time period (the + threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). + + This function wraps :func:`xclim.indicators.atmos.last_snowfall`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.last_snowfall`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_snowfall) + return wrapper(ds, **kwargs) + +def liquid_precip_ratio( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Fraction of liquid to total precipitation. + + The ratio of total liquid precipitation over the total precipitation. Liquid + precipitation is approximated from total precipitation on days where temperature is + above a given threshold. + + This function wraps :func:`xclim.indicators.atmos.liquid_precip_ratio`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.liquid_precip_ratio`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_ratio) + return wrapper(ds, **kwargs) + +def liquid_precip_average( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Averaged liquid precipitation. + + Averaged liquid precipitation. Precipitation is considered liquid when the average daily + temperature is above a given threshold. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.liquidprcpavg`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.liquidprcpavg`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_average) + return wrapper(ds, **kwargs) + +def liquid_precip_accumulation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Total accumulated liquid precipitation. + + Total accumulated liquid precipitation. Precipitation is considered liquid when the + average daily temperature is above a given threshold. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.liquidprcptot`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.liquidprcptot`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_accumulation) + return wrapper(ds, **kwargs) + +def max_n_day_precipitation_amount( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + maximum n-day total precipitation. + + Maximum of the moving sum of daily precipitation for a given period. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.max_n_day_precipitation_amount`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.max_n_day_precipitation_amount`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_n_day_precipitation_amount) + return wrapper(ds, **kwargs) + +def max_pr_intensity( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum precipitation intensity over time window. + + Maximum precipitation intensity over a given rolling time window. + + **Units:** mm h-1 + + This function wraps :func:`xclim.indicators.atmos.max_pr_intensity`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.max_pr_intensity`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_pr_intensity) + return wrapper(ds, **kwargs) + +def precip_average( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Averaged precipitation (solid and liquid). + + Averaged precipitation. If the average daily temperature is given, the phase parameter + can be used to restrict the calculation to precipitation of only one phase (liquid or + solid). Precipitation is considered solid if the average daily temperature is below 0°C + threshold (and vice versa). + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.prcpavg`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.prcpavg`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_average) + return wrapper(ds, **kwargs) + +def precip_accumulation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Total accumulated precipitation (solid and liquid). + + Total accumulated precipitation. If the average daily temperature is given, the phase + parameter can be used to restrict the calculation to precipitation of only one phase + (liquid or solid). Precipitation is considered solid if the average daily temperature is + below 0°C (and vice versa). + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.prcptot`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.prcptot`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_accumulation) + return wrapper(ds, **kwargs) + +def rain_on_frozen_ground_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of rain on frozen ground days. + + The number of days with rain above a given threshold after a series of seven days with + average daily temperature below 0°C. Precipitation is assumed to be rain when the daily + average temperature is above 0°C. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.rain_frzgr`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.rain_frzgr`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_on_frozen_ground_days) + return wrapper(ds, **kwargs) + +def rain_season( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Rain season. + + Start time, end time and length of the rain season, notably useful for West Africa + (sivakumar, 1998). The rain season starts with a period of abundant rainfall, followed + by a period without prolonged dry sequences, which must happen before a given date. The + rain season stops during a dry period happening after a given date. + + **Units:** ['', '', 'days'] + + This function wraps :func:`xclim.indicators.atmos.rain_season`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.rain_season`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_season) + return wrapper(ds, **kwargs) + +def rprctot( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Proportion of accumulated precipitation arising from convective processes. + + The proportion of total precipitation due to convective processes. Only days with + surpassing a minimum precipitation flux are considered. + + This function wraps :func:`xclim.indicators.atmos.rprctot`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.rprctot`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rprctot) + return wrapper(ds, **kwargs) + +def max_1day_precipitation_amount( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum 1-day total precipitation. + + Maximum total daily precipitation for a given period. + + **Units:** mm/day + + This function wraps :func:`xclim.indicators.atmos.rx1day`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.rx1day`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_1day_precipitation_amount) + return wrapper(ds, **kwargs) + +def daily_pr_intensity( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Simple Daily Intensity Index. + + Average precipitation for days with daily precipitation above a given threshold. + + **Units:** mm d-1 + + This function wraps :func:`xclim.indicators.atmos.sdii`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sdii`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) + return wrapper(ds, **kwargs) + +def snowfall_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Snowfall frequency. + + Percentage of days with snowfall above a given threshold (either a snowfall flux or a + liquid water equivalent snowfall rate). + + **Units:** % + + This function wraps :func:`xclim.indicators.atmos.snowfall_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.snowfall_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_frequency) + return wrapper(ds, **kwargs) + +def snowfall_intensity( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Snowfall intensity. + + Mean daily liquid water equivalent snowfall rate above threshold (either a snowfall flux + or a liquid water equivalent snowfall rate) + + **Units:** mm/day + + This function wraps :func:`xclim.indicators.atmos.snowfall_intensity`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.snowfall_intensity`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_intensity) + return wrapper(ds, **kwargs) + +def solid_precip_average( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Averaged solid precipitation. + + Averaged solid precipitation. Precipitation is considered solid when the average daily + temperature is at or below a given threshold. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.solidprcpavg`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.solidprcpavg`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_average) + return wrapper(ds, **kwargs) + +def solid_precip_accumulation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Total accumulated solid precipitation. + + Total accumulated solid precipitation. Precipitation is considered solid when the + average daily temperature is at or below a given threshold. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.solidprcptot`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.solidprcptot`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_accumulation) + return wrapper(ds, **kwargs) + +def warm_and_dry_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Warm and dry days. + + Number of days with temperature above a given percentile and precipitation below a given + percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.warm_and_dry_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.warm_and_dry_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_dry_days) + return wrapper(ds, **kwargs) + +def warm_and_wet_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Warm and wet days. + + Number of days with temperature above a given percentile and precipitation above a given + percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.warm_and_wet_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.warm_and_wet_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_wet_days) + return wrapper(ds, **kwargs) + +def water_cycle_intensity( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Water cycle intensity. + + The sum of precipitation and actual evapotranspiration. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.water_cycle_intensity`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.water_cycle_intensity`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.water_cycle_intensity) + return wrapper(ds, **kwargs) + +def wet_precip_accumulation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Total accumulated precipitation (solid and liquid) during wet days. + + Total accumulated precipitation on days with precipitation. A day is considered to have + precipitation if the precipitation is greater than or equal to a given threshold. + + **Units:** mm + + This function wraps :func:`xclim.indicators.atmos.wet_prcptot`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wet_prcptot`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_precip_accumulation) + return wrapper(ds, **kwargs) + +def wet_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Wet spell frequency. + + The frequency of wet periods of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is equal or above a given + threshold. + + This function wraps :func:`xclim.indicators.atmos.wet_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wet_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_frequency) + return wrapper(ds, **kwargs) + +def wet_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Wet spell maximum length. + + The maximum length of a wet period of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is equal or above a given + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.wet_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wet_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_max_length) + return wrapper(ds, **kwargs) + +def wet_spell_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Wet spell total length. + + The total length of dry periods of `N` days or more, during which the accumulated or + maximum precipitation over a given time window of days is equal or above a given + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.wet_spell_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wet_spell_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_total_length) + return wrapper(ds, **kwargs) + +def wetdays( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of wet days. + + The number of days with daily precipitation at or above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.wetdays`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wetdays`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays) + return wrapper(ds, **kwargs) + +def wetdays_prop( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Proportion of wet days. + + The proportion of days with daily precipitation at or above a given threshold. + + **Units:** 1 + + This function wraps :func:`xclim.indicators.atmos.wetdays_prop`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.wetdays_prop`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays_prop) + return wrapper(ds, **kwargs) + diff --git a/src/earthkit/climate/indicators/synoptic.py b/src/earthkit/climate/indicators/synoptic.py new file mode 100644 index 0000000..1fd8617 --- /dev/null +++ b/src/earthkit/climate/indicators/synoptic.py @@ -0,0 +1,51 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +"""Synoptic indices.""" + +from typing import Any + +import xarray +import xclim.indicators.atmos + +import earthkit.climate.utils.conversions as conversions +from earthkit.climate.api.wrapper import wrap_xclim_indicator + + +def jetstream_metric_woollings( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Strength and latitude of jetstream. + + Identify latitude and strength of maximum smoothed zonal wind speed in the region from + 15 to 75°N and -60 to 0°E, using the formula outlined in + :cite:p:`woollings_variability_2010`. Wind is smoothened using a Lanczos filter + approach. + + **Units:** ['degrees_north', 'm s-1'] + + This function wraps :func:`xclim.indicators.atmos.jetstream_metric_woollings`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.jetstream_metric_woollings`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.jetstream_metric_woollings) + return wrapper(ds, **kwargs) + diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index 7427d19..d00f828 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -6,7 +6,7 @@ # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. -"""Temperature-based climate indices.""" +"""Temperature indices.""" from typing import Any @@ -17,90 +17,2630 @@ from earthkit.climate.api.wrapper import wrap_xclim_indicator +def australian_hardiness_zones( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Australian hardiness zones. + + A climate indice based on a multi-year rolling average of the annual minimum + temperature. Developed specifically to aid in determining plant suitability of + geographic regions. The Australian National Botanical Gardens (ANBG) classification + scheme divides categories into 5-degree Celsius zones, starting from -15 degrees Celsius + and ending at 20 degrees Celsius. + + This function wraps :func:`xclim.indicators.atmos.australian_hardiness_zones`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.australian_hardiness_zones`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.australian_hardiness_zones) + return wrapper(ds, **kwargs) + +def biologically_effective_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Biologically effective degree days. + + Considers daily minimum and maximum temperature with a given base threshold between 1 + April and 31 October, with a maximum daily value for cumulative degree days (typically + 9°C), and integrates modification coefficients for latitudes between 40°N and 50°N as + well as for swings in daily temperature range. Metric originally published in Gladstones + (1992). + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.biologically_effective_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.biologically_effective_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.biologically_effective_degree_days) + return wrapper(ds, **kwargs) + +def cold_spell_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold spell days. + + The number of days that are part of a cold spell. A cold spell is defined as a minimum + number of consecutive days with mean daily temperature below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_spell_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_spell_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_days) + return wrapper(ds, **kwargs) + +def cold_spell_duration_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold Spell Duration Index (CSDI). + + Number of days part of a percentile-defined cold spell. A cold spell occurs when the + daily minimum temperature is below a given percentile for a given number of consecutive + days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_spell_duration_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_spell_duration_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_duration_index) + return wrapper(ds, **kwargs) + +def cold_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold spell frequency. + + The frequency of cold periods of `N` days or more, during which the temperature over a + given time window of days is below a given threshold. + + This function wraps :func:`xclim.indicators.atmos.cold_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_frequency) + return wrapper(ds, **kwargs) + +def cold_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold spell maximum length. + + The maximum length of a cold period of `N` days or more, during which the temperature + over a given time window of days is below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_max_length) + return wrapper(ds, **kwargs) + +def cold_spell_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cold spell total length. + + The total length of cold periods of `N` days or more, during which the temperature over + a given time window of days is below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.cold_spell_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cold_spell_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_total_length) + return wrapper(ds, **kwargs) + +def consecutive_frost_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Consecutive frost days. + + Maximum number of consecutive days where the daily minimum temperature is below 0°C + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.consecutive_frost_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.consecutive_frost_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.consecutive_frost_days) + return wrapper(ds, **kwargs) + +def maximum_consecutive_frost_free_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum consecutive frost free days. + + Maximum number of consecutive frost-free days: where the daily minimum temperature is + above or equal to 0°C + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.consecutive_frost_free_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.consecutive_frost_free_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_frost_free_days) + return wrapper(ds, **kwargs) + +def cool_night_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cool night index. + + A night coolness variable which takes into account the mean minimum night temperatures + during the month when ripening usually occurs beyond the ripening period. + + **Units:** degC + + This function wraps :func:`xclim.indicators.atmos.cool_night_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cool_night_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cool_night_index) + return wrapper(ds, **kwargs) + +def cooling_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cooling degree days. + + The cumulative degree days for days when the mean daily temperature is above a given + threshold and buildings must be air conditioned. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.cooling_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cooling_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days) + return wrapper(ds, **kwargs) + +def cooling_degree_days_approximation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Cooling degree days approximation. + + The cumulative degree days for days when temperatures are above a given threshold and + buildings must be air conditioned. This method integrates mean, minimum, and maximum + temperatures, accounting for asymmetry in the distributions of temperatures throughout + the diurnal cycle. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.cooling_degree_days_approximation`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cooling_degree_days_approximation`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days_approximation) + return wrapper(ds, **kwargs) + +def corn_heat_units( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Corn heat units. + + A temperature-based index used to estimate the development of corn crops. Corn growth + occurs when the daily minimum and maximum temperatures exceed given thresholds. + + This function wraps :func:`xclim.indicators.atmos.corn_heat_units`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.corn_heat_units`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.corn_heat_units) + return wrapper(ds, **kwargs) + +def chill_portions( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Chill portions. + + Chill portions are a measure to estimate the bud breaking potential of different crops. + The constants and functions are taken from Luedeling et al. (2009) which formalises the + method described in Fishman et al. (1987). The model computes the accumulation of cold + temperatures in a two-step process. First, cold temperatures contribute to an + intermediate product that is transformed to a chill portion once it exceeds a certain + concentration. The intermediate product can be broken down at higher temperatures but + the final product is stable even at higher temperature. Thus the dynamic model is more + accurate than other chill models like the Chilling hours or Utah model, especially in + moderate climates like Israel, California or Spain. + + This function wraps :func:`xclim.indicators.atmos.cp`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cp`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_portions) + return wrapper(ds, **kwargs) + +def chill_units( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Chill units. + + Chill units are a measure to estimate the bud breaking potential of different crop based + on Richardson et al. (1974). The Utah model assigns a weight to each hour depending on + the temperature recognising that high temperatures can actual decrease, the potential + for bud breaking. Providing `positive_only=True` will ignore days with negative chill + units. + + This function wraps :func:`xclim.indicators.atmos.cu`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.cu`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_units) + return wrapper(ds, **kwargs) + +def degree_days_exceedance_date( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Degree day exceedance date. + + The day of the year when the sum of degree days exceeds a threshold, occurring after a + given date. Degree days are calculated above or below a given temperature threshold. + + This function wraps :func:`xclim.indicators.atmos.degree_days_exceedance_date`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.degree_days_exceedance_date`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.degree_days_exceedance_date) + return wrapper(ds, **kwargs) + +def daily_freezethaw_cycles( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Daily freeze-thaw cycles. + + The number of days with a freeze-thaw cycle. A freeze-thaw cycle is defined as a day + where maximum daily temperature is above a given threshold and minimum daily temperature + is at or below a given threshold, usually 0°C for both. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.dlyfrzthw`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dlyfrzthw`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_freezethaw_cycles) + return wrapper(ds, **kwargs) + def daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Compute the daily temperature range (DTR) using the xclim indices module. + Mean of daily temperature range. + + The average difference between the daily maximum and minimum temperatures. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.dtr`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dtr`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) + return wrapper(ds, **kwargs) + +def max_daily_temperature_range( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum of daily temperature range. + + The maximum difference between the daily maximum and minimum temperatures. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.dtrmax`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dtrmax`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_daily_temperature_range) + return wrapper(ds, **kwargs) + +def daily_temperature_range_variability( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Variability of daily temperature range. + + The average day-to-day variation in daily temperature range. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.dtrvar`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.dtrvar`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range_variability) + return wrapper(ds, **kwargs) + +def extreme_temperature_range( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Extreme temperature range. + + The maximum of the maximum temperature minus the minimum of the minimum temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.etr`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.etr`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.extreme_temperature_range) + return wrapper(ds, **kwargs) + +def fire_season( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Fire season mask. + + Binary mask of the active fire season, defined by conditions on consecutive daily + temperatures and, optionally, snow depths. + + This function wraps :func:`xclim.indicators.atmos.fire_season`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.fire_season`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fire_season) + return wrapper(ds, **kwargs) + +def first_day_tg_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tg_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tg_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_above) + return wrapper(ds, **kwargs) + +def first_day_tg_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tg_below`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tg_below`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_below) + return wrapper(ds, **kwargs) + +def first_day_tn_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tn_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tn_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_above) + return wrapper(ds, **kwargs) + +def first_day_tn_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tn_below`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tn_below`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_below) + return wrapper(ds, **kwargs) + +def first_day_tx_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tx_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tx_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_above) + return wrapper(ds, **kwargs) + +def first_day_tx_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + This function wraps :func:`xclim.indicators.atmos.first_day_tx_below`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.first_day_tx_below`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_below) + return wrapper(ds, **kwargs) + +def freezethaw_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Freeze-thaw spell frequency. + + Frequency of daily freeze-thaw spells. A freeze-thaw spell is defined as a number of + consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a given threshold, usually 0°C for both. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.freezethaw_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_frequency) + return wrapper(ds, **kwargs) + +def freezethaw_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximal length of freeze-thaw spells. + + Maximal length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number + of consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a threshold, usually 0°C for both. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.freezethaw_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_max_length) + return wrapper(ds, **kwargs) + +def freezethaw_spell_mean_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Freeze-thaw spell mean length. + + Average length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number + of consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a given threshold, usually 0°C for both. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_mean_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.freezethaw_spell_mean_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_mean_length) + return wrapper(ds, **kwargs) + +def freezing_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Freezing degree days. + + The cumulative degree days for days when the average temperature is below a given + threshold, typically 0°C. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.freezing_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.freezing_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezing_degree_days) + return wrapper(ds, **kwargs) + +def freshet_start( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Day of year of spring freshet start. + + Day of year of the spring freshet start, defined as the first day when the temperature + exceeds a certain threshold for a given number of consecutive days. + + This function wraps :func:`xclim.indicators.atmos.freshet_start`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.freshet_start`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freshet_start) + return wrapper(ds, **kwargs) + +def frost_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost days. + + Number of days where the daily minimum temperature is below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.frost_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_days) + return wrapper(ds, **kwargs) + +def frost_free_season_end( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost free season end. + + First day when the temperature is below a given threshold for a given number of + consecutive days after a median calendar date. + + This function wraps :func:`xclim.indicators.atmos.frost_free_season_end`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_free_season_end`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_end) + return wrapper(ds, **kwargs) + +def frost_free_season_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost free season length. + + Duration of the frost free season, defined as the period when the minimum daily + temperature is above 0°C without a freezing window of `N` days, with freezing occurring + after a median calendar date. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.frost_free_season_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_free_season_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_length) + return wrapper(ds, **kwargs) + +def frost_free_season_start( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost free season start. + + First day when minimum daily temperature exceeds a given threshold for a given number of + consecutive days + + This function wraps :func:`xclim.indicators.atmos.frost_free_season_start`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_free_season_start`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_start) + return wrapper(ds, **kwargs) + +def frost_free_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost free spell maximum length. + + The maximum length of a frost free period of `N` days or more, during which the minimum + temperature over a given time window of days is above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.frost_free_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_free_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_spell_max_length) + return wrapper(ds, **kwargs) + +def frost_season_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Frost season length. + + Duration of the freezing season, defined as the period when the daily minimum + temperature is below 0°C without a thawing window of days, with the thaw occurring after + a median calendar date. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.frost_season_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.frost_season_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_season_length) + return wrapper(ds, **kwargs) + +def growing_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Growing degree days. + + The cumulative degree days for days when the average temperature is above a given + threshold. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.growing_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.growing_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_degree_days) + return wrapper(ds, **kwargs) + +def growing_season_end( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Growing season end. + + The first day when the temperature is below a certain threshold for a certain number of + consecutive days after a given calendar date. + + This function wraps :func:`xclim.indicators.atmos.growing_season_end`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.growing_season_end`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_end) + return wrapper(ds, **kwargs) + +def growing_season_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Growing season length. + + Number of days between the first occurrence of a series of days with a daily average + temperature above a threshold and the first occurrence of a series of days with a daily + average temperature below that same threshold, occurring after a given calendar date. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.growing_season_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.growing_season_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_length) + return wrapper(ds, **kwargs) + +def growing_season_start( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Growing season start. + + The first day when the temperature exceeds a certain threshold for a given number of + consecutive days. + + This function wraps :func:`xclim.indicators.atmos.growing_season_start`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.growing_season_start`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_start) + return wrapper(ds, **kwargs) + +def heat_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat spell frequency. + + Number of heat spells. A heat spell occurs when rolling averages of daily minimum and + maximumtemperatures exceed given thresholds for a number of days. + + This function wraps :func:`xclim.indicators.atmos.heat_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_frequency) + return wrapper(ds, **kwargs) + +def heat_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat spell maximum length. + + The longest heat spell of a period. A heat spell occurs when rolling averages of daily + minimum and maximum temperatures exceed given thresholds for a number of days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.heat_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_max_length) + return wrapper(ds, **kwargs) + +def heat_spell_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat spell total length. + + Total length of heat spells. A heat spell occurs when rolling averages of daily minimum + and maximum temperatures exceed given thresholds for a number of days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.heat_spell_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_spell_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_total_length) + return wrapper(ds, **kwargs) + +def heat_wave_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat wave frequency. + + Number of heat waves. A heat wave occurs when daily minimum and maximum temperatures + exceed given thresholds for a number of days. + + This function wraps :func:`xclim.indicators.atmos.heat_wave_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_wave_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_frequency) + return wrapper(ds, **kwargs) + +def heat_wave_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat wave index. + + Number of days that constitute heatwave events. A heat wave occurs when daily minimum + and maximum temperatures exceed given thresholds for a number of days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.heat_wave_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_wave_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_index) + return wrapper(ds, **kwargs) + +def heat_wave_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat wave maximum length. + + Maximal duration of heat waves. A heat wave occurs when daily minimum and maximum + temperatures exceed given thresholds for a number of days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.heat_wave_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_wave_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_max_length) + return wrapper(ds, **kwargs) + +def heat_wave_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heat wave total length. + + Total length of heat waves. A heat wave occurs when daily minimum and maximum + temperatures exceed given thresholds for a number of days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.heat_wave_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heat_wave_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_total_length) + return wrapper(ds, **kwargs) + +def heating_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heating degree days. + + The cumulative degree days for days when the mean daily temperature is below a given + threshold and buildings must be heated. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.heating_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heating_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) + return wrapper(ds, **kwargs) + +def heating_degree_days_approximation( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Heating degree days approximation. + + The cumulative degree days for days where temperatures are below a given threshold and + buildings must be heated. This method integrates mean, minimum, and maximum + temperatures, accounting for asymmetry in the distributions of temperatures throughout + the diurnal cycle. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days_approximation) + return wrapper(ds, **kwargs) + +def hot_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Hot days. + + Number of days where the daily maximum temperature is above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.hot_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.hot_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_days) + return wrapper(ds, **kwargs) + +def hot_spell_frequency( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Hot spell frequency. + + The frequency of hot periods of `N` days or more, during which the temperature over a + given time window of days is above a given threshold. + + This function wraps :func:`xclim.indicators.atmos.hot_spell_frequency`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.hot_spell_frequency`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_frequency) + return wrapper(ds, **kwargs) + +def hot_spell_max_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Hot spell maximum length. + + The maximum length of a hot period of `N` days or more, during which the temperature + over a given time window of days is above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.hot_spell_max_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.hot_spell_max_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_length) + return wrapper(ds, **kwargs) + +def hot_spell_max_magnitude( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Hot spell maximum magnitude. + + Magnitude of the most intensive heat wave per {freq}. A heat wave occurs when daily + maximum temperatures exceed given thresholds for a number of days. + + **Units:** K d + + This function wraps :func:`xclim.indicators.atmos.hot_spell_max_magnitude`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.hot_spell_max_magnitude`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_magnitude) + return wrapper(ds, **kwargs) + +def hot_spell_total_length( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Hot spell total length. + + The total length of hot periods of `N` days or more, during which the temperature over a + given time window of days is above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.hot_spell_total_length`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.hot_spell_total_length`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_total_length) + return wrapper(ds, **kwargs) + +def huglin_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Huglin heliothermal index. + + Heat-summation index for agroclimatic suitability estimation, developed specifically for + viticulture. Considers daily minimum and maximum temperature with a given base + threshold, typically between 1 April and 30September, and integrates a day-length + coefficient calculation for higher latitudes. Metric originally published in Huglin + (1978). Day-length coefficient based on Hall & Jones (2010). + + This function wraps :func:`xclim.indicators.atmos.huglin_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.huglin_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.huglin_index) + return wrapper(ds, **kwargs) + +def ice_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Ice days. + + Number of days where the daily maximum temperature is below 0°C + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.ice_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.ice_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.ice_days) + return wrapper(ds, **kwargs) + +def last_spring_frost( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Last spring frost. + + The last day when minimum temperature is below a given threshold for a certain number of + days, limited by a final calendar date. + + This function wraps :func:`xclim.indicators.atmos.last_spring_frost`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.last_spring_frost`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_spring_frost) + return wrapper(ds, **kwargs) + +def late_frost_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Late frost days. + + Number of days where the daily minimum temperature is below a given threshold between a + givenstart date and a given end date. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.late_frost_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.late_frost_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.late_frost_days) + return wrapper(ds, **kwargs) + +def latitude_temperature_index( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Latitude temperature index. + + A climate indice based on mean temperature of the warmest month and a latitude-based + coefficient to account for longer day-length favouring growing conditions. Developed + specifically for viticulture. Mean temperature of warmest month multiplied by the + difference of latitude factor coefficient minus latitude. Metric originally published in + Jackson, D. I., & Cherry, N. J. (1988). + + This function wraps :func:`xclim.indicators.atmos.latitude_temperature_index`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.latitude_temperature_index`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.latitude_temperature_index) + return wrapper(ds, **kwargs) + +def maximum_consecutive_warm_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum consecutive warm days. + + Maximum number of consecutive days where the maximum daily temperature exceeds a certain + threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.maximum_consecutive_warm_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.maximum_consecutive_warm_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_warm_days) + return wrapper(ds, **kwargs) + +def tg10p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with mean temperature below the 10th percentile. + + Number of days with mean temperature below the 10th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tg10p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg10p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg10p) + return wrapper(ds, **kwargs) + +def tg90p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with mean temperature above the 90th percentile. + + Number of days with mean temperature above the 90th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tg90p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg90p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg90p) + return wrapper(ds, **kwargs) + +def tg_days_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with mean temperature above a given threshold. + + The number of days with mean temperature above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tg_days_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg_days_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_above) + return wrapper(ds, **kwargs) + +def tg_days_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with mean temperature below a given threshold. + + The number of days with mean temperature below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tg_days_below`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg_days_below`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_below) + return wrapper(ds, **kwargs) + +def tg_max( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum of mean temperature. + + Maximum of daily mean temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tg_max`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg_max`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_max) + return wrapper(ds, **kwargs) + +def tg_mean( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Mean temperature. + + Mean of daily mean temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tg_mean`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg_mean`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_mean) + return wrapper(ds, **kwargs) + +def tg_min( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Minimum of mean temperature. + + Minimum of daily mean temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tg_min`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tg_min`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_min) + return wrapper(ds, **kwargs) + +def thawing_degree_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Thawing degree days. + + The cumulative degree days for days when the average temperature is above a given + threshold, typically 0°C. + + **Units:** K days + + This function wraps :func:`xclim.indicators.atmos.thawing_degree_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.thawing_degree_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.thawing_degree_days) + return wrapper(ds, **kwargs) + +def tn10p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with minimum temperature below the 10th percentile. + + Number of days with minimum temperature below the 10th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tn10p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tn10p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn10p) + return wrapper(ds, **kwargs) + +def tn90p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with minimum temperature above the 90th percentile. + + Number of days with minimum temperature above the 90th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tn90p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tn90p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn90p) + return wrapper(ds, **kwargs) + +def tn_days_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with minimum temperature above a given threshold. + + The number of days with minimum temperature above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tn_days_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tn_days_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_above) + return wrapper(ds, **kwargs) + +def tn_days_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with minimum temperature below a given threshold. + + The number of days with minimum temperature below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tn_days_below`. Parameters ---------- ds : conversions.EarthkitData | xarray.Dataset - Input data containing maximum and minimum daily temperature values. + Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indices.daily_temperature_range`. + :func:`xclim.indicators.atmos.tn_days_below`. Returns ------- conversions.EarthkitData - The computed daily temperature range converted back to an Earthkit-compatible type. + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_below) + return wrapper(ds, **kwargs) + +def tn_max( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum of minimum temperature. + + Maximum of daily minimum temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tn_max`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tn_max`. + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_max) return wrapper(ds, **kwargs) +def tn_mean( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Mean of minimum temperature. + + Mean of daily minimum temperature. -def heating_degree_days( + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tn_mean`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tn_mean`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_mean) + return wrapper(ds, **kwargs) + +def tn_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Compute the Heating Degree Days (HDD) using the approximation method - from the xclim indicators module. + Minimum temperature. + + Minimum of daily minimum temperature. - This version uses both daily maximum and minimum temperatures, following - the approach used in :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tn_min`. Parameters ---------- ds : conversions.EarthkitData | xarray.Dataset - Daily maximum, minimum and mean temperature data. + Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + :func:`xclim.indicators.atmos.tn_min`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_min) + return wrapper(ds, **kwargs) + +def tropical_nights( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Tropical nights. + + Number of days where minimum temperature is above a given threshold. - Common arguments include: + **Units:** days - - `thresh` : str, default "18.0 degC" - Base temperature threshold for heating. - - `freq` : str, default "YS" - Frequency for accumulation (e.g., "YS" = yearly sum). + This function wraps :func:`xclim.indicators.atmos.tropical_nights`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tropical_nights`. Returns ------- conversions.EarthkitData - The computed Heating Degree Days (HDD) converted back to an Earthkit-compatible type. + The computed index as an Earthkit-compatible field. """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tropical_nights) + return wrapper(ds, **kwargs) + +def tx10p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with maximum temperature below the 10th percentile. + + Number of days with maximum temperature below the 10th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tx10p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx10p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx10p) + return wrapper(ds, **kwargs) + +def tx90p( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Days with maximum temperature above the 90th percentile. + + Number of days with maximum temperature above the 90th percentile. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tx90p`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx90p`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx90p) + return wrapper(ds, **kwargs) + +def tx_days_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with maximum temperature above a given threshold. + + The number of days with maximum temperature above a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tx_days_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_days_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_above) + return wrapper(ds, **kwargs) + +def tx_days_below( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with maximum temperature below a given threshold. + + The number of days with maximum temperature below a given threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tx_days_below`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_days_below`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_below) + return wrapper(ds, **kwargs) + +def tx_max( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum temperature. + + Maximum of daily maximum temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tx_max`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_max`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_max) + return wrapper(ds, **kwargs) + +def tx_mean( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Mean of maximum temperature. + + Mean of daily maximum temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tx_mean`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_mean`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_mean) + return wrapper(ds, **kwargs) + +def tx_min( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Minimum of maximum temperature. + + Minimum of daily maximum temperature. + + **Units:** K + + This function wraps :func:`xclim.indicators.atmos.tx_min`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_min`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_min) + return wrapper(ds, **kwargs) + +def tx_tn_days_above( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Number of days with daily minimum and maximum temperatures exceeding thresholds. + + Number of days with daily maximum and minimum temperatures above given thresholds. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.tx_tn_days_above`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.tx_tn_days_above`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_tn_days_above) return wrapper(ds, **kwargs) +def usda_hardiness_zones( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + USDA hardiness zones. + + A climate indice based on a multi-year rolling average of the annual minimum + temperature. Developed specifically to aid in determining plant suitability of + geographic regions. The USDA classificationscheme divides categories into 10 degree + Fahrenheit zones, with 5-degree Fahrenheit half-zones, starting from -65 degrees + Fahrenheit and ending at 65 degrees Fahrenheit. + + This function wraps :func:`xclim.indicators.atmos.usda_hardiness_zones`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.usda_hardiness_zones`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.usda_hardiness_zones) + return wrapper(ds, **kwargs) def warm_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Compute the Warm Spell Duration Index (WSDI) using the xclim indices module. - The 90th percentile threshold must be pre-calculated and included in the input dataset `ds` - as a variable named `{variable}_per` (e.g., `tasmax_per`). + Warm spell duration index. + + Number of days part of a percentile-defined warm spell. A warm spell occurs when the + maximum daily temperature is above a given percentile for a given number of consecutive + days. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.warm_spell_duration_index`. Parameters ---------- ds : conversions.EarthkitData | xarray.Dataset - Daily maximum temperature data for the target period, including the pre-calculated percentile. + Input dataset. See xclim documentation for required variables. **kwargs : Any - Additional arguments forwarded to :func:`xclim.indicators.atmos.warm_spell_duration_index`. + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.warm_spell_duration_index`. Returns ------- conversions.EarthkitData - The computed WSDI index as an Earthkit-compatible field. + The computed index as an Earthkit-compatible field. """ - # Create wrapper inside the function wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) + return wrapper(ds, **kwargs) - return wrapper(earthkit_input=ds, **kwargs) diff --git a/src/earthkit/climate/indicators/wind.py b/src/earthkit/climate/indicators/wind.py new file mode 100644 index 0000000..a65c13c --- /dev/null +++ b/src/earthkit/climate/indicators/wind.py @@ -0,0 +1,251 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +"""Wind indices.""" + +from typing import Any + +import xarray +import xclim.indicators.atmos + +import earthkit.climate.utils.conversions as conversions +from earthkit.climate.api.wrapper import wrap_xclim_indicator + + +def calm_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Calm days. + + Number of days with surface wind speed below threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.calm_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.calm_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.calm_days) + return wrapper(ds, **kwargs) + +def sfcWind_max( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum near-surface mean wind speed. + + Maximum of daily mean near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWind_max`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWind_max`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_max) + return wrapper(ds, **kwargs) + +def sfcWind_mean( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Mean near-surface wind speed. + + Mean of daily near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWind_mean`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWind_mean`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_mean) + return wrapper(ds, **kwargs) + +def sfcWind_min( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Minimum near-surface mean wind speed. + + Minimum of daily mean near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWind_min`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWind_min`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_min) + return wrapper(ds, **kwargs) + +def sfcWindmax_max( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Maximum near-surface maximum wind speed. + + Maximum of daily maximum near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWindmax_max`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWindmax_max`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_max) + return wrapper(ds, **kwargs) + +def sfcWindmax_mean( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Mean near-surface maximum wind speed. + + Mean of daily maximum near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWindmax_mean`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWindmax_mean`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_mean) + return wrapper(ds, **kwargs) + +def sfcWindmax_min( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Minimum near-surface maximum wind speed. + + Minimum of daily maximum near-surface wind speed. + + **Units:** m s-1 + + This function wraps :func:`xclim.indicators.atmos.sfcWindmax_min`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.sfcWindmax_min`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_min) + return wrapper(ds, **kwargs) + +def windy_days( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + """ + Windy days. + + Number of days with surface wind speed at or above threshold. + + **Units:** days + + This function wraps :func:`xclim.indicators.atmos.windy_days`. + + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.windy_days`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """ + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.windy_days) + return wrapper(ds, **kwargs) + diff --git a/tools/__init__.py b/tools/__init__.py new file mode 100644 index 0000000..7eccc68 --- /dev/null +++ b/tools/__init__.py @@ -0,0 +1,9 @@ +#!/usr/bin/env python3 + +# (C) Copyright 2025 - ECMWF and individual contributors. +# +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py new file mode 100644 index 0000000..afc8b65 --- /dev/null +++ b/tools/xclim_wrappers_generator.py @@ -0,0 +1,235 @@ +#!/usr/bin/env python3 + +# (C) Copyright 2025 - ECMWF and individual contributors. +# +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +import inspect +import textwrap +from pathlib import Path +from typing import Any, List + +import xclim.indicators.atmos + +MODULE_TEMPLATE = """# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +\"\"\"{category_title} indices.\"\"\" + +from typing import Any + +import xarray +import xclim.indicators.atmos + +import earthkit.climate.utils.conversions as conversions +from earthkit.climate.api.wrapper import wrap_xclim_indicator + +{functions_code} +""" + +FUNCTION_TEMPLATE = """ +def {func_name}( + ds: conversions.EarthkitData | xarray.Dataset, + **kwargs: Any, +) -> conversions.EarthkitData: + \"\"\" + {docstring} + \"\"\" + wrapper = wrap_xclim_indicator(xclim.indicators.atmos.{xclim_func_name}) + return wrapper(ds, **kwargs) +""" + + +def generate_docstring(indicator: Any) -> str: + """Generate a docstring for the wrapper function based on the xclim indicator. + + Parameters + ---------- + indicator : xclim.core.indicator.Indicator + The xclim indicator object. + + Returns + ------- + str + The generated docstring for the wrapper function. + """ + identifier = indicator.identifier + + # Extract metadata + # Use title as the summary if available, otherwise fallback to docstring or identifier + summary = getattr(indicator, "title", "").strip() + if not summary: + summary = (indicator.__doc__ or "").split("\n")[0].strip() + + if not summary: + summary = f"Compute {identifier}." + + if not summary.endswith("."): + summary += "." + + description = getattr(indicator, "abstract", "") or getattr(indicator, "description", "") + units = getattr(indicator, "units", "") + + sections = [summary] + + if description: + # Wrap the description to avoid long lines + # We target a width of 88 to allow for indentation (4 spaces) and staying well under 110 + sections.append(textwrap.fill(description, width=88)) + + if units: + sections.append(f"**Units:** {units}") + + sections.append(f"This function wraps :func:`xclim.indicators.atmos.{identifier}`.") + + # Static footer + footer = inspect.cleandoc(f""" + Parameters + ---------- + ds : conversions.EarthkitData | xarray.Dataset + Input dataset. See xclim documentation for required variables. + **kwargs : Any + Additional keyword arguments forwarded to + :func:`xclim.indicators.atmos.{identifier}`. + + Returns + ------- + conversions.EarthkitData + The computed index as an Earthkit-compatible field. + """) + sections.append(footer) + + return "\n\n".join(sections) + + +def generate_module_content(category: str, indicators: List[Any]) -> str: + """Generate the content for a python module containing wrapper functions. + + Parameters + ---------- + category : str + The category of indicators (e.g. 'precipitation', 'temperature'). + indicators : list + List of xclim indicator objects to generate wrappers for. + + Returns + ------- + str + The complete source code for the module. + """ + functions_code = [] + + # Sort indicators by name for consistent output + # key=lambda x: x.identifier might be good, but we want to sort by the function name we use. + # We use xclim_func_name derived below, but let's just sort by identifier primarily. + indicators.sort(key=lambda x: x.identifier) + + for ind in indicators: + # We need the name of the attribute in xclim.indicators.atmos to generate the call + xclim_func_name = None + for name, obj in inspect.getmembers(xclim.indicators.atmos): + if obj is ind: + xclim_func_name = name + break + + if not xclim_func_name: + # Fallback + xclim_func_name = ind.identifier + + # Use the xclim variable name as the function name to match existing conventions + func_name = xclim_func_name + + docstring = generate_docstring(ind) + + # Indent the docstring correctly + lines = docstring.split("\n") + indented_doc = ( + lines[0] + "\n" + "\n".join([(" " + line if line.strip() else "") for line in lines[1:]]) + ) + + code = FUNCTION_TEMPLATE.format( + func_name=func_name, xclim_func_name=xclim_func_name, docstring=indented_doc + ) + functions_code.append(code) + + return MODULE_TEMPLATE.format( + category_title=category.capitalize(), functions_code="".join(functions_code) + ) + + +def main(): + output_dir = Path("src/earthkit/climate/indicators") + + # Discovery + module = xclim.indicators.atmos + + # Get all potential indicators from __all__ if present + if hasattr(module, "__all__"): + names = module.__all__ + else: + # Fallback to public members + names = [n for n, o in inspect.getmembers(module) if not n.startswith("_")] + + # Map internal xclim module names to our category names + module_to_category = { + "_precip": "precipitation", + "_temperature": "temperature", + "_wind": "wind", + "_synoptic": "synoptic", + } + + indicators_map = {cat: [] for cat in module_to_category.values()} + + for name in names: + # We need the object to check type/attributes and pass to generation + try: + obj = getattr(module, name) + except AttributeError: + continue + + # Basic check if it is likely an indicator (has identifier) + if not hasattr(obj, "identifier"): + continue + + # Check which module it comes from + # e.g. xclim.indicators.atmos._precip + obj_module = getattr(obj, "__module__", "") + # Extract the last part of the module path + module_name = obj_module.split(".")[-1] + + if module_name in module_to_category: + category = module_to_category[module_name] + indicators_map[category].append(obj) + else: + # Fallback or skip + # If we want to capture everything, we might need a default, but + # adhering to strict categories is checking checking the files mentioned. + # print(f"Skipping {name} from unknown module {module_name}") + continue + + for category, indicators in indicators_map.items(): + if not indicators: + continue + + filename = f"{category}.py" + filepath = output_dir / filename + + print(f"Generating {filepath} with {len(indicators)} indicators...") + content = generate_module_content(category, indicators) + + with open(filepath, "w") as f: + f.write(content) + print(f"Written {filepath}") + + +if __name__ == "__main__": + main() From 364d2453f2c24c2f0ddaeb343468654afb9cfbc3 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 19 Jan 2026 17:06:05 +0100 Subject: [PATCH 07/47] refactor: improve code formatting and docstring consistency in precipitation indicators. --- .../climate/indicators/precipitation.py | 51 ++++++++++++++++++- 1 file changed, 49 insertions(+), 2 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index 1ace9ed..d4f9303 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -47,6 +47,7 @@ def antecedent_precipitation_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.antecedent_precipitation_index) return wrapper(ds, **kwargs) + def maximum_consecutive_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -77,6 +78,7 @@ def maximum_consecutive_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_dry_days) return wrapper(ds, **kwargs) + def cffwis_indices( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -108,6 +110,7 @@ def cffwis_indices( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cffwis_indices) return wrapper(ds, **kwargs) + def cold_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -138,6 +141,7 @@ def cold_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_dry_days) return wrapper(ds, **kwargs) + def cold_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -168,6 +172,7 @@ def cold_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_wet_days) return wrapper(ds, **kwargs) + def maximum_consecutive_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -198,6 +203,7 @@ def maximum_consecutive_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_wet_days) return wrapper(ds, **kwargs) + def days_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -228,6 +234,7 @@ def days_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_doy_thresh) return wrapper(ds, **kwargs) + def days_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -258,6 +265,7 @@ def days_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_thresh) return wrapper(ds, **kwargs) + def days_with_snow( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -287,6 +295,7 @@ def days_with_snow( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_with_snow) return wrapper(ds, **kwargs) + def drought_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -315,6 +324,7 @@ def drought_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.drought_code) return wrapper(ds, **kwargs) + def griffiths_drought_factor( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -344,6 +354,7 @@ def griffiths_drought_factor( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.griffiths_drought_factor) return wrapper(ds, **kwargs) + def duff_moisture_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -373,6 +384,7 @@ def duff_moisture_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.duff_moisture_code) return wrapper(ds, **kwargs) + def dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -402,6 +414,7 @@ def dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_days) return wrapper(ds, **kwargs) + def dry_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -430,6 +443,7 @@ def dry_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_frequency) return wrapper(ds, **kwargs) + def dry_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -460,6 +474,7 @@ def dry_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_max_length) return wrapper(ds, **kwargs) + def dry_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -490,6 +505,7 @@ def dry_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_total_length) return wrapper(ds, **kwargs) + def dryness_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -521,6 +537,7 @@ def dryness_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dryness_index) return wrapper(ds, **kwargs) + def mcarthur_forest_fire_danger_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -548,6 +565,7 @@ def mcarthur_forest_fire_danger_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) return wrapper(ds, **kwargs) + def first_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -576,6 +594,7 @@ def first_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_snowfall) return wrapper(ds, **kwargs) + def fraction_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -605,6 +624,7 @@ def fraction_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_doy_thresh) return wrapper(ds, **kwargs) + def fraction_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -634,6 +654,7 @@ def fraction_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_thresh) return wrapper(ds, **kwargs) + def high_precip_low_temp( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -664,6 +685,7 @@ def high_precip_low_temp( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.high_precip_low_temp) return wrapper(ds, **kwargs) + def keetch_byram_drought_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -697,6 +719,7 @@ def keetch_byram_drought_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.keetch_byram_drought_index) return wrapper(ds, **kwargs) + def last_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -725,6 +748,7 @@ def last_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_snowfall) return wrapper(ds, **kwargs) + def liquid_precip_ratio( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -754,6 +778,7 @@ def liquid_precip_ratio( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_ratio) return wrapper(ds, **kwargs) + def liquid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -784,6 +809,7 @@ def liquid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_average) return wrapper(ds, **kwargs) + def liquid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -814,12 +840,13 @@ def liquid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_accumulation) return wrapper(ds, **kwargs) + def max_n_day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - maximum n-day total precipitation. + Maximum n-day total precipitation. Maximum of the moving sum of daily precipitation for a given period. @@ -843,6 +870,7 @@ def max_n_day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_n_day_precipitation_amount) return wrapper(ds, **kwargs) + def max_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -872,6 +900,7 @@ def max_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_pr_intensity) return wrapper(ds, **kwargs) + def precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -904,6 +933,7 @@ def precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_average) return wrapper(ds, **kwargs) + def precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -936,6 +966,7 @@ def precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_accumulation) return wrapper(ds, **kwargs) + def rain_on_frozen_ground_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -967,6 +998,7 @@ def rain_on_frozen_ground_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_on_frozen_ground_days) return wrapper(ds, **kwargs) + def rain_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -999,6 +1031,7 @@ def rain_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_season) return wrapper(ds, **kwargs) + def rprctot( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1027,6 +1060,7 @@ def rprctot( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rprctot) return wrapper(ds, **kwargs) + def max_1day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1056,6 +1090,7 @@ def max_1day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_1day_precipitation_amount) return wrapper(ds, **kwargs) + def daily_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1085,6 +1120,7 @@ def daily_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) return wrapper(ds, **kwargs) + def snowfall_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1115,6 +1151,7 @@ def snowfall_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_frequency) return wrapper(ds, **kwargs) + def snowfall_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1145,6 +1182,7 @@ def snowfall_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_intensity) return wrapper(ds, **kwargs) + def solid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1175,6 +1213,7 @@ def solid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_average) return wrapper(ds, **kwargs) + def solid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1205,6 +1244,7 @@ def solid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_accumulation) return wrapper(ds, **kwargs) + def warm_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1235,6 +1275,7 @@ def warm_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_dry_days) return wrapper(ds, **kwargs) + def warm_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1265,6 +1306,7 @@ def warm_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_wet_days) return wrapper(ds, **kwargs) + def water_cycle_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1294,6 +1336,7 @@ def water_cycle_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.water_cycle_intensity) return wrapper(ds, **kwargs) + def wet_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1324,6 +1367,7 @@ def wet_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_precip_accumulation) return wrapper(ds, **kwargs) + def wet_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1353,6 +1397,7 @@ def wet_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_frequency) return wrapper(ds, **kwargs) + def wet_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1384,6 +1429,7 @@ def wet_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_max_length) return wrapper(ds, **kwargs) + def wet_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1415,6 +1461,7 @@ def wet_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_total_length) return wrapper(ds, **kwargs) + def wetdays( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1444,6 +1491,7 @@ def wetdays( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays) return wrapper(ds, **kwargs) + def wetdays_prop( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1472,4 +1520,3 @@ def wetdays_prop( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays_prop) return wrapper(ds, **kwargs) - From afb021e54f778782297e07761136fa5229eab494 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 21 Jan 2026 12:24:18 +0100 Subject: [PATCH 08/47] docs: Update `pixi run` commands in README to explicitly use the `dev` environment. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 510ded9..585f145 100644 --- a/README.md +++ b/README.md @@ -98,19 +98,19 @@ This project uses `pixi` tasks to manage development workflows, replacing the le - **Quality Assurance**: Run pre-commit hooks to ensure code quality. ```bash - pixi run qa + pixi run -e dev qa ``` - **Unit Tests**: Run the test suite using pytest. ```bash - pixi run unit-tests + pixi run -e dev unit-tests ``` - **Type Checking**: Run static type analysis with mypy. ```bash - pixi run type-check + pixi run -e dev type-check ``` - **Build Documentation**: Build the Sphinx documentation. Note that this task runs in the `docs` environment. From 31b35028b9c17a8b40b864ba64daf23a3b664283 Mon Sep 17 00:00:00 2001 From: garciam Date: Wed, 21 Jan 2026 15:08:04 +0100 Subject: [PATCH 09/47] fix generated indices install location so it writes the packages in the location where the package is installed. --- README.md | 11 +++++++---- tools/xclim_wrappers_generator.py | 5 ++--- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 510ded9..7e9064a 100644 --- a/README.md +++ b/README.md @@ -104,14 +104,17 @@ This project uses `pixi` tasks to manage development workflows, replacing the le - **Unit Tests**: Run the test suite using pytest. ```bash - pixi run unit-tests ``` +pixi run -e dev unit-tests + +```` + - **Type Checking**: Run static type analysis with mypy. - ```bash - pixi run type-check - ``` +```bash +pixi run -e dev type-check +```` - **Build Documentation**: Build the Sphinx documentation. Note that this task runs in the `docs` environment. diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index afc8b65..fdde236 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -8,9 +8,9 @@ # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. +import importlib import inspect import textwrap -from pathlib import Path from typing import Any, List import xclim.indicators.atmos @@ -167,8 +167,7 @@ def generate_module_content(category: str, indicators: List[Any]) -> str: def main(): - output_dir = Path("src/earthkit/climate/indicators") - + output_dir = importlib.resources.files("earthkit.climate.indicators") # Discovery module = xclim.indicators.atmos From bb14434c7b5e536290f7fce97f24e736b9cc8776 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Thu, 22 Jan 2026 19:15:05 +0100 Subject: [PATCH 10/47] feat: Add a robust performance benchmarking notebook comparing Earthkit-Climate and Xclim for climate indicator calculations. --- .../robust_performance_benchmarking.ipynb | 1603 +++++++++++++++++ 1 file changed, 1603 insertions(+) create mode 100644 docs/notebooks/robust_performance_benchmarking.ipynb diff --git a/docs/notebooks/robust_performance_benchmarking.ipynb b/docs/notebooks/robust_performance_benchmarking.ipynb new file mode 100644 index 0000000..0a2adcd --- /dev/null +++ b/docs/notebooks/robust_performance_benchmarking.ipynb @@ -0,0 +1,1603 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fb33bbc9", + "metadata": {}, + "source": [ + "# Robust Performance Benchmarking: Earthkit-Climate vs Xclim\n", + "\n", + "This notebook provides a robust comparative analysis of climate indicator calculations using:\n", + "1. **Earthkit-Climate (Lazy)**: Standard wrapper usage.\n", + "2. **Earthkit-Climate (Optimized)**: With pre-computation of percentiles and manual re-chunking (`time: -1`).\n", + "3. **Xclim (Direct)**: Direct optimized usage of `xclim.indices`.\n", + "4. **Xclim + Flox**: `xclim` with `flox` optimization enabled for faster reductions.\n", + "\n", + "Key features:\n", + "- Statistical Sampling: $N$ runs per test.\n", + "- Resource Profiling: RAM peak & CPU usage.\n", + "- Visualization: Speedup comparison with error bars.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "410735a7", + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "import os\n", + "import time\n", + "import warnings\n", + "from typing import Any, Callable, Dict, List, Optional\n", + "\n", + "import earthkit.data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import xarray as xr\n", + "import xclim.indicators\n", + "from dask.diagnostics import Profiler, ResourceProfiler\n", + "from IPython.display import Markdown, display\n", + "from memory_profiler import memory_usage\n", + "\n", + "import earthkit.climate.indicators.precipitation as ek_pr\n", + "import earthkit.climate.indicators.temperature as ek_temp\n", + "from earthkit.climate.utils.percentile import percentile_doy\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "# Configure robust caching to avoid re-downloading\n", + "cache_dir = os.path.expanduser(\"~/.cache/earthkit/data\")\n", + "os.makedirs(cache_dir, exist_ok=True)\n", + "settings_earthkit = {\n", + " \"cache-policy\": \"user\",\n", + " \"temporary-directory-root\": cache_dir,\n", + "}\n", + "earthkit.data.config.set(settings_earthkit)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "68281ebe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "## Resources Used\n", + "\n", + "### Hardware Configuration\n", + "The performance analysis was conducted on the following hardware (dynamically detected):\n", + "- **CPU**: 12th Gen Intel(R) Core(TM) i5-12600KF\n", + "- **RAM**: 31.2 GB\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "def get_cpu_info() -> str:\n", + " \"\"\"\n", + " Extract the CPU model name from the system's /proc/cpuinfo.\n", + "\n", + " This function parses the Linux virtual filesystem to find the\n", + " hardware model name of the processor.\n", + "\n", + " Returns\n", + " -------\n", + " cpu_model : str\n", + " The name of the CPU model (e.g., 'Intel(R) Core(TM) i7-10700K').\n", + " Returns 'Unknown CPU' if the file is inaccessible or the entry\n", + " is missing.\n", + " \"\"\"\n", + " try:\n", + " if os.path.exists(\"/proc/cpuinfo\"):\n", + " with open(\"/proc/cpuinfo\", \"r\") as f:\n", + " for line in f:\n", + " if \"model name\" in line:\n", + " return line.split(\":\")[1].strip()\n", + " except Exception:\n", + " return \"Unknown CPU\"\n", + " return \"Unknown CPU\"\n", + "\n", + "\n", + "def get_ram_info() -> str:\n", + " \"\"\"\n", + " Extract the total system RAM from /proc/meminfo.\n", + "\n", + " Parses the system memory information and converts the total memory\n", + " from kilobytes to gigabytes.\n", + "\n", + " Returns\n", + " -------\n", + " ram_size : str\n", + " A formatted string representing total RAM in GB (e.g., '16.0 GB').\n", + " Returns 'Unknown RAM' if the file is inaccessible or the entry\n", + " is missing.\n", + " \"\"\"\n", + " try:\n", + " if os.path.exists(\"/proc/meminfo\"):\n", + " with open(\"/proc/meminfo\", \"r\") as f:\n", + " for line in f:\n", + " if \"MemTotal\" in line:\n", + " total_kb = int(line.split()[1])\n", + " return f\"{total_kb / 1024 / 1024:.1f} GB\"\n", + " except Exception:\n", + " return \"Unknown RAM\"\n", + " return \"Unknown RAM\"\n", + "\n", + "# --- Execution Logic ---\n", + "\n", + "cpu_model: str = get_cpu_info()\n", + "ram_size: str = get_ram_info()\n", + "\n", + "display(\n", + " Markdown(f\"\"\"\n", + "## Resources Used\n", + "\n", + "### Hardware Configuration\n", + "The performance analysis was conducted on the following hardware (dynamically detected):\n", + "- **CPU**: {cpu_model}\n", + "- **RAM**: {ram_size}\n", + "\"\"\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fba35188", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "\n", + "### Dataset Information\n", + "\n", + "The analysis uses the following climate datasets derived from CMIP6 projections\n", + "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", + "repository and are automatically downloaded or cached by **earthkit-data**.\n", + "\n", + "| Variable | Scenario | Dimensions | Size | Status | URL |\n", + "|----------|----------|------------|------|--------|-----|\n", + "| `tasmax` | historical | (time: 7305, lat: 48, lon: 84) | 67.3 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc) |\n", + "| `tasmax` | ssp585 | (time: 14610, lat: 48, lon: 84) | 126.9 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "| `tasmin` | ssp585 | (time: 14610, lat: 48, lon: 84) | 132.1 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "| `pr` | ssp585 | (time: 14610, lat: 48, lon: 84) | 111.5 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "\n", + "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", + "download the files.\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Dataset URLs\n", + "DATASET_URLS = [\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + "]\n", + "\n", + "\n", + "def format_size(size_bytes: float) -> str:\n", + " \"\"\"\n", + " Convert a byte size into a human-readable string.\n", + "\n", + " Parameters\n", + " ----------\n", + " size_bytes : float\n", + " File size in bytes.\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " Size formatted as B, KB, MB, or GB.\n", + " \"\"\"\n", + " for unit in [\"B\", \"KB\", \"MB\", \"GB\"]:\n", + " if size_bytes < 1024:\n", + " return f\"{size_bytes:.1f} {unit}\"\n", + " size_bytes /= 1024\n", + " return f\"{size_bytes:.1f} TB\"\n", + "\n", + "\n", + "def extract_dataset_info(url: str) -> Dict[str, Any]:\n", + " \"\"\"\n", + " Extract key metadata information from a NetCDF dataset available via URL.\n", + "\n", + " Parameters\n", + " ----------\n", + " url : str\n", + " URL to the NetCDF dataset.\n", + "\n", + " Returns\n", + " -------\n", + " dict\n", + " Dictionary containing:\n", + " - Variable\n", + " - Scenario\n", + " - Description\n", + " - Dimensions\n", + " - Size\n", + " - Status (\"Cached\" or \"Remote\")\n", + " - URL\n", + " \"\"\"\n", + " ds = earthkit.data.from_source(\"url\", url)\n", + "\n", + " # Detect file size & cache status\n", + " if getattr(ds, \"path\", None) and os.path.exists(ds.path):\n", + " size = format_size(os.path.getsize(ds.path))\n", + " status = \"Cached\"\n", + " else:\n", + " size = \"Unknown\"\n", + " status = \"Remote\"\n", + "\n", + " # Convert to xarray\n", + " xr_ds = ds.to_xarray()\n", + "\n", + " # Extract primary variable\n", + " variables = list(xr_ds.data_vars)\n", + " variable = f\"`{variables[0]}`\" if variables else \"Unknown\"\n", + "\n", + " # Scenario metadata\n", + " scenario = xr_ds.attrs.get(\"scenario\", \"Unknown\")\n", + "\n", + " # Dimension string, e.g. \"(time: 365, lat: 180, lon: 360)\"\n", + " dims_str = \"(\" + \", \".join(f\"{k}: {v}\" for k, v in xr_ds.dims.items()) + \")\"\n", + "\n", + " return {\n", + " \"Variable\": variable,\n", + " \"Scenario\": scenario,\n", + " \"Dimensions\": dims_str,\n", + " \"Size\": size,\n", + " \"Status\": status,\n", + " \"URL\": url,\n", + " }\n", + "\n", + "\n", + "def generate_dataset_table(urls: List[str]) -> str:\n", + " \"\"\"\n", + " Create a Markdown table summarizing metadata for a list of dataset URLs.\n", + "\n", + " Parameters\n", + " ----------\n", + " urls : list of str\n", + " List of dataset URLs.\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " A Markdown-formatted table of dataset metadata.\n", + " \"\"\"\n", + " header = (\n", + " \"| Variable | Scenario | Dimensions | Size | Status | URL |\\n\"\n", + " \"|----------|----------|------------|------|--------|-----|\\n\"\n", + " )\n", + "\n", + " rows = []\n", + " for url in urls:\n", + " info = extract_dataset_info(url)\n", + " rows.append(\n", + " f\"| {info['Variable']} | {info['Scenario']} | \"\n", + " f\"{info['Dimensions']} | {info['Size']} | {info['Status']} | \"\n", + " f\"[Download]({info['URL']}) |\"\n", + " )\n", + "\n", + " return header + \"\\n\".join(rows)\n", + "\n", + "\n", + "# Display Markdown report\n", + "display(\n", + " Markdown(\n", + " f\"\"\"\n", + "### Dataset Information\n", + "\n", + "The analysis uses the following climate datasets derived from CMIP6 projections\n", + "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", + "repository and are automatically downloaded or cached by **earthkit-data**.\n", + "\n", + "{generate_dataset_table(DATASET_URLS)}\n", + "\n", + "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", + "download the files.\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5922ba8f", + "metadata": {}, + "source": [ + "## 1. Benchmarking Engine\n", + "\n", + "Helper functions to run tests multiple times, capture statistics, and profile resources." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "5d1ba8a5", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import threading\n", + "from typing import Any, Dict, List\n", + "\n", + "import psutil\n", + "\n", + "\n", + "class ResourceMonitor(threading.Thread):\n", + " def __init__(self, interval=0.1):\n", + " self.interval = interval\n", + " self.stop_event = threading.Event()\n", + " self.memory_usage = []\n", + " self.cpu_usage = []\n", + " self.process = psutil.Process()\n", + " super().__init__()\n", + "\n", + " def run(self):\n", + " while not self.stop_event.is_set():\n", + " try:\n", + " # RSS Memory in MiB\n", + " mem = self.process.memory_info().rss / (1024 * 1024)\n", + " self.memory_usage.append(mem)\n", + " # CPU Percent (blocking for interval would slow main thread, so we sleep manually)\n", + " # self.cpu_usage.append(self.process.cpu_percent(interval=None)) \n", + " except Exception:\n", + " pass\n", + " time.sleep(self.interval)\n", + "\n", + " def stop(self):\n", + " self.stop_event.set()\n", + "\n", + "def benchmark_function(\n", + " func: Callable[..., Any],\n", + " kwargs: Dict[str, Any],\n", + " n_repeats: int = 5,\n", + " warmup: bool = True\n", + ") -> Dict[str, float]:\n", + " \"\"\"\n", + " Run a function N times with robust profiling (Warm-up + High-Freq Sampling).\n", + "\n", + " Parameters\n", + " ----------\n", + " func : Callable\n", + " The function to benchmark.\n", + " kwargs : Dict\n", + " Arguments for the function.\n", + " n_repeats : int\n", + " Number of measurement runs.\n", + " warmup : bool\n", + " If True, runs the function once silently before measuring to exclude JIT/Import costs.\n", + "\n", + " Returns\n", + " -------\n", + " stats : Dict\n", + " 'mean_time', 'median_time', 'std_time', 'max_mem', 'mean_mem_peak'\n", + " \"\"\"\n", + " # 1. Warm-up (Silent)\n", + " if warmup:\n", + " print(f\" Warm-up run for {func.__name__}...\")\n", + " try:\n", + " # Run without monitoring\n", + " res = func(**kwargs)\n", + " if hasattr(res, 'compute'):\n", + " res.compute()\n", + " elif hasattr(res, 'to_xarray'):\n", + " res.to_xarray().compute()\n", + " except Exception as e:\n", + " print(f\" Warm-up warning: {e}\")\n", + " # Force GC after warmup\n", + " gc.collect()\n", + "\n", + " times: List[float] = []\n", + " mem_peaks: List[float] = []\n", + "\n", + " print(f\"Benchmarking {func.__name__} ({n_repeats} runs)...\")\n", + "\n", + " for i in range(n_repeats):\n", + " gc.collect()\n", + "\n", + " # Start Monitor (10ms interval)\n", + " monitor = ResourceMonitor(interval=0.1)\n", + " # Capture baseline memory\n", + " baseline_mem = psutil.Process().memory_info().rss / (1024 * 1024)\n", + " monitor.start()\n", + "\n", + " start_time = time.perf_counter()\n", + "\n", + " # --- Computation ---\n", + " try:\n", + " res = func(**kwargs)\n", + " if hasattr(res, 'compute'):\n", + " res.compute()\n", + " elif hasattr(res, 'to_xarray'):\n", + " res.to_xarray().compute()\n", + " except Exception as e:\n", + " print(f\" Run {i+1} Failed: {e}\")\n", + " monitor.stop()\n", + " continue\n", + " # -------------------\n", + "\n", + " end_time = time.perf_counter()\n", + " monitor.stop()\n", + " monitor.join()\n", + "\n", + " duration = end_time - start_time\n", + "\n", + " # Calculate Peak Memory Delta\n", + " observed_mems = monitor.memory_usage\n", + " if observed_mems:\n", + " # We want the peak usage *induced* by the function\n", + " # Peak Delta = Max(Observed) - Baseline\n", + " # Or simplified: Peak Observed\n", + " # The previous 'memory_profiler' returned (Max - Min). reliable for single process.\n", + " # Let's use Normalized Peak: (Max - Baseline)\n", + " peak_delta = max(observed_mems) - baseline_mem\n", + " # Clamp to 0\n", + " if peak_delta < 0: peak_delta = 0\n", + " else:\n", + " peak_delta = 0.0\n", + "\n", + " times.append(duration)\n", + " mem_peaks.append(peak_delta)\n", + "\n", + " print(f\" Run {i+1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", + "\n", + " if not times:\n", + " return {\"mean_time\": 0.0, \"max_mem\": 0.0}\n", + "\n", + " return {\n", + " \"mean_time\": float(np.mean(times)),\n", + " \"median_time\": float(np.median(times)),\n", + " \"std_time\": float(np.std(times)),\n", + " \"max_mem\": float(np.max(mem_peaks)),\n", + " \"mean_mem\": float(np.mean(mem_peaks))\n", + " }\n" + ] + }, + { + "cell_type": "markdown", + "id": "d672e8b4", + "metadata": {}, + "source": [ + "## 2. Data Management\n", + "Load CMIP6 datasets and ensure unit compatibility." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "acf4d31d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading datasets via earthkit...\n" + ] + } + ], + "source": [ + "URLS = {\n", + " \"tasmax_hist\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + " \"tasmax_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"tasmin_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + "}\n", + "\n", + "def load_data() -> Dict[str, xr.Dataset]:\n", + " \"\"\"\n", + " Load climate datasets from remote URLs using earthkit-data.\n", + "\n", + " Iterates through a predefined global dictionary of URLs, downloads\n", + " the data sources, and converts them into xarray Datasets.\n", + "\n", + " Returns\n", + " -------\n", + " datasets : Dict[str, xr.Dataset]\n", + " A dictionary where keys match the source identifiers and values\n", + " are the loaded xarray Datasets.\n", + "\n", + " Raises\n", + " ------\n", + " NameError\n", + " If `URLS` is not defined in the global scope.\n", + " \"\"\"\n", + " print(\"Loading datasets via earthkit...\")\n", + " datasets: Dict[str, xr.Dataset] = {}\n", + " for key, url in URLS.items():\n", + " # earthkit.data.from_source returns a wrapper that to_xarray() converts\n", + " ds = earthkit.data.from_source(\"url\", url).to_xarray()\n", + " datasets[key] = ds\n", + " return datasets\n", + "\n", + "data_cache = load_data()" + ] + }, + { + "cell_type": "markdown", + "id": "37feb6c1", + "metadata": {}, + "source": [ + "## 3. Contender Implementations\n", + "\n", + "We wrap each library's call in a uniform function signature." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "66739804", + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import nullcontext\n", + "from typing import Any, Callable, Dict\n", + "\n", + "import xarray as xr\n", + "\n", + "\n", + "def run_climate_indicator(\n", + " func: Callable[..., Any],\n", + " kwargs: Dict[str, Any],\n", + " use_flox: Optional[bool] = None\n", + ") -> Any:\n", + " \"\"\"\n", + " Unified execution wrapper for climate indicator functions with configurable backend options.\n", + "\n", + " This function streamlines the benchmarking process by dynamically handling \n", + " Xarray global options (specifically 'flox' optimization) via a context manager.\n", + "\n", + " Parameters\n", + " ----------\n", + " func : Callable[..., Any]\n", + " The indicator function to execute (e.g., from Earthkit or Xclim).\n", + " kwargs : Dict[str, Any]\n", + " A dictionary of keyword arguments required by the `func` (e.g., input datasets, \n", + " thresholds, frequencies).\n", + " use_flox : bool, optional\n", + " Controls the 'use_flox' option in Xarray for accelerated GroupBy operations:\n", + " - True: Explicitly enables Flox optimization.\n", + " - False: Explicitly disables Flox (forces legacy implementation).\n", + " - None: Uses the current environment's default configuration (no context change).\n", + " By default, None.\n", + "\n", + " Returns\n", + " -------\n", + " Any\n", + " The result of the indicator calculation (typically an xarray.DataArray or Dataset).\n", + " \"\"\"\n", + " # Determine context: set options if boolean, otherwise do nothing (nullcontext)\n", + " ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext()\n", + "\n", + " with ctx:\n", + " return func(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "id": "f2f50cc5", + "metadata": {}, + "source": [ + "## 4. Execution Loop\n", + "\n", + "We define the specific configurations for WSDI, CWD, DTR, HDD, SDII." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "46022af7", + "metadata": {}, + "outputs": [], + "source": [ + "def get_named_runner(name: str, target_runner: Callable, **kwargs) -> Callable:\n", + " \"\"\"\n", + " Creates a 0-argument wrapper with a custom `__name__`.\n", + "\n", + " Renamed the second argument to 'target_runner' to avoid collision \n", + " with the 'func' argument inside **kwargs.\n", + " \"\"\"\n", + " def wrapper():\n", + " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", + " return target_runner(**kwargs)\n", + "\n", + " wrapper.__name__ = name\n", + " return wrapper" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e0be7a44", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre-calculating 90th percentile for Optimized WSDI...\n", + "Configuring Optimized Benchmarks (Chunk -1, No Persist)...\n", + "\n", + "=== Benchmarking WSDI ===\n", + " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", + "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", + " Run 1: 25.8927s, Mem Peak Delta: 898.79 MiB\n", + " Run 2: 27.1201s, Mem Peak Delta: 898.91 MiB\n", + " Run 3: 26.4929s, Mem Peak Delta: 898.83 MiB\n", + " Run 4: 26.0854s, Mem Peak Delta: 898.88 MiB\n", + " Run 5: 27.0777s, Mem Peak Delta: 898.79 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", + "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", + " Run 1: 23.6207s, Mem Peak Delta: 898.87 MiB\n", + " Run 2: 23.7128s, Mem Peak Delta: 898.91 MiB\n", + " Run 3: 24.3635s, Mem Peak Delta: 898.91 MiB\n", + " Run 4: 24.0506s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 23.8922s, Mem Peak Delta: 898.90 MiB\n", + " Warm-up run for run_earthkit_opt_flox_WSDI...\n", + "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", + " Run 1: 20.7526s, Mem Peak Delta: 49.98 MiB\n", + " Run 2: 20.8830s, Mem Peak Delta: 49.94 MiB\n", + " Run 3: 21.2385s, Mem Peak Delta: 12.00 MiB\n", + " Run 4: 21.2124s, Mem Peak Delta: 49.94 MiB\n", + " Run 5: 20.8216s, Mem Peak Delta: 49.94 MiB\n", + " Warm-up run for run_xclim_noflox_WSDI...\n", + "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", + " Run 1: 26.7390s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 26.7734s, Mem Peak Delta: 898.86 MiB\n", + " Run 3: 26.0276s, Mem Peak Delta: 898.84 MiB\n", + " Run 4: 25.8968s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 26.0709s, Mem Peak Delta: 898.92 MiB\n", + " Warm-up run for run_xclim_flox_WSDI...\n", + "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", + " Run 1: 23.6802s, Mem Peak Delta: 898.93 MiB\n", + " Run 2: 23.3310s, Mem Peak Delta: 898.80 MiB\n", + " Run 3: 23.4548s, Mem Peak Delta: 898.85 MiB\n", + " Run 4: 23.3895s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 23.9636s, Mem Peak Delta: 898.84 MiB\n", + " Warm-up run for run_xclim_opt_flox_WSDI...\n", + "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", + " Run 1: 19.9375s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 20.0916s, Mem Peak Delta: 898.79 MiB\n", + " Run 3: 20.7634s, Mem Peak Delta: 898.85 MiB\n", + " Run 4: 19.8487s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 20.0441s, Mem Peak Delta: 898.91 MiB\n", + "\n", + "=== Benchmarking CWD ===\n", + " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", + "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", + " Run 1: 29.3767s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 28.0602s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 28.0488s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 27.9270s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 28.1274s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_CWD...\n", + "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", + " Run 1: 26.1044s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 26.1309s, Mem Peak Delta: 8.25 MiB\n", + " Run 3: 28.3037s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 25.7714s, Mem Peak Delta: 0.05 MiB\n", + " Run 5: 25.5983s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_opt_flox_CWD...\n", + "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", + " Run 1: 18.8773s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 18.8134s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 19.0268s, Mem Peak Delta: 74.84 MiB\n", + " Run 4: 19.3369s, Mem Peak Delta: 49.94 MiB\n", + " Run 5: 19.7957s, Mem Peak Delta: 49.95 MiB\n", + " Warm-up run for run_xclim_noflox_CWD...\n", + "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", + " Run 1: 29.7120s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 29.0183s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 29.9438s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 29.2263s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 31.9159s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_flox_CWD...\n", + "Benchmarking run_xclim_flox_CWD (5 runs)...\n", + " Run 1: 28.6923s, Mem Peak Delta: 15.97 MiB\n", + " Run 2: 28.9293s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 29.0942s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 29.2740s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 29.0477s, Mem Peak Delta: 8.25 MiB\n", + " Warm-up run for run_xclim_opt_flox_CWD...\n", + "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", + " Run 1: 21.7682s, Mem Peak Delta: 24.98 MiB\n", + " Run 2: 22.0080s, Mem Peak Delta: 43.93 MiB\n", + " Run 3: 22.0705s, Mem Peak Delta: 49.88 MiB\n", + " Run 4: 21.9743s, Mem Peak Delta: 49.94 MiB\n", + " Run 5: 22.2275s, Mem Peak Delta: 49.94 MiB\n", + "\n", + "=== Benchmarking DTR ===\n", + " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", + "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", + " Run 1: 9.4351s, Mem Peak Delta: 0.38 MiB\n", + " Run 2: 9.5768s, Mem Peak Delta: 0.38 MiB\n", + " Run 3: 9.3402s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 9.4722s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 9.3636s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_DTR...\n", + "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", + " Run 1: 1.4357s, Mem Peak Delta: 0.10 MiB\n", + " Run 2: 1.4295s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 1.4219s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 1.4171s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.4218s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_opt_flox_DTR...\n", + "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", + " Run 1: 1.7658s, Mem Peak Delta: 31.94 MiB\n", + " Run 2: 1.6892s, Mem Peak Delta: 49.84 MiB\n", + " Run 3: 1.7469s, Mem Peak Delta: 49.94 MiB\n", + " Run 4: 1.7437s, Mem Peak Delta: 49.93 MiB\n", + " Run 5: 1.7736s, Mem Peak Delta: 49.94 MiB\n", + " Warm-up run for run_xclim_noflox_DTR...\n", + "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", + " Run 1: 9.5489s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 9.5487s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 9.5000s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 9.4695s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 9.4420s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_flox_DTR...\n", + "Benchmarking run_xclim_flox_DTR (5 runs)...\n", + " Run 1: 1.4329s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.4351s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 1.4164s, Mem Peak Delta: 0.06 MiB\n", + " Run 4: 1.4164s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.4251s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_opt_flox_DTR...\n", + "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", + " Run 1: 1.7975s, Mem Peak Delta: 50.02 MiB\n", + " Run 2: 1.7540s, Mem Peak Delta: 39.91 MiB\n", + " Run 3: 1.7573s, Mem Peak Delta: 50.03 MiB\n", + " Run 4: 1.7095s, Mem Peak Delta: 49.94 MiB\n", + " Run 5: 1.7366s, Mem Peak Delta: 73.91 MiB\n", + "\n", + "=== Benchmarking HDD ===\n", + " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", + "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", + " Run 1: 7.9535s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.9782s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 7.9025s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 7.9938s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 7.9317s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_HDD...\n", + "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", + " Run 1: 1.3291s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.3241s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 1.3409s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 1.3291s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.3446s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_opt_flox_HDD...\n", + "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", + " Run 1: 1.4934s, Mem Peak Delta: 67.85 MiB\n", + " Run 2: 1.4911s, Mem Peak Delta: 37.92 MiB\n", + " Run 3: 1.4917s, Mem Peak Delta: 49.86 MiB\n", + " Run 4: 1.4938s, Mem Peak Delta: 50.03 MiB\n", + " Run 5: 1.4861s, Mem Peak Delta: 21.92 MiB\n", + " Warm-up run for run_xclim_noflox_HDD...\n", + "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", + " Run 1: 7.8671s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.9274s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 7.8776s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 7.9001s, Mem Peak Delta: 0.25 MiB\n", + " Run 5: 7.9224s, Mem Peak Delta: 0.12 MiB\n", + " Warm-up run for run_xclim_flox_HDD...\n", + "Benchmarking run_xclim_flox_HDD (5 runs)...\n", + " Run 1: 1.3224s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.3168s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 1.3044s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 1.3087s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.3207s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_opt_flox_HDD...\n", + "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", + " Run 1: 1.5035s, Mem Peak Delta: 49.96 MiB\n", + " Run 2: 1.4986s, Mem Peak Delta: 53.77 MiB\n", + " Run 3: 1.5113s, Mem Peak Delta: 25.91 MiB\n", + " Run 4: 1.4709s, Mem Peak Delta: 3.94 MiB\n", + " Run 5: 1.5036s, Mem Peak Delta: 83.88 MiB\n", + "\n", + "=== Benchmarking SDII ===\n", + " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", + "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", + " Run 1: 10.8552s, Mem Peak Delta: 0.12 MiB\n", + " Run 2: 10.8570s, Mem Peak Delta: 17.31 MiB\n", + " Run 3: 10.7975s, Mem Peak Delta: 0.12 MiB\n", + " Run 4: 10.8643s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 10.9053s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_SDII...\n", + "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", + " Run 1: 0.8518s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 0.8436s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.8402s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 0.8451s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 0.8408s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_opt_flox_SDII...\n", + "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", + " Run 1: 1.0089s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.0014s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.9898s, Mem Peak Delta: 49.94 MiB\n", + " Run 4: 1.0295s, Mem Peak Delta: 0.09 MiB\n", + " Run 5: 1.0309s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_noflox_SDII...\n", + "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", + " Run 1: 10.7494s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 10.8059s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 10.6910s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 10.8391s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 10.7851s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_flox_SDII...\n", + "Benchmarking run_xclim_flox_SDII (5 runs)...\n", + " Run 1: 0.8291s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 0.8436s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.8257s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 0.8360s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 0.8336s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_opt_flox_SDII...\n", + "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", + " Run 1: 1.0077s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 0.9856s, Mem Peak Delta: 0.01 MiB\n", + " Run 3: 0.9891s, Mem Peak Delta: 0.19 MiB\n", + " Run 4: 0.9722s, Mem Peak Delta: 35.87 MiB\n", + " Run 5: 1.0106s, Mem Peak Delta: 49.94 MiB\n" + ] + } + ], + "source": [ + "results_data = []\n", + "\n", + "# --- 1. Data Preparation ---\n", + "tasmax_ssp = data_cache['tasmax_ssp']['tasmax']\n", + "tasmin_ssp = data_cache['tasmin_ssp']['tasmin']\n", + "pr_ssp = data_cache['pr_ssp']['pr']\n", + "\n", + "# Pre-calculate percentile for WSDI\n", + "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", + "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90).compute()\n", + "per_90.name = \"tasmax_per\"\n", + "\n", + "# --- 1b. Optimized Configuration (Chunk -1) ---\n", + "print(\"Configuring Optimized Benchmarks (Chunk -1, No Persist)...\")\n", + "# We remove .persist() to include I/O overhead and compare fairly with Lazy\n", + "tasmax_ssp_opt = tasmax_ssp.chunk({\"time\": -1})\n", + "tasmin_ssp_opt = tasmin_ssp.chunk({\"time\": -1})\n", + "pr_ssp_opt = pr_ssp.chunk({\"time\": -1})\n", + "\n", + "# --- 2. Define Benchmarks (Symmetric Structure) ---\n", + "benchmarks = [\n", + " {\n", + " \"name\": \"WSDI\",\n", + " \"ek_func\": ek_temp.warm_spell_duration_index,\n", + " \"xi_func\": xclim.indicators.atmos.warm_spell_duration_index,\n", + " # Earthkit Arguments\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], per_90]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"}\n", + " },\n", + " # Xclim Arguments (Now with dual structure)\n", + " \"xi_args\": {\n", + " \"lazy\": {\n", + " \"tasmax\": tasmax_ssp, \n", + " \"tasmax_per\": per_90, \n", + " \"freq\": \"MS\"\n", + " },\n", + " \"optimized\": {\n", + " \"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \n", + " \"tasmax_per\": per_90, \n", + " \"freq\": \"MS\"\n", + " },\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"CWD\",\n", + " \"ek_func\": ek_pr.maximum_consecutive_wet_days,\n", + " \"xi_func\": xclim.indicators.atmos.maximum_consecutive_wet_days,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"DTR\",\n", + " \"ek_func\": ek_temp.daily_temperature_range,\n", + " \"xi_func\": xclim.indicators.atmos.daily_temperature_range,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], data_cache['tasmin_ssp']]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmin\": tasmin_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\n", + " \"tasmax\": tasmax_ssp_opt,\n", + " \"tasmin\": tasmin_ssp_opt,\n", + " \"freq\": \"MS\"\n", + " }\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"HDD\",\n", + " \"ek_func\": ek_temp.heating_degree_days,\n", + " \"xi_func\": xclim.indicators.atmos.heating_degree_days,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp)/2).to_dataset(name='tas'), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt)/2).to_dataset(name='tas'), \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp)/2, \"freq\": \"MS\"},\n", + " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt)/2, \"freq\": \"MS\"}\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"SDII\",\n", + " \"ek_func\": ek_pr.daily_precipitation_intensity,\n", + " \"xi_func\": xclim.indicators.atmos.daily_pr_intensity,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " }\n", + "]\n", + "\n", + "# --- 3. Run Loop (Symmetric Comparison) ---\n", + "for b in benchmarks:\n", + " print(f\"\\n=== Benchmarking {b['name']} ===\")\n", + "\n", + " # =========================================================\n", + " # BLOCK 1: EARTHKIT\n", + " # =========================================================\n", + "\n", + " # 1. Earthkit: Standard (No Flox)\n", + " runner_ek_nf = get_named_runner(\n", + " f\"run_earthkit_lazy_noflox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['lazy'],\n", + " use_flox=False\n", + " )\n", + " res_ek_nf = benchmark_function(runner_ek_nf, {})\n", + " res_ek_nf.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " results_data.append(res_ek_nf)\n", + "\n", + " # 2. Earthkit: Standard (With Flox)\n", + " runner_ek_fl = get_named_runner(\n", + " f\"run_earthkit_lazy_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['lazy'],\n", + " use_flox=True\n", + " )\n", + " res_ek_fl = benchmark_function(runner_ek_fl, {})\n", + " res_ek_fl.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", + " results_data.append(res_ek_fl)\n", + "\n", + " # 3. Earthkit: Optimized (Chunk -1) + Flox\n", + " runner_ek_opt = get_named_runner(\n", + " f\"run_earthkit_opt_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['optimized'],\n", + " use_flox=True\n", + " )\n", + " res_ek_opt = benchmark_function(runner_ek_opt, {}, n_repeats=5)\n", + " res_ek_opt.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " results_data.append(res_ek_opt)\n", + "\n", + "\n", + " # =========================================================\n", + " # BLOCK 2: XCLIM\n", + " # =========================================================\n", + "\n", + " # 4. Xclim: Standard (No Flox)\n", + " runner_xc_nf = get_named_runner(\n", + " f\"run_xclim_noflox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", + " use_flox=False\n", + " )\n", + " res_xc_nf = benchmark_function(runner_xc_nf, {})\n", + " res_xc_nf.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " results_data.append(res_xc_nf)\n", + "\n", + " # 5. Xclim: Standard (With Flox)\n", + " runner_xc_fl = get_named_runner(\n", + " f\"run_xclim_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", + " use_flox=True\n", + " )\n", + " res_xc_fl = benchmark_function(runner_xc_fl, {})\n", + " res_xc_fl.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", + " results_data.append(res_xc_fl)\n", + "\n", + " # 6. Xclim: Optimized (Chunk -1) + Flox\n", + " runner_xc_opt = get_named_runner(\n", + " f\"run_xclim_opt_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['optimized'], # <--- USING OPTIMIZED ARGS\n", + " use_flox=True\n", + " )\n", + " res_xc_opt = benchmark_function(runner_xc_opt, {}, n_repeats=5)\n", + " res_xc_opt.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " results_data.append(res_xc_opt)" + ] + }, + { + "cell_type": "markdown", + "id": "08fa3d40", + "metadata": {}, + "source": [ + "## 5. Results & Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "75852284", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Benchmarking Results (Robust Stats):\n" + ] + }, + { + "data": { + "text/html": [ + "
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IndicatorLibraryModemedian_timestd_timemax_memSpeedup
0WSDIEarthkit1. No Flox (Standard)26.4929460.500665898.9140620.984069
1WSDIEarthkit2. Flox (Standard)23.8921940.263340898.9140621.091189
2WSDIEarthkit3. Flox + Opt (Chunk -1)20.8829970.20348249.9765621.248427
3WSDIXclim1. No Flox (Standard)26.0708930.375776898.9179691.000000
4WSDIXclim2. Flox (Standard)23.4548210.232307898.9335941.111537
5WSDIXclim3. Flox + Opt (Chunk -1)20.0440650.324348898.9140621.300679
6CWDEarthkit1. No Flox (Standard)28.0602080.5382420.0000001.058867
7CWDEarthkit2. Flox (Standard)26.1043750.9818238.2500001.138202
8CWDEarthkit3. Flox + Opt (Chunk -1)19.0268460.36133074.8398441.561585
9CWDXclim1. No Flox (Standard)29.7120421.0307600.0000001.000000
10CWDXclim2. Flox (Standard)29.0477150.19267515.9726561.022870
11CWDXclim3. Flox + Opt (Chunk -1)22.0079720.14882349.9414061.350058
12DTREarthkit1. No Flox (Standard)9.4350900.0843120.3750001.006879
13DTREarthkit2. Flox (Standard)1.4218640.0065900.0976566.681367
14DTREarthkit3. Flox + Opt (Chunk -1)1.7469480.02954149.9414065.438052
15DTRXclim1. No Flox (Standard)9.4999940.0425230.0000001.000000
16DTRXclim2. Flox (Standard)1.4251330.0079040.0625006.666041
17DTRXclim3. Flox + Opt (Chunk -1)1.7540160.02877973.9101565.416138
18HDDEarthkit1. No Flox (Standard)7.9534900.0325530.0000000.993293
19HDDEarthkit2. Flox (Standard)1.3290870.0078050.0000005.944043
20HDDEarthkit3. Flox + Opt (Chunk -1)1.4917420.00275267.8515625.295923
21HDDXclim1. No Flox (Standard)7.9001490.0237880.2500001.000000
22HDDXclim2. Flox (Standard)1.3167920.0069460.0000005.999541
23HDDXclim3. Flox + Opt (Chunk -1)1.5034890.01394183.8750005.254545
24SDIIEarthkit1. No Flox (Standard)10.8569740.03440217.3125000.993377
25SDIIEarthkit2. Flox (Standard)0.8435840.0041440.00000012.784815
26SDIIEarthkit3. Flox + Opt (Chunk -1)1.0088730.01597849.93750010.690221
27SDIIXclim1. No Flox (Standard)10.7850710.0507240.0000001.000000
28SDIIXclim2. Flox (Standard)0.8335840.0061290.00000012.938193
29SDIIXclim3. Flox + Opt (Chunk -1)0.9890740.01434349.93750010.904207
\n", + "
" + ], + "text/plain": [ + " Indicator Library Mode median_time std_time \\\n", + "0 WSDI Earthkit 1. No Flox (Standard) 26.492946 0.500665 \n", + "1 WSDI Earthkit 2. Flox (Standard) 23.892194 0.263340 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.882997 0.203482 \n", + "3 WSDI Xclim 1. No Flox (Standard) 26.070893 0.375776 \n", + "4 WSDI Xclim 2. Flox (Standard) 23.454821 0.232307 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 20.044065 0.324348 \n", + "6 CWD Earthkit 1. No Flox (Standard) 28.060208 0.538242 \n", + "7 CWD Earthkit 2. Flox (Standard) 26.104375 0.981823 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 19.026846 0.361330 \n", + "9 CWD Xclim 1. No Flox (Standard) 29.712042 1.030760 \n", + "10 CWD Xclim 2. Flox (Standard) 29.047715 0.192675 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 22.007972 0.148823 \n", + "12 DTR Earthkit 1. No Flox (Standard) 9.435090 0.084312 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.421864 0.006590 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.746948 0.029541 \n", + "15 DTR Xclim 1. No Flox (Standard) 9.499994 0.042523 \n", + "16 DTR Xclim 2. Flox (Standard) 1.425133 0.007904 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.754016 0.028779 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.953490 0.032553 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.329087 0.007805 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.491742 0.002752 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.900149 0.023788 \n", + "22 HDD Xclim 2. Flox (Standard) 1.316792 0.006946 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.503489 0.013941 \n", + "24 SDII Earthkit 1. No Flox (Standard) 10.856974 0.034402 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.843584 0.004144 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 1.008873 0.015978 \n", + "27 SDII Xclim 1. No Flox (Standard) 10.785071 0.050724 \n", + "28 SDII Xclim 2. Flox (Standard) 0.833584 0.006129 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.989074 0.014343 \n", + "\n", + " max_mem Speedup \n", + "0 898.914062 0.984069 \n", + "1 898.914062 1.091189 \n", + "2 49.976562 1.248427 \n", + "3 898.917969 1.000000 \n", + "4 898.933594 1.111537 \n", + "5 898.914062 1.300679 \n", + "6 0.000000 1.058867 \n", + "7 8.250000 1.138202 \n", + "8 74.839844 1.561585 \n", + "9 0.000000 1.000000 \n", + "10 15.972656 1.022870 \n", + "11 49.941406 1.350058 \n", + "12 0.375000 1.006879 \n", + "13 0.097656 6.681367 \n", + "14 49.941406 5.438052 \n", + "15 0.000000 1.000000 \n", + "16 0.062500 6.666041 \n", + "17 73.910156 5.416138 \n", + "18 0.000000 0.993293 \n", + "19 0.000000 5.944043 \n", + "20 67.851562 5.295923 \n", + "21 0.250000 1.000000 \n", + "22 0.000000 5.999541 \n", + "23 83.875000 5.254545 \n", + "24 17.312500 0.993377 \n", + "25 0.000000 12.784815 \n", + "26 49.937500 10.690221 \n", + "27 0.000000 1.000000 \n", + "28 0.000000 12.938193 \n", + "29 49.937500 10.904207 " + ] + }, + "metadata": {}, + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# 1. Create DataFrame\n", + "df_res = pd.DataFrame(results_data)\n", + "\n", + "# 2. Create a specific label for the Plot Legend (Library + Mode)\n", + "df_res['Label'] = df_res['Library'] + \": \" + df_res['Mode']\n", + "\n", + "# 3. Calculate Speedup relative to 'Earthkit No Flox (Standard)' using MEDIAN time\n", + "# Robustness Update: We use Median Time for speedup calculation to be resistant to outliers.\n", + "df_res['Speedup'] = 0.0\n", + "baseline_mode = \"1. No Flox (Standard)\"\n", + "\n", + "for indicator in df_res['Indicator'].unique():\n", + " # Find the baseline: Xclim + No Flox (as per original logic, though code comments said Earthkit)\n", + " # Let's double check the user intent. Usually Xclim NoFlox is the \"base\" reference.\n", + " # The original code searched for Library='Xclim'. We stick to that.\n", + " baseline_row = df_res.loc[\n", + " (df_res['Indicator'] == indicator) &\n", + " (df_res['Library'] == 'Xclim') &\n", + " (df_res['Mode'] == baseline_mode)\n", + " ]\n", + "\n", + " if not baseline_row.empty:\n", + " # Use median_time for robust speedup\n", + " baseline_time = baseline_row['median_time'].values[0]\n", + " mask = df_res['Indicator'] == indicator\n", + " df_res.loc[mask, 'Speedup'] = baseline_time / df_res.loc[mask, 'median_time']\n", + "\n", + "print(\"\\nBenchmarking Results (Robust Stats):\")\n", + "display(df_res[['Indicator', 'Library', 'Mode', 'median_time', 'std_time', 'max_mem', 'Speedup']])\n", + "\n", + "# --- Plots ---\n", + "# Define a consistent order for the bars\n", + "hue_order = sorted(df_res['Label'].unique())\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "\n", + "# Plot 1: Relative Speedup (Robust)\n", + "plt.subplot(1, 3, 1)\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"Speedup\",\n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(f\"Relative Speedup (via Median Time)\\n(Baseline: Xclim {baseline_mode})\")\n", + "plt.axhline(1.0, color='black', linestyle='--', alpha=0.5, linewidth=1)\n", + "plt.legend(title=\"Configuration\", fontsize='small')\n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "# Plot 2: Median Execution Time\n", + "plt.subplot(1, 3, 2)\n", + "# We plot the pre-calculated median. \n", + "# Note: standard sns.barplot aggregates data if there are duplicates. \n", + "# Here df_res has 1 row per case, so it just plots the value.\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"median_time\", \n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", + "plt.legend().remove() \n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "# Plot 3: Peak Memory Usage\n", + "plt.subplot(1, 3, 3)\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"max_mem\",\n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", + "plt.legend().remove()\n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 88a069674f716b45d0105d8554e66ae4164604a6 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 9 Feb 2026 13:44:18 +0100 Subject: [PATCH 11/47] Remove `robust_performance_benchmarking.ipynb`, update `performance_analysis.ipynb`, and refresh `pixi` dependencies. --- docs/notebooks/performance_analysis.ipynb | 1430 +++++++++++---- .../robust_performance_benchmarking.ipynb | 1603 ----------------- pixi.lock | 1080 +++++++---- pixi.toml | 2 + 4 files changed, 1839 insertions(+), 2276 deletions(-) delete mode 100644 docs/notebooks/robust_performance_benchmarking.ipynb diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index 4363256..bb0406e 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -2,41 +2,51 @@ "cells": [ { "cell_type": "markdown", - "id": "28ef162d4b412362", + "id": "fb33bbc9", "metadata": {}, "source": [ - "# Performance Analysis: Earthkit-Climate Indicators\n", - "\n", - "This notebook provides a comparative performance analysis of the `earthkit-climate` indicators on the SSP5-8.5 dataset. We compare two execution modes:\n", - "1. **Lazy Execution**: The baseline approach where dask graphs are built and executed without specific optimizations.\n", - "2. **Optimized Execution**: An enhanced approach using pre-computation of heavy statistics (like percentiles) and strategic re-chunking.\n", - "\n", - "We profile 5 key indicators:\n", - "- **WSDI**: Warm Spell Duration Index\n", - "- **CWD**: Maximum Consecutive Wet Days\n", - "- **DTR**: Daily Temperature Range\n", - "- **HDD**: Heating Degree Days\n", - "- **SDII**: Simple Daily Precipitation Intensity Index" + "# Robust Performance Benchmarking: Earthkit-Climate vs Xclim\n", + "\n", + "This notebook provides a robust comparative analysis of climate indicator calculations using:\n", + "1. **Earthkit-Climate (Lazy)**: Standard wrapper usage.\n", + "2. **Earthkit-Climate (Optimized)**: With pre-computation of percentiles and manual re-chunking (`time: -1`).\n", + "3. **Xclim (Direct)**: Direct optimized usage of `xclim.indices`.\n", + "4. **Xclim + Flox**: `xclim` with `flox` optimization enabled for faster reductions.\n", + "\n", + "Key features:\n", + "- Statistical Sampling: $N$ runs per test.\n", + "- Resource Profiling: RAM peak & CPU usage.\n", + "- Visualization: Speedup comparison with error bars.\n" ] }, { "cell_type": "code", "execution_count": 1, - "id": "cache_config", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:05.411512492Z", - "start_time": "2025-12-11T17:35:05.334785047Z" - } - }, + "id": "410735a7", + "metadata": {}, "outputs": [], "source": [ + "import gc\n", "import os\n", + "import time\n", "import warnings\n", + "from typing import Any, Callable, Dict, List, Optional\n", + "\n", + "import earthkit.data\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import xarray as xr\n", + "import xclim.indicators\n", + "from IPython.display import Markdown, display\n", "\n", - "from earthkit.data import config\n", + "import earthkit.climate.indicators.precipitation as ek_pr\n", + "import earthkit.climate.indicators.temperature as ek_temp\n", + "from earthkit.climate.utils.percentile import percentile_doy\n", "\n", "warnings.filterwarnings(\"ignore\")\n", + "sns.set_theme(style=\"whitegrid\")\n", "\n", "# Configure robust caching to avoid re-downloading\n", "cache_dir = os.path.expanduser(\"~/.cache/earthkit/data\")\n", @@ -45,65 +55,90 @@ " \"cache-policy\": \"user\",\n", " \"temporary-directory-root\": cache_dir,\n", "}\n", - "config.set(settings_earthkit)" + "earthkit.data.config.set(settings_earthkit)" ] }, { "cell_type": "code", "execution_count": 2, - "id": "dynamic_resources", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:05.451533026Z", - "start_time": "2025-12-11T17:35:05.415349573Z" - } - }, + "id": "68281ebe", + "metadata": {}, "outputs": [ { "data": { - "text/markdown": "\n## Resources Used\n\n### Hardware Configuration\nThe performance analysis was conducted on the following hardware (dynamically detected):\n- **CPU**: AMD Ryzen 5 5500H with Radeon Graphics\n- **RAM**: 15.0 GB\n", + "text/markdown": [ + "\n", + "## Resources Used\n", + "\n", + "### Hardware Configuration\n", + "The performance analysis was conducted on the following hardware (dynamically detected):\n", + "- **CPU**: 12th Gen Intel(R) Core(TM) i5-12600KF\n", + "- **RAM**: 31.2 GB\n" + ], "text/plain": [ "" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "import os\n", "\n", - "from IPython.display import Markdown, display\n", + "def get_cpu_info() -> str:\n", + " \"\"\"\n", + " Extract the CPU model name from the system's /proc/cpuinfo.\n", "\n", + " This function parses the Linux virtual filesystem to find the\n", + " hardware model name of the processor.\n", "\n", - "def get_cpu_info():\n", + " Returns\n", + " -------\n", + " cpu_model : str\n", + " The name of the CPU model (e.g., 'Intel(R) Core(TM) i7-10700K').\n", + " Returns 'Unknown CPU' if the file is inaccessible or the entry\n", + " is missing.\n", + " \"\"\"\n", " try:\n", - " with open(\"/proc/cpuinfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"model name\" in line:\n", - " return line.split(\":\")[1].strip()\n", + " if os.path.exists(\"/proc/cpuinfo\"):\n", + " with open(\"/proc/cpuinfo\", \"r\") as f:\n", + " for line in f:\n", + " if \"model name\" in line:\n", + " return line.split(\":\")[1].strip()\n", " except Exception:\n", " return \"Unknown CPU\"\n", " return \"Unknown CPU\"\n", "\n", "\n", - "def get_ram_info():\n", + "def get_ram_info() -> str:\n", + " \"\"\"\n", + " Extract the total system RAM from /proc/meminfo.\n", + "\n", + " Parses the system memory information and converts the total memory\n", + " from kilobytes to gigabytes.\n", + "\n", + " Returns\n", + " -------\n", + " ram_size : str\n", + " A formatted string representing total RAM in GB (e.g., '16.0 GB').\n", + " Returns 'Unknown RAM' if the file is inaccessible or the entry\n", + " is missing.\n", + " \"\"\"\n", " try:\n", - " with open(\"/proc/meminfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"MemTotal\" in line:\n", - " total_kb = int(line.split()[1])\n", - " return f\"{total_kb / 1024 / 1024:.1f} GB\"\n", + " if os.path.exists(\"/proc/meminfo\"):\n", + " with open(\"/proc/meminfo\", \"r\") as f:\n", + " for line in f:\n", + " if \"MemTotal\" in line:\n", + " total_kb = int(line.split()[1])\n", + " return f\"{total_kb / 1024 / 1024:.1f} GB\"\n", " except Exception:\n", " return \"Unknown RAM\"\n", " return \"Unknown RAM\"\n", "\n", + "# --- Execution Logic ---\n", "\n", - "cpu_model = get_cpu_info()\n", - "ram_size = get_ram_info()\n", + "cpu_model: str = get_cpu_info()\n", + "ram_size: str = get_ram_info()\n", "\n", "display(\n", " Markdown(f\"\"\"\n", @@ -120,37 +155,38 @@ { "cell_type": "code", "execution_count": 3, - "id": "dynamic_dataset_info", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:08.886634702Z", - "start_time": "2025-12-11T17:35:05.458533533Z" - } - }, + "id": "fba35188", + "metadata": {}, "outputs": [ { "data": { - "text/markdown": "\n### Dataset Information\n\nThe analysis uses the following climate datasets derived from CMIP6 projections\n(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\nrepository and are automatically downloaded or cached by **earthkit-data**.\n\n| Variable | Scenario | Dimensions | Size | Status | URL |\n|----------|----------|------------|------|--------|-----|\n| `tasmax` | historical | (time: 7305, lat: 48, lon: 84) | 67.3 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc) |\n| `tasmax` | ssp585 | (time: 14610, lat: 48, lon: 84) | 126.9 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n| `tasmin` | ssp585 | (time: 14610, lat: 48, lon: 84) | 132.1 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n| `pr` | ssp585 | (time: 14610, lat: 48, lon: 84) | 111.5 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n\n> **Note**: Dimensions and sizes are extracted dynamically. The first run may\ndownload the files.\n", + "text/markdown": [ + "\n", + "### Dataset Information\n", + "\n", + "The analysis uses the following climate datasets derived from CMIP6 projections\n", + "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", + "repository and are automatically downloaded or cached by **earthkit-data**.\n", + "\n", + "| Variable | Scenario | Dimensions | Size | Status | URL |\n", + "|----------|----------|------------|------|--------|-----|\n", + "| `tasmax` | historical | (time: 7305, lat: 48, lon: 84) | 67.3 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc) |\n", + "| `tasmax` | ssp585 | (time: 14610, lat: 48, lon: 84) | 126.9 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "| `tasmin` | ssp585 | (time: 14610, lat: 48, lon: 84) | 132.1 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "| `pr` | ssp585 | (time: 14610, lat: 48, lon: 84) | 111.5 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", + "\n", + "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", + "download the files.\n" + ], "text/plain": [ "" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "import os\n", - "from typing import Any, Dict, List\n", - "\n", - "import xarray as xr\n", - "from IPython.display import Markdown, display\n", - "\n", - "import earthkit.data\n", - "\n", "# Dataset URLs\n", "DATASET_URLS = [\n", " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", @@ -286,338 +322,600 @@ ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "47d74c7c204e37e", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:12.058087337Z", - "start_time": "2025-12-11T17:35:08.995389510Z" - } - }, - "outputs": [], + "cell_type": "markdown", + "id": "5922ba8f", + "metadata": {}, "source": [ - "import time\n", - "\n", - "import pandas as pd\n", + "## 1. Benchmarking Engine\n", "\n", - "from earthkit.climate.indicators.precipitation import (\n", - " daily_precipitation_intensity,\n", - " maximum_consecutive_wet_days,\n", - ")\n", - "from earthkit.climate.indicators.temperature import (\n", - " daily_temperature_range,\n", - " heating_degree_days,\n", - " warm_spell_duration_index,\n", - ")\n", - "from earthkit.climate.utils.percentile import percentile_doy" + "Helper functions to run tests multiple times, capture statistics, and profile resources." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "1c298d73e3b8c1b9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:12.095922736Z", - "start_time": "2025-12-11T17:35:12.075465775Z" - } - }, + "execution_count": 4, + "id": "5d1ba8a5", + "metadata": {}, "outputs": [], "source": [ - "# Data URLs (Access-CM2)\n", - "URLS = {\n", - " \"tasmax_hist\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"tasmin_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"tasmax_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - "}" + "\n", + "import threading\n", + "from typing import Any, Dict, List\n", + "\n", + "import psutil\n", + "\n", + "\n", + "class ResourceMonitor(threading.Thread):\n", + " def __init__(self, interval=0.1):\n", + " self.interval = interval\n", + " self.stop_event = threading.Event()\n", + " self.memory_usage = []\n", + " self.cpu_usage = []\n", + " self.process = psutil.Process()\n", + " super().__init__()\n", + "\n", + " def run(self):\n", + " while not self.stop_event.is_set():\n", + " try:\n", + " # RSS Memory in MiB\n", + " mem = self.process.memory_info().rss / (1024 * 1024)\n", + " self.memory_usage.append(mem)\n", + " # CPU Percent (blocking for interval would slow main thread, so we sleep manually)\n", + " # self.cpu_usage.append(self.process.cpu_percent(interval=None)) \n", + " except Exception:\n", + " pass\n", + " time.sleep(self.interval)\n", + "\n", + " def stop(self):\n", + " self.stop_event.set()\n", + "\n", + "def benchmark_function(\n", + " func: Callable[..., Any],\n", + " kwargs: Dict[str, Any],\n", + " n_repeats: int = 5,\n", + " warmup: bool = True\n", + ") -> Dict[str, float]:\n", + " \"\"\"\n", + " Run a function N times with robust profiling (Warm-up + High-Freq Sampling).\n", + "\n", + " Parameters\n", + " ----------\n", + " func : Callable\n", + " The function to benchmark.\n", + " kwargs : Dict\n", + " Arguments for the function.\n", + " n_repeats : int\n", + " Number of measurement runs.\n", + " warmup : bool\n", + " If True, runs the function once silently before measuring to exclude JIT/Import costs.\n", + "\n", + " Returns\n", + " -------\n", + " stats : Dict\n", + " 'mean_time', 'median_time', 'std_time', 'max_mem', 'mean_mem_peak'\n", + " \"\"\"\n", + " # 1. Warm-up (Silent)\n", + " if warmup:\n", + " print(f\" Warm-up run for {func.__name__}...\")\n", + " try:\n", + " # Run without monitoring\n", + " res = func(**kwargs)\n", + " if hasattr(res, 'compute'):\n", + " res.compute()\n", + " elif hasattr(res, 'to_xarray'):\n", + " res.to_xarray().compute()\n", + " except Exception as e:\n", + " print(f\" Warm-up warning: {e}\")\n", + " # Force GC after warmup\n", + " gc.collect()\n", + "\n", + " times: List[float] = []\n", + " mem_peaks: List[float] = []\n", + "\n", + " print(f\"Benchmarking {func.__name__} ({n_repeats} runs)...\")\n", + "\n", + " for i in range(n_repeats):\n", + " gc.collect()\n", + "\n", + " # Start Monitor (10ms interval)\n", + " monitor = ResourceMonitor(interval=0.1)\n", + " # Capture baseline memory\n", + " baseline_mem = psutil.Process().memory_info().rss / (1024 * 1024)\n", + " monitor.start()\n", + "\n", + " start_time = time.perf_counter()\n", + "\n", + " # --- Computation ---\n", + " try:\n", + " res = func(**kwargs)\n", + " if hasattr(res, 'compute'):\n", + " res.compute()\n", + " elif hasattr(res, 'to_xarray'):\n", + " res.to_xarray().compute()\n", + " except Exception as e:\n", + " print(f\" Run {i+1} Failed: {e}\")\n", + " monitor.stop()\n", + " continue\n", + " # -------------------\n", + "\n", + " end_time = time.perf_counter()\n", + " monitor.stop()\n", + " monitor.join()\n", + "\n", + " duration = end_time - start_time\n", + "\n", + " # Calculate Peak Memory Delta\n", + " observed_mems = monitor.memory_usage\n", + " if observed_mems:\n", + " # We want the peak usage *induced* by the function\n", + " # Peak Delta = Max(Observed) - Baseline\n", + " # Or simplified: Peak Observed\n", + " # The previous 'memory_profiler' returned (Max - Min). reliable for single process.\n", + " # Let's use Normalized Peak: (Max - Baseline)\n", + " peak_delta = max(observed_mems) - baseline_mem\n", + " # Clamp to 0\n", + " if peak_delta < 0: peak_delta = 0\n", + " else:\n", + " peak_delta = 0.0\n", + "\n", + " times.append(duration)\n", + " mem_peaks.append(peak_delta)\n", + "\n", + " print(f\" Run {i+1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", + "\n", + " if not times:\n", + " return {\"mean_time\": 0.0, \"max_mem\": 0.0}\n", + "\n", + " return {\n", + " \"mean_time\": float(np.mean(times)),\n", + " \"median_time\": float(np.median(times)),\n", + " \"std_time\": float(np.std(times)),\n", + " \"max_mem\": float(np.max(mem_peaks)),\n", + " \"mean_mem\": float(np.mean(mem_peaks))\n", + " }\n" ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "b15b477e2834eeb6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:13.776963639Z", - "start_time": "2025-12-11T17:35:12.100018881Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading datasets...\n" - ] - } - ], + "cell_type": "markdown", + "id": "d672e8b4", + "metadata": {}, "source": [ - "def load_data():\n", - " print(\"Loading datasets...\")\n", - " tasmax_hist = earthkit.data.from_source(\"url\", URLS[\"tasmax_hist\"]).to_xarray()\n", - " tasmax_ssp = earthkit.data.from_source(\"url\", URLS[\"tasmax_ssp\"]).to_xarray()\n", - " tasmin_ssp = earthkit.data.from_source(\"url\", URLS[\"tasmin_ssp\"]).to_xarray()\n", - " pr_ssp = earthkit.data.from_source(\"url\", URLS[\"pr_ssp\"]).to_xarray()\n", - " return tasmax_hist, tasmax_ssp, tasmin_ssp, pr_ssp\n", - "\n", - "\n", - "tasmax_hist, tasmax_ssp, tasmin_ssp, pr_ssp = load_data()" + "## 2. Data Management\n", + "Load CMIP6 datasets and ensure unit compatibility." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8ad3c950a1553b3e", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:13.872576032Z", - "start_time": "2025-12-11T17:35:13.810554635Z" - } - }, + "execution_count": 5, + "id": "acf4d31d", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Computing proxy 'tas' for HDD...\n" + "Loading datasets via earthkit...\n" ] } ], "source": [ - "# Create 'tas' for Heating Degree Days (Mean of Max and Min)\n", - "print(\"Computing proxy 'tas' for HDD...\")\n", - "tas_ssp = (tasmax_ssp[\"tasmax\"] + tasmin_ssp[\"tasmin\"]) / 2\n", - "tas_ssp.name = \"tas\"\n", - "tas_ssp_ds = tas_ssp.to_dataset()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "39501b21479a8265", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:13.893829550Z", - "start_time": "2025-12-11T17:35:13.875932760Z" - } - }, - "outputs": [], - "source": [ - "def profile_run(name, func, kwargs):\n", - " print(f\" Running {name}...\")\n", - " start = time.perf_counter()\n", - " res = func(**kwargs)\n", - " out = res.to_xarray()\n", - " if hasattr(out, \"compute\"):\n", - " out.compute()\n", - " elapsed = time.perf_counter() - start\n", - " print(f\" > Done in {elapsed:.4f}s\")\n", - " return elapsed" + "URLS = {\n", + " \"tasmax_hist\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + " \"tasmax_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"tasmin_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", + "}\n", + "\n", + "def load_data() -> Dict[str, xr.Dataset]:\n", + " \"\"\"\n", + " Load climate datasets from remote URLs using earthkit-data.\n", + "\n", + " Iterates through a predefined global dictionary of URLs, downloads\n", + " the data sources, and converts them into xarray Datasets.\n", + "\n", + " Returns\n", + " -------\n", + " datasets : Dict[str, xr.Dataset]\n", + " A dictionary where keys match the source identifiers and values\n", + " are the loaded xarray Datasets.\n", + "\n", + " Raises\n", + " ------\n", + " NameError\n", + " If `URLS` is not defined in the global scope.\n", + " \"\"\"\n", + " print(\"Loading datasets via earthkit...\")\n", + " datasets: Dict[str, xr.Dataset] = {}\n", + " for key, url in URLS.items():\n", + " # earthkit.data.from_source returns a wrapper that to_xarray() converts\n", + " ds = earthkit.data.from_source(\"url\", url).to_xarray()\n", + " datasets[key] = ds\n", + " return datasets\n", + "\n", + "data_cache = load_data()" ] }, { "cell_type": "markdown", - "id": "acbe90a484de3306", + "id": "37feb6c1", "metadata": {}, "source": [ - "## 1. Lazy Execution (Baseline)\n", - "In this mode, we simply merge datasets and pass them to the indicators without any specific handling of chunks or pre-computation." + "## 3. Contender Implementations\n", + "\n", + "We wrap each library's call in a uniform function signature." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "a3e5144d71920175", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:14.273558905Z", - "start_time": "2025-12-11T17:35:13.897784414Z" - } - }, + "execution_count": 6, + "id": "66739804", + "metadata": {}, "outputs": [], "source": [ - "results = []\n", + "from contextlib import nullcontext\n", + "from typing import Any, Callable, Dict\n", "\n", - "# WSDI (Lazy)\n", - "tasmax_per_lazy = percentile_doy(tasmax_hist[\"tasmax\"], per=90)\n", - "tasmax_per_lazy.name = \"tasmax_per\"\n", - "wsdi_ds_lazy = xr.merge([tasmax_ssp, tasmax_per_lazy])\n", + "import xarray as xr\n", "\n", - "# CWD (Lazy)\n", - "cwd_ds_lazy = pr_ssp\n", "\n", - "# DTR (Lazy)\n", - "dtr_ds_lazy = xr.merge([tasmax_ssp, tasmin_ssp])\n", + "def run_climate_indicator(\n", + " func: Callable[..., Any],\n", + " kwargs: Dict[str, Any],\n", + " use_flox: Optional[bool] = None\n", + ") -> Any:\n", + " \"\"\"\n", + " Unified execution wrapper for climate indicator functions with configurable backend options.\n", + "\n", + " This function streamlines the benchmarking process by dynamically handling \n", + " Xarray global options (specifically 'flox' optimization) via a context manager.\n", + "\n", + " Parameters\n", + " ----------\n", + " func : Callable[..., Any]\n", + " The indicator function to execute (e.g., from Earthkit or Xclim).\n", + " kwargs : Dict[str, Any]\n", + " A dictionary of keyword arguments required by the `func` (e.g., input datasets, \n", + " thresholds, frequencies).\n", + " use_flox : bool, optional\n", + " Controls the 'use_flox' option in Xarray for accelerated GroupBy operations:\n", + " - True: Explicitly enables Flox optimization.\n", + " - False: Explicitly disables Flox (forces legacy implementation).\n", + " - None: Uses the current environment's default configuration (no context change).\n", + " By default, None.\n", "\n", - "# HDD (Lazy)\n", - "hdd_ds_lazy = tas_ssp_ds\n", + " Returns\n", + " -------\n", + " Any\n", + " The result of the indicator calculation (typically an xarray.DataArray or Dataset).\n", + " \"\"\"\n", + " # Determine context: set options if boolean, otherwise do nothing (nullcontext)\n", + " ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext()\n", "\n", - "# SDII (Lazy)\n", - "sdii_ds_lazy = pr_ssp" + " with ctx:\n", + " return func(**kwargs)" ] }, { - "cell_type": "code", - "execution_count": 10, - "id": "4aca6c8dc0f0975d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:57.182908226Z", - "start_time": "2025-12-11T17:35:14.276631488Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Running WSDI (Lazy)...\n", - " > Done in 29.2237s\n", - " Running CWD (Lazy)...\n", - " > Done in 6.6821s\n", - " Running DTR (Lazy)...\n", - " > Done in 2.8461s\n", - " Running HDD (Lazy)...\n", - " > Done in 2.2843s\n", - " Running SDII (Lazy)...\n", - " > Done in 1.8180s\n" - ] - } - ], + "cell_type": "markdown", + "id": "f2f50cc5", + "metadata": {}, "source": [ - "t_wsdi_lazy = profile_run(\"WSDI (Lazy)\", warm_spell_duration_index, {\"ds\": wsdi_ds_lazy})\n", - "t_cwd_lazy = profile_run(\"CWD (Lazy)\", maximum_consecutive_wet_days, {\"ds\": cwd_ds_lazy})\n", - "t_dtr_lazy = profile_run(\"DTR (Lazy)\", daily_temperature_range, {\"ds\": dtr_ds_lazy})\n", - "t_hdd_lazy = profile_run(\"HDD (Lazy)\", heating_degree_days, {\"ds\": hdd_ds_lazy})\n", - "t_sdii_lazy = profile_run(\"SDII (Lazy)\", daily_precipitation_intensity, {\"ds\": sdii_ds_lazy})\n", - "\n", - "results.append({\"Indicator\": \"WSDI\", \"Mode\": \"Lazy\", \"Time\": t_wsdi_lazy})\n", - "results.append({\"Indicator\": \"CWD\", \"Mode\": \"Lazy\", \"Time\": t_cwd_lazy})\n", - "results.append({\"Indicator\": \"DTR\", \"Mode\": \"Lazy\", \"Time\": t_dtr_lazy})\n", - "results.append({\"Indicator\": \"HDD\", \"Mode\": \"Lazy\", \"Time\": t_hdd_lazy})\n", - "results.append({\"Indicator\": \"SDII\", \"Mode\": \"Lazy\", \"Time\": t_sdii_lazy})" + "## 4. Execution Loop\n", + "\n", + "We define the specific configurations for WSDI, CWD, DTR, HDD, SDII." ] }, { - "cell_type": "markdown", - "id": "39c8c972a9f62426", + "cell_type": "code", + "execution_count": 7, + "id": "46022af7", "metadata": {}, + "outputs": [], "source": [ - "## 2. Optimized Execution\n", - "In this mode, we apply two key optimizations:\n", - "1. **Pre-computing Percentiles**: We force the computation of the percentile threshold before passing it to the indicator. This simplifies the dask graph significantly.\n", - "2. **Re-chunking**: We re-chunk the data along the time dimension (`time=-1`) to ensure optimal processing for time-series based indicators." + "def get_named_runner(name: str, target_runner: Callable, **kwargs) -> Callable:\n", + " \"\"\"\n", + " Creates a 0-argument wrapper with a custom `__name__`.\n", + "\n", + " Renamed the second argument to 'target_runner' to avoid collision \n", + " with the 'func' argument inside **kwargs.\n", + " \"\"\"\n", + " def wrapper():\n", + " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", + " return target_runner(**kwargs)\n", + "\n", + " wrapper.__name__ = name\n", + " return wrapper" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "f515f3d9cd6338f8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:35:59.666352087Z", - "start_time": "2025-12-11T17:35:57.208333405Z" - } - }, + "execution_count": null, + "id": "e0be7a44", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Pre-computing percentile for WSDI...\n", - " > Percentile computed in 2.4377s\n" + "Pre-calculating 90th percentile for Optimized WSDI...\n", + "Configuring Optimized Benchmarks (Chunk -1, No Persist)...\n", + "\n", + "=== Benchmarking WSDI ===\n", + " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", + "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", + " Run 1: 26.8280s, Mem Peak Delta: 898.81 MiB\n", + " Run 2: 26.9964s, Mem Peak Delta: 898.86 MiB\n", + " Run 3: 27.1795s, Mem Peak Delta: 897.89 MiB\n", + " Run 4: 27.1633s, Mem Peak Delta: 897.86 MiB\n", + " Run 5: 27.8780s, Mem Peak Delta: 897.79 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", + "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", + " Run 1: 19.2791s, Mem Peak Delta: 898.82 MiB\n", + " Run 2: 19.3164s, Mem Peak Delta: 898.88 MiB\n", + " Run 3: 20.0295s, Mem Peak Delta: 898.91 MiB\n", + " Run 4: 20.0448s, Mem Peak Delta: 898.88 MiB\n", + " Run 5: 20.5861s, Mem Peak Delta: 898.79 MiB\n", + " Warm-up run for run_earthkit_opt_flox_WSDI...\n", + "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", + " Run 1: 21.2633s, Mem Peak Delta: 159.07 MiB\n", + " Run 2: 20.8128s, Mem Peak Delta: 108.58 MiB\n", + " Run 3: 20.5832s, Mem Peak Delta: 137.06 MiB\n", + " Run 4: 21.2587s, Mem Peak Delta: 73.79 MiB\n", + " Run 5: 22.3321s, Mem Peak Delta: 54.58 MiB\n", + " Warm-up run for run_xclim_noflox_WSDI...\n", + "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", + " Run 1: 28.1347s, Mem Peak Delta: 898.86 MiB\n", + " Run 2: 28.1445s, Mem Peak Delta: 898.86 MiB\n", + " Run 3: 28.1346s, Mem Peak Delta: 898.80 MiB\n", + " Run 4: 27.9559s, Mem Peak Delta: 898.89 MiB\n", + " Run 5: 28.2008s, Mem Peak Delta: 898.81 MiB\n", + " Warm-up run for run_xclim_flox_WSDI...\n", + "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", + " Run 1: 20.1094s, Mem Peak Delta: 898.79 MiB\n", + " Run 2: 20.4975s, Mem Peak Delta: 898.89 MiB\n", + " Run 3: 20.2302s, Mem Peak Delta: 898.79 MiB\n", + " Run 4: 20.2439s, Mem Peak Delta: 898.96 MiB\n", + " Run 5: 20.1670s, Mem Peak Delta: 898.79 MiB\n", + " Warm-up run for run_xclim_opt_flox_WSDI...\n", + "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", + " Run 1: 25.3983s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 25.8664s, Mem Peak Delta: 898.86 MiB\n", + " Run 3: 25.6448s, Mem Peak Delta: 898.79 MiB\n", + " Run 4: 25.7176s, Mem Peak Delta: 898.93 MiB\n", + " Run 5: 26.4639s, Mem Peak Delta: 898.93 MiB\n", + "\n", + "=== Benchmarking CWD ===\n", + " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", + "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", + " Run 1: 30.2380s, Mem Peak Delta: 99.16 MiB\n", + " Run 2: 30.7625s, Mem Peak Delta: 85.13 MiB\n", + " Run 3: 30.2546s, Mem Peak Delta: 71.05 MiB\n", + " Run 4: 30.6261s, Mem Peak Delta: 68.62 MiB\n", + " Run 5: 30.6804s, Mem Peak Delta: 59.89 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_CWD...\n", + "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", + " Run 1: 28.2647s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 26.0986s, Mem Peak Delta: 37.20 MiB\n", + " Run 3: 26.2005s, Mem Peak Delta: 48.67 MiB\n", + " Run 4: 26.3546s, Mem Peak Delta: 69.92 MiB\n", + " Run 5: 27.4395s, Mem Peak Delta: 91.67 MiB\n", + " Warm-up run for run_earthkit_opt_flox_CWD...\n", + "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", + " Run 1: 20.0539s, Mem Peak Delta: 89.59 MiB\n" ] } ], "source": [ - "# WSDI (Optimized)\n", - "print(\" Pre-computing percentile for WSDI...\")\n", - "start_per = time.perf_counter()\n", - "tasmax_per_opt = percentile_doy(tasmax_hist[\"tasmax\"], per=90)\n", - "tasmax_per_opt.name = \"tasmax_per\"\n", - "tasmax_per_opt = tasmax_per_opt.compute()\n", - "print(f\" > Percentile computed in {time.perf_counter() - start_per:.4f}s\")\n", + "results_data = []\n", "\n", - "tasmax_ssp_opt = tasmax_ssp.chunk({\"time\": -1})\n", - "wsdi_ds_opt = xr.merge([tasmax_ssp_opt, tasmax_per_opt])\n", + "# --- 1. Data Preparation ---\n", + "tasmax_ssp = data_cache['tasmax_ssp']['tasmax']\n", + "tasmin_ssp = data_cache['tasmin_ssp']['tasmin']\n", + "pr_ssp = data_cache['pr_ssp']['pr']\n", "\n", - "# CWD (Optimized)\n", - "cwd_ds_opt = pr_ssp.chunk({\"time\": -1})\n", + "# Pre-calculate percentile for WSDI\n", + "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", + "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90).compute()\n", + "per_90.name = \"tasmax_per\"\n", "\n", - "# DTR (Optimized)\n", + "# --- 1b. Optimized Configuration (Chunk -1) ---\n", + "print(\"Configuring Optimized Benchmarks (Chunk -1, No Persist)...\")\n", + "# We remove .persist() to include I/O overhead and compare fairly with Lazy\n", + "tasmax_ssp_opt = tasmax_ssp.chunk({\"time\": -1})\n", "tasmin_ssp_opt = tasmin_ssp.chunk({\"time\": -1})\n", - "dtr_ds_opt = xr.merge([tasmax_ssp_opt, tasmin_ssp_opt])\n", + "pr_ssp_opt = pr_ssp.chunk({\"time\": -1})\n", "\n", - "# HDD (Optimized)\n", - "hdd_ds_opt = tas_ssp_ds.chunk({\"time\": -1})\n", + "# --- 2. Define Benchmarks (Symmetric Structure) ---\n", + "benchmarks = [\n", + " {\n", + " \"name\": \"WSDI\",\n", + " \"ek_func\": ek_temp.warm_spell_duration_index,\n", + " \"xi_func\": xclim.indicators.atmos.warm_spell_duration_index,\n", + " # Earthkit Arguments\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], per_90]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"}\n", + " },\n", + " # Xclim Arguments (Now with dual structure)\n", + " \"xi_args\": {\n", + " \"lazy\": {\n", + " \"tasmax\": tasmax_ssp, \n", + " \"tasmax_per\": per_90, \n", + " \"freq\": \"MS\"\n", + " },\n", + " \"optimized\": {\n", + " \"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \n", + " \"tasmax_per\": per_90, \n", + " \"freq\": \"MS\"\n", + " },\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"CWD\",\n", + " \"ek_func\": ek_pr.maximum_consecutive_wet_days,\n", + " \"xi_func\": xclim.indicators.atmos.maximum_consecutive_wet_days,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"DTR\",\n", + " \"ek_func\": ek_temp.daily_temperature_range,\n", + " \"xi_func\": xclim.indicators.atmos.daily_temperature_range,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], data_cache['tasmin_ssp']]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmin\": tasmin_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\n", + " \"tasmax\": tasmax_ssp_opt,\n", + " \"tasmin\": tasmin_ssp_opt,\n", + " \"freq\": \"MS\"\n", + " }\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"HDD\",\n", + " \"ek_func\": ek_temp.heating_degree_days,\n", + " \"xi_func\": xclim.indicators.atmos.heating_degree_days,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp)/2).to_dataset(name='tas'), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt)/2).to_dataset(name='tas'), \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp)/2, \"freq\": \"MS\"},\n", + " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt)/2, \"freq\": \"MS\"}\n", + " },\n", + " },\n", + " {\n", + " \"name\": \"SDII\",\n", + " \"ek_func\": ek_pr.daily_precipitation_intensity,\n", + " \"xi_func\": xclim.indicators.atmos.daily_pr_intensity,\n", + " \"ek_args\": {\n", + " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " \"xi_args\": {\n", + " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", + " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " },\n", + " }\n", + "]\n", "\n", - "# SDII (Optimized)\n", - "sdii_ds_opt = pr_ssp.chunk({\"time\": -1})" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b94d03495270e0e9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:36:16.253595242Z", - "start_time": "2025-12-11T17:35:59.704488516Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Running WSDI (Optimized)...\n", - " > Done in 5.7216s\n", - " Running CWD (Optimized)...\n", - " > Done in 4.0565s\n", - " Running DTR (Optimized)...\n", - " > Done in 2.2120s\n", - " Running HDD (Optimized)...\n", - " > Done in 2.5596s\n", - " Running SDII (Optimized)...\n", - " > Done in 1.9346s\n" - ] - } - ], - "source": [ - "t_wsdi_opt = profile_run(\"WSDI (Optimized)\", warm_spell_duration_index, {\"ds\": wsdi_ds_opt})\n", - "t_cwd_opt = profile_run(\"CWD (Optimized)\", maximum_consecutive_wet_days, {\"ds\": cwd_ds_opt})\n", - "t_dtr_opt = profile_run(\"DTR (Optimized)\", daily_temperature_range, {\"ds\": dtr_ds_opt})\n", - "t_hdd_opt = profile_run(\"HDD (Optimized)\", heating_degree_days, {\"ds\": hdd_ds_opt})\n", - "t_sdii_opt = profile_run(\"SDII (Optimized)\", daily_precipitation_intensity, {\"ds\": sdii_ds_opt})\n", - "\n", - "results.append({\"Indicator\": \"WSDI\", \"Mode\": \"Optimized\", \"Time\": t_wsdi_opt})\n", - "results.append({\"Indicator\": \"CWD\", \"Mode\": \"Optimized\", \"Time\": t_cwd_opt})\n", - "results.append({\"Indicator\": \"DTR\", \"Mode\": \"Optimized\", \"Time\": t_dtr_opt})\n", - "results.append({\"Indicator\": \"HDD\", \"Mode\": \"Optimized\", \"Time\": t_hdd_opt})\n", - "results.append({\"Indicator\": \"SDII\", \"Mode\": \"Optimized\", \"Time\": t_sdii_opt})" + "# --- 3. Run Loop (Symmetric Comparison) ---\n", + "for b in benchmarks:\n", + " print(f\"\\n=== Benchmarking {b['name']} ===\")\n", + "\n", + " # =========================================================\n", + " # BLOCK 1: EARTHKIT\n", + " # =========================================================\n", + "\n", + " # 1. Earthkit: Standard (No Flox)\n", + " runner_ek_nf = get_named_runner(\n", + " f\"run_earthkit_lazy_noflox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['lazy'],\n", + " use_flox=False\n", + " )\n", + " res_ek_nf = benchmark_function(runner_ek_nf, {})\n", + " res_ek_nf.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " results_data.append(res_ek_nf)\n", + "\n", + " # 2. Earthkit: Standard (With Flox)\n", + " runner_ek_fl = get_named_runner(\n", + " f\"run_earthkit_lazy_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['lazy'],\n", + " use_flox=True\n", + " )\n", + " res_ek_fl = benchmark_function(runner_ek_fl, {})\n", + " res_ek_fl.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", + " results_data.append(res_ek_fl)\n", + "\n", + " # 3. Earthkit: Optimized (Chunk -1) + Flox\n", + " runner_ek_opt = get_named_runner(\n", + " f\"run_earthkit_opt_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['ek_func'],\n", + " kwargs=b['ek_args']['optimized'],\n", + " use_flox=True\n", + " )\n", + " res_ek_opt = benchmark_function(runner_ek_opt, {}, n_repeats=5)\n", + " res_ek_opt.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " results_data.append(res_ek_opt)\n", + "\n", + "\n", + " # =========================================================\n", + " # BLOCK 2: XCLIM\n", + " # =========================================================\n", + "\n", + " # 4. Xclim: Standard (No Flox)\n", + " runner_xc_nf = get_named_runner(\n", + " f\"run_xclim_noflox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", + " use_flox=False\n", + " )\n", + " res_xc_nf = benchmark_function(runner_xc_nf, {})\n", + " res_xc_nf.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " results_data.append(res_xc_nf)\n", + "\n", + " # 5. Xclim: Standard (With Flox)\n", + " runner_xc_fl = get_named_runner(\n", + " f\"run_xclim_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", + " use_flox=True\n", + " )\n", + " res_xc_fl = benchmark_function(runner_xc_fl, {})\n", + " res_xc_fl.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", + " results_data.append(res_xc_fl)\n", + "\n", + " # 6. Xclim: Optimized (Chunk -1) + Flox\n", + " runner_xc_opt = get_named_runner(\n", + " f\"run_xclim_opt_flox_{b['name']}\",\n", + " run_climate_indicator,\n", + " func=b['xi_func'],\n", + " kwargs=b['xi_args']['optimized'], # <--- USING OPTIMIZED ARGS\n", + " use_flox=True\n", + " )\n", + " res_xc_opt = benchmark_function(runner_xc_opt, {}, n_repeats=5)\n", + " res_xc_opt.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " results_data.append(res_xc_opt)" ] }, { "cell_type": "markdown", - "id": "b563555fb0100d6d", + "id": "08fa3d40", "metadata": {}, "source": [ - "## 3. Summary of Results" + "## 5. Results & Visualization" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "28459f4a38c7ce02", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-11T17:36:16.349760661Z", - "start_time": "2025-12-11T17:36:16.292768260Z" - } - }, + "execution_count": null, + "id": "75852284", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Benchmarking Results (Robust Stats):\n" + ] + }, { "data": { "text/html": [ @@ -638,73 +936,487 @@ "\n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
IndicatorLibraryModeLazyOptimizedmedian_timestd_timemax_memSpeedup
Indicator
CWD6.6820694.0564881.6472550WSDIEarthkit1. No Flox (Standard)22.9980220.151736898.9101560.984812
1WSDIEarthkit2. Flox (Standard)25.6811270.157031898.9531250.881921
2WSDIEarthkit3. Flox + Opt (Chunk -1)20.9107700.050164130.6757811.083113
3WSDIXclim1. No Flox (Standard)22.6487260.125794898.9296881.000000
4WSDIXclim2. Flox (Standard)25.6550040.385313898.9531250.882819
5WSDIXclim3. Flox + Opt (Chunk -1)19.3382930.038324899.0664061.171185
6CWDEarthkit1. No Flox (Standard)22.5892670.326003135.8164061.015740
7CWDEarthkit2. Flox (Standard)24.3604020.136368100.5507810.941890
8CWDEarthkit3. Flox + Opt (Chunk -1)23.3382270.764065107.8320310.983144
9CWDXclim1. No Flox (Standard)22.9448290.43491895.6835941.000000
10CWDXclim2. Flox (Standard)24.5392120.711479169.0585940.935027
11CWDXclim3. Flox + Opt (Chunk -1)24.4717740.28535281.9804690.937604
12DTREarthkit1. No Flox (Standard)9.4053110.30623547.2148440.944445
13DTREarthkit2. Flox (Standard)1.4201760.016578139.8203126.254715
14DTREarthkit3. Flox + Opt (Chunk -1)1.6533070.021712171.5859385.372745
15DTRXclim1. No Flox (Standard)8.8827970.09080565.8671881.000000
16DTRXclim2. Flox (Standard)1.3620440.01892791.5195316.521666
17DTRXclim3. Flox + Opt (Chunk -1)1.6313670.006617182.8984385.445001
18HDDEarthkit1. No Flox (Standard)7.2753660.12428261.3007811.005746
DTR2.8460732.2119511.28668019HDDEarthkit2. Flox (Standard)1.2671010.02366293.7890625.774732
HDD2.2842532.5595550.89244220HDDEarthkit3. Flox + Opt (Chunk -1)1.4101260.019725147.8007815.189017
SDII1.8179781.9345810.93972721HDDXclim1. No Flox (Standard)7.3171680.04400982.5195311.000000
WSDI29.2236515.7216425.10756322HDDXclim2. Flox (Standard)1.2667680.01029848.7734385.776251
23HDDXclim3. Flox + Opt (Chunk -1)1.4005950.013077149.6054695.224329
24SDIIEarthkit1. No Flox (Standard)9.7903770.28390980.6445310.975787
25SDIIEarthkit2. Flox (Standard)0.7848840.00381766.55468812.171627
26SDIIEarthkit3. Flox + Opt (Chunk -1)0.9264570.00857391.79296910.311668
27SDIIXclim1. No Flox (Standard)9.5533200.04380588.1289061.000000
28SDIIXclim2. Flox (Standard)0.7853510.00350697.51562512.164400
29SDIIXclim3. Flox + Opt (Chunk -1)0.9266580.01394066.05468810.309438
\n", "" ], "text/plain": [ - "Mode Lazy Optimized Speedup\n", - "Indicator \n", - "CWD 6.682069 4.056488 1.647255\n", - "DTR 2.846073 2.211951 1.286680\n", - "HDD 2.284253 2.559555 0.892442\n", - "SDII 1.817978 1.934581 0.939727\n", - "WSDI 29.223651 5.721642 5.107563" + " Indicator Library Mode median_time std_time \\\n", + "0 WSDI Earthkit 1. No Flox (Standard) 22.998022 0.151736 \n", + "1 WSDI Earthkit 2. Flox (Standard) 25.681127 0.157031 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.910770 0.050164 \n", + "3 WSDI Xclim 1. No Flox (Standard) 22.648726 0.125794 \n", + "4 WSDI Xclim 2. Flox (Standard) 25.655004 0.385313 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 19.338293 0.038324 \n", + "6 CWD Earthkit 1. No Flox (Standard) 22.589267 0.326003 \n", + "7 CWD Earthkit 2. Flox (Standard) 24.360402 0.136368 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 23.338227 0.764065 \n", + "9 CWD Xclim 1. No Flox (Standard) 22.944829 0.434918 \n", + "10 CWD Xclim 2. Flox (Standard) 24.539212 0.711479 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 24.471774 0.285352 \n", + "12 DTR Earthkit 1. No Flox (Standard) 9.405311 0.306235 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.420176 0.016578 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.653307 0.021712 \n", + "15 DTR Xclim 1. No Flox (Standard) 8.882797 0.090805 \n", + "16 DTR Xclim 2. Flox (Standard) 1.362044 0.018927 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.631367 0.006617 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.275366 0.124282 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.267101 0.023662 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.410126 0.019725 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.317168 0.044009 \n", + "22 HDD Xclim 2. Flox (Standard) 1.266768 0.010298 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.400595 0.013077 \n", + "24 SDII Earthkit 1. No Flox (Standard) 9.790377 0.283909 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.784884 0.003817 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.926457 0.008573 \n", + "27 SDII Xclim 1. No Flox (Standard) 9.553320 0.043805 \n", + "28 SDII Xclim 2. Flox (Standard) 0.785351 0.003506 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.926658 0.013940 \n", + "\n", + " max_mem Speedup \n", + "0 898.910156 0.984812 \n", + "1 898.953125 0.881921 \n", + "2 130.675781 1.083113 \n", + "3 898.929688 1.000000 \n", + "4 898.953125 0.882819 \n", + "5 899.066406 1.171185 \n", + "6 135.816406 1.015740 \n", + "7 100.550781 0.941890 \n", + "8 107.832031 0.983144 \n", + "9 95.683594 1.000000 \n", + "10 169.058594 0.935027 \n", + "11 81.980469 0.937604 \n", + "12 47.214844 0.944445 \n", + "13 139.820312 6.254715 \n", + "14 171.585938 5.372745 \n", + "15 65.867188 1.000000 \n", + "16 91.519531 6.521666 \n", + "17 182.898438 5.445001 \n", + "18 61.300781 1.005746 \n", + "19 93.789062 5.774732 \n", + "20 147.800781 5.189017 \n", + "21 82.519531 1.000000 \n", + "22 48.773438 5.776251 \n", + "23 149.605469 5.224329 \n", + "24 80.644531 0.975787 \n", + "25 66.554688 12.171627 \n", + "26 91.792969 10.311668 \n", + "27 88.128906 1.000000 \n", + "28 97.515625 12.164400 \n", + "29 66.054688 10.309438 " + ] + }, + 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+ "text/plain": [ + "
" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df = pd.DataFrame(results)\n", - "pivot = df.pivot(index=\"Indicator\", columns=\"Mode\", values=\"Time\")\n", - "pivot[\"Speedup\"] = pivot[\"Lazy\"] / pivot[\"Optimized\"]\n", - "pivot" + "\n", + "# 1. Create DataFrame\n", + "df_res = pd.DataFrame(results_data)\n", + "\n", + "# 2. Create a specific label for the Plot Legend (Library + Mode)\n", + "df_res['Label'] = df_res['Library'] + \": \" + df_res['Mode']\n", + "\n", + "# 3. Calculate Speedup relative to 'Earthkit No Flox (Standard)' using MEDIAN time\n", + "# Robustness Update: We use Median Time for speedup calculation to be resistant to outliers.\n", + "df_res['Speedup'] = 0.0\n", + "baseline_mode = \"1. No Flox (Standard)\"\n", + "\n", + "for indicator in df_res['Indicator'].unique():\n", + " # Find the baseline: Xclim + No Flox (as per original logic, though code comments said Earthkit)\n", + " # Let's double check the user intent. Usually Xclim NoFlox is the \"base\" reference.\n", + " # The original code searched for Library='Xclim'. We stick to that.\n", + " baseline_row = df_res.loc[\n", + " (df_res['Indicator'] == indicator) &\n", + " (df_res['Library'] == 'Xclim') &\n", + " (df_res['Mode'] == baseline_mode)\n", + " ]\n", + "\n", + " if not baseline_row.empty:\n", + " # Use median_time for robust speedup\n", + " baseline_time = baseline_row['median_time'].values[0]\n", + " mask = df_res['Indicator'] == indicator\n", + " df_res.loc[mask, 'Speedup'] = baseline_time / df_res.loc[mask, 'median_time']\n", + "\n", + "print(\"\\nBenchmarking Results (Robust Stats):\")\n", + "display(df_res[['Indicator', 'Library', 'Mode', 'median_time', 'std_time', 'max_mem', 'Speedup']])\n", + "\n", + "# --- Plots ---\n", + "# Define a consistent order for the bars\n", + "hue_order = sorted(df_res['Label'].unique())\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "\n", + "# Plot 1: Relative Speedup (Robust)\n", + "plt.subplot(1, 3, 1)\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"Speedup\",\n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(f\"Relative Speedup (via Median Time)\\n(Baseline: Xclim {baseline_mode})\")\n", + "plt.axhline(1.0, color='black', linestyle='--', alpha=0.5, linewidth=1)\n", + "plt.legend(title=\"Configuration\", fontsize='small')\n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "# Plot 2: Median Execution Time\n", + "plt.subplot(1, 3, 2)\n", + "# We plot the pre-calculated median. \n", + "# Note: standard sns.barplot aggregates data if there are duplicates. \n", + "# Here df_res has 1 row per case, so it just plots the value.\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"median_time\", \n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", + "plt.legend().remove() \n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "# Plot 3: Peak Memory Usage\n", + "plt.subplot(1, 3, 3)\n", + "sns.barplot(\n", + " data=df_res,\n", + " x=\"Indicator\",\n", + " y=\"max_mem\",\n", + " hue=\"Label\",\n", + " palette=\"Paired\",\n", + " hue_order=hue_order\n", + ")\n", + "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", + "plt.legend().remove()\n", + "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] } ], diff --git a/docs/notebooks/robust_performance_benchmarking.ipynb b/docs/notebooks/robust_performance_benchmarking.ipynb deleted file mode 100644 index 0a2adcd..0000000 --- a/docs/notebooks/robust_performance_benchmarking.ipynb +++ /dev/null @@ -1,1603 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb33bbc9", - "metadata": {}, - "source": [ - "# Robust Performance Benchmarking: Earthkit-Climate vs Xclim\n", - "\n", - "This notebook provides a robust comparative analysis of climate indicator calculations using:\n", - "1. **Earthkit-Climate (Lazy)**: Standard wrapper usage.\n", - "2. **Earthkit-Climate (Optimized)**: With pre-computation of percentiles and manual re-chunking (`time: -1`).\n", - "3. **Xclim (Direct)**: Direct optimized usage of `xclim.indices`.\n", - "4. **Xclim + Flox**: `xclim` with `flox` optimization enabled for faster reductions.\n", - "\n", - "Key features:\n", - "- Statistical Sampling: $N$ runs per test.\n", - "- Resource Profiling: RAM peak & CPU usage.\n", - "- Visualization: Speedup comparison with error bars.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "410735a7", - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "import os\n", - "import time\n", - "import warnings\n", - "from typing import Any, Callable, Dict, List, Optional\n", - "\n", - "import earthkit.data\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import xarray as xr\n", - "import xclim.indicators\n", - "from dask.diagnostics import Profiler, ResourceProfiler\n", - "from IPython.display import Markdown, display\n", - "from memory_profiler import memory_usage\n", - "\n", - "import earthkit.climate.indicators.precipitation as ek_pr\n", - "import earthkit.climate.indicators.temperature as ek_temp\n", - "from earthkit.climate.utils.percentile import percentile_doy\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "sns.set_theme(style=\"whitegrid\")\n", - "\n", - "# Configure robust caching to avoid re-downloading\n", - "cache_dir = os.path.expanduser(\"~/.cache/earthkit/data\")\n", - "os.makedirs(cache_dir, exist_ok=True)\n", - "settings_earthkit = {\n", - " \"cache-policy\": \"user\",\n", - " \"temporary-directory-root\": cache_dir,\n", - "}\n", - "earthkit.data.config.set(settings_earthkit)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "68281ebe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "## Resources Used\n", - "\n", - "### Hardware Configuration\n", - "The performance analysis was conducted on the following hardware (dynamically detected):\n", - "- **CPU**: 12th Gen Intel(R) Core(TM) i5-12600KF\n", - "- **RAM**: 31.2 GB\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "def get_cpu_info() -> str:\n", - " \"\"\"\n", - " Extract the CPU model name from the system's /proc/cpuinfo.\n", - "\n", - " This function parses the Linux virtual filesystem to find the\n", - " hardware model name of the processor.\n", - "\n", - " Returns\n", - " -------\n", - " cpu_model : str\n", - " The name of the CPU model (e.g., 'Intel(R) Core(TM) i7-10700K').\n", - " Returns 'Unknown CPU' if the file is inaccessible or the entry\n", - " is missing.\n", - " \"\"\"\n", - " try:\n", - " if os.path.exists(\"/proc/cpuinfo\"):\n", - " with open(\"/proc/cpuinfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"model name\" in line:\n", - " return line.split(\":\")[1].strip()\n", - " except Exception:\n", - " return \"Unknown CPU\"\n", - " return \"Unknown CPU\"\n", - "\n", - "\n", - "def get_ram_info() -> str:\n", - " \"\"\"\n", - " Extract the total system RAM from /proc/meminfo.\n", - "\n", - " Parses the system memory information and converts the total memory\n", - " from kilobytes to gigabytes.\n", - "\n", - " Returns\n", - " -------\n", - " ram_size : str\n", - " A formatted string representing total RAM in GB (e.g., '16.0 GB').\n", - " Returns 'Unknown RAM' if the file is inaccessible or the entry\n", - " is missing.\n", - " \"\"\"\n", - " try:\n", - " if os.path.exists(\"/proc/meminfo\"):\n", - " with open(\"/proc/meminfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"MemTotal\" in line:\n", - " total_kb = int(line.split()[1])\n", - " return f\"{total_kb / 1024 / 1024:.1f} GB\"\n", - " except Exception:\n", - " return \"Unknown RAM\"\n", - " return \"Unknown RAM\"\n", - "\n", - "# --- Execution Logic ---\n", - "\n", - "cpu_model: str = get_cpu_info()\n", - "ram_size: str = get_ram_info()\n", - "\n", - "display(\n", - " Markdown(f\"\"\"\n", - "## Resources Used\n", - "\n", - "### Hardware Configuration\n", - "The performance analysis was conducted on the following hardware (dynamically detected):\n", - "- **CPU**: {cpu_model}\n", - "- **RAM**: {ram_size}\n", - "\"\"\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "fba35188", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "### Dataset Information\n", - "\n", - "The analysis uses the following climate datasets derived from CMIP6 projections\n", - "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", - "repository and are automatically downloaded or cached by **earthkit-data**.\n", - "\n", - "| Variable | Scenario | Dimensions | Size | Status | URL |\n", - "|----------|----------|------------|------|--------|-----|\n", - "| `tasmax` | historical | (time: 7305, lat: 48, lon: 84) | 67.3 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc) |\n", - "| `tasmax` | ssp585 | (time: 14610, lat: 48, lon: 84) | 126.9 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "| `tasmin` | ssp585 | (time: 14610, lat: 48, lon: 84) | 132.1 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "| `pr` | ssp585 | (time: 14610, lat: 48, lon: 84) | 111.5 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "\n", - "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", - "download the files.\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Dataset URLs\n", - "DATASET_URLS = [\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - "]\n", - "\n", - "\n", - "def format_size(size_bytes: float) -> str:\n", - " \"\"\"\n", - " Convert a byte size into a human-readable string.\n", - "\n", - " Parameters\n", - " ----------\n", - " size_bytes : float\n", - " File size in bytes.\n", - "\n", - " Returns\n", - " -------\n", - " str\n", - " Size formatted as B, KB, MB, or GB.\n", - " \"\"\"\n", - " for unit in [\"B\", \"KB\", \"MB\", \"GB\"]:\n", - " if size_bytes < 1024:\n", - " return f\"{size_bytes:.1f} {unit}\"\n", - " size_bytes /= 1024\n", - " return f\"{size_bytes:.1f} TB\"\n", - "\n", - "\n", - "def extract_dataset_info(url: str) -> Dict[str, Any]:\n", - " \"\"\"\n", - " Extract key metadata information from a NetCDF dataset available via URL.\n", - "\n", - " Parameters\n", - " ----------\n", - " url : str\n", - " URL to the NetCDF dataset.\n", - "\n", - " Returns\n", - " -------\n", - " dict\n", - " Dictionary containing:\n", - " - Variable\n", - " - Scenario\n", - " - Description\n", - " - Dimensions\n", - " - Size\n", - " - Status (\"Cached\" or \"Remote\")\n", - " - URL\n", - " \"\"\"\n", - " ds = earthkit.data.from_source(\"url\", url)\n", - "\n", - " # Detect file size & cache status\n", - " if getattr(ds, \"path\", None) and os.path.exists(ds.path):\n", - " size = format_size(os.path.getsize(ds.path))\n", - " status = \"Cached\"\n", - " else:\n", - " size = \"Unknown\"\n", - " status = \"Remote\"\n", - "\n", - " # Convert to xarray\n", - " xr_ds = ds.to_xarray()\n", - "\n", - " # Extract primary variable\n", - " variables = list(xr_ds.data_vars)\n", - " variable = f\"`{variables[0]}`\" if variables else \"Unknown\"\n", - "\n", - " # Scenario metadata\n", - " scenario = xr_ds.attrs.get(\"scenario\", \"Unknown\")\n", - "\n", - " # Dimension string, e.g. \"(time: 365, lat: 180, lon: 360)\"\n", - " dims_str = \"(\" + \", \".join(f\"{k}: {v}\" for k, v in xr_ds.dims.items()) + \")\"\n", - "\n", - " return {\n", - " \"Variable\": variable,\n", - " \"Scenario\": scenario,\n", - " \"Dimensions\": dims_str,\n", - " \"Size\": size,\n", - " \"Status\": status,\n", - " \"URL\": url,\n", - " }\n", - "\n", - "\n", - "def generate_dataset_table(urls: List[str]) -> str:\n", - " \"\"\"\n", - " Create a Markdown table summarizing metadata for a list of dataset URLs.\n", - "\n", - " Parameters\n", - " ----------\n", - " urls : list of str\n", - " List of dataset URLs.\n", - "\n", - " Returns\n", - " -------\n", - " str\n", - " A Markdown-formatted table of dataset metadata.\n", - " \"\"\"\n", - " header = (\n", - " \"| Variable | Scenario | Dimensions | Size | Status | URL |\\n\"\n", - " \"|----------|----------|------------|------|--------|-----|\\n\"\n", - " )\n", - "\n", - " rows = []\n", - " for url in urls:\n", - " info = extract_dataset_info(url)\n", - " rows.append(\n", - " f\"| {info['Variable']} | {info['Scenario']} | \"\n", - " f\"{info['Dimensions']} | {info['Size']} | {info['Status']} | \"\n", - " f\"[Download]({info['URL']}) |\"\n", - " )\n", - "\n", - " return header + \"\\n\".join(rows)\n", - "\n", - "\n", - "# Display Markdown report\n", - "display(\n", - " Markdown(\n", - " f\"\"\"\n", - "### Dataset Information\n", - "\n", - "The analysis uses the following climate datasets derived from CMIP6 projections\n", - "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", - "repository and are automatically downloaded or cached by **earthkit-data**.\n", - "\n", - "{generate_dataset_table(DATASET_URLS)}\n", - "\n", - "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", - "download the files.\n", - "\"\"\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5922ba8f", - "metadata": {}, - "source": [ - "## 1. Benchmarking Engine\n", - "\n", - "Helper functions to run tests multiple times, capture statistics, and profile resources." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "5d1ba8a5", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import threading\n", - "from typing import Any, Dict, List\n", - "\n", - "import psutil\n", - "\n", - "\n", - "class ResourceMonitor(threading.Thread):\n", - " def __init__(self, interval=0.1):\n", - " self.interval = interval\n", - " self.stop_event = threading.Event()\n", - " self.memory_usage = []\n", - " self.cpu_usage = []\n", - " self.process = psutil.Process()\n", - " super().__init__()\n", - "\n", - " def run(self):\n", - " while not self.stop_event.is_set():\n", - " try:\n", - " # RSS Memory in MiB\n", - " mem = self.process.memory_info().rss / (1024 * 1024)\n", - " self.memory_usage.append(mem)\n", - " # CPU Percent (blocking for interval would slow main thread, so we sleep manually)\n", - " # self.cpu_usage.append(self.process.cpu_percent(interval=None)) \n", - " except Exception:\n", - " pass\n", - " time.sleep(self.interval)\n", - "\n", - " def stop(self):\n", - " self.stop_event.set()\n", - "\n", - "def benchmark_function(\n", - " func: Callable[..., Any],\n", - " kwargs: Dict[str, Any],\n", - " n_repeats: int = 5,\n", - " warmup: bool = True\n", - ") -> Dict[str, float]:\n", - " \"\"\"\n", - " Run a function N times with robust profiling (Warm-up + High-Freq Sampling).\n", - "\n", - " Parameters\n", - " ----------\n", - " func : Callable\n", - " The function to benchmark.\n", - " kwargs : Dict\n", - " Arguments for the function.\n", - " n_repeats : int\n", - " Number of measurement runs.\n", - " warmup : bool\n", - " If True, runs the function once silently before measuring to exclude JIT/Import costs.\n", - "\n", - " Returns\n", - " -------\n", - " stats : Dict\n", - " 'mean_time', 'median_time', 'std_time', 'max_mem', 'mean_mem_peak'\n", - " \"\"\"\n", - " # 1. Warm-up (Silent)\n", - " if warmup:\n", - " print(f\" Warm-up run for {func.__name__}...\")\n", - " try:\n", - " # Run without monitoring\n", - " res = func(**kwargs)\n", - " if hasattr(res, 'compute'):\n", - " res.compute()\n", - " elif hasattr(res, 'to_xarray'):\n", - " res.to_xarray().compute()\n", - " except Exception as e:\n", - " print(f\" Warm-up warning: {e}\")\n", - " # Force GC after warmup\n", - " gc.collect()\n", - "\n", - " times: List[float] = []\n", - " mem_peaks: List[float] = []\n", - "\n", - " print(f\"Benchmarking {func.__name__} ({n_repeats} runs)...\")\n", - "\n", - " for i in range(n_repeats):\n", - " gc.collect()\n", - "\n", - " # Start Monitor (10ms interval)\n", - " monitor = ResourceMonitor(interval=0.1)\n", - " # Capture baseline memory\n", - " baseline_mem = psutil.Process().memory_info().rss / (1024 * 1024)\n", - " monitor.start()\n", - "\n", - " start_time = time.perf_counter()\n", - "\n", - " # --- Computation ---\n", - " try:\n", - " res = func(**kwargs)\n", - " if hasattr(res, 'compute'):\n", - " res.compute()\n", - " elif hasattr(res, 'to_xarray'):\n", - " res.to_xarray().compute()\n", - " except Exception as e:\n", - " print(f\" Run {i+1} Failed: {e}\")\n", - " monitor.stop()\n", - " continue\n", - " # -------------------\n", - "\n", - " end_time = time.perf_counter()\n", - " monitor.stop()\n", - " monitor.join()\n", - "\n", - " duration = end_time - start_time\n", - "\n", - " # Calculate Peak Memory Delta\n", - " observed_mems = monitor.memory_usage\n", - " if observed_mems:\n", - " # We want the peak usage *induced* by the function\n", - " # Peak Delta = Max(Observed) - Baseline\n", - " # Or simplified: Peak Observed\n", - " # The previous 'memory_profiler' returned (Max - Min). reliable for single process.\n", - " # Let's use Normalized Peak: (Max - Baseline)\n", - " peak_delta = max(observed_mems) - baseline_mem\n", - " # Clamp to 0\n", - " if peak_delta < 0: peak_delta = 0\n", - " else:\n", - " peak_delta = 0.0\n", - "\n", - " times.append(duration)\n", - " mem_peaks.append(peak_delta)\n", - "\n", - " print(f\" Run {i+1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", - "\n", - " if not times:\n", - " return {\"mean_time\": 0.0, \"max_mem\": 0.0}\n", - "\n", - " return {\n", - " \"mean_time\": float(np.mean(times)),\n", - " \"median_time\": float(np.median(times)),\n", - " \"std_time\": float(np.std(times)),\n", - " \"max_mem\": float(np.max(mem_peaks)),\n", - " \"mean_mem\": float(np.mean(mem_peaks))\n", - " }\n" - ] - }, - { - "cell_type": "markdown", - "id": "d672e8b4", - "metadata": {}, - "source": [ - "## 2. Data Management\n", - "Load CMIP6 datasets and ensure unit compatibility." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "acf4d31d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading datasets via earthkit...\n" - ] - } - ], - "source": [ - "URLS = {\n", - " \"tasmax_hist\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - " \"tasmax_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"tasmin_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - "}\n", - "\n", - "def load_data() -> Dict[str, xr.Dataset]:\n", - " \"\"\"\n", - " Load climate datasets from remote URLs using earthkit-data.\n", - "\n", - " Iterates through a predefined global dictionary of URLs, downloads\n", - " the data sources, and converts them into xarray Datasets.\n", - "\n", - " Returns\n", - " -------\n", - " datasets : Dict[str, xr.Dataset]\n", - " A dictionary where keys match the source identifiers and values\n", - " are the loaded xarray Datasets.\n", - "\n", - " Raises\n", - " ------\n", - " NameError\n", - " If `URLS` is not defined in the global scope.\n", - " \"\"\"\n", - " print(\"Loading datasets via earthkit...\")\n", - " datasets: Dict[str, xr.Dataset] = {}\n", - " for key, url in URLS.items():\n", - " # earthkit.data.from_source returns a wrapper that to_xarray() converts\n", - " ds = earthkit.data.from_source(\"url\", url).to_xarray()\n", - " datasets[key] = ds\n", - " return datasets\n", - "\n", - "data_cache = load_data()" - ] - }, - { - "cell_type": "markdown", - "id": "37feb6c1", - "metadata": {}, - "source": [ - "## 3. Contender Implementations\n", - "\n", - "We wrap each library's call in a uniform function signature." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "66739804", - "metadata": {}, - "outputs": [], - "source": [ - "from contextlib import nullcontext\n", - "from typing import Any, Callable, Dict\n", - "\n", - "import xarray as xr\n", - "\n", - "\n", - "def run_climate_indicator(\n", - " func: Callable[..., Any],\n", - " kwargs: Dict[str, Any],\n", - " use_flox: Optional[bool] = None\n", - ") -> Any:\n", - " \"\"\"\n", - " Unified execution wrapper for climate indicator functions with configurable backend options.\n", - "\n", - " This function streamlines the benchmarking process by dynamically handling \n", - " Xarray global options (specifically 'flox' optimization) via a context manager.\n", - "\n", - " Parameters\n", - " ----------\n", - " func : Callable[..., Any]\n", - " The indicator function to execute (e.g., from Earthkit or Xclim).\n", - " kwargs : Dict[str, Any]\n", - " A dictionary of keyword arguments required by the `func` (e.g., input datasets, \n", - " thresholds, frequencies).\n", - " use_flox : bool, optional\n", - " Controls the 'use_flox' option in Xarray for accelerated GroupBy operations:\n", - " - True: Explicitly enables Flox optimization.\n", - " - False: Explicitly disables Flox (forces legacy implementation).\n", - " - None: Uses the current environment's default configuration (no context change).\n", - " By default, None.\n", - "\n", - " Returns\n", - " -------\n", - " Any\n", - " The result of the indicator calculation (typically an xarray.DataArray or Dataset).\n", - " \"\"\"\n", - " # Determine context: set options if boolean, otherwise do nothing (nullcontext)\n", - " ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext()\n", - "\n", - " with ctx:\n", - " return func(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "f2f50cc5", - "metadata": {}, - "source": [ - "## 4. Execution Loop\n", - "\n", - "We define the specific configurations for WSDI, CWD, DTR, HDD, SDII." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "46022af7", - "metadata": {}, - "outputs": [], - "source": [ - "def get_named_runner(name: str, target_runner: Callable, **kwargs) -> Callable:\n", - " \"\"\"\n", - " Creates a 0-argument wrapper with a custom `__name__`.\n", - "\n", - " Renamed the second argument to 'target_runner' to avoid collision \n", - " with the 'func' argument inside **kwargs.\n", - " \"\"\"\n", - " def wrapper():\n", - " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", - " return target_runner(**kwargs)\n", - "\n", - " wrapper.__name__ = name\n", - " return wrapper" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "e0be7a44", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-calculating 90th percentile for Optimized WSDI...\n", - "Configuring Optimized Benchmarks (Chunk -1, No Persist)...\n", - "\n", - "=== Benchmarking WSDI ===\n", - " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", - "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", - " Run 1: 25.8927s, Mem Peak Delta: 898.79 MiB\n", - " Run 2: 27.1201s, Mem Peak Delta: 898.91 MiB\n", - " Run 3: 26.4929s, Mem Peak Delta: 898.83 MiB\n", - " Run 4: 26.0854s, Mem Peak Delta: 898.88 MiB\n", - " Run 5: 27.0777s, Mem Peak Delta: 898.79 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", - "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", - " Run 1: 23.6207s, Mem Peak Delta: 898.87 MiB\n", - " Run 2: 23.7128s, Mem Peak Delta: 898.91 MiB\n", - " Run 3: 24.3635s, Mem Peak Delta: 898.91 MiB\n", - " Run 4: 24.0506s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 23.8922s, Mem Peak Delta: 898.90 MiB\n", - " Warm-up run for run_earthkit_opt_flox_WSDI...\n", - "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", - " Run 1: 20.7526s, Mem Peak Delta: 49.98 MiB\n", - " Run 2: 20.8830s, Mem Peak Delta: 49.94 MiB\n", - " Run 3: 21.2385s, Mem Peak Delta: 12.00 MiB\n", - " Run 4: 21.2124s, Mem Peak Delta: 49.94 MiB\n", - " Run 5: 20.8216s, Mem Peak Delta: 49.94 MiB\n", - " Warm-up run for run_xclim_noflox_WSDI...\n", - "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", - " Run 1: 26.7390s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 26.7734s, Mem Peak Delta: 898.86 MiB\n", - " Run 3: 26.0276s, Mem Peak Delta: 898.84 MiB\n", - " Run 4: 25.8968s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 26.0709s, Mem Peak Delta: 898.92 MiB\n", - " Warm-up run for run_xclim_flox_WSDI...\n", - "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", - " Run 1: 23.6802s, Mem Peak Delta: 898.93 MiB\n", - " Run 2: 23.3310s, Mem Peak Delta: 898.80 MiB\n", - " Run 3: 23.4548s, Mem Peak Delta: 898.85 MiB\n", - " Run 4: 23.3895s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 23.9636s, Mem Peak Delta: 898.84 MiB\n", - " Warm-up run for run_xclim_opt_flox_WSDI...\n", - "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", - " Run 1: 19.9375s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 20.0916s, Mem Peak Delta: 898.79 MiB\n", - " Run 3: 20.7634s, Mem Peak Delta: 898.85 MiB\n", - " Run 4: 19.8487s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 20.0441s, Mem Peak Delta: 898.91 MiB\n", - "\n", - "=== Benchmarking CWD ===\n", - " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", - "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", - " Run 1: 29.3767s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 28.0602s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 28.0488s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 27.9270s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 28.1274s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_CWD...\n", - "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", - " Run 1: 26.1044s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 26.1309s, Mem Peak Delta: 8.25 MiB\n", - " Run 3: 28.3037s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 25.7714s, Mem Peak Delta: 0.05 MiB\n", - " Run 5: 25.5983s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_CWD...\n", - "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 18.8773s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 18.8134s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 19.0268s, Mem Peak Delta: 74.84 MiB\n", - " Run 4: 19.3369s, Mem Peak Delta: 49.94 MiB\n", - " Run 5: 19.7957s, Mem Peak Delta: 49.95 MiB\n", - " Warm-up run for run_xclim_noflox_CWD...\n", - "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", - " Run 1: 29.7120s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 29.0183s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 29.9438s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 29.2263s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 31.9159s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_CWD...\n", - "Benchmarking run_xclim_flox_CWD (5 runs)...\n", - " Run 1: 28.6923s, Mem Peak Delta: 15.97 MiB\n", - " Run 2: 28.9293s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 29.0942s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 29.2740s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 29.0477s, Mem Peak Delta: 8.25 MiB\n", - " Warm-up run for run_xclim_opt_flox_CWD...\n", - "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", - " Run 1: 21.7682s, Mem Peak Delta: 24.98 MiB\n", - " Run 2: 22.0080s, Mem Peak Delta: 43.93 MiB\n", - " Run 3: 22.0705s, Mem Peak Delta: 49.88 MiB\n", - " Run 4: 21.9743s, Mem Peak Delta: 49.94 MiB\n", - " Run 5: 22.2275s, Mem Peak Delta: 49.94 MiB\n", - "\n", - "=== Benchmarking DTR ===\n", - " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", - "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", - " Run 1: 9.4351s, Mem Peak Delta: 0.38 MiB\n", - " Run 2: 9.5768s, Mem Peak Delta: 0.38 MiB\n", - " Run 3: 9.3402s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 9.4722s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 9.3636s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_DTR...\n", - "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", - " Run 1: 1.4357s, Mem Peak Delta: 0.10 MiB\n", - " Run 2: 1.4295s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 1.4219s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 1.4171s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.4218s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_DTR...\n", - "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.7658s, Mem Peak Delta: 31.94 MiB\n", - " Run 2: 1.6892s, Mem Peak Delta: 49.84 MiB\n", - " Run 3: 1.7469s, Mem Peak Delta: 49.94 MiB\n", - " Run 4: 1.7437s, Mem Peak Delta: 49.93 MiB\n", - " Run 5: 1.7736s, Mem Peak Delta: 49.94 MiB\n", - " Warm-up run for run_xclim_noflox_DTR...\n", - "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", - " Run 1: 9.5489s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 9.5487s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 9.5000s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 9.4695s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 9.4420s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_DTR...\n", - "Benchmarking run_xclim_flox_DTR (5 runs)...\n", - " Run 1: 1.4329s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.4351s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 1.4164s, Mem Peak Delta: 0.06 MiB\n", - " Run 4: 1.4164s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.4251s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_opt_flox_DTR...\n", - "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.7975s, Mem Peak Delta: 50.02 MiB\n", - " Run 2: 1.7540s, Mem Peak Delta: 39.91 MiB\n", - " Run 3: 1.7573s, Mem Peak Delta: 50.03 MiB\n", - " Run 4: 1.7095s, Mem Peak Delta: 49.94 MiB\n", - " Run 5: 1.7366s, Mem Peak Delta: 73.91 MiB\n", - "\n", - "=== Benchmarking HDD ===\n", - " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", - "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", - " Run 1: 7.9535s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.9782s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 7.9025s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 7.9938s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 7.9317s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_HDD...\n", - "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", - " Run 1: 1.3291s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.3241s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 1.3409s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 1.3291s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.3446s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_HDD...\n", - "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4934s, Mem Peak Delta: 67.85 MiB\n", - " Run 2: 1.4911s, Mem Peak Delta: 37.92 MiB\n", - " Run 3: 1.4917s, Mem Peak Delta: 49.86 MiB\n", - " Run 4: 1.4938s, Mem Peak Delta: 50.03 MiB\n", - " Run 5: 1.4861s, Mem Peak Delta: 21.92 MiB\n", - " Warm-up run for run_xclim_noflox_HDD...\n", - "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", - " Run 1: 7.8671s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.9274s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 7.8776s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 7.9001s, Mem Peak Delta: 0.25 MiB\n", - " Run 5: 7.9224s, Mem Peak Delta: 0.12 MiB\n", - " Warm-up run for run_xclim_flox_HDD...\n", - "Benchmarking run_xclim_flox_HDD (5 runs)...\n", - " Run 1: 1.3224s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.3168s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 1.3044s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 1.3087s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.3207s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_opt_flox_HDD...\n", - "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.5035s, Mem Peak Delta: 49.96 MiB\n", - " Run 2: 1.4986s, Mem Peak Delta: 53.77 MiB\n", - " Run 3: 1.5113s, Mem Peak Delta: 25.91 MiB\n", - " Run 4: 1.4709s, Mem Peak Delta: 3.94 MiB\n", - " Run 5: 1.5036s, Mem Peak Delta: 83.88 MiB\n", - "\n", - "=== Benchmarking SDII ===\n", - " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", - "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", - " Run 1: 10.8552s, Mem Peak Delta: 0.12 MiB\n", - " Run 2: 10.8570s, Mem Peak Delta: 17.31 MiB\n", - " Run 3: 10.7975s, Mem Peak Delta: 0.12 MiB\n", - " Run 4: 10.8643s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 10.9053s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_SDII...\n", - "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", - " Run 1: 0.8518s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 0.8436s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.8402s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 0.8451s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 0.8408s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_SDII...\n", - "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", - " Run 1: 1.0089s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.0014s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.9898s, Mem Peak Delta: 49.94 MiB\n", - " Run 4: 1.0295s, Mem Peak Delta: 0.09 MiB\n", - " Run 5: 1.0309s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_noflox_SDII...\n", - "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", - " Run 1: 10.7494s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 10.8059s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 10.6910s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 10.8391s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 10.7851s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_SDII...\n", - "Benchmarking run_xclim_flox_SDII (5 runs)...\n", - " Run 1: 0.8291s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 0.8436s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.8257s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 0.8360s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 0.8336s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_opt_flox_SDII...\n", - "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", - " Run 1: 1.0077s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 0.9856s, Mem Peak Delta: 0.01 MiB\n", - " Run 3: 0.9891s, Mem Peak Delta: 0.19 MiB\n", - " Run 4: 0.9722s, Mem Peak Delta: 35.87 MiB\n", - " Run 5: 1.0106s, Mem Peak Delta: 49.94 MiB\n" - ] - } - ], - "source": [ - "results_data = []\n", - "\n", - "# --- 1. Data Preparation ---\n", - "tasmax_ssp = data_cache['tasmax_ssp']['tasmax']\n", - "tasmin_ssp = data_cache['tasmin_ssp']['tasmin']\n", - "pr_ssp = data_cache['pr_ssp']['pr']\n", - "\n", - "# Pre-calculate percentile for WSDI\n", - "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", - "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90).compute()\n", - "per_90.name = \"tasmax_per\"\n", - "\n", - "# --- 1b. Optimized Configuration (Chunk -1) ---\n", - "print(\"Configuring Optimized Benchmarks (Chunk -1, No Persist)...\")\n", - "# We remove .persist() to include I/O overhead and compare fairly with Lazy\n", - "tasmax_ssp_opt = tasmax_ssp.chunk({\"time\": -1})\n", - "tasmin_ssp_opt = tasmin_ssp.chunk({\"time\": -1})\n", - "pr_ssp_opt = pr_ssp.chunk({\"time\": -1})\n", - "\n", - "# --- 2. Define Benchmarks (Symmetric Structure) ---\n", - "benchmarks = [\n", - " {\n", - " \"name\": \"WSDI\",\n", - " \"ek_func\": ek_temp.warm_spell_duration_index,\n", - " \"xi_func\": xclim.indicators.atmos.warm_spell_duration_index,\n", - " # Earthkit Arguments\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], per_90]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"}\n", - " },\n", - " # Xclim Arguments (Now with dual structure)\n", - " \"xi_args\": {\n", - " \"lazy\": {\n", - " \"tasmax\": tasmax_ssp, \n", - " \"tasmax_per\": per_90, \n", - " \"freq\": \"MS\"\n", - " },\n", - " \"optimized\": {\n", - " \"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \n", - " \"tasmax_per\": per_90, \n", - " \"freq\": \"MS\"\n", - " },\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"CWD\",\n", - " \"ek_func\": ek_pr.maximum_consecutive_wet_days,\n", - " \"xi_func\": xclim.indicators.atmos.maximum_consecutive_wet_days,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"DTR\",\n", - " \"ek_func\": ek_temp.daily_temperature_range,\n", - " \"xi_func\": xclim.indicators.atmos.daily_temperature_range,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], data_cache['tasmin_ssp']]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"}\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmin\": tasmin_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\n", - " \"tasmax\": tasmax_ssp_opt,\n", - " \"tasmin\": tasmin_ssp_opt,\n", - " \"freq\": \"MS\"\n", - " }\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"HDD\",\n", - " \"ek_func\": ek_temp.heating_degree_days,\n", - " \"xi_func\": xclim.indicators.atmos.heating_degree_days,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp)/2).to_dataset(name='tas'), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt)/2).to_dataset(name='tas'), \"freq\": \"MS\"}\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp)/2, \"freq\": \"MS\"},\n", - " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt)/2, \"freq\": \"MS\"}\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"SDII\",\n", - " \"ek_func\": ek_pr.daily_precipitation_intensity,\n", - " \"xi_func\": xclim.indicators.atmos.daily_pr_intensity,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " }\n", - "]\n", - "\n", - "# --- 3. Run Loop (Symmetric Comparison) ---\n", - "for b in benchmarks:\n", - " print(f\"\\n=== Benchmarking {b['name']} ===\")\n", - "\n", - " # =========================================================\n", - " # BLOCK 1: EARTHKIT\n", - " # =========================================================\n", - "\n", - " # 1. Earthkit: Standard (No Flox)\n", - " runner_ek_nf = get_named_runner(\n", - " f\"run_earthkit_lazy_noflox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['lazy'],\n", - " use_flox=False\n", - " )\n", - " res_ek_nf = benchmark_function(runner_ek_nf, {})\n", - " res_ek_nf.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", - " results_data.append(res_ek_nf)\n", - "\n", - " # 2. Earthkit: Standard (With Flox)\n", - " runner_ek_fl = get_named_runner(\n", - " f\"run_earthkit_lazy_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['lazy'],\n", - " use_flox=True\n", - " )\n", - " res_ek_fl = benchmark_function(runner_ek_fl, {})\n", - " res_ek_fl.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", - " results_data.append(res_ek_fl)\n", - "\n", - " # 3. Earthkit: Optimized (Chunk -1) + Flox\n", - " runner_ek_opt = get_named_runner(\n", - " f\"run_earthkit_opt_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['optimized'],\n", - " use_flox=True\n", - " )\n", - " res_ek_opt = benchmark_function(runner_ek_opt, {}, n_repeats=5)\n", - " res_ek_opt.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", - " results_data.append(res_ek_opt)\n", - "\n", - "\n", - " # =========================================================\n", - " # BLOCK 2: XCLIM\n", - " # =========================================================\n", - "\n", - " # 4. Xclim: Standard (No Flox)\n", - " runner_xc_nf = get_named_runner(\n", - " f\"run_xclim_noflox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", - " use_flox=False\n", - " )\n", - " res_xc_nf = benchmark_function(runner_xc_nf, {})\n", - " res_xc_nf.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", - " results_data.append(res_xc_nf)\n", - "\n", - " # 5. Xclim: Standard (With Flox)\n", - " runner_xc_fl = get_named_runner(\n", - " f\"run_xclim_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", - " use_flox=True\n", - " )\n", - " res_xc_fl = benchmark_function(runner_xc_fl, {})\n", - " res_xc_fl.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", - " results_data.append(res_xc_fl)\n", - "\n", - " # 6. Xclim: Optimized (Chunk -1) + Flox\n", - " runner_xc_opt = get_named_runner(\n", - " f\"run_xclim_opt_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['optimized'], # <--- USING OPTIMIZED ARGS\n", - " use_flox=True\n", - " )\n", - " res_xc_opt = benchmark_function(runner_xc_opt, {}, n_repeats=5)\n", - " res_xc_opt.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", - " results_data.append(res_xc_opt)" - ] - }, - { - "cell_type": "markdown", - "id": "08fa3d40", - "metadata": {}, - "source": [ - "## 5. Results & Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "75852284", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Benchmarking Results (Robust Stats):\n" - ] - }, - { - "data": { - "text/html": [ - "
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IndicatorLibraryModemedian_timestd_timemax_memSpeedup
0WSDIEarthkit1. No Flox (Standard)26.4929460.500665898.9140620.984069
1WSDIEarthkit2. Flox (Standard)23.8921940.263340898.9140621.091189
2WSDIEarthkit3. Flox + Opt (Chunk -1)20.8829970.20348249.9765621.248427
3WSDIXclim1. No Flox (Standard)26.0708930.375776898.9179691.000000
4WSDIXclim2. Flox (Standard)23.4548210.232307898.9335941.111537
5WSDIXclim3. Flox + Opt (Chunk -1)20.0440650.324348898.9140621.300679
6CWDEarthkit1. No Flox (Standard)28.0602080.5382420.0000001.058867
7CWDEarthkit2. Flox (Standard)26.1043750.9818238.2500001.138202
8CWDEarthkit3. Flox + Opt (Chunk -1)19.0268460.36133074.8398441.561585
9CWDXclim1. No Flox (Standard)29.7120421.0307600.0000001.000000
10CWDXclim2. Flox (Standard)29.0477150.19267515.9726561.022870
11CWDXclim3. Flox + Opt (Chunk -1)22.0079720.14882349.9414061.350058
12DTREarthkit1. No Flox (Standard)9.4350900.0843120.3750001.006879
13DTREarthkit2. Flox (Standard)1.4218640.0065900.0976566.681367
14DTREarthkit3. Flox + Opt (Chunk -1)1.7469480.02954149.9414065.438052
15DTRXclim1. No Flox (Standard)9.4999940.0425230.0000001.000000
16DTRXclim2. Flox (Standard)1.4251330.0079040.0625006.666041
17DTRXclim3. Flox + Opt (Chunk -1)1.7540160.02877973.9101565.416138
18HDDEarthkit1. No Flox (Standard)7.9534900.0325530.0000000.993293
19HDDEarthkit2. Flox (Standard)1.3290870.0078050.0000005.944043
20HDDEarthkit3. Flox + Opt (Chunk -1)1.4917420.00275267.8515625.295923
21HDDXclim1. No Flox (Standard)7.9001490.0237880.2500001.000000
22HDDXclim2. Flox (Standard)1.3167920.0069460.0000005.999541
23HDDXclim3. Flox + Opt (Chunk -1)1.5034890.01394183.8750005.254545
24SDIIEarthkit1. No Flox (Standard)10.8569740.03440217.3125000.993377
25SDIIEarthkit2. Flox (Standard)0.8435840.0041440.00000012.784815
26SDIIEarthkit3. Flox + Opt (Chunk -1)1.0088730.01597849.93750010.690221
27SDIIXclim1. No Flox (Standard)10.7850710.0507240.0000001.000000
28SDIIXclim2. Flox (Standard)0.8335840.0061290.00000012.938193
29SDIIXclim3. Flox + Opt (Chunk -1)0.9890740.01434349.93750010.904207
\n", - "
" - ], - "text/plain": [ - " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 26.492946 0.500665 \n", - "1 WSDI Earthkit 2. Flox (Standard) 23.892194 0.263340 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.882997 0.203482 \n", - "3 WSDI Xclim 1. No Flox (Standard) 26.070893 0.375776 \n", - "4 WSDI Xclim 2. Flox (Standard) 23.454821 0.232307 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 20.044065 0.324348 \n", - "6 CWD Earthkit 1. No Flox (Standard) 28.060208 0.538242 \n", - "7 CWD Earthkit 2. Flox (Standard) 26.104375 0.981823 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 19.026846 0.361330 \n", - "9 CWD Xclim 1. No Flox (Standard) 29.712042 1.030760 \n", - "10 CWD Xclim 2. Flox (Standard) 29.047715 0.192675 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 22.007972 0.148823 \n", - "12 DTR Earthkit 1. No Flox (Standard) 9.435090 0.084312 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.421864 0.006590 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.746948 0.029541 \n", - "15 DTR Xclim 1. No Flox (Standard) 9.499994 0.042523 \n", - "16 DTR Xclim 2. Flox (Standard) 1.425133 0.007904 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.754016 0.028779 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.953490 0.032553 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.329087 0.007805 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.491742 0.002752 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.900149 0.023788 \n", - "22 HDD Xclim 2. Flox (Standard) 1.316792 0.006946 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.503489 0.013941 \n", - "24 SDII Earthkit 1. No Flox (Standard) 10.856974 0.034402 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.843584 0.004144 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 1.008873 0.015978 \n", - "27 SDII Xclim 1. No Flox (Standard) 10.785071 0.050724 \n", - "28 SDII Xclim 2. Flox (Standard) 0.833584 0.006129 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.989074 0.014343 \n", - "\n", - " max_mem Speedup \n", - "0 898.914062 0.984069 \n", - "1 898.914062 1.091189 \n", - "2 49.976562 1.248427 \n", - "3 898.917969 1.000000 \n", - "4 898.933594 1.111537 \n", - "5 898.914062 1.300679 \n", - "6 0.000000 1.058867 \n", - "7 8.250000 1.138202 \n", - "8 74.839844 1.561585 \n", - "9 0.000000 1.000000 \n", - "10 15.972656 1.022870 \n", - "11 49.941406 1.350058 \n", - "12 0.375000 1.006879 \n", - "13 0.097656 6.681367 \n", - "14 49.941406 5.438052 \n", - "15 0.000000 1.000000 \n", - "16 0.062500 6.666041 \n", - "17 73.910156 5.416138 \n", - "18 0.000000 0.993293 \n", - "19 0.000000 5.944043 \n", - "20 67.851562 5.295923 \n", - "21 0.250000 1.000000 \n", - "22 0.000000 5.999541 \n", - "23 83.875000 5.254545 \n", - "24 17.312500 0.993377 \n", - "25 0.000000 12.784815 \n", - "26 49.937500 10.690221 \n", - "27 0.000000 1.000000 \n", - "28 0.000000 12.938193 \n", - "29 49.937500 10.904207 " - ] - }, - "metadata": {}, - 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# 1. Create DataFrame\n", - "df_res = pd.DataFrame(results_data)\n", - "\n", - "# 2. Create a specific label for the Plot Legend (Library + Mode)\n", - "df_res['Label'] = df_res['Library'] + \": \" + df_res['Mode']\n", - "\n", - "# 3. Calculate Speedup relative to 'Earthkit No Flox (Standard)' using MEDIAN time\n", - "# Robustness Update: We use Median Time for speedup calculation to be resistant to outliers.\n", - "df_res['Speedup'] = 0.0\n", - "baseline_mode = \"1. No Flox (Standard)\"\n", - "\n", - "for indicator in df_res['Indicator'].unique():\n", - " # Find the baseline: Xclim + No Flox (as per original logic, though code comments said Earthkit)\n", - " # Let's double check the user intent. Usually Xclim NoFlox is the \"base\" reference.\n", - " # The original code searched for Library='Xclim'. We stick to that.\n", - " baseline_row = df_res.loc[\n", - " (df_res['Indicator'] == indicator) &\n", - " (df_res['Library'] == 'Xclim') &\n", - " (df_res['Mode'] == baseline_mode)\n", - " ]\n", - "\n", - " if not baseline_row.empty:\n", - " # Use median_time for robust speedup\n", - " baseline_time = baseline_row['median_time'].values[0]\n", - " mask = df_res['Indicator'] == indicator\n", - " df_res.loc[mask, 'Speedup'] = baseline_time / df_res.loc[mask, 'median_time']\n", - "\n", - "print(\"\\nBenchmarking Results (Robust Stats):\")\n", - "display(df_res[['Indicator', 'Library', 'Mode', 'median_time', 'std_time', 'max_mem', 'Speedup']])\n", - "\n", - "# --- Plots ---\n", - "# Define a consistent order for the bars\n", - "hue_order = sorted(df_res['Label'].unique())\n", - "\n", - "plt.figure(figsize=(20, 6))\n", - "\n", - "# Plot 1: Relative Speedup (Robust)\n", - "plt.subplot(1, 3, 1)\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"Speedup\",\n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", - "plt.title(f\"Relative Speedup (via Median Time)\\n(Baseline: Xclim {baseline_mode})\")\n", - "plt.axhline(1.0, color='black', linestyle='--', alpha=0.5, linewidth=1)\n", - "plt.legend(title=\"Configuration\", fontsize='small')\n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", - "\n", - "# Plot 2: Median Execution Time\n", - "plt.subplot(1, 3, 2)\n", - "# We plot the pre-calculated median. \n", - "# Note: standard sns.barplot aggregates data if there are duplicates. \n", - "# Here df_res has 1 row per case, so it just plots the value.\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"median_time\", \n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", - "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", - "plt.legend().remove() \n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", - "\n", - "# Plot 3: Peak Memory Usage\n", - "plt.subplot(1, 3, 3)\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"max_mem\",\n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", - "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", - "plt.legend().remove()\n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pixi.lock b/pixi.lock index 3f769e3..c7c425e 100644 --- a/pixi.lock +++ b/pixi.lock @@ -35,6 +35,8 @@ environments: - 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0fafb53141024408f09bffe2e194afde1fdee1fd Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 9 Feb 2026 16:12:51 +0100 Subject: [PATCH 12/47] docs: refresh performance analysis notebook outputs with new benchmark results and execution counts. --- docs/notebooks/performance_analysis.ipynb | 531 ++++++++++++++-------- 1 file changed, 345 insertions(+), 186 deletions(-) diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index bb0406e..f0b6cf6 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -620,7 +620,7 @@ " with the 'func' argument inside **kwargs.\n", " \"\"\"\n", " def wrapper():\n", - " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", + " # Ahora llamamos a target_runner pas\u00e1ndole los kwargs (que contienen 'func')\n", " return target_runner(**kwargs)\n", "\n", " wrapper.__name__ = name\n", @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e0be7a44", "metadata": {}, "outputs": [ @@ -701,7 +701,164 @@ " Run 5: 27.4395s, Mem Peak Delta: 91.67 MiB\n", " Warm-up run for run_earthkit_opt_flox_CWD...\n", "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.0539s, Mem Peak Delta: 89.59 MiB\n" + " Run 1: 20.0539s, Mem Peak Delta: 89.59 MiB\n", + " Run 2: 20.6084s, Mem Peak Delta: 110.74 MiB\n", + " Run 3: 20.7335s, Mem Peak Delta: 107.82 MiB\n", + " Run 4: 20.2407s, Mem Peak Delta: 50.92 MiB\n", + " Run 5: 20.3195s, Mem Peak Delta: 91.41 MiB\n", + " Warm-up run for run_xclim_noflox_CWD...\n", + "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", + " Run 1: 28.8911s, Mem Peak Delta: 61.70 MiB\n", + " Run 2: 28.6861s, Mem Peak Delta: 77.30 MiB\n", + " Run 3: 29.5600s, Mem Peak Delta: 83.47 MiB\n", + " Run 4: 29.5330s, Mem Peak Delta: 65.57 MiB\n", + " Run 5: 29.8725s, Mem Peak Delta: 62.04 MiB\n", + " Warm-up run for run_xclim_flox_CWD...\n", + "Benchmarking run_xclim_flox_CWD (5 runs)...\n", + " Run 1: 26.4795s, Mem Peak Delta: 30.13 MiB\n", + " Run 2: 26.8512s, Mem Peak Delta: 84.45 MiB\n", + " Run 3: 27.5432s, Mem Peak Delta: 82.79 MiB\n", + " Run 4: 27.8137s, Mem Peak Delta: 41.73 MiB\n", + " Run 5: 27.4936s, Mem Peak Delta: 115.35 MiB\n", + " Warm-up run for run_xclim_opt_flox_CWD...\n", + "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", + " Run 1: 20.6049s, Mem Peak Delta: 20.08 MiB\n", + " Run 2: 21.2001s, Mem Peak Delta: 113.94 MiB\n", + " Run 3: 20.7782s, Mem Peak Delta: 52.78 MiB\n", + " Run 4: 20.3147s, Mem Peak Delta: 66.99 MiB\n", + " Run 5: 20.9418s, Mem Peak Delta: 94.09 MiB\n", + "\n", + "=== Benchmarking DTR ===\n", + " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", + "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", + " Run 1: 8.6815s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 8.6741s, Mem Peak Delta: 33.26 MiB\n", + " Run 3: 8.5244s, Mem Peak Delta: 8.30 MiB\n", + " Run 4: 8.5588s, Mem Peak Delta: 32.91 MiB\n", + " Run 5: 8.7207s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_DTR...\n", + "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", + " Run 1: 1.3558s, Mem Peak Delta: 100.01 MiB\n", + " Run 2: 1.3922s, Mem Peak Delta: 66.67 MiB\n", + " Run 3: 1.3871s, Mem Peak Delta: 108.08 MiB\n", + " Run 4: 1.4307s, Mem Peak Delta: 74.76 MiB\n", + " Run 5: 1.3847s, Mem Peak Delta: 113.68 MiB\n", + " Warm-up run for run_earthkit_opt_flox_DTR...\n", + "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", + " Run 1: 1.6725s, Mem Peak Delta: 191.08 MiB\n", + " Run 2: 1.6556s, Mem Peak Delta: 133.26 MiB\n", + " Run 3: 1.6982s, Mem Peak Delta: 66.37 MiB\n", + " Run 4: 1.6596s, Mem Peak Delta: 182.82 MiB\n", + " Run 5: 1.6782s, Mem Peak Delta: 74.75 MiB\n", + " Warm-up run for run_xclim_noflox_DTR...\n", + "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", + " Run 1: 8.7513s, Mem Peak Delta: 2.50 MiB\n", + " Run 2: 9.0851s, Mem Peak Delta: 24.75 MiB\n", + " Run 3: 8.8050s, Mem Peak Delta: 33.12 MiB\n", + " Run 4: 9.1217s, Mem Peak Delta: 41.42 MiB\n", + " Run 5: 8.6643s, Mem Peak Delta: 16.67 MiB\n", + " Warm-up run for run_xclim_flox_DTR...\n", + "Benchmarking run_xclim_flox_DTR (5 runs)...\n", + " Run 1: 1.3531s, Mem Peak Delta: 48.56 MiB\n", + " Run 2: 1.3507s, Mem Peak Delta: 40.51 MiB\n", + " Run 3: 1.3659s, Mem Peak Delta: 36.82 MiB\n", + " Run 4: 1.3639s, Mem Peak Delta: 41.49 MiB\n", + " Run 5: 1.3656s, Mem Peak Delta: 65.02 MiB\n", + " Warm-up run for run_xclim_opt_flox_DTR...\n", + "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", + " Run 1: 1.6826s, Mem Peak Delta: 150.31 MiB\n", + " Run 2: 1.6448s, Mem Peak Delta: 181.25 MiB\n", + " Run 3: 1.6264s, Mem Peak Delta: 118.98 MiB\n", + " Run 4: 1.6609s, Mem Peak Delta: 141.14 MiB\n", + " Run 5: 1.6225s, Mem Peak Delta: 132.98 MiB\n", + "\n", + "=== Benchmarking HDD ===\n", + " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", + "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", + " Run 1: 7.2806s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.2965s, Mem Peak Delta: 58.33 MiB\n", + " Run 3: 7.3453s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 7.3729s, Mem Peak Delta: 8.88 MiB\n", + " Run 5: 7.5313s, Mem Peak Delta: 32.25 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_HDD...\n", + "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", + " Run 1: 1.3970s, Mem Peak Delta: 58.03 MiB\n", + " Run 2: 1.3345s, Mem Peak Delta: 25.00 MiB\n", + " Run 3: 1.2926s, Mem Peak Delta: 25.32 MiB\n", + " Run 4: 1.3194s, Mem Peak Delta: 21.38 MiB\n", + " Run 5: 1.2983s, Mem Peak Delta: 33.42 MiB\n", + " Warm-up run for run_earthkit_opt_flox_HDD...\n", + "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", + " Run 1: 1.4519s, Mem Peak Delta: 233.26 MiB\n", + " Run 2: 1.4638s, Mem Peak Delta: 161.30 MiB\n", + " Run 3: 1.4553s, Mem Peak Delta: 157.99 MiB\n", + " Run 4: 1.4571s, Mem Peak Delta: 157.19 MiB\n", + " Run 5: 1.4779s, Mem Peak Delta: 207.99 MiB\n", + " Warm-up run for run_xclim_noflox_HDD...\n", + "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", + " Run 1: 7.5720s, Mem Peak Delta: 0.12 MiB\n", + " Run 2: 7.6712s, Mem Peak Delta: 8.38 MiB\n", + " Run 3: 7.3330s, Mem Peak Delta: 8.44 MiB\n", + " Run 4: 7.3025s, Mem Peak Delta: 50.75 MiB\n", + " Run 5: 7.3905s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_flox_HDD...\n", + "Benchmarking run_xclim_flox_HDD (5 runs)...\n", + " Run 1: 1.2945s, Mem Peak Delta: 25.85 MiB\n", + " Run 2: 1.2593s, Mem Peak Delta: 16.88 MiB\n", + " Run 3: 1.2741s, Mem Peak Delta: 25.09 MiB\n", + " Run 4: 1.2768s, Mem Peak Delta: 8.72 MiB\n", + " Run 5: 1.2742s, Mem Peak Delta: 8.38 MiB\n", + " Warm-up run for run_xclim_opt_flox_HDD...\n", + "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", + " Run 1: 1.4511s, Mem Peak Delta: 116.84 MiB\n", + " Run 2: 1.4302s, Mem Peak Delta: 107.97 MiB\n", + " Run 3: 1.4256s, Mem Peak Delta: 141.47 MiB\n", + " Run 4: 1.4202s, Mem Peak Delta: 116.32 MiB\n", + " Run 5: 1.4349s, Mem Peak Delta: 157.96 MiB\n", + "\n", + "=== Benchmarking SDII ===\n", + " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", + "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", + " Run 1: 10.1295s, Mem Peak Delta: 56.32 MiB\n", + " Run 2: 10.1441s, Mem Peak Delta: 57.98 MiB\n", + " Run 3: 10.1011s, Mem Peak Delta: 51.18 MiB\n", + " Run 4: 10.1489s, Mem Peak Delta: 50.16 MiB\n", + " Run 5: 10.1188s, Mem Peak Delta: 83.11 MiB\n", + " Warm-up run for run_earthkit_lazy_flox_SDII...\n", + "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", + " Run 1: 0.7854s, Mem Peak Delta: 33.62 MiB\n", + " Run 2: 0.8015s, Mem Peak Delta: 33.39 MiB\n", + " Run 3: 0.7988s, Mem Peak Delta: 32.85 MiB\n", + " Run 4: 0.8017s, Mem Peak Delta: 33.40 MiB\n", + " Run 5: 0.7937s, Mem Peak Delta: 33.34 MiB\n", + " Warm-up run for run_earthkit_opt_flox_SDII...\n", + "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", + " Run 1: 0.9294s, Mem Peak Delta: 74.69 MiB\n", + " Run 2: 0.9447s, Mem Peak Delta: 58.10 MiB\n", + " Run 3: 0.9245s, Mem Peak Delta: 74.85 MiB\n", + " Run 4: 0.9445s, Mem Peak Delta: 49.88 MiB\n", + " Run 5: 0.9290s, Mem Peak Delta: 99.82 MiB\n", + " Warm-up run for run_xclim_noflox_SDII...\n", + "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", + " Run 1: 10.0605s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 10.0377s, Mem Peak Delta: 53.90 MiB\n", + " Run 3: 10.1044s, Mem Peak Delta: 91.63 MiB\n", + " Run 4: 10.1368s, Mem Peak Delta: 95.63 MiB\n", + " Run 5: 10.0813s, Mem Peak Delta: 66.67 MiB\n", + " Warm-up run for run_xclim_flox_SDII...\n", + "Benchmarking run_xclim_flox_SDII (5 runs)...\n", + " Run 1: 0.8215s, Mem Peak Delta: 41.55 MiB\n", + " Run 2: 0.8043s, Mem Peak Delta: 41.53 MiB\n", + " Run 3: 0.7934s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 0.7862s, Mem Peak Delta: 25.10 MiB\n", + " Run 5: 0.7829s, Mem Peak Delta: 0.00 MiB\n", + " Warm-up run for run_xclim_opt_flox_SDII...\n", + "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", + " Run 1: 0.9232s, Mem Peak Delta: 33.03 MiB\n", + " Run 2: 0.9651s, Mem Peak Delta: 66.69 MiB\n", + " Run 3: 0.9470s, Mem Peak Delta: 58.07 MiB\n", + " Run 4: 0.9455s, Mem Peak Delta: 8.43 MiB\n", + " Run 5: 0.9539s, Mem Peak Delta: 50.02 MiB\n" ] } ], @@ -904,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "75852284", "metadata": {}, "outputs": [ @@ -952,39 +1109,39 @@ " WSDI\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 22.998022\n", - " 0.151736\n", - " 898.910156\n", - " 0.984812\n", + " 27.163342\n", + " 0.358026\n", + " 898.859375\n", + " 1.035759\n", " \n", " \n", " 1\n", " WSDI\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 25.681127\n", - " 0.157031\n", - " 898.953125\n", - " 0.881921\n", + " 20.029513\n", + " 0.494503\n", + " 898.914062\n", + " 1.404661\n", " \n", " \n", " 2\n", " WSDI\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 20.910770\n", - " 0.050164\n", - " 130.675781\n", - " 1.083113\n", + " 21.258730\n", + " 0.601173\n", + " 159.070312\n", + " 1.323441\n", " \n", " \n", " 3\n", " WSDI\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 22.648726\n", - " 0.125794\n", - " 898.929688\n", + " 28.134670\n", + " 0.082817\n", + " 898.890625\n", " 1.000000\n", " \n", " \n", @@ -992,59 +1149,59 @@ " WSDI\n", " Xclim\n", " 2. Flox (Standard)\n", - " 25.655004\n", - " 0.385313\n", - " 898.953125\n", - " 0.882819\n", + " 20.230234\n", + " 0.132923\n", + " 898.964844\n", + " 1.390724\n", " \n", " \n", " 5\n", " WSDI\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 19.338293\n", - " 0.038324\n", - " 899.066406\n", - " 1.171185\n", + " 25.717589\n", + " 0.356579\n", + " 898.933594\n", + " 1.093986\n", " \n", " \n", " 6\n", " CWD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 22.589267\n", - " 0.326003\n", - " 135.816406\n", - " 1.015740\n", + " 30.626105\n", + " 0.221556\n", + " 99.156250\n", + " 0.964307\n", " \n", " \n", " 7\n", " CWD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 24.360402\n", - " 0.136368\n", - " 100.550781\n", - " 0.941890\n", + " 26.354595\n", + " 0.846001\n", + " 91.671875\n", + " 1.120600\n", " \n", " \n", " 8\n", " CWD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 23.338227\n", - " 0.764065\n", - " 107.832031\n", - " 0.983144\n", + " 20.319477\n", + " 0.247352\n", + " 110.738281\n", + " 1.453431\n", " \n", " \n", " 9\n", " CWD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 22.944829\n", - " 0.434918\n", - " 95.683594\n", + " 29.532964\n", + " 0.445733\n", + " 83.472656\n", " 1.000000\n", " \n", " \n", @@ -1052,59 +1209,59 @@ " CWD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 24.539212\n", - " 0.711479\n", - " 169.058594\n", - " 0.935027\n", + " 27.493610\n", + " 0.492927\n", + " 115.347656\n", + " 1.074176\n", " \n", " \n", " 11\n", " CWD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 24.471774\n", - " 0.285352\n", - " 81.980469\n", - " 0.937604\n", + " 20.778197\n", + " 0.299679\n", + " 113.937500\n", + " 1.421344\n", " \n", " \n", " 12\n", " DTR\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 9.405311\n", - " 0.306235\n", - " 47.214844\n", - " 0.944445\n", + " 8.674074\n", + " 0.076174\n", + " 33.261719\n", + " 1.015091\n", " \n", " \n", " 13\n", " DTR\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.420176\n", - " 0.016578\n", - " 139.820312\n", - " 6.254715\n", + " 1.387059\n", + " 0.023953\n", + " 113.675781\n", + " 6.347944\n", " \n", " \n", " 14\n", " DTR\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.653307\n", - " 0.021712\n", - " 171.585938\n", - " 5.372745\n", + " 1.672507\n", + " 0.015136\n", + " 191.082031\n", + " 5.264536\n", " \n", " \n", " 15\n", " DTR\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 8.882797\n", - " 0.090805\n", - " 65.867188\n", + " 8.804974\n", + " 0.183875\n", + " 41.421875\n", " 1.000000\n", " \n", " \n", @@ -1112,59 +1269,59 @@ " DTR\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.362044\n", - " 0.018927\n", - " 91.519531\n", - " 6.521666\n", + " 1.363925\n", + " 0.006582\n", + " 65.019531\n", + " 6.455614\n", " \n", " \n", " 17\n", " DTR\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.631367\n", - " 0.006617\n", - " 182.898438\n", - " 5.445001\n", + " 1.644765\n", + " 0.022314\n", + " 181.246094\n", + " 5.353332\n", " \n", " \n", " 18\n", " HDD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 7.275366\n", - " 0.124282\n", - " 61.300781\n", - " 1.005746\n", + " 7.345279\n", + " 0.089340\n", + " 58.332031\n", + " 1.006162\n", " \n", " \n", " 19\n", " HDD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.267101\n", - " 0.023662\n", - " 93.789062\n", - " 5.774732\n", + " 1.319415\n", + " 0.037436\n", + " 58.027344\n", + " 5.601381\n", " \n", " \n", " 20\n", " HDD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.410126\n", - " 0.019725\n", - " 147.800781\n", - " 5.189017\n", + " 1.457090\n", + " 0.009215\n", + " 233.261719\n", + " 5.072124\n", " \n", " \n", " 21\n", " HDD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 7.317168\n", - " 0.044009\n", - " 82.519531\n", + " 7.390543\n", + " 0.143334\n", + " 50.746094\n", " 1.000000\n", " \n", " \n", @@ -1172,59 +1329,59 @@ " HDD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.266768\n", - " 0.010298\n", - " 48.773438\n", - " 5.776251\n", + " 1.274181\n", + " 0.011207\n", + " 25.851562\n", + " 5.800229\n", " \n", " \n", " 23\n", " HDD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.400595\n", - " 0.013077\n", - " 149.605469\n", - " 5.224329\n", + " 1.430180\n", + " 0.010546\n", + " 157.957031\n", + " 5.167563\n", " \n", " \n", " 24\n", " SDII\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 9.790377\n", - " 0.283909\n", - " 80.644531\n", - " 0.975787\n", + " 10.129517\n", + " 0.017331\n", + " 83.109375\n", + " 0.995237\n", " \n", " \n", " 25\n", " SDII\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 0.784884\n", - " 0.003817\n", - " 66.554688\n", - " 12.171627\n", + " 0.798818\n", + " 0.006143\n", + " 33.617188\n", + " 12.620229\n", " \n", " \n", " 26\n", " SDII\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.926457\n", - " 0.008573\n", - " 91.792969\n", - " 10.311668\n", + " 0.929431\n", + " 0.008475\n", + " 99.816406\n", + " 10.846709\n", " \n", " \n", " 27\n", " SDII\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 9.553320\n", - " 0.043805\n", - " 88.128906\n", + " 10.081266\n", + " 0.034378\n", + " 95.632812\n", " 1.000000\n", " \n", " \n", @@ -1232,20 +1389,20 @@ " SDII\n", " Xclim\n", " 2. Flox (Standard)\n", - " 0.785351\n", - " 0.003506\n", - " 97.515625\n", - " 12.164400\n", + " 0.793418\n", + " 0.013971\n", + " 41.550781\n", + " 12.706125\n", " \n", " \n", " 29\n", " SDII\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.926658\n", - " 0.013940\n", - " 66.054688\n", - " 10.309438\n", + " 0.947046\n", + " 0.013728\n", + " 66.691406\n", + " 10.644964\n", " \n", " \n", "\n", @@ -1253,68 +1410,68 @@ ], "text/plain": [ " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 22.998022 0.151736 \n", - "1 WSDI Earthkit 2. Flox (Standard) 25.681127 0.157031 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.910770 0.050164 \n", - "3 WSDI Xclim 1. No Flox (Standard) 22.648726 0.125794 \n", - "4 WSDI Xclim 2. Flox (Standard) 25.655004 0.385313 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 19.338293 0.038324 \n", - "6 CWD Earthkit 1. No Flox (Standard) 22.589267 0.326003 \n", - "7 CWD Earthkit 2. Flox (Standard) 24.360402 0.136368 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 23.338227 0.764065 \n", - "9 CWD Xclim 1. No Flox (Standard) 22.944829 0.434918 \n", - "10 CWD Xclim 2. Flox (Standard) 24.539212 0.711479 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 24.471774 0.285352 \n", - "12 DTR Earthkit 1. No Flox (Standard) 9.405311 0.306235 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.420176 0.016578 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.653307 0.021712 \n", - "15 DTR Xclim 1. No Flox (Standard) 8.882797 0.090805 \n", - "16 DTR Xclim 2. Flox (Standard) 1.362044 0.018927 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.631367 0.006617 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.275366 0.124282 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.267101 0.023662 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.410126 0.019725 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.317168 0.044009 \n", - "22 HDD Xclim 2. Flox (Standard) 1.266768 0.010298 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.400595 0.013077 \n", - "24 SDII Earthkit 1. No Flox (Standard) 9.790377 0.283909 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.784884 0.003817 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.926457 0.008573 \n", - "27 SDII Xclim 1. No Flox (Standard) 9.553320 0.043805 \n", - "28 SDII Xclim 2. Flox (Standard) 0.785351 0.003506 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.926658 0.013940 \n", + "0 WSDI Earthkit 1. No Flox (Standard) 27.163342 0.358026 \n", + "1 WSDI Earthkit 2. Flox (Standard) 20.029513 0.494503 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 21.258730 0.601173 \n", + "3 WSDI Xclim 1. No Flox (Standard) 28.134670 0.082817 \n", + "4 WSDI Xclim 2. Flox (Standard) 20.230234 0.132923 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 25.717589 0.356579 \n", + "6 CWD Earthkit 1. No Flox (Standard) 30.626105 0.221556 \n", + "7 CWD Earthkit 2. Flox (Standard) 26.354595 0.846001 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.319477 0.247352 \n", + "9 CWD Xclim 1. No Flox (Standard) 29.532964 0.445733 \n", + "10 CWD Xclim 2. Flox (Standard) 27.493610 0.492927 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 20.778197 0.299679 \n", + "12 DTR Earthkit 1. No Flox (Standard) 8.674074 0.076174 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.387059 0.023953 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.672507 0.015136 \n", + "15 DTR Xclim 1. No Flox (Standard) 8.804974 0.183875 \n", + "16 DTR Xclim 2. Flox (Standard) 1.363925 0.006582 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.644765 0.022314 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.345279 0.089340 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.319415 0.037436 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.457090 0.009215 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.390543 0.143334 \n", + "22 HDD Xclim 2. Flox (Standard) 1.274181 0.011207 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.430180 0.010546 \n", + "24 SDII Earthkit 1. No Flox (Standard) 10.129517 0.017331 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.798818 0.006143 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.929431 0.008475 \n", + "27 SDII Xclim 1. No Flox (Standard) 10.081266 0.034378 \n", + "28 SDII Xclim 2. Flox (Standard) 0.793418 0.013971 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.947046 0.013728 \n", "\n", " max_mem Speedup \n", - "0 898.910156 0.984812 \n", - "1 898.953125 0.881921 \n", - "2 130.675781 1.083113 \n", - "3 898.929688 1.000000 \n", - "4 898.953125 0.882819 \n", - "5 899.066406 1.171185 \n", - "6 135.816406 1.015740 \n", - "7 100.550781 0.941890 \n", - "8 107.832031 0.983144 \n", - "9 95.683594 1.000000 \n", - "10 169.058594 0.935027 \n", - "11 81.980469 0.937604 \n", - "12 47.214844 0.944445 \n", - "13 139.820312 6.254715 \n", - "14 171.585938 5.372745 \n", - "15 65.867188 1.000000 \n", - "16 91.519531 6.521666 \n", - "17 182.898438 5.445001 \n", - "18 61.300781 1.005746 \n", - "19 93.789062 5.774732 \n", - "20 147.800781 5.189017 \n", - "21 82.519531 1.000000 \n", - "22 48.773438 5.776251 \n", - "23 149.605469 5.224329 \n", - "24 80.644531 0.975787 \n", - "25 66.554688 12.171627 \n", - "26 91.792969 10.311668 \n", - "27 88.128906 1.000000 \n", - "28 97.515625 12.164400 \n", - "29 66.054688 10.309438 " + "0 898.859375 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1wAEHuL5q0NSpU91j1e+WWbNmud/Dn6eCfP823P5ff/11tseq/XSb7gs6+eSTs84+++zI73fccUfczxjB/vGcOXPc79OmTcvKyc8//+y2nTBhQo7bAgBQWGOs7777blbTpk2zDj30UDdWOHv2bLfN448/HvXYZcuWufHEESNGxNy3+kXap8YR9fi33347W19N/bmXX37ZjZWq/xHsv6RrnEw0rqfXtnz58sht6gNoO/WJgmOLes/X7e+88477Xa/h6KOPdv3D4OtR26of2a1bt2ztcO+992Z7nbpP7fLnn39m62t99tlnCdso2L6xhNtGffAOHTpkpUJ9YI0xn3DCCVFjirfffrvrM/lxYk/9qmCfKZX+fyy+X6c2SdR/1rjmaaed5n7WMekxM2fOjJz3YF8/1mcFPT7W5x1dB59//nnMY1NfU/1ajY8CJR0Z4EAuaYaZsi1VIuXcc891GbvKXvWlRzRbUTPQVLJFglkpmtWvmf6ajRaPZqepvLVKzygrRF+awaVZ/SrLkxvVqlVz2QnKZPAl9TQDUGVxlH3rj10ldfR6lFkcPG5lJmsGoGZGJqJZg5pF9/TTT7tZhCq/p3I8mm2n8juarRak+3fZZZfI75odp1lyakNfqk+Z0ZpVV7169WxtKf6Ykj12X/LGnx9PszTD5WNys3aRSm0GaRajyiqqrFA8Komj7JNYM02Vna+2UKaMp4xwvSbfBvGoDKJmg6r91A66bq+55hqX9RKuBpCIskR0bjW7VSWGdE7jlXUqrPOQ6t9JuDy8z/ryZbUKSsuWLaPK/yurScIzNf3t/nhU8UDtpr/PYDtut912rjRVrL9FXT/5WYoKAFB08d7836VilEkR/FJmUJD6wMokUZ9ImULKhlUpxGCGsJbw6dSpU9T7rfrLKq+oPo/6FuqnzZo1y/VTCnvdcT2/MpiUMaKs72C/WZnD6icGs19KSr8nSBk/Ej43fqkqrYWoTPlY/ST1XdWHV181+NwqH16qVKnIc2utR1FGejy+f6vrKUglPfWawxlF6hvrvvC5Cp4n7TPeZ4ygvfbay2UDKSNMZTkTZc37zxz0GwEARWmMVaW/NUaoTGl913u03ovV3wm+R+s+vS8G+wcqXa5scfVFND6kfWpcSmKNEanyjMpqq1KOKueEl21MR1/c0+3BLGtfZltje8E1usP9KY01q0+k/lTw9aj/qPLh6k+qTxuk28OUES/BUtrKCN9///1dvyw/qa+mjHdlbKtiTqz109VWOl8aL1b5eJ1ffde4ZvDcqq+uPtO+++6bsM+UbP8/1X5nmK6HuXPnuutDn1N0vaTafhUqVIh8zlH2v7LlVQ1TS9zEKlOvPp5eR7A6AlBS5S3KA5RgKo2iToYCcBpIUQkYlZ9RGRbxbzLaTl+x/P3333H3r5IuKqGukjXqCKijog6f3tzCAeRU6I33zTffdCV29IauMiwq2+LLn/sOo4Kx6kiketyeOll6Q/dv6uo0qMSh2kpv2MFBo2DwO3ybOiM1atRwx6TOojqviY4p2WPXfiW8VrmCrip3kxc5vZ54fLkiDfKFKcitY1XQWwNxmrig0vEq0aiBz3jUAVZb+zLhtWrVcvvXOoOakJBTOfsgXX+6TlSuXtegyv1rXZ5YCus8pPp3Et6nSgNJKu2QGxqIDFI50kS3+2P3/0dU+imWWB/OdH4L+vUAAIoG3pv/GyhMJhitNf00EKbBNQ3oackQz7/fqqR5POp76X1XEwjzs+RjstSvUhnucJ9JNEE0Vj+zJPR7gvx+wn1pfR7RpAj1YTUJVJ99NDCqCaJ+MFR9V02+zemzhsqXq+8d6zx4/jz48xKk28ITEGL1eXWugn1Z7XOPPfbI8XOHyqPqdWqAWJM+dN3qWBVYUCl33+b+OSQvny0BAMjPMVaNBSl4F3wP1Xu0+kAqfx6LljgRBfxUzlqBSY0RKVCrQLEeq/fBWO93Wm5E/bpY6zGnqy/uhftN/n07p/6U30+8PqPaSccRDKLH6rOoj6F20Xi3xthUcl7l02OVEg/z45Q++SpMgeZg0osmPahvrvPx7LPPuserHTUxwU9kvO2221wAXiXh1bdTO6j9NXEhmT5TONko2f5/8DNDsmO4QTpWXRdqxzfeeMOtN67jToX6wL4dPI0Na3Kp2kX7DvLHxNggQAAcyDV1zPybT9OmTd2bumZhad3qtm3bukwT/yau7NRYFJSMRWucKONZ6yCrk+FpsCav61PrDVIdGwVSFQDXdz8g6OnYNRDjg/lhwayTZKnDoLZQAFydpqBYM9L8bX5ASMekAU5lbsTiO2vJHrvfrzLxg4OY6oSFBw99x0Ht7zuciSYCJPN6YvH3xQqSq/On2ZvqVKujGmviQixvv/22m3yg9YIU/PY0+Jsbej6tN6QOqdZSjKcgzkNh/p0UFf7/iNq8Zs2aST1G7eYfBwDIfLw3J0fvpVovTwFO/azsGz9g6t83b7jhBtcvjsVnUqhPllPGbKp9x2QoY0iDX+ozxctAKe7v/7np98R6vPqB4YFcrVWpL50Trd+u9SU1kVL9Y1UP0mN13rQ+eaJ9a7KFJkHoPMQaLA72b3VeNJE3SLfl5jxpn4k+YwTpM5OC3xrwV7aRPu9pAoCyh4J9Zt9fLu7XDQAgs8ZYw/Q+pYChgp/BfpXnb1M/T2NdCggGq7D49cRj0ZiVxraUOKKqQsFxs3T1xfPKv6/H6zOqP6l+ZTKUdKMKn6ocqsxsPS5cwTEWP0FPfebwZD31T3RswYkACob36dPHfWnMU9Uv1ZdRxVWN+ylYr+B4x44dXfJZkPrXwdeTbJ8p2f5/Mv3OZK4RrS+u6zhcISi31Cb6LBNrfJc+HvA/ZIAD+eTqq6+2t956y3V6VDpGpWk0w0tvROE355zoDVEdgnDHTgF2DbjkJZPDB1KfeuopN3NPZVjCs/c0g+y1115zg3zxOgGJqEMVa0DIl6QJ36cygOqI+E6RXqMC5SoL4weNdEwzZsxwt4VnO+bm2FUySF599dWoTpdm4yn4GuQ7wDqXwfKEykiPRQF+bRssUaiAtTqz8bJK/LlU52XJkiVxO0zqKGtfGsjSYJ0vdRSPn1UYvJZ0bQVLGKVCQepzzjnHldhUx7Mwz0Ne/k6KK01Y0QeBxYsXu5Knyfj1119dVhMAoGTgvTlnqnz06KOPugxYZV2oD6NJlSoRrX6Eym1q4Ezlonv27JljFocmvOrx8TLPU+k76vmT6cdrMqn6VNOmTXNZzApmivpaGhBUnzne5NpM7vcE+dKgeny8vpDaW+VKdb5Vcl1l8dWnVt9VQXENmvqJEbGoKpO207XTv3//mNtocrTovATPvyow6fOQMs9TpT6zPgfE+oyRqK+sbVUFSktghZdi8p85cvo8AQBAOuk9Wv04BVMTZWrHGv8SBabj0YQ7BdYVePVBcI3lprMvnlfqD+qY1EfQcfl2UXKMxq0bNWoUlf2diMbp1E9SOXqNdSqTPl5GdLgvpOfV2G54HFTLyajEebyMfvXRlFim862JiUuXLnVJW9pfsJKNKDiu7bQETLCv/vjjj7t+fTDZS20flEr/Pxbff1K/Mye6LlR6Xo/Jr0pSqkir544VpFcfT33aWBVKgZKGADiQTxSU1Yx6rUesYJ6CzFpbUKVZ1OHQDC+9yWkWlgY+NAChYHkslSpVcm/YY8aMcTPKNIimtWBUOjw8S88P7iigqQCrMhdU6iXRTH6VQVfnRVkPGjwLdyBPOeUU9xr0es4880w3cKNOhtYW1PpzrVu3jpvVLiolqM6OBogUsFYpGr3RP/HEE+7NN1zWUMeqwUiVKFInbMKECa7jqNl+nkrSaAag1m3UMalDpwwOBfvUeVJba+Av2WNXp0PrB2kigAba1PFSZ05trvYP0tpB6jiohLsGujSJQINIy5Yti/n6FeDXAKsyk1VySINfGnhV6Z6cOpkakPNrC4bpmNXxVMdfzz1kyBDLiV6XXr8mYWjmpNpMA3aaUZlbeh05KYjzkJe/k+JKf8u69jVTVB1Y/U3ptWnCiNYj0vUULNekma9a/yg3nXcAQPFVkt+b1adW2ecwDXjpeDQxUxNVdVzqmynrRX1MvVeq367+nfrQKp84cOBA11dX8FWDSSp3rYCjvvs1w7VOpEqoawBQbam+rkpaamkabaPnTKXvqPKcGpDTAKHe99WX92t1h6k/p9KeysbRd50/9Zt1fkaOHJlyOcXi3u8J0zWtzzb63KHr2bv33nvddd6sWTP3eUH94LFjx7r2U99b9FlEg8K6Lnr37u3OgQaodc4UKFd7a7Ba5Tj1Oe+hhx5y512D2RpoVyBdx6e/LQXiu3XrZk8//bS73vQ6NHir49D689p/qnR8Wl9U15wmX+j61N9zeN13TbLQNaFsdwXyNVlUr0uv+aijjoraVu2kazO/1/EEACA/NW7c2L2vakKXknj0vqX3XGURf/HFF64v1b17d/f+q37ZXXfd5d7/NE6r90WNxyWiMTxVW9SYmfoBCp5qn+nqi+eV+h7q++qYVIlTbaexQPXP1R/QWHAq1O9UZrv6mWrnZOg8qC31nKreqL6Q+mjqz2lMU4H14Jrcmhyo8W3drgmm6jfps4g+S/jgtvpc6k/rPKufps8A2n+42o7vM2k8Xv1G9Zk0GUBro/v2kVT6/7HoedXXUn8qJ4oHPPjgg5Zb6pOqgpH/WUF/XbM6bn2+CdO26uMW988GQH4gAA7kI3VgNHNQb2o+CKxsVK3Bpllr6mhoMEwDiyeddFLCfanDNmzYMDcwp0xYzUxTAFmdlyC92aoTqEEcdUqU+Tp8+PCEpbEVPFYgdfbs2a50TXjQUAMhGtTRPlXqRp0T3aY3d3U0c+oIqjOlgSK9bg1Y6fg12KM2UWA4nAHeqlUrN0ipwS4NMuk13XnnnVGBeT1Gg6dqW3Vw9Gavzoo6Qyrl7gdVUzl2ta8C8upAqeNw4IEHulLh4Yx9DWRqwoDOoTqRaq8uXbq451VnKUz7UftrXwpG6tg1WJrMYJfOhzpqyhAJZox42q/K88SauBCLrjUdh9q2X79+7vrTedCxqDNYUAriPOTl76Q402vReVRbaoBcH1w0sUIfDDQAH6SyVPoAldP/FwBAyZOp780arIxFz6msWfVLNfij4/IDXsp80UDeiBEj3DYKFiqoqSwgZdnedNNNLqtCA3B67cFShcqoVZ9UE1m1T22n92X1+33GUSp9R/XPNICr27Uv9W0VTI9FA1nKTNK5UN9SA2A6Hp1XlXQvaf2eMLW/Bi/VHwpeqwpca8Bcny80mKnPDdqf2tJPJlY2kz7H6e9C6yhqkq362/oMo0kgwZKoKq1av35912dXVSZtp88ywWv85ptvdp9pdK0oIK1rQudf12NuSo6rDRRQ19+W9q2Bf123+lygScSeBon1+nQda/KH+oX67BcuB+uXSvKTDAAAKMpUuVLv53qPVlKH+kAaa1M/04+d6T1P45B6r7zxxhvdRE5NftP7vYKniajPp4CrD07rfTReSfbcyutYayo0tqi+gp5DfV49j9pPz602S4X6G+pjqc+cSna8JoKqT6e+kBKD9LlA/Sll2mtsOJipr32/+eabbgxd2eHq96j/pT6Oz/rW/nRO9ZqUza6+mPrEmmAYDjarz6R+uPr0vs+kYLiqKAXHwJPt/ydqZ/Ufw8se5TdVi9JEBk+BerWtlrjRawtSVriWA9BnDABmpbI0JQoA0kSz9tT5Uec0EyiYr4E0lUbMLXWg1CFNNNMQiEWzcTVQqwF5AACAkkaZRao2pepYBVletLjT4KiW7dLE4nBmOAAAgKeJmQpYK/CsKkfFlSYNKhNc2fb5FaxWcpYy9zWpNpkkpcKgBChNsNCySZowAJR0/50CDwAoMpQppIwrlUECkjVr1iw36KuymAAAACWRsrVUCScvZSZLAmWgKSuO4DcAAIhFa2PPmDHDbr/9dpcRraoxxcUDDzzgssk/+eQTVxlIwW/9rmqY+ZmprWxzlVxXv0pVCdJNlWdVeUhZ/wS/gf9iGggAFDHqVA4YMMCVXgyvZQPEs2rVKvfBROU2AQAASiqt5ahymyqhmdO69SWRSpCqv6gyrwAAALGoKuWXX37pSo1rKZXitJ60yqarrLkytNXvUel29Q8VrM5vyo5XmXU9lyoyppPGkdW/U2VRAP9FCXQAAAAAAAAAAAAAQEagBDoAAAAAAAAAAAAAICMQAAcAAAAAAAAAAAAAZAQC4EDAAw88YCeffLJt27Ytclu9evWivho1amQnnXSS23bdunVpb7+XXnrJHZfW+fC0rkmrVq2sqHj55ZfdMT733HPZ7tN6MgceeKBbuzgVsV6jftftBe3777+3m2++2bp16+auB722mTNn5mmf999/v9tPs2bN3HqFYXptF1xwgeUXtVP42vZf7777rttG15R+1zWWDlqb58Ybb4y67ccff7Srr77aWrdubQ0bNrQjjzzSOnXqZLfccktUu7366qv25JNPWlH72yxIei5dR97EiRPtmGOOyfZ/avPmzdamTZu0tw8AILX+qN7rMlF+9d/UF1M7TZ061fLLDz/84N5bY72XF4W+RvB1+y/1q5s2bWoXXnihzZkzJ099mNw+Prf0uUDtvWbNmqS2nzFjRlTfJ7+9//77bh1FtedBBx1kLVu2tGuuucZdF7m1fv16d8yxPjsUpc91q1evtsMPP9zefvvtQn9uAACKIvrouZNJfXTG04DihwA48P9+//13GzNmjF166aVWunT0n8aJJ57ogrf6evDBB93vo0aNcgMgRdHFF1/sOmZFRceOHV3A8rbbbovqnCgwp0Gdvffe2y677LI8P49es157QZs7d64bDKpataobEMtPK1eutMcee8wKQ4UKFSLXdfCrcePGlm5qXw2CXnLJJZHbvv32WzvttNNcEFy3q50GDx7sBiM//PBDW7VqVWTbKVOm2NixY60k08SA7bffPtv1VK5cOdd++l/2999/p+34AACp9UczVWH133I7uKbjW7p0abb7ilpf44orrnD9uHHjxrn2nD17tp155pn2888/W3GhY1Z7pxIAL6jPPCNGjLDzzjvPTUS56aab7IknnrC+ffu6SQHqY7311lu5DoDrmD/77LMi/blOn3N69+7t2mHTpk2F/vwAABQl9NGLlnT10RlPA4qfkjGqAiRBb46VK1e2E044Idt9u+yyi8v01Vfz5s1dsPbUU091QbqNGzcWufbdc889rX79+laUKGupfPnydu2111pWVpa7TQMqCogr+3u77bbL83PoNeu1F7QOHTq4gOujjz7qgvv5SRm7Tz31lK1YscIKmgbW/XUd/KpSpYql2yOPPGLHH3+87bbbbpHb1C46Zv2tKhCu7O+2bdu6v0cNQtaqVcsymQZMU1G2bFlXpUDtFX7sKaecYqVKlYpZlQEAUDT7o8XV1q1bEwbQCqv/Vlxs2LAhV4/ba6+9XD9OWbu9evVyfW69/0+ePDnfjzHTaeBUE1H+85//uImEqv51xBFHWJcuXeyFF16w/fbbzwYMGGBLliwpdp/rUulPnnHGGW5g+c0338y35wcAoDiijw7fR2c8DSheCIADZm5QToMZ7dq1SzrbRoOTCiAFt//oo4/soosushYtWrjyzArgqYSzsnqD9PsNN9zgMldVTk9ZxBpg+Pjjj6O20+8qA33YYYfZIYcc4rb55JNPcjy2WKXyfOlMlSPXII721759+0i56yBlilx55ZWuHLeOT9uPHz8+T9eKJhEoe0LZDspMUVs988wzLrPi4IMPjlmyRsG7Qw891H0p6KySzqmU0PRldrSvO+64w44++mi3L5WE/PPPP13JbJ0HBVL1pYHCf//9N8fXUpAZWQrmbtmyJalyjsp4Vil2Bc11npRlf/fddxd4lsbnn3/urku1pb8u33vvvaj7GzRokK2svS/rmNN5VKb3N9984855+PXusMMO7isW/T2Ksp10PBqwC5YE9TRLVAOYTZo0cX9byuLRMfmJGeGy8yp/qW10nSrgrv8VYV999ZVrB/3d6zq766673HkMe/311+3ss89222h/+tu68847s5Up13Ws9l24cKHbXj8rC0d03Q4aNMhds7r9nHPOsUWLFsVsE03U0favvfZa1O2ajKLnfv7557O9bgBA8emPptovUGa5Bm2C1C/S++Qbb7wRuW3evHnutunTp0du0+Q89WvVz9X+9T6p99Tg+51fPmX06NGu0oi20Xvjp59+mnT/TRm3vuKS3isV0NX7mSbCJUOTU4cPH25HHXWUe3zPnj1d3yJMmbx67eoP6Bg1qVHv08F+S//+/d3PCij7/oRuz6mvoTbXa1C/wff11c8MfybwfQ1N5NPz6zjyK9tXzyvq86bSjwtSJraOW22k4LraKxz0jVfCXm2kr2TPq/q+mhwrunZ9m8ZbZkjP6T+fBM+Brzal60D9MR2f2kJ/F6oclEx2+cMPP+wyoGNV+1J1HX1+UCA5WF4z/HpjfS7Tsenzleg8+2NOtARArM916rvptauvrLZUcF5/2+Fzo+PR/5NZs2a586zzfd1117n79JlS96s/qX0ce+yx1q9fv6gAuT6/afL3s88+m2ObAQCQqeij/xd99P/20RlPA4qXsuk+AKAoULBNg4YaAIhFgwx+cE+BKgVxJ02a5NZnVPkTb/HixW4wScE1Bcg1KKZyed27d3dBWL+t1jDWQNzll1/uyn9rIEa/B0s4v/LKK27QRQNACiQqm1PZmgp2KSPBD56kQoNbGuzTAIkvjaxSflqHpXbt2pEyMhog2X333d3z77rrri7beejQoa5csrb3NGiitlCQLhlqLw3wjRw50ipVquQGfIIlrr17773XDZAp+6lPnz6uLbXu9m+//Wa5ocFfnVsNhuqcqD1VJlJtqmPQ8aj9tZ2CqwoupkvNmjXd9fL000+7116nTp2Y22lQT4OxGujSYJVehwY0lZU+f/589z0Z4SCtgshlypSJu73OtwKy+++/vw0bNsx1/DSRQQOiakedYw1oasBYg476Wdewzp8mYGjShf4+EtGkDB2DHhukgVddw1dddZWbHKHBOpVxD9NECw1Mqm1iDSLrGtDj1dY+eK3rWyWtgte3LFiwwF0vmqihQUAFyq+//nqXZaXBRv83o+C0MtBV5l/HNGHCBJc9FGtyiQIHGniuWLGi/fTTTy5IoP9B4RJNWltIE2r096jnVwad/hf5sqb629FAuUrF6/5Y9Pe7zz77uBKhp59+etR9GszWufvuu++iBu0BAEWzP5pIsv0CBbOUzfnHH39Y9erVXT9A7+1679LES02OEv2sfpLeK3zwW+/fCszr/UdZqXoveuihh9z7qvpYQZrsqD6u+pLq8+l9M1nqn+r9W++B6gvoGPV+uXbt2qQer/6cMmb13q7HaF/qs2oSqO/vKiB/7rnnuoCgJg2or6ngt/rmyu5QpRkFBNVfVP9GgX9N7hO9dvVB4vU1NDio9+ovvvjC9ds12U5tpACvzvGLL74Y1X/RZAMt76LXu8cee7j+QX7wgeBgXzKZflyQ+jy6ZjRZb/ny5XbPPfe4tlRWeaoVg3I6r7q+tO60rh1tpz6M7LvvvjH3pzbW5zJdz8GKNrqufX9J51lreOv59HlF50D9Pm2v1x6L/jbUb1VbxDsX+ry38847uwm9qdCxqR107alf5vvEO+20U0r70fWoz6I6F+oXq920PJf6jPoMqT6rp79dffbUc+r61t+wrg1NvFC76DrQuVQ/+IMPPnD9z+Dr1v8AXRv6vFoUqkQBAFDY6KP/F3306P4R42lA8UAAHPj/9ebED2yFKZilryAFsRTQC1KZPE8DLxoc0Zvicccd57JIFQgUBaw04NG1a9fI9m3atIn8rJn3t956qxt402CGp4xxZaJqECKnLNp4g6MKyGsg0r9eZUMo40eDQ6IBTAWB9Ubut1MGjWY8avBUAy3KiBANoCQKlsaiLBINVOk1KrsiPPikgUSVv1ZGiAbbPB1Dbmm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" ] @@ -1367,10 +1524,8 @@ "# Define a consistent order for the bars\n", "hue_order = sorted(df_res['Label'].unique())\n", "\n", - "plt.figure(figsize=(20, 6))\n", - "\n", "# Plot 1: Relative Speedup (Robust)\n", - "plt.subplot(1, 3, 1)\n", + "plt.figure(figsize=(10, 6))\n", "sns.barplot(\n", " data=df_res,\n", " x=\"Indicator\",\n", @@ -1383,9 +1538,11 @@ "plt.axhline(1.0, color='black', linestyle='--', alpha=0.5, linewidth=1)\n", "plt.legend(title=\"Configuration\", fontsize='small')\n", "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "# Plot 2: Median Execution Time\n", - "plt.subplot(1, 3, 2)\n", + "plt.figure(figsize=(10, 6))\n", "# We plot the pre-calculated median. \n", "# Note: standard sns.barplot aggregates data if there are duplicates. \n", "# Here df_res has 1 row per case, so it just plots the value.\n", @@ -1400,9 +1557,11 @@ "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", "plt.legend().remove() \n", "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", "\n", "# Plot 3: Peak Memory Usage\n", - "plt.subplot(1, 3, 3)\n", + "plt.figure(figsize=(10, 6))\n", "sns.barplot(\n", " data=df_res,\n", " x=\"Indicator\",\n", @@ -1441,4 +1600,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file From 241ff9fe552eda86194b98bbf09caccf2e5fbcc9 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 9 Feb 2026 21:28:54 +0100 Subject: [PATCH 13/47] Re-run performance analysis notebook to update execution counts and benchmark results. --- docs/notebooks/performance_analysis.ipynb | 694 +++++++++++----------- 1 file changed, 357 insertions(+), 337 deletions(-) diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index f0b6cf6..d6cd40f 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "id": "410735a7", "metadata": {}, "outputs": [], @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "id": "68281ebe", "metadata": {}, "outputs": [ @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "id": "fba35188", "metadata": {}, "outputs": [ @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "id": "5d1ba8a5", "metadata": {}, "outputs": [], @@ -486,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "id": "acf4d31d", "metadata": {}, "outputs": [ @@ -547,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "id": "66739804", "metadata": {}, "outputs": [], @@ -607,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "id": "46022af7", "metadata": {}, "outputs": [], @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "id": "e0be7a44", "metadata": {}, "outputs": [ @@ -643,222 +643,222 @@ "=== Benchmarking WSDI ===\n", " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", - " Run 1: 26.8280s, Mem Peak Delta: 898.81 MiB\n", - " Run 2: 26.9964s, Mem Peak Delta: 898.86 MiB\n", - " Run 3: 27.1795s, Mem Peak Delta: 897.89 MiB\n", - " Run 4: 27.1633s, Mem Peak Delta: 897.86 MiB\n", - " Run 5: 27.8780s, Mem Peak Delta: 897.79 MiB\n", + " Run 1: 27.8730s, Mem Peak Delta: 899.18 MiB\n", + " Run 2: 27.8275s, Mem Peak Delta: 897.91 MiB\n", + " Run 3: 27.9897s, Mem Peak Delta: 898.84 MiB\n", + " Run 4: 27.9458s, Mem Peak Delta: 898.81 MiB\n", + " Run 5: 27.8991s, Mem Peak Delta: 898.84 MiB\n", " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", - " Run 1: 19.2791s, Mem Peak Delta: 898.82 MiB\n", - " Run 2: 19.3164s, Mem Peak Delta: 898.88 MiB\n", - " Run 3: 20.0295s, Mem Peak Delta: 898.91 MiB\n", - " Run 4: 20.0448s, Mem Peak Delta: 898.88 MiB\n", - " Run 5: 20.5861s, Mem Peak Delta: 898.79 MiB\n", + " Run 1: 19.7318s, Mem Peak Delta: 898.93 MiB\n", + " Run 2: 19.9195s, Mem Peak Delta: 898.84 MiB\n", + " Run 3: 19.9674s, Mem Peak Delta: 898.86 MiB\n", + " Run 4: 19.8348s, Mem Peak Delta: 898.81 MiB\n", + " Run 5: 20.0525s, Mem Peak Delta: 898.85 MiB\n", " Warm-up run for run_earthkit_opt_flox_WSDI...\n", "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", - " Run 1: 21.2633s, Mem Peak Delta: 159.07 MiB\n", - " Run 2: 20.8128s, Mem Peak Delta: 108.58 MiB\n", - " Run 3: 20.5832s, Mem Peak Delta: 137.06 MiB\n", - " Run 4: 21.2587s, Mem Peak Delta: 73.79 MiB\n", - " Run 5: 22.3321s, Mem Peak Delta: 54.58 MiB\n", + " Run 1: 20.6069s, Mem Peak Delta: 44.44 MiB\n", + " Run 2: 20.6198s, Mem Peak Delta: 114.90 MiB\n", + " Run 3: 20.7867s, Mem Peak Delta: 60.07 MiB\n", + " Run 4: 20.9424s, Mem Peak Delta: 110.00 MiB\n", + " Run 5: 20.5628s, Mem Peak Delta: 119.67 MiB\n", " Warm-up run for run_xclim_noflox_WSDI...\n", "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", - " Run 1: 28.1347s, Mem Peak Delta: 898.86 MiB\n", - " Run 2: 28.1445s, Mem Peak Delta: 898.86 MiB\n", - " Run 3: 28.1346s, Mem Peak Delta: 898.80 MiB\n", - " Run 4: 27.9559s, Mem Peak Delta: 898.89 MiB\n", - " Run 5: 28.2008s, Mem Peak Delta: 898.81 MiB\n", + " Run 1: 27.7358s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 27.8259s, Mem Peak Delta: 898.90 MiB\n", + " Run 3: 27.9684s, Mem Peak Delta: 898.84 MiB\n", + " Run 4: 27.9526s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 28.1973s, Mem Peak Delta: 898.84 MiB\n", " Warm-up run for run_xclim_flox_WSDI...\n", "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", - " Run 1: 20.1094s, Mem Peak Delta: 898.79 MiB\n", - " Run 2: 20.4975s, Mem Peak Delta: 898.89 MiB\n", - " Run 3: 20.2302s, Mem Peak Delta: 898.79 MiB\n", - " Run 4: 20.2439s, Mem Peak Delta: 898.96 MiB\n", - " Run 5: 20.1670s, Mem Peak Delta: 898.79 MiB\n", + " Run 1: 20.3087s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 20.8343s, Mem Peak Delta: 899.02 MiB\n", + " Run 3: 20.7178s, Mem Peak Delta: 898.84 MiB\n", + " Run 4: 20.5356s, Mem Peak Delta: 898.81 MiB\n", + " Run 5: 20.4841s, Mem Peak Delta: 898.84 MiB\n", " Warm-up run for run_xclim_opt_flox_WSDI...\n", "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", - " Run 1: 25.3983s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 25.8664s, Mem Peak Delta: 898.86 MiB\n", - " Run 3: 25.6448s, Mem Peak Delta: 898.79 MiB\n", - " Run 4: 25.7176s, Mem Peak Delta: 898.93 MiB\n", - " Run 5: 26.4639s, Mem Peak Delta: 898.93 MiB\n", + " Run 1: 25.4670s, Mem Peak Delta: 898.84 MiB\n", + " Run 2: 26.0923s, Mem Peak Delta: 898.85 MiB\n", + " Run 3: 25.7871s, Mem Peak Delta: 898.91 MiB\n", + " Run 4: 25.0722s, Mem Peak Delta: 898.79 MiB\n", + " Run 5: 25.5977s, Mem Peak Delta: 898.84 MiB\n", "\n", "=== Benchmarking CWD ===\n", " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", - " Run 1: 30.2380s, Mem Peak Delta: 99.16 MiB\n", - " Run 2: 30.7625s, Mem Peak Delta: 85.13 MiB\n", - " Run 3: 30.2546s, Mem Peak Delta: 71.05 MiB\n", - " Run 4: 30.6261s, Mem Peak Delta: 68.62 MiB\n", - " Run 5: 30.6804s, Mem Peak Delta: 59.89 MiB\n", + " Run 1: 28.8221s, Mem Peak Delta: 66.03 MiB\n", + " Run 2: 28.7667s, Mem Peak Delta: 57.00 MiB\n", + " Run 3: 29.4298s, Mem Peak Delta: 72.53 MiB\n", + " Run 4: 28.7520s, Mem Peak Delta: 65.12 MiB\n", + " Run 5: 28.7452s, Mem Peak Delta: 62.76 MiB\n", " Warm-up run for run_earthkit_lazy_flox_CWD...\n", "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", - " Run 1: 28.2647s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 26.0986s, Mem Peak Delta: 37.20 MiB\n", - " Run 3: 26.2005s, Mem Peak Delta: 48.67 MiB\n", - " Run 4: 26.3546s, Mem Peak Delta: 69.92 MiB\n", - " Run 5: 27.4395s, Mem Peak Delta: 91.67 MiB\n", + " Run 1: 27.0645s, Mem Peak Delta: 8.92 MiB\n", + " Run 2: 27.2851s, Mem Peak Delta: 50.64 MiB\n", + " Run 3: 27.9523s, Mem Peak Delta: 15.12 MiB\n", + " Run 4: 33.2643s, Mem Peak Delta: 128.43 MiB\n", + " Run 5: 27.4686s, Mem Peak Delta: 44.89 MiB\n", " Warm-up run for run_earthkit_opt_flox_CWD...\n", "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.0539s, Mem Peak Delta: 89.59 MiB\n", - " Run 2: 20.6084s, Mem Peak Delta: 110.74 MiB\n", - " Run 3: 20.7335s, Mem Peak Delta: 107.82 MiB\n", - " Run 4: 20.2407s, Mem Peak Delta: 50.92 MiB\n", - " Run 5: 20.3195s, Mem Peak Delta: 91.41 MiB\n", + " Run 1: 20.0614s, Mem Peak Delta: 63.48 MiB\n", + " Run 2: 20.3331s, Mem Peak Delta: 123.46 MiB\n", + " Run 3: 20.2189s, Mem Peak Delta: 33.37 MiB\n", + " Run 4: 20.3473s, Mem Peak Delta: 92.31 MiB\n", + " Run 5: 20.4886s, Mem Peak Delta: 83.07 MiB\n", " Warm-up run for run_xclim_noflox_CWD...\n", "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", - " Run 1: 28.8911s, Mem Peak Delta: 61.70 MiB\n", - " Run 2: 28.6861s, Mem Peak Delta: 77.30 MiB\n", - " Run 3: 29.5600s, Mem Peak Delta: 83.47 MiB\n", - " Run 4: 29.5330s, Mem Peak Delta: 65.57 MiB\n", - " Run 5: 29.8725s, Mem Peak Delta: 62.04 MiB\n", + " Run 1: 28.3228s, Mem Peak Delta: 61.27 MiB\n", + " Run 2: 29.4927s, Mem Peak Delta: 63.88 MiB\n", + " Run 3: 28.9596s, Mem Peak Delta: 70.38 MiB\n", + " Run 4: 28.8950s, Mem Peak Delta: 46.79 MiB\n", + " Run 5: 28.9882s, Mem Peak Delta: 69.48 MiB\n", " Warm-up run for run_xclim_flox_CWD...\n", "Benchmarking run_xclim_flox_CWD (5 runs)...\n", - " Run 1: 26.4795s, Mem Peak Delta: 30.13 MiB\n", - " Run 2: 26.8512s, Mem Peak Delta: 84.45 MiB\n", - " Run 3: 27.5432s, Mem Peak Delta: 82.79 MiB\n", - " Run 4: 27.8137s, Mem Peak Delta: 41.73 MiB\n", - " Run 5: 27.4936s, Mem Peak Delta: 115.35 MiB\n", + " Run 1: 26.6984s, Mem Peak Delta: 27.05 MiB\n", + " Run 2: 27.5815s, Mem Peak Delta: 41.75 MiB\n", + " Run 3: 27.4208s, Mem Peak Delta: 71.05 MiB\n", + " Run 4: 27.5665s, Mem Peak Delta: 62.85 MiB\n", + " Run 5: 26.8656s, Mem Peak Delta: 26.84 MiB\n", " Warm-up run for run_xclim_opt_flox_CWD...\n", "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.6049s, Mem Peak Delta: 20.08 MiB\n", - " Run 2: 21.2001s, Mem Peak Delta: 113.94 MiB\n", - " Run 3: 20.7782s, Mem Peak Delta: 52.78 MiB\n", - " Run 4: 20.3147s, Mem Peak Delta: 66.99 MiB\n", - " Run 5: 20.9418s, Mem Peak Delta: 94.09 MiB\n", + " Run 1: 19.8813s, Mem Peak Delta: 84.47 MiB\n", + " Run 2: 19.8554s, Mem Peak Delta: 58.26 MiB\n", + " Run 3: 19.9892s, Mem Peak Delta: 41.64 MiB\n", + " Run 4: 19.9504s, Mem Peak Delta: 24.76 MiB\n", + " Run 5: 20.1185s, Mem Peak Delta: 24.79 MiB\n", "\n", "=== Benchmarking DTR ===\n", " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", - " Run 1: 8.6815s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 8.6741s, Mem Peak Delta: 33.26 MiB\n", - " Run 3: 8.5244s, Mem Peak Delta: 8.30 MiB\n", - " Run 4: 8.5588s, Mem Peak Delta: 32.91 MiB\n", - " Run 5: 8.7207s, Mem Peak Delta: 0.00 MiB\n", + " Run 1: 8.5965s, Mem Peak Delta: 24.08 MiB\n", + " Run 2: 8.6182s, Mem Peak Delta: 26.71 MiB\n", + " Run 3: 8.6238s, Mem Peak Delta: 33.28 MiB\n", + " Run 4: 8.5962s, Mem Peak Delta: 0.38 MiB\n", + " Run 5: 8.6086s, Mem Peak Delta: 18.86 MiB\n", " Warm-up run for run_earthkit_lazy_flox_DTR...\n", "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", - " Run 1: 1.3558s, Mem Peak Delta: 100.01 MiB\n", - " Run 2: 1.3922s, Mem Peak Delta: 66.67 MiB\n", - " Run 3: 1.3871s, Mem Peak Delta: 108.08 MiB\n", - " Run 4: 1.4307s, Mem Peak Delta: 74.76 MiB\n", - " Run 5: 1.3847s, Mem Peak Delta: 113.68 MiB\n", + " Run 1: 1.3398s, Mem Peak Delta: 141.43 MiB\n", + " Run 2: 1.3505s, Mem Peak Delta: 91.47 MiB\n", + " Run 3: 1.3465s, Mem Peak Delta: 83.12 MiB\n", + " Run 4: 1.3422s, Mem Peak Delta: 83.28 MiB\n", + " Run 5: 1.3609s, Mem Peak Delta: 81.71 MiB\n", " Warm-up run for run_earthkit_opt_flox_DTR...\n", "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6725s, Mem Peak Delta: 191.08 MiB\n", - " Run 2: 1.6556s, Mem Peak Delta: 133.26 MiB\n", - " Run 3: 1.6982s, Mem Peak Delta: 66.37 MiB\n", - " Run 4: 1.6596s, Mem Peak Delta: 182.82 MiB\n", - " Run 5: 1.6782s, Mem Peak Delta: 74.75 MiB\n", + " Run 1: 1.6420s, Mem Peak Delta: 158.02 MiB\n", + " Run 2: 1.6677s, Mem Peak Delta: 33.20 MiB\n", + " Run 3: 1.6489s, Mem Peak Delta: 141.30 MiB\n", + " Run 4: 1.6321s, Mem Peak Delta: 108.11 MiB\n", + " Run 5: 1.6601s, Mem Peak Delta: 95.85 MiB\n", " Warm-up run for run_xclim_noflox_DTR...\n", "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", - " Run 1: 8.7513s, Mem Peak Delta: 2.50 MiB\n", - " Run 2: 9.0851s, Mem Peak Delta: 24.75 MiB\n", - " Run 3: 8.8050s, Mem Peak Delta: 33.12 MiB\n", - " Run 4: 9.1217s, Mem Peak Delta: 41.42 MiB\n", - " Run 5: 8.6643s, Mem Peak Delta: 16.67 MiB\n", + " Run 1: 8.5330s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 8.5588s, Mem Peak Delta: 24.89 MiB\n", + " Run 3: 8.5554s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 8.6053s, Mem Peak Delta: 16.55 MiB\n", + " Run 5: 8.5404s, Mem Peak Delta: 25.00 MiB\n", " Warm-up run for run_xclim_flox_DTR...\n", "Benchmarking run_xclim_flox_DTR (5 runs)...\n", - " Run 1: 1.3531s, Mem Peak Delta: 48.56 MiB\n", - " Run 2: 1.3507s, Mem Peak Delta: 40.51 MiB\n", - " Run 3: 1.3659s, Mem Peak Delta: 36.82 MiB\n", - " Run 4: 1.3639s, Mem Peak Delta: 41.49 MiB\n", - " Run 5: 1.3656s, Mem Peak Delta: 65.02 MiB\n", + " Run 1: 1.3459s, Mem Peak Delta: 66.66 MiB\n", + " Run 2: 1.3357s, Mem Peak Delta: 89.96 MiB\n", + " Run 3: 1.3427s, Mem Peak Delta: 50.05 MiB\n", + " Run 4: 1.3366s, Mem Peak Delta: 74.89 MiB\n", + " Run 5: 1.3452s, Mem Peak Delta: 66.19 MiB\n", " Warm-up run for run_xclim_opt_flox_DTR...\n", "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6826s, Mem Peak Delta: 150.31 MiB\n", - " Run 2: 1.6448s, Mem Peak Delta: 181.25 MiB\n", - " Run 3: 1.6264s, Mem Peak Delta: 118.98 MiB\n", - " Run 4: 1.6609s, Mem Peak Delta: 141.14 MiB\n", - " Run 5: 1.6225s, Mem Peak Delta: 132.98 MiB\n", + " Run 1: 1.6360s, Mem Peak Delta: 108.08 MiB\n", + " Run 2: 1.6222s, Mem Peak Delta: 125.05 MiB\n", + " Run 3: 1.6499s, Mem Peak Delta: 141.43 MiB\n", + " Run 4: 1.6070s, Mem Peak Delta: 41.93 MiB\n", + " Run 5: 1.6011s, Mem Peak Delta: 107.99 MiB\n", "\n", "=== Benchmarking HDD ===\n", " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", - " Run 1: 7.2806s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.2965s, Mem Peak Delta: 58.33 MiB\n", - " Run 3: 7.3453s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 7.3729s, Mem Peak Delta: 8.88 MiB\n", - " Run 5: 7.5313s, Mem Peak Delta: 32.25 MiB\n", + " Run 1: 7.3256s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.2703s, Mem Peak Delta: 25.00 MiB\n", + " Run 3: 7.1994s, Mem Peak Delta: 23.80 MiB\n", + " Run 4: 7.2199s, Mem Peak Delta: 9.50 MiB\n", + " Run 5: 7.1747s, Mem Peak Delta: 3.12 MiB\n", " Warm-up run for run_earthkit_lazy_flox_HDD...\n", "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", - " Run 1: 1.3970s, Mem Peak Delta: 58.03 MiB\n", - " Run 2: 1.3345s, Mem Peak Delta: 25.00 MiB\n", - " Run 3: 1.2926s, Mem Peak Delta: 25.32 MiB\n", - " Run 4: 1.3194s, Mem Peak Delta: 21.38 MiB\n", - " Run 5: 1.2983s, Mem Peak Delta: 33.42 MiB\n", + " Run 1: 1.2583s, Mem Peak Delta: 19.40 MiB\n", + " Run 2: 1.2613s, Mem Peak Delta: 12.25 MiB\n", + " Run 3: 1.2405s, Mem Peak Delta: 18.20 MiB\n", + " Run 4: 1.2473s, Mem Peak Delta: 40.62 MiB\n", + " Run 5: 1.2692s, Mem Peak Delta: 24.96 MiB\n", " Warm-up run for run_earthkit_opt_flox_HDD...\n", "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4519s, Mem Peak Delta: 233.26 MiB\n", - " Run 2: 1.4638s, Mem Peak Delta: 161.30 MiB\n", - " Run 3: 1.4553s, Mem Peak Delta: 157.99 MiB\n", - " Run 4: 1.4571s, Mem Peak Delta: 157.19 MiB\n", - " Run 5: 1.4779s, Mem Peak Delta: 207.99 MiB\n", + " Run 1: 1.4035s, Mem Peak Delta: 99.74 MiB\n", + " Run 2: 1.4150s, Mem Peak Delta: 149.53 MiB\n", + " Run 3: 1.4151s, Mem Peak Delta: 74.72 MiB\n", + " Run 4: 1.4000s, Mem Peak Delta: 91.61 MiB\n", + " Run 5: 1.4108s, Mem Peak Delta: 133.76 MiB\n", " Warm-up run for run_xclim_noflox_HDD...\n", "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", - " Run 1: 7.5720s, Mem Peak Delta: 0.12 MiB\n", - " Run 2: 7.6712s, Mem Peak Delta: 8.38 MiB\n", - " Run 3: 7.3330s, Mem Peak Delta: 8.44 MiB\n", - " Run 4: 7.3025s, Mem Peak Delta: 50.75 MiB\n", - " Run 5: 7.3905s, Mem Peak Delta: 0.00 MiB\n", + " Run 1: 7.2109s, Mem Peak Delta: 12.59 MiB\n", + " Run 2: 7.5437s, Mem Peak Delta: 44.88 MiB\n", + " Run 3: 7.5749s, Mem Peak Delta: 11.50 MiB\n", + " Run 4: 7.5326s, Mem Peak Delta: 0.62 MiB\n", + " Run 5: 7.5081s, Mem Peak Delta: 2.25 MiB\n", " Warm-up run for run_xclim_flox_HDD...\n", "Benchmarking run_xclim_flox_HDD (5 runs)...\n", - " Run 1: 1.2945s, Mem Peak Delta: 25.85 MiB\n", - " Run 2: 1.2593s, Mem Peak Delta: 16.88 MiB\n", - " Run 3: 1.2741s, Mem Peak Delta: 25.09 MiB\n", - " Run 4: 1.2768s, Mem Peak Delta: 8.72 MiB\n", - " Run 5: 1.2742s, Mem Peak Delta: 8.38 MiB\n", + " Run 1: 1.2734s, Mem Peak Delta: 51.93 MiB\n", + " Run 2: 1.2619s, Mem Peak Delta: 31.27 MiB\n", + " Run 3: 1.3011s, Mem Peak Delta: 15.02 MiB\n", + " Run 4: 1.2718s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.2659s, Mem Peak Delta: 35.42 MiB\n", " Warm-up run for run_xclim_opt_flox_HDD...\n", "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4511s, Mem Peak Delta: 116.84 MiB\n", - " Run 2: 1.4302s, Mem Peak Delta: 107.97 MiB\n", - " Run 3: 1.4256s, Mem Peak Delta: 141.47 MiB\n", - " Run 4: 1.4202s, Mem Peak Delta: 116.32 MiB\n", - " Run 5: 1.4349s, Mem Peak Delta: 157.96 MiB\n", + " Run 1: 1.4193s, Mem Peak Delta: 141.53 MiB\n", + " Run 2: 1.4112s, Mem Peak Delta: 116.27 MiB\n", + " Run 3: 1.4032s, Mem Peak Delta: 124.84 MiB\n", + " Run 4: 1.4345s, Mem Peak Delta: 69.43 MiB\n", + " Run 5: 1.4378s, Mem Peak Delta: 156.84 MiB\n", "\n", "=== Benchmarking SDII ===\n", " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", - " Run 1: 10.1295s, Mem Peak Delta: 56.32 MiB\n", - " Run 2: 10.1441s, Mem Peak Delta: 57.98 MiB\n", - " Run 3: 10.1011s, Mem Peak Delta: 51.18 MiB\n", - " Run 4: 10.1489s, Mem Peak Delta: 50.16 MiB\n", - " Run 5: 10.1188s, Mem Peak Delta: 83.11 MiB\n", + " Run 1: 10.7946s, Mem Peak Delta: 66.58 MiB\n", + " Run 2: 10.1700s, Mem Peak Delta: 49.73 MiB\n", + " Run 3: 10.0803s, Mem Peak Delta: 33.18 MiB\n", + " Run 4: 10.4312s, Mem Peak Delta: 24.82 MiB\n", + " Run 5: 10.2447s, Mem Peak Delta: 108.33 MiB\n", " Warm-up run for run_earthkit_lazy_flox_SDII...\n", "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", - " Run 1: 0.7854s, Mem Peak Delta: 33.62 MiB\n", - " Run 2: 0.8015s, Mem Peak Delta: 33.39 MiB\n", - " Run 3: 0.7988s, Mem Peak Delta: 32.85 MiB\n", - " Run 4: 0.8017s, Mem Peak Delta: 33.40 MiB\n", - " Run 5: 0.7937s, Mem Peak Delta: 33.34 MiB\n", + " Run 1: 0.8005s, Mem Peak Delta: 17.25 MiB\n", + " Run 2: 0.7971s, Mem Peak Delta: 8.23 MiB\n", + " Run 3: 0.8040s, Mem Peak Delta: 73.63 MiB\n", + " Run 4: 0.7929s, Mem Peak Delta: 41.66 MiB\n", + " Run 5: 0.7863s, Mem Peak Delta: 41.64 MiB\n", " Warm-up run for run_earthkit_opt_flox_SDII...\n", "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9294s, Mem Peak Delta: 74.69 MiB\n", - " Run 2: 0.9447s, Mem Peak Delta: 58.10 MiB\n", - " Run 3: 0.9245s, Mem Peak Delta: 74.85 MiB\n", - " Run 4: 0.9445s, Mem Peak Delta: 49.88 MiB\n", - " Run 5: 0.9290s, Mem Peak Delta: 99.82 MiB\n", + " Run 1: 0.9247s, Mem Peak Delta: 44.07 MiB\n", + " Run 2: 0.9545s, Mem Peak Delta: 106.60 MiB\n", + " Run 3: 0.9321s, Mem Peak Delta: 83.03 MiB\n", + " Run 4: 0.9278s, Mem Peak Delta: 58.23 MiB\n", + " Run 5: 0.9285s, Mem Peak Delta: 58.19 MiB\n", " Warm-up run for run_xclim_noflox_SDII...\n", "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", - " Run 1: 10.0605s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 10.0377s, Mem Peak Delta: 53.90 MiB\n", - " Run 3: 10.1044s, Mem Peak Delta: 91.63 MiB\n", - " Run 4: 10.1368s, Mem Peak Delta: 95.63 MiB\n", - " Run 5: 10.0813s, Mem Peak Delta: 66.67 MiB\n", + " Run 1: 9.9722s, Mem Peak Delta: 16.68 MiB\n", + " Run 2: 10.3954s, Mem Peak Delta: 57.97 MiB\n", + " Run 3: 10.2973s, Mem Peak Delta: 100.85 MiB\n", + " Run 4: 10.1024s, Mem Peak Delta: 24.98 MiB\n", + " Run 5: 10.0814s, Mem Peak Delta: 35.96 MiB\n", " Warm-up run for run_xclim_flox_SDII...\n", "Benchmarking run_xclim_flox_SDII (5 runs)...\n", - " Run 1: 0.8215s, Mem Peak Delta: 41.55 MiB\n", - " Run 2: 0.8043s, Mem Peak Delta: 41.53 MiB\n", - " Run 3: 0.7934s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 0.7862s, Mem Peak Delta: 25.10 MiB\n", - " Run 5: 0.7829s, Mem Peak Delta: 0.00 MiB\n", + " Run 1: 0.7954s, Mem Peak Delta: 41.46 MiB\n", + " Run 2: 0.7981s, Mem Peak Delta: 16.65 MiB\n", + " Run 3: 0.7888s, Mem Peak Delta: 58.35 MiB\n", + " Run 4: 0.7910s, Mem Peak Delta: 99.57 MiB\n", + " Run 5: 0.7826s, Mem Peak Delta: 33.39 MiB\n", " Warm-up run for run_xclim_opt_flox_SDII...\n", "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9232s, Mem Peak Delta: 33.03 MiB\n", - " Run 2: 0.9651s, Mem Peak Delta: 66.69 MiB\n", - " Run 3: 0.9470s, Mem Peak Delta: 58.07 MiB\n", - " Run 4: 0.9455s, Mem Peak Delta: 8.43 MiB\n", - " Run 5: 0.9539s, Mem Peak Delta: 50.02 MiB\n" + " Run 1: 0.9302s, Mem Peak Delta: 99.82 MiB\n", + " Run 2: 0.9391s, Mem Peak Delta: 66.50 MiB\n", + " Run 3: 0.9216s, Mem Peak Delta: 99.71 MiB\n", + " Run 4: 0.9339s, Mem Peak Delta: 116.29 MiB\n", + " Run 5: 0.9285s, Mem Peak Delta: 16.76 MiB\n" ] } ], @@ -872,7 +872,7 @@ "\n", "# Pre-calculate percentile for WSDI\n", "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", - "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90).compute()\n", + "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90)\n", "per_90.name = \"tasmax_per\"\n", "\n", "# --- 1b. Optimized Configuration (Chunk -1) ---\n", @@ -1061,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "id": "75852284", "metadata": {}, "outputs": [ @@ -1109,39 +1109,39 @@ " WSDI\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 27.163342\n", - " 0.358026\n", - " 898.859375\n", - " 1.035759\n", + " 27.899066\n", + " 0.056364\n", + " 899.175781\n", + " 1.001920\n", " \n", " \n", " 1\n", " WSDI\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 20.029513\n", - " 0.494503\n", - " 898.914062\n", - " 1.404661\n", + " 19.919541\n", + " 0.110188\n", + " 898.929688\n", + " 1.403277\n", " \n", " \n", " 2\n", " WSDI\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 21.258730\n", - " 0.601173\n", - " 159.070312\n", - " 1.323441\n", + " 20.619848\n", + " 0.141511\n", + " 119.671875\n", + " 1.355618\n", " \n", " \n", " 3\n", " WSDI\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 28.134670\n", - " 0.082817\n", - " 898.890625\n", + " 27.952629\n", + " 0.156093\n", + " 898.898438\n", " 1.000000\n", " \n", " \n", @@ -1149,59 +1149,59 @@ " WSDI\n", " Xclim\n", " 2. Flox (Standard)\n", - " 20.230234\n", - " 0.132923\n", - " 898.964844\n", - " 1.390724\n", + " 20.535628\n", + " 0.183507\n", + " 899.023438\n", + " 1.361177\n", " \n", " \n", " 5\n", " WSDI\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 25.717589\n", - " 0.356579\n", - " 898.933594\n", - " 1.093986\n", + " 25.597690\n", + " 0.338709\n", + " 898.910156\n", + " 1.091998\n", " \n", " \n", " 6\n", " CWD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 30.626105\n", - " 0.221556\n", - " 99.156250\n", - " 0.964307\n", + " 28.766718\n", + " 0.264687\n", + " 72.527344\n", + " 1.006706\n", " \n", " \n", " 7\n", " CWD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 26.354595\n", - " 0.846001\n", - " 91.671875\n", - " 1.120600\n", + " 27.468571\n", + " 2.346962\n", + " 128.429688\n", + " 1.054282\n", " \n", " \n", " 8\n", " CWD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 20.319477\n", - " 0.247352\n", - " 110.738281\n", - " 1.453431\n", + " 20.333059\n", + " 0.142748\n", + " 123.457031\n", + " 1.424263\n", " \n", " \n", " 9\n", " CWD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 29.532964\n", - " 0.445733\n", - " 83.472656\n", + " 28.959633\n", + " 0.371716\n", + " 70.375000\n", " 1.000000\n", " \n", " \n", @@ -1209,59 +1209,59 @@ " CWD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 27.493610\n", - " 0.492927\n", - " 115.347656\n", - " 1.074176\n", + " 27.420777\n", + " 0.371098\n", + " 71.046875\n", + " 1.056120\n", " \n", " \n", " 11\n", " CWD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 20.778197\n", - " 0.299679\n", - " 113.937500\n", - " 1.421344\n", + " 19.950392\n", + " 0.092965\n", + " 84.472656\n", + " 1.451582\n", " \n", " \n", " 12\n", " DTR\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 8.674074\n", - " 0.076174\n", - " 33.261719\n", - " 1.015091\n", + " 8.608574\n", + " 0.011151\n", + " 33.277344\n", + " 0.993819\n", " \n", " \n", " 13\n", " DTR\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.387059\n", - " 0.023953\n", - " 113.675781\n", - " 6.347944\n", + " 1.346492\n", + " 0.007435\n", + " 141.429688\n", + " 6.353818\n", " \n", " \n", " 14\n", " DTR\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.672507\n", - " 0.015136\n", - " 191.082031\n", - " 5.264536\n", + " 1.648928\n", + " 0.012676\n", + " 158.015625\n", + " 5.188437\n", " \n", " \n", " 15\n", " DTR\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 8.804974\n", - " 0.183875\n", - " 41.421875\n", + " 8.555362\n", + " 0.025209\n", + " 25.000000\n", " 1.000000\n", " \n", " \n", @@ -1269,59 +1269,59 @@ " DTR\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.363925\n", - " 0.006582\n", - " 65.019531\n", - " 6.455614\n", + " 1.342688\n", + " 0.004283\n", + " 89.957031\n", + " 6.371815\n", " \n", " \n", " 17\n", " DTR\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.644765\n", - " 0.022314\n", - " 181.246094\n", - " 5.353332\n", + " 1.622218\n", + " 0.018059\n", + " 141.433594\n", + " 5.273868\n", " \n", " \n", " 18\n", " HDD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 7.345279\n", - " 0.089340\n", - " 58.332031\n", - " 1.006162\n", + " 7.219920\n", + " 0.053929\n", + " 25.000000\n", + " 1.043309\n", " \n", " \n", " 19\n", " HDD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.319415\n", - " 0.037436\n", - " 58.027344\n", - " 5.601381\n", + " 1.258335\n", + " 0.010198\n", + " 40.621094\n", + " 5.986172\n", " \n", " \n", " 20\n", " HDD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.457090\n", - " 0.009215\n", - " 233.261719\n", - " 5.072124\n", + " 1.410802\n", + " 0.006139\n", + " 149.527344\n", + " 5.339240\n", " \n", " \n", " 21\n", " HDD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 7.390543\n", - " 0.143334\n", - " 50.746094\n", + " 7.532608\n", + " 0.133296\n", + " 44.878906\n", " 1.000000\n", " \n", " \n", @@ -1329,59 +1329,59 @@ " HDD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.274181\n", - " 0.011207\n", - " 25.851562\n", - " 5.800229\n", + " 1.271845\n", + " 0.013790\n", + " 51.933594\n", + " 5.922581\n", " \n", " \n", " 23\n", " HDD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.430180\n", - " 0.010546\n", - " 157.957031\n", - " 5.167563\n", + " 1.419286\n", + " 0.013256\n", + " 156.843750\n", + " 5.307321\n", " \n", " \n", " 24\n", " SDII\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 10.129517\n", - " 0.017331\n", - " 83.109375\n", - " 0.995237\n", + " 10.244741\n", + " 0.253102\n", + " 108.328125\n", + " 0.986106\n", " \n", " \n", " 25\n", " SDII\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 0.798818\n", - " 0.006143\n", - " 33.617188\n", - " 12.620229\n", + " 0.797117\n", + " 0.006139\n", + " 73.628906\n", + " 12.673680\n", " \n", " \n", " 26\n", " SDII\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.929431\n", - " 0.008475\n", - " 99.816406\n", - " 10.846709\n", + " 0.928537\n", + " 0.010764\n", + " 106.597656\n", + " 10.879918\n", " \n", " \n", " 27\n", " SDII\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 10.081266\n", - " 0.034378\n", - " 95.632812\n", + " 10.102402\n", + " 0.153982\n", + " 100.851562\n", " 1.000000\n", " \n", " \n", @@ -1389,20 +1389,20 @@ " SDII\n", " Xclim\n", " 2. Flox (Standard)\n", - " 0.793418\n", - " 0.013971\n", - " 41.550781\n", - " 12.706125\n", + " 0.791026\n", + " 0.005384\n", + " 99.566406\n", + " 12.771264\n", " \n", " \n", " 29\n", " SDII\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.947046\n", - " 0.013728\n", - " 66.691406\n", - " 10.644964\n", + " 0.930157\n", + " 0.005788\n", + " 116.285156\n", + " 10.860968\n", " \n", " \n", "\n", @@ -1410,68 +1410,68 @@ ], "text/plain": [ " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 27.163342 0.358026 \n", - "1 WSDI Earthkit 2. Flox (Standard) 20.029513 0.494503 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 21.258730 0.601173 \n", - "3 WSDI Xclim 1. No Flox (Standard) 28.134670 0.082817 \n", - "4 WSDI Xclim 2. Flox (Standard) 20.230234 0.132923 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 25.717589 0.356579 \n", - "6 CWD Earthkit 1. No Flox (Standard) 30.626105 0.221556 \n", - "7 CWD Earthkit 2. Flox (Standard) 26.354595 0.846001 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.319477 0.247352 \n", - "9 CWD Xclim 1. No Flox (Standard) 29.532964 0.445733 \n", - "10 CWD Xclim 2. Flox (Standard) 27.493610 0.492927 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 20.778197 0.299679 \n", - "12 DTR Earthkit 1. No Flox (Standard) 8.674074 0.076174 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.387059 0.023953 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.672507 0.015136 \n", - "15 DTR Xclim 1. No Flox (Standard) 8.804974 0.183875 \n", - "16 DTR Xclim 2. Flox (Standard) 1.363925 0.006582 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.644765 0.022314 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.345279 0.089340 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.319415 0.037436 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.457090 0.009215 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.390543 0.143334 \n", - "22 HDD Xclim 2. Flox (Standard) 1.274181 0.011207 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.430180 0.010546 \n", - "24 SDII Earthkit 1. No Flox (Standard) 10.129517 0.017331 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.798818 0.006143 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.929431 0.008475 \n", - "27 SDII Xclim 1. No Flox (Standard) 10.081266 0.034378 \n", - "28 SDII Xclim 2. Flox (Standard) 0.793418 0.013971 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.947046 0.013728 \n", + "0 WSDI Earthkit 1. No Flox (Standard) 27.899066 0.056364 \n", + "1 WSDI Earthkit 2. Flox (Standard) 19.919541 0.110188 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.619848 0.141511 \n", + "3 WSDI Xclim 1. No Flox (Standard) 27.952629 0.156093 \n", + "4 WSDI Xclim 2. Flox (Standard) 20.535628 0.183507 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 25.597690 0.338709 \n", + "6 CWD Earthkit 1. No Flox (Standard) 28.766718 0.264687 \n", + "7 CWD Earthkit 2. Flox (Standard) 27.468571 2.346962 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.333059 0.142748 \n", + "9 CWD Xclim 1. No Flox (Standard) 28.959633 0.371716 \n", + "10 CWD Xclim 2. Flox (Standard) 27.420777 0.371098 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 19.950392 0.092965 \n", + "12 DTR Earthkit 1. No Flox (Standard) 8.608574 0.011151 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.346492 0.007435 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.648928 0.012676 \n", + "15 DTR Xclim 1. No Flox (Standard) 8.555362 0.025209 \n", + "16 DTR Xclim 2. Flox (Standard) 1.342688 0.004283 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.622218 0.018059 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.219920 0.053929 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.258335 0.010198 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.410802 0.006139 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.532608 0.133296 \n", + "22 HDD Xclim 2. Flox (Standard) 1.271845 0.013790 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.419286 0.013256 \n", + "24 SDII Earthkit 1. No Flox (Standard) 10.244741 0.253102 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.797117 0.006139 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.928537 0.010764 \n", + "27 SDII Xclim 1. No Flox (Standard) 10.102402 0.153982 \n", + "28 SDII Xclim 2. Flox (Standard) 0.791026 0.005384 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.930157 0.005788 \n", "\n", " max_mem Speedup \n", - "0 898.859375 1.035759 \n", - "1 898.914062 1.404661 \n", - "2 159.070312 1.323441 \n", - "3 898.890625 1.000000 \n", - "4 898.964844 1.390724 \n", - "5 898.933594 1.093986 \n", - "6 99.156250 0.964307 \n", - "7 91.671875 1.120600 \n", - "8 110.738281 1.453431 \n", - "9 83.472656 1.000000 \n", - "10 115.347656 1.074176 \n", - "11 113.937500 1.421344 \n", - "12 33.261719 1.015091 \n", - "13 113.675781 6.347944 \n", - "14 191.082031 5.264536 \n", - "15 41.421875 1.000000 \n", - "16 65.019531 6.455614 \n", - "17 181.246094 5.353332 \n", - "18 58.332031 1.006162 \n", - "19 58.027344 5.601381 \n", - "20 233.261719 5.072124 \n", - "21 50.746094 1.000000 \n", - "22 25.851562 5.800229 \n", - "23 157.957031 5.167563 \n", - "24 83.109375 0.995237 \n", - "25 33.617188 12.620229 \n", - "26 99.816406 10.846709 \n", - "27 95.632812 1.000000 \n", - "28 41.550781 12.706125 \n", - "29 66.691406 10.644964 " + "0 899.175781 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", 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", 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", + "text/plain": [ + "
" ] }, "metadata": {}, From 37e165457e97e5fb7e5e31cd13d6345bd3a253d7 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 9 Feb 2026 22:41:28 +0100 Subject: [PATCH 14/47] feat: Add unit tests for synoptic and wind indicators, refactor precipitation indicator tests, and update unit mocking in conftest. --- tests/unit/conftest.py | 2 +- tests/unit/indicators/test_precipitation.py | 656 +++++++++++++++++++- tests/unit/indicators/test_synoptic.py | 39 ++ tests/unit/indicators/test_temperature.py | 60 +- tests/unit/indicators/test_wind.py | 129 ++++ 5 files changed, 824 insertions(+), 62 deletions(-) create mode 100644 tests/unit/indicators/test_synoptic.py create mode 100644 tests/unit/indicators/test_wind.py diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 0dc5662..4bf5a28 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -82,7 +82,7 @@ def common_mocks(mocker: MockerFixture, dummy_precip_ds: xr.Dataset) -> dict: mock_ensure_units = mocker.patch( "earthkit.climate.utils.units.ensure_units", - side_effect=lambda ds, var, units, strict=False: ds.assign_attrs({"ensured": True}), + side_effect=lambda ds, var, units, strict=False: ds, ) mock_add_prov = mocker.patch( diff --git a/tests/unit/indicators/test_precipitation.py b/tests/unit/indicators/test_precipitation.py index 8753814..49eac39 100644 --- a/tests/unit/indicators/test_precipitation.py +++ b/tests/unit/indicators/test_precipitation.py @@ -19,41 +19,655 @@ class MockEarthkitData: def test_maximum_consecutive_wet_days(mocker: MockerFixture, common_mocks): """Test maximum_consecutive_wet_days calls wrapper correctly.""" - mock_wrapper_factory = mocker.patch("earthkit.climate.indicators.precipitation.wrap_xclim_indicator") - mock_wrapped_fn = mocker.MagicMock() - mock_wrapper_factory.return_value = mock_wrapped_fn + mock_fn = mocker.patch("xclim.indicators.atmos.maximum_consecutive_wet_days") pr_in = MockEarthkitData() precipitation.maximum_consecutive_wet_days(pr_in, thresh="2 mm/day", freq="MS") - import xclim.indicators.atmos + common_mocks["mock_to_xr"].assert_called_once_with(pr_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] - mock_wrapper_factory.assert_called_once_with(xclim.indicators.atmos.maximum_consecutive_wet_days) - - mock_wrapped_fn.assert_called_once() - call_args = mock_wrapped_fn.call_args - ds_arg = call_args[0][0] - assert ds_arg is pr_in + mock_fn.assert_called_once() + call_args = mock_fn.call_args + assert call_args.kwargs["ds"] is ds_converted assert call_args.kwargs["thresh"] == "2 mm/day" assert call_args.kwargs["freq"] == "MS" def test_daily_precipitation_intensity(mocker: MockerFixture, common_mocks): """Test daily_precipitation_intensity calls wrapper correctly.""" - mock_wrapper_factory = mocker.patch("earthkit.climate.indicators.precipitation.wrap_xclim_indicator") - mock_wrapped_fn = mocker.MagicMock() - mock_wrapper_factory.return_value = mock_wrapped_fn + mock_fn = mocker.patch("xclim.indicators.atmos.daily_pr_intensity") pr_in = MockEarthkitData() - precipitation.daily_precipitation_intensity(pr_in, thresh="2 mm/day", freq="MS") - - import xclim.indicators.atmos + # Note: Validating against daily_pr_intensity as per existing code structure + try: + precipitation.daily_precipitation_intensity(pr_in, thresh="2 mm/day", freq="MS") + except AttributeError: + # Fallback if the function is actually named daily_pr_intensity in the module + precipitation.daily_pr_intensity(pr_in, thresh="2 mm/day", freq="MS") - mock_wrapper_factory.assert_called_once_with(xclim.indicators.atmos.daily_pr_intensity) + common_mocks["mock_to_xr"].assert_called_once_with(pr_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] - mock_wrapped_fn.assert_called_once() - call_args = mock_wrapped_fn.call_args - ds_arg = call_args[0][0] - assert ds_arg is pr_in + mock_fn.assert_called_once() + call_args = mock_fn.call_args + assert call_args.kwargs["ds"] is ds_converted assert call_args.kwargs["thresh"] == "2 mm/day" assert call_args.kwargs["freq"] == "MS" + +# New tests + +def test_antecedent_precipitation_index(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.antecedent_precipitation_index") + + ds_in = MockEarthkitData() + precipitation.antecedent_precipitation_index(ds_in, val="test") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + # No other kwargs passed in original test besides ds_in? Wait, val="test" was passed. + # The original test passed val="test" to the wrapper. + # Let's verify it's passed to the xclim fn. + assert mock_fn.call_args.kwargs["val"] == "test" + + +def test_maximum_consecutive_dry_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.maximum_consecutive_dry_days") + + ds_in = MockEarthkitData() + precipitation.maximum_consecutive_dry_days(ds_in, val="test") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["val"] == "test" + + +def test_cffwis_indices(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.cffwis_indices") + + ds_in = MockEarthkitData() + precipitation.cffwis_indices(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_cold_and_dry_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.cold_and_dry_days") + + ds_in = MockEarthkitData() + precipitation.cold_and_dry_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_cold_and_wet_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.cold_and_wet_days") + + ds_in = MockEarthkitData() + precipitation.cold_and_wet_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_days_over_precip_doy_thresh(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.days_over_precip_doy_thresh") + + ds_in = MockEarthkitData() + precipitation.days_over_precip_doy_thresh(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_days_over_precip_thresh(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.days_over_precip_thresh") + + ds_in = MockEarthkitData() + precipitation.days_over_precip_thresh(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_days_with_snow(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.days_with_snow") + + ds_in = MockEarthkitData() + precipitation.days_with_snow(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_drought_code(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.drought_code") + + ds_in = MockEarthkitData() + precipitation.drought_code(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_griffiths_drought_factor(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.griffiths_drought_factor") + + ds_in = MockEarthkitData() + precipitation.griffiths_drought_factor(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_duff_moisture_code(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.duff_moisture_code") + + ds_in = MockEarthkitData() + precipitation.duff_moisture_code(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_dry_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.dry_days") + + ds_in = MockEarthkitData() + precipitation.dry_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_dry_spell_frequency(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_frequency") + + ds_in = MockEarthkitData() + precipitation.dry_spell_frequency(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_dry_spell_max_length(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_max_length") + + ds_in = MockEarthkitData() + precipitation.dry_spell_max_length(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_dry_spell_total_length(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_total_length") + + ds_in = MockEarthkitData() + precipitation.dry_spell_total_length(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_dryness_index(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.dryness_index") + + ds_in = MockEarthkitData() + precipitation.dryness_index(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_mcarthur_forest_fire_danger_index(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.mcarthur_forest_fire_danger_index") + + ds_in = MockEarthkitData() + precipitation.mcarthur_forest_fire_danger_index(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_first_snowfall(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.first_snowfall") + + ds_in = MockEarthkitData() + precipitation.first_snowfall(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_fraction_over_precip_doy_thresh(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.fraction_over_precip_doy_thresh") + + ds_in = MockEarthkitData() + precipitation.fraction_over_precip_doy_thresh(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_fraction_over_precip_thresh(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.fraction_over_precip_thresh") + + ds_in = MockEarthkitData() + precipitation.fraction_over_precip_thresh(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_high_precip_low_temp(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.high_precip_low_temp") + + ds_in = MockEarthkitData() + precipitation.high_precip_low_temp(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_keetch_byram_drought_index(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.keetch_byram_drought_index") + + ds_in = MockEarthkitData() + precipitation.keetch_byram_drought_index(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_last_snowfall(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.last_snowfall") + + ds_in = MockEarthkitData() + precipitation.last_snowfall(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_liquid_precip_ratio(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_ratio") + + ds_in = MockEarthkitData() + precipitation.liquid_precip_ratio(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_liquid_precip_average(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_average") + + ds_in = MockEarthkitData() + precipitation.liquid_precip_average(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_liquid_precip_accumulation(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_accumulation") + + ds_in = MockEarthkitData() + precipitation.liquid_precip_accumulation(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_max_n_day_precipitation_amount(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.max_n_day_precipitation_amount") + + ds_in = MockEarthkitData() + precipitation.max_n_day_precipitation_amount(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_max_pr_intensity(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.max_pr_intensity") + + ds_in = MockEarthkitData() + precipitation.max_pr_intensity(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_precip_average(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.precip_average") + + ds_in = MockEarthkitData() + precipitation.precip_average(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_precip_accumulation(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.precip_accumulation") + + ds_in = MockEarthkitData() + precipitation.precip_accumulation(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_rain_on_frozen_ground_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.rain_on_frozen_ground_days") + + ds_in = MockEarthkitData() + precipitation.rain_on_frozen_ground_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_rain_season(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.rain_season") + + ds_in = MockEarthkitData() + precipitation.rain_season(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_rprctot(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.rprctot") + + ds_in = MockEarthkitData() + precipitation.rprctot(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_max_1day_precipitation_amount(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.max_1day_precipitation_amount") + + ds_in = MockEarthkitData() + precipitation.max_1day_precipitation_amount(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_snowfall_frequency(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.snowfall_frequency") + + ds_in = MockEarthkitData() + precipitation.snowfall_frequency(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_snowfall_intensity(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.snowfall_intensity") + + ds_in = MockEarthkitData() + precipitation.snowfall_intensity(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_solid_precip_average(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.solid_precip_average") + + ds_in = MockEarthkitData() + precipitation.solid_precip_average(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_solid_precip_accumulation(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.solid_precip_accumulation") + + ds_in = MockEarthkitData() + precipitation.solid_precip_accumulation(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_warm_and_dry_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.warm_and_dry_days") + + ds_in = MockEarthkitData() + precipitation.warm_and_dry_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_warm_and_wet_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.warm_and_wet_days") + + ds_in = MockEarthkitData() + precipitation.warm_and_wet_days(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_water_cycle_intensity(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.water_cycle_intensity") + + ds_in = MockEarthkitData() + precipitation.water_cycle_intensity(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wet_precip_accumulation(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wet_precip_accumulation") + + ds_in = MockEarthkitData() + precipitation.wet_precip_accumulation(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wet_spell_frequency(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_frequency") + + ds_in = MockEarthkitData() + precipitation.wet_spell_frequency(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wet_spell_max_length(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_max_length") + + ds_in = MockEarthkitData() + precipitation.wet_spell_max_length(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wet_spell_total_length(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_total_length") + + ds_in = MockEarthkitData() + precipitation.wet_spell_total_length(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wetdays(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wetdays") + + ds_in = MockEarthkitData() + precipitation.wetdays(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + + +def test_wetdays_prop(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.wetdays_prop") + + ds_in = MockEarthkitData() + precipitation.wetdays_prop(ds_in) + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted diff --git a/tests/unit/indicators/test_synoptic.py b/tests/unit/indicators/test_synoptic.py new file mode 100644 index 0000000..a02c4b2 --- /dev/null +++ b/tests/unit/indicators/test_synoptic.py @@ -0,0 +1,39 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +from pytest_mock import MockerFixture + +from earthkit.climate.indicators import synoptic + + +class MockEarthkitData: + """Mock object for Earthkit input.""" + + pass + + +def test_jetstream_metric_woollings(mocker: MockerFixture, common_mocks): + mock_metric = mocker.patch("xclim.indicators.atmos.jetstream_metric_woollings") + + ds_in = MockEarthkitData() + synoptic.jetstream_metric_woollings(ds_in, freq="MS") + + # Verify conversions were called (handled by common_mocks) + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + + # Verify xclim indicator was called with the converted dataset (which is common_mocks['dummy_precip_ds']) + # The first element of the return value of mock_to_xr is the dataset + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_metric.assert_called_once() + call_args = mock_metric.call_args + assert call_args.kwargs["ds"] is ds_converted + assert call_args.kwargs["freq"] == "MS" + + # Verify result conversion + common_mocks["mock_to_ek"].assert_called_once() diff --git a/tests/unit/indicators/test_temperature.py b/tests/unit/indicators/test_temperature.py index cecbfa1..173bd84 100644 --- a/tests/unit/indicators/test_temperature.py +++ b/tests/unit/indicators/test_temperature.py @@ -19,55 +19,45 @@ class MockEarthkitData: def test_daily_temperature_range(mocker: MockerFixture, common_mocks): """Test daily_temperature_range calls wrapper with merged dataset.""" - # Mock the wrapper creator and the wrapped function - mock_wrapper_factory = mocker.patch("earthkit.climate.indicators.temperature.wrap_xclim_indicator") - mock_wrapped_fn = mocker.MagicMock() - mock_wrapper_factory.return_value = mock_wrapped_fn + # Mock the underlying xclim function + mock_fn = mocker.patch("xclim.indicators.atmos.daily_temperature_range") # Call function with single dataset ds_in = MockEarthkitData() temperature.daily_temperature_range(ds_in, arg="val") - # Verify wrapper created with correct xclim function - import xclim.indicators.atmos - - mock_wrapper_factory.assert_called_once_with(xclim.indicators.atmos.daily_temperature_range) + # Verify conversions were called (handled by common_mocks) + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] # Verify wrapped function called with the dataset - call_args = mock_wrapped_fn.call_args + call_args = mock_fn.call_args assert call_args is not None - ds_arg = call_args[0][0] - # The wrapper receives the raw input, conversion happens inside the wrapper (which is mocked) - assert ds_arg is ds_in + # The first argument to the xclim function should be the converted dataset + assert call_args.kwargs["ds"] is ds_converted assert call_args.kwargs["arg"] == "val" def test_heating_degree_days(mocker: MockerFixture, common_mocks): """Test heating_degree_days calls wrapper with merged dataset.""" - mock_wrapper_factory = mocker.patch("earthkit.climate.indicators.temperature.wrap_xclim_indicator") - mock_wrapped_fn = mocker.MagicMock() - mock_wrapper_factory.return_value = mock_wrapped_fn + mock_fn = mocker.patch("xclim.indicators.atmos.heating_degree_days") ds_in = MockEarthkitData() temperature.heating_degree_days(ds_in, thresh="18 degC") - import xclim.indicators.atmos - - mock_wrapper_factory.assert_called_once_with(xclim.indicators.atmos.heating_degree_days) + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] - call_args = mock_wrapped_fn.call_args - ds_arg = call_args[0][0] - assert ds_arg is ds_in - assert call_args.kwargs["thresh"] == "18 degC" + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["thresh"] == "18 degC" def test_warm_spell_duration_index(mocker: MockerFixture, common_mocks): """Test warm_spell_duration_index passes merged dataset (tasmax + tasmax_per).""" # Mock wrapper factory - mock_wrapper_factory = mocker.patch("earthkit.climate.indicators.temperature.wrap_xclim_indicator") - mock_wrapped_fn = mocker.MagicMock() - mock_wrapper_factory.return_value = mock_wrapped_fn + mock_fn = mocker.patch("xclim.indicators.atmos.warm_spell_duration_index") # Create a dummy input that represents a merged dataset ds_merged_in = MockEarthkitData() @@ -75,24 +65,14 @@ def test_warm_spell_duration_index(mocker: MockerFixture, common_mocks): # Call with single merged input temperature.warm_spell_duration_index(ds_merged_in, window=10) - import xclim.indicators.atmos - - mock_wrapper_factory.assert_called_once_with(xclim.indicators.atmos.warm_spell_duration_index) + common_mocks["mock_to_xr"].assert_called_once_with(ds_merged_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] # Verify call args - mock_wrapped_fn.assert_called_once() - call_kwargs = mock_wrapped_fn.call_args.kwargs - - # We assume the first positional arg is handled by the wrapper as 'earthkit_input' - # Check positional args first - if mock_wrapped_fn.call_args.args: - assert mock_wrapped_fn.call_args.args[0] is ds_merged_in - else: - # Fallback if passed as keyword - # (though wrapper signature might not support it yet, test logic verifies call) - # In this test we called it positionally. - pass + mock_fn.assert_called_once() + call_kwargs = mock_fn.call_args.kwargs + assert call_kwargs["ds"] is ds_converted assert call_kwargs["window"] == 10 # Ensure reference_data is NOT passed assert "reference_data" not in call_kwargs diff --git a/tests/unit/indicators/test_wind.py b/tests/unit/indicators/test_wind.py new file mode 100644 index 0000000..a94c003 --- /dev/null +++ b/tests/unit/indicators/test_wind.py @@ -0,0 +1,129 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +from pytest_mock import MockerFixture + +from earthkit.climate.indicators import wind + + +class MockEarthkitData: + """Mock object for Earthkit input.""" + + pass + + +def test_calm_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.calm_days") + + ds_in = MockEarthkitData() + wind.calm_days(ds_in, thresh="2 m s-1") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["thresh"] == "2 m s-1" + + +def test_sfcWind_max(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_max") + + ds_in = MockEarthkitData() + wind.sfcWind_max(ds_in, freq="MS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "MS" + + +def test_sfcWind_mean(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_mean") + + ds_in = MockEarthkitData() + wind.sfcWind_mean(ds_in, freq="YS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "YS" + + +def test_sfcWind_min(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_min") + + ds_in = MockEarthkitData() + wind.sfcWind_min(ds_in, freq="MS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "MS" + + +def test_sfcWindmax_max(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_max") + + ds_in = MockEarthkitData() + wind.sfcWindmax_max(ds_in, freq="MS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "MS" + + +def test_sfcWindmax_mean(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_mean") + + ds_in = MockEarthkitData() + wind.sfcWindmax_mean(ds_in, freq="MS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "MS" + + +def test_sfcWindmax_min(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_min") + + ds_in = MockEarthkitData() + wind.sfcWindmax_min(ds_in, freq="MS") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["freq"] == "MS" + + +def test_windy_days(mocker: MockerFixture, common_mocks): + mock_fn = mocker.patch("xclim.indicators.atmos.windy_days") + + ds_in = MockEarthkitData() + wind.windy_days(ds_in, thresh="10 m s-1") + + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["thresh"] == "10 m s-1" From f5a1a17c4a2d3773a8dae06b39f745f3d2780560 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 10 Feb 2026 11:59:58 +0100 Subject: [PATCH 15/47] docs: Update performance analysis notebook with fresh benchmark results and minor comment clarifications. --- docs/notebooks/performance_analysis.ipynb | 689 +++++++++++----------- 1 file changed, 347 insertions(+), 342 deletions(-) diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index d6cd40f..573a8dc 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "410735a7", "metadata": {}, "outputs": [], @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "id": "68281ebe", "metadata": {}, "outputs": [ @@ -154,10 +154,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 3, "id": "fba35188", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, { "data": { "text/markdown": [ @@ -333,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "5d1ba8a5", "metadata": {}, "outputs": [], @@ -449,9 +456,7 @@ " if observed_mems:\n", " # We want the peak usage *induced* by the function\n", " # Peak Delta = Max(Observed) - Baseline\n", - " # Or simplified: Peak Observed\n", - " # The previous 'memory_profiler' returned (Max - Min). reliable for single process.\n", - " # Let's use Normalized Peak: (Max - Baseline)\n", + " # Normalized Peak: (Max - Baseline)\n", " peak_delta = max(observed_mems) - baseline_mem\n", " # Clamp to 0\n", " if peak_delta < 0: peak_delta = 0\n", @@ -486,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "id": "acf4d31d", "metadata": {}, "outputs": [ @@ -547,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "id": "66739804", "metadata": {}, "outputs": [], @@ -607,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "id": "46022af7", "metadata": {}, "outputs": [], @@ -620,7 +625,7 @@ " with the 'func' argument inside **kwargs.\n", " \"\"\"\n", " def wrapper():\n", - " # Ahora llamamos a target_runner pas\u00e1ndole los kwargs (que contienen 'func')\n", + " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", " return target_runner(**kwargs)\n", "\n", " wrapper.__name__ = name\n", @@ -629,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "id": "e0be7a44", "metadata": {}, "outputs": [ @@ -643,222 +648,222 @@ "=== Benchmarking WSDI ===\n", " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", - " Run 1: 27.8730s, Mem Peak Delta: 899.18 MiB\n", - " Run 2: 27.8275s, Mem Peak Delta: 897.91 MiB\n", - " Run 3: 27.9897s, Mem Peak Delta: 898.84 MiB\n", - " Run 4: 27.9458s, Mem Peak Delta: 898.81 MiB\n", - " Run 5: 27.8991s, Mem Peak Delta: 898.84 MiB\n", + " Run 1: 89.7710s, Mem Peak Delta: 523.52 MiB\n", + " Run 2: 92.9727s, Mem Peak Delta: 340.92 MiB\n", + " Run 3: 95.7104s, Mem Peak Delta: 333.95 MiB\n", + " Run 4: 95.0707s, Mem Peak Delta: 268.96 MiB\n", + " Run 5: 96.9543s, Mem Peak Delta: 185.86 MiB\n", " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", - " Run 1: 19.7318s, Mem Peak Delta: 898.93 MiB\n", - " Run 2: 19.9195s, Mem Peak Delta: 898.84 MiB\n", - " Run 3: 19.9674s, Mem Peak Delta: 898.86 MiB\n", - " Run 4: 19.8348s, Mem Peak Delta: 898.81 MiB\n", - " Run 5: 20.0525s, Mem Peak Delta: 898.85 MiB\n", + " Run 1: 91.2536s, Mem Peak Delta: 26.64 MiB\n", + " Run 2: 92.7830s, Mem Peak Delta: 196.10 MiB\n", + " Run 3: 95.7225s, Mem Peak Delta: 191.45 MiB\n", + " Run 4: 96.5830s, Mem Peak Delta: 210.40 MiB\n", + " Run 5: 95.0315s, Mem Peak Delta: 193.62 MiB\n", " Warm-up run for run_earthkit_opt_flox_WSDI...\n", "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", - " Run 1: 20.6069s, Mem Peak Delta: 44.44 MiB\n", - " Run 2: 20.6198s, Mem Peak Delta: 114.90 MiB\n", - " Run 3: 20.7867s, Mem Peak Delta: 60.07 MiB\n", - " Run 4: 20.9424s, Mem Peak Delta: 110.00 MiB\n", - " Run 5: 20.5628s, Mem Peak Delta: 119.67 MiB\n", + " Run 1: 59.0237s, Mem Peak Delta: 103.71 MiB\n", + " Run 2: 59.3023s, Mem Peak Delta: 95.52 MiB\n", + " Run 3: 59.4765s, Mem Peak Delta: 233.46 MiB\n", + " Run 4: 59.7330s, Mem Peak Delta: 112.57 MiB\n", + " Run 5: 61.9002s, Mem Peak Delta: 181.49 MiB\n", " Warm-up run for run_xclim_noflox_WSDI...\n", "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", - " Run 1: 27.7358s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 27.8259s, Mem Peak Delta: 898.90 MiB\n", - " Run 3: 27.9684s, Mem Peak Delta: 898.84 MiB\n", - " Run 4: 27.9526s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 28.1973s, Mem Peak Delta: 898.84 MiB\n", + " Run 1: 95.5910s, Mem Peak Delta: 113.41 MiB\n", + " Run 2: 97.3999s, Mem Peak Delta: 209.49 MiB\n", + " Run 3: 102.3339s, Mem Peak Delta: 183.56 MiB\n", + " Run 4: 105.1568s, Mem Peak Delta: 159.73 MiB\n", + " Run 5: 104.6472s, Mem Peak Delta: 295.09 MiB\n", " Warm-up run for run_xclim_flox_WSDI...\n", "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", - " Run 1: 20.3087s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 20.8343s, Mem Peak Delta: 899.02 MiB\n", - " Run 3: 20.7178s, Mem Peak Delta: 898.84 MiB\n", - " Run 4: 20.5356s, Mem Peak Delta: 898.81 MiB\n", - " Run 5: 20.4841s, Mem Peak Delta: 898.84 MiB\n", + " Run 1: 102.1373s, Mem Peak Delta: 211.81 MiB\n", + " Run 2: 107.0449s, Mem Peak Delta: 139.21 MiB\n", + " Run 3: 114.8249s, Mem Peak Delta: 205.55 MiB\n", + " Run 4: 111.2941s, Mem Peak Delta: 44.91 MiB\n", + " Run 5: 105.7781s, Mem Peak Delta: 74.06 MiB\n", " Warm-up run for run_xclim_opt_flox_WSDI...\n", "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", - " Run 1: 25.4670s, Mem Peak Delta: 898.84 MiB\n", - " Run 2: 26.0923s, Mem Peak Delta: 898.85 MiB\n", - " Run 3: 25.7871s, Mem Peak Delta: 898.91 MiB\n", - " Run 4: 25.0722s, Mem Peak Delta: 898.79 MiB\n", - " Run 5: 25.5977s, Mem Peak Delta: 898.84 MiB\n", + " Run 1: 60.1291s, Mem Peak Delta: 15.52 MiB\n", + " Run 2: 64.3885s, Mem Peak Delta: 62.09 MiB\n", + " Run 3: 67.2124s, Mem Peak Delta: 191.05 MiB\n", + " Run 4: 66.9474s, Mem Peak Delta: 52.80 MiB\n", + " Run 5: 67.1786s, Mem Peak Delta: 116.32 MiB\n", "\n", "=== Benchmarking CWD ===\n", " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", - " Run 1: 28.8221s, Mem Peak Delta: 66.03 MiB\n", - " Run 2: 28.7667s, Mem Peak Delta: 57.00 MiB\n", - " Run 3: 29.4298s, Mem Peak Delta: 72.53 MiB\n", - " Run 4: 28.7520s, Mem Peak Delta: 65.12 MiB\n", - " Run 5: 28.7452s, Mem Peak Delta: 62.76 MiB\n", + " Run 1: 28.5323s, Mem Peak Delta: 7.73 MiB\n", + " Run 2: 28.8032s, Mem Peak Delta: 25.11 MiB\n", + " Run 3: 28.8414s, Mem Peak Delta: 23.77 MiB\n", + " Run 4: 29.1343s, Mem Peak Delta: 14.91 MiB\n", + " Run 5: 29.0878s, Mem Peak Delta: 13.65 MiB\n", " Warm-up run for run_earthkit_lazy_flox_CWD...\n", "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", - " Run 1: 27.0645s, Mem Peak Delta: 8.92 MiB\n", - " Run 2: 27.2851s, Mem Peak Delta: 50.64 MiB\n", - " Run 3: 27.9523s, Mem Peak Delta: 15.12 MiB\n", - " Run 4: 33.2643s, Mem Peak Delta: 128.43 MiB\n", - " Run 5: 27.4686s, Mem Peak Delta: 44.89 MiB\n", + " Run 1: 23.1237s, Mem Peak Delta: 24.98 MiB\n", + " Run 2: 23.2226s, Mem Peak Delta: 16.63 MiB\n", + " Run 3: 24.0029s, Mem Peak Delta: 17.38 MiB\n", + " Run 4: 23.2542s, Mem Peak Delta: 33.37 MiB\n", + " Run 5: 22.9485s, Mem Peak Delta: 27.67 MiB\n", " Warm-up run for run_earthkit_opt_flox_CWD...\n", "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.0614s, Mem Peak Delta: 63.48 MiB\n", - " Run 2: 20.3331s, Mem Peak Delta: 123.46 MiB\n", - " Run 3: 20.2189s, Mem Peak Delta: 33.37 MiB\n", - " Run 4: 20.3473s, Mem Peak Delta: 92.31 MiB\n", - " Run 5: 20.4886s, Mem Peak Delta: 83.07 MiB\n", + " Run 1: 20.8623s, Mem Peak Delta: 16.62 MiB\n", + " Run 2: 21.5585s, Mem Peak Delta: 66.62 MiB\n", + " Run 3: 21.7855s, Mem Peak Delta: 16.76 MiB\n", + " Run 4: 22.5419s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 22.0640s, Mem Peak Delta: 8.27 MiB\n", " Warm-up run for run_xclim_noflox_CWD...\n", "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", - " Run 1: 28.3228s, Mem Peak Delta: 61.27 MiB\n", - " Run 2: 29.4927s, Mem Peak Delta: 63.88 MiB\n", - " Run 3: 28.9596s, Mem Peak Delta: 70.38 MiB\n", - " Run 4: 28.8950s, Mem Peak Delta: 46.79 MiB\n", - " Run 5: 28.9882s, Mem Peak Delta: 69.48 MiB\n", + " Run 1: 27.7460s, Mem Peak Delta: 24.90 MiB\n", + " Run 2: 27.7050s, Mem Peak Delta: 16.50 MiB\n", + " Run 3: 27.7064s, Mem Peak Delta: 24.98 MiB\n", + " Run 4: 27.6762s, Mem Peak Delta: 33.21 MiB\n", + " Run 5: 27.7167s, Mem Peak Delta: 33.09 MiB\n", " Warm-up run for run_xclim_flox_CWD...\n", "Benchmarking run_xclim_flox_CWD (5 runs)...\n", - " Run 1: 26.6984s, Mem Peak Delta: 27.05 MiB\n", - " Run 2: 27.5815s, Mem Peak Delta: 41.75 MiB\n", - " Run 3: 27.4208s, Mem Peak Delta: 71.05 MiB\n", - " Run 4: 27.5665s, Mem Peak Delta: 62.85 MiB\n", - " Run 5: 26.8656s, Mem Peak Delta: 26.84 MiB\n", + " Run 1: 21.8447s, Mem Peak Delta: 41.77 MiB\n", + " Run 2: 21.9301s, Mem Peak Delta: 83.01 MiB\n", + " Run 3: 22.2363s, Mem Peak Delta: 19.67 MiB\n", + " Run 4: 22.2319s, Mem Peak Delta: 24.80 MiB\n", + " Run 5: 22.3105s, Mem Peak Delta: 33.25 MiB\n", " Warm-up run for run_xclim_opt_flox_CWD...\n", "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", - " Run 1: 19.8813s, Mem Peak Delta: 84.47 MiB\n", - " Run 2: 19.8554s, Mem Peak Delta: 58.26 MiB\n", - " Run 3: 19.9892s, Mem Peak Delta: 41.64 MiB\n", - " Run 4: 19.9504s, Mem Peak Delta: 24.76 MiB\n", - " Run 5: 20.1185s, Mem Peak Delta: 24.79 MiB\n", + " Run 1: 21.7654s, Mem Peak Delta: 25.23 MiB\n", + " Run 2: 21.7366s, Mem Peak Delta: 49.93 MiB\n", + " Run 3: 22.0504s, Mem Peak Delta: 49.95 MiB\n", + " Run 4: 21.9013s, Mem Peak Delta: 66.38 MiB\n", + " Run 5: 20.2474s, Mem Peak Delta: 25.07 MiB\n", "\n", "=== Benchmarking DTR ===\n", " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", - " Run 1: 8.5965s, Mem Peak Delta: 24.08 MiB\n", - " Run 2: 8.6182s, Mem Peak Delta: 26.71 MiB\n", - " Run 3: 8.6238s, Mem Peak Delta: 33.28 MiB\n", - " Run 4: 8.5962s, Mem Peak Delta: 0.38 MiB\n", - " Run 5: 8.6086s, Mem Peak Delta: 18.86 MiB\n", + " Run 1: 8.9316s, Mem Peak Delta: 23.88 MiB\n", + " Run 2: 8.9837s, Mem Peak Delta: 33.29 MiB\n", + " Run 3: 8.9439s, Mem Peak Delta: 1.32 MiB\n", + " Run 4: 8.9590s, Mem Peak Delta: 24.88 MiB\n", + " Run 5: 8.9646s, Mem Peak Delta: 25.11 MiB\n", " Warm-up run for run_earthkit_lazy_flox_DTR...\n", "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", - " Run 1: 1.3398s, Mem Peak Delta: 141.43 MiB\n", - " Run 2: 1.3505s, Mem Peak Delta: 91.47 MiB\n", - " Run 3: 1.3465s, Mem Peak Delta: 83.12 MiB\n", - " Run 4: 1.3422s, Mem Peak Delta: 83.28 MiB\n", - " Run 5: 1.3609s, Mem Peak Delta: 81.71 MiB\n", + " Run 1: 1.3615s, Mem Peak Delta: 61.15 MiB\n", + " Run 2: 1.3698s, Mem Peak Delta: 33.58 MiB\n", + " Run 3: 1.3750s, Mem Peak Delta: 41.55 MiB\n", + " Run 4: 1.3665s, Mem Peak Delta: 68.60 MiB\n", + " Run 5: 1.3678s, Mem Peak Delta: 11.66 MiB\n", " Warm-up run for run_earthkit_opt_flox_DTR...\n", "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6420s, Mem Peak Delta: 158.02 MiB\n", - " Run 2: 1.6677s, Mem Peak Delta: 33.20 MiB\n", - " Run 3: 1.6489s, Mem Peak Delta: 141.30 MiB\n", - " Run 4: 1.6321s, Mem Peak Delta: 108.11 MiB\n", - " Run 5: 1.6601s, Mem Peak Delta: 95.85 MiB\n", + " Run 1: 1.6516s, Mem Peak Delta: 122.49 MiB\n", + " Run 2: 1.6276s, Mem Peak Delta: 100.05 MiB\n", + " Run 3: 1.6354s, Mem Peak Delta: 83.21 MiB\n", + " Run 4: 1.5959s, Mem Peak Delta: 59.10 MiB\n", + " Run 5: 1.6488s, Mem Peak Delta: 89.11 MiB\n", " Warm-up run for run_xclim_noflox_DTR...\n", "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", - " Run 1: 8.5330s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 8.5588s, Mem Peak Delta: 24.89 MiB\n", - " Run 3: 8.5554s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 8.6053s, Mem Peak Delta: 16.55 MiB\n", - " Run 5: 8.5404s, Mem Peak Delta: 25.00 MiB\n", + " Run 1: 8.9989s, Mem Peak Delta: 8.25 MiB\n", + " Run 2: 8.9958s, Mem Peak Delta: 0.08 MiB\n", + " Run 3: 9.1292s, Mem Peak Delta: 2.38 MiB\n", + " Run 4: 9.4178s, Mem Peak Delta: 6.00 MiB\n", + " Run 5: 9.2605s, Mem Peak Delta: 8.25 MiB\n", " Warm-up run for run_xclim_flox_DTR...\n", "Benchmarking run_xclim_flox_DTR (5 runs)...\n", - " Run 1: 1.3459s, Mem Peak Delta: 66.66 MiB\n", - " Run 2: 1.3357s, Mem Peak Delta: 89.96 MiB\n", - " Run 3: 1.3427s, Mem Peak Delta: 50.05 MiB\n", - " Run 4: 1.3366s, Mem Peak Delta: 74.89 MiB\n", - " Run 5: 1.3452s, Mem Peak Delta: 66.19 MiB\n", + " Run 1: 1.3580s, Mem Peak Delta: 49.94 MiB\n", + " Run 2: 1.3487s, Mem Peak Delta: 25.05 MiB\n", + " Run 3: 1.3596s, Mem Peak Delta: 27.66 MiB\n", + " Run 4: 1.3548s, Mem Peak Delta: 25.05 MiB\n", + " Run 5: 1.3798s, Mem Peak Delta: 24.96 MiB\n", " Warm-up run for run_xclim_opt_flox_DTR...\n", "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6360s, Mem Peak Delta: 108.08 MiB\n", - " Run 2: 1.6222s, Mem Peak Delta: 125.05 MiB\n", - " Run 3: 1.6499s, Mem Peak Delta: 141.43 MiB\n", - " Run 4: 1.6070s, Mem Peak Delta: 41.93 MiB\n", - " Run 5: 1.6011s, Mem Peak Delta: 107.99 MiB\n", + " Run 1: 1.6841s, Mem Peak Delta: 72.75 MiB\n", + " Run 2: 1.7539s, Mem Peak Delta: 39.25 MiB\n", + " Run 3: 1.7238s, Mem Peak Delta: 73.33 MiB\n", + " Run 4: 1.6859s, Mem Peak Delta: 89.11 MiB\n", + " Run 5: 1.7034s, Mem Peak Delta: 25.19 MiB\n", "\n", "=== Benchmarking HDD ===\n", " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", - " Run 1: 7.3256s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.2703s, Mem Peak Delta: 25.00 MiB\n", - " Run 3: 7.1994s, Mem Peak Delta: 23.80 MiB\n", - " Run 4: 7.2199s, Mem Peak Delta: 9.50 MiB\n", - " Run 5: 7.1747s, Mem Peak Delta: 3.12 MiB\n", + " Run 1: 7.5611s, Mem Peak Delta: 24.88 MiB\n", + " Run 2: 7.3306s, Mem Peak Delta: 42.62 MiB\n", + " Run 3: 7.4190s, Mem Peak Delta: 33.20 MiB\n", + " Run 4: 7.4930s, Mem Peak Delta: 8.88 MiB\n", + " Run 5: 7.4986s, Mem Peak Delta: 25.35 MiB\n", " Warm-up run for run_earthkit_lazy_flox_HDD...\n", "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", - " Run 1: 1.2583s, Mem Peak Delta: 19.40 MiB\n", - " Run 2: 1.2613s, Mem Peak Delta: 12.25 MiB\n", - " Run 3: 1.2405s, Mem Peak Delta: 18.20 MiB\n", - " Run 4: 1.2473s, Mem Peak Delta: 40.62 MiB\n", - " Run 5: 1.2692s, Mem Peak Delta: 24.96 MiB\n", + " Run 1: 1.2988s, Mem Peak Delta: 34.25 MiB\n", + " Run 2: 1.2915s, Mem Peak Delta: 64.59 MiB\n", + " Run 3: 1.2781s, Mem Peak Delta: 10.25 MiB\n", + " Run 4: 1.2996s, Mem Peak Delta: 41.48 MiB\n", + " Run 5: 1.3060s, Mem Peak Delta: 33.31 MiB\n", " Warm-up run for run_earthkit_opt_flox_HDD...\n", "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4035s, Mem Peak Delta: 99.74 MiB\n", - " Run 2: 1.4150s, Mem Peak Delta: 149.53 MiB\n", - " Run 3: 1.4151s, Mem Peak Delta: 74.72 MiB\n", - " Run 4: 1.4000s, Mem Peak Delta: 91.61 MiB\n", - " Run 5: 1.4108s, Mem Peak Delta: 133.76 MiB\n", + " Run 1: 1.4264s, Mem Peak Delta: 135.50 MiB\n", + " Run 2: 1.4128s, Mem Peak Delta: 40.84 MiB\n", + " Run 3: 1.4465s, Mem Peak Delta: 14.59 MiB\n", + " Run 4: 1.4055s, Mem Peak Delta: 49.93 MiB\n", + " Run 5: 1.4351s, Mem Peak Delta: 74.78 MiB\n", " Warm-up run for run_xclim_noflox_HDD...\n", "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", - " Run 1: 7.2109s, Mem Peak Delta: 12.59 MiB\n", - " Run 2: 7.5437s, Mem Peak Delta: 44.88 MiB\n", - " Run 3: 7.5749s, Mem Peak Delta: 11.50 MiB\n", - " Run 4: 7.5326s, Mem Peak Delta: 0.62 MiB\n", - " Run 5: 7.5081s, Mem Peak Delta: 2.25 MiB\n", + " Run 1: 7.4480s, Mem Peak Delta: 26.75 MiB\n", + " Run 2: 7.4481s, Mem Peak Delta: 24.88 MiB\n", + " Run 3: 7.8391s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 7.4711s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 7.5348s, Mem Peak Delta: 16.62 MiB\n", " Warm-up run for run_xclim_flox_HDD...\n", "Benchmarking run_xclim_flox_HDD (5 runs)...\n", - " Run 1: 1.2734s, Mem Peak Delta: 51.93 MiB\n", - " Run 2: 1.2619s, Mem Peak Delta: 31.27 MiB\n", - " Run 3: 1.3011s, Mem Peak Delta: 15.02 MiB\n", - " Run 4: 1.2718s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.2659s, Mem Peak Delta: 35.42 MiB\n", + " Run 1: 1.3109s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.2927s, Mem Peak Delta: 33.32 MiB\n", + " Run 3: 1.2930s, Mem Peak Delta: 16.63 MiB\n", + " Run 4: 1.2737s, Mem Peak Delta: 35.46 MiB\n", + " Run 5: 1.2796s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_opt_flox_HDD...\n", "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4193s, Mem Peak Delta: 141.53 MiB\n", - " Run 2: 1.4112s, Mem Peak Delta: 116.27 MiB\n", - " Run 3: 1.4032s, Mem Peak Delta: 124.84 MiB\n", - " Run 4: 1.4345s, Mem Peak Delta: 69.43 MiB\n", - " Run 5: 1.4378s, Mem Peak Delta: 156.84 MiB\n", + " Run 1: 1.5200s, Mem Peak Delta: 74.92 MiB\n", + " Run 2: 1.4439s, Mem Peak Delta: 4.97 MiB\n", + " Run 3: 1.4065s, Mem Peak Delta: 25.01 MiB\n", + " Run 4: 1.4434s, Mem Peak Delta: 0.11 MiB\n", + " Run 5: 1.4043s, Mem Peak Delta: 62.78 MiB\n", "\n", "=== Benchmarking SDII ===\n", " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", - " Run 1: 10.7946s, Mem Peak Delta: 66.58 MiB\n", - " Run 2: 10.1700s, Mem Peak Delta: 49.73 MiB\n", - " Run 3: 10.0803s, Mem Peak Delta: 33.18 MiB\n", - " Run 4: 10.4312s, Mem Peak Delta: 24.82 MiB\n", - " Run 5: 10.2447s, Mem Peak Delta: 108.33 MiB\n", + " Run 1: 9.8736s, Mem Peak Delta: 49.82 MiB\n", + " Run 2: 9.9811s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 9.7374s, Mem Peak Delta: 74.89 MiB\n", + " Run 4: 9.9493s, Mem Peak Delta: 16.62 MiB\n", + " Run 5: 10.3370s, Mem Peak Delta: 32.91 MiB\n", " Warm-up run for run_earthkit_lazy_flox_SDII...\n", "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", - " Run 1: 0.8005s, Mem Peak Delta: 17.25 MiB\n", - " Run 2: 0.7971s, Mem Peak Delta: 8.23 MiB\n", - " Run 3: 0.8040s, Mem Peak Delta: 73.63 MiB\n", - " Run 4: 0.7929s, Mem Peak Delta: 41.66 MiB\n", - " Run 5: 0.7863s, Mem Peak Delta: 41.64 MiB\n", + " Run 1: 0.8138s, Mem Peak Delta: 4.40 MiB\n", + " Run 2: 0.8061s, Mem Peak Delta: 29.55 MiB\n", + " Run 3: 0.8064s, Mem Peak Delta: 16.63 MiB\n", + " Run 4: 0.8126s, Mem Peak Delta: 40.15 MiB\n", + " Run 5: 0.8250s, Mem Peak Delta: 16.74 MiB\n", " Warm-up run for run_earthkit_opt_flox_SDII...\n", "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9247s, Mem Peak Delta: 44.07 MiB\n", - " Run 2: 0.9545s, Mem Peak Delta: 106.60 MiB\n", - " Run 3: 0.9321s, Mem Peak Delta: 83.03 MiB\n", - " Run 4: 0.9278s, Mem Peak Delta: 58.23 MiB\n", - " Run 5: 0.9285s, Mem Peak Delta: 58.19 MiB\n", + " Run 1: 0.9654s, Mem Peak Delta: 58.84 MiB\n", + " Run 2: 0.9568s, Mem Peak Delta: 49.93 MiB\n", + " Run 3: 0.9748s, Mem Peak Delta: 50.09 MiB\n", + " Run 4: 0.9490s, Mem Peak Delta: 49.86 MiB\n", + " Run 5: 0.9420s, Mem Peak Delta: 50.02 MiB\n", " Warm-up run for run_xclim_noflox_SDII...\n", "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", - " Run 1: 9.9722s, Mem Peak Delta: 16.68 MiB\n", - " Run 2: 10.3954s, Mem Peak Delta: 57.97 MiB\n", - " Run 3: 10.2973s, Mem Peak Delta: 100.85 MiB\n", - " Run 4: 10.1024s, Mem Peak Delta: 24.98 MiB\n", - " Run 5: 10.0814s, Mem Peak Delta: 35.96 MiB\n", + " Run 1: 10.1145s, Mem Peak Delta: 66.40 MiB\n", + " Run 2: 10.0157s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 10.3028s, Mem Peak Delta: 16.63 MiB\n", + " Run 4: 10.1469s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 10.0440s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_flox_SDII...\n", "Benchmarking run_xclim_flox_SDII (5 runs)...\n", - " Run 1: 0.7954s, Mem Peak Delta: 41.46 MiB\n", - " Run 2: 0.7981s, Mem Peak Delta: 16.65 MiB\n", - " Run 3: 0.7888s, Mem Peak Delta: 58.35 MiB\n", - " Run 4: 0.7910s, Mem Peak Delta: 99.57 MiB\n", - " Run 5: 0.7826s, Mem Peak Delta: 33.39 MiB\n", + " Run 1: 0.8203s, Mem Peak Delta: 0.02 MiB\n", + " Run 2: 0.7956s, Mem Peak Delta: 16.69 MiB\n", + " Run 3: 0.8210s, Mem Peak Delta: 33.43 MiB\n", + " Run 4: 0.8468s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 0.8123s, Mem Peak Delta: 16.05 MiB\n", " Warm-up run for run_xclim_opt_flox_SDII...\n", "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9302s, Mem Peak Delta: 99.82 MiB\n", - " Run 2: 0.9391s, Mem Peak Delta: 66.50 MiB\n", - " Run 3: 0.9216s, Mem Peak Delta: 99.71 MiB\n", - " Run 4: 0.9339s, Mem Peak Delta: 116.29 MiB\n", - " Run 5: 0.9285s, Mem Peak Delta: 16.76 MiB\n" + " Run 1: 0.9853s, Mem Peak Delta: 34.00 MiB\n", + " Run 2: 0.9825s, Mem Peak Delta: 1.79 MiB\n", + " Run 3: 0.9502s, Mem Peak Delta: 33.94 MiB\n", + " Run 4: 0.9560s, Mem Peak Delta: 25.59 MiB\n", + " Run 5: 0.9399s, Mem Peak Delta: 43.94 MiB\n" ] } ], @@ -1061,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "75852284", "metadata": {}, "outputs": [ @@ -1109,39 +1114,39 @@ " WSDI\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 27.899066\n", - " 0.056364\n", - " 899.175781\n", - " 1.001920\n", + " 95.070671\n", + " 2.517686\n", + " 523.523438\n", + " 1.076398\n", " \n", " \n", " 1\n", " WSDI\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 19.919541\n", - " 0.110188\n", - " 898.929688\n", - " 1.403277\n", + " 95.031452\n", + " 1.967204\n", + " 210.402344\n", + " 1.076842\n", " \n", " \n", " 2\n", " WSDI\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 20.619848\n", - " 0.141511\n", - " 119.671875\n", - " 1.355618\n", + " 59.476542\n", + " 1.032706\n", + " 233.464844\n", + " 1.720575\n", " \n", " \n", " 3\n", " WSDI\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 27.952629\n", - " 0.156093\n", - " 898.898438\n", + " 102.333867\n", + " 3.861991\n", + " 295.089844\n", " 1.000000\n", " \n", " \n", @@ -1149,59 +1154,59 @@ " WSDI\n", " Xclim\n", " 2. Flox (Standard)\n", - " 20.535628\n", - " 0.183507\n", - " 899.023438\n", - " 1.361177\n", + " 107.044898\n", + " 4.414015\n", + " 211.808594\n", + " 0.955990\n", " \n", " \n", " 5\n", " WSDI\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 25.597690\n", - " 0.338709\n", - " 898.910156\n", - " 1.091998\n", + " 66.947395\n", + " 2.734442\n", + " 191.054688\n", + " 1.528571\n", " \n", " \n", " 6\n", " CWD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 28.766718\n", - " 0.264687\n", - " 72.527344\n", - " 1.006706\n", + " 28.841368\n", + " 0.217333\n", + " 25.105469\n", + " 0.960648\n", " \n", " \n", " 7\n", " CWD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 27.468571\n", - " 2.346962\n", - " 128.429688\n", - " 1.054282\n", + " 23.222558\n", + " 0.362283\n", + " 33.371094\n", + " 1.193081\n", " \n", " \n", " 8\n", " CWD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 20.333059\n", - " 0.142748\n", - " 123.457031\n", - " 1.424263\n", + " 21.785499\n", + " 0.556947\n", + " 66.625000\n", + " 1.271782\n", " \n", " \n", " 9\n", " CWD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 28.959633\n", - " 0.371716\n", - " 70.375000\n", + " 27.706397\n", + " 0.022472\n", + " 33.214844\n", " 1.000000\n", " \n", " \n", @@ -1209,59 +1214,59 @@ " CWD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 27.420777\n", - " 0.371098\n", - " 71.046875\n", - " 1.056120\n", + " 22.231937\n", + " 0.186417\n", + " 83.011719\n", + " 1.246243\n", " \n", " \n", " 11\n", " CWD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 19.950392\n", - " 0.092965\n", - " 84.472656\n", - " 1.451582\n", + " 21.765449\n", + " 0.655963\n", + " 66.375000\n", + " 1.272953\n", " \n", " \n", " 12\n", " DTR\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 8.608574\n", - " 0.011151\n", - " 33.277344\n", - " 0.993819\n", + " 8.958993\n", + " 0.017841\n", + " 33.285156\n", + " 1.018993\n", " \n", " \n", " 13\n", " DTR\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.346492\n", - " 0.007435\n", - " 141.429688\n", - " 6.353818\n", + " 1.367763\n", + " 0.004411\n", + " 68.597656\n", + " 6.674512\n", " \n", " \n", " 14\n", " DTR\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.648928\n", - " 0.012676\n", - " 158.015625\n", - " 5.188437\n", + " 1.635447\n", + " 0.020017\n", + " 122.488281\n", + " 5.582052\n", " \n", " \n", " 15\n", " DTR\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 8.555362\n", - " 0.025209\n", - " 25.000000\n", + " 9.129151\n", + " 0.161529\n", + " 8.250000\n", " 1.000000\n", " \n", " \n", @@ -1269,59 +1274,59 @@ " DTR\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.342688\n", - " 0.004283\n", - " 89.957031\n", - " 6.371815\n", + " 1.357999\n", + " 0.010495\n", + " 49.941406\n", + " 6.722503\n", " \n", " \n", " 17\n", " DTR\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.622218\n", - " 0.018059\n", - " 141.433594\n", - " 5.273868\n", + " 1.703398\n", + " 0.026138\n", + " 89.105469\n", + " 5.359375\n", " \n", " \n", " 18\n", " HDD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 7.219920\n", - " 0.053929\n", - " 25.000000\n", - " 1.043309\n", + " 7.492971\n", + " 0.079038\n", + " 42.625000\n", + " 0.997077\n", " \n", " \n", " 19\n", " HDD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.258335\n", - " 0.010198\n", - " 40.621094\n", - " 5.986172\n", + " 1.298846\n", + " 0.009550\n", + " 64.585938\n", + " 5.752082\n", " \n", " \n", " 20\n", " HDD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.410802\n", - " 0.006139\n", - " 149.527344\n", - " 5.339240\n", + " 1.426414\n", + " 0.014806\n", + " 135.496094\n", + " 5.237655\n", " \n", " \n", " 21\n", " HDD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 7.532608\n", - " 0.133296\n", - " 44.878906\n", + " 7.471066\n", + " 0.148868\n", + " 26.750000\n", " 1.000000\n", " \n", " \n", @@ -1329,59 +1334,59 @@ " HDD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.271845\n", - " 0.013790\n", - " 51.933594\n", - " 5.922581\n", + " 1.292692\n", + " 0.012860\n", + " 35.457031\n", + " 5.779463\n", " \n", " \n", " 23\n", " HDD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.419286\n", - " 0.013256\n", - " 156.843750\n", - " 5.307321\n", + " 1.443442\n", + " 0.041851\n", + " 74.917969\n", + " 5.175870\n", " \n", " \n", " 24\n", " SDII\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 10.244741\n", - " 0.253102\n", - " 108.328125\n", - " 0.986106\n", + " 9.949329\n", + " 0.199215\n", + " 74.886719\n", + " 1.016605\n", " \n", " \n", " 25\n", " SDII\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 0.797117\n", - " 0.006139\n", - " 73.628906\n", - " 12.673680\n", + " 0.812625\n", + " 0.006871\n", + " 40.148438\n", + " 12.446758\n", " \n", " \n", " 26\n", " SDII\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.928537\n", - " 0.010764\n", - " 106.597656\n", - " 10.879918\n", + " 0.956808\n", + " 0.011624\n", + " 58.839844\n", + " 10.571128\n", " \n", " \n", " 27\n", " SDII\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 10.102402\n", - " 0.153982\n", - " 100.851562\n", + " 10.114541\n", + " 0.100703\n", + " 66.402344\n", " 1.000000\n", " \n", " \n", @@ -1389,20 +1394,20 @@ " SDII\n", " Xclim\n", " 2. Flox (Standard)\n", - " 0.791026\n", - " 0.005384\n", - " 99.566406\n", - " 12.771264\n", + " 0.820253\n", + " 0.016554\n", + " 33.425781\n", + " 12.331000\n", " \n", " \n", " 29\n", " SDII\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.930157\n", - " 0.005788\n", - " 116.285156\n", - " 10.860968\n", + " 0.956009\n", + " 0.018034\n", + " 43.937500\n", + " 10.579968\n", " \n", " \n", "\n", @@ -1410,68 +1415,68 @@ ], "text/plain": [ " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 27.899066 0.056364 \n", - "1 WSDI Earthkit 2. Flox (Standard) 19.919541 0.110188 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 20.619848 0.141511 \n", - "3 WSDI Xclim 1. No Flox (Standard) 27.952629 0.156093 \n", - "4 WSDI Xclim 2. Flox (Standard) 20.535628 0.183507 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 25.597690 0.338709 \n", - "6 CWD Earthkit 1. No Flox (Standard) 28.766718 0.264687 \n", - "7 CWD Earthkit 2. Flox (Standard) 27.468571 2.346962 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.333059 0.142748 \n", - "9 CWD Xclim 1. No Flox (Standard) 28.959633 0.371716 \n", - "10 CWD Xclim 2. Flox (Standard) 27.420777 0.371098 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 19.950392 0.092965 \n", - "12 DTR Earthkit 1. No Flox (Standard) 8.608574 0.011151 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.346492 0.007435 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.648928 0.012676 \n", - "15 DTR Xclim 1. No Flox (Standard) 8.555362 0.025209 \n", - "16 DTR Xclim 2. Flox (Standard) 1.342688 0.004283 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.622218 0.018059 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.219920 0.053929 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.258335 0.010198 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.410802 0.006139 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.532608 0.133296 \n", - "22 HDD Xclim 2. Flox (Standard) 1.271845 0.013790 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.419286 0.013256 \n", - "24 SDII Earthkit 1. No Flox (Standard) 10.244741 0.253102 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.797117 0.006139 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.928537 0.010764 \n", - "27 SDII Xclim 1. No Flox (Standard) 10.102402 0.153982 \n", - "28 SDII Xclim 2. Flox (Standard) 0.791026 0.005384 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.930157 0.005788 \n", + "0 WSDI Earthkit 1. No Flox (Standard) 95.070671 2.517686 \n", + "1 WSDI Earthkit 2. Flox (Standard) 95.031452 1.967204 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 59.476542 1.032706 \n", + "3 WSDI Xclim 1. No Flox (Standard) 102.333867 3.861991 \n", + "4 WSDI Xclim 2. Flox (Standard) 107.044898 4.414015 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 66.947395 2.734442 \n", + "6 CWD Earthkit 1. No Flox (Standard) 28.841368 0.217333 \n", + "7 CWD Earthkit 2. Flox (Standard) 23.222558 0.362283 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 21.785499 0.556947 \n", + "9 CWD Xclim 1. No Flox (Standard) 27.706397 0.022472 \n", + "10 CWD Xclim 2. Flox (Standard) 22.231937 0.186417 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 21.765449 0.655963 \n", + "12 DTR Earthkit 1. No Flox (Standard) 8.958993 0.017841 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.367763 0.004411 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.635447 0.020017 \n", + "15 DTR Xclim 1. No Flox (Standard) 9.129151 0.161529 \n", + "16 DTR Xclim 2. Flox (Standard) 1.357999 0.010495 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.703398 0.026138 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.492971 0.079038 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.298846 0.009550 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.426414 0.014806 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.471066 0.148868 \n", + "22 HDD Xclim 2. Flox (Standard) 1.292692 0.012860 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.443442 0.041851 \n", + "24 SDII Earthkit 1. No Flox (Standard) 9.949329 0.199215 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.812625 0.006871 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.956808 0.011624 \n", + "27 SDII Xclim 1. No Flox (Standard) 10.114541 0.100703 \n", + "28 SDII Xclim 2. Flox (Standard) 0.820253 0.016554 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.956009 0.018034 \n", "\n", " max_mem Speedup \n", - "0 899.175781 1.001920 \n", - "1 898.929688 1.403277 \n", - "2 119.671875 1.355618 \n", - "3 898.898438 1.000000 \n", - "4 899.023438 1.361177 \n", - "5 898.910156 1.091998 \n", - "6 72.527344 1.006706 \n", - "7 128.429688 1.054282 \n", - "8 123.457031 1.424263 \n", - "9 70.375000 1.000000 \n", - "10 71.046875 1.056120 \n", - "11 84.472656 1.451582 \n", - "12 33.277344 0.993819 \n", - "13 141.429688 6.353818 \n", - "14 158.015625 5.188437 \n", - "15 25.000000 1.000000 \n", - "16 89.957031 6.371815 \n", - "17 141.433594 5.273868 \n", - "18 25.000000 1.043309 \n", - "19 40.621094 5.986172 \n", - "20 149.527344 5.339240 \n", - "21 44.878906 1.000000 \n", - "22 51.933594 5.922581 \n", - "23 156.843750 5.307321 \n", - "24 108.328125 0.986106 \n", - "25 73.628906 12.673680 \n", - "26 106.597656 10.879918 \n", - "27 100.851562 1.000000 \n", - "28 99.566406 12.771264 \n", - "29 116.285156 10.860968 " + "0 523.523438 1.076398 \n", + "1 210.402344 1.076842 \n", + "2 233.464844 1.720575 \n", + "3 295.089844 1.000000 \n", + "4 211.808594 0.955990 \n", + "5 191.054688 1.528571 \n", + "6 25.105469 0.960648 \n", + "7 33.371094 1.193081 \n", + "8 66.625000 1.271782 \n", + "9 33.214844 1.000000 \n", + "10 83.011719 1.246243 \n", + "11 66.375000 1.272953 \n", + "12 33.285156 1.018993 \n", + "13 68.597656 6.674512 \n", + "14 122.488281 5.582052 \n", + "15 8.250000 1.000000 \n", + "16 49.941406 6.722503 \n", + "17 89.105469 5.359375 \n", + "18 42.625000 0.997077 \n", + "19 64.585938 5.752082 \n", + "20 135.496094 5.237655 \n", + "21 26.750000 1.000000 \n", + "22 35.457031 5.779463 \n", + "23 74.917969 5.175870 \n", + "24 74.886719 1.016605 \n", + "25 40.148438 12.446758 \n", + "26 58.839844 10.571128 \n", + "27 66.402344 1.000000 \n", + "28 33.425781 12.331000 \n", + "29 43.937500 10.579968 " ] }, "metadata": {}, @@ -1479,7 +1484,7 @@ }, { "data": { - "image/png": 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3tYrXMBKP6pr2Q2WrCedU//Q5aF/U+JMqqfgcKvM9SWc6Fn2OKkv1ClAjiRpulPHXPcODNGGhGjLKOr8AQCbJ0lTi1b0TAADUBBpXrUm4dH/gTKDGAvUqeOSRRyq8DgXZChSVlQfK4/DDD3eNWWp4AIDagtt0AQCAhJQlVzbX36IMSMa3337ruo0nmggQADIVATYAAEhI42wvvPBCN74cSJZmD9ctATXEAgBqE7qIAwAAAAAQAjLYAAAAAACEgAAbAAAAAIAQEGADQA11//33u1sfFRUVRc1yHXz07NnT3QJHy65YscKq26uvvur2Kzhe9+KLL3azWdcUr7/+utvHF154ocRr48aNc7dl0tjR8oh3jPpbz6ea7tN89dVX2yGHHOLqg45N93auDN2WSuvp06ePLVu2rMTrOrYwbz2lcoqt2/7x0UcfuWVUp/S36lh10L3RY2eX1z3kNQmc7qPdvXt323rrrd095q+99tqoctM9uJ9++mmrad/NVNK2VI883SJQt5KLPU+tWbPGdtlll2ovHwAIC/fBBoAaSPdV1v2VdQ/p7OzottDdd9/djjvuOPe7Lla/+eYbe+CBB2zSpElRF7Q1xWmnnebuiV1T7L///vbee+/ZzTffbNtuu6116NAhUpYK9Dp27BjKzMdq9NA9q1Ptl19+sffff9+6du3q7nXuA9IwLFiwwB5//PEqmQla99J+5plnSjy//vrrW3VT+arx5dZbb40899tvv7n7Puue0Keffrq7B/rChQtt4sSJ9s4777jvqP/833rrLdcQksy9xzOVGh5Ul/Q488wzI8/rPtkqP53r9ttvP2vevHm17icAVBYBNgDUQEOHDrXGjRvbbrvtVuK1Vq1auUyl17dvX5s1a5bLkq1atcrq1atnNcm6665rNY0yjHvvvbddcsklrqyzsrJc8KTs3vPPPx9KGSrgrQoKShS8yOjRo0MNsJVxVNCre4O3bt3aUkkNScF6XZPoPuK77rqrtW3bNvKcykX7rPoTbEjZY489XINEcXGxZbKVK1da/fr1k14+NzfX9bJ48MEH7cQTT4x6r76LavBSr5JTTjklRXsMAFWDLuIAUMOsXr3aXn75ZRswYECJ7HUiCsYVJAaX//zzz+3UU091t1lS91UFCOriqqxkkP6+4oorbIcddrBNN93UZUEPPfRQ++KLL6KW09/qJtu7d2/r0aOHW+bLL7+sUPdpdR9VkKvu2urirvXtu+++cYPDv/76y8477zzXXVn7p+WHDx9ulaFGiquuuspl/4cNG+bK6rnnnnMX/ptttlmJ5dV4oeCgV69e7qGgVl1eSxPbRVzdtnXcWtdtt91m2223nVuXAop58+a5LsX6HNTNWA8F/8uXLy/zWJKtIxWhQLGgoCCpnhG6LZO6qiso1+ekbtN33XWXq8+p9N1337l6qbL09fLjjz+Oer1bt24luv37LtNlfY7KVP/888/uM4893oYNG7pHPPo+ypFHHun2Z+bMmVFd34M9HQYNGmRbbbWV+26psUT7FBug+275Y8eOdcuoniqY17ki1o8//ujKQd971bM77rjDfY6xfKZdy2h9+m7dfvvtJbpxqx6rfNVLRsvrd5+NV729/PLLXZ3V88cff7xNnTo1bpnss88+bvm333476vm6deu6bb/44osZ3zABIPORwQaAGkYX87p41wVrPLoA9RfLvov4a6+95sZrq7ulN23aNHfBq4t3BeC6wH/qqafs8MMPd0GeX1ZjSBVEnHPOOa579JIlS9zf2gfvjTfesIsuusgFTQpUlI1StkkX0+rKruC3vBR0jB8/3nUXbdCgges6OmTIEJeF9ffO/eOPP1ygsPbaa7vtK4v62Wef2fXXX++642p5T4GMykJBQDJUXuoqfuedd7oMpIIedVWNdc8997ism3oTHHvssa4s1d1XvQYqQkGnPlt1idVnovI899xzXZlqH7Q/Kn8tp+BNwUt1adeunasvzz77rDv2Tp06xV1OPSc0DGD69Ol2xhlnuONQYPvoo4/ahAkT3M9kxAaBClJzcnISLq/PWwHfRhttZDfccIML1NRQokYLlaM+4y222MLOOussF2Tqd9VhfX5q4FGjjr4fpVGjj/ZB7w1Stl11+Pzzz3eNLwpQ1c09lhpy1HCislEwHUt1QO9XWfvgWPVbw0SC9VvU/Vz1RQ1BaiRSIH7ZZZfZeuutZ1tuuWXkO6PgV13WlRXWPo0YMcJ1U4/XeKUGODVQKKP8559/2mOPPebOQcrMx46VVoOdvo/afmFhoTsXaQjIDz/84L47CujVlV6vx6Pvr7r8f/LJJ3bQQQdFvaYGBn12v//+e1QDBACkGwJsAKhhdLEqyrrFo4tlPYJ0kayAIUjjQz1dCCvY1kXsjjvu6LJgCjREF8QKMg4++ODI8pp0KNgV9MYbb7T+/fu7sd6eMt7KpCmQKSsLmCgoU8Dvu9fqeJX9HDVqlJ100knuOQWhCjJ14e2X07hpZUUVtCmobtq0aSSTW1owFo+yxO+++647xocfftgFaEEKitQ9WJk3ZfY87UNFKRjUcXkKatTdWMeiRgS/fgVaagipzgBbFKy+8sorLuC/99574y6jBh41bNx9990uE+mPQQ0nKjf1ECirzNRYFFvnldHVZ5+IguYmTZq4Xgg+k6z6rXH2CkS1LwrSFfAp4FcmVt8dZebVaHPNNdeUefz6HBTAxmaq1bik8e8KXPVQ3VNg6APWFi1auOU22GADt4+qW/G6wAfrgiY01HdU31cFuApafSZc1Kik8vDBuILqr776ytUTH2DrO6r3q04pCBd9d9UjJpaCY0/vUXlrTPkRRxzhgvmNN944KsDW/hx44IGR53QeUc8MBfl+ngV9zmq8U31JNHQiXs8X/9nrfESADSCd0UUcAGqYOXPmuIvqRJP9KGhQt1A91FVaAZgu9E844YSo7rjz5893XcIVCOuiVhewCj787MeeMm8KkJSlVTChC+nYgF/ZbAXTyjD6h4IBBcTKQldkBnNlcYNjVxUMtGzZ0mX0fACu4EFd25WFC25bQYxe1/56CiiU+S0PBTG+S2psl3j/nDJ1GoMcFv8ZeApofBAU+7zKPZlu4qmkeqgAVQ0RP/30U9xl9DkpmFaX5aCBAwe6n8kMJdBn7Ou1fygrnYjqnPZHk/4Fg18FuspMz5492zVeiL5PCri1nAJEjbVXY4D2OZnvo+plLAXMCmbVzVoNNcqWa7iFGmr0HfXbLovKRhnnzTff3M1gr++pGjL02es7HKTXfXAtmitAvU6CvSkU8KpHiQ+ufZlo/2KpAUnDLxQU+20ruJZ4+6+yDvKz1asBKiheMO+pLHVcsb0VfBkrcw8A6YwMNgDUMAoc1V04UTZWmTF1xfTUdVXPqZuxxpWqC6eCX3WdVXCgLJWypuoCqmBSmWptw1Om6aGHHnIBjbpDK+hQUKuu4+rSqfHBEpz5N9bixYuTClaCmjVrFjdo8fumAEMX4cpO6hGPMnoVpYYDZdCVbdT+a5yxxrkq4+j58eprrbWWhcVn3D3fVT/R8yqPRON8q4rKSN3ENXZcP2Pps1JAF8y2+qBJdTk43CAR9UAI1uuyaCiD6nO8ydfatGkT2a9gQ4E+XzVKqX4nmyXNz8+PClZjqSHEN5L4zLGy0vou6VEadcVWJlxZ6+uuu87VM33umrVcgbq2XZ7vjD/mePsb+5wabtT9X0G6MvoK1NXIoYYJdU2P3bbOH7Gz4mtb+nxjGwNLmxBP21M5+fNc8DgkeCwAkI4IsAGghtHFqrLIytAlG7T6YEHdOkXjGPW7xmD6Gabl77//LvFeBefq4qmHMmEffvih63qrLJPGV/uLZ40j1SRS8cTL8FWWutWqkUGTSykQiMffYqu8FDyou7BmONfYc2X+1Y1Zz2lsuW/c8N18FXSoS3FtpcBLY6tVB4ITiAUDP2WTFTgFg2yfqUzFrZdUPxSUz507t8RraliS4Hb9RHbqsTFmzBiXkY/NyMajdSTTQCA6dmWjldnWOO+yaLIvBZkahhCcuV4BdkXps/CNYkGxz6nXgcpJjVcK8L2lS5fGXW9s44nflj5fNXQFyzreZ+KpLBVMxzYaqZFLuE0XgHRHF3EAqGH8RFKapCxZmkgqGOj6i+HYMcW6BVVp1P1UXUR16y/f3VrjMhXMaPIkZRjjPWK3EwZlzNSNXPuhBoR4263oxbgaENQ9Vt2GFTzq+JRBVHd3NSp46jqrYLu0ccC1hbpWK1OrslMPiSB1SVaDUGxgqFni/ethU+OTGnwULAezrdq3kSNHumyw/y4pkFSPDI1T1ndAmWw1KKkOlEWTcqlLeaIgPpa6OGumbJ9FF30/YjPCwUncgjPBazntf0XpO6Nu58GAWsMc1JU9dtt+38pzjojdlmgMeFC8CdU8lWWwl4jnPwvfGwAA0hUZbACoYfxFqzKCwUmGPF04+7HH6k6p4FpdvBUk+jGvCgqUnVUwpKyiuh9rNmRl8YKUrdLkRBozqfcoq6Qg89NPP3XdaMXPZK3srrJMyvopkFf3aWXJ9TOZyaIqQkGQstcaA61J2zQzsrq2qvFBmfbgTMfqxvztt9+WOQ5byyhrp3HFwYy8xkAr2x/sKq4MuW6NpPHpCnxUTppFXI0NytqV1m2+qmiCNs3KLH6MtI5R+6dGCo3B9/xnqqC0vBQIahiCn2k92MVak4qp67UmadMYeg1J+P77711mVttXg00qaH80FEJ1WD/VvVqTmCl7rMn3FEQquNQ4Y/2u74OOQz07tM/qvaDlS2sgUnZXk7zp1lPBWdQ1v4G6qWt2eR2vgmQ/YZ1+D86krdc1Y722pVuYaV/UQKSy0TAF7Z9mEld2Vw08lWmw0kzf+m7o+6DPSg1I+mxUT4I06aHOC5rlXF3ClUlXoJzsLPyi23up0UJDB7R+HZsmKdNdB+JR44e6xcfOIO7rrj4bP1kbAKQrAmwAqGHUFVnjqj/44AN30R1LXVv1EAUUytQpINSFtQJQ/7zGcGqSKAUCunhWFvHpp5+OmkxL3VLVZVYXxAqM1N1T21dwoEnTPHXTVnZbt9LSBbmCXHWf1sRIwS7oYVOQq3HlCnA1KZWCeQW4mtU5GDj6i3cFU6VRllUTUm244Yauy3OsSy+9tERXcd3iSdvT2GPdkknPabyqZv2uCdQNW/sY5O9brfqgYMsrq3zKotnlFZj5me6D9UiNHRrPrzqi4L5t27Yu6I291VSYFPyqTut49bmqDqhRSg1OfjI5TRimGcSffPLJyNhgBZYKwNVbQ8GhGnJKO2Zly/V9DH4n9F5lhTWDvrLWCjDVo0Llo54RwRnD1QCgoF/lo0YtNXopkNV3UjP069ZYmq1dZaY5EvywjYpQMK+gXfugBg8dqyZ9U8OYuvh72lc1gGg5ZffVGOPvXZ7sd1oNCSprjTnX566hLerxohn+/WzysZOi6fhjJ0UT9X7Q5IVqKASAdJZV7KdPBQDUGAqglV1T1lkX3QCqj4YPqNu1xkzHG4uM5CiQV1fw2G7o6pGingDK3lfmFngAUBMwBhsAaiBdbKoLqTJMAKqXeocoS+17jqD8FETrHvfqBRJLWXBl8wmuAWQCAmwAqIGUJVPWTBMlxU4oBaBq6RZXt99+e9yJypAc3aFAXdQ1/CVIw1LWWWcdN5QFADIBXcQBAAAAAAgBGWwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACEGtvQ+27uGpO5TpXrEAAAAAAMSzZs0aNwFtr169rCy1NsBWcM0twAEAAAAApSlP3FhrA2yfudZ9ZgEAAAAAiGf8+PGWLMZgAwAAAAAQAgJsAAAAAABCQIANAAAAAEAICLABAAAAAAhBrZ3kLNnZ4goKCqywsLC6dwVAhsnJybHc3Fx3ywcAAABkBgLsBFavXm3//POPrVixomo/EQC1RoMGDWzttde2unXrVveuAAAAIAQE2HEUFRXZ1KlTXYapXbt27uKXLBOAMHvHqBFv7ty57lyz4YYbWnY2I3YAAADSHQF2HLrwVZC9zjrruAwTAIStfv36VqdOHfv777/dOScvL49CBgAASHOkTEorHDJKAFJ5AuYcAwAAkFEIsAEAAAAACAEBNqrNxIkT7ZJLLrGddtrJunfvbr169bIDDjjAHnvsMVu0aFHKtvvbb7/ZEUccYZtvvrl16dLFnn76afv666/d7/pZ040bN87uu+8+W7JkSYnXjjzySPcAAAAAUPUYg41q8eKLL9o111xjnTp1suOPP9422GADd0u0X375xZ5//nn78ccf7YEHHkjJti+99FJbuXKl3Xnnnda0aVNr3769Gw/7wgsvuP2o6X744Qe7//77XWNEkyZNol676qqrqm2/AAAAgNqOABvVEiBeffXV1rdvX3vwwQejblG07bbb2rHHHmuffvppyrY/efJkGzRokO2www5Rz/fs2dOqg4J9BfhhSIcGAgAAACBT0UUcVe6RRx5xtz277rrr4t7/V8/tvPPO7nfN5q4u43vssYdtuumm1qdPH7vwwgtt9uzZUe9Rt+gBAwbYzz//bIcffrj16NHDrePRRx9165BXX33VdQNXpvy5555zv+shibqIK9O+++67u23vtdde9uabb9rFF1/surV7id47Y8YM97y26+m96go/adIkO+6449zvxxxzjHvt888/t1NPPdX69evnuszvuuuuduWVV9qCBQsi71fX8FtvvdX9ruPzx+C3Ha+LuLrbq0Fj++23d8eh9911111u5uogrefaa6+1119/3fbcc09Xhvvuu6999NFHSX2uAAAAQG1HBhtVqrCw0L766ivr1q2brb322mUur8BQXbc1Zrp///42c+ZMu+eee+ybb75xgWuLFi0iy+qewhdccIHLgA8ZMsTGjBljd9xxh7Vp08b2339/936t65BDDnFBswLc0mhZBbhaVmPFly5d6rpmr1mzplJloPcrkD700EPtxBNPdGUi06ZNcwG3suuNGzd2x/rUU0+5BgMF9rqlk15bvHixDRs2zO1L69atS81cr1q1yo466iibPn26nXHGGS6I/u6771zDw4QJE9zPoI8//tjGjx9vZ555prtF3eOPP+7KcvTo0e62dQAAAAASI8BGlVq4cKHrEt2hQ4cyl50yZYoLchVgXnHFFZHnu3bt6gLNZ555xs4555yoTK2y3Ztttpn7W13QFYgrOFWArWDcB+StWrUqtUu4st7KFiuLe++990ae18Rou+22mwvaKxNgn3766XbggQdGPX/YYYdFfi8uLnbB9lZbbWU77rijjR071mWe11prrUjDxCabbFJmOb722msuW3733Xe7rLTvhq/g+fbbb3dZc/0dDMgV1Ddq1Mj9rYYQZb5HjRplJ510UoWPGQAAAKgN6CKOGst3e9ZkXkEKoDt37mxffvll1PPK5vrg2lPGdtasWeXe9tSpU11G3AelXrt27VzgW1nKiseaP3++y5hrbLgaERTcKrj2jQ0Vod4CCqbVxT5o4MCB7mdsGW699daR4No3RLRs2dJl0wEAAACUjgw2qlTz5s3dhF4an1wWf6uueNliPRcbODdr1izueG5lZSuSaRcFl7EUdFYm4NTxB4NYnzFXl/U5c+bYaaedZhtttJFbTpnsgw8+uELH4MtQ+6sx70E6rtzc3BK3QwuzDAEAAIDahgAbVSonJ8e22WYbN0u4JipTl+dEfLCnoDN2OT2nYD1V/LqVVY41b968qL/r1avnfsZOGuaD9Fixwa78/vvv7r7gN998c1TG/u+//7bKUBn+9NNPLlAPblfHpcneUlmGAAAAQG1DF3FUuZNPPtkFfJdffnmJoNSPUf7www9dIC4jR46Mel0zhavLtH89FXR/bnU519jjIGXNdZuxIN1HWzTWOUjHkCwf/MbOqq57gsfyyySTVdas6ytWrLD3338/6nnNFO5fBwAAABAOMtiochrDrNnBr7nmGjfRl2bT3nDDDV1G9bfffnO3xtLfDzzwgJvx+9lnn7Xs7Gx3+yo/i7gm+vK3t0oFbU+zbmtMtGbU1n4uWbIkMnN3MBusvzWhmmbkbtq0qRunrbHNmsU8Weuvv76tu+66btZzNT5oPbo9liYhi6Xu46JJ3pTtVldvNQjEdjsXTe42fPhwu+iii1zZ6b3ff/+9u1WaxnprvwEAAGqjwqIiy8nOTvttoGYhwEa10LhiTUj29NNPu1tBaUIx3YaqY8eO7n7Wui2XKBDX7aFefvllGzFihAsiNav1eeedl/LuzQruFUhr/zTrtzLVmkn7gw8+sH/++SdqWd2bWvf11szcuu2WJidTsBw7U3giOvaHH37YbrjhBhfUK2hWdlnlo9uLxU5Epl4AmiH8pZdecuO3hw4d6p6Ppe7rek33vdZxqNt627Zt3Xhv3X4LAACgtlLge/mIT23qnMUpWX+nNk3t+sO3T8m6UXNlFStdVgvpXr/SvXv3Eq/l5+e7WaSVFczLy6uGvUNNpSy2ZgDfZZddXEANVAbnGgAAqtfgu9+yiTMXpGTdG7dvYcPPHpCSdaPmxI6xyGADCSirrqyyMsOaLEzjr5VRXr58uR111FGUGwAAAIAoBNhAAppMTOOWNVZ88eLFrjdDjx493N8aIw4AAAAAQQTYQAKaaEwZbAAAAKA2ir3Va7qtvzoQYAMAAAAASlDwu/yLL6xoSfgTwWU3aWoNM/CONgTYAAAAAIC4FFwXLlxI6SSJm7IBAAAAABACAmwAAAAAAEJAgA0AAAAAQCaMwf7777/tiSeesJ9++skmT55s66+/vr311luR1wsLC+3JJ5+0Tz75xP744w/390YbbWRDhgyxPn36VOu+AwAAAABQYzLYCqoVPK+33nrWuXPnEq/n5+fbI488YhtvvLHddNNNduedd1rbtm3t2GOPtY8++qha9hkAAAAAgBoXYO+0004uwL733nutW7duJV7Py8uzDz74wC6//HLbcccdbfvtt7c77rjDunbt6jLbVU33asvk7ZXXjBkzrEuXLjZhwoSEy7z66qu2xRZbJHz966+/dutYsmRJivYyvags3n///ZRv5/zzz6/R9/lOZb2IXbfOQfvtt58VFRWFvi0AAADUHtXeRTw7u/QYPycnx5o2bVrifmzKaH///fdW1bTtb6fOtqUr16R8W43r17EtO61VrvdcfPHF9tprr5V4frvttnNd8StD61ZA8uCDD1qYevXqZZ999pk1btw4EpDfeOON9t1335V7Xe+995698MIL9ssvv9iiRYvs9ddft0022aTcjQY777yztWjRwsaMGWONGjWKvKYgbJdddrEzzjjDKtOoNHPmzKjn2rdvbx9++KFVFTWIjB071q6++urIc9OnT7e7777bBZ+LFy+25s2bu0YvBeLqXeLLpSJlWtPtsMMOds8999jIkSNt//33r+7dAQAAQJqq9gC7IpRl+uGHH+J2Ka8KCq4Xr1xlNZWy/OpOH1S3bt0Kr6+goMA1LKSK9q1169ahrGvFihUuYN9jjz1cr4fKWL58ueslceaZZ1rYtM6DDz44qiGpKj377LOujHzjwZo1a+z44493cyA88MAD1qpVK5s9e7YLwpcuXWrpatWqVVavXr2klj3ggANs2LBhBNgAAABI3y7iFaGL4KlTp7px2JWlSdOCvwe7iKp7tu+iHfy9qsXuQ6J98n8rYFWA5B8KXps0aRJZ9qmnnrIBAwZYjx49rF+/fnbllVe6IMq//sorr7gu3eqav9dee9lmm21ml156qcuM6zl1rdVDmU7/HmU/jzzySLfOffbZx/UuSLS/CxYssIEDB9rpp5/uAiDfXVdZ06+++souueQStz9+O/fdd1/c9cQrB2Uftd5tttmm1GXLKkM54ogjXFnNmzcv6nMILqss+YUXXmhbbrmlO/YTTjjB/vrrr1LXq58NGzaMfDb6qWxxos984sSJdtRRR7nPYauttnINBwr+ReW16aabumy/38ajjz7qJgCcO3du3H1QPR89erQbcuFf11wImnDwiiuucMfRrl076927t51zzjnuby2n7LUvY30u+rzl559/dt/Frbfe2jbffHMbPHiwey64TS3/0ksvuc9G69ttt91cN/hguWhOBT2v49S6lTEPUr3R/qjOah2qw2+88UbUdvS+a6+91m644Qa3P/o89Hxp6/bvVc8C9Xzwn19Z9aM8damsZYPnHv8Z+df1fOx5KtF5K5n3smzp5UAZVm1dorxTU95876mzmXKuT/V2qlpNKe/yvLc2lUtRSOWQdgH2N998Y7fddpsdd9xxLqipDBWaD1R89lMBn38tWOjV9UX02463D/qwgx94afsbXFbZaAWxb775pt1yyy2RMg1auXKlPfbYY3b99de7brOXXXaZ7bnnni47/vHHH9unn37qMsWeJp/TZ6Lu3R07dnTdipUVjd0ndY0+/PDD3Uzwd911l9WpUydq/3v27OmCeWVWtR11Hdd6te8ap68gyC9bWrnEBi8VKUM1Lqy77rouo5tovSpHBWVaZvjw4e71k046ydWj2PUG3xvbkBP7xfXL6nM48cQTXQPJyy+/7Lpwf/nll3bddde51/UdUMCoIH/ZsmU2fvx4V0763Fq2bBn3WCdNmuSWVTdvvx0F+Bqu8e6770ZOMLH7/+KLL7rfldXXZ6OGD1FjyL777msjRoxw3fM1YeEpp5zithEsby2/6667uoYa1aMLLrjA5s+f75aZNWuW63av5/X6oEGD3FwLsXVSXdY16aHqpJZR+f/4449RZaj35+bmuv255pprIutWN3B1bz/ooIOi1u0/V3XTV2PHt99+m/CziVcHgsuWVbcS1UNN5qiHpzLV98f3IAk2gAWX1XN6TcuI3hPscaDlVG6ePpPS1ptoWe2rlvXHo/qt86Wn8+jq1asTLhs8z+p3f57VMlrWl4XWUdo5OfZYtY/B+pFsuWjZ4LEGyzvesrGfjV+vL8OKlnewDLVsaWWYqLx9GfplSyvDeOUdLEMtW1oZJirv8pZLWeWdaNny1NnYZaujzsYuW946W1Z5c47gHME5onLniOA5oaqk2znC72uqrVy5ssZfR5QnyE6rLuLK5J122mluDKwuzitLgaYyiV6DBg3cc/pw9DPYbTf276rku2fH7kPs+HX/moIfZRKDFPQp6JFjjjkm8iVTAHnWWWe5sbgKRjxVKD2nse5+WU04p7JZa63/xoUHu42re7HPiCqYURZbWW11OfbU60DL6fNTwB5L+6/uvBqLrXVrO34bWq/GRK+zzjpRx5qoXIL7Vtay8crQv3beeee5OqcMrbYdXK+OT+Omn3vuuUhjgxoqVA7KmKoLdrz1+gYJH6DK2WefbUcffXTUfooaQXRiUEOI6uqGG27ossynnnqqa8RQEK33KpOtngj6jijDrGyzyizesaqRQ8+3adMmsh2VtT6T22+/3e6//36XFVcvAAXOCjy1vMrfB+PBz6Zv376RfdY2Ffwr8Feg2r9//8g+qAu2xrDLueee67qp//bbby6oVhmqfLUPWo+Gf/z++++ukcdTVl31x29HWf3PP//cNQooo+33Rw08anDw+6Rj0roVjKsMOnXq5DL2ft3BOqAy+eeff6I+h+BnXlp9KU/d8sv6E7e+W3p4+g7496uxwH8n/LLB9QRfU4OVn8vALxtsEFDDlV823noTLat9De6TvqfBYSeqmxVZVssEl9VywUY3f04OHqtfVsvpGLz69etXaNnY8o4tQy0bLJdgmfkyrM7yLk8Zpqq8Y8swXrmEWd7JlGHsstVRZ2OXTVWd5RzBOYJzRMXOEfq+VrV0O0ckO8yusuoH9r+mXkeUNW9YWgbY06ZNc909NXv4rbfeGtqY4OBFb2wQFLy4TuUY5LIk2od4f+uhrrHByatEE8X55RWMKQuo+4qr5UatPgri1HLjK6Iqkrr1lrVd/3twWd1GzXfpVaCk59QipC7X6tYbL7gO7n+i7ShT67slJ1MOZa0zmfeqO7K6SmsCrNiM6p9//um+mMHgTkGoArgpU6aUeiwKFNVN3ovtIu6X1zZUtr4hSM+r8UStaGqwUMZVJx4F9gqGFYSqB0Bpx6rPWieg4IlCr+nzURCsLvvKCr/zzjuuu/lDDz3kupwnKlN9ziofvU/d6bVvqkvKHAeXU2ON/1vHo390eq+e03Gq90Jwn/R3kNar/dF+zZkzxzX26BH7T0WNA8G/VU7Bdeu14LqDy6osVT7JfOeSqUvJLqtzT3D/kg3UwwjyWbb0xlTKMLXlQHmnthz43lNn0/07VlXbqSo1rbzL+97aUi5ZlSyHtAiwNZZU3YQVTGgG68pM2FUbKOBQN914lL1UNvvQQw91mWsF3hovraDXZ9PiZRzKEmwt8u8LdqXQZ6ZMp7LrCi59FjwdKFN8yCGHuP0OCraKxj5fVtkpoE70GSW7ruDzmvRPNI5dj9JaZbVt3xUn9rukwFfd8PVQZlzHrO+cAuxELrroIlu4cKEL7BXga50qL981KV4d8fsfHOtSFs2C//TTT7vtqNFB9VyzzcduJ9gym+y6PZWdz9QDAAAA5VXtY7B1oa8Jl/RQ8KeMqv9b2S1lPpW51ljNIUOGuKyrsmv+gfLReGFlrHXLLWXxlG1VNjAZCpAqOtmBWn7U80BjaNUV+t9//y11O9U13j0eTYylscOxGewNNtjANUr4Cb1Ek55pkqywZrjXNtTtOziGaNy4ca481RXa9+5QoKmu2dpXfbalBZX+FlvKspdG29Bx+LEsPkCO/WzUQKOeBRrjrC7sCrAVcJf3OH/66aeo52L/1nGr67u6mSsbrm7fKusw1i3KXKvbv3rJAAAAABVR7RlsBc7KpAb5v4cOHerGfyrAEM1AHEsTNlU13Z+6Jm9HmUll/YP8GFplTRUUaiZ2ZSkVHD3//PNJrVefhSYdU3feZs2aRY3RSIb2QeNhNf5WQbb2Id7tubQdTUqgybx8plIPjdnVfamfeeaZhNtQgKsxtL7RQN2Dxc/YLRqfq27sGl+dLM1ere7twe4iCnAV8Gnss7rkK/ursdVat59xu7I0ll2TliloVv1Xo5MmMFOQqWNSsKvj0X3ODzzwQDeeWe9RpjfRLPuqB2ro0BhpH2zrvtjajtargFTBtCa/04zyauASjfdWdlgT3KkHgh8vr3LQpGPdu3d3DWRqSInNIpdFPSo0eZpuL6fs96+//lrifu6quxpvrUBbPS/8DO9lNWYks27RenXcsV3TAQAAgLQJsDt06FBmkFwdQXQiygxu2anqujcn0904lgIgBVxBylSrV4Ayf5rsSRM8KRjU7bgU8Kqbb1l032YFXQrkFAD7BpDy0JhlbVcBqw+yY2nMs4IidVFWwKyeC5o4TVlRZRhLo0nHdHyetiN+HaIAvLxjKVR+Om7Nkh2koE23hDr55JNdV2WVp8YJx3aHrig1LKhrtLah2a/1t243pYBbND5at5zSTz9JlzLZajxQl3w/Nj7eZ6kZ3zVRmKhRQJ+lZkPX+lTn9LfKTJPi+c9OtwjTMgrGdaz6/LRvmnhNk6upi7jKXEF2eeh9mvRN5anZv5WJ13r8eHLRxG76/NVtXeWgY9CEeWXdpzuZdYvGdqtxQusGAAAAKiKruDwDFDOIbmckyrrFUrd0ZT4VVJU3EwekA3WH1izn6vauBo3aTj0DdBs63QrNz1RfFTjXAABQvQbf/ZZNnLkgJeveuH0LG372AEt3S0ePssJyDv9LRk7z5tZ4jz0t3WPHGjcGG0DVU/du3fpLPQTw3+R/V111VZUG1wAAAMg81d5FHED12GqrrSj6/1FrZDItkgAAAEBpyGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAC7nAqLijJ6e+U1Y8YM69Kli02YMCHhMq+++qptscUWCV//+uuv3TqWLFli6aCq9nf16tW2yy672Pfff2811X333Wf77bdflaz7tttus+uvvz4l2wIAAADCkBvKWmqRnOxsu3zEpzZ1zuKUb6tTm6Z2/eHbl+s9F198sb322mslnt9uu+3siSeeqNT+aN0KKh988EELU69eveyzzz6zxo0bRwLyG2+80b777rtyrWfNmjV2991329ixY2369OnWqFEj69u3r5133nnWtm3bcjUa7LzzziWeP+CAA+zmm2+2qvL8889b+/btbfPNN488980339j9999vEydOtFWrVrnjUvldd911Vrdu3QqXXTo4/vjj3edy1FFH2brrrlvduwMAAACUQIBdAQquJ85cYDXV9ttvbzfddFPUcwq+KqqgoMCysrIsVbRvrVu3rvR68vPz7bfffrNTTz3VNt54Y9cYcMMNN7i/FXiW19NPP20bbLBB5O+8vDyrKsXFxfbss8/amWeeGXluypQpdsIJJ9jRRx9tV1xxhdWrV8/++usve++996yohvd0KCtTn0z9bNGihWsoeu655+yiiy6qkn0DAAAAyoMu4hnIB6zBR9OmTSOvP/XUU7bPPvtYjx49rF+/fnbllVfasmXLSnTp/uCDD2yvvfayzTbbzC677DKXGddz6h6th7pKe8oYH3nkkW6dWndp3ZoXLFhgAwcOtNNPP90FV8Eu1/r9kksusaVLl0a2o67CyVAGXMemfV5//fWtZ8+eLhD99ddfbdasWeUux2bNmkWVoc+wx/Puu+/a3nvvbZtuuqnttNNO9uSTT0ZeU8ZZgeHChQsjz5144omuvBIFxuPHj3dl2r9//8hzn376qbVq1cpl5DfccEOXxdXnp27TCv5LK7s33njDlbmy3dtuu62de+65Nnfu3Mi6/Wfw5ZdfuuX0OR5yyCEuqA96+OGHXa8ArefSSy91WfSgn3/+2Y499ljbeuutXeb9iCOOcMcSpO2MGDHCTjnlFPcZPfLII0mtW1S2b7/9dsLPAQAAAKhOBNi1kLLRCpjfeustu+WWW1y3Y41vDVI2+LHHHnPBm5a7/PLLbc8993TZcXXn1kOBkHfnnXfacccdZ6+//rp16tTJzj//fNdlO9Y///xjhx9+uG200UZ27733lshc+uBK3bv9drReUbCoAKs81HCQnZ1tTZo0sVT55Zdf7Oyzz3aB/ZtvvmlDhgyxe+65J5I1Vwa9Q4cOriFDlJn+4YcfXNlr3+IZN26cdezY0ZWDpyBfQfG3334b9z2llZ0+i7POOstGjhxpDzzwgGtwUDAeS5+jhgK88sorbt+0Pk+BrRoLzjnnHPe69keBctDy5ctt//33d8+/+OKLtt5669nJJ58c1YAjWs+uu+7qyuvAAw9Mat2ixp5///3XNT4AAAAANQ1dxDPQxx9/HBX8ioIcZQzlmGOOiTy/zjrruMDr6quvtmuuuSbyvAIyPaeu1p6ypMo4x+vOrfGxO+64o/v9jDPOsAEDBti0adOsc+fOkWX+/PNPF/Bp4i4F+PG6nSvgVqZYr8Vup3nz5m5/k6UM6O233+4yy8FANVmHHnpoVAA8fPhw69q1a4nllDXv06ePy8iLGhj++OMPN+Zd2eCcnBzXgKEJu7Q/w4YNcw0X7dq1K3UceJs2baKe22OPPVzQrMy3MtnKMivjq4C2YcOGpZbdQQcdFPldZajAedCgQS7wDZaNAtytttoqUmf0UDmqO/rQoUPd8eh9flllvIOZZpVDkMaGqzeEGgV8/RD1clBgHdxuWesWP5ZeDQTlqQsAAABAVSDAzkDqnqvgOCjYRfyrr75y3XIVBCrAKiwsdIHMihUrrEGDBm6ZOnXquK68yQou6wNDdQX3AbYy4oMHD3aBt4LrilB3Yz2SoQYCBWk6tquuuqpC27vrrruiGgjWXnvtuMup4SB2UrTevXu7gFTbV4CtYFDjhpXFVqZbAWZp1JChoDZI69HYeh3XF198YT/99JPLRj/++OP20ksvuaA7EY1NVw8ATY62aNEiN8ZbZs+eHTXOPNig4j/H+fPnu8YAHedhhx0WtV518Q4OFdCyyt7ruXnz5rku8CtXrizRRV9d6WPLsKx1iy8T1ScAAACgpqGLeAaqX7++65obfGg8scycOdNOOukkN4ZXAZe6Mfuuy5rMLJitLs/EZgrIPf++4PhiZVeVbVV2XUFdKvngWt2IlV0ubex0aRRQB8sw0URcPlgti7K4CpL1GQTLOh5l6xPdBkyBr7LWajh45513XACrib8SUcOJeg6o8USZ9Jdfftl1x5bYbvy5ubmlfo5lUSOCxrwrQ65Z0DVkQHUvdjuqoxWxePHiyIRnAAAAQE1DgF3LaLywsqoaZ6sMobozz5kzJ+kguqKzVaur9a233mrdunVzs2BrHG1p29E+VoQCOY2Hnjp1qpsFvCoCMWW5NaY6SH9rDLUCalEgPGbMGJfVVjZXE3qVZpNNNnETjJUVvPuJ2DT2OVHZKTusCdY0Ll7dtbW/yjRX5Dh//PHHqOeURQ/S5Hbqwr7DDju4Rhw1SgQnd6vMuuX33393x6h1AwAAADUNXcQreH/qmrwddS8OzhAtCvQUbCoTq+ypxgFrwjAFRMo0JkP3ZNYYYAVsCuzKmxnWPmgMsmawVpCtfYg3nlvbUdZVY3DV9VzZTj00OZiC1GeeeSbu+nVcuq2VukOrC7waA3w5qIu8z0BfeOGFbiyvZuMOg7LDGuP80EMPuYngFChqvLbvGaCMvbrs+wBX99LWeHjNAK5JuxJ181dmetKkSZFu2/qcJkyY4CYH0wzi6tavDLG6+msSukRlp+7dCkpV3uqGrSC1Ivcy1/2nlaFW927NEK4JyiZPnhw1FlqNCppIrXv37m74gRpVkrm9WTLr9r0A9HpV3jINAAAASBYBdjkVFhXZ9Ydvb1W5vZwEM00nots56bZQQcpUjx492gVrmj1aM4RrxmgFfAp4k7mv8MEHH+xmHNfkVArilI1VQFce6oKs7aoLtw+yY2n8siYYUyZa44U1K7cmTlMmtLTZoxXIfvjhh+53TSgWpH1V0OpnMk80e3dFKCt/9913u1nRNSZajQbaX03apQy0egso4PTjx/XZ6PguuOACd+szP+49tov4brvt5mZw9wG2gnE1iKhruHod6H3K5Gqb/tgSlZ2CepW7ylv7q89bs5uXh8aOa+I6NZIouN99991dwK5GF0/3Hdet0dSFXYG9PmcF2WGs2/cECN4bHAAAAKhJsoqTHUCaYfy9eRX4xNIESupirKCUTBmqizLNmvH9vffeq9As6Jnmk08+cbc2U3bbd71Pd5xrAACoXoPvfssmzlyQknVv3L6FDT97gKW7paNHWWESQ/7KK6d5c2u8x56W7rFjLMZgAzWU7hWuLLcmRYO5LvOaRT1TgmsAAABkHrqIAzXYAQccUN27UGPoPuAAAABATUYGGwAAAACAEBBgAwAAAAAQAgJsAAAAAABCQIANAAAAAEAICLABAAAAAAgBATYAAAAAACEgwEalzJgxw7p06WITJkxIuMyrr75qW2yxRcLXv/76a7eOJUuWpMWnkW77W9W+/PJLd0utoqKicr1vp512sqefftpS7eKLL7bTTjst5dv55JNPbL/99it3OQAAACB9EWCXU3FxUY3enoIHBX+xj+OPP77GBia9evWyzz77zBo3bpxUQF6a++67zwV3PXv2tC233NKOOeYY++mnnyrUaBD70PGnk0WLFtkNN9zgAtdNN93UtttuO7vkkkts1qxZ5V5XeYLfW2+91U455RTLzv7/08vq1avtkUcesX322cc222wz22abbWzw4MH25ptvpm0Aev3119vAgQNd2SqQjrXDDjtYTk6OjRw5slr2DwAAAFUvtxq2mdaysrLthxljbOmqhSnfVuN6za1Xh13L/b7tt9/ebrrppqjn6tatW+H9KCgosKysLEsV7Vvr1q1DWVfHjh3tyiuvtHXWWcfy8/PtqaeesuOOO87GjBljLVq0KNe6FFBusMEGkb/z8vKsuii4b9++vZ1xxhlJB9eHHHKI5ebmuvLYaKONXMPBXXfdZQcddJC98MILrozC9t1339m0adNszz33jAqujz32WPv777/trLPOsq222srVp3HjxtnDDz/sGlg6dOhg6ejAAw90DTiTJk2K+/oBBxxgw4YNs/3337/K9w0AAABVjwx2BSi4XpI/L+WPigbxPmANPpo2bRp5XUGnMok9evSwfv36uQBs2bJlkdd9BvmDDz6wvfbay2UcL7vsMnvttdfccz6jq67S3vTp0+3II49069S6v//++4T7t2DBApf5O/30013wFexyrd+VZV26dGlkO8pKJ0vb7tu3rwseN9xwQ7v00kvdsSUKgErTrFmzqDL0GfZ43n33Xdt7771dNlPZ3ieffDLy2v333++yxwsX/v/neeKJJ7rySlX2VoH0v//+6z7r/v37W7t27Vxgq/1SVvWaa66JLKv9uPbaa91j8803t6233tq9v7i4OPL6zJkzXaON/0wSefvtt23bbbe1evXqRTVUqD4omB40aJCtt956tu6667qgU3Ut2LiiRhF9/gq6td8jRowotWu+hiboOTUeBOvup59+6oJ8rUe9N1QWifz8888uo/7oo4+Wq4wvv/xyl4UvraFCdeGXX35xjQsAAADIfATYtZCyhwqY33rrLbvlllvsm2++sdtuuy1qGQU6jz32mOsGq+UUTChgUXZc3bn1UPDi3XnnnS5T/Prrr1unTp3s/PPPtzVr1pTY9j///GOHH364y6jee++9JTLrWqeC4kaNGkW2o/WKAm0FLMlS8K5MrQLj0oLCylIAdfbZZ7vGCHV5HjJkiN1zzz0u2JNTTz3VZWjVkCHPPvus/fDDD67sg92ow6Kg/Z133nGNDW3atIl6rX79+q78Va7KcntqPFHg/dJLL7m68cwzz7iy8+W+1lpr2Zlnnhn5TBJRIK1GhiDVHwXdsc+LAvFgMK4GAS2nenTooYfaddddZ1OmTCnX8avuPv744658lT1W44B+TzReXPVLn99JJ51kYVOvg1atWrnMPgAAADIfXcQz0McffxwV/MrJJ5/sxsWKxiV7yr6p2+7VV18dldVUcKznNt5446gu0gpa43XnVpZwxx13dL+rG/OAAQNcV+HOnTtHlvnzzz9dMLPLLru4IC5et3MF3AqI9Vrsdpo3b55Ut+aPPvrIzj33XFu5cqVbh7K25e0eLgrwggHw8OHDrWvXriWWU1DYp08fl5EXNTD88ccf9sQTT7hMvQJXNWBonO7tt9/ugj41XCirnArqIaAsb7Dsg9TtXdlpfT7K0osCaDVsqNzXX399+/33323o0KGuDLSMjqFhw4ZlduVXMBsb1Ct7q+x5MtSAo6ywr7PaBzUAJTqWeFR3lY1Xptxn4B988MESy73//vt2wQUXuM9CvQ9SReVRkXHvAAAASD8E2BlIXXwVHAcFu4h/9dVXbsIpBYHqPl1YWGirVq2yFStWWIMGDdwyderUKVfWN7isD7AU6PnASFlFBU4KvBVcV8QRRxzhHskcvzKg6pL94osvuuykMrMtW7Ys1/bUTToY2K299tpxl1PDwc477xz1XO/evV1wqLJVcKqGgYsuushlsZXpVna5NJoY66qrror8rYYNBb/BrufqYVCRyeB81+8gTQoXbPDQ32o48PufLNWjYEbaby/ZMfzBBh29R9nf+fPnW3koS++Da18fY9ehbuFqiFIvCjX4lOaEE06IDHlQo4i6wZeHykP1HwAAAJmPADsDxQYYsRlGdYVVZlKZawXeCh4U9Goys2C2ujwTmykg9/z7guOLlZnW2GgFNcp2K2OaKmok0PHroUBxt912s1deeaXcXYAVUCcqx7IC1ni+/fZbF6zqM1BZawKyRNQVXuPZPWW+27Zt67Kxnv6OR9n6Jk2aJOxarQYBfUYaBx029TKIvX2ZJp7TNpMRWybaT1++vjdBsLzjDUMobR2eGjyUmX/55ZfdPASlTQKomdh9gFzaZ5bI4sWLK9SDAgAAAOmHMdi1jMYLKyupWakVfKo785w5c5IOois6KZeCI92+qVu3bnb00UeXOumUtqN9DIuCK2VWU0VZbo2pDtLfCix99ldjojWTubLa6i6sCb9KozHovpFAD3XPVmNI8LlEs5qrrDVeXmOf586dG/WaAkVNHKZJ13z3cIm9lZn+1jb8/if72asLvXpGBKnXwhdffGG//fZbieWVmU/2s/FBavCYJk6caBVtCNA4c3WTP+ecc+IG6sGGDF/mGlNdHjo2TQAYb2gBAAAAMg8BdgZS0KIgJPhQd21RkKDsqcYB68JfXamff/75pNar4EKzcSsbqfWVFpTEo2BNmVh1J1eQHRv8Bbej7uqagErb0VhqPzmY3peI3qPJ1n788UeXJf71119dZn727Nnu3tjehRdeaHfccYeFRePKFUA+9NBD9tdff7ky1XhtPzmbtq8u+5r4TV26b775Zhdgq5tyqmgMurpGax8++eQTN7mcJtpS7wF9/sHu56LXNUu4PlsF5irro446KuozUQZeDSO+LsWjwD12Qi+N+Vc2XrfqUnCvoFh1TxPCaYx6onoQSxl39SrQpGtTp051vSGCXebLS0MGFGTrmM8777yoHhzJ0NhyzWKu/VfDhX7XQ98/T7ciU+OEGrMAAACQ+egiXsH7U9fk7egWRQp0gpSpHj16tBvjqtsgafyuglEFfArGND64LAcffLCbcEr3/lUwq2xseTN66mKr7SprqGBZgX4sjV9WF3aNndZM15qVWxOnaUy1ArPSAngFS5oRW8sqQ9u9e3cX7OqWXcFgMszZu5WVv/vuu9143gceeMBNBKb9VfCo7Ll6C2g//PhxfTY6Pk2wpX31497DpGNXw4n2R8G0gkA9p0nENOFa7ARrumWWgkTdRkvlqOBa99H2NIO4xo9rvLICyES3Pdt3331dI4q6p/vx6+p+rUBWwbACbAXyOmZ9JpphPdnJ3hSoqmFEjRWaME5lqjqioQ4Vpc9K+6au92oA0fqTHXOumfX1ffD8va51Kzt/X28/m7uGbQAAACDzZRUnO4A0w4wfP9791EV6LAUaypApKI3thltcXGRZWVWX+K/q7aH2UXCphpeKTj4XSwG8xmHrFlu1mTL96qqvcd6JZr8v7VwDAABSb/Ddb9nEmYl751XGxu1b2PCzB1i6Wzp6lBUuXBj6enOaN7fGe+xp6R47xiJyK6eqDnYJrpFudDs49WwIcxx9OtIwBfUeSObWcgAAAMgMdBEHECrdx9zfc702UwtnMq2cAAAAyBwE2EAtF28cPAAAAIDyo4s4AAAAAISsZeM8N59SKqV6/amUlZdnxVUwpLAqthFEBhsAAAAAQtY4r66bT+mHGWNs6aqFKbnjUK8Ou1q6yqpb17JycmzK5VdY/tS/UrKNvE4drfP1VTvxLgE2AAAAAKSIgusl+fMo3wQUXK9IcAvYdEQXcQAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2Srj44ovttNNOi/x95JFH2g033EBJJeG+++6z/fbbL+Vl9eeff9q2225ry5Ytq7GfSyrrTey6Dz74YHvvvfdSsi0AAAAgWQTY5VRYVFhjt1dYWGiHHHKInXHGGVHPL1682LbffnsX/FWE3nfWWWdZqq1atcoF9/vss4917do1KsgvD62jS5cu9uijj0Y9//7777vnK+PVV19164h96PmqdOedd9rhhx9ujRo1ijz30ksv2b777ms9e/a0LbbYwvbff3975JFHEjacZJJTTjnFbr/9disqSt9bVQAAACD9MYt4OeVk59gN715jfy9MzVTyQes172iX7X5V0svn5OTYzTff7AKrkSNHumBLrrvuOmvVqpULQiqiWbNmVhXUQFCvXj2XnXz33XcrtS6t57HHHnMNDk2bNrUwKagdPXp01HONGze2qjJr1iz76KOP7PLLL48898orr9iNN95oV1xxhQuu1VgxadIkmzx5sqUrBcuqE3Xq1Clz2R122MGVx9ixY61///5Vsn8AAABALALsClBwPXnu71YTderUyc455xy7/vrrbeutt7aff/7ZBYMKwIKBigKv2267zb777jsrLi62TTbZxAXn6667bol1KuDdeOON7bLLLnN/77TTTnbQQQfZX3/9ZWPGjHEBuF5T5lQ/v/76a1tnnXVcwNe9e/ek971BgwZ2zTXXuN/HjRtnS5YsqXA59O3b1/7++2+Xwb3wwgsTLqdA/t5773XLtmnTxo444gg77rjjSl13VlaWtW7dOukg8cEHH7QXX3zRFixYYJ07d7bzzjvP+vXr58r92GOPdQ0jjz/+uFvvokWLXMOIyvfMM8+Mu8533nnHfR5rrbVW5LkPPvjA9thjDxs4cGDkuQ033DCqF8Jrr73mfvdZ/KFDh7o6onqg7P4///zjGmIGDBhgQ4YMsbp160beq9e1ryor3yNCdcxn0FesWGFXXXWVqw96Ll4ZvvHGG/bMM8/Y1KlT3WetbV9yySWRslS9Oeqoo1zPg3vuucd+//13e+KJJ1wdKmvdKkMF2W+99RYBNgAAAKoNXcQz0NFHH+2Cq4suusgFJgrUgl2j//33XxdIKsurgEfdmw888EArKChIeht6X+/evV3QpsBGQay2p/HHek6Buv5WEOlVZVfq7OxsO/fcc+3ZZ5+12bNnx13ml19+sbPPPtv22msve/PNN11QqcAuzH1UEPvUU0+5slCvgu22285101bjhALqW265xcaPH2/Dhg1zy1955ZXWtm3bUrtyf//997bppptGPafA+Mcff7SZM2fGfY+C0j333NMFxp999pl79OrVy73WsGFDu+mmm1zgriywGmOefvrpqPdPmzbNBbgPPfSQe3zzzTdR3c/VOKPnHnjgARcU63eVb9CaNWvcUAOVg5ZTJl4Bdqw77rjDNUJof1Rnklm3bLbZZq5sAAAAgOpCBjsDKXC7+uqrXSZSmenjjz8+6vXhw4e7TKDG8fqstjLf5aEM7KGHHup+P/300+25555zmUYFq3LiiSe67tnz5s2LZCi1jarsSr3rrru641fWVdn0WAp8+/Tp4/bf798ff/zhgrhgJjjW0qVLI8GpKBv7+eefx11W61JZ7L333u7vCy64wGVq1UChxg8F08raKwCfO3euffrpp66BIjc38VdTQXRsgK1jUPCq3gUdO3Z0jR/qKr3bbru5+qAgOi8vz1avXl0i+x4M5jt06OAmUFNwe9JJJ0Vl4tUY4DPWakjRccjy5ctdo4Re18RroqBYDS9Bysp76uFw6aWX2qBBg9xEbcGx5DoOv55k1y3qgaDGFO2rGlgAAACAqkaAnaFefvllq1+/vk2fPt3mzJlja6+9duS1CRMmuHG6yYxtTSSYEVf2NPa5li1bup/z58+PBHSx45arwvnnn+8y+vG6FSuQ3HnnnaOeU2CqrLPG/qrbcTwKVn13a0kUzClwVNlrnbHbmDhxYuRvZZbVBVtdo9UwogC5NBpfrd4HQQrUn3/+edf1XxledbHXsasbttZbWsCpz0UBv7LU6uqtngzBgNcH3sHntD19tqI6pux0sNFBwwZiG21+++03191cx66u8L53g4LiDTbYILJcsPEg2XWLGhAUXKsRQb8DAAAAVY00TwbSuGp1OX744YetW7duUZNhiYIPZTUrI5hh9esKBuz+uWAX8eqw5ZZbum7ZytbHqui+KVhdb731Ig9lY0sTW9babvC5lStXui7PCugV5JalefPmCcena2jA4MGDXTdrZeiVEf/2228TrkvdytWVXj0SVF/UcKDJ8BTUBsXLqPvyS6YcFbirkUPZfo35VgPQ/fff716L3ZYahmK3kQyNDdd7Ca4BAABQXQiwM4wCGY1r1S2cttlmG9c1WtlM3cLJU6ZZQXhsYJOpNJ5Xs26rHII04dgPP/wQ9Zz+VgY5Ufa6PJTxVbfl2O0qqNW2PXV5VtCuWc+VPf/qq69KXa9uYaau7GXxWWF1s/YNILG3sdK+tWvXzk499VTXxV/HrrHR5aHx9lq3jisY7GqcebC3wMKFC11WXb0ndPw+A17ZdXuaNV1lAwAAANTaAFuzN2tiJ43p1MWxxg3H88knn7jbTykI0NhajSNGScoOKjuqoNJ37dW4XwVxmiValOHUOGIto8ypgpXXX3/dBUGppFmuNVFWaRQ4qgu7uhBrH/W7Hp5mRdd6NFFbstSgoHtra8KzIGVUv/jiCzdply8D1auyZhEvD41/V+CsMc2aPVuZdB2PZsuWjz/+2E0qpns4a4yxltf9qkubQV0ZeTUEqBu7p/HcmgRMk3xpjLYCUo3rbtGiRaR7dfv27V0Qqs9ZM5qrgUUZeNWLt99+22XPFeCru3p5qMu8JslT3fvyyy/d7N86hmCWXkG8AmX1rFC3b816rtnVw1i3p0YjP04bAAAAqJVjsDVmVMFzjx49XHYtXpdQBROaiElBuC6ulXXTLYJ0GyFNklTVdH/qmrgdBSAvvPCCC5KC3WwPO+wwe++991xXcU26pS7GGnOroEWziSt7qsnANt98c0slBZgKmkujibWCM2GrUUUUGPru1FpPebPvmjhr1KhRUc+p+/zdd9/tJkFTcKqx4meccUapE5yVlwJpjcVWA4e/TZcCS2WK9bdua6Ztal9Ev2uGb018pm7e8WiCL9V9Taymrt3+tmQK1DXZnBon9BkrsNZs4PpdDj74YDc+WwGrejqonmgMusaoX3vttW7ssiZGUzbbd99OlmaR1zr1XgXFuqWXjttToK8yUAODgmwdrxoAtHxl1+3Hcf/0008JywwAAACoClnF1TxINjjjr4JnZVR1L9ugE044wXULDXZzvuKKK1y337Fjx1ZoxmDdGkni3ac5Pz/fBXGaSCl2PGdhUaHlZFe++3Cyqnp7SA8jRoxwmeYnn3yyunelRlBjkbL+1113naWT0s41AAAg9Qbf/ZZNnLkgJeveo2dHu2FwPxs75UVbkj8v9PU3yWtl/TofbKm2dPQoK1y4MPT11llvPWvYd1v7dfCRtuJ/ybSwNejSxboN/+92uJVRWuxY47qIlxUcK6umMan+NkeeuvzqtkaambgqVXWwS3CNeJSN1gRusZnc2kqz1quXAgAAAFCdqj3ALovGhao78Prrrx93AqcpU6ZU054B1UezeqvLdOzttGorjZv3t4sDAAAAqkuND7DVNVyaNGkS9bz/279eUcGJovR7cJZl9Z4P3ooo2Js+3mthL1tV26mNy9bEfcrkZWviPtWkZYPnHj2vv/3rej72PJXovJXMe1m29HKgDKu2LlHeqSlvvvfU2Uw516d6O5kileeITFFcybqUUQG2l+i+zZW5n7MKzd/CSDSR0qpVqyKvBQs99m8VdLCw450MyrNs8AMs6yQT9j74v4PHGlw2dvK5VC6b6mOtCcumugyDy1b3sVbVstVZZ4PLVnT/NRZbD0+TAfqJ/AoKCtzf/r3BZfWcXtMyovcEJxLUcpoY0NOQgtLWm2hZ7auW9cej86TOl57OoxrOk2jZ4HlWv/vzrJbRsr4stI7SzsmxxxocIqF9T7ZctGzwWIPlHW/Z2M/Gr9eXYUXLO1iGWra0MkxU3r4M/bKllWG88g6WoZYtrQwTlXd5y6Ws8k60bHnqbOyy1VFnY5ctb50tq7w5R3CO4BxRuXNE8JyQ7nTeScU5wp/PMkFxJa8jyhNkV/ss4mVp2rRp3Ey1v41RbGa7PBSca1Zir0GDBu45/cPUz+C9kGP/jh07XtllfUNBcNmq2od47w02XJS1nVQtm8ryrs5l472XMqxceadrnfUnbk1wFpzkrHHjxpH3aziA/vb7EVxOzwVf063Q9LenZYMNAhpS4JeNt95Ey2pfg/tUr149N5O9p/NoRZbVMsFltZyOIfacHDxWv6yW0zF4unNCRZaNLe/YMtSywXIJlpkvw+os7/KUYarKO7YM45VLmOWdTBnGLlsddTZ22VTVWc4RnCM4R1TsHKHva6ZQHUjFOULrzRRZlbyOKM+k2jU+wF533XXdQenevf6WRP5+yaLbHlVG8KI3NggKXlzHZsqDf5f2WmWWrartsCzlQv2u3u+Czj3+xF3ZxprqbkBK92Vr4j5l8rI1cZ8yadmauE/pvmxN3KdMXraqtpPuquockc6yQqxLZanxpabW4W222abEPYx1Ky/dt7hr167Vtm8AAAAAANSYDLb6u3/yySfu95kzZ7q+8KNHj3Z/b7XVVtaiRQs7/fTT7YgjjrDLL7/c3Z5r3Lhx7p7Y1157bUa1rAAAAAAA0le1B9jz588vcf9a//fQoUNt6623tl69etmDDz5od955p73++uu21lpruWB70KBB1bTXAAAAAABEq/b0b4cOHWzSpElxHwquvR122MHeeOMN++WXX+z999+3wYMHV+t+Z7KLL77YTjvttMjfRx55pN1www2WKarqeNTLQvdnrsm6dOnivk+pXveCBQusT58+Nnv27JRsCwAAAKgJqj3ATjfB2Qhr2vZ0K5BDDjnEzjjjjKjnNQP79ttvb/fdd1+F9kHvi+1lkApff/21nXrqqbbddttZz549bb/99rORI0dWqIFAwV3sY8aMGVZVdFuDe+65J6qhQsMh7rjjDttll12se/fubm4BDX0IBrg77bSTPf3005ZpNNRDn6fKBAAAAMhU1d5FPN1oVrnlX3xhRUuibxuWCtlNmlrDvn2TXl6z3d188822//77u8B03333dc9fd9111qpVKzvllFMqtB/NmjWzqvDDDz+4QPjEE090+/vxxx/bRRdd5G6roMCzPNSgcNNNN5UI8qqK5hHQrV222GKLyHNXX321/fTTT3bllVe62e8XLlzojnnRokWWrnRLu+DtbEpzwAEH2EEHHWQXXnihNW/ePOX7BgAAAFQ1AuwKUHBduHCh1USdOnWyc845x66//nrXxf7nn392wd4rr7wSda/OyZMn22233Wbfffedy5JvsskmLjjXbdHidaneeOON7bLLLnN/K9hVoPTXX3/ZmDFjXACu15R11k9lotdZZx278cYbXaY2WbENAEcddZR99tlnbhvlDbAV9GmW+WQow68u4x999JELGLfccks3xr9jx46ua7Mm1tO+nHzyyW55TbKnvx977DHX7Tmet99+u8Q+f/DBB658/O3m2rdvb5tuumlUOWuiPzUM+MYBDZVQIK5GEn1W2leVrRohlBEOvleNEzrul19+2X3WBx98cFTPg6lTp7rjUp3QOvznGaQ6oYz6P//84xo5BgwYYEOGDIkE0erNoNeVeX/ooYds1qxZNnHixKTWrf3TZ6L3M38CAAAAMhFdxDPQ0UcfbRtuuKHL/l511VV25plnuuDG+/fff12ApJvHP/PMM/bqq6/agQceaAUFBUlvQ+/r3bu3vfbaa258vLKS2p6CPj2nQF1/B7u4ax+0rfJYunRpyjPo6lKusf0KGF944QW3zyeddJKtWbPGZb3VUKDAUstolvsLLrjAzQGQKLjW+xWExzYuKGDVjPlaRzzahibw0+elhgU9REF/t27d7JFHHnG3p9MwgEsuucR+/PHHqPer3PPy8twxnHfeeW5iwE8//dS9VlRU5AJlzbr/4osv2jXXXGO33357iX1Q1l3B/TvvvOMCZjXMxHZZnzZtmr377rt2//33u0kHk1239OjRwzUUAAAAAJmIDHaGdmNXd2RlH5WZPv7446NeHz58uOt2rVnZfVZbme/yUBb20EMPdb/rNmrPPfecCyj32msv95wyrAoE582bF8kkaxuNGzdOehvKvI8fP95lb8tL3cs1+3ywy/i9995bYjll4T/88EO3/2owEAWH/fv3d5nWPffc0zUgKOOqwFrl2aBBAxfAJrJkyRLXMNCmTZuo53UcWofGXquxQd3HtX5l/kUNCermryA3mH1v27Zt1GeobLUCZwW5/r2y0UYbueDcl7U+Z/Um0LF//vnnLsv8xBNPuCBe1NNBn1NQcMy4JiD8888/XbCtBgdPDQ+33nprpMu99iWZdYvK5LfffktYdgAAAEA6I8DOUOomXL9+fZs+fbrNmTPH1l577chrEyZMcMFdsMt4eQUz4srMxj7XsmXLyG3YfLDo72+ejG+++cZlaZUN3WCDDcq9f+oer0YGT2URz5QpUyw3N9dlVj2ND1aAqtc8ZePVYPHee++5rG5p4441wZnELqOu5wralXlWhvuLL75wPQEUjPru54kmr3v00UddoKvPUhltPWKPSd34Y4NZlb8oUFYd8AGwBBsgPH1G2idlqVesWOF6NagxJkhd24Pj2ZNdtyjDnp+fn/BYAQAAgHRGF/EMpC64w4YNs4cffth1LVZX39ggR1nuylBQ6vl1BQN2/1xFZl1XcK2AU9ledV2vCAWf6623XuQRm032Eu2fng+WkW+oUHdojZMujTLReq8y2fHKTY0bygir67VmfFfXcAXMiSgzrGVPOOEEF/yqW7ZmWlcmOXbdQdoHf3zxjjO2DijwP/fcc13vBNUddTnXuPjY7aj+BCWzbk8TulXlZHMAAABAVSLAzjDKOirze/jhh7uuyBo/rGyp7snsKdOsIDw2cKoJ1KVZwbW6YOsYUk3ZcWVpNTlXMAhU13HN9C0Kfs8//3zX/V2ThqnBwmeG41HmWuv9448/yty+xspr+z7AViOFgvggfX4777yzG9+uLLUmEdP+lfc4NXGZxt97msE8djvt2rVzt0pTd39N8qZJzMJYt/f777+7bvYAAABAJiLAzjCaBVrZQz9GWONolQnWDOEKgkQTdGmMsJbRxF0K1pQVVVffVNpjjz3cjOBlBdeagG333Xe3uXPnukfwNlYKhLWeYDBXGQoiFbzq1lnff/+9mxFbE7Zp3LOel7vuusuVlwJrjSvWe+LNkh2kDHPsZF4aO/3888+7Mtc9uTXhmcbBqzu774at7tfffvutOz7NYC7KwKs7uQJgdVvXvmpse3n07dvXdXtXV3cdo/ZNxxWk7aiOaAZ0dREfOnRo1D26K7Nu3/ij8dcqGwAAACATEWBXpNCaNLWc5s1T/tB2yuPLL790M0grax0cn3vYYYe5jKTvKq4xxupqvHz5chfMDhw40GW4KzMmOxmaCEuBaiLqkrxy5Uo33lhBmH+oG7Wn17WeMLPvmjVbWVUF95qYzY95Vnko6FegqUm9FARrpmz9riBYZZ2IJkUbO3ZsVDdxHYsaMjRhmbLhmvRMz919992RZTRJmbqg77LLLpFZypVR7tq1q3ufgnSNedfr5aH91qzfypTrFmtqINDY7yA1KGgG+muvvdZly5WF1rbDWLe/TZky5MF7gwMAAACZJKu4IoNkM4Bmp5Z492nWJEwK4pSVizfetLLjl8ujqreH8Jx99tmuS3fs/b1rK92XWw0Euq84yj7XAACA1Bt891s2ceZ/vQbDtkfPjnbD4H42dsqLtiS/fL0Pk9Ekr5X163ywpdrS0aOscOHC0NdbZ731rGHfbe3XwUfaikmTLBUadOli3YYPS2nsGIsMdjlVdbBLcJ2+1DVft9yCue7uu+66q5uJHQAAAMhU3KYLSBGNp1bGFuZmDo93X2wAAAAgk5DBBgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNkq4+OKL7bTTTov8rXs533DDDRlTUpl2PGE7/PDD7c033yzXe1599VXbYostLNVmzJhhXbp0sQkTJqR8WwcffLC99957Kd8OAAAAMgcBdjkVFxbW2O0VFhbaIYccYmeccUbU84sXL7btt9/e7rvvvgrtg9531llnWar9+eefLvjt27evde/e3XbeeWe76667bM2aNeVuIFAQFvtQcJZOPvroI1cevXr1sh49etiBBx7oAtnyKk/w+8EHH9j8+fNt7733jnp+/Pjxrl716dPHfTa77767XXPNNTZ9+nRLR99++62dcsoptt1227m68f7775dYRq/ffvvtVlRUVC37CAAAgPSTW907kG6ycnJsyuVXWP7Uv1K+rbxOHa3z9dclvXxOTo7dfPPNtv/++9vIkSNt3333dc9fd9111qpVKxcwVESzZs2sKtSpU8fte7du3axx48Y2ceJEu+KKK9xr55xzTrnWpQaFm266Keq5Fi1aWHVQYK/GgkmTJiX9nmHDhtmNN95oxx9/vF111VWubBQEXnnllTZ58mS76KKLUrKv2u7AgQMtOzs7KuhWA4uC6gceeMDWWmst+/fff+2tt96yRx55xK6//npLNytWrHCBtY41tkHK22GHHezyyy+3sWPHWv/+/at8HwEAAJB+CLArQMH1inIES1WpU6dOLhhV0LP11lvbzz//bKNHj7ZXXnnFBWmegrTbbrvNvvvuOysuLrZNNtnEBefrrrtuiXUqi7rxxhvbZZdd5v7eaaed7KCDDrK//vrLxowZ4wJwvdazZ0/38+uvv7Z11lnHBYjKdiZL79HDa9++vX3zzTcu21hedevWtdatWye1rDL86jKujPHq1attyy23dIFVx44dbcGCBbbPPvvYUUcdZSeffLJbfty4ce7vxx57zGV0w/bPP//YLbfc4sr9/PPPjzyvYDs3N9eV6x577OGy2ipr7YsC3TvuuMN9Jvqs1Kiin3r9kksuce9XQClDhgyJG1Qqc/3VV19FPmcfiF566aWuwULr99q1a+cy60uWLIlax6effur2b/bs2da7d2/3e9u2bePWI9FQhCZNmri65+uWumb//fffrt42bdrUTjrpJNdtPVGvDX1WP/zwgz311FO29tprJ1XGCp71KKvBSsuoIYEAGwAAAMmgi3gGOvroo23DDTd0WU5lP88888xIcCXKPh5xxBFWr149e+aZZ1wXYnU/LigoSHobep8CqNdee80FIRdeeKHb3n777eeeU6CuvxW8e9qH8nRxVpClgE0BbyqpS/kvv/xiDz30kL3wwgtunxXUqWu6st4KEtVNXsssW7bMLrjgAhs8eHBKgmt599133bYVUMc67LDDrEGDBi7oC7r11lvdcbz88svWsmVLO/XUU11jgYJgBciNGjWyzz77zD2OO+64uNtVw0H9+vWtc+fOkec+//xzW7RokSuPeBQce/n5+fb444+7xgFlwmfOnOl+Ly8Fyptuuqm9/vrrduihh7rGgilTppRYTsenzLp6OowYMSLp4Lo8NttsM/v+++9DXy8AAAAyExnsDJSVlWVXX321DRgwwGWmYwO14cOHu4DrzjvvjGS1lfkuj379+rngR04//XR77rnnXLZ6r732cs+deOKJbjz4vHnzIplkbUNdv8ui9f76668ugNI6KjL+++OPP3bBpacM7L333ltiOWV8P/zwQ7f/ajAQjbtVxlJdsvfcc0/XgDBo0CAXWKs8FeCed955lipTp051n4/P/MZm5pXl134H6TPYdttt3e/KBmuftf/6PFTmqhNlZfQVECs4D3YP99tZf/31y9xvNQpce+21tt5660Uy1g8++KCVlz4rNWCIeg0MHTrU9WQIBv7Lly93Qb/qiF5Ppl5VRJs2bVw2XuOwg+UCAAAAxEOAnaGUyVQ2UpNQzZkzJyq7pxmYNelVsMt4eQUz4hrfHfucAjXf7dgHdurymwxNbKYASplJZWafeOIJF7CXh7rHq5HBU1nEo8youl2ru7XXvHlz1xgQzJoqG68GC80qre72CnRLo0nCZs2a5X73WfxgwL/55pu7bG8iCogT0fpiXw+uW132tf+aNK48Vq1a5Xo1xG4rWSpjH1z74FSff3mpG7mn41T9il2PGjg0Flw9KfLy8hKuS0MggnVHE7P5uQmSoXUruFYgX9p2AAAAACHAzkAKKtRF98knn3QZRI1RVZDqKVAoLYBLhoJSz68rGLD758oToHm+MWCDDTZwY2w1sZey8OXJIMYGe4kk2r/YINY3VCjYUqY32JgQz6OPPhrpcq8u+crmqsuzV1qwpuB46dKl7n2xWWwFepo0bZtttrGwqWFB49Fj90UUrAeD+LLqhKj8guUb+7fEG5ZQ1npEGXpN5Kex16V11fddzWMbfpKl8lBdIrgGAABAMujzmGE0KZUmtdKkUArCNH5YY2tfeumlyDIKDhWEl/f2V9VBgZWCMAXaqaAgXuvXZHCexhyra7TvkqygVpONqbu1uqurwaKszKwmaFOAr4cmBBP/tx7xun97mq1bjRUaixzr+eefd5+xsulBP/74Y1RQqP333bq1rmTKT93f1aU/GGSr27ky4omy7bGTnJVG49nnzp0b+Vv7pMn2KkJj0ZXF1iRp6j6eiALjYLmr6315aOb3rl27VmgfAQAAUPsQYGcYzQyujJ8fI9yhQwc3dljjcjU7tWh8qzKkWkYTdykYU5avvF2Ky0szX2vW8USUkXznnXdc12xljEeNGuXGiWsctM+OKxDWepTdDYNmCtcttJQl12RW6pauCdsUAOt532Vd5aXAWt2N9Z7gTNhhUwZfXdLV/VnbVnlMmzbNBdz6fDVJWbBLu6inwpdffmm///67m+xM2ehddtklEuwrKNfrmhV95cqVcberQFJBsBpfPI0314z0GtOuYFbrUAZdn4NmXlcX/mSpweeTTz5x69Ixqbt2eQL0WOoVoAYPjdMO7nMyNARBQyX0EB2Tfvfd+j2t149tBwAAAMpCF/EK3p+6Jm5HwY9mwdakT8Exx8r2aeyw7yqu4EvBm4I1zSaurtfKXmpccCpp8i4Fqomoa7AypVpOlPlVY8AxxxwTWUbBoV4PM/uu+2UrWFSgpvVqfLq6eCuo122uVJ4qL5/9VFCp2dJV1pqELRUUPKpxRJ+Xtq9sr7LtGleuGd9jqbFEx6DGEn2WmhHdjxPX5G2aOO7ss8922flEt+nSbam0bs1Q7hsXZNddd3WzdKtMdAs4Baf6bBR4arbyZGndasBQ44G2pc9VY+UrQ+vws76r7viJ6sqihiXd3szz90w/4IADIrcM0+RmP/30U9TtyQAAAIDSZBVXZJBsBhg/frz7Ge8+zbrdkII4jT+NHXtZXFhoWTk5VbafVb09pBd/H2zdKzx4y6yKUtd3dYXX7dSU+a7N1AClDLtuE5YqpZ1rAABA6g2++y2bOHNBSta9R8+OdsPgfjZ2you2JH9e6OtvktfK+nU+2FJt6ehRVrhwYejrrbPeetaw77b26+AjbcWkSZYKDbp0sW7Dh6U0doxFF/Fyqupgl+AaVUmTgCkT7ocT1PayqMgt4gAAAFB70UUcQBQ/dru201h3AAAAoDwIsIE0pjHMmukaAAAAQPWjizgAAAAAACEgwAYAAAAAIAQE2KUoKioKo4wBgHMMAABALcAY7Dh0/2DdG3rWrFnWunVr93dWVlbVfzoAMpLujrh69WqbO3euO9f4e5YDAAAgvRFgx6ELXt2XVrcqUpANAKnQoEEDW3fddd05BwAAAOmPADsBZZR04VtQUGCFhYVV+6kAyHg5OTmWm5tL7xgAAIAMQoBdCnULr1OnjnsAAAAAAFAa+iUCAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAGpTgP3+++/boEGDrHfv3ta3b18bMmSI/fnnn9W9WwAAAAAApE+A/cUXX7iAulOnTnbffffZFVdcYVOnTrVjjz3Wli1bVt27BwAAAACA5aZDGbz99tvWrl07u+WWWywrK8s91759e5fR/v77722HHXao7l0EAAAAANRyaZHBLigosIYNG0aCa2ncuHG17hMAAAAAAGkXYB900EFuvPWwYcNsyZIlNmPGDJfN7ty5s/Xp06e6dw8AAAAAgPQIsLfccku7//777a677nK/77zzzjZ9+nR78sknrW7dupVad2FhYdTvRUVF7vfi4mL3t36Knq+qZWviPmXKsjVxnzJ52Zq4T5mwbE3cp0xZtibuUyYvWxP3KROWrYn7lCnL1sR9yuRlU72dTJHKc0SmKK5kXcq4AHvcuHF2wQUX2IEHHmhPP/20C7bz8vLsxBNPrNQkZyq05cuXR/5esWKFrVq1KlKQS5cujRT8mjVrora1cuVKy8/Pj6xHy6ore1nLipbVMqL36G//AWq50tarvz0tp3V72mZp6020rI5Ry/rKozJQWXgqo9WrVydcNliG+j1RGWodicq7PGVYVrlo2eCxBss73rKxn41fry/DipZ3sAy1bGllmKi8fRn6ZUsrw3jlHSxDLVuZOptsuZRV3omWLU+djV22Ouos5wjOEZwjOEdwjuA6guuI9L2OCF43pDtdm6Qi1vDXPJmguJKxRnmC7KxiX9o12MCBA90kZwqsvQULFli/fv3svPPOc7OJl9f48ePdz65du1pOTo77XRfVGuednZ3tClYFqd/1nH7Xc1WxrNS0fcqUZWviPmXysjVxnzJhWc4R1NmaUA85R9SMMuQcQZ2tqfUwHc4Rem3w3W/ZxJkLLBX26NnRbhjcz8ZOedGW5M8Lff1N8lpZv84Hp/Qcod+Xjh5lhQsXhr7/ddZbzxr23dZ+HXykrZg0yVKhQZcu1m34sErXpV9//dUt171798yYRXzKlCm20047RT3XokULa9OmjU2bNq1S6/aFGvu7CjP4two40ftStWxN3Kd0X7Ym7lMmL1sT9ymTlq2J+5Tuy9bEfcrkZWviPmXSsjVxn9J92Zq4T5m8bFVtJ91V1TkinWWFWJfKkhalpuy1bzXw5s6da3PmzHG36wIAAAAAoLqlRYA9ePBg+/DDD+3aa6+1zz//3EaNGuXGXzdo0MD23Xff6t49AAAAAADSo4u4Auw6derYiBEj7LXXXnOBtfq/61Zd6iYOAAAAAEB1S4sAW/3gDznkEPcAAAAAAKAmSosu4gAAAAAA1HQE2AAAAAAAhIAAGwAAAACAEBBgAwAAAAAQAgJsAAAAAABCQIANAAAAAEAICLABAAAAAAgBATYAAAAAACEgwAYAAAAAIAQE2AAAAAAAhIAAGwAAAACAEBBgAwAAAAAQAgJsAAAAAABCkFuZN8+ePdu+/fZbW7RokTVr1sy23HJLW2uttcLYLwAAAAAAMj/ALioqshtvvNGee+45KywsjDyfk5Njhx56qF122WWWnU1yHAAAAABQe1QowL7vvvvs2WeftYMPPtgGDBhgrVq1snnz5tmbb75pw4cPtyZNmthZZ50V/t4CAAAAAJBJAfYrr7xiRx11lF166aWR59Zff33baqutLC8vz71OgA0AAAAAqE0q1I978eLF1r9//7iv6Xm9DgAAAABAbVKhAHvjjTe2qVOnxn3tr7/+sg033LCy+wUAAAAAQOYH2BdccIE9+uij9vHHH0c9/+GHH7rnL7744rD2DwAAAACAzB2Dfc0119iqVavs1FNPtYYNG1rLli1t/vz5tnz5cne7Lr3uZWVl2ciRI8PcZwAAAAAAMiPAVhCtR1CbNm3C2icAAAAAAGpHgD1s2LDw9wQAAAAAgNo2BhsAAAAAAISQwf7222/LXGbLLbesyKoBAAAAAKg9AfaRRx7pJi8rzYQJEyq6TwAAAAAA1I4Ae+jQoSWeW7hwoX3wwQc2btw4u/LKK8PYNwAAAAAAMjvA3mqrreI+v/vuu7vg+tNPP7V+/fpVdt8AAAAAAKi9k5ztuuuu9s4774S9WgAAAAAAaleAvWTJElu9enXYqwUAAAAAIPO6iM+aNavEcwqqJ02aZHfccYf16NEjjH0DAAAAACCzA+yddtop7izixcXF1qlTJyY5AwAAAADUOhUKsG+88cYSAXa9evWsffv21r17d8vODr3nOQAAAAAAmRdgDxw4MPw9AQAAAAAgjZFqBgAAAACgKjPYRx11VNIrVffxZ555pqL7BAAAAABA5gbYmsAsaOrUqTZv3jxr166dtW7d2ubOnetmF9fvmugMAAAAAIDaJOkAe9iwYZHfx44da1dccYU999xz1qtXr8jz48aNs3POOceOP/748PcUAAAAAIBMG4N9991325AhQ6KCa+ndu7d7/q677gpr/wAAAAAAyNwA+48//rC111477mt6/s8//6zsfgEAAAAAkPkBdqtWrey9996L+9ro0aPd6wAAAMnM65Ju6wcAINT7YB922GF2xx132KJFi2yfffZxAbUmPHvzzTdtzJgxdu6551ZktQAApL3CoiLLyc5O2/VXBd1tZPkXX1jRksWhrzu7SVNr2Ldv6OsFACBlAfaJJ55o+fn59vjjj7uA2rcW16tXz0455RT3OgAAtZGC38tHfGpT54QfPHZq09SuP3x7ywQKrgsXLqzu3QAAoPoDbDnjjDPsmGOOcTOHL1682Jo1a2Y9e/a0Jk2ahLuHAACkGQXXE2cuqO7dAAAA6RJgS+PGjW2HHXYIb28AAAAAAEhTFR7EtWDBAjcO+5BDDrHdd9/dJk+e7J5//vnn7bfffgtzHwEAAAAAyMwAe/r06bbvvvvasGHD3EQl06ZNs9WrV7vXJk2a5J4HAAAAAKA2qVCAfdttt7mx1u+++649++yzUbfD2Hzzzd24bAAAAAAAapMKBdhfffWVDRkyxNq2besy2EGtW7e2OXPmhLV/AAAAAABkboC9atUqa9q0adzXVq5cWSLoBgAAAAAg01UowO7UqZN98cUXcV/79ttvbaONNqrsfgEAAAAAkPkB9qBBg2zo0KH2zDPPuHtgy5o1a2z06NE2YsQIN7M4AAAAAAC1SYXugz148GCbOHGi3XTTTXbLLbe45w4//HA32ZmC7wMOOCDs/QQAAAAAIPMCbLnuuuvswAMPtI8++sjdE7t58+bWv39/6927d7h7CAAAAABAJgfY0rNnT/cAAAAAAKC2q1SA/emnn9o333xjCxcutNNOO83atWtnP//8s3Xo0MFatGgR3l4CAAAAAJCJAbZuxaWA+ssvv4zckuuwww5zAfaTTz5pa6+9tl100UVh7ysAAAAAAJk1i/hdd91lv/zyi91333323XffucnNvG233TbhLbwAAAAAAMhUFcpg63ZcZ511lu26665WWFgY9Zqy2P/8809Y+wcAAAAAQOZmsDVr+AYbbBB/hdnZlp+fX9n9AgAAAAAg8wPstm3b2u+//x73tUmTJrlJzlLhpZdesn333de6d+9uffr0sVNOOSUl2wEAAAAAoEq6iO+222728MMP2xZbbGFdunRxz2mys5kzZ9rTTz9tAwcOtLBpvLfWraC6R48etnjxYjeLOQAAAAAAaRtgn3766W4G8UGDBtmGG27ogutLLrnEpk2bZp06dbKTTjop1J2cMmWKPfTQQ/boo4/adtttF3leY8ABAAAAAEjbLuKNGjWy559/3k101qBBA1t33XWtfv36dvLJJ9vw4cMtLy8v1J189dVXbZ111okKrgEAAAAASPsMtiiIVqY67Gx1PD/99JNttNFG9sADD9izzz5rS5cutZ49e9pll11mm2yyScq3DwAAAABASjLY3qpVq2zcuHH24Ycfup/6OxXmzp1rn332mb355pt2zTXXuPHYK1eutGOPPdaWLFlSqXUHbzOm34uKitzvure3/vb3+NbzVbVsTdynTFm2Ju5TJi9bE/cpE5atifuUKcuGuZ1UqsllmMyyVaGmHCvniJpTLjXtHMGy1V/emSKV1xGZoriSdalKAuynnnrKddkePHiwnXbaae7ntttua08++aSFTQe3YsUKF1hrgrUdd9zRjclevny5vfDCC5Var9bhaRu+kUAFqUy5L/g1a9bYsmXLIssqwPe3I9N6tGxBQUGZy4qW1TKi9+hv/wFqudLWq789Lad1e9pmaetNtKyOUcv6yqMyUFl4KqPVq1cnXDZYhvo9URlqHYnKuzxlWFa5aNngsQbLO96ysZ+NX68vw4qWd7AMtWxpZZiovH0Z+mVLK8N45R0sQy1bmTqbbLmUVd6Jli1PnY1dtjrqLOcIzhHxvgupamiO5b8L6XiOCH5XU0nb4RzBdUS8Ost1RM0+R1TVdURVnYuqgj7DVMQaVfU/rSpUNtYoT5BdoS7iw4YNs1tuucUF1AMGDLBWrVrZvHnzXIb5tttus9zcXDvqqKMsLE2bNnXb0IRqXps2bWz99de3P/74o8Lr1eRsDRs2jPyt8eR6zt/Pu3Hjxu6n1KlTxx2XpzHnfln9THZZCS6r5fS3fz04ft2v17+m9epvT8v6L4sfG++XjbfeRMvm5ORE7VO9evWsbt26kWVVRhVZNrYMtZyOIV55l6cMK1PesWWoZYPlEiwzX4bVWd7lKcNUlXdsGcYrlzDLO5kyjF22Ouos5wjOEWWdI1LJ18t0PEdoO1UheDycI7iO4Doifc4RVXUdUVXnoqqgc1wqYg2tN1NkVTLW8L+nLMB+5pln3P2ob7311qjnDzjgADv//PNt6NChoQbYnTt3tlmzZpV4Xl+a8hxsPLr4jve7Cjb4d+x2qmLZmrhP6b5sTdynTF62Ju5TJi1bE/cp3ZcNczup5LdVE8uwPMumUk07Vs4RNadcaso5gmVrTnmnu6q6jkhnWVX4/71CpTZnzhzbZ5994r623377udfD1L9/f5ch//333yPP/fvvv/bnn39G7sMNAAAAAEB1qlAGu2PHjjZ//vyEE5Ktt956Fibd77pbt252xhlnuFuDqcuKZhRv0aKFHXzwwaFuCwAAAACAiqhQBvvMM8+0e++9NyqjLBMnTrT777/fvR4mpegfe+wx23TTTe3KK6903dA1Jvvpp5/OqPETAAAAAIBalsF++eWX3QyD+++/v22wwQbWunVrl7nWhGOafOyVV15xD9+HXTN+V1bLli3tjjvuqPR6AAAAAACoMQG2MtfKKq+11lpuenM/xbn+9q97wRntAAAAAADIVBUKsD/88MPw9wQAAABIU7q7TSoTS6leP4BqDLCDFi1aZI8//rhNnjzZ2rZt627PpW7jAAAAyAyFRUWWk8Jb9qR6/VVBwe/yL76woiWLQ193dpOm1rBv39DXC6AaA+xbbrnFRo0aZR9//HHkuRUrVtiBBx5oM2fOjDz39ttv20svvWTrr79++HsLAACAKqfg9/IRn9rUOeEHj53aNLXrD9/eMoGC68KFC6t7NwCkQ4D9ww8/2F577RX13LPPPuuC62OOOcaGDBniJjnTrbQ04/dNN92Uiv0FAABANVBwPXHmAsoeAEqRdF+c6dOnu9tkBX300UfuXtQXXHCBNWrUyHr27GnHHnusff3118muFgAAAACA2hVgL1myxN2CyysoKLDx48fbVltt5WYU9zbZZBN3yy4AAAAAAGqTpAPsVq1a2Zw5cyJ///bbby7Ijs1qZ2dnW926dcPdSwAAAAAAMiXA7tatm5u8TLcIkJEjR7rZEvv06RO13J9//mmtW7cOf08BAKjlWjbOs+LiopRvpyq2AQBArZ7k7MQTT7TDDjvM9thjD2vevLn9+OOPtsUWW7jAO3Zcdvfu3VOxrwAA1GqN8+paVla2/TBjjC1dlZqZihvXa269OuyaknUDAJDpkg6we/ToYQ8++KA98cQT7t7XgwYNsvPOOy9qGY29nj17tg0cODAV+woAAMxccL0kfx5lAQBAugbY0r9/f/dIRF3D1XUcAAAAAIDaJukx2AAAAAAAIDECbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AADIGFl5eVZcWJjSbaR6/QCAWjKLOAAAQE2WVbeuZeXk2JTLr7D8qX+Fvv68Th2t8/XXhb5eAEBmIMAGAAAZR8H1ikmTqns3AAC1DF3EAQAAAAAIAQE2AAAAAAAhIMAGAABAtWnZOM+Ki4tSvp2q2AYAMAYbAAAA1aZxXl3Lysq2H2aMsaWrFqZmG/WaW68Ou6Zk3QAQRIANAACAaqfgekn+vOrejRp9+znNkJ9KVbENINMRYAMAAAC1+PZzwi3ogHAQYAMAAABpgNvPATUfk5wBAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAGprgL18+XLr16+fdenSxcaPH1/duwMAAAAAQHoG2A8++KAVFhZW924AAAAAAJC+AfaUKVNsxIgRdsYZZ1T3rgAAAAAAkL4B9g033GCHHnqoderUqbp3BQAAAACA9AywR48ebRMnTrTTTz+9uncFAAAAAID0DLBXrlxpN998s5177rnWqFGj0NYbHMut34uKitzvxcXF7m/9FD1fVcvWxH3KlGVr4j5l8rI1cZ8yYdmauE+ZsmyY20l3qSzvTFGbzhGZJJXniEyQqefkTD4XpfIckSmKK1mXMjLAfuihh6xly5Y2cODA0NapQtOM5N6KFSts1apVkYJcunRppODXrFljy5Ytiwr48/PzI+vRsgUFBWUuK1pWy4jeo7/9B6jlSluv/va0nNbtaZulrTfRsjpGLesrj8pAZeGpjFavXp1w2WAZ6vdEZah1JCrv8pRhWeWiZYPHGizveMvGfjZ+vb4MK1rewTLUsqWVYaLy9mXoly2tDOOVd7AMtWxl6myy5VJWeSdatjx1NnbZ6qiznCM4R8T7Lvj6kQlU31Nxjgh+V9NdbTpHZJJUXkdkgtpyHZFJ5yKdd1Jxjsik/2nFlYw1yhNk51oamDlzpj355JP2wAMPRArCfyn0U//cGjZsWO71ZmVlRb2vQYMG7jnJzs62xo0bu59Sp04dy839/+KqX79+ZFn9THZZCS6r5fS3fz0vLy9q/4Kvab3629OywX98yuz7ZeOtN9GyOTk5UftUr149q1u3bmRZlVFFlo0tQy2nY4hX3uUpw8qUd2wZatlguQTLzJdhdZZ3ecowVeUdW4bxyiXM8k6mDGOXrY46yzmCc0RZ54h0p/qeinOE1pspatM5IpOk8joiE+j/Zm24jsikc5GubVJxjtB6M0VWJWMN/3vGBNgzZsxwLQcnnXRSideOOuoo69Gjh7344ouVOonE/q6CDf4dW6hVsWxN3Kd0X7Ym7lMmL1sT9ymTlq2J+5Tuy4a5nXRXFeWd7mrTOSKTpPIckQmCjUSZdE7O5PpdVeeIdJZVhf/f0yLA3mSTTWzo0KFRz02YMMFuuukmu+aaa6x79+7Vtm8AAAAAAKRNgN2kSRPbeuut477WrVs39wAAAAAAoDplTt4fAAAAAIBqlBYZ7HiU0Z40aVJ17wYAAAAAAA4ZbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhIMAGAAAAACAEBNgAAAAAAISAABsAAAAAgBAQYAMAAAAAEAICbAAAAAAAQkCADQAAAABACAiwAQAAAAAIAQE2AAAAAAAhyLU0MGrUKHvzzTft119/tcWLF9s666xjhx12mB166KGWnU0bAQAAAACg+qVFgP3UU09Zu3bt7MILL7SWLVva119/bTfccINNnz7dLrroourePQAAAAAA0iPAfvjhh61FixaRv7fZZhtbsWKFDR8+3M455xyrW7dute4fAAAAAABp0b86GFx7m2yyia1atcoWLVpULfsEAAAAAEDaBdjxfP/999asWTPXZRwAAAAAgOqWlgH2+PHj7dVXX7Wjjz7acnJyKrWuwsLCqN+Liorc78XFxe5v/RQ9X1XL1sR9ypRla+I+ZfKyNXGfMmHZmrhPmbJsmNtJd6ks70xRm84RmSSV54hMkKnn5Ew+F6XyHJEpiitZlzI6wJ47d66deeaZ1r17dzvxxBMrtS4V2vLlyyN/a1y3up37gly6dGmk4NesWWPLli2LLLty5UrLz8+PrEfLFhQUlLmsaFktI3qP/vYfoJYrbb3629NyWrenbZa23kTL6hi1rK88KgOVhacyWr16dcJlg2Wo3xOVodaRqLzLU4ZllYuWDR5rsLzjLRv72fj1+jKsaHkHy1DLllaGicrbl6FftrQyjFfewTLUspWps8mWS1nlnWjZ8tTZ2GWro85yjuAcEe+74OtHJlB9T8U5IvhdTXe16RyRSVJ5HZEJast1RCadi3TeScU5IpP+pxVXMtYoT5CdFpOceTpQBdV5eXn20EMPWZ06dSq1vqysLGvYsGHk7wYNGrjnRLf/aty4ceQ2YNpWbu7/F1f9+vUjy+pnsstKcFktp7/96zq24P4FX9N69benZYP/+Bo1ahRZNt56Ey2rXgDBfapXr17UxHEqo4osG1uGWi74mQXLuzxlWJnyji1DLRssl2CZ+TKszvIuTxmmqrxjyzBeuYRZ3smUYeyy1VFnOUdwjijrHJHuVN9TcY7QejNFbTpHZJJUXkdkAt87NNOvIzLpXKRrm1ScI7TeTJFVyVijPLeGTpsAWy0op556qs2bN89eeOEFa968eSjrDXYxD/6ugg3+HVuoVbFsTdyndF+2Ju5TJi9bE/cpk5atifuU7suGuZ10VxXlne5q0zkik6TyHJEJgo1EmXROzuT6XVXniHSWVYX/39MiwFZ6/qyzzrKJEyfas88+a+3bt6/uXQIAAAAAIP0C7GuvvdY++ugju+CCC1zf+B9//DHy2gYbbOC6fAAAAAAAUJ3SIsD+7LPP3M/bbrutxGtDhw61rbfeuhr2CgAAAACANAuwP/zww+reBQAAAAAASpU5I9cBAAAAAKhGBNgAAAAAAISAABsAAAAAgBAQYMMpLi5K6/UDAAAAQHVLi0nOkHpZWdn2w4wxtnTVwtDX3bhec+vVYdfQ1wsAAAAANQkBNiIUXC/Jn0eJAAAAAEAF0EUcAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsIEkFRcWpvX6AQAAAKQWs4gDScrKybEpl19h+VP/Cr3M8jp1tM7XX8dnAQAAAKQxAmygHBRcr5g0iTIDAAAAUAJdxAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0gYxUXF6X1+qtCcWFhWq8fAACgJsmt7h0AgFTJysq2H2aMsaWrFoa+7sb1mluvDrtaqhUXF1tWVlbK1p+Vk2NTLr/C8qf+Ffq68zp1tM7XXxf6egEAAGoqAmwAGU3B9ZL8eZauFFwv/+ILK1qyOPR1567dzur36OGC6xWTJoW+fgAAgNqGABtAtSksKrKcbEaqlEXBdeHC8LPw2U2ahL5OAACA2owAG0C1UXB9+YhPbeqc8LOzfbu0s9P37B36egEAAIBECLABVCsF1xNnLgh9vR1bk50FAABA1aJvJgAAAAAAISDADmmW36oYqwoAAAAAqLnoIh7SLL/fTp1tS1eusVRo27S+dWvfirGqAAAAAFCD5db2zPM///wT9VxeXp41b97cCgoKbO7cuSXes/baa7uf8+bNszVr/j+g/mvaTCvMzbN6eXmWv3KFrVi+LOp9dXLrWONmza2oqMgWLSh5y6CmzVtaTk6OLV28yNasWR31WnZhW7P2rWzyjDk2YerM6DdmZ1udBv+NNV2zbFGJ9ebWb2RZOblWkL/ciguiGwCy69SznHr1rUOzPFcO8+cstKWrFkcaDZq3+m+9C+ctKZGlb9y0gdWpW8dWLFtp+Suj97duvTrWqEkDK1hTaEsWLbM19XLsnwb/lChDla/KOahZs2ZWv359W7ZsmS1dujTqtXr16lmLFi2ssLDQ5syZU+JY27Zta9nZ2TZ//nxbvTp6n5o0aWINGza0lStX2qJF0eVUp04da9Wqlfs9tj5I69atLTc31xYuXGhzV66wlYHPp352jjXMybHVRUW2pDD6WLIty1rUqeN+n79mjRVbdBk2zcm1OtnZtqyw0JavXBG17QYNGljTpk1dHVNdC9Jns9ZaayUsQ9Vf1eN4Zejrd6Iy1Hq1/nhlqP3Rfq1YscIWL46elKxu3brWsmVLV09mz55dYr1t2rRx9VtlmJ+fH/XamlUr3c+igtVWmL8icf1evshiijBSvwtXrbCimO/NqhXL/3vf6gJbMDd6f4P1e9H8pe57Gbd+L8+3/BWrYo61jjVq2sAKCgrj1pdE54hg/V6+fLktWbIkbhlqX/7999+o15YtWGAtiovdpHALly2zVTHrbVS/vjXKy7OVq1fb4uXLo8soJ8da/W+28NkxM5HXadjQ1v3fupYWFNiq4uhyyMvOsUYJ6neWZVnLUup3k5xca2DmjlPHG6QyUFnEq99VcY6I99mEdY7w9Xvl4gXunJxdt57l1K1vRQVrrDA/uhwsK9vqNPT1e7H+KUW9nFO/kWW7+r3Sitb8Vw+XLZzv9mHZ0hVmdSxyno1abZZZ81ZN3e+LFyy1wsLoz1XnZ52nV67It5XLS9bvJnlWJecI1evC/5V166ZNXf1etGy55cd8l339zl+92haVUr//XbTIrV/1usE//7jzdb3iIsvN0nm2wPKL4tfvNUVFtriU+r1gzRorCtTv+itX2KpVq1x9Ux1UXQzjOqIy54jgeXbBggVu/4IaN25sjRo1ilu/VXdVhxPVb9V9fQf0Pl+vY68j4tfvLKvTsGni+p3X0LJz67j67eu1vw6JvY6I1aJ14vrdsHF9q5dXt0T91rXIguYLUn4dMTtQryNl2KSJq6vx6nfDvDxrXL++O6/r/B5VRtnZ7rsh/y5YYPX/V6/9dUjwOiK/qDDqvXnZ2dZI13/FRbYo5jyqGt7qf/V74Zo1Vvi/+q26rc+hJl1H6G+tr7TrCF+/9bxeT1S/td7Y61n/ucW7jojU78ICK1y5rJT6vcQs5v+nr9+r/lemwWtsfx1RWFBoixeWUr8XLnPLxKvfujbRNUrwGjuV54i8//0eex0hLRs3tjq5ue76Q9chQQ3q1bMmDRrErd/6rrX/3+/zVq60FTHlr+uIugnqd72sbGucm6h+m7WqUzdSv2OvsSt6HaHnVH7JqNUBtirXI488EvXcZpttZgMHDnT/2GJfk6uvvtr9fP31123GjBmR56fOXWw9t9/ZOm64sU2fMtm+//yTqPet1WFd67/3/lZYsMbee+X5Euvd/6gTLKd+A/vxy09t5t9To17bbbfdrH/3zrZs/myb+9NHUa/VadTMWvfY0f0+b/wnVhxzAdG6587uAm7ZjEm24t+/o15r1GEja7JeN1u2aL471r8X/GqrCv4LcBo0yrP9j97Z/f7J29/aimXRJ7Od99/G2rZvab+P/9t+Gzcl6rXOXdexrXfczJYtWWGjX/zMGtRtaj80nWnZWdmuYl5xxRVuuVdffbXEP/NBgwZZt27dbPz48fbuu+9GvdalSxc77LDD3Ak03mdz4UUXWoP6Deydd96xKVOi92mvvfayrbbayiZPnuy2G9ShQwc74YQT3O/x1nvmmWe6L9tHH31k70+ebGvmz4+8tnnjxrZ54yb27+rVNmrB/z8vTXNz7ZA2bd3vb8+fV+Libr9Wraxt3Xo2ftkymzh5srUJbHvLLbe0vffe212Axe6TvvyXXHKJ+/3FF18scQGnMlJZ/fDDD/bBBx9Evda1a1c7+OCD3cVbvGO9/PLL3Yn0zTfftL/++ivqtX333dd69+5tEydOtJEjR0a91rFjRzvmmGPcySfees8991x3cTJmzBj77bffol5bOL++Wd7atnrJfFsw4auo1+o0aGyte+3ifp//y6dWFHMSVd3Xd2DZjN9t+ezo783MJjpR72kL5y1y9TCqDOvXtQOP29X9PnbUd7ZscXRg33+fLa3dum1syq/TbPy3k6OPdaP21nfXnlaQb/bQiIdcvU7mHCE6t+gc8+uvv7p6GtS5c2c78sgj3QV3bBmu+mOyDdl5Z3ch9sHPP9sfs6K/NztttplttdGG9tecOfbGV19Hvda2eTM7duf/vsvDPvo4arhJdrOmdvY2fdzvPyxbahNXRJdDz0aNbKsmTW3emjX21vzoQFgNS4Pb/tfQM3rBfFteGP3Pb0DLVtbMzL755hv77LPo8lc9Un3ShVDssabyHKHvjb4/o0aNsrHf/WSrAxcuW2+/k228WU+bMmmCffb+qKj3tWq7tu190GHu92ceuLPEeg8YfJw1adbMPh0zyv79+w9r06SBTf7qd5u3ZKU1Xmdja7zuJrZ66QJb8NsXUe/Lrd/Q2vTezf0+/9fPSlzcterez+o2aWnLZ/1hy2b94Z4b928ze2TeT1a/Q75167OuCz5i63edurk26MTd3e+fvTvOFi+Ivqjpt9cW1qFTW/tzwgz76avo+5+v03kt63TQxrZk6ZKUnyNUr4tX/ve/5bS99nQXYR/9Mt4mzYhuSO63aTfru/HGNn3ePHvliy+jXmvZpLGduNt/ZTj8k09s9ZoCV6/rjvvB5kyebPtkZ7sLrR+XLbPfYoLz7g0bWZ+mTW1BwRp7I6ahR8HJUWv9FwiPWbjAFgfOPXWKiqzD9Om2wQYb2Pfff28ff/xxKNcRlTlHyPnnn+8ukPW9mBRzX/vdd9/d+vTpY3/++ae99NJLJQL+k08+2f3++OOPu88o6LTTTnMX5mPHjrXJX4529Tr2OkINoPN/ia6HCkzabrGH+33BhC9dIB3UctPtrF7T1rZ89p82bsZXrl7765DY64ig7JxsO/SUPd3vX7z/oy2cG90Qse3uvWy9DdrZ37/PsnGfT4g8r2uRFVs2ssGDB5d5jqjMdcST77wTqdfeyXvsbs0bNbJPf/vNfp02Leq17bpuYtt17Woz58+3Fz/7POq15o0a2sl7/FeGI0aNsoLvvnf12l+HBK8jxsckdro2bGjbNW3mgo9XY64T6mZl2TFrt3O/v79wgS38X/1W3da1SE27jpi5cJntO/g4a9iosX08+k37e0r0/+Ve22xrm22+tU2fOsU+fOeNEgms/Q8/+r8yfOx+WxMIABvWq2NXXHCO+z3edUSjdhtYk07drWD5Yps3fmzUa9l16tpaW+3tfl846SsrWBl9fmnRta/lNW9rs6dOskce+SrqGttfRyhAjq3fcvjp/633qw9+svn/Rge7fXbpaZ26tLdpU/6x78b+GnWNXdY54oILLnCNRBU5Rxy+3rpxryPk+F13cQ1Bn0+caD9Pjf7Mt+nSxfp339Q1gI74JLoM1bB09mabud/f/muqLYypp7qOaFevnv22fJk7hwdt3KCB9WvW3JYUFJao3zlZWXb8/+r3R4sW2uKYa+yKXkfstNNOrnEpGVnFVTGAuAZSoerQfatWZVueP/s9dRnszu3augD7kFtfSUkGe5du7ezs3bvZV3+NtKWrFoSewW7bqKNt1r6/3fPRHTZj8XRr0Fx5LbOVS/KtOKbluW7DupZbN9fW5K+xNTFd7rPr5Fheo3quDPMXR//z2rhtV7tkv8tdGaYyg/3T8SfYysA/3bAy2FmdOlqXB+6vcRls33Kcygz2WUM/synzVoaewd6t94Z2+4l72gcTRtjMmMalMDLYbRp0snXyekbqtefrd/6SfCsqT/3Ozba8xnlWXFRsKxf/dyHaq/3mdviWR9qyjz5MTQa7Qwdbd8+97PdjjrN/f/019Ax2s002sXUeeqDGZbBVvz/8Zaotyf//OtOgYSPLq9/AVuXn2/Jl0Rfs+v43adbC/b5gXsn1Nm3WwnJyc23ZksXWsn6u9VqvjZ3+6Bj7Y3b4Gewdu61jFx+4jf3w77tWVGdFSjLYG62zmfVot7Nd9sJFUXVb6jer774/+Uvzraggpn43qGu59XKtYFWBrV6xOn79Li62jRt2jdTrlGSwO3SwBltuZZNOH2L1/v47/Ax2587W57kRNTKDPW7aXJv5z1xb87/64jVo0MjqN2zo6veypdHn7xx9V1u0dL/Pn/tv3PrdvlUTa98w1467Z6Sr12FnsHfYqI2r1/46JBUZbF2LbLn+7vbQt/fZ1Pl/lriOkPpN61tWdvz6Xad+HauTV8cKVhfY6uUx9Tsn23bYtL8d3/dkmzxieEoy2AsaN7b6m2/h6rW/Dgk9g925s7sWqUkZbH+NnVW/sbuO0Hl29ero+l2/QUP3WL1qVZz6neOusd2xzpsb9b9q7aYNbdfNu9oxD7xrv/45IyUZ7B27tLHz9u4RdY0dZgY7eI09a/nMEtcRQXlN89z/wPxlq6xoTWFS9VvXIUf1Odbyvv/OChcuDD+Dvdlm1rDvtvbJwINsxZT/GpLDzmDX7bx+1DV2Ra8jdN5VferevXuJ7cWq1RlsnQD8P7pYuqBK9Jr4gMxrubDAFq/87wuvizQ94lFlatGqTcL1Nm6qnE+0Bg0b/rdPdeu5bF0ipb2Wm9cw8Wt16rpjbbmiudXJj67A4gOReBo0qu8e8deb404SrZu0dOtfkD3fZhbMMCt5vfH/Srsdsr4/Jc8XzsYNN3GVfvkXX1jdJYvtv6/V/9Npz3+FGsV5f2mvaZO5a7ez5j16WOv6DWzF/760QToBtMou+bznL9Ti0QWeMu/x6puC/9LqYWwDUdR6GzVyj3hUVqWtV//kvMj8AgvVYh6dpf9/BWb/xgTHARMWzko4t0Cdeqo/Ky07t65lN0pchnUaJq7fOfUauEdQvQYNI9k8/88qnmYtGyd8rUHDPPeIJzc3J7pee5Wp3zHXexvX28RtY6lO9v/7p6YsSCL169Z1j0TWiml5rfO/rp+if1SJSqIy9VuNW3rEU531u2XrFZb7v3N2kBpJ9UiktPN3oyZNrVXzRrb22mtZ/aYtrE7gWkIXWtmlnb//d6EWjy7w9HDbaP7f+XTyiga2JH9F5DybSNMWiet3/QZ57hF3m+pGGFu3pWR7yP+LjgcT1u/1WnT8/3r9vwY8r1kjfW/j/7/Kq1vX1iqlfrdt1ixSrxvqu6nz9f96mCjQaJSgZ1+dMuq3byj1dL7WRZjvVqlHGNcRQWoQ1iPRdUSi9epcXVyvvuX+r754uuRd7ep7luXGqYf++iXea8sLimz5qgJr1r5ViXodVv329Tr2OiTM+q1rEV04/73wL5syP/pCPiLRvzh/oRB9Lf7/Csz+WfpfA/1acep1MvW7Xp06Jc7RQW1btPj/eh1zHaLrCD3iUQOTDzbiaR6o37HXImFdR8RSkK1HstfmwWtsnWcTqavgqF7ic3TzVtH/U3S+9v8D411HeGroLL1+J75Orve/Mo13jZ2TW0b9bp74/31eg3ruEbzGnp4/rcR1RGXrt65D9L/Yv1RaHW3asKF7VKR+t6pfP+71dRj1O9E1dnnrd7yEQCK1OsBG5ilasjgSiIQpO0GAUBvogs3/YwtTo7zEQRkAAACQjrhNFwAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYAAAAAACEgAAbAAAAAIAQEGADAAAAABACAmwAAAAAAEJAgA0AAAAAAAE2AAAAAAA1AxlsAAAAAABCQIANAAAAAEBtCrCnTp1qxx9/vPXs2dP69Olj119/veXn51f3bgEAAAAA4ORaGliyZIkdffTR1q5dO7v33nttwYIFdtNNN9miRYvs9ttvr+7dAwAAAAAgPQLs559/3gXZr7/+urVo0cI9l5OTY+eff76deuqp1rlz5+reRQAAAABALZcWXcTHjh3ruoX74Fp23313q1u3rn3yySfVum8AAAAAAKRNgD1lypQSWWoF1+uuu657DQAAAACA6pZVXFxcbDVct27d7KyzzrKTTjop6vnDDjvMWrZsaffff3+51zlu3DjToStQD8OqgkK3vlTIyc62OjnZtnBZvq0pLAp9/Xl1c61J/bq2unClFRWHv/6crFyrk1PPFq1caAVFBZYK9XLzrHG9xla8apVZUfjHYDk5llW3rhUsXGjFa8I/hqw6uZbbvLnVRKmq29TrslGvU4d6XX3nbOp16lCvE6Nep++1SLrW60y4xk7383WY9Xr16tWWlZVlvXv3zowx2Inoy6YDrYiKvi+Rerk5lmrNG+WldP11c+qndP3N6qf+pJ1Vr15K118T//GkWqrrNvW6bNTr8FGvq/+cTb0OH/W6bNTr9JPu9ToTrrE5X5uLHZONH9MiwG7SpImb5CzW0qVLKzzBWa9evULYMwAAAAAA0mgMtoLo2LHWStNPmzaNGcQBAAAAADVCWgTY/fr1s6+++soWLlwYeW7MmDEuyN5hhx2qdd8AAAAAAEibSc7UPXzAgAHWvn17O+2002z+/Pl2880323bbbWe33357de8eAAAAAADpEWDL1KlT7frrr7fvv//e8vLyXMB9/vnnu98BAAAAAKhuaRNgAwAAAABQk6XFGGwAAAAAAGo6AmwAAAAAAEJAgA0AAAAAQAgIsAEAAAAACAEBNgAAAAAAISDABgAAAAAgBATYiOu1116zLl262F9//RX1/PDhw93zd911V9Tzy5cvt65du9oDDzzg/v7kk0/siCOOsK233tp69uxpu+66q7tvue5n7l188cVuXXpssskmtuWWW9rAgQPttttus3/++afEPu2000527bXX8okhdKqvxx9/vKuvm266qe2444529dVX27Rp0+ySSy6xfv36lXjPWWed5eru559/XmJdev67776rUD0HwnLfffdF6t7GG29sm2++ue2zzz7uPDplyhS3zNdffx1ZprTHjBkz7NVXX416bosttnB1+fXXX+dDQ5XV6V69eiX1WrCu6ry+3XbbufP8Sy+9ZGvWrIl6b+z3QNctO++8s51zzjklzvFAKo0cOdIOOuggd77u3bu37bnnnnbZZZfZ/PnzI8sceeSRkbqqa29duxx66KHuGnzhwoUl1qnlnnjiiaS+RwhHbkjrQYbRF1vGjRtnHTt2jDz/ww8/WP369d3zQT/99JMVFha6k8Hbb79t5557rh1wwAF2wgknWJ06ddzF3KhRo9zPTp06Rd63zjrr2O233266HfvSpUvtl19+seeff949dALo27dvFR41aiM1Fj388MOuEeiaa66xli1b2syZM10j0zHHHGOnnnqqCywUYHTo0CHud2HbbbeNPK+/Vee7d+8eeY56juqSl5dnzzzzTKQh9Pfff7cXXnjBXnzxRbvhhhtcEKG/vV9//dUF4DfddJOtv/76kefbtGkT+f3xxx+3xo0b26JFi2zYsGF20UUXuTq/9957V/HRAaVTIDJgwAArKCiwOXPm2KeffuoaTxVkP/nkk9aoUaOo5X29X7VqlU2fPt1dzxx33HF2+OGH21VXXUVxI6UeffRRu/POO921x5lnnumujSdPnmxvvvmmq7+6PvF0va1zb1FRkS1evNhdkwwdOtSee+45d45WoyqqDwE24lp33XWtdevWLlhQhsLT3wqcFXCoBVgXVf753Nxc69Gjh/tnpNa0m2++OfI+BSBHHXWUOxHEXvyppdhTplD/yJT9VsvxBx98UOIfIBCWsWPHuuD65JNPdo1CnrLM+++/v3344Ye23nrrReq4D7AVgP/777+ursY2NulvZUvq1atHPUe1y87OjjrH6lysenvSSSe5rIgu0oKvK7CQDTfcMKqRKKhbt27WokUL97vO9f3793f/EwiwUdOsvfbaUfV7r732chlBnfN1jXL99ddHLR+s96rbyiQq4HnkkUdcxm/fffet8mNA7aEGS11jq+ebt8MOO7hkVez1c5MmTaLqtnreKYt98MEH29lnn23vvPOOO/+jelDySEj/TILBgwIKBRZqEVa2esKECZHXtJy6vzZo0MBlohWcx61wSXzZmzVrZhdccIHLjqj1GEgVZTBatWplZ5xxRtzXNSyhc+fO1rx586jvgn5v37697b777vbjjz+674MoSzJ+/HgXtJSFeo7qosafK664wjWSKpNXGerFoQbZWbNmhbZ/QCqpIX+33XZzQxuWLVtW5vLKJOqaZsSIEXwwSCldPwd7C5X3+rldu3au152GY37xxRcp2EMkiwAbpXYT//PPP12g64MKffHVfUoZDB9wqFVNQYYPKvTae++9Z0899ZTrVlsR22yzjcuIa71AKigYVh3u06dPpCdGso1N+l3PbbbZZrZ69WqbNGmSe/63336zlStXJhVgC/Uc1WWDDTawtm3bum6FlaHz/+zZs12QDVTl+Tv2EZvhK43GY6uBSefssuhaROdqDWGLHbsNhEnXzxoiqYbPuXPnVmgdqtvC9XP1IsBGQgoSNP7DBxa6EPOTIujn999/737XmD6N7fPjts877zzXzUrdrzS+T1/2yy+/3CZOnFiuDIuyhhU9wQBlUcORusOqC2Ey3wWNg1Lrsv8u6Dn12NDkIf674IOVZANs6jmqk+r+vHnzyv0+BTIKaPReTdan75K63AJVYcWKFS4QiX08+OCDSa9jrbXWcj+Trf/6rii41lhXIFU0zr9p06bumlnXzrqG1jCG8iSr/DUN18/VizHYSEhdvv0kTuoqq5+aLMQH2Jp0QXxw4YMKZUVefvll+/bbb92EIppN+ZVXXnHdsTTDocaTJEPBfVZWFp8QUkL1S5KpY6rbCip8YK1GJV/ffXZbQyf0Uz08/PjUZPeDeo7qUNG6F5zUTzQ5YLKNSkBlae6WZ599tsTzmrjvrbfeKtf5PxX/L4CK2mijjVwd/vLLL+2zzz5z19Eal605LnQXH12XU1fTAwE2EvIzIStoULdXZaCvvPLKSFChll/dxkiv+0nRgmNFNEGIHqJuWJq47O67704qwFZmUVkRjY8FUkE9JJRBTmbsqL4HdevWdXU9JyfHvU//CP13QVk8UQC+/fbbJ70P1HNUJ3XtDt4lIllPP/20NWzY0L1fd3tQhkWT7TBrLaqCri/iTcD38ccfJ70OzSkjieaLiaW6rmsiZReBVNK1hq6T/bWyElXqIaQE1f33359UXRWun6sXXcRRKmUlNO5IWWoFFr71TGOxNcmTAg4FFb57eCK6T5+yHv7eq2VR6526IJIVQapoXJ3qrepaWePq9A9PM4Orvuuh2fL1ffABtv6hqaVZF21lfReCqOeoLhryoPpakXuhaliE5h/QRFGPPfaYCzx0u0UgXSho0XldXcvLomuRr776ygX1+r8BVCU12qvxMtnrZ2W+hevn6kWAjVLpC6osm7qo6J9LcDIoXZiNHj3azSweDCrijWlS99q///47qRY1jXHSxZoyjLqlBpAqxx57rKuvahmO56OPPor6Lvz888/2zTffRP3jUkOThkXovpOSbIBNPUd10Tn9uuuucwHGoEGDKrUujWXVLRgVsCQzYRRQE27POGbMGHc7JM2jUZZ7773XjWdVLzwgleJdP+fn59s///yT1PWzeuRpLgLd/UQT86H60BSHUimQUHesTz75xE488cQSr+kizf/u6X596naoe/Ip+Fi4cKEbg62Zli+99NISJw4/06EmkFK2XDMo6tYZCnrUDRFI5e1aTjnlFHvooYfcjPm6j2/Lli1do9HIkSPdrS5Uj33grCBamWrdQzhI3WM1c77+Afr7ZlPPURP4uzz4yaE0f8ALL7xg06dPdxNR+nu7V7ahSo2wymbfddddIew1EA4FJv5WigqSFVy/8cYbrhfSRRddFLdnh5bV3SH0HdF4WN3uSHNscJ93pNo+++zjrjk0wZl6is6ZM8edW3UdffTRR0ctu2TJEle3NT+AGuzVm1TXz0qEaTgm98CuXgTYKFXjxo3d7Vx0URbblVB/64ut+/lqYidPgfioUaPsnnvucf/QtA69rrF66lIYpH9ghxxyiDsRKJjWWG6dYAYPHpzU7M5AZZ1zzjmuLuufmO4NrBnx9Y+tb9++dskll0TVd01wo4cC6iA1ML377rsJu2RRz1Fd1Iipc6zqrbJ1avTUrek0lk9ZjjDof4ACEAXYmpeDW3ahptB5XQ8FHaqnGt6gSfn233//uN29/TlfE6mpsVWBuG45qv8HQKoNGTLE9ZxT4+eCBQtcT07VWc17EZuR1nA1nds1XE3X2Z06dXJB+GGHHebeh+qVVVzeqRQBAAAAAEAJjMEGAAAAACAEBNgAAAAA/q+9ewuJao/iOL5MS6SiDJMEy1IxKksiyKSLmSQKhgmmXcEikaIyqUyEohchIsMoouihvNBNKaheIws1SkiwHqTsoayoLMtAupEe1oIZnLGyQ9ujc/p+YHD8773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", 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ocIwax3V5O8L3v//9RrfH34xRv5gGHMEz6lWcwxvXV47p9HGt7ygTYSRCU0xLjin0cRmkWKwtpgPHY69crCpGBOOAQUxjjn1GuTjHNUYli5H7CNlxDeSYbhzhPg4AxDWYY8G0CPKVDjrooDzdOLbHvuKgRMNrdhciDMaibvFcRNCPABj1iec1ZjBUg+gncZAn+mkceIhZBtG+sXZAw8tvNRTtHwdH4nrUlX01gvGTTz6ZF/6KgyUR2mN/0ZZxLnYxQ+DKK6/Mr4u4vFZc0zvCegTiOMhUzEoIp556al68LwJvTL+OcnFAp7KPx2W6IuBGX4nAG30inv/oj22Zkh1tEIE9Xlux7wjz0W/jtIZY76BykbN4fNGP4+BSHESImQ1R54aLrv3jH/+oO4gB0JPVxFLinV0JAICuJka6Y5G0a6+9tsnz6fl/0+g32WSTPNuk4cg2QE/jHGwAgEbE+dJxGbXzzjtP+zQjZj3ElHnhGkDABgBoUpxLH0F78uTJWqkRcepGTF8/7rjjtA+AKeIAAABQDlPEAQAAoAQCNgAAAJRAwAaocuecc076zne+ky/DVIjr8p5wwgmpGm244Yb5vNn2iut5RzvdeuutqSwvvvhivixWXLapobgOcVzqqbMVj7v4ikuLxWXD9ttvv7yqdlvE5adiX229f1s98sgjub3jus0tcffdd+fyHeWee+7J156O9oxLe8U1zo888sjcL9pqypQpuc7xvDXV7pX9LV4b8RqZ0+Ka8HHd77icF0A1E7ABqti7776bL51z8MEH1123uSccUKi8lm9XEkEq6vfmm2/OcttNN92Ur9fcVcS1n+MaznHt8mjPRx99NH33u99Nr776auouos7R3q0J2FG+I8R10vfZZ598oCuujR7XVj/wwAPzQYe4pvTf//73NgfsqPO///3vFpWP57KjHmNzhg4dmvbcc8/cDnE9coBq1TM+bQH0UBHYhgwZkq9RWy1mzJjR7Af0FVZYIY0YMWKO1qkrmzp1apvut9hii6Wvf/3redRx9913T0cffXQOczfccEPpdax2cfAkDnTtvPPO6eKLL86X/lp99dXT6NGj0x//+Mf0la98Jf34xz9Or7/+eofXJV4b8RopS/SJltppp53ywaW//e1vpf19gK5GwAaoUhFC48P75ptv3qbR648//jj97Gc/S9/4xjfydNaNNtoonXnmmfXCbYyMb7bZZvXuF1OJY1rqLbfcUrftqaeeytvuuOOOum3vv/9+vrTPyJEj8/5j2mqMrMVlfwoxtTXud9FFF+VrEUeZuGTSAw880OIp4jFiGPfddNNN09e+9rUcGLfYYot06aWXtqgdpk2blk455ZR8jd+4/2677ZaefvrpWcrFSGQ89jXWWCPXceutt05//etf603XPeSQQ/LPEViLKdixPUaG77rrrhw+KqdnF6LN4zF861vfym0VU4wj8H700UezPPZ99903j4bG3496lDVaGX83fPDBB/W2/+c//0l77LFHWmWVVdLKK6+cQ1Q8lsbESHLUO9oownu0V8NQ2dQU/2ij+Grp8xrTpmO0NETfLdq0sanUIf7mlVdemX+ufA6K6dXRD375y1/m+kVbxOvi+OOPb9Ho+AUXXJBHcGM6eENzzTVX+ulPf5qDauUpAg0fb2NTvKNucf3pEM9zUefmTpFobIp4bW1tfuxbbbVVbssI//HabvjcRH3i/eShhx7Kz3M838ccc0y+7V//+le+fc0118z7WH/99dNBBx1UL4B/6UtfSuuss066+uqrZ9tmAN1Vn86uAAAd4/HHH88hOT7wtlaEiQiB8QE7PiTHh/YIUhdeeGF65pln8vcQH5ZjNOq9995LCyywQA7HMVV1wIAB6f77788jdSF+7tOnTw5WRbiO0bsI/mPGjMmjajGd9/zzz88hMwJtpZimvPjii+eAMnjw4Dy62lIxYhjhY//9988hLOr48ssvp0mTJrXo/nFQIUb8xo4dm+8T+4ogcf311+fr/4YI/N///vdz4IiDEjFrIML1oYcemkeQt9122xw4Ytr1GWeckQ8srLjiivm+8dgjkETIivZuGIgjSMa03ocffjh973vfS6uuumpuowiQ8Rz/6U9/yu1deTDjpZdeyo93kUUWSQMHDkxlKILmEkssUbctnuu99947LbPMMumkk05K/fr1S3/4wx9ycI7HGef+V/rJT36S+8zpp5+e3nnnnfSrX/0qt2WMis8999ytqs/sntfoX3Heb/SdKDf//PPn7UsvvXSj+4s2/uyzz3J/jqnxhejXEUDj9nie4xzq+HvPPfdcfg7++9//5vLx2BsTr40XXnght0VTz0UcnJhvvvnSfffd16o2iLpFO0Tf23777fNjDsOGDWvVfqI/Xnfddfm5OPzww3O7nXvuuTlE/+Uvf8nBuBCv3SOOOCL/zejf8RqOvhEHdqJdoh/Ecxmnp/zzn/9M06dPr/e44z0g+kYcmGjtcw7QHQjYAFUqAmsoglxrxIftCBARgIqQHCO4MdoW4SiCQPweYakI0DFi+thjj6VPP/00f/iuXBwsRrdiNDXCcYhgEh/ib7755rTQQgvlbTESF0Hx5z//eQ6SlUGof//+eYpt375927TQVQTAOFBQiNHHloqwEmGjpqYm/77aaqvlUdPf/OY3OXSHGMmMab4xehoHEoq/MWHChBwmom1iP8WBgXhsMYJb+TcibERIq9weYiZABJVos8qp/sstt1wOVTECvssuu9Rtj1HtaNfKINwWEewjtMZXjNifeuqpud7bbbddXZkY0Y16R4gdNGhQ3rbBBhvkxxvPY/Sdot1CjPyefPLJdb/H/mLadIyeRlAu83kdPnx4+vKXv5x/joXa4mBDc+JARxEkGz4H0f733ntvXbAM0f/jb0TIjIMtO+ywQ6P7ffvtt/P32f39uD1ec60R/aV4fUddGta7JeIAwbXXXptHtvfaa6+67RGWo5/HueLxuAtx0C7eF4qR8xAHJeKgXExzj35ZiBkFDUV9o2/F343ZKwDVxhRxgCoVI2cRbuadd95W3zdG6iJMx5TkSjESWwTmIpQsvPDCdb9H0I7Qs+WWW+ZRrfHjx+fpzTH6WoTxEFOIY2S9GPUuvooP3A0XbIoprW0J1yGC/bPPPptHliMoTZ48uVX3jymxlSExHm+MOBZTjV977bU8clqEiYaPJ0b8XnnlldRWd955Zw6xEVwr9x2hMUZlG7ZVzDZob7gOERwjDMWofITgaLc4qFCMOsZobxxQiRBWhOvQu3fv/PzHCHW0S6WGgStG46M9m5q23ZHPa2sUpyQU/b8QBxDidVL0//aIUfLKfjanRP+KvxvPWWX/ioMNEZYb9q+Y6l4ZrkP0xXh9xiyMODjX3Lnkxeh6jHADVCMj2ABVKkaUYjQ1Ak9rxShVfMBu+IE/prHGPuP2QnzYjoBTBOwY2YuQF/eP32PUNqZJVwbsDz/8MH+wb2p0PUZ+KxXTe9sipq5GCIppyHHuZ7RHjM7FVNgIabNTOT22cluEu8pzkmPENr5a8nhaI9oqptMW50B3ZFtVivaJc73juYvR2zgtIKbzjxs3Lo+cRp0iFDb29+LASajsJ821ZcNyc+J5bY2oX/T7hlOv4/Uxu/oXo+iNXZqt0ltvvZVHoee06F/xPFa+PisVp0EUGnu+40BbnD8e09Xj8n9x8CXuF1PO4/z8SjEbpXh/AqhGAjZAlYqR6zj/MT7sRhBpjXnmmSePTjYcVYsP4zG6VTkqHgE7FlOL84Hjq5jqG+EsAnYEh/j7MRJaWbcI4T/84Q8b/ftFQCu0Z2QvglFMfY2vCIVRpzivOqb6xkj67M5RbrioV7Et2qh4LEXg23jjjRvdR3tGlGP/8bcivDSmcvQ4lDUKGgGpCKqx6FVM34+pwTEdPKbwx0h2nH8bI/SNzZ4o6t6Stqxc9T3Ce2OrxMeBhMr9tfd5bY1o/+j3Mf2+MmTH6yPq31ygj74cpw/EaRWx4Fdj9YrTOWI/lTNGoh0aG5Vvz8GaxkSbRp+JafqNnUfecFtT/SsObsRXrPL/5JNP5n4SpwPEAYjKhRDj1JDi7wJUI1PEAapUEepimnZrRWiOYP6Pf/yj3vY417S4vbJsfOg+66yz8vcIY8X2mPobwSK2VU7xjgW/nn/++RysIpw0/FpwwQVTR4hQGCEmzlmOUcfGrkfd2CWWIkgV4j4RiIoF25Zccsm8AFuMaDf2WCrPPS/CSmOXzorbGtsebRV1jfNWG9t3/P05IYJrzEaIkewIfsVBk9tuu61evaOeMaoco7ENDyzceOONs5xHHe1ZtGWIKeMNz0WOKfbNTbNv6nkt2rulo6VNPT9Ff294ibI49zheJw2nTDcUi75FsGxshkPcP87lj+Ad14mubIe45njlwYYI18XaCrOrc0tF/4r+HVO2G+tflavZt0TMJIh+Edf6Lhbdq1RMH19qqaXaVF+Ars4INkCVKlYPj5HoyoWHChG8Kxciq1x4KhapihGtWLU7wkqcVx3nUcc5uKNGjao3nTSmjccIXUwjjr9ZjNBFmQg78RWXZqoUlwCKEcdYpTimkUYQiyAR02jvueeevGhYWdNlI9xE/WKKdYw+xuOJxcgiwLRkNfIYtYyp0bGIVaxQHYuNRaiJEetC1HefffbJI7vbbLNNPkAQgSpW846A8etf/zqXi3qEWFQqRp5jumwsbhWjedHGcXmtq666Ktc1DlZEwInRvwimsXp1tFWsOB4HK+Ic5ziAEZegamrkvFLUO1bTjmujt2Vl+fibcV52zDqIfcSq2rEqeqwiHivOx/coE/WPVbNjcbeGo50xshkriUcYjvrHiHO0VeUibXGpqFhUK86tjvO74/mK0fuGI54teV6jTUNsj+clRr2jrxUHPBoqysdl4eL8+Rihj4AZpz2st956eYG/OLgQ547HQYB4XmOF+ajz7M7jj37wu9/9LtczFoqLkd04aBBTq+O1GAvGVU7Hjn3G6uQx5T36XryOoh0a1j1+j8d8++2356Af50hHW81uUbVCLNq344475sttxfMTB8PiNRwzE+I1H21S+fw0JlaOj/PUI6zHlPg4oBGr24eGU8/j/ShmBLQ2uAN0FwI2QJWKD7oxZTM+eMcH6IbivOni3OlKBx54YF6ZOUJUBKD4UB8jZxGEIkTF7Q3Fh+gYka78MB2rg8fIbozCNRzhi2mzMa08rmMcq4PH6FkEzggKsRJ0mZfviTAZI41x7nCEoziHNOoZAbElC6dFqIxrXMdBgrh/BNwIj5XTmmM6fOw/rncc02JjynKEiBilK1ZhDxGgIshE20Yojem0cUmyWDwrfo9gGm0eQT5GFSPExYhgXL4s7hOXTIoR5NgWByAiDBWhcHZipLQ4Z7it4rHEqtIRCiPsx8hz/BzhPdonRq/jYE7UNxZlaygu4RSPIYJ5HFCJ5yYCdzHdvlgILaaYx3nVsUJ6hOgI27GSe2uf1ygTB0Ji4a0oF/Vr7gBDBOEYVY+DBPH34jmI10+E1eir8TijTvE8R50jBMdjaeoSXZXiYFX0kzhwFaO7sdp+HBiIbTH7o+HlwyL4xoh3PN/xmKLvxIGeOADVcOGxaNe45necnhHtGgcTYtX3lorzpmPUOQJ9hOVop3iNxoGE6O+zE4ucxUyVaJ8I5jG7Ifpl9IM4MFGI9rzjjjvyc9wZC7oBzAk1tZXz3gCoKhFAIiDGgmIdNe2a7iEu6RUHPYrRdJjTYrX1OEgXp12YIg5UK+dgA1SxuG5yTDOOqd30XDHCG+eIH3LIIZ1dFXqwmAUQ0+OFa6CamSIOUMViGuaJJ56Yp2XGtM84p5SeJ87TjfNrobPEmgRxSsHszucG6O5MEQcAAIASGMoAAACAEgjYAAAAUAIBGwAAAEpgkbMWePTRR/O1G1tyvVQAAACqx/Tp0/PCsaussspsywrYLRDh2uXCAQAAep7a2toWlxWwW6AYuY5ryQIAANBzPPHEEy0u6xxsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDApkepra3t1vsHAAC6rj6dXQGYk2pqatKn99+fZn4ysfR995p7aBq0zjql7xcAAOgeBGx6nAjXMyZM6OxqAAAAVcYUcQAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYBNlzJj5ozOrgIAAECb9Gnb3aBj9O7VO530t+PTaxNeLX3fa45YK31vnX1L3y8AAEAQsOlyIly/8P7zpe93xLyLlb5PAACAginiAAAAUC0B+7XXXkvHHXdc2mqrrdIKK6yQNt9880bL3X333WnrrbdOK620Utp4443TlVde2Wi53/72t2nDDTfM5bbbbrv04IMPdvAjAAAAoKfrEgH7hRdeyOF5scUWS0sttVSjZR599NF0wAEH5AB+0UUXpW222SaNHTs2jRs3bpZwfeaZZ6Zdd901XXjhhXmf++yzT3ruuefm0KMBAACgJ+oS52DHaPM3v/nN/PNRRx2VnnzyyVnKnHvuuTlcn3zyyfn3tdZaK7399tvprLPOyqPUvXr1Sp9//nk6//zz0+67756+973v5XJrrLFG2mKLLdIFF1yQgzcAAABU7Qh2hOPmRHB+4IEH0mabbVZvewTn999/Pz399NP590ceeSRNmjSp3hTz3r17p+985zt5hLy2traDHgEAAAA9XZcI2LMzfvz4NH369LTkkkvW27700kvn7y+99FK97w3LxbTzTz/9NL377rtzrM4AAAD0LN0iYE+cODF/n3vuuettL34vbv/kk09Sv3790oABA+qVGzp0aP7+8ccft6seM2bMqPfzzJkz888xMh6/FyPksX1Ole2KdWpP2WrRXdq7K5XtinWqhrJdsU7VUrYr1qmay3bFOlVD2a5Yp2op2xXrVM1lu2KdqqFsV6zTzE4oW3UBu1BTUzPb7Y2VKRqnqfu3ROwjRsELn332WZo2bVpdo8fU9OJJitH2yZMn15WdMmVKmjp1at1+ouwXX3wx27IhykaZEPeJ34vHE+Wa22/8Xohyse9C/M3m9ttU2XiMUbboaNEG0RaFaKOY0t9U2co2jJ8r27ByP91da9u78rFH2ebasKn2LvphUTa2t7TPRtnKfhhl29Nni7LFY22qXSr7d8M+G+WaKtuaPtuwbJl9tmEbeo/o2PeIlrZ3a/rs7N47m+uHjZVt62vBe0Tj7eI9omV91ucI7xE+R/gc4T0idejniNaE7C6xyNnsFCPQxUh1IUasK0ey43s0THz1799/lnLFftoiwvmgQYPqfp9rrrnqAnucQz5kyJC6c8n79u2b+vT5v6YdOHBgXdn43tKyobJslIvfi9srR+qL/Ra3xX7j90KULQJGGDx4cF3ZxvbbVNk4p72yTtHOMWugEG3UlrJRJtq0WnRGezfsh1Eu+kFL+mxzZcvos5Xt0lTZhn029lvZLpVt1po2bFi2zD7b1vb2HtGx7d2aPtue/t2WPlvwHuE9wntE+Z8jvEfM+j/Q5wifI2SNmlLeI2a3Zli3C9gjRozID+zll19OI0eOrNv+4osv5u/Fpb2K73Eudqw4Xojf4014wQUXbFc94k2qsZ/jSaj8veETMCfKdsU6tadsd9cV2rC7le2Kdaqmsl2xTt29bFesUzWX7Yp1qqayXbFO3b1sV6xTNZftinWqprJdsU41nVS2KqaIxxGGuCzXLbfcUm/7TTfdlOaff/66ML3qqqvmow1//etf68rENIC436hRo9o1RRwAAAC6/Ah2nFsUl9EKb775Zj6X6NZbb627jvWwYcPSmDFj0m677ZaOPfbYfHmuuCTXuHHj0gknnFBvqH///ffP17uO+0TwjjKvv/56OuOMMzr1MQIAAFDdukTA/vDDD9MhhxxSb1vx+2WXXZbWXHPNtMoqq6TzzjsvB+Xrr78+DR8+PIft0aNH17vf3nvvnc+Bu/zyy9MHH3yQlllmmXThhRemZZdddo4+JgAAAHqWLhGwF1lkkfTcc8/NtlxM846v5sQ08O9///v5CwAAAOaUbnEONgAAAHR1AjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkE7Dmstra2W+8fAACAxvVpYjsdpKamJj30yjtp0pTppe97vsH901cX+VLqXVOTOsqMmTNT716OywAAADQkYHeCCNcTp0wrfb+DB/TN4ffYq/6ZXnlvYun7X2KBoWnsLt8ofb8AAADVQMCuQhGun33zo86uBgAAQI9iri8AAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAADoiQH7H//4Rxo9enRaddVV0zrrrJMOPPDA9PLLL89S7u67705bb711WmmlldLGG2+crrzyyk6pLwAAAD1DtwrY999/fw7USyyxRDr77LPTT3/60/TKK6+kvfbaK02ePLmu3KOPPpoOOOCAtMIKK6SLLroobbPNNmns2LFp3LhxnVp/AAAAqlef1I3cfPPNaaGFFko///nPU01NTd628MIL5xHthx9+OI0aNSpvO/fcc3O4Pvnkk/Pva621Vnr77bfTWWedlbbbbrvUq1e3Oq4AAABAN9CtkuYXX3yRBg0aVBeuw5AhQ+qV+fzzz9MDDzyQNttss3rbt9hii/T++++np59+eo7VFwAAgJ6jWwXs7bffPp9vffnll6dPPvkkvfHGG3k0e6mllkprr712LjN+/Pg0ffr0tOSSS9a779JLL52/v/TSS51SdwAAAKpbtwrYq6++ejrnnHPSmWeemX/eaKON0uuvv55+97vfpX79+uUyEydOzN/nnnvuevctfi9ub4sZM2bU+3nmzJn559ra2vx7fA+xvamy1aClj7U17VKUrRZlt0tPKNsV61QNZbtinaqlbFesUzWX7Yp1qoayXbFO1VK2K9apmst2xTpVQ9muWKeZnVC2agP2I488ko444oh8HvUll1ySw/aAAQPSPvvsU2+Rs1A5jbwl22cnGvjTTz+t+/2zzz5L06ZNq2v0SZMm1T1JMYJeWZ8pU6akqVOnpmoRj73oaNEG8Xsh2iim6Ydoj2iXyrKVbRg/V7Zh5X66u3i+43kvRH+IflGc6hDtUrxoo2zlY4+yzbVhU+1d9MOibGxvaZ+NspV9NsoWfTbqGWWj3i3p35Vli8faVLvEbUW7xPfKslGuqbKNtWFL27vMPtuwDct4j2hLezfXLs3tt7nnZnZ9tqmyremzHdXerWnD2bVLc/2wsbJtfS14j2i8XbxHtKzPeo/wHuFzhM8R3iNSh36OaE3IrqktPjF1A9tuu21e5CyCdeGjjz5KI0eOTIcddlheTfzFF1/M51/H6uGxvbJcTCP/xS9+kbbaaqtW/d0nnngif4+F03r37p1/jicnwnosmBZNGI0eP8e2+Dm2NVY23PH062nilP/3BJZp4XkHpzWWHJ52/dVN6dk3Pyp9/8stPCxd+cPN8+NpyWNtTbsUZeO2H1y9V3rh/edLr/9Gy2ycjt30Z2nSrbekGRMmlL7/3vPOm4Z869v557LbpSeU7Yp1qoayuW92sTpVS9muWKdqLtsV61QNZb1H6LNdoR96j+gabeg9oqbJdnnqqady+8UloKtqFfE4f3rDDTest23YsGFpgQUWyOdehxEjRqS+ffvmc7UrA3YE7xDna7dV0TEb/hwNX/l7w1XKK2+rBi19rK1pl4Zlu7uOapdqLtsV61RNZbtinbp72a5Yp2ou2xXrVE1lu2KdunvZrlinai7bFetUTWW7Yp1qOqlsVU0Rj9Hr4uhBIVYGf++99/LlukKcix2X5brlllvqlbvpppvS/PPPn0ehAQAAoGzdKmDvuuuu6Y477kgnnHBCuu+++3KIjvOv55prrrTlllvWlRszZkx68skn07HHHpsefPDBdP7556dx48alQw45pNVHIAAAAKAl+nS3gB3Tv6+66qp03XXX5WAd8+DjUl0xTbywyiqrpPPOOy+dccYZ6frrr0/Dhw/PYXv06NGdWn8AAACqV7cK2DEffscdd8xfszNq1Kj8BQAAAHOC+dIAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAIGADAABA12AEGwAAAEogYAMAAEBnB+yPPvoo/fKXv0w77rhj2nTTTdMLL7yQt1999dXp6aefLqN+AAAAUN0B+/XXX09bbrlluvzyy1NNTU0aP358+vzzz/Ntzz33XN4OAAAAPUWbA/Zpp52W5p577vS3v/0tXXHFFam2trbuttVWWy098sgjZdURAAAAqjdgP/DAA+nAAw9MCy64YB7BrjT//POn9957r4z6AQAAQHUH7GnTpqWhQ4c2etuUKVNmCd0AAABQzdocsJdYYol0//33N3rbQw89lJZZZpn21Au6nZoBA1LtjBkd+jc6ev8AAEDb9WnrHUePHp1OPfXUtMACC6Qtttgib5s+fXq69dZb01VXXZWOO+64dlQLup+afv1STe/e6aVjf5qmvvJq6fsfsMTiaamxJ5a+XwAAoJMD9q677pqeffbZdMopp6Sf//znedsuu+ySFzuL8L3NNtuUVEXoXiJcf/bcc51dDQAAoLsE7HDiiSem7bbbLt11113pww8/TPPOO29af/3106qrrlpeDQEAAKDaA3b4+te/nr8AAACgJ2t3wC5WDY9VxRuaZ555ytg9AAAAVG/AjlB9xhlnpBtvvDFNnDix0TLPPPNMe+oGAAAA1R+wTzjhhHTDDTekDTbYIC211FKpb9++5dYMAAAAekLAvvPOO9OPfvSj9L3vfa/cGgEAAEA31Ks9d15hhRXKqwkAAAD0xIC9ySabpHvvvbfc2gAAAEBPmyJ+5JFHpoMPPjidcsopadSoUWno0KGzlFlxxRXbWz8AAACo7oAdl+X64osv0qWXXpouu+yyerfV1tammpoaq4gDAADQY7Q5YB9zzDHpiSeeSHvssYdVxAEAAOjx2hywH3zwwXT00UenHXbYocc3IgAAALR5kbNBgwalhRdeWAsCAABAe1YR33rrrdPNN9+sEQEAAKA952Avt9xy6cwzz0xjxoxJ66+/fqOriMelvAAAAKAnaHPAPvzww/P3N998M91+++2z3G4VcQAAAHqSNgfshpfmAgAAgJ6szQF7jTXWKLcmAAAA0BMXOQMAAADaOIId170+4IAD0qKLLpp/bk6cg33yySe3ZvcAAADQMwL2gw8+mPbYY4+6n2cXsDvKuHHj0uWXX55eeeWVNHjw4LTyyiunCy64oO72u+++O69w/tJLL6Xhw4enPffcM+26664dVh8AAABoVcC+4447Gv15Tjr77LPTJZdckvbbb78crCdOnJj++c9/1t3+6KOP5lH2rbbaKh111FHpkUceSWPHjk39+vVLo0eP7pQ6AwAAUP3avMjZW2+9leaff/7Ut2/fWW774osv0nvvvZcWWmihVKYYkT7//PPThRdemNZbb7267RtvvHHdz+eee25aYYUV6qanr7XWWuntt99OZ511Vtpuu+1Sr15OOwcAAKB8bU6bG220UXrmmWcave3ZZ5/Nt5ftz3/+cz7/uzJcV/r888/TAw88kDbbbLN627fYYov0/vvvp6effrr0OgEAAEC7AnZtbW2Tt82cObNDzsF+7LHH0jLLLJNHqddee+301a9+Ne222251QX/8+PFp+vTpackll6x3v6WXXrpuBBwAAAA6QunzpWMU+Z577knzzjtv2bvOo9D33ntvuvHGG9Pxxx+fz8eeMmVK2muvvdInn3ySz8cOc889d737Fb8Xt7fVjBkz6v0cBxKKgw3xe3HQIbY3VbYatPSxtqZdirK0rP3Lau+uVLYr1qkaynbFOlVL2a5Yp2ou2xXrVA1lu2KdqqVsV6xTNZftinWqhrJdsU4zO6FshwXsc845Jy2//PL5K0aod9xxx7rfi69YeCxGmDtiing8yM8++ywH60022SRtsMEG+ZzsTz/9NF1zzTV15ZoaPW/PqHr87fg7hajHtGnT6hp90qRJdU9SjKJPnjy5rmwcBJg6dWqqFvHYi44WbRC/F6KN4iBLiPaIdqksW9mG8XNlG1buh6ZV9qXK9i76YdHesb2lfTbKVvbZKFv8nej7UTbWVmhJ/64sG9/j98q6R/nKsrG/Yr+VZaNcU2WL/RZvfA33G/VrqmyZfbZhG5bxHtGW9m6uXZrbb3PPTXNt2FzZxtpwTrd3a9pwdu3SXD9srGxbXwuza+/KNoyyzbVhU+3tPcJ7hPcI7xHeI3yO8DliRps+R7QmZLdqkbOVVlop7bLLLvmPXnXVVWnTTTdNX/rSl+qVidW6Yxr35ptvnso2dOjQ/Pe+8pWv1G1bYIEF8pTwF198MQfuxkaqY3S7sZHt1ohwPmjQoLrf55prrrrAHgunDRkypG4BtVj4rU+f/2vagQMHduhly+a0eOzFY+3fv39+zgvRRsVj7d27d712aa5slIn9MnsDBgxosg0r2zvaunIRwub6bHNl43tr+ndl2SgXv1fWvamysd/KsrHfImAUZYv7Fvstfo/9VpaNy/c1VbbMPtvW9m6uDcto78p2KRT7LW5r2N6tacPmyramDTuqvVvThu1p77b02c5sb+8R3iO8R3iP8B7hc4TPEb3a9DmiNQtltypgjxo1Kn8VRz/iclix6NicstRSS+XVyxuKDx7xoEeMGJEb4OWXX04jR46suz3Cd3H/9ogPMo39HE9C5e8Nn4DK26pBSx9ra9qlYVla1v5ltXdXKNsV61RNZbtinbp72a5Yp2ou2xXrVE1lu2KdunvZrlinai7bFetUTWW7Yp1qOqlsh52Dfcopp7Q4XMcUtpg+/tRTT6X2WH/99dMHH3yQnn/++bpt7777bg7Uyy67bD4SEZfluuWWW+rd76abbsqXFIvLd9F28w2JUZTqOZccAACgS1wHu7Uqp7e1VVzvesUVV0wHHXRQOuSQQ3KgjvO9hw0blnbYYYdcZsyYMXll8WOPPTZfnuuRRx5J48aNSyeccIJrYLfTkAH9Uk1Nr/ToG7elSdMmpLItMHhEWm7BtUrfLwAAQFUF7DLEUP1FF12UTj755HTcccflE89XX3319Mtf/rLu/N1VVlklnXfeeemMM85I119/fRo+fHgO26NHj+7s6leNCNefTP2g9P0O7jdP6fsEAACYU7pVwA7zzTdfDtQtPVccAAAAuuV1sAEAAKAnErABAACgBAI2AAAAlEDABgAAgO4SsOPi3Ntss02ad95558SfAwAAgO65ivhHH32Upk6dOsv2hRZaKH+vqalJp5xyShl/CgAAAKorYE+ePDmH5ptvvjlNmzat0TLPPPNMe+oGAAAA1R+wTz755HTTTTel7bffPi277LKpX79+5dYMAAAAekLAvvvuu9Nhhx2W9thjj3JrBAAAAD1pkbOYFr7MMsuUWxsAAADoaQF71KhR6eGHHy63NgAAANDTpojvv//+6eCDD06DBg1KG2ywQZpnnnlmKdPYNgAAAKhGbQ7Ym2++ef7+i1/8In81xiriAAAA9BRtDthjxozJ17cGAAAA2hGwDzroIO0HAAAA7V3kDAAAAChhBDvMmDEj3XPPPemll15KU6dOrXdbTB+PaeQAAADQE7Q5YE+YMCHtuuuu6eWXX85hura2Nm+vPC9bwAYAAKCnaPMU8TPPPDP1798/3XnnnTlcX3vttenvf/972nPPPdPiiy+e7rrrrnJrCgAAANUYsB944IEcphdYYIH/t6NevdKIESPSkUcemdZZZ53085//vMx6AgAAQHUG7HfeeSctvPDCqXfv3jlcT5kype62DTbYIN13331l1REAAACqN2DPO++8afLkyfnnGMV+/vnn626bOHFiXgANAAAAeoo2L3K24oorphdeeCGtv/76aeTIkem8885LgwcPTn379k1nnHFGWnnllcutKQAAAFRjwN5tt93S+PHj888//OEP02OPPZbPvw5xLvZPfvKT8moJAAAA1RqwYyGz+ArDhg1L119/fZ4mHpfpWnLJJVOfPu26xDYAAAB0K6Wl4AjWyy67bFm7AwAAgOoN2G+99Vaaf/7583nW8fPsLLTQQu2pGwAAAFRnwN5oo43SNddck772ta+lDTfcMI9aN+eZZ55pb/0AAACg+gL2ySefnBZddNG6n2cXsAEAAKCnaFXA3mabbep+3nbbbTuiPgAAANAt9ersCgAAAECPG8E+55xzWlw2po+PGTOmLXUCAACAnhWwI0TX1tbOsq0gYAMAANBTtCpgP/vss3U/v/rqq2mfffZJ22+/fdp8883z5bvef//9dOONN6Y//elP6aKLLuqI+gIAAED3D9iVTjrppLTVVlulfffdt27bwgsvnPbbb7/0xRdfpLFjx6aLL764rHoCAABAdS5y9p///Cetttpqjd626qqrpocffrg99QIAAICeEbD79euXnnjiiUZve/LJJ/PtAAAA0FO0eYr4xhtvnM4999w0aNCgfA720KFD08SJE/M52LF9iy22KLemAAAAUI0B+6ijjkrjx49PJ554Yj7funfv3mnGjBl5VfHVV1893w4AAAA9RZsD9uDBg9Nll12W7rnnnvTggw/m0et55pknrbnmmmm99dard7kuAAAAqHZtDtiFkSNH5i8AAADoydodsP/5z3+mf//732nChAnpgAMOSAsttFB6/PHH0yKLLJKGDRtWTi0BAACgWgP2lClTcqD+17/+VTcdfOedd84B+3e/+1368pe/nI488sgy6woAAADVd5muM888M1+O6+yzz87XxI7FzQrrrrtuuv/++8uqIwAAAFTvCPatt96aDjnkkHy5rlg9vFKMYr/99ttl1A8AAACqewT7o48+SksvvXTjO+3VK02dOrU99QIAAICeEbAXXHDB9Pzzzzd623PPPZcXOQMAAICeos0Be5NNNkkXXHBBevrpp+u2xWJnb775ZrrkkkvSt771rbLqCAAAANV7DvaYMWPyCuKjR49OX/nKV3K4Pvroo9P48ePTEksskX7wgx+UW1MAAACoxhHswYMHp6uvvjovdDbXXHOlESNGpIEDB6Z99903XXnllWnAgAHl1hQAAACqcQQ7RIiOkWqj1QAAAPR07QrY//jHP9INN9yQ3nrrrTRt2rR6t8WU8bgNAAAAeoI2B+yLL744nX766WnYsGF108MBAACgp2pzwL7qqqvSdtttl0444YTUu3fvcmsFAAAAPWWRs48//jhtvvnmwjUAAAC0J2Cvuuqq6eWXX9aIAAAA0J6Afcwxx+TLcd1+++3p888/15gAAAD0aG0+B3uxxRZL66yzTjrwwAPziuENr3sd2x5++OEy6ggAAADVG7BPO+20dMUVV6Tll18+Lbnkkqlfv37l1gwAAAB6QsC+7rrr0j777JMOO+ywcmsEAAAAPekc7BkzZuQp4gAAAEA7Ava6666bHnvsMW0IAAAA7ZkifsABB6RDDz00DRw4MK2//vpp6NChs5SZZ555NDIAAAA9QpsD9lZbbZW/n3rqqfmrMc8880zbawYAAAA9IWCPGTMmX4oLAAAAaEfAPuigg7QfAAAAtHeRMwAAAOD/CNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAA9OWB/+umnaeTIkWnZZZdNTzzxRL3b7r777rT11lunlVZaKW288cbpyiuv7LR6AgAA0DN024B93nnnpRkzZsyy/dFHH00HHHBAWmGFFdJFF12UttlmmzR27Ng0bty4TqknAAAAPUO3DNgvvfRSuuqqq9JBBx00y23nnntuDtcnn3xyWmuttXLY3n777dNZZ52VZs6c2Sn1BQAAoPp1y4B90kknpZ122iktscQS9bZ//vnn6YEHHkibbbZZve1bbLFFev/999PTTz89h2sKAABAT9HtAvatt96ann322TRmzJhZbhs/fnyaPn16WnLJJettX3rppetGvgEAACD19IA9ZcqUdOqpp6Yf/ehHafDgwbPcPnHixPx97rnnrre9+L24va0qz/mOn4sp57W1tfn3+B5ie1Nlob3K6IddsWxXrFM1lO2KdaqWsl2xTtVctivWqRrKdsU6VUvZrlinai7bFetUDWW7Yp1mdkLZqg3Y559/fppvvvnStttu22y5mpqaVm1viWjgWLm88Nlnn6Vp06bVNfqkSZPqnqQYRZ88eXK9AwNTp05t89+GSpV9KfpknBpR2Q+LN4HY3tI+G2Ur+2yULf5O9P0o+8UXX7Sof1eWje/xe2Xdo3xl2dhfsd/KslGuqbLFfos3vob7jfo1VTbaIB5fY20Y7VHZhlG2sg3j5+basIz3iLa0d3Pt0tx+m3tummvD5so21oZzur1b04aza5fm+mFjZdv6Wphde1e2YZRtrg2bam/vEd4jvEd4j/Ae4XOEzxEz2vQ5ojUhu0/qJt588830u9/9Li9iVnxQKj5ExPdooKFDhzY6Uv3JJ580OrLdGhHOBw0aVPf7XHPNVRfYe/XqlYYMGZK/h759+6Y+ff6vaQcOHNiucA+VBgwYUPdz9Mmm+mG/fv1yX2xJn22ubHxvTf+uLBvl4vfKujdVNvZbWTb2WwSMomxx32K/xe+x38qyMcOlqbL9+/fPj7exNuzdu3e9OjVXtj3t3VwbltHele1SKPZb3NawvVvThs2VbU0bdlR7t6YN29Pebemzndne3iO8R3iP8B7hPcLnCJ8jerXpc0Txc1UF7DfeeCMfPfjBD34wy2277757WnnlldMVV1yRG+Dll1/O18guvPjii/n7Ukst1a46xAeZxn6OJ6Hy94ZPQOVt0F5l9MOuWLYr1qmaynbFOnX3sl2xTtVctivWqZrKdsU6dfeyXbFO1Vy2K9apmsp2xTrVdFLZqgnYyy+/fLrsssvqbXvmmWfSKaecko4//vi00kor5SMRcWmuW265Je2555515W666aY0//zz58t3AQAAQEfoNgE7pnevueaajd624oor5q8Qq4vvtttu6dhjj82X53rkkUfSuHHj0gknnNDqow8AAABQdQG7pVZZZZV03nnnpTPOOCNdf/31afjw4Tlsjx49urOrBgAAQBXr1gE7RrSfe+65WbaPGjUqfwEAAMCcYs40AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAbarLa2tlvvHwAAytSn1L0BPUpNTU166JV30qQp00vf95CBfdPqSwwvfb8AANBRBGygXSJcT5wyTSsCANDjmSIOAAAAJRCwAQAAoAQCNkAHmTFzRrfePwAAreMcbIAO0rtX73TS345Pr014tfR9Lzbv4uknm/5v6fsFAKDtBGyADhTh+oX3n9fGAAA9gCniAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2ECX1L9P7zRj5swO/RsdvX8AAHqWPp1dAYDG9O3TK/Xu1Ssde9U/0yvvTSy9kZZYYGgau8s3ND4AAKURsIEuLcL1s29+1NnVAACA2TJFHAAAAEogYAN0Q8PmGpZqa2s79G909P4BAKqNKeIA3dDg/kNSTU1N+vT++9PMT8o/R73X3EPToHXWKX2/AADVTMAGeqT5hgxItbUzU01N957IE+F6xoQJpe+3ZsCAVDtjRqrp3Tt1lI7ePwDAnNatAvYtt9ySbrzxxvTUU0+liRMnpkUXXTTtvPPOaaeddkq9ev3fh+S77747nXnmmemll15Kw4cPT3vuuWfaddddO7XuQNcyZEC/HK4ffeO2NGla+QF1gcEj0nILrpW6q5p+/XL4fenYn6apr7xa+v4HLLF4WmrsiaXvFwCgM3WrgP373/8+LbTQQunHP/5xmm+++dKDDz6YTjrppPT666+nI488Mpd59NFH0wEHHJC22mqrdNRRR6VHHnkkjR07NvXr1y+NHj26sx8C0MVEuP5k6gel73dwv3lSNYhw/dlzz3V2NQAAuoVuFbAvuOCCNGzYsLrf11prrfTZZ5+lK6+8Mh166KE5RJ977rlphRVWSCeffHJdmbfffjudddZZabvttqs30g0AAABl6VZpszJcF5Zffvk0bdq09PHHH6fPP/88PfDAA2mzzTarV2aLLbZI77//fnr66afnYG0BAADoSbpVwG7Mww8/nOaZZ548ZXz8+PFp+vTpackll6xXZumll87f45xsAAAA6AjdOmA/8cQT6c9//nPaY489Uu/evfPCZ2HuueeuV674vbi9rWbMmFHv55kzZ9ZdKzZ+L64ZG9ubKgvtVUY/LLMstLc/z+k+W1bZrlinai7bFetUDWW7Yp2qpWxXrFM1l+2KdaqGsl2xTjM7oWyPCNgx5fvggw9OK620Utpnn33q3RbXhm1MU9tbIhr4008/rfs9zv2OqelFo0+aNKnuSYpR9MmTJ9eVnTJlSpo6dWqb/zZUquxL0Sfj1IjKfli8CcT2lvbZKFvZZ6Ns8Xei70fZL774otH+De0Rfa3os9E/K/ts/Nxcn22qf8+uz1a+JzdWNn6vLBtfhbgtyjRVtvL1Wbnf+F5ZNspV7jfqV+y3KFv8Y4+y8fgqyxav+2iPytd9tEFl2a7wHtGedpldezdVtrE2bGl7N9eGjbV3GX22NZ8j2tLezbVLS18Lre2zTZVtTZ/tqPb2HuE9wnuE94jPW/ke0ZqQ3a0WOSvEA45QPWDAgHT++eenvn375u1Dhw5tdKT6k08+aXRkuzUinA8aNKju97nmmqsusMfCaUOGDKlbQC3q06fP/zXtwIED2xXuoVL0+0L0yab6YSz6V7w2Ztdnmysb35vr39Ae0deKvtW/f//cF9vbv2fXZyvfk1tTNjQsG79Xli0CRlG2uG/ss7JsvI4ryw4ePHiWssXvzZWN2VuVdSqrDTuqvRu2YWPtUmZ7t6QNG5Ztrg3nVHt3VJ9trF0KxX6L2xq2d2f02Y5qb+8R3iO8R3iP6NfK94jWLJTd7T4lx5GF/fffP33wwQfpmmuuSfPOO2/dbSNGjMgN8PLLL6eRI0fWbX/xxRfz96WWWqpdfzve6Bv7OZ6Eyt8bPgGVt0F7ldEPyywL7dEZfbassl2xTtVctivWqZrKdsU6dfeyXbFO1Vy2K9apmsp2xTrVdNHPvt3qk3IM0x9yyCHp2WefTRdffHFaeOGF690eRyLisly33HJLve033XRTmn/++fPluwAAAKAjdKsR7BNOOCHdeeed6Ygjjsjn9/z3v/+tt1J4TEEaM2ZM2m233dKxxx6bL8/1yCOPpHHjxuX7GnkDAACgo3SrgH3vvffm76eddtost1122WVpzTXXTKussko677zz0hlnnJGuv/76NHz48By2R48e3Qk1BgAAoKfoVgH7jjvuaFG5UaNG5S8AAACYU7rVOdgAAADQVQnYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAdFMzZs7o1vuvNn06uwIAAABNqa2tTTU1Nd12/x2td6/e6aS/HZ9em/Bq6ftebN7F0082/d/S91vNBGwAAKDLivD70CvvpElTppe+7yED+6bVlxieursI1y+8/3xnVwMBGwAA6OoiXE+cMq2zqwGz5RxsAGC2nOMHALNnijgAOMdvtpzjBwCzJ2ADgHP8WsQ5fgDQPAEbAP5/zvEDANrDOdgAAABQAgEbAAAASiBgAwAAQAkEbACAKlNbW9ut9w/QXVnkDACgytTU1KSHXnknL9xXtiED+6bVlxieuvt13ePSc911/0DXJWADAFQhq+I3zXXdgY4iYAMA0OO4rjvQEZyDDQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAACNgAAADQNRjBBgAAgBII2ABQBWprZ3Z2Fbq02hkzuvX+Aege+qQq9Morr6SxY8emhx9+OA0cODBtttlm6fDDD08DBgzo7KoB0AP179M7zZg5M/Xu1XHHtWtqeqVH37gtTZo2ofR9LzB4RFpuwbVSRxk217BUW1ubampqOuxv1PTunV469qdp6iuvlr7vAUssnpYae2Lp++3pB4yiT9NE+8yYkft0d90/VLOqC9iffPJJ2mOPPdJCCy2Ufv3rX6ePPvoonXLKKenjjz9Op59+emdXD4AeqG+fXjlcH3vVP9Mr700sff/rLLtQGvPtVXO4/mTqB6Xvf3C/eUrfZ7399x+Sw/Wn99+fZn5Sfvv0+fJCaeDKK+dw/dlzz5W+/57GAaPmOWAEPVvVBeyrr746h+zrr78+DRs2LG/r3bt3HsHef//901JLLdXZVQSgh4pw/eybH5W+38XnnztVgwjXMyaUPwLfa+7qaJ+uwgGj5jlgRENmZPSsGRlVF7DvueeetPbaa9eF67DpppumY445Jt19990CNgBACRwwap4DRt2DGRnNMyOj9aouYL/00ktpu+22q7etX79+acSIEfk2AACAYEZG88zIaL2a2lhVpIqsuOKK6ZBDDkk/+MEP6m3feeed03zzzZfOOeecVu/zkUceyYuvRFAvw7QvZuT9lS3O7+vbu1eaMHlqmj6j/NVkB/Trk+Ye2C99PmNKmtkBq9X2rumT+vbunz6eMiF9MfOL0vffv8+ANKT/kFQ7bVpKMztgtd3evVNNv37piwkTUu308utf07dP6jPvvKmr0Z8bpz83T38ul/fn2fD+XCqfN5rn80bH8HmjcT5vzJnPG59//nleK2TVVVfteSPYTWnP6qRlr2oaU1E60ryDO3a19H69B3bo/ucZ2LEhsqZ//w7df1cMwR1Jf26e/ty96M/N05+7F/25efpz96I/N09/7liRB1uaCasuYM8999x5kbOGJk2a1Obzr1dZZZUSagYAAEA1q7oLDEaIbniudQzpjx8/3gJnAAAAdJiqC9gjR45MDzzwQJpQcZmP2267LYfsUaNGdWrdAAAAqF5Vt8hZTA/ffPPN08ILL5wOOOCA9OGHH6ZTTz01rbfeeun000/v7OoBAABQpaouYIdXXnkljR07Nj388MNpwIABOXAffvjh+WcAAADoCFUZsAEAAGBOq7pzsAEAAKAzCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELBpl+uuuy4tu+yy6dVXX623/corr8zbzzzzzHrbP/3007TCCiukc889N/9+9913p9122y2tueaa6etf/3raeOON8zXL41rmhaOOOirvK76WX375tPrqq6dtt902nXbaaentt9+epU4bbrhhOuGEEzyzlCL66Pe+973cR7/61a+mDTbYIP3sZz9L48ePT0cffXQaOXLkLPc55JBDcn+97777ZtlXbP/Pf/7Tpr4NZTj77LPr+t1yyy2XVltttbTFFlvk982XXnopl3nwwQfryjT39cYbb6Q///nP9bb9z//8T+7H119/vSeMDu3Hq6yySotuq+yf8T6+3nrr5ff1cePGpenTp9e7b8O+H59NNtpoo3TooYfO8p4OHeGGG25I22+/fX5vXnXVVdO3v/3t9JOf/CR9+OGHdWW++93v1vXR+Fwdn1F22mmn/Pl6woQJs+wzyv32t79t0euH9utTwj7oweLFHx555JG0+OKL121/9NFH08CBA/P2So899liaMWNGfsO4+eab049+9KO0zTbbpO9///upb9+++cPdLbfckr8vscQSdfdbdNFF0+mnn57isu2TJk1KTz75ZLr66qvzV7xJrLPOOnPwUdNTxAGiCy64IB/4Of7449N8882X3nzzzXxgac8990z7779/DhcRMhZZZJFG+/+6665btz1+j36+0kor1W3Tt+kMAwYMSJdeemndgc/nn38+XXPNNenaa69NJ510Ug4U8XvhqaeeygH8lFNOSUsuuWTd9gUWWKDu54svvjgNGTIkffzxx+nyyy9PRx55ZO7vm2222Rx+dDCrCCSbb755+uKLL9J7772X/vnPf+aDpRGyf/e736XBgwfXK1/09WnTpqXXX389f2bZe++90y677JL+93//VxPTIS688MJ0xhln5M8YBx98cP7c+8ILL6Qbb7wx99v4HFKIz9LxPjtz5sw0ceLE/NnjsssuS3/4wx/y+3EcQKVzCNi0y4gRI9L888+fg0OMWBTi9wjOET7i6HB8yCq29+nTJ6288sr5H1UccTv11FPr7hdhZPfdd89vFg0/DMZR5EKMGsY/uRj9jqPKt99++yz/HKE97rnnnhyu991333wgqBCjzFtvvXW644470mKLLVbXr4uAHQH83Xffzf2z4QGm+D1GT/r3769v06l69epV7z013nujz/7gBz/IIyXxwa3y9ggZ4Stf+Uq9A0SVVlxxxTRs2LD8c7y3r7/++vl/gIBNV/DlL3+5Xp/+zne+k0cG4z0+PoeMHTu2XvnKvh79OUYUI/j85je/ySN/W2655Rx/DFS/ODgZn59jhlth1KhReSCq4Wfjueeeu16fjhl2MYq9ww47pB/+8Ifpr3/9a36vZ87T6rRb/KOpDBIRLiJkxNHiGK1+5pln6m6LcjEVdq655soj0RHOG+2YLXhDmGeeedIRRxyRR0viyDKUKUY0vvSlL6WDDjqo0dvjVISllloqzTvvvPX6f/y88MILp0033TT997//za+BEKMmTzzxRA4us6Nv0xniwM9Pf/rTfFA0RvXaI2ZwxAHYt956q7T6QdniYP0mm2yST2eYPHnybMvHiGJ8brnqqqs8GXSI+GxcOTOotZ+NF1pooTy7Lk61vP/++zughrSEgE0p08RffvnlHHSLgBFvDjG1KkY0ivARR94icBQBI277+9//nn7/+9/nKbZtsdZaa+UR8dgvlCXCcPTbtddeu272RUsPMMXPse1rX/ta+vzzz9Nzzz2Xtz/99NNpypQpLQrYQd+mMyy99NJpwQUXzFMN2yPe7995550csqGj368bfjUc6WtOnI8dB5XiPXp24vNGvDfHaWoNz92GMsRn4zj9MQ5yvv/++23aR/Tp4LNx5xGwabcIDHGOSBEy4oNZsXBCfH/44Yfzz3GOX5zrV5y3fdhhh+UpWDE1K873izeEY489Nj377LOtGnGJEcS2vglBY+JgUUyJjSmFLen/cX5UHHUu+n9si1kasahI0f+LwNLSgK1v01mi33/wwQetvl+Emgg3cd9YqC9eRzH9FjrKZ599lgNJw6/zzjuvxfsYPnx4/t7SPh+vjwjXcc4rlC3O7x86dGj+PByfi+PzcZy+0JqBqOKzi8/Gncc52LRbTPkuFnSKabPxPRYSKQJ2LMwQiqBRBIwYJfnjH/+YHnroobzYSKys/Kc//SlP1YpVEOOck5aIcF9TU+OZpDTRp0JL+lX05wgWRbCOA0lFHy9Gt+N0ifgeszqKc1RbWg99mzmtrf2uckG/EAsDtvSAErRFrM9yxRVXzLI9Fuu76aabWvV+3xH/H6C1lllmmdx3//Wvf6V77703f0aO87JjPYu4Qk985tZHuz4Bm3YrVkWOABFTYGME+rjjjqsLGHFUOC5pFLcXi6JVnk8Si4fEV4gpWrFw2a9+9asWBewYZYxRkjhXFsoSsyJiBLkl549G3+/Xr1/u37179873i3+QRf+PkbwQAfwb3/hGi+ugb9NZYmp35VUhWuqSSy5JgwYNyvePqzvEqEsswGMlWzpKfIZobNG9u+66q8X7iHVjQlNrwjQU/Ts+98QoI3SE+EwRn4GLz8ExCBWzgWLw6ZxzzmlRHw0+G3ceU8QpRYxSxDlJMUodIaM4whbnYseCTxE+ImAU08ObEtfyi1GQ4lqssxNH+GJKolESyhTn2UVfjf41u/Ps4h9hrAwefTy+YoX8eA0UATv+0cUR6PgQN7v+X0nfpjPE6Q7RV9tyfdQ4JSLWHohFoy666KIcQuLyitCVRXiJ9/GYWj478XnjgQceyKE+/k/AnBAH5+NAZUs/G8fId/DZuPMI2JQiXsQx4hbTWOIfT+XCUPFB7dZbb80ri1cGjMbOd4qptq+99lqLjrrF+U/x4S1GG+NyG1CmvfbaK/fROGLcmDvvvLNe/3/88cfTv//973r/0OLgUpwKEdejDC0N2Po2nSHew0888cQcNkaPHt2ufcV5rXHJxQgvLVk8Cjrrcoy33XZbvixSrJsxO7/+9a/zea0x0w46QmOfjadOnZrefvvtFn02jpl3sQZBXOUkFuSjczj8RikiVMRUrbvvvjvts88+s9wWH9qKnwtxTb+YhhjX7YsgMmHChHwOdqy6fMwxx8zy5lKshhiLScVoeayyGJfViAAU0xKh7Mu37Lfffun888/Pq+THtXznm2++fKDohhtuyJfAiL5bBOcI0TFSHdcRrhRTZGO1/PjHWFw3W9+msxVXdSgWioq1A6655pr0+uuv54Uni+u6t/cgVRx0jdHsM888s4RaQ9tFQCkunRghOcL1X/7ylzzr6Mgjj2x0NkeUjatBxOsizouNyx7Fmhqu7U5H2WKLLfJni1jgLGaBvvfee/l9ND4j77HHHvXKfvLJJ7lPx7oAcWA+ZorGZ+MY5IpTLV0Du/MI2JRiyJAh+fIu8SGt4dTC+D1e/HFt31jkqRBB/JZbbklnnXVW/mcX+4jb49y9mGJYKf657bjjjvnNIsJ0nMsdb0K77rpri1Z6hrY49NBDc/+Nf25xfeBYBT/+4a2zzjrp6KOPrtfHY8Gb+IpAXSkOKv3tb39rcqqWvk1niIOW8Z4afTZG7uIgZ1yWLs7vi5GPMsR7foSRCNixDodLdtGZ4n08viJ8RN+MUxpiIb6tt9660enexXt8LKQWB1cjiMdlReP9HzrKgQcemGfIxYHOjz76KM/SjL4aa1w0HJGO09LifTxOS4vP0EsssUQO4TvvvHO+H52npra1yycCAAAAs3AONgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AHRRf/7zn9Oyyy6bnnjiiVL298Ybb+T9xX4LZ599dt7WUR555JH8Nz755JMO+xsA0FUI2ADQg40ePTpdc801Hbb/Rx99NJ1zzjkCNgA9Qp/OrgAA0HmGDx+ev7qbKVOmpIEDB3Z2NQCgHiPYANBNHHXUUWmVVVZJr732Wtpnn33yz6NGjUqnnnpq+vzzz+uVfffdd9MhhxySy6y22mrphz/8Yfrggw9m2WdTU8RvvPHGtOOOO+b7x9dWW22Vxo0bV3f7fffdl/bff/80cuTItNJKK6WNN944HXfccemjjz6qt+9f/OIX+eeNNtoo/534evDBB/O2mTNnposuuih961vfSl/96lfT2muvnX784x+nd955p15dvvvd76bNN988PfTQQ2mnnXZKK6+8cjrmmGNKaFEAKJcRbADoRqZPn56D7fbbb5/23nvvHDrPO++8NHjw4HTggQfmMlOnTk177bVXeu+999Jhhx2WFl988XTXXXelQw89tEV/46yzzsr73GSTTfJ+hgwZkl544YX01ltv1ZUZP358Dt4xxTxuf/PNN9Pvf//7tMsuu+Rw3rdv33zbxIkT0+WXX56nic8///z5vksvvXT+/rOf/SxPT99tt93S+uuvn/cRf/vf//53Pk982LBhdX/v/fffT0cccUT6/ve/nx9Hr17GCADoegRsAOhmAfuggw5K3/72t/PvMer75JNPpptuuqkuYF933XXppZdeyiE5Ro7Deuutl6ZNm5auvfbaZvf/+uuvp9/85jdpiy22SKeffnrd9nXXXbdeuZ133rnu59ra2hy211hjjbTBBhuke+65J//dmHr+5S9/OZdZfvnl0yKLLFJ3n6hfhOsI5D/96U/rtq+wwgo5mF966aX1Dgh8/PHH6Ve/+lV+vADQVTn8CwDdSE1NTdpwww3rbYtp15WjyzEFe9CgQXXhuhDTrGfn/vvvTzNmzEi77rprs+U+/PDDPCU8pqhHKF5xxRVzuC7C8+wU08S32Wabetu/9rWvpaWWWir961//qrd96NChwjUAXZ4RbADoRmJhr/79+9fb1q9fvzw6XTna+6UvfWmW+za2raHiHOrmFj6Lc6djenpMQT/ggAPSMsssk+sVI9k77LBDvbo0JeoYFlhggVlui22VBwxCMb0cALoyARsAqsw888yTHn/88Vm2N7bIWUPFec+x0Fgxvbuh559/Pj377LN5cbXKEehYfK01dQwR0huG+dg277zzzjJyDwBdnSniAFBl1lxzzfTpp5+m22+/vd72OE97duJc6969e6c//OEPTZYpwm6MnFe6+uqrZylblGk4qr3WWmvl7zfccEO97XFgIKaYF7cDQHdiBBsAqszWW2+dLrnkknTkkUfmhcIWW2yxdPfdd6d77713tveNhcj23XffvEBarEYe523HKuEvvvhimjBhQjr44IPTkksumUaMGJF++ctf5mnhcX70nXfemS/d1VBMHw+xaFmMdvfp0yctscQSeR9xGbArrrgirwgel/sqVhGPkfM999yzQ9oGADqSgA0AVSbOh77sssvSSSedlFcCjxHnWEX8jDPOyNeRnp24fnaE8gi/hx9+eB7Rjkt9xfWoQ1yC64ILLsj7j4XOIjTH6t4R6uNyWw1H0yOwx8rmcR3tOH876hbb4zJdiy66aPrjH/+YrrrqqnypsW984xv50mINp4gDQHdQUxuHngEAAIB2cQ42AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAFL7/X9Aeoobq80xcAAAAABJRU5ErkJggg==", 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", 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OckevEt2eJxTiGtSdUQTsqF+scN5QXIYrrvXdVgunjR07Nl1++eVp6tSpbfIzAKge3eMTAwC0UgS2gQMHpu23375q2m7+/Plp7ty5TT4eK4cPGzasXevUmc2ePTvf7rzzznk19VtvvbWjqwRAJydgA0ADEUJ//vOfp1122WWxeq+nTZuWTjvttLTFFluk9dZbL2277bbpoosuqhduo2c8glulww47LA+tvueee+q2PfXUU3nbn/70p7ptkydPTqeeemq+RnXsP4Z2R+9uXJqqEEOp43lXX3117n2NMuuvv356+OGHWzxEPIbGx3N32GGH9PGPfzx96lOfypeCuu6661rUDnPmzEnnnHNO+sxnPpOfv99++6Wnn356oXJPPvlkfu0bbbRRruPuu++e7r777rrHb7/99nTMMcfk7w844ID8uuIrtu+///75etfRs11sj69CtHm8hrhedbTVJptskr7zne+kd999d6HX/vWvfz39/ve/zz8/6hFtGuIa2jvuuGO67bbbUm1tbYteOwDdk+tgA0ADTzzxRA7JG2+8cavbJkJlhMBJkyalo446Koe9f/zjH2ncuHHpmWeeybdhs802S7/73e/S22+/nT760Y/mcPzoo4+mfv36pYceeigHuhDf9+rVK4fPIlzvs88+OfjH0OXocZ4wYUK64oorcsiMQFvphhtuyNeDPuGEE9LSSy+dhg8f3uLXEteUjpAZ13yOcB11fPHFF9PMmTNb9Pw4qRC94meeeWZ+TuwrAvEdd9yRVl111VwmAv8hhxySNthgg3xSIkYNRLg+7rjjcg/ynnvumbbaaqv0jW98I1144YX5xMK6666bnxuvPYL7Kaecktu7CMSVJwhiyPtjjz2WDj744LThhhvmNoq53PE7/sUvfpHbu/JkxsSJE/PrXWWVVfJ1rgvR/j/72c/Sf/7zn3oBHgAqCdgA0EAE1lAEudb45S9/mZ577rl08cUX14Xk6MFdaqml0gUXXJD++te/5vsRsIsAHT2m//rXv9L777+fw2bl4mB/+9vfcm9qhOMQ4XD69OnpN7/5TVpppZXytk033TQHxR/84Ac5SK655pp1z+/bt2+eSx5ziVvr8ccfTyNHjswnCgrRK99SQ4YMSZdddlkeXh0++clP5t7wq666KofucPrpp6ePfexjuVc8TiQUPyPmO0egjraJ/RQnBuK1feITn6j3MwYNGpR7mSu3hxgJ8OCDD+Y2qxzqv9Zaa6W9994794B/+ctfrtsevdrRrquvvvpCr6U4FqJNBGwAmmKIOAA0EL3KEQqXXXbZVrdN9MhGmI4hyZWiJ7YIzEXv68orr1x3P4J2hNnPfe5zeXj3K6+8koc3R+9rEcZDDIeOnvWi17v4iuHiIXrBGw59XpxwHSLYP/vss7lnOYLqe++916rnxxD7IlyHeL2jR4/Oq4yHl19+OfeIx7Dz0PD1RG/9Sy+9lBbXfffdl8P31ltvXW/fa6+9dho6dOhCbRXBubFwHZZbbrm6xe8AoCl6sAGgkWHe0Zvas2fPVrdNDC3/yEc+Ui9YFgEt9hmPF6LnOYJrEbCjZztCXjw/7kevbQyTrgzY77zzTg6OTfWuN1zpOoLk4oo5yXGy4M4770y33HJLbo8YKn788cfn8L0o8Toa2xahPUyZMiXfRs97fDVmSVbujraaMWNGnnu9pG0VPeTFsQEATRGwAaCB6LmO6x9/8MEHOWC2xjLLLJOHe8diWJUhO8Je9J5W9opHwI7F1GI+cHzF3N8QC3FFwH799dfzz4/5yZV1ixB+7LHHNvrzo2e7UsOg3xpxQuCrX/1q/oqgGnWKedUxjD160ivnKDemCNANt0UbFa+lCPLbbbddo/toqke5JWL/8bNiLnljBgwY0OK2imH5lXUGgMYI2ADQRKiLYdoxX7c1IjTH3N8//vGP9UJjLOxVPF5ZNkLdJZdckm8//elP120///zz84Jcsa1yiHcs+HX//ffnIeaDBw9ut99dDLWOYe8xRPrss8/Odauc692YuD51hPMiuMZzYn77brvtlu+vscYaeQG26NGORcyaU/QgF5fOavhYY9ujrWJOdSx2VnmSYnHEImphxIgRS7QfAKqbgA0ADRSrh0dPdGMBO4J35UJkhQicsSjXTTfdlFftjkAZ86pjHnUs7DVmzJh6w71j2Hgs8PWXv/wl/8yiRzjKxFDy+IpLSlWKy3tFT/IXv/jFvCJ3nAyIudoxb/uBBx7Ii4atsMIKpfxO49JZUb8YYh2LicXricXIYi51S1Yjj0XDYqXzz3/+83kV8VhsLMJw9FgXor6HHnpoXpxtjz32SMsvv3zuLY7VvGNV7x/+8Ie5XNQjxKWyouc5Fm+Llb6jRznaOC6vdfPNN+e6RqCPIexxGbRf//rX6Wtf+1puq1hxPE5WvPnmm3keeFw+rame84biWIgh8sVJEABojIANAA2suOKKea7xvffem77whS8s1D4xb7qYO13pyCOPzCtuX3/99XkodQxNjnm+ERoPOuig/HhDEabj0k+VwTtWB4+e3f/+97/1eryLIeAxrDyu7Ryrg0ePcgTOCL2x+nb0NJclQn9cSmz8+PF5gbOYoxz1jEtftWThtLjUVlzjOk4SxPMj4MbK4NH7Xojh8LH/K6+8MveMx1D0GNYdPcXFKuwhLut10kkn5baNy6DNnz8/X5IsFo+L+88//3xu8wjyMTw/VnKPQByXL4vn/OpXv8qXSIttcQIignIE85aKEQmx8FqZ7QtA9ampjf9CAEA9ESwjIMaCYhGQ6b5ixEJc5itOaMRCdADQFJfpAoBGRKCKYcYxtJvuLXrBYySBcA3AogjYANCImMf7/e9/Pw/JjkWy6J5i5fcYnn7qqad2dFUA6AIMEQcAAIAS6MEGAACAEgjYAAAAUAIBGwAAAErgOtiLMGHChHw9zZZc7xMAAIDqMm/evLz46ejRoxdZVsBehAjXLhUOAADQPdXW1ra4rIC9CEXPdVwLFQAAgO7lySefbHFZc7ABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgG7HdXW1nbp/QMAANC0Xs08RslqamrS3196M82cNa/0th3Yv3f69OorlL5fAAAAWkbAbmcRrqfPmtPePxYAAIA2Zog4AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAKohYN9+++1p1KhRC31dcMEF9crdf//9affdd0/rr79+2m677dJNN93U6P6uvfbatM022+Rye+21V3rkkUfa6ZUAAADQnfVKncQ111yTBg4cWHd/+eWXr/t+woQJ6Ygjjki77bZbOvHEE9Pjjz+ezjzzzNSnT5+0zz771AvXF110UTruuOPSOuusk8aPH58OPfTQfBuhHQAAAKo+YK+77rppyJAhjT522WWX5cB89tln5/ubbLJJeuONN9Ill1ySe6l79OiR5s6dm6644op0wAEHpIMPPjiX22ijjdKuu+6arrzyyhy8AQAAoGqHiC9KBOeHH3447bzzzvW2R3CePHlyevrpp/P96NWeOXNm2mWXXerK9OzZM+200055eHltbW271x0AAIDuo9ME7AjGa6+9dtp2223TVVddlebPn5+3v/LKK2nevHlpjTXWqFd+zTXXzLcTJ06sd9uw3IgRI9L777+f3nrrrXZ6JQAAAHRHHR6whw4dmo466qj0gx/8IF199dVpzJgx6eKLL05nnXVWfnz69On5dtCgQfWeV9wvHp8xY0aek92vX7965QYPHpxvp02btkT1LAJ/8f2CBQvy99EzHveLHvLY3lTZ9tKaOinbeDtol/Y9PrR327S3v3vHbLW813fGOlVD2c5Yp2op2xnrVM1lO2OdqqFsZ6zTgg4u2yUC9hZbbJGOPPLIfLv55punU089NR144IHplltuSW+//XZduZqamkafX7m9sTJF4zT1/JaIfUQveOGDDz5Ic+bMqWv0GJpeNHz0tr/33nt1ZWfNmpVmz56d2lPUIepUiJ8f9ShE/aJM+PDDD3PZop2aKxuvMcoWB2C0QbRFIdoohvQ3VbayDeP7ptow9tFUe0c9o2zUe1Ht3VjZynaJspWvNR4rXmtjZSt/j5X7Ldpwcdu7sg2jbHNt2FR7F21YlG2uDRtr78o2jLLNtWFzx3dr2mVR7d1U2dYcsw3LdsQx25r3iMVp7+bapaV/C94jvEd4j/Ae4T2iZe/JPkf4HOFzRPf+HNESNbWdcHLyE088kVcHHzduXFp55ZXz/Ovo3d5yyy3ryrz77rtp0003Teedd15eXTwu23XGGWfk5/bt27eu3D333JOOPfbYPA97hRVWaHVdnnzyyXwbi6zFnO4Qv5wI7LG4WjRf/NLi+9gW38e2xsqGPz09KU2f9X+/wDIN7t83bbPOqvn71tRJ2cbbQbu07/GhvdumvYO/e8dsNbzXd8Y6VUNZ7xGO2c5wHHqP6Bxt6D2iptl2Kdb9iktBd5lVxJsybNiw1Lt37/Tiiy/WC9gvvPBC3RzrytuYix1huBD3BwwYUO+yX4ujODAbfh8NX3m/CNKNlW0vramTso23g3Zp33bQ3m3bDv7uHbNd/W+sM9apmsp2xjp19bKdsU7VXLYz1qmaynbGOtV0UNkuMUS8MXfffXd+YRGUY151XJYreqIr3XXXXXn+dhGmN9xww3wd7XhuIc7SxPNiXveSDBEHAACATt+DHdesjgA9cuTIfP/ee+9Nt912W76edQToMHbs2LTffvulk08+OV+eKy7JNX78+DwkvDirEEH88MMPz9e7jutpR/COMpMmTUoXXnhhh75GAAAAql+HB+zVV189/fznP09vvvlmHuO+2mqrpZNOOintv//+dWVGjx6dLr/88hyU77jjjjyXOsJ2zNOudNBBB+Ux8jfccEOaMmVKDu0xj3vUqFEd8MoAAADoTjrlImedSbHIWUsmtLdEeyxyBgAAQPtnwk45BxsAAAC6GgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAAqi1gv//++2nLLbdMo0aNSk8++WS9x+6///60++67p/XXXz9tt9126aabbmp0H9dee23aZpttcrm99torPfLII+1UewAAALqzThWwL7/88jR//vyFtk+YMCEdccQRaZ111klXX3112mOPPdKZZ56Zxo8fv1C4vuiii9K+++6bxo0bl4YPH54OPfTQ9Nxzz7XjqwAAAKA76jQBe+LEienmm29ORx111EKPXXbZZTlcn3322WmTTTbJYXvvvfdOl1xySVqwYEEuM3fu3HTFFVekAw44IB188MFp0003Teeff35aZZVV0pVXXtkBrwgAAIDupNME7LPOOit98YtfTKuvvnq97RGcH3744bTzzjvX277rrrumyZMnp6effjrff/zxx9PMmTPTLrvsUlemZ8+eaaeddsrDy2tra9vplQAAANAddYqA/dvf/jY9++yzaezYsQs99sorr6R58+alNdZYo972Nddcs67nu/K2YbkRI0bkud1vvfVWG74CAAAAursOD9izZs1K5557bvrGN76Rll566YUenz59er4dNGhQve3F/eLxGTNmpD59+qR+/frVKzd48OB8O23atCWqZ+Xc8Pi+GJoePeNxv+ghj+1NlW0vramTso23g3Zp3+NDe7dNe/u7d8xWy3t9Z6xTNZTtjHWqlrKdsU7VXLYz1qkaynbGOi3o4LJdImDHvOnlllsu7bnnns2Wq6mpWeT2xsoUjdPU81si9hG94IUPPvggzZkzp67RY2h60fDR2/7ee+/VO4Ewe/bs1J6iDlGnQvz8qEch6hdlwocffpjLFu3UXNl4jVG2OACjDaItCtFGMaS/qbKVbRjfN9WGsY+m2jvqGWWj3otq78bKVrZLlK18rfFY8VobK1v5e6zcb9GGi9velW0YZZtrw6bau2jDyjUJWnrMRtnKNoyyzbVhc8d3a9plUe3dVNnWHLMNy3bEMdua94jFae/m2qWlfwveI7xHeI/wHuE9omXvyT5H+Bzhc0T3/hzREjW1HTg5+bXXXks77LBDXsRs9OjRedtjjz2WDjvssHT99den9dZbL73xxht5/nWsHh6X8Cq8++67eSGz8847L+222275sl1nnHFGeuKJJ1Lfvn3ryt1zzz3p2GOPzfOwV1hhhVbXsbhcWCyyFnO6Q/xyIrD36NEj/xLilxbfx7b4PrY1Vjb86elJafqs//sFlmlw/75pm3VWzd+3pk7KNt4O2qV9jw/t3TbtHfzdO2ar4b2+M9apGsp6j3DMdobj0HtE52hD7xE1zbZLse5XXAp6UXqlDvTqq6/mswJf+9rXFnosVgPfYIMN0o033ph69+6dXnzxxXoB+4UXXqibY115G3OxIwwX4v6AAQPS8ssvv0R1LQ7Mht9Hw1feL4J0Y2XbS2vqpGzj7aBd2rcdtHfbtoO/e8dsV/8b64x1qqaynbFOXb1sZ6xTNZftjHWqprKdsU41HVS2JTo0YK+99tq5p7rSM888k84555x0+umn5zMEMa86Ls0VPdEHHnhgXbm77rorDR06tC5Mb7jhhmngwIHp7rvvrtsWZ2nieWPGjFmiIeIAAADQqQN2LFS28cYbN/rYuuuum79CrC6+3377pZNPPjlfnisuyTV+/Pg8JLw4qxBB/PDDD08XXXRRGjJkSA7ZUWbSpEnpwgsvbNfXBQAAQPfToQG7pWJ+9uWXX56D8h133JHnUkfY3meffeqVO+igg/IY+RtuuCFNmTIljRw5Mo0bNy6NGjWqw+oOAABA99Chi5x1BcUiZy2Z0N4S7bHIGQAAAO2fCTv8Ml0AAABQDQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAAAEbAAAAOgc9GADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAS9luTJs2bNSnPmzFlo+zLLLLMkuwUAAIDqD9gRqi+88ML061//Ok2fPr3RMs8880wZdQMAAIDqDdhnnHFGuvPOO9PWW2+dRowYkXr37t02NQMAAIBqDtj33Xdf+sY3vpEOPvjgtqkRAAAAdJdFztZZZ53yawIAAADdKWBvv/326S9/+Uvb1AYAAAC6yxDxE044IR199NHpnHPOSWPGjEmDBw9eqMy6665bVv0AAACgOgN2XJbrww8/TNddd126/vrr6z1WW1ubampqrCIOAABAt9PqgH3SSSelJ598Mn3lK1+xijgAAAAsbsB+5JFH0ne+8530+c9/vrVPBQAAgKrV6kXOBgwYkFZeeeW2qQ0AAAB0l4C9++67p9/85jdtUxsAAADoLkPE11prrXTRRRelsWPHpq222qrRVcTjUl4AAADQnbQ6YB9//PH59rXXXkv33nvvQo+3dhXxBx98MF111VXphRdeSO+9915afvnl02c/+9l05JFHpoEDB9aVu//++3OwnzhxYlphhRXSgQcemPbdd9+F9nfttdemm266KU2ePDmNHDkyffvb304bb7xxa18mAAAAtG3AbnhpriU1ffr0NHr06Lwq+aBBg9Lzzz+fLr300nz74x//OJeZMGFCOuKII9Juu+2WTjzxxPT444+nM888M/Xp0yfts88+9cJ1hPDjjjsurbPOOmn8+PHp0EMPzbejRo0qtd4AAABQqaY2Ll7dydx2223plFNOSQ888EDu0T7kkENyEI+gXIjH77vvvlymR48eae7cuWmzzTbLq5tHr3WYP39+2nXXXXO4juC9OOKSZGH99dcv5bX96elJafqsOalsg/v3Tduss2rp+wUAAOjOnmxFJmz1ImeFmTNn5uHdd955Zw6/ZVpmmWXy7YcffpiD88MPP5x23nnnemUiOMcw8Keffjrfj17tqNMuu+xSV6Znz55pp512ysPLO+F5BAAAAKrIYgXsyy67LG2xxRZ5+PUJJ5yQXn311bw9hnmPGzdusSoSvc1z5sxJTz31VN7/1ltvnS8H9sorr6R58+alNdZYo175NddcM9/GnOzK24blRowYkd5///301ltvLVa9AAAAoE0CdiwgFgF47733zouTVfYMRyj+85//3Npd1j334x//eNpzzz3T0KFD04UXXpi3F73jMT+7UnG/eHzGjBl5Tna/fv3qlStWOZ82bVpaEnECoPL7BQsW5O/j9cf9oh1ie1Nl20tr6qRs4+2gXdr3+NDebdPe/u4ds9XyXt8Z61QNZTtjnaqlbGesUzWX7Yx1qoaynbFOCzq4bJsF7FjB++STT06bb755vceGDx+eXn755bQ4ouf7Zz/7Wfr+97+fVxQ/7LDD6r2YWJ28MZXbGytTNE5Tz2+J2Ef0ghc++OCD3NteNHoMTS/qGr3tsRp6YdasWWn27NmpPUUdok6F+PlRj0LUL8oUw/CjbNFOzZWN1xhliwMw2iDaohBtFEP6mypb2YbxfVNtGPtoqr2jnlE26r2o9m6sbGW7RNnK1xqPFa+1sbKVv8fK/RZtuLjtXdmGUba5NmyqvYs2LMo214aNtXdlG0bZ5tqwueO7Ne2yqPZuqmxrjtmGZTvimG3Ne8TitHdz7dLSvwXvEd4jvEd4j/Ae0bL3ZJ8jfI7wOaJ7f45ok0XOYmJ3hOFNN900V3LddddNv/jFL/Lto48+mg4++OC6SeCL69///nfaa6+90iWXXJKHgsf866uvvjptueWWdWXefffdXIfzzjsvry4ewf+MM85ITzzxROrbt29duXvuuScde+yxeR52XN6rtYrXEquSx5zuEK87AnssrhbNF7+0+D62xfexrbGy7bXIWWvqpGzj7aBd2vf40N5t097B371jthre6ztjnaqhrPcIx2xnOA69R3SONvQeUdNsuxTrfrVkkbNWX6Yrrk09ZcqURh+La2Mvt9xyaUmtvfba+UCI+dfbbLNN6t27d3rxxRfrBezo5S7mWFfexlzsCMOFuD9gwIC8GvmSKA7Mht9Hw1feL4J0Y2XbS2vqpGzj7aBd2rcdtHfbtoO/e8dsV/8b64x1qqaynbFOXb1sZ6xTNZftjHWqprKdsU41HVS2JVr9jOg1vuaaa+p110dFohs9hng3HDa+OOK613GGZZVVVsnzqjfZZJPcE13prrvuynO1izC94YYb5vB/991315WJfcTzxowZs0RDxAEAAKD0Huyjjz46L3AWw7Y/+9nP5uB64403pmeeeSa9/vrr6eKLL27V/o488si03nrr5WtVxwJlzz77bA7wcT/2H8aOHZv222+/PO87Ls8Vl+SKa2LHkPDirEIE8cMPPzxf73rIkCE5eEeZSZMm1S2YRuc2f8H81LNHzy67fwAAoHtr9RzsYnj2Oeeckx555JHccx3d6BtvvHH67ne/WzdUu6ViPnf0Osdw8KhKXJpru+22y3O5l1566bpyMYc6gnIM+Y651F/96lfTvvvuW29f8fxrr702z8eOYewjR45M3/rWt3IPeHtcVLwl2mMOdld21u9OTy9P/W/p+x2+7Grpuzt8r/T9AgAA1e3JVmTCxQrYhViBberUqflSWA0vj1UtBOz29bVbvpqen/yf0vf7saEj07gv/qT0/QIAANXtyVYE7FYPEa8Uw7KXdPEwAAAAqAaLFbAnT56cfv/73+dVw4trkVWKudIAAADQnbQ6YD/44IN5YbLiYtwNxaJnAjYAAADdTasD9nnnnZevU33aaaflBc3iGtUAAADQ3bU6YMdlry699NK01lprtU2NAAAAoAv6v4tIt8Iaa6yR3nvvvbapDQAAAHSXgH300UenK6+8Ml9nGgAAAFjMIeJbbbVVeuqpp9J2222Xh4nHNbAbLnJ2xRVXtHa3AAAA0L0C9u23357nYPfs2TO9+uqr6a233looYAMAAEB30+qA/aMf/ShtvfXW6dxzz12o9xoAAAC6q1bPwX7nnXfS/vvvL1wDAADAkgTsuAb2m2++2dqnAQAAQFVrdcA+8cQT0zXXXJOeeeaZtqkRAAAAdIc52Kecckp6991305577pmGDh3a6Crid955Z5l1BAAAgOoL2Msss0z+AgAAAJYgYN9www2tfQoAAABUvVbPwW6NBQsWpG233TY9//zzbfljAAAAoLoDdm1tbXrttdfS3Llz2/LHAAAAQHUHbAAAAOguBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIDOHrBramrSpz/96TRgwIC2/DEAAADQ9QL23/72t2Yfv/766//fznv0SDfccENabbXVFq92AAAAUK0B++CDD06XXnppqq2trbd95syZaezYsemcc84ps34AAABQnQH7sMMOS1dccUU68MAD05QpU/K2J554Iu2+++7p0UcfTRdffHFb1BMAAAA6tV6tfcLRRx+d51Uff/zxOVTvuuuu6cYbb0wjR45MP/3pT9Oqq67aNjUFAACAalvkbNNNN81zrWNYeITqddZZJ916663CNQAAAN3WYgXsp556Kg8V7927dxozZkz617/+lU455ZQ0e/bs8msIAAAA1Riwb7rppvSlL30pDRw4MN1+++3pyiuvTN///vfTPffck/bZZ5/04osvtk1NAQAAoJoCdoTpPffcM91yyy1p2LBheVsE6xgi/uGHH6a99tqrLeoJAAAA1bXI2YUXXph22mmnhbaPGjUq/eIXv0innXZaWXUDAACA6u3BbixcF5Zaaql03nnnLWmdAAAAoHsscgYAAAAs4RDx8Ktf/Spdd911eUGzOXPmLPT4M888szi7BQAAgO7Tg33vvfemk046KV/7Oi7LFQue7bzzzql///5p+PDhaezYsW1TUwAAAKimgH311VenAw88MJ1++un5/pe//OV0wQUXpN/97ndpwYIFaYUVVmiLegIAAEB1BeyXXnopbbbZZqmmpibfnz9/fr4dOnRoOvzww9NPf/rT8msJAAAA1RawI1D37t079ejRIw8Lnzx5ct1jK664Ypo0aVLZdQQAAIDqC9irrLJKevvtt/P3a621VvrNb35T91gME4+ebAAAAOhuWr2K+KabbpoeeuihtMsuu6QDDjggHXfccenJJ5/MvdoxfPyb3/xm29QUAAAAqilgR6CeO3du/n7HHXdMPXv2THfeeWceMn7IIYfkVcUBAACgu2l1wO7Tp0/+Kmy//fb5CwAAALqzVgfs8Mc//jH3Wr/++utpzpw59R6L1cXjMQAAAOhOWh2wr7nmmnzd6yFDhqRhw4bllcQBAACgu2t1wL755pvTXnvtlc4444w8/xoAAABYjMt0TZs2La8gLlwDAADAEgTsDTfcML344outfRoAAABUtVYH7JNOOinddNNN6d577627XBfdQ23tgo6uAgAAQPXMwR4+fHjabLPN0pFHHplXDO/Xr1+9x2PbY489VmYd6SRqanqkCa/+Ic2cM7X0fX906WFpreU3KX2/AAAAnTZgn3/++enGG29Ma6+9dlpjjTXqXROb6hfhesbsKaXvd+k+y5S+TwAAgE4dsH/5y1+mQw89NH3zm99smxoBAABAd5iDPX/+/DxEHAAAAFiCgP2Zz3wm/etf/2rt0wAAAKCqtXqI+BFHHJGOO+641L9//7TVVlulwYMHL1RmmWXMpwUAAKB7aXXA3m233fLtueeem78a88wzzyx5zQAAAKCaA/bYsWPzpbgAAACAJQjYRx11VGufAgAAAFWv1YucAQAAAAsTsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCw6RaGLDUk1dbWtunPaOv9AwAAnVuvjq4A5ejbq2eav2BB6tnDOZPGLN13YKqpqUnvP/RQWjBjeumHXY9Bg9OAzTYrfb8AAEDXIWBXid69euRwffLND6aX3i4/QG42aqU0dscNU1cX4Xr+1KkdXQ0AAKAKCdhVJsL1s6+9W/p+Vxs6qPR9AgAAVBPjiQEAAKAaAvY999yTjjjiiDRmzJj0iU98Iu26667p5ptvTgsWLKhX7v7770+77757Wn/99dN2222Xbrrppkb3d+2116Ztttkml9trr73SI4880k6vBAAAgO6swwP2T37yk9SnT5/07W9/O1155ZXps5/9bDrrrLPS+eefX1dmwoQJOYSvs8466eqrr0577LFHOvPMM9P48eMXCtcXXXRR2nfffdO4cePS8OHD06GHHpqee+65DnhlAAAAdCcdPgc7QvWQIUPq7m+yySbpgw8+yD3Uxx13XA7fl112WQ7XZ599dl2ZN954I11yySW5l7pHjx5p7ty56YorrkgHHHBAOvjgg3O5jTbaKPeIx8+I4A0AAABV24NdGa4La6+9dpozZ06aNm1aDs4PP/xw2nnnneuVieA8efLk9PTTT+f7jz/+eJo5c2baZZdd6sr07Nkz7bTTTnl4uWsUAwAAUNUBuzGPPfZYWmaZZdJyyy2XXnnllTRv3ry0xhpr1Cuz5ppr5tuJEyfWu21YbsSIEen9999Pb731VrvVHwAAgO6n0wXsJ598Mt1+++3pK1/5Su6Bnj79/67pPGhQ/ctEFfeLx2fMmJGHk/fr169eucGDB+fb6A1fEvPnz6/3fbEIW/SMx/2ihzy2N1WW7qG5Y6A1x0t3KtsZ61QNZTtjnaqlbGesUzWX7Yx1qoaynbFO1VK2M9apmst2xjpVQ9nOWKcFHVy2ywXsGPJ99NFH5xXAY3GySjU1NY0+p3J7Y2WKxmnq+S0R+4he8ELMEY8h7EWjx9D0ouGjt/29996rKztr1qw0e/bsxf7ZdD3x+47feyGOhzguwocffpiPl+K4jLJxPFWWjWkRIY6pKFv8wccxV1k2jsmibHEcFmVje0uP2ShbecxG2eKYjXpG2ah3S47vyrLFa22qXeKxol3itrJslGuqbGNt2NL2bq4NG2vvyjaM75trwzLeIxanvZtrl+b229zvZlHHbFNlW3PMtlV7t6YNF9UuzR2HjZVd3L8F7xGNt4v3iJYds94jvEf4HOFzhPeI1G6fI7rEImeFeCERqqMHOhYr6927d70e6KKnuhA91pU92XEbDRNfffv2XahcsZ/FEeF8wIABdfeXWmqpusAeC6wNHDgw34aod69e/69Z+/fvv0Thnq4njuEijISll1667hiIYyOOl+J+c2VjBEflsRXHdYzSKMQx2dRxGOWKv6FFHbPNlY3b1hzflWWL11rZLk2Vjf1Wlo39VrZLZZu1pg0blm2uDdurvZtrwzLau7JdCsV+i8catndHHLNt1d6tacMlae/FOWY7sr29R3iP8B7hPcJ7hM8RPkf0WOLPEV0mYEcoPvzww9OUKVPSrbfempZddtm6x4YNG5Zf2Isvvpi23HLLuu0vvPBC3RzrytuYix0rjhfifnzIWH755ZeojvFBprHv45dQeb/4ZTRWlu6huWOgNcdLdyrbGetUTWU7Y526etnOWKdqLtsZ61RNZTtjnbp62c5Yp2ou2xnrVE1lO2OdajqobJcYIh7d78ccc0x69tln0zXXXJNWXnnleo/HGYa4LNc999xTb/tdd92Vhg4dWhemN9xww3y24e67764rE8MA4nljxozRiwwAAECb6vAe7DPOOCPdd9996Vvf+laeP/DPf/6z3krhMRxu7Nixab/99ksnn3xyvjxXXJJr/Pjx+bmVQwajFzyudx2X/orgHWUmTZqULrzwwg58hQAAAHQHHR6w//KXv+Tb888/f6HHrr/++rTxxhun0aNHp8svvzwH5TvuuCOtsMIKOWzvs88+9cofdNBBea7aDTfckIebjxw5Mo0bNy6NGjWq3V4PAAAA3VOHB+w//elPLSoXw7zjqzkxZv6QQw7JXwAAANCeOnwONlSDmljpt5XXyGuttt4/AADQxXuwoRrU9OmTanr2TBNPPiXNfum/pe+/3+qrpRFnfr/0/QIAAOURsKFEEa4/eO45bQoAAN2QIeIAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgAwAAQAkEbAAAACiBgA0AAAAlELABAACgBAI2AAAAlEDABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAnUpt7YIuvX+g++rV0RUAAIBKNTU90oRX/5BmzplaesMM7LtsGr3KdhocaBMCNgAAnU6E6xmzp3R0NQBaxRBxAAAAKIGADQAAACUQsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwA6OJqaxdUxc8AgK6uV0dXAABYMjU1PdKEV/+QZs6Z2iZNObDvsmn0Ktu1yb4BoJoI2ABQBSJcz5g9paOrAQDdmiHiAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAAqiFgv/zyy+nUU09Nu+22W1pnnXXSLrvs0mi5+++/P+2+++5p/fXXT9ttt1266aabGi137bXXpm222SaX22uvvdIjjzzSxq8AAAAAOkHAfv7553N4Hj58eBoxYkSjZSZMmJCOOOKIHMCvvvrqtMcee6QzzzwzjR8/fqFwfdFFF6V99903jRs3Lu/z0EMPTc8991w7vRoAAAC6q14dXYHobf7sZz+bvz/xxBPTv//974XKXHbZZTlcn3322fn+Jptskt544410ySWX5F7qHj16pLlz56YrrrgiHXDAAenggw/O5TbaaKO06667piuvvDIHbwAAAKjaHuwIx82J4Pzwww+nnXfeud72CM6TJ09OTz/9dL7/+OOPp5kzZ9YbYt6zZ8+000475R7y2traNnoFAAAA0AkC9qK88sorad68eWmNNdaot33NNdfMtxMnTqx327BcDDt///3301tvvdVudQYAAKD76fQBe/r06fl20KBB9bYX94vHZ8yYkfr06ZP69etXr9zgwYPz7bRp05aoHvPnz6/3/YIFC/L30TMe94se8tjeVFlYUmUch52xbGesUzWU7Yx1qpaynbFO7UV7d57feTW/R7SX7vQeUc1lO2OdqqFsZ6zTgg4uWxUBu1BTU7PI7Y2VKRqnqee3ROwjesELH3zwQZozZ05do8fQ9KLho7f9vffeqys7a9asNHv27MX+2VCp8liKYzKmUFQeh8WbQ2xv6TEbZSuP2Shb/Jw49qPshx9+2KLju7Js3Mb9yrpH+cqysb9iv5Vlo1xTZYv9Fn/bDfcb9WuqbLRBvL7G2jDao7INo2xlG8b3zbVhGe8Ri9PezbVLc/tt7nfTXBs2V7axNmzv9m5NGy6qXZo7Dhsru7h/C4tq78o2jLJNtWF78R7hPaI93iPaS3d6j2iuvX2O8DnC54g5Tf7dV75HdIlFzhal6IEueqoL0WNd2ZMdt/HGEV99+/ZdqFyxn8UR4XzAgAF195daaqm6wB5zyAcOHFg3l7x3796pV6//16z9+/dfonAPlSpHaMQx2dRxGKM54lhsyTHbXNm4bc3xXVk2ysX9yro3VTb2W1k29lvZKxePFc8t9lvcj/1Wll166aWbLBvvDfF6G2vDWLOhsk7NlV2S9m6uDcto78p2KRT7LR5r2N6tacPmyramDduqvVvThkvS3otzzLZ1e7cX7xHeI9rjPaK9dKf3iLb6v+ZzhM8R3elzRFUE7GHDhuUX9uKLL6Ytt9yybvsLL7yQb4tLexW3MRc7VhwvxP1o4OWXX36J6hG/xMa+j19C5f2GH3QqH4MlVcZx2BnLdsY6VVPZzlinrl62M9apvWjvzvM7r+b3iPbSnd4jqrlsZ6xTNZXtjHWq6aCyVTFEPM4wxGW57rnnnnrb77rrrjR06NC6ML3hhhvmsw133313XZkYGhPPGzNmjF5kAAAA2lSH92DHHJO4jFZ47bXX8hj33/72t3XXsR4yZEgaO3Zs2m+//dLJJ5+cL88Vl+QaP358OuOMM+p19R9++OH5etfxnAjeUWbSpEnpwgsv7NDXCAAAQPXr8ID9zjvvpGOOOabetuL+9ddfnzbeeOM0evTodPnll+egfMcdd6QVVlghh+199tmn3vMOOuigPKb/hhtuSFOmTEkjR45M48aNS6NGjWrX1wQAAED30+EBe5VVVknPPffcIsvFMO/4ak6MmT/kkEPyFwAAALSnTj8HGwAAALoCARsAAABKIGADAABACQRsAKDq1c6f36X3D0DX0OGLnAEAtLWanj3TxJNPSbNf+m/p++63+mppxJnfL32/AHQ9AjYA0C1EuP6gBVcuAYDFZYg4AAAAlEDABgAAgBII2ABdUG1tbZfePwBANTIHG6ALqqmpSe8/9FBaMGN66fvuMWhwGrDZZqXvFwCg2gnYAF1UhOv5U6d2dDUAAPj/GSIOAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAANCF1M6f36X331ouTUlXYhVxAADoQmp69kwTTz4lzX7pv6Xvu9/qq6URZ34/dbZLU/79pTfTzFnzSt/3wP6906dXX6H0/dJ9CdgAANDFRLj+4LnnUncR4Xr6rDkdXQ1YJEPEAQAAoAQCNgAAAJRAwAYAAIASCNgA0MbmL1igjQGgG7DIGdAt1dYuSDU1Pbrs/ulaevbokU6++cH00tvTS9/3ZqNWSmN33LD0/QIArSdgA91ShN8Jr/4hzZwztfR9D+y7bBq9ynapq6rp1y9fAzUuA9NW2nr/nVGE62dfe7f0/a42dFDp+wSg65i/YH7q2aNnl91/tRGwgW4rwvWM2VM6uhqdTk2fPt3uGqtA66c9xMgMoONF+D3rd6enl6eW/z97+LKrpe/u8L3S91vNBGwAGtXdrrEKtJxpD9C5RLh+fvJ/OroaCNgAACwO0x4AFmZsDwAAAJRAwAYAAIASCNgAAABteOlOug+LnAEAAHTBS4N+dOlhaa3lNyl9vyw+ARsAAKALXhp06T7LlL5Plowh4gAAAFACARsAAABKIGADAABACQRsAAAAKIGADQAAACUQsAGADldbW9vRVQCAJeYyXQBAh6upqUnvP/RQWjBjeun77rXiSqn/BhuUvl8AaEjABgA6hQjX86dOLX2/PQYNKn2fANAYQ8QBAACgBAI2AAAAlEDABgAAoEPUzp/fpfffkDnYAABVuCp7LBwH0NnV9OyZJp58Spr90n9L33e/1VdLI878fmpPAjYAQJWJcP33l95MM2fNK33fyw/un9Zd+SOl7xfovma/9N/0wXPPpWogYAMAVKEI19NnzSl9v0v36136PqGj9O3VM81fsCD17GHmLOUQsAEAgG6pd68eOVyffPOD6aW3p5e+/81GrZTG7rhh6ful8xKwAQCAbi3C9bOvvVv6flcbOqj0fdK5GQsBAAAAJRCwAQCg5FXcge7JEHEAACh5Fff3H3ooLZhR/pzeXiuulPpvsEHp+wXKIWADnVJXXtGzb6+l0vwF81PPHj07uiq0kGsGA2WLcD1/6tTS99tjkDm9tJ8hSw3xP7KVBGygU+rKK3r27tEnh+uzfnd6ennqf0vf/8bDNkkHb/b10vfbnblmMAAsbOm+A43IaCUBG+i0uvqKnhGun5/8n9L3O2zZ4aXvE9cMBoCmGJHRcl1z/CUAAAB0MgI2AAAAlEDABgAAgBII2AAAdBvFlR4A2oJFzoDF4rJGAHRFrvQAtCUBG1gsLmsEQFfmSg9AWxCwgcU2c9a8NH3WnNJbcOl+vUvfJwAAtDVzsAEAAKAEAjYAAACUQMAGAACAEgjYAAAAUAIBGwAAAEogYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAAjYAAAA0DnowQYAAIASCNgAQLP69loqzV8wXysBwCL0SlXmpZdeSmeeeWZ67LHHUv/+/dPOO++cjj/++NSvX7+OrhoAdEm9e/RJPXv0TGf97vT08tT/lr7/jYdtkg7e7Oul7xcA2ltVBewZM2akr3zlK2mllVZKP/zhD9O7776bzjnnnDRt2rR0wQUXdHT1AKBLi3D9/OT/lL7fYcsOL32fANARqipg33LLLTlk33HHHWnIkCF5W8+ePXMP9uGHH55GjBjR0VUEAACgSlXVHOwHHnggbbrppnXhOuywww6pT58+6f777+/QugEAAFDdqipgT5w4caFe6gjXw4YNy48BAABAW6mpra2tTVVi3XXXTcccc0z62te+Vm/7l770pbTccsulH/3oR63e5+OPP56iiSKol2HOh/Pz/srWs0eP1LtnjzT1vdlp3vwFpe+/X59eaVD/Pmnu/FlpQW35++9Z0yv17tk3TZs1NX244MPS99+3V780sO/AVDtnTkoLyq9/6tkz1fTpkz6cOjXVziu//jW9e6Veyy6bOhvHc+Mcz81zPHet9+fgmG6eY7pcPnMsgs8cpfIZunk+Q/+fuXPnppqamrThhhumbjUHuykRaKNBFsfiPq8pfXv1TG1p2aXbdrX0Pj37t+n+l+nftiGypm/fNt1/ZwzBbcnx3DzHc9fieF40x3TX4phunuO5a3E8N8/x3LYiE7Y0F1ZVwB40aFBe5KyhmTNnLvYCZ6NHjy6hZgAAAFS7qpqDHSG64Vzr6M5/5ZVXrCAOAABAm6qqgL3lllumhx9+OE2dOrVu2x/+8IccsseMGdOhdQMAAKC6VdUiZzE8fJdddkkrr7xyOuKII9I777yTzj333LT55punCy64oKOrBwAAQBWrqoAdXnrppXTmmWemxx57LPXr1y8H7uOPPz5/DwAAAG2l6gI2AAAAdISqmoMNAAAAHUXABgAAgBII2AAAAFACARsAAABKIGADAABACQRsAAAAKIGAzWL75S9/mUaNGpX++9//1tt+00035e0XXXRRve3vv/9+WmedddJll12W799///1pv/32SxtvvHH6xCc+kbbbbrt8zfK4lnnhxBNPzPuKr7XXXjt9+tOfTnvuuWc6//zz0xtvvLFQnbbZZpt0xhln+K1SijhGDz744HyMrrfeemnrrbdOp512WnrllVfSd77znbTlllsu9JxjjjkmH69//etfF9pXbP/HP/6xWMc2lOHSSy+tO+7WWmut9MlPfjLtuuuu+X1z4sSJucwjjzxSV6a5r1dffTXdfvvt9bZ96lOfysfxHXfc4RdGmx/Lo0ePbtFjlcdovJdvvvnm+b19/Pjxad68efWe2/D4j88n2267bTruuOMWel+HtnDnnXemvffeO78/b7jhhmnHHXdM3/3ud9M777xTV2b//fevO0bjs3V8TvniF7+YP2NPnTp1oX1GuWuvvbZFfz8suV4l7INuKv7ww+OPP55WW221uu0TJkxI/fv3z9sr/etf/0rz58/Pbxa/+c1v0je+8Y20xx57pEMOOST17t07f7i755578u3qq69e97xVV101XXDBBSku2T5z5sz073//O91yyy35K94gNttss3Z81XQXcYLoyiuvzCd+Tj/99LTccsul1157LZ9YOvDAA9Phhx+ew0WEjFVWWaXR4/8zn/lM3fa4H8f5+uuvX7fNsU1H6NevX7ruuuvqTnz+5z//Sbfeemu67bbb0llnnZXDRNwvPPXUUzmAn3POOWmNNdao2/7Rj3607vtrrrkmDRw4ME2bNi3dcMMN6YQTTsjH+84779zOrw4aF4Fkl112SR9++GF6++2304MPPphPmEbI/vGPf5yWXnrpeuWL433OnDlp0qRJ+XPLQQcdlL785S+n733ve5qZNjFu3Lh04YUX5s8ZRx99dP7s+/zzz6df//rX+biNzyKF+Dwd77ULFixI06dPz58/rr/++vSzn/0svyfHSVQ6hoDNYhs2bFgaOnRoDg7RY1GI+xGcI3zEmeH4kFVs79WrV9pggw3yP6k423buuefWPS/CyAEHHJDfKBp+GIwzyIXoNYx/cNH7HWeU77333oX+McKSeOCBB3K4/vrXv55PBBWil3n33XdPf/rTn9Lw4cPrjusiYEcAf+utt/Lx2fAEU9yPnpO+ffs6tulQPXr0qPeeGu+9ccx+7Wtfy70k8aGt8vEIGOFjH/tYvRNEldZdd900ZMiQ/H28t2+11Vb5f4CATWex4oor1juud9ppp9wzGO/z8VnkzDPPrFe+8niPYzp6FCP4XHXVVbnn73Of+1y7vwaqX5ygjM/QMcqtMGbMmNwZ1fDz8aBBg+od0zHKLnqxP//5z6djjz023X333fn9nvan1Vki8U+mMkhEuIiQEWeKo7f6mWeeqXssysVQ2KWWWir3REc4b/SgbMGbwTLLLJO+9a1v5d6SOKsMZYrejI985CPpqKOOavTxmIowYsSItOyyy9Y7/uP7lVdeOe2www7pn//8Z/4bCNFj8uSTT+bgsiiObTpCnPg55ZRT8knR6NFbEjGCI07Avv7666XVD9pCnLDffvvt85SG9957b5Hlo0cxPrvcfPPNfiG0ifh8XDk6qLWfj1daaaU8wi6mWz700ENtUENaQsBmiYeJv/jiiznoFgEj3hhiWFX0aBThI866ReAoAkY89vvf/z795Cc/yUNsF8cmm2ySe8Rjv1CWCMNx3G666aZ1oy9aeoIpvo9tH//4x9PcuXPTc889l7c//fTTadasWS0K2MGxTUdYc8010/LLL5+HGS6JeL9/8803c8iG9njPbvjVsKevOTEfO04sxfv0osRnjnh/jqlqDeduQxni83FMgYwTnZMnT16sfcQxHXw+7jgCNkskAkPMDylCRnwwKxZNiNvHHnssfx9z/GKuXzFv+5vf/GYefhXDsmK+X7wZnHzyyenZZ59tVY9L9CAu7hsQNCZOFsWQ2BhO2JLjP+ZGxRnn4viPbTFKIxYUKY7/IrC0NGA7tukocdxPmTKl1c+LQBPBJp4bC/XF31EMvYW29MEHH+RA0vDr8ssvb/E+VlhhhXzb0uM+/kYiXMecVyhbzO8fPHhw/kwcn43jM3JMX2hNZ1Tx+cXn445jDjZLJIZ8Fws6xbDZuI1FRIqAHYsyhCJoFAEjekl+/vOfp7///e95oZFYWfkXv/hFHqYVKyDGfJOWiHBfU1Pjt0hp4pgKLTmu4niOYFEE6ziRVBzjRe92TJeI2xjVUcxRbWk9HNu0t8U97ioX9AuxMGBLTyjB4oo1Wm688caFtseCfXfddVer3vPb4n8EtNbIkSPzsfu3v/0t/eUvf8mfk2NedqxpEVfpic/djtHOT8BmiRSrIkeAiCGw0QN96qmn1gWMOCMclzSKx4tF0SrnksTCIfEVYnhWLFx28cUXtyhgRy9j9JLEXFkoS4yKiB7klswfjWO/T58++fju2bNnfl78cyyO/+jJCxHAt9hiixbXwbFNR4mh3ZVXhWipn/70p2nAgAH5+XF1h+hxicV3rGJLW4rPEY0tvPfnP/+5xfuItWNCU+vCNBTHeHz2iV5GaAvxuSI+BxefhaMjKkYERQfUj370oxYdo8Hn445jiDhLLHopYj5S9FJHyCjOrsVc7FjwKcJHBIxieHhT4jp+0QtSXIt1UeLsXgxJ1EtCmWKOXRyrcXwtao5d/BOMlcHjGI+vWCE//gaKgB3/5OLsc3yAW9TxX8mxTUeI6Q5xrC7OtVFjSkSsPRALRl199dU5gMTlFaGzi/AS7+UxtHxR4jPHww8/nEN9/K+A9hAn6ONkZUs/H0fPd/D5uOMI2Cyx+AOOHrcYwhL/dCoXhooPar/97W/zyuKVAaOxuU4x1Pbll19u0Rm3mPsUH96itzEutQFl+upXv5qP0Thb3Jj77ruv3vH/xBNPpEcffbTeP7M4uRRTIeJalKGlAduxTUeI9/Dvf//7OWjss88+S7SvmNMal1yM4NKShaOgIy/J+Ic//CFfFinWzliUH/7wh3lea4y2g7bQ2Ofj2bNnpzfeeKNFn49j9F2sQRBXOokF+egYTr+xxCJUxDCt+++/Px166KELPRYf2orvC3E9vxiGGNfsiyAyderUPAc7Vl0+6aSTFnpjKVZCjMWkorc8VliMS2pEAIphiVD2pVsOO+ywdMUVV+RV8uNavsstt1w+UXTnnXfmy1/EsVsE5wjR0VMd1xGuFENkY7X8+KdYXDfbsU1HK67qUCwSFWsH3HrrrWnSpEl54cniuu5LepIqTrpGb/ZFF11UQq1hyURAKS6fGCE5wvWvfvWrPPLohBNOaHRER5SNK0LE30bMi43LHsW6Gq7vTlvZdddd8+eLWOAsRoK+/fbb+b00Pid/5StfqVd2xowZ+ZiOdQHi5HyMFo3Px9HRFdMtXQO74wjYLLGBAwfmy7vEh7SGQwvjfvzhx7V9Y5GnQgTxe+65J11yySX5H13sIx6PuXsxxLBS/GP7whe+kN8oIkzHXO54A9p3331btNIzLI7jjjsuH7/xjy2uDxyr4Mc/u8022yx95zvfqXeMx2I38RWBulKcVPrd737X5DAtxzYdIU5axntqHLPRaxcnOeOydDG3L3o9yhDv+RFEImDHOhwu2UVHi/fy+IrwEcdnTGuIxfh23333Rod7F+/zsZBanGCNIB6XFo3/AdBWjjzyyDxKLk52vvvuu3mkZhyrsc5Fwx7pmJoW7+UxNS0+R6+++uo5hH/pS1/Kz6Pj1NS2dvlEAAAAYCHmYAMAAEAJBGwAAAAogYANAAAAJRCwAQAAoAQCNgAAAJRAwAYAAIASCNgAAABQAgEbAAAASiBgA0AndPvtt6dRo0alJ598spT9vfrqq3l/sd/CpZdemre1lccffzz/jBkzZrTZzwCAzkTABoBuap999km33nprm+1/woQJ6Uc/+pGADUC30aujKwAAdIwVVlghf3U1s2bNSv379+/oagDAQvRgA0AXcOKJJ6bRo0enl19+OR166KH5+zFjxqRzzz03zZ07t17Zt956Kx1zzDG5zCc/+cl07LHHpilTpiy0z6aGiP/6179OX/jCF/Lz42u33XZL48ePr3v8r3/9azr88MPTlltumdZff/203XbbpVNPPTW9++679fZ93nnn5e+33Xbb/HPi65FHHsnbFixYkK6++ur0P//zP2m99dZLm266afr2t7+d3nzzzXp12X///dMuu+yS/v73v6cvfvGLaYMNNkgnnXRSCS0KAOXTgw0AXcS8efNysN17773TQQcdlEPn5ZdfnpZeeul05JFH5jKzZ89OX/3qV9Pbb7+dvvnNb6bVVlst/fnPf07HHXdci37GJZdckve5/fbb5/0MHDgwPf/88+n111+vK/PKK6/k4B1DzOPx1157Lf3kJz9JX/7yl3M47927d35s+vTp6YYbbsjDxIcOHZqfu+aaa+bb0047LQ9P32+//dJWW22V9xE/+9FHH83zxIcMGVL38yZPnpy+9a1vpUMOOSS/jh499A8A0DkJ2ADQhQL2UUcdlXbcccd8P3p9//3vf6e77rqrLmD/8pe/TBMnTswhOXqOw+abb57mzJmTbrvttmb3P2nSpHTVVVelXXfdNV1wwQV12z/zmc/UK/elL32p7vva2toctjfaaKO09dZbpwceeCD/3Bh6vuKKK+Yya6+9dlpllVXqnhP1i3AdgfyUU06p277OOuvkYH7dddfVOyEwbdq0dPHFF+fXCwCdmVPAANBF1NTUpG222ab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" ] @@ -1620,4 +1625,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From 1ad1f0a8f3e2ef99116ae7d11d1d172b1408575d Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 10 Feb 2026 12:27:31 +0100 Subject: [PATCH 16/47] Remove `wind` and `synoptic` indicator modules and their tests, and update the xclim wrapper generator tool. --- src/earthkit/climate/indicators/synoptic.py | 51 ---- src/earthkit/climate/indicators/wind.py | 251 -------------------- tests/unit/indicators/test_synoptic.py | 39 --- tests/unit/indicators/test_wind.py | 129 ---------- tools/xclim_wrappers_generator.py | 4 +- 5 files changed, 2 insertions(+), 472 deletions(-) delete mode 100644 src/earthkit/climate/indicators/synoptic.py delete mode 100644 src/earthkit/climate/indicators/wind.py delete mode 100644 tests/unit/indicators/test_synoptic.py delete mode 100644 tests/unit/indicators/test_wind.py diff --git a/src/earthkit/climate/indicators/synoptic.py b/src/earthkit/climate/indicators/synoptic.py deleted file mode 100644 index 1fd8617..0000000 --- a/src/earthkit/climate/indicators/synoptic.py +++ /dev/null @@ -1,51 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -"""Synoptic indices.""" - -from typing import Any - -import xarray -import xclim.indicators.atmos - -import earthkit.climate.utils.conversions as conversions -from earthkit.climate.api.wrapper import wrap_xclim_indicator - - -def jetstream_metric_woollings( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Strength and latitude of jetstream. - - Identify latitude and strength of maximum smoothed zonal wind speed in the region from - 15 to 75°N and -60 to 0°E, using the formula outlined in - :cite:p:`woollings_variability_2010`. Wind is smoothened using a Lanczos filter - approach. - - **Units:** ['degrees_north', 'm s-1'] - - This function wraps :func:`xclim.indicators.atmos.jetstream_metric_woollings`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.jetstream_metric_woollings`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.jetstream_metric_woollings) - return wrapper(ds, **kwargs) - diff --git a/src/earthkit/climate/indicators/wind.py b/src/earthkit/climate/indicators/wind.py deleted file mode 100644 index a65c13c..0000000 --- a/src/earthkit/climate/indicators/wind.py +++ /dev/null @@ -1,251 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -"""Wind indices.""" - -from typing import Any - -import xarray -import xclim.indicators.atmos - -import earthkit.climate.utils.conversions as conversions -from earthkit.climate.api.wrapper import wrap_xclim_indicator - - -def calm_days( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Calm days. - - Number of days with surface wind speed below threshold. - - **Units:** days - - This function wraps :func:`xclim.indicators.atmos.calm_days`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.calm_days`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.calm_days) - return wrapper(ds, **kwargs) - -def sfcWind_max( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Maximum near-surface mean wind speed. - - Maximum of daily mean near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWind_max`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWind_max`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_max) - return wrapper(ds, **kwargs) - -def sfcWind_mean( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Mean near-surface wind speed. - - Mean of daily near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWind_mean`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWind_mean`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_mean) - return wrapper(ds, **kwargs) - -def sfcWind_min( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Minimum near-surface mean wind speed. - - Minimum of daily mean near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWind_min`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWind_min`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWind_min) - return wrapper(ds, **kwargs) - -def sfcWindmax_max( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Maximum near-surface maximum wind speed. - - Maximum of daily maximum near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWindmax_max`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWindmax_max`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_max) - return wrapper(ds, **kwargs) - -def sfcWindmax_mean( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Mean near-surface maximum wind speed. - - Mean of daily maximum near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWindmax_mean`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWindmax_mean`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_mean) - return wrapper(ds, **kwargs) - -def sfcWindmax_min( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Minimum near-surface maximum wind speed. - - Minimum of daily maximum near-surface wind speed. - - **Units:** m s-1 - - This function wraps :func:`xclim.indicators.atmos.sfcWindmax_min`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sfcWindmax_min`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.sfcWindmax_min) - return wrapper(ds, **kwargs) - -def windy_days( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: - """ - Windy days. - - Number of days with surface wind speed at or above threshold. - - **Units:** days - - This function wraps :func:`xclim.indicators.atmos.windy_days`. - - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.windy_days`. - - Returns - ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.windy_days) - return wrapper(ds, **kwargs) - diff --git a/tests/unit/indicators/test_synoptic.py b/tests/unit/indicators/test_synoptic.py deleted file mode 100644 index a02c4b2..0000000 --- a/tests/unit/indicators/test_synoptic.py +++ /dev/null @@ -1,39 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -from pytest_mock import MockerFixture - -from earthkit.climate.indicators import synoptic - - -class MockEarthkitData: - """Mock object for Earthkit input.""" - - pass - - -def test_jetstream_metric_woollings(mocker: MockerFixture, common_mocks): - mock_metric = mocker.patch("xclim.indicators.atmos.jetstream_metric_woollings") - - ds_in = MockEarthkitData() - synoptic.jetstream_metric_woollings(ds_in, freq="MS") - - # Verify conversions were called (handled by common_mocks) - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - - # Verify xclim indicator was called with the converted dataset (which is common_mocks['dummy_precip_ds']) - # The first element of the return value of mock_to_xr is the dataset - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_metric.assert_called_once() - call_args = mock_metric.call_args - assert call_args.kwargs["ds"] is ds_converted - assert call_args.kwargs["freq"] == "MS" - - # Verify result conversion - common_mocks["mock_to_ek"].assert_called_once() diff --git a/tests/unit/indicators/test_wind.py b/tests/unit/indicators/test_wind.py deleted file mode 100644 index a94c003..0000000 --- a/tests/unit/indicators/test_wind.py +++ /dev/null @@ -1,129 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -from pytest_mock import MockerFixture - -from earthkit.climate.indicators import wind - - -class MockEarthkitData: - """Mock object for Earthkit input.""" - - pass - - -def test_calm_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.calm_days") - - ds_in = MockEarthkitData() - wind.calm_days(ds_in, thresh="2 m s-1") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["thresh"] == "2 m s-1" - - -def test_sfcWind_max(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_max") - - ds_in = MockEarthkitData() - wind.sfcWind_max(ds_in, freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "MS" - - -def test_sfcWind_mean(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_mean") - - ds_in = MockEarthkitData() - wind.sfcWind_mean(ds_in, freq="YS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "YS" - - -def test_sfcWind_min(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWind_min") - - ds_in = MockEarthkitData() - wind.sfcWind_min(ds_in, freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "MS" - - -def test_sfcWindmax_max(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_max") - - ds_in = MockEarthkitData() - wind.sfcWindmax_max(ds_in, freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "MS" - - -def test_sfcWindmax_mean(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_mean") - - ds_in = MockEarthkitData() - wind.sfcWindmax_mean(ds_in, freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "MS" - - -def test_sfcWindmax_min(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.sfcWindmax_min") - - ds_in = MockEarthkitData() - wind.sfcWindmax_min(ds_in, freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["freq"] == "MS" - - -def test_windy_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.windy_days") - - ds_in = MockEarthkitData() - wind.windy_days(ds_in, thresh="10 m s-1") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["thresh"] == "10 m s-1" diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index fdde236..12f5913 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -182,8 +182,8 @@ def main(): module_to_category = { "_precip": "precipitation", "_temperature": "temperature", - "_wind": "wind", - "_synoptic": "synoptic", + # "_wind": "wind", + # "_synoptic": "synoptic", } indicators_map = {cat: [] for cat in module_to_category.values()} From 18fc9d3cac7a28e64c57dab01e4d2362a953e630 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 10 Feb 2026 12:54:13 +0100 Subject: [PATCH 17/47] style: Apply code formatting and update dependencies in the performance analysis notebook. --- docs/notebooks/performance_analysis.ipynb | 224 +++++++----------- pixi.lock | 14 +- pixi.toml | 4 +- .../climate/indicators/temperature.py | 89 ++++++- tests/unit/indicators/test_precipitation.py | 2 + 5 files changed, 190 insertions(+), 143 deletions(-) diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index 573a8dc..756cc01 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -32,7 +32,6 @@ "import warnings\n", "from typing import Any, Callable, Dict, List, Optional\n", "\n", - "import earthkit.data\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", @@ -43,6 +42,7 @@ "\n", "import earthkit.climate.indicators.precipitation as ek_pr\n", "import earthkit.climate.indicators.temperature as ek_temp\n", + "import earthkit.data\n", "from earthkit.climate.utils.percentile import percentile_doy\n", "\n", "warnings.filterwarnings(\"ignore\")\n", @@ -84,7 +84,6 @@ } ], "source": [ - "\n", "def get_cpu_info() -> str:\n", " \"\"\"\n", " Extract the CPU model name from the system's /proc/cpuinfo.\n", @@ -135,6 +134,7 @@ " return \"Unknown RAM\"\n", " return \"Unknown RAM\"\n", "\n", + "\n", "# --- Execution Logic ---\n", "\n", "cpu_model: str = get_cpu_info()\n", @@ -345,7 +345,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "import threading\n", "from typing import Any, Dict, List\n", "\n", @@ -368,7 +367,7 @@ " mem = self.process.memory_info().rss / (1024 * 1024)\n", " self.memory_usage.append(mem)\n", " # CPU Percent (blocking for interval would slow main thread, so we sleep manually)\n", - " # self.cpu_usage.append(self.process.cpu_percent(interval=None)) \n", + " # self.cpu_usage.append(self.process.cpu_percent(interval=None))\n", " except Exception:\n", " pass\n", " time.sleep(self.interval)\n", @@ -376,11 +375,9 @@ " def stop(self):\n", " self.stop_event.set()\n", "\n", + "\n", "def benchmark_function(\n", - " func: Callable[..., Any],\n", - " kwargs: Dict[str, Any],\n", - " n_repeats: int = 5,\n", - " warmup: bool = True\n", + " func: Callable[..., Any], kwargs: Dict[str, Any], n_repeats: int = 5, warmup: bool = True\n", ") -> Dict[str, float]:\n", " \"\"\"\n", " Run a function N times with robust profiling (Warm-up + High-Freq Sampling).\n", @@ -407,9 +404,9 @@ " try:\n", " # Run without monitoring\n", " res = func(**kwargs)\n", - " if hasattr(res, 'compute'):\n", + " if hasattr(res, \"compute\"):\n", " res.compute()\n", - " elif hasattr(res, 'to_xarray'):\n", + " elif hasattr(res, \"to_xarray\"):\n", " res.to_xarray().compute()\n", " except Exception as e:\n", " print(f\" Warm-up warning: {e}\")\n", @@ -435,12 +432,12 @@ " # --- Computation ---\n", " try:\n", " res = func(**kwargs)\n", - " if hasattr(res, 'compute'):\n", + " if hasattr(res, \"compute\"):\n", " res.compute()\n", - " elif hasattr(res, 'to_xarray'):\n", + " elif hasattr(res, \"to_xarray\"):\n", " res.to_xarray().compute()\n", " except Exception as e:\n", - " print(f\" Run {i+1} Failed: {e}\")\n", + " print(f\" Run {i + 1} Failed: {e}\")\n", " monitor.stop()\n", " continue\n", " # -------------------\n", @@ -459,14 +456,15 @@ " # Normalized Peak: (Max - Baseline)\n", " peak_delta = max(observed_mems) - baseline_mem\n", " # Clamp to 0\n", - " if peak_delta < 0: peak_delta = 0\n", + " if peak_delta < 0:\n", + " peak_delta = 0\n", " else:\n", " peak_delta = 0.0\n", "\n", " times.append(duration)\n", " mem_peaks.append(peak_delta)\n", "\n", - " print(f\" Run {i+1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", + " print(f\" Run {i + 1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", "\n", " if not times:\n", " return {\"mean_time\": 0.0, \"max_mem\": 0.0}\n", @@ -476,8 +474,8 @@ " \"median_time\": float(np.median(times)),\n", " \"std_time\": float(np.std(times)),\n", " \"max_mem\": float(np.max(mem_peaks)),\n", - " \"mean_mem\": float(np.mean(mem_peaks))\n", - " }\n" + " \"mean_mem\": float(np.mean(mem_peaks)),\n", + " }" ] }, { @@ -511,6 +509,7 @@ " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", "}\n", "\n", + "\n", "def load_data() -> Dict[str, xr.Dataset]:\n", " \"\"\"\n", " Load climate datasets from remote URLs using earthkit-data.\n", @@ -537,6 +536,7 @@ " datasets[key] = ds\n", " return datasets\n", "\n", + "\n", "data_cache = load_data()" ] }, @@ -564,14 +564,12 @@ "\n", "\n", "def run_climate_indicator(\n", - " func: Callable[..., Any],\n", - " kwargs: Dict[str, Any],\n", - " use_flox: Optional[bool] = None\n", + " func: Callable[..., Any], kwargs: Dict[str, Any], use_flox: Optional[bool] = None\n", ") -> Any:\n", " \"\"\"\n", " Unified execution wrapper for climate indicator functions with configurable backend options.\n", "\n", - " This function streamlines the benchmarking process by dynamically handling \n", + " This function streamlines the benchmarking process by dynamically handling\n", " Xarray global options (specifically 'flox' optimization) via a context manager.\n", "\n", " Parameters\n", @@ -579,7 +577,7 @@ " func : Callable[..., Any]\n", " The indicator function to execute (e.g., from Earthkit or Xclim).\n", " kwargs : Dict[str, Any]\n", - " A dictionary of keyword arguments required by the `func` (e.g., input datasets, \n", + " A dictionary of keyword arguments required by the `func` (e.g., input datasets,\n", " thresholds, frequencies).\n", " use_flox : bool, optional\n", " Controls the 'use_flox' option in Xarray for accelerated GroupBy operations:\n", @@ -621,9 +619,10 @@ " \"\"\"\n", " Creates a 0-argument wrapper with a custom `__name__`.\n", "\n", - " Renamed the second argument to 'target_runner' to avoid collision \n", + " Renamed the second argument to 'target_runner' to avoid collision\n", " with the 'func' argument inside **kwargs.\n", " \"\"\"\n", + "\n", " def wrapper():\n", " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", " return target_runner(**kwargs)\n", @@ -871,13 +870,13 @@ "results_data = []\n", "\n", "# --- 1. Data Preparation ---\n", - "tasmax_ssp = data_cache['tasmax_ssp']['tasmax']\n", - "tasmin_ssp = data_cache['tasmin_ssp']['tasmin']\n", - "pr_ssp = data_cache['pr_ssp']['pr']\n", + "tasmax_ssp = data_cache[\"tasmax_ssp\"][\"tasmax\"]\n", + "tasmin_ssp = data_cache[\"tasmin_ssp\"][\"tasmin\"]\n", + "pr_ssp = data_cache[\"pr_ssp\"][\"pr\"]\n", "\n", "# Pre-calculate percentile for WSDI\n", "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", - "per_90 = percentile_doy(data_cache['tasmax_hist']['tasmax'], per=90)\n", + "per_90 = percentile_doy(data_cache[\"tasmax_hist\"][\"tasmax\"], per=90)\n", "per_90.name = \"tasmax_per\"\n", "\n", "# --- 1b. Optimized Configuration (Chunk -1) ---\n", @@ -895,21 +894,13 @@ " \"xi_func\": xclim.indicators.atmos.warm_spell_duration_index,\n", " # Earthkit Arguments\n", " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], per_90]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"}\n", + " \"lazy\": {\"ds\": xr.merge([data_cache[\"tasmax_ssp\"], per_90]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"},\n", " },\n", " # Xclim Arguments (Now with dual structure)\n", " \"xi_args\": {\n", - " \"lazy\": {\n", - " \"tasmax\": tasmax_ssp, \n", - " \"tasmax_per\": per_90, \n", - " \"freq\": \"MS\"\n", - " },\n", - " \"optimized\": {\n", - " \"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \n", - " \"tasmax_per\": per_90, \n", - " \"freq\": \"MS\"\n", - " },\n", + " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmax_per\": per_90, \"freq\": \"MS\"},\n", + " \"optimized\": {\"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \"tasmax_per\": per_90, \"freq\": \"MS\"},\n", " },\n", " },\n", " {\n", @@ -917,29 +908,22 @@ " \"ek_func\": ek_pr.maximum_consecutive_wet_days,\n", " \"xi_func\": xclim.indicators.atmos.maximum_consecutive_wet_days,\n", " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " \"lazy\": {\"ds\": data_cache[\"pr_ssp\"], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"},\n", " },\n", + " \"xi_args\": {\"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"}, \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}},\n", " },\n", " {\n", " \"name\": \"DTR\",\n", " \"ek_func\": ek_temp.daily_temperature_range,\n", " \"xi_func\": xclim.indicators.atmos.daily_temperature_range,\n", " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache['tasmax_ssp'], data_cache['tasmin_ssp']]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"}\n", + " \"lazy\": {\"ds\": xr.merge([data_cache[\"tasmax_ssp\"], data_cache[\"tasmin_ssp\"]]), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"},\n", " },\n", " \"xi_args\": {\n", " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmin\": tasmin_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\n", - " \"tasmax\": tasmax_ssp_opt,\n", - " \"tasmin\": tasmin_ssp_opt,\n", - " \"freq\": \"MS\"\n", - " }\n", + " \"optimized\": {\"tasmax\": tasmax_ssp_opt, \"tasmin\": tasmin_ssp_opt, \"freq\": \"MS\"},\n", " },\n", " },\n", " {\n", @@ -947,12 +931,12 @@ " \"ek_func\": ek_temp.heating_degree_days,\n", " \"xi_func\": xclim.indicators.atmos.heating_degree_days,\n", " \"ek_args\": {\n", - " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp)/2).to_dataset(name='tas'), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt)/2).to_dataset(name='tas'), \"freq\": \"MS\"}\n", + " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp) / 2).to_dataset(name=\"tas\"), \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt) / 2).to_dataset(name=\"tas\"), \"freq\": \"MS\"},\n", " },\n", " \"xi_args\": {\n", - " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp)/2, \"freq\": \"MS\"},\n", - " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt)/2, \"freq\": \"MS\"}\n", + " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp) / 2, \"freq\": \"MS\"},\n", + " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt) / 2, \"freq\": \"MS\"},\n", " },\n", " },\n", " {\n", @@ -960,14 +944,11 @@ " \"ek_func\": ek_pr.daily_precipitation_intensity,\n", " \"xi_func\": xclim.indicators.atmos.daily_pr_intensity,\n", " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache['pr_ssp'], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"}\n", + " \"lazy\": {\"ds\": data_cache[\"pr_ssp\"], \"freq\": \"MS\"},\n", + " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"},\n", " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}\n", - " },\n", - " }\n", + " \"xi_args\": {\"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"}, \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}},\n", + " },\n", "]\n", "\n", "# --- 3. Run Loop (Symmetric Comparison) ---\n", @@ -982,39 +963,38 @@ " runner_ek_nf = get_named_runner(\n", " f\"run_earthkit_lazy_noflox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['lazy'],\n", - " use_flox=False\n", + " func=b[\"ek_func\"],\n", + " kwargs=b[\"ek_args\"][\"lazy\"],\n", + " use_flox=False,\n", " )\n", " res_ek_nf = benchmark_function(runner_ek_nf, {})\n", - " res_ek_nf.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " res_ek_nf.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", " results_data.append(res_ek_nf)\n", "\n", " # 2. Earthkit: Standard (With Flox)\n", " runner_ek_fl = get_named_runner(\n", " f\"run_earthkit_lazy_flox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['lazy'],\n", - " use_flox=True\n", + " func=b[\"ek_func\"],\n", + " kwargs=b[\"ek_args\"][\"lazy\"],\n", + " use_flox=True,\n", " )\n", " res_ek_fl = benchmark_function(runner_ek_fl, {})\n", - " res_ek_fl.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", + " res_ek_fl.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", " results_data.append(res_ek_fl)\n", "\n", " # 3. Earthkit: Optimized (Chunk -1) + Flox\n", " runner_ek_opt = get_named_runner(\n", " f\"run_earthkit_opt_flox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['ek_func'],\n", - " kwargs=b['ek_args']['optimized'],\n", - " use_flox=True\n", + " func=b[\"ek_func\"],\n", + " kwargs=b[\"ek_args\"][\"optimized\"],\n", + " use_flox=True,\n", " )\n", " res_ek_opt = benchmark_function(runner_ek_opt, {}, n_repeats=5)\n", - " res_ek_opt.update({\"Indicator\": b['name'], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " res_ek_opt.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", " results_data.append(res_ek_opt)\n", "\n", - "\n", " # =========================================================\n", " # BLOCK 2: XCLIM\n", " # =========================================================\n", @@ -1023,36 +1003,36 @@ " runner_xc_nf = get_named_runner(\n", " f\"run_xclim_noflox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", - " use_flox=False\n", + " func=b[\"xi_func\"],\n", + " kwargs=b[\"xi_args\"][\"lazy\"], # <--- USING LAZY ARGS\n", + " use_flox=False,\n", " )\n", " res_xc_nf = benchmark_function(runner_xc_nf, {})\n", - " res_xc_nf.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", + " res_xc_nf.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", " results_data.append(res_xc_nf)\n", "\n", " # 5. Xclim: Standard (With Flox)\n", " runner_xc_fl = get_named_runner(\n", " f\"run_xclim_flox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['lazy'], # <--- USING LAZY ARGS\n", - " use_flox=True\n", + " func=b[\"xi_func\"],\n", + " kwargs=b[\"xi_args\"][\"lazy\"], # <--- USING LAZY ARGS\n", + " use_flox=True,\n", " )\n", " res_xc_fl = benchmark_function(runner_xc_fl, {})\n", - " res_xc_fl.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", + " res_xc_fl.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", " results_data.append(res_xc_fl)\n", "\n", " # 6. Xclim: Optimized (Chunk -1) + Flox\n", " runner_xc_opt = get_named_runner(\n", " f\"run_xclim_opt_flox_{b['name']}\",\n", " run_climate_indicator,\n", - " func=b['xi_func'],\n", - " kwargs=b['xi_args']['optimized'], # <--- USING OPTIMIZED ARGS\n", - " use_flox=True\n", + " func=b[\"xi_func\"],\n", + " kwargs=b[\"xi_args\"][\"optimized\"], # <--- USING OPTIMIZED ARGS\n", + " use_flox=True,\n", " )\n", " res_xc_opt = benchmark_function(runner_xc_opt, {}, n_repeats=5)\n", - " res_xc_opt.update({\"Indicator\": b['name'], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", + " res_xc_opt.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", " results_data.append(res_xc_opt)" ] }, @@ -1514,93 +1494,71 @@ } ], "source": [ - "\n", "# 1. Create DataFrame\n", "df_res = pd.DataFrame(results_data)\n", "\n", "# 2. Create a specific label for the Plot Legend (Library + Mode)\n", - "df_res['Label'] = df_res['Library'] + \": \" + df_res['Mode']\n", + "df_res[\"Label\"] = df_res[\"Library\"] + \": \" + df_res[\"Mode\"]\n", "\n", "# 3. Calculate Speedup relative to 'Earthkit No Flox (Standard)' using MEDIAN time\n", "# Robustness Update: We use Median Time for speedup calculation to be resistant to outliers.\n", - "df_res['Speedup'] = 0.0\n", + "df_res[\"Speedup\"] = 0.0\n", "baseline_mode = \"1. No Flox (Standard)\"\n", "\n", - "for indicator in df_res['Indicator'].unique():\n", + "for indicator in df_res[\"Indicator\"].unique():\n", " # Find the baseline: Xclim + No Flox (as per original logic, though code comments said Earthkit)\n", " # Let's double check the user intent. Usually Xclim NoFlox is the \"base\" reference.\n", " # The original code searched for Library='Xclim'. We stick to that.\n", " baseline_row = df_res.loc[\n", - " (df_res['Indicator'] == indicator) &\n", - " (df_res['Library'] == 'Xclim') &\n", - " (df_res['Mode'] == baseline_mode)\n", + " (df_res[\"Indicator\"] == indicator)\n", + " & (df_res[\"Library\"] == \"Xclim\")\n", + " & (df_res[\"Mode\"] == baseline_mode)\n", " ]\n", "\n", " if not baseline_row.empty:\n", " # Use median_time for robust speedup\n", - " baseline_time = baseline_row['median_time'].values[0]\n", - " mask = df_res['Indicator'] == indicator\n", - " df_res.loc[mask, 'Speedup'] = baseline_time / df_res.loc[mask, 'median_time']\n", + " baseline_time = baseline_row[\"median_time\"].values[0]\n", + " mask = df_res[\"Indicator\"] == indicator\n", + " df_res.loc[mask, \"Speedup\"] = baseline_time / df_res.loc[mask, \"median_time\"]\n", "\n", "print(\"\\nBenchmarking Results (Robust Stats):\")\n", - "display(df_res[['Indicator', 'Library', 'Mode', 'median_time', 'std_time', 'max_mem', 'Speedup']])\n", + "display(df_res[[\"Indicator\", \"Library\", \"Mode\", \"median_time\", \"std_time\", \"max_mem\", \"Speedup\"]])\n", "\n", "# --- Plots ---\n", "# Define a consistent order for the bars\n", - "hue_order = sorted(df_res['Label'].unique())\n", + "hue_order = sorted(df_res[\"Label\"].unique())\n", "\n", "# Plot 1: Relative Speedup (Robust)\n", "plt.figure(figsize=(10, 6))\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"Speedup\",\n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", + "sns.barplot(data=df_res, x=\"Indicator\", y=\"Speedup\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", "plt.title(f\"Relative Speedup (via Median Time)\\n(Baseline: Xclim {baseline_mode})\")\n", - "plt.axhline(1.0, color='black', linestyle='--', alpha=0.5, linewidth=1)\n", - "plt.legend(title=\"Configuration\", fontsize='small')\n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "plt.axhline(1.0, color=\"black\", linestyle=\"--\", alpha=0.5, linewidth=1)\n", + "plt.legend(title=\"Configuration\", fontsize=\"small\")\n", + "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Plot 2: Median Execution Time\n", "plt.figure(figsize=(10, 6))\n", - "# We plot the pre-calculated median. \n", - "# Note: standard sns.barplot aggregates data if there are duplicates. \n", + "# We plot the pre-calculated median.\n", + "# Note: standard sns.barplot aggregates data if there are duplicates.\n", "# Here df_res has 1 row per case, so it just plots the value.\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"median_time\", \n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", + "sns.barplot(data=df_res, x=\"Indicator\", y=\"median_time\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", - "plt.legend().remove() \n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "plt.legend().remove()\n", + "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Plot 3: Peak Memory Usage\n", "plt.figure(figsize=(10, 6))\n", - "sns.barplot(\n", - " data=df_res,\n", - " x=\"Indicator\",\n", - " y=\"max_mem\",\n", - " hue=\"Label\",\n", - " palette=\"Paired\",\n", - " hue_order=hue_order\n", - ")\n", + "sns.barplot(data=df_res, x=\"Indicator\", y=\"max_mem\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", "plt.legend().remove()\n", - "plt.grid(axis='y', linestyle=':', alpha=0.3)\n", + "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", "\n", "plt.tight_layout()\n", - "plt.show()\n" + "plt.show()" ] } ], diff --git a/pixi.lock b/pixi.lock index c7c425e..66a8711 100644 --- a/pixi.lock +++ b/pixi.lock @@ -5792,23 +5792,23 @@ packages: timestamp: 1733217860944 - pypi: ./ name: earthkit-climate - version: 0.1.1.dev50+gbb14434c7 - sha256: 5dcf2bf388bcb38e295e30042e888abeefa55fc8f10e6fa1061beccf0eae2396 + version: 0.1.1.dev68 + sha256: 64129c6c22973e64d5797cb9def633140cd35600b2c405644fb07fd05536c5a1 requires_dist: - earthkit-data>=0.17.0 - numpy>=1.22 - xarray>=2023.1 - xclim>=0.59.1 - xsdba>=0.5,<0.6 - - pytest ; extra == 'dev' - - pytest-cov ; extra == 'dev' - - pytest-mock ; extra == 'dev' - - ipython ; extra == 'dev' - - ipykernel ; extra == 'dev' - black ; extra == 'dev' - ruff ; extra == 'dev' - mypy ; extra == 'dev' - pre-commit ; extra == 'dev' + - ipython ; extra == 'dev' + - pytest ; extra == 'dev' + - pytest-cov ; extra == 'dev' + - pytest-mock ; extra == 'dev' + - ipykernel ; extra == 'dev' - nbsphinx ; extra == 'docs' - roman-numerals-py>=3.1.0,<4 ; extra == 'docs' - sphinx ; extra == 'docs' diff --git a/pixi.toml b/pixi.toml index 4d41c78..363e448 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,12 +1,12 @@ [dependencies] earthkit-data = ">=0.17.0" +flox = ">=0.11.0,<0.12" numpy = ">=1.22" python = "3.12.*" +seaborn = ">=0.13.2,<0.14" xarray = ">=2023.1" xclim = ">=0.59.1" xsdba = ">=0.5,<0.6" -seaborn = ">=0.13.2,<0.14" -flox = ">=0.11.0,<0.12" [environments] dev = ["dev"] diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index d00f828..7db1ee8 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -48,6 +48,7 @@ def australian_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.australian_hardiness_zones) return wrapper(ds, **kwargs) + def biologically_effective_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -81,6 +82,7 @@ def biologically_effective_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.biologically_effective_degree_days) return wrapper(ds, **kwargs) + def cold_spell_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -111,6 +113,7 @@ def cold_spell_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_days) return wrapper(ds, **kwargs) + def cold_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -142,6 +145,7 @@ def cold_spell_duration_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_duration_index) return wrapper(ds, **kwargs) + def cold_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -170,6 +174,7 @@ def cold_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_frequency) return wrapper(ds, **kwargs) + def cold_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -200,6 +205,7 @@ def cold_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_max_length) return wrapper(ds, **kwargs) + def cold_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -230,6 +236,7 @@ def cold_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_total_length) return wrapper(ds, **kwargs) + def consecutive_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -259,6 +266,7 @@ def consecutive_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.consecutive_frost_days) return wrapper(ds, **kwargs) + def maximum_consecutive_frost_free_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -289,6 +297,7 @@ def maximum_consecutive_frost_free_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_frost_free_days) return wrapper(ds, **kwargs) + def cool_night_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -319,6 +328,7 @@ def cool_night_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cool_night_index) return wrapper(ds, **kwargs) + def cooling_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -349,6 +359,7 @@ def cooling_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days) return wrapper(ds, **kwargs) + def cooling_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -381,6 +392,7 @@ def cooling_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days_approximation) return wrapper(ds, **kwargs) + def corn_heat_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -409,6 +421,7 @@ def corn_heat_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.corn_heat_units) return wrapper(ds, **kwargs) + def chill_portions( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -444,6 +457,7 @@ def chill_portions( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_portions) return wrapper(ds, **kwargs) + def chill_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -475,6 +489,7 @@ def chill_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_units) return wrapper(ds, **kwargs) + def degree_days_exceedance_date( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -503,6 +518,7 @@ def degree_days_exceedance_date( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.degree_days_exceedance_date) return wrapper(ds, **kwargs) + def daily_freezethaw_cycles( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -534,6 +550,7 @@ def daily_freezethaw_cycles( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_freezethaw_cycles) return wrapper(ds, **kwargs) + def daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -563,6 +580,7 @@ def daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) return wrapper(ds, **kwargs) + def max_daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -592,6 +610,7 @@ def max_daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_daily_temperature_range) return wrapper(ds, **kwargs) + def daily_temperature_range_variability( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -621,6 +640,7 @@ def daily_temperature_range_variability( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range_variability) return wrapper(ds, **kwargs) + def extreme_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -650,6 +670,7 @@ def extreme_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.extreme_temperature_range) return wrapper(ds, **kwargs) + def fire_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -678,6 +699,7 @@ def fire_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fire_season) return wrapper(ds, **kwargs) + def first_day_tg_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -706,6 +728,7 @@ def first_day_tg_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_above) return wrapper(ds, **kwargs) + def first_day_tg_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -734,6 +757,7 @@ def first_day_tg_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_below) return wrapper(ds, **kwargs) + def first_day_tn_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -762,6 +786,7 @@ def first_day_tn_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_above) return wrapper(ds, **kwargs) + def first_day_tn_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -790,6 +815,7 @@ def first_day_tn_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_below) return wrapper(ds, **kwargs) + def first_day_tx_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -818,6 +844,7 @@ def first_day_tx_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_above) return wrapper(ds, **kwargs) + def first_day_tx_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -846,6 +873,7 @@ def first_day_tx_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_below) return wrapper(ds, **kwargs) + def freezethaw_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -877,6 +905,7 @@ def freezethaw_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_frequency) return wrapper(ds, **kwargs) + def freezethaw_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -908,6 +937,7 @@ def freezethaw_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_max_length) return wrapper(ds, **kwargs) + def freezethaw_spell_mean_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -939,6 +969,7 @@ def freezethaw_spell_mean_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_mean_length) return wrapper(ds, **kwargs) + def freezing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -969,6 +1000,7 @@ def freezing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezing_degree_days) return wrapper(ds, **kwargs) + def freshet_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -997,6 +1029,7 @@ def freshet_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freshet_start) return wrapper(ds, **kwargs) + def frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1026,6 +1059,7 @@ def frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_days) return wrapper(ds, **kwargs) + def frost_free_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1054,6 +1088,7 @@ def frost_free_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_end) return wrapper(ds, **kwargs) + def frost_free_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1085,6 +1120,7 @@ def frost_free_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_length) return wrapper(ds, **kwargs) + def frost_free_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1113,6 +1149,7 @@ def frost_free_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_start) return wrapper(ds, **kwargs) + def frost_free_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1143,6 +1180,7 @@ def frost_free_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_spell_max_length) return wrapper(ds, **kwargs) + def frost_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1174,6 +1212,7 @@ def frost_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_season_length) return wrapper(ds, **kwargs) + def growing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1204,6 +1243,7 @@ def growing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_degree_days) return wrapper(ds, **kwargs) + def growing_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1232,6 +1272,7 @@ def growing_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_end) return wrapper(ds, **kwargs) + def growing_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1263,6 +1304,7 @@ def growing_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_length) return wrapper(ds, **kwargs) + def growing_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1291,6 +1333,7 @@ def growing_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_start) return wrapper(ds, **kwargs) + def heat_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1319,6 +1362,7 @@ def heat_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_frequency) return wrapper(ds, **kwargs) + def heat_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1349,6 +1393,7 @@ def heat_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_max_length) return wrapper(ds, **kwargs) + def heat_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1379,6 +1424,7 @@ def heat_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_total_length) return wrapper(ds, **kwargs) + def heat_wave_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1407,6 +1453,7 @@ def heat_wave_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_frequency) return wrapper(ds, **kwargs) + def heat_wave_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1437,6 +1484,7 @@ def heat_wave_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_index) return wrapper(ds, **kwargs) + def heat_wave_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1467,6 +1515,7 @@ def heat_wave_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_max_length) return wrapper(ds, **kwargs) + def heat_wave_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1497,6 +1546,7 @@ def heat_wave_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_total_length) return wrapper(ds, **kwargs) + def heating_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1527,6 +1577,7 @@ def heating_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) return wrapper(ds, **kwargs) + def heating_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1559,6 +1610,7 @@ def heating_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days_approximation) return wrapper(ds, **kwargs) + def hot_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1588,6 +1640,7 @@ def hot_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_days) return wrapper(ds, **kwargs) + def hot_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1616,6 +1669,7 @@ def hot_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_frequency) return wrapper(ds, **kwargs) + def hot_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1646,6 +1700,7 @@ def hot_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_length) return wrapper(ds, **kwargs) + def hot_spell_max_magnitude( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1676,6 +1731,7 @@ def hot_spell_max_magnitude( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_magnitude) return wrapper(ds, **kwargs) + def hot_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1706,6 +1762,7 @@ def hot_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_total_length) return wrapper(ds, **kwargs) + def huglin_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1737,6 +1794,7 @@ def huglin_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.huglin_index) return wrapper(ds, **kwargs) + def ice_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1766,6 +1824,7 @@ def ice_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.ice_days) return wrapper(ds, **kwargs) + def last_spring_frost( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1794,6 +1853,7 @@ def last_spring_frost( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_spring_frost) return wrapper(ds, **kwargs) + def late_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1824,6 +1884,7 @@ def late_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.late_frost_days) return wrapper(ds, **kwargs) + def latitude_temperature_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1855,6 +1916,7 @@ def latitude_temperature_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.latitude_temperature_index) return wrapper(ds, **kwargs) + def maximum_consecutive_warm_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1885,6 +1947,7 @@ def maximum_consecutive_warm_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_warm_days) return wrapper(ds, **kwargs) + def tg10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1914,6 +1977,7 @@ def tg10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg10p) return wrapper(ds, **kwargs) + def tg90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1943,6 +2007,7 @@ def tg90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg90p) return wrapper(ds, **kwargs) + def tg_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1972,6 +2037,7 @@ def tg_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_above) return wrapper(ds, **kwargs) + def tg_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2001,6 +2067,7 @@ def tg_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_below) return wrapper(ds, **kwargs) + def tg_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2030,6 +2097,7 @@ def tg_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_max) return wrapper(ds, **kwargs) + def tg_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2059,6 +2127,7 @@ def tg_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_mean) return wrapper(ds, **kwargs) + def tg_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2088,6 +2157,7 @@ def tg_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_min) return wrapper(ds, **kwargs) + def thawing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2118,6 +2188,7 @@ def thawing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.thawing_degree_days) return wrapper(ds, **kwargs) + def tn10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2147,6 +2218,7 @@ def tn10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn10p) return wrapper(ds, **kwargs) + def tn90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2176,6 +2248,7 @@ def tn90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn90p) return wrapper(ds, **kwargs) + def tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2205,6 +2278,7 @@ def tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_above) return wrapper(ds, **kwargs) + def tn_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2234,6 +2308,7 @@ def tn_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_below) return wrapper(ds, **kwargs) + def tn_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2263,6 +2338,7 @@ def tn_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_max) return wrapper(ds, **kwargs) + def tn_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2292,6 +2368,7 @@ def tn_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_mean) return wrapper(ds, **kwargs) + def tn_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2321,6 +2398,7 @@ def tn_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_min) return wrapper(ds, **kwargs) + def tropical_nights( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2350,6 +2428,7 @@ def tropical_nights( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tropical_nights) return wrapper(ds, **kwargs) + def tx10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2379,6 +2458,7 @@ def tx10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx10p) return wrapper(ds, **kwargs) + def tx90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2408,6 +2488,7 @@ def tx90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx90p) return wrapper(ds, **kwargs) + def tx_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2437,6 +2518,7 @@ def tx_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_above) return wrapper(ds, **kwargs) + def tx_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2466,6 +2548,7 @@ def tx_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_below) return wrapper(ds, **kwargs) + def tx_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2495,6 +2578,7 @@ def tx_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_max) return wrapper(ds, **kwargs) + def tx_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2524,6 +2608,7 @@ def tx_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_mean) return wrapper(ds, **kwargs) + def tx_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2553,6 +2638,7 @@ def tx_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_min) return wrapper(ds, **kwargs) + def tx_tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2582,6 +2668,7 @@ def tx_tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_tn_days_above) return wrapper(ds, **kwargs) + def usda_hardiness_zones( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2613,6 +2700,7 @@ def usda_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.usda_hardiness_zones) return wrapper(ds, **kwargs) + def warm_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2643,4 +2731,3 @@ def warm_spell_duration_index( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) return wrapper(ds, **kwargs) - diff --git a/tests/unit/indicators/test_precipitation.py b/tests/unit/indicators/test_precipitation.py index 49eac39..c70cc6d 100644 --- a/tests/unit/indicators/test_precipitation.py +++ b/tests/unit/indicators/test_precipitation.py @@ -55,8 +55,10 @@ def test_daily_precipitation_intensity(mocker: MockerFixture, common_mocks): assert call_args.kwargs["thresh"] == "2 mm/day" assert call_args.kwargs["freq"] == "MS" + # New tests + def test_antecedent_precipitation_index(mocker: MockerFixture, common_mocks): mock_fn = mocker.patch("xclim.indicators.atmos.antecedent_precipitation_index") From d433ae26d242c0ae542dc78dda56af887d55d6d2 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 10 Feb 2026 20:43:48 +0100 Subject: [PATCH 18/47] chore: Re-execute performance analysis notebook, updating cell execution counts and benchmark results. --- docs/notebooks/performance_analysis.ipynb | 702 +++++++++++----------- 1 file changed, 337 insertions(+), 365 deletions(-) diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb index 756cc01..7c6a56a 100644 --- a/docs/notebooks/performance_analysis.ipynb +++ b/docs/notebooks/performance_analysis.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "id": "410735a7", "metadata": {}, "outputs": [], @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "id": "68281ebe", "metadata": {}, "outputs": [ @@ -154,17 +154,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "fba35188", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, { "data": { "text/markdown": [ @@ -340,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "5d1ba8a5", "metadata": {}, "outputs": [], @@ -489,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "acf4d31d", "metadata": {}, "outputs": [ @@ -552,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "66739804", "metadata": {}, "outputs": [], @@ -610,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "46022af7", "metadata": {}, "outputs": [], @@ -633,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "id": "e0be7a44", "metadata": {}, "outputs": [ @@ -647,222 +640,222 @@ "=== Benchmarking WSDI ===\n", " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", - " Run 1: 89.7710s, Mem Peak Delta: 523.52 MiB\n", - " Run 2: 92.9727s, Mem Peak Delta: 340.92 MiB\n", - " Run 3: 95.7104s, Mem Peak Delta: 333.95 MiB\n", - " Run 4: 95.0707s, Mem Peak Delta: 268.96 MiB\n", - " Run 5: 96.9543s, Mem Peak Delta: 185.86 MiB\n", + " Run 1: 95.0477s, Mem Peak Delta: 85.38 MiB\n", + " Run 2: 97.7674s, Mem Peak Delta: 136.30 MiB\n", + " Run 3: 94.7928s, Mem Peak Delta: 160.32 MiB\n", + " Run 4: 96.2381s, Mem Peak Delta: 76.02 MiB\n", + " Run 5: 100.2789s, Mem Peak Delta: 108.48 MiB\n", " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", - " Run 1: 91.2536s, Mem Peak Delta: 26.64 MiB\n", - " Run 2: 92.7830s, Mem Peak Delta: 196.10 MiB\n", - " Run 3: 95.7225s, Mem Peak Delta: 191.45 MiB\n", - " Run 4: 96.5830s, Mem Peak Delta: 210.40 MiB\n", - " Run 5: 95.0315s, Mem Peak Delta: 193.62 MiB\n", + " Run 1: 100.9231s, Mem Peak Delta: 51.39 MiB\n", + " Run 2: 95.6108s, Mem Peak Delta: 88.91 MiB\n", + " Run 3: 106.4386s, Mem Peak Delta: 87.94 MiB\n", + " Run 4: 100.7570s, Mem Peak Delta: 2.66 MiB\n", + " Run 5: 104.0698s, Mem Peak Delta: 166.26 MiB\n", " Warm-up run for run_earthkit_opt_flox_WSDI...\n", "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", - " Run 1: 59.0237s, Mem Peak Delta: 103.71 MiB\n", - " Run 2: 59.3023s, Mem Peak Delta: 95.52 MiB\n", - " Run 3: 59.4765s, Mem Peak Delta: 233.46 MiB\n", - " Run 4: 59.7330s, Mem Peak Delta: 112.57 MiB\n", - " Run 5: 61.9002s, Mem Peak Delta: 181.49 MiB\n", + " Run 1: 59.4010s, Mem Peak Delta: 23.42 MiB\n", + " Run 2: 60.0811s, Mem Peak Delta: 99.04 MiB\n", + " Run 3: 60.3694s, Mem Peak Delta: 10.18 MiB\n", + " Run 4: 60.4882s, Mem Peak Delta: 128.80 MiB\n", + " Run 5: 60.5862s, Mem Peak Delta: 113.94 MiB\n", " Warm-up run for run_xclim_noflox_WSDI...\n", "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", - " Run 1: 95.5910s, Mem Peak Delta: 113.41 MiB\n", - " Run 2: 97.3999s, Mem Peak Delta: 209.49 MiB\n", - " Run 3: 102.3339s, Mem Peak Delta: 183.56 MiB\n", - " Run 4: 105.1568s, Mem Peak Delta: 159.73 MiB\n", - " Run 5: 104.6472s, Mem Peak Delta: 295.09 MiB\n", + " Run 1: 94.4468s, Mem Peak Delta: 179.88 MiB\n", + " Run 2: 100.3204s, Mem Peak Delta: 117.65 MiB\n", + " Run 3: 98.2519s, Mem Peak Delta: 158.38 MiB\n", + " Run 4: 98.7358s, Mem Peak Delta: 148.62 MiB\n", + " Run 5: 96.9592s, Mem Peak Delta: 140.45 MiB\n", " Warm-up run for run_xclim_flox_WSDI...\n", "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", - " Run 1: 102.1373s, Mem Peak Delta: 211.81 MiB\n", - " Run 2: 107.0449s, Mem Peak Delta: 139.21 MiB\n", - " Run 3: 114.8249s, Mem Peak Delta: 205.55 MiB\n", - " Run 4: 111.2941s, Mem Peak Delta: 44.91 MiB\n", - " Run 5: 105.7781s, Mem Peak Delta: 74.06 MiB\n", + " Run 1: 90.7513s, Mem Peak Delta: 115.07 MiB\n", + " Run 2: 94.4029s, Mem Peak Delta: 52.20 MiB\n", + " Run 3: 96.7050s, Mem Peak Delta: 114.93 MiB\n", + " Run 4: 95.9556s, Mem Peak Delta: 149.26 MiB\n", + " Run 5: 96.8507s, Mem Peak Delta: 77.72 MiB\n", " Warm-up run for run_xclim_opt_flox_WSDI...\n", "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", - " Run 1: 60.1291s, Mem Peak Delta: 15.52 MiB\n", - " Run 2: 64.3885s, Mem Peak Delta: 62.09 MiB\n", - " Run 3: 67.2124s, Mem Peak Delta: 191.05 MiB\n", - " Run 4: 66.9474s, Mem Peak Delta: 52.80 MiB\n", - " Run 5: 67.1786s, Mem Peak Delta: 116.32 MiB\n", + " Run 1: 59.5290s, Mem Peak Delta: 89.22 MiB\n", + " Run 2: 60.0085s, Mem Peak Delta: 131.08 MiB\n", + " Run 3: 60.2594s, Mem Peak Delta: 30.26 MiB\n", + " Run 4: 60.3024s, Mem Peak Delta: 29.35 MiB\n", + " Run 5: 60.6037s, Mem Peak Delta: 91.17 MiB\n", "\n", "=== Benchmarking CWD ===\n", " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", - " Run 1: 28.5323s, Mem Peak Delta: 7.73 MiB\n", - " Run 2: 28.8032s, Mem Peak Delta: 25.11 MiB\n", - " Run 3: 28.8414s, Mem Peak Delta: 23.77 MiB\n", - " Run 4: 29.1343s, Mem Peak Delta: 14.91 MiB\n", - " Run 5: 29.0878s, Mem Peak Delta: 13.65 MiB\n", + " Run 1: 25.7984s, Mem Peak Delta: 7.62 MiB\n", + " Run 2: 25.9442s, Mem Peak Delta: 0.38 MiB\n", + " Run 3: 26.0592s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 26.2207s, Mem Peak Delta: 0.12 MiB\n", + " Run 5: 26.1019s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_earthkit_lazy_flox_CWD...\n", "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", - " Run 1: 23.1237s, Mem Peak Delta: 24.98 MiB\n", - " Run 2: 23.2226s, Mem Peak Delta: 16.63 MiB\n", - " Run 3: 24.0029s, Mem Peak Delta: 17.38 MiB\n", - " Run 4: 23.2542s, Mem Peak Delta: 33.37 MiB\n", - " Run 5: 22.9485s, Mem Peak Delta: 27.67 MiB\n", + " Run 1: 20.5405s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 20.5765s, Mem Peak Delta: 21.62 MiB\n", + " Run 3: 20.8313s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 20.8417s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 20.8313s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_earthkit_opt_flox_CWD...\n", "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.8623s, Mem Peak Delta: 16.62 MiB\n", - " Run 2: 21.5585s, Mem Peak Delta: 66.62 MiB\n", - " Run 3: 21.7855s, Mem Peak Delta: 16.76 MiB\n", - " Run 4: 22.5419s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 22.0640s, Mem Peak Delta: 8.27 MiB\n", + " Run 1: 20.4005s, Mem Peak Delta: 0.67 MiB\n", + " Run 2: 20.4653s, Mem Peak Delta: 49.93 MiB\n", + " Run 3: 19.9873s, Mem Peak Delta: 0.07 MiB\n", + " Run 4: 20.3613s, Mem Peak Delta: 3.94 MiB\n", + " Run 5: 20.1712s, Mem Peak Delta: 49.94 MiB\n", " Warm-up run for run_xclim_noflox_CWD...\n", "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", - " Run 1: 27.7460s, Mem Peak Delta: 24.90 MiB\n", - " Run 2: 27.7050s, Mem Peak Delta: 16.50 MiB\n", - " Run 3: 27.7064s, Mem Peak Delta: 24.98 MiB\n", - " Run 4: 27.6762s, Mem Peak Delta: 33.21 MiB\n", - " Run 5: 27.7167s, Mem Peak Delta: 33.09 MiB\n", + " Run 1: 25.9901s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 26.2468s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 26.1138s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 26.3420s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 26.2693s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_flox_CWD...\n", "Benchmarking run_xclim_flox_CWD (5 runs)...\n", - " Run 1: 21.8447s, Mem Peak Delta: 41.77 MiB\n", - " Run 2: 21.9301s, Mem Peak Delta: 83.01 MiB\n", - " Run 3: 22.2363s, Mem Peak Delta: 19.67 MiB\n", - " Run 4: 22.2319s, Mem Peak Delta: 24.80 MiB\n", - " Run 5: 22.3105s, Mem Peak Delta: 33.25 MiB\n", + " Run 1: 20.8982s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 20.7771s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 20.9738s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 21.0002s, Mem Peak Delta: 17.87 MiB\n", + " Run 5: 21.7872s, Mem Peak Delta: 8.28 MiB\n", " Warm-up run for run_xclim_opt_flox_CWD...\n", "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", - " Run 1: 21.7654s, Mem Peak Delta: 25.23 MiB\n", - " Run 2: 21.7366s, Mem Peak Delta: 49.93 MiB\n", - " Run 3: 22.0504s, Mem Peak Delta: 49.95 MiB\n", - " Run 4: 21.9013s, Mem Peak Delta: 66.38 MiB\n", - " Run 5: 20.2474s, Mem Peak Delta: 25.07 MiB\n", + " Run 1: 21.3257s, Mem Peak Delta: 49.94 MiB\n", + " Run 2: 21.9894s, Mem Peak Delta: 8.00 MiB\n", + " Run 3: 21.2518s, Mem Peak Delta: 7.94 MiB\n", + " Run 4: 21.2220s, Mem Peak Delta: 23.94 MiB\n", + " Run 5: 21.4322s, Mem Peak Delta: 1.12 MiB\n", "\n", "=== Benchmarking DTR ===\n", " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", - " Run 1: 8.9316s, Mem Peak Delta: 23.88 MiB\n", - " Run 2: 8.9837s, Mem Peak Delta: 33.29 MiB\n", - " Run 3: 8.9439s, Mem Peak Delta: 1.32 MiB\n", - " Run 4: 8.9590s, Mem Peak Delta: 24.88 MiB\n", - " Run 5: 8.9646s, Mem Peak Delta: 25.11 MiB\n", + " Run 1: 9.2593s, Mem Peak Delta: 16.62 MiB\n", + " Run 2: 9.2828s, Mem Peak Delta: 16.62 MiB\n", + " Run 3: 9.2531s, Mem Peak Delta: 7.25 MiB\n", + " Run 4: 9.2653s, Mem Peak Delta: 16.62 MiB\n", + " Run 5: 9.3646s, Mem Peak Delta: 16.62 MiB\n", " Warm-up run for run_earthkit_lazy_flox_DTR...\n", "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", - " Run 1: 1.3615s, Mem Peak Delta: 61.15 MiB\n", - " Run 2: 1.3698s, Mem Peak Delta: 33.58 MiB\n", - " Run 3: 1.3750s, Mem Peak Delta: 41.55 MiB\n", - " Run 4: 1.3665s, Mem Peak Delta: 68.60 MiB\n", - " Run 5: 1.3678s, Mem Peak Delta: 11.66 MiB\n", + " Run 1: 1.3767s, Mem Peak Delta: 24.89 MiB\n", + " Run 2: 1.3803s, Mem Peak Delta: 41.28 MiB\n", + " Run 3: 1.3897s, Mem Peak Delta: 41.25 MiB\n", + " Run 4: 1.3913s, Mem Peak Delta: 49.84 MiB\n", + " Run 5: 1.3873s, Mem Peak Delta: 41.55 MiB\n", " Warm-up run for run_earthkit_opt_flox_DTR...\n", "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6516s, Mem Peak Delta: 122.49 MiB\n", - " Run 2: 1.6276s, Mem Peak Delta: 100.05 MiB\n", - " Run 3: 1.6354s, Mem Peak Delta: 83.21 MiB\n", - " Run 4: 1.5959s, Mem Peak Delta: 59.10 MiB\n", - " Run 5: 1.6488s, Mem Peak Delta: 89.11 MiB\n", + " Run 1: 1.6778s, Mem Peak Delta: 107.46 MiB\n", + " Run 2: 1.7182s, Mem Peak Delta: 41.16 MiB\n", + " Run 3: 1.6794s, Mem Peak Delta: 91.50 MiB\n", + " Run 4: 1.6648s, Mem Peak Delta: 74.91 MiB\n", + " Run 5: 1.6657s, Mem Peak Delta: 91.50 MiB\n", " Warm-up run for run_xclim_noflox_DTR...\n", "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", - " Run 1: 8.9989s, Mem Peak Delta: 8.25 MiB\n", - " Run 2: 8.9958s, Mem Peak Delta: 0.08 MiB\n", - " Run 3: 9.1292s, Mem Peak Delta: 2.38 MiB\n", - " Run 4: 9.4178s, Mem Peak Delta: 6.00 MiB\n", - " Run 5: 9.2605s, Mem Peak Delta: 8.25 MiB\n", + " Run 1: 9.2579s, Mem Peak Delta: 16.62 MiB\n", + " Run 2: 9.2941s, Mem Peak Delta: 23.08 MiB\n", + " Run 3: 9.2964s, Mem Peak Delta: 8.25 MiB\n", + " Run 4: 9.3419s, Mem Peak Delta: 8.18 MiB\n", + " Run 5: 9.3060s, Mem Peak Delta: 16.83 MiB\n", " Warm-up run for run_xclim_flox_DTR...\n", "Benchmarking run_xclim_flox_DTR (5 runs)...\n", - " Run 1: 1.3580s, Mem Peak Delta: 49.94 MiB\n", - " Run 2: 1.3487s, Mem Peak Delta: 25.05 MiB\n", - " Run 3: 1.3596s, Mem Peak Delta: 27.66 MiB\n", - " Run 4: 1.3548s, Mem Peak Delta: 25.05 MiB\n", - " Run 5: 1.3798s, Mem Peak Delta: 24.96 MiB\n", + " Run 1: 1.3991s, Mem Peak Delta: 41.55 MiB\n", + " Run 2: 1.3745s, Mem Peak Delta: 33.95 MiB\n", + " Run 3: 1.3680s, Mem Peak Delta: 41.54 MiB\n", + " Run 4: 1.3830s, Mem Peak Delta: 49.48 MiB\n", + " Run 5: 1.3745s, Mem Peak Delta: 16.72 MiB\n", " Warm-up run for run_xclim_opt_flox_DTR...\n", "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6841s, Mem Peak Delta: 72.75 MiB\n", - " Run 2: 1.7539s, Mem Peak Delta: 39.25 MiB\n", - " Run 3: 1.7238s, Mem Peak Delta: 73.33 MiB\n", - " Run 4: 1.6859s, Mem Peak Delta: 89.11 MiB\n", - " Run 5: 1.7034s, Mem Peak Delta: 25.19 MiB\n", + " Run 1: 1.6835s, Mem Peak Delta: 91.50 MiB\n", + " Run 2: 1.6887s, Mem Peak Delta: 91.55 MiB\n", + " Run 3: 1.7050s, Mem Peak Delta: 91.44 MiB\n", + " Run 4: 1.7212s, Mem Peak Delta: 52.20 MiB\n", + " Run 5: 1.6964s, Mem Peak Delta: 91.56 MiB\n", "\n", "=== Benchmarking HDD ===\n", " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", - " Run 1: 7.5611s, Mem Peak Delta: 24.88 MiB\n", - " Run 2: 7.3306s, Mem Peak Delta: 42.62 MiB\n", - " Run 3: 7.4190s, Mem Peak Delta: 33.20 MiB\n", - " Run 4: 7.4930s, Mem Peak Delta: 8.88 MiB\n", - " Run 5: 7.4986s, Mem Peak Delta: 25.35 MiB\n", + " Run 1: 7.6595s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.7498s, Mem Peak Delta: 24.73 MiB\n", + " Run 3: 7.6312s, Mem Peak Delta: 30.79 MiB\n", + " Run 4: 7.6334s, Mem Peak Delta: 10.93 MiB\n", + " Run 5: 7.6688s, Mem Peak Delta: 30.75 MiB\n", " Warm-up run for run_earthkit_lazy_flox_HDD...\n", "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", - " Run 1: 1.2988s, Mem Peak Delta: 34.25 MiB\n", - " Run 2: 1.2915s, Mem Peak Delta: 64.59 MiB\n", - " Run 3: 1.2781s, Mem Peak Delta: 10.25 MiB\n", - " Run 4: 1.2996s, Mem Peak Delta: 41.48 MiB\n", - " Run 5: 1.3060s, Mem Peak Delta: 33.31 MiB\n", + " Run 1: 1.3248s, Mem Peak Delta: 46.03 MiB\n", + " Run 2: 1.2938s, Mem Peak Delta: 16.59 MiB\n", + " Run 3: 1.3048s, Mem Peak Delta: 41.57 MiB\n", + " Run 4: 1.3243s, Mem Peak Delta: 0.07 MiB\n", + " Run 5: 1.3109s, Mem Peak Delta: 56.11 MiB\n", " Warm-up run for run_earthkit_opt_flox_HDD...\n", "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4264s, Mem Peak Delta: 135.50 MiB\n", - " Run 2: 1.4128s, Mem Peak Delta: 40.84 MiB\n", - " Run 3: 1.4465s, Mem Peak Delta: 14.59 MiB\n", - " Run 4: 1.4055s, Mem Peak Delta: 49.93 MiB\n", - " Run 5: 1.4351s, Mem Peak Delta: 74.78 MiB\n", + " Run 1: 1.4628s, Mem Peak Delta: 16.78 MiB\n", + " Run 2: 1.4637s, Mem Peak Delta: 74.63 MiB\n", + " Run 3: 1.4424s, Mem Peak Delta: 71.88 MiB\n", + " Run 4: 1.4518s, Mem Peak Delta: 74.81 MiB\n", + " Run 5: 1.4362s, Mem Peak Delta: 99.88 MiB\n", " Warm-up run for run_xclim_noflox_HDD...\n", "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", - " Run 1: 7.4480s, Mem Peak Delta: 26.75 MiB\n", - " Run 2: 7.4481s, Mem Peak Delta: 24.88 MiB\n", - " Run 3: 7.8391s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 7.4711s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 7.5348s, Mem Peak Delta: 16.62 MiB\n", + " Run 1: 7.6122s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 7.6125s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 7.6941s, Mem Peak Delta: 8.38 MiB\n", + " Run 4: 7.7262s, Mem Peak Delta: 11.88 MiB\n", + " Run 5: 7.5845s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_flox_HDD...\n", "Benchmarking run_xclim_flox_HDD (5 runs)...\n", - " Run 1: 1.3109s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.2927s, Mem Peak Delta: 33.32 MiB\n", - " Run 3: 1.2930s, Mem Peak Delta: 16.63 MiB\n", - " Run 4: 1.2737s, Mem Peak Delta: 35.46 MiB\n", - " Run 5: 1.2796s, Mem Peak Delta: 0.00 MiB\n", + " Run 1: 1.2948s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 1.2911s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 1.2848s, Mem Peak Delta: 33.34 MiB\n", + " Run 4: 1.2892s, Mem Peak Delta: 16.48 MiB\n", + " Run 5: 1.2930s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_opt_flox_HDD...\n", "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.5200s, Mem Peak Delta: 74.92 MiB\n", - " Run 2: 1.4439s, Mem Peak Delta: 4.97 MiB\n", - " Run 3: 1.4065s, Mem Peak Delta: 25.01 MiB\n", - " Run 4: 1.4434s, Mem Peak Delta: 0.11 MiB\n", - " Run 5: 1.4043s, Mem Peak Delta: 62.78 MiB\n", + " Run 1: 1.4343s, Mem Peak Delta: 0.09 MiB\n", + " Run 2: 1.4694s, Mem Peak Delta: 44.04 MiB\n", + " Run 3: 1.4299s, Mem Peak Delta: 49.88 MiB\n", + " Run 4: 1.4282s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 1.4291s, Mem Peak Delta: 71.88 MiB\n", "\n", "=== Benchmarking SDII ===\n", " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", - " Run 1: 9.8736s, Mem Peak Delta: 49.82 MiB\n", - " Run 2: 9.9811s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 9.7374s, Mem Peak Delta: 74.89 MiB\n", - " Run 4: 9.9493s, Mem Peak Delta: 16.62 MiB\n", - " Run 5: 10.3370s, Mem Peak Delta: 32.91 MiB\n", + " Run 1: 10.1665s, Mem Peak Delta: 6.00 MiB\n", + " Run 2: 10.2258s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 10.2883s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 10.2388s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 10.1664s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_earthkit_lazy_flox_SDII...\n", "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", - " Run 1: 0.8138s, Mem Peak Delta: 4.40 MiB\n", - " Run 2: 0.8061s, Mem Peak Delta: 29.55 MiB\n", - " Run 3: 0.8064s, Mem Peak Delta: 16.63 MiB\n", - " Run 4: 0.8126s, Mem Peak Delta: 40.15 MiB\n", - " Run 5: 0.8250s, Mem Peak Delta: 16.74 MiB\n", + " Run 1: 0.8125s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 0.8110s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.8148s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 0.8239s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 0.8146s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_earthkit_opt_flox_SDII...\n", "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9654s, Mem Peak Delta: 58.84 MiB\n", - " Run 2: 0.9568s, Mem Peak Delta: 49.93 MiB\n", - " Run 3: 0.9748s, Mem Peak Delta: 50.09 MiB\n", - " Run 4: 0.9490s, Mem Peak Delta: 49.86 MiB\n", - " Run 5: 0.9420s, Mem Peak Delta: 50.02 MiB\n", + " Run 1: 0.9460s, Mem Peak Delta: 31.91 MiB\n", + " Run 2: 0.9741s, Mem Peak Delta: 0.52 MiB\n", + " Run 3: 0.9659s, Mem Peak Delta: 49.93 MiB\n", + " Run 4: 0.9523s, Mem Peak Delta: 37.75 MiB\n", + " Run 5: 0.9575s, Mem Peak Delta: 50.05 MiB\n", " Warm-up run for run_xclim_noflox_SDII...\n", "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", - " Run 1: 10.1145s, Mem Peak Delta: 66.40 MiB\n", - " Run 2: 10.0157s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 10.3028s, Mem Peak Delta: 16.63 MiB\n", - " Run 4: 10.1469s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 10.0440s, Mem Peak Delta: 0.00 MiB\n", + " Run 1: 10.1138s, Mem Peak Delta: 18.46 MiB\n", + " Run 2: 10.2198s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 10.1793s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 10.1764s, Mem Peak Delta: 0.00 MiB\n", + " Run 5: 10.1534s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_flox_SDII...\n", "Benchmarking run_xclim_flox_SDII (5 runs)...\n", - " Run 1: 0.8203s, Mem Peak Delta: 0.02 MiB\n", - " Run 2: 0.7956s, Mem Peak Delta: 16.69 MiB\n", - " Run 3: 0.8210s, Mem Peak Delta: 33.43 MiB\n", - " Run 4: 0.8468s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 0.8123s, Mem Peak Delta: 16.05 MiB\n", + " Run 1: 0.8329s, Mem Peak Delta: 0.00 MiB\n", + " Run 2: 0.8163s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.8052s, Mem Peak Delta: 0.00 MiB\n", + " Run 4: 0.8053s, Mem Peak Delta: 6.38 MiB\n", + " Run 5: 0.8217s, Mem Peak Delta: 0.00 MiB\n", " Warm-up run for run_xclim_opt_flox_SDII...\n", "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9853s, Mem Peak Delta: 34.00 MiB\n", - " Run 2: 0.9825s, Mem Peak Delta: 1.79 MiB\n", - " Run 3: 0.9502s, Mem Peak Delta: 33.94 MiB\n", - " Run 4: 0.9560s, Mem Peak Delta: 25.59 MiB\n", - " Run 5: 0.9399s, Mem Peak Delta: 43.94 MiB\n" + " Run 1: 0.9713s, Mem Peak Delta: 49.05 MiB\n", + " Run 2: 0.9530s, Mem Peak Delta: 0.00 MiB\n", + " Run 3: 0.9497s, Mem Peak Delta: 49.94 MiB\n", + " Run 4: 0.9755s, Mem Peak Delta: 49.93 MiB\n", + " Run 5: 0.9685s, Mem Peak Delta: 49.94 MiB\n" ] } ], @@ -1046,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "id": "75852284", "metadata": {}, "outputs": [ @@ -1094,39 +1087,39 @@ " WSDI\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 95.070671\n", - " 2.517686\n", - " 523.523438\n", - " 1.076398\n", + " 96.238054\n", + " 2.022403\n", + " 160.316406\n", + " 1.020926\n", " \n", " \n", " 1\n", " WSDI\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 95.031452\n", - " 1.967204\n", - " 210.402344\n", - " 1.076842\n", + " 100.923075\n", + " 3.648089\n", + " 166.261719\n", + " 0.973533\n", " \n", " \n", " 2\n", " WSDI\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 59.476542\n", - " 1.032706\n", - " 233.464844\n", - " 1.720575\n", + " 60.369373\n", + " 0.427161\n", + " 128.804688\n", + " 1.627512\n", " \n", " \n", " 3\n", " WSDI\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 102.333867\n", - " 3.861991\n", - " 295.089844\n", + " 98.251902\n", + " 1.968112\n", + " 179.878906\n", " 1.000000\n", " \n", " \n", @@ -1134,59 +1127,59 @@ " WSDI\n", " Xclim\n", " 2. Flox (Standard)\n", - " 107.044898\n", - " 4.414015\n", - " 211.808594\n", - " 0.955990\n", + " 95.955562\n", + " 2.264089\n", + " 149.257812\n", + " 1.023931\n", " \n", " \n", " 5\n", " WSDI\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 66.947395\n", - " 2.734442\n", - " 191.054688\n", - " 1.528571\n", + " 60.259380\n", + " 0.359515\n", + " 131.078125\n", + " 1.630483\n", " \n", " \n", " 6\n", " CWD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 28.841368\n", - " 0.217333\n", - " 25.105469\n", - " 0.960648\n", + " 26.059164\n", + " 0.143695\n", + " 7.625000\n", + " 1.007200\n", " \n", " \n", " 7\n", " CWD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 23.222558\n", - " 0.362283\n", - " 33.371094\n", - " 1.193081\n", + " 20.831257\n", + " 0.135888\n", + " 21.617188\n", + " 1.259971\n", " \n", " \n", " 8\n", " CWD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 21.785499\n", - " 0.556947\n", - " 66.625000\n", - " 1.271782\n", + " 20.361283\n", + " 0.174882\n", + " 49.937500\n", + " 1.289053\n", " \n", " \n", " 9\n", " CWD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 27.706397\n", - " 0.022472\n", - " 33.214844\n", + " 26.246779\n", + " 0.125185\n", + " 0.000000\n", " 1.000000\n", " \n", " \n", @@ -1194,59 +1187,59 @@ " CWD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 22.231937\n", - " 0.186417\n", - " 83.011719\n", - " 1.246243\n", + " 20.973772\n", + " 0.358413\n", + " 17.867188\n", + " 1.251410\n", " \n", " \n", " 11\n", " CWD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 21.765449\n", - " 0.655963\n", - " 66.375000\n", - " 1.272953\n", + " 21.325712\n", + " 0.282060\n", + " 49.937500\n", + " 1.230757\n", " \n", " \n", " 12\n", " DTR\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 8.958993\n", - " 0.017841\n", - " 33.285156\n", - " 1.018993\n", + " 9.265321\n", + " 0.040986\n", + " 16.625000\n", + " 1.003354\n", " \n", " \n", " 13\n", " DTR\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.367763\n", - " 0.004411\n", - " 68.597656\n", - " 6.674512\n", + " 1.387280\n", + " 0.005597\n", + " 49.835938\n", + " 6.701169\n", " \n", " \n", " 14\n", " DTR\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.635447\n", - " 0.020017\n", - " 122.488281\n", - " 5.582052\n", + " 1.677764\n", + " 0.019465\n", + " 107.460938\n", + " 5.540945\n", " \n", " \n", " 15\n", " DTR\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 9.129151\n", - " 0.161529\n", - " 8.250000\n", + " 9.296399\n", + " 0.026865\n", + " 23.082031\n", " 1.000000\n", " \n", " \n", @@ -1254,59 +1247,59 @@ " DTR\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.357999\n", - " 0.010495\n", - " 49.941406\n", - " 6.722503\n", + " 1.374482\n", + " 0.010758\n", + " 49.476562\n", + " 6.763564\n", " \n", " \n", " 17\n", " DTR\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.703398\n", - " 0.026138\n", - " 89.105469\n", - " 5.359375\n", + " 1.696448\n", + " 0.013268\n", + " 91.562500\n", + " 5.479920\n", " \n", " \n", " 18\n", " HDD\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 7.492971\n", - " 0.079038\n", - " 42.625000\n", - " 0.997077\n", + " 7.659471\n", + " 0.043146\n", + " 30.785156\n", + " 0.993867\n", " \n", " \n", " 19\n", " HDD\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 1.298846\n", - " 0.009550\n", - " 64.585938\n", - " 5.752082\n", + " 1.310911\n", + " 0.011830\n", + " 56.113281\n", + " 5.807026\n", " \n", " \n", " 20\n", " HDD\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.426414\n", - " 0.014806\n", - " 135.496094\n", - " 5.237655\n", + " 1.451769\n", + " 0.010897\n", + " 99.875000\n", + " 5.243598\n", " \n", " \n", " 21\n", " HDD\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 7.471066\n", - " 0.148868\n", - " 26.750000\n", + " 7.612493\n", + " 0.054374\n", + " 11.875000\n", " 1.000000\n", " \n", " \n", @@ -1314,59 +1307,59 @@ " HDD\n", " Xclim\n", " 2. Flox (Standard)\n", - " 1.292692\n", - " 0.012860\n", - " 35.457031\n", - " 5.779463\n", + " 1.291052\n", + " 0.003433\n", + " 33.339844\n", + " 5.896351\n", " \n", " \n", " 23\n", " HDD\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 1.443442\n", - " 0.041851\n", - " 74.917969\n", - " 5.175870\n", + " 1.429851\n", + " 0.015745\n", + " 71.875000\n", + " 5.323978\n", " \n", " \n", " 24\n", " SDII\n", " Earthkit\n", " 1. No Flox (Standard)\n", - " 9.949329\n", - " 0.199215\n", - " 74.886719\n", - " 1.016605\n", + " 10.225849\n", + " 0.046377\n", + " 6.000000\n", + " 0.995162\n", " \n", " \n", " 25\n", " SDII\n", " Earthkit\n", " 2. Flox (Standard)\n", - " 0.812625\n", - " 0.006871\n", - " 40.148438\n", - " 12.446758\n", + " 0.814624\n", + " 0.004503\n", + " 0.000000\n", + " 12.492111\n", " \n", " \n", " 26\n", " SDII\n", " Earthkit\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.956808\n", - " 0.011624\n", - " 58.839844\n", - " 10.571128\n", + " 0.957500\n", + " 0.009923\n", + " 50.054688\n", + " 10.628070\n", " \n", " \n", " 27\n", " SDII\n", " Xclim\n", " 1. No Flox (Standard)\n", - " 10.114541\n", - " 0.100703\n", - " 66.402344\n", + " 10.176372\n", + " 0.034716\n", + " 18.457031\n", " 1.000000\n", " \n", " \n", @@ -1374,20 +1367,20 @@ " SDII\n", " Xclim\n", " 2. Flox (Standard)\n", - " 0.820253\n", - " 0.016554\n", - " 33.425781\n", - " 12.331000\n", + " 0.816276\n", + " 0.010467\n", + " 6.378906\n", + " 12.466829\n", " \n", " \n", " 29\n", " SDII\n", " Xclim\n", " 3. Flox + Opt (Chunk -1)\n", - " 0.956009\n", - " 0.018034\n", - " 43.937500\n", - " 10.579968\n", + " 0.968494\n", + " 0.010326\n", + " 49.937500\n", + " 10.507419\n", " \n", " \n", "\n", @@ -1395,68 +1388,68 @@ ], "text/plain": [ " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 95.070671 2.517686 \n", - "1 WSDI Earthkit 2. Flox (Standard) 95.031452 1.967204 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 59.476542 1.032706 \n", - "3 WSDI Xclim 1. No Flox (Standard) 102.333867 3.861991 \n", - "4 WSDI Xclim 2. Flox (Standard) 107.044898 4.414015 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 66.947395 2.734442 \n", - "6 CWD Earthkit 1. No Flox (Standard) 28.841368 0.217333 \n", - "7 CWD Earthkit 2. Flox (Standard) 23.222558 0.362283 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 21.785499 0.556947 \n", - "9 CWD Xclim 1. No Flox (Standard) 27.706397 0.022472 \n", - "10 CWD Xclim 2. Flox (Standard) 22.231937 0.186417 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 21.765449 0.655963 \n", - "12 DTR Earthkit 1. No Flox (Standard) 8.958993 0.017841 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.367763 0.004411 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.635447 0.020017 \n", - "15 DTR Xclim 1. No Flox (Standard) 9.129151 0.161529 \n", - "16 DTR Xclim 2. Flox (Standard) 1.357999 0.010495 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.703398 0.026138 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.492971 0.079038 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.298846 0.009550 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.426414 0.014806 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.471066 0.148868 \n", - "22 HDD Xclim 2. Flox (Standard) 1.292692 0.012860 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.443442 0.041851 \n", - "24 SDII Earthkit 1. No Flox (Standard) 9.949329 0.199215 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.812625 0.006871 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.956808 0.011624 \n", - "27 SDII Xclim 1. No Flox (Standard) 10.114541 0.100703 \n", - "28 SDII Xclim 2. Flox (Standard) 0.820253 0.016554 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.956009 0.018034 \n", + "0 WSDI Earthkit 1. No Flox (Standard) 96.238054 2.022403 \n", + "1 WSDI Earthkit 2. Flox (Standard) 100.923075 3.648089 \n", + "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 60.369373 0.427161 \n", + "3 WSDI Xclim 1. No Flox (Standard) 98.251902 1.968112 \n", + "4 WSDI Xclim 2. Flox (Standard) 95.955562 2.264089 \n", + "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 60.259380 0.359515 \n", + "6 CWD Earthkit 1. No Flox (Standard) 26.059164 0.143695 \n", + "7 CWD Earthkit 2. Flox (Standard) 20.831257 0.135888 \n", + "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.361283 0.174882 \n", + "9 CWD Xclim 1. No Flox (Standard) 26.246779 0.125185 \n", + "10 CWD Xclim 2. Flox (Standard) 20.973772 0.358413 \n", + "11 CWD Xclim 3. Flox + Opt (Chunk -1) 21.325712 0.282060 \n", + "12 DTR Earthkit 1. No Flox (Standard) 9.265321 0.040986 \n", + "13 DTR Earthkit 2. Flox (Standard) 1.387280 0.005597 \n", + "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.677764 0.019465 \n", + "15 DTR Xclim 1. No Flox (Standard) 9.296399 0.026865 \n", + "16 DTR Xclim 2. Flox (Standard) 1.374482 0.010758 \n", + "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.696448 0.013268 \n", + "18 HDD Earthkit 1. No Flox (Standard) 7.659471 0.043146 \n", + "19 HDD Earthkit 2. Flox (Standard) 1.310911 0.011830 \n", + "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.451769 0.010897 \n", + "21 HDD Xclim 1. No Flox (Standard) 7.612493 0.054374 \n", + "22 HDD Xclim 2. Flox (Standard) 1.291052 0.003433 \n", + "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.429851 0.015745 \n", + "24 SDII Earthkit 1. No Flox (Standard) 10.225849 0.046377 \n", + "25 SDII Earthkit 2. Flox (Standard) 0.814624 0.004503 \n", + "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.957500 0.009923 \n", + "27 SDII Xclim 1. No Flox (Standard) 10.176372 0.034716 \n", + "28 SDII Xclim 2. Flox (Standard) 0.816276 0.010467 \n", + "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.968494 0.010326 \n", "\n", " max_mem Speedup \n", - "0 523.523438 1.076398 \n", - "1 210.402344 1.076842 \n", - "2 233.464844 1.720575 \n", - "3 295.089844 1.000000 \n", - "4 211.808594 0.955990 \n", - "5 191.054688 1.528571 \n", - "6 25.105469 0.960648 \n", - "7 33.371094 1.193081 \n", - "8 66.625000 1.271782 \n", - "9 33.214844 1.000000 \n", - "10 83.011719 1.246243 \n", - "11 66.375000 1.272953 \n", - "12 33.285156 1.018993 \n", - "13 68.597656 6.674512 \n", - "14 122.488281 5.582052 \n", - "15 8.250000 1.000000 \n", - "16 49.941406 6.722503 \n", - "17 89.105469 5.359375 \n", - "18 42.625000 0.997077 \n", - "19 64.585938 5.752082 \n", - "20 135.496094 5.237655 \n", - "21 26.750000 1.000000 \n", - "22 35.457031 5.779463 \n", - "23 74.917969 5.175870 \n", - "24 74.886719 1.016605 \n", - "25 40.148438 12.446758 \n", - "26 58.839844 10.571128 \n", - "27 66.402344 1.000000 \n", - "28 33.425781 12.331000 \n", - "29 43.937500 10.579968 " + "0 160.316406 1.020926 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", 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", + "image/png": 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", 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" ] @@ -1538,19 +1521,8 @@ "plt.tight_layout()\n", "plt.show()\n", "\n", - "# Plot 2: Median Execution Time\n", - "plt.figure(figsize=(10, 6))\n", - "# We plot the pre-calculated median.\n", - "# Note: standard sns.barplot aggregates data if there are duplicates.\n", - "# Here df_res has 1 row per case, so it just plots the value.\n", - "sns.barplot(data=df_res, x=\"Indicator\", y=\"median_time\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", - "plt.title(\"Median Execution Time (seconds)\\n(Lower is better, Robust to Outliers)\")\n", - "plt.legend().remove()\n", - "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()\n", "\n", - "# Plot 3: Peak Memory Usage\n", + "# Plot 2: Peak Memory Usage\n", "plt.figure(figsize=(10, 6))\n", "sns.barplot(data=df_res, x=\"Indicator\", y=\"max_mem\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", From fc5b533b4169824301fe6c2c22cc235f202e7d1b Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Feb 2026 09:45:05 +0100 Subject: [PATCH 19/47] refactor: Update and expand unit tests for precipitation and temperature indicators. --- tests/unit/indicators/test_precipitation.py | 761 +++----------------- tests/unit/indicators/test_temperature.py | 196 +++-- 2 files changed, 251 insertions(+), 706 deletions(-) diff --git a/tests/unit/indicators/test_precipitation.py b/tests/unit/indicators/test_precipitation.py index c70cc6d..f14243a 100644 --- a/tests/unit/indicators/test_precipitation.py +++ b/tests/unit/indicators/test_precipitation.py @@ -6,6 +6,9 @@ # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. +from typing import Any, Callable, Dict + +import pytest from pytest_mock import MockerFixture from earthkit.climate.indicators import precipitation @@ -17,659 +20,105 @@ class MockEarthkitData: pass -def test_maximum_consecutive_wet_days(mocker: MockerFixture, common_mocks): - """Test maximum_consecutive_wet_days calls wrapper correctly.""" - mock_fn = mocker.patch("xclim.indicators.atmos.maximum_consecutive_wet_days") - - pr_in = MockEarthkitData() - precipitation.maximum_consecutive_wet_days(pr_in, thresh="2 mm/day", freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(pr_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - call_args = mock_fn.call_args - assert call_args.kwargs["ds"] is ds_converted - assert call_args.kwargs["thresh"] == "2 mm/day" - assert call_args.kwargs["freq"] == "MS" - - -def test_daily_precipitation_intensity(mocker: MockerFixture, common_mocks): - """Test daily_precipitation_intensity calls wrapper correctly.""" - mock_fn = mocker.patch("xclim.indicators.atmos.daily_pr_intensity") - - pr_in = MockEarthkitData() - # Note: Validating against daily_pr_intensity as per existing code structure - try: - precipitation.daily_precipitation_intensity(pr_in, thresh="2 mm/day", freq="MS") - except AttributeError: - # Fallback if the function is actually named daily_pr_intensity in the module - precipitation.daily_pr_intensity(pr_in, thresh="2 mm/day", freq="MS") - - common_mocks["mock_to_xr"].assert_called_once_with(pr_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - call_args = mock_fn.call_args - assert call_args.kwargs["ds"] is ds_converted - assert call_args.kwargs["thresh"] == "2 mm/day" - assert call_args.kwargs["freq"] == "MS" - - -# New tests - - -def test_antecedent_precipitation_index(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.antecedent_precipitation_index") - - ds_in = MockEarthkitData() - precipitation.antecedent_precipitation_index(ds_in, val="test") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - # No other kwargs passed in original test besides ds_in? Wait, val="test" was passed. - # The original test passed val="test" to the wrapper. - # Let's verify it's passed to the xclim fn. - assert mock_fn.call_args.kwargs["val"] == "test" - - -def test_maximum_consecutive_dry_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.maximum_consecutive_dry_days") - - ds_in = MockEarthkitData() - precipitation.maximum_consecutive_dry_days(ds_in, val="test") - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["val"] == "test" - - -def test_cffwis_indices(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.cffwis_indices") - - ds_in = MockEarthkitData() - precipitation.cffwis_indices(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_cold_and_dry_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.cold_and_dry_days") - - ds_in = MockEarthkitData() - precipitation.cold_and_dry_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_cold_and_wet_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.cold_and_wet_days") - - ds_in = MockEarthkitData() - precipitation.cold_and_wet_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_days_over_precip_doy_thresh(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.days_over_precip_doy_thresh") - - ds_in = MockEarthkitData() - precipitation.days_over_precip_doy_thresh(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_days_over_precip_thresh(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.days_over_precip_thresh") - - ds_in = MockEarthkitData() - precipitation.days_over_precip_thresh(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_days_with_snow(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.days_with_snow") - - ds_in = MockEarthkitData() - precipitation.days_with_snow(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_drought_code(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.drought_code") - - ds_in = MockEarthkitData() - precipitation.drought_code(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_griffiths_drought_factor(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.griffiths_drought_factor") - - ds_in = MockEarthkitData() - precipitation.griffiths_drought_factor(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_duff_moisture_code(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.duff_moisture_code") - - ds_in = MockEarthkitData() - precipitation.duff_moisture_code(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_dry_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.dry_days") - - ds_in = MockEarthkitData() - precipitation.dry_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_dry_spell_frequency(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_frequency") - - ds_in = MockEarthkitData() - precipitation.dry_spell_frequency(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_dry_spell_max_length(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_max_length") - - ds_in = MockEarthkitData() - precipitation.dry_spell_max_length(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_dry_spell_total_length(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.dry_spell_total_length") - - ds_in = MockEarthkitData() - precipitation.dry_spell_total_length(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_dryness_index(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.dryness_index") - - ds_in = MockEarthkitData() - precipitation.dryness_index(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_mcarthur_forest_fire_danger_index(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.mcarthur_forest_fire_danger_index") - - ds_in = MockEarthkitData() - precipitation.mcarthur_forest_fire_danger_index(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_first_snowfall(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.first_snowfall") - - ds_in = MockEarthkitData() - precipitation.first_snowfall(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_fraction_over_precip_doy_thresh(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.fraction_over_precip_doy_thresh") - - ds_in = MockEarthkitData() - precipitation.fraction_over_precip_doy_thresh(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_fraction_over_precip_thresh(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.fraction_over_precip_thresh") - - ds_in = MockEarthkitData() - precipitation.fraction_over_precip_thresh(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_high_precip_low_temp(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.high_precip_low_temp") - - ds_in = MockEarthkitData() - precipitation.high_precip_low_temp(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_keetch_byram_drought_index(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.keetch_byram_drought_index") - - ds_in = MockEarthkitData() - precipitation.keetch_byram_drought_index(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_last_snowfall(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.last_snowfall") - - ds_in = MockEarthkitData() - precipitation.last_snowfall(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_liquid_precip_ratio(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_ratio") - - ds_in = MockEarthkitData() - precipitation.liquid_precip_ratio(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_liquid_precip_average(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_average") - - ds_in = MockEarthkitData() - precipitation.liquid_precip_average(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_liquid_precip_accumulation(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.liquid_precip_accumulation") - - ds_in = MockEarthkitData() - precipitation.liquid_precip_accumulation(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_max_n_day_precipitation_amount(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.max_n_day_precipitation_amount") - - ds_in = MockEarthkitData() - precipitation.max_n_day_precipitation_amount(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_max_pr_intensity(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.max_pr_intensity") - - ds_in = MockEarthkitData() - precipitation.max_pr_intensity(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_precip_average(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.precip_average") - - ds_in = MockEarthkitData() - precipitation.precip_average(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_precip_accumulation(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.precip_accumulation") - - ds_in = MockEarthkitData() - precipitation.precip_accumulation(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_rain_on_frozen_ground_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.rain_on_frozen_ground_days") - - ds_in = MockEarthkitData() - precipitation.rain_on_frozen_ground_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_rain_season(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.rain_season") - - ds_in = MockEarthkitData() - precipitation.rain_season(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_rprctot(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.rprctot") - - ds_in = MockEarthkitData() - precipitation.rprctot(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_max_1day_precipitation_amount(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.max_1day_precipitation_amount") - - ds_in = MockEarthkitData() - precipitation.max_1day_precipitation_amount(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_snowfall_frequency(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.snowfall_frequency") - - ds_in = MockEarthkitData() - precipitation.snowfall_frequency(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_snowfall_intensity(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.snowfall_intensity") - - ds_in = MockEarthkitData() - precipitation.snowfall_intensity(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_solid_precip_average(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.solid_precip_average") - - ds_in = MockEarthkitData() - precipitation.solid_precip_average(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_solid_precip_accumulation(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.solid_precip_accumulation") - - ds_in = MockEarthkitData() - precipitation.solid_precip_accumulation(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_warm_and_dry_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.warm_and_dry_days") - - ds_in = MockEarthkitData() - precipitation.warm_and_dry_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_warm_and_wet_days(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.warm_and_wet_days") - - ds_in = MockEarthkitData() - precipitation.warm_and_wet_days(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_water_cycle_intensity(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.water_cycle_intensity") - - ds_in = MockEarthkitData() - precipitation.water_cycle_intensity(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wet_precip_accumulation(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wet_precip_accumulation") - - ds_in = MockEarthkitData() - precipitation.wet_precip_accumulation(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wet_spell_frequency(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_frequency") - - ds_in = MockEarthkitData() - precipitation.wet_spell_frequency(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wet_spell_max_length(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_max_length") - - ds_in = MockEarthkitData() - precipitation.wet_spell_max_length(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wet_spell_total_length(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wet_spell_total_length") - - ds_in = MockEarthkitData() - precipitation.wet_spell_total_length(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wetdays(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wetdays") - - ds_in = MockEarthkitData() - precipitation.wetdays(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted - - -def test_wetdays_prop(mocker: MockerFixture, common_mocks): - mock_fn = mocker.patch("xclim.indicators.atmos.wetdays_prop") - - ds_in = MockEarthkitData() - precipitation.wetdays_prop(ds_in) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted +INDICATORS = [ + (precipitation.antecedent_precipitation_index, "antecedent_precipitation_index", {"val": "test"}), + (precipitation.maximum_consecutive_dry_days, "maximum_consecutive_dry_days", {"val": "test"}), + ( + precipitation.maximum_consecutive_wet_days, + "maximum_consecutive_wet_days", + {"thresh": "2 mm/day", "freq": "MS"}, + ), + # The original test used specific args for daily_pr_intensity, preserving those. + (precipitation.daily_pr_intensity, "daily_pr_intensity", {"thresh": "2 mm/day", "freq": "MS"}), + (precipitation.cffwis_indices, "cffwis_indices", {"arg1": "val1"}), + (precipitation.cold_and_dry_days, "cold_and_dry_days", {"arg1": "val1"}), + (precipitation.cold_and_wet_days, "cold_and_wet_days", {"arg1": "val1"}), + (precipitation.days_over_precip_doy_thresh, "days_over_precip_doy_thresh", {"arg1": "val1"}), + (precipitation.days_over_precip_thresh, "days_over_precip_thresh", {"arg1": "val1"}), + (precipitation.days_with_snow, "days_with_snow", {"arg1": "val1"}), + (precipitation.drought_code, "drought_code", {"arg1": "val1"}), + (precipitation.griffiths_drought_factor, "griffiths_drought_factor", {"arg1": "val1"}), + (precipitation.duff_moisture_code, "duff_moisture_code", {"arg1": "val1"}), + (precipitation.dry_days, "dry_days", {"arg1": "val1"}), + (precipitation.dry_spell_frequency, "dry_spell_frequency", {"arg1": "val1"}), + (precipitation.dry_spell_max_length, "dry_spell_max_length", {"arg1": "val1"}), + (precipitation.dry_spell_total_length, "dry_spell_total_length", {"arg1": "val1"}), + (precipitation.dryness_index, "dryness_index", {"arg1": "val1"}), + (precipitation.mcarthur_forest_fire_danger_index, "mcarthur_forest_fire_danger_index", {"arg1": "val1"}), + (precipitation.first_snowfall, "first_snowfall", {"arg1": "val1"}), + (precipitation.fraction_over_precip_doy_thresh, "fraction_over_precip_doy_thresh", {"arg1": "val1"}), + (precipitation.fraction_over_precip_thresh, "fraction_over_precip_thresh", {"arg1": "val1"}), + (precipitation.high_precip_low_temp, "high_precip_low_temp", {"arg1": "val1"}), + (precipitation.keetch_byram_drought_index, "keetch_byram_drought_index", {"arg1": "val1"}), + (precipitation.last_snowfall, "last_snowfall", {"arg1": "val1"}), + (precipitation.liquid_precip_ratio, "liquid_precip_ratio", {"arg1": "val1"}), + (precipitation.liquid_precip_average, "liquid_precip_average", {"arg1": "val1"}), + (precipitation.liquid_precip_accumulation, "liquid_precip_accumulation", {"arg1": "val1"}), + (precipitation.max_n_day_precipitation_amount, "max_n_day_precipitation_amount", {"arg1": "val1"}), + (precipitation.max_pr_intensity, "max_pr_intensity", {"arg1": "val1"}), + (precipitation.precip_average, "precip_average", {"arg1": "val1"}), + (precipitation.precip_accumulation, "precip_accumulation", {"arg1": "val1"}), + (precipitation.rain_on_frozen_ground_days, "rain_on_frozen_ground_days", {"arg1": "val1"}), + (precipitation.rain_season, "rain_season", {"arg1": "val1"}), + (precipitation.rprctot, "rprctot", {"arg1": "val1"}), + (precipitation.max_1day_precipitation_amount, "max_1day_precipitation_amount", {"arg1": "val1"}), + (precipitation.snowfall_frequency, "snowfall_frequency", {"arg1": "val1"}), + (precipitation.snowfall_intensity, "snowfall_intensity", {"arg1": "val1"}), + (precipitation.solid_precip_average, "solid_precip_average", {"arg1": "val1"}), + (precipitation.solid_precip_accumulation, "solid_precip_accumulation", {"arg1": "val1"}), + (precipitation.warm_and_dry_days, "warm_and_dry_days", {"arg1": "val1"}), + (precipitation.warm_and_wet_days, "warm_and_wet_days", {"arg1": "val1"}), + (precipitation.water_cycle_intensity, "water_cycle_intensity", {"arg1": "val1"}), + (precipitation.wet_precip_accumulation, "wet_precip_accumulation", {"arg1": "val1"}), + (precipitation.wet_spell_frequency, "wet_spell_frequency", {"arg1": "val1"}), + (precipitation.wet_spell_max_length, "wet_spell_max_length", {"arg1": "val1"}), + (precipitation.wet_spell_total_length, "wet_spell_total_length", {"arg1": "val1"}), + (precipitation.wetdays, "wetdays", {"arg1": "val1"}), + (precipitation.wetdays_prop, "wetdays_prop", {"arg1": "val1"}), +] + + +@pytest.mark.parametrize("earthkit_fn, xclim_name, kwargs", INDICATORS) +def test_precipitation_indicator( + mocker: MockerFixture, + common_mocks: dict, + earthkit_fn: Callable, + xclim_name: str, + kwargs: Dict[str, Any], +): + """ + Test that the earthkit function wraps the xclim function correctly. + + Parameters + ---------- + mocker : MockerFixture + The pytest-mock fixture. + common_mocks : dict + Dictionary containing common mocks used in tests. + earthkit_fn : Callable + The earthkit indicator function to test. + xclim_name : str + The name of the corresponding xclim function. + kwargs : Dict[str, Any] + Arguments to pass to the function. + """ + xclim_func_name = xclim_name + + mock_path = f"xclim.indicators.atmos.{xclim_func_name}" + + mock_fn = mocker.patch(mock_path) + + ds_in = MockEarthkitData() + + # Call the earthkit function + earthkit_fn(ds_in, **kwargs) + + # Verify conversions were called (handled by common_mocks) + common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) + ds_converted = common_mocks["mock_to_xr"].return_value[0] + + # Verify wrapped function called with the dataset and arguments + mock_fn.assert_called_once() + assert mock_fn.call_args.kwargs["ds"] is ds_converted + for k, v in kwargs.items(): + assert mock_fn.call_args.kwargs[k] == v diff --git a/tests/unit/indicators/test_temperature.py b/tests/unit/indicators/test_temperature.py index 173bd84..fcd9daa 100644 --- a/tests/unit/indicators/test_temperature.py +++ b/tests/unit/indicators/test_temperature.py @@ -6,6 +6,9 @@ # granted to it by virtue of its status as an intergovernmental organisation nor # does it submit to any jurisdiction. +from typing import Callable + +import pytest from pytest_mock import MockerFixture from earthkit.climate.indicators import temperature @@ -17,62 +20,155 @@ class MockEarthkitData: pass -def test_daily_temperature_range(mocker: MockerFixture, common_mocks): - """Test daily_temperature_range calls wrapper with merged dataset.""" - # Mock the underlying xclim function - mock_fn = mocker.patch("xclim.indicators.atmos.daily_temperature_range") - - # Call function with single dataset - ds_in = MockEarthkitData() - temperature.daily_temperature_range(ds_in, arg="val") - - # Verify conversions were called (handled by common_mocks) - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - # Verify wrapped function called with the dataset - call_args = mock_fn.call_args - assert call_args is not None - # The first argument to the xclim function should be the converted dataset - assert call_args.kwargs["ds"] is ds_converted - assert call_args.kwargs["arg"] == "val" - - -def test_heating_degree_days(mocker: MockerFixture, common_mocks): - """Test heating_degree_days calls wrapper with merged dataset.""" - mock_fn = mocker.patch("xclim.indicators.atmos.heating_degree_days") +INDICATORS = [ + (temperature.australian_hardiness_zones, "australian_hardiness_zones"), + ( + temperature.biologically_effective_degree_days, + "biologically_effective_degree_days", + ), + (temperature.cold_spell_days, "cold_spell_days"), + (temperature.cold_spell_duration_index, "cold_spell_duration_index"), + (temperature.cold_spell_frequency, "cold_spell_frequency"), + (temperature.cold_spell_max_length, "cold_spell_max_length"), + (temperature.cold_spell_total_length, "cold_spell_total_length"), + (temperature.consecutive_frost_days, "consecutive_frost_days"), + ( + temperature.maximum_consecutive_frost_free_days, + "maximum_consecutive_frost_free_days", + ), + (temperature.cool_night_index, "cool_night_index"), + (temperature.cooling_degree_days, "cooling_degree_days"), + ( + temperature.cooling_degree_days_approximation, + "cooling_degree_days_approximation", + ), + (temperature.corn_heat_units, "corn_heat_units"), + (temperature.chill_portions, "chill_portions"), + (temperature.chill_units, "chill_units"), + (temperature.degree_days_exceedance_date, "degree_days_exceedance_date"), + (temperature.daily_freezethaw_cycles, "daily_freezethaw_cycles"), + (temperature.daily_temperature_range, "daily_temperature_range"), + (temperature.max_daily_temperature_range, "max_daily_temperature_range"), + ( + temperature.daily_temperature_range_variability, + "daily_temperature_range_variability", + ), + (temperature.extreme_temperature_range, "extreme_temperature_range"), + (temperature.fire_season, "fire_season"), + (temperature.first_day_tg_above, "first_day_tg_above"), + (temperature.first_day_tg_below, "first_day_tg_below"), + (temperature.first_day_tn_above, "first_day_tn_above"), + (temperature.first_day_tn_below, "first_day_tn_below"), + (temperature.first_day_tx_above, "first_day_tx_above"), + (temperature.first_day_tx_below, "first_day_tx_below"), + (temperature.freezethaw_spell_frequency, "freezethaw_spell_frequency"), + (temperature.freezethaw_spell_max_length, "freezethaw_spell_max_length"), + (temperature.freezethaw_spell_mean_length, "freezethaw_spell_mean_length"), + (temperature.freezing_degree_days, "freezing_degree_days"), + (temperature.freshet_start, "freshet_start"), + (temperature.frost_days, "frost_days"), + (temperature.frost_free_season_end, "frost_free_season_end"), + (temperature.frost_free_season_length, "frost_free_season_length"), + (temperature.frost_free_season_start, "frost_free_season_start"), + (temperature.frost_free_spell_max_length, "frost_free_spell_max_length"), + (temperature.frost_season_length, "frost_season_length"), + (temperature.growing_degree_days, "growing_degree_days"), + (temperature.growing_season_end, "growing_season_end"), + (temperature.growing_season_length, "growing_season_length"), + (temperature.growing_season_start, "growing_season_start"), + (temperature.heat_spell_frequency, "heat_spell_frequency"), + (temperature.heat_spell_max_length, "heat_spell_max_length"), + (temperature.heat_spell_total_length, "heat_spell_total_length"), + (temperature.heat_wave_frequency, "heat_wave_frequency"), + (temperature.heat_wave_index, "heat_wave_index"), + (temperature.heat_wave_max_length, "heat_wave_max_length"), + (temperature.heat_wave_total_length, "heat_wave_total_length"), + (temperature.heating_degree_days, "heating_degree_days"), + ( + temperature.heating_degree_days_approximation, + "heating_degree_days_approximation", + ), + (temperature.hot_days, "hot_days"), + (temperature.hot_spell_frequency, "hot_spell_frequency"), + (temperature.hot_spell_max_length, "hot_spell_max_length"), + (temperature.hot_spell_max_magnitude, "hot_spell_max_magnitude"), + (temperature.hot_spell_total_length, "hot_spell_total_length"), + (temperature.huglin_index, "huglin_index"), + (temperature.ice_days, "ice_days"), + (temperature.last_spring_frost, "last_spring_frost"), + (temperature.late_frost_days, "late_frost_days"), + (temperature.latitude_temperature_index, "latitude_temperature_index"), + (temperature.maximum_consecutive_warm_days, "maximum_consecutive_warm_days"), + (temperature.tg10p, "tg10p"), + (temperature.tg90p, "tg90p"), + (temperature.tg_days_above, "tg_days_above"), + (temperature.tg_days_below, "tg_days_below"), + (temperature.tg_max, "tg_max"), + (temperature.tg_mean, "tg_mean"), + (temperature.tg_min, "tg_min"), + (temperature.thawing_degree_days, "thawing_degree_days"), + (temperature.tn10p, "tn10p"), + (temperature.tn90p, "tn90p"), + (temperature.tn_days_above, "tn_days_above"), + (temperature.tn_days_below, "tn_days_below"), + (temperature.tn_max, "tn_max"), + (temperature.tn_mean, "tn_mean"), + (temperature.tn_min, "tn_min"), + (temperature.tropical_nights, "tropical_nights"), + (temperature.tx10p, "tx10p"), + (temperature.tx90p, "tx90p"), + (temperature.tx_days_above, "tx_days_above"), + (temperature.tx_days_below, "tx_days_below"), + (temperature.tx_max, "tx_max"), + (temperature.tx_mean, "tx_mean"), + (temperature.tx_min, "tx_min"), + (temperature.tx_tn_days_above, "tx_tn_days_above"), + (temperature.usda_hardiness_zones, "usda_hardiness_zones"), + (temperature.warm_spell_duration_index, "warm_spell_duration_index"), +] + + +@pytest.mark.parametrize("earthkit_fn, xclim_name", INDICATORS) +def test_temperature_indicator( + mocker: MockerFixture, + common_mocks: dict, + earthkit_fn: Callable, + xclim_name: str, +): + """ + Test that the earthkit function wraps the xclim function correctly. + + Parameters + ---------- + mocker : MockerFixture + The pytest-mock fixture. + common_mocks : dict + Dictionary containing common mocks used in tests. + earthkit_fn : Callable + The earthkit indicator function to test. + xclim_name : str + The name of the corresponding xclim function. + """ + xclim_func_name = xclim_name + + mock_path = f"xclim.indicators.atmos.{xclim_func_name}" + + mock_fn = mocker.patch(mock_path) + + # Use a dummy argument dictionary + kwargs = {"arg1": "val1", "arg2": 2} ds_in = MockEarthkitData() - temperature.heating_degree_days(ds_in, thresh="18 degC") + # Call the earthkit function + earthkit_fn(ds_in, **kwargs) + # Verify conversions were called (handled by common_mocks) common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) ds_converted = common_mocks["mock_to_xr"].return_value[0] + # Verify wrapped function called with the dataset and arguments mock_fn.assert_called_once() assert mock_fn.call_args.kwargs["ds"] is ds_converted - assert mock_fn.call_args.kwargs["thresh"] == "18 degC" - - -def test_warm_spell_duration_index(mocker: MockerFixture, common_mocks): - """Test warm_spell_duration_index passes merged dataset (tasmax + tasmax_per).""" - # Mock wrapper factory - mock_fn = mocker.patch("xclim.indicators.atmos.warm_spell_duration_index") - - # Create a dummy input that represents a merged dataset - ds_merged_in = MockEarthkitData() - - # Call with single merged input - temperature.warm_spell_duration_index(ds_merged_in, window=10) - - common_mocks["mock_to_xr"].assert_called_once_with(ds_merged_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - - # Verify call args - mock_fn.assert_called_once() - call_kwargs = mock_fn.call_args.kwargs - - assert call_kwargs["ds"] is ds_converted - assert call_kwargs["window"] == 10 - # Ensure reference_data is NOT passed - assert "reference_data" not in call_kwargs + for k, v in kwargs.items(): + assert mock_fn.call_args.kwargs[k] == v From 5654f7b4d84c85266879939e19818aa6a1e1d3e3 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 13 Feb 2026 11:10:03 +0100 Subject: [PATCH 20/47] docs: Update xclim wrapper docstrings with specific function names, URLs, and detailed units, and remove extraneous blank lines. --- .../climate/indicators/precipitation.py | 234 ++++++------ .../climate/indicators/temperature.py | 337 ++++++++---------- tools/xclim_wrappers_generator.py | 56 ++- 3 files changed, 301 insertions(+), 326 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index d4f9303..ffc5d2a 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -22,14 +22,14 @@ def antecedent_precipitation_index( **kwargs: Any, ) -> conversions.EarthkitData: """ - Antecedent Precipitation Index. + Antecedent precipitation index. Calculate the running weighted sum of daily precipitation values given a window and weighting exponent. This index serves as an indicator for soil moisture. **Units:** mm - This function wraps :func:`xclim.indicators.atmos.api`. + This function wraps `xclim.indicators.atmos.antecedent_precipitation_index `_. Parameters ---------- @@ -37,7 +37,7 @@ def antecedent_precipitation_index( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.api`. + :func:`xclim.indicators.atmos.antecedent_precipitation_index`. Returns ------- @@ -47,7 +47,6 @@ def antecedent_precipitation_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.antecedent_precipitation_index) return wrapper(ds, **kwargs) - def maximum_consecutive_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -60,7 +59,7 @@ def maximum_consecutive_dry_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cdd`. + This function wraps `xclim.indicators.atmos.maximum_consecutive_dry_days `_. Parameters ---------- @@ -68,7 +67,7 @@ def maximum_consecutive_dry_days( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cdd`. + :func:`xclim.indicators.atmos.maximum_consecutive_dry_days`. Returns ------- @@ -78,21 +77,26 @@ def maximum_consecutive_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_dry_days) return wrapper(ds, **kwargs) - def cffwis_indices( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Canadian Fire Weather Index System indices. + Canadian fire weather index system indices. Computes the six (6) fire weather indexes, as defined by the Canadian Forest Service: - The Drought Code - The Duff-Moisture Code - The Fine Fuel Moisture Code - The Initial Spread Index - The Build Up Index - The Fire Weather Index. - **Units:** ['', '', '', '', '', ''] + **Units:** + - dc: dimensionless + - dmc: dimensionless + - ffmc: dimensionless + - isi: dimensionless + - bui: dimensionless + - fwi: dimensionless - This function wraps :func:`xclim.indicators.atmos.cffwis`. + This function wraps `xclim.indicators.atmos.cffwis_indices `_. Parameters ---------- @@ -100,7 +104,7 @@ def cffwis_indices( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cffwis`. + :func:`xclim.indicators.atmos.cffwis_indices`. Returns ------- @@ -110,7 +114,6 @@ def cffwis_indices( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cffwis_indices) return wrapper(ds, **kwargs) - def cold_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -123,7 +126,7 @@ def cold_and_dry_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_and_dry_days`. + This function wraps `xclim.indicators.atmos.cold_and_dry_days `_. Parameters ---------- @@ -141,7 +144,6 @@ def cold_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_dry_days) return wrapper(ds, **kwargs) - def cold_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -154,7 +156,7 @@ def cold_and_wet_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_and_wet_days`. + This function wraps `xclim.indicators.atmos.cold_and_wet_days `_. Parameters ---------- @@ -172,7 +174,6 @@ def cold_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_wet_days) return wrapper(ds, **kwargs) - def maximum_consecutive_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -185,7 +186,7 @@ def maximum_consecutive_wet_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cwd`. + This function wraps `xclim.indicators.atmos.maximum_consecutive_wet_days `_. Parameters ---------- @@ -193,7 +194,7 @@ def maximum_consecutive_wet_days( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cwd`. + :func:`xclim.indicators.atmos.maximum_consecutive_wet_days`. Returns ------- @@ -203,7 +204,6 @@ def maximum_consecutive_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_wet_days) return wrapper(ds, **kwargs) - def days_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -216,7 +216,7 @@ def days_over_precip_doy_thresh( **Units:** days - This function wraps :func:`xclim.indicators.atmos.days_over_precip_doy_thresh`. + This function wraps `xclim.indicators.atmos.days_over_precip_doy_thresh `_. Parameters ---------- @@ -234,7 +234,6 @@ def days_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_doy_thresh) return wrapper(ds, **kwargs) - def days_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -247,7 +246,7 @@ def days_over_precip_thresh( **Units:** days - This function wraps :func:`xclim.indicators.atmos.days_over_precip_thresh`. + This function wraps `xclim.indicators.atmos.days_over_precip_thresh `_. Parameters ---------- @@ -265,7 +264,6 @@ def days_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_thresh) return wrapper(ds, **kwargs) - def days_with_snow( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -277,7 +275,7 @@ def days_with_snow( **Units:** days - This function wraps :func:`xclim.indicators.atmos.days_with_snow`. + This function wraps `xclim.indicators.atmos.days_with_snow `_. Parameters ---------- @@ -295,7 +293,6 @@ def days_with_snow( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_with_snow) return wrapper(ds, **kwargs) - def drought_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -306,7 +303,9 @@ def drought_code( The Drought Index is part of the Canadian Forest-Weather Index system. It is a numerical code that estimates the average moisture content of organic layers. - This function wraps :func:`xclim.indicators.atmos.dc`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.drought_code `_. Parameters ---------- @@ -314,7 +313,7 @@ def drought_code( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dc`. + :func:`xclim.indicators.atmos.drought_code`. Returns ------- @@ -324,7 +323,6 @@ def drought_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.drought_code) return wrapper(ds, **kwargs) - def griffiths_drought_factor( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -336,7 +334,9 @@ def griffiths_drought_factor( deep litter bed. It is often used in the calculation of the McArthur Forest Fire Danger Index. The method implemented here follows :cite:t:`ffdi-finkele_2006`. - This function wraps :func:`xclim.indicators.atmos.df`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.griffiths_drought_factor `_. Parameters ---------- @@ -344,7 +344,7 @@ def griffiths_drought_factor( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.df`. + :func:`xclim.indicators.atmos.griffiths_drought_factor`. Returns ------- @@ -354,19 +354,20 @@ def griffiths_drought_factor( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.griffiths_drought_factor) return wrapper(ds, **kwargs) - def duff_moisture_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Duff moisture code (FWI component). + Duff moisture code (fwi component). The duff moisture code is part of the Canadian Forest Fire Weather Index System. It is a numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. - This function wraps :func:`xclim.indicators.atmos.dmc`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.duff_moisture_code `_. Parameters ---------- @@ -374,7 +375,7 @@ def duff_moisture_code( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dmc`. + :func:`xclim.indicators.atmos.duff_moisture_code`. Returns ------- @@ -384,7 +385,6 @@ def duff_moisture_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.duff_moisture_code) return wrapper(ds, **kwargs) - def dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -396,7 +396,7 @@ def dry_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.dry_days`. + This function wraps `xclim.indicators.atmos.dry_days `_. Parameters ---------- @@ -414,7 +414,6 @@ def dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_days) return wrapper(ds, **kwargs) - def dry_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -425,7 +424,9 @@ def dry_spell_frequency( The frequency of dry periods of `N` days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold. - This function wraps :func:`xclim.indicators.atmos.dry_spell_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.dry_spell_frequency `_. Parameters ---------- @@ -443,7 +444,6 @@ def dry_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_frequency) return wrapper(ds, **kwargs) - def dry_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -456,7 +456,7 @@ def dry_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.dry_spell_max_length`. + This function wraps `xclim.indicators.atmos.dry_spell_max_length `_. Parameters ---------- @@ -474,7 +474,6 @@ def dry_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_max_length) return wrapper(ds, **kwargs) - def dry_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -487,7 +486,7 @@ def dry_spell_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.dry_spell_total_length`. + This function wraps `xclim.indicators.atmos.dry_spell_total_length `_. Parameters ---------- @@ -505,7 +504,6 @@ def dry_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_total_length) return wrapper(ds, **kwargs) - def dryness_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -519,7 +517,7 @@ def dryness_index( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.dryness_index`. + This function wraps `xclim.indicators.atmos.dryness_index `_. Parameters ---------- @@ -537,17 +535,18 @@ def dryness_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dryness_index) return wrapper(ds, **kwargs) - def mcarthur_forest_fire_danger_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - McArthur forest fire danger index (FFDI) Mark 5. + Mcarthur forest fire danger index (ffdi) mark 5. The FFDI is a numeric indicator of the potential danger of a forest fire. - This function wraps :func:`xclim.indicators.atmos.ffdi`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.mcarthur_forest_fire_danger_index `_. Parameters ---------- @@ -555,7 +554,7 @@ def mcarthur_forest_fire_danger_index( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.ffdi`. + :func:`xclim.indicators.atmos.mcarthur_forest_fire_danger_index`. Returns ------- @@ -565,7 +564,6 @@ def mcarthur_forest_fire_danger_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) return wrapper(ds, **kwargs) - def first_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -576,7 +574,9 @@ def first_snowfall( The first day where snowfall exceeded a given threshold during a time period (the threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). - This function wraps :func:`xclim.indicators.atmos.first_snowfall`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_snowfall `_. Parameters ---------- @@ -594,7 +594,6 @@ def first_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_snowfall) return wrapper(ds, **kwargs) - def fraction_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -606,7 +605,9 @@ def fraction_over_precip_doy_thresh( precipitation is above a threshold defining wet days and above a given percentile for that day. - This function wraps :func:`xclim.indicators.atmos.fraction_over_precip_doy_thresh`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.fraction_over_precip_doy_thresh `_. Parameters ---------- @@ -624,7 +625,6 @@ def fraction_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_doy_thresh) return wrapper(ds, **kwargs) - def fraction_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -636,7 +636,9 @@ def fraction_over_precip_thresh( precipitation is above a threshold defining wet days and above a given percentile for that day. - This function wraps :func:`xclim.indicators.atmos.fraction_over_precip_thresh`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.fraction_over_precip_thresh `_. Parameters ---------- @@ -654,7 +656,6 @@ def fraction_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_thresh) return wrapper(ds, **kwargs) - def high_precip_low_temp( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -667,7 +668,7 @@ def high_precip_low_temp( **Units:** days - This function wraps :func:`xclim.indicators.atmos.high_precip_low_temp`. + This function wraps `xclim.indicators.atmos.high_precip_low_temp `_. Parameters ---------- @@ -685,13 +686,12 @@ def high_precip_low_temp( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.high_precip_low_temp) return wrapper(ds, **kwargs) - def keetch_byram_drought_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Keetch-Byram drought index (KBDI) for soil moisture deficit. + Keetch-byram drought index (kbdi) for soil moisture deficit. The KBDI indicates the amount of water necessary to bring the soil moisture content back to field capacity. It is often used in the calculation of the McArthur Forest Fire @@ -701,7 +701,7 @@ def keetch_byram_drought_index( **Units:** mm/day - This function wraps :func:`xclim.indicators.atmos.kbdi`. + This function wraps `xclim.indicators.atmos.keetch_byram_drought_index `_. Parameters ---------- @@ -709,7 +709,7 @@ def keetch_byram_drought_index( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.kbdi`. + :func:`xclim.indicators.atmos.keetch_byram_drought_index`. Returns ------- @@ -719,7 +719,6 @@ def keetch_byram_drought_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.keetch_byram_drought_index) return wrapper(ds, **kwargs) - def last_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -730,7 +729,9 @@ def last_snowfall( The last day where snowfall exceeded a given threshold during a time period (the threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). - This function wraps :func:`xclim.indicators.atmos.last_snowfall`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.last_snowfall `_. Parameters ---------- @@ -748,7 +749,6 @@ def last_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_snowfall) return wrapper(ds, **kwargs) - def liquid_precip_ratio( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -760,7 +760,9 @@ def liquid_precip_ratio( precipitation is approximated from total precipitation on days where temperature is above a given threshold. - This function wraps :func:`xclim.indicators.atmos.liquid_precip_ratio`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.liquid_precip_ratio `_. Parameters ---------- @@ -778,7 +780,6 @@ def liquid_precip_ratio( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_ratio) return wrapper(ds, **kwargs) - def liquid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -791,7 +792,7 @@ def liquid_precip_average( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.liquidprcpavg`. + This function wraps `xclim.indicators.atmos.liquid_precip_average `_. Parameters ---------- @@ -799,7 +800,7 @@ def liquid_precip_average( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.liquidprcpavg`. + :func:`xclim.indicators.atmos.liquid_precip_average`. Returns ------- @@ -809,7 +810,6 @@ def liquid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_average) return wrapper(ds, **kwargs) - def liquid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -822,7 +822,7 @@ def liquid_precip_accumulation( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.liquidprcptot`. + This function wraps `xclim.indicators.atmos.liquid_precip_accumulation `_. Parameters ---------- @@ -830,7 +830,7 @@ def liquid_precip_accumulation( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.liquidprcptot`. + :func:`xclim.indicators.atmos.liquid_precip_accumulation`. Returns ------- @@ -840,7 +840,6 @@ def liquid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_accumulation) return wrapper(ds, **kwargs) - def max_n_day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -852,7 +851,7 @@ def max_n_day_precipitation_amount( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.max_n_day_precipitation_amount`. + This function wraps `xclim.indicators.atmos.max_n_day_precipitation_amount `_. Parameters ---------- @@ -870,7 +869,6 @@ def max_n_day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_n_day_precipitation_amount) return wrapper(ds, **kwargs) - def max_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -882,7 +880,7 @@ def max_pr_intensity( **Units:** mm h-1 - This function wraps :func:`xclim.indicators.atmos.max_pr_intensity`. + This function wraps `xclim.indicators.atmos.max_pr_intensity `_. Parameters ---------- @@ -900,7 +898,6 @@ def max_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_pr_intensity) return wrapper(ds, **kwargs) - def precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -915,7 +912,7 @@ def precip_average( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.prcpavg`. + This function wraps `xclim.indicators.atmos.precip_average `_. Parameters ---------- @@ -923,7 +920,7 @@ def precip_average( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.prcpavg`. + :func:`xclim.indicators.atmos.precip_average`. Returns ------- @@ -933,7 +930,6 @@ def precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_average) return wrapper(ds, **kwargs) - def precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -948,7 +944,7 @@ def precip_accumulation( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.prcptot`. + This function wraps `xclim.indicators.atmos.precip_accumulation `_. Parameters ---------- @@ -956,7 +952,7 @@ def precip_accumulation( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.prcptot`. + :func:`xclim.indicators.atmos.precip_accumulation`. Returns ------- @@ -966,7 +962,6 @@ def precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_accumulation) return wrapper(ds, **kwargs) - def rain_on_frozen_ground_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -980,7 +975,7 @@ def rain_on_frozen_ground_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.rain_frzgr`. + This function wraps `xclim.indicators.atmos.rain_on_frozen_ground_days `_. Parameters ---------- @@ -988,7 +983,7 @@ def rain_on_frozen_ground_days( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.rain_frzgr`. + :func:`xclim.indicators.atmos.rain_on_frozen_ground_days`. Returns ------- @@ -998,7 +993,6 @@ def rain_on_frozen_ground_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_on_frozen_ground_days) return wrapper(ds, **kwargs) - def rain_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1011,9 +1005,12 @@ def rain_season( by a period without prolonged dry sequences, which must happen before a given date. The rain season stops during a dry period happening after a given date. - **Units:** ['', '', 'days'] + **Units:** + - rain_season_start: dimensionless + - rain_season_end: dimensionless + - rain_season_length: days - This function wraps :func:`xclim.indicators.atmos.rain_season`. + This function wraps `xclim.indicators.atmos.rain_season `_. Parameters ---------- @@ -1031,7 +1028,6 @@ def rain_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_season) return wrapper(ds, **kwargs) - def rprctot( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1042,7 +1038,9 @@ def rprctot( The proportion of total precipitation due to convective processes. Only days with surpassing a minimum precipitation flux are considered. - This function wraps :func:`xclim.indicators.atmos.rprctot`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.rprctot `_. Parameters ---------- @@ -1060,7 +1058,6 @@ def rprctot( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rprctot) return wrapper(ds, **kwargs) - def max_1day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1072,7 +1069,7 @@ def max_1day_precipitation_amount( **Units:** mm/day - This function wraps :func:`xclim.indicators.atmos.rx1day`. + This function wraps `xclim.indicators.atmos.max_1day_precipitation_amount `_. Parameters ---------- @@ -1080,7 +1077,7 @@ def max_1day_precipitation_amount( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.rx1day`. + :func:`xclim.indicators.atmos.max_1day_precipitation_amount`. Returns ------- @@ -1090,19 +1087,18 @@ def max_1day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_1day_precipitation_amount) return wrapper(ds, **kwargs) - def daily_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Simple Daily Intensity Index. + Simple daily intensity index. Average precipitation for days with daily precipitation above a given threshold. **Units:** mm d-1 - This function wraps :func:`xclim.indicators.atmos.sdii`. + This function wraps `xclim.indicators.atmos.daily_pr_intensity `_. Parameters ---------- @@ -1110,7 +1106,7 @@ def daily_pr_intensity( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.sdii`. + :func:`xclim.indicators.atmos.daily_pr_intensity`. Returns ------- @@ -1120,7 +1116,6 @@ def daily_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) return wrapper(ds, **kwargs) - def snowfall_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1133,7 +1128,7 @@ def snowfall_frequency( **Units:** % - This function wraps :func:`xclim.indicators.atmos.snowfall_frequency`. + This function wraps `xclim.indicators.atmos.snowfall_frequency `_. Parameters ---------- @@ -1151,7 +1146,6 @@ def snowfall_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_frequency) return wrapper(ds, **kwargs) - def snowfall_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1164,7 +1158,7 @@ def snowfall_intensity( **Units:** mm/day - This function wraps :func:`xclim.indicators.atmos.snowfall_intensity`. + This function wraps `xclim.indicators.atmos.snowfall_intensity `_. Parameters ---------- @@ -1182,7 +1176,6 @@ def snowfall_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_intensity) return wrapper(ds, **kwargs) - def solid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1195,7 +1188,7 @@ def solid_precip_average( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.solidprcpavg`. + This function wraps `xclim.indicators.atmos.solid_precip_average `_. Parameters ---------- @@ -1203,7 +1196,7 @@ def solid_precip_average( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.solidprcpavg`. + :func:`xclim.indicators.atmos.solid_precip_average`. Returns ------- @@ -1213,7 +1206,6 @@ def solid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_average) return wrapper(ds, **kwargs) - def solid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1226,7 +1218,7 @@ def solid_precip_accumulation( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.solidprcptot`. + This function wraps `xclim.indicators.atmos.solid_precip_accumulation `_. Parameters ---------- @@ -1234,7 +1226,7 @@ def solid_precip_accumulation( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.solidprcptot`. + :func:`xclim.indicators.atmos.solid_precip_accumulation`. Returns ------- @@ -1244,7 +1236,6 @@ def solid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_accumulation) return wrapper(ds, **kwargs) - def warm_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1257,7 +1248,7 @@ def warm_and_dry_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.warm_and_dry_days`. + This function wraps `xclim.indicators.atmos.warm_and_dry_days `_. Parameters ---------- @@ -1275,7 +1266,6 @@ def warm_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_dry_days) return wrapper(ds, **kwargs) - def warm_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1288,7 +1278,7 @@ def warm_and_wet_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.warm_and_wet_days`. + This function wraps `xclim.indicators.atmos.warm_and_wet_days `_. Parameters ---------- @@ -1306,7 +1296,6 @@ def warm_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_wet_days) return wrapper(ds, **kwargs) - def water_cycle_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1318,7 +1307,7 @@ def water_cycle_intensity( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.water_cycle_intensity`. + This function wraps `xclim.indicators.atmos.water_cycle_intensity `_. Parameters ---------- @@ -1336,7 +1325,6 @@ def water_cycle_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.water_cycle_intensity) return wrapper(ds, **kwargs) - def wet_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1349,7 +1337,7 @@ def wet_precip_accumulation( **Units:** mm - This function wraps :func:`xclim.indicators.atmos.wet_prcptot`. + This function wraps `xclim.indicators.atmos.wet_precip_accumulation `_. Parameters ---------- @@ -1357,7 +1345,7 @@ def wet_precip_accumulation( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wet_prcptot`. + :func:`xclim.indicators.atmos.wet_precip_accumulation`. Returns ------- @@ -1367,7 +1355,6 @@ def wet_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_precip_accumulation) return wrapper(ds, **kwargs) - def wet_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1379,7 +1366,9 @@ def wet_spell_frequency( maximum precipitation over a given time window of days is equal or above a given threshold. - This function wraps :func:`xclim.indicators.atmos.wet_spell_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.wet_spell_frequency `_. Parameters ---------- @@ -1397,7 +1386,6 @@ def wet_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_frequency) return wrapper(ds, **kwargs) - def wet_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1411,7 +1399,7 @@ def wet_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.wet_spell_max_length`. + This function wraps `xclim.indicators.atmos.wet_spell_max_length `_. Parameters ---------- @@ -1429,7 +1417,6 @@ def wet_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_max_length) return wrapper(ds, **kwargs) - def wet_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1443,7 +1430,7 @@ def wet_spell_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.wet_spell_total_length`. + This function wraps `xclim.indicators.atmos.wet_spell_total_length `_. Parameters ---------- @@ -1461,7 +1448,6 @@ def wet_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_total_length) return wrapper(ds, **kwargs) - def wetdays( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1473,7 +1459,7 @@ def wetdays( **Units:** days - This function wraps :func:`xclim.indicators.atmos.wetdays`. + This function wraps `xclim.indicators.atmos.wetdays `_. Parameters ---------- @@ -1491,7 +1477,6 @@ def wetdays( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays) return wrapper(ds, **kwargs) - def wetdays_prop( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1503,7 +1488,7 @@ def wetdays_prop( **Units:** 1 - This function wraps :func:`xclim.indicators.atmos.wetdays_prop`. + This function wraps `xclim.indicators.atmos.wetdays_prop `_. Parameters ---------- @@ -1520,3 +1505,4 @@ def wetdays_prop( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays_prop) return wrapper(ds, **kwargs) + diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index 7db1ee8..612d880 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -30,7 +30,9 @@ def australian_hardiness_zones( scheme divides categories into 5-degree Celsius zones, starting from -15 degrees Celsius and ending at 20 degrees Celsius. - This function wraps :func:`xclim.indicators.atmos.australian_hardiness_zones`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.australian_hardiness_zones `_. Parameters ---------- @@ -48,7 +50,6 @@ def australian_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.australian_hardiness_zones) return wrapper(ds, **kwargs) - def biologically_effective_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -64,7 +65,7 @@ def biologically_effective_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.biologically_effective_degree_days`. + This function wraps `xclim.indicators.atmos.biologically_effective_degree_days `_. Parameters ---------- @@ -82,7 +83,6 @@ def biologically_effective_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.biologically_effective_degree_days) return wrapper(ds, **kwargs) - def cold_spell_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -95,7 +95,7 @@ def cold_spell_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_spell_days`. + This function wraps `xclim.indicators.atmos.cold_spell_days `_. Parameters ---------- @@ -113,13 +113,12 @@ def cold_spell_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_days) return wrapper(ds, **kwargs) - def cold_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - Cold Spell Duration Index (CSDI). + Cold spell duration index (csdi). Number of days part of a percentile-defined cold spell. A cold spell occurs when the daily minimum temperature is below a given percentile for a given number of consecutive @@ -127,7 +126,7 @@ def cold_spell_duration_index( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_spell_duration_index`. + This function wraps `xclim.indicators.atmos.cold_spell_duration_index `_. Parameters ---------- @@ -145,7 +144,6 @@ def cold_spell_duration_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_duration_index) return wrapper(ds, **kwargs) - def cold_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -156,7 +154,9 @@ def cold_spell_frequency( The frequency of cold periods of `N` days or more, during which the temperature over a given time window of days is below a given threshold. - This function wraps :func:`xclim.indicators.atmos.cold_spell_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.cold_spell_frequency `_. Parameters ---------- @@ -174,7 +174,6 @@ def cold_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_frequency) return wrapper(ds, **kwargs) - def cold_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -187,7 +186,7 @@ def cold_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_spell_max_length`. + This function wraps `xclim.indicators.atmos.cold_spell_max_length `_. Parameters ---------- @@ -205,7 +204,6 @@ def cold_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_max_length) return wrapper(ds, **kwargs) - def cold_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -218,7 +216,7 @@ def cold_spell_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.cold_spell_total_length`. + This function wraps `xclim.indicators.atmos.cold_spell_total_length `_. Parameters ---------- @@ -236,7 +234,6 @@ def cold_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_total_length) return wrapper(ds, **kwargs) - def consecutive_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -248,7 +245,7 @@ def consecutive_frost_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.consecutive_frost_days`. + This function wraps `xclim.indicators.atmos.consecutive_frost_days `_. Parameters ---------- @@ -266,7 +263,6 @@ def consecutive_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.consecutive_frost_days) return wrapper(ds, **kwargs) - def maximum_consecutive_frost_free_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -279,7 +275,7 @@ def maximum_consecutive_frost_free_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.consecutive_frost_free_days`. + This function wraps `xclim.indicators.atmos.maximum_consecutive_frost_free_days `_. Parameters ---------- @@ -287,7 +283,7 @@ def maximum_consecutive_frost_free_days( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.consecutive_frost_free_days`. + :func:`xclim.indicators.atmos.maximum_consecutive_frost_free_days`. Returns ------- @@ -297,7 +293,6 @@ def maximum_consecutive_frost_free_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_frost_free_days) return wrapper(ds, **kwargs) - def cool_night_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -310,7 +305,7 @@ def cool_night_index( **Units:** degC - This function wraps :func:`xclim.indicators.atmos.cool_night_index`. + This function wraps `xclim.indicators.atmos.cool_night_index `_. Parameters ---------- @@ -328,7 +323,6 @@ def cool_night_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cool_night_index) return wrapper(ds, **kwargs) - def cooling_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -341,7 +335,7 @@ def cooling_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.cooling_degree_days`. + This function wraps `xclim.indicators.atmos.cooling_degree_days `_. Parameters ---------- @@ -359,7 +353,6 @@ def cooling_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days) return wrapper(ds, **kwargs) - def cooling_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -374,7 +367,7 @@ def cooling_degree_days_approximation( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.cooling_degree_days_approximation`. + This function wraps `xclim.indicators.atmos.cooling_degree_days_approximation `_. Parameters ---------- @@ -392,7 +385,6 @@ def cooling_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days_approximation) return wrapper(ds, **kwargs) - def corn_heat_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -403,7 +395,9 @@ def corn_heat_units( A temperature-based index used to estimate the development of corn crops. Corn growth occurs when the daily minimum and maximum temperatures exceed given thresholds. - This function wraps :func:`xclim.indicators.atmos.corn_heat_units`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.corn_heat_units `_. Parameters ---------- @@ -421,7 +415,6 @@ def corn_heat_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.corn_heat_units) return wrapper(ds, **kwargs) - def chill_portions( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -439,7 +432,9 @@ def chill_portions( accurate than other chill models like the Chilling hours or Utah model, especially in moderate climates like Israel, California or Spain. - This function wraps :func:`xclim.indicators.atmos.cp`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.chill_portions `_. Parameters ---------- @@ -447,7 +442,7 @@ def chill_portions( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cp`. + :func:`xclim.indicators.atmos.chill_portions`. Returns ------- @@ -457,7 +452,6 @@ def chill_portions( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_portions) return wrapper(ds, **kwargs) - def chill_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -471,7 +465,9 @@ def chill_units( for bud breaking. Providing `positive_only=True` will ignore days with negative chill units. - This function wraps :func:`xclim.indicators.atmos.cu`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.chill_units `_. Parameters ---------- @@ -479,7 +475,7 @@ def chill_units( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cu`. + :func:`xclim.indicators.atmos.chill_units`. Returns ------- @@ -489,7 +485,6 @@ def chill_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_units) return wrapper(ds, **kwargs) - def degree_days_exceedance_date( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -500,7 +495,9 @@ def degree_days_exceedance_date( The day of the year when the sum of degree days exceeds a threshold, occurring after a given date. Degree days are calculated above or below a given temperature threshold. - This function wraps :func:`xclim.indicators.atmos.degree_days_exceedance_date`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.degree_days_exceedance_date `_. Parameters ---------- @@ -518,7 +515,6 @@ def degree_days_exceedance_date( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.degree_days_exceedance_date) return wrapper(ds, **kwargs) - def daily_freezethaw_cycles( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -532,7 +528,7 @@ def daily_freezethaw_cycles( **Units:** days - This function wraps :func:`xclim.indicators.atmos.dlyfrzthw`. + This function wraps `xclim.indicators.atmos.daily_freezethaw_cycles `_. Parameters ---------- @@ -540,7 +536,7 @@ def daily_freezethaw_cycles( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dlyfrzthw`. + :func:`xclim.indicators.atmos.daily_freezethaw_cycles`. Returns ------- @@ -550,7 +546,6 @@ def daily_freezethaw_cycles( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_freezethaw_cycles) return wrapper(ds, **kwargs) - def daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -562,7 +557,7 @@ def daily_temperature_range( **Units:** K - This function wraps :func:`xclim.indicators.atmos.dtr`. + This function wraps `xclim.indicators.atmos.daily_temperature_range `_. Parameters ---------- @@ -570,7 +565,7 @@ def daily_temperature_range( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dtr`. + :func:`xclim.indicators.atmos.daily_temperature_range`. Returns ------- @@ -580,7 +575,6 @@ def daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) return wrapper(ds, **kwargs) - def max_daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -592,7 +586,7 @@ def max_daily_temperature_range( **Units:** K - This function wraps :func:`xclim.indicators.atmos.dtrmax`. + This function wraps `xclim.indicators.atmos.max_daily_temperature_range `_. Parameters ---------- @@ -600,7 +594,7 @@ def max_daily_temperature_range( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dtrmax`. + :func:`xclim.indicators.atmos.max_daily_temperature_range`. Returns ------- @@ -610,7 +604,6 @@ def max_daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_daily_temperature_range) return wrapper(ds, **kwargs) - def daily_temperature_range_variability( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -622,7 +615,7 @@ def daily_temperature_range_variability( **Units:** K - This function wraps :func:`xclim.indicators.atmos.dtrvar`. + This function wraps `xclim.indicators.atmos.daily_temperature_range_variability `_. Parameters ---------- @@ -630,7 +623,7 @@ def daily_temperature_range_variability( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dtrvar`. + :func:`xclim.indicators.atmos.daily_temperature_range_variability`. Returns ------- @@ -640,7 +633,6 @@ def daily_temperature_range_variability( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range_variability) return wrapper(ds, **kwargs) - def extreme_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -652,7 +644,7 @@ def extreme_temperature_range( **Units:** K - This function wraps :func:`xclim.indicators.atmos.etr`. + This function wraps `xclim.indicators.atmos.extreme_temperature_range `_. Parameters ---------- @@ -660,7 +652,7 @@ def extreme_temperature_range( Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.etr`. + :func:`xclim.indicators.atmos.extreme_temperature_range`. Returns ------- @@ -670,7 +662,6 @@ def extreme_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.extreme_temperature_range) return wrapper(ds, **kwargs) - def fire_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -681,7 +672,9 @@ def fire_season( Binary mask of the active fire season, defined by conditions on consecutive daily temperatures and, optionally, snow depths. - This function wraps :func:`xclim.indicators.atmos.fire_season`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.fire_season `_. Parameters ---------- @@ -699,7 +692,6 @@ def fire_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fire_season) return wrapper(ds, **kwargs) - def first_day_tg_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -710,7 +702,9 @@ def first_day_tg_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tg_above`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tg_above `_. Parameters ---------- @@ -728,7 +722,6 @@ def first_day_tg_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_above) return wrapper(ds, **kwargs) - def first_day_tg_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -739,7 +732,9 @@ def first_day_tg_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tg_below`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tg_below `_. Parameters ---------- @@ -757,7 +752,6 @@ def first_day_tg_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_below) return wrapper(ds, **kwargs) - def first_day_tn_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -768,7 +762,9 @@ def first_day_tn_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tn_above`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tn_above `_. Parameters ---------- @@ -786,7 +782,6 @@ def first_day_tn_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_above) return wrapper(ds, **kwargs) - def first_day_tn_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -797,7 +792,9 @@ def first_day_tn_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tn_below`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tn_below `_. Parameters ---------- @@ -815,7 +812,6 @@ def first_day_tn_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_below) return wrapper(ds, **kwargs) - def first_day_tx_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -826,7 +822,9 @@ def first_day_tx_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tx_above`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tx_above `_. Parameters ---------- @@ -844,7 +842,6 @@ def first_day_tx_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_above) return wrapper(ds, **kwargs) - def first_day_tx_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -855,7 +852,9 @@ def first_day_tx_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - This function wraps :func:`xclim.indicators.atmos.first_day_tx_below`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tx_below `_. Parameters ---------- @@ -873,7 +872,6 @@ def first_day_tx_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_below) return wrapper(ds, **kwargs) - def freezethaw_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -887,7 +885,7 @@ def freezethaw_spell_frequency( **Units:** days - This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_frequency`. + This function wraps `xclim.indicators.atmos.freezethaw_spell_frequency `_. Parameters ---------- @@ -905,7 +903,6 @@ def freezethaw_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_frequency) return wrapper(ds, **kwargs) - def freezethaw_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -919,7 +916,7 @@ def freezethaw_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_max_length`. + This function wraps `xclim.indicators.atmos.freezethaw_spell_max_length `_. Parameters ---------- @@ -937,7 +934,6 @@ def freezethaw_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_max_length) return wrapper(ds, **kwargs) - def freezethaw_spell_mean_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -951,7 +947,7 @@ def freezethaw_spell_mean_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.freezethaw_spell_mean_length`. + This function wraps `xclim.indicators.atmos.freezethaw_spell_mean_length `_. Parameters ---------- @@ -969,7 +965,6 @@ def freezethaw_spell_mean_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_mean_length) return wrapper(ds, **kwargs) - def freezing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -982,7 +977,7 @@ def freezing_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.freezing_degree_days`. + This function wraps `xclim.indicators.atmos.freezing_degree_days `_. Parameters ---------- @@ -1000,7 +995,6 @@ def freezing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezing_degree_days) return wrapper(ds, **kwargs) - def freshet_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1011,7 +1005,9 @@ def freshet_start( Day of year of the spring freshet start, defined as the first day when the temperature exceeds a certain threshold for a given number of consecutive days. - This function wraps :func:`xclim.indicators.atmos.freshet_start`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.freshet_start `_. Parameters ---------- @@ -1029,7 +1025,6 @@ def freshet_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freshet_start) return wrapper(ds, **kwargs) - def frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1041,7 +1036,7 @@ def frost_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.frost_days`. + This function wraps `xclim.indicators.atmos.frost_days `_. Parameters ---------- @@ -1059,7 +1054,6 @@ def frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_days) return wrapper(ds, **kwargs) - def frost_free_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1070,7 +1064,9 @@ def frost_free_season_end( First day when the temperature is below a given threshold for a given number of consecutive days after a median calendar date. - This function wraps :func:`xclim.indicators.atmos.frost_free_season_end`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.frost_free_season_end `_. Parameters ---------- @@ -1088,7 +1084,6 @@ def frost_free_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_end) return wrapper(ds, **kwargs) - def frost_free_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1102,7 +1097,7 @@ def frost_free_season_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.frost_free_season_length`. + This function wraps `xclim.indicators.atmos.frost_free_season_length `_. Parameters ---------- @@ -1120,7 +1115,6 @@ def frost_free_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_length) return wrapper(ds, **kwargs) - def frost_free_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1131,7 +1125,9 @@ def frost_free_season_start( First day when minimum daily temperature exceeds a given threshold for a given number of consecutive days - This function wraps :func:`xclim.indicators.atmos.frost_free_season_start`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.frost_free_season_start `_. Parameters ---------- @@ -1149,7 +1145,6 @@ def frost_free_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_start) return wrapper(ds, **kwargs) - def frost_free_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1162,7 +1157,7 @@ def frost_free_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.frost_free_spell_max_length`. + This function wraps `xclim.indicators.atmos.frost_free_spell_max_length `_. Parameters ---------- @@ -1180,7 +1175,6 @@ def frost_free_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_spell_max_length) return wrapper(ds, **kwargs) - def frost_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1194,7 +1188,7 @@ def frost_season_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.frost_season_length`. + This function wraps `xclim.indicators.atmos.frost_season_length `_. Parameters ---------- @@ -1212,7 +1206,6 @@ def frost_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_season_length) return wrapper(ds, **kwargs) - def growing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1225,7 +1218,7 @@ def growing_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.growing_degree_days`. + This function wraps `xclim.indicators.atmos.growing_degree_days `_. Parameters ---------- @@ -1243,7 +1236,6 @@ def growing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_degree_days) return wrapper(ds, **kwargs) - def growing_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1254,7 +1246,9 @@ def growing_season_end( The first day when the temperature is below a certain threshold for a certain number of consecutive days after a given calendar date. - This function wraps :func:`xclim.indicators.atmos.growing_season_end`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.growing_season_end `_. Parameters ---------- @@ -1272,7 +1266,6 @@ def growing_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_end) return wrapper(ds, **kwargs) - def growing_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1286,7 +1279,7 @@ def growing_season_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.growing_season_length`. + This function wraps `xclim.indicators.atmos.growing_season_length `_. Parameters ---------- @@ -1304,7 +1297,6 @@ def growing_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_length) return wrapper(ds, **kwargs) - def growing_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1315,7 +1307,9 @@ def growing_season_start( The first day when the temperature exceeds a certain threshold for a given number of consecutive days. - This function wraps :func:`xclim.indicators.atmos.growing_season_start`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.growing_season_start `_. Parameters ---------- @@ -1333,7 +1327,6 @@ def growing_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_start) return wrapper(ds, **kwargs) - def heat_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1344,7 +1337,9 @@ def heat_spell_frequency( Number of heat spells. A heat spell occurs when rolling averages of daily minimum and maximumtemperatures exceed given thresholds for a number of days. - This function wraps :func:`xclim.indicators.atmos.heat_spell_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.heat_spell_frequency `_. Parameters ---------- @@ -1362,7 +1357,6 @@ def heat_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_frequency) return wrapper(ds, **kwargs) - def heat_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1375,7 +1369,7 @@ def heat_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.heat_spell_max_length`. + This function wraps `xclim.indicators.atmos.heat_spell_max_length `_. Parameters ---------- @@ -1393,7 +1387,6 @@ def heat_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_max_length) return wrapper(ds, **kwargs) - def heat_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1406,7 +1399,7 @@ def heat_spell_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.heat_spell_total_length`. + This function wraps `xclim.indicators.atmos.heat_spell_total_length `_. Parameters ---------- @@ -1424,7 +1417,6 @@ def heat_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_total_length) return wrapper(ds, **kwargs) - def heat_wave_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1435,7 +1427,9 @@ def heat_wave_frequency( Number of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days. - This function wraps :func:`xclim.indicators.atmos.heat_wave_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.heat_wave_frequency `_. Parameters ---------- @@ -1453,7 +1447,6 @@ def heat_wave_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_frequency) return wrapper(ds, **kwargs) - def heat_wave_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1466,7 +1459,7 @@ def heat_wave_index( **Units:** days - This function wraps :func:`xclim.indicators.atmos.heat_wave_index`. + This function wraps `xclim.indicators.atmos.heat_wave_index `_. Parameters ---------- @@ -1484,7 +1477,6 @@ def heat_wave_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_index) return wrapper(ds, **kwargs) - def heat_wave_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1497,7 +1489,7 @@ def heat_wave_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.heat_wave_max_length`. + This function wraps `xclim.indicators.atmos.heat_wave_max_length `_. Parameters ---------- @@ -1515,7 +1507,6 @@ def heat_wave_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_max_length) return wrapper(ds, **kwargs) - def heat_wave_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1528,7 +1519,7 @@ def heat_wave_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.heat_wave_total_length`. + This function wraps `xclim.indicators.atmos.heat_wave_total_length `_. Parameters ---------- @@ -1546,7 +1537,6 @@ def heat_wave_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_total_length) return wrapper(ds, **kwargs) - def heating_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1559,7 +1549,7 @@ def heating_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.heating_degree_days`. + This function wraps `xclim.indicators.atmos.heating_degree_days `_. Parameters ---------- @@ -1577,7 +1567,6 @@ def heating_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) return wrapper(ds, **kwargs) - def heating_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1592,7 +1581,7 @@ def heating_degree_days_approximation( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + This function wraps `xclim.indicators.atmos.heating_degree_days_approximation `_. Parameters ---------- @@ -1610,7 +1599,6 @@ def heating_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days_approximation) return wrapper(ds, **kwargs) - def hot_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1622,7 +1610,7 @@ def hot_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.hot_days`. + This function wraps `xclim.indicators.atmos.hot_days `_. Parameters ---------- @@ -1640,7 +1628,6 @@ def hot_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_days) return wrapper(ds, **kwargs) - def hot_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1651,7 +1638,9 @@ def hot_spell_frequency( The frequency of hot periods of `N` days or more, during which the temperature over a given time window of days is above a given threshold. - This function wraps :func:`xclim.indicators.atmos.hot_spell_frequency`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.hot_spell_frequency `_. Parameters ---------- @@ -1669,7 +1658,6 @@ def hot_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_frequency) return wrapper(ds, **kwargs) - def hot_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1682,7 +1670,7 @@ def hot_spell_max_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.hot_spell_max_length`. + This function wraps `xclim.indicators.atmos.hot_spell_max_length `_. Parameters ---------- @@ -1700,7 +1688,6 @@ def hot_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_length) return wrapper(ds, **kwargs) - def hot_spell_max_magnitude( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1713,7 +1700,7 @@ def hot_spell_max_magnitude( **Units:** K d - This function wraps :func:`xclim.indicators.atmos.hot_spell_max_magnitude`. + This function wraps `xclim.indicators.atmos.hot_spell_max_magnitude `_. Parameters ---------- @@ -1731,7 +1718,6 @@ def hot_spell_max_magnitude( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_magnitude) return wrapper(ds, **kwargs) - def hot_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1744,7 +1730,7 @@ def hot_spell_total_length( **Units:** days - This function wraps :func:`xclim.indicators.atmos.hot_spell_total_length`. + This function wraps `xclim.indicators.atmos.hot_spell_total_length `_. Parameters ---------- @@ -1762,7 +1748,6 @@ def hot_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_total_length) return wrapper(ds, **kwargs) - def huglin_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1776,7 +1761,9 @@ def huglin_index( coefficient calculation for higher latitudes. Metric originally published in Huglin (1978). Day-length coefficient based on Hall & Jones (2010). - This function wraps :func:`xclim.indicators.atmos.huglin_index`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.huglin_index `_. Parameters ---------- @@ -1794,7 +1781,6 @@ def huglin_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.huglin_index) return wrapper(ds, **kwargs) - def ice_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1806,7 +1792,7 @@ def ice_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.ice_days`. + This function wraps `xclim.indicators.atmos.ice_days `_. Parameters ---------- @@ -1824,7 +1810,6 @@ def ice_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.ice_days) return wrapper(ds, **kwargs) - def last_spring_frost( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1835,7 +1820,9 @@ def last_spring_frost( The last day when minimum temperature is below a given threshold for a certain number of days, limited by a final calendar date. - This function wraps :func:`xclim.indicators.atmos.last_spring_frost`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.last_spring_frost `_. Parameters ---------- @@ -1853,7 +1840,6 @@ def last_spring_frost( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_spring_frost) return wrapper(ds, **kwargs) - def late_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1866,7 +1852,7 @@ def late_frost_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.late_frost_days`. + This function wraps `xclim.indicators.atmos.late_frost_days `_. Parameters ---------- @@ -1884,7 +1870,6 @@ def late_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.late_frost_days) return wrapper(ds, **kwargs) - def latitude_temperature_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1898,7 +1883,9 @@ def latitude_temperature_index( difference of latitude factor coefficient minus latitude. Metric originally published in Jackson, D. I., & Cherry, N. J. (1988). - This function wraps :func:`xclim.indicators.atmos.latitude_temperature_index`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.latitude_temperature_index `_. Parameters ---------- @@ -1916,7 +1903,6 @@ def latitude_temperature_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.latitude_temperature_index) return wrapper(ds, **kwargs) - def maximum_consecutive_warm_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1929,7 +1915,7 @@ def maximum_consecutive_warm_days( **Units:** days - This function wraps :func:`xclim.indicators.atmos.maximum_consecutive_warm_days`. + This function wraps `xclim.indicators.atmos.maximum_consecutive_warm_days `_. Parameters ---------- @@ -1947,7 +1933,6 @@ def maximum_consecutive_warm_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_warm_days) return wrapper(ds, **kwargs) - def tg10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1959,7 +1944,7 @@ def tg10p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tg10p`. + This function wraps `xclim.indicators.atmos.tg10p `_. Parameters ---------- @@ -1977,7 +1962,6 @@ def tg10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg10p) return wrapper(ds, **kwargs) - def tg90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1989,7 +1973,7 @@ def tg90p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tg90p`. + This function wraps `xclim.indicators.atmos.tg90p `_. Parameters ---------- @@ -2007,7 +1991,6 @@ def tg90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg90p) return wrapper(ds, **kwargs) - def tg_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2019,7 +2002,7 @@ def tg_days_above( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tg_days_above`. + This function wraps `xclim.indicators.atmos.tg_days_above `_. Parameters ---------- @@ -2037,7 +2020,6 @@ def tg_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_above) return wrapper(ds, **kwargs) - def tg_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2049,7 +2031,7 @@ def tg_days_below( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tg_days_below`. + This function wraps `xclim.indicators.atmos.tg_days_below `_. Parameters ---------- @@ -2067,7 +2049,6 @@ def tg_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_below) return wrapper(ds, **kwargs) - def tg_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2079,7 +2060,7 @@ def tg_max( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tg_max`. + This function wraps `xclim.indicators.atmos.tg_max `_. Parameters ---------- @@ -2097,7 +2078,6 @@ def tg_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_max) return wrapper(ds, **kwargs) - def tg_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2109,7 +2089,7 @@ def tg_mean( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tg_mean`. + This function wraps `xclim.indicators.atmos.tg_mean `_. Parameters ---------- @@ -2127,7 +2107,6 @@ def tg_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_mean) return wrapper(ds, **kwargs) - def tg_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2139,7 +2118,7 @@ def tg_min( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tg_min`. + This function wraps `xclim.indicators.atmos.tg_min `_. Parameters ---------- @@ -2157,7 +2136,6 @@ def tg_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_min) return wrapper(ds, **kwargs) - def thawing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2170,7 +2148,7 @@ def thawing_degree_days( **Units:** K days - This function wraps :func:`xclim.indicators.atmos.thawing_degree_days`. + This function wraps `xclim.indicators.atmos.thawing_degree_days `_. Parameters ---------- @@ -2188,7 +2166,6 @@ def thawing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.thawing_degree_days) return wrapper(ds, **kwargs) - def tn10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2200,7 +2177,7 @@ def tn10p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tn10p`. + This function wraps `xclim.indicators.atmos.tn10p `_. Parameters ---------- @@ -2218,7 +2195,6 @@ def tn10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn10p) return wrapper(ds, **kwargs) - def tn90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2230,7 +2206,7 @@ def tn90p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tn90p`. + This function wraps `xclim.indicators.atmos.tn90p `_. Parameters ---------- @@ -2248,7 +2224,6 @@ def tn90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn90p) return wrapper(ds, **kwargs) - def tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2260,7 +2235,7 @@ def tn_days_above( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tn_days_above`. + This function wraps `xclim.indicators.atmos.tn_days_above `_. Parameters ---------- @@ -2278,7 +2253,6 @@ def tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_above) return wrapper(ds, **kwargs) - def tn_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2290,7 +2264,7 @@ def tn_days_below( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tn_days_below`. + This function wraps `xclim.indicators.atmos.tn_days_below `_. Parameters ---------- @@ -2308,7 +2282,6 @@ def tn_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_below) return wrapper(ds, **kwargs) - def tn_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2320,7 +2293,7 @@ def tn_max( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tn_max`. + This function wraps `xclim.indicators.atmos.tn_max `_. Parameters ---------- @@ -2338,7 +2311,6 @@ def tn_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_max) return wrapper(ds, **kwargs) - def tn_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2350,7 +2322,7 @@ def tn_mean( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tn_mean`. + This function wraps `xclim.indicators.atmos.tn_mean `_. Parameters ---------- @@ -2368,7 +2340,6 @@ def tn_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_mean) return wrapper(ds, **kwargs) - def tn_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2380,7 +2351,7 @@ def tn_min( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tn_min`. + This function wraps `xclim.indicators.atmos.tn_min `_. Parameters ---------- @@ -2398,7 +2369,6 @@ def tn_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_min) return wrapper(ds, **kwargs) - def tropical_nights( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2410,7 +2380,7 @@ def tropical_nights( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tropical_nights`. + This function wraps `xclim.indicators.atmos.tropical_nights `_. Parameters ---------- @@ -2428,7 +2398,6 @@ def tropical_nights( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tropical_nights) return wrapper(ds, **kwargs) - def tx10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2440,7 +2409,7 @@ def tx10p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tx10p`. + This function wraps `xclim.indicators.atmos.tx10p `_. Parameters ---------- @@ -2458,7 +2427,6 @@ def tx10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx10p) return wrapper(ds, **kwargs) - def tx90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2470,7 +2438,7 @@ def tx90p( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tx90p`. + This function wraps `xclim.indicators.atmos.tx90p `_. Parameters ---------- @@ -2488,7 +2456,6 @@ def tx90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx90p) return wrapper(ds, **kwargs) - def tx_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2500,7 +2467,7 @@ def tx_days_above( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tx_days_above`. + This function wraps `xclim.indicators.atmos.tx_days_above `_. Parameters ---------- @@ -2518,7 +2485,6 @@ def tx_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_above) return wrapper(ds, **kwargs) - def tx_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2530,7 +2496,7 @@ def tx_days_below( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tx_days_below`. + This function wraps `xclim.indicators.atmos.tx_days_below `_. Parameters ---------- @@ -2548,7 +2514,6 @@ def tx_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_below) return wrapper(ds, **kwargs) - def tx_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2560,7 +2525,7 @@ def tx_max( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tx_max`. + This function wraps `xclim.indicators.atmos.tx_max `_. Parameters ---------- @@ -2578,7 +2543,6 @@ def tx_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_max) return wrapper(ds, **kwargs) - def tx_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2590,7 +2554,7 @@ def tx_mean( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tx_mean`. + This function wraps `xclim.indicators.atmos.tx_mean `_. Parameters ---------- @@ -2608,7 +2572,6 @@ def tx_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_mean) return wrapper(ds, **kwargs) - def tx_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2620,7 +2583,7 @@ def tx_min( **Units:** K - This function wraps :func:`xclim.indicators.atmos.tx_min`. + This function wraps `xclim.indicators.atmos.tx_min `_. Parameters ---------- @@ -2638,7 +2601,6 @@ def tx_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_min) return wrapper(ds, **kwargs) - def tx_tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2650,7 +2612,7 @@ def tx_tn_days_above( **Units:** days - This function wraps :func:`xclim.indicators.atmos.tx_tn_days_above`. + This function wraps `xclim.indicators.atmos.tx_tn_days_above `_. Parameters ---------- @@ -2668,13 +2630,12 @@ def tx_tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_tn_days_above) return wrapper(ds, **kwargs) - def usda_hardiness_zones( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, ) -> conversions.EarthkitData: """ - USDA hardiness zones. + Usda hardiness zones. A climate indice based on a multi-year rolling average of the annual minimum temperature. Developed specifically to aid in determining plant suitability of @@ -2682,7 +2643,9 @@ def usda_hardiness_zones( Fahrenheit zones, with 5-degree Fahrenheit half-zones, starting from -65 degrees Fahrenheit and ending at 65 degrees Fahrenheit. - This function wraps :func:`xclim.indicators.atmos.usda_hardiness_zones`. + **Units:** dimensionless + + This function wraps `xclim.indicators.atmos.usda_hardiness_zones `_. Parameters ---------- @@ -2700,7 +2663,6 @@ def usda_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.usda_hardiness_zones) return wrapper(ds, **kwargs) - def warm_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2714,7 +2676,7 @@ def warm_spell_duration_index( **Units:** days - This function wraps :func:`xclim.indicators.atmos.warm_spell_duration_index`. + This function wraps `xclim.indicators.atmos.warm_spell_duration_index `_. Parameters ---------- @@ -2731,3 +2693,4 @@ def warm_spell_duration_index( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) return wrapper(ds, **kwargs) + diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index 12f5913..9bbb15e 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -49,24 +49,26 @@ def {func_name}( """ -def generate_docstring(indicator: Any) -> str: +def generate_docstring(indicator: Any, xclim_func_name: str) -> str: """Generate a docstring for the wrapper function based on the xclim indicator. Parameters ---------- indicator : xclim.core.indicator.Indicator The xclim indicator object. + xclim_func_name : str + The name of the function in xclim.indicators.atmos. Returns ------- str The generated docstring for the wrapper function. """ - identifier = indicator.identifier + identifier = indicator.identifier.capitalize() # Extract metadata # Use title as the summary if available, otherwise fallback to docstring or identifier - summary = getattr(indicator, "title", "").strip() + summary = getattr(indicator, "title", "").strip().capitalize() if not summary: summary = (indicator.__doc__ or "").split("\n")[0].strip() @@ -78,6 +80,7 @@ def generate_docstring(indicator: Any) -> str: description = getattr(indicator, "abstract", "") or getattr(indicator, "description", "") units = getattr(indicator, "units", "") + outputs = getattr(indicator, "var_name", None) sections = [summary] @@ -86,10 +89,38 @@ def generate_docstring(indicator: Any) -> str: # We target a width of 88 to allow for indentation (4 spaces) and staying well under 110 sections.append(textwrap.fill(description, width=88)) - if units: - sections.append(f"**Units:** {units}") - - sections.append(f"This function wraps :func:`xclim.indicators.atmos.{identifier}`.") + # Units handling + units_section = "" + if isinstance(units, str): + units = units.strip() + if not units: + units = "dimensionless" + units_section = f"**Units:** {units}" + elif isinstance(units, (list, tuple)): + if units: + # Treat empty units as "dimensionless" + processed_units = [u.strip() if u else "dimensionless" for u in units] + + if len(processed_units) == 1: + units_section = f"**Units:** {processed_units[0]}" + else: + # Check if outputs align with units + if isinstance(outputs, (list, tuple)) and len(outputs) == len(processed_units): + lines = ["**Units:**"] + for out, unit in zip(outputs, processed_units): + lines.append(f"- {out}: {unit}") + units_section = "\n".join(lines) + else: + # Fallback to comma-separated + units_section = f"**Units:** {', '.join(processed_units)}" + + if units_section: + sections.append(units_section) + + sections.append( + f"This function wraps `xclim.indicators.atmos.{xclim_func_name} " + f"`_." + ) # Static footer footer = inspect.cleandoc(f""" @@ -99,7 +130,7 @@ def generate_docstring(indicator: Any) -> str: Input dataset. See xclim documentation for required variables. **kwargs : Any Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.{identifier}`. + :func:`xclim.indicators.atmos.{xclim_func_name}`. Returns ------- @@ -129,8 +160,6 @@ def generate_module_content(category: str, indicators: List[Any]) -> str: functions_code = [] # Sort indicators by name for consistent output - # key=lambda x: x.identifier might be good, but we want to sort by the function name we use. - # We use xclim_func_name derived below, but let's just sort by identifier primarily. indicators.sort(key=lambda x: x.identifier) for ind in indicators: @@ -148,7 +177,7 @@ def generate_module_content(category: str, indicators: List[Any]) -> str: # Use the xclim variable name as the function name to match existing conventions func_name = xclim_func_name - docstring = generate_docstring(ind) + docstring = generate_docstring(ind, xclim_func_name) # Indent the docstring correctly lines = docstring.split("\n") @@ -209,10 +238,7 @@ def main(): category = module_to_category[module_name] indicators_map[category].append(obj) else: - # Fallback or skip - # If we want to capture everything, we might need a default, but - # adhering to strict categories is checking checking the files mentioned. - # print(f"Skipping {name} from unknown module {module_name}") + print(f"Skipping {name} from unknown module {module_name}") continue for category, indicators in indicators_map.items(): From 0f45bff818208bc95514ddfc11bff1b76e7dc4e5 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 13 Feb 2026 11:12:48 +0100 Subject: [PATCH 21/47] style: run pre-commit hooks --- .../climate/indicators/precipitation.py | 49 +++++++++- .../climate/indicators/temperature.py | 89 ++++++++++++++++++- 2 files changed, 136 insertions(+), 2 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index ffc5d2a..d89a1b9 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -47,6 +47,7 @@ def antecedent_precipitation_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.antecedent_precipitation_index) return wrapper(ds, **kwargs) + def maximum_consecutive_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -77,6 +78,7 @@ def maximum_consecutive_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_dry_days) return wrapper(ds, **kwargs) + def cffwis_indices( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -114,6 +116,7 @@ def cffwis_indices( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cffwis_indices) return wrapper(ds, **kwargs) + def cold_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -144,6 +147,7 @@ def cold_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_dry_days) return wrapper(ds, **kwargs) + def cold_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -174,6 +178,7 @@ def cold_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_wet_days) return wrapper(ds, **kwargs) + def maximum_consecutive_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -204,6 +209,7 @@ def maximum_consecutive_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_wet_days) return wrapper(ds, **kwargs) + def days_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -234,6 +240,7 @@ def days_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_doy_thresh) return wrapper(ds, **kwargs) + def days_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -264,6 +271,7 @@ def days_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_thresh) return wrapper(ds, **kwargs) + def days_with_snow( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -293,6 +301,7 @@ def days_with_snow( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_with_snow) return wrapper(ds, **kwargs) + def drought_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -323,6 +332,7 @@ def drought_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.drought_code) return wrapper(ds, **kwargs) + def griffiths_drought_factor( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -354,6 +364,7 @@ def griffiths_drought_factor( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.griffiths_drought_factor) return wrapper(ds, **kwargs) + def duff_moisture_code( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -385,6 +396,7 @@ def duff_moisture_code( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.duff_moisture_code) return wrapper(ds, **kwargs) + def dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -414,6 +426,7 @@ def dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_days) return wrapper(ds, **kwargs) + def dry_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -444,6 +457,7 @@ def dry_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_frequency) return wrapper(ds, **kwargs) + def dry_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -474,6 +488,7 @@ def dry_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_max_length) return wrapper(ds, **kwargs) + def dry_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -504,6 +519,7 @@ def dry_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_total_length) return wrapper(ds, **kwargs) + def dryness_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -535,6 +551,7 @@ def dryness_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dryness_index) return wrapper(ds, **kwargs) + def mcarthur_forest_fire_danger_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -564,6 +581,7 @@ def mcarthur_forest_fire_danger_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) return wrapper(ds, **kwargs) + def first_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -594,6 +612,7 @@ def first_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_snowfall) return wrapper(ds, **kwargs) + def fraction_over_precip_doy_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -625,6 +644,7 @@ def fraction_over_precip_doy_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_doy_thresh) return wrapper(ds, **kwargs) + def fraction_over_precip_thresh( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -656,6 +676,7 @@ def fraction_over_precip_thresh( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_thresh) return wrapper(ds, **kwargs) + def high_precip_low_temp( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -686,6 +707,7 @@ def high_precip_low_temp( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.high_precip_low_temp) return wrapper(ds, **kwargs) + def keetch_byram_drought_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -719,6 +741,7 @@ def keetch_byram_drought_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.keetch_byram_drought_index) return wrapper(ds, **kwargs) + def last_snowfall( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -749,6 +772,7 @@ def last_snowfall( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_snowfall) return wrapper(ds, **kwargs) + def liquid_precip_ratio( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -780,6 +804,7 @@ def liquid_precip_ratio( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_ratio) return wrapper(ds, **kwargs) + def liquid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -810,6 +835,7 @@ def liquid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_average) return wrapper(ds, **kwargs) + def liquid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -840,6 +866,7 @@ def liquid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_accumulation) return wrapper(ds, **kwargs) + def max_n_day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -869,6 +896,7 @@ def max_n_day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_n_day_precipitation_amount) return wrapper(ds, **kwargs) + def max_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -898,6 +926,7 @@ def max_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_pr_intensity) return wrapper(ds, **kwargs) + def precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -930,6 +959,7 @@ def precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_average) return wrapper(ds, **kwargs) + def precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -962,6 +992,7 @@ def precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_accumulation) return wrapper(ds, **kwargs) + def rain_on_frozen_ground_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -993,6 +1024,7 @@ def rain_on_frozen_ground_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_on_frozen_ground_days) return wrapper(ds, **kwargs) + def rain_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1028,6 +1060,7 @@ def rain_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_season) return wrapper(ds, **kwargs) + def rprctot( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1058,6 +1091,7 @@ def rprctot( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rprctot) return wrapper(ds, **kwargs) + def max_1day_precipitation_amount( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1087,6 +1121,7 @@ def max_1day_precipitation_amount( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_1day_precipitation_amount) return wrapper(ds, **kwargs) + def daily_pr_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1116,6 +1151,7 @@ def daily_pr_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) return wrapper(ds, **kwargs) + def snowfall_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1146,6 +1182,7 @@ def snowfall_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_frequency) return wrapper(ds, **kwargs) + def snowfall_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1176,6 +1213,7 @@ def snowfall_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_intensity) return wrapper(ds, **kwargs) + def solid_precip_average( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1206,6 +1244,7 @@ def solid_precip_average( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_average) return wrapper(ds, **kwargs) + def solid_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1236,6 +1275,7 @@ def solid_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_accumulation) return wrapper(ds, **kwargs) + def warm_and_dry_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1266,6 +1306,7 @@ def warm_and_dry_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_dry_days) return wrapper(ds, **kwargs) + def warm_and_wet_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1296,6 +1337,7 @@ def warm_and_wet_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_wet_days) return wrapper(ds, **kwargs) + def water_cycle_intensity( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1325,6 +1367,7 @@ def water_cycle_intensity( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.water_cycle_intensity) return wrapper(ds, **kwargs) + def wet_precip_accumulation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1355,6 +1398,7 @@ def wet_precip_accumulation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_precip_accumulation) return wrapper(ds, **kwargs) + def wet_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1386,6 +1430,7 @@ def wet_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_frequency) return wrapper(ds, **kwargs) + def wet_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1417,6 +1462,7 @@ def wet_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_max_length) return wrapper(ds, **kwargs) + def wet_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1448,6 +1494,7 @@ def wet_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_total_length) return wrapper(ds, **kwargs) + def wetdays( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1477,6 +1524,7 @@ def wetdays( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays) return wrapper(ds, **kwargs) + def wetdays_prop( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1505,4 +1553,3 @@ def wetdays_prop( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays_prop) return wrapper(ds, **kwargs) - diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index 612d880..c294c44 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -50,6 +50,7 @@ def australian_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.australian_hardiness_zones) return wrapper(ds, **kwargs) + def biologically_effective_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -83,6 +84,7 @@ def biologically_effective_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.biologically_effective_degree_days) return wrapper(ds, **kwargs) + def cold_spell_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -113,6 +115,7 @@ def cold_spell_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_days) return wrapper(ds, **kwargs) + def cold_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -144,6 +147,7 @@ def cold_spell_duration_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_duration_index) return wrapper(ds, **kwargs) + def cold_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -174,6 +178,7 @@ def cold_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_frequency) return wrapper(ds, **kwargs) + def cold_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -204,6 +209,7 @@ def cold_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_max_length) return wrapper(ds, **kwargs) + def cold_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -234,6 +240,7 @@ def cold_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_total_length) return wrapper(ds, **kwargs) + def consecutive_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -263,6 +270,7 @@ def consecutive_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.consecutive_frost_days) return wrapper(ds, **kwargs) + def maximum_consecutive_frost_free_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -293,6 +301,7 @@ def maximum_consecutive_frost_free_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_frost_free_days) return wrapper(ds, **kwargs) + def cool_night_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -323,6 +332,7 @@ def cool_night_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cool_night_index) return wrapper(ds, **kwargs) + def cooling_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -353,6 +363,7 @@ def cooling_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days) return wrapper(ds, **kwargs) + def cooling_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -385,6 +396,7 @@ def cooling_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days_approximation) return wrapper(ds, **kwargs) + def corn_heat_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -415,6 +427,7 @@ def corn_heat_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.corn_heat_units) return wrapper(ds, **kwargs) + def chill_portions( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -452,6 +465,7 @@ def chill_portions( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_portions) return wrapper(ds, **kwargs) + def chill_units( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -485,6 +499,7 @@ def chill_units( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_units) return wrapper(ds, **kwargs) + def degree_days_exceedance_date( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -515,6 +530,7 @@ def degree_days_exceedance_date( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.degree_days_exceedance_date) return wrapper(ds, **kwargs) + def daily_freezethaw_cycles( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -546,6 +562,7 @@ def daily_freezethaw_cycles( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_freezethaw_cycles) return wrapper(ds, **kwargs) + def daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -575,6 +592,7 @@ def daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) return wrapper(ds, **kwargs) + def max_daily_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -604,6 +622,7 @@ def max_daily_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_daily_temperature_range) return wrapper(ds, **kwargs) + def daily_temperature_range_variability( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -633,6 +652,7 @@ def daily_temperature_range_variability( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range_variability) return wrapper(ds, **kwargs) + def extreme_temperature_range( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -662,6 +682,7 @@ def extreme_temperature_range( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.extreme_temperature_range) return wrapper(ds, **kwargs) + def fire_season( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -692,6 +713,7 @@ def fire_season( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fire_season) return wrapper(ds, **kwargs) + def first_day_tg_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -722,6 +744,7 @@ def first_day_tg_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_above) return wrapper(ds, **kwargs) + def first_day_tg_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -752,6 +775,7 @@ def first_day_tg_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_below) return wrapper(ds, **kwargs) + def first_day_tn_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -782,6 +806,7 @@ def first_day_tn_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_above) return wrapper(ds, **kwargs) + def first_day_tn_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -812,6 +837,7 @@ def first_day_tn_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_below) return wrapper(ds, **kwargs) + def first_day_tx_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -842,6 +868,7 @@ def first_day_tx_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_above) return wrapper(ds, **kwargs) + def first_day_tx_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -872,6 +899,7 @@ def first_day_tx_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_below) return wrapper(ds, **kwargs) + def freezethaw_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -903,6 +931,7 @@ def freezethaw_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_frequency) return wrapper(ds, **kwargs) + def freezethaw_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -934,6 +963,7 @@ def freezethaw_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_max_length) return wrapper(ds, **kwargs) + def freezethaw_spell_mean_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -965,6 +995,7 @@ def freezethaw_spell_mean_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_mean_length) return wrapper(ds, **kwargs) + def freezing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -995,6 +1026,7 @@ def freezing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezing_degree_days) return wrapper(ds, **kwargs) + def freshet_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1025,6 +1057,7 @@ def freshet_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freshet_start) return wrapper(ds, **kwargs) + def frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1054,6 +1087,7 @@ def frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_days) return wrapper(ds, **kwargs) + def frost_free_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1084,6 +1118,7 @@ def frost_free_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_end) return wrapper(ds, **kwargs) + def frost_free_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1115,6 +1150,7 @@ def frost_free_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_length) return wrapper(ds, **kwargs) + def frost_free_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1145,6 +1181,7 @@ def frost_free_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_start) return wrapper(ds, **kwargs) + def frost_free_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1175,6 +1212,7 @@ def frost_free_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_spell_max_length) return wrapper(ds, **kwargs) + def frost_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1206,6 +1244,7 @@ def frost_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_season_length) return wrapper(ds, **kwargs) + def growing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1236,6 +1275,7 @@ def growing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_degree_days) return wrapper(ds, **kwargs) + def growing_season_end( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1266,6 +1306,7 @@ def growing_season_end( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_end) return wrapper(ds, **kwargs) + def growing_season_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1297,6 +1338,7 @@ def growing_season_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_length) return wrapper(ds, **kwargs) + def growing_season_start( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1327,6 +1369,7 @@ def growing_season_start( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_start) return wrapper(ds, **kwargs) + def heat_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1357,6 +1400,7 @@ def heat_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_frequency) return wrapper(ds, **kwargs) + def heat_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1387,6 +1431,7 @@ def heat_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_max_length) return wrapper(ds, **kwargs) + def heat_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1417,6 +1462,7 @@ def heat_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_total_length) return wrapper(ds, **kwargs) + def heat_wave_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1447,6 +1493,7 @@ def heat_wave_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_frequency) return wrapper(ds, **kwargs) + def heat_wave_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1477,6 +1524,7 @@ def heat_wave_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_index) return wrapper(ds, **kwargs) + def heat_wave_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1507,6 +1555,7 @@ def heat_wave_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_max_length) return wrapper(ds, **kwargs) + def heat_wave_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1537,6 +1586,7 @@ def heat_wave_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_total_length) return wrapper(ds, **kwargs) + def heating_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1567,6 +1617,7 @@ def heating_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) return wrapper(ds, **kwargs) + def heating_degree_days_approximation( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1599,6 +1650,7 @@ def heating_degree_days_approximation( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days_approximation) return wrapper(ds, **kwargs) + def hot_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1628,6 +1680,7 @@ def hot_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_days) return wrapper(ds, **kwargs) + def hot_spell_frequency( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1658,6 +1711,7 @@ def hot_spell_frequency( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_frequency) return wrapper(ds, **kwargs) + def hot_spell_max_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1688,6 +1742,7 @@ def hot_spell_max_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_length) return wrapper(ds, **kwargs) + def hot_spell_max_magnitude( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1718,6 +1773,7 @@ def hot_spell_max_magnitude( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_magnitude) return wrapper(ds, **kwargs) + def hot_spell_total_length( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1748,6 +1804,7 @@ def hot_spell_total_length( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_total_length) return wrapper(ds, **kwargs) + def huglin_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1781,6 +1838,7 @@ def huglin_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.huglin_index) return wrapper(ds, **kwargs) + def ice_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1810,6 +1868,7 @@ def ice_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.ice_days) return wrapper(ds, **kwargs) + def last_spring_frost( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1840,6 +1899,7 @@ def last_spring_frost( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_spring_frost) return wrapper(ds, **kwargs) + def late_frost_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1870,6 +1930,7 @@ def late_frost_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.late_frost_days) return wrapper(ds, **kwargs) + def latitude_temperature_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1903,6 +1964,7 @@ def latitude_temperature_index( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.latitude_temperature_index) return wrapper(ds, **kwargs) + def maximum_consecutive_warm_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1933,6 +1995,7 @@ def maximum_consecutive_warm_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_warm_days) return wrapper(ds, **kwargs) + def tg10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1962,6 +2025,7 @@ def tg10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg10p) return wrapper(ds, **kwargs) + def tg90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -1991,6 +2055,7 @@ def tg90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg90p) return wrapper(ds, **kwargs) + def tg_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2020,6 +2085,7 @@ def tg_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_above) return wrapper(ds, **kwargs) + def tg_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2049,6 +2115,7 @@ def tg_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_below) return wrapper(ds, **kwargs) + def tg_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2078,6 +2145,7 @@ def tg_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_max) return wrapper(ds, **kwargs) + def tg_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2107,6 +2175,7 @@ def tg_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_mean) return wrapper(ds, **kwargs) + def tg_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2136,6 +2205,7 @@ def tg_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_min) return wrapper(ds, **kwargs) + def thawing_degree_days( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2166,6 +2236,7 @@ def thawing_degree_days( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.thawing_degree_days) return wrapper(ds, **kwargs) + def tn10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2195,6 +2266,7 @@ def tn10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn10p) return wrapper(ds, **kwargs) + def tn90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2224,6 +2296,7 @@ def tn90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn90p) return wrapper(ds, **kwargs) + def tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2253,6 +2326,7 @@ def tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_above) return wrapper(ds, **kwargs) + def tn_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2282,6 +2356,7 @@ def tn_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_below) return wrapper(ds, **kwargs) + def tn_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2311,6 +2386,7 @@ def tn_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_max) return wrapper(ds, **kwargs) + def tn_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2340,6 +2416,7 @@ def tn_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_mean) return wrapper(ds, **kwargs) + def tn_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2369,6 +2446,7 @@ def tn_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_min) return wrapper(ds, **kwargs) + def tropical_nights( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2398,6 +2476,7 @@ def tropical_nights( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tropical_nights) return wrapper(ds, **kwargs) + def tx10p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2427,6 +2506,7 @@ def tx10p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx10p) return wrapper(ds, **kwargs) + def tx90p( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2456,6 +2536,7 @@ def tx90p( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx90p) return wrapper(ds, **kwargs) + def tx_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2485,6 +2566,7 @@ def tx_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_above) return wrapper(ds, **kwargs) + def tx_days_below( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2514,6 +2596,7 @@ def tx_days_below( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_below) return wrapper(ds, **kwargs) + def tx_max( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2543,6 +2626,7 @@ def tx_max( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_max) return wrapper(ds, **kwargs) + def tx_mean( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2572,6 +2656,7 @@ def tx_mean( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_mean) return wrapper(ds, **kwargs) + def tx_min( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2601,6 +2686,7 @@ def tx_min( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_min) return wrapper(ds, **kwargs) + def tx_tn_days_above( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2630,6 +2716,7 @@ def tx_tn_days_above( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_tn_days_above) return wrapper(ds, **kwargs) + def usda_hardiness_zones( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2663,6 +2750,7 @@ def usda_hardiness_zones( wrapper = wrap_xclim_indicator(xclim.indicators.atmos.usda_hardiness_zones) return wrapper(ds, **kwargs) + def warm_spell_duration_index( ds: conversions.EarthkitData | xarray.Dataset, **kwargs: Any, @@ -2693,4 +2781,3 @@ def warm_spell_duration_index( """ wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) return wrapper(ds, **kwargs) - From 2b1c68b44d5145fc2fc349323e851d55d965784c Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 13 Feb 2026 11:26:13 +0100 Subject: [PATCH 22/47] refactor: standardize indicator docstring unit formatting to explicitly list units per output. --- .../climate/indicators/precipitation.py | 190 +++++++--- .../climate/indicators/temperature.py | 356 +++++++++++++----- tools/xclim_wrappers_generator.py | 38 +- 3 files changed, 429 insertions(+), 155 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index d89a1b9..1561b57 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -27,7 +27,9 @@ def antecedent_precipitation_index( Calculate the running weighted sum of daily precipitation values given a window and weighting exponent. This index serves as an indicator for soil moisture. - **Units:** mm + **Units:** + + - api: mm This function wraps `xclim.indicators.atmos.antecedent_precipitation_index `_. @@ -58,7 +60,9 @@ def maximum_consecutive_dry_days( The longest number of consecutive days where daily precipitation below a given threshold. - **Units:** days + **Units:** + + - cdd: days This function wraps `xclim.indicators.atmos.maximum_consecutive_dry_days `_. @@ -91,6 +95,7 @@ def cffwis_indices( Spread Index - The Build Up Index - The Fire Weather Index. **Units:** + - dc: dimensionless - dmc: dimensionless - ffmc: dimensionless @@ -127,7 +132,9 @@ def cold_and_dry_days( Number of days with temperature below a given percentile and precipitation below a given percentile. - **Units:** days + **Units:** + + - cold_and_dry_days: days This function wraps `xclim.indicators.atmos.cold_and_dry_days `_. @@ -158,7 +165,9 @@ def cold_and_wet_days( Number of days with temperature below a given percentile and precipitation above a given percentile. - **Units:** days + **Units:** + + - cold_and_wet_days: days This function wraps `xclim.indicators.atmos.cold_and_wet_days `_. @@ -189,7 +198,9 @@ def maximum_consecutive_wet_days( The longest number of consecutive days where daily precipitation is at or above a given threshold. - **Units:** days + **Units:** + + - cwd: days This function wraps `xclim.indicators.atmos.maximum_consecutive_wet_days `_. @@ -220,7 +231,9 @@ def days_over_precip_doy_thresh( Number of days in a period where precipitation is above a given daily percentile and a fixed threshold. - **Units:** days + **Units:** + + - days_over_precip_doy_thresh: days This function wraps `xclim.indicators.atmos.days_over_precip_doy_thresh `_. @@ -251,7 +264,9 @@ def days_over_precip_thresh( Number of days in a period where precipitation is above a given percentile, calculated over a given period and a fixed threshold. - **Units:** days + **Units:** + + - days_over_precip_thresh: days This function wraps `xclim.indicators.atmos.days_over_precip_thresh `_. @@ -281,7 +296,9 @@ def days_with_snow( Number of days with snow between a lower and upper limit. - **Units:** days + **Units:** + + - days_with_snow: days This function wraps `xclim.indicators.atmos.days_with_snow `_. @@ -312,7 +329,9 @@ def drought_code( The Drought Index is part of the Canadian Forest-Weather Index system. It is a numerical code that estimates the average moisture content of organic layers. - **Units:** dimensionless + **Units:** + + - dc: dimensionless This function wraps `xclim.indicators.atmos.drought_code `_. @@ -344,7 +363,9 @@ def griffiths_drought_factor( deep litter bed. It is often used in the calculation of the McArthur Forest Fire Danger Index. The method implemented here follows :cite:t:`ffdi-finkele_2006`. - **Units:** dimensionless + **Units:** + + - df: dimensionless This function wraps `xclim.indicators.atmos.griffiths_drought_factor `_. @@ -376,7 +397,9 @@ def duff_moisture_code( numeric rating of the average moisture content of loosely compacted organic layers of moderate depth. - **Units:** dimensionless + **Units:** + + - dmc: dimensionless This function wraps `xclim.indicators.atmos.duff_moisture_code `_. @@ -406,7 +429,9 @@ def dry_days( The number of days with daily precipitation under a given threshold. - **Units:** days + **Units:** + + - dry_days: days This function wraps `xclim.indicators.atmos.dry_days `_. @@ -437,7 +462,9 @@ def dry_spell_frequency( The frequency of dry periods of `N` days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold. - **Units:** dimensionless + **Units:** + + - dry_spell_frequency: dimensionless This function wraps `xclim.indicators.atmos.dry_spell_frequency `_. @@ -468,7 +495,9 @@ def dry_spell_max_length( The maximum length of a dry period of `N` days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold. - **Units:** days + **Units:** + + - dry_spell_max_length: days This function wraps `xclim.indicators.atmos.dry_spell_max_length `_. @@ -499,7 +528,9 @@ def dry_spell_total_length( The total length of dry periods of `N` days or more, during which the accumulated or maximum precipitation over a given time window of days is below a given threshold. - **Units:** days + **Units:** + + - dry_spell_total_length: days This function wraps `xclim.indicators.atmos.dry_spell_total_length `_. @@ -531,7 +562,9 @@ def dryness_index( which considers the precipitation and evapotranspiration factors without deduction for surface runoff or drainage. Metric originally published in Riou et al. (1994). - **Units:** mm + **Units:** + + - dryness_index: mm This function wraps `xclim.indicators.atmos.dryness_index `_. @@ -561,7 +594,9 @@ def mcarthur_forest_fire_danger_index( The FFDI is a numeric indicator of the potential danger of a forest fire. - **Units:** dimensionless + **Units:** + + - ffdi: dimensionless This function wraps `xclim.indicators.atmos.mcarthur_forest_fire_danger_index `_. @@ -592,7 +627,9 @@ def first_snowfall( The first day where snowfall exceeded a given threshold during a time period (the threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). - **Units:** dimensionless + **Units:** + + - first_snowfall: dimensionless This function wraps `xclim.indicators.atmos.first_snowfall `_. @@ -624,7 +661,9 @@ def fraction_over_precip_doy_thresh( precipitation is above a threshold defining wet days and above a given percentile for that day. - **Units:** dimensionless + **Units:** + + - fraction_over_precip_doy_thresh: dimensionless This function wraps `xclim.indicators.atmos.fraction_over_precip_doy_thresh `_. @@ -656,7 +695,9 @@ def fraction_over_precip_thresh( precipitation is above a threshold defining wet days and above a given percentile for that day. - **Units:** dimensionless + **Units:** + + - fraction_over_precip_thresh: dimensionless This function wraps `xclim.indicators.atmos.fraction_over_precip_thresh `_. @@ -687,7 +728,9 @@ def high_precip_low_temp( Number of days with precipitation above a given threshold and temperature below a given threshold. - **Units:** days + **Units:** + + - high_precip_low_temp: days This function wraps `xclim.indicators.atmos.high_precip_low_temp `_. @@ -721,7 +764,9 @@ def keetch_byram_drought_index( the maximum KBDI to 203.2 mm, rather than 200 mm, in order to align best with the majority of the literature. - **Units:** mm/day + **Units:** + + - kbdi: mm/day This function wraps `xclim.indicators.atmos.keetch_byram_drought_index `_. @@ -752,7 +797,9 @@ def last_snowfall( The last day where snowfall exceeded a given threshold during a time period (the threshold can be given as a snowfall flux or a liquid water equivalent snowfall rate). - **Units:** dimensionless + **Units:** + + - last_snowfall: dimensionless This function wraps `xclim.indicators.atmos.last_snowfall `_. @@ -784,7 +831,9 @@ def liquid_precip_ratio( precipitation is approximated from total precipitation on days where temperature is above a given threshold. - **Units:** dimensionless + **Units:** + + - liquid_precip_ratio: dimensionless This function wraps `xclim.indicators.atmos.liquid_precip_ratio `_. @@ -815,7 +864,9 @@ def liquid_precip_average( Averaged liquid precipitation. Precipitation is considered liquid when the average daily temperature is above a given threshold. - **Units:** mm + **Units:** + + - liquidprcpavg: mm This function wraps `xclim.indicators.atmos.liquid_precip_average `_. @@ -846,7 +897,9 @@ def liquid_precip_accumulation( Total accumulated liquid precipitation. Precipitation is considered liquid when the average daily temperature is above a given threshold. - **Units:** mm + **Units:** + + - liquidprcptot: mm This function wraps `xclim.indicators.atmos.liquid_precip_accumulation `_. @@ -876,7 +929,9 @@ def max_n_day_precipitation_amount( Maximum of the moving sum of daily precipitation for a given period. - **Units:** mm + **Units:** + + - rx{window}day: mm This function wraps `xclim.indicators.atmos.max_n_day_precipitation_amount `_. @@ -906,7 +961,9 @@ def max_pr_intensity( Maximum precipitation intensity over a given rolling time window. - **Units:** mm h-1 + **Units:** + + - max_pr_intensity: mm h-1 This function wraps `xclim.indicators.atmos.max_pr_intensity `_. @@ -939,7 +996,9 @@ def precip_average( solid). Precipitation is considered solid if the average daily temperature is below 0°C threshold (and vice versa). - **Units:** mm + **Units:** + + - prcpavg: mm This function wraps `xclim.indicators.atmos.precip_average `_. @@ -972,7 +1031,9 @@ def precip_accumulation( (liquid or solid). Precipitation is considered solid if the average daily temperature is below 0°C (and vice versa). - **Units:** mm + **Units:** + + - prcptot: mm This function wraps `xclim.indicators.atmos.precip_accumulation `_. @@ -1004,7 +1065,9 @@ def rain_on_frozen_ground_days( average daily temperature below 0°C. Precipitation is assumed to be rain when the daily average temperature is above 0°C. - **Units:** days + **Units:** + + - rain_frzgr: days This function wraps `xclim.indicators.atmos.rain_on_frozen_ground_days `_. @@ -1038,6 +1101,7 @@ def rain_season( rain season stops during a dry period happening after a given date. **Units:** + - rain_season_start: dimensionless - rain_season_end: dimensionless - rain_season_length: days @@ -1071,7 +1135,9 @@ def rprctot( The proportion of total precipitation due to convective processes. Only days with surpassing a minimum precipitation flux are considered. - **Units:** dimensionless + **Units:** + + - rprctot: dimensionless This function wraps `xclim.indicators.atmos.rprctot `_. @@ -1101,7 +1167,9 @@ def max_1day_precipitation_amount( Maximum total daily precipitation for a given period. - **Units:** mm/day + **Units:** + + - rx1day: mm/day This function wraps `xclim.indicators.atmos.max_1day_precipitation_amount `_. @@ -1131,7 +1199,9 @@ def daily_pr_intensity( Average precipitation for days with daily precipitation above a given threshold. - **Units:** mm d-1 + **Units:** + + - sdii: mm d-1 This function wraps `xclim.indicators.atmos.daily_pr_intensity `_. @@ -1162,7 +1232,9 @@ def snowfall_frequency( Percentage of days with snowfall above a given threshold (either a snowfall flux or a liquid water equivalent snowfall rate). - **Units:** % + **Units:** + + - snowfall_frequency: % This function wraps `xclim.indicators.atmos.snowfall_frequency `_. @@ -1193,7 +1265,9 @@ def snowfall_intensity( Mean daily liquid water equivalent snowfall rate above threshold (either a snowfall flux or a liquid water equivalent snowfall rate) - **Units:** mm/day + **Units:** + + - snowfall_intensity: mm/day This function wraps `xclim.indicators.atmos.snowfall_intensity `_. @@ -1224,7 +1298,9 @@ def solid_precip_average( Averaged solid precipitation. Precipitation is considered solid when the average daily temperature is at or below a given threshold. - **Units:** mm + **Units:** + + - solidprcpavg: mm This function wraps `xclim.indicators.atmos.solid_precip_average `_. @@ -1255,7 +1331,9 @@ def solid_precip_accumulation( Total accumulated solid precipitation. Precipitation is considered solid when the average daily temperature is at or below a given threshold. - **Units:** mm + **Units:** + + - solidprcptot: mm This function wraps `xclim.indicators.atmos.solid_precip_accumulation `_. @@ -1286,7 +1364,9 @@ def warm_and_dry_days( Number of days with temperature above a given percentile and precipitation below a given percentile. - **Units:** days + **Units:** + + - warm_and_dry_days: days This function wraps `xclim.indicators.atmos.warm_and_dry_days `_. @@ -1317,7 +1397,9 @@ def warm_and_wet_days( Number of days with temperature above a given percentile and precipitation above a given percentile. - **Units:** days + **Units:** + + - warm_and_wet_days: days This function wraps `xclim.indicators.atmos.warm_and_wet_days `_. @@ -1347,7 +1429,9 @@ def water_cycle_intensity( The sum of precipitation and actual evapotranspiration. - **Units:** mm + **Units:** + + - water_cycle_intensity: mm This function wraps `xclim.indicators.atmos.water_cycle_intensity `_. @@ -1378,7 +1462,9 @@ def wet_precip_accumulation( Total accumulated precipitation on days with precipitation. A day is considered to have precipitation if the precipitation is greater than or equal to a given threshold. - **Units:** mm + **Units:** + + - wet_prcptot: mm This function wraps `xclim.indicators.atmos.wet_precip_accumulation `_. @@ -1410,7 +1496,9 @@ def wet_spell_frequency( maximum precipitation over a given time window of days is equal or above a given threshold. - **Units:** dimensionless + **Units:** + + - wet_spell_frequency: dimensionless This function wraps `xclim.indicators.atmos.wet_spell_frequency `_. @@ -1442,7 +1530,9 @@ def wet_spell_max_length( maximum precipitation over a given time window of days is equal or above a given threshold. - **Units:** days + **Units:** + + - wet_spell_max_length: days This function wraps `xclim.indicators.atmos.wet_spell_max_length `_. @@ -1474,7 +1564,9 @@ def wet_spell_total_length( maximum precipitation over a given time window of days is equal or above a given threshold. - **Units:** days + **Units:** + + - wet_spell_total_length: days This function wraps `xclim.indicators.atmos.wet_spell_total_length `_. @@ -1504,7 +1596,9 @@ def wetdays( The number of days with daily precipitation at or above a given threshold. - **Units:** days + **Units:** + + - wetdays: days This function wraps `xclim.indicators.atmos.wetdays `_. @@ -1534,7 +1628,9 @@ def wetdays_prop( The proportion of days with daily precipitation at or above a given threshold. - **Units:** 1 + **Units:** + + - wetdays_prop: 1 This function wraps `xclim.indicators.atmos.wetdays_prop `_. diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index c294c44..989b0c6 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -30,7 +30,9 @@ def australian_hardiness_zones( scheme divides categories into 5-degree Celsius zones, starting from -15 degrees Celsius and ending at 20 degrees Celsius. - **Units:** dimensionless + **Units:** + + - hz: dimensionless This function wraps `xclim.indicators.atmos.australian_hardiness_zones `_. @@ -64,7 +66,9 @@ def biologically_effective_degree_days( well as for swings in daily temperature range. Metric originally published in Gladstones (1992). - **Units:** K days + **Units:** + + - bedd: K days This function wraps `xclim.indicators.atmos.biologically_effective_degree_days `_. @@ -95,7 +99,9 @@ def cold_spell_days( The number of days that are part of a cold spell. A cold spell is defined as a minimum number of consecutive days with mean daily temperature below a given threshold. - **Units:** days + **Units:** + + - cold_spell_days: days This function wraps `xclim.indicators.atmos.cold_spell_days `_. @@ -127,7 +133,9 @@ def cold_spell_duration_index( daily minimum temperature is below a given percentile for a given number of consecutive days. - **Units:** days + **Units:** + + - csdi_{window}: days This function wraps `xclim.indicators.atmos.cold_spell_duration_index `_. @@ -158,7 +166,9 @@ def cold_spell_frequency( The frequency of cold periods of `N` days or more, during which the temperature over a given time window of days is below a given threshold. - **Units:** dimensionless + **Units:** + + - cold_spell_frequency: dimensionless This function wraps `xclim.indicators.atmos.cold_spell_frequency `_. @@ -189,7 +199,9 @@ def cold_spell_max_length( The maximum length of a cold period of `N` days or more, during which the temperature over a given time window of days is below a given threshold. - **Units:** days + **Units:** + + - cold_spell_max_length: days This function wraps `xclim.indicators.atmos.cold_spell_max_length `_. @@ -220,7 +232,9 @@ def cold_spell_total_length( The total length of cold periods of `N` days or more, during which the temperature over a given time window of days is below a given threshold. - **Units:** days + **Units:** + + - cold_spell_total_length: days This function wraps `xclim.indicators.atmos.cold_spell_total_length `_. @@ -250,7 +264,9 @@ def consecutive_frost_days( Maximum number of consecutive days where the daily minimum temperature is below 0°C - **Units:** days + **Units:** + + - consecutive_frost_days: days This function wraps `xclim.indicators.atmos.consecutive_frost_days `_. @@ -281,7 +297,9 @@ def maximum_consecutive_frost_free_days( Maximum number of consecutive frost-free days: where the daily minimum temperature is above or equal to 0°C - **Units:** days + **Units:** + + - consecutive_frost_free_days: days This function wraps `xclim.indicators.atmos.maximum_consecutive_frost_free_days `_. @@ -312,7 +330,9 @@ def cool_night_index( A night coolness variable which takes into account the mean minimum night temperatures during the month when ripening usually occurs beyond the ripening period. - **Units:** degC + **Units:** + + - cool_night_index: degC This function wraps `xclim.indicators.atmos.cool_night_index `_. @@ -343,7 +363,9 @@ def cooling_degree_days( The cumulative degree days for days when the mean daily temperature is above a given threshold and buildings must be air conditioned. - **Units:** K days + **Units:** + + - cooling_degree_days: K days This function wraps `xclim.indicators.atmos.cooling_degree_days `_. @@ -376,7 +398,9 @@ def cooling_degree_days_approximation( temperatures, accounting for asymmetry in the distributions of temperatures throughout the diurnal cycle. - **Units:** K days + **Units:** + + - cooling_degree_days_approximation: K days This function wraps `xclim.indicators.atmos.cooling_degree_days_approximation `_. @@ -407,7 +431,9 @@ def corn_heat_units( A temperature-based index used to estimate the development of corn crops. Corn growth occurs when the daily minimum and maximum temperatures exceed given thresholds. - **Units:** dimensionless + **Units:** + + - chu: dimensionless This function wraps `xclim.indicators.atmos.corn_heat_units `_. @@ -445,7 +471,9 @@ def chill_portions( accurate than other chill models like the Chilling hours or Utah model, especially in moderate climates like Israel, California or Spain. - **Units:** dimensionless + **Units:** + + - cp: dimensionless This function wraps `xclim.indicators.atmos.chill_portions `_. @@ -479,7 +507,9 @@ def chill_units( for bud breaking. Providing `positive_only=True` will ignore days with negative chill units. - **Units:** dimensionless + **Units:** + + - cu: dimensionless This function wraps `xclim.indicators.atmos.chill_units `_. @@ -510,7 +540,9 @@ def degree_days_exceedance_date( The day of the year when the sum of degree days exceeds a threshold, occurring after a given date. Degree days are calculated above or below a given temperature threshold. - **Units:** dimensionless + **Units:** + + - degree_days_exceedance_date: dimensionless This function wraps `xclim.indicators.atmos.degree_days_exceedance_date `_. @@ -542,7 +574,9 @@ def daily_freezethaw_cycles( where maximum daily temperature is above a given threshold and minimum daily temperature is at or below a given threshold, usually 0°C for both. - **Units:** days + **Units:** + + - dlyfrzthw: days This function wraps `xclim.indicators.atmos.daily_freezethaw_cycles `_. @@ -572,7 +606,9 @@ def daily_temperature_range( The average difference between the daily maximum and minimum temperatures. - **Units:** K + **Units:** + + - dtr: K This function wraps `xclim.indicators.atmos.daily_temperature_range `_. @@ -602,7 +638,9 @@ def max_daily_temperature_range( The maximum difference between the daily maximum and minimum temperatures. - **Units:** K + **Units:** + + - dtrmax: K This function wraps `xclim.indicators.atmos.max_daily_temperature_range `_. @@ -632,7 +670,9 @@ def daily_temperature_range_variability( The average day-to-day variation in daily temperature range. - **Units:** K + **Units:** + + - dtrvar: K This function wraps `xclim.indicators.atmos.daily_temperature_range_variability `_. @@ -662,7 +702,9 @@ def extreme_temperature_range( The maximum of the maximum temperature minus the minimum of the minimum temperature. - **Units:** K + **Units:** + + - etr: K This function wraps `xclim.indicators.atmos.extreme_temperature_range `_. @@ -693,7 +735,9 @@ def fire_season( Binary mask of the active fire season, defined by conditions on consecutive daily temperatures and, optionally, snow depths. - **Units:** dimensionless + **Units:** + + - fire_season: dimensionless This function wraps `xclim.indicators.atmos.fire_season `_. @@ -724,7 +768,9 @@ def first_day_tg_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - **Units:** dimensionless + **Units:** + + - first_day_tg_above: dimensionless This function wraps `xclim.indicators.atmos.first_day_tg_above `_. @@ -755,7 +801,9 @@ def first_day_tg_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - **Units:** dimensionless + **Units:** + + - first_day_tg_below: dimensionless This function wraps `xclim.indicators.atmos.first_day_tg_below `_. @@ -786,7 +834,9 @@ def first_day_tn_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - **Units:** dimensionless + **Units:** + + - first_day_tn_above: dimensionless This function wraps `xclim.indicators.atmos.first_day_tn_above `_. @@ -817,7 +867,9 @@ def first_day_tn_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - **Units:** dimensionless + **Units:** + + - first_day_tn_below: dimensionless This function wraps `xclim.indicators.atmos.first_day_tn_below `_. @@ -848,7 +900,9 @@ def first_day_tx_above( Returns first day of period where temperature is superior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: January 1st). - **Units:** dimensionless + **Units:** + + - first_day_tx_above: dimensionless This function wraps `xclim.indicators.atmos.first_day_tx_above `_. @@ -879,7 +933,9 @@ def first_day_tx_below( Returns first day of period where temperature is inferior to a threshold over a given number of days (default: 1), limited to a starting calendar date (default: July 1st). - **Units:** dimensionless + **Units:** + + - first_day_tx_below: dimensionless This function wraps `xclim.indicators.atmos.first_day_tx_below `_. @@ -911,7 +967,9 @@ def freezethaw_spell_frequency( consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a given threshold, usually 0°C for both. - **Units:** days + **Units:** + + - freezethaw_spell_frequency: days This function wraps `xclim.indicators.atmos.freezethaw_spell_frequency `_. @@ -943,7 +1001,9 @@ def freezethaw_spell_max_length( of consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a threshold, usually 0°C for both. - **Units:** days + **Units:** + + - freezethaw_spell_max_length: days This function wraps `xclim.indicators.atmos.freezethaw_spell_max_length `_. @@ -975,7 +1035,9 @@ def freezethaw_spell_mean_length( of consecutive days where maximum daily temperatures are above a given threshold and minimum daily temperatures are at or below a given threshold, usually 0°C for both. - **Units:** days + **Units:** + + - freezethaw_spell_mean_length: days This function wraps `xclim.indicators.atmos.freezethaw_spell_mean_length `_. @@ -1006,7 +1068,9 @@ def freezing_degree_days( The cumulative degree days for days when the average temperature is below a given threshold, typically 0°C. - **Units:** K days + **Units:** + + - freezing_degree_days: K days This function wraps `xclim.indicators.atmos.freezing_degree_days `_. @@ -1037,7 +1101,9 @@ def freshet_start( Day of year of the spring freshet start, defined as the first day when the temperature exceeds a certain threshold for a given number of consecutive days. - **Units:** dimensionless + **Units:** + + - freshet_start: dimensionless This function wraps `xclim.indicators.atmos.freshet_start `_. @@ -1067,7 +1133,9 @@ def frost_days( Number of days where the daily minimum temperature is below a given threshold. - **Units:** days + **Units:** + + - frost_days: days This function wraps `xclim.indicators.atmos.frost_days `_. @@ -1098,7 +1166,9 @@ def frost_free_season_end( First day when the temperature is below a given threshold for a given number of consecutive days after a median calendar date. - **Units:** dimensionless + **Units:** + + - frost_free_season_end: dimensionless This function wraps `xclim.indicators.atmos.frost_free_season_end `_. @@ -1130,7 +1200,9 @@ def frost_free_season_length( temperature is above 0°C without a freezing window of `N` days, with freezing occurring after a median calendar date. - **Units:** days + **Units:** + + - frost_free_season_length: days This function wraps `xclim.indicators.atmos.frost_free_season_length `_. @@ -1161,7 +1233,9 @@ def frost_free_season_start( First day when minimum daily temperature exceeds a given threshold for a given number of consecutive days - **Units:** dimensionless + **Units:** + + - frost_free_season_start: dimensionless This function wraps `xclim.indicators.atmos.frost_free_season_start `_. @@ -1192,7 +1266,9 @@ def frost_free_spell_max_length( The maximum length of a frost free period of `N` days or more, during which the minimum temperature over a given time window of days is above a given threshold. - **Units:** days + **Units:** + + - frost_free_spell_max_length: days This function wraps `xclim.indicators.atmos.frost_free_spell_max_length `_. @@ -1224,7 +1300,9 @@ def frost_season_length( temperature is below 0°C without a thawing window of days, with the thaw occurring after a median calendar date. - **Units:** days + **Units:** + + - frost_season_length: days This function wraps `xclim.indicators.atmos.frost_season_length `_. @@ -1255,7 +1333,9 @@ def growing_degree_days( The cumulative degree days for days when the average temperature is above a given threshold. - **Units:** K days + **Units:** + + - growing_degree_days: K days This function wraps `xclim.indicators.atmos.growing_degree_days `_. @@ -1286,7 +1366,9 @@ def growing_season_end( The first day when the temperature is below a certain threshold for a certain number of consecutive days after a given calendar date. - **Units:** dimensionless + **Units:** + + - growing_season_end: dimensionless This function wraps `xclim.indicators.atmos.growing_season_end `_. @@ -1318,7 +1400,9 @@ def growing_season_length( temperature above a threshold and the first occurrence of a series of days with a daily average temperature below that same threshold, occurring after a given calendar date. - **Units:** days + **Units:** + + - growing_season_length: days This function wraps `xclim.indicators.atmos.growing_season_length `_. @@ -1349,7 +1433,9 @@ def growing_season_start( The first day when the temperature exceeds a certain threshold for a given number of consecutive days. - **Units:** dimensionless + **Units:** + + - growing_season_start: dimensionless This function wraps `xclim.indicators.atmos.growing_season_start `_. @@ -1380,7 +1466,9 @@ def heat_spell_frequency( Number of heat spells. A heat spell occurs when rolling averages of daily minimum and maximumtemperatures exceed given thresholds for a number of days. - **Units:** dimensionless + **Units:** + + - heat_spell_frequency: dimensionless This function wraps `xclim.indicators.atmos.heat_spell_frequency `_. @@ -1411,7 +1499,9 @@ def heat_spell_max_length( The longest heat spell of a period. A heat spell occurs when rolling averages of daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** days + **Units:** + + - heat_spell_max_length: days This function wraps `xclim.indicators.atmos.heat_spell_max_length `_. @@ -1442,7 +1532,9 @@ def heat_spell_total_length( Total length of heat spells. A heat spell occurs when rolling averages of daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** days + **Units:** + + - heat_spell_total_length: days This function wraps `xclim.indicators.atmos.heat_spell_total_length `_. @@ -1473,7 +1565,9 @@ def heat_wave_frequency( Number of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** dimensionless + **Units:** + + - heat_wave_frequency: dimensionless This function wraps `xclim.indicators.atmos.heat_wave_frequency `_. @@ -1504,7 +1598,9 @@ def heat_wave_index( Number of days that constitute heatwave events. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** days + **Units:** + + - heat_wave_index: days This function wraps `xclim.indicators.atmos.heat_wave_index `_. @@ -1535,7 +1631,9 @@ def heat_wave_max_length( Maximal duration of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** days + **Units:** + + - heat_wave_max_length: days This function wraps `xclim.indicators.atmos.heat_wave_max_length `_. @@ -1566,7 +1664,9 @@ def heat_wave_total_length( Total length of heat waves. A heat wave occurs when daily minimum and maximum temperatures exceed given thresholds for a number of days. - **Units:** days + **Units:** + + - heat_wave_total_length: days This function wraps `xclim.indicators.atmos.heat_wave_total_length `_. @@ -1597,7 +1697,9 @@ def heating_degree_days( The cumulative degree days for days when the mean daily temperature is below a given threshold and buildings must be heated. - **Units:** K days + **Units:** + + - heating_degree_days: K days This function wraps `xclim.indicators.atmos.heating_degree_days `_. @@ -1630,7 +1732,9 @@ def heating_degree_days_approximation( temperatures, accounting for asymmetry in the distributions of temperatures throughout the diurnal cycle. - **Units:** K days + **Units:** + + - heating_degree_days_approximation: K days This function wraps `xclim.indicators.atmos.heating_degree_days_approximation `_. @@ -1660,7 +1764,9 @@ def hot_days( Number of days where the daily maximum temperature is above a given threshold. - **Units:** days + **Units:** + + - hot_days: days This function wraps `xclim.indicators.atmos.hot_days `_. @@ -1691,7 +1797,9 @@ def hot_spell_frequency( The frequency of hot periods of `N` days or more, during which the temperature over a given time window of days is above a given threshold. - **Units:** dimensionless + **Units:** + + - hot_spell_frequency: dimensionless This function wraps `xclim.indicators.atmos.hot_spell_frequency `_. @@ -1722,7 +1830,9 @@ def hot_spell_max_length( The maximum length of a hot period of `N` days or more, during which the temperature over a given time window of days is above a given threshold. - **Units:** days + **Units:** + + - hot_spell_max_length: days This function wraps `xclim.indicators.atmos.hot_spell_max_length `_. @@ -1753,7 +1863,9 @@ def hot_spell_max_magnitude( Magnitude of the most intensive heat wave per {freq}. A heat wave occurs when daily maximum temperatures exceed given thresholds for a number of days. - **Units:** K d + **Units:** + + - hot_spell_max_magnitude: K d This function wraps `xclim.indicators.atmos.hot_spell_max_magnitude `_. @@ -1784,7 +1896,9 @@ def hot_spell_total_length( The total length of hot periods of `N` days or more, during which the temperature over a given time window of days is above a given threshold. - **Units:** days + **Units:** + + - hot_spell_total_length: days This function wraps `xclim.indicators.atmos.hot_spell_total_length `_. @@ -1818,7 +1932,9 @@ def huglin_index( coefficient calculation for higher latitudes. Metric originally published in Huglin (1978). Day-length coefficient based on Hall & Jones (2010). - **Units:** dimensionless + **Units:** + + - hi: dimensionless This function wraps `xclim.indicators.atmos.huglin_index `_. @@ -1848,7 +1964,9 @@ def ice_days( Number of days where the daily maximum temperature is below 0°C - **Units:** days + **Units:** + + - ice_days: days This function wraps `xclim.indicators.atmos.ice_days `_. @@ -1879,7 +1997,9 @@ def last_spring_frost( The last day when minimum temperature is below a given threshold for a certain number of days, limited by a final calendar date. - **Units:** dimensionless + **Units:** + + - last_spring_frost: dimensionless This function wraps `xclim.indicators.atmos.last_spring_frost `_. @@ -1910,7 +2030,9 @@ def late_frost_days( Number of days where the daily minimum temperature is below a given threshold between a givenstart date and a given end date. - **Units:** days + **Units:** + + - late_frost_days: days This function wraps `xclim.indicators.atmos.late_frost_days `_. @@ -1944,7 +2066,9 @@ def latitude_temperature_index( difference of latitude factor coefficient minus latitude. Metric originally published in Jackson, D. I., & Cherry, N. J. (1988). - **Units:** dimensionless + **Units:** + + - lti: dimensionless This function wraps `xclim.indicators.atmos.latitude_temperature_index `_. @@ -1975,7 +2099,9 @@ def maximum_consecutive_warm_days( Maximum number of consecutive days where the maximum daily temperature exceeds a certain threshold. - **Units:** days + **Units:** + + - maximum_consecutive_warm_days: days This function wraps `xclim.indicators.atmos.maximum_consecutive_warm_days `_. @@ -2005,7 +2131,9 @@ def tg10p( Number of days with mean temperature below the 10th percentile. - **Units:** days + **Units:** + + - tg10p: days This function wraps `xclim.indicators.atmos.tg10p `_. @@ -2035,7 +2163,9 @@ def tg90p( Number of days with mean temperature above the 90th percentile. - **Units:** days + **Units:** + + - tg90p: days This function wraps `xclim.indicators.atmos.tg90p `_. @@ -2065,7 +2195,9 @@ def tg_days_above( The number of days with mean temperature above a given threshold. - **Units:** days + **Units:** + + - tg_days_above: days This function wraps `xclim.indicators.atmos.tg_days_above `_. @@ -2095,7 +2227,9 @@ def tg_days_below( The number of days with mean temperature below a given threshold. - **Units:** days + **Units:** + + - tg_days_below: days This function wraps `xclim.indicators.atmos.tg_days_below `_. @@ -2125,7 +2259,9 @@ def tg_max( Maximum of daily mean temperature. - **Units:** K + **Units:** + + - tg_max: K This function wraps `xclim.indicators.atmos.tg_max `_. @@ -2155,7 +2291,9 @@ def tg_mean( Mean of daily mean temperature. - **Units:** K + **Units:** + + - tg_mean: K This function wraps `xclim.indicators.atmos.tg_mean `_. @@ -2185,7 +2323,9 @@ def tg_min( Minimum of daily mean temperature. - **Units:** K + **Units:** + + - tg_min: K This function wraps `xclim.indicators.atmos.tg_min `_. @@ -2216,7 +2356,9 @@ def thawing_degree_days( The cumulative degree days for days when the average temperature is above a given threshold, typically 0°C. - **Units:** K days + **Units:** + + - thawing_degree_days: K days This function wraps `xclim.indicators.atmos.thawing_degree_days `_. @@ -2246,7 +2388,9 @@ def tn10p( Number of days with minimum temperature below the 10th percentile. - **Units:** days + **Units:** + + - tn10p: days This function wraps `xclim.indicators.atmos.tn10p `_. @@ -2276,7 +2420,9 @@ def tn90p( Number of days with minimum temperature above the 90th percentile. - **Units:** days + **Units:** + + - tn90p: days This function wraps `xclim.indicators.atmos.tn90p `_. @@ -2306,7 +2452,9 @@ def tn_days_above( The number of days with minimum temperature above a given threshold. - **Units:** days + **Units:** + + - tn_days_above: days This function wraps `xclim.indicators.atmos.tn_days_above `_. @@ -2336,7 +2484,9 @@ def tn_days_below( The number of days with minimum temperature below a given threshold. - **Units:** days + **Units:** + + - tn_days_below: days This function wraps `xclim.indicators.atmos.tn_days_below `_. @@ -2366,7 +2516,9 @@ def tn_max( Maximum of daily minimum temperature. - **Units:** K + **Units:** + + - tn_max: K This function wraps `xclim.indicators.atmos.tn_max `_. @@ -2396,7 +2548,9 @@ def tn_mean( Mean of daily minimum temperature. - **Units:** K + **Units:** + + - tn_mean: K This function wraps `xclim.indicators.atmos.tn_mean `_. @@ -2426,7 +2580,9 @@ def tn_min( Minimum of daily minimum temperature. - **Units:** K + **Units:** + + - tn_min: K This function wraps `xclim.indicators.atmos.tn_min `_. @@ -2456,7 +2612,9 @@ def tropical_nights( Number of days where minimum temperature is above a given threshold. - **Units:** days + **Units:** + + - tropical_nights: days This function wraps `xclim.indicators.atmos.tropical_nights `_. @@ -2486,7 +2644,9 @@ def tx10p( Number of days with maximum temperature below the 10th percentile. - **Units:** days + **Units:** + + - tx10p: days This function wraps `xclim.indicators.atmos.tx10p `_. @@ -2516,7 +2676,9 @@ def tx90p( Number of days with maximum temperature above the 90th percentile. - **Units:** days + **Units:** + + - tx90p: days This function wraps `xclim.indicators.atmos.tx90p `_. @@ -2546,7 +2708,9 @@ def tx_days_above( The number of days with maximum temperature above a given threshold. - **Units:** days + **Units:** + + - tx_days_above: days This function wraps `xclim.indicators.atmos.tx_days_above `_. @@ -2576,7 +2740,9 @@ def tx_days_below( The number of days with maximum temperature below a given threshold. - **Units:** days + **Units:** + + - tx_days_below: days This function wraps `xclim.indicators.atmos.tx_days_below `_. @@ -2606,7 +2772,9 @@ def tx_max( Maximum of daily maximum temperature. - **Units:** K + **Units:** + + - tx_max: K This function wraps `xclim.indicators.atmos.tx_max `_. @@ -2636,7 +2804,9 @@ def tx_mean( Mean of daily maximum temperature. - **Units:** K + **Units:** + + - tx_mean: K This function wraps `xclim.indicators.atmos.tx_mean `_. @@ -2666,7 +2836,9 @@ def tx_min( Minimum of daily maximum temperature. - **Units:** K + **Units:** + + - tx_min: K This function wraps `xclim.indicators.atmos.tx_min `_. @@ -2696,7 +2868,9 @@ def tx_tn_days_above( Number of days with daily maximum and minimum temperatures above given thresholds. - **Units:** days + **Units:** + + - tx_tn_days_above: days This function wraps `xclim.indicators.atmos.tx_tn_days_above `_. @@ -2730,7 +2904,9 @@ def usda_hardiness_zones( Fahrenheit zones, with 5-degree Fahrenheit half-zones, starting from -65 degrees Fahrenheit and ending at 65 degrees Fahrenheit. - **Units:** dimensionless + **Units:** + + - hz: dimensionless This function wraps `xclim.indicators.atmos.usda_hardiness_zones `_. @@ -2762,7 +2938,9 @@ def warm_spell_duration_index( maximum daily temperature is above a given percentile for a given number of consecutive days. - **Units:** days + **Units:** + + - warm_spell_duration_index: days This function wraps `xclim.indicators.atmos.warm_spell_duration_index `_. diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index 9bbb15e..ab0aeb7 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -90,29 +90,29 @@ def generate_docstring(indicator: Any, xclim_func_name: str) -> str: sections.append(textwrap.fill(description, width=88)) # Units handling - units_section = "" + units_list = [] if isinstance(units, str): units = units.strip() if not units: - units = "dimensionless" - units_section = f"**Units:** {units}" + units_list = ["dimensionless"] + else: + units_list = [units] elif isinstance(units, (list, tuple)): - if units: - # Treat empty units as "dimensionless" - processed_units = [u.strip() if u else "dimensionless" for u in units] - - if len(processed_units) == 1: - units_section = f"**Units:** {processed_units[0]}" - else: - # Check if outputs align with units - if isinstance(outputs, (list, tuple)) and len(outputs) == len(processed_units): - lines = ["**Units:**"] - for out, unit in zip(outputs, processed_units): - lines.append(f"- {out}: {unit}") - units_section = "\n".join(lines) - else: - # Fallback to comma-separated - units_section = f"**Units:** {', '.join(processed_units)}" + units_list = [u.strip() if u else "dimensionless" for u in units] + else: + units_list = ["dimensionless"] + + outputs_list = [] + if isinstance(outputs, str): + outputs_list = [outputs.strip()] + elif isinstance(outputs, (list, tuple)): + outputs_list = [o.strip() for o in outputs] + + units_section = "" + lines = ["**Units:**", ""] + for out, unit in zip(outputs_list, units_list): + lines.append(f"- {out}: {unit}") + units_section = "\n".join(lines) if units_section: sections.append(units_section) From 5da452a18d4ffe248b218d5eee4ba632d50c7b33 Mon Sep 17 00:00:00 2001 From: garciam Date: Tue, 17 Feb 2026 11:13:40 +0100 Subject: [PATCH 23/47] Set pre-commit to run with python 3.13 --- .github/workflows/qa.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/qa.yml b/.github/workflows/qa.yml index 8800cff..26cfd36 100644 --- a/.github/workflows/qa.yml +++ b/.github/workflows/qa.yml @@ -12,7 +12,7 @@ jobs: uses: ecmwf/reusable-workflows/.github/workflows/qa-precommit-run.yml@v2 with: skip-hooks: "no-commit-to-branch" - + python-version: 3.13 tests: strategy: matrix: From 3ac66dab86faaaa6c67139e0c8ca66cfc1e47604 Mon Sep 17 00:00:00 2001 From: garciam Date: Tue, 17 Feb 2026 11:20:52 +0100 Subject: [PATCH 24/47] revert since the CI blocks us from touching this --- .github/workflows/qa.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/qa.yml b/.github/workflows/qa.yml index 26cfd36..f9cb228 100644 --- a/.github/workflows/qa.yml +++ b/.github/workflows/qa.yml @@ -12,7 +12,6 @@ jobs: uses: ecmwf/reusable-workflows/.github/workflows/qa-precommit-run.yml@v2 with: skip-hooks: "no-commit-to-branch" - python-version: 3.13 tests: strategy: matrix: From 33f3e8c241db531e09c39f1e9d08b06f6fa822c5 Mon Sep 17 00:00:00 2001 From: Christopher Polster Date: Fri, 20 Feb 2026 09:12:58 +0100 Subject: [PATCH 25/47] Put empty line back back so qa.yml remains untouched --- .github/workflows/qa.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/qa.yml b/.github/workflows/qa.yml index f9cb228..8800cff 100644 --- a/.github/workflows/qa.yml +++ b/.github/workflows/qa.yml @@ -12,6 +12,7 @@ jobs: uses: ecmwf/reusable-workflows/.github/workflows/qa-precommit-run.yml@v2 with: skip-hooks: "no-commit-to-branch" + tests: strategy: matrix: From 013592ed89a1dec640e0df1855328e2287927cda Mon Sep 17 00:00:00 2001 From: Christopher Polster Date: Fri, 20 Feb 2026 09:14:32 +0100 Subject: [PATCH 26/47] Update pre-commit --- .pre-commit-config.yaml | 13 +++++++++---- docs/notebooks/climate_indices_analysis.ipynb | 4 ++-- src/earthkit/climate/utils/conversions.py | 3 +-- tests/unit/utils/test_conversions.py | 2 +- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 218274e..191a013 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,3 +1,8 @@ +default_language_version: + python: python3 +default_stages: +- pre-commit +- pre-push repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 @@ -14,9 +19,9 @@ repos: - id: no-commit-to-branch args: [--branch, main] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.8 + rev: v0.15.2 hooks: - - id: ruff + - id: ruff-check args: [--fix, --show-fixes] - id: ruff-format - repo: https://github.com/executablebooks/mdformat @@ -24,13 +29,13 @@ repos: hooks: - id: mdformat - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.11.0 + rev: v2.16.0 hooks: - id: pretty-format-yaml args: [--autofix, --preserve-quotes] - id: pretty-format-toml args: [--autofix] - repo: https://github.com/gitleaks/gitleaks - rev: v8.18.1 + rev: v8.30.0 hooks: - id: gitleaks diff --git a/docs/notebooks/climate_indices_analysis.ipynb b/docs/notebooks/climate_indices_analysis.ipynb index aa9d235..4779524 100644 --- a/docs/notebooks/climate_indices_analysis.ipynb +++ b/docs/notebooks/climate_indices_analysis.ipynb @@ -37,10 +37,10 @@ "import warnings\n", "\n", "import cartopy.crs as ccrs\n", - "import matplotlib.pyplot as plt\n", - "\n", "import earthkit.data as ekd\n", "import earthkit.plots as ekp\n", + "import matplotlib.pyplot as plt\n", + "\n", "from earthkit.climate.indicators.precipitation import (\n", " daily_precipitation_intensity,\n", " maximum_consecutive_wet_days,\n", diff --git a/src/earthkit/climate/utils/conversions.py b/src/earthkit/climate/utils/conversions.py index 6712e7f..f949567 100644 --- a/src/earthkit/climate/utils/conversions.py +++ b/src/earthkit/climate/utils/conversions.py @@ -12,9 +12,8 @@ from typing import Any, Dict, Mapping, Tuple -import xarray - import earthkit.data as ekd +import xarray EarthkitData = ekd.FieldList | ekd.Field MetadataDict = Dict[str, Any] diff --git a/tests/unit/utils/test_conversions.py b/tests/unit/utils/test_conversions.py index 6d6870a..b1780b0 100644 --- a/tests/unit/utils/test_conversions.py +++ b/tests/unit/utils/test_conversions.py @@ -10,12 +10,12 @@ import pytest import xarray as xr +from earthkit.data.wrappers.xarray import XArrayDatasetWrapper from earthkit.climate.utils.conversions import ( to_earthkit_field, to_xarray_dataset, ) -from earthkit.data.wrappers.xarray import XArrayDatasetWrapper @pytest.mark.parametrize( From cbde504394a89086ddad8cc1532f09a8815868bd Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 24 Feb 2026 09:05:00 +0100 Subject: [PATCH 27/47] feat: add performance benchmarking script and integration tests for climate indicators --- docs/notebooks/performance_analysis.ipynb | 1558 -------------------- tests/integration/__init__.py | 0 tests/integration/benchmark_performance.py | 521 +++++++ tests/integration/test_performance.py | 310 ++++ 4 files changed, 831 insertions(+), 1558 deletions(-) delete mode 100644 docs/notebooks/performance_analysis.ipynb create mode 100644 tests/integration/__init__.py create mode 100644 tests/integration/benchmark_performance.py create mode 100644 tests/integration/test_performance.py diff --git a/docs/notebooks/performance_analysis.ipynb b/docs/notebooks/performance_analysis.ipynb deleted file mode 100644 index 7c6a56a..0000000 --- a/docs/notebooks/performance_analysis.ipynb +++ /dev/null @@ -1,1558 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fb33bbc9", - "metadata": {}, - "source": [ - "# Robust Performance Benchmarking: Earthkit-Climate vs Xclim\n", - "\n", - "This notebook provides a robust comparative analysis of climate indicator calculations using:\n", - "1. **Earthkit-Climate (Lazy)**: Standard wrapper usage.\n", - "2. **Earthkit-Climate (Optimized)**: With pre-computation of percentiles and manual re-chunking (`time: -1`).\n", - "3. **Xclim (Direct)**: Direct optimized usage of `xclim.indices`.\n", - "4. **Xclim + Flox**: `xclim` with `flox` optimization enabled for faster reductions.\n", - "\n", - "Key features:\n", - "- Statistical Sampling: $N$ runs per test.\n", - "- Resource Profiling: RAM peak & CPU usage.\n", - "- Visualization: Speedup comparison with error bars.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "410735a7", - "metadata": {}, - "outputs": [], - "source": [ - "import gc\n", - "import os\n", - "import time\n", - "import warnings\n", - "from typing import Any, Callable, Dict, List, Optional\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import xarray as xr\n", - "import xclim.indicators\n", - "from IPython.display import Markdown, display\n", - "\n", - "import earthkit.climate.indicators.precipitation as ek_pr\n", - "import earthkit.climate.indicators.temperature as ek_temp\n", - "import earthkit.data\n", - "from earthkit.climate.utils.percentile import percentile_doy\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "sns.set_theme(style=\"whitegrid\")\n", - "\n", - "# Configure robust caching to avoid re-downloading\n", - "cache_dir = os.path.expanduser(\"~/.cache/earthkit/data\")\n", - "os.makedirs(cache_dir, exist_ok=True)\n", - "settings_earthkit = {\n", - " \"cache-policy\": \"user\",\n", - " \"temporary-directory-root\": cache_dir,\n", - "}\n", - "earthkit.data.config.set(settings_earthkit)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "68281ebe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "## Resources Used\n", - "\n", - "### Hardware Configuration\n", - "The performance analysis was conducted on the following hardware (dynamically detected):\n", - "- **CPU**: 12th Gen Intel(R) Core(TM) i5-12600KF\n", - "- **RAM**: 31.2 GB\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def get_cpu_info() -> str:\n", - " \"\"\"\n", - " Extract the CPU model name from the system's /proc/cpuinfo.\n", - "\n", - " This function parses the Linux virtual filesystem to find the\n", - " hardware model name of the processor.\n", - "\n", - " Returns\n", - " -------\n", - " cpu_model : str\n", - " The name of the CPU model (e.g., 'Intel(R) Core(TM) i7-10700K').\n", - " Returns 'Unknown CPU' if the file is inaccessible or the entry\n", - " is missing.\n", - " \"\"\"\n", - " try:\n", - " if os.path.exists(\"/proc/cpuinfo\"):\n", - " with open(\"/proc/cpuinfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"model name\" in line:\n", - " return line.split(\":\")[1].strip()\n", - " except Exception:\n", - " return \"Unknown CPU\"\n", - " return \"Unknown CPU\"\n", - "\n", - "\n", - "def get_ram_info() -> str:\n", - " \"\"\"\n", - " Extract the total system RAM from /proc/meminfo.\n", - "\n", - " Parses the system memory information and converts the total memory\n", - " from kilobytes to gigabytes.\n", - "\n", - " Returns\n", - " -------\n", - " ram_size : str\n", - " A formatted string representing total RAM in GB (e.g., '16.0 GB').\n", - " Returns 'Unknown RAM' if the file is inaccessible or the entry\n", - " is missing.\n", - " \"\"\"\n", - " try:\n", - " if os.path.exists(\"/proc/meminfo\"):\n", - " with open(\"/proc/meminfo\", \"r\") as f:\n", - " for line in f:\n", - " if \"MemTotal\" in line:\n", - " total_kb = int(line.split()[1])\n", - " return f\"{total_kb / 1024 / 1024:.1f} GB\"\n", - " except Exception:\n", - " return \"Unknown RAM\"\n", - " return \"Unknown RAM\"\n", - "\n", - "\n", - "# --- Execution Logic ---\n", - "\n", - "cpu_model: str = get_cpu_info()\n", - "ram_size: str = get_ram_info()\n", - "\n", - "display(\n", - " Markdown(f\"\"\"\n", - "## Resources Used\n", - "\n", - "### Hardware Configuration\n", - "The performance analysis was conducted on the following hardware (dynamically detected):\n", - "- **CPU**: {cpu_model}\n", - "- **RAM**: {ram_size}\n", - "\"\"\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "fba35188", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "\n", - "### Dataset Information\n", - "\n", - "The analysis uses the following climate datasets derived from CMIP6 projections\n", - "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", - "repository and are automatically downloaded or cached by **earthkit-data**.\n", - "\n", - "| Variable | Scenario | Dimensions | Size | Status | URL |\n", - "|----------|----------|------------|------|--------|-----|\n", - "| `tasmax` | historical | (time: 7305, lat: 48, lon: 84) | 67.3 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc) |\n", - "| `tasmax` | ssp585 | (time: 14610, lat: 48, lon: 84) | 126.9 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "| `tasmin` | ssp585 | (time: 14610, lat: 48, lon: 84) | 132.1 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "| `pr` | ssp585 | (time: 14610, lat: 48, lon: 84) | 111.5 MB | Cached | [Download](https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc) |\n", - "\n", - "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", - "download the files.\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Dataset URLs\n", - "DATASET_URLS = [\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - "]\n", - "\n", - "\n", - "def format_size(size_bytes: float) -> str:\n", - " \"\"\"\n", - " Convert a byte size into a human-readable string.\n", - "\n", - " Parameters\n", - " ----------\n", - " size_bytes : float\n", - " File size in bytes.\n", - "\n", - " Returns\n", - " -------\n", - " str\n", - " Size formatted as B, KB, MB, or GB.\n", - " \"\"\"\n", - " for unit in [\"B\", \"KB\", \"MB\", \"GB\"]:\n", - " if size_bytes < 1024:\n", - " return f\"{size_bytes:.1f} {unit}\"\n", - " size_bytes /= 1024\n", - " return f\"{size_bytes:.1f} TB\"\n", - "\n", - "\n", - "def extract_dataset_info(url: str) -> Dict[str, Any]:\n", - " \"\"\"\n", - " Extract key metadata information from a NetCDF dataset available via URL.\n", - "\n", - " Parameters\n", - " ----------\n", - " url : str\n", - " URL to the NetCDF dataset.\n", - "\n", - " Returns\n", - " -------\n", - " dict\n", - " Dictionary containing:\n", - " - Variable\n", - " - Scenario\n", - " - Description\n", - " - Dimensions\n", - " - Size\n", - " - Status (\"Cached\" or \"Remote\")\n", - " - URL\n", - " \"\"\"\n", - " ds = earthkit.data.from_source(\"url\", url)\n", - "\n", - " # Detect file size & cache status\n", - " if getattr(ds, \"path\", None) and os.path.exists(ds.path):\n", - " size = format_size(os.path.getsize(ds.path))\n", - " status = \"Cached\"\n", - " else:\n", - " size = \"Unknown\"\n", - " status = \"Remote\"\n", - "\n", - " # Convert to xarray\n", - " xr_ds = ds.to_xarray()\n", - "\n", - " # Extract primary variable\n", - " variables = list(xr_ds.data_vars)\n", - " variable = f\"`{variables[0]}`\" if variables else \"Unknown\"\n", - "\n", - " # Scenario metadata\n", - " scenario = xr_ds.attrs.get(\"scenario\", \"Unknown\")\n", - "\n", - " # Dimension string, e.g. \"(time: 365, lat: 180, lon: 360)\"\n", - " dims_str = \"(\" + \", \".join(f\"{k}: {v}\" for k, v in xr_ds.dims.items()) + \")\"\n", - "\n", - " return {\n", - " \"Variable\": variable,\n", - " \"Scenario\": scenario,\n", - " \"Dimensions\": dims_str,\n", - " \"Size\": size,\n", - " \"Status\": status,\n", - " \"URL\": url,\n", - " }\n", - "\n", - "\n", - "def generate_dataset_table(urls: List[str]) -> str:\n", - " \"\"\"\n", - " Create a Markdown table summarizing metadata for a list of dataset URLs.\n", - "\n", - " Parameters\n", - " ----------\n", - " urls : list of str\n", - " List of dataset URLs.\n", - "\n", - " Returns\n", - " -------\n", - " str\n", - " A Markdown-formatted table of dataset metadata.\n", - " \"\"\"\n", - " header = (\n", - " \"| Variable | Scenario | Dimensions | Size | Status | URL |\\n\"\n", - " \"|----------|----------|------------|------|--------|-----|\\n\"\n", - " )\n", - "\n", - " rows = []\n", - " for url in urls:\n", - " info = extract_dataset_info(url)\n", - " rows.append(\n", - " f\"| {info['Variable']} | {info['Scenario']} | \"\n", - " f\"{info['Dimensions']} | {info['Size']} | {info['Status']} | \"\n", - " f\"[Download]({info['URL']}) |\"\n", - " )\n", - "\n", - " return header + \"\\n\".join(rows)\n", - "\n", - "\n", - "# Display Markdown report\n", - "display(\n", - " Markdown(\n", - " f\"\"\"\n", - "### Dataset Information\n", - "\n", - "The analysis uses the following climate datasets derived from CMIP6 projections\n", - "(ACCESS-CM2 model, DeepESD downscaling). These datasets are hosted in the ECMWF\n", - "repository and are automatically downloaded or cached by **earthkit-data**.\n", - "\n", - "{generate_dataset_table(DATASET_URLS)}\n", - "\n", - "> **Note**: Dimensions and sizes are extracted dynamically. The first run may\n", - "download the files.\n", - "\"\"\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5922ba8f", - "metadata": {}, - "source": [ - "## 1. Benchmarking Engine\n", - "\n", - "Helper functions to run tests multiple times, capture statistics, and profile resources." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5d1ba8a5", - "metadata": {}, - "outputs": [], - "source": [ - "import threading\n", - "from typing import Any, Dict, List\n", - "\n", - "import psutil\n", - "\n", - "\n", - "class ResourceMonitor(threading.Thread):\n", - " def __init__(self, interval=0.1):\n", - " self.interval = interval\n", - " self.stop_event = threading.Event()\n", - " self.memory_usage = []\n", - " self.cpu_usage = []\n", - " self.process = psutil.Process()\n", - " super().__init__()\n", - "\n", - " def run(self):\n", - " while not self.stop_event.is_set():\n", - " try:\n", - " # RSS Memory in MiB\n", - " mem = self.process.memory_info().rss / (1024 * 1024)\n", - " self.memory_usage.append(mem)\n", - " # CPU Percent (blocking for interval would slow main thread, so we sleep manually)\n", - " # self.cpu_usage.append(self.process.cpu_percent(interval=None))\n", - " except Exception:\n", - " pass\n", - " time.sleep(self.interval)\n", - "\n", - " def stop(self):\n", - " self.stop_event.set()\n", - "\n", - "\n", - "def benchmark_function(\n", - " func: Callable[..., Any], kwargs: Dict[str, Any], n_repeats: int = 5, warmup: bool = True\n", - ") -> Dict[str, float]:\n", - " \"\"\"\n", - " Run a function N times with robust profiling (Warm-up + High-Freq Sampling).\n", - "\n", - " Parameters\n", - " ----------\n", - " func : Callable\n", - " The function to benchmark.\n", - " kwargs : Dict\n", - " Arguments for the function.\n", - " n_repeats : int\n", - " Number of measurement runs.\n", - " warmup : bool\n", - " If True, runs the function once silently before measuring to exclude JIT/Import costs.\n", - "\n", - " Returns\n", - " -------\n", - " stats : Dict\n", - " 'mean_time', 'median_time', 'std_time', 'max_mem', 'mean_mem_peak'\n", - " \"\"\"\n", - " # 1. Warm-up (Silent)\n", - " if warmup:\n", - " print(f\" Warm-up run for {func.__name__}...\")\n", - " try:\n", - " # Run without monitoring\n", - " res = func(**kwargs)\n", - " if hasattr(res, \"compute\"):\n", - " res.compute()\n", - " elif hasattr(res, \"to_xarray\"):\n", - " res.to_xarray().compute()\n", - " except Exception as e:\n", - " print(f\" Warm-up warning: {e}\")\n", - " # Force GC after warmup\n", - " gc.collect()\n", - "\n", - " times: List[float] = []\n", - " mem_peaks: List[float] = []\n", - "\n", - " print(f\"Benchmarking {func.__name__} ({n_repeats} runs)...\")\n", - "\n", - " for i in range(n_repeats):\n", - " gc.collect()\n", - "\n", - " # Start Monitor (10ms interval)\n", - " monitor = ResourceMonitor(interval=0.1)\n", - " # Capture baseline memory\n", - " baseline_mem = psutil.Process().memory_info().rss / (1024 * 1024)\n", - " monitor.start()\n", - "\n", - " start_time = time.perf_counter()\n", - "\n", - " # --- Computation ---\n", - " try:\n", - " res = func(**kwargs)\n", - " if hasattr(res, \"compute\"):\n", - " res.compute()\n", - " elif hasattr(res, \"to_xarray\"):\n", - " res.to_xarray().compute()\n", - " except Exception as e:\n", - " print(f\" Run {i + 1} Failed: {e}\")\n", - " monitor.stop()\n", - " continue\n", - " # -------------------\n", - "\n", - " end_time = time.perf_counter()\n", - " monitor.stop()\n", - " monitor.join()\n", - "\n", - " duration = end_time - start_time\n", - "\n", - " # Calculate Peak Memory Delta\n", - " observed_mems = monitor.memory_usage\n", - " if observed_mems:\n", - " # We want the peak usage *induced* by the function\n", - " # Peak Delta = Max(Observed) - Baseline\n", - " # Normalized Peak: (Max - Baseline)\n", - " peak_delta = max(observed_mems) - baseline_mem\n", - " # Clamp to 0\n", - " if peak_delta < 0:\n", - " peak_delta = 0\n", - " else:\n", - " peak_delta = 0.0\n", - "\n", - " times.append(duration)\n", - " mem_peaks.append(peak_delta)\n", - "\n", - " print(f\" Run {i + 1}: {duration:.4f}s, Mem Peak Delta: {peak_delta:.2f} MiB\")\n", - "\n", - " if not times:\n", - " return {\"mean_time\": 0.0, \"max_mem\": 0.0}\n", - "\n", - " return {\n", - " \"mean_time\": float(np.mean(times)),\n", - " \"median_time\": float(np.median(times)),\n", - " \"std_time\": float(np.std(times)),\n", - " \"max_mem\": float(np.max(mem_peaks)),\n", - " \"mean_mem\": float(np.mean(mem_peaks)),\n", - " }" - ] - }, - { - "cell_type": "markdown", - "id": "d672e8b4", - "metadata": {}, - "source": [ - "## 2. Data Management\n", - "Load CMIP6 datasets and ensure unit compatibility." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "acf4d31d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading datasets via earthkit...\n" - ] - } - ], - "source": [ - "URLS = {\n", - " \"tasmax_hist\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - " \"tasmax_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"tasmin_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - " \"pr_ssp\": \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - "}\n", - "\n", - "\n", - "def load_data() -> Dict[str, xr.Dataset]:\n", - " \"\"\"\n", - " Load climate datasets from remote URLs using earthkit-data.\n", - "\n", - " Iterates through a predefined global dictionary of URLs, downloads\n", - " the data sources, and converts them into xarray Datasets.\n", - "\n", - " Returns\n", - " -------\n", - " datasets : Dict[str, xr.Dataset]\n", - " A dictionary where keys match the source identifiers and values\n", - " are the loaded xarray Datasets.\n", - "\n", - " Raises\n", - " ------\n", - " NameError\n", - " If `URLS` is not defined in the global scope.\n", - " \"\"\"\n", - " print(\"Loading datasets via earthkit...\")\n", - " datasets: Dict[str, xr.Dataset] = {}\n", - " for key, url in URLS.items():\n", - " # earthkit.data.from_source returns a wrapper that to_xarray() converts\n", - " ds = earthkit.data.from_source(\"url\", url).to_xarray()\n", - " datasets[key] = ds\n", - " return datasets\n", - "\n", - "\n", - "data_cache = load_data()" - ] - }, - { - "cell_type": "markdown", - "id": "37feb6c1", - "metadata": {}, - "source": [ - "## 3. Contender Implementations\n", - "\n", - "We wrap each library's call in a uniform function signature." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "66739804", - "metadata": {}, - "outputs": [], - "source": [ - "from contextlib import nullcontext\n", - "from typing import Any, Callable, Dict\n", - "\n", - "import xarray as xr\n", - "\n", - "\n", - "def run_climate_indicator(\n", - " func: Callable[..., Any], kwargs: Dict[str, Any], use_flox: Optional[bool] = None\n", - ") -> Any:\n", - " \"\"\"\n", - " Unified execution wrapper for climate indicator functions with configurable backend options.\n", - "\n", - " This function streamlines the benchmarking process by dynamically handling\n", - " Xarray global options (specifically 'flox' optimization) via a context manager.\n", - "\n", - " Parameters\n", - " ----------\n", - " func : Callable[..., Any]\n", - " The indicator function to execute (e.g., from Earthkit or Xclim).\n", - " kwargs : Dict[str, Any]\n", - " A dictionary of keyword arguments required by the `func` (e.g., input datasets,\n", - " thresholds, frequencies).\n", - " use_flox : bool, optional\n", - " Controls the 'use_flox' option in Xarray for accelerated GroupBy operations:\n", - " - True: Explicitly enables Flox optimization.\n", - " - False: Explicitly disables Flox (forces legacy implementation).\n", - " - None: Uses the current environment's default configuration (no context change).\n", - " By default, None.\n", - "\n", - " Returns\n", - " -------\n", - " Any\n", - " The result of the indicator calculation (typically an xarray.DataArray or Dataset).\n", - " \"\"\"\n", - " # Determine context: set options if boolean, otherwise do nothing (nullcontext)\n", - " ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext()\n", - "\n", - " with ctx:\n", - " return func(**kwargs)" - ] - }, - { - "cell_type": "markdown", - "id": "f2f50cc5", - "metadata": {}, - "source": [ - "## 4. Execution Loop\n", - "\n", - "We define the specific configurations for WSDI, CWD, DTR, HDD, SDII." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "46022af7", - "metadata": {}, - "outputs": [], - "source": [ - "def get_named_runner(name: str, target_runner: Callable, **kwargs) -> Callable:\n", - " \"\"\"\n", - " Creates a 0-argument wrapper with a custom `__name__`.\n", - "\n", - " Renamed the second argument to 'target_runner' to avoid collision\n", - " with the 'func' argument inside **kwargs.\n", - " \"\"\"\n", - "\n", - " def wrapper():\n", - " # Ahora llamamos a target_runner pasándole los kwargs (que contienen 'func')\n", - " return target_runner(**kwargs)\n", - "\n", - " wrapper.__name__ = name\n", - " return wrapper" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e0be7a44", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pre-calculating 90th percentile for Optimized WSDI...\n", - "Configuring Optimized Benchmarks (Chunk -1, No Persist)...\n", - "\n", - "=== Benchmarking WSDI ===\n", - " Warm-up run for run_earthkit_lazy_noflox_WSDI...\n", - "Benchmarking run_earthkit_lazy_noflox_WSDI (5 runs)...\n", - " Run 1: 95.0477s, Mem Peak Delta: 85.38 MiB\n", - " Run 2: 97.7674s, Mem Peak Delta: 136.30 MiB\n", - " Run 3: 94.7928s, Mem Peak Delta: 160.32 MiB\n", - " Run 4: 96.2381s, Mem Peak Delta: 76.02 MiB\n", - " Run 5: 100.2789s, Mem Peak Delta: 108.48 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_WSDI...\n", - "Benchmarking run_earthkit_lazy_flox_WSDI (5 runs)...\n", - " Run 1: 100.9231s, Mem Peak Delta: 51.39 MiB\n", - " Run 2: 95.6108s, Mem Peak Delta: 88.91 MiB\n", - " Run 3: 106.4386s, Mem Peak Delta: 87.94 MiB\n", - " Run 4: 100.7570s, Mem Peak Delta: 2.66 MiB\n", - " Run 5: 104.0698s, Mem Peak Delta: 166.26 MiB\n", - " Warm-up run for run_earthkit_opt_flox_WSDI...\n", - "Benchmarking run_earthkit_opt_flox_WSDI (5 runs)...\n", - " Run 1: 59.4010s, Mem Peak Delta: 23.42 MiB\n", - " Run 2: 60.0811s, Mem Peak Delta: 99.04 MiB\n", - " Run 3: 60.3694s, Mem Peak Delta: 10.18 MiB\n", - " Run 4: 60.4882s, Mem Peak Delta: 128.80 MiB\n", - " Run 5: 60.5862s, Mem Peak Delta: 113.94 MiB\n", - " Warm-up run for run_xclim_noflox_WSDI...\n", - "Benchmarking run_xclim_noflox_WSDI (5 runs)...\n", - " Run 1: 94.4468s, Mem Peak Delta: 179.88 MiB\n", - " Run 2: 100.3204s, Mem Peak Delta: 117.65 MiB\n", - " Run 3: 98.2519s, Mem Peak Delta: 158.38 MiB\n", - " Run 4: 98.7358s, Mem Peak Delta: 148.62 MiB\n", - " Run 5: 96.9592s, Mem Peak Delta: 140.45 MiB\n", - " Warm-up run for run_xclim_flox_WSDI...\n", - "Benchmarking run_xclim_flox_WSDI (5 runs)...\n", - " Run 1: 90.7513s, Mem Peak Delta: 115.07 MiB\n", - " Run 2: 94.4029s, Mem Peak Delta: 52.20 MiB\n", - " Run 3: 96.7050s, Mem Peak Delta: 114.93 MiB\n", - " Run 4: 95.9556s, Mem Peak Delta: 149.26 MiB\n", - " Run 5: 96.8507s, Mem Peak Delta: 77.72 MiB\n", - " Warm-up run for run_xclim_opt_flox_WSDI...\n", - "Benchmarking run_xclim_opt_flox_WSDI (5 runs)...\n", - " Run 1: 59.5290s, Mem Peak Delta: 89.22 MiB\n", - " Run 2: 60.0085s, Mem Peak Delta: 131.08 MiB\n", - " Run 3: 60.2594s, Mem Peak Delta: 30.26 MiB\n", - " Run 4: 60.3024s, Mem Peak Delta: 29.35 MiB\n", - " Run 5: 60.6037s, Mem Peak Delta: 91.17 MiB\n", - "\n", - "=== Benchmarking CWD ===\n", - " Warm-up run for run_earthkit_lazy_noflox_CWD...\n", - "Benchmarking run_earthkit_lazy_noflox_CWD (5 runs)...\n", - " Run 1: 25.7984s, Mem Peak Delta: 7.62 MiB\n", - " Run 2: 25.9442s, Mem Peak Delta: 0.38 MiB\n", - " Run 3: 26.0592s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 26.2207s, Mem Peak Delta: 0.12 MiB\n", - " Run 5: 26.1019s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_CWD...\n", - "Benchmarking run_earthkit_lazy_flox_CWD (5 runs)...\n", - " Run 1: 20.5405s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 20.5765s, Mem Peak Delta: 21.62 MiB\n", - " Run 3: 20.8313s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 20.8417s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 20.8313s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_CWD...\n", - "Benchmarking run_earthkit_opt_flox_CWD (5 runs)...\n", - " Run 1: 20.4005s, Mem Peak Delta: 0.67 MiB\n", - " Run 2: 20.4653s, Mem Peak Delta: 49.93 MiB\n", - " Run 3: 19.9873s, Mem Peak Delta: 0.07 MiB\n", - " Run 4: 20.3613s, Mem Peak Delta: 3.94 MiB\n", - " Run 5: 20.1712s, Mem Peak Delta: 49.94 MiB\n", - " Warm-up run for run_xclim_noflox_CWD...\n", - "Benchmarking run_xclim_noflox_CWD (5 runs)...\n", - " Run 1: 25.9901s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 26.2468s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 26.1138s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 26.3420s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 26.2693s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_CWD...\n", - "Benchmarking run_xclim_flox_CWD (5 runs)...\n", - " Run 1: 20.8982s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 20.7771s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 20.9738s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 21.0002s, Mem Peak Delta: 17.87 MiB\n", - " Run 5: 21.7872s, Mem Peak Delta: 8.28 MiB\n", - " Warm-up run for run_xclim_opt_flox_CWD...\n", - "Benchmarking run_xclim_opt_flox_CWD (5 runs)...\n", - " Run 1: 21.3257s, Mem Peak Delta: 49.94 MiB\n", - " Run 2: 21.9894s, Mem Peak Delta: 8.00 MiB\n", - " Run 3: 21.2518s, Mem Peak Delta: 7.94 MiB\n", - " Run 4: 21.2220s, Mem Peak Delta: 23.94 MiB\n", - " Run 5: 21.4322s, Mem Peak Delta: 1.12 MiB\n", - "\n", - "=== Benchmarking DTR ===\n", - " Warm-up run for run_earthkit_lazy_noflox_DTR...\n", - "Benchmarking run_earthkit_lazy_noflox_DTR (5 runs)...\n", - " Run 1: 9.2593s, Mem Peak Delta: 16.62 MiB\n", - " Run 2: 9.2828s, Mem Peak Delta: 16.62 MiB\n", - " Run 3: 9.2531s, Mem Peak Delta: 7.25 MiB\n", - " Run 4: 9.2653s, Mem Peak Delta: 16.62 MiB\n", - " Run 5: 9.3646s, Mem Peak Delta: 16.62 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_DTR...\n", - "Benchmarking run_earthkit_lazy_flox_DTR (5 runs)...\n", - " Run 1: 1.3767s, Mem Peak Delta: 24.89 MiB\n", - " Run 2: 1.3803s, Mem Peak Delta: 41.28 MiB\n", - " Run 3: 1.3897s, Mem Peak Delta: 41.25 MiB\n", - " Run 4: 1.3913s, Mem Peak Delta: 49.84 MiB\n", - " Run 5: 1.3873s, Mem Peak Delta: 41.55 MiB\n", - " Warm-up run for run_earthkit_opt_flox_DTR...\n", - "Benchmarking run_earthkit_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6778s, Mem Peak Delta: 107.46 MiB\n", - " Run 2: 1.7182s, Mem Peak Delta: 41.16 MiB\n", - " Run 3: 1.6794s, Mem Peak Delta: 91.50 MiB\n", - " Run 4: 1.6648s, Mem Peak Delta: 74.91 MiB\n", - " Run 5: 1.6657s, Mem Peak Delta: 91.50 MiB\n", - " Warm-up run for run_xclim_noflox_DTR...\n", - "Benchmarking run_xclim_noflox_DTR (5 runs)...\n", - " Run 1: 9.2579s, Mem Peak Delta: 16.62 MiB\n", - " Run 2: 9.2941s, Mem Peak Delta: 23.08 MiB\n", - " Run 3: 9.2964s, Mem Peak Delta: 8.25 MiB\n", - " Run 4: 9.3419s, Mem Peak Delta: 8.18 MiB\n", - " Run 5: 9.3060s, Mem Peak Delta: 16.83 MiB\n", - " Warm-up run for run_xclim_flox_DTR...\n", - "Benchmarking run_xclim_flox_DTR (5 runs)...\n", - " Run 1: 1.3991s, Mem Peak Delta: 41.55 MiB\n", - " Run 2: 1.3745s, Mem Peak Delta: 33.95 MiB\n", - " Run 3: 1.3680s, Mem Peak Delta: 41.54 MiB\n", - " Run 4: 1.3830s, Mem Peak Delta: 49.48 MiB\n", - " Run 5: 1.3745s, Mem Peak Delta: 16.72 MiB\n", - " Warm-up run for run_xclim_opt_flox_DTR...\n", - "Benchmarking run_xclim_opt_flox_DTR (5 runs)...\n", - " Run 1: 1.6835s, Mem Peak Delta: 91.50 MiB\n", - " Run 2: 1.6887s, Mem Peak Delta: 91.55 MiB\n", - " Run 3: 1.7050s, Mem Peak Delta: 91.44 MiB\n", - " Run 4: 1.7212s, Mem Peak Delta: 52.20 MiB\n", - " Run 5: 1.6964s, Mem Peak Delta: 91.56 MiB\n", - "\n", - "=== Benchmarking HDD ===\n", - " Warm-up run for run_earthkit_lazy_noflox_HDD...\n", - "Benchmarking run_earthkit_lazy_noflox_HDD (5 runs)...\n", - " Run 1: 7.6595s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.7498s, Mem Peak Delta: 24.73 MiB\n", - " Run 3: 7.6312s, Mem Peak Delta: 30.79 MiB\n", - " Run 4: 7.6334s, Mem Peak Delta: 10.93 MiB\n", - " Run 5: 7.6688s, Mem Peak Delta: 30.75 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_HDD...\n", - "Benchmarking run_earthkit_lazy_flox_HDD (5 runs)...\n", - " Run 1: 1.3248s, Mem Peak Delta: 46.03 MiB\n", - " Run 2: 1.2938s, Mem Peak Delta: 16.59 MiB\n", - " Run 3: 1.3048s, Mem Peak Delta: 41.57 MiB\n", - " Run 4: 1.3243s, Mem Peak Delta: 0.07 MiB\n", - " Run 5: 1.3109s, Mem Peak Delta: 56.11 MiB\n", - " Warm-up run for run_earthkit_opt_flox_HDD...\n", - "Benchmarking run_earthkit_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4628s, Mem Peak Delta: 16.78 MiB\n", - " Run 2: 1.4637s, Mem Peak Delta: 74.63 MiB\n", - " Run 3: 1.4424s, Mem Peak Delta: 71.88 MiB\n", - " Run 4: 1.4518s, Mem Peak Delta: 74.81 MiB\n", - " Run 5: 1.4362s, Mem Peak Delta: 99.88 MiB\n", - " Warm-up run for run_xclim_noflox_HDD...\n", - "Benchmarking run_xclim_noflox_HDD (5 runs)...\n", - " Run 1: 7.6122s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 7.6125s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 7.6941s, Mem Peak Delta: 8.38 MiB\n", - " Run 4: 7.7262s, Mem Peak Delta: 11.88 MiB\n", - " Run 5: 7.5845s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_HDD...\n", - "Benchmarking run_xclim_flox_HDD (5 runs)...\n", - " Run 1: 1.2948s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 1.2911s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 1.2848s, Mem Peak Delta: 33.34 MiB\n", - " Run 4: 1.2892s, Mem Peak Delta: 16.48 MiB\n", - " Run 5: 1.2930s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_opt_flox_HDD...\n", - "Benchmarking run_xclim_opt_flox_HDD (5 runs)...\n", - " Run 1: 1.4343s, Mem Peak Delta: 0.09 MiB\n", - " Run 2: 1.4694s, Mem Peak Delta: 44.04 MiB\n", - " Run 3: 1.4299s, Mem Peak Delta: 49.88 MiB\n", - " Run 4: 1.4282s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 1.4291s, Mem Peak Delta: 71.88 MiB\n", - "\n", - "=== Benchmarking SDII ===\n", - " Warm-up run for run_earthkit_lazy_noflox_SDII...\n", - "Benchmarking run_earthkit_lazy_noflox_SDII (5 runs)...\n", - " Run 1: 10.1665s, Mem Peak Delta: 6.00 MiB\n", - " Run 2: 10.2258s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 10.2883s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 10.2388s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 10.1664s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_lazy_flox_SDII...\n", - "Benchmarking run_earthkit_lazy_flox_SDII (5 runs)...\n", - " Run 1: 0.8125s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 0.8110s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.8148s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 0.8239s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 0.8146s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_earthkit_opt_flox_SDII...\n", - "Benchmarking run_earthkit_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9460s, Mem Peak Delta: 31.91 MiB\n", - " Run 2: 0.9741s, Mem Peak Delta: 0.52 MiB\n", - " Run 3: 0.9659s, Mem Peak Delta: 49.93 MiB\n", - " Run 4: 0.9523s, Mem Peak Delta: 37.75 MiB\n", - " Run 5: 0.9575s, Mem Peak Delta: 50.05 MiB\n", - " Warm-up run for run_xclim_noflox_SDII...\n", - "Benchmarking run_xclim_noflox_SDII (5 runs)...\n", - " Run 1: 10.1138s, Mem Peak Delta: 18.46 MiB\n", - " Run 2: 10.2198s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 10.1793s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 10.1764s, Mem Peak Delta: 0.00 MiB\n", - " Run 5: 10.1534s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_flox_SDII...\n", - "Benchmarking run_xclim_flox_SDII (5 runs)...\n", - " Run 1: 0.8329s, Mem Peak Delta: 0.00 MiB\n", - " Run 2: 0.8163s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.8052s, Mem Peak Delta: 0.00 MiB\n", - " Run 4: 0.8053s, Mem Peak Delta: 6.38 MiB\n", - " Run 5: 0.8217s, Mem Peak Delta: 0.00 MiB\n", - " Warm-up run for run_xclim_opt_flox_SDII...\n", - "Benchmarking run_xclim_opt_flox_SDII (5 runs)...\n", - " Run 1: 0.9713s, Mem Peak Delta: 49.05 MiB\n", - " Run 2: 0.9530s, Mem Peak Delta: 0.00 MiB\n", - " Run 3: 0.9497s, Mem Peak Delta: 49.94 MiB\n", - " Run 4: 0.9755s, Mem Peak Delta: 49.93 MiB\n", - " Run 5: 0.9685s, Mem Peak Delta: 49.94 MiB\n" - ] - } - ], - "source": [ - "results_data = []\n", - "\n", - "# --- 1. Data Preparation ---\n", - "tasmax_ssp = data_cache[\"tasmax_ssp\"][\"tasmax\"]\n", - "tasmin_ssp = data_cache[\"tasmin_ssp\"][\"tasmin\"]\n", - "pr_ssp = data_cache[\"pr_ssp\"][\"pr\"]\n", - "\n", - "# Pre-calculate percentile for WSDI\n", - "print(\"Pre-calculating 90th percentile for Optimized WSDI...\")\n", - "per_90 = percentile_doy(data_cache[\"tasmax_hist\"][\"tasmax\"], per=90)\n", - "per_90.name = \"tasmax_per\"\n", - "\n", - "# --- 1b. Optimized Configuration (Chunk -1) ---\n", - "print(\"Configuring Optimized Benchmarks (Chunk -1, No Persist)...\")\n", - "# We remove .persist() to include I/O overhead and compare fairly with Lazy\n", - "tasmax_ssp_opt = tasmax_ssp.chunk({\"time\": -1})\n", - "tasmin_ssp_opt = tasmin_ssp.chunk({\"time\": -1})\n", - "pr_ssp_opt = pr_ssp.chunk({\"time\": -1})\n", - "\n", - "# --- 2. Define Benchmarks (Symmetric Structure) ---\n", - "benchmarks = [\n", - " {\n", - " \"name\": \"WSDI\",\n", - " \"ek_func\": ek_temp.warm_spell_duration_index,\n", - " \"xi_func\": xclim.indicators.atmos.warm_spell_duration_index,\n", - " # Earthkit Arguments\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache[\"tasmax_ssp\"], per_90]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, per_90]).chunk({\"time\": -1}), \"freq\": \"MS\"},\n", - " },\n", - " # Xclim Arguments (Now with dual structure)\n", - " \"xi_args\": {\n", - " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmax_per\": per_90, \"freq\": \"MS\"},\n", - " \"optimized\": {\"tasmax\": tasmax_ssp.chunk({\"time\": -1}), \"tasmax_per\": per_90, \"freq\": \"MS\"},\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"CWD\",\n", - " \"ek_func\": ek_pr.maximum_consecutive_wet_days,\n", - " \"xi_func\": xclim.indicators.atmos.maximum_consecutive_wet_days,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache[\"pr_ssp\"], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"},\n", - " },\n", - " \"xi_args\": {\"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"}, \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}},\n", - " },\n", - " {\n", - " \"name\": \"DTR\",\n", - " \"ek_func\": ek_temp.daily_temperature_range,\n", - " \"xi_func\": xclim.indicators.atmos.daily_temperature_range,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": xr.merge([data_cache[\"tasmax_ssp\"], data_cache[\"tasmin_ssp\"]]), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": xr.merge([tasmax_ssp_opt, tasmin_ssp_opt]), \"freq\": \"MS\"},\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"tasmax\": tasmax_ssp, \"tasmin\": tasmin_ssp, \"freq\": \"MS\"},\n", - " \"optimized\": {\"tasmax\": tasmax_ssp_opt, \"tasmin\": tasmin_ssp_opt, \"freq\": \"MS\"},\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"HDD\",\n", - " \"ek_func\": ek_temp.heating_degree_days,\n", - " \"xi_func\": xclim.indicators.atmos.heating_degree_days,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": ((tasmax_ssp + tasmin_ssp) / 2).to_dataset(name=\"tas\"), \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": ((tasmax_ssp_opt + tasmin_ssp_opt) / 2).to_dataset(name=\"tas\"), \"freq\": \"MS\"},\n", - " },\n", - " \"xi_args\": {\n", - " \"lazy\": {\"tas\": (tasmax_ssp + tasmin_ssp) / 2, \"freq\": \"MS\"},\n", - " \"optimized\": {\"tas\": (tasmax_ssp_opt + tasmin_ssp_opt) / 2, \"freq\": \"MS\"},\n", - " },\n", - " },\n", - " {\n", - " \"name\": \"SDII\",\n", - " \"ek_func\": ek_pr.daily_precipitation_intensity,\n", - " \"xi_func\": xclim.indicators.atmos.daily_pr_intensity,\n", - " \"ek_args\": {\n", - " \"lazy\": {\"ds\": data_cache[\"pr_ssp\"], \"freq\": \"MS\"},\n", - " \"optimized\": {\"ds\": pr_ssp_opt, \"freq\": \"MS\"},\n", - " },\n", - " \"xi_args\": {\"lazy\": {\"pr\": pr_ssp, \"freq\": \"MS\"}, \"optimized\": {\"pr\": pr_ssp_opt, \"freq\": \"MS\"}},\n", - " },\n", - "]\n", - "\n", - "# --- 3. Run Loop (Symmetric Comparison) ---\n", - "for b in benchmarks:\n", - " print(f\"\\n=== Benchmarking {b['name']} ===\")\n", - "\n", - " # =========================================================\n", - " # BLOCK 1: EARTHKIT\n", - " # =========================================================\n", - "\n", - " # 1. Earthkit: Standard (No Flox)\n", - " runner_ek_nf = get_named_runner(\n", - " f\"run_earthkit_lazy_noflox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"ek_func\"],\n", - " kwargs=b[\"ek_args\"][\"lazy\"],\n", - " use_flox=False,\n", - " )\n", - " res_ek_nf = benchmark_function(runner_ek_nf, {})\n", - " res_ek_nf.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"1. No Flox (Standard)\"})\n", - " results_data.append(res_ek_nf)\n", - "\n", - " # 2. Earthkit: Standard (With Flox)\n", - " runner_ek_fl = get_named_runner(\n", - " f\"run_earthkit_lazy_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"ek_func\"],\n", - " kwargs=b[\"ek_args\"][\"lazy\"],\n", - " use_flox=True,\n", - " )\n", - " res_ek_fl = benchmark_function(runner_ek_fl, {})\n", - " res_ek_fl.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"2. Flox (Standard)\"})\n", - " results_data.append(res_ek_fl)\n", - "\n", - " # 3. Earthkit: Optimized (Chunk -1) + Flox\n", - " runner_ek_opt = get_named_runner(\n", - " f\"run_earthkit_opt_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"ek_func\"],\n", - " kwargs=b[\"ek_args\"][\"optimized\"],\n", - " use_flox=True,\n", - " )\n", - " res_ek_opt = benchmark_function(runner_ek_opt, {}, n_repeats=5)\n", - " res_ek_opt.update({\"Indicator\": b[\"name\"], \"Library\": \"Earthkit\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", - " results_data.append(res_ek_opt)\n", - "\n", - " # =========================================================\n", - " # BLOCK 2: XCLIM\n", - " # =========================================================\n", - "\n", - " # 4. Xclim: Standard (No Flox)\n", - " runner_xc_nf = get_named_runner(\n", - " f\"run_xclim_noflox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"xi_func\"],\n", - " kwargs=b[\"xi_args\"][\"lazy\"], # <--- USING LAZY ARGS\n", - " use_flox=False,\n", - " )\n", - " res_xc_nf = benchmark_function(runner_xc_nf, {})\n", - " res_xc_nf.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"1. No Flox (Standard)\"})\n", - " results_data.append(res_xc_nf)\n", - "\n", - " # 5. Xclim: Standard (With Flox)\n", - " runner_xc_fl = get_named_runner(\n", - " f\"run_xclim_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"xi_func\"],\n", - " kwargs=b[\"xi_args\"][\"lazy\"], # <--- USING LAZY ARGS\n", - " use_flox=True,\n", - " )\n", - " res_xc_fl = benchmark_function(runner_xc_fl, {})\n", - " res_xc_fl.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"2. Flox (Standard)\"})\n", - " results_data.append(res_xc_fl)\n", - "\n", - " # 6. Xclim: Optimized (Chunk -1) + Flox\n", - " runner_xc_opt = get_named_runner(\n", - " f\"run_xclim_opt_flox_{b['name']}\",\n", - " run_climate_indicator,\n", - " func=b[\"xi_func\"],\n", - " kwargs=b[\"xi_args\"][\"optimized\"], # <--- USING OPTIMIZED ARGS\n", - " use_flox=True,\n", - " )\n", - " res_xc_opt = benchmark_function(runner_xc_opt, {}, n_repeats=5)\n", - " res_xc_opt.update({\"Indicator\": b[\"name\"], \"Library\": \"Xclim\", \"Mode\": \"3. Flox + Opt (Chunk -1)\"})\n", - " results_data.append(res_xc_opt)" - ] - }, - { - "cell_type": "markdown", - "id": "08fa3d40", - "metadata": {}, - "source": [ - "## 5. Results & Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "75852284", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Benchmarking Results (Robust Stats):\n" - ] - }, - { - "data": { - "text/html": [ - "
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IndicatorLibraryModemedian_timestd_timemax_memSpeedup
0WSDIEarthkit1. No Flox (Standard)96.2380542.022403160.3164061.020926
1WSDIEarthkit2. Flox (Standard)100.9230753.648089166.2617190.973533
2WSDIEarthkit3. Flox + Opt (Chunk -1)60.3693730.427161128.8046881.627512
3WSDIXclim1. No Flox (Standard)98.2519021.968112179.8789061.000000
4WSDIXclim2. Flox (Standard)95.9555622.264089149.2578121.023931
5WSDIXclim3. Flox + Opt (Chunk -1)60.2593800.359515131.0781251.630483
6CWDEarthkit1. No Flox (Standard)26.0591640.1436957.6250001.007200
7CWDEarthkit2. Flox (Standard)20.8312570.13588821.6171881.259971
8CWDEarthkit3. Flox + Opt (Chunk -1)20.3612830.17488249.9375001.289053
9CWDXclim1. No Flox (Standard)26.2467790.1251850.0000001.000000
10CWDXclim2. Flox (Standard)20.9737720.35841317.8671881.251410
11CWDXclim3. Flox + Opt (Chunk -1)21.3257120.28206049.9375001.230757
12DTREarthkit1. No Flox (Standard)9.2653210.04098616.6250001.003354
13DTREarthkit2. Flox (Standard)1.3872800.00559749.8359386.701169
14DTREarthkit3. Flox + Opt (Chunk -1)1.6777640.019465107.4609385.540945
15DTRXclim1. No Flox (Standard)9.2963990.02686523.0820311.000000
16DTRXclim2. Flox (Standard)1.3744820.01075849.4765626.763564
17DTRXclim3. Flox + Opt (Chunk -1)1.6964480.01326891.5625005.479920
18HDDEarthkit1. No Flox (Standard)7.6594710.04314630.7851560.993867
19HDDEarthkit2. Flox (Standard)1.3109110.01183056.1132815.807026
20HDDEarthkit3. Flox + Opt (Chunk -1)1.4517690.01089799.8750005.243598
21HDDXclim1. No Flox (Standard)7.6124930.05437411.8750001.000000
22HDDXclim2. Flox (Standard)1.2910520.00343333.3398445.896351
23HDDXclim3. Flox + Opt (Chunk -1)1.4298510.01574571.8750005.323978
24SDIIEarthkit1. No Flox (Standard)10.2258490.0463776.0000000.995162
25SDIIEarthkit2. Flox (Standard)0.8146240.0045030.00000012.492111
26SDIIEarthkit3. Flox + Opt (Chunk -1)0.9575000.00992350.05468810.628070
27SDIIXclim1. No Flox (Standard)10.1763720.03471618.4570311.000000
28SDIIXclim2. Flox (Standard)0.8162760.0104676.37890612.466829
29SDIIXclim3. Flox + Opt (Chunk -1)0.9684940.01032649.93750010.507419
\n", - "
" - ], - "text/plain": [ - " Indicator Library Mode median_time std_time \\\n", - "0 WSDI Earthkit 1. No Flox (Standard) 96.238054 2.022403 \n", - "1 WSDI Earthkit 2. Flox (Standard) 100.923075 3.648089 \n", - "2 WSDI Earthkit 3. Flox + Opt (Chunk -1) 60.369373 0.427161 \n", - "3 WSDI Xclim 1. No Flox (Standard) 98.251902 1.968112 \n", - "4 WSDI Xclim 2. Flox (Standard) 95.955562 2.264089 \n", - "5 WSDI Xclim 3. Flox + Opt (Chunk -1) 60.259380 0.359515 \n", - "6 CWD Earthkit 1. No Flox (Standard) 26.059164 0.143695 \n", - "7 CWD Earthkit 2. Flox (Standard) 20.831257 0.135888 \n", - "8 CWD Earthkit 3. Flox + Opt (Chunk -1) 20.361283 0.174882 \n", - "9 CWD Xclim 1. No Flox (Standard) 26.246779 0.125185 \n", - "10 CWD Xclim 2. Flox (Standard) 20.973772 0.358413 \n", - "11 CWD Xclim 3. Flox + Opt (Chunk -1) 21.325712 0.282060 \n", - "12 DTR Earthkit 1. No Flox (Standard) 9.265321 0.040986 \n", - "13 DTR Earthkit 2. Flox (Standard) 1.387280 0.005597 \n", - "14 DTR Earthkit 3. Flox + Opt (Chunk -1) 1.677764 0.019465 \n", - "15 DTR Xclim 1. No Flox (Standard) 9.296399 0.026865 \n", - "16 DTR Xclim 2. Flox (Standard) 1.374482 0.010758 \n", - "17 DTR Xclim 3. Flox + Opt (Chunk -1) 1.696448 0.013268 \n", - "18 HDD Earthkit 1. No Flox (Standard) 7.659471 0.043146 \n", - "19 HDD Earthkit 2. Flox (Standard) 1.310911 0.011830 \n", - "20 HDD Earthkit 3. Flox + Opt (Chunk -1) 1.451769 0.010897 \n", - "21 HDD Xclim 1. No Flox (Standard) 7.612493 0.054374 \n", - "22 HDD Xclim 2. Flox (Standard) 1.291052 0.003433 \n", - "23 HDD Xclim 3. Flox + Opt (Chunk -1) 1.429851 0.015745 \n", - "24 SDII Earthkit 1. No Flox (Standard) 10.225849 0.046377 \n", - "25 SDII Earthkit 2. Flox (Standard) 0.814624 0.004503 \n", - "26 SDII Earthkit 3. Flox + Opt (Chunk -1) 0.957500 0.009923 \n", - "27 SDII Xclim 1. No Flox (Standard) 10.176372 0.034716 \n", - "28 SDII Xclim 2. Flox (Standard) 0.816276 0.010467 \n", - "29 SDII Xclim 3. Flox + Opt (Chunk -1) 0.968494 0.010326 \n", - "\n", - " max_mem Speedup \n", - "0 160.316406 1.020926 \n", - "1 166.261719 0.973533 \n", - "2 128.804688 1.627512 \n", - "3 179.878906 1.000000 \n", - "4 149.257812 1.023931 \n", - "5 131.078125 1.630483 \n", - "6 7.625000 1.007200 \n", - "7 21.617188 1.259971 \n", - "8 49.937500 1.289053 \n", - "9 0.000000 1.000000 \n", - "10 17.867188 1.251410 \n", - "11 49.937500 1.230757 \n", - "12 16.625000 1.003354 \n", - "13 49.835938 6.701169 \n", - "14 107.460938 5.540945 \n", - "15 23.082031 1.000000 \n", - "16 49.476562 6.763564 \n", - "17 91.562500 5.479920 \n", - "18 30.785156 0.993867 \n", - "19 56.113281 5.807026 \n", - "20 99.875000 5.243598 \n", - "21 11.875000 1.000000 \n", - "22 33.339844 5.896351 \n", - "23 71.875000 5.323978 \n", - "24 6.000000 0.995162 \n", - "25 0.000000 12.492111 \n", - "26 50.054688 10.628070 \n", - "27 18.457031 1.000000 \n", - "28 6.378906 12.466829 \n", - "29 49.937500 10.507419 " - ] - }, - "metadata": {}, - 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11VXZdZQ7epbo9jygENegLkcRsKN+McN5fXEZrrjWd1tNnDZixIh0ySWXpAkTJrTJewBQObrGHgMAtFAEtoEDB6Ztt922Ytquuro6TZ8+vdHnY+bwIUOGtGudytnUqVOz3zvssEM2m/ott9zS0VUCoMwJ2ABQT4TQ2267LQ0fPnyueq+//PLLdMopp6RNNtkkrbbaammrrbZK5513Xp1wGz3jEdxK/fjHP86GVt9///21j7388svZY3/7299qH/v000/TySefnF2jOpYfQ7ujdzcuTZWLodTxuiuuuCLrfY0yq6++enrqqaeaPUQ8hsbHa7fbbrv0rW99K62zzjrZpaCuueaaZrXDtGnT0plnnpm+/e1vZ6/fe++90yuvvDJbuRdffDH77Outt15Wx5133jndd999tc/ffvvt6eijj85u77vvvtnnip94fJ999smudx092/nj8ZOLNo/PENerjrbaYIMN0i9+8Yv0xRdfzPbZf/SjH6W//OUv2ftHPaJNQ1xDe/vtt09//OMfU01NTbM+OwBdk+tgA0A9L7zwQhaS119//Ra3TYTKCIHjxo1LRx55ZBb2/vWvf6XLL788vfrqq9nvsNFGG6UHH3wwffLJJ2mhhRbKwvEzzzyT+vTpk5588sks0IW43aNHjyx85uF69913z4J/DF2OHufnnnsujRo1KguZEWhLXXfdddn1oI8//vg0YMCAtNRSSzX7s8Q1pSNkxjWfI1xHHd955500adKkZr0+DipEr/hpp52WvSaWFYH4zjvvTEsuuWRWJgL/wQcfnIYNG5YdlIhRAxGujznmmKwHedddd02bb755+ulPf5rOPffc7MDCqquumr02PnsE95NOOilr7zwQlx4giCHvzz77bDrooIPSWmutlbVRnMsd3/Gf/vSnrL1LD2a8/fbb2eddYoklsutc56L9b7rppvTGG2/UCfAAUErABoB6IrCGPMi1xB133JFef/31dP7559eG5OjB7devXzrnnHPS3//+9+x+BOw8QEeP6fPPP58mT56chc3SycH+8Y9/ZL2pEY5DhMOJEyeme++9Ny222GLZYxtuuGEWFH/zm99kQXK55ZarfX3v3r2zc8njXOKWGjNmTFphhRWyAwW56JVvrsGDB6eLL744G14d1l577aw3/LLLLstCd/jVr36Vll9++axXPA4k5O8R5ztHoI62ieXkBwbis62xxhp13mOeeebJeplLHw8xEuDxxx/P2qx0qP9KK62Uvve972U94HvuuWft49GrHe26zDLLzPZZ8nUh2kTABqAxhogDQD3RqxyhcNCgQS1um+iRjTAdQ5JLRU9sHpjz3tfFF1+89n4E7Qiz3/3ud7Ph3WPHjs2GN0fvax7GQwyHjp71vNc7/4nh4iF6wesPfZ6bcB0i2L/22mtZz3IE1a+//rpFr48h9nm4DvF511xzzWyW8fD+++9nPeIx7DzU/zzRW//uu++mufXII49k4XuLLbaos+yVV145LbjggrO1VQTnhsJ1mH/++WsnvwOAxujBBoAGhnlHb2r37t1b3DYxtHyBBRaoEyzzgBbLjOdz0fMcwTUP2NGzHSEvXh/3o9c2hkmXBuzPP/88C46N9a7Xn+k6guTcinOS42DBXXfdlW6++easPWKo+LHHHpuF7zmJz9HQYxHaw2effZb9jp73+GlIa2bujrb66quvsnOvW9tW0UOerxsA0BgBGwDqiZ7ruP7xN998kwXMlphvvvmy4d4xGVZpyI6wF72npb3iEbBjMrU4Hzh+4tzfEBNxRcD+8MMPs/eP85NL6xYh/Cc/+UmD7x8926XqB/2WiAMCBxxwQPYTQTXqFOdVxzD26EkvPUe5IXmArv9YtFH+WfIgv8022zS4jMZ6lJsjlh/vFeeSN6R///7NbqsYll9aZwBoiIANAI2EuhimHefrtkSE5jj396GHHqoTGmNir/z50rIR6i644ILs97rrrlv7+Nlnn51NyBWPlQ7xjgm/Ro8enQ0xn3feedvtu4uh1jHsPYZIn3HGGVndSs/1bkhcnzrCeR5c4zVxfvtOO+2U3V922WWzCdiiRzsmMWtK3oOcXzqr/nMNPR5tFedUx2RnpQcp5kZMohaGDh3aquUAUNkEbACoJ589PHqiGwrYEbxLJyLLReCMSbluuOGGbNbuCJRxXnWcRx0Te2222WZ1hnvHsPGY4OuJJ57I3jPvEY4yMZQ8fuKSUqXi8l7Rk/zDH/4wm5E7DgbEudpx3vZjjz2WTRq2yCKLFPKdxqWzon4xxDomE4vPE5ORxbnUzZmNPCYNi5nOv//972eziMdkYxGGo8c6F/U95JBDssnZdtlll7TwwgtnvcUxm3fM6n3hhRdm5aIeIS6VFT3PMXlbzPQdPcrRxnF5rRtvvDGrawT6GMIel0G7++6706GHHpq1Vcw4HgcrPvroo+w88Lh8WmM95/XFuhBD5PODIADQEAEbAOpZdNFFs3ONH3744fSDH/xgtvaJ86bzc6dLHXHEEdmM29dee202lDqGJsd5vhEaDzzwwOz5+iJMx6WfSoN3zA4ePbvvvfdenR7vfAh4DCuPazvH7ODRoxyBM0JvzL4dPc1FidAflxK79dZbswnO4hzlqGdc+qo5E6fFpbbiGtdxkCBeHwE3ZgaP3vdcDIeP5V966aVZz3gMRY9h3dFTnM/CHuKyXieccELWtnEZtOrq6uySZDF5XNx/8803szaPIB/D82Mm9wjEcfmyeM2f//zn7BJp8VgcgIigHMG8uWJEQky8VmT7AlB5qmrivxAAUEcEywiIMaFYBGS6rhixEJf5igMaMREdADTGZboAoAERqGKYcQztpmuLXvAYSSBcAzAnAjYANCDO4/31r3+dDcmOSbLommLm9xiefvLJJ3d0VQDoBAwRBwAAgALowQYAAIACCNgAAABQAAEbAAAACuA62HPw3HPPZdfTbM71PgEAAKgsM2bMyCY/XXPNNedYVsCegwjXLhUOAADQNdXU1DS7rIA9B3nPdVwLFQAAgK7lxRdfbHZZ52ADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2ZaN6VnWnXj4AANC19ejoCkCue7fu6fQHf5Xen/Be4Y2y1KCl039v90uNDQAAtBkBm7IS4frNT9/o6GoAAAC0mCHiAAAAUAABGwAAAAogYAMAAEABBGwAAACohEnO3n///XTVVVel559/Pr355ptp2WWXTffcc0+dMiuuuGKjr3/88cfTQgstlN3ecsst0/jx42cr88ILL6TevXu3Qe0BAACgTAJ2hOrRo0enYcOGpVmzZqWamprZytxyyy2zPXb88cenvn371obr3HbbbZcOPPDAOo/16tWrDWoOAAAAZRSwo9d56623zm6PHDkyvfTSS7OVWWONNerc/+CDD9J7772XjjvuuNnKLrDAArOVBwAAgIo/B7tbt5ZXIYaQV1VVpeHDh7dJnQAAAKDTBey5ce+996Z11103LbLIIrM9d/fdd6fVVlstrbnmmumQQw5Jr7/+eofUEQAAgK6l0wXs1157Lb3xxhsN9l7HcPOTTjopXX311enkk09OY8eOTXvuuWcaN25cq9+3urq6zu04XzzEOeNxPz93PB5vr7LlWKfWlG0Pza1vObVLVyhbjnWqhLLlWKdKKVuOdarksuVYp0ooW451qpSy5VinSi5bjnWqhLLlWKdZHVy2IgN29FD37Nkzm8ysvhNPPDF997vfTeuss07aZZdd0nXXXZc9HrOUt0Y08OTJk2vvf/PNN2natGm1jT5p0qTahp8xY0b6+uuva8tOmTIlTZ06tXY5UXbmzJlzLBuibJQJ8Zq4n3/ZUa6p5cb9XJSLZefiPZtabmNl4zNG2XwFjDaItshFG02fPr3RsqVtGLdL27B0OW2p9H3ic5Z+1tL2rt+GDX03eXvnbTi37V1apyjbVBs21t75epiXjcebu85G2dL1MMq2Zp1tbrvMqb0bK9uSdbZ+2SLX2fptaBvRttuI5rZ3S9bZOW07bSNsI2wjOs9+hG3E/7W3/Qj7EbJGarNtRHNU1eRbxDKQT3JW/zJduahq9FKvtNJKadSoUc1a5qGHHpomTJiQbr311rmq04svvpj9XmWVVVL37t2z2/HlxDngcf541Cm+tLgdj8XteKw9yoZyq1NrysZzh958QHrz0zdS0ZZfcIV0+Q//kL1vc+pbTu3SFcqWY50qoWylbSPKqWw51qmSy5ZjnSqhrG2EdbYc1kPbiPJoQ9uIqibb5ZVXXsnacPXVVy//WcRb4tlnn00ffvhhg7OHN6ao4wf5iln/djR86f36k7a1R9lyrFNryral5n6P5dgulVy2HOtUSWXLsU6dvWw51qmSy5ZjnSqpbDnWqbOXLcc6VXLZcqxTJZUtxzpVdVDZihsiHsPD+/Xrl7bYYotmlf/444/TmDFjmnWkAQAAAFqjw3uw4zyd0aNHZ7fHjx+fjXF/4IEHsvvrrbdeGjx4cHY7xsE/+OCD2TWz+/btO9tyYlj5o48+mjbddNO00EILZRObXX755dkRiAMOOKCdPxUAAABdTYcH7M8//zwdffTRdR7L71977bVp/fXXz24/8cQT2bnUjV37eokllsh6rM8444zsxPSBAwemDTbYIB111FFpySWXbIdPAgAAQFfW4QE7gnFzrlW9+eabN1lujTXWqJ01HAAAANpbpzoHGwAAAMqVgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYFeQ6lmzOvXyAQAAOrMeHV0BitO9W7d04o2Pp3c/mVh4sy6z0LzptD03KXy5AAAAlULArjARrl8b/0VHVwMAAKDLMUQcAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsNtRTU1Ne74dAAAA7ahHe75ZV1dVVZX++e5HadKUGYUve+F5+6ZVF1+g8OUCAADQPAJ2O4twPXHKtMKXO6BPz8KXCQAAQPMZIg4AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAAlRCw33///XTyySennXbaKa2yyipp+PDhs5UZOXJkWnHFFWf7eeyxx2Yre9VVV6Utt9wyrb766mm33XZLTz/9dDt9EgAAALqyHh1dgTfffDONHj06DRs2LM2aNSvV1NQ0WG7JJZdM55xzTp3Hhg4dOlu4Pu+889IxxxyThfVbb701HXLIIdnvCOQAAABQsQE7epu33nrr2p7ql156qcFyffr0SWussUajy5k+fXoaNWpU2nfffdNBBx2UPbbeeuulHXfcMV166aVZ8AYAAICKHSLerVsxVRgzZkyaNGlSnSHm3bt3T9/5zneyHvLGesYBAACgIgJ2c40dOzats846abXVVku77rpreuihh+o8//bbb2e/l1122dmGkU+ePDl9/PHH7VpfAAAAupZOEbBXXnnldPzxx6eLL744nX/++WnQoEFpxIgR6YEHHqgt89VXX6VevXplQ8lLzTvvvNnvL7/8slV1qK6urnM7zhcP0TMe9/Me8ni8sbKVoLmftSXtkpdtr/o3p75zU39l574dtGHbrEvW2bb727XOtu/2T3vbRnS2/8PlWKdKLluOdaqEsuVYp1kdXLZiAvZ+++2X9tprr7T++utn52tfccUV2aRoF154YZ1yVVVVs702b5yGnmuuWEb0gue++eabNG3atNpGj6HpecPPmDEjff3117Vlp0yZkqZOnZoqRXz2fAWMNoj7uWijOBc+RHtEu5SWLW3DuF3ahqXLaev6l3438ZOL+sb3F+J33G/se4znZs6cmd2O36Vlo1zpcmN9yJebl83XyyhbWqco21QbNtbe+XqYl43Hm7vORtnSdTbK5p816ln6Wee0frekXebU3o2VbagNm9veRa6z9duwiG3E3LR3U+3S1HJbs842VrYl62xbtXdL2nBO7WIbYRthG2EbYRsx5/9r9iPsR3Sl/YjmqKopo5OT80nO7rnnnjmWvfLKK9PZZ5+dnn/++azX+oYbbkinnnpqeuGFF1Lv3r1ry91///3pJz/5SXYe9iKLLNLiOr344ovZ75iVPM7pDvHlRGCP88ej+eJLi9vxWD4TekNlw99eGZcmTvnfL7BIiw8akNZbdpG01/n3pNfGf1H48ldafHC64SfDs8/TnM/aknbJy8Zzh958QHrz0zcKr//yC66QLv/hH7L3bU5956b+ys59O2jDtlmXgnW2bf52rbPtu/3T3rYRne3/cDnWqZLLlmOdKqGs/Yiq2nZ55ZVXsvaIS0GX/Szic6v+cYH8kl1xLnaE4Vzc79+/f1p44YVb9X75ylb/dqyQpffrT9pW+lwlaO5nbUm71C/bFgb3G1xnozGnOsxN/fN1sq3apZLLlmOdKqlsOdaps5ctxzpVctlyrFMllS3HOnX2suVYp0ouW451qqSy5Vinqg4q2xydMmDHkYQHH3wwLb/88rXnXK+11lpp4MCB6b777qsN2HHkJXqwN9tss6yx6LoG9B6YrQOTn3wyzfpqYuHL7zbPvKn/RhsVvlwAAKDz6PCAHWPwY/h2GD9+fDbGPZ+8LK5jHc/H0PG4/NaQIUPSxIkT00033ZQNJb/oootqlxMTnB122GHZ9a4HDx6chexbb701jRs3Lp177rkd9vkoLxGuqydM6OhqAAAAFajDA/bnn3+ejj766DqP5fevvfbatOKKK6YBAwZkM4h/8cUXqWfPntmlumKis0022aTO6w488MBsmO51112XPvvss7TCCiukyy+/PFsGAAAAVHTAXmKJJdLrr7/eZJlRo0Y1a1kxBPjggw/OfgAAAKA9dYrLdAEAAEC5E7ABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwoQBVffqkmurqNm3Ltl4+AADQOj1a+XogAnavXqmqe/f09oknpanvvld4m/RZZuk09LRfa2sAAChjAjYUKML1N6+/rk0BAKALMkQcAAAACiBgAwAAQAEEbAAAACiAgE2zzD+wT6qpmaW1AAAAGmGSM5plYJ9eqaqqW3rug7+mSdMmFN5qCw0YklZaeAPfBgAA0GkJ2LRIhOuvpn5WeKsN6DWfbwIAAOjUOjxgv//+++mqq65Kzz//fHrzzTfTsssum+65557a56urq9Pvf//7NHr06PTWW29l91dYYYV0xBFHpA033LDOsrbccss0fvz42d7jhRdeSL17926XzwMAAEDX1OEBO0J1hOdhw4alWbNmpZqamjrPT506NV122WVp5513TgcddFDq0aNHuuOOO9IBBxyQRo0albbYYos65bfbbrt04IEH1nmsV69e7fJZAAAA6Lo6PGBHr/PWW2+d3R45cmR66aWX6jzfp0+f9PDDD6d555239rGNN944vffee1nPdv2AvcACC6Q11lijnWoPAAAAZTKLeLduTVehe/fudcJ1qKqqSiuttFL65JNP2rh2AAAA0EkC9tyIoeTPPfdcGjp06GzP3X333Wm11VZLa665ZjrkkEPS66+/3iF1BAAAoGvplAH7uuuuS++++252Hnb94eYnnXRSuvrqq9PJJ5+cxo4dm/bcc880bty4Vr9nTK5WejtCfohzxuN+fu54PN5YWSiH9bAcy5ZjnSqhbDnWqVLKlmOdKrlsOdapEsqWY50qpWw51qmSy5ZjnSqhbDnWaVYHl63IgP3MM8+ks88+O5vIbN11163z3Iknnpi++93vpnXWWSftsssuWRAPMUt5a0QDT548ufb+N998k6ZNm1bb6JMmTapt+BkzZqSvv/66tuyUKVOyidqgCKXrUqyT06dPr7Me5huHeLy562yULV1no2z+PrHuR9mZM2c2a/0uLRu/435p3aN8adlYXr7c0rJRrrGy+XLzDV/95Ub9GisbbRCfr6E2jPYobcMoW9qGcbupNixiGzE37d1UuzS13Ka+m6basKmyDbVhe7d3S9pwTu3S1HrYUNm5/VuYU3uXtmGUbaoNG2tv2wjbCNsI2wjbCPsR9iOqW70f0SkmOWuJ1157LR1++OHZpGjHHXfcHMsvtNBCae21104vv/xyq943zvnu379/7f1+/fplj+XnkA8cOLD2XPKePXtmM53n+vbtW1sWWism/cvFOtnYehgz58e62Jx1tqmy8bsl63dp2SgX90vr3ljZWG5p2VhuHjDysvlr8+Xm92O5pWUHDBjQaNm4XF/pVQVK2zDmeyitU1NlW9PeTbVhEe1d2i65fLn5c/XbuyVt2FTZlrRhW7V3S9qwNe09N+tsR7a3bYRthG2EbYRthP0I+xHdWr0fUVEBO4Z7H3zwwWmVVVZJv/3tb5sdWkt3TFojdmQauh31KL1ff9K20uegHNbDcixbjnWqpLLlWKfOXrYc61TJZcuxTpVUthzr1NnLlmOdKrlsOdapksqWY52qOqhsxQwR//TTT7Mh4XEJrksuuaTZ17X++OOP05gxY9Lqq6/e5nUEAACga+vwHuw4F2D06NHZ7fHjx2dj3B944IHs/nrrrZd12UfP9eeff55dJ/utt96q8/r8mtf33HNPevTRR9Omm26aDQ2Pic0uv/zy7AhE/cnQAAAAoOICdgTno48+us5j+f1rr702Lb744tm512HEiBGzvT6/DNcSSyyR9VifccYZ2YnpMXZ+gw02SEcddVRacskl2+WzAAAA0HV1eMCOYDyna1U351rW0ZOdzxoOAAAA7a1TnIMNAAAA5U7ABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAAAjYAAAAUB70YAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAD1a8+IpU6akadOmzfb4fPPN15rFAgAAQOUH7AjV5557brr77rvTxIkTGyzz6quvFlE3AAAAqNyAfeqpp6a77rorbbHFFmno0KGpZ8+ebVMzAAAAqOSA/cgjj6Sf/vSn6aCDDmqbGgEAAEBXmeRslVVWKb4mAAAA0JUC9rbbbpueeOKJtqkNAAAAdJUh4scff3w66qij0plnnpk222yzNO+8885WZtVVVy2qfgAAAFCZATsuyzVz5sx0zTXXpGuvvbbOczU1Namqqsos4gAAAHQ5LQ7YJ5xwQnrxxRfTfvvtZxZxACgDNTWzUlVVt07/HgDQ5QL2008/nX7xi1+k73//+21TIwCgRSL4PvfBX9OkaRPapOUG9h6U1lxiG98KABQdsPv3758WX3zxlr4MAGhDEa6/mvqZNgaADtTisV4777xzuvfee9umNgAAANBVerBXWmmldN5556URI0akzTffvMFZxONSXgAAANCVtDhgH3vssdnv8ePHp4cffni2580iDgAAQFfU4oBd/9JcAAAAwFwE7PXWW0+7AQAAQGsDdm7SpEnp3//+d5owYULabLPNGjwXGwAAALqKuQrYF198cbriiivS1KlTs3Oub7vttixg77fffunb3/52OvTQQ4uvKQAAAFTSZbpuuOGGLGB/73vfS5dddlmqqampfW6LLbZIjz76aNF1BAAAgMrrwY6Avf/++6ef//znqbq6us5zSy21VHr//feLrB8AAABUZg/2uHHj0iabbNLgc/37909fffVVi5YXgfzkk09OO+20U1pllVXS8OHDGyw3evTotPPOO6fVV189bbPNNlnQb8hVV12Vttxyy6zcbrvtlp5++ukW1QcAAADaJWAPHDgwffbZZw0+F9fGnn/++Vu0vDfffDMLz9H7PXTo0AbLPPfcc+nwww/PAnic+73LLruk0047Ld16662zhevzzjsv7bXXXunyyy/PlnnIIYek119/vUV1AgAAgDYP2BtuuGG68sor0zfffFP7WEx0NnPmzHTTTTeljTfeuEXLi97mCNgXXnhhWnXVVRssE+d8R7g+44wz0gYbbJCF7TgH/IILLkizZs3KykyfPj2NGjUq7bvvvumggw7K6nn22WenJZZYIl166aUt/ZgAAADQtgH7qKOOSh9++GHaYYcd0llnnZWF6+uvvz7tvvvu2XDvCL8tqkC3pqsQwfmpp57K3q/UjjvumD799NP0yiuvZPfHjBmTXTqsdIh59+7d03e+850swJdOxgYAAAAdHrBj2HX0VC+77LLZ7wiuf/7zn9OgQYPSjTfemBZbbLFCKzh27Ng0Y8aM7P1KLbfcctnvt99+u87v+uVi2PnkyZPTxx9/XGi9AAAAoNXXwY5wG+c7R+/yhAkTsmtg9+nTJ7WFiRMnZr/nmWeeOo/n9/PnY3K1Xr16zVaPqFv48ssv0yKLLDLX9YgZ06NHPL8dPffR+x4HGGKYetyOx+J2PNZQWWitItbDcixbjnWqhLKh3OpUKWXLsU7tRXuXz3duG9HxbdiZthGVXLYc61QJZUO51amqg8q2RKtSXwTahRdeuM3CdanGdh5KH2+oTN4grdn5iGVEL3guzj+fNm1adjsaPYam5yth9LZ//fXXtWWnTJmSpk6dOtfvDaVK16VYJ+MgV+l6WDonQXPX2Shbus5G2fx9Yt2PsjHHQnPW79Ky8Tvul9Y9ypeWjeXlyy0tG+UaK5svN//brr/cqF9jZaMNSuePKG3DaI/SNoyypW0Yt5tqwyK2EXPT3k21S1PLbeq7aaoNmyrbUBu2d3u3pA3n1C5NrYcNlZ3bv4U5tXdpG0bZxtqwvdhG2Ea01zYiniv9v1a63Lhd+j8wypZuZ0vLRp3yslHPKNsVtxFNtbf9CPsR9iOmNfp3X7qNaLMe7Dj3+S9/+Us2a3j+h1nqxBNPTEXJe6DznupcfjmwvCc7fseGI3569+49W7l8OXMjwnlcgizXr1+/2sAeRzdiZvW8h7pnz56pR4//a9a+ffu2a88Cla30YFask42th3HwK9bF5qyzTZWN3y1Zv0vLRrm4X1r3xsrGckvLxnJLjxbGc/lr8+Xm92O5pWUHDBjQaNnYNsTnbagN4yhmaZ2aKtua9m6qDYto79J2yeXLzZ+r394tacOmyrakDduqvVvShq1p77lZZ9u6vduLbYRtRPtsI/737z5XvzMnlpvLl5uL5ZbuC5buw+Xbk5qaWV1uG9FW/9fsR9iP6Er7EW0SsB9//PF0xBFH1PYW1BeVKjJgDxkyJPtg77zzTtp0001rH3/rrbey3/mlvfLfcS52zDiei/vRwNHT3hr5EIL6t+Pzlt6vv6NT+hy0VhHrYTmWLcc6VVLZcqxTZy9bjnVqL9q7fL7zyt5GdEvPffDXNGnahFS0gb0HpTWX2KbLbSMquWw51qmSypZjnao6qGybBOzf/va3aeWVV06nnHJKFmpL039biCMMcWmu+++/P+2///61j99zzz1pwQUXrA3Ta621Vna04b777qt9LIbGxOs222wzvcgAAJ1IhOuvpn7W0dUAaNuAPW7cuHTRRRellVZaKRUhzjGJy2iFGHIeY9wfeOCB7P56662XBg8enEaMGJH23nvvrGc8Ls8Vl+S69dZb06mnnlqnq/+www5L5513XvaaCNlRJup77rnnFlJXAAAAKCxgx2WwWnqid1M+//zzdPTRR9d5LL9/7bXXpvXXXz+tueaa6ZJLLsmC8p133pnNBh5hO669XerAAw/MxvRfd9116bPPPksrrLBCuvzyy9OKK65YWH0BAACgkIB91FFHZb3E6667blpggQVSay2xxBLp9ddfn2O5GOYdP02JMfMHH3xw9gMAAABlHbA333zz9PLLL6dtttkmGyZef3buCLmjRo0qso4AAABQeQH79ttvz87BjtnVPvjgg/Txxx/Xed4lqQAAAOiKWhywf/e736UtttginXXWWa26tjQAAABUkm5zMynZPvvsI1wDAABAawJ2XAP7o48+aunLAAAAoKK1OGCPHDkyXXnllenVV19tmxoBAABAVzgH+6STTkpffPFF2nXXXdOCCy7Y4Czid911V5F1BAAAgMoL2PPNN1/2AwAAALQiYF933XUtfQkAAABUvBafg90Ss2bNSltttVV688032/JtAAAAoLIDdk1NTRo/fnyaPn16W74NAAAAVHbABgAAgK5CwAYAAIACCNgAAABQAAEbAAAACiBgAwAVr6a6ulMvH4AKvQ42AEBnU9W9e3r7xJPS1HffK3zZfZZZOg097deFLxeAzqdNA3ZVVVVad911U//+/dvybQAA5ijC9Tevv66lACifIeL/+Mc/mnz+2muv/b+Fd+uWrrvuurT00kvPXe0AAACgUgP2QQcdlC666KJUU1NT5/FJkyalESNGpDPPPLPI+gEAAEBlBuwf//jHadSoUWn//fdPn332WfbYCy+8kHbeeef0zDPPpPPPP78t6gkAAACVdQ72UUcdlZ1Xfeyxx2ahescdd0zXX399WmGFFdLVV1+dllxyybapKQAAAFTaZbo23HDD7FzrGBYeoXqVVVZJt9xyi3ANAABAlzVXAfvll1/Ohor37NkzbbbZZun5559PJ510Upo6dWrxNQQAAIBKDNg33HBD2mOPPdLAgQPT7bffni699NL061//Ot1///1p9913T++8807b1BQAAAAqKWBHmN51113TzTffnIYMGZI9FsE6hojPnDkz7bbbbm1RTwAAAKisSc7OPffc9J3vfGe2x1dcccX0pz/9KZ1yyilF1Q0AAAAqtwe7oXCd69evX/rtb3/b2joBAABA15jkDAAAAGjlEPHw5z//OV1zzTXZhGbTpk2b7flXX311bhYLAAAAXacH++GHH04nnHBCdu3ruCxXTHi2ww47pL59+6allloqjRgxom1qCgAAAJUUsK+44oq0//77p1/96lfZ/T333DOdc8456cEHH0yzZs1KiyyySFvUEwAAACorYL/77rtpo402SlVVVdn96urq7PeCCy6YDjvssHT11VcXX0sAAACotIAdgbpnz56pW7du2bDwTz/9tPa5RRddNI0bN67oOgIAAEDlBewlllgiffLJJ9ntlVZaKd177721z8Uw8ejJBgAAgK6mxbOIb7jhhunJJ59Mw4cPT/vuu2865phj0osvvpj1asfw8Z/97GdtU1MAAACopIAdgXr69OnZ7e233z5179493XXXXdmQ8YMPPjibVRwAAAC6mhYH7F69emU/uW233Tb7AQAAgK6sxQE7PPTQQ1mv9YcffpimTZtW57mYXTyeAwAAgK6kxQH7yiuvzK57PXjw4DRkyJBsJnEAAADo6locsG+88ca02267pVNPPTU7/xoAAACYi8t0ffnll9kM4sI1AAAAtCJgr7XWWumdd95p6csAAACgorU4YJ9wwgnphhtuSA8//HDt5boAAACgq2vxOdhLLbVU2mijjdIRRxyRzRjep0+fOs/HY88++2yRdQQAAIDKC9hnn312uv7669PKK6+cll122TrXxAYAAICuqsUB+4477kiHHHJI+tnPftY2NQIAAICucA52dXV1NkQcAAAAaEXA/va3v52ef/75lr4MAAAAKlqLh4gffvjh6Zhjjkl9+/ZNm2++eZp33nlnKzPffPMVVT8AAACozIC90047Zb/POuus7Kchr776autrBgAAAJUcsEeMGJFdiqu97bPPPumZZ55p8Llzzz037bDDDmnkyJHZJGz1XXHFFWnTTTdth1oCAADQVbU4YB955JGpI/zyl79MX3/9dZ3HrrnmmvSXv/wlbbjhhrWPLbnkkumcc86pU27o0KHtVk8AAAC6phYH7I6y3HLLzfZYXCosJl0bPHhw7WN9+vRJa6yxRjvXDgAAgK6uxbOIl4sxY8akDz74IO24444dXRUAAADovAH7nnvuyWYy32qrreo8Pnbs2LTOOuuk1VZbLe26667poYce6rA6AgAA0HV0yoA9c+bM9MADD2Thul+/frWPr7zyyun4449PF198cTr//PPToEGDsknZomxrVVdX17k9a9as7HZNTU12P36HeLyxslAO62E5li3HOlVC2XKsU6WULcc6tZfO3t5tqZzX2c62jWgvnWGdVVZ720aUz99CxQbsv//97+nzzz9Pw4cPr/P4fvvtl/baa6+0/vrrp6233jqbPXzYsGHpwgsvbNX7RQNPnjy59v4333yTpk2bVtvokyZNqm34GTNm1JmMbcqUKWnq1Kmten/Ila5LsU5Onz69znqYbxzi8eaus1G2dJ2Nsvn7xLofZeOgVnPW79Ky8Tvul9Y9ypeWjeXlyy0tG+UaK5svN9/w1V9u1K+xstEG8fkaasNoj9I2jLKlbRi3m2rDIrYRc9PeTbVLU8tt6rtpqg2bKttQG7Z3e7ekDefULk2thw2Vndu/hTm1d2kbRtnG2rC9dMZtRL5+tLX622TbiLnfRrSXrrSNaKq97UfYj7AfMa3Rv/v6E21XzCRn9YeHzzfffGnjjTdusly3bt3Stttum84+++xsAxQToM2NuCxZ//79a+9Hr3l+qbJ4j4EDB2a/Q8+ePVOPHv/XrDGMvSMua0ZlKl2HY51sbD3s1atXti42Z51tqmz8bsn6XVo2ysX90ro3VjaWW1o2llvaKxfP5a/Nl5vfj+WWlh0wYECjZXv37p193obasHv37nXq1FTZ1rR3U21YRHuXtksuX27+XP32bkkbNlW2JW3YVu3dkjZsTXvPzTrb1u3dXjr7NqIt5euwbUTrtxHtpSttI9rq/5r9CPsRXWk/oiIDdgTlhx9+OJvcrLRhGlPUP9X4Ehu6HV9C6f36Ozqlz0E5rIflWLYc61RJZcuxTp29bDnWqb109vZuS/l7leP60dm2Ee2lM6yzymrvjl4/rIepsoeI/+1vf8uGtDRn9vAY7vLggw+m5Zdffq57rwEAAKAie7DvvvvutNhii6W11167zuPjx49PI0eOzM7LHjJkSJo4cWK66aab0ksvvZQuuuiiDqsvAAAAXUOnCtgRmh9//PFsMrP65+jEOPsYwx8ziH/xxRfZ8PG4VFdMdLbJJpt0WJ0BAADoGjpVwJ533nmzHumGxKRno0aNavc6AQAAQKc8BxsAAADKkYANAACdSE11dadePlSyTjVEHAAAurqq7t3T2yeelKa++17hy+6zzNJp6Gm/Lny50FUI2AAA0MlEuP7m9dc7uhpAPYaIAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAC1SPWuWFgNoQI+GHgQAgMZ079YtnXjj4+ndTyYW3kgbrbhYGrH9Whof6JQEbAAAWizC9Wvjvyi85ZZecB7fBtBpGSIOAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQFcJ2LfffntaccUVZ/s555xz6pQbPXp02nnnndPqq6+ettlmm3TDDTd0WJ0BAADoWnqkTuTKK69MAwcOrL2/8MIL195+7rnn0uGHH5522mmnNHLkyDRmzJh02mmnpV69eqXdd9+9g2oMAABAV9GpAvaqq66aBg8e3OBzF198cVpllVXSGWeckd3fYIMN0n/+8590wQUXpN122y1169YpOusBAADopCoidU6fPj099dRTaYcddqjz+I477pg+/fTT9Morr3RY3QAAAOgaOlXAHj58eFp55ZXTVlttlS677LJUXV2dPT527Ng0Y8aMtOyyy9Ypv9xyy2W/33777Q6pLwAAAF1HpwjYCy64YDryyCPTb37zm3TFFVekzTbbLJ1//vnp9NNPz56fOHFi9nueeeap87r8fv58a+RhPr89a9as7HZNTU12P36HeLyxslAO62E5li3HOlVC2XKsU6WULcc6tZfO3t5tqSttIypFZ1hnGyrbHspxne1M2+RKKFuOdZrVwWUrJmBvsskm6Ygjjsh+b7zxxunkk09O+++/f7r55pvTJ598Uluuqqqqwdc39nhzRQNPnjy59v4333yTpk2bVtvokyZNqm346En/+uuva8tOmTIlTZ06tVXvD7nSdSnWyTg9onQ9zDcO8Xhz19koW7rORtn8fWLdj7IzZ85s1vpdWjZ+x/3Sukf50rKxvHy5pWWjXGNl8+XmG776y436NVY22iA+3/9+tlmpe/futfMzxHYi7ufbi3g87udaWjb9/81OS7YRc9PeTbVLU8tt6ruJdafq/y+n6HapnQ9j1qzss+XrbHw3pets3G5qnW1s/W5JG86pXZpaDxsq29TfQqwQRbdh/bLtpa22EfEd5t9j/j6lZfO/3eaWLf27z7eVba3+Nrk562xn3EaUfhedXblsI5pq7/r/19qr/et/5/Yj6u5H1N8Xi7/h0n2x0v9r7bXPUS7biK+b2BfryH2OIvYjKm6Ss1Lbb799+v3vf59effXVtPjiizfYU/3VV1812LPdUvHF9e/fv/Z+v3796nyZMbN5/gX27Nkz9ejxf83at2/fdt/5oXL16dOn9nask42thzF7fqyLzVlnmyobv1uyfpeWjXKls/5H3RsrG8stLRvLLe2Vi+fy1+bLze/HckvLDhgwoNGyvXv3zj7v/362bum5D/6aJk2bkIo2sPegtOYS27R4G1FEe5e2Sy5fbv5c/fau34bZutW9e3r7xJPS1HffK7x9+iyzdBp62q+z7yqvf+l3U1uHNl5nW9PeLV1n4/aJNz6e3v2k9SOq6ttoxcXSiO3XSu2lrbYRfWMbUbLjVPpcLLf0O4/llmpJ2baUr8Ot2SZ3hm1E1L9SlMs2oqn2rv9/rb3av/Rvx37E7PsR9f9XRaArXQc6Yp+jXLYRA5rYFyuHfY7W7EdUdMAuNWTIkOzDv/POO2nTTTetffytt97Kfg8dOrTV7zHbkaL/Lz96kqs/W3npc1AO62E5lu2oOsU/uq+mfpbaUjm099y+Nv7RffP666mtdKV1NkS4fm38F6loSy/YuoPILdVW7R3hevKTT6ZZXxV/EKLHooulvsOGpbZWv2el/uPlun639rWdWblsI+a2bFvqTPsc5VinStrnaM1rqypsn6OiA/Z9992Xffi4NFcchYjLct1///3Z0PHcPffck52/HWUAgPIW4bp6QvE9PN1aOZINACoqYB900EFZgF5hhRWy+w8//HD64x//mPbdd98sQIcRI0akvffeO5144onZ5bnGjBmTbr311nTqqae6BjYAAABtrlME7GWWWSbddttt6aOPPsomDlh66aXTCSeckPbZZ5/aMmuuuWa65JJL0rnnnpvuvPPOtMgii2Rhe/fdd+/QugMAANA1dIqAHUG5OeLyXfEDAAAA7a1TXKYLAAAAyp2ADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYAAAAUAABGwAAAAogYAMAAEABBGwAAAAogIANAAAABRCwAQAAoAACNgAAFKimpkZ7QhfVo6MrAAAAlaSqqipNfvLJNOuriYUvu8eii6W+w4YVvlygGAI2AAAULMJ19YQJhbdrt3nmKXyZQHEMEQcAAIACCNgAAABQAAEbAAAACiBgAwBUGLNYA3QMk5wBAFTgLNb/fPejNGnKjMKXvfC8fdOqiy9Q+HIBKoGADQBQgSJcT5wyrfDlDujTs/BlAlQKQ8QBAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAV7lM1/3335/uvvvu9PLLL6eJEyemJZdcMu2xxx7phz/8YerW7X+PEYwcOTLdcccds732iiuuSJtuumkH1BoAAICupFME7D/84Q9pscUWSz//+c/T/PPPn55++ul0+umnp3HjxqXjjz++tlwE73POOafOa4cOHdoBNQYAAKCr6RQB+9JLL02DBw+uvb/BBhukb775Jt1www3pmGOOSb169coe79OnT1pjjTU6sKYAAAB0VZ3iHOzScJ1beeWV07Rp09KXX37ZIXUCAACAThewG/Lss8+m+eabLxsynhs7dmxaZ5110mqrrZZ23XXX9NBDD3VoHQEAAOg6OmXAfvHFF9Ptt9+e9ttvv9S9e/faHu04H/viiy9O559/fho0aFAaMWJEeuCBBwp5z+rq6jq3Z82ald2uqanJ7sfvEI83VhbKYT0sx7IdVaf20JHt3ZrXtoeutM5Wks7899jWymmdpfnfWVt8N5Wgo7eznWE/oty2ceW0HzGrQvc5KjJgf/rpp+moo45Kq6++ejrkkENqH4+wvddee6X1118/bb311tns4cOGDUsXXnhhq98zGnjy5Mm19+P87xienjf6pEmTaht+xowZ6euvv64tO2XKlDR16tRW1wFC6boU6+T06dPrrIf5xiEeb+46G2VL19kom79PrPtRdubMmc1av0vLxu+4n4tyUb60bCwvX25p2SjXWNl8ufmGr/5yo36NlY02iM/XnlqyjZib9m6qXZpablPfTXu1UbxPvs7Gd1O6zsbtptbZxtbvlrThnNqlqfWwobJN/S1UkrbaRlSC+tvk5qyzbbmNYM7aahtRCer/X7AfMft+ROm+WPwNl+6L1f+/1h7KZT/i6yb2xTpyn6OI/YiKmeQsFx82QnVMZjZq1KjUs2fPRsvG5bu23XbbdPbZZ2dfZLxmblVVVaX+/fvX3u/Xr1/2WP4+AwcOrL1cWNSpR4//a9a+ffvWloXWKl2PY51sbD2Mif9K/z6aWmebKhu/W7J+l5aNcnG/tO6NlY3llpaN5Zb2AsRz+Wvz5eb3Y7mlZQcMGNBo2d69e9dOitheWrKNKKK9S9slly83f65+e9dvw1gH2kO8T17/+t/N3K7fLWnD1rR3S9fZStJW24hKkK/DrdkmF7nOMmdttY2oBPH5cvYjqub4vypG1ZauAx2xz1Eu+xEDmtgXK4d9jtbsRzRHp/mvH0cVDjvssPTZZ5+lW265JRsCPidFDtPJh6LXvx1fQun9+hvW0uegHNbDcizbUXVqD+XQ3q19bVvpSutsJenMf49tLf9M5bDO0rLvrOjvphJ0pn2OcqxTJe1zVMJ2q3s7fo+dImBHF/3RRx+dXnvttXT99denxRdffI6vieEZDz74YFp++eVb1XsNAAAAFROwTz311PTII4+k4447Lhvu/e9//7v2ueWWWy5NnDgxjRw5Mg0fPjwNGTIku3/TTTell156KV100UUdWncAAAC6hk4RsJ944onsd5xPXd+1116bVlxxxWysf8wg/sUXX2Rj5eNSXTHR2SabbNIBNQaAytG7R79UPas6de9WWUNgAaBLBuy//e1vcywTk54BAMXr2a1XFq5Pf/BX6f0J7xW+/PWHbJAO2uhHhS8XANpbpwjYAEDHi3D95qdvFL7cIYOWKnyZANARKm8KTwAAAOgAAjZQlqpnzeroKgAAQIsYIg6Upe7duqUTb3w8vfvJxMKXvdGKi6UR269V+HIBAOjaBGygbEW4fm38F4Uvd+kF5yl8mQAAYIg4AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADAABAAQRsAACATqh3j36pelZ1m75HWy+/0rhMF0AnVFNTk6qqqjq6GgBAB+rZrVfq3q17Ov3BX6X3J7xX+PKXGrR0+u/tfln4ciuZgA3QCUW4nvzkk2nWVxMLX3aPRRdLfYcNK3y5AEDbiHD95qdvaN4yIGADdFIRrqsnTCh8ud3mmafwZQIAdAXOwQagy4sh9wAAraUHG4AuL4bc//Pdj9KkKTMKb4uF5+2bVl18gS7fxgDQFQjYAJBSFq4nTplWeFsM6NNT+wJAF2GIOAAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgCgy+jdo1+qnlXd0dUAKlSPjq4AAAC0l57deqXu3bqn0x/8VXp/wnuFL3/9IRukgzb6UeHLBToHARsAgC4nwvWbn75R+HKHDFqq8GUCnYch4gAAAFAAARsAAAAKIGADAABAAQRsAAAAKICADQAAAAUQsAEAAKAAAjYAAAAUQMAGAACAAgjYwFypqanRcgAAUKJH6R2A5qqqqkr/fPejNGnKjMIbbeF5+6ZVF1/AlwEAQKciYANzLcL1xCnTCm/BAX16Fr5MAABoa4aIAwAAMJvB/QY7LbCF9GADAABlPe9LnJrWWZffmQ3oPTBrm8lPPplmfTWx8OX3WHSx1HfYsFRJBGwAAKBLzvsysG/PtO4yixS+3EoT4bp6woTCl9ttnnlSpRGwAQCALjnvCxTNOdgAAABQAAEbAAAACiBgAwAAgIANAAAA5UEPNgAAABRAwAYAAIACVFzAfvfdd9NBBx2U1lhjjbThhhum0047LU2dOrWjqwUAAECFq6jrYH/11Vdpv/32S4sttli68MIL0xdffJHOPPPM9OWXX6Zzzjmno6sHAABABauogH3zzTdnIfvOO+9MgwcPzh7r3r17OvbYY9Nhhx2Whg4d2tFVBAAAoEJV1BDxxx57LBsWnofrsN1226VevXql0aNHd2jdAAAAqGwVFbDffvvt2XqpI1wPGTIkew4AAADaSlVNTU1NqhCrrrpqOvroo9Ohhx5a5/E99tgjzT///Ol3v/tdi5c5ZsyYFE0UQb0I02ZWZ8srWvdu3VLP7t3ShK+nphnVswpffp9ePdI8fXul6dVT0qya4pffvapH6tm9d/pyyoQ0c9bMwpffu0efNLD3wFQzbVpKs4qvf+rePVX16pVmTpiQamYUX/+qnj1Sj0GDUrmxPjesW1W31Kt73zZvf+tzsazPjbONbpptdLHsc8yBfY5CVVVVpd49uqe2Zh+6c6/P06dPz9aVtdZaq2udg92YCLTRIHNjbl/XmLb+Ax40oE+bLr+tQ8N8fds2RFb17t2myy/HENyWrM8dy/pcLOvznNlGdy7W6aZZnzuX9gjBbck+dOfeh45M2NxcWFEBe5555skmOatv0qRJcz3B2ZprrllAzQAAAKh0FXUOdoTo+udaR3f+2LFjzSAOAABAm6qogL3pppump556Kk2YMKH2sb/+9a9ZyN5ss806tG4AAABUtoqa5CyGhw8fPjwtvvji6fDDD0+ff/55Ouuss9LGG2+czjnnnI6uHgAAABWsogJ2ePfdd9Npp52Wnn322dSnT58scB977LHZbQAAAGgrFRewAQAAoCNU1DnYAAAA0FEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2My1O+64I6244orpvffeq/P4DTfckD1+3nnn1Xl88uTJaZVVVkkXX3xxdn/06NFp7733Tuuvv35aY4010jbbbJNdszyuZZ4bOXJktqz4WXnlldO6666bdt1113T22Wen//znP7PVacstt0ynnnqqb5VCxDp60EEHZevoaqutlrbYYot0yimnpLFjx6Zf/OIXadNNN53tNUcffXS2vv7973+fbVnx+L/+9a+5WrehCBdddFHterfSSiultddeO+24447ZdvPtt9/Oyjz99NO1ZZr6+eCDD9Ltt99e57F11lknW4/vvPNOXxhtvi6vueaazXqudB2NbfnGG2+cbdtvvfXWNGPGjDqvrb/+x/7JVlttlY455pjZtuvQFu666670ve99L9s+r7XWWmn77bdP//3f/50+//zz2jL77LNP7Toa+9axn/LDH/4w28eeMGHCbMuMcldddVWz/n5ovR4FLIMuKv7ww5gxY9LSSy9d+/hzzz2X+vbtmz1e6vnnn0/V1dXZxuLee+9NP/3pT9Muu+ySDj744NSzZ89s5+7+++/Pfi+zzDK1r1tyySXTOeeck+KS7ZMmTUovvfRSuvnmm7Of2EBstNFG7fip6SriANGll16aHfj51a9+leaff/40fvz47MDS/vvvnw477LAsXETIWGKJJRpc/7/97W/XPh73Yz1fffXVax+zbtMR+vTpk6655praA59vvPFGuuWWW9If//jHdPrpp2dhIu7nXn755SyAn3nmmWnZZZetfXyhhRaqvX3llVemgQMHpi+//DJdd9116fjjj8/W9x122KGdPx00LALJ8OHD08yZM9Mnn3ySHn/88eyAaYTs3//+92nAgAF1yufr+7Rp09K4ceOy/ZYDDzww7bnnnumXv/ylZqZNXH755encc8/N9jOOOuqobN/3zTffTHfffXe23sa+SC72p2NbO2vWrDRx4sRs/+Paa69NN910U7ZNjoOodAwBm7k2ZMiQtOCCC2bBIXoscnE/gnOEjzgyHDtZ+eM9evRIw4YNy/5JxdG2s846q/Z1EUb23XffbENRf2cwjiDnotcw/sFF73ccUX744Ydn+8cIrfHYY49l4fpHP/pRdiAoF73MO++8c/rb3/6Wllpqqdr1Og/YEcA//vjjbP2sf4Ap7kfPSe/eva3bdKhu3brV2abGtjfW2UMPPTTrJYmdttLnI2CE5Zdfvs4BolKrrrpqGjx4cHY7tu2bb7559j9AwKZcLLroonXW6+985ztZz2Bs52Nf5LTTTqtTvnR9j3U6ehQj+Fx22WVZz993v/vddv8MVL44QBn70DHKLbfZZptlnVH194/nmWeeOut0jLKLXuzvf//76Sc/+Um67777su097U+r0yrxT6Y0SES4iJARR4qjt/rVV1+tfS7KxVDYfv36ZT3REc4bXCmbsTGYb7750nHHHZf1lsRRZShS9GYssMAC6cgjj2zw+TgVYejQoWnQoEF11v+4vfjii6ftttsu/fvf/87+BkL0mLz44otZcJkT6zYdIQ78nHTSSdlB0ejRa40YwREHYD/88MPC6gdtIQ7Yb7vtttkpDV9//fUcy0ePYuy73Hjjjb4Q2kTsH5eODmrp/vFiiy2WjbCL0y2ffPLJNqghzSFg0+ph4u+8804WdPOAERuGGFYVPRp5+IijbhE48oARz/3lL39Jf/jDH7IhtnNjgw02yHrEY7lQlAjDsd5uuOGGtaMvmnuAKW7HY9/61rfS9OnT0+uvv549/sorr6QpU6Y0K2AH6zYdYbnllksLL7xwNsywNWJ7/9FHH2UhG9pjm13/p35PX1PifOw4sBTb6TmJfY7YPsepavXP3YYixP5xnAIZBzo//fTTuVpGrNPB/nHHEbBplQgMcX5IHjJixyyfNCF+P/vss9ntOMcvzvXLz9v+2c9+lg2/imFZcb5fbAxOPPHE9Nprr7WoxyV6EOd2AwQNiYNFMSQ2hhM2Z/2Pc6PiiHO+/sdjMUojJhTJ1/88sDQ3YFu36Six3n/22Wctfl0Emgg28dqYqC/+jmLoLbSlb775Jgsk9X8uueSSZi9jkUUWyX43d72Pv5EI13HOKxQtzu+fd955s33i2DeOfeQ4faElnVH5/ov9447jHGxaJYZ85xM6xbDZ+B2TiOQBOyZlCHnQyANG9JLcdttt6Z///Gc20UjMrPynP/0pG6YVMyDG+SbNEeG+qqrKt0hhYp0KzVmvYn2OYJEH6ziQlK/jee92nC4Rv2NUR36OanPrYd2mvc3telc6oV+IiQGbe0AJ5lbM0XL99dfP9nhM2HfPPfe0aJvfFv8joKVWWGGFbN39xz/+kZ544olsPznOy445LeIqPbHfbR0tfwI2rZLPihwBIobARg/0ySefXBsw4ohwXNIons8nRSs9lyQmDomfEMOzYuKy888/v1kBO3oZo5ckzpWFosSoiOhBbs75o7Hu9+rVK1u/u3fvnr0u/jnm63/05IUI4Jtsskmz62DdpqPE0O7Sq0I019VXX5369++fvT6u7hA9LjH5jllsaUuxH9HQxHuPPvpos5cRc8eExuaFqS/W8dj3iV5GaAuxXxH7wfm+cHRExYig6ID63e9+16x1NNg/7jiGiNNq0UsR5yNFL3WEjPzoWpyLHRM+RfiIgJEPD29MXMcvekHya7HOSRzdiyGJekkoUpxjF+tqrF9zOscu/gnGzOCxjsdPzJAffwN5wI5/cnH0OXbg5rT+l7Ju0xHidIdYV+fm2qhxSkTMPRATRl1xxRVZAInLK0K5i/AS2/IYWj4nsc/x1FNPZaE+/ldAe4gD9HGwsrn7x9HzHewfdxwBm1aLP+DocYshLPFPp3RiqNhRe+CBB7KZxUsDRkPnOsVQ2/fff79ZR9zi3KfYeYvexrjUBhTpgAMOyNbROFrckEceeaTO+v/CCy+kZ555ps4/szi4FKdCxLUoQ3MDtnWbjhDb8F//+tdZ0Nh9991btaw4pzUuuRjBpTkTR0FHXpLxr3/9a3ZZpJg7Y04uvPDC7LzWGG0HbaGh/eOpU6em//znP83aP47RdzEHQVzpJCbko2M4/EarRaiIYVqjR49OhxxyyGzPxU5bfjsX1/OLYYhxzb4IIhMmTMjOwY5Zl0844YTZNiz5TIgxmVT0lscMi3FJjQhAMSwRir50y49//OM0atSobJb8uJbv/PPPnx0ouuuuu7LLX8S6mwfnCNHRUx3XES4VQ2Rjtvz4p5hfN9u6TUfLr+qQTxIVcwfccsstady4cdnEk/l13Vt7kCoOukZv9nnnnVdAraF1IqDkl0+MkBzh+s9//nM28uj4449vcERHlI0rQsTfRpwXG5c9ink1XN+dtrLjjjtm+xcxwVmMBP3kk0+ybWnsJ++33351yn711VfZOh3zAsTB+RgtGvvH0dEVp1u6BnbHEbBptYEDB2aXd4mdtPpDC+N+/OHHtX1jkqdcBPH7778/XXDBBdk/ulhGPB/n7sUQw1Lxj+0HP/hBtqGIMB3ncscGaK+99mrWTM8wN4455phs/Y1/bHF94JgFP/7ZbbTRRukXv/hFnXU8JruJnwjUpeKg0oMPPtjoMC3rNh0hDlrGNjXW2ei1i4OccVm6OLcvej2KENv8CCIRsGMeDpfsoqPFtjx+InzE+hmnNcRkfDvvvHODw73z7XxMpBYHWCOIx6VF438AtJUjjjgiGyUXBzu/+OKLbKRmrKsxz0X9Huk4NS225XFqWuxHL7PMMlkI32OPPbLX0XGqalo6fSIAAAAwG+dgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgAII2AAAAFAAARsAAAAKIGADQBm6/fbb04orrphefPHFQpb3wQcfZMuL5eYuuuii7LG2MmbMmOw9vvrqqzZ7DwAoJwI2AHRRu+++e7rlllvabPnPPfdc+t3vfidgA9Bl9OjoCgAAHWORRRbJfjqbKVOmpL59+3Z0NQBgNnqwAaATGDlyZFpzzTXT+++/nw455JDs9mabbZbOOuusNH369DplP/7443T00UdnZdZee+30k5/8JH322WezLbOxIeJ33313+sEPfpC9Pn522mmndOutt9Y+//e//z0ddthhadNNN02rr7562mabbdLJJ5+cvvjiizrL/u1vf5vd3mqrrbL3iZ+nn346e2zWrFnpiiuuSP/1X/+VVltttbThhhumn//85+mjjz6qU5d99tknDR8+PP3zn/9MP/zhD9OwYcPSCSecUECLAkDx9GADQCcxY8aMLNh+73vfSwceeGAWOi+55JI0YMCAdMQRR2Rlpk6dmg444ID0ySefpJ/97Gdp6aWXTo8++mg65phjmvUeF1xwQbbMbbfdNlvOwIED05tvvpk+/PDD2jJjx47NgncMMY/nx48fn/7whz+kPffcMwvnPXv2zJ6bOHFiuu6667Jh4gsuuGD22uWWWy77fcopp2TD0/fee++0+eabZ8uI937mmWey88QHDx5c+36ffvppOu6449LBBx+cfY5u3fQPAFCeBGwA6EQB+8gjj0zbb799dj96fV966aV0zz331AbsO+64I7399ttZSI6e47DxxhunadOmpT/+8Y9NLn/cuHHpsssuSzvuuGM655xzah//9re/XafcHnvsUXu7pqYmC9vrrbde2mKLLdJjjz2WvW8MPV900UWzMiuvvHJaYoklal8T9YtwHYH8pJNOqn18lVVWyYL5NddcU+eAwJdffpnOP//87PMCQDlzCBgAOomqqqq05ZZb1nkshl2X9i7HEOz+/fvXhutcDLOekyeffDJVV1envfbaq8lyn3/+eTYkPIaoRyheddVVs3Cdh+c5yYeJ77LLLnUe/9a3vpWGDh2a/vGPf9R5fN555xWuAegU9GADQCcRE3v17t27zmO9evXKeqdLe3sXWGCB2V7b0GP15edQNzXxWZw7HcPTYwj64YcfnlZYYYWsXtGT/f3vf79OXRoTdQwLLbTQbM/FY6UHDEI+vBwAyp2ADQAVZL755ksvvPDCbI83NMlZffl5zzHRWD68u7433ngjvfbaa9nkaqU90DH5WkvqGCKk1w/z8digQYNm67kHgM7AEHEAqCDrr79+mjx5cnr44YfrPB7nac9JnGvdvXv3dNNNNzVaJg+70XNe6uabb56tbF6mfq/2BhtskP2+66676jweBwZiiHn+PAB0NnqwAaCC7Lzzzunqq69Oxx9/fDZR2FJLLZVGjx6dnnjiiTm+NiYi+9GPfpRNkBazkcd52zFL+FtvvZUmTJiQjjrqqLTsssumIUOGpP/5n//JhoXH+dGPPPJIdumu+mL4eIhJy6K3u0ePHmmZZZbJlhGXAbv++uuzGcHjcl/5LOLRc77//vu3SdsAQFsTsAGggsT50Ndee206/fTTs5nAo8c5ZhE/99xzs+tIz0lcPztCeYTfY489NuvRjkt9xfWoQ1yC69JLL82WHxOdRWiO2b0j1Mfltur3pkdgj5nN4zracf521C0ej8t0Lbnkkum2225LN954Y3apsU022SS7tFj9IeIA0FlU1cThZwAAAKBVnIMNAAAABRCwAQAAoAACNgAAABRAwAYAAIACCNgAAABQAAEbAAAACiBgAwAAQAEEbAAAACiAgA0AAAAFELABAACgAAI2AAAAFEDABgAAgNR6/w9BjtkEb470cAAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 1. Create DataFrame\n", - "df_res = pd.DataFrame(results_data)\n", - "\n", - "# 2. Create a specific label for the Plot Legend (Library + Mode)\n", - "df_res[\"Label\"] = df_res[\"Library\"] + \": \" + df_res[\"Mode\"]\n", - "\n", - "# 3. Calculate Speedup relative to 'Earthkit No Flox (Standard)' using MEDIAN time\n", - "# Robustness Update: We use Median Time for speedup calculation to be resistant to outliers.\n", - "df_res[\"Speedup\"] = 0.0\n", - "baseline_mode = \"1. No Flox (Standard)\"\n", - "\n", - "for indicator in df_res[\"Indicator\"].unique():\n", - " # Find the baseline: Xclim + No Flox (as per original logic, though code comments said Earthkit)\n", - " # Let's double check the user intent. Usually Xclim NoFlox is the \"base\" reference.\n", - " # The original code searched for Library='Xclim'. We stick to that.\n", - " baseline_row = df_res.loc[\n", - " (df_res[\"Indicator\"] == indicator)\n", - " & (df_res[\"Library\"] == \"Xclim\")\n", - " & (df_res[\"Mode\"] == baseline_mode)\n", - " ]\n", - "\n", - " if not baseline_row.empty:\n", - " # Use median_time for robust speedup\n", - " baseline_time = baseline_row[\"median_time\"].values[0]\n", - " mask = df_res[\"Indicator\"] == indicator\n", - " df_res.loc[mask, \"Speedup\"] = baseline_time / df_res.loc[mask, \"median_time\"]\n", - "\n", - "print(\"\\nBenchmarking Results (Robust Stats):\")\n", - "display(df_res[[\"Indicator\", \"Library\", \"Mode\", \"median_time\", \"std_time\", \"max_mem\", \"Speedup\"]])\n", - "\n", - "# --- Plots ---\n", - "# Define a consistent order for the bars\n", - "hue_order = sorted(df_res[\"Label\"].unique())\n", - "\n", - "# Plot 1: Relative Speedup (Robust)\n", - "plt.figure(figsize=(10, 6))\n", - "sns.barplot(data=df_res, x=\"Indicator\", y=\"Speedup\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", - "plt.title(f\"Relative Speedup (via Median Time)\\n(Baseline: Xclim {baseline_mode})\")\n", - "plt.axhline(1.0, color=\"black\", linestyle=\"--\", alpha=0.5, linewidth=1)\n", - "plt.legend(title=\"Configuration\", fontsize=\"small\")\n", - "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "\n", - "# Plot 2: Peak Memory Usage\n", - "plt.figure(figsize=(10, 6))\n", - "sns.barplot(data=df_res, x=\"Indicator\", y=\"max_mem\", hue=\"Label\", palette=\"Paired\", hue_order=hue_order)\n", - "plt.title(\"Peak Memory Usage (MiB)\\n(Lower is better)\")\n", - "plt.legend().remove()\n", - "plt.grid(axis=\"y\", linestyle=\":\", alpha=0.3)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/integration/benchmark_performance.py b/tests/integration/benchmark_performance.py new file mode 100644 index 0000000..beae8e5 --- /dev/null +++ b/tests/integration/benchmark_performance.py @@ -0,0 +1,521 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +""" +Performance analysis script for climate indicators. + +This script migrates the benchmarking and profiling logic from +docs/notebooks/performance_analysis.ipynb. It allows for repeatable +performance measurements of Earthkit-Climate indicators compared to Xclim. + +Features: +- Timing statistics (mean, median, std). +- Memory profiling (peak Induced RAM usage using psutil). +- Comparative analysis between Earthkit (Lazy/Optimized) and Xclim. +- CLI interface to run specific indicators and configuration. + +Run with: + export PYTHONPATH="." + pixi run -e dev python tests/integration/benchmark_performance.py --help +""" + +import argparse +import gc +import os +import threading +import time +import warnings +from contextlib import nullcontext +from typing import Any, Callable, Optional + +import earthkit.data +import pandas as pd +import psutil +import xarray as xr +from tqdm import tqdm + +import earthkit.climate.indicators.precipitation as ek_pr +import earthkit.climate.indicators.temperature as ek_temp +from earthkit.climate.utils.percentile import percentile_doy + +warnings.filterwarnings("ignore") + + +# --------------------------------------------------------------------------- +# Constants & Dataset URLs +# --------------------------------------------------------------------------- + +_URLS: dict[str, str] = { + "tasmax_hist": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc" + ), + "tasmax_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), + "tasmin_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), + "pr_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), +} + +# --------------------------------------------------------------------------- +# Resource Monitor +# --------------------------------------------------------------------------- + + +class ResourceMonitor(threading.Thread): + """ + Background thread to monitor system resource usage during execution. + + ## Parameters + + interval : float, default 0.1 + Sampling interval in seconds. + """ + + def __init__(self, interval: float = 0.1) -> None: + self.interval: float = interval + self.stop_event: threading.Event = threading.Event() + self.memory_usage: list[float] = [] + self.process: psutil.Process = psutil.Process() + super().__init__() + + def run(self) -> None: + """ + Periodically record resident set size (RSS) memory in MiB. + + ## Returns + + None + """ + while not self.stop_event.is_set(): + try: + # RSS Memory in MiB + mem: float = self.process.memory_info().rss / (1024 * 1024) + self.memory_usage.append(mem) + except Exception: + pass + time.sleep(self.interval) + + def stop(self) -> None: + """ + Signal the monitor to stop sampling. + + ## Returns + + None + """ + self.stop_event.set() + + +# --------------------------------------------------------------------------- +# Data Loading +# --------------------------------------------------------------------------- + + +def load_datasets() -> dict[str, xr.Dataset]: + """ + Download or use cached CMIP6 datasets required for benchmarking. + + Uses earthkit-data's URL source with a user-level cache. + + ## Returns + + dict[str, xr.Dataset] + Mapping from short name to xarray Dataset. + """ + cache_dir: str = os.path.expanduser("~/.cache/earthkit/data") + os.makedirs(cache_dir, exist_ok=True) + earthkit.data.config.set( + { + "cache-policy": "user", + "temporary-directory-root": cache_dir, + } + ) + + datasets: dict[str, xr.Dataset] = {} + print(f"Loading {len(_URLS)} datasets via earthkit-data...") + for key, url in _URLS.items(): + ds = earthkit.data.from_source("url", url).to_xarray() + datasets[key] = ds + return datasets + + +# --------------------------------------------------------------------------- +# Benchmark Engine +# --------------------------------------------------------------------------- + + +def benchmark_function( + func: Callable[..., Any], + kwargs: dict[str, Any], + n_repeats: int = 5, + warmup: bool = True, + label: str = "Function", +) -> dict[str, float]: + """ + Execute a function multiple times and profile time and memory usage. + + ## Parameters + + func : Callable + The function to benchmark. + kwargs : dict[str, Any] + Arguments to pass to func. + n_repeats : int, default 5 + Number of measurement runs. + warmup : bool, default True + Perform a silent initial run to avoid JIT/caching bias. + label : str, default "Function" + Human-readable label for logging. + + ## Returns + + dict[str, float] + Statistics including 'mean_time', 'median_time', 'std_time', 'max_mem'. + """ + # 1. Warm-up + if warmup: + try: + res: Any = func(**kwargs) + if hasattr(res, "compute"): + res.compute() + except Exception as e: + print(f" Warm-up failed for {label}: {e}") + gc.collect() + + times: list[float] = [] + mem_peaks: list[float] = [] + + for _ in range(n_repeats): + gc.collect() + monitor: ResourceMonitor = ResourceMonitor(interval=0.1) + baseline_mem: float = psutil.Process().memory_info().rss / (1024 * 1024) + monitor.start() + + start_time: float = time.perf_counter() + try: + res = func(**kwargs) + if hasattr(res, "compute"): + res.compute() + except Exception as e: + print(f" Execution failed for {label}: {e}") + monitor.stop() + continue + end_time: float = time.perf_counter() + + monitor.stop() + monitor.join() + + duration: float = end_time - start_time + observed_mems: list[float] = monitor.memory_usage + peak_delta: float = 0.0 + if observed_mems: + peak_delta = max(max(observed_mems) - baseline_mem, 0.0) + + times.append(duration) + mem_peaks.append(peak_delta) + + if not times: + return {"mean_time": 0.0, "median_time": 0.0, "std_time": 0.0, "max_mem": 0.0} + + import numpy as np + + return { + "mean_time": float(np.mean(times)), + "median_time": float(np.median(times)), + "std_time": float(np.std(times)), + "max_mem": float(np.max(mem_peaks)), + } + + +def _run_indicator( + func: Callable[..., Any], + kwargs: dict[str, Any], + use_flox: Optional[bool] = None, +) -> Any: + """ + Wrapper to set xarray options while running an indicator. + + ## Parameters + + func : Callable + Target function. + kwargs : dict[str, Any] + Arguments. + use_flox : bool or None + Sets xarray's use_flox option. + + ## Returns + + Any + Result of the indicator call. + """ + ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext() + with ctx: + return func(**kwargs) + + +# --------------------------------------------------------------------------- +# Define Benchmarks +# --------------------------------------------------------------------------- + + +def get_benchmarks( + data_cache: dict[str, xr.Dataset], +) -> list[dict[str, Any]]: + """ + Construct the list of benchmarks as defined in the performance analysis notebook. + + ## Parameters + + data_cache : dict[str, xr.Dataset] + Dictionary of loaded datasets. + + ## Returns + + list[dict[str, Any]] + List of benchmark configurations. + """ + import xclim.indicators + + tasmax_ssp = data_cache["tasmax_ssp"]["tasmax"] + tasmin_ssp = data_cache["tasmin_ssp"]["tasmin"] + pr_ssp = data_cache["pr_ssp"]["pr"] + + # Pre-calculate percentile for WSDI + per_90: xr.DataArray = percentile_doy(data_cache["tasmax_hist"]["tasmax"], per=90) + per_90.name = "tasmax_per" + + # Optimized views (Chunk -1) + tasmax_opt = tasmax_ssp.chunk({"time": -1}) + tasmin_opt = tasmin_ssp.chunk({"time": -1}) + pr_opt = pr_ssp.chunk({"time": -1}) + + benchmarks: list[dict[str, Any]] = [ + { + "name": "WSDI", + "ek_func": ek_temp.warm_spell_duration_index, + "xi_func": xclim.indicators.atmos.warm_spell_duration_index, + "ek_args": { + "lazy": {"ds": xr.merge([tasmax_ssp, per_90]), "freq": "MS"}, + "optimized": { + "ds": xr.merge([tasmax_opt, per_90]).chunk({"time": -1}), + "freq": "MS", + }, + }, + "xi_args": { + "lazy": {"tasmax": tasmax_ssp, "tasmax_per": per_90, "freq": "MS"}, + "optimized": { + "tasmax": tasmax_opt, + "tasmax_per": per_90, + "freq": "MS", + }, + }, + }, + { + "name": "CWD", + "ek_func": ek_pr.maximum_consecutive_wet_days, + "xi_func": xclim.indicators.atmos.maximum_consecutive_wet_days, + "ek_args": { + "lazy": {"ds": pr_ssp, "freq": "MS"}, + "optimized": {"ds": pr_opt, "freq": "MS"}, + }, + "xi_args": { + "lazy": {"pr": pr_ssp, "freq": "MS"}, + "optimized": {"pr": pr_opt, "freq": "MS"}, + }, + }, + { + "name": "DTR", + "ek_func": ek_temp.daily_temperature_range, + "xi_func": xclim.indicators.atmos.daily_temperature_range, + "ek_args": { + "lazy": {"ds": xr.merge([tasmax_ssp, tasmin_ssp]), "freq": "MS"}, + "optimized": {"ds": xr.merge([tasmax_opt, tasmin_opt]), "freq": "MS"}, + }, + "xi_args": { + "lazy": {"tasmax": tasmax_ssp, "tasmin": tasmin_ssp, "freq": "MS"}, + "optimized": {"tasmax": tasmax_opt, "tasmin": tasmin_opt, "freq": "MS"}, + }, + }, + { + "name": "HDD", + "ek_func": ek_temp.heating_degree_days, + "xi_func": xclim.indicators.atmos.heating_degree_days, + "ek_args": { + "lazy": {"ds": ((tasmax_ssp + tasmin_ssp) / 2).to_dataset(name="tas"), "freq": "MS"}, + "optimized": { + "ds": ((tasmax_opt + tasmin_opt) / 2).to_dataset(name="tas"), + "freq": "MS", + }, + }, + "xi_args": { + "lazy": {"tas": (tasmax_ssp + tasmin_ssp) / 2, "freq": "MS"}, + "optimized": {"tas": (tasmax_opt + tasmin_opt) / 2, "freq": "MS"}, + }, + }, + { + "name": "SDII", + "ek_func": ek_pr.daily_pr_intensity, + "xi_func": xclim.indicators.atmos.daily_pr_intensity, + "ek_args": { + "lazy": {"ds": pr_ssp, "freq": "MS"}, + "optimized": {"ds": pr_opt, "freq": "MS"}, + }, + "xi_args": { + "lazy": {"pr": pr_ssp, "freq": "MS"}, + "optimized": {"pr": pr_opt, "freq": "MS"}, + }, + }, + ] + return benchmarks + + +# --------------------------------------------------------------------------- +# Main Execution +# --------------------------------------------------------------------------- + + +def run_benchmarks( + indicators: Optional[list[str]] = None, + n_repeats: int = 5, +) -> None: + """ + Execute the performance analysis loop. + + ## Parameters + + indicators : list[str] or None + Subset of indicators to run (e.g. ['WSDI', 'CWD']). + If None, all 5 are run. + n_repeats : int, default 5 + Number of repeats per configuration. + + ## Returns + + None + """ + data_cache: dict[str, xr.Dataset] = load_datasets() + all_benchmarks: list[dict[str, Any]] = get_benchmarks(data_cache) + + if indicators: + benchmarks: list[dict[str, Any]] = [b for b in all_benchmarks if b["name"] in indicators] + else: + benchmarks = all_benchmarks + + results: list[dict[str, Any]] = [] + + for b in tqdm(benchmarks, desc="Indicators"): + name: str = b["name"] + + # Define configurations + configs: list[dict[str, Any]] = [ + { + "lib": "Earthkit", + "mode": "1. No Flox (Lazy)", + "func": b["ek_func"], + "args": b["ek_args"]["lazy"], + "use_flox": False, + }, + { + "lib": "Earthkit", + "mode": "2. Flox (Lazy)", + "func": b["ek_func"], + "args": b["ek_args"]["lazy"], + "use_flox": True, + }, + { + "lib": "Earthkit", + "mode": "3. Flox + Opt", + "func": b["ek_func"], + "args": b["ek_args"]["optimized"], + "use_flox": True, + }, + { + "lib": "Xclim", + "mode": "1. No Flox (Lazy)", + "func": b["xi_func"], + "args": b["xi_args"]["lazy"], + "use_flox": False, + }, + { + "lib": "Xclim", + "mode": "2. Flox (Lazy)", + "func": b["xi_func"], + "args": b["xi_args"]["lazy"], + "use_flox": True, + }, + { + "lib": "Xclim", + "mode": "3. Flox + Opt", + "func": b["xi_func"], + "args": b["xi_args"]["optimized"], + "use_flox": True, + }, + ] + + for cfg in configs: + label: str = f"{name} / {cfg['lib']} / {cfg['mode']}" + stats: dict[str, float] = benchmark_function( + _run_indicator, + {"func": cfg["func"], "kwargs": cfg["args"], "use_flox": cfg["use_flox"]}, + n_repeats=n_repeats, + label=label, + ) + res: dict[str, Any] = { + "Indicator": name, + "Library": cfg["lib"], + "Mode": cfg["mode"], + **stats, + } + results.append(res) + + # Summarize with Pandas + df: pd.DataFrame = pd.DataFrame(results) + + # Add speedup relative to Xclim No Flox for each indicator + def calculate_speedup(group: pd.DataFrame) -> pd.DataFrame: + reference_time: float = group[ + (group["Library"] == "Xclim") & (group["Mode"] == "1. No Flox (Lazy)") + ]["mean_time"].values[0] + group["Speedup"] = reference_time / group["mean_time"] + return group + + df = df.groupby("Indicator", group_keys=False).apply(calculate_speedup) + + print("\n" + "=" * 80) + print(" PERFORMANCE ANALYSIS SUMMARY") + print("=" * 80) + print(df.to_string(index=False, formatters={"mean_time": "{:.3f}s".format, "Speedup": "{:.2f}x".format})) + print("=" * 80) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Earthkit-Climate indicator performance analysis") + parser.add_argument( + "--indicators", + nargs="+", + help="Sub-list of indicators to run (WSDI CWD DTR HDD SDII)", + ) + parser.add_argument( + "-n", + "--n-repeats", + type=int, + default=5, + help="Number of iterations per test", + ) + + args = parser.parse_args() + run_benchmarks(indicators=args.indicators, n_repeats=args.n_repeats) diff --git a/tests/integration/test_performance.py b/tests/integration/test_performance.py new file mode 100644 index 0000000..2422593 --- /dev/null +++ b/tests/integration/test_performance.py @@ -0,0 +1,310 @@ +# (C) Copyright 2025 - ECMWF and individual contributors. + +# This software is licensed under the terms of the Apache Licence Version 2.0 +# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. +# In applying this licence, ECMWF does not waive the privileges and immunities +# granted to it by virtue of its status as an intergovernmental organisation nor +# does it submit to any jurisdiction. + +""" +Integration tests for climate indicator performance analysis. + +Migrated from docs/notebooks/performance_analysis.ipynb. + +These tests verify that the earthkit-climate indicators produce results that are +consistent with their xclim counterparts when applied to real CMIP6 datasets +downloaded via earthkit-data. They run in the optimised mode (flox enabled, +time axis rechunked to -1) which avoids the multi-minute timing runs from the +original notebook. + +Run with: + export PYTHONPATH="." + pixi run -e dev python -m pytest tests/integration/test_performance.py -vv +""" + +import os +import warnings +from contextlib import nullcontext +from typing import Any + +import earthkit.data +import pytest +import xarray as xr + +import earthkit.climate.indicators.precipitation as ek_pr +import earthkit.climate.indicators.temperature as ek_temp +from earthkit.climate.utils.percentile import percentile_doy + +warnings.filterwarnings("ignore") + + +# --------------------------------------------------------------------------- +# Dataset URLs (same as the notebook) +# --------------------------------------------------------------------------- + +_URLS: dict[str, str] = { + "tasmax_hist": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc" + ), + "tasmax_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), + "tasmin_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), + "pr_ssp": ( + "https://sites.ecmwf.int/repository/earthkit-climate/" + "pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc" + ), +} + + +# --------------------------------------------------------------------------- +# Session-scoped fixture: download / cache datasets once per test session +# --------------------------------------------------------------------------- + + +@pytest.fixture(scope="session") +def data_cache() -> dict[str, xr.Dataset]: + """ + Download (or use cached) CMIP6 datasets required by the indicator tests. + + Uses earthkit-data's URL source with a user-level cache so that large + files are only downloaded once across test runs. + + ## Returns + + dict[str, xr.Dataset] + Mapping from short name to xarray Dataset. + """ + cache_dir: str = os.path.expanduser("~/.cache/earthkit/data") + os.makedirs(cache_dir, exist_ok=True) + earthkit.data.config.set( + { + "cache-policy": "user", + "temporary-directory-root": cache_dir, + } + ) + + datasets: dict[str, xr.Dataset] = {} + for key, url in _URLS.items(): + ds = earthkit.data.from_source("url", url).to_xarray() + datasets[key] = ds + return datasets + + +# --------------------------------------------------------------------------- +# Helper: run a climate indicator with optional flox setting +# --------------------------------------------------------------------------- + + +def _run_indicator( + func: Any, + kwargs: dict[str, Any], + use_flox: bool | None = None, +) -> Any: + """ + Execute a climate indicator function with configurable xarray flox backend. + + ## Parameters + + func : callable + The indicator function to execute (earthkit or xclim). + kwargs : dict[str, Any] + Keyword arguments forwarded to *func*. + use_flox : bool or None + If True/False, sets xarray's ``use_flox`` option for the call. + If None (default) the environment's current setting is used unchanged. + + ## Returns + + Any + The result returned by *func* (typically an xr.DataArray or Dataset). + """ + ctx = xr.set_options(use_flox=use_flox) if use_flox is not None else nullcontext() + with ctx: + return func(**kwargs) + + +# --------------------------------------------------------------------------- +# Parametrised benchmark fixture +# --------------------------------------------------------------------------- + + +@pytest.fixture( + params=["WSDI", "CWD", "DTR", "HDD", "SDII"], + ids=["WSDI", "CWD", "DTR", "HDD", "SDII"], +) +def indicator_config( + request: pytest.FixtureRequest, + data_cache: dict[str, xr.Dataset], +) -> dict[str, Any]: + """ + Build the indicator configuration for the parametrised performance test. + + Mirrors the ``benchmarks`` list from the notebook, but only the *optimised* + mode (``time: -1`` chunking + flox) is included because that is the + deterministic variant used for correctness checking. + + ## Parameters + + request : pytest.FixtureRequest + Pytest fixture request, carries the indicator name via ``request.param``. + data_cache : dict[str, xr.Dataset] + Session-scoped mapping of downloaded CMIP6 datasets. + + ## Returns + + dict[str, Any] + Dictionary with keys ``name``, ``ek_func``, ``xi_func``, + ``ek_args`` and ``xi_args``. + """ + import xclim.indicators + + name: str = request.param + + tasmax_ssp = data_cache["tasmax_ssp"]["tasmax"] + tasmin_ssp = data_cache["tasmin_ssp"]["tasmin"] + pr_ssp = data_cache["pr_ssp"]["pr"] + + # Rechunk time to -1 for the optimised variant + tasmax_opt = tasmax_ssp.chunk({"time": -1}) + tasmin_opt = tasmin_ssp.chunk({"time": -1}) + pr_opt = pr_ssp.chunk({"time": -1}) + + if name == "WSDI": + per_90 = percentile_doy(data_cache["tasmax_hist"]["tasmax"], per=90) + per_90.name = "tasmax_per" + return { + "name": name, + "ek_func": ek_temp.warm_spell_duration_index, + "xi_func": xclim.indicators.atmos.warm_spell_duration_index, + "ek_args": { + "ds": xr.merge([tasmax_opt, per_90]).chunk({"time": -1}), + "freq": "MS", + }, + "xi_args": { + "tasmax": tasmax_opt, + "tasmax_per": per_90, + "freq": "MS", + }, + } + + if name == "CWD": + return { + "name": name, + "ek_func": ek_pr.maximum_consecutive_wet_days, + "xi_func": xclim.indicators.atmos.maximum_consecutive_wet_days, + "ek_args": {"ds": pr_opt, "freq": "MS"}, + "xi_args": {"pr": pr_opt, "freq": "MS"}, + } + + if name == "DTR": + return { + "name": name, + "ek_func": ek_temp.daily_temperature_range, + "xi_func": xclim.indicators.atmos.daily_temperature_range, + "ek_args": { + "ds": xr.merge([tasmax_opt, tasmin_opt]), + "freq": "MS", + }, + "xi_args": { + "tasmax": tasmax_opt, + "tasmin": tasmin_opt, + "freq": "MS", + }, + } + + if name == "HDD": + tas_opt = ((tasmax_opt + tasmin_opt) / 2).chunk({"time": -1}) + return { + "name": name, + "ek_func": ek_temp.heating_degree_days, + "xi_func": xclim.indicators.atmos.heating_degree_days, + "ek_args": {"ds": tas_opt.to_dataset(name="tas"), "freq": "MS"}, + "xi_args": {"tas": tas_opt, "freq": "MS"}, + } + + if name == "SDII": + return { + "name": name, + "ek_func": ek_pr.daily_pr_intensity, + "xi_func": xclim.indicators.atmos.daily_pr_intensity, + "ek_args": {"ds": pr_opt, "freq": "MS"}, + "xi_args": {"pr": pr_opt, "freq": "MS"}, + } + + raise ValueError(f"Unknown indicator: {name}") + + +# --------------------------------------------------------------------------- +# Main correctness test +# --------------------------------------------------------------------------- + + +@pytest.mark.integration +def test_indicator_correctness(indicator_config: dict[str, Any]) -> None: + """ + Verify that the earthkit indicator result matches the xclim result. + + Runs both calls in optimised mode (flox=True, time rechunked to -1) and + asserts: + + * Both results are non-empty xarray DataArrays. + * Both results contain at least one finite value. + * The results agree within a relative tolerance of 1 %. + + ## Parameters + + indicator_config : dict[str, Any] + Configuration dict produced by the ``indicator_config`` fixture. + Keys: ``name``, ``ek_func``, ``xi_func``, ``ek_args``, ``xi_args``. + + ## Returns + + None + Asserts raise on failure; the test passes silently on success. + """ + name: str = indicator_config["name"] + + # --- Run earthkit --- + ek_result = _run_indicator( + indicator_config["ek_func"], + indicator_config["ek_args"], + use_flox=True, + ) + if hasattr(ek_result, "compute"): + ek_result = ek_result.compute() + if isinstance(ek_result, xr.Dataset): + # earthkit may return a Dataset; take the first data variable + ek_da: xr.DataArray = ek_result[list(ek_result.data_vars)[0]] + else: + ek_da = ek_result # type: ignore[assignment] + + # --- Run xclim --- + xc_result = _run_indicator( + indicator_config["xi_func"], + indicator_config["xi_args"], + use_flox=True, + ) + if hasattr(xc_result, "compute"): + xc_result = xc_result.compute() + xc_da: xr.DataArray = xc_result + + # --- Assertions: both results must be non-empty DataArrays --- + assert isinstance(ek_da, xr.DataArray), ( + f"[{name}] earthkit result is not a DataArray: {type(ek_da)}" + ) + assert isinstance(xc_da, xr.DataArray), ( + f"[{name}] xclim result is not a DataArray: {type(xc_da)}" + ) + assert ek_da.size > 0, f"[{name}] earthkit result is empty" + assert xc_da.size > 0, f"[{name}] xclim result is empty" + assert ek_da.notnull().any().item(), f"[{name}] earthkit result is all-NaN" + assert xc_da.notnull().any().item(), f"[{name}] xclim result is all-NaN" + + # --- Assertions: results agree within 1 % --- + xr.testing.assert_allclose(ek_da, xc_da, rtol=1e-2) From 6172f83291346ed6700b7af681df6b96951e48e4 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 24 Feb 2026 09:13:28 +0100 Subject: [PATCH 28/47] chore: remove pixi sections in pyproject.toml. --- pyproject.toml | 36 ------------------------------------ 1 file changed, 36 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 9a44f6b..16cd164 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,42 +55,6 @@ docs = [ [tool.coverage.run] branch = true -[tool.pixi.dependencies] -ipykernel = ">=7.1.0,<8" -pre-commit = "*" -pytest = "*" -pytest-cov = "*" -pytest-mock = ">=3.15.1,<4" -python = "3.12.*" - -# ---- Pixi environments ---- -[tool.pixi.environments] -dev = {features = ["dev"]} -docs = {features = ["docs"]} - -[tool.pixi.feature.docs.dependencies] -nbsphinx = ">=0.9.8,<0.10" -pandoc = "*" -sphinx = "*" -sphinx-autoapi = ">=3.6.1,<4" -sphinx-rtd-theme = ">=3.0.2,<4" - -[tool.pixi.feature.docs.tasks] -docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_build" - -[tool.pixi.pypi-dependencies] -earthkit-plots = ">=0.5.0" - -[tool.pixi.tasks] -qa = "pre-commit run --all-files" -type-check = "python -m mypy . --no-namespace-packages" -unit-tests = "python -m pytest -vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" - -# ---- Pixi CONFIGURATION ---- -[tool.pixi.workspace] -channels = ["conda-forge"] -platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] - [tool.pytest.ini_options] addopts = "-vv --cov=. --cov-report=html --doctest-glob='*.md' --doctest-glob='*.rst'" From aeb95c8b12ede7e86f0496109e8c869b9bb2c121 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 24 Feb 2026 09:47:44 +0100 Subject: [PATCH 29/47] chore: update pixi environment dependencies and lock file. (earthkit ata and utils decorators) --- pixi.lock | 2245 +++++++++++++++++++---------------------------------- pixi.toml | 3 +- 2 files changed, 794 insertions(+), 1454 deletions(-) diff --git a/pixi.lock b/pixi.lock index 66a8711..d43d129 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,9 +9,6 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.13.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.3-hef928c7_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.6-hb03c661_0.conda @@ -31,7 +28,6 @@ environments: - 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version: 0.18.5.dev54+ga51125492 + version: 0.19.0 + sha256: 3697238beeeec94209ecb86ac133bdd6c3f66dad734955edd3c0fd07bc5875e1 requires_dist: - cfgrib>=0.9.10.1 - dask - deprecation - - earthkit-meteo>=0.0.1 - - earthkit-utils>=0.1.1 + - earthkit-meteo>=0.6 + - earthkit-utils>=0.2 - eccodes>=1.7 - entrypoints - filelock @@ -5706,7 +5707,7 @@ packages: - lru-dict - markdown - multiurl>=0.3.3 - - netcdf4!=1.7.4 + - netcdf4 - pandas - pdbufr>=0.11 - pyyaml @@ -5754,16 +5755,18 @@ packages: - hda>=2.22 ; extra == 'wekeo' - zarr>=3 ; extra == 'zarr' requires_python: '>=3.10' -- pypi: https://files.pythonhosted.org/packages/c0/5c/b2f6d6221834f8df6912b8623bea7070b5cb593d098a606b376badc21678/earthkit_meteo-0.5.1-py3-none-any.whl +- pypi: https://files.pythonhosted.org/packages/34/eb/c10deda542516f679a20f1cb641a13094fc9e80bc13e54f4cebcfb9f8173/earthkit_meteo-0.6.1-py3-none-any.whl name: earthkit-meteo - version: 0.5.1 - sha256: 02ae1ed7471749b3ee18b286a84a7f41e2bf3cdb54f923928f455eb4ecb988ca + version: 0.6.1 + sha256: fcb425e23d1827d2fd85ad4b31bfd41eb8f1dcfc7fb78da4b03d69037e8e1d49 requires_dist: - - earthkit-utils<0.2 + - earthkit-utils>=0.2 - numpy + - cupy ; extra == 'gpu' + - torch ; extra == 'gpu' - pytest ; extra == 'test' - pytest-cov ; extra == 'test' - requires_python: '>=3.9' + requires_python: '>=3.10' - pypi: https://files.pythonhosted.org/packages/b9/de/d4453d754be718d06a393344dca41fb62be4e2d72bbf84b8be559870061c/earthkit_plots-0.5.2-py3-none-any.whl name: earthkit-plots version: 0.5.2 @@ -5794,12 +5797,11 @@ packages: version: 0.1.3 sha256: 676560092a1e2956ec396b9536cd7256d5e7eb893db6e044c127341ee5dc15b8 requires_python: '>=3.8' -- pypi: git+https://github.com/ecmwf/earthkit-utils.git?branch=feature%2Fproc-hackathon-units#fc59c86e4114067bd78e12d64fae08e13b0d7578 +- pypi: git+https://github.com/ecmwf/earthkit-utils.git?branch=develop#125dc1974651720e2fa89bd1bcc1ac7bc78df58a name: earthkit-utils - version: 0.1.3.dev73 + version: 0.2.2.dev4 requires_dist: - array-api-compat - - pint - numpy ; extra == 'dev' - pytest ; extra == 'dev' - pytest-cov ; extra == 'dev' @@ -5811,6 +5813,7 @@ packages: - sphinx-issues ; extra == 'docs' - sphinx-rtd-theme ; extra == 'docs' - sphinx-tabs ; extra == 'docs' + - earthkit-data ; extra == 'test' - nbconvert ; extra == 'test' - nbformat ; extra == 'test' - pytest ; extra == 'test' diff --git a/pixi.toml b/pixi.toml index 233ad9c..4032bb3 100644 --- a/pixi.toml +++ b/pixi.toml @@ -34,9 +34,9 @@ docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_bu [pypi-dependencies] earthkit-climate = {path = ".", editable = true} -earthkit-data = {git = "https://github.com/ecmwf/earthkit-data.git", branch = "883-hackathon-20260127-inputs-transforms-decorator-freeze"} +earthkit-data = ">=0.17.0" earthkit-plots = ">=0.5.0" -earthkit-utils = {git = "https://github.com/ecmwf/earthkit-utils.git", branch = "feature/proc-hackathon-units"} +earthkit-utils = {git = "https://github.com/ecmwf/earthkit-utils.git", branch = "develop"} [tasks] qa = "pre-commit run --all-files" From 4caecdf917a6c373aa04b8fd27017a56844c9568 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 25 Feb 2026 11:44:07 +0100 Subject: [PATCH 31/47] refactor: remove xclim wrapper API and associated utility modules for conversions, units, and provenance. --- src/earthkit/climate/__init__.py | 5 +- src/earthkit/climate/api/__init__.py | 7 - src/earthkit/climate/api/wrapper.py | 77 - .../climate/indicators/precipitation.py | 2643 ++++++--- .../climate/indicators/temperature.py | 4778 ++++++++++++----- src/earthkit/climate/utils/conversions.py | 133 - src/earthkit/climate/utils/provenance.py | 58 - src/earthkit/climate/utils/units.py | 57 - tests/unit/conftest.py | 49 - tests/unit/indicators/test_precipitation.py | 36 +- tests/unit/indicators/test_temperature.py | 59 +- tests/unit/utils/test_conversions.py | 75 - tests/unit/utils/test_provenance.py | 121 - tests/unit/utils/test_units.py | 87 - .../unit/wrapper/test_wrap_xclim_indicator.py | 82 - tools/xclim_wrappers_generator.py | 198 +- 16 files changed, 5675 insertions(+), 2790 deletions(-) delete mode 100644 src/earthkit/climate/api/__init__.py delete mode 100644 src/earthkit/climate/api/wrapper.py delete mode 100644 src/earthkit/climate/utils/conversions.py delete mode 100644 src/earthkit/climate/utils/provenance.py delete mode 100644 src/earthkit/climate/utils/units.py delete mode 100644 tests/unit/utils/test_conversions.py delete mode 100644 tests/unit/utils/test_provenance.py delete mode 100644 tests/unit/utils/test_units.py delete mode 100644 tests/unit/wrapper/test_wrap_xclim_indicator.py diff --git a/src/earthkit/climate/__init__.py b/src/earthkit/climate/__init__.py index 0a0f2f2..11bb8d3 100644 --- a/src/earthkit/climate/__init__.py +++ b/src/earthkit/climate/__init__.py @@ -14,8 +14,5 @@ # Local copy or not installed with setuptools __version__ = "999" -# Avoid importing optional heavy submodules at package import time to keep -# test collection lightweight and not require optional dependencies (e.g., xclim). -from .utils import conversions -__all__ = [conversions, __version__] +__all__ = [__version__] diff --git a/src/earthkit/climate/api/__init__.py b/src/earthkit/climate/api/__init__.py deleted file mode 100644 index b86dcd7..0000000 --- a/src/earthkit/climate/api/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. diff --git a/src/earthkit/climate/api/wrapper.py b/src/earthkit/climate/api/wrapper.py deleted file mode 100644 index 417d500..0000000 --- a/src/earthkit/climate/api/wrapper.py +++ /dev/null @@ -1,77 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -from functools import wraps -from typing import Any, Callable, Dict, Union - -import xarray as xr - -from earthkit.climate.utils import conversions, provenance, units - - -def wrap_xclim_indicator(xclim_fn: Callable) -> Callable: - """ - Wraps an xclim indicator to handle Earthkit inputs and unit alignment. - - Parameters - ---------- - xclim_fn : Callable - The xclim indicator function to be wrapped. - - Returns - ------- - Callable - The wrapped function which accepts Earthkit inputs. - """ - - @wraps(xclim_fn) - def wrapper( - earthkit_input: Union[conversions.EarthkitData, xr.Dataset], - *args, - **kwargs, - ) -> conversions.EarthkitData: - """ - Wrapper function that processes Earthkit inputs and calls the xclim indicator. - - Parameters - ---------- - earthkit_input : Union[conversions.EarthkitData, xr.Dataset] - The input data, either as an Earthkit object or an xarray Dataset. - *args - Variable length argument list passed to the xclim indicator. - **kwargs - Arbitrary keyword arguments passed to the xclim indicator. - - Returns - ------- - conversions.EarthkitData - The result of the indicator calculation wrapped as an Earthkit object. - """ - metadata: Dict[str, Any] = {} - - # --- STEP 1: Load & Standardize Main Data --- - # Convert Earthkit object to xarray Dataset - dataset, metadata = conversions.to_xarray_dataset(earthkit_input, metadata) - - # Standardize units for common variables to Kelvin - for var in ["tas", "tasmin", "tasmax"]: - if var in dataset: - dataset = units.ensure_units(dataset, var, "degC", strict=False) - if "pr" in dataset: - dataset = units.ensure_units(dataset, "pr", "mm/day", strict=False) - - # --- STEP 2: Execution --- - # We pass the single merged dataset (ds) and the variable name mappings - output_dataset: xr.Dataset = xclim_fn(ds=dataset, *args, **kwargs) - - # --- STEP 3: Provenance & Output --- - metadata = provenance.add_indicator_provenance(metadata, xclim_fn, dataset, **kwargs) - - return conversions.to_earthkit_field(output_dataset, metadata) - - return wrapper diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index 1561b57..fa43c82 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -8,19 +8,25 @@ """Precipitation indices.""" -from typing import Any +from typing import Any, Literal import xarray import xclim.indicators.atmos +from earthkit.utils.decorators.format_handlers import format_handler -import earthkit.climate.utils.conversions as conversions -from earthkit.climate.api.wrapper import wrap_xclim_indicator +# from earthkit.climate.utils.decorators import metadata_handler +@format_handler() +# @metadata_handler(xclim.indicators.atmos.antecedent_precipitation_index) def antecedent_precipitation_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + window: int = 7, + p_exp: float = 0.935, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Antecedent precipitation index. @@ -31,29 +37,46 @@ def antecedent_precipitation_index( - api: mm - This function wraps `xclim.indicators.atmos.antecedent_precipitation_index `_. + This function wraps `xclim.indicators.atmos.antecedent_precipitation_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation data. + window : int + Window for the days of precipitation data to be weighted and summed, default is 7. + p_exp : float + Weighting exponent, default is 0.935. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.antecedent_precipitation_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.antecedent_precipitation_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.antecedent_precipitation_index( + pr=pr, + window=window, + p_exp=p_exp, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_dry_days) def maximum_consecutive_dry_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum consecutive dry days. @@ -64,29 +87,60 @@ def maximum_consecutive_dry_days( - cdd: days - This function wraps `xclim.indicators.atmos.maximum_consecutive_dry_days `_. + This function wraps `xclim.indicators.atmos.maximum_consecutive_dry_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + thresh : Any + Threshold precipitation on which to base evaluation. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.maximum_consecutive_dry_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_dry_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_dry_days( + pr=pr, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cffwis_indices) def cffwis_indices( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + sfcWind: xarray.DataArray | str = 'sfcWind', + hurs: xarray.DataArray | str = 'hurs', + lat: xarray.DataArray | str = 'lat', + snd: xarray.DataArray | str | None = None, + ffmc0: xarray.DataArray | str | None = None, + dmc0: xarray.DataArray | str | None = None, + dc0: xarray.DataArray | str | None = None, + season_mask: xarray.DataArray | str | None = None, + *, + season_method: str | None = None, + overwintering: bool = False, + dry_start: str | None = None, + initial_start_up: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Canadian fire weather index system indices. @@ -103,29 +157,86 @@ def cffwis_indices( - bui: dimensionless - fwi: dimensionless - This function wraps `xclim.indicators.atmos.cffwis_indices `_. + This function wraps `xclim.indicators.atmos.cffwis_indices + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Noon temperature. + pr : xarray.DataArray | str + Rain fall in open over previous 24 hours, at noon. + sfcWind : xarray.DataArray | str + Noon wind speed. + hurs : xarray.DataArray | str + Noon relative humidity. + lat : xarray.DataArray | str + Latitude coordinate. + snd : xarray.DataArray | str | None + Noon snow depth, only used if `season_method='LA08'` is passed. + ffmc0 : xarray.DataArray | str | None + Initial values of the fine fuel moisture code. + dmc0 : xarray.DataArray | str | None + Initial values of the Duff moisture code. + dc0 : xarray.DataArray | str | None + Initial values of the drought code. + season_mask : xarray.DataArray | str | None + Boolean mask, True where/when the fire season is active. + season_method : str | None + How to compute the start-up and shutdown of the fire season. If "None", no start-ups + or shutdowns are computed, similar to the R fire function. Ignored if `season_mask` + is given. + overwintering : bool + Whether to activate DC overwintering or not. If True, either season_method or + season_mask must be given. + dry_start : str | None + Whether to activate the DC and DMC "dry start" mechanism or not, see + :py:func:`fire_weather_ufunc`. + initial_start_up : bool + If True (default), gridpoints where the fire season is active on the first timestep + go through a start_up phase for that time step. Otherwise, previous codes must be + given as a continuing fire season is assumed for those points. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cffwis_indices`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cffwis_indices) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cffwis_indices( + tas=tas, + pr=pr, + sfcWind=sfcWind, + hurs=hurs, + lat=lat, + snd=snd, + ffmc0=ffmc0, + dmc0=dmc0, + dc0=dc0, + season_mask=season_mask, + season_method=season_method, + overwintering=overwintering, + dry_start=dry_start, + initial_start_up=initial_start_up, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_and_dry_days) def cold_and_dry_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold and dry days. @@ -136,29 +247,53 @@ def cold_and_dry_days( - cold_and_dry_days: days - This function wraps `xclim.indicators.atmos.cold_and_dry_days `_. + This function wraps `xclim.indicators.atmos.cold_and_dry_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature values. + pr : xarray.DataArray | str + Daily precipitation. + tas_per : xarray.DataArray | str + First quartile of daily mean temperature computed by month. + pr_per : xarray.DataArray | str + First quartile of daily total precipitation computed by month. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_and_dry_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_dry_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_and_dry_days( + tas=tas, + pr=pr, + tas_per=tas_per, + pr_per=pr_per, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_and_wet_days) def cold_and_wet_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold and wet days. @@ -169,29 +304,52 @@ def cold_and_wet_days( - cold_and_wet_days: days - This function wraps `xclim.indicators.atmos.cold_and_wet_days `_. + This function wraps `xclim.indicators.atmos.cold_and_wet_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature values. + pr : xarray.DataArray | str + Daily precipitation. + tas_per : xarray.DataArray | str + First quartile of daily mean temperature computed by month. + pr_per : xarray.DataArray | str + Third quartile of daily total precipitation computed by month. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_and_wet_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_and_wet_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_and_wet_days( + tas=tas, + pr=pr, + tas_per=tas_per, + pr_per=pr_per, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_wet_days) def maximum_consecutive_wet_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum consecutive wet days. @@ -202,29 +360,52 @@ def maximum_consecutive_wet_days( - cwd: days - This function wraps `xclim.indicators.atmos.maximum_consecutive_wet_days `_. + This function wraps `xclim.indicators.atmos.maximum_consecutive_wet_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + thresh : Any + Threshold precipitation on which to base evaluation. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.maximum_consecutive_wet_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_wet_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_wet_days( + pr=pr, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.days_over_precip_doy_thresh) def days_over_precip_doy_thresh( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with precipitation above a given daily percentile. @@ -235,29 +416,63 @@ def days_over_precip_doy_thresh( - days_over_precip_doy_thresh: days - This function wraps `xclim.indicators.atmos.days_over_precip_doy_thresh `_. + This function wraps `xclim.indicators.atmos.days_over_precip_doy_thresh + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + pr_per : xarray.DataArray | str + Percentile of wet day precipitation flux. Either computed daily (one value per day + of year) or computed over a period (one value per spatial point). + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.days_over_precip_doy_thresh`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_doy_thresh) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.days_over_precip_doy_thresh( + pr=pr, + pr_per=pr_per, + thresh=thresh, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.days_over_precip_thresh) def days_over_precip_thresh( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with precipitation above a given percentile. @@ -268,29 +483,61 @@ def days_over_precip_thresh( - days_over_precip_thresh: days - This function wraps `xclim.indicators.atmos.days_over_precip_thresh `_. + This function wraps `xclim.indicators.atmos.days_over_precip_thresh + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + pr_per : xarray.DataArray | str + Percentile of wet day precipitation flux. Either computed daily (one value per day + of year) or computed over a period (one value per spatial point). + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.days_over_precip_thresh`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_over_precip_thresh) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.days_over_precip_thresh( + pr=pr, + pr_per=pr_per, + thresh=thresh, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.days_with_snow) def days_with_snow( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + prsn: xarray.DataArray | str = 'prsn', + *, + low: Any = '0 kg m-2 s-1', + high: Any = '1E6 kg m-2 s-1', + freq: str = 'YS-JUL', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with snowfall. @@ -300,29 +547,55 @@ def days_with_snow( - days_with_snow: days - This function wraps `xclim.indicators.atmos.days_with_snow `_. + This function wraps `xclim.indicators.atmos.days_with_snow + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + prsn : xarray.DataArray | str + Snowfall flux. + low : Any + Minimum threshold snowfall flux or liquid water equivalent snowfall rate. + high : Any + Maximum threshold snowfall flux or liquid water equivalent snowfall rate. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.days_with_snow`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.days_with_snow) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.days_with_snow( + prsn=prsn, + low=low, + high=high, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.drought_code) def drought_code( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + lat: xarray.DataArray | str = 'lat', + snd: xarray.DataArray | str | None = None, + dc0: xarray.DataArray | str | None = None, + season_mask: xarray.DataArray | str | None = None, + *, + season_method: str | None = None, + overwintering: bool = False, + dry_start: str | None = None, + initial_start_up: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Daily drought code. @@ -333,29 +606,72 @@ def drought_code( - dc: dimensionless - This function wraps `xclim.indicators.atmos.drought_code `_. + This function wraps `xclim.indicators.atmos.drought_code + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Noon temperature. + pr : xarray.DataArray | str + Rain fall in open over previous 24 hours, at noon. + lat : xarray.DataArray | str + Latitude coordinate. + snd : xarray.DataArray | str | None + Noon snow depth. + dc0 : xarray.DataArray | str | None + Initial values of the drought code. + season_mask : xarray.DataArray | str | None + Boolean mask, True where/when the fire season is active. + season_method : str | None + How to compute the start-up and shutdown of the fire season. If "None", no start-ups + or shutdowns are computed, similar to the R fire function. Ignored if `season_mask` + is given. + overwintering : bool + Whether to activate DC overwintering or not. If True, either season_method or + season_mask must be given. + dry_start : str | None + Whether to activate the DC and DMC "dry start" mechanism and which method to use. + See :py:func:`fire_weather_ufunc`. + initial_start_up : bool + If True (default), grid points where the fire season is active on the first timestep + go through a start_up phase for that time step. Otherwise, previous codes must be + given as a continuing fire season is assumed for those points. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.drought_code`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.drought_code) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.drought_code( + tas=tas, + pr=pr, + lat=lat, + snd=snd, + dc0=dc0, + season_mask=season_mask, + season_method=season_method, + overwintering=overwintering, + dry_start=dry_start, + initial_start_up=initial_start_up, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.griffiths_drought_factor) def griffiths_drought_factor( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + smd: xarray.DataArray | str = 'smd', + *, + limiting_func: str = 'xlim', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Griffiths drought factor based on the soil moisture deficit. @@ -367,29 +683,55 @@ def griffiths_drought_factor( - df: dimensionless - This function wraps `xclim.indicators.atmos.griffiths_drought_factor `_. + This function wraps `xclim.indicators.atmos.griffiths_drought_factor + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Total rainfall over previous 24 hours [mm/day]. + smd : xarray.DataArray | str + Daily soil moisture deficit (often KBDI) [mm/day]. + limiting_func : str + How to limit the values of the drought factor. If "xlim" (default), use equation + (14) in :cite:t:`ffdi-finkele_2006`. If "discrete", use equation Eq (13) in + :cite:t:`ffdi-finkele_2006`, but with the lower limit of each category bound + adjusted to match the upper limit of the previous bound. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.griffiths_drought_factor`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.griffiths_drought_factor) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.griffiths_drought_factor( + pr=pr, + smd=smd, + limiting_func=limiting_func, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.duff_moisture_code) def duff_moisture_code( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + hurs: xarray.DataArray | str = 'hurs', + lat: xarray.DataArray | str = 'lat', + snd: xarray.DataArray | str | None = None, + dmc0: xarray.DataArray | str | None = None, + season_mask: xarray.DataArray | str | None = None, + *, + season_method: str | None = None, + dry_start: str | None = None, + initial_start_up: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Duff moisture code (fwi component). @@ -401,29 +743,72 @@ def duff_moisture_code( - dmc: dimensionless - This function wraps `xclim.indicators.atmos.duff_moisture_code `_. + This function wraps `xclim.indicators.atmos.duff_moisture_code + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Noon temperature. + pr : xarray.DataArray | str + Rain fall in open over previous 24 hours, at noon. + hurs : xarray.DataArray | str + Noon relative humidity. + lat : xarray.DataArray | str + Latitude coordinate. + snd : xarray.DataArray | str | None + Noon snow depth. + dmc0 : xarray.DataArray | str | None + Initial values of the duff moisture code. + season_mask : xarray.DataArray | str | None + Boolean mask, True where/when the fire season is active. + season_method : str | None + How to compute the start-up and shutdown of the fire season. If "None", no start-ups + or shutdowns are computed, similar to the R fire function. Ignored if `season_mask` + is given. + dry_start : str | None + Whether to activate the DC and DMC "dry start" mechanism and which method to use. + See :py:func:`fire_weather_ufunc`. + initial_start_up : bool + If True (default), grid points where the fire season is active on the first timestep + go through a start_up phase for that time step. Otherwise, previous codes must be + given as a continuing fire season is assumed for those points. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.duff_moisture_code`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.duff_moisture_code) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.duff_moisture_code( + tas=tas, + pr=pr, + hurs=hurs, + lat=lat, + snd=snd, + dmc0=dmc0, + season_mask=season_mask, + season_method=season_method, + dry_start=dry_start, + initial_start_up=initial_start_up, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.dry_days) def dry_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '0.2 mm/d', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of dry days. @@ -433,29 +818,51 @@ def dry_days( - dry_days: days - This function wraps `xclim.indicators.atmos.dry_days `_. + This function wraps `xclim.indicators.atmos.dry_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Threshold precipitation on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dry_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.dry_days( + pr=pr, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.dry_spell_frequency) def dry_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 3, + freq: str = 'YS', + resample_before_rl: bool = True, + op: Literal['sum', 'max', 'min', 'mean'] = 'sum', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Dry spell frequency. @@ -466,29 +873,63 @@ def dry_spell_frequency( - dry_spell_frequency: dimensionless - This function wraps `xclim.indicators.atmos.dry_spell_frequency `_. + This function wraps `xclim.indicators.atmos.dry_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Precipitation amount under which a period is considered dry. The value against which + the threshold is compared depends on `op`. + window : int + Minimum length of the spells. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + op : Literal['sum', 'max', 'min', 'mean'] + Operation to perform on the window. Default is "sum", which checks that the sum of + accumulated precipitation over the whole window is less than the threshold. "max" + checks that the maximal daily precipitation amount within the window is less than + the threshold. This is the same as verifying that each individual day is below the + threshold. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dry_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.dry_spell_frequency( + pr=pr, + thresh=thresh, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.dry_spell_max_length) def dry_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 1, + op: Literal['max', 'sum'] = 'sum', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Dry spell maximum length. @@ -499,29 +940,58 @@ def dry_spell_max_length( - dry_spell_max_length: days - This function wraps `xclim.indicators.atmos.dry_spell_max_length `_. + This function wraps `xclim.indicators.atmos.dry_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Accumulated precipitation value under which a period is considered dry. + window : int + Number of days when the maximum or accumulated precipitation is under the threshold. + op : Literal['max', 'sum'] + Reduce operation. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dry_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.dry_spell_max_length( + pr=pr, + thresh=thresh, + window=window, + op=op, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.dry_spell_total_length) def dry_spell_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 3, + op: Literal['sum', 'max', 'min', 'mean'] = 'sum', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Dry spell total length. @@ -532,29 +1002,61 @@ def dry_spell_total_length( - dry_spell_total_length: days - This function wraps `xclim.indicators.atmos.dry_spell_total_length `_. + This function wraps `xclim.indicators.atmos.dry_spell_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Accumulated precipitation value under which a period is considered dry. + window : int + Number of days when the maximum or accumulated precipitation is under the threshold. + op : Literal['sum', 'max', 'min', 'mean'] + Operation to perform on the window. Default is "sum", which checks that the sum of + accumulated precipitation over the whole window is less than the threshold. "max" + checks that the maximal daily precipitation amount within the window is less than + the threshold. This is the same as verifying that each individual day is below the + threshold. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dry_spell_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dry_spell_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.dry_spell_total_length( + pr=pr, + thresh=thresh, + window=window, + op=op, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.dryness_index) def dryness_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + evspsblpot: xarray.DataArray | str = 'evspsblpot', + lat: xarray.DataArray | str | None = None, + *, + wo: Any = '200 mm', + freq: Literal['YS', 'YS-JAN'] = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Dryness index. @@ -566,29 +1068,52 @@ def dryness_index( - dryness_index: mm - This function wraps `xclim.indicators.atmos.dryness_index `_. + This function wraps `xclim.indicators.atmos.dryness_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Precipitation. + evspsblpot : xarray.DataArray | str + Potential evapotranspiration. + lat : xarray.DataArray | str | None + Latitude coordinate as an array, float or string. If None, a CF-conformant + "latitude" field must be available within the passed DataArray. + wo : Any + The initial soil water reserve accessible to root systems [length]. Default: 200 mm. + freq : Literal['YS', 'YS-JAN'] + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.dryness_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.dryness_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.dryness_index( + pr=pr, + evspsblpot=evspsblpot, + lat=lat, + wo=wo, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) def mcarthur_forest_fire_danger_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + drought_factor: xarray.DataArray | str = 'drought_factor', + tasmax: xarray.DataArray | str = 'tasmax', + hurs: xarray.DataArray | str = 'hurs', + sfcWind: xarray.DataArray | str = 'sfcWind', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Mcarthur forest fire danger index (ffdi) mark 5. @@ -598,29 +1123,57 @@ def mcarthur_forest_fire_danger_index( - ffdi: dimensionless - This function wraps `xclim.indicators.atmos.mcarthur_forest_fire_danger_index `_. + This function wraps `xclim.indicators.atmos.mcarthur_forest_fire_danger_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + drought_factor : xarray.DataArray | str + The drought factor, often the daily Griffiths drought factor (see + :py:func:`griffiths_drought_factor`). + tasmax : xarray.DataArray | str + The daily maximum temperature near the surface, or similar. Different applications + have used different inputs here, including the previous/current day's maximum daily + temperature at a height of 2m, and the daily mean temperature at a height of 2m. + hurs : xarray.DataArray | str + The relative humidity near the surface and near the time of the maximum daily + temperature, or similar. Different applications have used different inputs here, + including the mid-afternoon relative humidity at a height of 2m, and the daily mean + relative humidity at a height of 2m. + sfcWind : xarray.DataArray | str + The wind speed near the surface and near the time of the maximum daily temperature, + or similar. Different applications have used different inputs here, including the + mid-afternoon wind speed at a height of 10m, and the daily mean wind speed at a + height of 10m. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.mcarthur_forest_fire_danger_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.mcarthur_forest_fire_danger_index( + drought_factor=drought_factor, + tasmax=tasmax, + hurs=hurs, + sfcWind=sfcWind, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_snowfall) def first_snowfall( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + prsn: xarray.DataArray | str = 'prsn', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day where snowfall exceeded a given threshold. @@ -631,29 +1184,49 @@ def first_snowfall( - first_snowfall: dimensionless - This function wraps `xclim.indicators.atmos.first_snowfall `_. + This function wraps `xclim.indicators.atmos.first_snowfall + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + prsn : xarray.DataArray | str + Snowfall flux. + thresh : Any + Threshold snowfall flux or liquid water equivalent snowfall rate. (default: 1 + mm/day). + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_snowfall`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_snowfall) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_snowfall( + prsn=prsn, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.fraction_over_precip_doy_thresh) def fraction_over_precip_doy_thresh( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Fraction of precipitation due to wet days with daily precipitation over a given daily percentile. @@ -665,29 +1238,63 @@ def fraction_over_precip_doy_thresh( - fraction_over_precip_doy_thresh: dimensionless - This function wraps `xclim.indicators.atmos.fraction_over_precip_doy_thresh `_. + This function wraps `xclim.indicators.atmos.fraction_over_precip_doy_thresh + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + pr_per : xarray.DataArray | str + Percentile of wet day precipitation flux. Either computed daily (one value per day + of year) or computed over a period (one value per spatial point). + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.fraction_over_precip_doy_thresh`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_doy_thresh) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.fraction_over_precip_doy_thresh( + pr=pr, + pr_per=pr_per, + thresh=thresh, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.fraction_over_precip_thresh) def fraction_over_precip_thresh( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Fraction of precipitation due to wet days with daily precipitation over a given percentile. @@ -699,29 +1306,62 @@ def fraction_over_precip_thresh( - fraction_over_precip_thresh: dimensionless - This function wraps `xclim.indicators.atmos.fraction_over_precip_thresh `_. + This function wraps `xclim.indicators.atmos.fraction_over_precip_thresh + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + pr_per : xarray.DataArray | str + Percentile of wet day precipitation flux. Either computed daily (one value per day + of year) or computed over a period (one value per spatial point). + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.fraction_over_precip_thresh`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fraction_over_precip_thresh) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.fraction_over_precip_thresh( + pr=pr, + pr_per=pr_per, + thresh=thresh, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.high_precip_low_temp) def high_precip_low_temp( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + pr_thresh: Any = '0.4 mm/d', + tas_thresh: Any = '-0.2 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with precipitation and cold temperature. @@ -732,29 +1372,51 @@ def high_precip_low_temp( - high_precip_low_temp: days - This function wraps `xclim.indicators.atmos.high_precip_low_temp `_. + This function wraps `xclim.indicators.atmos.high_precip_low_temp + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Daily mean, minimum or maximum temperature. + pr_thresh : Any + Precipitation threshold to exceed. + tas_thresh : Any + Temperature threshold not to exceed. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.high_precip_low_temp`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.high_precip_low_temp) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.high_precip_low_temp( + pr=pr, + tas=tas, + pr_thresh=pr_thresh, + tas_thresh=tas_thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.keetch_byram_drought_index) def keetch_byram_drought_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tasmax: xarray.DataArray | str = 'tasmax', + pr_annual: xarray.DataArray | str = 'pr_annual', + kbdi0: xarray.DataArray | str | None = None, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Keetch-byram drought index (kbdi) for soil moisture deficit. @@ -768,29 +1430,49 @@ def keetch_byram_drought_index( - kbdi: mm/day - This function wraps `xclim.indicators.atmos.keetch_byram_drought_index `_. + This function wraps `xclim.indicators.atmos.keetch_byram_drought_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Total rainfall over previous 24 hours [mm/day]. + tasmax : xarray.DataArray | str + Maximum temperature near the surface over previous 24 hours [degC]. + pr_annual : xarray.DataArray | str + Mean (over years) annual accumulated rainfall [mm/year]. + kbdi0 : xarray.DataArray | str | None + Previous KBDI values used to initialise the KBDI calculation [mm/day]. Defaults to + 0. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.keetch_byram_drought_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.keetch_byram_drought_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.keetch_byram_drought_index( + pr=pr, + tasmax=tasmax, + pr_annual=pr_annual, + kbdi0=kbdi0, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.last_snowfall) def last_snowfall( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + prsn: xarray.DataArray | str = 'prsn', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Last day where snowfall exceeded a given threshold. @@ -801,29 +1483,47 @@ def last_snowfall( - last_snowfall: dimensionless - This function wraps `xclim.indicators.atmos.last_snowfall `_. + This function wraps `xclim.indicators.atmos.last_snowfall + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + prsn : xarray.DataArray | str + Snowfall flux. + thresh : Any + Threshold snowfall flux or liquid water equivalent snowfall rate (default: 1 + mm/day). + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.last_snowfall`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_snowfall) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.last_snowfall( + prsn=prsn, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.liquid_precip_ratio) def liquid_precip_ratio( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'QS-DEC', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Fraction of liquid to total precipitation. @@ -835,29 +1535,49 @@ def liquid_precip_ratio( - liquid_precip_ratio: dimensionless - This function wraps `xclim.indicators.atmos.liquid_precip_ratio `_. + This function wraps `xclim.indicators.atmos.liquid_precip_ratio + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature under which precipitation is assumed to be solid. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.liquid_precip_ratio`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_ratio) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.liquid_precip_ratio( + pr=pr, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.liquid_precip_average) def liquid_precip_average( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Averaged liquid precipitation. @@ -868,29 +1588,49 @@ def liquid_precip_average( - liquidprcpavg: mm - This function wraps `xclim.indicators.atmos.liquid_precip_average `_. + This function wraps `xclim.indicators.atmos.liquid_precip_average + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean, maximum or minimum daily temperature. + thresh : Any + Threshold of `tas` over which the precipication is assumed to be liquid rain. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.liquid_precip_average`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_average) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.liquid_precip_average( + pr=pr, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.liquid_precip_accumulation) def liquid_precip_accumulation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Total accumulated liquid precipitation. @@ -901,29 +1641,48 @@ def liquid_precip_accumulation( - liquidprcptot: mm - This function wraps `xclim.indicators.atmos.liquid_precip_accumulation `_. + This function wraps `xclim.indicators.atmos.liquid_precip_accumulation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean, maximum or minimum daily temperature. + thresh : Any + Threshold of `tas` over which the precipication is assumed to be liquid rain. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.liquid_precip_accumulation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.liquid_precip_accumulation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.liquid_precip_accumulation( + pr=pr, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.max_n_day_precipitation_amount) def max_n_day_precipitation_amount( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum n-day total precipitation. @@ -933,29 +1692,45 @@ def max_n_day_precipitation_amount( - rx{window}day: mm - This function wraps `xclim.indicators.atmos.max_n_day_precipitation_amount `_. + This function wraps `xclim.indicators.atmos.max_n_day_precipitation_amount + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation values. + window : int + Window size in days. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.max_n_day_precipitation_amount`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_n_day_precipitation_amount) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.max_n_day_precipitation_amount( + pr=pr, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.max_pr_intensity) def max_pr_intensity( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum precipitation intensity over time window. @@ -965,29 +1740,45 @@ def max_pr_intensity( - max_pr_intensity: mm h-1 - This function wraps `xclim.indicators.atmos.max_pr_intensity `_. + This function wraps `xclim.indicators.atmos.max_pr_intensity + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Hourly precipitation values. + window : int + Window size in hours. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.max_pr_intensity`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_pr_intensity) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.max_pr_intensity( + pr=pr, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.precip_average) def precip_average( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Averaged precipitation (solid and liquid). @@ -1000,29 +1791,44 @@ def precip_average( - prcpavg: mm - This function wraps `xclim.indicators.atmos.precip_average `_. + This function wraps `xclim.indicators.atmos.precip_average + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + thresh : Any + Threshold of `tas` over which the precipication is assumed to be liquid rain. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.precip_average`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_average) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.precip_average( + pr=pr, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.precip_accumulation) def precip_accumulation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Total accumulated precipitation (solid and liquid). @@ -1035,29 +1841,44 @@ def precip_accumulation( - prcptot: mm - This function wraps `xclim.indicators.atmos.precip_accumulation `_. + This function wraps `xclim.indicators.atmos.precip_accumulation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.precip_accumulation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.precip_accumulation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.precip_accumulation( + pr=pr, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.rain_on_frozen_ground_days) def rain_on_frozen_ground_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '1 mm/d', + window: int = 7, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of rain on frozen ground days. @@ -1069,29 +1890,64 @@ def rain_on_frozen_ground_days( - rain_frzgr: days - This function wraps `xclim.indicators.atmos.rain_on_frozen_ground_days `_. + This function wraps `xclim.indicators.atmos.rain_on_frozen_ground_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Precipitation threshold to consider a day as a rain event. + window : int + Minimum number of days below freezing temperature needed to consider the ground + frozen. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.rain_on_frozen_ground_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_on_frozen_ground_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.rain_on_frozen_ground_days( + pr=pr, + tas=tas, + thresh=thresh, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.rain_season) def rain_season( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh_wet_start: Any = '25.0 mm', + window_wet_start: int = 3, + window_not_dry_start: int = 30, + thresh_dry_start: Any = '1.0 mm', + window_dry_start: int = 7, + method_dry_start: str = 'per_day', + date_min_start: str = '05-01', + date_max_start: str = '12-31', + thresh_dry_end: Any = '0.0 mm', + window_dry_end: int = 20, + method_dry_end: str = 'per_day', + date_min_end: str = '09-01', + date_max_end: str = '12-31', + freq: Any = 'YS-JAN', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Rain season. @@ -1106,29 +1962,100 @@ def rain_season( - rain_season_end: dimensionless - rain_season_length: days - This function wraps `xclim.indicators.atmos.rain_season `_. + This function wraps `xclim.indicators.atmos.rain_season + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Precipitation data. + thresh_wet_start : Any + Accumulated precipitation threshold associated with `window_wet_start`. + window_wet_start : int + Number of days when accumulated precipitation is above `thresh_wet_start`. Defines + the first condition to start the rain season. + window_not_dry_start : int + Number of days, after `window_wet_start` days, during which no dry period must be + found as a second and last condition to start the rain season. A dry sequence is + defined with `thresh_dry_start`, `window_dry_start` and `method_dry_start`. + thresh_dry_start : Any + Threshold length defining a dry day in the sequence related to `window_dry_start`. + window_dry_start : int + Number of days used to define a dry sequence in the start of the season. Daily + precipitations lower than `thresh_dry_start` during `window_dry_start` days are + considered a dry sequence. The precipitations must be lower than `thresh_dry_start` + for either every day in the sequence (`method_dry_start == "per_day"`) or for the + total (`method_dry_start == "total"`). + method_dry_start : str + Method used to define a dry sequence associated with `window_dry_start`. The + threshold `thresh_dry_start` is either compared to every daily precipitation + (`method_dry_start == "per_day"`) or to total precipitations (`method_dry_start == + "total"`) in the sequence `window_dry_start` days. + date_min_start : str + First day of year when season can start ("mm-dd"). + date_max_start : str + Last day of year when season can start ("mm-dd"). + thresh_dry_end : Any + Threshold length defining a dry day in the sequence related to `window_dry_end`. + window_dry_end : int + Number of days used to define a dry sequence in the end of the season. Daily + precipitations lower than `thresh_dry_end` during `window_dry_end` days are + considered a dry sequence. The precipitations must be lower than `thresh_dry_end` + for either every day in the sequence (`method_dry_end == "per_day"`) or for the + total (`method_dry_end == "total"`). + method_dry_end : str + Method used to define a dry sequence associated with `window_dry_end`. The threshold + `thresh_dry_end` is either compared to every daily precipitation (`method_dry_end == + "per_day"`) or to total precipitations (`method_dry_end == "total"`) in the sequence + `window_dry` days. + date_min_end : str + First day of year when season can end ("mm-dd"). + date_max_end : str + Last day of year when season can end ("mm-dd"). + freq : Any + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.rain_season`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rain_season) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.rain_season( + pr=pr, + thresh_wet_start=thresh_wet_start, + window_wet_start=window_wet_start, + window_not_dry_start=window_not_dry_start, + thresh_dry_start=thresh_dry_start, + window_dry_start=window_dry_start, + method_dry_start=method_dry_start, + date_min_start=date_min_start, + date_max_start=date_max_start, + thresh_dry_end=thresh_dry_end, + window_dry_end=window_dry_end, + method_dry_end=method_dry_end, + date_min_end=date_min_end, + date_max_end=date_max_end, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.rprctot) def rprctot( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + prc: xarray.DataArray | str = 'prc', + *, + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Proportion of accumulated precipitation arising from convective processes. @@ -1139,29 +2066,50 @@ def rprctot( - rprctot: dimensionless - This function wraps `xclim.indicators.atmos.rprctot `_. + This function wraps `xclim.indicators.atmos.rprctot + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + prc : xarray.DataArray | str + Daily convective precipitation. + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.rprctot`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.rprctot) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.rprctot( + pr=pr, + prc=prc, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.max_1day_precipitation_amount) def max_1day_precipitation_amount( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum 1-day total precipitation. @@ -1171,29 +2119,43 @@ def max_1day_precipitation_amount( - rx1day: mm/day - This function wraps `xclim.indicators.atmos.max_1day_precipitation_amount `_. + This function wraps `xclim.indicators.atmos.max_1day_precipitation_amount + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation values. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.max_1day_precipitation_amount`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_1day_precipitation_amount) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.max_1day_precipitation_amount( + pr=pr, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_pr_intensity) def daily_pr_intensity( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Simple daily intensity index. @@ -1203,29 +2165,48 @@ def daily_pr_intensity( - sdii: mm d-1 - This function wraps `xclim.indicators.atmos.daily_pr_intensity `_. + This function wraps `xclim.indicators.atmos.daily_pr_intensity + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.daily_pr_intensity`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_pr_intensity) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.daily_pr_intensity( + pr=pr, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.snowfall_frequency) def snowfall_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + prsn: xarray.DataArray | str = 'prsn', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Snowfall frequency. @@ -1236,29 +2217,46 @@ def snowfall_frequency( - snowfall_frequency: % - This function wraps `xclim.indicators.atmos.snowfall_frequency `_. + This function wraps `xclim.indicators.atmos.snowfall_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + prsn : xarray.DataArray | str + Snowfall flux. + thresh : Any + Threshold snowfall flux or liquid water equivalent snowfall rate (default: 1 + mm/day). + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.snowfall_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.snowfall_frequency( + prsn=prsn, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.snowfall_intensity) def snowfall_intensity( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + prsn: xarray.DataArray | str = 'prsn', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Snowfall intensity. @@ -1269,29 +2267,47 @@ def snowfall_intensity( - snowfall_intensity: mm/day - This function wraps `xclim.indicators.atmos.snowfall_intensity `_. + This function wraps `xclim.indicators.atmos.snowfall_intensity + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + prsn : xarray.DataArray | str + Snowfall flux. + thresh : Any + Threshold snowfall flux or liquid water equivalent snowfall rate (default: 1 + mm/day). + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.snowfall_intensity`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.snowfall_intensity) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.snowfall_intensity( + prsn=prsn, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.solid_precip_average) def solid_precip_average( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Averaged solid precipitation. @@ -1302,29 +2318,49 @@ def solid_precip_average( - solidprcpavg: mm - This function wraps `xclim.indicators.atmos.solid_precip_average `_. + This function wraps `xclim.indicators.atmos.solid_precip_average + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean, maximum or minimum daily temperature. + thresh : Any + Threshold of `tas` over which the precipication is assumed to be liquid rain. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.solid_precip_average`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_average) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.solid_precip_average( + pr=pr, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.solid_precip_accumulation) def solid_precip_accumulation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Total accumulated solid precipitation. @@ -1335,29 +2371,50 @@ def solid_precip_accumulation( - solidprcptot: mm - This function wraps `xclim.indicators.atmos.solid_precip_accumulation `_. + This function wraps `xclim.indicators.atmos.solid_precip_accumulation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Mean daily precipitation flux. + tas : xarray.DataArray | str + Mean, maximum or minimum daily temperature. + thresh : Any + Threshold of `tas` over which the precipication is assumed to be liquid rain. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.solid_precip_accumulation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.solid_precip_accumulation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.solid_precip_accumulation( + pr=pr, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.warm_and_dry_days) def warm_and_dry_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Warm and dry days. @@ -1368,29 +2425,53 @@ def warm_and_dry_days( - warm_and_dry_days: days - This function wraps `xclim.indicators.atmos.warm_and_dry_days `_. + This function wraps `xclim.indicators.atmos.warm_and_dry_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature values. + pr : xarray.DataArray | str + Daily precipitation. + tas_per : xarray.DataArray | str + Third quartile of daily mean temperature computed by month. + pr_per : xarray.DataArray | str + First quartile of daily total precipitation computed by month. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.warm_and_dry_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_dry_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.warm_and_dry_days( + tas=tas, + pr=pr, + tas_per=tas_per, + pr_per=pr_per, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.warm_and_wet_days) def warm_and_wet_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Warm and wet days. @@ -1401,29 +2482,51 @@ def warm_and_wet_days( - warm_and_wet_days: days - This function wraps `xclim.indicators.atmos.warm_and_wet_days `_. + This function wraps `xclim.indicators.atmos.warm_and_wet_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature values. + pr : xarray.DataArray | str + Daily precipitation. + tas_per : xarray.DataArray | str + Third quartile of daily mean temperature computed by month. + pr_per : xarray.DataArray | str + Third quartile of daily total precipitation computed by month. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.warm_and_wet_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_and_wet_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.warm_and_wet_days( + tas=tas, + pr=pr, + tas_per=tas_per, + pr_per=pr_per, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.water_cycle_intensity) def water_cycle_intensity( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + evspsbl: xarray.DataArray | str = 'evspsbl', + *, + freq: Any = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Water cycle intensity. @@ -1433,29 +2536,45 @@ def water_cycle_intensity( - water_cycle_intensity: mm - This function wraps `xclim.indicators.atmos.water_cycle_intensity `_. + This function wraps `xclim.indicators.atmos.water_cycle_intensity + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Precipitation flux. + evspsbl : xarray.DataArray | str + Actual evapotranspiration flux. + freq : Any + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.water_cycle_intensity`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.water_cycle_intensity) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.water_cycle_intensity( + pr=pr, + evspsbl=evspsbl, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wet_precip_accumulation) def wet_precip_accumulation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1 mm/day', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Total accumulated precipitation (solid and liquid) during wet days. @@ -1466,29 +2585,48 @@ def wet_precip_accumulation( - wet_prcptot: mm - This function wraps `xclim.indicators.atmos.wet_precip_accumulation `_. + This function wraps `xclim.indicators.atmos.wet_precip_accumulation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. + thresh : Any + Threshold over which precipitation starts being cumulated. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wet_precip_accumulation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_precip_accumulation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.wet_precip_accumulation( + pr=pr, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wet_spell_frequency) def wet_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 3, + freq: str = 'YS', + resample_before_rl: bool = True, + op: Literal['sum', 'min', 'max', 'mean'] = 'sum', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Wet spell frequency. @@ -1500,29 +2638,63 @@ def wet_spell_frequency( - wet_spell_frequency: dimensionless - This function wraps `xclim.indicators.atmos.wet_spell_frequency `_. + This function wraps `xclim.indicators.atmos.wet_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Precipitation amount over which a period is considered dry. The value against which + the threshold is compared depends on `op`. + window : int + Minimum length of the spells. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + op : Literal['sum', 'min', 'max', 'mean'] + Operation to perform on the window. Default is "sum", which checks that the sum of + accumulated precipitation over the whole window is more than the threshold. "min" + checks that the maximal daily precipitation amount within the window is more than + the threshold. This is the same as verifying that each individual day is above the + threshold. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wet_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.wet_spell_frequency( + pr=pr, + thresh=thresh, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wet_spell_max_length) def wet_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 1, + op: Literal['min', 'sum', 'max', 'mean'] = 'sum', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Wet spell maximum length. @@ -1534,29 +2706,61 @@ def wet_spell_max_length( - wet_spell_max_length: days - This function wraps `xclim.indicators.atmos.wet_spell_max_length `_. + This function wraps `xclim.indicators.atmos.wet_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Accumulated precipitation value over which a period is considered wet. + window : int + Number of days when the maximum or accumulated precipitation is over threshold. + op : Literal['min', 'sum', 'max', 'mean'] + Reduce operation. `min` means that all days within the minimum window must exceed + the threshold. `sum` means that the accumulated precipitation within the window must + exceed the threshold. In all cases, the whole window is marked a part of a wet + spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wet_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.wet_spell_max_length( + pr=pr, + thresh=thresh, + window=window, + op=op, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wet_spell_total_length) def wet_spell_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm', + window: int = 3, + op: Literal['min', 'sum', 'max', 'mean'] = 'sum', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Wet spell total length. @@ -1568,29 +2772,59 @@ def wet_spell_total_length( - wet_spell_total_length: days - This function wraps `xclim.indicators.atmos.wet_spell_total_length `_. + This function wraps `xclim.indicators.atmos.wet_spell_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Accumulated precipitation value over which a period is considered wet. + window : int + Number of days when the maximum or accumulated precipitation is over the threshold. + op : Literal['min', 'sum', 'max', 'mean'] + Reduce operation. `min` means that all days within the minimum window must exceed + the threshold. `sum` means that the accumulated precipitation within the window must + exceed the threshold. In all cases, the whole window is marked a part of a wet + spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wet_spell_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wet_spell_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.wet_spell_total_length( + pr=pr, + thresh=thresh, + window=window, + op=op, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wetdays) def wetdays( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of wet days. @@ -1600,29 +2834,49 @@ def wetdays( - wetdays: days - This function wraps `xclim.indicators.atmos.wetdays `_. + This function wraps `xclim.indicators.atmos.wetdays + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wetdays`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.wetdays( + pr=pr, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.wetdays_prop) def wetdays_prop( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + pr: xarray.DataArray | str = 'pr', + *, + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Proportion of wet days. @@ -1632,20 +2886,35 @@ def wetdays_prop( - wetdays_prop: 1 - This function wraps `xclim.indicators.atmos.wetdays_prop `_. + This function wraps `xclim.indicators.atmos.wetdays_prop + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + pr : xarray.DataArray | str + Daily precipitation. + thresh : Any + Precipitation value over which a day is considered wet. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.wetdays_prop`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.wetdays_prop) - return wrapper(ds, **kwargs) + Any + The computed index. + """ + return xclim.indicators.atmos.wetdays_prop( + pr=pr, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index 989b0c6..a5d7a66 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -8,19 +8,25 @@ """Temperature indices.""" -from typing import Any +from typing import Any, Literal import xarray import xclim.indicators.atmos +from earthkit.utils.decorators.format_handlers import format_handler -import earthkit.climate.utils.conversions as conversions -from earthkit.climate.api.wrapper import wrap_xclim_indicator +# from earthkit.climate.utils.decorators import metadata_handler +@format_handler() +# @metadata_handler(xclim.indicators.atmos.australian_hardiness_zones) def australian_hardiness_zones( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + window: int = 30, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Australian hardiness zones. @@ -34,29 +40,54 @@ def australian_hardiness_zones( - hz: dimensionless - This function wraps `xclim.indicators.atmos.australian_hardiness_zones `_. + This function wraps `xclim.indicators.atmos.australian_hardiness_zones + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum temperature. + window : int + The length of the averaging window, in years. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.australian_hardiness_zones`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.australian_hardiness_zones) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.australian_hardiness_zones( + tasmin=tasmin, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.biologically_effective_degree_days) def biologically_effective_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + lat: xarray.DataArray | str = 'lat', + *, + thresh_tasmin: Any = '10 degC', + method: Literal['gladstones', 'icclim', 'jones', 'smoothed', 'stepwise'] = 'gladstones', + cap_value: float = 1.0, + low_dtr: Any = '10 degC', + high_dtr: Any = '13 degC', + max_daily_degree_days: Any = '9 degC', + start_date: str | str = '04-01', + end_date: str | str = '11-01', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Biologically effective degree days. @@ -70,29 +101,91 @@ def biologically_effective_degree_days( - bedd: K days - This function wraps `xclim.indicators.atmos.biologically_effective_degree_days `_. + This function wraps `xclim.indicators.atmos.biologically_effective_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None and method is not "icclim", a CF-conformant "latitude" + field must be available within the passed DataArray. + thresh_tasmin : Any + The minimum temperature threshold. + method : Literal['gladstones', 'icclim', 'jones', 'smoothed', 'stepwise'] + The formula to use for the daily temperature range and latitude coefficient. The + "gladstones" method uses a temperature range adjustment and a latitude coefficient + based on :cite:t:`gladstones_wine_2011`. End_date should be "11-01" for the Northern + Hemisphere. The "huglin" method uses a temperature range adjustment and a stepwise + latitude coefficient for values between 40° and 50° based on + :cite:t:`huglin_nouveau_1978`. End_date should be "11-01" for the Northern + Hemisphere. The "icclim" method does not implement daily temperature range and nor a + latitude coefficient based on :cite:t:`project_team_eca&d_algorithm_2013`. End date + should be "10-01" for the Northern Hemisphere. The "interpolated" method uses a + temperature range adjustment and a smoothed curve latitude coefficient for values + between 40° and 50° based on :cite:t:`huglin_nouveau_1978`. The "jones" method uses + a temperature range adjustment and integrates axial tilt, latitude, and day-of-year + based on :cite:t:`hall_spatial_2010`. End_date should be "11-01" for the Northern + Hemisphere. + cap_value : float + The value to use for the latitude coefficient for latitudes north of 50°N or south + of 50°S. Only applicable for methods "huglin" and "interpolated". + low_dtr : Any + The lower bound for daily temperature range adjustment. + high_dtr : Any + The higher bound for daily temperature range adjustment. + max_daily_degree_days : Any + The maximum number of biologically effective degrees days that can be summed daily. + start_date : str | str + The hemisphere-based start date to consider (north = April, south = October). + end_date : str | str + The hemisphere-based start date to consider (north = October, south = April). This + date is non-inclusive. + freq : str + Resampling frequency (For Southern Hemisphere, should be "YS-JUL"). **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.biologically_effective_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.biologically_effective_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.biologically_effective_degree_days( + tasmin=tasmin, + tasmax=tasmax, + lat=lat, + thresh_tasmin=thresh_tasmin, + method=method, + cap_value=cap_value, + low_dtr=low_dtr, + high_dtr=high_dtr, + max_daily_degree_days=max_daily_degree_days, + start_date=start_date, + end_date=end_date, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_days) def cold_spell_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '-10 degC', + window: int = 5, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold spell days. @@ -103,29 +196,60 @@ def cold_spell_days( - cold_spell_days: days - This function wraps `xclim.indicators.atmos.cold_spell_days `_. + This function wraps `xclim.indicators.atmos.cold_spell_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature below which a cold spell begins. + window : int + Minimum number of days with temperature below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_spell_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_days( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_duration_index) def cold_spell_duration_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + *, + window: int = 6, + freq: str = 'YS', + resample_before_rl: bool = True, + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold spell duration index (csdi). @@ -137,29 +261,66 @@ def cold_spell_duration_index( - csdi_{window}: days - This function wraps `xclim.indicators.atmos.cold_spell_duration_index `_. + This function wraps `xclim.indicators.atmos.cold_spell_duration_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmin_per : xarray.DataArray | str + The nth percentile of daily minimum temperature with `dayofyear` coordinate. + window : int + Minimum number of days with temperature below threshold to qualify as a cold spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Keep + bootstrap to `False` when there is no common period, as bootstrapping is + computationally expensive, and it might provide the wrong results. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_spell_duration_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_duration_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_duration_index( + tasmin=tasmin, + tasmin_per=tasmin_per, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_frequency) def cold_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '-10 degC', + window: int = 5, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold spell frequency. @@ -170,29 +331,58 @@ def cold_spell_frequency( - cold_spell_frequency: dimensionless - This function wraps `xclim.indicators.atmos.cold_spell_frequency `_. + This function wraps `xclim.indicators.atmos.cold_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature below which a cold spell begins. + window : int + Minimum number of days with temperature below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_frequency( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_max_length) def cold_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '-10 degC', + window: int = 1, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold spell maximum length. @@ -203,29 +393,59 @@ def cold_spell_max_length( - cold_spell_max_length: days - This function wraps `xclim.indicators.atmos.cold_spell_max_length `_. + This function wraps `xclim.indicators.atmos.cold_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + The temperature threshold needed to trigger a cold spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_max_length( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_total_length) def cold_spell_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '-10 degC', + window: int = 3, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cold spell total length. @@ -236,29 +456,57 @@ def cold_spell_total_length( - cold_spell_total_length: days - This function wraps `xclim.indicators.atmos.cold_spell_total_length `_. + This function wraps `xclim.indicators.atmos.cold_spell_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + The temperature threshold needed to trigger a cold spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cold_spell_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cold_spell_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_total_length( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.consecutive_frost_days) def consecutive_frost_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + freq: str = 'YS-JUL', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Consecutive frost days. @@ -268,29 +516,50 @@ def consecutive_frost_days( - consecutive_frost_days: days - This function wraps `xclim.indicators.atmos.consecutive_frost_days `_. + This function wraps `xclim.indicators.atmos.consecutive_frost_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.consecutive_frost_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.consecutive_frost_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.consecutive_frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_frost_free_days) def maximum_consecutive_frost_free_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum consecutive frost free days. @@ -301,29 +570,49 @@ def maximum_consecutive_frost_free_days( - consecutive_frost_free_days: days - This function wraps `xclim.indicators.atmos.maximum_consecutive_frost_free_days `_. + This function wraps `xclim.indicators.atmos.maximum_consecutive_frost_free_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.maximum_consecutive_frost_free_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_frost_free_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_frost_free_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cool_night_index) def cool_night_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + lat: xarray.DataArray | str | None = None, + *, + freq: Literal['YS', 'YS-JAN'] = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cool night index. @@ -334,29 +623,46 @@ def cool_night_index( - cool_night_index: degC - This function wraps `xclim.indicators.atmos.cool_night_index `_. + This function wraps `xclim.indicators.atmos.cool_night_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + lat : xarray.DataArray | str | None + Latitude coordinate as an array, float or string. If None, a CF-conformant + "latitude" field must be available within the passed DataArray. + freq : Literal['YS', 'YS-JAN'] + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cool_night_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cool_night_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cool_night_index( + tasmin=tasmin, + lat=lat, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cooling_degree_days) def cooling_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '18.0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cooling degree days. @@ -367,29 +673,47 @@ def cooling_degree_days( - cooling_degree_days: K days - This function wraps `xclim.indicators.atmos.cooling_degree_days `_. + This function wraps `xclim.indicators.atmos.cooling_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Temperature threshold above which air is cooled. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cooling_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cooling_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cooling_degree_days_approximation) def cooling_degree_days_approximation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + tasmin: xarray.DataArray | str = 'tasmin', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '18.0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Cooling degree days approximation. @@ -402,29 +726,52 @@ def cooling_degree_days_approximation( - cooling_degree_days_approximation: K days - This function wraps `xclim.indicators.atmos.cooling_degree_days_approximation `_. + This function wraps `xclim.indicators.atmos.cooling_degree_days_approximation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Temperature threshold above which air is cooled. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.cooling_degree_days_approximation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.cooling_degree_days_approximation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.cooling_degree_days_approximation( + tasmax=tasmax, + tasmin=tasmin, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.corn_heat_units) def corn_heat_units( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '4.44 degC', + thresh_tasmax: Any = '10 degC', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Corn heat units. @@ -435,29 +782,47 @@ def corn_heat_units( - chu: dimensionless - This function wraps `xclim.indicators.atmos.corn_heat_units `_. + This function wraps `xclim.indicators.atmos.corn_heat_units + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed for corn growth. + thresh_tasmax : Any + The maximum temperature threshold needed for corn growth. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.corn_heat_units`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.corn_heat_units) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.corn_heat_units( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.chill_portions) def chill_portions( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Chill portions. @@ -475,29 +840,42 @@ def chill_portions( - cp: dimensionless - This function wraps `xclim.indicators.atmos.chill_portions `_. + This function wraps `xclim.indicators.atmos.chill_portions + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Hourly temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.chill_portions`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_portions) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.chill_portions( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.chill_units) def chill_units( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + positive_only: bool = False, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Chill units. @@ -511,29 +889,49 @@ def chill_units( - cu: dimensionless - This function wraps `xclim.indicators.atmos.chill_units `_. + This function wraps `xclim.indicators.atmos.chill_units + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Hourly temperature. + positive_only : bool + If `True`, only positive daily chill units are aggregated. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.chill_units`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.chill_units) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.chill_units( + tas=tas, + positive_only=positive_only, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.degree_days_exceedance_date) def degree_days_exceedance_date( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + sum_thresh: Any = '25 K days', + op: Literal['>', 'gt', '<', 'lt', '>=', 'ge', '<=', 'le'] = '>', + after_date: str | None = None, + never_reached: str | int | None = None, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Degree day exceedance date. @@ -544,29 +942,66 @@ def degree_days_exceedance_date( - degree_days_exceedance_date: dimensionless - This function wraps `xclim.indicators.atmos.degree_days_exceedance_date `_. + This function wraps `xclim.indicators.atmos.degree_days_exceedance_date + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base degree-days evaluation. + sum_thresh : Any + Threshold of the degree days sum. + op : Literal['>', 'gt', '<', 'lt', '>=', 'ge', '<=', 'le'] + If equivalent to '>', degree days are computed as `tas - thresh` and if equivalent + to '<', they are computed as `thresh - tas`. + after_date : str | None + Date at which to start the cumulative sum. In "MM-DD" format, defaults to the start + of the sampling period. + never_reached : str | int | None + What to do when `sum_thresh` is never exceeded. If an int, the value to assign as a + day-of-year. If a string, must be in "MM-DD" format, the day-of-year of that date is + assigned. Default (None) assigns "NaN". + freq : str + Resampling frequency. If `after_date` is given, `freq` should be annual. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.degree_days_exceedance_date`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.degree_days_exceedance_date) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.degree_days_exceedance_date( + tas=tas, + thresh=thresh, + sum_thresh=sum_thresh, + op=op, + after_date=after_date, + never_reached=never_reached, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_freezethaw_cycles) def daily_freezethaw_cycles( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Daily freeze-thaw cycles. @@ -578,29 +1013,61 @@ def daily_freezethaw_cycles( - dlyfrzthw: days - This function wraps `xclim.indicators.atmos.daily_freezethaw_cycles `_. + This function wraps `xclim.indicators.atmos.daily_freezethaw_cycles + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.daily_freezethaw_cycles`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_freezethaw_cycles) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.daily_freezethaw_cycles( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_temperature_range) def daily_temperature_range( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Mean of daily temperature range. @@ -610,29 +1077,45 @@ def daily_temperature_range( - dtr: K - This function wraps `xclim.indicators.atmos.daily_temperature_range `_. + This function wraps `xclim.indicators.atmos.daily_temperature_range + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.daily_temperature_range`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.daily_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.max_daily_temperature_range) def max_daily_temperature_range( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum of daily temperature range. @@ -642,29 +1125,45 @@ def max_daily_temperature_range( - dtrmax: K - This function wraps `xclim.indicators.atmos.max_daily_temperature_range `_. + This function wraps `xclim.indicators.atmos.max_daily_temperature_range + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.max_daily_temperature_range`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.max_daily_temperature_range) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.max_daily_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_temperature_range_variability) def daily_temperature_range_variability( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Variability of daily temperature range. @@ -674,29 +1173,45 @@ def daily_temperature_range_variability( - dtrvar: K - This function wraps `xclim.indicators.atmos.daily_temperature_range_variability `_. + This function wraps `xclim.indicators.atmos.daily_temperature_range_variability + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.daily_temperature_range_variability`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range_variability) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.daily_temperature_range_variability( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.extreme_temperature_range) def extreme_temperature_range( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Extreme temperature range. @@ -706,29 +1221,51 @@ def extreme_temperature_range( - etr: K - This function wraps `xclim.indicators.atmos.extreme_temperature_range `_. + This function wraps `xclim.indicators.atmos.extreme_temperature_range + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.extreme_temperature_range`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.extreme_temperature_range) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.extreme_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.fire_season) def fire_season( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + snd: xarray.DataArray | str | None = None, + *, + method: str = 'WF93', + freq: str | None = None, + temp_start_thresh: Any = '12 degC', + temp_end_thresh: Any = '5 degC', + temp_condition_days: int = 3, + snow_condition_days: int = 3, + snow_thresh: Any = '0.01 m', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Fire season mask. @@ -739,29 +1276,71 @@ def fire_season( - fire_season: dimensionless - This function wraps `xclim.indicators.atmos.fire_season `_. + This function wraps `xclim.indicators.atmos.fire_season + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Daily surface temperature, cffdrs recommends using maximum daily temperature. + snd : xarray.DataArray | str | None + Snow depth, used with method == 'LA08'. + method : str + Which method to use. "LA08" and "GFWED" need the snow depth. + freq : str | None + If given only the longest fire season for each period defined by this frequency, + Every "seasons" are returned if None, including the short shoulder seasons. + temp_start_thresh : Any + Minimal temperature needed to start the season. Must be scalar. + temp_end_thresh : Any + Maximal temperature needed to end the season. Must be scalar. + temp_condition_days : int + Number of days with temperature above or below the thresholds to trigger a start or + an end of the fire season. + snow_condition_days : int + Parameters for the fire season determination. See :py:func:`fire_season`. + Temperature is in degC, snow in m. The `snow_thresh` parameters is also used when + `dry_start` is set to "GFWED". + snow_thresh : Any + Minimal snow depth level to end a fire season, only used with method "LA08". Must be + scalar. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.fire_season`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.fire_season) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.fire_season( + tas=tas, + snd=snd, + method=method, + freq=freq, + temp_start_thresh=temp_start_thresh, + temp_end_thresh=temp_end_thresh, + temp_condition_days=temp_condition_days, + snow_condition_days=snow_condition_days, + snow_thresh=snow_thresh, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tg_above) def first_day_tg_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures superior to a given temperature threshold. @@ -772,29 +1351,58 @@ def first_day_tg_above( - first_day_tg_above: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tg_above `_. + This function wraps `xclim.indicators.atmos.first_day_tg_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tg_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tg_above( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tg_below) def first_day_tg_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures inferior to a given temperature threshold. @@ -805,29 +1413,58 @@ def first_day_tg_below( - first_day_tg_below: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tg_below `_. + This function wraps `xclim.indicators.atmos.first_day_tg_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tg_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tg_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tg_below( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tn_above) def first_day_tn_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures superior to a given temperature threshold. @@ -838,29 +1475,58 @@ def first_day_tn_above( - first_day_tn_above: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tn_above `_. + This function wraps `xclim.indicators.atmos.first_day_tn_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tn_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tn_above( + tasmin=tasmin, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tn_below) def first_day_tn_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures inferior to a given temperature threshold. @@ -871,29 +1537,58 @@ def first_day_tn_below( - first_day_tn_below: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tn_below `_. + This function wraps `xclim.indicators.atmos.first_day_tn_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tn_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tn_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tn_below( + tasmin=tasmin, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tx_above) def first_day_tx_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures superior to a given temperature threshold. @@ -904,29 +1599,58 @@ def first_day_tx_above( - first_day_tx_above: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tx_above `_. + This function wraps `xclim.indicators.atmos.first_day_tx_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tx_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tx_above( + tasmax=tasmax, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tx_below) def first_day_tx_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ First day of temperatures inferior to a given temperature threshold. @@ -937,29 +1661,61 @@ def first_day_tx_below( - first_day_tx_below: dimensionless - This function wraps `xclim.indicators.atmos.first_day_tx_below `_. + This function wraps `xclim.indicators.atmos.first_day_tx_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.first_day_tx_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.first_day_tx_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tx_below( + tasmax=tasmax, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_frequency) def freezethaw_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Freeze-thaw spell frequency. @@ -971,29 +1727,70 @@ def freezethaw_spell_frequency( - freezethaw_spell_frequency: days - This function wraps `xclim.indicators.atmos.freezethaw_spell_frequency `_. + This function wraps `xclim.indicators.atmos.freezethaw_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.freezethaw_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_frequency( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_max_length) def freezethaw_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximal length of freeze-thaw spells. @@ -1005,29 +1802,68 @@ def freezethaw_spell_max_length( - freezethaw_spell_max_length: days - This function wraps `xclim.indicators.atmos.freezethaw_spell_max_length `_. + This function wraps `xclim.indicators.atmos.freezethaw_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.freezethaw_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_max_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_mean_length) def freezethaw_spell_mean_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Freeze-thaw spell mean length. @@ -1039,29 +1875,58 @@ def freezethaw_spell_mean_length( - freezethaw_spell_mean_length: days - This function wraps `xclim.indicators.atmos.freezethaw_spell_mean_length `_. + This function wraps `xclim.indicators.atmos.freezethaw_spell_mean_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.freezethaw_spell_mean_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezethaw_spell_mean_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_mean_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezing_degree_days) def freezing_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Freezing degree days. @@ -1072,29 +1937,48 @@ def freezing_degree_days( - freezing_degree_days: K days - This function wraps `xclim.indicators.atmos.freezing_degree_days `_. + This function wraps `xclim.indicators.atmos.freezing_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.freezing_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freezing_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.freezing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freshet_start) def freshet_start( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 5, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Day of year of spring freshet start. @@ -1105,29 +1989,55 @@ def freshet_start( - freshet_start: dimensionless - This function wraps `xclim.indicators.atmos.freshet_start `_. + This function wraps `xclim.indicators.atmos.freshet_start + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.freshet_start`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.freshet_start) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.freshet_start( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_days) def frost_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost days. @@ -1137,29 +2047,48 @@ def frost_days( - frost_days: days - This function wraps `xclim.indicators.atmos.frost_days `_. + This function wraps `xclim.indicators.atmos.frost_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_end) def frost_free_season_end( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost free season end. @@ -1170,29 +2099,58 @@ def frost_free_season_end( - frost_free_season_end: dimensionless - This function wraps `xclim.indicators.atmos.frost_free_season_end `_. + This function wraps `xclim.indicators.atmos.frost_free_season_end + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date what must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_free_season_end`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_end) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_end( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_length) def frost_free_season_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost free season length. @@ -1204,29 +2162,58 @@ def frost_free_season_length( - frost_free_season_length: days - This function wraps `xclim.indicators.atmos.frost_free_season_length `_. + This function wraps `xclim.indicators.atmos.frost_free_season_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date what must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_free_season_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_length( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_start) def frost_free_season_start( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost free season start. @@ -1237,29 +2224,58 @@ def frost_free_season_start( - frost_free_season_start: dimensionless - This function wraps `xclim.indicators.atmos.frost_free_season_start `_. + This function wraps `xclim.indicators.atmos.frost_free_season_start + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date that must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_free_season_start`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_season_start) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_start( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_spell_max_length) def frost_free_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0.0 degC', + window: int = 1, + freq: str = 'YS-JUL', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost free spell maximum length. @@ -1270,29 +2286,59 @@ def frost_free_spell_max_length( - frost_free_spell_max_length: days - This function wraps `xclim.indicators.atmos.frost_free_spell_max_length `_. + This function wraps `xclim.indicators.atmos.frost_free_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + The temperature threshold needed to trigger a frost-free spell. + window : int + Minimum number of days with temperatures above thresholds to qualify as a frost-free + day. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_free_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_free_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_spell_max_length( + tasmin=tasmin, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_season_length) def frost_season_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + window: int = 5, + mid_date: str | None = '01-01', + thresh: Any = '0 degC', + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Frost season length. @@ -1304,29 +2350,56 @@ def frost_season_length( - frost_season_length: days - This function wraps `xclim.indicators.atmos.frost_season_length `_. + This function wraps `xclim.indicators.atmos.frost_season_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + window : int + Minimum number of days with temperature below threshold to mark the beginning and + end of frost season. + mid_date : str | None + The date must be included in the season. It is the earliest the end of the season + can be. ``None`` removes that constraint. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.frost_season_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.frost_season_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.frost_season_length( + tasmin=tasmin, + window=window, + mid_date=mid_date, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_degree_days) def growing_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '4.0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Growing degree days. @@ -1337,29 +2410,48 @@ def growing_degree_days( - growing_degree_days: K days - This function wraps `xclim.indicators.atmos.growing_degree_days `_. + This function wraps `xclim.indicators.atmos.growing_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.growing_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.growing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_end) def growing_season_end( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '5.0 degC', + mid_date: str | None = '07-01', + window: int = 5, + freq: str = 'YS', + op: Literal['>', '>=', 'lt', 'le'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Growing season end. @@ -1370,29 +2462,60 @@ def growing_season_end( - growing_season_end: dimensionless - This function wraps `xclim.indicators.atmos.growing_season_end `_. + This function wraps `xclim.indicators.atmos.growing_season_end + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + mid_date : str | None + Date of the year after which to look for the end of the season. Should have the + format '%m-%d'. ``None`` removes that constraint. + window : int + Minimum number of days with temperature below threshold needed for evaluation. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'lt', 'le'] + Comparison operation. Default: ">". Note that this comparison is what defines the + season. The end of the season happens when the condition is NOT met for `window` + consecutive days. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.growing_season_end`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_end) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_end( + tas=tas, + thresh=thresh, + mid_date=mid_date, + window=window, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_length) def growing_season_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '5.0 degC', + window: int = 6, + mid_date: str | None = '07-01', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Growing season length. @@ -1404,29 +2527,59 @@ def growing_season_length( - growing_season_length: days - This function wraps `xclim.indicators.atmos.growing_season_length `_. + This function wraps `xclim.indicators.atmos.growing_season_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above the threshold to mark the beginning + and end of growing season. + mid_date : str | None + Date of the year before which the season must start and after which it can end. + Should have the format '%m-%d'. Setting `None` removes that constraint. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.growing_season_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_length( + tas=tas, + thresh=thresh, + window=window, + mid_date=mid_date, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_start) def growing_season_start( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '5.0 degC', + mid_date: str | None = '07-01', + window: int = 5, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Growing season start. @@ -1437,29 +2590,61 @@ def growing_season_start( - growing_season_start: dimensionless - This function wraps `xclim.indicators.atmos.growing_season_start `_. + This function wraps `xclim.indicators.atmos.growing_season_start + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + mid_date : str | None + Date of the year before which the season must start. Should have the format '%m-%d'. + ``None`` removes that constraint. + window : int + Minimum number of days with temperature above threshold needed for evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.growing_season_start`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.growing_season_start) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_start( + tas=tas, + thresh=thresh, + mid_date=mid_date, + window=window, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_frequency) def heat_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat spell frequency. @@ -1470,29 +2655,72 @@ def heat_spell_frequency( - heat_spell_frequency: dimensionless - This function wraps `xclim.indicators.atmos.heat_spell_frequency `_. + This function wraps `xclim.indicators.atmos.heat_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_frequency( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_max_length) def heat_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat spell maximum length. @@ -1503,29 +2731,72 @@ def heat_spell_max_length( - heat_spell_max_length: days - This function wraps `xclim.indicators.atmos.heat_spell_max_length `_. + This function wraps `xclim.indicators.atmos.heat_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_max_length( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_total_length) def heat_spell_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat spell total length. @@ -1536,29 +2807,71 @@ def heat_spell_total_length( - heat_spell_total_length: days - This function wraps `xclim.indicators.atmos.heat_spell_total_length `_. + This function wraps `xclim.indicators.atmos.heat_spell_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_spell_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_spell_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_total_length( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_frequency) def heat_wave_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat wave frequency. @@ -1569,29 +2882,64 @@ def heat_wave_frequency( - heat_wave_frequency: dimensionless - This function wraps `xclim.indicators.atmos.heat_wave_frequency `_. + This function wraps `xclim.indicators.atmos.heat_wave_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_wave_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_frequency( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_index) def heat_wave_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25 degC', + window: int = 5, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat wave index. @@ -1602,29 +2950,61 @@ def heat_wave_index( - heat_wave_index: days - This function wraps `xclim.indicators.atmos.heat_wave_index `_. + This function wraps `xclim.indicators.atmos.heat_wave_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_wave_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_index( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_max_length) def heat_wave_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat wave maximum length. @@ -1635,29 +3015,66 @@ def heat_wave_max_length( - heat_wave_max_length: days - This function wraps `xclim.indicators.atmos.heat_wave_max_length `_. + This function wraps `xclim.indicators.atmos.heat_wave_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_wave_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_max_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_total_length) def heat_wave_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heat wave total length. @@ -1668,29 +3085,61 @@ def heat_wave_total_length( - heat_wave_total_length: days - This function wraps `xclim.indicators.atmos.heat_wave_total_length `_. + This function wraps `xclim.indicators.atmos.heat_wave_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heat_wave_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heat_wave_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_total_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heating_degree_days) def heating_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '17.0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heating degree days. @@ -1701,29 +3150,47 @@ def heating_degree_days( - heating_degree_days: K days - This function wraps `xclim.indicators.atmos.heating_degree_days `_. + This function wraps `xclim.indicators.atmos.heating_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heating_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heating_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heating_degree_days_approximation) def heating_degree_days_approximation( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + tasmin: xarray.DataArray | str = 'tasmin', + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '17.0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Heating degree days approximation. @@ -1736,29 +3203,51 @@ def heating_degree_days_approximation( - heating_degree_days_approximation: K days - This function wraps `xclim.indicators.atmos.heating_degree_days_approximation `_. + This function wraps `xclim.indicators.atmos.heating_degree_days_approximation + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days_approximation) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.heating_degree_days_approximation( + tasmax=tasmax, + tasmin=tasmin, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_days) def hot_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Hot days. @@ -1768,29 +3257,48 @@ def hot_days( - hot_days: days - This function wraps `xclim.indicators.atmos.hot_days `_. + This function wraps `xclim.indicators.atmos.hot_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.hot_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.hot_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_frequency) def hot_spell_frequency( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Hot spell frequency. @@ -1801,29 +3309,58 @@ def hot_spell_frequency( - hot_spell_frequency: dimensionless - This function wraps `xclim.indicators.atmos.hot_spell_frequency `_. + This function wraps `xclim.indicators.atmos.hot_spell_frequency + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature below which a hot spell begins. + window : int + Minimum number of days with temperature above the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.hot_spell_frequency`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_frequency) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_frequency( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_max_length) def hot_spell_max_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '30 degC', + window: int = 1, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Hot spell maximum length. @@ -1834,29 +3371,57 @@ def hot_spell_max_length( - hot_spell_max_length: days - This function wraps `xclim.indicators.atmos.hot_spell_max_length `_. + This function wraps `xclim.indicators.atmos.hot_spell_max_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below thresholds to qualify as a hot spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.hot_spell_max_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_max_length( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_max_magnitude) def hot_spell_max_magnitude( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25.0 degC', + window: int = 3, + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Hot spell maximum magnitude. @@ -1867,29 +3432,56 @@ def hot_spell_max_magnitude( - hot_spell_max_magnitude: K d - This function wraps `xclim.indicators.atmos.hot_spell_max_magnitude `_. + This function wraps `xclim.indicators.atmos.hot_spell_max_magnitude + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to designate a heatwave. + window : int + Minimum number of days with temperature above the threshold to qualify as a + heatwave. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.hot_spell_max_magnitude`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_max_magnitude) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_max_magnitude( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_total_length) def hot_spell_total_length( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Hot spell total length. @@ -1900,29 +3492,62 @@ def hot_spell_total_length( - hot_spell_total_length: days - This function wraps `xclim.indicators.atmos.hot_spell_total_length `_. + This function wraps `xclim.indicators.atmos.hot_spell_total_length + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.hot_spell_total_length`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.hot_spell_total_length) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_total_length( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.huglin_index) def huglin_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + tasmax: xarray.DataArray | str = 'tasmax', + lat: xarray.DataArray | str = 'lat', + *, + thresh: Any = '10 degC', + method: str = 'jones', + cap_value: float = 1.0, + start_date: str | str = '04-01', + end_date: str | str = '10-01', + freq: Literal['YS', 'YS-JAN', 'YS-JUL'] = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Huglin heliothermal index. @@ -1936,29 +3561,73 @@ def huglin_index( - hi: dimensionless - This function wraps `xclim.indicators.atmos.huglin_index `_. + This function wraps `xclim.indicators.atmos.huglin_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None, a CF-conformant "latitude" field must be available + within the passed DataArray. + thresh : Any + The temperature threshold. + method : str + The formula to use for the latitude coefficient calculation. The "huglin" method + uses a stepwise latitude coefficient for values between 40° and 50° based on + :cite:t:`huglin_nouveau_1978`. The "interpolated" method uses a smoothed curve + latitude coefficient for values based on the intervals set in + :cite:t:`huglin_nouveau_1978`. The "jones" method integrates axial tilt, latitude, + and day-of-year based on :cite:t:`hall_spatial_2010`. The "icclim" method is + deprecated but is identical to method "huglin". + cap_value : float + The value to use for the latitude coefficient when latitude is above 50°N or below + 50°S. Only applicable for methods "huglin", "icclim", and "interpolated" (default: + 1.0). + start_date : str | str + The hemisphere-based start date to consider (north = April, south = October). + end_date : str | str + The hemisphere-based start date to consider (north = October, south = April). This + date is non-inclusive. + freq : Literal['YS', 'YS-JAN', 'YS-JUL'] + Resampling frequency (default: "YS"; For Southern Hemisphere, should be "YS-JUL"). **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.huglin_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.huglin_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.huglin_index( + tas=tas, + tasmax=tasmax, + lat=lat, + thresh=thresh, + method=method, + cap_value=cap_value, + start_date=start_date, + end_date=end_date, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.ice_days) def ice_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Ice days. @@ -1968,29 +3637,48 @@ def ice_days( - ice_days: days - This function wraps `xclim.indicators.atmos.ice_days `_. + This function wraps `xclim.indicators.atmos.ice_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.ice_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.ice_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.ice_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.last_spring_frost) def last_spring_frost( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + before_date: str = '07-01', + window: int = 1, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Last spring frost. @@ -2001,29 +3689,55 @@ def last_spring_frost( - last_spring_frost: dimensionless - This function wraps `xclim.indicators.atmos.last_spring_frost `_. + This function wraps `xclim.indicators.atmos.last_spring_frost + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + before_date : str + Date of the year before which to look for the final frost event. Should have the + format '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.last_spring_frost`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.last_spring_frost) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.last_spring_frost( + tasmin=tasmin, + thresh=thresh, + op=op, + before_date=before_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.late_frost_days) def late_frost_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Late frost days. @@ -2034,29 +3748,45 @@ def late_frost_days( - late_frost_days: days - This function wraps `xclim.indicators.atmos.late_frost_days `_. + This function wraps `xclim.indicators.atmos.late_frost_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.late_frost_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.late_frost_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.late_frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.latitude_temperature_index) def latitude_temperature_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + lat: xarray.DataArray | str = 'lat', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Latitude temperature index. @@ -2070,29 +3800,47 @@ def latitude_temperature_index( - lti: dimensionless - This function wraps `xclim.indicators.atmos.latitude_temperature_index `_. + This function wraps `xclim.indicators.atmos.latitude_temperature_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None, a CF-conformant "latitude" field must be available + within the passed DataArray. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.latitude_temperature_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.latitude_temperature_index) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.latitude_temperature_index( + tas=tas, + lat=lat, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_warm_days) def maximum_consecutive_warm_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25 degC', + freq: str = 'YS', + resample_before_rl: bool = True, **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum consecutive warm days. @@ -2103,29 +3851,51 @@ def maximum_consecutive_warm_days( - maximum_consecutive_warm_days: days - This function wraps `xclim.indicators.atmos.maximum_consecutive_warm_days `_. + This function wraps `xclim.indicators.atmos.maximum_consecutive_warm_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Max daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.maximum_consecutive_warm_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.maximum_consecutive_warm_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_warm_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg10p) def tg10p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + tas_per: xarray.DataArray | str = 'tas_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with mean temperature below the 10th percentile. @@ -2135,29 +3905,58 @@ def tg10p( - tg10p: days - This function wraps `xclim.indicators.atmos.tg10p `_. + This function wraps `xclim.indicators.atmos.tg10p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + tas_per : xarray.DataArray | str + 10th percentile of daily mean temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg10p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg10p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg10p( + tas=tas, + tas_per=tas_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg90p) def tg90p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + tas_per: xarray.DataArray | str = 'tas_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with mean temperature above the 90th percentile. @@ -2167,29 +3966,57 @@ def tg90p( - tg90p: days - This function wraps `xclim.indicators.atmos.tg90p `_. + This function wraps `xclim.indicators.atmos.tg90p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + tas_per : xarray.DataArray | str + 90th percentile of daily mean temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg90p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg90p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg90p( + tas=tas, + tas_per=tas_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_days_above) def tg_days_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with mean temperature above a given threshold. @@ -2199,29 +4026,49 @@ def tg_days_above( - tg_days_above: days - This function wraps `xclim.indicators.atmos.tg_days_above `_. + This function wraps `xclim.indicators.atmos.tg_days_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg_days_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg_days_above( + tas=tas, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_days_below) def tg_days_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with mean temperature below a given threshold. @@ -2231,29 +4078,47 @@ def tg_days_below( - tg_days_below: days - This function wraps `xclim.indicators.atmos.tg_days_below `_. + This function wraps `xclim.indicators.atmos.tg_days_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg_days_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_days_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg_days_below( + tas=tas, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_max) def tg_max( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum of mean temperature. @@ -2263,29 +4128,41 @@ def tg_max( - tg_max: K - This function wraps `xclim.indicators.atmos.tg_max `_. + This function wraps `xclim.indicators.atmos.tg_max + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg_max`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_max) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg_max( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_mean) def tg_mean( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Mean temperature. @@ -2295,29 +4172,41 @@ def tg_mean( - tg_mean: K - This function wraps `xclim.indicators.atmos.tg_mean `_. + This function wraps `xclim.indicators.atmos.tg_mean + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg_mean`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_mean) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg_mean( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_min) def tg_min( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Minimum of mean temperature. @@ -2327,29 +4216,42 @@ def tg_min( - tg_min: K - This function wraps `xclim.indicators.atmos.tg_min `_. + This function wraps `xclim.indicators.atmos.tg_min + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tg_min`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tg_min) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tg_min( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.thawing_degree_days) def thawing_degree_days( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tas: xarray.DataArray | str = 'tas', + *, + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Thawing degree days. @@ -2360,29 +4262,47 @@ def thawing_degree_days( - thawing_degree_days: K days - This function wraps `xclim.indicators.atmos.thawing_degree_days `_. + This function wraps `xclim.indicators.atmos.thawing_degree_days + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.thawing_degree_days`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.thawing_degree_days) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.thawing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn10p) def tn10p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with minimum temperature below the 10th percentile. @@ -2392,29 +4312,58 @@ def tn10p( - tn10p: days - This function wraps `xclim.indicators.atmos.tn10p `_. + This function wraps `xclim.indicators.atmos.tn10p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Mean daily temperature. + tasmin_per : xarray.DataArray | str + 10th percentile of daily minimum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn10p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn10p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn10p( + tasmin=tasmin, + tasmin_per=tasmin_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn90p) def tn90p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with minimum temperature above the 90th percentile. @@ -2424,29 +4373,57 @@ def tn90p( - tn90p: days - This function wraps `xclim.indicators.atmos.tn90p `_. + This function wraps `xclim.indicators.atmos.tn90p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmin_per : xarray.DataArray | str + 90th percentile of daily minimum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn90p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn90p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn90p( + tasmin=tasmin, + tasmin_per=tasmin_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_days_above) def tn_days_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '20.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with minimum temperature above a given threshold. @@ -2456,29 +4433,49 @@ def tn_days_above( - tn_days_above: days - This function wraps `xclim.indicators.atmos.tn_days_above `_. + This function wraps `xclim.indicators.atmos.tn_days_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn_days_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn_days_above( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_days_below) def tn_days_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '-10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with minimum temperature below a given threshold. @@ -2488,29 +4485,47 @@ def tn_days_below( - tn_days_below: days - This function wraps `xclim.indicators.atmos.tn_days_below `_. + This function wraps `xclim.indicators.atmos.tn_days_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn_days_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_days_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn_days_below( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_max) def tn_max( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum of minimum temperature. @@ -2520,29 +4535,41 @@ def tn_max( - tn_max: K - This function wraps `xclim.indicators.atmos.tn_max `_. + This function wraps `xclim.indicators.atmos.tn_max + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn_max`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_max) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn_max( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_mean) def tn_mean( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Mean of minimum temperature. @@ -2552,29 +4579,41 @@ def tn_mean( - tn_mean: K - This function wraps `xclim.indicators.atmos.tn_mean `_. + This function wraps `xclim.indicators.atmos.tn_mean + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn_mean`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_mean) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn_mean( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_min) def tn_min( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Minimum temperature. @@ -2584,29 +4623,43 @@ def tn_min( - tn_min: K - This function wraps `xclim.indicators.atmos.tn_min `_. + This function wraps `xclim.indicators.atmos.tn_min + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tn_min`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tn_min) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tn_min( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tropical_nights) def tropical_nights( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + thresh: Any = '20.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Tropical nights. @@ -2616,29 +4669,50 @@ def tropical_nights( - tropical_nights: days - This function wraps `xclim.indicators.atmos.tropical_nights `_. + This function wraps `xclim.indicators.atmos.tropical_nights + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tropical_nights`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tropical_nights) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tropical_nights( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx10p) def tx10p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with maximum temperature below the 10th percentile. @@ -2648,29 +4722,58 @@ def tx10p( - tx10p: days - This function wraps `xclim.indicators.atmos.tx10p `_. + This function wraps `xclim.indicators.atmos.tx10p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + 10th percentile of daily maximum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx10p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx10p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx10p( + tasmax=tasmax, + tasmax_per=tasmax_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx90p) def tx90p( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Days with maximum temperature above the 90th percentile. @@ -2680,29 +4783,57 @@ def tx90p( - tx90p: days - This function wraps `xclim.indicators.atmos.tx90p `_. + This function wraps `xclim.indicators.atmos.tx90p + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + 90th percentile of daily maximum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx90p`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx90p) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx90p( + tasmax=tasmax, + tasmax_per=tasmax_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_days_above) def tx_days_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with maximum temperature above a given threshold. @@ -2712,29 +4843,49 @@ def tx_days_above( - tx_days_above: days - This function wraps `xclim.indicators.atmos.tx_days_above `_. + This function wraps `xclim.indicators.atmos.tx_days_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_days_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_days_above( + tasmax=tasmax, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_days_below) def tx_days_below( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh: Any = '25.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with maximum temperature below a given threshold. @@ -2744,29 +4895,47 @@ def tx_days_below( - tx_days_below: days - This function wraps `xclim.indicators.atmos.tx_days_below `_. + This function wraps `xclim.indicators.atmos.tx_days_below + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_days_below`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_days_below) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_days_below( + tasmax=tasmax, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_max) def tx_max( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Maximum temperature. @@ -2776,29 +4945,41 @@ def tx_max( - tx_max: K - This function wraps `xclim.indicators.atmos.tx_max `_. + This function wraps `xclim.indicators.atmos.tx_max + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_max`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_max) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_max( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_mean) def tx_mean( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Mean of maximum temperature. @@ -2808,29 +4989,41 @@ def tx_mean( - tx_mean: K - This function wraps `xclim.indicators.atmos.tx_mean `_. + This function wraps `xclim.indicators.atmos.tx_mean + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_mean`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_mean) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_mean( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_min) def tx_min( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + *, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Minimum of maximum temperature. @@ -2840,29 +5033,45 @@ def tx_min( - tx_min: K - This function wraps `xclim.indicators.atmos.tx_min `_. + This function wraps `xclim.indicators.atmos.tx_min + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_min`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_min) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_min( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_tn_days_above) def tx_tn_days_above( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + *, + thresh_tasmin: Any = '22 degC', + thresh_tasmax: Any = '30 degC', + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Number of days with daily minimum and maximum temperatures exceeding thresholds. @@ -2872,29 +5081,54 @@ def tx_tn_days_above( - tx_tn_days_above: days - This function wraps `xclim.indicators.atmos.tx_tn_days_above `_. + This function wraps `xclim.indicators.atmos.tx_tn_days_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + Threshold temperature for tasmin on which to base evaluation. + thresh_tasmax : Any + Threshold temperature for tasmax on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.tx_tn_days_above`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.tx_tn_days_above) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.tx_tn_days_above( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.usda_hardiness_zones) def usda_hardiness_zones( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmin: xarray.DataArray | str = 'tasmin', + *, + window: int = 30, + freq: str = 'YS', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Usda hardiness zones. @@ -2908,29 +5142,49 @@ def usda_hardiness_zones( - hz: dimensionless - This function wraps `xclim.indicators.atmos.usda_hardiness_zones `_. + This function wraps `xclim.indicators.atmos.usda_hardiness_zones + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmin : xarray.DataArray | str + Minimum temperature. + window : int + The length of the averaging window, in years. + freq : str + Resampling frequency. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.usda_hardiness_zones`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.usda_hardiness_zones) - return wrapper(ds, **kwargs) - - + Any + The computed index. + """ + return xclim.indicators.atmos.usda_hardiness_zones( + tasmin=tasmin, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.warm_spell_duration_index) def warm_spell_duration_index( - ds: conversions.EarthkitData | xarray.Dataset, + ds: xarray.Dataset | Any, + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', + *, + window: int = 6, + freq: str = 'YS', + resample_before_rl: bool = True, + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, -) -> conversions.EarthkitData: +) -> Any: """ Warm spell duration index. @@ -2942,20 +5196,50 @@ def warm_spell_duration_index( - warm_spell_duration_index: days - This function wraps `xclim.indicators.atmos.warm_spell_duration_index `_. + This function wraps `xclim.indicators.atmos.warm_spell_duration_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. + ds : xarray.Dataset | Any + Input dataset. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + Percentile(s) of daily maximum temperature. + window : int + Minimum number of days with temperature above threshold to qualify as a warm spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.warm_spell_duration_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. - """ - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) - return wrapper(ds, **kwargs) + Any + The computed index. + """ + return xclim.indicators.atmos.warm_spell_duration_index( + tasmax=tasmax, + tasmax_per=tasmax_per, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + diff --git a/src/earthkit/climate/utils/conversions.py b/src/earthkit/climate/utils/conversions.py deleted file mode 100644 index f949567..0000000 --- a/src/earthkit/climate/utils/conversions.py +++ /dev/null @@ -1,133 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -"""Utilities to bridge Earthkit data objects and xarray structures.""" - -from __future__ import annotations - -from typing import Any, Dict, Mapping, Tuple - -import earthkit.data as ekd -import xarray - -EarthkitData = ekd.FieldList | ekd.Field -MetadataDict = Dict[str, Any] - - -def to_xarray_dataset( - earthkit_input: EarthkitData | xarray.Dataset, - metadata: Mapping[str, Any] | None = None, -) -> Tuple[xarray.Dataset, MetadataDict]: - """ - Convert Earthkit-like data to an ``xarray.Dataset`` and gather metadata. - - Parameters - ---------- - earthkit_input : EarthkitData | xarray.Dataset - Input data in any supported Earthkit or xarray representation. - metadata : Mapping[str, Any], optional - Existing metadata to propagate and enrich during the conversion. - - Returns - ------- - tuple[xr.Dataset, dict[str, Any]] - Dataset ready to be consumed by xclim and an updated metadata mapping. - - Raises - ------ - TypeError - If the input cannot be converted to an ``xarray.Dataset``. - """ - meta: MetadataDict = dict(metadata or {}) - earthkit_internal = dict(meta.get("earthkit_internal", {})) - earthkit_internal["input_type"] = _describe_type(earthkit_input) - - if isinstance(earthkit_input, xarray.Dataset): - dataset = earthkit_input - elif isinstance(earthkit_input, xarray.DataArray): - variable_name = earthkit_input.name or "variable" - dataset = earthkit_input.to_dataset(name=variable_name) - earthkit_internal["dataarray_name"] = variable_name - elif hasattr(earthkit_input, "to_xarray"): - dataset = earthkit_input.to_xarray() - if isinstance(dataset, xarray.DataArray): - variable_name = dataset.name or "variable" - dataset = dataset.to_dataset(name=variable_name) - earthkit_internal["dataarray_name"] = variable_name - elif not isinstance(dataset, xarray.Dataset): - raise TypeError("The object returned by 'to_xarray' is not an xarray.Dataset instance.") - else: - raise TypeError( - "Unsupported input type for conversion to xarray. " - "Expected an xarray object or an Earthkit field exposing 'to_xarray'." - ) - - meta["earthkit_internal"] = earthkit_internal - return dataset, meta - - -def _describe_type(obj: Any) -> str: - """ - Return a human-readable description of an object's type, with special handling for xarray objects. - - Parameters - ---------- - obj : Any - The object to describe. - - Returns - ------- - str - A string describing the object type. - """ - if isinstance(obj, xarray.Dataset): - return "xarray.Dataset" - if isinstance(obj, xarray.DataArray): - return "xarray.DataArray" - obj_type = type(obj) - return f"{obj_type.__module__}.{obj_type.__qualname__}" - - -def to_earthkit_field( - output: xarray.Dataset | xarray.DataArray, - metadata: Mapping[str, Any] | None = None, -) -> EarthkitData: - """ - Convert an xarray result back into an Earthkit representation. - - Parameters - ---------- - output : xarray.Dataset or xarray.DataArray - Resulting data returned by an xclim indicator. - metadata : Mapping[str, Any], optional - Provenance metadata gathered during the conversion and call workflow. - - Returns - ------- - EarthkitData - The indicator output converted to the closest possible Earthkit type. - """ - meta: MetadataDict = dict(metadata or {}) - - # Ensure we always have a dataset - dataset: xarray.Dataset - if isinstance(output, xarray.DataArray): - dataset = output.to_dataset(name=output.name or "variable") - else: - dataset = output - - dataset = dataset.copy() - - # Attach provenance metadata - provenance = dict(meta) - if provenance: - dataset.attrs.setdefault("earthkit_provenance", provenance) - - # --- Use Earthkit’s official wrapper system --- - ek_object = ekd.from_object(dataset) - return ek_object diff --git a/src/earthkit/climate/utils/provenance.py b/src/earthkit/climate/utils/provenance.py deleted file mode 100644 index 73ddc61..0000000 --- a/src/earthkit/climate/utils/provenance.py +++ /dev/null @@ -1,58 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -"""Helpers for collecting provenance information from xclim indicators.""" - -from __future__ import annotations - -import inspect -from typing import Any - -import xarray as xr - -from earthkit.climate.utils.conversions import MetadataDict - - -def add_indicator_provenance( - metadata: MetadataDict, - indicator: Any, - dataset: xr.Dataset, - **kwargs: Any, -) -> MetadataDict: - """ - Add provenance information from an xclim indicator call to the metadata. - - Parameters - ---------- - metadata : MetadataDict - Metadata dictionary to update. - indicator : Callable - xclim indicator function used to compute the index. - dataset : xarray.Dataset - Dataset passed to the indicator. - **kwargs : Any - Keyword arguments passed to the indicator. - - Returns - ------- - MetadataDict - The updated metadata dictionary. - """ - metadata["indicator_definition"] = getattr(indicator, "parameters", None) - metadata["cf_attrs"] = getattr(indicator, "cf_attrs", None) - - signature = inspect.signature(indicator) - bound_args = signature.bind_partial(ds=dataset, **kwargs) - bound_args.apply_defaults() - - metadata["call_info"] = { - "xclim_function": indicator.compute.__name__, - "parameters": dict(bound_args.arguments), - } - - return metadata diff --git a/src/earthkit/climate/utils/units.py b/src/earthkit/climate/utils/units.py deleted file mode 100644 index 9b5f03b..0000000 --- a/src/earthkit/climate/utils/units.py +++ /dev/null @@ -1,57 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -import warnings - -import xarray -from xclim.core.units import convert_units_to - - -def ensure_units(ds: xarray.Dataset, var: str, expected_units: str, strict: bool = False) -> xarray.Dataset: - """ - Ensure that a variable in the dataset has the expected units for xclim indicators. - - Parameters - ---------- - ds : xarray.Dataset - Dataset containing the variable. - var : str - Variable name (e.g. "tas", "pr"). - expected_units : str - Units expected by xclim (e.g. "degC", "mm/day"). - strict : bool, default False - If True, attempt to physically convert units using pint. - If False, only overwrite the unit attribute and issue a warning. - - Returns - ------- - xarray.Dataset - The dataset with corrected units. - """ - current_units = ds[var].attrs.get("units") - - if current_units != expected_units: - if strict: - try: - ds[var] = convert_units_to(ds[var], expected_units) - ds[var].attrs["units"] = expected_units - warnings.warn( - f"Variable '{var}' converted from {current_units} to {expected_units}.", - UserWarning, - ) - except Exception as e: - raise ValueError(f"Failed to convert {var} from {current_units} to {expected_units}: {e}") - else: - warnings.warn( - f"Variable '{var}' has units '{current_units}', expected '{expected_units}'. " - f"Overwriting without conversion.", - UserWarning, - ) - ds[var].attrs["units"] = expected_units - - return ds diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 4bf5a28..1f9ef24 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -10,7 +10,6 @@ import pandas as pd import pytest import xarray as xr -from pytest_mock import MockerFixture @pytest.fixture @@ -54,51 +53,3 @@ def daily_temperature_ds() -> xr.Dataset: data = rng.normal(loc=10.0, scale=2.0, size=time.size) ds = xr.Dataset({"tas": ("time", data)}, coords={"time": time}) return ds - - -@pytest.fixture -def common_mocks(mocker: MockerFixture, dummy_precip_ds: xr.Dataset) -> dict: - """ - Fixture that sets up common mocks used across indicators tests. - - Parameters - ---------- - mocker : MockerFixture - Pytest-mock fixture used to create and manage mocks. - dummy_precip_ds : xr.Dataset - The dummy precipitation dataset fixture. - - Returns - ------- - dict[str, Any]+ - Dictionary with references to key mock objects for assertions. - """ - object_ek = object() - - mock_to_xr = mocker.patch( - "earthkit.climate.utils.conversions.to_xarray_dataset", - return_value=(dummy_precip_ds, {"earthkit_internal": {}}), - ) - - mock_ensure_units = mocker.patch( - "earthkit.climate.utils.units.ensure_units", - side_effect=lambda ds, var, units, strict=False: ds, - ) - - mock_add_prov = mocker.patch( - "earthkit.climate.utils.provenance.add_indicator_provenance", - side_effect=lambda md, *a, **k: {**md, "prov": True}, - ) - - mock_to_ek = mocker.patch( - "earthkit.climate.utils.conversions.to_earthkit_field", - return_value=object_ek, - ) - - return { - "mock_to_xr": mock_to_xr, - "mock_ensure_units": mock_ensure_units, - "mock_add_prov": mock_add_prov, - "mock_to_ek": mock_to_ek, - "object_ek": object_ek, - } diff --git a/tests/unit/indicators/test_precipitation.py b/tests/unit/indicators/test_precipitation.py index f14243a..2c60abc 100644 --- a/tests/unit/indicators/test_precipitation.py +++ b/tests/unit/indicators/test_precipitation.py @@ -9,17 +9,11 @@ from typing import Any, Callable, Dict import pytest +import xarray from pytest_mock import MockerFixture from earthkit.climate.indicators import precipitation - -class MockEarthkitData: - """Mock object for Earthkit input.""" - - pass - - INDICATORS = [ (precipitation.antecedent_precipitation_index, "antecedent_precipitation_index", {"val": "test"}), (precipitation.maximum_consecutive_dry_days, "maximum_consecutive_dry_days", {"val": "test"}), @@ -28,7 +22,6 @@ class MockEarthkitData: "maximum_consecutive_wet_days", {"thresh": "2 mm/day", "freq": "MS"}, ), - # The original test used specific args for daily_pr_intensity, preserving those. (precipitation.daily_pr_intensity, "daily_pr_intensity", {"thresh": "2 mm/day", "freq": "MS"}), (precipitation.cffwis_indices, "cffwis_indices", {"arg1": "val1"}), (precipitation.cold_and_dry_days, "cold_and_dry_days", {"arg1": "val1"}), @@ -81,44 +74,25 @@ class MockEarthkitData: @pytest.mark.parametrize("earthkit_fn, xclim_name, kwargs", INDICATORS) def test_precipitation_indicator( mocker: MockerFixture, - common_mocks: dict, + dummy_precip_ds: xarray.Dataset, earthkit_fn: Callable, xclim_name: str, kwargs: Dict[str, Any], ): - """ - Test that the earthkit function wraps the xclim function correctly. - - Parameters - ---------- - mocker : MockerFixture - The pytest-mock fixture. - common_mocks : dict - Dictionary containing common mocks used in tests. - earthkit_fn : Callable - The earthkit indicator function to test. - xclim_name : str - The name of the corresponding xclim function. - kwargs : Dict[str, Any] - Arguments to pass to the function. - """ + """Test that the earthkit function wraps the xclim function correctly.""" xclim_func_name = xclim_name mock_path = f"xclim.indicators.atmos.{xclim_func_name}" mock_fn = mocker.patch(mock_path) - ds_in = MockEarthkitData() + ds_in = dummy_precip_ds # Call the earthkit function earthkit_fn(ds_in, **kwargs) - # Verify conversions were called (handled by common_mocks) - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - # Verify wrapped function called with the dataset and arguments mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted + assert mock_fn.call_args.kwargs["ds"] is not None for k, v in kwargs.items(): assert mock_fn.call_args.kwargs[k] == v diff --git a/tests/unit/indicators/test_temperature.py b/tests/unit/indicators/test_temperature.py index fcd9daa..efa3361 100644 --- a/tests/unit/indicators/test_temperature.py +++ b/tests/unit/indicators/test_temperature.py @@ -9,39 +9,24 @@ from typing import Callable import pytest +import xarray from pytest_mock import MockerFixture from earthkit.climate.indicators import temperature - -class MockEarthkitData: - """Mock object for Earthkit input.""" - - pass - - INDICATORS = [ (temperature.australian_hardiness_zones, "australian_hardiness_zones"), - ( - temperature.biologically_effective_degree_days, - "biologically_effective_degree_days", - ), + (temperature.biologically_effective_degree_days, "biologically_effective_degree_days"), (temperature.cold_spell_days, "cold_spell_days"), (temperature.cold_spell_duration_index, "cold_spell_duration_index"), (temperature.cold_spell_frequency, "cold_spell_frequency"), (temperature.cold_spell_max_length, "cold_spell_max_length"), (temperature.cold_spell_total_length, "cold_spell_total_length"), (temperature.consecutive_frost_days, "consecutive_frost_days"), - ( - temperature.maximum_consecutive_frost_free_days, - "maximum_consecutive_frost_free_days", - ), + (temperature.maximum_consecutive_frost_free_days, "maximum_consecutive_frost_free_days"), (temperature.cool_night_index, "cool_night_index"), (temperature.cooling_degree_days, "cooling_degree_days"), - ( - temperature.cooling_degree_days_approximation, - "cooling_degree_days_approximation", - ), + (temperature.cooling_degree_days_approximation, "cooling_degree_days_approximation"), (temperature.corn_heat_units, "corn_heat_units"), (temperature.chill_portions, "chill_portions"), (temperature.chill_units, "chill_units"), @@ -49,10 +34,7 @@ class MockEarthkitData: (temperature.daily_freezethaw_cycles, "daily_freezethaw_cycles"), (temperature.daily_temperature_range, "daily_temperature_range"), (temperature.max_daily_temperature_range, "max_daily_temperature_range"), - ( - temperature.daily_temperature_range_variability, - "daily_temperature_range_variability", - ), + (temperature.daily_temperature_range_variability, "daily_temperature_range_variability"), (temperature.extreme_temperature_range, "extreme_temperature_range"), (temperature.fire_season, "fire_season"), (temperature.first_day_tg_above, "first_day_tg_above"), @@ -84,10 +66,7 @@ class MockEarthkitData: (temperature.heat_wave_max_length, "heat_wave_max_length"), (temperature.heat_wave_total_length, "heat_wave_total_length"), (temperature.heating_degree_days, "heating_degree_days"), - ( - temperature.heating_degree_days_approximation, - "heating_degree_days_approximation", - ), + (temperature.heating_degree_days_approximation, "heating_degree_days_approximation"), (temperature.hot_days, "hot_days"), (temperature.hot_spell_frequency, "hot_spell_frequency"), (temperature.hot_spell_max_length, "hot_spell_max_length"), @@ -131,24 +110,11 @@ class MockEarthkitData: @pytest.mark.parametrize("earthkit_fn, xclim_name", INDICATORS) def test_temperature_indicator( mocker: MockerFixture, - common_mocks: dict, + dummy_temp_ds: xarray.Dataset, earthkit_fn: Callable, xclim_name: str, ): - """ - Test that the earthkit function wraps the xclim function correctly. - - Parameters - ---------- - mocker : MockerFixture - The pytest-mock fixture. - common_mocks : dict - Dictionary containing common mocks used in tests. - earthkit_fn : Callable - The earthkit indicator function to test. - xclim_name : str - The name of the corresponding xclim function. - """ + """Test that the earthkit function wraps the xclim function correctly.""" xclim_func_name = xclim_name mock_path = f"xclim.indicators.atmos.{xclim_func_name}" @@ -158,17 +124,14 @@ def test_temperature_indicator( # Use a dummy argument dictionary kwargs = {"arg1": "val1", "arg2": 2} - ds_in = MockEarthkitData() + ds_in = dummy_temp_ds # Call the earthkit function earthkit_fn(ds_in, **kwargs) - # Verify conversions were called (handled by common_mocks) - common_mocks["mock_to_xr"].assert_called_once_with(ds_in, {}) - ds_converted = common_mocks["mock_to_xr"].return_value[0] - # Verify wrapped function called with the dataset and arguments mock_fn.assert_called_once() - assert mock_fn.call_args.kwargs["ds"] is ds_converted + # The dataset might be the same or transformed by the decorator + assert mock_fn.call_args.kwargs["ds"] is not None for k, v in kwargs.items(): assert mock_fn.call_args.kwargs[k] == v diff --git a/tests/unit/utils/test_conversions.py b/tests/unit/utils/test_conversions.py deleted file mode 100644 index b1780b0..0000000 --- a/tests/unit/utils/test_conversions.py +++ /dev/null @@ -1,75 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -from typing import Any - -import pytest -import xarray as xr -from earthkit.data.wrappers.xarray import XArrayDatasetWrapper - -from earthkit.climate.utils.conversions import ( - to_earthkit_field, - to_xarray_dataset, -) - - -@pytest.mark.parametrize( - "xr_input", - [ - xr.Dataset({"tas": ("time", [1.0, 2.0])}, coords={"time": [0, 1]}), - xr.DataArray([1.0, 2.0], dims=["time"], name="tas"), - ], -) -def test_to_xarray_dataset_accepts_xarray_and_propagates_metadata(xr_input: Any) -> None: - """ - Test that `to_xarray_dataset` correctly handles both xarray.Dataset and xarray.DataArray inputs, - returning a Dataset and propagating the correct metadata about the input type. - """ - ds, meta = to_xarray_dataset(xr_input, {"a": 1}) - assert isinstance(ds, xr.Dataset) - assert meta["earthkit_internal"]["input_type"] in {"xarray.Dataset", "xarray.DataArray"} - - -def test_to_xarray_dataset_uses_to_xarray_on_wrapped_object() -> None: - """ - Test that `to_xarray_dataset` calls `to_xarray()` method when provided with an object - implementing that method, and correctly extracts the Dataset and metadata. - """ - - class HasToXarray: - def to_xarray(self) -> xr.Dataset: - return xr.Dataset({"pr": ("time", [0.1, 0.2])}, coords={"time": [0, 1]}) - - ds, meta = to_xarray_dataset(HasToXarray(), {}) - assert set(ds.data_vars) == {"pr"} - assert meta["earthkit_internal"]["input_type"].endswith("HasToXarray") - - -def test_to_xarray_dataset_rejects_invalid_to_xarray_return() -> None: - """ - Test that `to_xarray_dataset` raises a TypeError when an object's `to_xarray()` method - does not return a valid xarray object. - """ - - class BadToXarray: - def to_xarray(self) -> int: - return 123 - - with pytest.raises(TypeError): - to_xarray_dataset(BadToXarray(), {}) - - -def test_to_earthkit_field_wraps_dataset_and_attaches_provenance() -> None: - """ - Test that `to_earthkit_field` wraps an xarray.DataArray inside a DummyWrapper - and attaches provenance metadata correctly. - """ - da = xr.DataArray([1, 2, 3], dims=["time"], name="tas") - ek_obj = to_earthkit_field(da, {"provenance": {"step": "x"}}) - assert isinstance(ek_obj, XArrayDatasetWrapper) - assert "earthkit_provenance" in ek_obj.to_xarray().attrs diff --git a/tests/unit/utils/test_provenance.py b/tests/unit/utils/test_provenance.py deleted file mode 100644 index 3919556..0000000 --- a/tests/unit/utils/test_provenance.py +++ /dev/null @@ -1,121 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -from __future__ import annotations - -import inspect -from typing import Any, Dict - -import xarray as xr - -from earthkit.climate.utils.provenance import add_indicator_provenance - - -class DummyIndicator: - """ - Minimal indicator stub exposing parameters, cf_attrs, and a compute method. - Behaves like an xclim indicator in terms of inspect.signature(). - """ - - parameters = {"threshold": {"default": 2.0}, "window": {"default": 3}} - cf_attrs = [{"standard_name": "dummy"}] - - def compute(self, ds: xr.Dataset, threshold: float = 2.0, window: int = 3) -> xr.DataArray: - """Fake compute method similar to xclim indicators.""" - return ds[list(ds.data_vars)[0]].mean(dim="time") - - __signature__ = inspect.signature(compute) - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - """Make the indicator callable, like real xclim indicators.""" - return self.compute(*args, **kwargs) - - -class NoAttrsIndicator: - """Stub indicator without parameters or cf_attrs.""" - - def compute(self, ds: xr.Dataset, alpha: float = 0.5) -> xr.DataArray: - return ds[list(ds.data_vars)[0]] - - def __call__(self, *args: Any, **kwargs: Any) -> Any: - return self.compute(*args, **kwargs) - - -def test_populates_indicator_definition_and_cf_attrs() -> None: - """ - Test that `add_indicator_provenance` correctly populates the metadata with - the indicator definition and CF attributes when they are present in the indicator. - """ - ds: xr.Dataset = xr.Dataset({"tas": ("time", [1.0, 2.0, 3.0])}, coords={"time": [0, 1, 2]}) - meta: Dict[str, Any] = {} - - ind = DummyIndicator() - out: Dict[str, Any] = add_indicator_provenance(meta, ind, ds, threshold=10.0) - - assert out["indicator_definition"] == DummyIndicator.parameters - assert out["cf_attrs"] == DummyIndicator.cf_attrs - - -def test_call_info_contains_compute_name_and_bound_args() -> None: - """ - Test that `add_indicator_provenance` stores function call metadata correctly, - including the function name and bound parameters (e.g., Dataset and arguments). - """ - ds: xr.Dataset = xr.Dataset({"pr": ("time", [0.1, 0.2])}, coords={"time": [0, 1]}) - meta: Dict[str, Any] = {} - - ind = DummyIndicator() - out: Dict[str, Any] = add_indicator_provenance(meta, ind, ds, window=5) - - assert "call_info" in out - call = out["call_info"] - assert call["xclim_function"] == ind.compute.__name__ - assert isinstance(call["parameters"]["ds"], xr.Dataset) - assert call["parameters"]["window"] == 5 - # default threshold applied - assert call["parameters"]["threshold"] == 2.0 - - -def test_handles_missing_optional_attributes() -> None: - """ - Test that `add_indicator_provenance` handles indicators without optional - attributes (`parameters` or `cf_attrs`) without raising errors. - """ - ds: xr.Dataset = xr.Dataset({"tas": ("time", [1.0])}, coords={"time": [0]}) - meta: Dict[str, Any] = {} - - ind = NoAttrsIndicator() - out: Dict[str, Any] = add_indicator_provenance(meta, ind, ds) - - assert out["indicator_definition"] is None - assert out["cf_attrs"] is None - assert out["call_info"]["xclim_function"] == ind.compute.__name__ - - -def test_uses_indicator_signature() -> None: - """ - Test that the `compute` method of the indicator exposes the expected - function signature, including the `ds` parameter. - """ - ind = DummyIndicator() - sig = inspect.signature(ind.compute) - assert "ds" in sig.parameters - - -def test_mutates_metadata_in_place() -> None: - """ - Test that `add_indicator_provenance` mutates the provided metadata dictionary - in place instead of returning a new object. - """ - ds: xr.Dataset = xr.Dataset({"tas": ("time", [1.0, 2.0])}, coords={"time": [0, 1]}) - meta: Dict[str, Any] = {"pre": True} - ind = DummyIndicator() - - out: Dict[str, Any] = add_indicator_provenance(meta, ind, ds) - assert out is meta - assert "call_info" in meta diff --git a/tests/unit/utils/test_units.py b/tests/unit/utils/test_units.py deleted file mode 100644 index 939a408..0000000 --- a/tests/unit/utils/test_units.py +++ /dev/null @@ -1,87 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -import warnings -from typing import Any - -import pytest -import xarray - -from earthkit.climate.utils.units import ensure_units - - -def test_ensure_units_non_strict_overwrites_and_warns() -> None: - """ - Test that `ensure_units` overwrites units without conversion in non-strict mode - and emits a warning message. - """ - ds = xarray.Dataset({"tas": ("time", [273.15, 274.15])}) - ds["tas"].attrs["units"] = "K" - - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - out = ensure_units(ds, "tas", "degC", strict=False) - assert out["tas"].attrs["units"] == "degC" - assert any("Overwriting without conversion" in str(ww.message) for ww in w) - - -def test_ensure_units_strict_uses_xclim_convert_units_to(monkeypatch: Any) -> None: - """ - Test that `ensure_units` calls `convert_units_to` (from xclim) in strict mode, - performing unit conversion and emitting a conversion warning. - """ - ds = xarray.Dataset({"tas": ("time", [273.15, 274.15])}) - ds["tas"].attrs["units"] = "K" - - # Mock convert_units_to to avoid requiring pint configuration - from earthkit.climate.utils import units as units_mod - - def fake_convert(var: xarray.DataArray, units: str) -> xarray.DataArray: - # fake conversion K -> degC - data = var - 273.15 - data.attrs = dict(var.attrs) - data.attrs["units"] = units - return data - - monkeypatch.setattr(units_mod, "convert_units_to", fake_convert) - - with warnings.catch_warnings(record=True) as w: - warnings.simplefilter("always") - out = ensure_units(ds, "tas", "degC", strict=True) - assert pytest.approx(out["tas"].values.tolist()) == [0.0, 1.0] - assert out["tas"].attrs["units"] == "degC" - assert any("converted from" in str(ww.message) for ww in w) - - -def test_ensure_units_strict_raises_on_conversion_error(monkeypatch: Any) -> None: - """Test that `ensure_units` raises a ValueError when conversion fails in strict mode.""" - ds = xarray.Dataset({"tas": ("time", [1.0, 2.0])}) - ds["tas"].attrs["units"] = "unknown" - - from earthkit.climate.utils import units as units_mod - - def failing_convert(*args: Any, **kwargs: Any) -> None: - raise RuntimeError("no conversion") - - monkeypatch.setattr(units_mod, "convert_units_to", failing_convert) - - with pytest.raises(ValueError) as exc: - ensure_units(ds, "tas", "degC", strict=True) - assert "Failed to convert" in str(exc.value) - - -def test_ensure_units_noop_when_units_already_expected() -> None: - """ - Test that `ensure_units` does nothing (no conversion or overwrite) - when the variable already has the expected units. - """ - ds = xarray.Dataset({"pr": ("time", [0.1, 0.2])}) - ds["pr"].attrs["units"] = "mm/day" - out = ensure_units(ds, "pr", "mm/day", strict=True) - # identical object references are ok since we don't copy in function - assert out["pr"].attrs["units"] == "mm/day" diff --git a/tests/unit/wrapper/test_wrap_xclim_indicator.py b/tests/unit/wrapper/test_wrap_xclim_indicator.py deleted file mode 100644 index 56864f6..0000000 --- a/tests/unit/wrapper/test_wrap_xclim_indicator.py +++ /dev/null @@ -1,82 +0,0 @@ -# (C) Copyright 2025 - ECMWF and individual contributors. - -# This software is licensed under the terms of the Apache Licence Version 2.0 -# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0. -# In applying this licence, ECMWF does not waive the privileges and immunities -# granted to it by virtue of its status as an intergovernmental organisation nor -# does it submit to any jurisdiction. - -import numpy as np -import pytest -import xarray as xr -from pytest_mock import MockerFixture - -from earthkit.climate.api.wrapper import wrap_xclim_indicator - - -class MockEarthkitData: - """Mock object for Earthkit input.""" - - pass - - -@pytest.fixture -def mock_xclim_indicator(mocker: MockerFixture): - """Creates a mock xclim indicator function.""" - mock_fn = mocker.MagicMock() - # Setup return value as an xarray Dataset - ds_out = xr.Dataset( - {"out_var": (("time", "lat", "lon"), np.random.rand(10, 10, 10))}, - coords={"time": np.arange(10), "lat": np.arange(10), "lon": np.arange(10)}, - ) - mock_fn.return_value = ds_out - mock_fn.__name__ = "mock_indicator" - return mock_fn - - -def test_wrapper_call(mock_xclim_indicator, common_mocks): - """Test that the wrapper calls the underlying xclim function.""" - wrapped_fn = wrap_xclim_indicator(mock_xclim_indicator) - input_data = MockEarthkitData() - - mock_to_xr = common_mocks["mock_to_xr"] - mock_ensure_units = common_mocks["mock_ensure_units"] - mock_add_prov = common_mocks["mock_add_prov"] - mock_to_ek = common_mocks["mock_to_ek"] - object_ek = common_mocks["object_ek"] - - result = wrapped_fn(input_data, arg1="value1") - - # Check conversions called - mock_to_xr.assert_called_once() - - # Check units called (dummy_precip_ds has 'pr', so ensure_units should be called) - mock_ensure_units.assert_called() - - # Check xclim function called - mock_xclim_indicator.assert_called_once() - - # Check provenance called - mock_add_prov.assert_called_once() - - # Check output conversion - mock_to_ek.assert_called_once() - assert result is object_ek - - -def test_wrapper_units_conversion(mock_xclim_indicator, common_mocks): - """Test that units are ensured for specific variables.""" - wrapped_fn = wrap_xclim_indicator(mock_xclim_indicator) - input_data = MockEarthkitData() - - mock_to_xr = common_mocks["mock_to_xr"] - mock_ensure_units = common_mocks["mock_ensure_units"] - - # Setup mock dataset with 'tas' and 'pr' - ds_in = xr.Dataset({"tas": (("x"), [1]), "pr": (("x"), [1])}) - mock_to_xr.return_value = (ds_in, {}) - - wrapped_fn(input_data) - - # Verify ensure_units called for both - assert mock_ensure_units.call_count >= 2 diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index ab0aeb7..f329cf9 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -25,30 +25,57 @@ \"\"\"{category_title} indices.\"\"\" -from typing import Any +from typing import Any, Literal import xarray import xclim.indicators.atmos +from earthkit.utils.decorators.format_handlers import format_handler -import earthkit.climate.utils.conversions as conversions -from earthkit.climate.api.wrapper import wrap_xclim_indicator +# from earthkit.climate.utils.decorators import metadata_handler {functions_code} """ FUNCTION_TEMPLATE = """ +@format_handler() +# @metadata_handler({xclim_obj_ref}) def {func_name}( - ds: conversions.EarthkitData | xarray.Dataset, - **kwargs: Any, -) -> conversions.EarthkitData: +{signature_params} +) -> Any: \"\"\" {docstring} \"\"\" - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.{xclim_func_name}) - return wrapper(ds, **kwargs) + return {xclim_obj_ref}({call_params}) """ +def simplify_type(type_obj: Any) -> str: + """Simplify complex types to strings that can be used in the generated code.""" + if type_obj == inspect.Parameter.empty: + return "Any" + + type_str = str(type_obj) + + # Common replacements + replacements = { + "xarray.core.dataarray.DataArray": "xarray.DataArray", + "xarray.core.dataset.Dataset": "xarray.Dataset", + "xarray.core.datatree.DataTree": "Any", + "Quantified": "Any", # xclim specific, hard to import reliably + "DayOfYearStr": "str", + "Indexer": "Any", + } + + for old, new in replacements.items(): + type_str = type_str.replace(old, new) + + # Remove quotes + if type_str.startswith("'") and type_str.endswith("'"): + type_str = type_str[1:-1] + + return type_str + + def generate_docstring(indicator: Any, xclim_func_name: str) -> str: """Generate a docstring for the wrapper function based on the xclim indicator. @@ -117,31 +144,140 @@ def generate_docstring(indicator: Any, xclim_func_name: str) -> str: if units_section: sections.append(units_section) - sections.append( - f"This function wraps `xclim.indicators.atmos.{xclim_func_name} " - f"`_." - ) - - # Static footer - footer = inspect.cleandoc(f""" - Parameters - ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input dataset. See xclim documentation for required variables. - **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.{xclim_func_name}`. - + link_prefix = f"This function wraps `xclim.indicators.atmos.{xclim_func_name}" + link_url = f"`_." + sections.append(f"{link_prefix}\n {link_url}") + + # Parameters section + params_lines = [ + "Parameters", + "----------", + "ds : xarray.Dataset | Any", + " Input dataset.", + ] + + try: + sig = inspect.signature(indicator) + for name, param in sig.parameters.items(): + if name == "ds": + continue + + # Skip VAR parameters as they are handled by docstring **kwargs + if param.kind in (inspect.Parameter.VAR_KEYWORD, inspect.Parameter.VAR_POSITIONAL): + continue + + # Try to get description from indicator.parameters + param_meta = indicator.parameters.get(name) + description = getattr(param_meta, "description", "") if param_meta else "" + + type_hint = simplify_type(param.annotation) + + params_lines.append(f"{name} : {type_hint}") + if description: + # Wrap description with hanging indent + wrapped_description = textwrap.fill( + description, width=88, initial_indent=" ", subsequent_indent=" " + ) + params_lines.append(wrapped_description) + except Exception: + pass + + params_lines.append("**kwargs : Any") + params_lines.append(" Additional keyword arguments.") + + sections.append("\n".join(params_lines)) + + # Returns section + returns_section = inspect.cleandoc(""" Returns ------- - conversions.EarthkitData - The computed index as an Earthkit-compatible field. + Any + The computed index. """) - sections.append(footer) + sections.append(returns_section) return "\n\n".join(sections) +def format_signature_params(indicator: Any) -> str: + """Format the parameters for the function signature.""" + params = [" ds: xarray.Dataset | Any,"] + + try: + sig = inspect.signature(indicator) + + sig_params = list(sig.parameters.values()) + + pos_no_default = [] + pos_with_default = [] + kw_only = [] + + for p in sig_params: + if p.name == "ds": + continue + + if p.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.POSITIONAL_ONLY): + if p.default == inspect.Parameter.empty: + pos_no_default.append(p) + else: + pos_with_default.append(p) + elif p.kind == inspect.Parameter.KEYWORD_ONLY: + kw_only.append(p) + # Skip VAR_KEYWORD and VAR_POSITIONAL as we add our own **kwargs + + for p in pos_no_default: + type_hint = simplify_type(p.annotation) + params.append(f" {p.name}: {type_hint},") + + for p in pos_with_default: + type_hint = simplify_type(p.annotation) + default_val = repr(p.default) + params.append(f" {p.name}: {type_hint} = {default_val},") + + if kw_only: + params.append(" *,") + for p in kw_only: + type_hint = simplify_type(p.annotation) + if p.default != inspect.Parameter.empty: + default_val = repr(p.default) + params.append(f" {p.name}: {type_hint} = {default_val},") + else: + params.append(f" {p.name}: {type_hint},") + + except Exception: + pass + + params.append(" **kwargs: Any,") + + return "\n".join(params) + + +def format_call_params(indicator: Any) -> str: + """Format the parameters for the xclim call.""" + try: + sig = inspect.signature(indicator) + call_args = [] + for name, param in sig.parameters.items(): + if name == "ds": + call_args.append("ds=ds") + elif param.kind in (inspect.Parameter.VAR_KEYWORD, inspect.Parameter.VAR_POSITIONAL): + continue # Skip VAR parameters, they are covered by **kwargs + else: + call_args.append(f"{name}={name}") + call_args.append("**kwargs") + + # If the total length is likely to exceed 88 chars (indent=4 + total), or many parameters + total_len = ( + sum(len(arg) for arg in call_args) + 2 * len(call_args) + 30 + ) # 30 for the 'return xclim...' part + if len(call_args) > 3 or total_len > 80: + return "\n " + ",\n ".join(call_args) + ",\n " + + return ", ".join(call_args) + except Exception: + return "ds=ds, **kwargs" + + def generate_module_content(category: str, indicators: List[Any]) -> str: """Generate the content for a python module containing wrapper functions. @@ -185,8 +321,16 @@ def generate_module_content(category: str, indicators: List[Any]) -> str: lines[0] + "\n" + "\n".join([(" " + line if line.strip() else "") for line in lines[1:]]) ) + signature_params = format_signature_params(ind) + call_params = format_call_params(ind) + xclim_obj_ref = f"xclim.indicators.atmos.{xclim_func_name}" + code = FUNCTION_TEMPLATE.format( - func_name=func_name, xclim_func_name=xclim_func_name, docstring=indented_doc + func_name=func_name, + signature_params=signature_params, + call_params=call_params, + xclim_obj_ref=xclim_obj_ref, + docstring=indented_doc, ) functions_code.append(code) From b740a95f1d8b5d767d1c9fb25474068f746d4a04 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 6 Mar 2026 10:54:12 +0100 Subject: [PATCH 32/47] refactor: function arguments order --- .../climate/indicators/precipitation.py | 294 +++++----- .../climate/indicators/temperature.py | 534 +++++++++--------- tools/xclim_wrappers_generator.py | 9 +- 3 files changed, 420 insertions(+), 417 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index fa43c82..26f5c3e 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -20,8 +20,8 @@ @format_handler() # @metadata_handler(xclim.indicators.atmos.antecedent_precipitation_index) def antecedent_precipitation_index( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, window: int = 7, p_exp: float = 0.935, @@ -42,14 +42,14 @@ def antecedent_precipitation_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation data. window : int Window for the days of precipitation data to be weighted and summed, default is 7. p_exp : float Weighting exponent, default is 0.935. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -69,8 +69,8 @@ def antecedent_precipitation_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_dry_days) def maximum_consecutive_dry_days( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -92,8 +92,6 @@ def maximum_consecutive_dry_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. thresh : Any @@ -103,6 +101,8 @@ def maximum_consecutive_dry_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -123,7 +123,6 @@ def maximum_consecutive_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cffwis_indices) def cffwis_indices( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', sfcWind: xarray.DataArray | str = 'sfcWind', @@ -134,6 +133,7 @@ def cffwis_indices( dmc0: xarray.DataArray | str | None = None, dc0: xarray.DataArray | str | None = None, season_mask: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, season_method: str | None = None, overwintering: bool = False, @@ -162,8 +162,6 @@ def cffwis_indices( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Noon temperature. pr : xarray.DataArray | str @@ -198,6 +196,8 @@ def cffwis_indices( If True (default), gridpoints where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -228,11 +228,11 @@ def cffwis_indices( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_and_dry_days) def cold_and_dry_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', tas_per: xarray.DataArray | str = 'tas_per', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -252,8 +252,6 @@ def cold_and_dry_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature values. pr : xarray.DataArray | str @@ -264,6 +262,8 @@ def cold_and_dry_days( First quartile of daily total precipitation computed by month. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -285,11 +285,11 @@ def cold_and_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_and_wet_days) def cold_and_wet_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', tas_per: xarray.DataArray | str = 'tas_per', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -309,8 +309,6 @@ def cold_and_wet_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature values. pr : xarray.DataArray | str @@ -321,6 +319,8 @@ def cold_and_wet_days( Third quartile of daily total precipitation computed by month. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -342,8 +342,8 @@ def cold_and_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_wet_days) def maximum_consecutive_wet_days( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -365,8 +365,6 @@ def maximum_consecutive_wet_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. thresh : Any @@ -376,6 +374,8 @@ def maximum_consecutive_wet_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -396,9 +396,9 @@ def maximum_consecutive_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_over_precip_doy_thresh) def days_over_precip_doy_thresh( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -421,8 +421,6 @@ def days_over_precip_doy_thresh( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. pr_per : xarray.DataArray | str @@ -441,6 +439,8 @@ def days_over_precip_doy_thresh( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -463,9 +463,9 @@ def days_over_precip_doy_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_over_precip_thresh) def days_over_precip_thresh( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -488,8 +488,6 @@ def days_over_precip_thresh( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. pr_per : xarray.DataArray | str @@ -508,6 +506,8 @@ def days_over_precip_thresh( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -530,8 +530,8 @@ def days_over_precip_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_with_snow) def days_with_snow( - ds: xarray.Dataset | Any, prsn: xarray.DataArray | str = 'prsn', + ds: xarray.Dataset | Any = None, *, low: Any = '0 kg m-2 s-1', high: Any = '1E6 kg m-2 s-1', @@ -552,8 +552,6 @@ def days_with_snow( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. prsn : xarray.DataArray | str Snowfall flux. low : Any @@ -562,6 +560,8 @@ def days_with_snow( Maximum threshold snowfall flux or liquid water equivalent snowfall rate. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -582,13 +582,13 @@ def days_with_snow( @format_handler() # @metadata_handler(xclim.indicators.atmos.drought_code) def drought_code( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', lat: xarray.DataArray | str = 'lat', snd: xarray.DataArray | str | None = None, dc0: xarray.DataArray | str | None = None, season_mask: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, season_method: str | None = None, overwintering: bool = False, @@ -611,8 +611,6 @@ def drought_code( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Noon temperature. pr : xarray.DataArray | str @@ -639,6 +637,8 @@ def drought_code( If True (default), grid points where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -665,9 +665,9 @@ def drought_code( @format_handler() # @metadata_handler(xclim.indicators.atmos.griffiths_drought_factor) def griffiths_drought_factor( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', smd: xarray.DataArray | str = 'smd', + ds: xarray.Dataset | Any = None, *, limiting_func: str = 'xlim', **kwargs: Any, @@ -688,8 +688,6 @@ def griffiths_drought_factor( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Total rainfall over previous 24 hours [mm/day]. smd : xarray.DataArray | str @@ -699,6 +697,8 @@ def griffiths_drought_factor( (14) in :cite:t:`ffdi-finkele_2006`. If "discrete", use equation Eq (13) in :cite:t:`ffdi-finkele_2006`, but with the lower limit of each category bound adjusted to match the upper limit of the previous bound. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -718,7 +718,6 @@ def griffiths_drought_factor( @format_handler() # @metadata_handler(xclim.indicators.atmos.duff_moisture_code) def duff_moisture_code( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', hurs: xarray.DataArray | str = 'hurs', @@ -726,6 +725,7 @@ def duff_moisture_code( snd: xarray.DataArray | str | None = None, dmc0: xarray.DataArray | str | None = None, season_mask: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, season_method: str | None = None, dry_start: str | None = None, @@ -748,8 +748,6 @@ def duff_moisture_code( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Noon temperature. pr : xarray.DataArray | str @@ -775,6 +773,8 @@ def duff_moisture_code( If True (default), grid points where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -801,8 +801,8 @@ def duff_moisture_code( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_days) def dry_days( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0.2 mm/d', freq: str = 'YS', @@ -823,8 +823,6 @@ def dry_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -833,6 +831,8 @@ def dry_days( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -853,8 +853,8 @@ def dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_frequency) def dry_spell_frequency( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 3, @@ -878,8 +878,6 @@ def dry_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -898,6 +896,8 @@ def dry_spell_frequency( checks that the maximal daily precipitation amount within the window is less than the threshold. This is the same as verifying that each individual day is below the threshold. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -920,8 +920,8 @@ def dry_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_max_length) def dry_spell_max_length( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 1, @@ -945,8 +945,6 @@ def dry_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -960,6 +958,8 @@ def dry_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -982,8 +982,8 @@ def dry_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_total_length) def dry_spell_total_length( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 3, @@ -1007,8 +1007,6 @@ def dry_spell_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -1026,6 +1024,8 @@ def dry_spell_total_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1048,10 +1048,10 @@ def dry_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.dryness_index) def dryness_index( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', evspsblpot: xarray.DataArray | str = 'evspsblpot', lat: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, wo: Any = '200 mm', freq: Literal['YS', 'YS-JAN'] = 'YS', @@ -1073,8 +1073,6 @@ def dryness_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Precipitation. evspsblpot : xarray.DataArray | str @@ -1086,6 +1084,8 @@ def dryness_index( The initial soil water reserve accessible to root systems [length]. Default: 200 mm. freq : Literal['YS', 'YS-JAN'] Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1107,11 +1107,11 @@ def dryness_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) def mcarthur_forest_fire_danger_index( - ds: xarray.Dataset | Any, drought_factor: xarray.DataArray | str = 'drought_factor', tasmax: xarray.DataArray | str = 'tasmax', hurs: xarray.DataArray | str = 'hurs', sfcWind: xarray.DataArray | str = 'sfcWind', + ds: xarray.Dataset | Any = None, **kwargs: Any, ) -> Any: """ @@ -1128,8 +1128,6 @@ def mcarthur_forest_fire_danger_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. drought_factor : xarray.DataArray | str The drought factor, often the daily Griffiths drought factor (see :py:func:`griffiths_drought_factor`). @@ -1147,6 +1145,8 @@ def mcarthur_forest_fire_danger_index( or similar. Different applications have used different inputs here, including the mid-afternoon wind speed at a height of 10m, and the daily mean wind speed at a height of 10m. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1167,8 +1167,8 @@ def mcarthur_forest_fire_danger_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_snowfall) def first_snowfall( - ds: xarray.Dataset | Any, prsn: xarray.DataArray | str = 'prsn', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS-JUL', @@ -1189,8 +1189,6 @@ def first_snowfall( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. prsn : xarray.DataArray | str Snowfall flux. thresh : Any @@ -1198,6 +1196,8 @@ def first_snowfall( mm/day). freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1217,9 +1217,9 @@ def first_snowfall( @format_handler() # @metadata_handler(xclim.indicators.atmos.fraction_over_precip_doy_thresh) def fraction_over_precip_doy_thresh( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -1243,8 +1243,6 @@ def fraction_over_precip_doy_thresh( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. pr_per : xarray.DataArray | str @@ -1263,6 +1261,8 @@ def fraction_over_precip_doy_thresh( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1285,9 +1285,9 @@ def fraction_over_precip_doy_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.fraction_over_precip_thresh) def fraction_over_precip_thresh( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -1311,8 +1311,6 @@ def fraction_over_precip_thresh( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. pr_per : xarray.DataArray | str @@ -1331,6 +1329,8 @@ def fraction_over_precip_thresh( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1353,9 +1353,9 @@ def fraction_over_precip_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.high_precip_low_temp) def high_precip_low_temp( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, pr_thresh: Any = '0.4 mm/d', tas_thresh: Any = '-0.2 degC', @@ -1377,8 +1377,6 @@ def high_precip_low_temp( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -1389,6 +1387,8 @@ def high_precip_low_temp( Temperature threshold not to exceed. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1410,11 +1410,11 @@ def high_precip_low_temp( @format_handler() # @metadata_handler(xclim.indicators.atmos.keetch_byram_drought_index) def keetch_byram_drought_index( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tasmax: xarray.DataArray | str = 'tasmax', pr_annual: xarray.DataArray | str = 'pr_annual', kbdi0: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, **kwargs: Any, ) -> Any: """ @@ -1435,8 +1435,6 @@ def keetch_byram_drought_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Total rainfall over previous 24 hours [mm/day]. tasmax : xarray.DataArray | str @@ -1446,6 +1444,8 @@ def keetch_byram_drought_index( kbdi0 : xarray.DataArray | str | None Previous KBDI values used to initialise the KBDI calculation [mm/day]. Defaults to 0. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1466,8 +1466,8 @@ def keetch_byram_drought_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.last_snowfall) def last_snowfall( - ds: xarray.Dataset | Any, prsn: xarray.DataArray | str = 'prsn', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS-JUL', @@ -1488,8 +1488,6 @@ def last_snowfall( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. prsn : xarray.DataArray | str Snowfall flux. thresh : Any @@ -1497,6 +1495,8 @@ def last_snowfall( mm/day). freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1516,9 +1516,9 @@ def last_snowfall( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_ratio) def liquid_precip_ratio( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'QS-DEC', @@ -1540,8 +1540,6 @@ def liquid_precip_ratio( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -1550,6 +1548,8 @@ def liquid_precip_ratio( Threshold temperature under which precipitation is assumed to be solid. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1570,9 +1570,9 @@ def liquid_precip_ratio( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_average) def liquid_precip_average( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -1593,8 +1593,6 @@ def liquid_precip_average( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -1603,6 +1601,8 @@ def liquid_precip_average( Threshold of `tas` over which the precipication is assumed to be liquid rain. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1623,9 +1623,9 @@ def liquid_precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_accumulation) def liquid_precip_accumulation( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -1646,8 +1646,6 @@ def liquid_precip_accumulation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -1656,6 +1654,8 @@ def liquid_precip_accumulation( Threshold of `tas` over which the precipication is assumed to be liquid rain. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1676,8 +1676,8 @@ def liquid_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_n_day_precipitation_amount) def max_n_day_precipitation_amount( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, window: int = 1, freq: str = 'YS', @@ -1697,14 +1697,14 @@ def max_n_day_precipitation_amount( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation values. window : int Window size in days. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1724,8 +1724,8 @@ def max_n_day_precipitation_amount( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_pr_intensity) def max_pr_intensity( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, window: int = 1, freq: str = 'YS', @@ -1745,14 +1745,14 @@ def max_pr_intensity( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Hourly precipitation values. window : int Window size in hours. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1772,8 +1772,8 @@ def max_pr_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.precip_average) def precip_average( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -1796,14 +1796,14 @@ def precip_average( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. thresh : Any Threshold of `tas` over which the precipication is assumed to be liquid rain. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1823,8 +1823,8 @@ def precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.precip_accumulation) def precip_accumulation( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -1846,12 +1846,12 @@ def precip_accumulation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1870,9 +1870,9 @@ def precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.rain_on_frozen_ground_days) def rain_on_frozen_ground_days( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/d', window: int = 7, @@ -1895,8 +1895,6 @@ def rain_on_frozen_ground_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -1908,6 +1906,8 @@ def rain_on_frozen_ground_days( frozen. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1929,8 +1929,8 @@ def rain_on_frozen_ground_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.rain_season) def rain_season( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh_wet_start: Any = '25.0 mm', window_wet_start: int = 3, @@ -1967,8 +1967,6 @@ def rain_season( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Precipitation data. thresh_wet_start : Any @@ -2016,6 +2014,8 @@ def rain_season( Last day of year when season can end ("mm-dd"). freq : Any Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2047,9 +2047,9 @@ def rain_season( @format_handler() # @metadata_handler(xclim.indicators.atmos.rprctot) def rprctot( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', prc: xarray.DataArray | str = 'prc', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm/day', freq: str = 'YS', @@ -2071,8 +2071,6 @@ def rprctot( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. prc : xarray.DataArray | str @@ -2083,6 +2081,8 @@ def rprctot( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2104,8 +2104,8 @@ def rprctot( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_1day_precipitation_amount) def max_1day_precipitation_amount( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -2124,12 +2124,12 @@ def max_1day_precipitation_amount( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation values. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2148,8 +2148,8 @@ def max_1day_precipitation_amount( @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_pr_intensity) def daily_pr_intensity( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -2170,8 +2170,6 @@ def daily_pr_intensity( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2180,6 +2178,8 @@ def daily_pr_intensity( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2200,8 +2200,8 @@ def daily_pr_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.snowfall_frequency) def snowfall_frequency( - ds: xarray.Dataset | Any, prsn: xarray.DataArray | str = 'prsn', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS-JUL', @@ -2222,8 +2222,6 @@ def snowfall_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. prsn : xarray.DataArray | str Snowfall flux. thresh : Any @@ -2231,6 +2229,8 @@ def snowfall_frequency( mm/day). freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2250,8 +2250,8 @@ def snowfall_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.snowfall_intensity) def snowfall_intensity( - ds: xarray.Dataset | Any, prsn: xarray.DataArray | str = 'prsn', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS-JUL', @@ -2272,8 +2272,6 @@ def snowfall_intensity( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. prsn : xarray.DataArray | str Snowfall flux. thresh : Any @@ -2281,6 +2279,8 @@ def snowfall_intensity( mm/day). freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2300,9 +2300,9 @@ def snowfall_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.solid_precip_average) def solid_precip_average( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -2323,8 +2323,6 @@ def solid_precip_average( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -2333,6 +2331,8 @@ def solid_precip_average( Threshold of `tas` over which the precipication is assumed to be liquid rain. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2353,9 +2353,9 @@ def solid_precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.solid_precip_accumulation) def solid_precip_accumulation( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -2376,8 +2376,6 @@ def solid_precip_accumulation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Mean daily precipitation flux. tas : xarray.DataArray | str @@ -2386,6 +2384,8 @@ def solid_precip_accumulation( Threshold of `tas` over which the precipication is assumed to be liquid rain. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2406,11 +2406,11 @@ def solid_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_and_dry_days) def warm_and_dry_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', tas_per: xarray.DataArray | str = 'tas_per', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -2430,8 +2430,6 @@ def warm_and_dry_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature values. pr : xarray.DataArray | str @@ -2442,6 +2440,8 @@ def warm_and_dry_days( First quartile of daily total precipitation computed by month. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2463,11 +2463,11 @@ def warm_and_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_and_wet_days) def warm_and_wet_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', pr: xarray.DataArray | str = 'pr', tas_per: xarray.DataArray | str = 'tas_per', pr_per: xarray.DataArray | str = 'pr_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -2487,8 +2487,6 @@ def warm_and_wet_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature values. pr : xarray.DataArray | str @@ -2499,6 +2497,8 @@ def warm_and_wet_days( Third quartile of daily total precipitation computed by month. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2520,9 +2520,9 @@ def warm_and_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.water_cycle_intensity) def water_cycle_intensity( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', evspsbl: xarray.DataArray | str = 'evspsbl', + ds: xarray.Dataset | Any = None, *, freq: Any = 'YS', **kwargs: Any, @@ -2541,14 +2541,14 @@ def water_cycle_intensity( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Precipitation flux. evspsbl : xarray.DataArray | str Actual evapotranspiration flux. freq : Any Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2568,8 +2568,8 @@ def water_cycle_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_precip_accumulation) def wet_precip_accumulation( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1 mm/day', freq: str = 'YS', @@ -2590,14 +2590,14 @@ def wet_precip_accumulation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. thresh : Any Threshold over which precipitation starts being cumulated. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2617,8 +2617,8 @@ def wet_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_frequency) def wet_spell_frequency( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 3, @@ -2643,8 +2643,6 @@ def wet_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2663,6 +2661,8 @@ def wet_spell_frequency( checks that the maximal daily precipitation amount within the window is more than the threshold. This is the same as verifying that each individual day is above the threshold. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2685,8 +2685,8 @@ def wet_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_max_length) def wet_spell_max_length( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 1, @@ -2711,8 +2711,6 @@ def wet_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2729,6 +2727,8 @@ def wet_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2751,8 +2751,8 @@ def wet_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_total_length) def wet_spell_total_length( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm', window: int = 3, @@ -2777,8 +2777,6 @@ def wet_spell_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2795,6 +2793,8 @@ def wet_spell_total_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2817,8 +2817,8 @@ def wet_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.wetdays) def wetdays( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm/day', freq: str = 'YS', @@ -2839,8 +2839,6 @@ def wetdays( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2849,6 +2847,8 @@ def wetdays( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2869,8 +2869,8 @@ def wetdays( @format_handler() # @metadata_handler(xclim.indicators.atmos.wetdays_prop) def wetdays_prop( - ds: xarray.Dataset | Any, pr: xarray.DataArray | str = 'pr', + ds: xarray.Dataset | Any = None, *, thresh: Any = '1.0 mm/day', freq: str = 'YS', @@ -2891,8 +2891,6 @@ def wetdays_prop( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. pr : xarray.DataArray | str Daily precipitation. thresh : Any @@ -2901,6 +2899,8 @@ def wetdays_prop( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index a5d7a66..a82d0d2 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -20,8 +20,8 @@ @format_handler() # @metadata_handler(xclim.indicators.atmos.australian_hardiness_zones) def australian_hardiness_zones( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, window: int = 30, freq: str = 'YS', @@ -45,14 +45,14 @@ def australian_hardiness_zones( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum temperature. window : int The length of the averaging window, in years. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -72,10 +72,10 @@ def australian_hardiness_zones( @format_handler() # @metadata_handler(xclim.indicators.atmos.biologically_effective_degree_days) def biologically_effective_degree_days( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '10 degC', method: Literal['gladstones', 'icclim', 'jones', 'smoothed', 'stepwise'] = 'gladstones', @@ -106,8 +106,6 @@ def biologically_effective_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -148,6 +146,8 @@ def biologically_effective_degree_days( date is non-inclusive. freq : str Resampling frequency (For Southern Hemisphere, should be "YS-JUL"). + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -176,8 +176,8 @@ def biologically_effective_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_days) def cold_spell_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '-10 degC', window: int = 5, @@ -201,8 +201,6 @@ def cold_spell_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -217,6 +215,8 @@ def cold_spell_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -239,9 +239,9 @@ def cold_spell_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_duration_index) def cold_spell_duration_index( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, *, window: int = 6, freq: str = 'YS', @@ -266,8 +266,6 @@ def cold_spell_duration_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmin_per : xarray.DataArray | str @@ -288,6 +286,8 @@ def cold_spell_duration_index( computationally expensive, and it might provide the wrong results. op : Literal['<', '<=', 'lt', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -311,8 +311,8 @@ def cold_spell_duration_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_frequency) def cold_spell_frequency( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '-10 degC', window: int = 5, @@ -336,8 +336,6 @@ def cold_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -351,6 +349,8 @@ def cold_spell_frequency( Comparison operation. Default: "<". resample_before_rl : bool Determines if the resampling should take place before or after the run. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -373,8 +373,8 @@ def cold_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_max_length) def cold_spell_max_length( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '-10 degC', window: int = 1, @@ -398,8 +398,6 @@ def cold_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -414,6 +412,8 @@ def cold_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -436,8 +436,8 @@ def cold_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_total_length) def cold_spell_total_length( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '-10 degC', window: int = 3, @@ -461,8 +461,6 @@ def cold_spell_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -477,6 +475,8 @@ def cold_spell_total_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -499,8 +499,8 @@ def cold_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.consecutive_frost_days) def consecutive_frost_days( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS-JUL', @@ -521,8 +521,6 @@ def consecutive_frost_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -532,6 +530,8 @@ def consecutive_frost_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -552,8 +552,8 @@ def consecutive_frost_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_frost_free_days) def maximum_consecutive_frost_free_days( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -575,8 +575,6 @@ def maximum_consecutive_frost_free_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -586,6 +584,8 @@ def maximum_consecutive_frost_free_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -606,9 +606,9 @@ def maximum_consecutive_frost_free_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cool_night_index) def cool_night_index( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', lat: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, freq: Literal['YS', 'YS-JAN'] = 'YS', **kwargs: Any, @@ -628,8 +628,6 @@ def cool_night_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. lat : xarray.DataArray | str | None @@ -637,6 +635,8 @@ def cool_night_index( "latitude" field must be available within the passed DataArray. freq : Literal['YS', 'YS-JAN'] Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -656,8 +656,8 @@ def cool_night_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.cooling_degree_days) def cooling_degree_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '18.0 degC', freq: str = 'YS', @@ -678,14 +678,14 @@ def cooling_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any Temperature threshold above which air is cooled. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -705,10 +705,10 @@ def cooling_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cooling_degree_days_approximation) def cooling_degree_days_approximation( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', tasmin: xarray.DataArray | str = 'tasmin', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '18.0 degC', freq: str = 'YS', @@ -731,8 +731,6 @@ def cooling_degree_days_approximation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. tasmin : xarray.DataArray | str @@ -743,6 +741,8 @@ def cooling_degree_days_approximation( Temperature threshold above which air is cooled. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -764,9 +764,9 @@ def cooling_degree_days_approximation( @format_handler() # @metadata_handler(xclim.indicators.atmos.corn_heat_units) def corn_heat_units( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '4.44 degC', thresh_tasmax: Any = '10 degC', @@ -787,8 +787,6 @@ def corn_heat_units( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -797,6 +795,8 @@ def corn_heat_units( The minimum temperature threshold needed for corn growth. thresh_tasmax : Any The maximum temperature threshold needed for corn growth. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -817,8 +817,8 @@ def corn_heat_units( @format_handler() # @metadata_handler(xclim.indicators.atmos.chill_portions) def chill_portions( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -845,12 +845,12 @@ def chill_portions( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Hourly temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -869,8 +869,8 @@ def chill_portions( @format_handler() # @metadata_handler(xclim.indicators.atmos.chill_units) def chill_units( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, positive_only: bool = False, freq: str = 'YS', @@ -894,14 +894,14 @@ def chill_units( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Hourly temperature. positive_only : bool If `True`, only positive daily chill units are aggregated. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -921,8 +921,8 @@ def chill_units( @format_handler() # @metadata_handler(xclim.indicators.atmos.degree_days_exceedance_date) def degree_days_exceedance_date( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', sum_thresh: Any = '25 K days', @@ -947,8 +947,6 @@ def degree_days_exceedance_date( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -967,6 +965,8 @@ def degree_days_exceedance_date( assigned. Default (None) assigns "NaN". freq : str Resampling frequency. If `after_date` is given, `freq` should be annual. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -990,9 +990,9 @@ def degree_days_exceedance_date( @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_freezethaw_cycles) def daily_freezethaw_cycles( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '0 degC', thresh_tasmax: Any = '0 degC', @@ -1018,8 +1018,6 @@ def daily_freezethaw_cycles( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -1037,6 +1035,8 @@ def daily_freezethaw_cycles( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1061,9 +1061,9 @@ def daily_freezethaw_cycles( @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_temperature_range) def daily_temperature_range( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -1082,14 +1082,14 @@ def daily_temperature_range( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1109,9 +1109,9 @@ def daily_temperature_range( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_daily_temperature_range) def max_daily_temperature_range( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -1130,14 +1130,14 @@ def max_daily_temperature_range( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1157,9 +1157,9 @@ def max_daily_temperature_range( @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_temperature_range_variability) def daily_temperature_range_variability( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -1178,14 +1178,14 @@ def daily_temperature_range_variability( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1205,9 +1205,9 @@ def daily_temperature_range_variability( @format_handler() # @metadata_handler(xclim.indicators.atmos.extreme_temperature_range) def extreme_temperature_range( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -1226,14 +1226,14 @@ def extreme_temperature_range( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1253,9 +1253,9 @@ def extreme_temperature_range( @format_handler() # @metadata_handler(xclim.indicators.atmos.fire_season) def fire_season( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', snd: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, *, method: str = 'WF93', freq: str | None = None, @@ -1281,8 +1281,6 @@ def fire_season( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Daily surface temperature, cffdrs recommends using maximum daily temperature. snd : xarray.DataArray | str | None @@ -1306,6 +1304,8 @@ def fire_season( snow_thresh : Any Minimal snow depth level to end a fire season, only used with method "LA08". Must be scalar. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1331,8 +1331,8 @@ def fire_season( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tg_above) def first_day_tg_above( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['>', 'gt', '>=', 'ge'] = '>', @@ -1356,8 +1356,6 @@ def first_day_tg_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Daily temperature. thresh : Any @@ -1371,6 +1369,8 @@ def first_day_tg_above( Minimum number of days with temperature above the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1393,8 +1393,8 @@ def first_day_tg_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tg_below) def first_day_tg_below( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['<', 'lt', '<=', 'le'] = '<', @@ -1418,8 +1418,6 @@ def first_day_tg_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Daily temperature. thresh : Any @@ -1433,6 +1431,8 @@ def first_day_tg_below( Minimum number of days with temperature below the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1455,8 +1455,8 @@ def first_day_tg_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tn_above) def first_day_tn_above( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['>', 'gt', '>=', 'ge'] = '>', @@ -1480,8 +1480,6 @@ def first_day_tn_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum surface temperature. thresh : Any @@ -1495,6 +1493,8 @@ def first_day_tn_above( Minimum number of days with temperature above the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1517,8 +1517,8 @@ def first_day_tn_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tn_below) def first_day_tn_below( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['<', 'lt', '<=', 'le'] = '<', @@ -1542,8 +1542,6 @@ def first_day_tn_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum surface temperature. thresh : Any @@ -1557,6 +1555,8 @@ def first_day_tn_below( Minimum number of days with temperature below the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1579,8 +1579,8 @@ def first_day_tn_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tx_above) def first_day_tx_above( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['>', 'gt', '>=', 'ge'] = '>', @@ -1604,8 +1604,6 @@ def first_day_tx_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum surface temperature. thresh : Any @@ -1619,6 +1617,8 @@ def first_day_tx_above( Minimum number of days with temperature above the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1641,8 +1641,8 @@ def first_day_tx_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tx_below) def first_day_tx_below( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['<', 'lt', '<=', 'le'] = '<', @@ -1666,8 +1666,6 @@ def first_day_tx_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum surface temperature. thresh : Any @@ -1681,6 +1679,8 @@ def first_day_tx_below( Minimum number of days with temperature below the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1703,9 +1703,9 @@ def first_day_tx_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_frequency) def freezethaw_spell_frequency( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '0 degC', thresh_tasmax: Any = '0 degC', @@ -1732,8 +1732,6 @@ def freezethaw_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -1753,6 +1751,8 @@ def freezethaw_spell_frequency( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1778,9 +1778,9 @@ def freezethaw_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_max_length) def freezethaw_spell_max_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '0 degC', thresh_tasmax: Any = '0 degC', @@ -1807,8 +1807,6 @@ def freezethaw_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -1828,6 +1826,8 @@ def freezethaw_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1853,9 +1853,9 @@ def freezethaw_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_mean_length) def freezethaw_spell_mean_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '0 degC', thresh_tasmax: Any = '0 degC', @@ -1880,8 +1880,6 @@ def freezethaw_spell_mean_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -1897,6 +1895,8 @@ def freezethaw_spell_mean_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1920,8 +1920,8 @@ def freezethaw_spell_mean_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.freezing_degree_days) def freezing_degree_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -1942,14 +1942,14 @@ def freezing_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any Threshold temperature on which to base evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -1969,8 +1969,8 @@ def freezing_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.freshet_start) def freshet_start( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['>', 'gt', '>=', 'ge'] = '>', @@ -1994,8 +1994,6 @@ def freshet_start( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Daily temperature. thresh : Any @@ -2009,6 +2007,8 @@ def freshet_start( Minimum number of days with temperature above the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2031,8 +2031,8 @@ def freshet_start( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_days) def frost_days( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -2052,14 +2052,14 @@ def frost_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any Freezing temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2079,8 +2079,8 @@ def frost_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_end) def frost_free_season_end( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', window: int = 5, @@ -2104,8 +2104,6 @@ def frost_free_season_end( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -2119,6 +2117,8 @@ def frost_free_season_end( How to compare tasmin and the threshold. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2141,8 +2141,8 @@ def frost_free_season_end( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_length) def frost_free_season_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', window: int = 5, @@ -2167,8 +2167,6 @@ def frost_free_season_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -2182,6 +2180,8 @@ def frost_free_season_length( How to compare tasmin and the threshold. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2204,8 +2204,8 @@ def frost_free_season_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_start) def frost_free_season_start( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', window: int = 5, @@ -2229,8 +2229,6 @@ def frost_free_season_start( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -2244,6 +2242,8 @@ def frost_free_season_start( How to compare tasmin and the threshold. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2266,8 +2266,8 @@ def frost_free_season_start( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_spell_max_length) def frost_free_spell_max_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0.0 degC', window: int = 1, @@ -2291,8 +2291,6 @@ def frost_free_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -2307,6 +2305,8 @@ def frost_free_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2329,8 +2329,8 @@ def frost_free_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_season_length) def frost_season_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, window: int = 5, mid_date: str | None = '01-01', @@ -2355,8 +2355,6 @@ def frost_season_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. window : int @@ -2371,6 +2369,8 @@ def frost_season_length( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2393,8 +2393,8 @@ def frost_season_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_degree_days) def growing_degree_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '4.0 degC', freq: str = 'YS', @@ -2415,14 +2415,14 @@ def growing_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any Threshold temperature on which to base evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2442,8 +2442,8 @@ def growing_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_end) def growing_season_end( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '5.0 degC', mid_date: str | None = '07-01', @@ -2467,8 +2467,6 @@ def growing_season_end( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -2484,6 +2482,8 @@ def growing_season_end( Comparison operation. Default: ">". Note that this comparison is what defines the season. The end of the season happens when the condition is NOT met for `window` consecutive days. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2506,8 +2506,8 @@ def growing_season_end( @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_length) def growing_season_length( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '5.0 degC', window: int = 6, @@ -2532,8 +2532,6 @@ def growing_season_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -2548,6 +2546,8 @@ def growing_season_length( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2570,8 +2570,8 @@ def growing_season_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_start) def growing_season_start( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '5.0 degC', mid_date: str | None = '07-01', @@ -2595,8 +2595,6 @@ def growing_season_start( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -2610,6 +2608,8 @@ def growing_season_start( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2632,9 +2632,9 @@ def growing_season_start( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_frequency) def heat_spell_frequency( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, window: int = 3, win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', @@ -2660,8 +2660,6 @@ def heat_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum surface temperature. tasmax : xarray.DataArray | str @@ -2683,6 +2681,8 @@ def heat_spell_frequency( Threshold for tasmin thresh_tasmax : Any Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2708,9 +2708,9 @@ def heat_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_max_length) def heat_spell_max_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, window: int = 3, win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', @@ -2736,8 +2736,6 @@ def heat_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum surface temperature. tasmax : xarray.DataArray | str @@ -2759,6 +2757,8 @@ def heat_spell_max_length( Threshold for tasmin thresh_tasmax : Any Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2784,9 +2784,9 @@ def heat_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_total_length) def heat_spell_total_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, window: int = 3, win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', @@ -2812,8 +2812,6 @@ def heat_spell_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum surface temperature. tasmax : xarray.DataArray | str @@ -2835,6 +2833,8 @@ def heat_spell_total_length( Threshold for tasmin thresh_tasmax : Any Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2860,9 +2860,9 @@ def heat_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_frequency) def heat_wave_frequency( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '22.0 degC', thresh_tasmax: Any = '30 degC', @@ -2887,8 +2887,6 @@ def heat_wave_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -2906,6 +2904,8 @@ def heat_wave_frequency( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2930,8 +2930,8 @@ def heat_wave_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_index) def heat_wave_index( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25 degC', window: int = 5, @@ -2955,8 +2955,6 @@ def heat_wave_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -2971,6 +2969,8 @@ def heat_wave_index( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -2993,9 +2993,9 @@ def heat_wave_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_max_length) def heat_wave_max_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '22.0 degC', thresh_tasmax: Any = '30 degC', @@ -3020,8 +3020,6 @@ def heat_wave_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -3039,6 +3037,8 @@ def heat_wave_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3063,9 +3063,9 @@ def heat_wave_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_total_length) def heat_wave_total_length( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '22.0 degC', thresh_tasmax: Any = '30 degC', @@ -3090,8 +3090,6 @@ def heat_wave_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -3109,6 +3107,8 @@ def heat_wave_total_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3133,8 +3133,8 @@ def heat_wave_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.heating_degree_days) def heating_degree_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '17.0 degC', freq: str = 'YS', @@ -3155,14 +3155,14 @@ def heating_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any Threshold temperature on which to base evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3182,10 +3182,10 @@ def heating_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.heating_degree_days_approximation) def heating_degree_days_approximation( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', tasmin: xarray.DataArray | str = 'tasmin', tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '17.0 degC', freq: str = 'YS', @@ -3208,8 +3208,6 @@ def heating_degree_days_approximation( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. tasmin : xarray.DataArray | str @@ -3220,6 +3218,8 @@ def heating_degree_days_approximation( Threshold temperature on which to base evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3241,8 +3241,8 @@ def heating_degree_days_approximation( @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_days) def hot_days( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25 degC', freq: str = 'YS', @@ -3262,14 +3262,14 @@ def hot_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any Threshold temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3289,8 +3289,8 @@ def hot_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_frequency) def hot_spell_frequency( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '30 degC', window: int = 3, @@ -3314,8 +3314,6 @@ def hot_spell_frequency( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -3329,6 +3327,8 @@ def hot_spell_frequency( Comparison operation. Default: ">". resample_before_rl : bool Determines if the resampling should take place before or after the run. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3351,8 +3351,8 @@ def hot_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_max_length) def hot_spell_max_length( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '30 degC', window: int = 1, @@ -3376,8 +3376,6 @@ def hot_spell_max_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -3391,6 +3389,8 @@ def hot_spell_max_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3413,8 +3413,8 @@ def hot_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_max_magnitude) def hot_spell_max_magnitude( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25.0 degC', window: int = 3, @@ -3437,8 +3437,6 @@ def hot_spell_max_magnitude( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -3451,6 +3449,8 @@ def hot_spell_max_magnitude( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3472,8 +3472,8 @@ def hot_spell_max_magnitude( @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_total_length) def hot_spell_total_length( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '30 degC', window: int = 3, @@ -3497,8 +3497,6 @@ def hot_spell_total_length( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -3513,6 +3511,8 @@ def hot_spell_total_length( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3535,10 +3535,10 @@ def hot_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.huglin_index) def huglin_index( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', tasmax: xarray.DataArray | str = 'tasmax', lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, *, thresh: Any = '10 degC', method: str = 'jones', @@ -3566,8 +3566,6 @@ def huglin_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. tasmax : xarray.DataArray | str @@ -3596,6 +3594,8 @@ def huglin_index( date is non-inclusive. freq : Literal['YS', 'YS-JAN', 'YS-JUL'] Resampling frequency (default: "YS"; For Southern Hemisphere, should be "YS-JUL"). + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3621,8 +3621,8 @@ def huglin_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.ice_days) def ice_days( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -3642,14 +3642,14 @@ def ice_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any Freezing temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3669,8 +3669,8 @@ def ice_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.last_spring_frost) def last_spring_frost( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', op: Literal['<', 'lt', '<=', 'le'] = '<', @@ -3694,8 +3694,6 @@ def last_spring_frost( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -3709,6 +3707,8 @@ def last_spring_frost( Minimum number of days with temperature below the threshold needed for evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3731,8 +3731,8 @@ def last_spring_frost( @format_handler() # @metadata_handler(xclim.indicators.atmos.late_frost_days) def late_frost_days( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -3753,14 +3753,14 @@ def late_frost_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any Freezing temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3780,9 +3780,9 @@ def late_frost_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.latitude_temperature_index) def latitude_temperature_index( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -3805,8 +3805,6 @@ def latitude_temperature_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. lat : xarray.DataArray | str @@ -3814,6 +3812,8 @@ def latitude_temperature_index( within the passed DataArray. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3833,8 +3833,8 @@ def latitude_temperature_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_warm_days) def maximum_consecutive_warm_days( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25 degC', freq: str = 'YS', @@ -3856,8 +3856,6 @@ def maximum_consecutive_warm_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Max daily temperature. thresh : Any @@ -3867,6 +3865,8 @@ def maximum_consecutive_warm_days( resample_before_rl : bool Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3887,9 +3887,9 @@ def maximum_consecutive_warm_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg10p) def tg10p( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', tas_per: xarray.DataArray | str = 'tas_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -3910,8 +3910,6 @@ def tg10p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. tas_per : xarray.DataArray | str @@ -3927,6 +3925,8 @@ def tg10p( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -3948,9 +3948,9 @@ def tg10p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg90p) def tg90p( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', tas_per: xarray.DataArray | str = 'tas_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -3971,8 +3971,6 @@ def tg90p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. tas_per : xarray.DataArray | str @@ -3988,6 +3986,8 @@ def tg90p( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4009,8 +4009,8 @@ def tg90p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_days_above) def tg_days_above( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '10.0 degC', freq: str = 'YS', @@ -4031,8 +4031,6 @@ def tg_days_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -4041,6 +4039,8 @@ def tg_days_above( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4061,8 +4061,8 @@ def tg_days_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_days_below) def tg_days_below( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '10.0 degC', freq: str = 'YS', @@ -4083,8 +4083,6 @@ def tg_days_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any @@ -4093,6 +4091,8 @@ def tg_days_below( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4113,8 +4113,8 @@ def tg_days_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_max) def tg_max( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4133,12 +4133,12 @@ def tg_max( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4157,8 +4157,8 @@ def tg_max( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_mean) def tg_mean( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4177,12 +4177,12 @@ def tg_mean( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4201,8 +4201,8 @@ def tg_mean( @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_min) def tg_min( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4221,12 +4221,12 @@ def tg_min( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4245,8 +4245,8 @@ def tg_min( @format_handler() # @metadata_handler(xclim.indicators.atmos.thawing_degree_days) def thawing_degree_days( - ds: xarray.Dataset | Any, tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, *, thresh: Any = '0 degC', freq: str = 'YS', @@ -4267,14 +4267,14 @@ def thawing_degree_days( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tas : xarray.DataArray | str Mean daily temperature. thresh : Any Threshold temperature on which to base evaluation. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4294,9 +4294,9 @@ def thawing_degree_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn10p) def tn10p( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -4317,8 +4317,6 @@ def tn10p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Mean daily temperature. tasmin_per : xarray.DataArray | str @@ -4334,6 +4332,8 @@ def tn10p( results. Note that bootstrapping is computationally expensive. op : Literal['<', '<=', 'lt', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4355,9 +4355,9 @@ def tn10p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn90p) def tn90p( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -4378,8 +4378,6 @@ def tn90p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmin_per : xarray.DataArray | str @@ -4395,6 +4393,8 @@ def tn90p( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4416,8 +4416,8 @@ def tn90p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_days_above) def tn_days_above( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '20.0 degC', freq: str = 'YS', @@ -4438,8 +4438,6 @@ def tn_days_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -4448,6 +4446,8 @@ def tn_days_above( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4468,8 +4468,8 @@ def tn_days_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_days_below) def tn_days_below( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '-10.0 degC', freq: str = 'YS', @@ -4490,8 +4490,6 @@ def tn_days_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -4500,6 +4498,8 @@ def tn_days_below( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4520,8 +4520,8 @@ def tn_days_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_max) def tn_max( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4540,12 +4540,12 @@ def tn_max( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4564,8 +4564,8 @@ def tn_max( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_mean) def tn_mean( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4584,12 +4584,12 @@ def tn_mean( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4608,8 +4608,8 @@ def tn_mean( @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_min) def tn_min( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4628,12 +4628,12 @@ def tn_min( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4652,8 +4652,8 @@ def tn_min( @format_handler() # @metadata_handler(xclim.indicators.atmos.tropical_nights) def tropical_nights( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, thresh: Any = '20.0 degC', freq: str = 'YS', @@ -4674,8 +4674,6 @@ def tropical_nights( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. thresh : Any @@ -4684,6 +4682,8 @@ def tropical_nights( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4704,9 +4704,9 @@ def tropical_nights( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx10p) def tx10p( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', tasmax_per: xarray.DataArray | str = 'tasmax_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -4727,8 +4727,6 @@ def tx10p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. tasmax_per : xarray.DataArray | str @@ -4744,6 +4742,8 @@ def tx10p( results. Note that bootstrapping is computationally expensive. op : Literal['<', '<=', 'lt', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4765,9 +4765,9 @@ def tx10p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx90p) def tx90p( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', tasmax_per: xarray.DataArray | str = 'tasmax_per', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', bootstrap: bool = False, @@ -4788,8 +4788,6 @@ def tx90p( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. tasmax_per : xarray.DataArray | str @@ -4805,6 +4803,8 @@ def tx90p( results. Note that bootstrapping is computationally expensive. op : Literal['<', '<=', 'lt', 'le'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4826,8 +4826,8 @@ def tx90p( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_days_above) def tx_days_above( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25.0 degC', freq: str = 'YS', @@ -4848,8 +4848,6 @@ def tx_days_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -4858,6 +4856,8 @@ def tx_days_above( Resampling frequency. op : Literal['>', 'gt', '>=', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4878,8 +4878,8 @@ def tx_days_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_days_below) def tx_days_below( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh: Any = '25.0 degC', freq: str = 'YS', @@ -4900,8 +4900,6 @@ def tx_days_below( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. thresh : Any @@ -4910,6 +4908,8 @@ def tx_days_below( Resampling frequency. op : Literal['<', 'lt', '<=', 'le'] Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4930,8 +4930,8 @@ def tx_days_below( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_max) def tx_max( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4950,12 +4950,12 @@ def tx_max( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -4974,8 +4974,8 @@ def tx_max( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_mean) def tx_mean( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -4994,12 +4994,12 @@ def tx_mean( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -5018,8 +5018,8 @@ def tx_mean( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_min) def tx_min( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, freq: str = 'YS', **kwargs: Any, @@ -5038,12 +5038,12 @@ def tx_min( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -5062,9 +5062,9 @@ def tx_min( @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_tn_days_above) def tx_tn_days_above( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, *, thresh_tasmin: Any = '22 degC', thresh_tasmax: Any = '30 degC', @@ -5086,8 +5086,6 @@ def tx_tn_days_above( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum daily temperature. tasmax : xarray.DataArray | str @@ -5100,6 +5098,8 @@ def tx_tn_days_above( Resampling frequency. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -5122,8 +5122,8 @@ def tx_tn_days_above( @format_handler() # @metadata_handler(xclim.indicators.atmos.usda_hardiness_zones) def usda_hardiness_zones( - ds: xarray.Dataset | Any, tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, *, window: int = 30, freq: str = 'YS', @@ -5147,14 +5147,14 @@ def usda_hardiness_zones( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmin : xarray.DataArray | str Minimum temperature. window : int The length of the averaging window, in years. freq : str Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. @@ -5174,9 +5174,9 @@ def usda_hardiness_zones( @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_spell_duration_index) def warm_spell_duration_index( - ds: xarray.Dataset | Any, tasmax: xarray.DataArray | str = 'tasmax', tasmax_per: xarray.DataArray | str = 'tasmax_per', + ds: xarray.Dataset | Any = None, *, window: int = 6, freq: str = 'YS', @@ -5201,8 +5201,6 @@ def warm_spell_duration_index( Parameters ---------- - ds : xarray.Dataset | Any - Input dataset. tasmax : xarray.DataArray | str Maximum daily temperature. tasmax_per : xarray.DataArray | str @@ -5223,6 +5221,8 @@ def warm_spell_duration_index( results. Note that bootstrapping is computationally expensive. op : Literal['>', '>=', 'gt', 'ge'] Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any Additional keyword arguments. diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index f329cf9..be9dedc 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -152,8 +152,6 @@ def generate_docstring(indicator: Any, xclim_func_name: str) -> str: params_lines = [ "Parameters", "----------", - "ds : xarray.Dataset | Any", - " Input dataset.", ] try: @@ -182,6 +180,8 @@ def generate_docstring(indicator: Any, xclim_func_name: str) -> str: except Exception: pass + params_lines.append("ds : xarray.Dataset | Any") + params_lines.append(" Input dataset.") params_lines.append("**kwargs : Any") params_lines.append(" Additional keyword arguments.") @@ -201,7 +201,7 @@ def generate_docstring(indicator: Any, xclim_func_name: str) -> str: def format_signature_params(indicator: Any) -> str: """Format the parameters for the function signature.""" - params = [" ds: xarray.Dataset | Any,"] + params = [] try: sig = inspect.signature(indicator) @@ -234,6 +234,9 @@ def format_signature_params(indicator: Any) -> str: default_val = repr(p.default) params.append(f" {p.name}: {type_hint} = {default_val},") + # Add ds here + params.append(" ds: xarray.Dataset | Any = None,") + if kw_only: params.append(" *,") for p in kw_only: From ad725ed7397eebd8cc00e6e15c5fb549ac58b025 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 6 Mar 2026 11:06:31 +0100 Subject: [PATCH 33/47] refactor: align climate indices notebook with updated indicator function names `daily_pr_intensity` and `heating_degree_days_approximation`. --- docs/notebooks/climate_indices_analysis.ipynb | 445 +++++------------- 1 file changed, 125 insertions(+), 320 deletions(-) diff --git a/docs/notebooks/climate_indices_analysis.ipynb b/docs/notebooks/climate_indices_analysis.ipynb index 4779524..8c6c614 100644 --- a/docs/notebooks/climate_indices_analysis.ipynb +++ b/docs/notebooks/climate_indices_analysis.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 4, "id": "1b36bd42f74117db", "metadata": { "ExecuteTime": { @@ -42,12 +42,12 @@ "import matplotlib.pyplot as plt\n", "\n", "from earthkit.climate.indicators.precipitation import (\n", - " daily_precipitation_intensity,\n", + " daily_pr_intensity,\n", " maximum_consecutive_wet_days,\n", ")\n", "from earthkit.climate.indicators.temperature import (\n", " daily_temperature_range,\n", - " heating_degree_days,\n", + " heating_degree_days_approximation,\n", " warm_spell_duration_index,\n", ")\n", "from earthkit.climate.utils.percentile import calculate_percentile_doy\n", @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "5c6692d99197d4af", "metadata": { "ExecuteTime": { @@ -77,15 +77,7 @@ "start_time": "2025-12-01T21:25:56.199912Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], + "outputs": [], "source": [ "# Load precipitation\n", "pr_hist = ekd.from_source(\n", @@ -129,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "51a81669f2d9f6fd", "metadata": { "ExecuteTime": { @@ -140,14 +132,11 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -210,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "id": "4f6e09420f604684", "metadata": { "ExecuteTime": { @@ -220,11 +209,11 @@ }, "outputs": [], "source": [ - "# SDII\n", - "sdii = daily_precipitation_intensity(pr_ssp585, thresh=\"1 mm/day\", freq=\"YS\")\n", + "# SDII (using dataset)\n", + "sdii = daily_pr_intensity(ds=pr_ssp585, thresh=\"1 mm/day\", freq=\"YS\")\n", "\n", - "# CWD\n", - "cwd = maximum_consecutive_wet_days(pr_ssp585, thresh=\"1 mm/day\")" + "# CWD (using DataArray)\n", + "cwd = maximum_consecutive_wet_days(pr_ssp585.to_xarray().pr, thresh=\"1 mm/day\")" ] }, { @@ -247,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "id": "26d2a1e8539a1c63", "metadata": { "ExecuteTime": { @@ -261,84 +250,39 @@ "output_type": "stream", "text": [ "SDII fields:\n", - " variable level valid_datetime units\n", - "0 sdii None 2015-01-01T00:00:00 mm d-1\n", - "1 sdii None 2016-01-01T00:00:00 mm d-1\n", - "2 sdii None 2017-01-01T00:00:00 mm d-1\n", - "3 sdii None 2018-01-01T00:00:00 mm d-1\n", - "4 sdii None 2019-01-01T00:00:00 mm d-1\n", - "5 sdii None 2020-01-01T00:00:00 mm d-1\n", - "6 sdii None 2021-01-01T00:00:00 mm d-1\n", - "7 sdii None 2022-01-01T00:00:00 mm d-1\n", - "8 sdii None 2023-01-01T00:00:00 mm d-1\n", - "9 sdii None 2024-01-01T00:00:00 mm d-1\n", - "10 sdii None 2025-01-01T00:00:00 mm d-1\n", - "11 sdii None 2026-01-01T00:00:00 mm d-1\n", - "12 sdii None 2027-01-01T00:00:00 mm d-1\n", - "13 sdii None 2028-01-01T00:00:00 mm d-1\n", - "14 sdii None 2029-01-01T00:00:00 mm d-1\n", - "15 sdii None 2030-01-01T00:00:00 mm d-1\n", - "16 sdii None 2031-01-01T00:00:00 mm d-1\n", - "17 sdii None 2032-01-01T00:00:00 mm d-1\n", - "18 sdii None 2033-01-01T00:00:00 mm d-1\n", - "19 sdii None 2034-01-01T00:00:00 mm d-1\n", - "20 sdii None 2035-01-01T00:00:00 mm d-1\n", - "21 sdii None 2036-01-01T00:00:00 mm d-1\n", - "22 sdii None 2037-01-01T00:00:00 mm d-1\n", - "23 sdii None 2038-01-01T00:00:00 mm d-1\n", - "24 sdii None 2039-01-01T00:00:00 mm d-1\n", - "25 sdii None 2040-01-01T00:00:00 mm d-1\n", - "26 sdii None 2041-01-01T00:00:00 mm d-1\n", - "27 sdii None 2042-01-01T00:00:00 mm d-1\n", - "28 sdii None 2043-01-01T00:00:00 mm d-1\n", - "29 sdii None 2044-01-01T00:00:00 mm d-1\n", - "30 sdii None 2045-01-01T00:00:00 mm d-1\n", - "31 sdii None 2046-01-01T00:00:00 mm d-1\n", - "32 sdii None 2047-01-01T00:00:00 mm d-1\n", - "33 sdii None 2048-01-01T00:00:00 mm d-1\n", - "34 sdii None 2049-01-01T00:00:00 mm d-1\n", - "35 sdii None 2050-01-01T00:00:00 mm d-1\n", - "36 sdii None 2051-01-01T00:00:00 mm d-1\n", - "37 sdii None 2052-01-01T00:00:00 mm d-1\n", - "38 sdii None 2053-01-01T00:00:00 mm d-1\n", - "39 sdii None 2054-01-01T00:00:00 mm d-1\n", - "\n", - "SDII metadata:\n", - "XArrayMetadata({'units': 'mm d-1', 'cell_methods': '', 'history': \"[2025-12-01 22:46:06] sdii: SDII(pr=pr, thresh='1 mm/day', freq='YS', op='>=') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)', 'description': 'Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.', 'date': 20150101, 'time': 0, 'variable': 'sdii', 'level': None, 'levtype': 'sfc'})\n", - "\n", - "SDII xarray attributes:\n", - "{'earthkit_provenance': {'earthkit_internal': {'input_type': 'earthkit.data.readers.netcdf.NetCDFFieldListReader'}, 'indicator_definition': {'pr': Parameter(kind=, default='pr', compute_name='pr', description='Daily precipitation.', units='[precipitation]', choices=, value=), 'thresh': Parameter(kind=, default='1 mm/day', compute_name='thresh', description='Precipitation value over which a day is considered wet.', units='[precipitation]', choices=, value=), 'freq': Parameter(kind=, default='YS', compute_name='freq', description='Resampling frequency.', units=, choices=, value=), 'op': Parameter(kind=, default='>=', compute_name='op', description='Comparison operation. Default: \">=\".', units=, choices={'>', '>=', 'ge', 'gt'}, value=), 'ds': Parameter(kind=, default=None, compute_name=, description='A dataset with the variables given by name.', units=, choices=, value=), 'indexer': Parameter(kind=, default=, compute_name=, description='Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.', units=, choices=, value=)}, 'cf_attrs': [{'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over {thresh} (Simple Daily Intensity Index: SDII)', 'units': 'mm d-1', 'description': '{freq} Simple Daily Intensity Index (SDII) or {freq} average precipitation for days with daily precipitation over {thresh}.', 'var_name': 'sdii'}], 'call_info': {'xclim_function': 'daily_pr_intensity', 'parameters': {'pr': 'pr', 'thresh': '1 mm/day', 'freq': 'YS', 'op': '>=', 'ds': Size: 236MB\n", - "Dimensions: (time: 14610, lat: 48, lon: 84)\n", + " Size: 1MB\n", + "dask.array\n", "Coordinates:\n", - " * time (time) datetime64[ns] 117kB 2015-01-01 2015-01-02 ... 2054-12-31\n", + " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", " height float64 8B ...\n", - "Data variables:\n", - " pr (time, lat, lon) float32 236MB dask.array\n", "Attributes:\n", - " CDI: Climate Data Interface version 2.4.4 (https://mpimet.mpg...\n", - " Conventions: CF-1.6\n", - " history: Fri Mar 21 11:42:19 2025: cdo splityear ACCESS-CM2_ssp58...\n", - " CDO: Climate Data Operators version 2.4.4 (https://mpimet.mpg...\n", - " model: ACCESS-CM2_r1i1p1f1_deepESD\n", - " scenario: ssp585\n", - " project_format: gridded\n", - " project_name: cmip6\n", - " project_type: projections, 'indexer': {}}}}}\n" + " units: mm d-1\n", + " cell_methods: \n", + " history: [2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day'...\n", + " standard_name: lwe_thickness_of_precipitation_amount\n", + " long_name: Average precipitation during days with daily precipitatio...\n", + " description: Annual simple daily intensity index (sdii) or annual aver...\n", + "\n", + "SDII metadata:\n", + "{'units': 'mm d-1', 'cell_methods': '', 'history': \"[2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day', freq='YS', op='>=') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)', 'description': 'Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.'}\n", + "\n", + "SDII xarray attributes:\n", + "{'units': 'mm d-1', 'cell_methods': '', 'history': \"[2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day', freq='YS', op='>=') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)', 'description': 'Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.'}\n" ] } ], "source": [ "# Inspect the SDII object (Simple Daily Intensity Index)\n", "print(\"SDII fields:\")\n", - "print(sdii.ls())\n", + "print(sdii)\n", "\n", "print(\"\\nSDII metadata:\")\n", - "print(sdii.metadata()[0]) # show first metadata entry\n", + "print(sdii.attrs) # show first metadata entry\n", "\n", "print(\"\\nSDII xarray attributes:\")\n", - "print(sdii.to_xarray().attrs)" + "print(sdii.attrs)" ] }, { @@ -355,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "id": "fb325cae63b747a", "metadata": { "ExecuteTime": { @@ -369,84 +313,39 @@ "output_type": "stream", "text": [ "CWD fields:\n", - " variable level valid_datetime units\n", - "0 cwd None 2015-01-01T00:00:00 days\n", - "1 cwd None 2016-01-01T00:00:00 days\n", - "2 cwd None 2017-01-01T00:00:00 days\n", - "3 cwd None 2018-01-01T00:00:00 days\n", - "4 cwd None 2019-01-01T00:00:00 days\n", - "5 cwd None 2020-01-01T00:00:00 days\n", - "6 cwd None 2021-01-01T00:00:00 days\n", - "7 cwd None 2022-01-01T00:00:00 days\n", - "8 cwd None 2023-01-01T00:00:00 days\n", - "9 cwd None 2024-01-01T00:00:00 days\n", - "10 cwd None 2025-01-01T00:00:00 days\n", - "11 cwd None 2026-01-01T00:00:00 days\n", - "12 cwd None 2027-01-01T00:00:00 days\n", - "13 cwd None 2028-01-01T00:00:00 days\n", - "14 cwd None 2029-01-01T00:00:00 days\n", - "15 cwd None 2030-01-01T00:00:00 days\n", - "16 cwd None 2031-01-01T00:00:00 days\n", - "17 cwd None 2032-01-01T00:00:00 days\n", - "18 cwd None 2033-01-01T00:00:00 days\n", - "19 cwd None 2034-01-01T00:00:00 days\n", - "20 cwd None 2035-01-01T00:00:00 days\n", - "21 cwd None 2036-01-01T00:00:00 days\n", - "22 cwd None 2037-01-01T00:00:00 days\n", - "23 cwd None 2038-01-01T00:00:00 days\n", - "24 cwd None 2039-01-01T00:00:00 days\n", - "25 cwd None 2040-01-01T00:00:00 days\n", - "26 cwd None 2041-01-01T00:00:00 days\n", - "27 cwd None 2042-01-01T00:00:00 days\n", - "28 cwd None 2043-01-01T00:00:00 days\n", - "29 cwd None 2044-01-01T00:00:00 days\n", - "30 cwd None 2045-01-01T00:00:00 days\n", - "31 cwd None 2046-01-01T00:00:00 days\n", - "32 cwd None 2047-01-01T00:00:00 days\n", - "33 cwd None 2048-01-01T00:00:00 days\n", - "34 cwd None 2049-01-01T00:00:00 days\n", - "35 cwd None 2050-01-01T00:00:00 days\n", - "36 cwd None 2051-01-01T00:00:00 days\n", - "37 cwd None 2052-01-01T00:00:00 days\n", - "38 cwd None 2053-01-01T00:00:00 days\n", - "39 cwd None 2054-01-01T00:00:00 days\n", - "\n", - "CWD metadata:\n", - "XArrayMetadata({'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2025-12-01 22:46:10] cwd: CWD(pr=pr, thresh='1 mm/day', freq='YS', resample_before_rl=True) with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above 1 mm/day', 'description': 'Annual maximum number of consecutive days with daily precipitation at or above 1 mm/day.', 'date': 20150101, 'time': 0, 'variable': 'cwd', 'level': None, 'levtype': 'sfc'})\n", - "\n", - "CWD xarray attributes:\n", - "{'earthkit_provenance': {'earthkit_internal': {'input_type': 'earthkit.data.readers.netcdf.NetCDFFieldListReader'}, 'indicator_definition': {'pr': Parameter(kind=, default='pr', compute_name='pr', description='Mean daily precipitation flux.', units='[precipitation]', choices=, value=), 'thresh': Parameter(kind=, default='1 mm/day', compute_name='thresh', description='Threshold precipitation on which to base evaluation.', units='[precipitation]', choices=, value=), 'freq': Parameter(kind=, default='YS', compute_name='freq', description='Resampling frequency.', units=, choices=, value=), 'resample_before_rl': Parameter(kind=, default=True, compute_name='resample_before_rl', description='Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs.', units=, choices=, value=), 'ds': Parameter(kind=, default=None, compute_name=, description='A dataset with the variables given by name.', units=, choices=, value=)}, 'cf_attrs': [{'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above {thresh}', 'units': 'days', 'cell_methods': 'time: sum over days', 'description': '{freq} maximum number of consecutive days with daily precipitation at or above {thresh}.', 'var_name': 'cwd'}], 'call_info': {'xclim_function': 'maximum_consecutive_wet_days', 'parameters': {'pr': 'pr', 'thresh': '1 mm/day', 'freq': 'YS', 'resample_before_rl': True, 'ds': Size: 236MB\n", - "Dimensions: (time: 14610, lat: 48, lon: 84)\n", + " Size: 1MB\n", + "dask.array\n", "Coordinates:\n", - " * time (time) datetime64[ns] 117kB 2015-01-01 2015-01-02 ... 2054-12-31\n", + " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", " height float64 8B ...\n", - "Data variables:\n", - " pr (time, lat, lon) float32 236MB dask.array\n", "Attributes:\n", - " CDI: Climate Data Interface version 2.4.4 (https://mpimet.mpg...\n", - " Conventions: CF-1.6\n", - " history: Fri Mar 21 11:42:19 2025: cdo splityear ACCESS-CM2_ssp58...\n", - " CDO: Climate Data Operators version 2.4.4 (https://mpimet.mpg...\n", - " model: ACCESS-CM2_r1i1p1f1_deepESD\n", - " scenario: ssp585\n", - " project_format: gridded\n", - " project_name: cmip6\n", - " project_type: projections}}}}\n" + " units: days\n", + " cell_methods: time: sum over days\n", + " history: [2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', ...\n", + " standard_name: number_of_days_with_lwe_thickness_of_precipitation_amount...\n", + " long_name: Maximum consecutive days with daily precipitation at or a...\n", + " description: Annual maximum number of consecutive days with daily prec...\n", + "\n", + "CWD metadata:\n", + "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', freq='YS', resample_before_rl=True) with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above 1 mm/day', 'description': 'Annual maximum number of consecutive days with daily precipitation at or above 1 mm/day.'}\n", + "\n", + "CWD xarray attributes:\n", + "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', freq='YS', resample_before_rl=True) with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above 1 mm/day', 'description': 'Annual maximum number of consecutive days with daily precipitation at or above 1 mm/day.'}\n" ] } ], "source": [ "# Inspect the CWD object (Maximum Consecutive Wet Days)\n", "print(\"CWD fields:\")\n", - "print(cwd.ls())\n", + "print(cwd)\n", "\n", "print(\"\\nCWD metadata:\")\n", - "print(cwd.metadata()[0])\n", + "print(cwd.attrs)\n", "\n", "print(\"\\nCWD xarray attributes:\")\n", - "print(cwd.to_xarray().attrs)" + "print(cwd.attrs)" ] }, { @@ -463,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "2dfb967617587dfd", "metadata": { "ExecuteTime": { @@ -474,14 +373,11 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -505,7 +401,7 @@ "\n", "# PLOT: Each index climatology\n", "for col, (name, index_obj) in enumerate(indices.items()):\n", - " ds = index_obj.to_xarray().mean(\"time\")\n", + " ds = index_obj.mean(\"time\")\n", " cmap = cmaps[name]\n", "\n", " style = ekp.styles.Style(colors=cmap, units=units[name])\n", @@ -535,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 11, "id": "93b725acd1e4d5a4", "metadata": { "ExecuteTime": { @@ -558,17 +454,13 @@ "wsdi = warm_spell_duration_index(ds=ds_merged)\n", "\n", "# HDD (approximation)\n", - "tas = (tasmax_ssp585.to_xarray()[\"tasmax\"] + tasmin_ssp585.to_xarray()[\"tasmin\"]) / 2\n", - "tas.attrs[\"units\"] = \"degC\"\n", - "tas = tas.to_dataset(name=\"tas\")\n", - "hdd = heating_degree_days(\n", - " ds=ekd.from_source(\n", - " \"multi\",\n", - " ekd.from_object(tasmax_ssp585.to_xarray()),\n", - " ekd.from_object(tasmin_ssp585.to_xarray()),\n", - " ekd.from_object(tas),\n", - " )\n", - ")" + "tas_da = (tasmax_ssp585.to_xarray()[\"tasmax\"] + tasmin_ssp585.to_xarray()[\"tasmin\"]) / 2\n", + "tas_da.attrs[\"units\"] = \"degC\"\n", + "hdd = heating_degree_days_approximation(\n", + " tasmax_ssp585.to_xarray().tasmax,\n", + " tasmin=tasmin_ssp585.to_xarray().tasmin,\n", + " tas=tas_da,\n", + ")\n" ] }, { @@ -576,7 +468,7 @@ "id": "19bb9271df9f8a95", "metadata": {}, "source": [ - "ekd.from_object(tas)## Inspecting the temperature-based indices\n", + "## Inspecting the temperature-based indices\n", "\n", "Now let's explore the three temperature indices we calculated:\n", "\n", @@ -594,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 12, "id": "eb9c38817b8d9d71", "metadata": { "ExecuteTime": { @@ -608,80 +500,40 @@ "output_type": "stream", "text": [ "DTR fields:\n", - " variable level valid_datetime units\n", - "0 dtr None 2015-01-01T00:00:00 K\n", - "1 dtr None 2016-01-01T00:00:00 K\n", - "2 dtr None 2017-01-01T00:00:00 K\n", - "3 dtr None 2018-01-01T00:00:00 K\n", - "4 dtr None 2019-01-01T00:00:00 K\n", - "5 dtr None 2020-01-01T00:00:00 K\n", - "6 dtr None 2021-01-01T00:00:00 K\n", - "7 dtr None 2022-01-01T00:00:00 K\n", - "8 dtr None 2023-01-01T00:00:00 K\n", - "9 dtr None 2024-01-01T00:00:00 K\n", - "10 dtr None 2025-01-01T00:00:00 K\n", - "11 dtr None 2026-01-01T00:00:00 K\n", - "12 dtr None 2027-01-01T00:00:00 K\n", - "13 dtr None 2028-01-01T00:00:00 K\n", - "14 dtr None 2029-01-01T00:00:00 K\n", - "15 dtr None 2030-01-01T00:00:00 K\n", - "16 dtr None 2031-01-01T00:00:00 K\n", - "17 dtr None 2032-01-01T00:00:00 K\n", - "18 dtr None 2033-01-01T00:00:00 K\n", - "19 dtr None 2034-01-01T00:00:00 K\n", - "20 dtr None 2035-01-01T00:00:00 K\n", - "21 dtr None 2036-01-01T00:00:00 K\n", - "22 dtr None 2037-01-01T00:00:00 K\n", - "23 dtr None 2038-01-01T00:00:00 K\n", - "24 dtr None 2039-01-01T00:00:00 K\n", - "25 dtr None 2040-01-01T00:00:00 K\n", - "26 dtr None 2041-01-01T00:00:00 K\n", - "27 dtr None 2042-01-01T00:00:00 K\n", - "28 dtr None 2043-01-01T00:00:00 K\n", - "29 dtr None 2044-01-01T00:00:00 K\n", - "30 dtr None 2045-01-01T00:00:00 K\n", - "31 dtr None 2046-01-01T00:00:00 K\n", - "32 dtr None 2047-01-01T00:00:00 K\n", - "33 dtr None 2048-01-01T00:00:00 K\n", - "34 dtr None 2049-01-01T00:00:00 K\n", - "35 dtr None 2050-01-01T00:00:00 K\n", - "36 dtr None 2051-01-01T00:00:00 K\n", - "37 dtr None 2052-01-01T00:00:00 K\n", - "38 dtr None 2053-01-01T00:00:00 K\n", - "39 dtr None 2054-01-01T00:00:00 K\n", - "\n", - " DTR metadata:\n", - "XArrayMetadata({'units': 'K', 'units_metadata': 'temperature: difference', 'cell_methods': ' time range within days time: mean over days', 'history': \"[2025-12-01 23:20:37] dtr: DTR(tasmin=tasmin, tasmax=tasmax, freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmin: \\ntasmax: \", 'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'description': 'Annual mean diurnal temperature range.', 'date': 20150101, 'time': 0, 'variable': 'dtr', 'level': None, 'levtype': 'sfc'})\n", - "\n", - " DTR xarray attributes:\n", - "{'earthkit_provenance': {'earthkit_internal': {'input_type': 'earthkit.data.readers.netcdf.fieldlist.NetCDFMultiFieldList'}, 'indicator_definition': {'tasmin': Parameter(kind=, default='tasmin', compute_name='tasmin', description='Minimum daily temperature.', units='[temperature]', choices=, value=), 'tasmax': Parameter(kind=, default='tasmax', compute_name='tasmax', description='Maximum daily temperature.', units='[temperature]', choices=, value=), 'freq': Parameter(kind=, default='YS', compute_name='freq', description='Resampling frequency.', units=, choices=, value=), 'ds': Parameter(kind=, default=None, compute_name=, description='A dataset with the variables given by name.', units=, choices=, value=), 'indexer': Parameter(kind=, default=, compute_name=, description='Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.', units=, choices=, value=)}, 'cf_attrs': [{'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'units': 'K', 'cell_methods': 'time range within days time: mean over days', 'description': '{freq} mean diurnal temperature range.', 'var_name': 'dtr'}], 'call_info': {'xclim_function': 'daily_temperature_range', 'parameters': {'tasmin': 'tasmin', 'tasmax': 'tasmax', 'freq': 'YS', 'ds': Size: 471MB\n", - "Dimensions: (time: 14610, lat: 48, lon: 84)\n", + " Size: 645kB\n", + "dask.array\n", "Coordinates:\n", - " * time (time) datetime64[ns] 117kB 2015-01-01 2015-01-02 ... 2054-12-31\n", + " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", " height float64 8B 2.0\n", - "Data variables:\n", - " tasmax (time, lat, lon) float32 236MB dask.array\n", - " tasmin (time, lat, lon) float32 236MB dask.array\n", "Attributes:\n", - " model: ACCESS-CM2_r1i1p1f1_deepESD\n", - " scenario: ssp585\n", - " project_name: cmip6\n", - " project_type: projections, 'indexer': {}}}}}\n" + " units: K\n", + " units_metadata: temperature: difference\n", + " cell_methods: time range within days time: mean over days\n", + " history: [2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tas...\n", + " standard_name: air_temperature\n", + " long_name: Mean diurnal temperature range\n", + " description: Annual mean diurnal temperature range.\n", + "\n", + " DTR metadata:\n", + "{'units': 'K', 'units_metadata': 'temperature: difference', 'cell_methods': ' time range within days time: mean over days', 'history': \"[2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tasmax, freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmin: \\ntasmax: \", 'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'description': 'Annual mean diurnal temperature range.'}\n", + "\n", + " DTR xarray attributes:\n", + "{'units': 'K', 'units_metadata': 'temperature: difference', 'cell_methods': ' time range within days time: mean over days', 'history': \"[2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tasmax, freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmin: \\ntasmax: \", 'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'description': 'Annual mean diurnal temperature range.'}\n" ] } ], "source": [ "# DTR (Daily Temperature Range)\n", "print(\"DTR fields:\")\n", - "print(dtr.ls())\n", + "print(dtr)\n", "\n", "print(\"\\n DTR metadata:\")\n", - "print(dtr.metadata()[0])\n", + "print(dtr.attrs)\n", "\n", "print(\"\\n DTR xarray attributes:\")\n", - "print(dtr.to_xarray().attrs)" + "print(dtr.attrs)" ] }, { @@ -698,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 13, "id": "e0a036138ac7e2a7", "metadata": { "ExecuteTime": { @@ -714,44 +566,28 @@ "WSDI fields:\n", "\n", " WSDI metadata:\n", - "XArrayMetadata({'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2025-12-01 23:20:39] warm_spell_duration_index: WARM_SPELL_DURATION_INDEX(tasmax=tasmax, tasmax_per=tasmax_per, window=6, freq='YS', resample_before_rl=True, bootstrap=False, op='>') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmax_per: [2025-12-01 23:20:38] per: percentile_doy(arr=tasmax, window=6, per=90, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True) - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s)', 'description': \"Annual number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s). a 6 day(s) window, centred on each calendar day in the ['1995-01-01', '2014-12-31'] period, is used to compute the [90]th percentile(s).\", 'date': 20150101, 'time': 0, 'variable': 'warm_spell_duration_index', 'level': None, 'levtype': 'sfc'})\n", + "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] warm_spell_duration_index: WARM_SPELL_DURATION_INDEX(tasmax=tasmax, tasmax_per=tasmax_per, window=6, freq='YS', resample_before_rl=True, bootstrap=False, op='>') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmax_per: [2026-03-06 11:02:56] per: percentile_doy(arr=tasmax, window=5, per=90, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True) - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s)', 'description': \"Annual number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s). a 5 day(s) window, centred on each calendar day in the ['1995-01-01', '2014-12-31'] period, is used to compute the [90]th percentile(s).\"}\n", "\n", " WSDI xarray attributes:\n", - "{'earthkit_provenance': {'earthkit_internal': {'input_type': 'earthkit.data.readers.netcdf.NetCDFFieldListReader'}, 'indicator_definition': {'tasmax': Parameter(kind=, default='tasmax', compute_name='tasmax', description='Maximum daily temperature.', units='[temperature]', choices=, value=), 'tasmax_per': Parameter(kind=, default='tasmax_per', compute_name='tasmax_per', description='Percentile(s) of daily maximum temperature.', units='[temperature]', choices=, value=), 'window': Parameter(kind=, default=6, compute_name='window', description='Minimum number of days with temperature above threshold to qualify as a warm spell.', units=, choices=, value=), 'freq': Parameter(kind=, default='YS', compute_name='freq', description='Resampling frequency.', units=, choices=, value=), 'resample_before_rl': Parameter(kind=, default=True, compute_name='resample_before_rl', description='Determines if the resampling should take place before or after the run length encoding (or a similar algorithm) is applied to runs.', units=, choices=, value=), 'bootstrap': Parameter(kind=, default=False, compute_name='bootstrap', description='Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Do not enable bootstrap when there is no common period, otherwise it will provide the wrong results. Note that bootstrapping is computationally expensive.', units=, choices=, value=), 'op': Parameter(kind=, default='>', compute_name='op', description='Comparison operation. Default: \">\".', units=, choices={'>', '>=', 'ge', 'gt'}, value=), 'ds': Parameter(kind=, default=None, compute_name=, description='A dataset with the variables given by name.', units=, choices=, value=)}, 'cf_attrs': [{'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least {window} consecutive days where the maximum daily temperature is above the {tasmax_per_thresh}th percentile(s)', 'units': 'days', 'cell_methods': 'time: sum over days', 'description': '{freq} number of days with at least {window} consecutive days where the maximum daily temperature is above the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).', 'var_name': 'warm_spell_duration_index'}], 'call_info': {'xclim_function': 'warm_spell_duration_index', 'parameters': {'tasmax': 'tasmax', 'tasmax_per': 'tasmax_per', 'window': 6, 'freq': 'YS', 'resample_before_rl': True, 'bootstrap': False, 'op': '>', 'ds': Size: 242MB\n", - "Dimensions: (time: 14610, lat: 48, lon: 84, percentiles: 1, dayofyear: 366)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 117kB 2015-01-01 ... 2054-12-31\n", - " * lat (lat) float64 384B 41.62 41.67 41.72 ... 43.87 43.92 43.97\n", - " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.475 -5.425\n", - " * percentiles (percentiles) int64 8B 90\n", - " * dayofyear (dayofyear) int64 3kB 1 2 3 4 5 6 7 ... 361 362 363 364 365 366\n", - " height float64 8B 2.0\n", - "Data variables:\n", - " tasmax (time, lat, lon) float32 236MB dask.array\n", - " tasmax_per (lat, lon, dayofyear, percentiles) float32 6MB dask.array\n", - "Attributes:\n", - " model: ACCESS-CM2_r1i1p1f1_deepESD\n", - " scenario: ssp585\n", - " project_name: cmip6\n", - " project_type: projections}}}}\n" + "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] warm_spell_duration_index: WARM_SPELL_DURATION_INDEX(tasmax=tasmax, tasmax_per=tasmax_per, window=6, freq='YS', resample_before_rl=True, bootstrap=False, op='>') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmax_per: [2026-03-06 11:02:56] per: percentile_doy(arr=tasmax, window=5, per=90, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True) - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s)', 'description': \"Annual number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s). a 5 day(s) window, centred on each calendar day in the ['1995-01-01', '2014-12-31'] period, is used to compute the [90]th percentile(s).\"}\n" ] } ], "source": [ "# WSDI (Warm Spell Duration Index)\n", "print(\"WSDI fields:\")\n", - "wsdi.ls()\n", + "wsdi\n", "\n", "print(\"\\n WSDI metadata:\")\n", - "print(wsdi.metadata()[0])\n", + "print(wsdi.attrs)\n", "\n", "print(\"\\n WSDI xarray attributes:\")\n", - "print(wsdi.to_xarray().attrs)" + "print(wsdi.attrs)" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 14, "id": "c2303c3c80f8ae82", "metadata": { "ExecuteTime": { @@ -765,81 +601,41 @@ "output_type": "stream", "text": [ "HDD fields:\n", - " variable level valid_datetime units\n", - "0 heating_degree_days None 2015-01-01T00:00:00 K days\n", - "1 heating_degree_days None 2016-01-01T00:00:00 K days\n", - "2 heating_degree_days None 2017-01-01T00:00:00 K days\n", - "3 heating_degree_days None 2018-01-01T00:00:00 K days\n", - "4 heating_degree_days None 2019-01-01T00:00:00 K days\n", - "5 heating_degree_days None 2020-01-01T00:00:00 K days\n", - "6 heating_degree_days None 2021-01-01T00:00:00 K days\n", - "7 heating_degree_days None 2022-01-01T00:00:00 K days\n", - "8 heating_degree_days None 2023-01-01T00:00:00 K days\n", - "9 heating_degree_days None 2024-01-01T00:00:00 K days\n", - "10 heating_degree_days None 2025-01-01T00:00:00 K days\n", - "11 heating_degree_days None 2026-01-01T00:00:00 K days\n", - "12 heating_degree_days None 2027-01-01T00:00:00 K days\n", - "13 heating_degree_days None 2028-01-01T00:00:00 K days\n", - "14 heating_degree_days None 2029-01-01T00:00:00 K days\n", - "15 heating_degree_days None 2030-01-01T00:00:00 K days\n", - "16 heating_degree_days None 2031-01-01T00:00:00 K days\n", - "17 heating_degree_days None 2032-01-01T00:00:00 K days\n", - "18 heating_degree_days None 2033-01-01T00:00:00 K days\n", - "19 heating_degree_days None 2034-01-01T00:00:00 K days\n", - "20 heating_degree_days None 2035-01-01T00:00:00 K days\n", - "21 heating_degree_days None 2036-01-01T00:00:00 K days\n", - "22 heating_degree_days None 2037-01-01T00:00:00 K days\n", - "23 heating_degree_days None 2038-01-01T00:00:00 K days\n", - "24 heating_degree_days None 2039-01-01T00:00:00 K days\n", - "25 heating_degree_days None 2040-01-01T00:00:00 K days\n", - "26 heating_degree_days None 2041-01-01T00:00:00 K days\n", - "27 heating_degree_days None 2042-01-01T00:00:00 K days\n", - "28 heating_degree_days None 2043-01-01T00:00:00 K days\n", - "29 heating_degree_days None 2044-01-01T00:00:00 K days\n", - "30 heating_degree_days None 2045-01-01T00:00:00 K days\n", - "31 heating_degree_days None 2046-01-01T00:00:00 K days\n", - "32 heating_degree_days None 2047-01-01T00:00:00 K days\n", - "33 heating_degree_days None 2048-01-01T00:00:00 K days\n", - "34 heating_degree_days None 2049-01-01T00:00:00 K days\n", - "35 heating_degree_days None 2050-01-01T00:00:00 K days\n", - "36 heating_degree_days None 2051-01-01T00:00:00 K days\n", - "37 heating_degree_days None 2052-01-01T00:00:00 K days\n", - "38 heating_degree_days None 2053-01-01T00:00:00 K days\n", - "39 heating_degree_days None 2054-01-01T00:00:00 K days\n", - "\n", - " HDD metadata:\n", - "XArrayMetadata({'units': 'K days', 'units_metadata': 'temperature: unknown', 'cell_methods': ' time: sum over days', 'history': \"[2025-12-01 23:20:41] heating_degree_days: HEATING_DEGREE_DAYS(tas=tas, thresh='17.0 degC', freq='YS') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for mean daily temperature below 17.0 degc', 'description': 'Annual cumulative heating degree days (mean temperature below 17.0 degc).', 'date': 20150101, 'time': 0, 'variable': 'heating_degree_days', 'level': None, 'levtype': 'sfc'})\n", - "\n", - " HDD xarray attributes:\n", - "{'earthkit_provenance': {'earthkit_internal': {'input_type': 'earthkit.data.readers.netcdf.fieldlist.XArrayMultiFieldList'}, 'indicator_definition': {'tas': Parameter(kind=, default='tas', compute_name='tas', description='Mean daily temperature.', units='[temperature]', choices=, value=), 'thresh': Parameter(kind=, default='17.0 degC', compute_name='thresh', description='Threshold temperature on which to base evaluation.', units='[temperature]', choices=, value=), 'freq': Parameter(kind=, default='YS', compute_name='freq', description='Resampling frequency.', units=, choices=, value=), 'ds': Parameter(kind=, default=None, compute_name=, description='A dataset with the variables given by name.', units=, choices=, value=), 'indexer': Parameter(kind=, default=, compute_name=, description='Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.', units=, choices=, value=)}, 'cf_attrs': [{'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for mean daily temperature below {thresh}', 'units': 'K days', 'cell_methods': 'time: sum over days', 'description': '{freq} cumulative heating degree days (mean temperature below {thresh}).', 'var_name': 'heating_degree_days'}], 'call_info': {'xclim_function': 'heating_degree_days', 'parameters': {'tas': 'tas', 'thresh': '17.0 degC', 'freq': 'YS', 'ds': Size: 707MB\n", - "Dimensions: (time: 14610, lat: 48, lon: 84)\n", + " Size: 645kB\n", + "dask.array\n", "Coordinates:\n", - " * time (time) datetime64[ns] 117kB 2015-01-01 2015-01-02 ... 2054-12-31\n", + " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", " height float64 8B 2.0\n", - "Data variables:\n", - " tasmax (time, lat, lon) float32 236MB dask.array\n", - " tasmin (time, lat, lon) float32 236MB dask.array\n", - " tas (time, lat, lon) float32 236MB dask.array\n", "Attributes:\n", - " model: ACCESS-CM2_r1i1p1f1_deepESD\n", - " scenario: ssp585\n", - " project_name: cmip6\n", - " project_type: projections, 'indexer': {}}}}}\n" + " units: K days\n", + " units_metadata: temperature: difference\n", + " cell_methods: time: sum over days\n", + " history: [2026-03-06 11:02:57] heating_degree_days_approximation:...\n", + " standard_name: integral_of_air_temperature_deficit_wrt_time\n", + " long_name: Cumulative sum of temperature degrees for daily temperat...\n", + " description: Annual cumulative heating degree days (temperature below...\n", + "\n", + " HDD metadata:\n", + "{'units': 'K days', 'units_metadata': 'temperature: difference', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] heating_degree_days_approximation: HEATING_DEGREE_DAYS_APPROXIMATION(tasmax=tasmax, tasmin=tasmin, tas=, thresh='17.0 degC', freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmin: \\ntas: \", 'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for daily temperatures below 17.0 degc', 'description': 'Annual cumulative heating degree days (temperature below 17.0 degc) using a combination of minimum, maximum, and mean daily temperatures.'}\n", + "\n", + " HDD xarray attributes:\n", + "{'units': 'K days', 'units_metadata': 'temperature: difference', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] heating_degree_days_approximation: HEATING_DEGREE_DAYS_APPROXIMATION(tasmax=tasmax, tasmin=tasmin, tas=, thresh='17.0 degC', freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmin: \\ntas: \", 'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for daily temperatures below 17.0 degc', 'description': 'Annual cumulative heating degree days (temperature below 17.0 degc) using a combination of minimum, maximum, and mean daily temperatures.'}\n" ] } ], "source": [ "# HDD (Heating Degree Days)\n", "print(\"HDD fields:\")\n", - "print(hdd.ls())\n", + "print(hdd)\n", "\n", "print(\"\\n HDD metadata:\")\n", - "print(hdd.metadata()[0])\n", + "print(hdd.attrs)\n", "\n", "print(\"\\n HDD xarray attributes:\")\n", - "print(hdd.to_xarray().attrs)" + "print(hdd.attrs)\n" ] }, { @@ -856,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "id": "ab5cf6b21d4cbb4f", "metadata": { "ExecuteTime": { @@ -867,14 +663,11 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "jetTransient": { - "display_id": null - }, "metadata": {}, "output_type": "display_data" } @@ -898,7 +691,7 @@ "\n", "# PLOT: Each index climatology\n", "for col, (name, index_obj) in enumerate(indices.items()):\n", - " ds = index_obj.to_xarray().mean(\"time\")\n", + " ds = index_obj.mean(\"time\")\n", " cmap = cmaps[name]\n", "\n", " style = ekp.styles.Style(colors=cmap, units=units[name])\n", @@ -915,9 +708,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "dev", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" } }, "nbformat": 4, From 9e7fdb5be10b0999a30778363bba00ad9cb06323 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Fri, 6 Mar 2026 15:41:30 +0100 Subject: [PATCH 34/47] feat: add notebooks demonstrating heatwave evolution analysis and tropical nights cooling demand. --- docs/notebooks/heatwave_evolution.ipynb | 254 ++++++++++++++++++ .../tropical_nights_cooling_demand.ipynb | 220 +++++++++++++++ 2 files changed, 474 insertions(+) create mode 100644 docs/notebooks/heatwave_evolution.ipynb create mode 100644 docs/notebooks/tropical_nights_cooling_demand.ipynb diff --git a/docs/notebooks/heatwave_evolution.ipynb b/docs/notebooks/heatwave_evolution.ipynb new file mode 100644 index 0000000..c3f6ce7 --- /dev/null +++ b/docs/notebooks/heatwave_evolution.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use Case: Heatwave Evolution Analysis\n", + "\n", + "This notebook demonstrates how to use `earthkit-climate` to analyze whether heatwaves are becoming more frequent and longer over time. We will compute heatwave indices from daily minimum (`tasmin`) and maximum (`tasmax`) temperature data and compare different decades.\n", + "\n", + "We'll use:\n", + "- **`heat_wave_frequency`**: Number of heatwaves in a given period.\n", + "- **`heat_wave_max_length`**: Maximum length of a heatwave in a given period.\n", + "\n", + "A heatwave is defined as a period where both the daily **minimum** temperature exceeds a threshold (e.g., 22°C) **and** the daily **maximum** temperature exceeds another threshold (e.g., 30°C) for a minimum number of consecutive days.\n", + "\n", + "The analysis will involve loading CMIP6 data, computing indices, aggregating results by decade, and visualizing spatial anomalies." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "46d7c451", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import numpy\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " heat_wave_frequency,\n", + " heat_wave_max_length,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "7fec24a5", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "We will load historical daily minimum (`tasmin`) and maximum (`tasmax`) temperature data from the ACCESS-CM2 CMIP6 model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7aec26b6", + "metadata": {}, + "outputs": [], + "source": [ + "tasmax_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + ")\n", + "ds_tasmax = tasmax_hist.to_xarray()\n", + "\n", + "tasmin_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + ")\n", + "ds_tasmin = tasmin_hist.to_xarray()" + ] + }, + { + "cell_type": "markdown", + "id": "90561dd6", + "metadata": {}, + "source": [ + "## Computing Heatwave Indices\n", + "\n", + "We define a heatwave as a period of at least 5 consecutive days where:\n", + "- The daily **minimum** temperature exceeds **22°C** (`thresh_tasmin`)\n", + "- The daily **maximum** temperature exceeds **30°C** (`thresh_tasmax`)\n", + "\n", + "Both conditions must be satisfied simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ba4943e3", + "metadata": {}, + "outputs": [], + "source": [ + "thresh_tasmin = \"22.0 degC\"\n", + "thresh_tasmax = \"30 degC\"\n", + "window = 5\n", + "\n", + "# Compute annual heatwave frequency\n", + "hwf = heat_wave_frequency(\n", + " ds_tasmin.tasmin,\n", + " ds_tasmax.tasmax,\n", + " thresh_tasmin=thresh_tasmin,\n", + " thresh_tasmax=thresh_tasmax,\n", + " window=window,\n", + " freq=\"YS\"\n", + ")\n", + "\n", + "# Compute annual maximum heatwave length\n", + "hwl = heat_wave_max_length(\n", + " ds_tasmin.tasmin,\n", + " ds_tasmax.tasmax,\n", + " thresh_tasmin=thresh_tasmin,\n", + " thresh_tasmax=thresh_tasmax,\n", + " window=window,\n", + " freq=\"YS\"\n", + ")\n", + "\n", + "# xclim sets units=\"1\" for dimensionless indices; replace with \"dimensionless\"\n", + "if hwf.attrs.get(\"units\") == \"1\":\n", + " hwf.attrs[\"units\"] = \"dimensionless\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8a3fa64", + "metadata": {}, + "source": [ + "## Decadal Aggregation and Anomalies\n", + "\n", + "We compare the 1980s (1980-1989) with the 2000s (2000-2009) to see how heatwaves have evolved." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "053a9f9d", + "metadata": {}, + "outputs": [], + "source": [ + "hwf_1980s = hwf.sel(time=slice(\"1980\", \"1989\")).mean(dim=\"time\", keep_attrs=True)\n", + "hwf_2000s = hwf.sel(time=slice(\"2000\", \"2009\")).mean(dim=\"time\", keep_attrs=True)\n", + "hwf_anomaly = hwf_2000s - hwf_1980s\n", + "hwf_anomaly.attrs.update(hwf.attrs)\n", + "\n", + "hwl_1980s = hwl.sel(time=slice(\"1980\", \"1989\")).mean(dim=\"time\", keep_attrs=True)\n", + "hwl_2000s = hwl.sel(time=slice(\"2000\", \"2009\")).mean(dim=\"time\", keep_attrs=True)\n", + "hwl_anomaly = hwl_2000s - hwl_1980s\n", + "hwl_anomaly.attrs.update(hwl.attrs)" + ] + }, + { + "cell_type": "markdown", + "id": "650559e9", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "Let's visualize the anomalies in heatwave frequency and length." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5ad5fa", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "No points given", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m 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\u001b[43m \u001b[49m\u001b[43mauto_style\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauto_style\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 584\u001b[39m \u001b[43m \u001b[49m\u001b[43mregrid\u001b[49m\u001b[43m=\u001b[49m\u001b[43mregrid\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 585\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 586\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 587\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/components/extractors.py:455\u001b[39m, in \u001b[36mextract_plottables_3D\u001b[39m\u001b[34m(subplot, method_name, args, x, y, z, style, no_style, units, xunits, yunits, every, source_units, extract_domain, auto_style, regrid, metadata, **kwargs)\u001b[39m\n\u001b[32m 453\u001b[39m \u001b[38;5;66;03m# Step 11: Create the plot with or without interpolation\u001b[39;00m\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_style:\n\u001b[32m--> \u001b[39m\u001b[32m455\u001b[39m mappable = \u001b[43mplot_with_interpolation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 456\u001b[39m \u001b[43m \u001b[49m\u001b[43msubplot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 457\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 458\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 459\u001b[39m \u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 460\u001b[39m \u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 461\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 462\u001b[39m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 463\u001b[39m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 464\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 465\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 466\u001b[39m warnings.warn(\u001b[33m\"\u001b[39m\u001b[33mStyle not set - using raw matplotlib method.\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/components/extractors.py:1019\u001b[39m, in \u001b[36mplot_with_interpolation\u001b[39m\u001b[34m(subplot, style, method_name, x_values, y_values, z_values, source_crs, kwargs)\u001b[39m\n\u001b[32m 1016\u001b[39m interpolate = Interpolate(**interpolate)\n\u001b[32m 1018\u001b[39m \u001b[38;5;66;03m# Apply interpolation\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1019\u001b[39m x_values, y_values, z_values = \u001b[43minterpolate\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1020\u001b[39m \u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1021\u001b[39m \u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1022\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1023\u001b[39m \u001b[43m \u001b[49m\u001b[43msource_crs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msource_crs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1024\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_crs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1025\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1027\u001b[39m \u001b[38;5;66;03m# Handle transform settings after interpolation\u001b[39;00m\n\u001b[32m 1028\u001b[39m _ = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33mtransform_first\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/resample.py:250\u001b[39m, in \u001b[36mInterpolate.apply\u001b[39m\u001b[34m(self, x, y, z, source_crs, target_crs)\u001b[39m\n\u001b[32m 247\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 248\u001b[39m target_x, target_y = x, y\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgrids\u001b[49m\u001b[43m.\u001b[49m\u001b[43minterpolate_unstructured\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 251\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 255\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_resolution\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtarget_resolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 256\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 257\u001b[39m \u001b[43m \u001b[49m\u001b[43mdistance_threshold\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdistance_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/geo/grids.py:275\u001b[39m, in \u001b[36minterpolate_unstructured\u001b[39m\u001b[34m(x, y, z, target_shape, target_resolution, method, distance_threshold)\u001b[39m\n\u001b[32m 272\u001b[39m z_filtered = z[mask]\n\u001b[32m 274\u001b[39m \u001b[38;5;66;03m# Interpolate the filtered data onto the structured grid\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m275\u001b[39m grid_z = \u001b[43mgriddata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 276\u001b[39m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcolumn_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_filtered\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_filtered\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 277\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_filtered\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 278\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid_y\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 279\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 280\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m np.isnan(grid_z).any() \u001b[38;5;129;01mand\u001b[39;00m is_global(x, y, np.max(np.diff(np.unique(y))) * \u001b[32m2\u001b[39m):\n\u001b[32m 283\u001b[39m warnings.warn(\n\u001b[32m 284\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mInterpolation produced NaN values in the global output grid, reinterpolating with `nearest`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 285\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/scipy/interpolate/_ndgriddata.py:320\u001b[39m, in \u001b[36mgriddata\u001b[39m\u001b[34m(points, values, xi, method, fill_value, rescale)\u001b[39m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[32m 319\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m method == \u001b[33m'\u001b[39m\u001b[33mlinear\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m320\u001b[39m ip = \u001b[43mLinearNDInterpolator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 321\u001b[39m \u001b[43m \u001b[49m\u001b[43mrescale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrescale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 322\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[32m 323\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m method == \u001b[33m'\u001b[39m\u001b[33mcubic\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ndim == \u001b[32m2\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:306\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.LinearNDInterpolator.__init__\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:97\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.NDInterpolatorBase.__init__\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:310\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.LinearNDInterpolator._calculate_triangulation\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_qhull.pyx:1889\u001b[39m, in \u001b[36mscipy.spatial._qhull.Delaunay.__init__\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_qhull.pyx:280\u001b[39m, in \u001b[36mscipy.spatial._qhull._Qhull.__init__\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: No points given" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = ekp.Figure(\n", + " rows=1, columns=2, size=(14, 5)\n", + ")\n", + "\n", + "indices = {\n", + " \"HWF Anomaly\": hwf_anomaly,\n", + " \"HWL Anomaly\": hwl_anomaly,\n", + "}\n", + "\n", + "cmaps = {\n", + " \"HWF Anomaly\": \"RdBu_r\",\n", + " \"HWL Anomaly\": \"RdBu_r\",\n", + "}\n", + "\n", + "for col, (name, data) in enumerate(indices.items()):\n", + " # Explicitly set units for the style to avoid inference issues\n", + " style = ekp.styles.Style(colors=cmaps[name])\n", + "\n", + " map_plot = figure.add_map(row=0, column=col)\n", + " # Use quickplot via Map object\n", + " map_plot.quickplot(data, style=style)\n", + "\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.title(f\"{name} (2000s minus 1980s)\")\n", + " map_plot.legend(location=\"right\")\n", + "\n", + "figure.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/tropical_nights_cooling_demand.ipynb b/docs/notebooks/tropical_nights_cooling_demand.ipynb new file mode 100644 index 0000000..eef5da7 --- /dev/null +++ b/docs/notebooks/tropical_nights_cooling_demand.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use Case: Tropical Nights and Cooling Demand Analysis\n", + "\n", + "This notebook explores how increasing temperatures affect nighttime heat stress and the demand for cooling. We use `earthkit-climate` to compute tropical nights and cooling degree days, analyzing their trends and correlation.\n", + "\n", + "We'll use:\n", + "- **`tropical_nights`**: Number of days where $T_{min} > 20^{\\circ}C$.\n", + "- **`cooling_degree_days`**: Cumulative degrees above a comfort threshold ($18^{\\circ}C$).\n", + "\n", + "The workflow includes loading temperature data, computing indices, performing trend analysis, and visualizing the relationship between the two indicators." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "11c39356", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " cooling_degree_days,\n", + " tropical_nights,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.rcParams[\"figure.figsize\"] = (12, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "37803e37", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "We load historical daily minimum temperature (`tasmin`) from CMIP6 simulations. We will use `tasmin` also as a proxy for `tas` to calculate cooling degree days if `tas` is not available, or just focus on minimum temperature trends if preferred. Here we load `tasmin` to compute tropical nights." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e3a9b72d", + "metadata": {}, + "outputs": [], + "source": [ + "tasmin_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + ")\n", + "ds_tasmin = tasmin_hist.to_xarray()" + ] + }, + { + "cell_type": "markdown", + "id": "b6d9669e", + "metadata": {}, + "source": [ + "## Computing Indices\n", + "\n", + "We compute the indices on an annual basis. For demonstration, we'll use `tasmin` for both, noting that `cooling_degree_days` usually uses `tas`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "59afd49a", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute annual tropical nights\n", + "tn = tropical_nights(ds_tasmin.tasmin, thresh=\"293.15 K\", freq=\"YS\")\n", + "\n", + "# Compute annual cooling degree days (using tasmin for this example)\n", + "cdd = cooling_degree_days(ds_tasmin.tasmin, thresh=\"291.15 K\", freq=\"YS\")" + ] + }, + { + "cell_type": "markdown", + "id": "48782406", + "metadata": {}, + "source": [ + "## Trend Analysis\n", + "\n", + "We estimate the trend for both indicators over the available period." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6b05e705", + "metadata": {}, + "outputs": [], + "source": [ + "# Simple linear trend using xarray's polyfit\n", + "tn_trend = tn.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10\n", + "cdd_trend = cdd.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10" + ] + }, + { + "cell_type": "markdown", + "id": "17759260", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "### Trend Maps\n", + "\n", + "We visualize the spatial patterns of trends in tropical nights and cooling degree days." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "28c4e7f1", + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'earthkit.plots' has no attribute 'plot'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m fig, (ax1, ax2) = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, subplot_kw={\u001b[33m'\u001b[39m\u001b[33mprojection\u001b[39m\u001b[33m'\u001b[39m: ccrs.PlateCarree()})\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mekp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m(tn_trend, ax=ax1, title=\u001b[33m\"\u001b[39m\u001b[33mTrend in Tropical Nights (days/decade)\u001b[39m\u001b[33m\"\u001b[39m, cmap=\u001b[33m\"\u001b[39m\u001b[33mYlOrRd\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m ekp.plot(cdd_trend, ax=ax2, title=\u001b[33m\"\u001b[39m\u001b[33mTrend in Cooling Degree Days (K days/decade)\u001b[39m\u001b[33m\"\u001b[39m, cmap=\u001b[33m\"\u001b[39m\u001b[33mYlOrRd\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6\u001b[39m plt.show()\n", + "\u001b[31mAttributeError\u001b[39m: module 'earthkit.plots' has no attribute 'plot'" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAADnCAYAAAAqwMpXAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAABiFJREFUeJzt3EGK4gAURdFURwg4ENyMa3DJrsHlOBAykBTpFcQ2RdeFc8Yin0wel4hfy7IsAwAAAET9+d8HAAAAwCeELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApB22fvD5fA7zPP/sNQDwhmmahuPx6JntxNYDUN36w9ahu91uw+v12uM2ANjFOI7D9XoVtzuw9QCUt35T2K5vateovVwuw+l02utGAPhnj8djuN/vfzfKW9vP2XoAylu/+afIqzVqz+fzp/cBAL+UrQegyJ9HAQAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJAmbAEAAEgTtgAAAKQJWwAAANKELQAAAGnCFgAAgDRhCwAAQJqwBQAAIE3YAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAEDa4Z0PPx6Pn7sEAN5gk36G5wpAcZM2he00TcM4jsP9fv/kLgDY1bpN60bxOVsPQHnrv5ZlWbZ84fP5HOZ53uM2ANjFOnTH49HT3ImtB6C69ZvDFgAAAH4jfx4FAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABD2Td5xmbowuzbpwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "\n", + "ekp.plot(tn_trend, ax=ax1, title=\"Trend in Tropical Nights (days/decade)\", cmap=\"YlOrRd\")\n", + "ekp.plot(cdd_trend, ax=ax2, title=\"Trend in Cooling Degree Days (K days/decade)\", cmap=\"YlOrRd\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Correlation Analysis\n", + "\n", + "Finally, we analyze the relationship between tropical nights and cooling demand by plotting their correlation over time." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Aggregating spatially for a simple correlation plot\n", + "tn_mean = tn.mean(dim=(\"lat\", \"lon\"))\n", + "cdd_mean = cdd.mean(dim=(\"lat\", \"lon\"))\n", + "\n", + "plt.scatter(tn_mean, cdd_mean)\n", + "plt.xlabel(\"Average Annual Tropical Nights\")\n", + "plt.ylabel(\"Average Annual Cooling Degree Days (K days)\")\n", + "plt.title(\"Relationship Between Nighttime Heat Stress and Cooling Demand\")\n", + "plt.grid(True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 11fc738b21c379b60d58b860762aa6fec29a34a3 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 13:41:51 +0100 Subject: [PATCH 35/47] fix: percentile dimension drop --- src/earthkit/climate/utils/percentile.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/earthkit/climate/utils/percentile.py b/src/earthkit/climate/utils/percentile.py index 30856bc..32a8a20 100644 --- a/src/earthkit/climate/utils/percentile.py +++ b/src/earthkit/climate/utils/percentile.py @@ -159,4 +159,8 @@ def calculate_percentile_doy( per_name = f"{variable}_per" per = per.rename(per_name) + # If the percentile is a singleton, squeeze the dimension to avoid downstream plotting issues + if "percentiles" in per.dims and per.sizes["percentiles"] == 1: + per = per.squeeze("percentiles", drop=True) + return per.to_dataset() From 98c42aa281e4e62c879b052ca276798406f98c05 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 14:59:51 +0100 Subject: [PATCH 36/47] deps: fix earthkit-plots version --- pixi.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pixi.toml b/pixi.toml index 4032bb3..bb8f575 100644 --- a/pixi.toml +++ b/pixi.toml @@ -35,7 +35,7 @@ docs-build = "rm -rf docs/_api docs/_build && sphinx-build -M html docs docs/_bu [pypi-dependencies] earthkit-climate = {path = ".", editable = true} earthkit-data = ">=0.17.0" -earthkit-plots = ">=0.5.0" +earthkit-plots = ">=0.5.0,<0.6" earthkit-utils = {git = "https://github.com/ecmwf/earthkit-utils.git", branch = "develop"} [tasks] From fdf483c2acd5a959ae38ceff4138e97c933b08cb Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 16:11:43 +0100 Subject: [PATCH 37/47] feat: Split the generic climate indices analysis notebook in one for precipitation and other for temperature indicators --- docs/notebooks/climate_indices_analysis.ipynb | 730 ----- .../intro_precipitation_indices.ipynb | 1617 +++++++++++ .../notebooks/intro_temperature_indices.ipynb | 2366 +++++++++++++++++ 3 files changed, 3983 insertions(+), 730 deletions(-) delete mode 100644 docs/notebooks/climate_indices_analysis.ipynb create mode 100644 docs/notebooks/intro_precipitation_indices.ipynb create mode 100644 docs/notebooks/intro_temperature_indices.ipynb diff --git a/docs/notebooks/climate_indices_analysis.ipynb b/docs/notebooks/climate_indices_analysis.ipynb deleted file mode 100644 index 8c6c614..0000000 --- a/docs/notebooks/climate_indices_analysis.ipynb +++ /dev/null @@ -1,730 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "392259143f051b53", - "metadata": {}, - "source": [ - "# Climate Indices with Earthkit & Xclim\n", - "\n", - "This notebook demonstrates how to compute and visualize **climate indices** from CMIP6 datasets using the `earthkit-climate` and `xclim` packages.\n", - "\n", - "We'll use:\n", - "- **Precipitation-based indices**:\n", - " - *SDII*: Simple Daily Intensity Index (average precipitation on wet days)\n", - " - *CWD*: Consecutive Wet Days (max number of wet days in a row)\n", - "\n", - "- **Temperature-based indices**:\n", - " - *DTR*: Daily Temperature Range (Tmax - Tmin)\n", - " - *WSDI*: Warm Spell Duration Index (≥6 consecutive days above 90th percentile)\n", - " - *HDD*: Heating Degree Days (based on temperature below threshold)\n", - "\n", - "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1b36bd42f74117db", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:58:48.501709Z", - "start_time": "2025-12-01T21:58:48.498192Z" - } - }, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import cartopy.crs as ccrs\n", - "import earthkit.data as ekd\n", - "import earthkit.plots as ekp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from earthkit.climate.indicators.precipitation import (\n", - " daily_pr_intensity,\n", - " maximum_consecutive_wet_days,\n", - ")\n", - "from earthkit.climate.indicators.temperature import (\n", - " daily_temperature_range,\n", - " heating_degree_days_approximation,\n", - " warm_spell_duration_index,\n", - ")\n", - "from earthkit.climate.utils.percentile import calculate_percentile_doy\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "plt.rcParams[\"figure.figsize\"] = (8, 5)" - ] - }, - { - "cell_type": "markdown", - "id": "d1552f095264a74f", - "metadata": {}, - "source": [ - "## Loading CMIP6 data\n", - "\n", - "We’ll use *daily gridded data* from the ACCESS-CM2 model for precipitation (`pr`), maximum (`tasmax`) and minimum (`tasmin`) temperature, for both historical and SSP585 future scenarios.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5c6692d99197d4af", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:27:58.826458Z", - "start_time": "2025-12-01T21:25:56.199912Z" - } - }, - "outputs": [], - "source": [ - "# Load precipitation\n", - "pr_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - ")\n", - "pr_ssp585 = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - ")\n", - "\n", - "# Load temperature\n", - "tasmin_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - ")\n", - "tasmin_ssp585 = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - ")\n", - "\n", - "tasmax_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - ")\n", - "tasmax_ssp585 = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_ssp585.nc\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c4c8b8fc1f899c6a", - "metadata": {}, - "source": [ - "## Inspect and visualize the raw data\n", - "\n", - "Before computing indices, let’s plot a few example grids to see how the raw variables look.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "51a81669f2d9f6fd", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:28:17.485423Z", - "start_time": "2025-12-01T21:27:58.838384Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define domain (Spain north coast)\n", - "domain = [-9.6, -5.4, 41.6, 44.0]\n", - "\n", - "# Create the figure with 2x2 maps\n", - "figure = ekp.Figure(\n", - " crs=ccrs.NearsidePerspective(central_longitude=-5, central_latitude=43), rows=2, columns=3, size=(15, 10)\n", - ")\n", - "\n", - "# Define variables and their datasets\n", - "variables = {\n", - " \"tasmin\": (tasmin_hist, tasmin_ssp585, \"celsius\"),\n", - " \"tasmax\": (tasmax_hist, tasmax_ssp585, \"celsius\"),\n", - " \"pr\": (pr_hist, pr_ssp585, \"mm/day\"),\n", - "}\n", - "\n", - "# HISTORICAL (row 0)\n", - "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", - " hist_clim = hist.to_xarray().mean(\"time\")\n", - " cmap = \"winter_r\" if var == \"pr\" else \"autumn\"\n", - " style = ekp.styles.Style(colors=cmap, units=units)\n", - " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", - " map_plot.quickplot(hist_clim, style=style)\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.legend(location=\"right\")\n", - " map_plot.title(f\"{var} Climatology (Historical)\")\n", - "\n", - "# SSP585 (row 1)\n", - "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", - " ssp_clim = ssp.to_xarray().mean(\"time\")\n", - " cmap = \"winter_r\" if var == \"pr\" else \"autumn_r\"\n", - " style = ekp.styles.Style(colors=cmap, units=units)\n", - " map_plot = figure.add_map(row=1, column=col, domain=domain)\n", - " map_plot.quickplot(ssp_clim, style=style)\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.legend(location=\"right\")\n", - " map_plot.title(f\"{var} Climatology (SSP585)\")\n", - "\n", - "# Final layout\n", - "figure.show()" - ] - }, - { - "cell_type": "markdown", - "id": "908b3743011b9663", - "metadata": {}, - "source": [ - "## Precipitation-based indices\n", - "\n", - "We'll compute:\n", - "- **SDII** – Simple Daily Intensity Index (average rain on wet days)\n", - "- **CWD** – Consecutive Wet Days (max length of a wet period)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4f6e09420f604684", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:48:39.507484Z", - "start_time": "2025-12-01T21:48:37.823637Z" - } - }, - "outputs": [], - "source": [ - "# SDII (using dataset)\n", - "sdii = daily_pr_intensity(ds=pr_ssp585, thresh=\"1 mm/day\", freq=\"YS\")\n", - "\n", - "# CWD (using DataArray)\n", - "cwd = maximum_consecutive_wet_days(pr_ssp585.to_xarray().pr, thresh=\"1 mm/day\")" - ] - }, - { - "cell_type": "markdown", - "id": "5bb30114dfe9aba3", - "metadata": {}, - "source": [ - "## Inspecting the computed precipitation indices\n", - "\n", - "Now that we’ve calculated the precipitation-based indices (**SDII** and **CWD**),\n", - "let’s take a closer look at their structure and metadata.\n", - "\n", - "We'll explore:\n", - "1. The list of fields available in each index object (`.ls()`).\n", - "2. The associated provenance and processing metadata (`.metadata()`).\n", - "3. The attributes of the resulting `xarray.Dataset` (`.to_xarray().attrs`).\n", - "\n", - "This helps confirm that the computations ran correctly and to understand what information Earthkit keeps about each field." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "26d2a1e8539a1c63", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:46:17.795728Z", - "start_time": "2025-12-01T21:46:17.779112Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SDII fields:\n", - " Size: 1MB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", - " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", - " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", - " height float64 8B ...\n", - "Attributes:\n", - " units: mm d-1\n", - " cell_methods: \n", - " history: [2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day'...\n", - " standard_name: lwe_thickness_of_precipitation_amount\n", - " long_name: Average precipitation during days with daily precipitatio...\n", - " description: Annual simple daily intensity index (sdii) or annual aver...\n", - "\n", - "SDII metadata:\n", - "{'units': 'mm d-1', 'cell_methods': '', 'history': \"[2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day', freq='YS', op='>=') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)', 'description': 'Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.'}\n", - "\n", - "SDII xarray attributes:\n", - "{'units': 'mm d-1', 'cell_methods': '', 'history': \"[2026-03-06 11:00:55] sdii: SDII(pr=pr, thresh='1 mm/day', freq='YS', op='>=') with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'lwe_thickness_of_precipitation_amount', 'long_name': 'Average precipitation during days with daily precipitation over 1 mm/day (simple daily intensity index: sdii)', 'description': 'Annual simple daily intensity index (sdii) or annual average precipitation for days with daily precipitation over 1 mm/day.'}\n" - ] - } - ], - "source": [ - "# Inspect the SDII object (Simple Daily Intensity Index)\n", - "print(\"SDII fields:\")\n", - "print(sdii)\n", - "\n", - "print(\"\\nSDII metadata:\")\n", - "print(sdii.attrs) # show first metadata entry\n", - "\n", - "print(\"\\nSDII xarray attributes:\")\n", - "print(sdii.attrs)" - ] - }, - { - "cell_type": "markdown", - "id": "e1862f19d777129b", - "metadata": {}, - "source": [ - "### Notes on SDII metadata\n", - "\n", - "- The **metadata** includes details such as the processing history, indicator name, variable units, and temporal frequency.\n", - "- `earthkit-climate` attaches provenance automatically through its integration with `xclim`, ensuring full traceability.\n", - "- You can also explore the `sdii.to_xarray()` object directly to see the data variables and dimensions.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "fb325cae63b747a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:46:21.211487Z", - "start_time": "2025-12-01T21:46:21.200733Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CWD fields:\n", - " Size: 1MB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", - " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", - " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", - " height float64 8B ...\n", - "Attributes:\n", - " units: days\n", - " cell_methods: time: sum over days\n", - " history: [2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', ...\n", - " standard_name: number_of_days_with_lwe_thickness_of_precipitation_amount...\n", - " long_name: Maximum consecutive days with daily precipitation at or a...\n", - " description: Annual maximum number of consecutive days with daily prec...\n", - "\n", - "CWD metadata:\n", - "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', freq='YS', resample_before_rl=True) with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above 1 mm/day', 'description': 'Annual maximum number of consecutive days with daily precipitation at or above 1 mm/day.'}\n", - "\n", - "CWD xarray attributes:\n", - "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:00:57] cwd: CWD(pr=pr, thresh='1 mm/day', freq='YS', resample_before_rl=True) with options check_missing=any - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold', 'long_name': 'Maximum consecutive days with daily precipitation at or above 1 mm/day', 'description': 'Annual maximum number of consecutive days with daily precipitation at or above 1 mm/day.'}\n" - ] - } - ], - "source": [ - "# Inspect the CWD object (Maximum Consecutive Wet Days)\n", - "print(\"CWD fields:\")\n", - "print(cwd)\n", - "\n", - "print(\"\\nCWD metadata:\")\n", - "print(cwd.attrs)\n", - "\n", - "print(\"\\nCWD xarray attributes:\")\n", - "print(cwd.attrs)" - ] - }, - { - "cell_type": "markdown", - "id": "201ffa0886c9e415", - "metadata": {}, - "source": [ - "### Notes on CWD metadata\n", - "\n", - "- The **CWD** (Consecutive Wet Days) index records the longest sequence of wet days (above a threshold, typically 1 mm/day) per period.\n", - "- Similar to SDII, it retains detailed provenance, so you know exactly which dataset, variable, and indicator were used.\n", - "- These attributes are critical for reproducibility in climate data analysis.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2dfb967617587dfd", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T21:49:05.664741Z", - "start_time": "2025-12-01T21:48:44.596435Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define domain (Spain north coast)\n", - "domain = [-9.6, -5.4, 41.6, 44.0]\n", - "\n", - "# Create figure: 1 row (2 indices) × 2 scenarios (historical + ssp585)\n", - "figure = ekp.Figure(\n", - " crs=ccrs.NearsidePerspective(central_longitude=-5, central_latitude=43), rows=1, columns=2, size=(12, 6)\n", - ")\n", - "\n", - "# Define indices and corresponding datasets\n", - "indices = {\"SDII\": sdii, \"CWD\": cwd}\n", - "\n", - "# Define color maps for each index\n", - "cmaps = {\"SDII\": \"winter_r\", \"CWD\": \"winter_r\"}\n", - "\n", - "units = {\"SDII\": \"mm/day\", \"CWD\": \"days\"}\n", - "\n", - "# PLOT: Each index climatology\n", - "for col, (name, index_obj) in enumerate(indices.items()):\n", - " ds = index_obj.mean(\"time\")\n", - " cmap = cmaps[name]\n", - "\n", - " style = ekp.styles.Style(colors=cmap, units=units[name])\n", - " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", - " map_plot.quickplot(ds, style=style)\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.title(f\"{name} Climatology (SSP585)\")\n", - " map_plot.legend(location=\"right\")\n", - "\n", - "# Final layout\n", - "figure.show()" - ] - }, - { - "cell_type": "markdown", - "id": "fcf31bde1257fad3", - "metadata": {}, - "source": [ - "## Temperature-based indices\n", - "\n", - "Now we’ll compute:\n", - "- **DTR** – Daily Temperature Range\n", - "- **WSDI** – Warm Spell Duration Index (based on 90th percentile)\n", - "- **HDD** – Heating Degree Days\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "93b725acd1e4d5a4", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T22:20:41.322501Z", - "start_time": "2025-12-01T22:20:37.611062Z" - } - }, - "outputs": [], - "source": [ - "# DTR\n", - "dtr = daily_temperature_range(ds=(tasmax_ssp585 + tasmin_ssp585))\n", - "\n", - "# WSDI (using historical baseline)\n", - "# Calculate 90th percentile from historical data\n", - "tasmax_per = calculate_percentile_doy(tasmax_hist.to_xarray(), variable=\"tasmax\", percentile=90)\n", - "\n", - "# Merge percentile with target dataset\n", - "ds_merged = tasmax_ssp585.to_xarray().merge(tasmax_per)\n", - "\n", - "wsdi = warm_spell_duration_index(ds=ds_merged)\n", - "\n", - "# HDD (approximation)\n", - "tas_da = (tasmax_ssp585.to_xarray()[\"tasmax\"] + tasmin_ssp585.to_xarray()[\"tasmin\"]) / 2\n", - "tas_da.attrs[\"units\"] = \"degC\"\n", - "hdd = heating_degree_days_approximation(\n", - " tasmax_ssp585.to_xarray().tasmax,\n", - " tasmin=tasmin_ssp585.to_xarray().tasmin,\n", - " tas=tas_da,\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "19bb9271df9f8a95", - "metadata": {}, - "source": [ - "## Inspecting the temperature-based indices\n", - "\n", - "Now let's explore the three temperature indices we calculated:\n", - "\n", - "1. **DTR (Daily Temperature Range)** — Difference between daily maximum and minimum temperatures.\n", - "2. **WSDI (Warm Spell Duration Index)** — Number of warm spells: consecutive periods (≥6 days) above the 90th percentile of the historical period.\n", - "3. **HDD (Heating Degree Days)** — Heating degree days, estimating heating energy demand based on temperatures below a threshold.\n", - "\n", - "For each index, we’ll check:\n", - "- The available fields (`.ls()`).\n", - "- The metadata and provenance (`.metadata()`).\n", - "- The dataset attributes (`.to_xarray().attrs`).\n", - "\n", - "This helps us confirm that the results and units are consistent and properly documented.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "eb9c38817b8d9d71", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T22:22:11.616721Z", - "start_time": "2025-12-01T22:22:11.605070Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DTR fields:\n", - " Size: 645kB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", - " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", - " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", - " height float64 8B 2.0\n", - "Attributes:\n", - " units: K\n", - " units_metadata: temperature: difference\n", - " cell_methods: time range within days time: mean over days\n", - " history: [2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tas...\n", - " standard_name: air_temperature\n", - " long_name: Mean diurnal temperature range\n", - " description: Annual mean diurnal temperature range.\n", - "\n", - " DTR metadata:\n", - "{'units': 'K', 'units_metadata': 'temperature: difference', 'cell_methods': ' time range within days time: mean over days', 'history': \"[2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tasmax, freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmin: \\ntasmax: \", 'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'description': 'Annual mean diurnal temperature range.'}\n", - "\n", - " DTR xarray attributes:\n", - "{'units': 'K', 'units_metadata': 'temperature: difference', 'cell_methods': ' time range within days time: mean over days', 'history': \"[2026-03-06 11:02:56] dtr: DTR(tasmin=tasmin, tasmax=tasmax, freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmin: \\ntasmax: \", 'standard_name': 'air_temperature', 'long_name': 'Mean diurnal temperature range', 'description': 'Annual mean diurnal temperature range.'}\n" - ] - } - ], - "source": [ - "# DTR (Daily Temperature Range)\n", - "print(\"DTR fields:\")\n", - "print(dtr)\n", - "\n", - "print(\"\\n DTR metadata:\")\n", - "print(dtr.attrs)\n", - "\n", - "print(\"\\n DTR xarray attributes:\")\n", - "print(dtr.attrs)" - ] - }, - { - "cell_type": "markdown", - "id": "c8949a87890c256f", - "metadata": {}, - "source": [ - "### Notes on DTR\n", - "\n", - "- Represents the **daily temperature range**, a key measure of local temperature variability.\n", - "- The metadata contains information about the input variables (`tasmax`, `tasmin`), their units, and the indicator method used.\n", - "- Always check that the output units (`degC`) are correct and consistent with the inputs.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e0a036138ac7e2a7", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T22:22:14.171154Z", - "start_time": "2025-12-01T22:22:14.161783Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WSDI fields:\n", - "\n", - " WSDI metadata:\n", - "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] warm_spell_duration_index: WARM_SPELL_DURATION_INDEX(tasmax=tasmax, tasmax_per=tasmax_per, window=6, freq='YS', resample_before_rl=True, bootstrap=False, op='>') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmax_per: [2026-03-06 11:02:56] per: percentile_doy(arr=tasmax, window=5, per=90, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True) - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s)', 'description': \"Annual number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s). a 5 day(s) window, centred on each calendar day in the ['1995-01-01', '2014-12-31'] period, is used to compute the [90]th percentile(s).\"}\n", - "\n", - " WSDI xarray attributes:\n", - "{'units': 'days', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] warm_spell_duration_index: WARM_SPELL_DURATION_INDEX(tasmax=tasmax, tasmax_per=tasmax_per, window=6, freq='YS', resample_before_rl=True, bootstrap=False, op='>') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmax_per: [2026-03-06 11:02:56] per: percentile_doy(arr=tasmax, window=5, per=90, alpha=0.3333333333333333, beta=0.3333333333333333, copy=True) - xclim version: 0.59.1\\n\", 'standard_name': 'number_of_days_with_air_temperature_above_threshold', 'long_name': 'Number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s)', 'description': \"Annual number of days with at least 6 consecutive days where the maximum daily temperature is above the [90]th percentile(s). a 5 day(s) window, centred on each calendar day in the ['1995-01-01', '2014-12-31'] period, is used to compute the [90]th percentile(s).\"}\n" - ] - } - ], - "source": [ - "# WSDI (Warm Spell Duration Index)\n", - "print(\"WSDI fields:\")\n", - "wsdi\n", - "\n", - "print(\"\\n WSDI metadata:\")\n", - "print(wsdi.attrs)\n", - "\n", - "print(\"\\n WSDI xarray attributes:\")\n", - "print(wsdi.attrs)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c2303c3c80f8ae82", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T22:22:16.312218Z", - "start_time": "2025-12-01T22:22:16.300952Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HDD fields:\n", - " Size: 645kB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 320B 2015-01-01 2016-01-01 ... 2054-01-01\n", - " * lat (lat) float64 384B 41.62 41.67 41.72 41.77 ... 43.87 43.92 43.97\n", - " * lon (lon) float64 672B -9.575 -9.525 -9.475 ... -5.525 -5.475 -5.425\n", - " height float64 8B 2.0\n", - "Attributes:\n", - " units: K days\n", - " units_metadata: temperature: difference\n", - " cell_methods: time: sum over days\n", - " history: [2026-03-06 11:02:57] heating_degree_days_approximation:...\n", - " standard_name: integral_of_air_temperature_deficit_wrt_time\n", - " long_name: Cumulative sum of temperature degrees for daily temperat...\n", - " description: Annual cumulative heating degree days (temperature below...\n", - "\n", - " HDD metadata:\n", - "{'units': 'K days', 'units_metadata': 'temperature: difference', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] heating_degree_days_approximation: HEATING_DEGREE_DAYS_APPROXIMATION(tasmax=tasmax, tasmin=tasmin, tas=, thresh='17.0 degC', freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmin: \\ntas: \", 'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for daily temperatures below 17.0 degc', 'description': 'Annual cumulative heating degree days (temperature below 17.0 degc) using a combination of minimum, maximum, and mean daily temperatures.'}\n", - "\n", - " HDD xarray attributes:\n", - "{'units': 'K days', 'units_metadata': 'temperature: difference', 'cell_methods': ' time: sum over days', 'history': \"[2026-03-06 11:02:57] heating_degree_days_approximation: HEATING_DEGREE_DAYS_APPROXIMATION(tasmax=tasmax, tasmin=tasmin, tas=, thresh='17.0 degC', freq='YS') with options check_missing=any - xclim version: 0.59.1\\ntasmax: \\ntasmin: \\ntas: \", 'standard_name': 'integral_of_air_temperature_deficit_wrt_time', 'long_name': 'Cumulative sum of temperature degrees for daily temperatures below 17.0 degc', 'description': 'Annual cumulative heating degree days (temperature below 17.0 degc) using a combination of minimum, maximum, and mean daily temperatures.'}\n" - ] - } - ], - "source": [ - "# HDD (Heating Degree Days)\n", - "print(\"HDD fields:\")\n", - "print(hdd)\n", - "\n", - "print(\"\\n HDD metadata:\")\n", - "print(hdd.attrs)\n", - "\n", - "print(\"\\n HDD xarray attributes:\")\n", - "print(hdd.attrs)\n" - ] - }, - { - "cell_type": "markdown", - "id": "eda0cc918a6fbca6", - "metadata": {}, - "source": [ - "### Notes on HDD\n", - "\n", - "- The **Heating Degree Days** index estimates heating demand based on temperatures below a threshold (typically 18 °C).\n", - "- Metadata document the base temperature, frequency of accumulation, and calculation method (approximation).\n", - "- HDD is a valuable indicator for **energy and climate impact assessments**.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "ab5cf6b21d4cbb4f", - "metadata": { - "ExecuteTime": { - "end_time": "2025-12-01T22:24:03.287174Z", - "start_time": "2025-12-01T22:22:19.467624Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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FxcXy2WefyYsvvujxsZljnhBCCCGEEEJI2IO0MmPHjq22Lj4+XpYuXSp//PGHTJs2TbZv366KtE2aNElGjx7t8WdkZWUpp9weFHyz2Ww+nT8hhBBCQg8I8suWLZN169bJtm3b1IB8hw4dVI55jcWLF3t1bArzhBBCCCGEEELCnszMTElKSpLZs2dXrbNYLGq6Oaag33jjjSqyHSL9008/LU2bNvV42vn48eNVPnlEyGnH37Rpk9x6660yatQo078TIYQQQkKjvszMmTNVOpvvv/9eGjZsqPoDDz/8sMP880ZhKhtCCCGEEEIIIWEPotlTUlIkLS2takF0+7fffiudOnWSa665Rlq0aCFjxoyR/v37y5IlSzz+jNdee03llMc09oKCAunbt69069ZNmjVrpiLyCSGEEBKZ9WWGDx+uhHkz68swYp4QQgghhBBCSNjjLM3MwIEDZcCAAdXW1apVSznVnpKQkCBz585VU9nXrFmj1mEqe9euXX04c0IIIYSEen2ZM888Ux577LFq9WXs+xeeQmGeEEIIIYQQQkhEpLI5cOCAXHrppZKbmyunnHKK3HfffdK5c+dq+x09elR+//13ue666zz+jG+++UZGjBgh7du3VwshhBBCIpssP9aXYSobQgghhBBCCCHBIVlEUjxYsL8TWrVqJT179pT/+7//U9POMd38oYceqrZPeXm5inZDFD1S0HjKxIkTVYqcq666Sr788kspKiry5lsTQgghJEzQ6stAoDe7vgyFeUIIIYQQQgghYc95552nirr26tVL+vTpo/LBIjL+yJEjVfu8+uqrsm/fPnnwwQe9+gxE23/++edKnL/rrrskPT1dLr/8cpkzZ46cOHHCxG9DCCGEkFDAn/VlmMqGEEIIIYQQQkjE0aZNG/V46NAhJaDPmzdPLR988IEkJSV5dUw45kOHDlXLK6+8Ihs2bFAR+IimQ/7548ePm/wtCCGEkBCknoikerBvGJPgx/oyFOYJIYQQQgghhIQ9yBl/xRVXyLnnnqte7927Vz1ClP/zzz/l+eeflxdffLFKsPeW0tJS+eWXX+SLL75Q6WwQPYfPnjBhginfgxBCCCGhhz/qy1CYJ4QQQgghhBAS9gwYMEDefPNNJcQnJibKCy+8oKabl5SUqNywyAvfoUMHlY5GAylpPAHC/7fffisxMTEyZswYeffdd9VAAF4TQgghJDJo06aNyiVvhB07dnj9ORTmCSGEEEIIIYSEPddee63K837//fdLcXGxEuXxHPnfc3Nz5b333lOLnpUrV3r0GXXq1JFPPvlEpbJBWhtCCCGERB6PP/54tb7C//73P7nvvvuqBuIx6I+ZeEhl5wvsSRBCCCGEEEIICXsglN9+++1q0XPTTTepxQzef/999VhYWCibN29Wjx07dvQ6Zz0hhBBCQo9JkyZVPcdsvA8//FBOP/30avsgz/wdd9whU6dO9fpzrD6dJSGEEEIIIYQQEiUgvzyi8Bs0aCA9evSQU089VRo2bCh///vf1TZCCCGERBZ//fWXJCcn11hfv359VQTeFyjME0IIIYQQQgghBrj77rvls88+k88//1ylx8nOzlbPf/jhB3nwwQfZhoQQQkiE0b9/fxUZrxWVB/v27VN9gtNOO82nY1OYJ4QQQgghhBBCDDBr1iw1nf38889X6Wvq1aunniPFzUcffcQ2JIQQQiKMjz76SKWua926tSoa36hRI2nZsqUUFRWpPoEvMMc8IYQQQgghhBBiAKSrcTSdPTExUTnthBBCCIksWrRoIYsWLZL169fL1q1bxWKxqPoyyDHvKxTmCSGEEEIIIYQQA4wdO1b+9re/qQg5OOVg27Zt8o9//ENGjx7NNiSEEEIilG7duqnFTJjKhhBCCCGEEEIIMcBrr70mKSkp0rlzZ/WIwq+dOnWSZs2ayeuvv842JIQQQohhGDFPCCGEEEIIIYQYoG7duvL111/Lli1bZO3atVKrVi3p2rWrtGvXju1HCCGEEI+gME8IIYQQQgghhHgACr916NBBbDabKgRHCCGEEOIpTGVDCCGEEEIIIYQY4NChQzJ+/Hglxp9yyinSp08f9fyKK66Qo0ePsg0JIYSQCOH111+XM844Q6Wui42NlZiYGImPj1dp7IYMGSLvv/++z5/BiHlCCCGRQ0MRKfFwf0IIIYQQg1x99dWSnZ0tCxculN69e0tZWZmsWrVK7rrrLpk0aZJ88803bEtCCCEkzLnnnnvko48+kr///e9y7733SoMGDcRisaiZcjk5OSqd3f/93//Jjh075Omnn/b6cyjME0IIIYQQQgghBliyZIn8+uuvSpTXGDRokLzzzjty1llnsQ0JIYSQCGD69Okya9YsGTZsmMPtY8aMUdH0l19+uU/CPFPZEEIIIYQQQgghBtDyyttTXl4uLVq0YBsSQgghEcCJEyekSZMmLvdBKjvs5wuMmCeEEEIIIYQQQpywaNGiqufjxo2Tm266SZ555hmVZxYUFRXJ/fffL9dffz3bkBBCCIkARowYIbfccou88MIL0r17d0lKSqrahpR2f/zxhzzwwANqP1+gME8IIYQQQgghhDhh8ODBNdYNHz68xro1a9bIfffdx3YkhBBCwpx3331XCfPoA5SWltbYjkKwo0ePln/9618+fQ6FeUIIIYQQQgghxAlIU0MIIYSQ6CElJUU+/fRTKSgokG3btkleXp4q+G61WlX0PFLbJSYm+vw5FOYJIYQQQgghhBCD/Pjjj7JixQopLi6uWldYWCi5ubnyxhtvsB0JIYSQCCEhIUF69uzpt+NTmCeEEEIIIYQQQgzw7LPPypNPPil9+vSR5cuXS9++fSU2NlaWLVsm1157LduQEEIIiSLmzJmj6s94i9XUsyGEEEIIIYQQQiIURMR/9tlnsmTJEqlTp47MmjVLPZ86dWqwT40QQgghAQQz55544gmfjkFhnhBCCCGEEEIIMUBWVpZ07dpVPW/QoIEcPXpUPb/00kvliy++YBsSQgghEcD+/fvd7hMfH68Kv/sChXlCCCGEEEIIIcQAgwYNkhdeeEHllO/Xr5+8/fbbkp+fr6ayJycnsw0JIYSQCKB58+bSq1cvNVMONWT8BYV5QgghhBBCCCHEAK+88orMnz9f1q5dK1OmTJEFCxYoQf7hhx9Wgj0hhBBCIoMbbrhB/vvf/0rTpk1l0qRJsnTpUtM/g8I8IYQQQgghhBBigM6dO8uOHTtU0VektNm+fbusXLlS9uzZIxdffDHbkBBCCIkQLr30Ulm0aJH8/vvvahD+/PPPl27dusmrr76qUtuZAYV5QgghhBBCCCHEIBaLRWJiYtTzWrVqySmnnCJpaWlsP0IIISQC6datm0ybNk3lnZ88ebLMmDFDmjVrJhMnTpSFCxf6dGwK84QQQgghhBBCCCGEEEKIVAzC25OQkCA33XSTmin3yy+/qOKvF1xwAYV5QgghhBBCCCFhSD0RSfFgwf6EEEIIiRgOHTok99xzjwwePFhGjRolH374YdW2P/74Q6655hoZOHCgjBs3TubNm1ftvd9++62MHj1aBgwYIFdddZVs2LChapvNZpPXXntNzj33XDnzzDPlrrvukuzsbEPnhPe64rTTTpMPPvhAMjIyxBcYMU8IIYQQQgghhBBCCCEkoJSXlytRHhHqEOQfeughlSrmu+++U6L37bffrgT7WbNmydVXXy1PP/20rFixQr1327Zt8sQTT6j1//nPf1QdmLvvvluKiorU9s8++0y+/vpr9Z5///vfkpmZKVOnTjV0Xo899pjUrVvX7X7169f36ftTmCeEEEIIIYQQQgyyatUq2bRpk3q+b98+efnll+Wbb75h+xFCCCEesmfPHtm4caPcf//90rp1azn99NNl+PDhsnjxYhUN36lTJxUx36JFCxkzZoz0799flixZot67YMEC6dOnjyq+jvdClD927JiKsgd4/4QJE1R0O45z2223qWKuBQUFhoR5pK7xNxTmCSGEEEIIIYQQA3z88cfKwUd+2RMnTsgZZ5wh7777rioA99JLL7ENCSGEEA9o0aKF/Pzzz9WKqKPAemFhoUpfg/QzelB0PTY2Vj3fuXOntGvXrmpb7dq1pXnz5rJr166q7e3bt6/a3qFDBykrK5O9e/eGzG9EYZ4QQgghhBBCCDHAU089JW+99ZZceeWVMnv2bGnUqJHKZ4sp9K+//jrbkBBCCPGAmJgYSUpKqnqNGWmIdEeueaSm6d69e9W2o0ePyu+//y7nnHOOep2fn18j3UxycrJa72g7tmnrQ4WKIQZCCCGEEEIIIYS4BPluUUQOIH3NZZddpvLidu3aVQ4ePMjWI4QQEh0gtfoJD/Z1w48//iiPPvqoyg8PUR555e1z0SO9DKLou3XrptYh+t0R2Ff/qAF77Wi9I1As9sCBA+5PXER27Ngh3kJhnhBCCCGEEEIIMUCvXr1U6przzz9f5s+frwrKAUTPI38tIYQQQjxnwIABMnPmTFXQ9fnnn1eFW8ePH1+1/dVXX1V1XZ599tmqdVarVWw2m8MofE2I12/XnmupcFyBPPeYIffcc8/59eekME8IIYQQQgghhBgAwgAi+Z555hm58847Ve7aKVOmKIF+3rx5bENCCCHECxISElQBVyyYnTZnzpwqYR72FcsHH3xQLe0N3pOXl1ftOCj+qu1jvx3bgH36G0fcfPPNMnXqVDUg37t3b7/9pswxTwghhBBCCCGEGKBfv34qYu/IkSPy4osvqnVjx46VzZs3y4gRI9iGhBBCiAesWLFCJkyYUC2yHZHwKPIK/vzzTxVBjwHxNm3aVHtv27ZtVU56DRSMhaivFYS1345o/Pj4eFUg1h2NGzeWa665RubOnevX35MR84QQQgghhBBCiBP00+k/+ugjl+109dVXsx0JIYQQg3Ts2FGys7Pl5ZdfVgL9oUOH5NNPP1U1XCCy33vvvXLVVVdJhw4dVPFXjbS0NDUgPmPGDJk1a5acccYZ6rF+/fpVEe7nnXee/Otf/1JR7yjWPm3aNBkyZIjUrl3b0Lm9/fbbfv8dKcwTQrxm79690qJFi6howdLSUjl8+LA0bdo02KdCCCGEmAaikxD9ayRyKBLAdGYU/ILTRohRnnrqKRk9erTExcWpwnPOQC5bCvOEEH9QUFCgFoiR0QD6JhBSjeQCJ+FNvXr15LXXXpNXXnlFJk6cKMnJyXLRRRfJ5ZdfLu+//77k5ubKe++9pxY9K1euVGI97DLEd4jueI3ZbNp1c8kll6jC7NgH0fQQ7++77z4JJXiFE0K8AjfOjRs3qmIY0QBu5tdee60yFt27dw/26RBCCCGmiPIPPfSQihp69NFHo6JFt27dqr4rIqCiZTCC+M7q1aurnu/cuZNNSggJKDk5OXLTTTfJpEmT5IILLoiK1kdx7T/++EPpDkg9QiKbLl26yDvvvFNjPa57LK5AMXYsjkBKnMmTJ6slVKEwTzzm8ccfl6+//roqKiQ1NVVNC0F0SLdu3ar2w58HuaBcgVErFE/CcuDAgar1KNSASOxLL71U/cHwOa747rvv5JNPPpEdO3aofXv06CE33nijOi/9+QDtz37XXXepSs0vvPBCWOSyxPdB8YlQAL8/2vzf//53tVxdaNt169apaDT8fogswlQkrSI2WLx4sZoCvH37dikrK5NOnTqpa+ess86q2sf+esD7mzVrJqeddpr6HRs0aFC1bfr06Wpk1J5hw4apHGQgKytL3nzzTVm2bJk6N+Qlu+6662Tw4MHV3nP77bfL0qVLaxzrgQceUNOXb7nlFrn77rvlP//5j6SkpPjUhoQQ4i9QGAmFCGEXUZRQf/9FocJ//vOf1e65iDbB/Q0DjwMHDlQzhGbPnq2Og+mjEG1hh2CDtLyO+/fvV5EsGtgHIufIkSNVdIvegdL21Wy+MxDFjM/94osvZM+ePeqYffr0kb/97W8qP6QGjtG3b1/VHwFXXHGFOj/Y9VDGaDsEknfffVf1nfCoFyARnYR82YjMQ45ORBtdeOGF1QT9b775Rk0z3r17t3J6evbsKddff7161MDvogfXBew5phAjZycKcmk8++yzKl2IPRAhbrvttqrouddff131LxH1hD4Erl1cJ3pwvo7EUzj3iJTCdYrrBVOftfylhBASSsDGQZT86quvDPmHet8b9+T09HTp37+/uoeikKIeva8FPwt5lGH/4ZM1bNjQ7bmheCL8wJ9++knNKIZfNGjQILn11lurCi7a2zwEOSES9v/+7/9k6NChEsqgzZ944gn58ssvQ2K2NGwu/FEIl5r4aNQOw47D1v3vf/9Tvzl+H9hBXDeIRtf3A/UkJiZKq1atquy/Xo9xZ2O1Y2IAfMuWLVX9OdhyfXvCRx8+fLjD7/zrr7+qoLjly5er/kG0BA+Q6ITCPPEKiPHPPfecep6ZmSnff/+9MgKPPPJI1QgupoccP3686j3333+/cqAgiGq0bNmy6jlu4to2GPtFixYpQw7nHE65M2AAkEcKQgAMCj4Tjh2MDaa6OItuRqcgkMAwwfGFkQpnIGxDvIFAjU4c2LBhg+oYIo8X1sOQ//XXXyqaHlH1Tz75pNoPHQcU7YAxRycRwjyEojvuuENdOxDyHV0P2A/GHxW4lyxZon5vrdOHwlsQgzSBRkOboo5Oyz333KOceVxH6KRCbEKeMgj6p59+etV7cCx0FHEt6dEi6i6++GLVQYPI//DDD/uphQkhxDdOOeUU9bhmzZpqwrw28AgnRy/Mr127Vjlc2mA2Iqh/++035RB17txZTR/9/PPPlcMORxxTRDWwz5lnninFxcXK+YLz9+OPP6r7P2yBJ+Bz4YjBPmCAHTkkZ86cqWwB7vvOnGNMV61bt66EilgSTunoYFfxW2k2FX0v2EzYQvSTMA0Y/RYM9GCQRnPc0ffCIDWuCdjWEydOqMg29AVeffXVarYVgoxm30tKSpTgj+sIg+X4fG2qMcQd9CP+8Y9/VDtPTTjAZ/z9739X1yoGmCDqf/zxx8rRxyC9/lrHsRDcgUF6PVohMAhY3377rXo/+q+EEBIpeZrhg2OgG/dB+C24TyMQDQFOejRfC34W/CQMjF955ZVq8BPHceUL4n24n6MP0KRJE+Wn4b6+fv169ego9Qj8RvjCgUyDaj+QH65AgMdsL/R3NIHciB3Gbwu7CUEeg+EIroC/i74V3gcbCN9YA/Yf/T5N0IedRhov2G1sM2pjca4IeIMY//TTT6t+JNKMwL6jP6fl9sZxAER3vS6kDeTjuz744IPKN8cgj5YznJBggP+TPuDUTCjME6/AjVJ/Y4QD99JLL8nUqVOVYw8hU+8gae/BaLqzG6r9Noza5+fnK4MDA+PIwENMgAgA0V8ryATOPvtsFUGH0VUYHEdES242s5kzZ44SQFBEQwO/EfKCoROg/U4DBgxQbQxjDMOP6+HDDz9UBhy/l/63QtQbOoHoPGG039H1gE4Vri0YZgjr6DgCFAaBIXd2XUF4wHWiF/7RMcVzdHL04gE6Byge4sroo+gIxCOIE7yGCCGhCO6JGEDHvQ8DihoQ23FvxaMe7AdnCvkccc+EsK7NFNJAhDMGVeFUY4BV/1naPROReeeee65y1mAPPBkAxwA/ornwPgj9GhhAwHfAgLCzGW6hEM0WjqD/BLuqt3kQuBGRp804034D5NXGNhThgkON2RjoZyE6Un+N3HDDDfLGG29Us60QY/SfceqppyphBgP5CxcurIqchA3GzEtnNhgDTRgcwEACjqH1DdCvwLWj9TshJqD/aP/d7PukuE4xMAC7zinyhJBIAD6a/r4HvwZCPYRPBK5pg7D2vhbupZhJBJ8bwUfw7ZwJULjHQ9yFT4i+huZbQdCFDcDnwFY4Qj/7jRgHtgptqgUhwHc2YocxsI7Z7I76VvCFIdAjQE4DdlR//WDwBuI4PguD2Ji1bsTGok+H92EgQRPhca1AmEefE/1FvTCPc9OuJXvQP4WugAEfDEYQEixw/SPjAnwd9D3RXzaLCgWMEBNA9BFEWYimZoGbPW7+yKnmCESroVMxZsyYauvhQGJUFZWdMarv7Hz1uaogCmNEGGI+/mwwEIjCgsHAd4JAgXXjxo2rSuWjgVFhRGCfc845akAB0X5aZCKm8WGqoTZNHM/1o/a7du1SI9AwpJgCCOOIaeWuQBQ4Okw4FxhMfH84zFivB9MLcd4wZkgpA7EFn49227Rpk3oOQ6sHAjacZKQycARG4nGemoAOMEJfp06dGoMnEN31MySwn6OoRnQ0xo4dK0VFRS6/N6I0MQCA6Xoa6Bhq0XSOQMcF6D8X54nXOB8NfDZG87VZAM5ARwbvRyofQggJVWA/IWRqQNCE6I77LSLbNGcIwGnTouy1+6L9vRrCJZw/+5Qh9uAeis/AwKd+1pw7ENWHWXV6xxFgsADCAu779jZOb7/1dhW2DU4pBlHRgYZdRsQVZuPBVqJ/gM+BQ4sIfT1IA4DoMtgvLJh9hxlgAA4ujo0+AKLP8Byz4TSwH9oINhI2HZ+PwWNXwNYiigzfAfYcthptYQ+EDpw39sFgN+w4Ph/n9PPPP6vnELn1wIm2nwGm/1yI2ThXPfj99cKNBmZDQsCGrcR7ESlpvx+ccHwm2tsdmrCOWZG+2HNEzaPPh76ihnZtuzoWQF8H1wRm4hFCSCSC+zIil5Ey5IcffnC5L+6lsGHwQ1esWOFwH9z74QNB1LUXUtHvwOAqoqsdofnE2mwzzaYifR18cthlpDWBXcW9GcFduE/DFsMu6/0/gPchNQ7eh34CBhRgRwCOgWPDVsNma/ZS78+iD4D3wr5hFjVm/rkCs/gwm1/TCXDO+n4WwDEQRIDzhi2Ej6+dC4BQjUEMvbYBuwvbjm2OwCwEfHe9vTZqh7U+nf1+mFkOncM+kNKZvcYMDPQhjdpY2Gv4y/pUcfDhgb29xnXnLkUs2hyzPXEdExIs5s6dW+UHYOALwciYiYJAYKTq8gUK88Q0cMPHiKa+OJKvwAjhZq2lJbEHhh+f6SiaHg4khAE49UaBMwwDgo4ARnThoGOKHnKiI4IA6XsQmYfULMipDtABwMg0BAhEIyCSEBGEiARDxwZR1Uipo+WTxXNNrEYHBcdHpwGiAaIL4cTDoEJEccbLL7+scgTD6OPzYKwgQugNOgQCRDxiehoi33DT0I8yI6oBQre+kwYjD5ECI4CO2hSdCHwnRFXogQCB3wrthtRGGjCy6OBpRh/7oTOGwQDNwQZIN4T9IO67AsI5Omp64432R6cIue+0SHi9qIHviJQIyJeL3wzvx80TwhQGA/TH0dLtoF1xLLQZBCs9GPVH27mrn0AIIcEEQjvsiHZPxmAx7DQcUaSY0aLmMTiMe6sW9YRoNuQUhZMKwVIvhsPxdRYFZ//ZEHCRyswosOfa9Gl74Nji3uyu3owe3OfxXWC3IWTD1uHcIWIjtzjsFaIBYXsxiA8wYI3p4GgnbMeCAQmIGtgH5wcbDgcaggSeazOxYHO1wX7kpcVnwDahf+AsQADg8xG5jkF02HNEoKGPoZ/th0F1BA107dpV7YPBeL09hwOOfpLenuN8Yb/0s9vsAwpgD+3tOQIMcG1gsEA/sIL8xLDTcMQhjMNRxyAH0sHoB/LRNu4KdAGtj6PVjMEx4HDj98F1BhuMQQq9aA4nCPnpcW3i/eiToC+E9+pz9muiAfpFEHQQnIBzx7WuB8dCLmXac+IN+N/iWsX/EoIV7nkI0sH9lBAzwT3O0WIUzFCCP7pq1Sq3+6IvAFvrzJ/HtY5oaWf2GnYB4rUnQLiGHwc/FjYJwWyw1xC5ETQHnxqp8vQ5xuFLYqY+AqbgFyO9GVKrwvcFsM2w0bDVsEt4rp0zgttwLORhRx8BswFxPO29joA9hP+ONkQQHdK7oZ3QZ0AEuAZsElICYaYfBHroFPqabPieSIfxyy+/VK1D/wzf1Zm9xmfC7iJ4QcOoHUZ/DHYW7Wj/m6Lfoa8d485ea/63ERuL42LgAO0BW41jwHbD7tqnkYXmAO0D543joQ+Fa8z+ukS72Q+EEBJIoBHh3oEZIehn416Caxt+Bq5tX2AqG2IqyDEHo+gNcP41owLjh2hv5LSFYXMkEgN0fu2L2fgCRr9h2AGcQnQCICQjF5qWaw+PKLoCRw6CM4R8TA/DcxhJjCjDeMBIwrmFCILXGOUF+ilfWiFaOMDaSDaMEjoT2KblZteDHIDoPGCgQDO62jRzdDrQkUEHDGljYIhxbtoUbYgtiObXQIQAtsMoQsCH4YeAgKmMjtAGI+xTuOA4OC9Ms4Mojk4VBgtwHL3YDnEFvy06XsjTjvOGU4MIAHvBRX89wBAjqg6pDCCk4D360XcMvmDUH9cJOkNoN5yjNv0O78MNUy8o4Vz0kZlwqCA4oAOFzgU6EegoYoAGo6P6wQDk5/v9998dthEhhIQCmq2Bw4hBUeQJhTOkFXKFfUIEtjb4qO2P+yhS00EwhvMJ24COKO7zRqdsoi+gRZcZBfZcn+fUVzDwquUOhz1HTnjYOEzb1gb78QjhHNFosEewG7jvQ2iD3YQ9x2A8BGI4g4i+RzvBHtin9INQDhuCR63Pgu1wfNGHsC+qBrRod0T7a84xzgPOLM4DAjz6FRhYhkMOcV4Ddk8TEfB5sIuI4IFzj3NDrnjgSph3ZM8xII0+AYQEBCXgmkEEI2y6vi8GUQLBBJhlB5uOtoHNty/2am/P8d3w2RBD0O+BEw7QN0Bb49pBZCT2Q58CggzaD7YZ1y4ce5yjNlNSmyqvz4eMz4MYg0EOzHhA8AMEBAywoI+gj+BDH1JrC0KMAkEQ4iH6nojSxfWK/weEqcmTJ6s+OCFmgPuXXsj0Ftxbjdhk2A/cP53tqw08mWmvtbomAD4k7BfsM2yj5h/CLiGNmTZLG22C/xnsteYrwo7ATmLQGbP3sNin0sX74YNiMFezobC7+M4QhDFTQJvRpQd9BwTPYfaaliMf/itEfdgl3A8wuAyfEWnStPQyOE/41rA9+sA4DLhrdgyz3RDU56wuHmyUo/SpRuww/GT06XCfQn8H5462Rp/DkYaCfo9mrxHZjvaAvqClKjZqY9FngT+ONkbbAfymGEjQUtsAPMc5QluBz42+FtoSfjnusxoQPaF1oC3QpyUkWCC4E8G/mHWD/y4G+HCta3U2vYXCPDEVpDfxJKJNDy5wLBo4DkRtdHoDhTbFSkNLa6J3+LRK9doUNDjHWjFciPXoOMCoAXQMXAFhBJFu+ullOB4cVYgojoDAD4OoVWTXQAcDhgyCMYwuIhUhPOvzpmqRaRo4BjoT6BxAtMbNBYZPKwBoj/adHc1gwCABOhj4DRG1B2OKAQdEHGodHEw5R7QfohJxM8PnYsQRnRR0KPRpZOyvB+13gOihz08Ih10Pctbhu6CzBmEebYWOHX4TTbDHFEzktEdnTqtaD6ff/vMgQkFUQScDHQ8N/F7O0isRQkgogPslIuMhzMPphDCNPLMA0U24P+P+iO247+unJMNZgyOFKK4FCxZUzXSCyItIODhHrtBSnXnSH3CWpsZMew77rbdf9vYc9gFiBAYlEAEPJ1s7L1f2HA4kRH8MBOjFa9hi2FPYbUfCPNZjsNlePIfDDJEdgyawn9u3b1eD8Xrs7TD6AP/9739VvwJiAew50g45m2ruzJ7j/PH90f/S7DkcelwviP7TCv+irRBAgOsKNhUDDEj7h0EQzNLTz1aEU49FD84NIovWL8F52KdCRKQf+hUYIIBggpl2SA+E3w3nhD4FvjMiAdEv0MQSnAOuWz3Yht8H6zEgpYHzhJNFiCfgesasFvSztehg/JchlOH+SohZ4F6LiHBHIOjIKLDHRm2yK3/ebFttb681IR22Rn8OensNYR79FPiR8B9xD9fbaDx3lJINQPiFOG/vR0PQhi2BXXYkzGM9fEZ94VrYS9hvDJ7DPsFWY5AOM8/02NtZLXUOBjkw+I7Z6q5mI+I7O/K9jdphnDeCHbEdKewQ5Qu7ilR3CMDQt7M+Xz3Q+igYcNQwYmMxSAktACI6giwxaIKAAwwiwPZrA+TIHIBF3zfANYbBE5yLNhiAaxJtRf+bBBNoQwjcxf0D/x/8j8yqOUhhnpgKpsw7K9zhDgjUmuMJcRnCKQRqZ9HyADdoTyLyzETrmODzEb2F7w3HG4YJnQojnSVMgdE6GnpwLGeGR5tyb/8+rd2192E/d78FOj4QrzVhHhEK9h0VPe6mTeKcNAOLmxYcaURBoJOg71BAiMeC2QkQD7QRf/1UP/31ABEI4oKRtEToQCA6AcI/QGcHU+HRcdFyI0O8x7QjdHRdRTVBrEJ0vDZTQAO/r7s8hIQQEkzgxEAUhhOK6ctaDlMA4QhRZUgfA2HeUfEuvB+CPhYIz3DkEBGlRTS7Qkuf40l/AE50sO057vWYqg77hFQ0+K6wqXjuCjjXOIajCEK0AQajHQE7Ddto38/ROvn4bCyIBHSXfxXnjJl7ENJh4yAiaAMxruy5M/EFM+zgFGOB06+l50E0oP58Ed2IBUXdYXdxXWEAHgKHfvaCljoO9hPtikEjd6CPh2sYYgfQ6uNgZp42KwORgRhAhxOvL1hrD46D42nH0kD/AkIKIZ6AQTtH9zf8p/0hXJLoBfcoLSrcF2CXtWAkV8A2wO44s9+a4B1sew1bhOAv+K3wOzHIC59Pq+nmDM2PtrfXsGuwx678b0ezBtFOCP5CVLyW/9ydvcYgAIR5pLOBr4k+hLPZ6tpv4mpQxYgdxnWE2W9YNJEcARgY4NCndsXAvPY9YTNha90FYziysRjMR3viXLQiwuinYLAdthx22xkY8IRNR/pcTZjX+g+01ySYwEfAINjixYur7pX47xqp1eAO5pgnpoHpTogwc1ad28hoOToeWJAfHoIojJa7iEAYAEeCMSKpURzVXdS6ryDlDgwqRF5ErOH7G+1A4Tvrc7JrIOreWV59LarAvviJliNdex86HEZGlRGZB+EGnRnkvHfVMdAKrumLpuI5fif7nG/4bTB9DTni4NRD8MB++o4cOhmYdoffG+KQ/nz11wOiJhyJ8khvg6hCiBZ69K81Ud3+N0FxIv30dUQ7OioAh2PZd4Zwns4iMQghJFSAPYL4jhlYcLQ0ZxGOIKZNYzAWkWZa4VcAR9HesUVkEwagMeippUhxBURhvMdo6huA+7wzARsp16688kq/C17arCkMQCA1DEQMI51t2AMMZDgqSgab58yew66h/2Bvw/T2HGIDnFqj9hy/DxwGoKUGcGXP9UXYME0fdto+ghwOPwZjMF0ddhOzA+0L5UHUQJScdg72Aw2aPUd7OhLl8dtjBp09EDw0G4zPRptoorz2uZgyr7fn6M+g32EPrh97e65FLBLiCRCVkE9e+8/jusI1jIEsfb0DQkIBpBvFYsRHx0A+7u3O9sWgLey7M3uNoCp9ChJ/AWEe54jZ0AjmQhoYpENzh+ZP2ttrTWRzZa+d2Xj8/+G3amK/O3ut5b1HYJyWCkMvQNsDG6W31cCoHdZmPNofD2niIMrb22v8vpq9Rj/RkShvxMbCJkML0ER5gO+I9tXsNew7/Hj7+i9a5gFH/jftNQkmsPv43+J+ivoSSOGNwS74MPpZJd5AYZ6YBowDnEv9FGFvwU0cTiCmceuLo9iDqWAQtvXFPjXjiiht3Lz9LaBqxkPvaNpHWOun9usdcES1wSDqC5xgFBtONbY5AtPrYKiQw14PRu+0YwIYVBhO/ciyow4FpryjjZCKB++BQXaGVtQCzrkGOmdof0zlsUcbFNHyzCOvnCPHG/vhN9en3TECDDTS1Og7B+gUIG8vUtPoIw8Rwa8HYpU+GgQdUUTt6wdyUF0bRW21Y+mjJox0/gghJJhoRVgxdVlfU0OLmocQrdVF0YuziKRy5HTDPrkr0o0BU0TXY5DXEwcKjiQ+UytKq4F7MiLAYH+8TZVnFNhnOKF6W+TInuM8NNsP0CYQBWDf9DYeHXcMOjuz51iP/gp+Hz2w77CtiECDbUSUmX1fSIv604Pp5tpgOYQKV+2v2XPYOQ18Jn67OXPmuLTn6LPAniPVkaP93F0jjoBjDhuMwoIa+C7oB+rtOYR0rfAcQHvjN9Lbc7QVxBp90AYCR3Dejuy5rwW7SPSh1ZLAbE4IZhi8QsCHVpyYkFAB90jkGIcgilohrkA6FlzbEGwdpXMBsI/Dhg1Tfra9HcL9GoFOZkSPGvle9oO8zvxvvV2GXUVec3s/GilYsJ8re40gNL0PjP1h93FM2E/YFzwasdfQMJAhALPcXAXFAdxX9LYaGLXDCGBEFgK93QSwj7Cx+nzvRjFiY2Gv8dn6vhK+A2y4Zq/x22C2PFIB6dHq8ulT1+I+i/4s7TUJBXAtQ1fDPRM+AV7bz8j0FKayIV6B0VkImbjZ4kYPpxk3UeT+1Ode8wVEUmMEGZFrcDD1I64amMKMvGXIL4cIM4gQuOFjFB3igH1OU38AcQOdFOSYRDoYRJ1r0Yb60W0tfzoEbJx3jx49VF52iPAYcUPUOETlGTNmqHbV5zTXA+Ec3xlTxGCgcBw4/kjJggI0WvujyCvS6SB3HArXwVjiPfZoU9uQRx05ZV2Bz4ajD5Ebgr5mVDHqjgrVmOaOzoWWsxXnBAONzh0+B+cNhwWdGrQB1uE6wlQ6DOggX6wnQAiBMwRxHu0FQ49BAkx9w3UDkOdPy2mHojfYBx0KdIYw6qmBz4cggdFOtBs6G+gooGOD9tODQoFawTpCCAlVIBTBPuGe60iYR35uRGTrC4Ch+DgimFDECzYB0ciwNXA+4XTrp0YD2Frcx9E5hWOG+yiiomB7PAHCPARqzLTCvRqfi/4FIr0wwOxrJIoRYNdw/nA4Yctg67R+hH4AHfYcAxgQJjArAFFhaC+ks8N5IpoWgxjoC8A5dWZbMQMBvwum4yPqDr8DnGzYMRxPs4mYpo+0cBCu4czjszUbpwefhd8Vs79gl91dG3AmMEitiSgQbtAvwbFx/uh74RwwYIJCrDhfnCP6AlpubfR70A8BCAbAb+gu1ZGzPh+OjX4E8tRCLMBvAYFBK1qv2WlcW5jBgXNDW+EaRHtpIO8n+qWw8eh3YBAf/R849Pri8VpOYP06QoyAaw++Bq4f3P8A7gVmpBwhxBdw78Y1CdEYPikGWhGhjBoh9rOPISxhX/g8GBxFnwAiMoRcR363Buq/ISgKthr2CZHi8L3g98E306dG8RewGbCx+EwMXiPyXCuwqve/MXiGc8UMd5wb0q4iqh/+KPxQRK7DZ0XKU9g1Z8L8xIkTlV2Bz473Y1AAn4c2hg8MEDWP2fqYtQ8bBsEeke24V9gDPxLHQHtjoMMVOG/oBxD/ND0BaQaN2GHMNkRueZwzbCMyEsCu4pzQ74B24ClGbCz6Pei3IDUOZhHhN0E/AjqCPogTthwpbdF/Qduj34XfFX0wvQiP/qXWdyEkWKCwM/rY+O9Ag8JgJ65r9L2NpHxyBYV54hWIUocxhiiLqfEQ0OG8akbBLGD4YUgwDUurXG4PnHiMqKKoCUaOITKgYwyBOxAj9nBQMTCATgwMEKZYwzGHw458cxowuhA20E4w4mgrTBGD+IvoBAgB6ARhcAG5XF1NaUPeWBhmGFV8ZzxHJXN0GjTQSUFeNzjYcObRRhCcsU4ruKKBGws6bhDoXYHfGx0hCAf4/fVRevje6DQg8h4j8OgIIe8fHGrtRoXfCtcKPkub5YDviXbzphOH80FHE0Vk0KFCBxOOPaZQojCN5jzhhol2QKcBvwn2gbikL7gHwR7HwG+BDgKEKJwrOjz6ornouEIUsS/sQwghoQZEedhDzPyxF4xw38d2++nqcCbhFKGwIRwvPMd9FNM0YecgzurB/RUL3of7OZxAOG2eRmFBJIYthR357LPP1H0WDjTOD3YlELOUMGCMgV7YZTi2cGBhm2Fj9DPO4IgiPRByuCLfJIR5nCf2Q15UBCng+6OjDsHYvhCtHhwDtgkD1AgsQL8AdkefDgN2F/YT9h6D+Ri0gG2FvbJvZ9RSwQCK/UCMPRDh0d/A94DYr3eS0XeCsABbiEERtD3W43fVfitEYOJ3wqwLnDuuJdhWDDJ4I3Tje6DtIJa88sorSlzSCr7jdwDo60CEwXWIz4ewjnPFe/TXJfo7aFMcD0ViEfSAAAEI+vrZEIjsxPdDcTpCjILgGfwfcW0h+MR+FgYhwQTCJnw0+JQQO3HvQ3CSPvpYA/4cFm32B0Rq2BZN/HUGfCbYfdg83KMxcA0RFb663u/zJxgQgA3A4D2EZs1+47wwW03TADBgjvVYkHYW/QqcI+wybBd8aWgZELfti6zb20z0DZBaD74nBj4gEiP4CwK8XrvAPQKDHBDu0f4QorXaZxpoIwju8GWdpc/RQF8C/TDYa81fNmqHcQ2g7wDbiXNC3woDNBhIxPfBOXiKERuLgQfYZuge2AfaA/xq6BD66wttg2sV5//pp58q4R7XEXQNPQioQ18E+gYhwQL3Sdz30G80OyuHxcYKNYRELPZV6REtAFEfDjccXg2MeGMWBDob7sCINTpD6AhFozMCgR9RGejc+DutAjEOIk4wHfTcl+6XBvt2Gn9fszbyw93PqRFv/QAMIYSEChAAIEDop+1j4AQ2G3Zdb+cxMIKBfxSBcwfumRgEgFPvThiIRCAWYLAewhIJsu1+/X5psN8D2920jfzwj+DZbgxCYvDOkzoahJDIB4PKsNV6H/HOO+9Ugjj8bw0M9mOmIAb/3aUYAghQwIxtR6ljo6EPhNmciO7XZtCRELLfb94vDQ4Ys9/ZTdrID7eGv99dWDnjEo/Q1MwQ6RkxT0iEAvEYkeNa9BsiJhH5iFF3TZTHVBxM70NqF4z+GQEj6xiFhyAQbc4s0ipgNB+dI4ryhBBCAgGi7jAYDIcUEWNIP4MoNETOac4AREJEPiLHLqLljADHCKlhYP8x3TyaQBtiej9mVxLiKYjoRKFXpLBwlPJDS/dICIkuMOsdMwiQ0gYCPXxtzHTT7DLETNTSgV1H9D5mxBkBaeZwTMy+dzfDPdJAW+E+q83aIyRYIO0XZsVCA0N2BYDZIJg9C20Ms4+8hcI8IREKnALk3UeKH+RfxbRDGHJ97nrcVDB1HlH0mAJoFEzhhUCAgi24EUVLBATaCbmD7VM5EEIIIf4CqXOQn1WfYxaR8frIMUTdIeodzr+rIu72YH8cB1PxkX4uWgbZMaMAUYyO0jsQ4g6kcABDhw6tsQ2BG/pik4SQ6AGD3EjzgnQ3EOjtU8ugwDnSxCFoDqlojAp5iC7G+zDLDXYrEOl6Q4GVK1eqwELMWNenoiMkGKB+E+pwQV9DHSakrUJdB/Qn8d/H/95bKMwTEqHAMYCIjMUZiP72BuR/Q6cDN6ZoAVOVUGgHRelI9IDiLqiJgOgWDERpoFgW8iCj2BXyaiIXIq4PQggxG+SixSC4q4FwROR5A0R+5MhdsWKFRAtIIYDZhM5qF5HIAX1VzC5BXSPMNkGRR+Q43rhxo7r2kQ8azrWnwBknhBB7kK8etd2wOAJ51jG7zRvgZ2CWDmr9RYswj2AE+FvMLU9CAaSjgi6g7zcgqAV1GxBEQ2GeEBJw0tPTVa75aAHFc/QFb0nkgwgXFJ5Gqgh9gUVEW6LA81VXXaX2gWiP6EsMdEGkJ4SQcAIRfViiBTj4dPIjn4yMjGqpijAF/Z577lG/PdYjlZFmu5Ez3hNcDZIhMAZFFQkhxGxQVDWaQB5+QkIF9CMgwtuDtFUI4vQFq0/vJoQQQiI46uXDDz+U22+/vUZkKlJDYaAGzjxmpUCQX758edDOlRBCCCEnQeSaPtUMUkig3hKmnCPdE2ZNIGoeg+ueYrPZaiwlJSVK5M/JyeHPQAghhEQYY8eOlb/97W+yZcuWqnWo7YSZ8yhQ7AtMZUMIIYQ4AIYXbN26tdr6UaNGyciRI6utQ1EiX0fKCSGEEOI7v/zyi0o1h1pK8+fPV+u0Qm36GXB4rq33hA8++MDh+k8++URmzpzp9XkTQgghJDRBn+Kyyy5TM+9Q1wk1IpBaCoWZp02b5tOxo0qYR+EsbzpfhBBC/AeqmSckJIRNE6P4kFaACAXevvvuO9m/f7+cc845wT61iIS2mxBCQo9Qtd0YJH/55ZdVkTaco0aHDh3ULLfp06fLzTffLMuWLVMR9GamhhgwYIDccsstph0v3KH9JoSQ0CNU7XeogzqLqLGIoL01a9aoduzatau0a9fO52PHRlPHAOIJRBRCCCGhA6LNzzvvPHM6CI09TNLW0PuPeumll2T27Nkq39zjjz+upsQTc6HtJoSQKLDd9RDS7uH+TkChdtRMGDx4sBo015/vk08+KTfeeKMS51HAFTnnPc0vD/bs2ePQXiFijn2Bk+1B35sQQiLcfkc4ixYtcri+YcMKEWHfvn1qAYMGDfL6c6JGmEekPET5/v37O0zY70+QdxCdP3w+HrXF/rX9OrzP0bGsVmvVgj8VigzhUb/efp/c3Fw11aJePRc9WeKWrKws1Y6BvoYiDUz5QcQximoS7zl69KjUqVNHFRwJV/Ly8uT3339X9+hw6xyg+NuYMWNU1N2zzz6rovE6deoU7NOKKELBdruy085suTOc2Wz9ev12PEcBw2gqzOmv3xJCGvJKE+/BtY3rsWXLlmxGH8A9A4JxixYtwrYdQ9V243/+3//+t1rRV43jx4/LI488IhMnTpQRI0bI+vXr1bT0Xr16SZcuXTz6HNyTcX+2v8/gv+EszU20EUz7rfnRmn1253Nrr53hysd2ZLuxHDx4UBo1aiRxcXEB/e6RBmxOkyZNVNsStmMo2BgM5uI/Hq6Eqv0OVTDIbwTYAF+CwKNGmNdAx6BBgwYSzug7ENqif63vbKAQEZ6jMwqDhj+g1pFEhwXPNbFf60y4e9Se64UD+85ppIL2gxDKAQ7fpxlDTOYAh2+cOHFCTaliOwYH2BIsmL6Gwq+Y2kZh3j9Egu22Fwlc2e7i4mL1HLMxYMcPHz6sjuHI1hq13fY2XLPf0QDaEvfKcL+GQqEdUdiS7egb+F8jYIbtaD7I746+EcR3oAU5TZgwQSZNmqTue1pBd9jrdevWqfc89dRTHn3Ozp07a6xDsAmKxpPIst/6QXp3thv/be05rsPs7Gx1DEe21t4ee2LDw1kU9CYICf+raPrO/oDtaF474n7G6zF6KHcRcGUmUSfMRwKaQUbktlEwvQJiMhxTRx0OR1EF+kcIAxBTHW3XLlZN5Neea4/2kQWOOheOBANtCSXhAN+ZN2Lf0QaFiO+wHQMPpsnv3btXHnvssap1uC/oc9kS4ui/6ondBhDo8Z42bdo43O7OdmPBMZzZbnubrX9txD672hZKthLfNZTOJ1xBO9Lm+A77QP4DueOvvPLKqtcY1MS6V199Vf74448a0cso/pqfn+/x5+B/gBkP9v8H3G8xG4KznCIHLRIeiyfR7xDpMZPS0T3Tfja9I/uMYDBn2xwJRfa+tzP77MqOh6Lvzfsl2zGU4PVI/AWF+SgBxtzZFDAt4t0fTquzaYP6zoVe9HeX0se+o6A/d0cdC1cpfuw7IEY6IdrsAuIbNGrmwHYMDr1795Z3331X+vTpo5bVq1fLihUrVN5aQgJlu70V+72JEHT0HKKDJvobSeljb2NdpQYwYrc9ERAozJt3XYSSYEOIPYis1Ueta/dPpMIYPny4fPjhh/LRRx+pYu3bt2+X+fPny3333edxQ2KwFKlK0tPTq61HMbhzzz1XzYgg0Y2r+6Ve7Df7Mx2l0LV/Dt/ble3Wz6p35HubYbuN+tO0O+ZB+812JL6DVHkff/yxSmv0/fffq1zzDz/8sFp8yapBYT5KcOfc+wt/R805y99vv6ADoo8sdNQBMWLMjh07pqYmIj+6K5HAnYgQatEIgYYiCQlnIMaj2Ov777+vcssj1+CUKVOke/fuwT41EoH3ymDYbm8jBM3I36+31XrxwNni7nNgb3E82G5gROh3N4gfrfabAgnbMZxBFPsLL7wgb731lrzzzjuSmpoqN910k1xwwQWGjzFkyJAqwXLs2LHKH9CzceNGpzOcSHQRDDuhD1jzx6A9cGaL3Yn/9nbfiO+N74F82Lt27XLrX7uz4dFsu4l5GLluSeQydepUFZx32223KSEe9znca3GPmjx5shr89xYK81FCpAqh/oo4cBaBgD8domMgVLgbCHA2AOBOTLB3fB11NNw9N7I9mB0UOvdsx3Bi1KhRatGDwnFYCPEnkZo+LVC2G8Deos4Ocv02btzYoUCgFxKcDRpo6105ZXrb5onNdme/7dcFy3ZHal8y0LAPFDiaNm0qK1eurHp95plnqsXXInALFy6UAQMGVEsRiv/luHHjVD57QiJVwPN30J2GZnNhlzHjRRu0d2Sj7f1uR/u4s9367+et/TZiy0l4w98wunnvvfdUtDz6EVpKW/gyCNBDn8AXKMxHCcGKmI8UNEcYhhv5KP0RQegIvRDgrMNhv81eWHD0XBtocPW5rtIGOXp0t07/HHkTtULEjvZjVIPx64MQErnQdvuOZltgtwNVB0KfTsCVzdbP+nNmq/XrtNeuPhfo7bcjO+uN3dZsN/oYmH1gb7ODPWhASCDQHHFExT3wwAMq6p4Q4r8BfNiWQNtuT/xvZ363o0ejgwP2A/zu7LYR+63Zbld+N+038Qpkbyn2YN8wJisry6HdR/CPr7oMhfkogdE55rVjIKPFtE5JsHHWUXHW6dA/1zos+vUotIVpP2hLR+/RFmcdBGfbtN/HUUfDXwvQrgln2/3Z4WEnipDIJVipbCKNQEd66x3fYGJvU+3ttDO77WwwH/WACgoK5MiRIw7ttvbaW7vkzt76Yt+NHN+V3TbT1rJPHv7s3LlTfvzxR1VfBrU2NPAfQX75N954I6jnR0ikEEg/R3/PD3bfy53f7ey5NtBvvx73Js12O/O7vfG9gaf2WD97QN9XMtO269eb/buQ6GX8+PFy7733yvTp09VrXF+bNm2SW2+9tcbsek+hME+IB0TrNG6zOyqIukNebrNnHjjqYPiyOOu02C/OPtvReg0jHR93zjvyLqLuAX4TRwKCo0cj+zh6tH+/o23O1rnaH9+BEOIYOFiBmqEVydB2mwPsDWYNIj2I2Zhlj43s6+7z9Nu159qjK9sNjIgAKJyMAQ70hTSM2mlPbbbRfZxtd/Y62m33c889J0888YSqObN8+XLp27evCjhZtmyZXHvttcE+PRJkOPhGQm2AHyn9WrZsKWZjhq+tP44ntt7Z59uvd2ebPfG9c3JyZMuWLVX7+cPnNuJ7u7Pjjr4zfW/fee2112TixInSqFEjda3C9mMwfuTIkTJt2jSfjk1hnpAQHrmPVPzVYfXHyHgos3v3bmUYIJS4ExP0gwKutht9dHUs+22OXmsdMXfFGwmJdmEe/2/iG9EqzIeT2BRN9hui/NGjR5VI4srWGrHHRmywO/vsrI/g7LW7WknRABzwzz77TM4//3xJTk6WWbNmSdu2bZVgv3Xr1mCfHgkyTENHQg3abnOAKN+xY0dDdtrINmfbHT06e+5uuwZ9b99JSEiQuXPnyrZt22TNmjVqXZcuXaRr164+H5vCPCEk4FAkMR9/TLcPBIgwI4Q4hvdKc2BKIHOAk8cBDnNwFSEXDkS77UY+Wc0Rb9CggRpogTB/6aWXSr9+/VSBOBK90Habg72wSEgoEa4BBdFuv80gPT1d2rdvr+5RzZo1M+WYDB8ihAQcTvFkOxJC3MOoO3OgSGJeO4ajExpqsA8U/gwaNEheeOEFlbcZQvzbb7+t6ifNmTNHRdCT6Ia22xx4rySEhBKHDh1SeebT0tLklFNOUens8PyKK65QA/S+QGE+CuBoMwlF6Nz7DjushEQ2dO7NgcK8OdDmmNeOJLx55ZVXZP78+bJ27VqZMmWKLFiwQAnyDz/8sBLsSXQD283ZRb5D200ICSWuvvpq2bNnjyxcuFDVbUB+eRSCR4qjSZMm+XRszmOIAjj1mJDIhM49IZENU7CY144USXyH/UnzYHBCeNO5c2fZsWNH1T16+/btsmHDBmnRooWKniPRDW23ee1I201CCfre0c2SJUvk119/ld69e1ebQffOO+/IWWed5dOxGTEfBXDUnpDIhc49IZEL7bc50Lk3rx1pc3yHMw/C/39w3333ybFjx5QoD2rVqqWmtVOUJ4Cz3cz7r1GYJ6EE+0DRTYcOHRwOzuBehYF5X2DEfBTAUXvz4CgpCSXo3BMS+dAJ8B069+ZAm2NeO5LwBULhrFmz5Nprr2U+eeIQ2hxzYDuaA20OId6zaNGiqufjxo2Tm266SZ555hmJj49X64qKiuT++++X66+/3odPoTAfFXDU3hzokBJCCCHhB517c2AqG/PggFt4M2bMGLn11lvl0UcfrYqa14Op7SS6fe+4uLhgn0bYQ9ttDtQwzIODHNHH4MGDa6wbPnx4jXVr1qxRs+m8hRHzUQCnwpsDjRoJNXhNEkKIe+jcmwNT2ZgDbXf48+abb6rHoUOHOhx0ge9Fohf8/rVr1w72aYQ9tN3mwEF18+CgenTehwIBhfkogKlszGtH5rkjoQSde0IIcQ/tN21OKEHbHf4EylEn4QmD4syBttu8dqSgTEhow+KvUQBT2ZgDOweEEEJI+EEhlO1IiNnMmDFDzjvvPOnatavs3btX5Zm99957JTc3l40d5TAozrx2ZFCc77APZB5MZUP8BYX5KICj9ubAzoF50KiZ146MgCAkMuF90lx4r/Qd9oPMgbY7/Jk6darKL488s7t27ZKSkhKJjY1VzydPnhzs0yNBhkFx5kCbYw60OebBviTxFxTmowCO2pvXjhy1NwcaNUIIcQ0dKfPgIId57Uj7bU47kvDmvffek48//ljuuuuuquKveJwyZYrMmzcv2KdHggx9RrZjKMHrkRDzWLVqlWzatEk937dvn7z88svyzTff+HxcCvNRAEftzYFGjYQaFEkIiVw4qE5CDRaQMw8OcIQ3WVlZkpqaWmN9dnY2B14IfW+ToO9tDvQXzYMD69HNxx9/LKeddpqsXLlSTpw4IWeccYa8++67MnHiRHnppZd8OjaF+SiAqWzMgYVTSKhBx56QyIW2m4Qa7AeZA0WS8Gf8+PEqnzwEeq0/hgi6W2+9VUaNGhXs0yNBhv9xc6Awbw68Hs2Dvnd089RTT8lbb70lV155pcyePVsaNWokGzZskP/85z/y+uuv+3RsCvNRAKPu2I6hBEeaCSHEPZztRkINOveEVPDaa6+pnPJwygsKCqRv377SrVs3adasmUybNo3NRIgJUJg3B7YjIeaQkZEh5557rnqO9DWXXXaZGqxBEfiDBw/6dOxYk86RhDB07s2BRs0c6NibBwc5CIlcaLvNgxFO5sBUNua1I6/J8CYhIUHmzp0r27ZtkzVr1qh1Xbp0Uc45Ifx/mwN9b3OgzTEP+t7RTa9evVTqmvPPP1/mz58vTz/9tFqP6PlOnTr5dGwK81EAjRrbMZRg58A82PEnJHKh7SahBu23ee1otXLSciSQnp4u7du3V78pouUJIebBfpA50HabB33v6ObVV19V6eqeeeYZueOOO5T9R9F3CPS+Fn6nMB8F0KiZ146Ytkp8gw4pIYS4hxHz5sDoJvNgjnlCKjh8+LDKJw9HHPdqEBMTo3LPI5VNWloam4oQE2wOBzF9h+1IiDn069dP9u3bJzk5OZKSkqLWjR07Vq6++mpp1aqVT8dmuEaUwNE9c5x7dLqJb9CxNw8KToRELnSkzIGRYubC/qTv8JoMfyZNmqSKvX7//fdy7Ngxyc7Olm+//Vb++usvtY0Q4jvsB5kDbY557UjIzJkz5YorrlCp6/bu3SsdOnRQhV9zc3N9ahwK84QYBBExdEh9h50D8+D1SEjkwoh5c6Bjbx50Ss1rR9rv8GbhwoXy73//W4YMGSKJiYlSr149VRDuvffek19++SXYp0eCCAOQzG1LRsz7Dm2OObAdydSpU+XRRx+V4cOHy65du6SkpERl1MDzyZMn+9RAzMtBiKedgwyL723WPHpHXGnUCCHEPRTmTbbd3zqx3VkGDqLtc1v02m5AMZn4jXoiUurh/kEEeWUhyDsqCtuuXbugnBMJHZvDGdYm251MA753qrsdotd+85okxBww+P7xxx/LmWeeKY899phah/s98swPGDDAp2NTmCfEE+f+cAeReBOazF7c3ysiLUSKS6wSH1du3m8SggMAjH4wD0YvEhK58F4Zgu04zbE4UGarWB9jcWFznQ0AZDoQFB4LPdsNaHPMa0cOcoQ311xzjfz973+XJ554ouq3xL3m8ccfV4XhFi1aVLXvoEGDgnimJBiD6ozyNhEjorwhHB+nuNgq8cfKfRD89YSu7abNYTsS38nKypLU1Jo3BaSz87WPTGGeEE9yzFtK/NJe+7KSZN6CTrJ4Uyvp3uGwXHHBOmnTPCcifxt2DsyDnSxCIhdGzJsozG+6TfxBYVmM/HSwjXyV0UnKxSITWq2XwY12uRbojTj9T1hCVpwnvsN+UPhzzz33qEeksrEHovwzzzxT1U/TisOS6IC220ROvCz+APrZhg3pMnduZ/nrr4Yy5IxdcskF6yWlfqGxwXOX4n/o2W7aHPOg7x3djB8/Xu69916ZPn161fWAejMoBo9BeV+gMB/hMLrJPMqy/08sDYpNPKLIjr31Ze5XXWTd3oYyrMcOefXBb2XZmhYy5V+DpHfng3LpyPXSKDXft8j8EIuaZ+5FQkQOHjwon3/+uSxbtky2bt2qCsbEx8dLo0aNpFevXjJy5Eg555xz2AGMYujcm0P5wm5itXTy7SAp1SPej5fEyYID7WX+vg7SNilbbuu8XMptFpm5s6d8ndFRLm+9Tk5N3S+VgbQOjxGO0CE1B/bNwx/0ZQlxdm0wYt4cbE6i3L0Ff9s//2wic+d2kayjdeSiYZvlpkv+kC//10nufOI8GXH2Nhk9fLMkJpRElDjPa9IcaLvJa6+9JhMnTlT+Ov5Xffv2VT48/PZp06b51EAU5iMc3ohNIsMiNtsksZoQMY9R+o3b02Tuj11kR0YDOb/XVrl52EqpW7tEGf3R52yWoafvkC9+7Cz3vTRMzu63Sy4etlHqJRVFhDgPo8YOK4lWjhw5Ig888IDKT9evXz+1nHrqqZKUlCSFhYVy9OhRNfJ+1VVXqfy1zz77rFx88cXBPm0SBGi/TeBbi5RbOopVTJjtliKSc7CWfL2vo/xwoK30bHBIHuqxSNrWPTm7bWrvH2X50WYyc1dPmZfRWSa2WStd6x2tdgyFO4E+pTJtTpTntI9kOMgR/sAZ37Jli5w4caLGbztw4MCgnRcJLhxUNzOFzS2mHKqszCLLljVXgnxZsUWJ7wP775bY2Aobe8Plq+TCoVvkky+7y+2PjpQx521SIn219LIeifOhBSPm2Y7hwoEDB+TFF1+UVatWSa1atWTYsGHyj3/8QwWv7dy5U/nFGzdulMaNG8ttt91WzdZ+++238q9//UsOHz6s6sA8+OCD0rVr16r/AITzL7/8Utns0047TR555BFp0KCBR+eHOjJz586V7du3y+rVq9W6Ll26VH2OL1CYj3CY584ABou5ltvixWop9kmQ/3NDE5n7Y2fJzEmQUYM3y93XLJXah8tq5Jqvm1AiV45aJ+edtU1mL+gmk585Ty44e6tcePYWqVO7VO1X2sQiB4/WlYxDybLvcLLsO5Skjjty4FY5ree+6pF6IQQ7ByRa+fPPP5XIfsUVV8ju3btVp8IV3333nTz55JPy448/yptvvhmw8yShAYV5N8y0VBe7nbWjxIlVfJvtdjg/Qb7c1kkWZ7SS01IzlADfNOF4jf1gd09P3yf9UvfLz4fayD83nq4i6q9ovU5aJuZV9QWyE2vLPtjuoiTJKEqW/cVJ0qp2rkxIXy910nUVMENMnGe0mHntSGE+vJkxY4bcfPPNakDdHqaviW4ozAcyb7xrkD9+4cLW8uWXnSQhoUQuOW+99O+9TxyVnWncMF/uuGG57NhdX2bO7Snzf+ogl476Swadtrtq/8L9MbK/MEkyMpJl3z4sSVJcHCOXXfaXtG2bUyHeKywiqaFlu2lzSKhTWloqkydPVgXU33//fRWs9vDDD6tAteuvv16lkOvcubOyvz///LPcf//98umnn0rz5s1l27ZtquYL0swgin3mzJly9913yxdffKEE/s8++0y+/vprefrpp6V+/foydepUtbzwwgse+WUIrsM54RzNLvROYT7CYRVuc0R51Za2GLFayr0bpV/TXEXAl5TGyJihm2Rgn8pRegjxLkhrcEL+dtlKGdV5s8z6tYfcvnikdGySKfuzk+RgTl1JrlskzRvlSbNGedKxdabUrnVIZnzVSxaubC3Xj/tTvT/UouYZMU+iFXQiMMq+dOlSt6I8OO+881SkAMT5Xbt2SevWrQNyniR0oCPlRpTXR587EuhTRMpLIcyXiFTo4h6xNy9ZvtjaWVYebCqDW+6Sl4YskLSE6tGxjs4jVmwyLG2HDOy0W+Zv7yCPrRsiHRtkyvGSeNmXlyRlNqs0S8iTZjHHpFmtPOmeeFiWFraQO3eMkOssq6R/2n4JRXg9ElIBHHOIAlhq167NZiFVcFDdrSU5+VSLQK8StH1Ad4wThbHyv8Vt5esfOkrTRsfkhgl/Ss8uhwwFrbVtlSOP3LFI1m5sqAT6r/7XSVIbFEjGwWTJyqkj6akF0qxFnjRvnid9+x6Q/Pw4mTLlbDn7tF1y2ai/pHbtspODDyEizvOaNAcOcPiXtWvXyp49e1T+9jp16kibNm3ksssuUwFqw4cPV0Ftb7/9tqSlpakC7Ih+/+2331Te9wULFkifPn2qZplDlEcE/R9//CFnnHGGej5hwgQVKQ8QbY8C7gUFBSoK3gjI+DBr1iy59tprJTk52fTvT2E+wuGovTmiPLB4mC+upNQqC1e0knk/dZaE2iUyfvgG6d9DN0rvTJR3sL556jG576KlsmV/ihLlm6Uck2YpeZJQq1RF2OsZ0CtDZi/oKve+OFwuHr5BRp61TWL2YxDAIjIg+B0E5pg3D0YvhhfoJIAzzzxTTjnlFBkwYIDLHPQQ72NiYlQEACHEDVmOxflyia8Q5p2lkXGQ+31rVorM3dpZNmamy4g222TaufMluZabqHsHn107tkzGddok57beISsONpPUOgXSvG6epJw4IVZL9fM+XfbJH5lN5P3tp8jCw63l2r6rJK3OiYpBiInBt93EPOjchz+ZmZkyadIkivLEoe+NCE3iiErDp08Jk2me733seLx8+3N7+W5he+nYNlPuvmmZdGrnnerfs8th6d7pB/ljXVMpKbFKs8bHpEmjYyfT2+gGFc7oslc++PQUufPJ8+T6S/+Ufr0OSChBm8N2DAeaNm2q0thAlNfAvTQ2NlaKiipSOusHwvFcW480N/oIdmxDJD0C2yDMY/vVV19dtb1Dhw7qXr13717p1Ml4HaoxY8aoQq+PPvqo8tHtGTRokHgLhfkIh6lszBHltennx/LjJe94rYrH/FqVz0++Pna0luSdqFhyC2pJu0bZcv3Ff0qvTofEkiEi+3z7PTs2zVJLNSrT32jUii9TaXAG9t0j73zcVxYvayU3Df1D2jXOllCAnQPzYPRieIJpdBjRR/48FI+xB+tHjx6togYIIXYgvUuKcXG+zBYvUl6k0tEcO1FLjp2Il7wjtSSvqJbkFdaSY2qJV69hz/OKa4nVYpML2m2Rf/T5XRLidKllvASi/tBWO0+uSHAc6d839YB0q39YZh/uJvf+PFwu6bRezmu7TazfWkRGBl+c52Cwee1I+x3ejB07VubNm6em3ROih763MzzzvW02i5TbRHJyK/xt5XOrJb7a64rH+Ar7fbyW9O+1Tx67Y6G0ap7r84WJYLpTezmZwabT+1PqF6pBgJVrm8j7n/SRX37LkusuXSUpIZLShjaHhAONGzeuNqMcwZyIdMfscQjpENoRTY80csuWLVMR9GeffbbaNz8/X+rWrVvteIhqx3pH27WId227UbTUskOHDjU9jR2F+QiHqWx8Jyevlnw4r7fYaneUFb9fKMl1iiSpTnHlY5F6xNIoOV+SGhZJcsLJdfUTKwu2QpT3J1qUvU6gb1WWK1Mu/Ul+WNdWnpozSAZ12S2XFcdJnbNNKILnA0xlQ6KdO+64Q1asWKHE+V9++UVFAmjA0Ue191GjRgX1HAkJWVHeRXS8Hjj0//u1nfyWc5ocySyQE8daVdjshAobnmQtkuTaRdKsXp4kNSpWz5NqVaxLSTghsTE214VaXQ0OuNpfL8hn1dxeW8rkqvS1MrD5bnlndV9ZtLeV3DToD2krwYdiMiEVIDctCr598MEHkpJS82bw008/samiFPrevovyuzLqyfufnCbJKe1k7drzVepWLEmJRZKcVCRJdYulfnKhtGyWW7m+WK3HusSE4Pm5/XoekO4dv5NPv+4mdz85QiaMWi8jzraINT244jxT2ZgDBzic0MA/+/773/+WvLw8lYIG0elI7XrjjTcqcR7XNHLOQ6wHzgRx7Kd/tO/P2q93h6f7ewKF+QiHqWx8KPZaLvLT8jYy66seclbnPXJqj+1yx2lzQraoqqM0OBjpH95rh/Rrt18+/KW33Dl9hPzjYEPpfunhYJ0hO6wmwujF8OW9995TU+uQ4+6tt95S61CA5v/+7/9UlXhMkSOEOBDlNVyI83uykuXtpf2kpMwql12wQ9qnrJbkWF3Euv1xXOFmAMDj/fWCvIt9W9fLladG/yTfb24nUxacLUN39pQrHl6tclwGA9obc9uSgxzhzXXXXafy3CLvLQrTEaJB39t7iopjZPbXXeXHJW3lklE7pX2HdXLvdXPD6uJCjvmrx6+Vs/rvkXdn9ZVFy1vJ5OvrSuPONQvGBwraHLZjuLFw4UI18P3uu++qtDTHjx9X/jGC10aMGCHr16+X1157TXr16qUGydE3dtRP1dLNoM+l36491wfHBZvQORPiFzhq750on3EwSd6Z3VcKjsXJg2OWSIcmWbK56PTQFuVdkFK3UO668Df5fVtTeenrM+T+XptUVetgwM6BedCxD1+QP2/OnDly6qmnSs+ePVVxmk8++UQtWuEaEr1QBLXjCYtjIdtOnC8us8pnf3SV7ze1k4t7bZTzu26VA8nnS4zVRQSdo2MaJcXH9znKd68DEvx5XbZL/5b75IWfzpQPnu0n1z34R1Du/bTdbEtyEhRxR0H3/v37s1lINZjKxh4X9kqXDmb1hkby3qw+0qJpnrzw8PeSlFRbDmR1qb7/UTcXW1roXIxtW+bI0/f9KJ/P7ypTXj1bpjyd5XB2TSCg/TavHYn/2bp1qxLhUWS9a9euat3333+vxPfbb79dvUZe+HXr1snMmTPlqaeeUgVcEV2v59ixY5KUlKSe22/HNmCf/sbIoLy7KH9voTAfBZ2D+Pj4YJ9GaLCssmNgVyxVT3GJVeb+0EW+XdRexpy6SS7ss+XkdPYIoH975MlbIc9PvV8effJVad26dcDPgZ0DEu3ccMMN0rdvX1UAFpHzqCbfrFkzWbJkifTu3TvYp0dCAE479py1hxvKu2v7StP6x+T5i/4nDZMKqhd/NYojv9lByhlD77N/r9H32O+WWCgPDlssjy0YIp9++qlceumlEmhouwk5SceOHascfkL0MChOj8VQytjps3vLhq3pcu2EVXLaKftUINyJomSxWoqNi/L6fZwJ9AEW9jHB7ZILN0hJqVWefuJyeeLpzz0WAs2AaWTNg0Fx/i+sfuedd8rll18uI0eOrFpfXFwscXFx1fZFJL2WI75t27by559/Vm0rLCyUjIyMqoKw2L5p06aq3PDbtm1TGqmWCseXwZnS0lKZO3euiuT3BQrzEQ6n09mJ8i5Yvy1d3vlPX2mYnC/PXfGDNKqf71Nl+FAW548XrpWpj98mT0z4RZo00E2tG+D/70jBiUQ7W7ZskdmzZ6vRekyhQyeve/fusnjxYtXBgDjPqfHRDSPuHETLOyGvKF4++quXrDnSWK7tsUoGdM+oNrutvCBOrMk+5pz1JcjN26h6O5KbFsvD4xfJo5/ESN3MR+SCvltPbjzN/7abjr25bUnnPrzBdHqkokNEn6P0UoMGDQrKeZHgQz9Hw7Xthr7186+tZebcnjKg71555bHvJKHOyWLr5bY4sWqz3YyI8no83d/V+0wQ6y8f/Zccz4+XZ54cLY9MXqjS3VQRgOKwuCZpc3yHttu/FBcXy7333istW7aUSy65RI4ePfmHPP3001Xqmo8++kjOOecc2b59u8yfP1/uu+8+tR2i+IwZM2TWrFkqVSwe69evXxXwdt5558m//vUvlfqmUaNGMm3aNBkyZIgS9z0B6XUcgRnviN73BQrzEQ47B+45lh8vMz7pKat2NpGrB61R+eTtZ4mjMnwkcU73XVJQFKeKwqJALFLdBAoaNRLtLFq0qGqqHlLYYIQfj4899pjk5OQoJx/ReBs2bAj2qZIgwYg7J6K8LnUNnPqFh1vJjF295LQmGfLKOd9J3fiSGlqAipjXR90FE08EfgdR+qlJJ+ShixfJY/8dIom1i2Vwt90V+31rERnpX+eejr15sB8U/mhigKMirxDAnBWiI9EBRVDXfvO+fUnyzpt9lVh9/61LpGPbmqPWSpgPBdvtrcivE/WhK9xw+Z/y6r9PlxfePlMeuHWJxMX5r4ikI3hNklBn3bp1snbt2iohXQ9EddRiQ122d955R1JTU+Wmm26SCy64QG3v0KGD8qOxH0R3vH7xxRercshD6D948KDaB9H0EO81O24GAwYMkFtuucWnY1CYj3AYMe8gWn7vSad+8aaWMmNRL+nT5oC8Muk7qVvbcVRduaBzEKQq7xl2rz2bceOUC/tulWOFteSpzwepyPmkOsUVbeXnqHk6pMHAyMBSZMwICSfQacBy2WWXVa3buXOnrFy5UlatWhXUcyPBhbbbdaT8/oK68u62vpJTXFvu7f+rdE7VJaoVB8J8dolvUe+OSHWwzvlpeI6T822aclz+b9ximfLZIEkoLpH+rZCizv/iPG13EMBvauT6CMCMCVJzoIoQ4hklSBk7t7PMn99BRg/bLKOGbXaaMra8HIPqJb4J48FGl1oHE2tuu2a5PPfWWfLaB6fJnTcsU+sk0xKQqHkS4H4Qfld38HevRt++fZUP7IozzzzT6bbzzz9fLY5A0NvkyZPV4gt79uypsa6goEANBiAS3xcozEc4Ue/cO0lhs2lfqnyytLvk5NeWOy9YJl2bu7b6SpiX4uAI8c62myDQX3bGX3K8MF6e+eIseeTiRVInvtTv4jxncQS4AI1W8Njt9YL9IqBj2FBEPCmrUV8CBiLrMKXOVfGnNm3aqAUj+xpIcYPiN4gOINFB1N8nnYjyeaXx8sWOzvLTwTZyYfMtMrr5ZomzlAfefqe6WW+mQO+ANo1y5N7RS+X5uWdKQvxS6d7kiE9pcozAVDZBcO7tihs73bbcQnGeEBIi1Ly3YRxr2bIW8umn3SQtrUCeffYHaRxXPWVsjffY4itS2UTCGBhkhjSR2Fib3H3TUnnqtUHyzsy+cvOVKOZOcT7ibLcRUV7bj+J8WNG6desa1wCuC6TfcZbmxig1k+KRiCKqnXsHovyOQ/XlmblnyUtfnyGntd8nL1z5P7eiPCjfHyfW3JIKUdzd4i2evt+Xz6oE95Xrh/wpDevly4tfnqGK0xjNye8tjLoLEoaur8hK2RRq7N69W8466yz59ddfDe2fm5srd911l9x9990e58Aj4U3UD6rbkV8WJ58c6ia3bx0px0pqyXOn/CDjW26UOKt7j90mFnNrxBgZHwvAGFqXhKNy26Dl8vLPA2T79gYVK2f67x7OVDYB7gdpv6WrARc/D8YQ1yCfLfLadunSRfbu3StFRUUqPy5sN4leDAfORCT2gpXIihVN5b77hqlI+SuvXCsPP7zIrSgPystDJJWNWVTKDbVrlalUNtt2p8jMuT0C8tHRfU0GuB2NivLe7k+CCma2IxXtjh07qhbkwt+8ebMKrAtKxPz333+vov+QmwfRf7t27ZJvvvlGDh06pF4jL1Dnzp2dOpwolmN/cT/00EOqwv2PP/4oy5cvl9NOO62qcu7bb78tJSUlcuutt1YTmjHd4YcffpAHHnjA268S0US1MK9j79Fk+XRZN9mQkS4X9dssd124TGrF2eV/dCFa2ixxYhGDqWwyzEs3E4jPwuXxjxG/y/Nfnimvfnua3HnBbxJj5XT4iHDstWj5ys7xgSN1JSGvROonF7m4biIkcj4Eufbaa6VZs2ZyzTXXSOPGjeXCCy+U/v37S8OGDZXtQ3552FAY+a+//lp++eUXmTRpkixcuFDq1Klj2nnQfoc+US3M344IooqnheUx8m1mB/kqs6N0TzwsT53ykzRPOObdcV1FH/tDXNe/P9PLz8h0Lcj2aXFQruu+Sp757Sx5/KxfpHnSsQpBd6L593AOqgcQuwGW/ENxciQuQVo1yK1RA6lKnGfUfECZOnWqvPvuu1UFYOGjIpct/GFMlf/www9N/0zabhLaVPc51q1rKJ980l3y8+PlkkvWyxln7K1I3WIQ5JiPsZogzLubwRaEyah1E0vkodsWy6MvDpG6icUyZsRmv0ZPM798cNoSM0X27KsnjdOPVy/4S8KWtm3bqlz16enp1davWLFCzj33XJ8G5r0S5jMzM9XUeg2cAEL3UYEeuXIxivDxxx+rSD9HU/aPHz+uOvj33HOP1KpVq2p9YmKiZGVlqUJ4V111lapue8opp1QdIyMjQ31pCPbEGFF7I66M+D6Ykyizl3WTP3c2kZGnbJW/DV8hCbVOVnyvwk0kcZklXmJsETRqbwfy+9194TJVDPbtH/rK34atFIufUtpwsChAIkmGRQqLYuSvrQ1l9abGsmpTEzlRGCtlZVYZ2G+3jDlnk6Q1OFGxb6AGkogMHz5cNm7cKJ9//rl8+umn8uqrryoDj98Sv2lcXJx0795dDW6/8sor0r59e1NbjfY7PIja4q8Q5UWkuNwq/8tuJ18c6Szt6mTJI60WSZs6OSIJBkR3OwHe42h5fzjp3h7TXWqcLJGzmu+V48Xx8vTSQfLkwJ8lvbBA/AGF+cC1ZblNZHdufVl1uLGsPtRYtuekSO3YUmlS75iM771BejU75FigpzgfMN577z3l6yLfLYrJAdyzp0yZoorAmQ1td3jA+6TIpk2p8p//9JAjRxJk/PgNcvbZuyXGSR55d8J8XN5xkZNSkXEyvdw31cdjpxpLaQMa1CuUh29fJI++NETqJhTLuQN3MrVJuP+/My2Sk1dL1mxoLKvWN5a1GxtJbEy5lJVb5cKhW2TE2dskoY4DHYopbUKeIUOGVPnqY8eOlfj46nlz4dsHJWL+yy+/VMKBVpxu06ZNKtpPi25HHtylS5cqgd6RiA5hHlEFaWmVdyYdmAaYkJCgkufjEa81EDG4YMEC9dkQ8Yl7onLq0myLZNarI58v7yK/bm4pw3psl1ev/VaSUdzUy5Qw5ZZ44xHz4RA17yBPPWYQPDB6iTz+2WCZsbinXDVwrV/EeXZa/Qfadt++fbLqi6Gyeucg2bQ/TVo3y5HeXQ7KHVf9Ju1aZElWbh2Z91Nnufv5ETKg914Ze+4maST5dtcDo+b9CezfpZdeqhaQn5+vBrgxUF2/fn2/CrK03+ETMW/f6Yt4brdIqc0iv5xoLZ8f7SqNax2Xe1oulU4JlV6w2cVbHRGKZRwy3acvOa/tdskviZenIM53/1nq+SFqnqls/At8o7Vr18qqWXfI6txREmMpl94ND8r57bZKz/RDEh9TJguzWsv7v/WRurWK5eJeG6RviwPVBXqmtgkYCCRzVPclOzvbL74XbXd4EL3BRxbZsaO+ipDftau+jBu3UYYO3SlxceVei+ZVxV89wdf6Lma834N+RKP0fHnotkXyxD8HS0JCiZzR14Q8tQ6ISj0ogP11aJ6rl42X1euHSsaBZOnS4aic0u2ATLhwvTRpeFx27Gkgn3/bRb764XwZOXibjByyVc2aqAbF+ZBm8ODB6hGz2DH4Xrdu3aptEOzHjRsnEyZMCKwwv379ejXV/rrrrqsS5lFBFwXt9MAoYVqfs86n/svoadKkiRL5EX2AVDh4rYEp/+i0fvfdd3LxxRd7euokGphtkflb28t/N3SXQV12yz8nfScN6hbW3M9Du4ficWEdMZ9hbH2ilMhDYxfJo58OkaTaxTK2/ybTT4XCvLORdptH77fZEsRy7BI5sW2mrENU/MbGsnp9YykujZFererLkO47ZfIFv50ckGpR8YAo+esvXiVjz90oX/7cSe57cZj077lPxg7dKE3luJ047+05Ek/AQHMgBptpv8OHaExlk1GaJM9lnyXJ1iL5e7PfpXvdI94fTBc1jxzzYSvKe8C4jhvleEm8PL1poDye+oskmCzOR33xV0SjOxK+R3rQxt9W2m/rDWLb8qFs35GkouLXHG4sO3IaSMekTOndIE9GddssLZLyakTFD03bKYPH7ZIlO1rKjBW95L+ruiuBvn+rfWK1VP8Mr8+RGGL8+PEqn/z06dOrHHMEqiHl6qhRo0xtRdru8CEahfnycou89dap8uefTeSiizbL3Xcvk1q1ynwWvhExb7hwu58LrvuTls3y5P6/LZFn3hgoCXVKpHdX81PaRG0GBXc53I22c+UxbMe7iaUkVbJ3/C5rNjSSVeubqKj4pMQi6d0tTiaMWi/dOh6RWvHVr/92rbLlvluWyq6MejLn2y5y2yPnq+j5C4ZulaS6xa7PlcVhQwJtZhzS1CGFuqOBeV/xSJiH0I68t+hwIOKv6iCxsVWvYZBWr16tIgYQ2e6IY8eOSXFxsbz++usqpy5y7l500UVVX/Dqq692KN7jM/DZKLYDkb5Fi0qliUTvjXh29e+3K6eezF7fTaae86M062qXh9aHQWiboHPg4ah9sPBxsL1+dpE8fMYiefTnIZJ5rI6ckZEuncYdNE0kimrnXsv53tzR72QRae6+g2DbK7L3QLKs2txWMk+0kB9/uUjaNM+W3p0PqnREbRpln3TQ9ezVPW8hklKvUK4Zs0bGDN0kX//SUR7857nSp8sBGTdso7Tol+fk0xlJH67QfocXEe/cV6as0afueDt3iJxdZ5dcnLhRLPaxG/6Olg9HUd5OJEZ376pua+StVafKk7+erSKt+7gIhPGUqB5Uhyjv7Do0mj7mW4vkFcbL2n2NZH+dpvL8wpESay2X3ukHZVS7zdI9/bAkHCt1fa2niMSITc5uv1sGtt0tS3e1kE9XdZPZq7rJ2F4b5YzWTnI4Q6ynOG8qr732mkycOFHN8Mb9GkFqmPk2cuRImTZtmmmfQ9sdXkT+oHpNG/DLL61lx44GMm3afElIcJCmw0sMR8yHmijvYdQ86Ng2S+68YZm88t4AGXXuZunXs5606JETvTY3lMi0SFmZRbbsSJW/trWT3fuay5p1qdK1wxHp3e2gXH7ROmnc0H0xY9C6ea7cdeNvknEgSQn0/3jkfBk2cLtceO6WivpvJCyKv/oLj4R5FHtFovtu3bqpKXz2fPXVV/Lbb78po3TJJZeoKfmOqFevnsrBg2kAmLqP9DQYfbjzzjurHFFnjkTXrl2lU6dO8sUXX8jf//53T06fRDhw7N/5o6+M77pBmiUf81mgrnZsS7xYPY2YD2Q6G+3zTKJhYoE8MfgX+X57O3n7f/3k+FfjpM9pw6Vfv37Sq1cvqV27ttfHhgPjqziAY3iy4J6kPUd6CBTb1A8umgnOD4OPR44cUQsqdavne9+TzOxz5WhOgtSKL5UGyYXSIPlE1WP95EJJObpa3TdRVwP3QO1+WFBQoGYLrf71H7Jm8wVSWmaVPt1ypUuXLJnw2FcV0+H0wrs7dPvWlyK5ctQ6uWjIZpm/qIM8Om2I9Oh4SMadu1FaN3NQwIT56MMS2u/wIvKd++r8cKKtFNliZGzippq5s70p3Co+5JgPJCkeph9xsy8GZW/pvVJ+2N1WFu5tLW/fMEk6dumpbDcWiIjBHCzy1HbrF9hWBPCYNdDgCKTP1Gy2/vHojsFyJC9BzUjDLMz6iYXSIPGEeq4etzaXBv1/kgYNGigbjloh2vdFMe9Vn46W1fvOkV1Z9aVTw6Nyav9ieeS8hdKifmVUvPa7uhHl9eCnOKvtXjmjzV75fXcz+XxN1wqBvudGGdhuj8RY7a57ivOmgusCtWLw+65Zs0at69Kli7Rr104OHDjg1Af2FNru8CLabHdOTi2ZObOn3H//EveivIcCevnxOLE6S0Pr5TFDWZzv2eWwPHDrEvl5aWuZ8urZUjvhdjXgd+qpp6osEr5cV76msrH3vfV+tZEFs4Jhv/0VcILPQFBwNb/7yBHJPDBDjmQNl5y82iqPP/L6N6h3QurXK5QU9dhKUlp+U+V7I222NhgCrRPBxquWD5B1mxpKvaQiOb1frAw8bafcfvUCiY/3Xs9o3uSY3H7d77L/UF2Z+10XmfzYSBlyxk65aNhmSalvl+mBKW5Cjh9//FHVPUWguUZhYaEanH/jjTe8Pq5hZQoXOfLGo/q8q6T4iGTfsmWLEs6RhqZp06Y19uvQoYNaNFAw9umnn5a9e/dKq1at3J4LouZRFO/333/3WFxDpxuNiJsbbg6RPBIZbfnEFmxrL+U2i4zssNX0YyOVjUc55l3kcvcLfkhJ16huvlzVa61cJWtlX0KSrCi5Vg2+YaYLBufg5KPDAEfUU+z/dzB+GzZsUNN1MRU4Ly/PpXNuD/7L2qL9t50tuAfg+BDntXuU9ogF38fVfQGdEc3413DeK5/jHFFDAwsGM9NOvCZtmxVI2qACSa1fIMUlMaqTkJVXp+Ixt47s2lZfshfdJdllXdXx8V3RUYAIsX//fmmbfkBOaZ0v9173q7RumiOl5fVkX04zz0V5R+wVSZZiuez89XLh4C3y7eIO8sSbg6VL2yNy8fCN0q5FdvWofwOR/SR0iAT7jQ4P/rv4f2OJZNsdbc59Vllt+c+xHvJQyiKJsZh/b3GbysaZ8+xMJDUjj3eKk+cmHBuC7Ig229WSnxgnq/atkJVb66li07BJcPJhv9u2beuRk+woYh79adwzYL+xoMaJK4fdHhzPma22t+X4fPQV4OA7st0YdNAEcWfnj3oermw3BtVhd6tsd1qadGzwqpwxtEDSJV/i48okJ7+2ZB+vI9mVj9sOpqjHnFVvq/PDZyAlJ46DWcG1LQeld5PaSjDv1vWI1DleKlusXaRleZ7xAZoU14Mxp7feJ6e12id/7G2iBPrPVneVsb02ydntdkmsF0UXiXsQYIbC7SjQri/SDmf93HPPVQ66r0SC7Qaw3Yj812x3JNvviJ/tZsf06b1lwIC90rFjlrkC+lHcs+NcR8wbPaa7rHjpElhxXlcA1p5O7TLVUl7+h2zdmSortpymCk3DlvTp00fZbwTIQUA2oyaZZruRIx39bKO+tyvb7WhBJgz0Fxo3blzDdmtprF2B96IAdg3hvXIdHhF4p7fdjawvS7euBZLeOl8J8fkF8ZKdU1uyc+tIdm5tOXS0rmzanibZP98h2cfaqzbGvQ06AB7xOd067JTe3Q7LlWPXqnoAWcd6S1l5bZ9EeT1NGx2Xv09aIePP3yBzF3SWO584Twb23y1jRmyStJQTJ3ekOB8yPPvss/Lkk0+q/+Py5cuVFobrZdmyZXLttdf6dGzDlnXx4sXqT4Gpe0D7g8JIn3POOapjAAEJCzrH+IP/8ccfDjsH9uA96Ggb7cRgxO3ss89Wkfb4XE+A4HXixAnlOGAB9h0EzSFwtdjvE4qdjGhy7I8W1JFP13eTR89e6DiNhwkR83G2Au8PYCR63mF6Ez+8xwuaFRyTZjJWxvQSyelUS/48sERWfPW1TH+/obSslyt9m+6XU5vul+bJdjlRL6npEOK/AmO34cO+suFIulqyT9SRzmlHpUv6ETm74xFJqXNCrBab8+XigmoG31NwD0BEEwRvLKikjdFPrMO9Te/047XeeYfxx4wBzfDjEfv17Nmzal1ycnL181r2t5PPKztjLZvonPJqwvrPYju9XDn36CRgEKHlod5St3Zl5xRNahWxlVvEklsuorPbPrNXpG6LErlkxAa54OwtsmBJe5n6zkBp3yJLLh6+QTq2rux4U5w3DaRm+/jjj5VzDJuGAaOHH35YLZhdZgaRYL9xfDj3etvt6N5ib6fRWXJmt7V1oUg0Ofcf5vVWKWzax+kGAMWcqHmbWMUilQ5UigmivKNtzvQIM9LvOBMeDAr4ibVKVFT1WXKnlLa3yIaD6bJy40/y0oJmKi9w35b7pV+L/dK99mGJj7FzNO3y0+O+UVpaKmufbCQbctPVsv14ijSpe0y6ph6R89KOSOt+OSo9i7LTDZC2zs52n3fSdnsj0OG/D3us2W7YbEQr4xH2EvZXs924f8Je64V3iIPVBs3T0pRzpT3H/amauJ9VM698YxRLd/i7LFIpbXCvxbnA38C9sVlGi+r9IlwXCIiz71LqxXkvrh18Rr+WB1RB2NX7Gsvnq7uoZXSPzTKkw06Jjy1n1LwJwG7husX/YezYsTWKdKM/CdHeDCLBdgMMWGHmpzZo5+h/78pOO/PD6XsHl9WrG8mGDenyyivfmXvgo5X3extS2RR7L8obLVPjQzkbf4n66P5ViPRXyZUjRUVWr1yzVObPayqvT2ug0qj067Vf+vXcXzPC2klOcvgZ638fIhu2psvGremC20nXjkfUscYMPiqJCSUVNluz21bdkna8mt/t6X9Pm02ut90YxMQjBjgx0AC7jQXiPe4XetuN+xR8a73txuyk008/vcqm435WdV6bLTX7eqkF0sZZFuzUH9S9CT437DcGKdrW7ybxdsWLbTZdn9JEIPrfcuUfSqD/YkFnuXvKCDmj314ZO2KjNEzzQX8ipoOI+M8++0zOP/98dU3OmjVLBbo899xzygYHRJgfNmyYDBw4sOo1Ltx33nlHjQzs3r1bZs+erdLXaOCP4SxyBV8A+eG14+Hixx8QX86TyrgoPovKuK4iZOzBn91VhK82VUdz/u0XdJC05/r99O/X36zcdTTs13srNEa7Yw9xNz0xX1rXz/HL8RExb7X5mGPeSPS8O6E9BNKI1K9dJOe02aWWwtIYWXeokazY31Qe/2WwJMSVSD+I9M32SafUTIlBDYBLKoRtRMNjVB7X5NJFC6RzWkvpmn5EzmmzU9o2yJZY+2nXrphXy7uTr2w/xBm0xYJxKxjpSkONTgqi4fZnJ8mB7Lqyf2eS6px0KC+QM1IKJL1FvqQlFEhCm1LnnTt3Hby9lZ/nLMo9Q8TymVXqXmI7OWV/fUn13x/vLcfJu2gzdwM2zV2fX0LtUhl77iYZOXCrfL+0nbzw3plqRkp6Ur6kJRdIeqfp1QYnsFTrFBG3TJ06Vd59910VDQchHqIRhORdu3bJ5MmTVYo3M4gE+w3Rwd3sHGe2G+thuyEq2ttuRyIB7Lg7gcCZ7Tbr+o8W+40UdH8WNZEnU382FmnmTJxPcfxa2W5ns928EeVd7e+DqGoIT6Pp7c4DkdM9mx1Wy7WnrVZpVVbuaSr/XdFd/nm8rvRqeEj6Nd4vfRofkKT44qr/9+bNm5XtPnTokOzZs0disntJ13pH5MJmW6Rzq6OSXMuBcOKsDX700HbbHQdWr3Hl0geCCJaeFdsKi2PkAGz33rqy/2CS5OyqJekJJ6Rr0wJJ71xht5CCpip4I9XB59iVJqralmXw2tlmEci0Ddvb1CCryj9vMXjdOPi+3oBb0CnND0rvZgflrwMN5fM1XeSjFb0kvW6+pCcWSNqv/SS9z/9Vs924t0ZLII+vwH4B2C+kZNWnVsL9f9y4cTJhwgRTPisSbDeA2GbE99bbaDzXbLXe99bbbkdokbxG7LZ+nVm2G+cWDbYbrFzZVE49dZ/pKWw0EDFvMZJj3kyh3ZvP8lfEvV1k9UXDt6gl71i8/LGuqaxY21RmfN5LmjfJUwL9qb32SYumeWLJtEh5gzLJyMio8r2ROnrN6u+la4d0VZx0woXrpVnjY45TBzrieG2PB1b04GNwp0lOF+mM9up1chv+ykcyE+XA4bqy/1CSHDxUVxXB7dm2QNJPzZe0lAJJa1BwMkrd/vhIzb5PfCPTgpg3qZ9qq0hD5rRgrFXEYr4wr4Eo+RsuXyXjRm6UL7/vJHc/NUIS6xRLOtog5XRJb357NduN576kFyaeg4FmpFYHsGsYPIIwf+mll6rZqJjh4ndhXhuR19CMjpZD74cfflARAjgxiArbt2+XoUOHVu0PA6tNfUOO+Pnz51c52v/73/9UXidPirmiQ4CCsRAuzMrjZx9x5yvoaNh3IvSvIcI46mg4S0GjnZu+Y+HoufaIjowW1RHpYtnpzTPkv391lz8ONJF+TQ+Yfvwy5Jg3WhneV4E+BMR3o9SOLZNTm0GI368Eli2ZqbJyf1N5e2U/OVYcLx1TM2XvNxfIcVuayo+HFDiNi3+SG0fPq5n/NBC4mbmAv4nKGVu3ULq1OOJc3PY1RZELUd5ZYeOq7c1PpmewOOocGJ1Bod+vufPzqy1lclH7LXJ+m62SeSxBjhxLkKN5CXIk83nZtStBVuQlqtdHjyVIXEyZpEO0b5SvOlHpWMorhfykAqmnF0XAgOieYg/jjWj5M888s6raO+7dU6ZMUQ6/WUSL/dbEcU8EA3cD9I7Efmcigav6GZ7Ybiw4h2iw3bgfjErcIp8d7yr3NVhq+vHLBba7pKbgaZYob9Z7AwwuqzapOWq5pNUGNetw5cGmsjijpbyzpq+0b5AlpYuGyq7jjaV58+bKCUG6jEtrfyBNSta6HAwxBS+OVTu+TNo0ylGLW7E71Y+1ACBCOXXqfTy2h79xj6aH1ZJ7opYcOZ4gR/MT5MjxRDmyZopsLUpUdhu58wtLYiU1+YSk1a+w3+mpBZKeWiGIQBBIlYIakYOS6oeT9gP/+te/lK398ssva0SS33TTTSpiE2kajaLZativBx54QEWS+4tosd1m+t76HNjeBNjZo0+tZcSOawEW0WC/L7hgqzzwwLkyevRmSU8vMC2FjUY5UtlIqfFjBlKQ9xUX6WzckZxULEPO2KWW4mKrrN3USFauaary0qOGGYT6rbtvVNch6l5gRndq3S/k75d95V6IdyCqV5HmxXuc7as7Fm5riBjH0rvbIe/Pz4z3HbW4/F1UxLzF+f3CLDAL4poJa+TyMX/JkcwEOZqVIEeyEuVI5jOyaU2CLFbPE1RaHuTOV7Y6tdJ2p1Ta7srnmAlR7Xd3MqOCGGPQoEHywgsvyEsvvaSE+LffflvpW3PmzPFooNsRplQ/RIcA0QFIBTFv3jxl6DFq0LJlS7Ud00x/+eUXFQEIkIsHuabwBZBSAp2CSZMmeWyQIfThhoNOVSgCg4ybollFJh11NvSv9UI/1qNtsegLE+hBp8FeEPDkMSTyBFamSkGE0lUHmstHa3pJ78YHPYu+NoBNRcybJMybWRw2AGlsPBFYOqdlquXKnutk/7G6sulomlzSdYO0vuFI1f97y9fzJeZEEI2C0XbPCHCBX49/S/z3ys25HgwMNCDaslH9fLU4orxytsGRvETl6EOsP7wnUTbkpavXR44lSlm5RdKSKkR6Jdbv+8y0dC3hOuruyKlHCoRA1Qih/favSOBsJp697Ub0on49IiMxJVK7DpxN/ffGdodECoDXTl7fowsLZfI1l8i6oobSo9Zh9+/1IKWNipi3Lx7nD1E+zElLOCHntd2uloKSWFlzuLHEFZRJl3qLJfHOivY7OP0cqRNTKGoCgj9EeXfHSPVA5HF1LG91VKMCuifnqT828JPWXa9OkVrap2fXbJ9UkYLCWDmSnShHchLkaHGFALB6feMqMSDvWC2pV7dQ0usXVIj39fOlQVvzB9LMBhGjSBfnCIjUiP7GTGpv2LlzpwQT2m7HaDbODN9bX/DSWZCdJvTrbTc+H2m0nNlZZ2K/fX0NR9uCH41/0nY3aSIydGgvmTmzh9xxx3LfD+0o0tq+7kwoivIBipp3BKLI+/U8oBbkpd+2K0VFnV89fo006YL0shYpO1Jbdh68wrUob0S49lYUd3csowMUZn6+l+eGoDirHyPm7akVX6YKxWJxRGmpRTKzYacrxfvMRNmVUV9Wrm2qbDfWxcWWVUTca6J98y+i2vf2FaSSGzlypNKuEUg3fPhwJchjVoqz/oZRvLZaEN+R/F4DhSiwOMLRNuSpw2KUm2++2eF6NEq04GlnA50D5KnGFEJHaBF5+g6F/aMmGDja7ixPoH1REEedDSPbPBUOTrtjr3z793Pku23t5cKO5haALbe4mA7vC2YKu95EaxsPlPGYpknH1aKYE1sxiIIo8DqO/8sBxV27ZwS4wK+nn9ccnYOYk3nuMoJ/PWJgJqVuoVo6Na3Zc4a+eLwwvkKkh3iPyPvjx1XxISP5UCOR8ePHy7333ivTp09Xr3HPQ/HjW2+9VRVa8xe036Ev9MP2duzY0el2fVEu+6h9PEekJAQDZ7bbVTSfr7bbU+EA03Avv+Vumf7OVHk+/n8Vs2pSfRBwU+yEeX2O2tQIEOT9HKicEFcqA5plVP+caRYpr3euWGxlvovynu6v/80c/X6eiOC+/v5GBXRn4rw70T7Fj7+3i5kDSF3XqkmutOrmON84ojKP7kqQo9kJcjgnUT0irRGm0YcyiGpDBDlEeD0IDkPedjjXEOfDBdru4NluozPx0KeFSOOsmKW92O/oUaunY7+ffmaeI/utT+PjqR331ve++OKlcvvtV8iWLSk1C8B6cm82Irr6S5T31YZ4K877EDXvCHS9OrbNUosiy6qio202pGoqDa7Y7WEUvcPtIXFu6N8GTph3R2ysrWq2gSNwu8jJq10l2kPAR3rhHTt2eDRbilQPDEf74V6MeyhmqiFdFNrT1z6ROaHcJCRxV/xVi3j3x+i7fTVvR8KAXvR31vFwde6Oqn6Pu/mfMufT6dJTfpe6sQUqyh2CesVjsd3rErHsLRVLC4PT4c2OmA+GOL/XyetA3JtftgTmc3xtd1+jzsWD39OHz7JZLWKJs1Xk1jMTMwYaHIC+flKdYrW0RaoBMOAaFR2O6djRCISBiRMnqqnluOdhNhkKHEEsmDZtWrBPj4Qw/oqccyQa2D/Xi/6ObLe72R6ObHeHDh2kaZuO8lNMopwSt1esdYvFWo6l0l5rz/FowboSsWiReynOxcfyevFiLascVPdfxgn/42N+eZ+YViHU2CRWrDY7595Ivn9vSfVwv0yTRXlXIrwRgV4T4e0/N1XEhqKyKQbP22ixYaPv15+fB1GZTTsel6ZZx0/O7Eq9LKRtN2Zqw3mGnbUX5jH1/JRTTpFTTz01rIR5EvpoYo2ZYr8RtGA7V7ZbC7hzZLfx2p3ttve9NWF/woRb5Ztvmkr9+n9KbGyxWK3FYj0GW13xPMaKVBrFEmMpEQu22Ucc+yLKe0umj+9LNVGc9xRPtD/km7ehXlpp6Any9hz1w++aau652QqsYrGWu/a9Q2i8Gm4C0uJgqRqwSb0xqn1vX8C9EinsUBdOS1uDSHn0J8yAwnwUdw78ib+n2znqfGBBhEJa4zay7ehG6dGkVEosiVJuiVdRcxWP8Scfj8WJrUmM2EqhGCIxiE1NUdLQnH4IADmxXaTMUktibfmVAn9JpcCvPWqCP8QC/fbSqu0WPErpSTEh2JHzjgqS+vP42qPzINDA469UQAFIMWQTa0X0or/wk0BPTpKQkCBz586Vbdu2qZRvAOnZtKIyhAQaf4kGjoR/e1Hg9AuukKWfvyO9W2ZIiTVZyq2w23FSHlOr4hGvEyttuOXkudlqV9htS+3qNtxau1RKiutKiSRLaWyCWPNKKoQCCPuwx5bKJa5yHRYravNoz0vt1gUgSspZuhT9ej/lJHeHzRIrlnqlIprZ8Uc6G1+caFcCvbeivLv2dvdbOPk+1fqBqR6KRfpzyzJRlDfy+wXp2vMUBP28/PLLcvfddyunWQ/sLXLK//e//5Xff//d689YsGCBisY3K10piY6gOH+hD7bzxzWpF/7tF9Qf+fnnLyQ7O15SUpBnP1nKC+OlvDxOymzxYiuPk3Kb9rqWytNddd4nnPjelhLJLe4iu/IuFWtBZTCdpURipNKf1ux4ToWN1nxu5YNX+efwwbE/1pss8rsS6O33MzsowMNI+4oiumWhKcabQWbg2t9pfTdPZgCQsMVqtapC6ijA7ms+eUewNxHhnQN/ONahnhpAifUFmyU9zU3+x4Pu/wm4AZdLrGyvfZ00Lfq2Iqt3ldAfpxP849QggM0SpwrF2qptr3jU1jnDlmkRS92KDorVUqY6EqpzoTojuueqQ1JW8TxO64hogwAnn1c84j1llQMDOCbeX169g+Jvcd4uSl/fASO+EKDpdBTo/U779u3VQggIVH2BULPdGFjPrt1M0nIqUjs5FAVPOFmvFZupxNagIt1XTllXKajTQtITlkuZLU5sEAdslbYZj3XjpaystpTYkipel2Ofym1KTKhYbOWx6ngunTWdoKBstrXk5PNq60qd7FNaMQBQUrkdfQDNlivbrhMXXAn4ZmH3GeWJMWIpLjNXlDfqMHuSg90+XYwZ6YvcifNGz80dvoj0+nMwo+Ct0c80C/i4ntz6XPjE//73v6V169YyePBg2b9/f7Vtzz33nFxzzTVqppovoAhqYmKiekRaOuSZjY/X3YRIVAKhOvh54M3H3Sz7Y8esUqfOWklPz6u4dxm9TzgQi202RHnHS8nBRGlS/L2Ux1TabATVwR5rz3Phe9ep7pPr/G21j8SqQeUq0Ieo79h2w09WAXbwpysD7Cqe63zqym0V9hkzALQAvMp9jpaKNaXSlms++dFSsaTZgiPOHxWxlcaI5USZCkKMKIwOsJgqznvge3uTR5+EPGPGjFEpZx999FGHvgyKw3oLhfkIBgI1crdGGyo/Xt93RLKHGX+TE1EaxjoGueUtIrVtRyuMtz81k0rDgWiCig4GxIBKcUB7Xe15rHpeVjkoUG6p3F9iTz5XjxX72RrHiM1S/Sai75RIqYgtSfdatYHWKdE9orNR9bxMLJUDAJZqzyv2sdap3NeGDk+ZlFnqSKElVbf/yePA2FG2N0a13y0QeJOqh7ikTZs2LvN8o4A6Ut1cf/31bMkoIpiz3YIJ/gteD0rYRTjjX6XsSz2R+LJcqRXrxIMzOeAFggLyuaroQFusihSsGBCI1Yn+sRXbbHGVgwJ1K9dXvM92onI77HZsnNgKYL9jxWat3mW3pVnEUlgZbajr6ulFBmVbYYMhOGhif0GFPa4Q/MvEkloqluOw7zq7rR7LxNKwVKw5pSpSvtjaQEos9aQopeik7W+gvaesemE5Z9Hr3jjHRqPXjX6OBzUKaqzPMiea3FCAgjf59L0V5MOtzoILkPse0fCOirB9/fXXqign7KqvHD16VEXNf/vtt6oOGvLWX3DBBXLJJZfIeeedJ3Xq1PH5M0j4EayI+dBgnYi0Mr67kwhuFHyNySqSGEux1LJkVxh0R3g6adjF/dNWORPZpoLt4EPHiS07Vsqs8UrYV6/hZzeotMeWOCmx1hFbfoyU18WMvpP+ty03tmKd7eQ6ybbUuP9rtlrz56rWW8orbLA2YK89t1unbDn85noVAXvYprbnVjxW2P0yKSyrL2XldaSoLLWiX6C9vzIIsEaBXTMxOrPAm2MG4TzUbHVvguL8EUXPyPyg8Oabb6pHzJizRxVbLvM+mwGF+QgmUkft3WGocM1eL44bCBG0MqUNjHKMFKml8sMd46SWi0/fWXdXQCehQtSviDioGChAByVGPVY8r/6IoYyKWQOJYsuySnmtyvdiuyVGTlibyqH4IWo//XuwDQbPHfYdGIDXFSJ/pdhfJfpXPFqdbivXvS43uB0GuWKQQXtesV95jX0qzstf100QC9DYp+ppHhrpfcINRO3NnDlT5avTHDnk/3z++edl2LBhkp6errYhF+A999wT7NMlAQKdumi03cCpMO9KLHX2OlWk/ES8EqQDBRxczYH2GvufXuvj13Ig/GoBuuUORIYG2qB8TOWAfoXdLk+OFVtOdVtejhkBlfa4zFqrwi4rmx8jtnREIMbI8dg2YqlTqoIVbHViKrYVY7DBenI2gaZFauep67som51VU4xQtlMJBGU1HxPLxHKk+voKAaJMLMf176uwvco2q3XlInbbLPUr31eE91Uu2mdjmj+ep5SLpbxinTpDTyMFDIrzXvcL3BWPdbR/hAvx9sCmnjhxokp81+4pEyZMUClukObjnHPOUeu0nNsDBw5Uueg9yROLGT6IlMcCli9froq54zVEedSKueGGG2TEiBF++Z4kNHFVWD2SMfU7G7nHHTH3mGow/ygMaaGLuXEiUuBAXC1wcK+1nyBfblwUVsF52uB8pbhfEbBXGZxnq/TBtUH+YxjUh72uXRmwB5uvvTdGCfL5JS3lQP45Ff525Xptu5xw/9spe137pO0Gyn6rwYCTvrP1hJ2vrdnzQ/p15WJJ0uy6zo477Auc9KstObrnykbr/O8qfx0Nje1+ip63YeaID369J1H0RlMPGdmPEfum4a4Opi9QmI9gonnU3rbyZpF25onyASXYqUN0swcqnOaK6XzV/EgjNslBO+NtJ+KbSqvYz0w73YrjVsxtqFqU2KAt1krxwdG2mq9VVIRuoMBmqRAd1HPVXas4Hh6rH8Oq28eie1/1yHb9a/vBBWeDDo7eh8jFEkuyFMS0UAMCFbMNbFWPFZ0Tm25b5Wv9fsphrf6+k7MW7LfrBxl063D8g/p12nPtF698X2xlFKc6n8ptOTkqyixaQUG66dOny4ABA6qtR575O++8U+W/RUHYO+64g8J8FBHNtlv2rXBuu70As81iLIUSVviSx7tScK2YMYDBAQjpDgb3tc/QbtlJ7j8Tef+bxXwl8XKsYpBAj5eOLkyMspuVAgFmG1Q91q981BbYXt1raVD9tYpMPFZbbOWV9hkzECqPJYlWJV5U7Fcxq+HkZ6FxrGKrHSO2XLzGe601Gs2mOeSFziMetefYx5ndB9klPWVrwQ2VuWpt1R9P6Gw4BnpEv0+5KvpuyT9pzyu2Vdjq6jZe20+q2/Uk2F6cvG4/9X6dva72uuIbVJyLtl7kuCU0bTei16+88sqq14cPH1brXn31VWnatKkqXq3x008/qeh6FIPFQLinHDt2TL755huZN2+eipxHKpsbb7xRxo0bJxs2bFCz3a666ip55plnTPt+JLSJVmEe2Gw9jQ8cHg1QkVcjHDUhjYy94OtNIdhKVHCepVhEsPjO8eLWkhvTWZrV/c5xOxvNwFXuYIZg5YC/ss94HquzrY4WbdtxnQ+NAYgk1B6o9KW1R7W/VWz52jotgE//Xv22Sluuluo2uZo9PnBykMF+uzvbXlDSUuJicqRWTFaljdbstTaYr/nNJ+32Sf+7cpu2z3Hd67oVj5ZjlY2st8+a3VXrHPjb9va7ah+d7d6nrbeJxES3720GmJGHIADM0MPMuYYNG6qCsFjq1avn9XEpzEcw0TodXvbMFFuaF4K8vwugmi3Q+zMC2de2cNrWXk4Bc0OFUawQIRwOHIRLymZ316juN8mN6SD5Ma2lcfHPuo5KhRxTMVAA9B0V/TZtnbYPwCCE3b6V+518XjHgABxuq3zUjlmxb1Xm5YptFu29FpGiIikqclXaPrL566+/HBpwrFu/fr163qNHDzl06FAQzo4Ei2ie7ebTrdouWl4T5uOsec7fkxVm0cNBPNfyOrFiLS9z2M7eApNT4dwWm/Md6xnIte4ITz8zy4t9Uk4ORmzOuEXaNf3wpC3WBgfU7AOLSHbloxIcpPo2PK+j20dnfyv6ApX71KtcXznIoLYl6Wy3ElUqjl2xn85247lNZ9crizWe3F+kyBaatjslJUUtGpof1KRJE7XY74vtLVp43uFFTvlFixZJ3bp1Vc7Z2bNnq0h87fOwvVu3bkqkpzAfPUSrKG+x7FD3NrMot8FfLDMnWt4fGMnxbi/O+yOtiwEgmCOyvdo5mIASkvW+d9UGLw6GWQfOZoa5UitdXQuuBkYaiFfsOz5ckuJ2SN34XZW29eRAAOxkdZ/bzm7r9qnKEKC9Lqq03XGaLdb74hX2urqfrfO9K/ev6E3pt1ccX5/gkL6370ydOlXeffddue2225QQj1l3mIm3a9cumTx5snz44YdeH5vCfAQTjdPhj8+MlyP5Z0nntKPBiZLXf04LPwr03ojyLTxsh70efg8Dx0akOVLEEONt5vw3sVQV/K2BfefYm+s/EINUjRpFdbG0fv36ye233y7vv/++tGpVkZczIyND7rrrLjn11FPVa0Tiac9JdBCNEfMYjNj18liR0paeTzV2IapCmHebysaROJ8VZDFcHzXvafoSbz7D0Wu7fRHVZmlQ6lkOd08i/80uXBuMzzVwXOh2VotNrFYX6Y6M/P0burguvC1468FvF28JsLoUYiD6fu7cuSrtHJxyR8B2w4EnJJI5eLCuFBTUzLfsMbr7GQq3qtnaoSjKO8NR38VR5LwZ6cg8ADPSrMdL1UyusMCsVEauZi14ndLGonL8YwnbonhR7nv7ynvvvScff/yxnHnmmfLYY4+pdfDZpkyZUmMGvKdQmI9gwt65X1Z5xxvgfggeU3JQ1Om7+edL78YH5bRm+0wV5A0V6trrZ5E+WDm6XQn0HrYviuVQmDfhusT761hF4m0iuQ5+GzOue08HZojHYFT9iiuuUEVgGzRooJz7I0eOyFlnnaWmyRUXF8tnn30m//znP9m6UUTYD6o/UWkvH7MZEuRXrlwps//5sJTa2sqk5NW+5wHVvQ/FVA3lmM/yMYVMJInzTrDVroxg9KSwaqjPRvDm/AJxjXhS5DbTi5zyJov0oS6g4x7jiFGjRqnFG4xExdWvX18uu+wyr45PSHDQ/F339vvgwYPy+ecXye+/D5Nhw7ZLy5a5Itlepo6xs2/lEicWe9sdaqK8kZQ2Pqa18TknOn7J3Fjnsw/cta23521/XF+PY388M/Cqba0iueU1awFpRPd4dVSQlZUlqak1f2jUg3NaJ8sgFOYjnLCdUqeJ8vbP7QTq48Vx8vWWjvLdtvZKkJ8yZKO0qJcXvCh5I/sEU+j0NGpejwntiTQqFptJhfhcnU+oiskmXpNIE1OVFsif13qopXiKICDIL1u2TNatWyfbtm1TA6kdOnRQOeY1Fi9eHNRzJIEnrNPQaaI8HJ7bnfc/ym0iK4uayuzj3aTUZpWL626SM2rvFavFy8KvKT5EzLsTH4OZ7iYY4rwTkMPVpXMfTiK8N0JzlnepawIizrs7jqvXRs8lAkR6X0HqGqMMGjTIr+dCiPnoDbBz+33wYKLMmdNFli9vLueeWySvvvqt1K/vQ2orB3ZNDao7ipgPNUJZnM88mcqmxuzqI34W1o+4eJ0eQEHel3Z3ANLK+Jh0kYQ548ePV4XeUSNO01o3bdokt956q9cD/RoU5iMYX0dtgoa9EO8ACPLfVAryvZQg//NJQT6QIqQ3gmgUC51IZWP1NpWNN2l4xMu2tv8sM34v08Vz5KrzX2XwakTxNevvUfeXX35Z1qxZI/n5+TW2ozAdic6I+bi4OAlrnIjINQX5Dc4FeROE8YqIeRMKqIWSOK/HLKE+xYXoqm1DQdO0MBXl/X2+jtot2OmQHJFi4jGMRMZGGIMHDza0Hxx13MdJdBG2frfCve9dXZDf4bsg7wJEzFv1xU9DLVreHcEU5+36BSoNnaXMtzZ0J9B7cmxPRPoQ+90r8sqX+3WGg2GCVLMg2nnttddk4sSJ0qhRIxVI1bdvX8nNzZWRI0fKtGnTfDo2hXkSWsy2uCx4mpNfSxb81b5KkH/SLEE+0BHowUwT4kvUvI/YLLFiqVcm4mk/bm8AROW9fhKm/dTWFRHzAXQCmNrGdJDGZseOHao4XJ06dcz/ABKWhG3xVy1a3gElNqusKmosnx3vKiW2GGOCvAlCo0cR8+EizutJ9ZNA7whnt6hQEZ39SZaJhWAD2V7R8NsE+N5MSMTZ7kyLU4EPYw379yfJvHmdAiLIV8sxb5btDlYhWGfivHgp0LsSfV30AZQwn2vSQKHZQnmICe/usYqlQXlFGtlgkGlgHYV6v5KQkKDqy2CmOwLrAGa6d+3a1edjU5gnIcvBnETZdbi+7Dpycjl2opac2nSffwV5OxG2YtpSeWRFImufGWCBvhzCvKNipa4wK1+6rznyAzGbwgP8el2aNRshSANA4cKSJUtk6dKl0rNnz2CfCgkhwrI+jE6ULyiLlR0nGsiu/Pqyq6S+7C6tLxmlydIsNk/GJLpIWeMHTIuYDwVcpTbxV5ob/fHNTtUSrQQqFUy45fsPA1DzBdPYwUcffeQyYv6qq64K4JmRUCAsbbcmyquBBZE9e+rJrl311bJ7d8UjxPmhQ3cGRJB3GDHvb/E2zUneezNwJqZ7Gz3vhZ0vz42RWJuByq+MwDZUczAoqWw8+d35OwaE9PR0ad++vZop1axZM1OOSWGehA4vW5TQtyO7vvzn9x6y7WCKtG2ULa3Tc+TMTntl4lnrpGnKMYnZH4Abok6EVZ0Ds/KiO/ssPRGcMgQ5aq02D4R5M4VdvbDuS579UBGjLdbApbJxBoV3n2jXrp0q8EpIWDv3laJ8flmcfHW0o3yb1UEaxx+XVpYcaRuXLeck7JRWsbmSaA189JupEfPBjpp3hz/EeaPFRCMZX6PlHWFAIyGhx1NPPSWjR49WqcYee+wxp/tRmI9OwjViHsL7ih1N5b//7S75+XHStm22tG6dIyNHblWP6ekF4reSdU5sli3QOebDSZw3SuWAhi0mVqz6+jDu+gmBTMfiCe7O29s8/KmeBsUFME2ZL326QAr09Svq4hoiWcKaw4cPq3zy8+bNq0pZB58Ng/ZIZZOW5izno3sozJPQ4GWL7DuRJP9d1k3+OtxQRvffLPeMWiq14oKfo7Hc4qZzYHZqmFApFOsHbE0MVIb3p+BrVvR9CMACNOHPDTfcIFdeeaW88sorkpiYWGM7i8dFJ2Hl3D9hkaLyGPkuq73MO9pJuicelqltf5RmtY6ZKxLbC+IGhWE1hTscCsiZVRDUTHE+FB3zSMLdII8vkfWMlvcLq1evrnq+c+dO/3wICVvCblA90yJ/bUqX/8zrIcfy4+XSK9bLgAF7xa/dD4Pid5mKmC8JbKoTX8R5Z+lsjIjzYrJAf8RRP6jU86jrUOkDZAbg+Ea/q8oxbwuv760/Tqj8pmHMpEmTZO/evfL9999L//79pbS0VFasWCF33nmn2vbNN994fWwK8xFKODn2R59JkNkZ/eS3rOYysvFWuXnkH5IYXyKi1b7LCJJwWhkdrfLc2YIU1RphIr3KMe9OJAkR4TssptMFO2Ke+MTkyZPV4wUXXFBjG4vHRS/h4tyjM/pTVlv5/EhXaVk7Vx5utUja1smpcAKOGzyIn3Nvq/ukxQuBO5Sj5v0tzkeq46a1mSe/nbtrxMvrCNdl1fFTApDuJtjXLCFRQLjYbrDtjxT5z7xBknEgWS65YL0MPmOXxDa0hYyAalM55oPge/szct4VZkXPOxjIKM+PEUuxh2lkfRHnnV0HZh7L2b7e9l8MvlcFxVls1ff1h/CdGaaDHFHAwoUL5ZdfflGivMa5554r7733npxzzjk+HZvCfIQSDsJ8Xl6ezH1ukPy0Y4Scnb5LXu31rdSPLxI5VClCGxHk/S3i7hUpb2VXGT5YhLtIrwY5kMrGScQ8BXkvUtl40dEiIQMLyZFwtN84P9RG+O+/pkq9mFYyuflv0jXxqPFOf1YIRGxlhbnQ6cvAgitSw+T7+0Og9/Y7efFbVMtRG6ic88R0fvzxRxUpp09JV1hYKLm5ufLGG2+wxaMM2MZQF+YzMjLkvzMmyIYtA2X0iE1y/9+WSHx8uTnCpslp6GIsBRIUtMj3owGKmjdDnHc2syBTxJYYUz2VjT8w8vt7en2ZdU0ZbVdDqV8wqG7Xlmb3UymehzTIK+9oljuKwiJFrS9QmI/yUfuDBw/K22+/rQoXNG/evNpSr149FbVpNgUFBfL111/L/Pnz5dT0eHmhx/fSsHaB+cU6TaL8YJxYm4fYVPi9YSbQt9BPp3PQOaAo7zEYtbcyYp6QqBTmYeP/85//yNq1a1XRIb3tbtSokcTGmt+9Qz9h1apV6nNtR9bIpMZ/Sd+6BzzLPZsVQWJvsM/X7GKwqQajvCMFR22XFWIDJd6QEqLXa4Tx7LPPypNPPil9+vSR5cuXS9++fdV9d9myZXLttdcG+/RIEIBdNjKo/tdff8mMGTOkfv361ew3njsSfMzg6NGj8umnn8rvy7+XkUPy5G9XrZCEOg6CexzZjdTAC4mo7xaXY5LvnRlC0fNmi/MuBPlqs9W9DeRyFQ2e6ce858EUp12co9/TyFKUD3muueYa+fvf/y5PPPFElU4Kv+3xxx+XUaNGyaJFi7xOSUthPho7B8sqLqJVOxvL6//rLyN7bpVW6bmSkZksW5cmy89ZybIvK0niYsqleese1Rx+dBpSU1MNC/bHjx+XPXv2qFxM2uPu3bulR48e8tTAOdI8+ZhzUTZExNpyS5xYs4pF6obgefoSRR+E87ZZYmqmsgmR3zncsKn/IFPZhDuMuCOeFlnNLa0l/9x7upSKVSamb5BDOxIlY0eybDiRLBkFyZJXUkuaNGtZY7C9SZMmqmih0TQ1+/fvr2a/saBfMaH+l3Jmnz1izfbQoTAiXNqLh3on1gth0a8OlKPvFInCZ+V3smTZfE8NE8kYzBePQosk/EFE/GeffSbnn3++JCcny6xZs6Rt27by3HPPydatW4N9eiRIvrdzG2tR//2vvuooX3zRWSZNWiOxseWSkZEsq1Yly1dfJcvBg3UlOblImjfvX8N+JyUlGR5Az87OrrLZmg0/cOCASrPw6uPfSb3kouCKswZQaWRDYba6J9HzaQEW5w2I8lVBcRDmHX0HT+pUmvlbO7qmzDi+mTMtHQj0NhuEeQO+NwX2iOWee+5Rj0OGDKmxDaL8M88843VKWgrzUTidrtwmMvf3LjJ/VQe57bzl0rs1cseInNpuf7V9juYlSEbWciXY716ZLEuXDldT4IB9hF7Tpk1VJHyVA7/2XdmbmSy5BbWlWUqetEzLlRapedKnU660apcjaQkfmfdlPc1B39wLYb68xLEIHkqisv25uBLqg3Te5RJ7MpVNKLVdWBLDHPNhDiPuiCNcDXxvO9FAXtp7hpyatE+ubrRGYq22GqLg8ZI42QeRfk+yZGxNlp+bX6JsN5x1RNPrB9rxiAi9qsHzxc/Lnrx6sv94ktSvVSgtknOlZXKuDEjOlQnN86RFQu7JzzRblNfvS7E3sKT68NtRoHdPir6t/DjzwN3/hv8t08jKypKuXbuq5w0aNFARyRDmL730UunXr5/KN0uiC1ez3QoLY+Stt06VvXuT5amnfpKmTWsWYikttShxPiNjlRLs169Plu+/Hyj79u1Tdlpvt7XZcZmZmZUC/AOyd2892bOnnpSWWqVly1xp0SJX2jbKlcGn5ErrFjlSN3GG718yQIJj+bE43+u7mXmungjY/uaIZ9+53BIr1qwy79LvBIJQF7GrCfSoW+TntEAkatPQUpiPslQ2EM9f/+oMJbo/c/kP0rCe4/xtVouobVj6tDlYuXal2E4vV51ROPlY0FlYuXKleqxTp460bNlSWlhnyOBuudIyNVeaNDgmsTG26iJ6fICFeGfvNyjQl1tR/NWEKO8McwYKDOPsHIOR/qYyNVFVKhuK8qZEzBsatSchCyPuiCf8lN1aZhzsJdc0WS1n19/tdL+6cSXSKS5TOiVr3sQakbdscuLECWWrNfuNPPF4nZ+fr5z8loVfS5fUXBneZru0SMqVuijCrseXYqnevCfF9wiogKdfCbTwaUYKlVST2tJeoHd1XlE88FIxFT6Ijj3FeVPAFPUXXnhBXnrpJSXEIy1ot27dZM6cOSqCnkQfznxvpI194YWh0qxZnjz99E9Sx1EKGYgysUgre0wtIvsq1/6mjnv48OEq240ZGT///LM6blpamrRo0UJapp2Q3mcflJbNciUtpUBCuEyN4VQ2VvsZ1uEs7nqKs6h5D0V5YCtAIFeUi8lau/lSYBf5+otjREps1cfXnRWCDST214UZhYSJU1BHZsuWLcqvsg+sGjhwoHgLhfkoGrWHMUcnsn29Uply6c9SK87zmzQuOKSywdKrVy+naXL8IqibdQz9sQyI4uUWB8K8N5/lyTZ/ifUgiKK4LTNWrGUeFvPJCGDbOPrsQH6eR0CY55z4cIYRd8QIJSUl8sEHH8iaI13lkdYLpW2dHM9FzmkWqXObTRUtwlKDmQYEbHvBVS8K+zIV2WyhUCfkB+UeGaHCp+G2NJqySFwUYXW0PhLAd8pE39zk6zIS2yrEeeWVV2TkyJEyadIklWt++PDhKt1I7dq15aOPTJwVTMI6jeyff/4pr7/+uowevVsuumiz85osTm2nRWJSbSoVHZZTTz3VwXsjrP5HVSqbksgV4r0JOPBClEdEvK0xUtkEqFaeli4nLUTb9ohv4jUCFGoExYWKGO9uG8V6U0B9kJtvvlkVerfHm/Q1eijMR8mo/W+//aaiOSZMmCDn1XvNs2JtenF0tkXkEicOhSNR3mwhPQgglU1Mec0/n18JphjtL/aK2JJjxGqkAE2QfmuHn+vhDItA5usXFn8NaxhxR4wM3iAiEzPSnu37gyTFhUDOVb1Abx+x7UigD1JhzKDm8g62OJ9pcrS8Df07PzWos2sjQgV6WwOrWHI8aMuUCLw+I4DOnTvLjh07VE5viLF4vmHDBhW9jChmEt1pZPH8888/lwULFsgdd3wjPXse9v5eDeE91ck9I5xEeaOFVI+IlMchlU1J5AnyZgmvBkV5UG6B710W2N9Vvy5Ub4deidcYeCsPj+vCrPeRajz88MNy//33qwUD8WZCYT6Chfn4+HjVMfjkk0/UlLd77723Ih/iMj8IohDs/Rm9nRG8iOhya5zElR3z7TN8RX+MEBOIPQGdA5fpVzKCFMUerM/1dTr8oXKRPCc7hNC5EvcRd1OmTFERd5gCX6tWLTUiT6I7d+HGjRvVNTJ48GC57LLLxPrGI74dfJpF5Dabd9HyjkhxIPDZR9BrTqM7cd4PQqENdTiCnTLEGaEginoQrYc0dFaLgUF1fxBhIjIGOSx1DFyXKUFqV0PtjfuIffXnyKdNmzYua4DogVBPojMoDunhpk2bpuq6TJ06VRo2fL/mzqYUugwjUd4RmT5EzPtCgArYBgQ3ojywWRAx78LmeJtn3uhASzDy2HubAtFNSpiKVHRBjPqguB50UNcDfrvZojygMB/Bzn1RUZHqECD/0XPPPScpKSnmRLW7ipr3B8GKnq6WyiYEohRDPILbCBWdg5LQj5IPg7a3ZeO/7MUgR3MT2yUE2iESIu60KKvt27cz4i7K0dLQzZ8/X2bPnq2mS55++ukVoroZ2Ivz7kR5Z8KdfrujtCRmRMi7Ewi1bU72KbfFidUSoOnbnhJm0eDltsr6MMEiosT5ylQ2ofB9omy2gq88/vjjVc9RX+t///uf3HfffVVR0kg79vzzz8v48eODeJYkmPZ7//79apZbhw4d5K677lJBcqaJ8Pqo+XAT5Y2KuNVyzNv53ma0oyOxNhRyhPtJlNdmWFuMzFb34+8ZVuK8i9Q3FcJ8ECLmKciHDGPHjpV58+bJ5MmTTT82hfkIBUViZs2apXLJXnPNNRIXF1chypslfnobIR+GIJWNtTwEnfsQEomN4nDU3ptr0qzo9YwwnMFQ9bujo+VF5yBY6aXC6DoNlAP3wAMPqClxWqE4RMqfcsopwT41EkQwkP7777/L8uXL1SwKFGU1Hb3I70x4MyKI2wumzgT7VBdR835McRPSwrxZAqiz9tPa3BkeOqs2CPPBipiPMHHeZrOKxRImhdsp0FcDUXIab775pnz44YcVA6c6unTpInfccYcKjCLRBYqpT58+Xc1wwwxINbvCTAHdl2O5ElJDKdXIEZ2YLEGwOeESTZ9p4uzBND+L8qHQTr4K9Nq1GW8RyS0PXM52CvIhB2w77Dxqf6mgZzt++uknr49NYT4CWbRokSxbtkx1CoYOHVqx0kxRPspAKhuvi78Gos3DSKBXqWz0wnwwr8mMMGz/DLsCNEFNouwhvP9UA1HRGDy99tprq4R5Et1gQP2NN96Q1q1byzPPPCMJCQkVG8yKlrfHG1He0b724p2j1+6EYk8weH7ltnixWkJotpvZaW+8HdRIDcO0QJ7gqrBsCADbHfQctVE6KGImf/31l0PbXb9+fTXzjURXCpv//Oc/kpeXp/IOYzZkSES1e5JqJARFeksoCOShGE2f6U3B0hAQ5YMRNe9JmxnsG1kMpr3xGorxIc11112n6shAZ01MTDT12BTmI4hDhw7J3LlzZc2aNXL99ddLz549T26kKBY5qWzCWKBHBIQq/hrs69Efn+/vHPR252yzhEABGuITY8aMkVtvvVUeffTRasW69cVhSeRTXFwsS5YskZkzZ8qFF14o/fr1868obzQa3pPjuUpv425fM8/FLmLeEuoR896KokZEefvBEB8ixsptMcHLMe+JQOxsRkYoicq2GLFYwmhQPZTbMoj0799fRca///77quCrFjF99913y2mnnRbs0yMBAIV/t27dqkT50tJSueSSS0JDlPdVPBWTBdSjBoVSfwuSvkZNOypuH2gyPW9r03Kih1OkvFltm+poYN1Y2huP8eX6D5VBoyhg6dKlqnYn+gBmQ2E+AtIhrF27Vr777jtVKO6ss86SZ599Vo4cOXJS6AnVtDNGC7+GayqbUMiZ3jy0jqcKyO03SSgJsWKsfh0ccXQtWazepbIhHnH06FGZM2eO/Pbbb/Lvf/+72rYtW7aoPOCxsbEqSspTMBUeVM1s0oEp0IjCIpEdIf/999+raY8QdpCvENHyBQUFxkV5TwVbfwnhrsR57bk+pY27c01x4BileHauFRHzESDMu0oZZAQTpnCHRCobdwS4uLC3VEQvhvG9PYTaMph89NFHMnHiRHXPbtCggfK50F/AgDrSmZDIHkyHOAPfGwVeETk5evRoVScoqJgpnJol0EeimBusKHqzPyuEZkeELHZtbqlvE8kx0K8yGk3v62AUBfmA07FjR0lKSvLLsSnMhymo+P7zh6fI92vaCVLYjei1TW6/dpck1PpIZMNNcqjoZrEefkdkny2wgnpF0EhEUW71ImI+2BHhZgnFGeYerzwvNvhicqDSCzX37/mGXSqbMOTpp5+Wr776ShlgffX1Y8eOyZ133inr1q1T09i8jWzHwCqJzsH0Bf/6h2zITZcz0/fI4x23ScvEPJGFT0r27z3EGpsscuxXc6LX9evdvc+T9Vk+ivPO9rfH3vlJ8TTHfBjMdjOKN6lrzMirWjmo7lBMdheR74vTmGpQGM4KM0E5nHLME6dgMBWpQ9evX6+ipjGYDocduWdJJA+mnys//9xGmjfPk4su2ib9+++T2Nx3RHJFpPAWkcx/Bf7E/Cl++yLQOzsvA9HyLiOTo1GkD7YofzR8UyIFK7rer7NBKMgHDQzI33bbbapGHNLSmjnbncJ8mIEIygULFqgozS6N0uWGc/6U7i0Pi9WB/TJ1quxeD/YLJ3G+ucGIeU9yzIeKKO+rUJxhvvBss0KYD2LUXSB/G1/FeTfnqlLZ2BfSJaaCoi4o7gan+5133qkWLYV0IxDu33rrLbY6McS2bdtU2oP8favlvKaH5B+dfpfE2Oq2pcxaS2LKC82PXne3v5F1zrZn+SjOi0FB3sPztEVSxHyQMZTKRl9kzd9OY1b4pmOxCRw5DqpHCt26dVMLiVwwi+3TTz+Vn3+eJ2f2jZNHb18orZpDiZcKQd7slCH+FOUzvRy89TQ/uCeivAOC0pYS4FQ3mQbbPzOKZycEYjDJg/1dDhiZWWjW1fFJULnvvvucFnn1dbY7hfkwYtOmTcqpj4+Pl0dGfyFtG2EuTQgKxOEmzrvBZoGYXBK+orwn0e4ZfhSeM0Rs7WPEUm6iMO/JOQTjtzFyft6eF1LZhGPHNYz429/+ph4hzOtJTU2VW265JUhnRcINzLBADlqkQ5rQaJEM77fd4WA6KE+uLXHlOSK+6skpXu5nhmDpKse8uBFvjUTIGy3+KnFiPVYsUujk2MS34q+ZYew8BlGgt9ksjJgnJExyyC9evFhmzJihBl9eefQ7Salf6GRfi0gga0d4IsJmmiAOGxUwvRGHI63YpSeD0+7a3x/21B/R7MEcFDhq0jkZ2F+lsnGHJ/8pI8ciIYU/Z7tTmA8DcnJy5OOPP5bVq1fLFVdcIYNrD3Xq1FdxPMi54N2J82GSX95tNfNwEOT9ea5GhfGMk8VfLdEW5e2nawMR80FPC0QIcdl5Q0QFRPm+ffvKK91mSb34Isc7VwqEZZZ4qR2IYuNmCpLuhHhHUfNiwHHxMKd8NTJFykuRyqbEP86Sr3gbtRgkauSYD4SzGIjfy5OZJaZGzNN2ExLK7N69WwXDYWAd9V+6N+nhcv+y8loSY3Fi30MpQt7VPp4K9J7igd2oFpkcjuKkJ9HzzmydSd+7WltGSoqZYA0EnHDx2Wkm9l/C8ZqPImbMmCEzZ86UPXv2qEwmDRs2VKltsNSrV8/r49ZMjENCBkyFmD9/vspljPzG//znP+WcOjpRPkO3hKJIvNckAd5fBT4NHtdtVHKotHewyDC+vdyKQrpBSGUTkb+RRYTCfEQY9xEjRkjXrl1l7969UlRUJPfee6/k5urmR5OwTFvz0EMPqeKumPZ46623uhXlQbmlllhtjiPyarxHv3iCp/nkvTmmq9fuztl+u5HnDiiXeLHaTz0IBWcn08k6bQlBym2xJ1PZhOg5miLSa4s/YY55QkI6bc0HH3wgjz/+uEpZ+MILL7gV5UF5eW2xWj0U5o96sRjFG3sSjBzpDqLl4XVHzIxgo+Ksvg8Qan2BUEqR4+n/wGRcprKxPy9vf0czf3tf7iHEIVOnTpVHH31UFf3etWuXlJSUSGxsrHqOQVxfYMR8CEfaPXbXMDU17pHRf0rbRh+KrLvRucgYysKjJs6HUnobPxfmjDocpclx0DY2q4GIef37jEbjm5WeJ4yomH3AqLsaNBKRuh40ZKIE1bi/++67VUVk7I078tuT8OOHH36QWe9Pkwnp62VY+x0S8/0LIt872dlOXC6T2hLjTJh3J2b7gj+ihV3lm9d/prOinO7EfQPFX+OsDnICBTNy3pPoxVCJps8UsZUipV/ZybRAkU6W//4fiJi3MGK+JskiEhMetlsDA+hbtmyREycQRmleATgSHLKzs+X++6+Wrl2PyEsvrZEU24fVcsi7oswWLzFGhPlACGO+CHv+yJPtgShfVWxc7yuaUbckNYxyz/uLcI+WDwdR2VEtBk/+U5kR3DYRwnvvvacymZx55pny2GOPqXUxMTEyZcoUGTBggE/HpjAfomyZ01Cy80+TadfNr562xoDIaDOzmrmZKWdCJX0NRXn/kWEkLVAAoyAiVJSvSmUTKRElUYo/jTsJXk7arz98USY3Xy296h5SE1sc4kToK7PUkhh9KptApNdI8WNKG1divKvP91GUPxkx7yQtUDDEeW8drhAYSFA55t0Vf41kTEx5Y2PEfMTMdrv55pulsLDQ9AJwJDgsXny6dOmSInfcsdzj+3VZmZuI+VAX5P1ld7w4J4ez3SKBQBRGDwb+vrZDSHR26nfrBXlnhZLdCfSZ4d020UJWVpaqNedoYBc+oC8wlU2IsnhTKxnYebf7XPJORpqtEsUOlL9FeUfpg4gh/CYkR+HvoQbfQj1ff4abJcrxp3EnwWHngylyojxOeiQe8ipljEpl06DQuxQ13mD2Zxgt1uooXY2j1Dyu0toYiJh36dwH0kH29bOC4czrPpP9SicpbxwtbgnxHPPuvou/U/2ECZjldv/996vUJ5jlrF8oyocjFlm8uJWcffYur95dbqtVM2I+kOkj/GEjfE2pkuldwVdVuN3ZoHo4kxlE3ztco+UD8d9x9x/V/Y9thRbn6WH0xzjqwXXg6f+MaWmCyvjx41XKWfjw2kD8pk2bVMrSUaNG+XRsCvMhSOkSqyzb0lzO6rzHq/djpNnpVHhPCaX0M6EiyhP//07a4s1vEy3Cryr+GoLibbS0f4gbdxIEpllkUWErObPRHrE6i4hxk46mrE5tiTHikHqbX97X93qLs89zdR7u8tYbibqzuGnLQBUxDaXj2B/TwHHLkVpAQnwgOEyEe1uuVSyWELTdjsT4rADn3w8jMjMzZdKkSaoGGAl3LLJnT7JkZdWRnj0PeXWvVcVfNWE+kLmcA5GP3NPPyPRelAflljix6mcOhkJKN1/x92/k7nrz9noMVkS2mf8ho3UbDNR1cOt3eyLOeyvIk6Dy2muvqbSzjRo1UgPzffv2lW7dukmzZs1k2rRpPh2bqWxCjWUWWbWrqTRMzpfmqce8OkSZpXZNg+arOB8qaWiCIMhXpQWi0OgzpqVYckYU/UYVqWxCIOouitrcH8Z94sSJyrgjyg7GHTlrR44c6bNxJwFmmkXKbBb59UhLeaj7Iq/zw1ek+yr3PSI9VEnxIo+3l9+vIuquJLKccTPT2mS6OK7dedtssWKxBEGYD3Q6hQCIP0hlI/nlHqe58isU3D1m7NixMm/ePJ+LvZHQANHyZ565R2JjvRs0Ky+vJdZjhRLQCeuBjsA2IxWHG1E+olPZRKLf7alQ7CzNiy/H9PdxfD2HtAj4HqSKhIQEmTt3rmzfvl1Wr16t1nXp0kW6du0qvkJhPphkVN4sm9uqvV68saUM7OJdtHxVjlqzK3SFqzjvsygfQZXhQwC2pbmtKcEq/hplYjyi151FsD/++OMhadyJH5mmc3Rus1W9XpfTUJLjiqRVosFqcQ5EOKf3yHAR3z0R+Ix8Jx++t9tUNv7O4e4v0cSM8810cdxMxwVLreGaY97TSE+/i/RuBtWdFUP2FYrvphdvh73+4IMPJCWl5g/2008/mfuBxCQ0+63ZWouUl4ssWdJS7rprmef37UrRrKygtsRZ8wLzKwU7T7m3n29AlA+ZQfUI+b1slhix2MLEdh8NffHaZrEETpyPpAGKCGLPnj3SokULadeunVo0iouLZf/+/dK6dWuvj01hPpQEehHJL4yTVTubyHVDVnl9uHKpJVabgcrw4SLOZwQvdY0qeBbqebxJVFLR2WIh3XAFEfIPPPCAylNrb9xJeIr0iw+3koENd0uNfru3Ue7hIsb7S/jz8fsbSmXjLwKRWiCAgwkVqWzCxLnX46/8yHo8/B0qZsW4sd1mifO+/ifD7R4UQK677jpJS0uT4cOHS2JiYrBPh3jMSUP9/+xdB5gb1dU9M5K2e73e4rrrhrsJzabYgOk99N4DKSQQIEBoCWBCMS1AAoHQO/wphBZKgFBsU0LHYMDgir02Lqtdd++uVjP/d99otLPakTS9SO9836ykmdGUN7O6884979xvvmlALJbEqFGt5v5nNISXJJf09pgPK/HuM5iVTSYxX+RtYhWyEIVolZh3i9ANm62OBob63XpEvFlyPgRJimLFiBEjsHLlSjQ0NPSYP2fOHOy7775s5LtVcGI+SFgGfLBgCMYNaUFNZYdlgpop5p20svGTnPeRlCdIIvncBShr3+zcuXGEG5S198TKpsjU8V5BFEU89dRTOOOMM1BdXe334XDYRHsygo/ig3HisC+tF0NFqrBU2CyLA6rENaW601NKx00SsgVMHMi5POazqOx9RVAK+9bpW9kYsgWyS84H9P+yUPDee+/hrbfewk477eT3oXAYhr7adfbsodh996UQLJLyhKRcBlEwScwH7XfTTRhUy6eT6m7xGF7Di2ucg4iVBBcFhtn262Sh2UIimY2S8y1F3k4BxV577cVqwcmyzKzsSkpKeiz/5ptvGGlvB5yYDwpSZPfsb4Zhr60X29qUK1Y2egVhlwWQEGx0Pmsv+E3MN2f5zAn6wkfmtW/02MqGk/Ku4ogjjmCFXq+66ipEIpFey6dNm+buAXA4ho/iQzCyqg31ZVu6Zxol1Wo9sPvK4R8eSOLPAaWuLEesqbzjISFVrKrm4w63pd/tYOQ4nLZYsnNMXSI1KLI+ptcZOI9aD/4vuVo+J8aMGYM+ffo41NgcfqGzU8T//teIGy/9ry0SLCmXImKUmA/Kb2YASXndpHo8wKPM/EZLfsW8JSubIBC9QTgGq7UXspHw6jnlWlaIbWQCP/zwA/74xz/is88+Q2lpKfbbbz/8+te/7kWE/+IXv2Dr/vvf/07Pe+WVV3DPPfdg9erVGDVqFC6//PK0PSwR6lS/7YUXXsCWLVuw884748orr0S/fv0MHdeee+7JXmfOnIkpU6agqqoqvYwI+6OOOgrHHXecrXPnxHwQkCK4W9aXY+Gqfrj08HdsEWQSytyxsvECASDjtZAFytongtkWXD3vXPsGNcnRmD0Ro1jZuEjMc1Leddx9993sdZ999um1jIJ8MslttMKC2auHYvf+S10rZGoLbnRG3SLlHW4fK3agoUImYeES8UBWNqJbxV/zkc25CJW4C/eqEfLbBsiv33ACrtVHNTxX3OcEFW4/99xzmR0djYDLBE+shwOffjoIQ4ZswMD+m2wRYZJcClEwIIrjpLw5K5swt1c8YFY2ThQiNUIIB63gqZHrUGdwO+05tmcm+eOHnU8A7sd86OrqYgXVyd71wQcfREtLC4uxZBd31llnpdd7+eWXGXE/cODA9LwFCxbgD3/4Ay6++GJMmjQJTz75JC666CI899xzjOB/+umn8eKLL+L6669HTU0NqxND0y233GLo2KZPn85eH3nkEWZFW1fn/AM3J+YDhHe+HYrJW61AWUnStmK+VDL4gBEE2CUAXSSok2RlIyWC2yacnC98kj7b8QgG1LXZ7qNGk0kBDtd85jnCj7WdpfhmXQPOH/eBeUJPs65EFhdm7KnqLBC1QX4wd5gIJS/vooPe9a1zqN5ONisbp4hfo9YtcY+IZZf82ZX7MhlOZafa9qN8Po4A4JJLLsla5JUn1sODWbOGYfcdvrdNipGVTSRfTZMgx98AgRTzUdkFHsPL31afLWx6JILDViuvxefC74XSDkE8pzz44osvWHHVRx99FOXl5cwa5oQTTsAbb7yRJuY3btyIO+64AwcddBAj51W8+uqr2GGHHXD00Uezz0TKk4L+k08+wdSpU9l7UrSTUp5AifVzzjkHmzdvRkVFheFjXLzYnrNJLnBiPiBqeRrVSjY2p06b03udZvPEvIgQKOadJP1cIqgVxXxnsNuFk/Put3GhnaPeMr3/H07Oc3DkxXtrmrBdv5WojCZyE4u1uT+z0W5GhsLXWVwWZAsbF9TJrtkC+QWrXu4OkBGKx7yDIoV8SvDM+yEeQrV3tmMQRAh9PLw3Q9g5DwN4Yj382LChBF9+MQC/PO5j2yQZFX/Nqpjn/4OmICOGCMUb3m6O1MozbWXjpz1Krn2H7X7IZVtj5HtWELY2ysDgwYOZjQ2R8ipI7R6NdlPW9957L7bffnvsuOOOPYh5IsxJaa+irKwMjY2NWLJkCSPmaflpp52WXj569Gg2Mn3ZsmUYO3YsggDPiPnXXnuNqQpIYVBbW8sa6aWXXsKqVavY5wMPPBDjxo1j61Lm4oknnsD69etZloQatbW1FTfffDMOO+ww1rha0AUaOXIk8yAKFTQe7d+39MX6LaX40dDVtglECVT81UWP+aASoy4Q1Gw4ndTpHUlZDISxYQ91j/ZZaLB6fl4meII2KsEnnHnmmTmXP/TQQ/AbPHbnx+zVw3Bk0zfmSHlk8ajNl1T3W0kbBMKzWBH3r7NGHvOiE4p5o/eP1cKnrTbX8cBqiikYN0j0Jvv+nVJ2hryTzmEPPH5ngv7pukdSvf/fRkwcsxrVfewLsKiosyjojHjj/4OmkRRKILSFuPhrQNTy3R7zDivm3SLu9bYbtv8fvdhtxNrHC3V8CNpy4MCBPexpKAFOSneV4yW7GvKU//vf/44PP/ywx3c3bdrUw/edUF1dzebrLadl6vygoLcpnguIx+OYPXt2+vO6devw8MMPMyKefIR22WUXRsQT+U549913MWDAAOa7Sx5CmQ8ZNISh0EBq+aljliEiyrYJNlb8Ncge880ub7vZYWJe6zHvJqHYXNhks5x5jfSOOdeyoKJRM4UdRpX0xd5ODoEK0WROiUQC//jHP1ic9Bs8dmfBud1xevnmPljZXoXta1eaa1wdAjAJ8qh1KXYH+YFc2xatzpH/RWllA/c85gW7HvNuJ3VyqfC1U75tmLkHM7dtaF/U9ZLcbyM7//OWzqv4Euu5Jr/B43d+zP5wGHbfSVMbxmnSLMhxN+CKedfquxXCNTFxP0qCCzZ0YSbl41kmv65ZiwMK+Vye916dY99UQsLIROsaxEMPPcSE2mpR1Ztuugk/+clPGE+ciWx12dTRbZmj3MhyTm9+PpBlDnnhh5aYp+q3W2+9dfrzvHnzWCV7It7JOJ+IeVLNz58/ny3v6OhAQ0MDa/T29vZejZhJ1ocWU5TOPd0P78wbit3HazzubBCTkhdWNhq1vyl4Rbg6RPBKokfFX8NERFuEvDwCYX2ycNpEj2T2m3R2os3caHe/2yWgoAR15vT444/jgQceQGen/2ohHrvzk/NU9HVK/TLERPv1AiQjinkrCLKFjXbb2u1zAjBQUKxsLHZErF5Ls+S4nW3k2q4dglpn/bzFX1s9GiHBifeCTqzz+J0Nyv/eqnmVWLq8LyZvuyJ3Q3JS3nMkN/lgIxsWtJivD5Mu/lofULW8W6S8GQI+bnDKVfw1H1p0JrvHk+u7IcfMmTNZv/iGG25gtjRUuJUSzlR4XQ9UhJ3icCYikUiaQ9YuV99rbXKMgNxb+vfvzxIEdExO9tddt7L56quvmF0NqQdUHyCqlLvddtv1akx6oCGQKT/Z02zZsgXHH398j/UOOOAAPP/889hpp50wfPhwhB5TZHz19wEoK+nCVgPaHCHFSHXnqmI+6KR8tv02OqCYV7cTRNI4yF7zzYAciUKQQpC1N4JGA8v8uEfcujf9PKcixJQpU/DLX/7S12PgsTs/WG2Y1cPw67E9h1Pait3ZPGr9LgzpNimfb5kHFiMceTr3Voh5Jwqw1rpgWeMXWgG5RIBIivnOHPe1et5xE4Wes23HaQSxXX0AEQZ6+Nvf/oYnn3wSfoLH73yQ8c5HE7DTdstRWuJwv6QACDG/IVPf28maJl7CzetvgRRXrGy6nCHlzcCIfYu6Xhj/tzKPpc6B53S755fr+2sQKsyfPx9XXnklrrjiCkyYMIHNe+SRR7BixQrsvffeaYU8cce77747KwZLBVxJXa/Fhg0bmBickLmclhEy7W/yoaWlhanmyWKHCtKSk8shhxyCY489llmza/3xA0XMU2NRJuHQQw/tkY2g9+pnGj7w+eefo62tLa2qJ7X8ZZddxoYJUIZEizFjxrAL9Nxzz+G8885jhH7YMW95Pbav/wHCcme2pyjmXco0h42Ut0lck2I+ktziLgEahLZxAxnnJYuR4BPzRu4Po/eQXwkcu/s1knRQkW8/QU0UBQRUeT4TVGPlzjvv1B2m5xV47DaG1pNbsOH9n2FsdZ6eRa1xxbyh2G3moT9enEScjmiGw1Z7RiC0ZYnfuchlJ+Cm2t4PCKIyVNZM4inucTKEI7SJdR6/jWHewnrsNWWxs0RokIjDsCIOJKtdHq3ulsAhYKQ8QSYrm6okoENl+AqjRV6dalOzxHSDyfUzk+hePacXEBmvglTxF1xwAU488UQcdNBB6fnUN9ZayFDtUvKaJzE3ccdUb/TTTz9NLyfXlebm5nRBWFpOri3k2KL61ZeUlLBapmZARP8xxxzDJsIHH3yAiy++mH0mUp6O+Wc/+xkTkweKmKcGo4aaOHFi2j9eCzLv/9///scyHpRlqKmp0SXvM0FE/6233or33nsPu+22m6ljokQADV1QfYWCgC31l6Jy/U2ObjPnMFkvCfmgwSQ5T4r5mOzikNTmAlTNZzknSYxAlLqKqyCpi0rzQPgn+32vhRw06iszFlF8Gjp0aFY1nhcIYuymfQUudm/ZgspIAqJDh2RIMe81zJB0bqrbTRYDJesVSwpvDv32bCf7lTy+6Jm1AryEGdubfMi8z6ycS457VbGykbLvo9bCPV9oyYuQJ9a1xeu8RhDjt1kPXy/QntwDlRXfObMxIscop17izOaKHUwxz61sHLGOkWroWSjpz7HoqebzbcMuKe8UGa2zHZn6H5s1y8yS93rIPEcnEhEhJeQJZAtDJDf1gyk+kTpdRX19PWKxWPoz2aCTTU1TUxP7TEQ42cE+9dRTmDp1Knul+Ka6tJCa/Z577sG2227LxG8Uq/faa69eInAjILX9Sy+9xFxcSDlPBP/Pf/5zHHXUUfj666/x05/+FKeeeiqz4QkEMU8NScH73HPPzboONQZZ0nz33XdMAT9o0CAMHjw477apkWkYw3//+1/WuGZAmRPKxOiB1Pd0gY1OtL4TJEHH0j+jJhrADuSyAlWEmyB7JSGEPnd+kvM5rrUskpVNAO9zsyp4q9s383+QbT/NBpNvQbVb4uiBxYsXs86x6n+nZuIrKyvxww8/+NJaQY3dNHxRj2QgUBw2G7udGG1H9XBKIxZ+07KQbZJchhKhzdg2cllcZK7jFoz4evtkQSNR8TghpEPhgwT1Whr5d1GJZC8I31YPEkx2kCNJRUn1rEkO9btacl4LI+3LCXdPE+uZnrZ+JtbD2Pe2Erud6Hu3b/oApaVd9knIOAkq6AeSD9NyCro2sk7DL1tAL/3c6zVWNmE4l3iwSWci5gVZE7v1CHrtfaU9nzoPCHntMbmxbQ/w5Zdf4osvvkgT6VoQqT558uSs3x09ejSmT5/O1iPSnT7/8Y9/TCebiehfuXIlW4fU9ETeX3LJJaaPcf/998esWbOYBc4RRxyBf/7znyw2qn15Wk6JcSLpA0PMz549m2U9yPOHoD643H777ezg6cGATogmylqQl9Ann3xi6OGAQH5CtL7ZQrD0wNSvX79e8+n4KKNPJIneROeSOU9V3xO0Dwn0Xo/kz5zHPn8+BJ3JrVFWus5/Ja0bqviQk4OSGIMohbBz7wc5n+daK8R8AK1swqTId/tYvbxnuNqeDaujhwRSt2nx0UcfYd999/WlgFxQYzcpIvRiNyFX7CbyXC92axV82vhtOHZHItj84L4oF0az7rhgQdWtp5gXBZOJYDcfuJ0oqGmzTaxCksmjNmRJ9aDAbsFUo8ut3BdhU4br7buPCHTJ3dYCtQ5463My3pfEeiYosU4qPr8Qxr53tthNljxEnmTOVwvtamM3fc4Wp/XnDUKiaxpKYzb7JHHNaDc3CrcXG1LtKbFi4x70F50m5+sCQoLW96wPY7lwuxMwkeDK+dkqIe/k9YiI+j6JmQS93n2Vr/Cs3vbyoSHHukG4Dy1g0qRJ+Pjjjw2tS6O4aNLi4IMPZpMe6Pf//PPPZ5MdULx89tlnsd9++2UdYbbjjjvi/vvvN71t14h5OlgK4CrIbP++++7DGWecge+//55lFyhzoYKCq3Z4Qj5QQ1BV3IceeoiZ+RPBYQdaxZ1dqJ1+PTIgk+SXEgeg7+BRiFZ24LvyDfrHhi5WzDUit0NER/q98rmTfRZBn5VlpBSjSUAiP0VfKBY1LhHXrmbtm4Nb9NbSfvKArGwsKeadTjJo7WVCTA7LVUJuZXxQVfMhbnOnQJ1jVWl35JFHsiFwWnzzzTcYMWKEL8cWtthNUFV0Zo5DD9kS9Oo8Lckv/edCrG86EGMHDML8/ppz6DEiUlbisqiJ3WiHmGhHROhU3gs0r4NZ2HRINagUvocsCxAEn9R3bhWLrPVgO5pjl2oCrpgPwKiCXvDLfiabBY5dS5mAktWyIPZU3eUi4Ws9VvjnQlDu04Bg2LBhLHlOynOyNcvEtGnTPD+msMVv2n8u+xwzyBW7ieTvjt/nIpk8GCO32hpr24fhu2b9OCG2k01dOyIixecO5T2b1M8diEhK/1tigV+CJEcgCgEUIPmJXORhFtCIYM9MC4OqnLeqls+wjpGECGJSwCwSzZDyVgh5l0hpmUbrbJKyj1hdY8ObXv2+GZgh5e2OvihyzJo1K/3+zDPPZK80Ok0PFPtphNkJJ5wQHGJezcirUIeNq152NBSOiAcK6kuWLMHChQvTZvxGQYVgqWDs3LlzESSoJEHeB433BeZH98S3u2P0kEUYE+td/ZW65jJiSAqlLCMvCWUZ78vQKfTtMX9DZCssLD+dZUm7tyP0sL8gsllc14FIfQdEIgukTogSkfv0mnrYSM9XPyvvDXuVBZEU1MIAMZuTmA8q8ZmJgBDQpJgXg6SYD0Cb2EHayiYs92EBtLlT2HPPPdnrzJkzWaE4baykjioNfzvuuON8ObZijt2GE/TnKfF0ebQRP6wdjjHiO7rkFXlJs7hcSrG5DBJK2WcWr1GKDrkfs69Jykrsbktuh3Z5IMSurizdUgEiiCwgUj9FDKRfOxXigL1mzqe4TVMCWR0AAkpiGiIrdY5dWlsCsSqgxHyrBZK+1UXC1MC1d4QmMav2dlO57yuoLSV7Cnmn6jwEpYZECEFetmeddRZTdevFEiKBvUYxx2/jCfpn2d+PPz4Cpx76H/Tr2/v6UXJcWhNjsTkdo+UyVqCdXhNyJaR1dSxu07IOuQ4bpFFYmDiDqb3VZ3TFtqo7yc7EdKl4rMTojJidXq7OT8V6dPiXrOcIDzL93FMcUiCsbNwg0OM+FG7XJtVzJXWMJHzykfJGLCtzbZPACXlH++35YDf2u1r8NRvogYBIhzfeeIOZ5tOwv+OPP54NdTOLH//4x0ytEHgQCZ8FHYkoYhH9i0jfIuU7kcMxbMxrYUeLO8Q6jN7yQM51pOYSSCKRBCVIimWQxBJIRCCIpWx+R7QCkliGZGo+LWfrCTFI5FeW5UAEOckI/EhbB8ThnRCTHRClTkSSKYKfvXbPF5OdCvmfWk7FQR0vXGuDtGYe89msbMJChrpJzptog0B7zHMUlk1OwEH+doRHHnkEl112GerqgijX6Y2ijN0pEl4P7VIUsRzqOPKRjspbEE17VqQCcJYnL0kuwZDYf1Airs26TUmOQkIJknIJU+kx4iBFDtD8hFwFSSbCoISRCMq6lBSgz7FeRAFUTkKkNAKRAJ0pEr9TieXsc4okUKcojdRTlrMEPr2iM7dvtpNoNegxH0Yrm0zy002C2dS2bRLzXhLlfnrcGyCtFWsBydmRJbmSPU4j0EkP73DFFVfg0ksvZZOV4nFeoyjjd5bfLXKj6OiMIhbN0vcWZEREJf4BG/TJL00c3ygNRbm0Eo3R7BY/jOynWJ2KzUxgl47dKcIf1Upsp2QAW07vS1hiPxfUhL0aoxVyX4nlNDpPfa8Q/anPauKexe6u7El7H+ChXt46AeqmnU2LM6Q8QRYo3gRIFGfG1sULv/QW423KFPOZVjaZ945WNZ/NYz6fdY1T/vOclHcMXhUw94yYpweAG2+8Mf2ZCseYKR6T+X0VpAK45pprEGaMHhzHO0uHYodBK21vSwZV385NfgrLwIIxkeT2Bv73BpH20g9E4pdAiigkfzJCSQD6rLwmYlW685VXhTjQqg20IOKeiHJG6qcJfZXc7z2/+72yLEKvyQwCIQdpzTzmw1b81SuYTEz0sLIJiIrfFAJ2vD3+N7Kp5p1U02stgDhc86gNGnjszo5R5a14fOW2WNdZir4l9v1lqXNOCrpcEIUuFuOjwmZHCTb6PWEkPhEBjDxIkQKC+rmEkQZsnWhqPptSRINAdkxCr9+mtGqQEf+JbtKf2fB1puZ19p5oVF86UdC9DiMQDJwaHaewKcFGJQYKRklNuz7+uYhjS8SqHHwitzUA2zegKlcKyGVpT71ir7mWcZLcN1Ax09NPPz3QpHxxx+/skYJI6DEj4njno6E4cM+FtvdEMZKs6XIeDZH9LMndbjvPqQX9lLCR9WrsZkl75b1C6meS/qnYnk7wl6ZH2OvFbgI9c/Qk+XsmAtT3ETYqT3ntnq+N38biiKcCPbcIei+RhZRXrWzEICnmrZLyfiQ7Mr4jVwgQNibzK+T1LG2MHr8TiQdOyIcWvijmOXriqJ2+wXkPHoRFbTUY2S+7Us4IKNCS97xfEJelSAM4QBro2foIkRSJT6R/LPU+llL6xxi5n4yUIRHroywjpX+kBLK6bmod8vjshSrloURr99NWsTUj9Zmfv5SAINP7TggtCYj9iewnoj/1mpoElhDImCdnkAl+kJteec7nsrJpSXYfh7YN8h1TGIl8M+dnAZ4+uDZmeW/0Pg7btfMQpF6jYq9Ue0QFDY0n79q77rrL12PjyI2hZeuxY/VyPL10An466jPbqlfqPLtaQK419+8J87pn+9coBPV+ZirME4JyiUL8pwkBVgunREP+K1MCFUqdHBrNx+ar6ymvJD7Q3b4mdpM6rFOoZgREorNvihggwiCByEYiBxIQ+yjzyIe+m2joSr+ndbu/59BvrdsqZiMq+0Ikct0oOGu3nXLsk1JUhhWMxXYtQwSqD0PKc7uF5Dj8wSlHfYFb7pmKaTt/j4pye8Qlqd4Vdb33oCQDi2mg0d2bHCX9u4n/1Eg9isFydxKARuF1k/yVoLH96cRAOkEQgxSjOE9SwN4HpxXjCTUS62evKxmPJX2OU0blpVwDuiclca+6CaiflWR+V/dytk7SWnPY9Z33sghsDlKeQNfO1+KvVoqfml3uFTnNrGzk7m3XmyTn9bDGgg1NtmvOCXnX8PTTT+OYY45h7x977LGc65522mmW98OJea/QlL3oalVZAkeMm4fH52yLq/aYaWs4GXnO09ByX4q9ukw2C6pVTnILQJNbaEypB4UYugb9AoPa/gOZWfikyP01sR5Ef6KEbH9iqSnK5hMJnU4E0CgAsadnsdykr5hi59dF5D4R+l0K6Z9MfU4qowUE9trVPY8tT40kSHbPV7aR7P6sMixOkNwWrrXcEoHQx6LnbxCI3ebgkvTp7Vo9xkYXVfRBuHYBBSnRSHW2ww474IMPPmDV6Kk2yfvvv8+KtXEEHyf0n4sLFh6IgwbPx+CKjba356iPbICIu57EfwaynbKFplDq8kQRF7ZnCsHajXNYIVhGEqxLxXHEIG8gkqECiaoUscAIhqjG8kf7XiH9M2v1ZO65J7Hf1SMhIG5MsiSAQqBQPE6RCPSeEQq0TP1Ossd6lNLw22XAciLYbxW7nsWL3aKyZpG5/ZoMn1qOUGLGjBkYP348Hn74YaYsz8Sbb77py3FxGMPYneIY998WPP/aOJx4+Fz7ivk8o93CCoX414jurASjGmOFLeWICKk1hkTfKgza/EYqSa+dFIKfJe2FStY3p/fpPnpqfZlG77PP3X7/bPuZVn7qOcrK+alEP/scTcVglfBXY3SPeN0dv5V4Tcu747vREX5ugtrClMe80+SuFdI9F1ntIxnNrGy0liZ6BL0Zcj7bebY4eM5e+/AXKK677jocfvjhrIaJakebzWOeE/MFgAP3WIBXHxyFz1YOtGVpQwHKVcWdHgrN2qI5RSI0Kln3sq62Hsuc2kfWEQGRGORIN6FPtgEK0a/Oj0KKRFlyIBGrZBYx7DtsfsZrahv0OXMItdyn5wOKlnxgxD8lCZjav/uVJQDiXRBGq6R/VyopkO298iqsSKJjcD2S0VJ0lvdJrZNkSQZhsEVFQ1jR7AxxnR5y2myTPHeCOM+1jzCOdvAIpIinLPzBBx+M6upqPPXUU8wH9qabbsL8+fP9PjyOXEg9eNfG2hkp/9SSH+G3E9431mZ2vKSNbr9IodTloQ6zjBKsQxn1YERNm2T2yTfksAsxcY1kWUyp+mOQWR2AaOp9DNI6hSRQyH41AaCoC+m9Qiqk1mfLoul16ZUU1plJAV0P3rVKXQNRJDJAS+5TeyRTBIJ2frKbXEi/V9ajz93f7UIXytCJvt3rpL+Tg7APqs+5z/8f8iYBwnoZrPQEL6TqOu655x488MADeOGFFzB48GDMmzePkeoUYxsaGvCLX/yCeaabxZlnnon6+nrsv//+qKysdOXYOdzFyUd+ictu2Bf7T1uIun5bbCnmY4KOFz2HKVD8ilDdGiRQmvSGUZRTsgGF9E/FY4q/7THINSrB3x2rk0IpEqhi9kFkE8Ne08s1cXxgDHI7Ldfva+t9ZnGX4m+U4nCK5Ne89pyXhNAn9Z4lCFIxXF2XbasLHUI1uoRKJMQ+6XWU5SYT/mYJcKsqeDOkvMeQRaEnMa9tm1zkfD64dZ4Ba78w4/PPP/fEhpYr5v1U0GvU6yVRCSf+6Es88cU22HbAKkREa+okKgaXUzHvJAqNkNc7vz7enWd6REBXEujySHnRmD1BwB4wRCUBwB4+6D3NWx1JJQhS81gCIPUaLUUiPY+WRyCTSnFMBJsahrEEQXtNgzKf1otEIFdHsnqvpj/30X+gYeS+3D2pCQVloiGRqQSA9rPuJPXaVs/1Jc1rz+0gc5lZZaHF+0voI5v7biZ53mgzmWBm34X+W2ERra2tmDBhAnvfr18/tLS0MGKeCrJNnjyZkQkcwcdhjd/i/I8Pwrx1dRjXN+5fwTO7ZKUZkjDg/tbUOY6olnpmVdV68/O0jSBI3SMC9C5jvrDg0EAJIvG7iX2FNGCkQ3oevVJ8j6WWK/OpuGD6O0RO9HgfwSZhGJaLByvf1Wwvqy8x2ShXyTk8i4kgSMVR7WuW92z9DfQ+FW8rtcs1MVl9T8vWSkBZ6tmAYjUyY3XP2M6WwwOw4fCSYU/6HutlQi+pxMn+NJqbm/H444+nPycSCVx00UXYbbfd2Gg1spG79tprMWbMGDaZwXvvvYe33noLO+20k6nvcQQHg/pvxB67LMHf/z0RZ5/2sb36MNxHwhF47S7PLHRShHavnStD8KyDurdxswkCJbbKbZQUSPW71T556jN7X5OK70IMCaE8HavTyYLU5w2lYyGJZcyGl/XtWVyn125L33TfmnJTQ3r2tdPva7v75EqSQO13a/rNG1OvNK+vTv+aXtenXsszl0kQWpMQBqSW0/t6Wp5aR9K+Vz6DXvur/XLZfRU9uR9kqw+TSc4bBSfPQ4k3XLKh5cS8F2g29qg/dcoyvPjdGLy1ZDj2HWktG0OZXMo2e25jwxFOklJHzawmCCAnEZE67B27Zv2W0TuyYFq38NOe6zRaTSAIqQRCKgEgaCcxlUzInK+Z0stSSYSo8j3tNrrXI8WimP4Oe5TTrqN5r+3ZZ1NGZJIVudbVzEgTD/RgsLZmAhaOPjX1kJQiFZgZpJReJ50o0L420rrqd+hV7rG+Oi+9vfS66H7fLkOoSz2xsu8rr+qjZfp96jVN3fTYjgxs2IBNmzahWDFt2jTccsstuPXWWxkRf++992LixIl45plnmIKeIxyoiHbhmKFf4/HF2+K6bd/0dgRQq4PbsKgWz4taf4j5GPPcdbGdjZxXqw+KQ+Z1nMfv2CjZkFqvXeyPEdL/OeKLziJCraghCNRkQYo0oGmdNpanRg1oSYXUezYqML1Mjcci5E1K0j8zTlNKoEfcpv9WNb6z5ZpT144wzCYayLNumsjQxNgNlVthY/lQlHStT8fmdOwt18ZnCcKm1HcH6MRyTWxmz2x19Kp8n323Cj3juMoypQUEPeN09zzajrIN9l6zrhLvEZrYTfF1n332wcsvv8w+f/LJJ1i/fj0uvPBClJaWYvjw4Xj99dfx6quvmibmaf0+fVKqEY5wog445uCvcf70g7Bk7/kY3rjO0mYosVmoVjaOocGgnU1dFELSYA0Ot2HXa96E33yPBAEhl/sMEcAGNZg0EmDQptdQKhmoYdiSf1mPvjer+5d6v1anP84Efd19Z3lDajlNrD4g9ZkjkDcq60j9U58jIuR6dV1altoGvVJiO/1ZXa7T996SPz4b+cxiXiop0FVWhs2DhyJZWgpBUuazeErLKfZWqPNScbUiNV/tj0up+Jvuq8vAes1nFmYlCOtSsZZ9NzWPrZPabo/1NeshY506zXs1rqf63ps3O18Lslhwo4s2tJyYD5BqXhSAU/f5An9+cWfsNnQpyqJJa8S8F1Y2QSKXOUIBVvw1YeHezELcK51SUjh0qf3HgoU6yJE9kKRIhES0AsMW/ytFKBC5oCEZUsWNlYcjelpR1lHmp9bvsT2Cuh3VNkFdrr5XXtX53dtLpQ9SpIbMHo7U/Sv7pq91b1N9ehJD07l3C7fffjsOOuggnH766Uy1R0PiiZAnwkCr8uMIGDI7aq3APgMX4eUVo/G/lkZMqWt2387GKcLXiFpci9pwqOapOB15xbqKfNfQTNsEWO1sSjBo4JxZJGiVgFrqTCZ67sjJoqsBgUJ5d8fmZQMPQ13bxyjrXKNJCnTH6XTyIJ00UOO7orZXiBElllN8VmJrRlyn13WpOJzajhKr6Ts04iEVz3t8n4pepbaRWpZ+n47f7CBC0bF/++23sWjRItxxxx1pYp6GoDc1NbEYq2L06NFYsmSJ6e2ffPLJOPfcc3HFFVdAFLvVp9rEO0fwUd2nE4ft/y2efGYb/P682cYUtxlkq6vFX+uKS21LtV3EMgeT6kGAlWKw9c4ovymxTSPOnCDle/W9CUmD55bPSz7fulZgs/3SMZeR/wLa6/pj9U67oun1F1NJAoGp6JXX1Hq1qfcUWykupPrL6KfpY6uv61Nxla3X3c+Wt6T63uo8Qbsv6lursT+l4k/Z7CgxPdX3pnUqFV/89Lo0v48SvzdutF8Tq1hxl4s2tJyYDxgmNq3ByEFteDr5CE6JnmrRysblzign5YMFox7jub5vRLVuQy1PkCIRxKSAqCDcgIse7gqVLadHMhAicheiXcHuGOfF4MFoa9PUcCgyjBs3jhEHkiQhEolg4cKF+PrrrxlxQN61HOFBVJRx8vAv8Oii7bDtyJWoiHWZJnY99etudYiMzkfO+0Q001BuKsTqOpyoF6BtvwC2pZJolZ1LPlhd7tR3PIZCbSsKN3ohW51YchObAoe4wXsv4LGbhpTfdtttzLZGS8KTEKCqig0lSIM61hR7zeKSSy7JWuSVCsAlg6L65ciLQ/b+Dq/N2goffj4YO223wpqVjeCwKK7OxDrxwlHNS2rstkJmFxr0yPl6K8Vfc/wW2S006oSXvJnteZjcUEd6C0mJJSDEZBKRzk5Et+Toe8ezXCMz5SOtHnM8x+9CXXjidzHb0PZO8XN4r5rPwM8ufo75Fn6+coBFK5t292xsioyUt+3562b7NRvcnlPr2C1uK9LwxC5zpHXQi4aq1yBX+xXZ/wyHMRAZTx37DRs2MFKeQATC9ttvz0n5MKIV2LFuBcZM3h/3tt2c1YZS73sESY4wz+se87VTkFGbZZ6P6m8avi06aWWTC3rXx8g1M3ttfboXyByHisvZOpZsbaRno1RIiPee5M0i0CZ3zwsDvPg9qkmRB0YnWj8LHnroIWZTs+eee/aYn40sp3hsFvSdbBMn5UOEOqCkRMKvzpmBe586EGviFYa/pyIplzk7Wt2sfYr6PxEWcj5f7FYFhn6fk9/7RwbJa0GvQ1ZxZJGjS/7aIeXjLpDybqDe4eKvhh/uLcIuKa++15L0QbiPC9CGtr29PW1DS0l/J2xouWI+gJY2pJQ855xz8Je7BdxU/RTqKraYIubFzIcDTspbhulCnl7BinrdjnLegUKfciTKPOYLgpR3o/39ThpxeAoa+k7D38iPjvvJFwaE82T8YssWXHbZZXht01Y4YIRBFWZrSnEndkKtV+oqnLAL0Ss6mWudXMfgAoGvqO48HA5v9ny8LNRrE0rHPmlvlIWd5U5/z4jSzOr3DYCGoffo3DvhY8yRxtKlS/H3v/9d1w6O4q6sQ6yQP6wR0PD1Y445hr1/7LHHsq5HivlTTzU/6pnDJ9TJ2KYOzE7w9kcG4JqTb0Q0aqD/l/q/lX6gRLADHvNO+JkjRAk/I7G7UJTzds7DBrmsxJsu8+RvtnWs2tb4Qco7DGYLYyGJ64eFEUc4bWg5MR805Xyj8iBABQX23ntv/OmbQZi+9fVsmLwRUOe+R/FXXvDVMgJKyQdbgZ3j2KhATJqYDyspb6ftHSbnXUsa2bVGsrv9IsIRRxyBs88+G1dddVVaNa8F96gNEaYr/4/l5eWssODVV6/H6H5xjKxZa9gT3ZP6MF5a4mQuy2Z744K/eno4vB9wi4jO3EatN4S+3BaFUGHSlsNtBbxT24/78H3m957RnpycdwxPPvkktmzZwjzgCSoRf9xxx7FEOBV/1YJGrWXa22TDddddh8MPPxyxWAzTp0/Puh4n5sOE7mfpY489FvPmzcMTr22Dnxw8x/AWpLIIxH6S9d8KpxNzQSezc1ja6CbVg34+AQYVSWcFxO2S8kbb3851igec7KYkRz5ivt6n4+XJ/YKwoeXEfJCQIuVVkFcRVf3929ytcco2X5q3snGSlC8kMq3ZGNFIhTN7KLyd3q/X27Cqmje6zzzrUdV0MdPKplhIee02Gh28b5sdbj87tkdG7i2OHrj77rvZ6z777NOrZbhHbUBRl52UVzFs2DCmlrz92SrcuM+9qDSg3Gb1YZxQ3HkFN/zAHSToWQE5LxXzfhDWZtqrNc93chwXPQuJ2iKt+faBEJPyRglyGwRCL8W82X2bQbz4OvNnnXUWTjnllPTn1atXs3l//vOfEY/HsWzZMlb4TiXjFyxYwCzkjODzzz9Pv6dCshyFBRpRcf755+OSS5ZjwvA12GnCCueEMl7/r4WUnJeELKPdgn4+AYbgBSlvZJ0Qq+UJVFRVoFox2eBkaTDttriCPnCgPnqmDa0T4MS8D4S7UdAFpweESy9dgXH1LZg8+Ie835FUKxuulDdODmYhDKVoDOKAAHbs3YYV8tgA6cqsbIiYbyxSUl67rUb7+1Wqt+f4jtdtqT1Wdd+cjM8JK762HB7jDmvxe6+99mIKirvf2REX7fUeaASsroJcq5jXjnZzA4Xq4x0EKxs/2z+XjU6rAfV8nuOSSKSQqfDOt/0wK+VzEeQOEEMKMZ/ltz9bsTYjx5ZtO2bWKwCivra2lk0q1M7zoEGD8KMf/Qg1NTXMH5bU85988gkj2y+99FLT+/n000/Z6GY9vPHGG7oJdw4vYS120/1x3nnn4bbbutA04J8YVLcRoUXQrW10yHmWVFc95v0k5934LfQzuWCFlLfqIR9EQt4J1byelY2TZHw26O3D6rl4cbwFiBEjRjAy3ghITW8VnJgPOOjhksj5W29Yg59P+gRTm3IzXUmyslnm8PDtIJNrZtS7Js9DisQgJBP6hKMfCPJ1MAA5kmcEglHC3st2cGtf+e6pfKMPYKAAjdkEi9PJBw5DID86GnpPvrivvvoq+vfvjyuuuIJNffv25a0YUtAD3M9+9jNce9Gz+NPru+DXO3yIkkjGA71GvSwJDhePCzOMWq7kUIwrw7cNjNAKGqGcI3nj6H5MrMs85jM9av1I8jixTzOEtWskigDkUt1pjyHX/GzEvR1k+34BEPaEkpIS3Hrrrbjhhhtw4oknoqGhAVdffTXrdJvFTjvthF/+8pe4/vrr07F65cqV+M1vfsOKwHV2+mSlxWEbW2+9NY4++mhc/eAaXH7qbAwftC7cNZ/qwkPO502qB/lcAgjD96YZUr5I2z892i0I5LZZRX0QjjnEuPrqq9PvP/74Y7z++uu45JJL0on/RCKBm2++OV2Dxio4MR+SB4TLd5+Nm97ZDevay3DQ6AVZ15U2lkJ0W3XnJ8zaqthQ7sqREohEzOfarldEpVNEp1Gy1oVipZIYhTggab64oV/JEK/I5UySvtmB4XRWrjeH55gxYwbuv/9+nHvuuYyIp8BOReiWLFnCErKPPPIIvyohRllZGa7caSZu/2gKZry/Oy7Z+V1UxHTI4lYgGaPirx3Iyc3XFpFavtWi+luDQFAlreG/NrIQhahNchQ6KW91fYNgNolO1IjxkhyxWxzXRwwePJh1pFWMHz8+Z+FWo3jrrbcYCT927FjcdNNNWLt2LasXM3XqVHz22We2t8/hLw4Z9WOU7zMc1zy8By484X1sPXKN9zWfnERIyPmsVjZejgQI4e9cznuzxQHrmriH96jLdnJWIdeKQGUARzrrjQbgRLyjoEKvWhta6p/vsssuPdahZwt6JqC+vVXQ4GqOEGBMXSv+sNdb+Pd3Y/DUl1ujl1iWrGuWpZS0YXhAMItmi+Sh1e/R6INIrDcx78B2TaNASFNmZSNlUTBmI4/9sGPx8trq7d9o8bh8inmj2/TjXP1s4wDhgQcewBNPPMGKhapZd3qlKu/PP/+834fH4QDKoklcvPO7aKjYjOnv7IXWLWU56sN0hJrAzXncuaZc3zO7DzvbcBpGPPVDcE0lNvogWVykvIswnFgPIgLUjn5j9913Z4S/WlRWKfp9NV555RVMnDjR78PjcAB7T1qCc47+CLf+31S8P7cxK9kVeMW8iroAk84N3Yp5IZuVjRcIavu4ASOkfNyB3/2G7uubF26R8nZtbOh/XzRQ/NUv1GdMHK5h7ty5qK6u1rVBIwtTO+CK+RChsXoDrtv7TVw/axrWtpfhrEmfICLKhe0n7yN5pyjmOz1XlvfaftBh8Bip+KuulY1ZUt6gqjx07eiUR61Dqvys39eDA575xYjW1lbU1fV+Cm1ra4NsNPHCEXhERRlnb/8Rnvr6R7hy5t74/dazMLixp28tWdnEpNzD5S0XSDVadDUTtT7YtzhQ+JWB6uhuNGmN4yScTDh4fex6Cm856QxB7gexHzgymRLrAe3ch7I9/UFzczN+97vf4emnn8bll1/O4ja9UlFYIui1Pvcc4cWksT/g8nNn46a/7oZ1KMWBey7sQfTJcggFcXXB/X+XzdSHcXoUQF3hjVyQtwjWSPm4dwV/TcEvUj6dVPf4fz3znrR7XYop8eQSyMaOlPEPPvggmpqa2Lzly5fjoosuws4772xr21wxHzLUlrfjmr3ewqqNVbjlvanoWKKoLAsOAVDUSrkU814okv0mMR3ev1wTgZipmHdbKe/3fdSoMzkBwUbH3o2khnakQXPARiEEGORFd/HFFzOCXvUlnzdvHs4++2wceuihfh8eh4OgmkEnD/oSBw+Zj6u+2Avzl9T2ICkVxTyxyQ4rwa2S8mb24eR3W22S9qn2Co2CMQRgRMlGBwrpclLeWmKdI5AYM2YM85Sn4rHkM0/D29955x28//77GDVqlN+Hx+EU6oExI1vxhwvfwvOvjcPfnp+oDFhNKVOTtaWIVLb3VqwWimrVY5V9Uijxp3B7gRKWukkjP0h5K+p5J+AQKU+QmWI+R608L+7JOp3JyHaCPFomZHjsscfQ3t6O4cOHo76+HgMGDMDQoUPR0dFh24KWK+ZDiMqSBH4/bRbu+GBnXPPNHjh/1P/Qv0wx7g5d1l4PASHwJFLMSz48HBQoJDHW08rGC1LeD+Q7fiuK9WwFaNxGWJNKIcEdd9yBk08+mQV1SZIwadIkrFu3DgcddBDuvPNOvw+PwwUcMmQ+amLtmPHV7jhr9MeYXLOCKeqJmE/Xh2k14ble6xIpb3QfVrZpB7XhsH8pJEiqYt4q/Lpe8QBn6cCJ+bCDOuBkY6PF5MmT8eGHH+Kee+7x7bg4HMJYGYh3J3gbB23A9Ze8gevvnIa29eU49ag5qKpMQJLKlPowuUg9u+RgEOC2r3sKEkog+mllU0hoAeQhBkQKRkh5I0p3M4S7dl27KvpMOPX/Vm/DRtYuzJDonHD3FKSSnzVrFr766ivMnz+fieooUU8e83bBifmQoiQi4cIp7+Pxd7bFxV/uj4pIAuP6tGB8dQ1KN/VFU8U6iIIPxVazIYQFKA0r5t2wtAlCWxg9HyNEc2MOKxuzCKJnOkxef5vkvDKczuWsfRDuwQJHRUUFnn32WSxYsABz5sxh8yiwT5gwwe9D43AKJ8vAkz2D8a79l6FPrAP3L5iEu77dCaP7xLHtj4ahacv3GFcSRUU0Sy0OJ+G0nYqT5Gvm/jI/WyHnnbSEsZsQCVlyQV4fhRC1SCS3FjEB73di3QisWGNxMGSS8iqok/6rX/2Kt1IBoramnSnn73p0J5x12aEY2LARE8d1YcSIClRGK9BQt1nJuxUyXLZiIY95XxTzxQKzpLXTpLmbJL2TCbB6k7Hbyf8LTrSHAlRLxul6MpyYDwuOlYF/9oz2YjNw+vA5OHXYHCzd3BfzNtSjNVGOGfN2R7sUxdiqFoyrbmGE/VaVbSiNeDj8xihx7Qf512hs36TwNkzMh4ncNJJEaDRPvKe3nWU7jExWR3RYVcsHkZS3mpCxQ85ny9pneskX0n1bwGhoaGBD38lXfsiQIX4fDodb5LyGkN2m32rcueMraO0ow7fr67FcmIRnlmyFRa1j0VSxHmP7puJ3dQtqS3UsbvwiRENEKLt6/nqEppm20SNC9b7vN2FKtkAVUQhyIpj3SchIeQLZLPk+urXY/48dwhtvvIGPPvoInZ3dCl8a4k4j3+666y5fj43DAdT1VM0TSCV/6dnvorNTxILva/Hd4olYsKQKj/7ffigtSWLsVi0YR9OoFgwbsg6RSECScEEn59dY8Jh3CoVIhBolqXNdR6MkeUMAfOidIOXz2E/JpHjVCgwz7xvt53gA70UvLYQ4LIET8yEn5wn0OzG8ch2GVm7Ewr7NOH6HF7GmowLfbqzHvPX1uHfNZIiQcft2r8JXBIHsM0FWStESRBJbjH/BKdW83+3U6M53XVWIWSXsXTpXb4rxSbnP2+3CxBy2sXr1auYn//zzzyOZVB72IpEI854nKxvyruMobBDpPqWhGQvrW3HExNeQSHRh/oZaFrvfWDkSd367M67e5m2MrY47R9q2FgCRl0N1TpHGFeLTjtd/GNHabWUjygZHcXjZHiEk5QMBo1ZZHDlx44034pprrsEOO+yADz74gFnRRaNR5jF/xhln8NYrYHKeUFIiYcLoFjQOWY5N7RH89Og30fxDNeYtqMe8RfV49tXx2H7iDzj7tI99OeywQiKPeW5l4xh6PAu15ImhcZ9Jeasx3yopb7aLxfresnFfdzPPKnUOtLP2mnESPpTgxV8LCJKoeNTSMDrynN+9fil+PvJTHDLwOwypWO/34QUPjUYU89znzkhbGQEp5m1tq9kFFX2uoqW54DPhzUYfrDVgLWD2vLwo0rpMMxU5Tj/9dFbs9bXXXsOGDRvQ1taGV155BXPnzmXLOApMNY/s5FdSpOKvnSiPdjE1/XHDvsbFE96FKMgYXL7B3L4KmSzWIktbyohCgI8jBK0ioMSoLJBi3kB7Fst9ZxOGk0bZCj9bhdPbK3KQIv7pp59mBV/Ly8vx1FNPsfczZszw+9A4PIQklSIidkIUgaFD1mP/PRbhvDM+xLbjV6JxEO97GyIT12Ra2fC+ty209Byh5Rrykb96hUqdLkRqhZS3WJyZFX+tsmDrV2exLdRCuUZJdrPrc5gG1YO75JJLsH69O7/tnJgPo2oeOTr2Uu8CNJ+vG4jt+q5075j8Vni76THf1WmdrC3QdrGKvIr5RodJeSsksxHLHr9V6HSMK8nKxsTDgZHkQ7NFcj3blG09jjRmzpyJhx56CHvttRcqKyvRt29f7LvvvnjggQfw9ttv85YqVHJeF2Rv0fN/+qu1/TGsci36xDqNk7h2C762hp/Ak4QYxEzrlYCS3r0QlONszSTm8yjmvb5fwqSWb82YjDhTadvTif/HEP8/BxWtra3pejD9+vVDS4vCEB1//PF47rnnfD46DsdV81mQZMVfe/5TSxIw55uB2G6Ci31vP+HU7+8amx7zYYoDXh1zBimfTgSbUcs7RcrnQzZS2k1i2epA5DpA7itAoH9uL8DJ9UBCFEWWhF++fLkr2+dWNgVkaaNHzCckEXPX9ceZwz/z8AALiJiXDDwc+E3UOolG9wqW5t2+2ToEVhX0+RBkC5hmbQEaiw8HdtrHDLHuFwnfn374TKwfg28gX3ki5PWKwm611Va+HBOHPzYseqqmz9oGYtuaVfrb0LOeaHXJpsVrsrjWXlv26tg7cfytRUTS97o3RYhuFxs3g7CQMVmtloTc/1vZ7jUrdjNOF3nmSGPatGm45ZZbcOutt2Ly5Mm49957WSG4Z555BtXV1bylisTSJimVIhbtOaptSXMNIqKEpsFcMa+LNXmsOo2MeAtLHHACLQbI5ZZstqddzhd8dYKUz1w/3/WMu1zs1ci5UH03yaCtX7GR8jXUeTW4bglCjSOOOILZ0F511VXMflbv2cAqODEfZjT1JL8koQxiBjH/1NIfYXSfVtSXmvBKLybkIJsZMZ+r+KsVhbfNY3IETviqN9uwsjEDr0n5XHCarLeh5mdWNlaJeSvgSndX8JOf/ATnnHMO/vCHP0BI/X/QMLmrr74ahx56KGbNmuVIoOcIdiFYPUw1DZwAAMfcSURBVCzaWIN31wzFddu+aZ7Ay4ZchJ/d7WQ7HyeIvlorivnUKIMwEo1+HrPONZRIMZ+LKOFqbFPt0cPKRrtunt+EHusbuUf4dXEVt99+Ow466CBmO3fttddi//33Z4R8aWkpHn/8cXd3zhEoKxtR6GYNu5ICHvnHdthr6hJmLesJeRgWGPQtF7wm5eM5yGS9fVm1YTF73NnunRaHbejMFGDNRRrbsaepy2gjq0VgnVLL65wLieLEbH1vvXZZEyBSvhALHPuEu+++m73us88+vZZRX16tGWcFnJgPu2peQ84rHrXdxPyHrYPxbrwJN/3ov9b2UeRWLFKkRCHmg6Kedtsqp9Hd5IFQJZtTqweJlHcaNo+VfO7YWFkvwEl51/Db3/6WvZKVTSaIlL/hhhscCfQcAUUWIm5TVwy3fzMFp46YgyEVG9xXfNcGiMirtd+ORMwLZGUTRlI+iApv1rnPIlLgFjbOtYdT6na7x8D/bwxh3LhxWLRoEUumk2pu4cKF+Prrr9HU1MQLtxeRaj4pk8d8t5XN317Ymr0effDXhUnKWyHFnSRYvVLKx00sc5r0dOi+YYXbN9lUeBsliusMzrN6/fRU9fUG28omId9DFJfZ987VPg0OjkLQgpPsvoJivlvgxHwBkfOs+KukPBysbK/EPYsm48LR76NfiRFDywArvN0mnrXzNech1cYgbkrAdA2aZo9Icr897W2o53PCrA96GO49u8ea+T2ysvGCmOekfGiDO0cIVPPQkGBlynsqxXH3hztibEML9hq/xIB0zEFkEvTZCDqz8z2GhBLjHrWFCgeV1cpw+KT/iZsgWRfYaAvHivH5cT38vgcCBioA991332HLlu5RyUTOE/got+Ig58nKRhQVUdzHXwzCzPeH46bfvY5om8Eiz2GC2d/gNSGKAVrVvNn9xA0Qpka36WAyJ6tiPu4yIW90vhPXMxc5b9ZPPh/hLUa667s1eKyE52R84NDe3s4S8h0dHRgzZgyqqqpsb5MT84WCJiAZLUVkTSc6JRG3fzcFPx70Hbbu63RUDCkaza8nieQxH6LK8EaI8kb/9mu7M2qHlFcJ5iYT+3KyrZqd/a5iZePyQz8n5T3BunXrenXsVZX87rvv7s1BcPhLzmvw0qLRWLmpCtdPe0MZBu8HmeYFwV7rnjo4bWWT77wDkkhwHK3OKquZlc1aPmLHKVK6h5VNEJFpr5M5j4OB7GrOOuss1jnPBB/lVlxWNhGxA6vjFfjrYzvi/J/+D7U17YWnli9kUt6pfeSyxPEBclsUQplLsdssIZ9t3bgD61st6KrdrgGwvndfyVsP+ADdTxwKEokErrjiCtxxxx2MlCeQhd2ZZ56JP//5z4hGrdPrnJgvoEKwklCKkvo2PLpoO/SNteOIwfOsbzdMKuRcsEGuyoyYT4Sv3bKNYMhX2NWJ/WrR7GBn1Aopv8wBgt4u3LofBE3W3g1wUt4T8I49B0Gi4ppI4tvWOvzr2wm4dvc3URZ1qDMVJELNDhFuotitLJRAzGa9ovf92hC1od0ivhbORd4cgUgF5PyE06SMUcuWMF17N1Ds558D1DG/9NJL2VRWRkOeOIpRNU+KeSmZwO33T8GBey7ANuNXc1I+jKS8U9Aj5306fuaJbpbHsEMUO0Ei17nsM5+5L6OgY+krQNjgUWKdE/KBtqF98cUX8a9//YsJ6Gj0+7vvvosLLrgAl19+OSsKbxWcmC8gJIVSfLGyBp/+UIObJrwO0csh8EGBgypnUswLTgc0v8h5P3zyndqnU6R85vKmEJDyWbZhuPirth2MnC8n5D0F79gXOVKqeUqqJ7qSuPOjXfDTbT5FY58NhUOquaFKz3NeEmLmrGwKwVe71aX7oRWQB9NweJ+JebdGE6io9e7/xjErGw5fEY/HWeFXTsoXN0gx/3/Pj0FFeULxlS8k+O0nb/UY/IbVY3ZylEULIJVbLP7qJSHvlGrezPfNoKGnrZ+rojgCJ+QDj6eeegrPPPNMj1HtBx98MCv+fvTRR3NinkNBvL0v/jW3CRdMeQHVmz22YPHaZ94jojn03ScrXvV6y/waCeAGKW/nWIy0p1Nt1Zy7+GtWK5ts559rtAAn5H0B79hzELpQig+a+2OHgZ9jt0YH/hmLlJBXIRlVzGduuzagbZkPbhPJqsd82JGrnTy8zoG3suEwhCOPPBLPP/88zj//fN5iRYzlq2rw4eeDcNPl/4UoojAQFDI8KMcRNLSY8JiXDCbVs6n88xHFuZbXGzxet8l5K2R3gw1RnFlwMj5U6OrqYiR8JiorK3Wt7cyAK+YLBO1dEby5bAz2H/EmxtS1Apt9OAg3SVw/FN9mEQQbG7fazGbiRQ47KZ+5/0aXr3++bQkiICWtnT8n4QMD3rHnILyyeAI2xWT8YuvP7TVIEEjkACjPyWM+IvWs2WCJnA9Ce+ZCqzfbJo950W9i3q5KjoPDYcyYMQPjx4/Hww8/jNra3j98b775Jm/zAseKVVX4ct4A/OanD6NvteI1bAt6XtleetXb/Y21qZbv0Vcstt/7bNfZxvWXxYj12O0EIZ/5ucWinY3eveUmmd2Q3Rooq2Kek+tF1Xf/1a9+hUceeYQVfSUsWLAAv/71r3H44Yfb2jYn5gsAJJx94NMdUDNMxr4DvvX7cJxRz4eBiC8EmPGdt3FdKZgJekSyX6R8k02FvJtJGAPb9qT4K4fr4B17jrlrGjBz+Wictfu3KJFsKHH8JpEDQMhrrWxiWGftyyo573d75oMbx5fNs18MiJVNgZDz3MqmMECF3urr67H//vszpRxHcaGzU8Rt90/BPnvFMW6U5ofJCPloplhlvUfkvJ3fVofsa2Q4aL2SDVoCNejxxOZ1Z4Xb9WK3nVhqhpDXW57tnDKPKfNzNoLeaeQq7EpcRiYxzwn5osMdd9yBE044AePGjUNNTQ0r9koj4MnG5i9/+YutbXNivgDw5uIR+LalHufsthqRTgcy9k6g0SDR6DYBb9R+hMMYLJLzbDgdBTOrhLbfSnm3YCfRkflwENY2KGLwjn1xo+3Bctzxyb44Y5f5qCkpB9oDQtDWmth2gAj5nlY2nd61gZdo9X67MgrEyiYgMGxlY7Q4MYcveO+99/DWW29hp5124legCPHg33dAQ+1mjBzWZvxLZgh5r8j5ABDyverDuEWYB5VE1bu2LQGyoatz8N7OpZ7PR87rEedrPCLl9URxQb2fOFxFVVUVK/46f/58zJkzB6WlpZgwYQK22mor29vmxHzIsWTJEjzxxTa4ao+ZSESORwQBIea18JsYz2U/Usxo9IicbwbkWBRCQyIYpHyTB4kdt22NyMjS7QI0HK6Dd+yLF8lkEn/+eGfs1rgU4xpkJOSAmNNqScBcyvEAk4VkZWPaYz6bpU0Y1PN20WpgOLxRn9p4ls5qZufaaofWqtKvtQCL1HL4ChrC3qdPH34VihBvvzgcc7+diJsufx0r2rZ2l5R3i5wPQmFXPWLerRp5dYU7CisbaLRb1tid79zrfEo26ZHzKuLmyXRHobWyCQspH5bjDDhOPPFE3HzzzWhqasJjjz2G448/HqNHj2aTk+DEfMhx/40/xfFbf48R/dbiO15UKjc56qd6Xo+oDWuiwCQp392xT4bTt9/MfePR8SqKeU3WnqvlQwnesS9evH3TaHQkR+KkCV9inTAJotzuDNloR2Wr953a8BGDadVdIcLpRIGBbcmgpJGBRLAZgiMbgW8EISdT8lrZmC1SG4L/yULEySefjHPPPRdXXHEFRJ2qn9OmTfPluDjcRfuKKB76+6G46jczUVWZANo8IOWdJufN/H66bR3iZFK9UMhJhxIwUj7FvF4sdVId7xQ5r52vhcfPAenR6kG6n4J0LAWMd955B99++y0j5s844wwcdNBBaGhwPivEifkw458C2toPxsQGD6NmWJCNIPWDnA/SsdiFReJZEiPmPOaN7stNtbzZa+VlEoGG06l+1JyUDy14x75IcaeAtrbxGFfZgqgoIymUIiatd3YfZgn6XOuFjPhzvHMfdtW8zWNnw7fzrRR3iHQPC2wkwHJa2Vi5VplFizk8wSWXXJK1yKsgCGxUFEfhYfOWGD2CY9TwNsiy4J8nvFUYJTN9oBakthjEcoeJ+aDHIhfvFcVGNulc+zhFyNsh5zPX0YNLhL1cJULoKwMb4S+Cfk8XII466igcc8wx2G677SDLMisAW1JSoruuncLvnJgPK/6pPAwkkhHEIvzhzxSCRIj7dSwe27Ow4nFGh8IHFUFQ7xupDM8RGvCOfRHiTiV2d0kiYkKSkWpSdQkiZR3OqeX15hURcSeBPOZtdu4Lhex0IKGQt9B4vk6w053keIDa08nRJFavVSHcpyGEZKdYN0c4ERfQ1VWBaFS59pJcgohopTiMDdgh/gNMytOxSVEH6sOEicB0yVu+Z9/bgUSHG4S8UXLeyv3rRoFf2ibre/vIuQX9fi5g/OlPf8Juu+2Gb775BrNmzcKUKVOY17zT4MR8GPF+d4aeOvdR0d7DYZck4It1A9ApRbBL3XIEAo0ukqKNxoZu9yqumQvNPpPz2fbvFOlvs/0ls8R8kNTyAYQspqxsuFo+1OAd++JFQoqgNKL8JibFMohr2wELz3grNlfhk9bB2H/QQpSCJ+nTinm45FMbFjio8CfFvGn4qZp3IxnV6lwyR9fKJswjMjg4igIC+01LrIwgFlVibVIqhShaizVb2qP44LMhGDJwA0aP8OAHIMikvCZ2C4VqQ+cV1Otcp1jZxKzYJFol5OttJBlyFYVVYaR+Tbbv2CHo67SjBw0Wb3cSnJD3HTQK7thjj2XvFy9ejKuuusqVGjOcmA85mOouk5g3QNYRp/f95r54e81wvBsfijKxi/3U+ErMNxpY1uxSgdIMSGLMmSyzm+S82wruZue+b8hj3sz+ip2QZj53nIQrBDz++ON48sknsXTpUrz66qvo378/86ylqW/fvn4fHocLanlClyyiUlBiN1nZMJWYQWJvw6oSvLemCbNWD8OKLcqD4QipDVtXrSl8BbiXVjZBajMzvI2XJK+dzq4V8j7uw/VrdXafvTr2dq5XUO5PJ1ANoMLE+rwHy+EZNLG7SkRMVcwzYr7DMFlJgyzmftsfb/9vOD6eMxh9qjoxdmQLRo/40NzhuGWX4xcpn/pdl4USRJxUzHsJN1TaRpBtX3FArohCKEm6S8rX2yTbc22vxcF2t3J96rJ4zHsJTsoHDg8//LBr2+aPNWFFinhOEDFvwsqmrbMMs1uGYlbLMKxLlGG3uqX4/bhZGFy+Aad/dCTakxGUeW2NY4aQtkm4G92XFCmB6AQxb7ZQaqNP23QZOa1svLCIKSC1fNrKZrUPWXsORzFjxgzcf//96QJyiUQC0WgUS5Yswfnnn49HHnmEt3iBIqFJqktiKSJSe061L41s+2zVIMxcOAxz2gZiYs1qHDrkW+xQ9wPumzsJzR3VuYl5ddvZCrwWEGREIcBB67QwqZndOtYgFFuN+9AeTnm+tzq4/QL7f+XgCBMSCRHR0lRSXSo1ZGXT/EMfRsbP/mAYyssS2GOX73HS4V9idUslHvnndsH47fSZlE8r5t0o/upWMXG/SdO4gb53W5c2r+QcKV/vItmeax9GFPV2VfTZrqvXNrJ+318cnoMT8yGGJNOkWNlIZL2iDmNv6qkq7pREfLjLTOaJRN5IO+ywA04qvxnb9F3FCs+paCjdxNR3I9eudf/g7ZDFdsl5g4p5R4h5s8hHpDt13h4T9lmJeSvnU+xqeWrPFsH7rD2H43jggQfwxBNPYNddd8X06dPZvEgkgmuvvZb513EULkgxHxNTw+GF0t4qsVZA7gcsmrIAM2fOxLvvvov6+nrs0fcZ/HzUp+hb0qGQemuBxtL1jJgvCGW4Q3BsuHGYSHk3EAeEgbL/nU6vEwMuEvpyu+COn7zeNs3+Xzvpmc/BUYDo6hIRiyXZ71dyfSkim3UU3vXA+tg6vPPOO5j18CysXr2aPedd/MuLsdWwNlY8llBSksTyVdVMSS+KcEc1H2RS3u3C7W6hzkWy3+xx5NgPWdmI1PeOmNimEQW8HVhR0ec6hlze9FaLx2aBopj3QBTHCfmiBSfmQ+wvT4VfCQoxX9EjmBFpP6+sHrO+H4b3V07E0HWvYo899mAqzMrKSuC2C3tut5l42vVoXl6NkZUuE/NOEMIuk/OMmE8GaDid3x72NiHpWdl4ScovKyDVPJ1LPxHoCsHDK0dOtLa2oq6u9xNYW1sbq/rOUdge89HNUqr4aynEeAfQT1kW7yjH7NVDMWvxwdg0+2bsvvvuLHEzdOhQADcBf+gpfyJi/vONA3t4ixYzdD28w4xWn5IIcYfa0y5JYee7tRbapTXASSM752KFpOfkfF4rOkquL1u2jFvRFQkSiUh38VepDGJ1Rw81/SdfDsKsz87Dl1+eg2222QZHHXUUE8TRaEjEf9ZjW9VVnSgvTTDl/MD+m1B0yPhtl8jKRtqMwMKLZysHbYrIRpbZnhq1jXOblM+2PTvn65atky48EMW5fY/dzPuWTuCzzz5DeXk5xo0bh+XLl+Pvf/87xo4di0MOOcTWdjkxH2KIgoyqkg5c/Nr+mDxsPbYaLaJ8cxXeWTqUEfKyLGCPg0/HTdOmYeDAgT2/fKEM3Cb0IEf7RzdhZZfzFYZ7wE+C2MS+JdEhKxu7RLqbIwM8JOxzWtl4pZI3Q85n7i9gpD5l7UVO3IYexxxzDC6++GI8+uij6eIy8+bNw9lnn41DDz3U78PjcBF9k+14snUbfL25AT8aXIO6zhjmLhqCmWuH47stddhx6p44bdo0/OhHP2KjKLIiDgxIbMLK9irFozk1z/TDPSfhwg2XSHnHRh9kkvN1LpLynEy2XhRXTWTwNswLbkVXnKis7MT339dg+vQ9MXlyX4wevRbrI7WY+eowvPvxUAxs2Ig99tkGv/zlL1FdnTGSrU4G4j0TnQPqN2HlmiprxLynpKTDiOsr5mPZFPMtLpDDbpKlXqnmc0AWqO9tk8vwos2dIum9sJE1SsxbKTpb5KKasICS8WeeeSYeeughDBs2DFOnTkVFRQV++OEHXHnllbjooossb5sT8yFGLCLhvkP/ja/XNGBO20S8u2QQPv9iX0xpWoazd/wI4365CmKusXFEzl/Y/YCwPFGNPSqXIDTIp5rXLm8MiZUNXCLkfVbNy0KkJzHvV6FXI+T8sgAr7lPHJosCBBr7yhFq3HHHHTj55JMxYMAASJKESZMmYd26dTjooINw5513+n14HG6hFfjJoDnYv3YhPt0wCMva++K++YdgTHkce9QswUV3vcIe8rJiuqyo5lMP/Mu6qjEkuoFfr2KFi6S8o1CJiiB3Pl1Wy8thIOg5KW8I3IquODFy5Frcf/8LmDNnIL79di+8995gtLVVYdq073Htb99E49brc29AQ87TY/zyVX0weKCP8XuNA/HBoSLeEkog6hV/dZuszUai+xWrHEq4KKK4pHUC3o9EiF2rGxeTI8zKJl8Uz7xngvy8w2EJ1113Hf7617/ilFNOwWOPPcb68B988AH+85//MGGdL8T8a6+9hjfffBOXXHIJamtrsWjRIrzyyitYtWoVyxCTbcqOO+6o+91kMskK3WUO1f/973+PPn364I033mAnuPPOO2OfffZhy+69915WGI9OWEs2f/zxx/jvf/+Lyy67DMUI8ojfZsBqjBxUhrXRMpw/8hWIFO+PNff43yULmNdZj1/2+9i1Y/WFGLa4T4WYD5CVjVuwmLiw8nDQy8rGLz/5XCR7rv2py/wg6DOPS4woT/QcoQaRr88++ywWLlyIzz//nM0bP348JkyY4Op+efwOBvE3uHQjBpfOx7d9m3HK+H8psZuQi5TXkvPnKV/4JtGA8SUZvetsBGguNSxXyvpH/AaFDNXpxGa1sjGiwEaIOqme1BMQAsLOW7x+HL5a0fHYHQxUVHRhypRmjBixDIcfvhJ9+85LecSbu+7fL69BeVkXGmpt2Ld4oZrPR24aHQ0VN+Ix71Pf243Esc+qeWYjS1Y2XpDyVhTiXhH0TkEgK5tkuJ5rOBxHc3Mz9t13X/b+pZdewgknnMBGvFPffeXKlba2bYmYj8fjmD17do/PJOengzz++OOxePFiPPPMM+jXrx9GjRrV6/sbN25kDy2//e1vUVpamp5P3uf0oPPpp5/i1FNPxd/+9jdsv/32jPhXG+Kjjz5ihH3RYorcw2dehSSUISq3d3fsjeI2RTW/JFGDGrEdddEtjh1qmOGrlY1byKWad1lRTw8HhrP2XkCPZDeaBPCSoM9yTDJ7OODEfNixdOlSNDU1YauttmKTis7OTqxYsQLDhw93fJ88fgeP+KNiUunYTYS7UdyhkPPfdNbjjD5KYqcHgq5OLjbkIn6DkBSJu1xI1yx8tgFwA7JIxeMC9CwUlHsvhPDaio7Hbj9Bv4G9O9jJZClisXbzpHxKNf/N/HqMH70mXQw2kL+hcZvrmyCnGTEPH/veXj8v5UqqOJBwsW0jawR1HhD0LcGwdKLR/9AmXfnzdVFi2223xf3334+DDz4YL7/8Mq6//no2/5///CfzmbcDszXAGV544QVsvfXWPQzwBw8ejD333BP19fVMKU+EPD2g6IGIeSqCQuuSQl6dSAnf0dHBVIQ0LIBe6bMKMtl/9dVXsWlTERZIyYOkUAoR1rPM33Q0YFxpkNKSBuESmSxFfLaysULgqlMuNOdZljk5BE8eDtxsNz+OJ9cxETFPFZ45Qo0RI0agpaX37+6cOXNY4HcDPH77CDOku0FslGL4oasPRpfo9ILqgqoadhe+Ecl22zXoavqwodXHwrnZwDxqQ3p/cvSyoqO+LPVXN2/ezKzoSDE3ZMgQtsxp8NgdPEhSKUSxm6cwi28WNGD8KAf63vWFQewrivmMvncIqYkeqHP5utaZsJE1uo96B86tzkPiusWb5D6NHhT6Sd6eG0fgQPH9wQcfxO67746zzjqLcd7XXnstc4O56aabvFXMf/XVV8yuhkzviZBXh95nZghisVjWgmVEzFdV6RcZHTRoECPpp0+fzird0mcVO+20E7744gvm4XP00UejaKGjmpdQClG2/nCwJlmBQdyjtrs9xRgiSevt6Rmy+aEjh6rbjDreIasbRsybVYkFhSzPhBtKeZPnKpOVDVfMhxZ77bUXU9fRyLEjjzwSJSUlPZZ/8803jLR3Gjx+BwCqP3zGCBiriCcrUBvZghLBwRE0XEHrH+npV9tn6cBmtbFR4eaxWrUBMNveHoJ51AaRmOcItBUdj93BVM2TYj4SsS6Ka2mtwKD+LvrLB6AAqRlIAnnMh0gU5wTyqb1tqMFl1crGDRK5zoN7sCUgpLx6rhWkZ+aj1YsdkyZNwvLly7F27dq0qwv15U877TRWDNYzYp483l988UU2RI9UAipIHaDF+vXrsWDBAkY+6GHDhg1sqP5f/vIXdlL0/cMOOyzt1Ucnpkfe0z5p348//jgj6ckCoKjJ+X8KPRTzMTlPwZkc2CzFePG4DGI+ltiIQMMOcW3Wusam1Q0N3xaTAVTMm0FTcK4js7LhHvOhBY0uI8ycORNTpkzpEeuIsD/qqKNw3HHHObpPHr+DCabAIaLOYidikxxDhVBkHdkckEDFuQLUcWoNd2JEqQ+T5f7KdYxhUJP5oBJXbOgCZmXDYQlkvXrbbbexEW56I7mpDpsT4LE7uOS8ophvt7y1TZtjqKzg8TunYt4OgmLnl4+cdsmKRRJMFH/VHks+BKFNvSDlM86TJ9Y5VJDLi0rKE7ROMp4R8/SQ0dDQgIkTJ7IHEj1IksQ8dkg1kI0479u3L1MDEiFBHvNkT/PII4/gggsuSBd2zaaoJyUCqfOfe+45nHPOOWYOv6BBlcwjNhTzm6QSVIohLHbqoN2KFjIr/pqwfwyNLh2jGT/0Jv/JeSmoVjYhJOXVRAdXzIcXNCKMQHGPCpfrFZBzGjx+B1M1L4mliGy02LE/T8BmaZB+7LZ7SwWIHO4F7eNnxjHKKIFAHfvMR9Raj61jWt07R8eRoxMrsaHwISKS7VgGFbOVTVD/1wOMk046CYsWLWKJdLJadQs8dgcXimLeRt97Swkqy0PY93YJEnSKv7pl0xM0uOA3n9dG1krbWnm2tKKab/GRlM92jry+G0cKJBJ/4oknsGzZMsZj9+/fn1nZ0EQ8t+vEPPngvvfeezj33HNzrkcm+ETa0wNLNowePZpNKqiaLRnn08kZGQJAqvnbb78dH374YQ/lvhF8//33WLNmTY95ZLmTbaJEAb3SftT39EqqRt9xbLdqPimUdVvZ0HwLivnQqe5cIuUJSVb8NaAPS05avDhkVeOKlU2hw851pOHwXDEfelChdC9QCPFbjd1k/6NCG6Mz43S2KRCxW0POJ8VSW7Fmk1wSvthtBa3GEghMcbcl4Yy9SW0R+HTHiyh2m/Wbd4GsVhR3ARrRwWEZ77zzDour22yzjWutWAixO7PvTTGc4nS+WK0Xu4MRv7tV85IUgyBYEx3Ro8zmzTFUlDsUv7ORtyGys5FJ4Q0HRVxBUXY7cR0skPNkZSPUOhi//faM95OUZ//53MrGC/zwww/44x//yCzTScC933774de//jWzfaX6pTNmzMD8+fOZWPwXv/gFfvzjH6e/+8orr+Cee+7B6tWrmff75ZdfnraXo9hz5513snotW7Zswc4774wrr7wS/fr1M3V8tH8q/kpxmYh4GtVGMXHJkiU4//zzmejOKgxH1tmzZzP7GbWgjdo5piC99957M9uajz76CB9//DGrSG9GPUDq+MrKSqxbt87Q+qQu3GOPPViGIptdTjbQw4f2AtB5JJNJ3YkamkYAdHV19XilKRPqg0YmoZ/ts3aerQeNFAmfXLoUkYYGqpBr7vsXCunh8JViEXTuTSjmhSAWf7VC5uZSzZsl6C2q5pnPnRnVXZD85QPgKZ8JWUzZX3BwFEn81ovdFIspVqvxWRu/Mz+rUzbki9N689QRfpYxXYbU3o7I6tXA0KcsbWKTpBO76xxSwwdFNd9qnEiV1pVArHYodhslaN0m5J3Yflzn/ogbJEqsjHZzwkIg7mDyxso2XCDoWT0JTswXBLbaaisWV91EIcRuvfit9qXVWK2+V/ve7e3tvWK3NimvguZZjd32SH71WL6FIPzV/NfjAjo6I0hKonPEfIEgCKkX32DV0iYH6S+4UfTVTbRYXOYyKa+iqO9PD9DV1cXIbYqvVGCVktNEflOsovqmF110EXbbbTdcc801LPZR0dUxY8awiWzU//CHP+Diiy9mPvBPPvkkW59cVojgf/rpp5klOyWka2pqGMFO0y233GLqGB944AGmlt91113TI+AprtCxkBuMHRgm5ilbQdVntT7y9913H8444wwMHDiQDeejDMSpp57K5Py58NRTTzGbG3V7FICpon11dbUpj17KpJBHLxWatQoKzJTlMJv914OWJMh8oKAbjR6utPPU9bRQHzzouDIVBbne07bVBx0rZIEnVjY2fcp7bcv14q+d7hyHVXsbL8hqI9fI6DrZrGxcvnaOIqhlLNhweK664yje+E0xUo2BdqEm6PViN83r6OjQjd1aokAbu9X4nStJr77SttX9myILzlPW21wWQyUp7s2osYJAtsMFArQVkGI6Q+HdOr6gKORr8xxL3HoHVhaoeJxLNnTxHJ3huMeEvFcgayCnkup652flfyhovwcBxqxZs9LvqQ7MySefjD/96U+MNMjEtGnTbO+vEGM3Qe3fUt+bCBOrUBP0mbFb/ZxJ8mv76dptqNDG7nz9bnpV+/fmRHap2L0lhvKyBOzm911DSFT2oYKR5zSrljZ6CfeyHNsxizqX7yGrKnmz+wnD6Aqr0F5XF2oWeIEvvvgCS5cuxaOPPsoSzWR9TqO73njjDWy77bYsBl544YUsbgwfPhyvv/46SxYTMU+vO+ywA44++mi2LSLlSUH/ySefYOrUqew9xW1SyhNI8U626BQHqZi7UdDoND0L2ra2Nt0EshlEzWTWtb7vKvlLGQcaDkCZAwr2gwYNYsVdVfTp04e9UuBSyW/yiKdhdwMGDGAZdGpUMtA3U8yVHgioYCwNF6BjCALUBw0noPewkfngoRL9NF9VPND3sqkKepH9b/0a4pD9EUm2ozYyGl3Va7BW3ghR6mCkNM0nn3Ux2c4+2y6o5hcp32z+uCSxJL9i3uxxNOq8b/aIlDeimncjgaLp3Bv2qQ2KWj5gvvKZHvPcyobDKHj8Npagdwq5YreW6Kf3VDSQiAN1hJ4eMof+i8/+BpGhByJS3oFk62BUS5vRVi1DrKLY3aHEcKkz9doBQe4Kpsqn1YBCv9Xn4nE2jiUQsNlhlWgovGplY5YI1qrm4y4doxekvIMjR5Tirw4l1WtNrmek7gKHocLtWhxyyCG6MSXXCC2j4LHbeILeTqIg20i8TLKf4rQ2UU/xm2x6aFkuQkYl85UYfgjEzR1Yu07EyJGD0bphO0TEDohCB0SxAxGxkxWUZa9CJwTBgUReiOxsHEMhEK1unHe9BbLeTFvGPVTJW91fIaI+gKMfLGDw4MHMxkY7+otIeOqjkf0rccXaZC7Zs5GFDIGWk9JeRVlZGRobG9lyIuZp+Wmnndbju/Q7TnZuxE0bxTHHHMNU+ZQ8UOMQWezQqDWyfLMDR3qilD2nbAMVqMmsQn/jjTeyivVvv/02G5pAoOEFGzduxDPPPMNIfWrk008/3bTybdy4cazILHkRFRrMPmxQx37kyJE5EwM9yP7nByO5sRTS+lIkxDJsSsgojUbQEalHMlLCCtJJkRIkI2WMpKb35I2pu10IEKWuNKEvMmKAiP0UMZD6LFZ0MiWbShiQJz57L3c6Txw021tXGqVT/NWq0rvRhnreL5I6Hzlvkrwnn1q6R0JDyrsFh84vlJXhwzRSwiPFXT44objLBx6/nYdWDZgPa9euZR19Eipkg5pwZ7H78gFKjI6UIimUYvOWOvQtTyIRrWaxmjzrk5FSJYanPpMdSXYFlcwKx7MYzeIzxeZU7KbXEpqvzGOfoXnPPlPSXvNbZKfoqgNwlZgvQqSLxxn0+Pes05zrHnLj/lLP025RXj9Hu2mPn5PylpAteeoHeOx2HmZH4lHfW0sE5Sf7G5GMlyNZXoI1LQ0oLelEUipHoqsaSakMSakUklwCSSpV3kslObctiglG3qcJ/U0diBDBL9B8eu1EhJbTe4rpoPcJJY6z91mSR5zsDKdqHj6R8vGQWtcUShInpCS8HgYOHMgmFfTbSUp3Gj1GiVCtSJxAI74WLlzI3mdbTvP1lqujxdTlRkHWcjRajvptdHzEa5NA+qCDDmIe9r4Q86RwJ9KdsO+++7IpG2joAU1akE8dTUZx1lln6c4nQp+jW1Fn6IHjmSgY1U+jvTcAG6UYVq8Yh4HC+1Rw2jSoS04df0bkM0K/NEXup8gDIgciZUjEqpEUUvPZq7KM3vcgDtLb7T4YGkrNSIC1nRCHp8h9RvynJvX9SlL4d0KsUV4jydTnLmWdHgRCDkibSiAuSwBO1EzJ5t/e7DFJbUY17zA5zwrQkGI+LORsvnbSO28vryf9r5vx7PcbYbnuPinu9OCU4k4PPH4HB3SN85EAWnucaEdrd0diIxBfVYetqpejv7TU2A4zCDkJYjoe06sSk+l9Kj6Dkvc1jPBOQlmHrUevqc8M7akN9u+O3Uq8lVNEv4bM75t6VQl+GpnHPicgbupkHvHscxsRDgn2XSoIJ4SRmPeKCNWzs3Gg48o85jcm/a9FYLZwqxfHYOG8WVLda3JXe5yckHcMNNyehGWZFiY0mnnFihVsmL3T4LE7zGR/tEdyPNm5Ee2bStDQ931L25VlAZIcYyQ+kflJqQRSVSmSrSWQ5FJIKEFSrkCnVKN8TpYgCYrb6vIY5C0RQIf7l2sEFr9ZTFZjcypBT4S+EsvVZTneg94nDPe9i4ZotUvOe0XK50I8xAVeCwUFRMhnw0MPPcTsa8iCRlWoZ0uYZ+szq8szE+tq7DabcCfbm2effZYlBD7//HM2j4TiapFZO3Bu7DaHr8g6hO59waC/fMISKU+gr5HiXSS7ImzOvmLuxH9+4v+HFKkvxrqJf3WipMC6SiSqUsvSU0x5jSqf8+9MZiRB26BtEN36CIXQZ3Y+2qkz/Sr0WkYkgvIq0LQs2U0mmCEnlwXEmsUIOY+MdfRGIJDqTh0OH3S1fLZ2anSpdoDV4fBSSBTznJQPrOKOIxj3Q1ZiPuUjny9+V0SsE9EiUfNyO5BUmfUMZPMoNUiEyiniXyH8YyliP5ZKBKQ+iyXoEioV8p8+l6SW1ahJ/Bjp+vOfi5xAR7SWPS/Qq5IQ0BILqYmNDkhASM/rTCX/u9ehZYG0//EY0oYIhBKfksBWi7YGGI5a2XD4CvK+XblyJRoaGnrMp1HiJFYzWlSVI5zI7SVsIHZvKUFFhfXYTRY3pIYnpXwPZLPs1wvx67ITn3R2RN4riXo1dqtT9+eE2Kf3cvSM971QnjHqnuLvlgTWlYzD932O6Y7NyIjd2kQBlDhNSQJl/S4lEVBLsTsEv7F27IWcJu2NJjicIMWDRsgHPYkTFlKeXMWN/tsZdP2eOXMmHn74Ydx///3MlobESXq/u+ro5GzL1T4WEfF6tcGsWJk+/vjjrLgsWZm99tprrMYL2dtQodq+ffua3l76XCx/kyNQMF1dXkOCRiChQ46gpasc9dEtCBq0xD+yEf8OkX+kZiISoHNKHwya/4ZC7ItE7quTSvbH0FVS2Wuedn05EoU80dhwSLGLiPwuhdiPJyCMpfNNsHOm5IDQlVTWoXm0HnvfvY7ARgQkISZS26F5LDmQSgzY8Uu3UOxVdzj8sq7gk/IhAfOYD0PnnpPyeUEFbaiyPKnstMOjqVN/1113uXt9OHwHKTxKSkosdyRiQhLLO6oxuY9Lln7ZVMEGiVDqIEdkqlOThfjXgwU+izr3khBFvHJHZvHTb8vcNKmvEATdJEEikiISiPDXJgs0E5H7tM18Sj8ByRSRT3Z+qjpQfd8FgSn+uyBWqokASgCQ+j+VxKf3jGTo6rmcrWMi+eqSfYtcGzFmQ2dx+2nUhpSUN6map9itUF4ew4tRDUWCvfbaK93BP/LII3v9fn/zzTeMtOco/KS6nbpysWgSq1ZXoauLatwERGijIT+p7xgBEd8J+z9ZmeSnJpegjKuLMAFdR6QGAza/lUoIKHFZ1sTnpFiGBNREgGadypjyHfY9SuQr1yV3DJVTZL4yIk8h+1OxmSn91fcK4c9ifOqz0GvdZGo71OdWEgOOJPadsLSpt0HuekHEG1mXk/LBJuVdwPz583HllVcyoltVopNSndTzWlBdU9WeJttytd5p5nK1Jmqm/U0+zJgxgyULqHgsHR/VHiFyn7zsybad6p9aBSfmiw06RBmR8ftXLsQ9bZPx+/rZ+ZXz2cg2hwuGGtpnvmUmQWQneeKTP37pZpuRoNm4goqIaymaIvRXpd6zKQo59UqfiexPlpQhUVEFOUbrd6/LEgGp9dj36HONkhjoaS2gfO71wCKDeckS8a8kCbqUzyrJX5NUkgJSgg3FZqMFpCRbh61H72k9ekBJzWcjB2QJXZ0V6IqVMl90Zf1k8DzScyUv7N7bTiceyKeWK69DD7KDu+aaa1gV+Q8++ID51FFwf//993HGGWf4fXgcAbGyyYVTBnyBqxbvhcl9VmBIqfKQ6ThCoEameEYEAnWIS5NtKOta4/o+VUJBIfdpiiqj+9IJgSjkdkoARCHJtIySAhWQoLyn5cpogGg6GaC8RtIqw8xYnS1ZIPShmKqS+hSDExCqU5/VGC0nu+O2rJ3XBWEtxfDUumoMr+hCZ3ktkmIUXdHyVPIhyTzSezwm1jpYrFXPkscreLFfP6xsVHBy3lE7OlLzTZkypUenngj7o446ig275yhsWCbmU0rpnbZbjv+8PQrPvDIexx36NQoSdQbFd5TgxhZE0IGyGgt974TFZH6KzJcZoR9lsZnU/iwWs/nKK1smVCDB1o9CpvieWpaO30zmqLxSDNfG68w+eBqDAGxREgTMri+duO9O0IuVXRDWaz6nYnx6WVsqZquxXlbjehLJihJ0ra1Ir68kDjwgd50g4wnctqYoSfl4PI4LLrgAJ554IvNtV0G1NKlQK9UpVePuggULsP3226eXf/rppz1Ebs3Nzek6ILScirTus88+6e9SYp0KxJrBAw88gCeeeAK77rorpk+fzuZRP+7aa69lzwR2wIn5YkKzDkmYst84vu9XuGzVvnh900jsX7UIoUEBKHIZWZ2ywWFY69CGTark0wkCIvQjmlcxRfJ3RlLviWiIKOuyhEJZ6n2ELWOkgrqcvidEsH7QaCzf6VDmNa9M0e6RnikuyTABQaMAKDFAryx5QK/J9CuNHMic1+NV/a46ryE1jy3TrKO+H0jvpdSDjWY+KdbVebkadplbivmAJTY4TIMU8U8//TQOPvhgVoTmqaeeYg8ON910E1MLcBQ+THfuMzq6Q8vW44j6ebhr+Y64dsRbiAhyUZNz1EGOyS4lKLIQChQHaFRATqI36nKCoI3IACIIUqSBEIG8LvVKcZslAWgZrROBvCnG1kuI5crogOpUfGdT9/vNfYYgUVKJjqqB3fGbEsPa/ZcZjN1bJAjDKWZqYq2s81qrvCoJhFQ81i5fp/2+BKG/5n2P1+5YTtconVRgy1PreKxeZ23j52g3MzUPjBRyLkKoHXFSxV122WWoqwurFwKHn0l1Cvtnn/YRLr1hP+zwox8wanhbYV0Qk/8WVO+GCGVPk/nkl6/K97WhwMOwIK8luz8aoZeK3ynFv0L2p+J1RyqWp5P/FM8rkKB5A9TvUVzXxPqqCDaWj8T3A45OjQDsThhkWglpkwcE3fiNVExlCQRKpKifU68bU6809dPMZ0kDuq5qPzoJca2mD81EBd3L2PLWVKwu08T0HuvQd22OSgjjz3YRkPKdnZ3MEmbo0KE49thj0dLSnbkh8VpNTQ1uueUWJl775JNPmMf7pZdeypYfcMABzGKG+tJTp05lr7T+dtttx5YfeOCBuOeee1jdUyrcSoVaaQQc2eSYQWtrq27cb2try2Nxlh+cmC8W5CKwG8FywL+u/RDXfL0Hthm+CgNbNlnbh0PFQn0j7VPHZ4v0tJMscIrEbXIgQZCJFovXrxnoOLAOI996vHcQXWaBgIhQAkAhD9j79KsIKeuy7nXY+4H04FKaIhpS89KTqDzg0Ptaep9ars6jz0RMpOenSIqNgLx1NyGRJieycER5bRLY6ALlYQSS+j7J3q8dPRFdZRWItm9W1Hcq0UD3rfqamsf2xEiI1Dy2XXrooUXadeVuwkC7DfYqa15pfz3nKftQXnusS8Nz1fepc+rxefNmltEuVlBwV4fo9evXjz2AEDF//PHHY/LkySwrz1HYsNu5JxxW/y0+2TAYz7WMw9EN3+T/QgGT84Er/uqZ3V8qQYAOpWh93BlVeOvA7ZCMlaNhWZYChQbvI5n2y2KnmrhPkQya2Cr1iLOp1w2a2JyKuVIsplmnOw73eNUsp/3StrtjdnecVx9K5HZjyQXdZWW9lykxVomFyrVRYmeirA/a+/RHV0lFOpYqcVcT77VxWX2l5exZQHnfHWd7rtMrZmvnsW2o8VeZ33M9GQJZo9L662SFuEg9E6RjfV/lvIUij92ExYsX+30IHCGP3f3rN+OUI7/AXY/uhJsufx0lJSGwqHSJ8JRRwmxgig1CnYRInJIDndkTAhUWPOU3gdXa2eqHx3uTux3mNqVEjFTspJEBqVf1s7y2m/hPT+wzJRgozkYgr1dGFyaFMsglqSRBarn6HfZ5cwRyUypes/lir/43W7dD6I65ZcZit976umukEgFKMkGN4RLW9R2HJcOPT6+jxF01vqrJg1Q8ZzFTGaGg1Njr7mN3r5P5qonZakwm43YWm1PLNOul43g6mdJzedovIXV8bL3NiiV0UOP3l19+iS+++CJNpGtBpPqtt96KG264ganpqb7L1VdfnbaOGz16NEuc03pEutPnP/7xj2kPeSL6qS4MrUPnT+T9JZdcYvoYjznmGJY8UIvR0kg5UuKfffbZOPTQQ22dPyfmCwB5szPNxojQEZVrcejgb3HTvN1w0Zj30NjqjerMcQLcDjGufnejT2r8poD7resVezWwPutUZi6zcJ4swFHV7SyVtw0nK3pakOmDznGFie3r3S8WryX7j2YjGOhBRGDSGoVgUOaRF+PA/72B2JbNynLNut3rUcJA/Uzfp/XogUbQfBZSDzmpfTAfK+3+6AFM/R5JfFLDM9V9ptdPXWF1Hk1rRcjUye+xbsbneBybNllIAhYIpk2bxjL/9KBBRPy9996LiRMn4plnnmEKeo7ChynFfJYOL/0rnzPkQ1y+aF/0iXRgv36L8lvSFSg5rxDzncYJaDfbwE9blkxYPA6KK2R/Y3ffSudQ6bSSzY6R73gGu/vKuIdYd5nFVJVIUGOoiI11w7B+4FgM/Pbt7vnaOM3iKZEaiqSWlJPdMTsV0zXb08bhHp9T21CEA5rnAXYh1G2q39O8Z4sjkNVkA9um2r1Pxe5KHrs5OGx5zGsKf+6962J89MVg3Hr/VPz69A/RpyqjmKtX8Nk2hGxgyK+dw7mCr70IaouKa6WH1wXIqXozss1Crlbuv7iDCaEs6yqnpZL/akxW4iV97iitx6AVryqJhnSfV3mvxFpNvIYmvqtxWPtK302vo3zuXkZ9b8261eqV1MTsVB2F9DZTy7tHNKoxXdP3ps9xpSGD2veeNGkSPv7445zrPPbYY1mX0Qh0mvRAv9fkAU+THdxxxx04+eSTmeqe4gAdM9WFI9sdSgjYASfmCwB2C9BoceTgeSgTu3DlV3vj9GGfY8/O74NpHaN3HA4emw6NHB7YKfTqFEHfnKc9lwW4PSyOCugFG+eoZrjJqkcPNKqhdF0bYpspgxRQGPl/fOEFNvSLMuTFiNtvv50F8tNPP5150+2///6MkC8tLWXD8TgKH4ZVd3k6HQNLN+HqEW/jtmVTMHdTf/xy8MeoiHQVHTlPiiwqzOZ4cVur7eRW+3pEXDNiPtsoOit2KF6fmw+JEUXdLgPJ3urXaOdmxDo2oXSTRwfmVnLoyuKO3RwcthXzKXKe+LILf/Y+Hv7n9rhkxn74zU//h7FbhdRc24Y9CNVmKUpiPu4eOd+j7+20DUqu4zByjHGH2yZu8P7LsY7SWurItd7LI1IHSjvXupZI0d2G7KDFMaHp3+yFx2/roCKyzz77LBYuXMisdAjjx49Pj4C3A07MF/rDwRQZ+KdxkpkeEA4etABj+sTxp/m7YG6f/vhZyacoEw0qlN2wqzFC8DmcMPDadzRUqnkL7e5be5oh5b20WbIJypb7VkCOwzGMGzcOixYtYslV+g2nIP/111+jqakJ9fVFYCbIwUa80TBIJzCsbB1uGvk67v9hEi5duB8uaHofI8vXFiY5n+W4STEfae20t61Wm97cbqDVuiLTDkFLXrWxxBZYgt+jBfzevw6Ymo1sbrxAGP+vOTgKQhRHfR7B8O80WdicdfIneOejJtx492444oB5OGy/b/OPfLMDp+uj1zmgmKfRbsUCq7kXtWuQh/SVtX3v+oAQ8kbP2a28lFe+8mp7Z7a7GaKedwED+7tPtWWuuOIKVlRWLSzrFJyRWXP4CpXUcRKjqtpw44/+i0/ig/FJ++BQkpehRlOA1fJ24VXSwWhbNNq8rx1WyxsCde79LCBXJFi+fDnOOeccVnn98MMPx3//+1/H90GkrPr7TUp5qi7PSfniQlZi/g7zCc2ySBK/HvIhJlauxpOrtgktgWnoeHWOWxJLzCnmDWxTdz2v0OrgPi0QteQDz/zSzcJIO2ZORr7n1P79giB6J1Rw8t4JGVatWoXf/va32HPPPZnnKxVqzfSHJ4/YffbZp9cyM6CRbVRwjpRyy5YtQ0dHB9suDWvnKGw44TGfid12XIbf/Xo2nnx2G6yJk7F4SOAA4VnQVjZxncku8hC35PHOitM7RfC22CDljZyzk23jJ+rzLFMnq9vg8BWUjKWissQPuAGumC+Qh4OcVjbHmlPNq/ikbTBqI1uwc7lJOXou1bzXBWAtIO2F7Tf8Vs4vc47w99wayMjxBvw+zAXyi7VVoJjDUMLzggsuwLBhw1jn+9NPP8WVV16JUaNGYfjw4Y4Vf73tttswZ84cXb+/N998k1+pYgeR8+eZ+/3cJMXw8YbBuKgpS8HOsCKfkr0VkCpjxjzMjW4/23peqJHdIFVNKueZlQ0rXGYCrS4sc2r/gYjdPKnuduwmUn7gwIGMdKdib7///e/ZZyomR6T8GWecgT322AN33XUXhg4damk/M2bMwP33349zzz2XqecSiQQrMrdkyRLmYWuH8OcIx30Wi8VyrGFQNZ+Bt98fjj12WcIKw1qCUQuNBhdU8zYg1aZit1VmKkDnkobbJHMO9TxLqpdJpgu99kKLxXXsesaHDfUOXTtOygceRxxxBCv0etVVV+kmZ6l+nFVwYr5YsvZEzt9m/AFhU1cMTyzdBr+p+R+iQnERgFIkBrErET5y3km1/LIsny3uI60Q8yLR0OQhIe+HWl6t9sitbFzF3Llz8f333+O+++5DTU0NRo4ciQ8++AD/+Mc/LFVx18NJJ53ErGyOOuoolJeXO7JNDo6/rdoa21WtxPhKE+Nmg2ppY4YsV31qrSjmzcLt9goIuSyJEYhSnloFXhPvYQazsuHEvJtYunQpvvnmG1bDhUafUSKd6rfMnj2bEfM0n0am/eEPf7C1nwceeABPPPEEG1E3ffp0No/6YlQvZsqUKQ6dDUe4FfPmyPmF3/fD+5824rarXoXjyLQ38xqUCMgBKWlTMd8QIILe63bWIXnlelLMJ7wn5POdfzy8ozp6CTedINGd8KLn8BR33303e6URd3ojoCk2WAUn5gsAThZ/VfH35on4UXQVJpZZjHBOKuMbHfCQX2ac0JWiRMwHyOfOa3uaZS6p6MNCyqvr5Lvnmv31qeWKeXexYsUKRsjTpGK77bbD22+/7dg+3nnnHbz33nvYZhuDliMcxaua/4Oxzv2iLTV4d/1Q3L7Vf8zvJyjkvA0ClzzRLSvmg9BeXpDXJlTzshCBYJSY95uUDwHxT6MHeex2F1Sj5a233kKfPn3S84hAbW9vZxYzlGB/8MEHHRnxVlfXm+2honpUO4SjsOF035vydQ/83w447sdfoabarsw5QMhDyKuQ4GPx17hDRK7fKnANUSwJEYhmR7u5Rcrna5c1zt1HbiIdu51WtXOVfOh++90C95gvJp+7C409KC7Z1Bez1wzDKTVf6K9gl3D3itBcpplMrC+tikGMB0QxH2SYaFtPrGyabPjIN+qsY+U+X+bhcPggq+58TFo4BSLkqRNPnXkVNGx5zRrn5DhUNKazM0BJQI7gYnr++C3JwIM/7IBjG75CTSykHXvb5KrgT7HxTK90q98361Prsi+rLObwmNfziM+2XlDb3WNwKxv3Qf0hLSk/b948vPLKK8xrfv78+UzRRuQ8jVSjiVTvVnDMMccwyxwi6Am0XdoXDXGnfXEUNox7zOeIR5rf7TffG4GupIj9dl+EgkGDSY95wYHnYbMEbrbYaTS+BtEbvQWQW6MQNiW7veEzpyzfy0vKtzhEyq/RTEaQb12vC7tycLgArpgvtuKvF+a2tGEd+8U74Oiqr1Eb6SakiglyNAaBrGyWFWjxVR/AiBK3PPPdbHMnRmu4ARpOx31q9THQmSYmdXxVVRUbskYesgsXLmSesRUVzhXk+tnPfoZTTjmFDa2vrKx01KeOI/hwWlX59trh6JAiOKB2IUIJI+RqPHenLGuNmLhLHbpcvvfQUdRbJZDjBtdRzyXuJDFvwspGC6fJ8pCQ7znBC7dnR9/cHGbvtsy9+I033mAesFSQlYhyKgT76quvoqurC19//TWznCG7uuuvv55Z3pDNjRnccccdOPnkkzFgwADWD5s0aRJL5h900EG48847TW2Lo9CLv+pY2mh+ozdsLMH/Pf8jXPzLdxGJFMBoCwvqZiLmI/BYqGImTgaJeM+FFo3HvJwjdpuxTwm71YoTpH09IJcHpAYhh68YMWIES8TrgUZRUd0aejb46U9/anrbnJgvkIeDkpIS29tZ21mKuxbuBKlTwIENC+Aq3C4Ca4MATsZKICYsKubNeLHnOn8nyeBGl7e/zAQ5rq63LGBkfAiKEmeCPx64i7KyMlx33XWs4Ovf/vY3DBkyBDvvvDPryDsFKhBHOOSQQxz3qeMowKHw0/UtbSih/nJ8NP61ZgIuG/YOInbqwgTFzsaqb+kIk9/LXF4XIDLZChGgJecd85i38DtUTKS8if8ZbkPnHcjn/cknn8SCBQtw88034+mnn2Zxvbq6GjfccAPrN02cOJGR9C+88IIpYp5+u6+++mqWrKdRdFTAnTB+/HhMmDDBxbPiCLWVjc5v+qKlNfjzg7tg18lLMW6rsLC/zkOSY4gJG53ZmJHCtmFp6jprx0xWNjmJeafJeLNqeatYkyXx4/Czj55K3pfRmByBw09+8hP2bHHZZZelk7NU/J2eM/bbbz80NDSwZWRrR6PqzIAT8wUAIm9MPRzoqOY/bRuIvy7aEbvWLcXJVV86U/DVTZ95F5XMcswDj/l87eLU+WXbT6O35HwvKxsr6vmm4iLeOfzHLrvswobAx+NxFmhvueUWZj8TBp86jgLt2GeQ82sTpbhr+U5YmyzDtSPfRGPpBniKbIRpbZ71zSrJvepEu9m5M3MMbn6/1aRi3qxPbWuREPIWQCM6srZnkJNiIQSNbqPCrzQ1NzfjmWeeYaPfKOmtFTPRcrK2MQP63X7qqadwxhlnMDJ+1KhRLpwBR2HF756qeXr8e+nNMXjm5fE4+agvsM+ui+E6vCgAa9ELXEbMXrFSMwgLKW/j+slWPebNEPJWirwGoTgvB4cNvPzyy3j00Ud7FXmnZ4ELLrgAH374IRtB95vf/MY0Mc895ovNykZLzhNZ2Qy88M0Y/HX+jji7+iP8JDoHMcEjsshNixAbJK4ULUEkYZOYz0U6GyWI8/miG/l+QKCbZTajsm/y6X4Moo2NV579HAzRaJQNVd+8eTPefPNNNhzeqd/tSy65BOvXr+ctXaQwNxS+N37oqMLFC/fHkNL1uH7EG8Eh5dVlrXk+O406TXEuu6pzPxA0n9p08ddEbz95PX95J73eQ+Qbz9BqxspG53lI24aF0iY+4aOPPsJxxx3XwyqMCNTS0lKWWN+4cWOPkW/Lly9nMd4sjjjiCOYnT4VmZ82a1WviKHxkszPIirrue/JPD+6CWR8Mw7UXv4l9d1vMHCo9RwAKaqpIogQRJzzmCx11xkj1vFY2et81Qsq75alvpmaOl+S+xlOe9705CHPnzkXfvuS91xM076uvvmLvf/SjH2HVqlUwC66YL+bO/W0ycKGA9zc34ZzaD7FdmfkbKDBwUAEukce8VSsbN6Al2I2coxni3wPimXn+ZvNSzmdt4yQhb0Q1HxJlPR9O5x2o004F4+666y6MHTsWu+66qyPb1SruaGg9R/HBcuxOqea/2tSA8ZVr8JNBio1CIJGPaDSiEPaDrPZaOR8wQl6F3BGBGDegunOSkA8rDNzPipWNlPuc820n26gTDoYxY8awIeS33XYbI+ipc/yPf/wDJ5xwAvr374/9998f11xzDRtqvmLFCqak/93vfme69aj+DGGfffbptYxb0XHkQleXgA8+a8TDtz6HinIbdiNhU9PngCx7qJj3Gi3eFxTNScw7pYo3+501Fmxw/B7BqAHve3MQJk+ejPPOOw8PPvgghg0bxubRqLwLL7wQO+64I/v8/PPPp9+bASfmi3U4PCFlZ7M6WYnBUY+VdlaIUI/sbCRmZRPQh4NcJH1ACWWleFyejn0mQe+WQt4MOR9QtXxONAf7XggjTjrpJNTW1uKAAw5gvnJOQlXcUYE6PYKWF38tbFiO3SmsSlRhcIlPsTugBGqP4q9OWcK43TGMB/S6tZLHPMVvqejuI8vIR5rT/altz2znn0nOZysyzMl5XdUaFWaloupUgI0S34cddhhOPPFEtvzSSy/FjBkzWFKcYvvPf/5zFt/NglvRcVjFmtZKVPfpCA4pHwD4UvzVTbS4JBKIG/eY72Fl4zYZ78R3s22vzoTXvJNwIWHCEX488sgjjBugIrD9+vVjI+tbWlqYcO/xxx9HZ2cnq2nzpz/9yfS2OTFfrKq7FCm/WYpikxRDXWSLue8bJS5DqFKW7BR/tVoU1QoC1Ga5IEUiEJMGHz6bLLaBGRI9M7mTb50Agg+n8w4zZ850bdtccVfcsKyYT3nMr+qsxHZVK50/MC0J6Dbxl49cNNnJS1vZxH1Qz8fdKeLmFylPkCNEzHcVNiHv4egIxfe309lRJwRO0Pfyer3vvvt0m6yqqooR8xwcniOeit0tlRhQ71Ch00y0BFsZnw0SSiAIARXFuU3It+QhgePm96so5pPOFHH14rtBg8714H1vDgIR8u+//z6+/PJLVlye+nGjR49mzx0qZs+eDSvgHvMFAPJRNO1zl8Lquko0RDYj4kSxV/hMcmaSrE3WrWwCq5gPIblvSDFvBqr3fqMD55GqsxB0It7wcDq7dQmcQhCOIQQgxV22iUhbjsKGJcW8pvDrqs4qDCjZBFcRIl9rmUiPdgudw0yvdCtFzuI+erO6RMoTZJE85pOFfz85fU1acxV/TSnmWx1sKyP3MIfjIHUcqe2pQ75s2TJ0dHTg4osvxrp163hrc2SgO3avXlOJ/vWbgkEKa1XHdf4q5kWEeASBUY92L+vDrDPRnvmeTeIGpnzIpnDPdd8FxMpGKd3sIlfGEbrYT7GebPC23357jBw50pHYzxXzxUjMp9TyhFUdVait2IKNEg0hkxERJPYqUl4w3ybN2H3kU8UbVc17YGdTVMS822gE5DKHiPl894fdeyFgIzc4Ch9nnnlm1mX0m07+dRyFC9OF2zWkPGF1ZyUqxU5sTkZZcl2N364UkctU5tYGz1dcEmMQkwn39p1pbRMGdZhFQl6rEHOkM9oaULV73EXrIr1zHigCmyR3CXSuovcEpLq///77ce655+KKK65AIpFgQ9qXLFmC888/nw135yjcfre2uLBhpH5bVrVUoaZPOzZtjiEi0nOAjEiEEvU2DsoMKZxLNe+Tol6SSyCG0WPeKTI+n2reJOT1UQhinr53PiLeDTRk8Zq38lzlhZ1NGn5UZ+YottjPifkCgClS/p9CDyW5HAcWbuqHn284DMmMARQRSOgjduCQPvNxQOUClOn9wDtJzgcEzGPezeKvXrSD3vZ9UoWT4k6sSvQ+JicL2YaRnLfhB8+H0xUG9Dp2XV1dePbZZy153nKECzQqgh7mrJDySVlAiZjE7xfvgy6ZpdLTy0RIjKif3GcFju3/FRpLDfjQGyXaW10i52GfJGDEvGTSJsTKOXhdGNYqzKits8A2Ke/G/ZF5PzhNqLt4fVnxVzc9+3O1Pbe7cRQPPPAAnnjiCeYrO336dDaPEq3XXnstpkyZ4uzOOAL37GZutFvP+C1UyPjPK6PxylujkZS6tyMItF0Zg/pvwNEHfYMpk5YZI+udVmr7QM4rivmQeMy7pYy3Ss5nHk8LINWRbVpXcAh5I+Q8AvJspWtjI0IAH8nMAVdjPyfmixy7bLccu9Q9y97Ly5ShOkTQU6efXpcn+uDp9RPw4oYxOLr6axxYtdDeDnMRn26Qok2agqLFoJhvtEBau1zoVMo2FD4XUe9nAscLct5me4diOF1YC+h6iIcfflh3/t/+9jc8+eSTnh8PR0g85ukhUJBx79gX058lmSYhHb+3SDG83joSVyzaG5P6/IBTB85BTf8OZ3zkA1p0UooQMR/S2O20OtohQlw3CWyU8HWalI87TKjnIydcIAgUKxuf4jdX0jvbnK2tqKvrfZO0tbVZU1NzFOhot96/oaec8iWb6HeGbhVJEpCkKSkimRTw1Xf98Y8XJ+KZV8bj1GPmYLsJq5wnibXkey6i1ENQYsITWEk8WPWO97rIaEu3jayYJG+/gJHybsJN0YQgKv+sHEWPVhdjP/eYLyaQWj4HhCZAFICYIDF1fKWYwJjSVlxe/w5Gl8Tx2satWOe/F8ySmM0uk88OFH8VulzO2jc74K+uNxn9fi5Y8ebX+06jSY95K+eht26jj9enycK2w05eW/XpD8HoGb9AGXerhWM4CtxjPgsodkdFGaViEhWRLtTFtuCEAV/h10M+xLvrmrCio09vMlWd7CBABL0sxiBYIeYDdA6GkeknrvUYt2lfY3p9L3zN4z6s50Y9APp/Vz3m/QL3oXcExxxzDPOUpU66Onp53rx5OPvss3HooYc6sxOOwCbVnYrdNOidrGxKYhLKy7pQVZnAztsvx3UXv4mOzgje+zhHx8JNT3OjBKdDRGigRwK3uOw33+LQd/WKv9oh5dcYnKzAMwsaZ+K4opjnxDwHXI39nJjnyAki4h9YuwNWJysxvWEm6/z3ghViMdt3rGzLYaKZFPMRN61srJyr20U97Ww7T/sqxLwDBX2ykfFmj32ZZsoFtwjzxgJ/gM2HoBSoDQCWLl3aa6LgfvPNN2PgwIF+Hx5HgBXzRvDVpgbctWInXND0P0yobMlOyFkpGhnAYpOSWGJPMW+GoHdLWZarTf1s83z71VvuVMLDbFvrFaMzW6TOJfQo/soRatxxxx3MimzAgAHYvHkzJk2ahAkTJmDIkCFsGUfhwu3YvXlLFDP+sjtGDmvDL07+BL4hCLYiVrCmAArAtthbh2zTRG3f2wwpb5Zwt0PQh+Q+VmI3t7LhgG7snzhxIov9d955p60m4lY2IYcjwyWz2L0QKX9P22QsTfTF9PqZ6BMJiPebywpjpphPBORc/bIZsWABlMvKpsfDgRm4TeAuy5Ng0POBd/r+s3COgczaZxu9EBSLogBi+PDhvWqE0G/60KFDeeG4IoCbnfvPNw7An5ftgnMbP8AOfVZ2LwgQke70sHOl+KvF2B001XxrMPZhO9bYrUNgl0SPB2t4PJElvivmORxBRUUFqwezcOFCfP7552ze+PHjGTnPUdhwcrRbJjZuijFSfvCADfjVqR8zNb0nanmzRTnrfFZBewWbBHneeFSXw1++JYfVTZ59SqSYNyJU0CPlrWKNyfsgIBZKxmyF6P89gH1vDt9i/4IFCzBnzhxHYz8n5kMOxx8OUkRalyzgrtadsCZZgSsbZjFbG114XWhTb3/NLhR/dcJjvsmhNnCLzNQjTZtzHP8yayMQZCGLx3y+YwsS3CDjw25ho8Kv+7cAsHjx4l7z+vTpg9raoLGEHGHq3H+0fjDuXr4jLmh6H9tUrUaxgCnm1WJn2ciEICOASRM2OsusNU7mz5cXRYLdhlPkvJfFX0NYJyJMoGHst912G+uYb9q0qdfyN99805fj4ghvUn39hhJcd8c0RSl/0ifZC7+2uJT0tlOUMyik/BoHE/52SXmjlmm5yHmjx5GxHhutriq84xZJeSPHr3dfrPHofvDwnmOF23lSnUODUaNGsclJcGI+5HCjY0+k/J/iu2CDVIrf189GuaijdnaTVMxGVntEZMqRKES7ivkmn4uNNrrQZla8541a2TQWKdGtp8Rv9NnKxovitxwMw4YN4y1RxHAjfr+/rhH3/TAJFw99V7GvMYNcaq4QgBV/JcW89pjDQtC3BnN/QrtDCjEr5HzQr5kF+Fr81QrCnlBxESeddBIWLVqEo446CuXl5X4fDkcYY7eGHG5bV4Zr/zwNW49djZ8c+7n7pHzA4MhIYK/V1y3BjV1MFJfLeiUXKR93IGntFTnv0fOq0u8OQFKdwxeMGDGi1wj3bKDnAqvgxHzI4XTWvlMSceumqUjKAn5XP5sVkuuBZp9IQLv7NWHNIkfIekXylsDO1Q5midJGEwR9PtW8A5CZlY3Ow4Hbnvlmz2OZ9eSD18mTrA+wdkn1Zg/J+WUhIiccxNNPP80KxxAee+yxnOuedtppHh0Vh18w+qBnBLPWDsUjK7fD5UNnY0yFSUYtn6IqBAQ9s7KpTACd1u1wPEcxEZ+1IT93J1TzQSj+yuEI3nnnHbz33nvYZptteIsWGZzue7e0luPaP++BHbddjpOP/JIVhPUNVuxFgqKWNws3ngusbM+Iat4kJDEKkYh5I8djlZTP/E6dTWsbM3Bjm7nanSU6irPfygFcffXV6Wb4+OOP8frrr+OSSy5Jx4FEIsHqw6n9e6vgxHzI4aTijkj5m77dDTEhiUvq30OJ4GPnIWzqZy8J3VxotEBUu6w2p4cDWwVTvBo9EZRrGBRw5bwruO6663D44YcjFoth+vTpOQlbTsxzGMUbbSPw1Kof4YphszCyfK25hjM63NpPgj5fB7oOkGrIyiaHDZ3f5Hyri9+tdeE4Wi2OzjJqk1IIFjcmwYfDFw622mordHYWeD0qjqx9b3qGs404sCZegatv3xN77LwEx/7469ykfEsAyfmgkPJBUMs79XzhADmfUzFv1NpGezywcUxrstwzQfOXzwF6FhK4Yr5ocfrpp6ff33333awO3C677NJjHfKZ/81vfoMZM2ZY3g8n5kMOp7L2VOj1z/N3QYmYxEUV7yEqWMwK6hGmjS4SgM0eEvpNASdCnVI4O0zUG7KyyXUshU7AW2jvnGQJJ9QDDbVIXDaPeQ4OsyTmh+sHM1L+quEzMaxsnbsN6GAhzPT2YHObqe9KQgyx5Jb86/pBzrd6sH6t89sNZKFxP2Hz/leI+YC0ab57psiSJkYwa9as9PvjjjsOJ598Mv70pz+hsrKy17rTpk3z+Og4vOx7l5WV2d7Oho0luP7O3bHXlMU45pBvcq9sh5Svz/H9bDExHznvICEvyzaHCASB4DVLduvZ7Tn4bCUbLf6are1aDMzLJOqNnoOd62X2vnOqTXnhdo4U5s6di+rqamSipqYGX3/9NeyAE/MFkLW3S8xTH+HhJdtjbaIMV42fiegPDncaTHhnuwoTdjbp9b2GVVLVyHd8Us2TlY2hh4NMmG0HD2x5XEOjA2SJE+faGKD/1yL3qaN1Fi5c6MkxcfgD2SxBV9ubMPt2cx3+unxHXDr0HWukfNwCues0OW9kmwb875mVjZFY4zU5b0KhXjBEa+axtFpQ9vs9wsGN+58693rWfhyhwJ577tlr3iGHHKIbv4m85ShMOCGK6+wUcfNfd8XEMWtw9MEukPL1Jsh5IySoi7YkEqIQkfCHkLcSZzLbUe/7cZvxxKxqvqW3Yl6MJ60lEMwUm7VKzltBrnvP7j7ztLUMkSvmORh22mknpox/8MEH0dSkkIXLly/HRRddhJ133hl2wIn5Ang4MGxlk0m0pQi4F74diy/WDcC1E99EacTGg2Q+YtBpJa8VIlIl25dlJ+DlPkLwVNVOtZ1RstrB4rGSGEG0q11/+/n2n2t5WEj3sIET8gXhU8dRQLhT0FXPr+iowi1Lp+KswR9jXKVNBrM1B3lrhky1g2wdOoOnRor5nFY2fsAIGe4kYa5HzlvcviMSDSMWPK3FQ84HrvhrtmQOV8tnFUNxcNi1kaXb6I4/7oyqyk6cefxnztvXZCMZrZDzHljWyIhBMEPMB0Ehnwtxh+KKDUsbQzaya0JSXNjqvefoCASK3fz3Pyv6Ug0dg41JzWjRSCEIoPpwNFpu+PDh6NevH+u/t7S0sFFyjz76qK1tc2K+mBXzjcA77zXhxe/G4Nrxb6I61mmd7Gz2mGB2ohhsMQ7dbjZJdjtA0JOVjWjVyoZDv02t+P5yFJVPHUdhF35dW1WKGd/tjiMb5mGXvsvd7TxqSbpaB1VRZrztDSCtmPfbD98MWgO6zVZ1tFvS2eMwOiqjQMl5WSQrG965LwQ4XQCUoziuPeXlHnlkO7SuLcdVv5mJSMThPqcVItfn31gJJRCFRDhJ+IBCsbLJ6HvH8xR8tULKu62aN0LKu6yWVxXzDskVOEKOpqYmZmv31VdfYf78+awvN2bMGNZ3twtOzBfAw0E0au0yzv20AQ9+ugN+P3YWBpZtsn4QZklbtzywC11BbbfdvGyfxkyPeROdeyfvDa+V9Waskmwg8Mkj7nPvu08dR/CT6laJ+fZkBDd+tRt2rF6BQ+rme0vkaolVNy1tjPqyaiCJJRBaOx1T4LvarkFSJGc5FkbMu2HHob2HgtQOVmD2f4D51AYsfmezHeLICVLKkbXNvvvui/3228+RTjlH4SvmX3xxDD77bBCuu/BNlJXm+X1tcYGUt6OadwprdEa7RRLBU9EaaasgJo0pfrdTYj1oDRpAUt4oWH0YnlTn6MbEiRPZ5CSsj8PiCJ+VTXP3tPSratz2/hScu/MHGFXV5vZhGjs2o+u5TLSGUo2subZZl/sEsrLpQcy7NWLC6DwOd8Hb3LRP3bJl3Rkdp3zqOAqzY5+UBdz+zRQMKNuEU8fPQUHCYkdX3hJFJJnIva0gdKJbbXxPnTw4FjliMqludr9GziMekGvmEAJV/FULTsabxrPPPovJkyfjxRdfZKPeGhsb8ZOf/ARPPPEEVq5c6cZV4gi5Yv7dd5vwwgtj8fuzZ6FvdYf3pHyudes8IuTXZFHMQ5NU9xpuFLT3Ei1ZEutaK5t8avmgtaddUr7O2f8b4oYE5sHCweEeODFfhFY28c3luGH27jjpR19ihy4TD49uE59GPOq1+IfznRs5DGpkMyS9B4mMfKCHA8NWNo0hJn49UsuHJnkU1OsUMJ+69vZ25lNXX1+PAQMGYOjQoejo6GAWNxyFCysde+Lz7l+wA9qTUZwz9kOIdn4G4j4SctNtxtgsKnimmJc6s59rEAq+tjpExjtkV5M3did9VNwVECGfBv3TBrX4KyfnTWGfffbBVVddhddeew1tbW34v//7PzQ3N+O0007DkCFD3LpKHAGB2RFvX33VgAce2AGXnPUuBvbflJtk9UvRXpeH0JwqK1O+9Uxa0kiIWSv+yqGPuJoENkEkW73n6guflGcQIgBXzHO4DG5lU2Sd+82JKGbM3h17DF+CfUcu9pRMtGWBkY2U15Lzxwm2yUBbCjEjx12EFiGKlU2qc2/32M2MCChgYlg3eRSy+4LDXZ86jgIa7ZbCv5aNx7fr6nHttm+iRJSMFc80A6P+33bsbFRSXkvOnyc4QuCmPeZzrOMrzFoGGVnHamHeVhOj3fzw7A/C9SomxbwKTs6bwuLFi/HSSy/h5ZdfxsyZM7HNNtvgmmuuwSGHHOLWFeIIG+LAshXVuO22KTjn1A8xeoQL/2T1Dtu06P3Wj5V7v3/PQOxeE9LC7QUAIV88dTrOhomUNwlFMR/g2M1REODEfBENh++SBNzy7q4Y2a8Nx0/8yj4p3+wSwaxdprePXEp5m8VKpUgMYpeFh4NCJkWb7bVF2mPeLVK+6NTyAUaQr1GR+dRxFNZot7dWDsfrP2yF67Z9E1Uxmx1Yvc5Yqw2y1yhxm00pf4dsjJyvy30eLH4HwVO11SfPf6PbqjUhVNigMxTebYI+TKR8rhoGOstMqxg5AgtKoH/33Xc4+OCDceKJJ7IRcDTyjYNDi9a1ZZjxl91x/GFzMXnbH5xXLft5y5F6Xo+cN1mw1XfFfDzA8USvuKqBc0mPrI67WEzX63sv37NHnXvHTrGb7lQODsLjjz/ObOvIivbVV19F//79ccUVV7Cpb9++sApuZVMkinkiAe7+aEdERAlnTf4YpmvOeU24NVsg5bXLGq1NUpNJYl79rpdoDtd2SXVniCyx2o5OqOUz74Ugg/55A83Oc3BwODna7fO2AXhs8ba4fOJsNJRt7r2C06ppq8RyLj9wJ+1rsuxHEqIQZJ+Jeafb1OoxtNo/FmUEYVdxEiRunQuL3yHr3BeYz79TOPPMM1nx19mzZ+Opp55iVjYLFizw+7A4AoTNmzfjhr/sjmk7f4/9py0KHilv9PtatbweOa/1j7dA/Coe8wWimDebuLab6M5y3+RVd8cdvvfqbKrk1SmApLwCnlTnUDBjxgxmY3fAAQdgyZIlSCQSiEaj7P35558PO+DEfJEo5pn3YUs1Lmx6H9Hlsr9qeasw4ilv03eeKe6qOo2T+U63kVFS2A2LHJf2wxTzA5PeHJ9VT307nvwe20HJouheQT4ODo5Axe5Fixbhz/N2wW/G/Q/Dq9ZlX9EoOW+UYGu1UXTUColHqnkHOly+Vt/wwPfdy8Kw0vosHvNxhwlf7eQ2nC6em4k8xYaVgnwhyqxrj58T9D1w8cUX44033mAF23/1q1/h66+/xv7774/Ro0fb7pxzBBuygf/hrq4u3HrDQRjWuBYnHDY398p+ecobISlzkfIOgVnZ+Fn81Q7qbDyz1HmsSndKLW+VlNcS8EbIeKPb98BmL9BWNnQ9+GAtz/DAAw8wtfyFF16YFljR67XXXovnn3/e1ra5lU0BPBzkK0Dz+eef45133sH1495BRbTLF3LWNlwo9JqNmBeSIcnaq9fFS9/2fNA5FlnIqAzvJinvNfyo0UDD6cLUsefg4LCsmL/llltw2og52LbfKvutaJb4bLVhoaL1njeqlteztMljX+MbguTF7dKxMBI56VIS2K/raHV0iR1o/hdkQYAghUwxj4D+DwYEK1euRGtrKyvkTv0x+rxw4UK/D4vDZzz77LOQZAG/PMXCKHUj8IKIM0rKk2r+ecHyb4fUP4aYvBGBhJ4Xfy5//nzPLnUukst5EsOekfJGyHajcNJP3sb/jGJDFxBRXLbzMHJfctgGxfu6ut43HxWBN5K0zQVOzBdBVXi6gcaOHYtaqR2hgF0Snr5PhWBzIYsXvRQpgegEMe8lSWylIKyHxyfXRCCuzdOmueoNGJkfhOSRR2Ade07MFwTot/m2227DnDlzsGnTpl7L33zzTV+Oi8MbxXwsFsu7Xjwex5Sxy/RJ2FofydxaC8p3KzDRsfRMzdQaYhLfKOKAPCQKQU8xb3O7BYM6C+fdLgJxCT1cG7wsqsvhGI4//ngmemppacHkyZOxzz774OGHH8aUKVMM/bZzFK4gTo3dO267HNGoHHxfedpenc34ebgOOW/w917amLKR7fTh99CJmFRnYDtWCOZ819lLErY+z3E7ScZnbtvKcsf/X+je9ugZk6vfA41jjjkGv/3tb1ldGQLFg3nz5uHss8/GoYceamvbnJgvAtjN3oQWjSbXaU5Z2SRtDqdrDqB63uwxOWXDQ779avFXswg6Ke+HWp5AWXtuZVMQOOmkk5hVyVFHHYXy8nK/D4fDY8V8WVmZudidSfCaKdTqBSHqNCnvJdS2rfWRZHdy23ZIghSkaFTfysYq3LgHrRQrdgK5VI/xPKq7TMW8dn1O0ocGAwYMwF//+lfstdde6NOnj9+HwxGwwu2G+t5BIOXhAClv8zeerGwEuTNcv4dhUyevCQkpb+Sae07Ke1S4nRPyocAdd9yBk08+mT0DUDyYNGkS1q1bh4MOOgh33nlnOIj51157jan/LrnkEtTW1jIy4pVXXsGqVatQXV2NPfbYAzvuuGO6WAp596xfvx4nnHACGhsbmbLw5ptvxmGHHYapU6f22Pa9996LkSNHYr/99vPqdIqHmG8OqWWNFXuXRkDutKmY91u57cT+HT4HmRXkS5q7VnbsdbSEeRMKi5RPecxzK5vCAKnt3nvvPWyzzTYIKnjs9tdjniA43Vl2g1wOKymvl+zIRK2NdvNaLW+UGNFaDWXZBiv+6pSVjVukvPbVD4LeSHtqIYaw+KtXqCajVhPr0625Bb52zoMOHr/dSaobid15+95BIXWdIuVtgBV/lXX63vnsX7yKS0ata6zEOavnlu942h0q/JrPuiYbKe/WNavzi+Sm/3kXY3d9kSaMQth3u/rqq/HII49gzZo1bMQ7Yfz48ZgwYYLt7XtS/JWGc1HVeu3nhx56CBMnTsR5553HSPlnnnkmXc3+3XffZVkIGhb48ssv93rI2LgxoD5kAYbw7d+cI2Obw+0jnwvSwJh1Yt5vUt4JWD2HLGr57mJnOTr3RhXwzQbmLdP5rJ1CTsoXjEctB8NWW22Fzs7gFrzisdtfj3m1Y88sWlotEr9cKW+fNHe7YKiZY3HyWsdzz8vpMW+0EKhbBUNbfUyCZPMONkAiKao7OTxWP0FWrHLkBI/f/irmCY57yxeoopYVf1UV83oIauFpPdJanYygLuDX2iwpb/b8zcDodl0qhJo3dttBEK41hyFQUvapp57CihUrMGrUKBx99NFscoKU90wx/8ILL2DrrbfGZ599xj7T6+DBg7Hnnnuyz/X19fjiiy+YPw+dZEdHBxoaGhg5T8V0tCAfHyLrjzvuOC8OvXD88Kz4YnlJNDtNyjdaOz9JjEGULBDzxUzKG0De+88Jn3wjhPkym0p6n0l5BqaY58R8IeBnP/sZTjnlFNx+++2orKzstXzatGnwEzx2B6Nzj7Y8MopshGTcA/KyUJTyfu7DD1Je+706/W2QYj6vUCEXGR1EIsVtxB2oEWNUfc/BkQM8fvuvmBcEB+NjkC1sCA/pZCEMqrFlIuZ7FN7IArcV9FaUyNnm2zlGJ7zlywyq+43eV7lIeTevhw+2NXogJk1wWjHPCflQ4ogjjmB+8ldddZVuP85O3911Yv6rr75idjVnnnlmmpgnuT8VI9WCCuWoJ7fzzjsze5otW7aw4jpaHHDAAXj++eex0047Yfjw4Sj2jr2RAjQMblSEt4r3AtKRb9Qp/irGEKltDwYBWyCkPAUz28fg9PGZJegDdD8wFWNQFfM6/1Mc2XH++eez10MOOaTXMvptpw6gX+Cx2//OvS0bOjdI0ZcDErt1IGeLNWEsvOrHtdbZhhQhxbzBWBMEEt4rKxtOnHOkQP1aqg8zbtw4LF++HH//+99Z/1YvpnsJHr/9J+YdsZyoD6FlTYv5daVqUswnvP0NjgfQJsTB693reajBhs+8UfsaJ1EXMJJbyDP6HwE6Xm5n4yruvvtu9krOLk733V0l5hOJBF588UVWoTYa7d7VkCFDeqxHXvJkY0MFdAiklr/sssvQ1dXVqzjamDFj2HCB5557jtngOBIcC9yjVlHM54ARi5BCI+WzgIj5WOd6cwSjX0Sk3n7tqs5dgKXRGm6o5c0S9AEi400r7jhC8xseRPDYHYz43W1l41CH06odjtdoNU/AykIEotwVnPNyUi3vNukdz54EFhMdCCRqM9rOT395q97CHKEG1UIj0RlZsw4bNozVP6uoqMAPP/yAK6+8EhdddJEvx8Xjd3CKv1qysgm6Mt4FSEIWj/kgJkj1SE8niND6gPa9jdjXGP2uW/BAeS476f7NlfKhhuRi391VVpuKvRLJTl7yuU7un//8J1PRNzV1s2NE5GeS8iqI6CfvPCqaV8ww4lHrKjlaYKS8ZSsbK4Vm7WynOY8Pu5nJy+O2Ci8SH5le9AEl5RmIzAsooevpfcHhGnjsDlrxV9k9Uj5oavlWa57vLHbbKdzuFJz0o/fZV1cp/pqR7AgSan0k5S1eF55ULxxcd911+Otf/8rs6KgfS/arX3/9Nf7v//4Pf/nLX3w7Lh6/Q9T3rteZioyU7/aYT3hr4WYHetfJzjWs92Gkep2LpLxbPvM+ktyKKM6Bvjcn5Tn8UMy3tLQw4vzcc8/NuR75xbe2tuKkk04yvO2amhrsvffe+O9//4ttt93W1HF9//33WL16NRtqQIE110SdZkoQ0Kv62bB1TMAU84FQywcN2Tzm6eEgqG0Q1ONywsrGa7V8SCHT71AYiHkOQ3jjjTfw0Ucf9SgCS7VV1q1bh7vuusvzVgxq7F6yZEk6dqsxOTNe54rnQYrdRpGO3UYP3S0SN6gWNq3dpCzFbkGbVPdSLR+0EQcOQWZWNj7ZaWW2aTby3U+lfLFY2vARAbpobm7Gvvvuy96/9NJLOOGEE1icoVHdK1euhB8Icvxes2ZN1vidL3YHKX6bUcz7biMbAlKeICMCAQFOAushl0o+k3zNpaavd8vyNNn7t9uMnY0VUt7reOgpyU18m417lBPyBYMzzzwz53IaRRc4Yn727NmMaLjjjjt6dDCpyB0FdrKtITLi448/Zgb65NFnBrvvvjs++eQT9nBhBjTcsF+/fux4KOutN9EwQCpAS+/JToeCML3PNnRBJfmNkgXqPLsPGkaz9nmtbLwghr1WyzdbU+1KYgmEFQFQ3YWJlM9WuDU139ZojaCes49wLGvvFrjPvGHceOONuOaaa7DDDjvggw8+wKRJk1gy+P3338cZZ5wBPxDU2E01ZdTYrY3JFKPV2E2f1didOWWDldjthIWekfhvysom7Ep5q0idnzyoxFrhdgf27aglS8CIedEPYr41dxImzOQ1xW9H4GcRRA4GIqfvv/9+HHzwwSyeXX/99Ww+qecz66h5haDHb4rRavzW63uTKCFzvp64jOZlxulchL+TAjs6ppKSEvesbMJMyluxc6lXC2t6lCB1UsBg1MLGY1JWFqLGnoeyxa187ek3Ke8DyS0LIkSrojhOyhcUZJ2YRH3RZ599ltVCtQPXiPn99tuPBXCtj/x9993HyIaBAwdi0aJFrGL8qaeeiv79+5vePpEXhx12GMtKkKffyJEjTX2fAjNtQ+t9bxW5HjToQtFDknaeum6vzncqgtODQ7aHDe37zZs3px9m8qkKdCvDO6GWzyTcpwr+k/IXCpbPR4qUINLVrVz1jIxsLFCCOhtpz2FLMQ/uMV8QIEX8008/zTr21dXVeOqpp1gsu+mmmzB//nxfjikMsVuNgXahJfkzJz2SX5sQoOPQPpyp8Tdf7FZf1WcDI2SB5eRmPsL3iYztHiyEi5TXILkhBnFjqiPqBNEdBLLcZ6WyFHQrmyAgbu46OW5l44Vqn/vn6+LPf/4zs1a94YYbcMEFF2DUqFG49tprGUH//PPPww8EPX6r/Vu7fW+KvdkEdhSfM0n+TIFdZuxWj81I7N6yZQsj5imGq+fjuY1sJuke91ua7zOMJCrjBVRoM9s+U7FIUhXzmnlZVfNG4ke+Yq8FZlujB0XiapKY54R8QeLhhx/Wnf+3v/0NTz75ZDCJ+aqqKjapUAMXDYWjoEZFc+jhYdCgQdiwYUN6vT59+hjeBxWC3XrrrTF37lz4CaceNLREgZYsyCQOVKJ/48aN7OGDJlonm2UNPXTUTdgfi/qOQUTqhCh3ICJ1INLVzjKqYrIdkWQHRKkTkb4dEJO0rDP12pFdnatHuIfARz4XpA0Zw+GDoBRuDjcBzxRiVgj6YrZZygUhBFY2PBljCDSUnIa9E0hNRsPQqaN7/PHHY/LkyXjggQfgNYopdjtJ8hNyxW4t0U/vKbFOVgjqd/RA6+68885Y1K8MUYliNMXuTkSSFLs7U587ILZ1INKnMxXLUzG9pRMCEtlVaJmkfMiI+EzIkRKIyc5wkvK5VPN1/pGjzGNe7dx7hSAkRMzCjWtjlHDnpLxvoBi9fPlyrF27FrW1ynCOI488Eqeddhobne0HiiV+axPhsVjM9vYyR+JlxnC1n03vqd3oc1tbW86+N0t8lByGxStXQxQ7EBE7IAr02omI2A5R7FTmCx2p5Z2a9Tr1BXXZlPAhsaxxHdl+N92On36Q89lQB8gboxBkTey2Y2ljptirWwgCwS1EjNWMCAqCdE8WCaZMmYJf/vKXwSTmc+Gzzz5jnVIqUENT5tB+M/jxj3+M7777DoUCLVGQ72GDCuAS6upy/0ouWLAArfPeRNOor5AUy5AUSiCtKYEUKUMyUsrsWzpiVZDWlSI5qARStBTJaCkkWhYtYcN30gRr93MchO++66EQ1Cr9M+dlWydo3oGyGLWmmFfBbTyyK8Qs2gtxhMzKhsMwpk2bhltuuQW33nor6+Tfe++9rFj6M888wxT0QQOP3c4l6ekZKJ/akIj8Dz/8EOdt/SKLx0mR4nVpKm6XQtpYikSkD6TK+vS8ZKQEUmcpkk2lLJ6pUIaKy4rq58DfsPitF5ONxu7A1dyJOFT8NajEsA8EveIx76FiPlfbB9nGxgQcs7Lhvu++49VXX2Ue8yopTyDCOqjg8duZJD2R8pTYKC0tzbke2f3Ul/8HQ+pWQpJLkJRK2SSlXpNSGRJd1d3zZHotSX8mqEa0aUKw/EIg/p1ufDYat13te5slA+tdGFng1wgflZT1igzN0dZyHSXVM2K3FXI+n1Je3a6bCALZTXZL5QKEpASU+X0wHH5j6dKlun26O++8k41MCwUxTw8uKulODzJqwRwr39eCVADk0VuMoMy9YaXAhi7Evt+AmJZZt6JEVtd7TxlGqKcyUBWAWqWB3vxcSgPFm69bEZHt4SJzYpY+5QMRoZEApChMkqqQVIPGiEyppgTCoATQ+3/OOBpNtm0uotqMQjyzCGoTvIVRVbwRgp4r47NDjJA01+TF4QgiyPf1oIMOwumnn87iGHnTESFPHb7HH38cQQCP3c4jZ0H2jPVoikmbAZqMdjxzEZwppbxqA5AZk7XvtSpBvfWyHbOeLV+umN1jXkVDOm7TK3UujdAHrHC7XY95P0n5AHrNk5UN85g3c1wFQqC7BUetbLha3leQpUtlZSV7Pfroo1n8NuI97iV4/HYeFPuM1pkR0ImS2DpndpxSxWtH1meLz+roer11svW99WJ33nitnSIDIMqdEBs6IbYkIMrU95aDTbgWAkGfzQ+9XCepboacb3A45pgpiKu3vtfI3D/73+B9bw6wmimZiU36/Rw6dGhWm5tAK+Y5vK8M71g2msjUf3R74qvEuRM2PpnQEv/ZHi50yf/+uzCveOqo02gAelUr8KiqQZ2dsU59W+02iA4/EmITEQMpckDqgrhGIQlIkaeQBfSqndelrNuVWqZVNLutEM8k5MMCLfmubaNiJ+XznL8cESDU82GrhYBx48Yxz1f6naPf0YULF+Lrr79GU1MT6uv9firlCELHvhccIOW1dXbcgjZpn5kE0E40KqDH8uHTlPjNJorhyjGqan+9+E0xuKO8FrIYYa/p2JyO0xTHu18FTSxPz4+TX7DxREAxgNpTaDVpZWO1SGvAkhKBhBd+8tp9ceQEWc+Rav6VV15hw9fJXvSQQw7BscceiwMPPNB0YVWOAux7Z7OjCZAFXy7/fr2YnZm4T08VUyGhBJIQg1RdAqk9llb8p4+d4nfq34KWiehiBP66knH4vs8xEOQEIrJiw0fze05kxZuAmF7WydbXznPN0z9kBL1EtivEQ+iNrMocfWdEFa/9Xr55WtQbXKZtoyB0fXSOQQZZ2XBingNYvHhxr2YgOzjt6Dmr4MR8iEHB0UjnnmXGnepppkh5L6Al/k3h++dMq7tliIzAT4yvxKDlb6RIfc3URyEJkmIJEmIl5LZYN3GgTrReJAaZvY/kVEcpdiQyIwQUkr9L8747GSBUp5Z10TLllT4LjFRIQmjuhNiP5ndBUJexdSUIywKsmtf7XrGj2UTxV1KrZlufWwWFCuT5SsViaGgcdfLJc/6KK65gU9++ff0+PA6XYrelpLodskzPU95FWCYPvv6XqdWphUjZHR+yI7P66bdmrpKQ7xGXFaI/UVraHafT82OQOmKQRlASP8p81bMm8DXWAixGU8xWE/OpBAD73GtZVzq2kzWMEuOTqSR/snvd+i4ILcFJDMgdUfY8ESq0FpB6P+4zOZ8Nfu8/IKCO+DHHHMMmwgcffICLL76YfSZSnkbD/exnP2NKeo7CgVr83dO+d10I+t4bnjW2XofyIqfITkkoQWdNNQZsfgsSYuwzkfty6pWmpFiOBPqkP6fXQebn7LFb2SctlyDKlBDoZIS+wN6rxD69dqWXC0gq89nyLs36XZp16Xvqq+yt13eWbcsCecx35bY9c9sOzQzBHgQyPs+x5Lu3AgnuM+8K6PeRBHSZcYBGKa1YsYIp6q2CE/NFQMwThJCR8q6gObulCgVqVhBXSqC0I+4JqcwoFzGqIRCiGsIgBikehRxVyf4okiUVSJRHFOKfpvVRyNvEINE6UYVQUF/JG5bto0+GP6HOMVBWXSH7iTxIkQgqmZAmFTSfSVFISYEUuSCk56feb+qC2D+pKA+lLnZe5HvMPstSYEiH0IL8IbmVTUFgxowZuP/++3HuuecyIp4USKRiXrJkCc4//3w88sgjfh8ih4+KeaOWN8UMim1U8JYI7tLONpRtMlrRzLpSmxEKYkSJ1RR/NXGcxex0LKeYTHV7aFlFOs4rMVyzDovZqWWJ3nUBemBLz+Q+xWKF2E8qCXwWr5OaZL0Si9nnXrFdE78z37d0IVFenj4+2qYhG5ZaH618WguYpPcSXC1vGFQI9KWXXsLzzz/PlPNkZfPzn/8cRx11FBv99tOf/hSnnnoqbrjhBjevGIeH0Fq+5IMj/Z0CLexKbaMQ31uYSr4s6c0PDxPiCZSAj6YJffpMSQE59Z6IbUX9H0WXWJlKGGiX0brR7nlsOcXuLKPzazLiNxstQDGVJiWZnyb8e72m1mGv3cuU90kIjQk2so29T20nUVIN6m0nmcVfEqhL9b311PNxi4nYuhCQ7A6C1Vvk9d04AIwYMQIrV65EQ0PP4SZz5sxhVu3r1lm3L+PEfBEMpyMCYF0yd5Gagiblm42rvDOH3bniKZ8CBW2moiNfXD1R2gqHLGya8gcbhRhIEQSMOIikiAb6rCYD1PfKelK0EolIallqvtKJT73vF2HqxPX1o7F4h1OY0lsWUgmD1EOt2tFXSYbsBylDWJ+EOEYhHdiIAEb8JyHEiVCgeamJCIrUK1tPXZbzVer9Xvua3r4EqPPgD/K2ldVRCxye44EHHmCK+V133RXTp09n8+g3/dprr2XV3TmKWzGvkvfrukrRd11KZhYCtbxlnGL9V5XiVaxzo7kvWSSChVaFUACSiKDdefI313HFe8YCislKQp5idCSVzFfishLHM5L2YgTJWCnkskplOVtfifksvnd0r7tu7ESA7I5kpRCsXvzp8TlLUTS9uKsk/OkzxVc1ridZnFcS/+p6mhhc2R2He8R8+tyahNCQsTy1Do0wEzak4nmN5KzPezGq0jlxz7D//vtj1qxZqKqqwhFHHIF//vOf2HvvvdO/7bScirkTSc+J+eID63tvKC0OUv4hwfJvnAQRQixlu+LB7wsT4smdpG8FZE3tHiu/yxaPlSX3W6hPHINcT6MGVKI/Cmktkf+RHkkC+qy+T4ilqXnK+ux9VQTS5u51t5Q1oKO0Hp1l/ZW4DrE7Xm9RbAG72yN1j2keZdLLSileamItSw4kIZQrr0pCgd6n1qvSrJNOPKifU+vkXS5pPqvvlfl0t7je927JllwQw2llE4CaB4WCvfbaiyVlKTl75JFH9qop88033zDS3g44MV8EnfupU6fi5cf74qUNo3FIn/np+W3JMizs7IeFnbVYjUr8Ch+B8r6hxoWCNQV7isx0ZJiSRaLe1Hcc9JWnYBfp6gRochhJ6vQfWopRHz1keRvsiggi5BUKccBIB1GdopBXEQmhmZ/jVYqVKAkC7Xz63GNdelgSdZdDFLrn5bE70C4zNLxyg6J+ZEkIlhSgJIBCIqhEAy3rqKtDok8fJKqqFIJBXVddj+b1VdSN7EFGnacmE9jDlUKWsO/QMjZp1kvNY9/XrKO8qvPowGmbNC/1PvXKlrMTS63PyBll4GqaFGlvTw/9chT9AZipv0a7Xw1f0Nrairq63gxMW1sbV0sXMIyOdisrK8OBtQtw09JdcVWfmSgTFb/vTlnEkkQNFiRqsSjRD3tXLMaEkpaiJeUJpF4n5bhhWFVnt7rgr24RimJeUcDr9qptnluyrBwj/vUEIgkDv9FZzltNHvSKxZkxWk38b+yOw0qCIRX3S0ogJ0XIfTXxn5apsbq9O17TqLLubSuCAGUfqdhP5ERpjnicK/mQZV0l1iqv6djK4nIqhlOSQJawfuhoLN3ryO75mTGa5lfKmvisjcFSlteM5Uz00fN76dirzkOW97RuJCWaULeVju+pNqDX9nbnY3fIMHjwYDz77LPYb7/9stbr2HHHHdmoOI7CgVG1PNUbmHHd2xgzIo4JY1rS/zor11Rh4ff9sPD7WpSWdOGEw74KNylvM+Eoo4TZwgQa+VTjcQujBerpd7lD9fTpRh/Ne7Nkamr9ddVjsaViMAaufCv7uvHc8+U6RbjH4if1tdkrxc8I5DpKFKTeq1M/Suyrn4lNSn2XEggsmaAkCJQkA3FXqfitfh9Z3rPPgsIDwNm+t4pu4j8Vw8uTqlGiQsbLMtaXjMKm2FC0dW2T2qayrDthkJona9+r63W/pveTuvDq5/Ty1P6610n13TO/p944an+bJQ0071PxOr3+AJf73kWAPffck73OnDmTiecoKa+NC5SEP+6442ztgxPzRTAcvqamBr+rn40r1+yN1clKrOmqYJ35TVIJRsTasCzRF6PQSj+R4VbLHxdQk5RGh33UzZLyfnjNp0DBXFiX7H3uJhTdakAiZR2SCUQyn9/KQ1wAVwN5udBNKqQmKlqskA9CmmxYP3o0tvTvj4YPP0yRHqn11O+q7weqy5R57FVdV/tZ9a1nD0ua5altpQsnU2JCUC2YlPVYyBeV1/T3CKnERfq9Oj+9jgCsWsVmUcG0YgV50ZIn7aOPPpoO7PPmzcPZZ5+NQw891O/D4/B5tBvh9IGf47aFU3Dz2l3RP7IJCxO1aO6qxsDIRkQFCSuTVTiial5Rk/IEsksjz/a8sGOXYuS7Zsj5bPYt2ea7qSTU2R8R2UT850Vt/uQB1nQZb5d87Vzr0DU1cjyt+ddNiwfScbv7vRJLlRhOJMSm/o1omPOeEjcZ6YDu5fTdGhHyxlR8VuO0Ns6n52ljNcVulbjo3p4Sl1ND8Ht8VxOzU8euxnY2b3OqX5H+Tsa2CKtWYdOmTShmGLGao/7XCSec4MnxcAQLo0ePxtmnf4Rb75uKvaYuxuJl/bDo+34QRRnDGtfiq+/64+iDvkbokUstb2DkD9nCkHe7pRjX4oF9ipHRS275tVv0CCcyO+0xnw25vOfV5AFLDFPyONFzuZ4OIMuIuSCjm7pOxdTUK3tf150IoFhNa/fpnI+KrpVKTGajENTlmnXT20qtk15X86pZriQfNN9NmTwp31PmKceqxm71u9As647T6e+n36diOcLT925pacEzzzyD//3vf3jooYd6FV69++678emnnzKbuJ/85CfpZWQnd88992D16tUYNWoULr/8cla3jUAq9zvvvBMvvPACtmzZgp133hlXXnkl+vXrZ/i41JHtFPsvu+wyXWGdXXBiPsSgm8yox/zg2EZcXj8b72weip3Kl+OEvnPRGF2P97c04fF12+Dc5Af6w4M4Ke8cGg2S82EphPqeDukzteddRCo4pvzKdY6NBdQmVtGcCqGkiqcpB2IDBjD1YPnKlbm3acP5whM8+HRaHU5erMWIO+64AyeffDIG0DWVJEyaNIl501HROHqA4Cju0W4E4u/Oq/kA/9g4EdViB3YvW8oS6kmIuLRlX/y8+hM0RjeEl5R3CGTdQtZwWeG0f7mbcHtf83XujbreT4CUjDVUzyRXMkJ7LtmSEGah3Z/dREuu/bcaW7eneCD3LqMdW1DeqnSMe0Dt3+UJ667DKLl0z9Msdn/1VRa1b5HgjTfewEcffdRDfdje3s7i+F133eXrsXH4jx23XYFE16eMkN93t0XY6pRWNNRuxqP/3JYtP+aQr8Otln9esG27RTYujJg3Q2xnktUtAfA694qcv0Tn3rhZ6HHOclUUwqY8wciK4r/AfOW7KwLQCLVUe6nNm/HoE5U3szoI5Um/g7RNDAt23/v666/Hv//9b1ZcnUYNZ5LyZ5xxBvbYYw8WX4cOHZpetmDBAvzhD39gYjfqSz/55JO46KKL8Nxzz6G0tBRPP/00XnzxRbZ9SphTjTeabrnlFtPHSMfhVuznxHyx4DYZowA2qaDKwQ/8/ve4dPqlqB43DqGFU0p5In/HIxjkfBhJ+cz5UwU2XD2vvQD3RDcHyoobIUt4uwYeFRUVbCg8PVBQ0RjC+PHj0xl+jsKEGcU8ptNAb+CUjO/fdNNN2G6bOkz7xUsINRxQy6se871ijVGFuwo94tUK8euxpY0jiMu6RL3hq2O0nTIJdb/bSu/6u5gYSY8e89NHnnvEO4Ibb7wR11xzDXbYYQd88MEHjAwgS5v333+fkQcchQfTBdnrZEzdH5iqmUUq0Pc/f5jFcLFGrQoaQhAp7wCYYp55vhuAGfV4EMh6p8l5PVIeGfNvFpj3vNinq5tkztdudorAhrVNTXrNK2r3EHrMhwy1tbVMkT5//nzcd999PZbdfvvt2H777RkBn4lXX32VxeKjjz6afSZSnhT0n3zyCbP0pvdkM0NKecK5556Lc845B5s3b2Z9cTOg3246BjdiPyfmixQdHR247bbbWPGCcWEj5c0S8dmI8CAWyPTQW95xUl5nPfnABkUFbodELoREhoMJGxrSHoridRyGQUPuaOIoHsV8LBaz/H0aikn1CejBsxhJeD1IYolSSN0oqdrqsyo+SGp5IySyE8ilqveAFM+LfPt2IInQI3bHfSLn3VJ2FhlIFUcqvIMPPhjV1dV46qmnMHLkSNZpJ1KBozCJeaMe83pYuXIl7r33XqbqJNVmaPCei7EbJRA35RFwOVW40qz1TV2AfluzkfI668n37MyKpqZRb6At81jbFCV6kPMhLf4aMvzqV79ir5kxlJToRII/+OCDWVXsW221Vfozqe0bGxuxZMkSRszT8tNOO62H1Rj1xZYtW4axY8eaOkYa0e5W7Dfmg8JRcCDPpoaGBvz4xz/2+1ACA22VckOwSuz7kRDwidCXn11qzKMWWYjpYiDlTd4XjJg3opgPYuKJgymljU4chQlTivkM0NDT559/HhdeeCFKSsxUOS5sSJ0xiC0GCsi1ekT+BoHkt0HK91LQO4lsSREzbRbE9rUCvwjyYiZbHAIlR9XRbeRTS764hOOPP54Nn+cobhu6TJDlAQniqH5QQYyKbNBMNiCtI8V8l/ukvNvbdBNnmovH8tEvZveYr88TFzKnXChUtbzOvcIKyXJRnG+YP38+S4oSOU9FVml64okn0sup5o22GCuBSHO1Fk7mclqmzjcLsgFyK/ZzxXwR4u2338bcuXOVYXQGPepDbVtjkKCUhuoMhc+3zcYiI5KNquW1dRAuuhM4zuAPVaG3nxOg/1kjxDxHIEEZfMrQjxkzBqeccgp22203WwosjuLp3JNi5M9//jN+8YtfYNCgQa4cW+iQImglVvw1ESwy16zCujUgpHwKwq8uAB67x5n9h83axyU4PgrBDrhy3hamTZvG/GlvvfVWTJ48mSmhJ06cyIrWqZ1+jsJLqlvtNz/66KNMJX/EEUc4flxhTkpKJVmsbFq8tSoppN9V1vfOleywWFS2aNECyH1IMW/AAaBYQTVUjWqF6N99jbnNx+NxdHV1MXHStddei++//575xdfX1+PAAw9k/apsv9naVxVqvztzvt+xnxPzxeR1B2Dp0qXs4eD3v/99r8xSUaORhsLHIEqd9oh+I5YkzcVByqs/eJ6Qjk0BsfmxCwP3D3XsedY+vKCHiVmzZrFMP6mnaFjeSSedxIrAFoSKisOVzj19h4oFkz/ilClTeCu36njMd2WJ337boxgpjIpgkfLpZ0xSzmcWhq0tIMW6x9CN3X5Z2kBnv9zixjDI75YKtZ9++unMa37//fdPF6x77LHHnL5SHCFOqr/zzjv49NNPwymIyweTBFvm74wklECUM5LqYSeNnSTnTarl033vU94F7q1xj5wvBrV8D9BzELeR9QuJRIKR3jfccAMbLUxEOJH0ZO1JxDz9rurxourvNXFR2uXqe/KGtxP7KUlAsZ+OjYrMPv7447bOkxPzReRzR9WC6Wai4gfcz7g30a4Q8wn71iC5yNXmAiTsjdynuUj9qQFSkIWBnDdqZcMRWFC2naa//OUv7KGCqsdTERki5klFf+KJJ3JFdAHDSuf+X//6FytSdOqpp7p2XKFBa5aEpV6nKQgEst8FTi0i/YyZzdYmk7DPhhCeu+fwk5znsASqz7Vo0SJFnSqK7D0RBU1NTUzFx1GYsdsssb58+XImwLjsssv4SAr0JqwlQaOYN0sUa9fn/3K9+97ZfOlvTsVurpw3DHmTCEHgxLxfqKurY/e01sJz+PDhzNqGQAVc169f3+M7GzZsYMlyveW0jGBFpKzGftWWdOHChY7Ffk7MF4nijn6kqbox3TSUWeLoTbbLYgxCNmK+0QFytQCJd0cSSBaU+KbQVFjEP/OY78rj28/95UMBesA45phj2ESedX//+99Ztv3SSy/FXnvthddee83vQ+QIQPz+4osv8J///IcpRewUjQ09cpDsvUj5IBDyTpLzQVQyu+lDn4v4rw3vdcxpZRMEcj4ANgxBxogRIwyLoqjjzlHc9WE6OjrYyMijjz7adIHBgoTOb4uMKMR1CUDhyYxBj8DnJL3xvrfRQrJ2oJL/hQJBBLQFdTk8xVZbbYWNGzeyUefDhg1LJz0HDBjA3lPxVRqVpBUjNzc3pwvC0vJ58+Zhn332YZ8XLFjA+uBkL2s2BlCS9YorrkgnWkkpv/322ztynpyYL5KO/RtvvMFuwhtvvJH7GavI+F+UxJLeinmnCM4iJOWtjuxwHMvk8JHzOVTz1LEXuWK+4LBy5Ur2wEEPEpWVlRg6dKjfh8QRgPhNBQbvvPNO/OpXv0L//v0L+5qoZHqtTYK91SeywW9StdBAxP/oAMZuo/eXDoGf14YuCOR8AWDVqlXMA/bjjz9mijkiRn/yk5+wZZ988gn7TSWVW0NDAxuOfvjhhxva7tVXX51+T9t+/fXXcckll6TJWhpuf/PNN7NkO0fhwexoN1LKE3F0yCGHoOBg1sJGL+HXAkg1JYhIm/N/v8UF/3i3fOadSnJasLHx1Ea2iCBDhNAqAzncgTjcA/V/yDKGbOOIGF+xYgXzdP/d737Hlh9wwAFM2PbUU09h6tSp7JVqemy33XZsOYmS77nnHmy77bbsN5meAUgAR9ZzZkB9N9r2GWec4coIKE7MF8HDweLFi5lVwvTp09lQjlDjH7JrBWF7WNlwxbHvhZKKHuo92Gyy+Cu/d0NFHlCAp4eJr776ij04/PGPf8Rhhx3GMvAcxZ2wpDhPxV732GMPVmAo9HhCJ34fLDhCrMvM/9MHxE2Qqn5Y2oSd5J2fh5wvRL97nuix/dz529/+FgMHDsQjjzzCkt5UV4s+b7311jjvvPPw85//nHnDEklPBeQGDx6MHXfcMe+2icRXcffdd7Pt77LLLj3WGT9+PH7zm99gxowZ9k6EI9R9mrfeeovZGxSEIG6qTux+3uA5xXMT7YqVTZ7C7RymoNpr+QpS5ReQal6p78ZtZP3EpZdeyuIqkeK1tbUsjhMhTxg9ejTjOYl8J9KdPlN/WvWQP/bYY9mzAK1Danoi7ympbgVUwPvss8/GVVddpcvFklWtVXBivsCJeXqIIF95KixIXkwFCSLrjZDzeQhLaVAMYmnq4eBCD4Z5WUkohAyBUMyHVTWfRT3PrGwsFH7mCAbIJ5yy/FT89c0332TFPH/xi1+w2h/0oMHBoeL5559nMfyEE07gjWIUL3v027hLCOJJ2En5sCMjEZPTxkYPXD1vCUuXLsU333zD+j7k90p9H1LazZ49G8uWLWOWIqp6nuw9//vf/7LinEaIeS3mzp2rq5gjlR4RshyF2fc2IppYvXo1KwBMdgdWPIxDgcPl3OR8HkK+Z/HXlMc8t1gprL63lpzXG5kQqiK/lOjgfW+vcOihh7JJC/otzZXwPvjgg9mkB0pUnX/++WyyC0rKE1RbHC3o/47ihFVwYr7As/Zr1qzBpk2bdG+egiPnLxTsFX9Vs/ZekPJmEgpuIJuvu8OFWAPzcFAI5HwKcr0AoUSTtecK+VCBhtAROU8FuEnFR561hBdffLHXuqeddpoPR8gRFHz55ZfswVRVfBQska6nmjdLgLajOEAK8mzQKsvrnInfgUCYVfMacj40iruQ+8wT2U5qZbXoG4GETKSS23333TFlypQe6xPRauU3dqeddmLKeLIroX2qnrcXXXQRS7hzFG/f+9tvv2UJINXfuKiQ67ejJcdodS9Iea+V3Nrf0ly2NA8VcN+b2jvb+dWHh6SXBRLFSe7aH3GEJg64hQLu7RU2jBagIQ8mGqIZmB/oIHvMU9b+sKe9PQa75LwVsjlXsVWHC7EGYjhdoYHakwgTTsiHtrI8qfg6OzvZMPhsoN9sTswXJozGYzV+FzzskPOtPqjlCf+T7anm1WM1c965SHkjy00iUM+NhUDOU/E4KSDJjgIG9Y20pDwVfHvllVdw5ZVXYty4cT3WbWlpwYcffogzzzzT9H5IEa2ORu7Xrx/bL22PhrE/+uijjpwLRzhHqxdN7FZV8/kSeTlIVyaK+8V38BR2yfl8owU88pIvKBvZ+qCT83S9Q5BY5/AEZEFLNuE0Qu/VV19lHvg0Qoqmvn37Wt4uJ+ZD/HBg5Ef3hx9+wKBBg1AUuC2Paj6flQ1l7cMUyIiUN0POL9N8J2AJJM8QZtW8NmvPi7+GFkuWLPH7EDhCgC1btmDdunXMF7lokItYrc29vnDiBfAc/7NYnFQvgdAaLFuawKjljSKopDwybeh4xz4rqC9bYqJByf1iS/bFb7zxBvOA7ejoYCOP9txzz17Pp+Q3Syr6iRMnwixIJT9r1ixWH2b+/PkskTVmzBjmMc9RmDBKeFLfm+oZFAWIpM6l+M5Dtkp73Rauvjedrxlyvk7zHY8QKMW8GahK9AAS9LIQ4TayHAxkp3P//ffj3HPPZUQ8FX2nUXfUvyernFyiu3wI0S8hhxVinrL2RUPMq+Q8EfB6Ux5Ih7/oz8OBXkHbfMgk2I0Q7h6T8qF+OAgyRAHgnXsOjlDCKOFJHfuGhobCtrGBDbW7hoiV3/eR7LSrUjd63pQE8BChit2tITlGUszz2O0ZyLKGFG1UfPPdd9/F00/3HBFLhbXJeubyyy+3tR8i9akY3OGHH85J+QKHUcV8UYnicqElwOpu1Ton1zFmjgTIJNiNEO4ek/Khi996CKBVjAwBAlfMcwB44IEHWJ24Cy+8MB0P6JUKylNtMDsokh5f4YECWSwWy7teUWXtVVj0iJdWrvQvkBE577C/exAQ+ocDBDRrzxXzHBwF/ZtIsbsohsI7QD7LkhSeOKNHxL+cxxbHY1I+sLE7o5hq2CCvFSAEzcqmgAvMVlRUMJsZmpqbm1nR9WOOOYYto84zTQ8//HAP2xsODrskMv12Fp0ozoYVi7RggX+KeTOWNlYIdh9I+YKxkQ2atQ09D/HEOgeA1tZWZkubiba2NtujTTkxXwRZ+6Lr3IfVky2fvzsR90051stl0+KDWl5t08B17kMOWQxg556Dg8NRey+uuAtR7DaimifiPRfBns2z3gdSPrDEvFYdXxsitbwKKv66OWk8waBHmDt53vHCJOc/+ugj3HLLLfj73/+evofp94GKvBI+/fRT3HzzzfjjH/+YLr7OweFU35vIGbrvqO4ARwj6ifmKzpJdTS6C3Wm/+UKO3yEm52VEuGKeg4ES/BdffHG6lgz9r1Etm7PPPpvZ5tkBJ+YLuCNKxQUpq1NUHrVh7tzng5HCrD4R8KHJ2ofcX171qbWVtafkTmbtAQ4OjsDZ0I0dO9aTYwo7Ah+7jRLsPpHwgSRL8iFMhHwKshjpLv5qRf1f62Cx21zFGvMVcgw4yOedyNHbbrsNxx13HFatWoV//OMfOOGEE5hynjrUp556KkaPHs2KtaqgouwcHHYJTzWpHujfz4Ah0PE7oHY1BVUjJiTkPNHzHBx33HEHK/w+YMAA9qw8adIkVhPsoIMOwp133mmrgTgxX8BZ+w0bNhiyu+EIUec+ZCiYrH2QYKf4ayYpz8HBEUhinuJ3SYmZSojFCx67iyCpHhSFfrbludbpMdrNYOw2omC3S85rUUCq+b59+7KO8+233846z9XV1TjssMNw4okn4sEHH2QdaPKIpUmLjz/+2PS+Hn/8ceY1u2zZMrz66qvo378/KwZHEx0HR2HBSH9m/fr1PHYXK4kcIBRU3zsgRWELqEU5bNrkPfvss1iwYAHmzJnD5lHR9wkTJsAuODFfwB1R8j8aNWoUZs2ahX333dezYwsreEfUnTYtqIeDAECm4fBOPsgSWc9V8xwcgbKy2X333fHKK69gzz335L+hBtqUk8jOgsfuFJz0tKfir1YVdyH21vcD1Em+7777es3/xS9+wSYnMGPGDNx///0499xzGRGfSCRYse4lS5bg/PPPxyOPPOLIfjjChe23357dF99//z2GDRvm9+FwcHA4VPyVg0OLhoYGxrPS8/KQIUPgBLgcpsA95kkl8u9//5t1XDlyg3funQdvUxdACTkp6caWOTg4AhK7p06dirVr1+Lrr7/m1yQPeJxxHpyYd6FNRRuj3dxGgajlvQQp7kktf+GFF6Z/0+n12muvZYVlOYoTZWVlOOCAA/DCCy/4fSihABdvOY+CHYXgo9uYoCbVueNZ0WP16tXMZ57s7ygRu8MOO7D3NCpPa49nBZyYL/CO6HbbbccUHFQMicOZNuUwDt65dx4yWdkU6kMXB0eBw2icobj94x//mHfuHWxTDuPgberSaLegEPO5iHhO0hsC1fCikcmZIH/7giXGOAzhwAMPZNZIa9as4S2WB/x/xXnwZAcHh3s4/fTTWbHX1157jdmOUsynEc5z585ly+yA92QK/IeX1jn88MOZeoMHv9zgHVHnwYn5ABZ/5eDgCLxinrDPPvvgu+++Y0PiObKDx27nwWO3C2Cx22HC1o7FjcopcyLeEkgxR4VkiaBX+1vUWT/77LNx6KGH2rgwHGEH1RcgO7qXXnrJ70MJPDiJzGEK9T5a2XC1PAeAmTNn4qGHHsJee+2FyspK9ntPluE0iu7tt9+21UacmC8C0JB4yuZ88803fh9KoME7ou60KVcyOgyrqjte+JWDI1QksjoknuzoOHK3Ke/cOwv+POQ8MR5IKxtOylsGFZilkU0DBgzA5s2bMWnSJEycOJF5zd55551OXiWOAMCsuI1GvL311ltMUcnBweEg6rPMMzrZsbLhKHqMGjWKEfJ6RWG32morW+3Di78WAejB8ZBDDsFzzz2HcePGcaI0B3jn3llwwsR5cCsbDo5wK+aJcDczJP68887DqlWrGAHE0Rs8Aew8+CgEd6xsDI1242R5KECd8GeffRYLFizAnDlz0kVnJ0yY8P/t3Ql4zWf2wPEjCCG2hFIV+xr9V1uqjRkpmiKWqcRaTGlmplqlSkfRsQxRbTG0aOmIpbVUUUZradVSZdTQ1lJbRUutqSX2NcH/OW/n3kki4ebe303u8v08z32S/O7NL9crue99z++85wj8t3G7TZkyZaROnTqmxEH79u1ZXwLuUNLF73G0JHiQiFxz4mfB53Tv3l1efPFFGT58uP11XeeHv//972a33Ndff21/bGRkZLbOTWDeT+iW+C+//FK6dOli3izo7d577013K1GiBEF7WIqsOzcI8KA6tQDcGvAsXry4Cc736dPHbJfUuTrj/K0B+8DAQL/9n8huwAR3x9zthsz5gLzWz92/VVFBLtAyJbqjSbPn9Abfv6ie3R3AsbGxJniju95sc3fZsmXTfSxSpIjfBu0psYtcV9KBAD0lbJDGX//6V/NRS9lkpEH5N954w3yur+s6b2QHgXk/mcyCgoLknXfekYsXL8rx48fNLSkpSXbt2iWrV6+WAwcOmF+w559/3m3PGf6Hxb0bxjRPgEaisvdNlLEBvK7GvE3nzp1Nxt2JEyfs87fetm3bZurPp6SkyNSpU7OVie9rgfn8+fPn9tPwKczd1pez+a35K9vhfYUmOinNkNPXZw3SFyhQILefFjxoF1HFihVNLWItJ6trbtvcvWXLFjl69KgcO3bMrLubNGnil/9vzDPuG1e/YGXAnOA7sjEXuAuBeT8THBws1apVM7e0WR9nz56VmJgY8Wd+M5HlMH/NBHEXU6c2O7+rBOUBry8RooFnrV2sNxtd1Gs2XseOHf02KK8ou2I9AiZubtzuStNWG7Llc9WpU6dME7ilS5dKv379zIVTLRuqQfro6GiTEAX/vqhuWwOFhISYm63Mkc5Z2odAd8FpHzh/xdztHn6x7iaQjlx07tw52bdvn1y5cuW2vz1t/O0sAvN+PpGtXLlSPv30Uxk2bJjf16/1i4kMvrG4v3nD2qC8Pu6wK88KgDsX9xlpzfn4+Hj5wx/+YDI1/RmLe+tRt98NrGz+ereg/Ok0n1Oz3m39u7RMqN7Gjx8vu3fvNmupdu3amfrzukMZ/l3KJqv5asqUKeZCzuDBg7moblE8A3CnW0KMCL+ZNWuW9OjRQ65evSoZOVO+Ji1eDf14EbpmzRpZsGCBDBkyxNS6A6zGLgQ3yONAxrwG2smUB3xy/j558qSMGDHCBOQ1Q9PfEZh3z5h6XbJCsmdnkd9yJTCf7MC/8XSaW8bjcIvU1FRZtWqV9OrVy/QCWbt2rcTFxcmiRYsYcR9jRS8TXRMlJCTIoUOH5LXXXvP7XRXM3e7B2tt6eYSqCviNXlAdMGCAXL582byGpb25EpRXZMz76ZsDbU4wZ84cGTp0qJQrV86y5wYglxf3BOQBn81ETk5ONkF57QnTpk0bS5+bt2Jxbz2vK2WTnOFzK0rFuLOUjTNcuehwOhuZ8wTyHe79sWLFCrMe09di7fMRFRVFI2of5epuN31NnTFjhiQmJpqdFYULFxZ/55UXgD2c183dgJc5ffq0dOvWzS27nQjM++F2Om08o81oNChfoUIFS58bADfTN1yZLe4JyAM+T+vSPvroo9K2bdvcfioeg8C8ny/uMwtYe2RwXpu/3rzz83K27AzB9BynNeTnzZtnStloWRv4NleT4v7973/L9u3bTQk67fcG5m7x97nbi1DKBjbak3PJkiXSp08fsRrvJPwwMH/hwgUpWbKkVK5c2dLn5c3Y9gVvcSsgb/pSNlYF5KkzD3hFw6HHHnuMhVcaBOb9eHF/pyxyDwvO/7bbLZe2w5Mtb4mFCxeaGvLq8ccfN70+5s6dm+ljn3nmGWt+KDxm7a1N2J119uxZqV27thQtWtTS5+XNmLv9eO72MpSygc2oUaOkVq1aZgeUNvXOrFS4swjM++FVe4LQmY8pDWisx5sD693K89+sO0WWPOBXmL8zHxPmGj98T5TsxGNyM1CvgflCd6g/6q4mrVmdlyz7bBs5cqQ89dRTJkCr5Uiyoq9HBOZ9C2tv69FknDEFvE1cXJxJcG7atKnlJckIzPtpjXkWsV64CAXSlrJxR1CerHnA4zF/p8f87UcXO5It+v6Q3Lmo7pag/OkcCsp7cGPdnLJt2zb75wcOHMjV5wLvqjGP2zF3W4+6/e5BKRvYbNy40TR6r1+/vliNSKQflrIh4+52vDlwD37X3FTK5r47NJArl+HmTHCeTHzALXhNdA+PDCJ7MY8MzHt5YPhW8Swat3t6pnyy94+9Oxw6dCjT1/Pr16/LwYMHc+U5wXPXicz91o8pvGTu9gGUsoFN9erVpUiRIuIOZMz7YZ073I43B+7hcW8ODltU3zUsF/9dWp4yk8Wg4UwgPitpg/OHLTwv4KesWDCx6Mp8TOAH74ms+H/OzfckeQJub9zualA+O0F2suQtValSJUlKSpJSpUqlO64NPqOiokw/EPgOKzLmPW5N5AMVAOAF7xHjLJi7p3vYvwl+q0uXLtK7d28ZPHhwpu+TIyMjnT43gXkv5JELJi/HmMKbttM5/PbkiAU/kKA8YAnmGffwuEWoD/DIxb1Vwf3QXPh3hfy3+auVF5FOO3C/LfBPPXnLNG7c2Pxt6N9ITEyMBAYGprt/z549JmgP38L87Z4xJdHQ+jH1yRiRBvdzMThPKRvYvPrqq1k2edX3BnoR11kE5r0QV+2t57OLULgv8z6Xsuaz3E6XMVueoDzgcwsmssORE3z6PdHpHAzOh2Rs3O78gi0dRwPtBOQt16hRI/Pxq6++koiICAkODrbfp38zsbGx0qFDB+t/MLx+/vbZ11Qn0fzVPWPqs79nuRicp5TNXRQTkUIODuZlETkpXj0XuAuBeS9EnTv3jCnb6eDxwXlqvwNei+Zx7sHFCveMqc8u7nNCSFalbCzImM+pYDt15TM1bNgw83HmzJkycOBACQ11V5MAeBpXXhOZp6zvmQc/nLtzOXMeULNmzZI5c+aYXjNffPGF3HPPPaa0jd6KFdOrFM4hMO+FWNxbjy7m1vP5Nwe5nDl/R2TLu+z999+XqVOnZnrfsmXLpHTp0q7/EPgVFqHu4fPzTC7w2e3wOZE1H5JV4/Ysmr86iux3j8uc79+/f5b3T58+PUefD+BtWHtbzy/W3kAuGjVqlIkP2OrMp6SkSL58+UzT9z59+piL9s4iMO+F2E7nmWOK9HhzYDGy5XPUH//4R2nbtm26Y6tWrZLFixcTlEeu7sxi0QV384v5OydL2igdz4zNX+HVMmZBp6ammvcIzZo1y7XnBHgLdqu7Z0x9Pp5B1jxyUUJCgsyePVt+97vf2XfQ6douPj7elLdzBYF5P8yYL1iwoFy6dMnS5+Tt/GIiy2F+kwmhWfM5ad8+kaFTcvZn+qFChQqZW8bAvDZ7A3JrtxvzN3KCXwTmbcH5HHLr5EkJ0L//kCxS6uFVZsyYkenxefPmmS3uQMa5+/JlLa4Mv5tncpDfjKkG53N67V2dtTdEkpOTMy1hd+bMGZdLlhGJ9MMXXS3BcOrUKZPZgd8QmLee37w5yGHUqcwdP/30k+zZs0datGiRS88A3s6KeUbn719//dWy5+TteD1037gyfzOmyD7NmFu/fj1Dh3SYu2/H2tt6zN3uGVPApl27dqaMnQbolb5X3rt3r/Ts2VNat24trvC7jPnz58+Lt9Mr7mfPnnXpHPfee6+phUTTov/9XhQoUMCS/x/8Rmtu6e+qXkGEda5cueJTY+otr8kLFy6UJk2aSNGiRXP7qfglb/k9uZNz586ZN3Bai9BZZcqUMYF5X3oNcHUXwtWrVxkPi+muSlffZyK9CxcuuPS372l84TXZFdr0LSN9zztx4kTzOg3f+l1xdT0THBzMmiiDixcvmvdFVpT4w290PDWQzHtE6+h4+lo8wxdek3PLhAkTpEuXLuZiq15crFu3rvm7i46ONvO/K3znHeJdaNBVX/g3b94sviAxMdGl72/QoIFs3brVsucDZEWzjOFZf/+eRl+bLbswpj1Zg7Px+IsicuLuF0OWL18ub7/9tqvPDn4+d7sqf/789rJK+J8ff/yR4bDY/v37GVPk3NxdTGvIZePxWhUkSXJNxYoVb9tVogGc8uXLu9T8zZf42vxtxdqbuTs95m54C9beUFrmVnvJ6E76bdu2mWO1atWS8PBwcVU+fxrE5s2by7Vr13L7qQAAMizeMtZz9yQrVqyQUqVKyUMPPZTbT8XvMHcDgGfy9LnbnQ4cOHDbsSJFikgIPQTsmL8BwDP58/zt6m65sLAwqVKlirnZXL9+XY4dO2Yu2jvLbwLzWTXzAwDgThYtWiRt2rRhkHIJczcAwJNUqFAht5+CV2D+BgD4ikqVKklSUpJJ2Etr+/btEhUVZcraOMuvAvMAAGTHzp07zXa1Vq1aMXAAAMCUrRk3bpzJnJ80aZIZkdGjR5u+F4MGDWKEAADwEY0bNzbl63Tuj4mJkcDAwNtKN2vQ3hUE5gEAyMInn3xiJuPixYszRgAAQIYMGSIffPCBvPnmm/bRqFevnjz33HOmIfXw4cMZJQAAfECjRo3Mx3Xr1klERIRp6G2jAfvY2Fjp0KGDSz+DwDwAAFl0rV+5ciVNXwEAgJ02eJ07d65ERkbajzVp0sQcb9euHYF5AAB8xLBhw8xHneMHDhwooaGhlv8MAvMAAGSiaNGi8u9//5uxAQAAdhcvXsx0YV6sWDG5dOkSIwUAgB80frcKgXkPkpycLIsXL5aDBw9KkSJFJDo6Wv7v//7P3KfHtXbRk08+KY888oisXbvWNBl4+eWX7d+vAaQ1a9aY7ZU2W7Zskc8//zzdMX+i20mXLFkie/fuNbWgHnvsMVOWQjGm2ffll1/K6tWrM71Pa2rq7x+/p867fPmyrFq1Snbt2iX33nuvdO/end9VwMMxd1uPudtazN3uxdztf5o1a2bWYJo9d99995ljx44dk1deecXcB8/H3O0ezN/WYv52L+ZveAoC8x7i5s2b5s2ddvjt1auXuRozb948KVOmjCmncPz4cenatat8+OGH8uCDD0r58uXNC/X169ftzQd+/vlnk6Xx66+/SunSpc2xI0eOSFhYmPirpUuXmrH7y1/+ImfPnjVjWqJECXPhgzHNPt2yqxc30tqxY4ds3rxZTp06xZi6ICUlRaZOnSr58uWT9u3bm8C80saj/K4Cnom52z2Yu63F3O0+zN3+6b333pM2bdqYNZau3bTGrL4PjoqKksmTJ+f208NdMHe7D/O3tZi/3Yf5G54kILefAH5z+PBh84ZOGwdoUF2DnzVr1pSNGzfKtWvXTEkFPZ43b165ceOGlCtXznQF1uwMpZ9rMF+3VWqA3ubo0aMmiO+vNFNeM+TLli0r4eHh5qLG7t27GVMnFShQwFzUSHv74YcfpH79+oypi/RvXbdG//nPf5aqVatK4cKFzXH+/gHPxdztHszd1mLudh/mbv+k663169fLtm3bZOLEiTJlyhTZuXOn2aWsgXp4NuZu92H+thbzt/swf8MRTz/9tJkzlCZJa2zGHQjMe4gzZ86YQJwtGKcqVqxoMmWrV68u586dk7///e9Su3ZtKViwoHmRvueee+y/JPo4DdjXq1fPHpjXr5OSkvw6MK9XQjUD2SZ//vzmGGNqDd2dobsyHnroIcbURbq4+/3vf2/+ttPidxXwXMzd7sHc7V7M3dZh7vZfs2bNkldffdU0hatbt65UqlRJ+vfvb9Zs8GzM3e7D/O1ezN/WYf6GIzZs2CA//vij+fzZZ5811UzcgVI2HqJQoUKmxlXa0jQaUL5w4YL52LNnT1OmJjg42P49GnDXoKit3EXlypVNpq2tWaEtWO/PpWz0QsY333xjxkXHUsuutGzZkjG1yKZNm0wfBP39VfyeOkf/TvWNlr4GaOaVZs5rQL5169bm9YBxBTwTc7d7MHe7F3O3NZi7/deoUaNM+cHevXvL4MGD7cFI7RPWp08fU54Unou5232Yv92L+dsazN9wlFY0adeunam8oVVKYmJi7PHajLTforPImPcQmh2vmfArV66U1NRUU4JGG7wGBPz2X6S1C9MG5ZUG3NMG5jX4rA2I9M3hiRMnzDk0qz5jBq4/adWqlZw8edLsNhg7dqy5mKF/VIoxdY1eRPr+++9NGRsbxtQ5GpDXepfaOLd58+bSoUMHs/NF6zQyroDnYu52D+Zu92Hutg5zt/9KSEiQ2bNnS79+/UyZUaUf4+PjZcmSJbn99HAXzN3uw/ztPszf1mH+hqPefvttcyG+SZMm5uuIiAh5/PHHM725gox5D6FXXTp27Cgff/yxyXgPCQmRatWqmbrzWdEg8+nTp00mvWZoREdHm0C+bqXUoJ6/15dXn3zyianNr41zdZwWL15sMuj1DyozjKnjtm7danof6O/bnTCmjl21V9r01bbDRf+etVmxNhezXaBjXAHPwtztHszd7sPcbR3mbv+VnJxs6sxnViJFM+rg2Zi73Yf5232Yv63D/A1HaeKpxmiU9vQcOnSo6bNoNTLmPYiWrnjttddk4MCB8sorr5h66BpUzootG37Lli3pHqslbTQwr41h/Tkwr5nyu3btkrZt25pgpzbTbdq0qdmJkBXG1HGbN29Oly3PmDpPd8Poi77+Haf9XdTdM3pFPzP8rgKegbnbWszd7sXcbR3mbv+l29q1nrwG6JW+h9Oml1p6UMsQwvMxd1uP+du9mL+tw/wNZ8yYMcME5a9evWrijN99950pl20FAvMeRrdBFitWzGxV2rlzp6nTlhXNotXSNVpOpEqVKvbj+rmWuNHJ0Z8D87YroWmbv2qGhAY7s8KYOubQoUOmsfDDDz9818cypnenv6MlS5Y042qjiz09bqvfz7gCnou52zrM3e7D3G0t5m7/NWHCBPP/r0lRmkChzV/Dw8PNukx7BcE7MHdbi/nbfZi/rcX8DWdoHHHAgAFSokQJ02fxkUceMcmSL7744h1jjI4gMO9hNBinV1/ee+89KVu2rNSoUeOOj9fAu9aT1/ryNvfee695k6jZG6VKlRJ/pf923Wb66aefmosUOqGtXr3avHG+E8b07v7zn//I/fffL4ULF3bo/4IxvbuGDRvKl19+KYmJiebC2ueff24WepmVsWFcAc/C3G0d5m73Ye62HnO3f9KkCS2PqVny8+fPN/VnNaFKewMVL148t58eHMTcbS3mb/dh/rYe8zeyS6uaLFy40JTsOnfunClfp5+vWrVKBg0aJK6gxryHeeedd8zWmjp16kjjxo3v+nhbPeq0gXkN5Gk5G826v1NQzx+yILp3727eJL/77rvmyqjuQGjRosUdv48xvTO96LNjxw7p1q2bw/8XjOndaVmg8+fPm7ryit9VwHswd1uHuds9mLvdg7nbv+s9BwUFmZKZ2tdL+4Rpz6+7rTPgOZi7rcX87R7M3+7B/I3smjt3rixatMhc1LHROV/7Lup7gTFjxoiz8tyiQw0AwMvpFWu9Wh31fwOkRPABx7/vYiVZ9cNbEhUVZbalAQCAHJ67awyQEoWyMXdfriSrfsy9uXv27NkSFxcn06dPN4vxWrVqmSD98ePHZciQISarDgAAn5+/qzo+f5u5e7/3rrtLlCghX331lUmiznihvlGjRiaL3ln+m04NAAAAAEA2jBw5UiZPnixdu3aVBQsWmBqzu3fvlo8++kgmTZrEWAIA4GNiYmLkhRdekH379tmP7d+/X3r16iVPPfWUS+cmMA8AAAAAgAO0F5Bm/Klly5ZJp06dTG8v7WOVlJTEGAIA4ION30NCQqRmzZrmo16U156g2vjd1Yvy1JgHAAAAAMABuo1dG75qbdnly5fL66+/bo5r9rwu0gEAgG8JDg42/SsTExNl+/btUqBAAXNBvkqVKi6fm8A8AAAAAAAOZs21atVK3njjDenbt69UrVpV4uPjTYB+yZIljCEAANl06tQp01x106ZNpoeLzXfffScTJ06Un376SUqVKiXdunVLVzpmxYoVMmXKFDlx4oSZjwcNGmQC5kpbqur3fvrpp3LlyhV59NFHTS8YV2rcV6tWzdysRCkbAAAAAAAcULduXTl69KicPHlSxo4da689++OPP0qzZs0YQwAAsuH11183F7x155nOrWlLx7300kumuercuXPlmWeeMY/dsmWLvcb78OHDzXHt86JlZrQB+7Vr18z9CxcuNFnu+j0a7D99+rSMGjXK4/5vCMwDAAAAAODoIjogwNSYtbn//vulQoUKjB8AANkUEhIiM2fONEH4tDQbXkvEde/eXcLCwqRNmzZSv3592bBhg7n/iy++kIcffljatm0rFStWNEH5CxcumCx72/d36NDBZMrreXr37i1ff/21XL582aP+jyhlAwAAAACAA5KTk2XcuHGmxuylS5duu3/NmjWMIwAADnrhhRfMR63fnlbDhg0lIiIi3TGt7Z4v32+h7AMHDqSr8V6wYEEpV66cHDx4UBo0aGDu12x6Gy1Bc+PGDTl8+LBH9YQhMA8AAAAA8HrHjx835WW2bt1qFu9PPvmk9OrVSwIDA80C/c0335Q9e/ZImTJlTOacLvqzq3PnzvLzzz9LbGysBAUFueXfAQCAv6tZs+Ztdeg3b94scXFx5mu9OK5NWdMqWrSo/aJ5xvv1PttxZ5w7d0727dtn6tVnFBkZKc4iMA8AAAAA8GqpqanSp08fkz03bdo0s4AfPHiwFC5cWP70pz/JX//6V7PInzVrlqxdu1YGDBgg8+fPN9l12aFb6Ddu3CgPPPCA2/4tAAB4PO2hWsTBx15w7UfdvHlThg0bZi6o165d2xzT7PesHpv2o02ePHkyPe4Ife/Qo0cPuXr16m336Xmzei6OoMY8AAAAAMCr7dixQw4dOiRDhw6VSpUqySOPPCKdOnWS9evXm23rv/zyi/Tt29fUgtd6tZo1v2nTpmz/HA38X79+3S3/BgAAcLt33nnHNF4fNGhQun4vt27duu2xefPmtQfM095v+9xWCic79EK/XtDX+vQa2E97cyUob56PS98NAAAAAEAuK1u2rCljk7a8jK0W7bVr1+z1Z230c9vx7Pjzn/8sXbt2lfHjx5tsfCu3swMAgPSWLFlibjNmzJAiRf6Xol+oUCE5f/58usdq81fbYzLer/epjOVvHHH69Gnp1q1buvcRViEwDwAAAADwapoBrzcbzWJbsWKFqTOvDd+0ZM0HH3xgtqJ/8803JoP+8ccfz/bP0XI5qmXLlpZvZwcAAP/z/fffy+jRo82Fd90Nl1blypXN/TZaZubIkSP2hrB6/969e+WJJ54wX+/fv9/0nMluCTsVExNjLg7Y3gNYicA8AAAAACB3aC+23/qxWbqCnT59usmU69Chg9nWPmLECPnLX/5igvMatNea884szp2pTQsAALLnyJEj0r9/f/njH/9oLrBr7xibkiVLSrNmzUzt97lz50qDBg3Mx+LFi8uDDz5oHtO8eXOZMmWK1KlTR0qXLi0TJ06Uxo0bO5X1PmrUKKlVq5bJ2g8JCbnt/jVr1jj930tgHgAAAADgM9atW2cWz1OnTjUL8IsXL8qQIUOkS5cuZiG/a9cumTBhglms60LbGZqZ99NPP5lyONWrV3dqazwAAMjc8uXL5dy5c5KQkGBuaX377bcmWK8NYTX4rkF3/Voz62015Nu3by9JSUnmMTpna/D+1VdfFWfExcWZiwFNmzbNtIydKwjMAwAAAAB8QmJiognCa6O28PBwc2zlypWmSdxLL71kvq5Ro4b88MMPMmfOHBk5cmS2zp+SkmLOrYF9W416rWWvi3ZtTudMUzkAAPxd69atzc3mueeeM7c7adGihbllRud9LT1jRfmZjRs3ytq1a6V+/fpitQDLzwgAAAAAQA7T5mx9+/aVp59+WqKjo+3Hr1+/Lvnz50/3WM2k1+PZpSVwFi5cKJ988onJ5Dtz5oz5fNWqVTJo0CBL/h0AAMBz6M64tI1nrcTlfAAAAACAV9Mgu9aiLV++vNm+nrYW7WOPPWYy3D/88ENp0qSJKUGjW+Sd2dKuNWwXLVokDRs2tB/TbL2iRYtK27ZtZcyYMZb9mwAAQO7TUni9e/c2O+Y0Ez+jyMhIp89NYB4AAAAA4NW0NM2OHTvsDd/S0vqzGjCfPHmy/POf/5TQ0FCzPb5ly5bZ/jmpqakmCJ+R1pzVGrYAAMC3vPrfC/mZNXnNkyeP3Lhxw+lzE5gHAAAAAHi1unXrmmZwd/K73/3O5Z8TExMjL7zwgsycOdNsbVf79++XXr16yVNPPeXy+QEAgGe5efOm285NYB4AAAAAAAdoSZxOnTpJzZo1pXjx4qbZq9a2j42NlUmTJjGGAAD4mLi4uCzv04z5adOmOX1uAvMAAAAAADggODhYli5dKomJibJ9+3YpUKCAhIeHS5UqVRg/AAB80K1btzItbbd48WJp1qyZS+cmMA8AAAAAgIOL83HjxsmBAwfsGfKjR48229wHDhzIGAIA4GNmzJiR6fF58+bJnDlzXDr37a1kAQAAAADAbYYMGSJvv/22RERE2I/Vq1dPEhISZNiwYYwYAAB+IiIiQtavX+/SOciYBwAAAADAAdr0de7cuRIZGWk/1qRJE3O8Xbt2Mnz4cMYRAAAfcujQoduOXb58WSZOnChlypRx6dwE5gEAAAAAcMDFixclNDT0tuPFihWTS5cuMYYAAPiYihUrmiavGUvblS9f3lyYdwWBeQAAAAAAHKBN3l5++WWzEL/vvvvMsWPHjskrr7zicgM4AADgebSvTEZFihSRkJAQl89NYB4AAAAAAAe899570qZNG5MlV7JkSZNBd+rUKXnyySdl8uTJjCEAAD6mQoUKbjs3gXkAAAAAABygZWy00duOHTtk7969EhgYKDVr1jQ3AADgm7Zu3SpBQUFmvj969Kh8/PHHUqNGDWnZsqVL5yUwDwDwHaVFpEQ2Hn9GRH5w4/MBAAA+6YEHHjA3AADg22bPni1xcXEyffp0kz3foEEDKVSokBw/flyGDBliytk5K8DSZwoAAAAAAAAAgA8YOXKkKVfXtWtXWbBggZQuXVp2794tH330kUyaNMmlcxOYBwAAAAAAAAAggyNHjkhUVJT5fNmyZdKpUyfTYyY8PFySkpLEFQTmAQAAAABwgjZ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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define domain (Spain north coast)\n", - "domain = [-9.6, -5.4, 41.6, 44.0]\n", - "\n", - "# Create figure: 1 row (2 indices) × 2 scenarios (historical + ssp585)\n", - "figure = ekp.Figure(\n", - " crs=ccrs.NearsidePerspective(central_longitude=-5, central_latitude=43), rows=1, columns=3, size=(15, 6)\n", - ")\n", - "\n", - "# Define indices and corresponding datasets\n", - "indices = {\"DTR\": dtr, \"WSDI\": wsdi, \"HDD\": hdd}\n", - "\n", - "# Define color maps for each index\n", - "cmaps = {\"DTR\": \"autumn_r\", \"WSDI\": \"autumn_r\", \"HDD\": \"autumn_r\"}\n", - "\n", - "units = {\"DTR\": \"K\", \"WSDI\": \"days\", \"HDD\": \"K days\"}\n", - "\n", - "# PLOT: Each index climatology\n", - "for col, (name, index_obj) in enumerate(indices.items()):\n", - " ds = index_obj.mean(\"time\")\n", - " cmap = cmaps[name]\n", - "\n", - " style = ekp.styles.Style(colors=cmap, units=units[name])\n", - " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", - " map_plot.quickplot(ds, style=style)\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.title(f\"{name} Climatology (SSP585)\")\n", - " map_plot.legend(location=\"right\")\n", - "\n", - "figure.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/notebooks/intro_precipitation_indices.ipynb b/docs/notebooks/intro_precipitation_indices.ipynb new file mode 100644 index 0000000..b053001 --- /dev/null +++ b/docs/notebooks/intro_precipitation_indices.ipynb @@ -0,0 +1,1617 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "392259143f051b53", + "metadata": {}, + "source": [ + "# Introduction to Precipitation-based Climate Indices\n", + "\n", + "This notebook demonstrates how to compute and visualize **precipitation-based climate indices** from CMIP6 datasets using the `earthkit-climate` and `xclim` packages.\n", + "\n", + "We'll use:\n", + "- **Precipitation-based indices**:\n", + " - *SDII*: Simple Daily Intensity Index (average precipitation on wet days)\n", + " - *CWD*: Consecutive Wet Days (max number of wet days in a row)\n", + "\n", + "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1b36bd42f74117db", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from earthkit.climate.indicators.precipitation import (\n", + " daily_pr_intensity,\n", + " maximum_consecutive_wet_days,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (8, 5)" + ] + }, + { + "cell_type": "markdown", + "id": "d1552f095264a74f", + "metadata": {}, + "source": [ + "## Loading CMIP6 data\n", + "\n", + "We’ll use *daily gridded data* from the ACCESS-CM2 model for precipitation (`pr`), for both historical and SSP585 future scenarios.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5c6692d99197d4af", + "metadata": {}, + "outputs": [], + "source": [ + "# Load precipitation\n", + "pr_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_ACCESS-CM2_historical_reference.nc\",\n", + ")\n", + "pr_ssp585 = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/pr_ACCESS-CM2_ssp585_far_future.nc\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c4c8b8fc1f899c6a", + "metadata": {}, + "source": [ + "## Inspect and visualize the raw data\n", + "\n", + "Before computing indices, let’s plot an example grid to see how the raw precipitation looks.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "51a81669f2d9f6fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define domain (Spain north coast)\n", + "domain = [-7.5, 0, 35.8, 40.2]\n", + "\n", + "# Create the figure with 2x3 maps\n", + "figure = ekp.Figure(\n", + " crs=ccrs.NearsidePerspective(central_longitude=-3.75, central_latitude=38.0),\n", + " rows=2,\n", + " columns=1,\n", + " size=(15, 10)\n", + ")\n", + "\n", + "# Define variables and their datasets\n", + "variables = {\n", + " \"pr\": (pr_hist, pr_ssp585, \"mm/day\"),\n", + "}\n", + "\n", + "# HISTORICAL (row 0)\n", + "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", + " hist_clim = hist.to_xarray().mean(\"time\")\n", + " cmap = \"winter_r\"\n", + " style = ekp.styles.Style(colors=cmap, units=units)\n", + " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", + " map_plot.quickplot(hist_clim, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.legend(location=\"right\")\n", + " map_plot.title(f\"{var} Climatology (Historical)\")\n", + "\n", + "# SSP585 (row 1)\n", + "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", + " ssp_clim = ssp.to_xarray().mean(\"time\")\n", + " cmap = \"winter_r\"\n", + " style = ekp.styles.Style(colors=cmap, units=units)\n", + " map_plot = figure.add_map(row=1, column=col, domain=domain)\n", + " map_plot.quickplot(ssp_clim, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.legend(location=\"right\")\n", + " map_plot.title(f\"{var} Climatology (SSP585)\")\n", + "\n", + "# Final layout\n", + "figure.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5963b6526e03cbd7", + "metadata": {}, + "source": [ + "## Compute Simple Daily Intensity Index (SDII)\n", + "\n", + "**SDII** measures the average precipitation amount on wet days (days with precipitation ≥ 1mm). We'll compute it for the SSP585 far-future scenario.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4d1d91636c7a72d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'sdii' (time: 30, lat: 88, lon: 150)> Size: 3MB\n",
+       "dask.array<where, shape=(30, 88, 150), dtype=float64, chunksize=(3, 40, 40), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n",
+       "  * lat      (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n",
+       "  * lon      (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n",
+       "    height   float64 8B ...\n",
+       "Attributes:\n",
+       "    units:          mm d-1\n",
+       "    cell_methods:   \n",
+       "    history:        [2026-03-11 16:06:08] sdii: SDII(pr=pr, thresh='1 mm/day'...\n",
+       "    standard_name:  lwe_thickness_of_precipitation_amount\n",
+       "    long_name:      Average precipitation during days with daily precipitatio...\n",
+       "    description:    Annual simple daily intensity index (sdii) or annual aver...
" + ], + "text/plain": [ + " Size: 3MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n", + " * lat (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n", + " * lon (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n", + " height float64 8B ...\n", + "Attributes:\n", + " units: mm d-1\n", + " cell_methods: \n", + " history: [2026-03-11 16:06:08] sdii: SDII(pr=pr, thresh='1 mm/day'...\n", + " standard_name: lwe_thickness_of_precipitation_amount\n", + " long_name: Average precipitation during days with daily precipitatio...\n", + " description: Annual simple daily intensity index (sdii) or annual aver..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute SDII\n", + "sdii = daily_pr_intensity(pr_ssp585.to_xarray())\n", + "sdii" + ] + }, + { + "cell_type": "markdown", + "id": "902d13b733671239", + "metadata": {}, + "source": [ + "## Compute Consecutive Wet Days (CWD)\n", + "\n", + "**CWD** calculates the maximum number of consecutive days with precipitation ≥ 1mm per period (annually in this case).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e963ee8391584c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'cwd' (time: 30, lat: 88, lon: 150)> Size: 3MB\n",
+       "dask.array<where, shape=(30, 88, 150), dtype=float64, chunksize=(1, 40, 40), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n",
+       "  * lat      (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n",
+       "  * lon      (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n",
+       "    height   float64 8B ...\n",
+       "Attributes:\n",
+       "    units:          days\n",
+       "    cell_methods:    time: sum over days\n",
+       "    history:        [2026-03-11 16:06:13] cwd: CWD(pr=pr, thresh='1 mm/day', ...\n",
+       "    standard_name:  number_of_days_with_lwe_thickness_of_precipitation_amount...\n",
+       "    long_name:      Maximum consecutive days with daily precipitation at or a...\n",
+       "    description:    Annual maximum number of consecutive days with daily prec...
" + ], + "text/plain": [ + " Size: 3MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n", + " * lat (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n", + " * lon (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n", + " height float64 8B ...\n", + "Attributes:\n", + " units: days\n", + " cell_methods: time: sum over days\n", + " history: [2026-03-11 16:06:13] cwd: CWD(pr=pr, thresh='1 mm/day', ...\n", + " standard_name: number_of_days_with_lwe_thickness_of_precipitation_amount...\n", + " long_name: Maximum consecutive days with daily precipitation at or a...\n", + " description: Annual maximum number of consecutive days with daily prec..." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute CWD\n", + "cwd = maximum_consecutive_wet_days(pr_ssp585.to_xarray())\n", + "cwd" + ] + }, + { + "cell_type": "markdown", + "id": "893c72b270a48721", + "metadata": {}, + "source": [ + "## Visualization of Precipitation Indices\n", + "\n", + "Let’s visualize the climatologies (average across years) of these precipitation indices.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "89b94098923a139a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define domain (Southern Spain)\n", + "domain = [-7.5, 0, 35.8, 40.2]\n", + "\n", + "# Create figure: 1 row (2 indices)\n", + "figure = ekp.Figure(\n", + " crs=ccrs.NearsidePerspective(central_longitude=-5, central_latitude=43), rows=1, columns=2, size=(12, 6)\n", + ")\n", + "\n", + "# Define indices and corresponding datasets\n", + "indices = {\"SDII\": sdii, \"CWD\": cwd}\n", + "cmaps = {\"SDII\": \"YlGnBu\", \"CWD\": \"Blues\"}\n", + "units = {\"SDII\": \"mm d-1\", \"CWD\": \"days\"}\n", + "\n", + "# PLOT: Each index climatology\n", + "for col, (name, index_obj) in enumerate(indices.items()):\n", + " ds = index_obj.mean(\"time\")\n", + " cmap = cmaps[name]\n", + "\n", + " style = ekp.styles.Style(colors=cmap, units=units[name])\n", + " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", + " map_plot.quickplot(ds, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.title(f\"{name} Climatology (SSP585)\")\n", + " map_plot.legend(location=\"right\")\n", + "\n", + "figure.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/intro_temperature_indices.ipynb b/docs/notebooks/intro_temperature_indices.ipynb new file mode 100644 index 0000000..766f7be --- /dev/null +++ b/docs/notebooks/intro_temperature_indices.ipynb @@ -0,0 +1,2366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "392259143f051b53", + "metadata": {}, + "source": [ + "# Introduction to Temperature-based Climate Indices\n", + "\n", + "This notebook demonstrates how to compute and visualize **temperature-based climate indices** from CMIP6 datasets using the `earthkit-climate` and `xclim` packages.\n", + "\n", + "We'll use:\n", + "- **Temperature-based indices**:\n", + " - *DTR*: Daily Temperature Range (Tmax - Tmin)\n", + " - *WSDI*: Warm Spell Duration Index (≥6 consecutive days above 90th percentile)\n", + " - *HDD*: Heating Degree Days (based on temperature below threshold)\n", + "\n", + "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1b36bd42f74117db", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " daily_temperature_range,\n", + " heating_degree_days_approximation,\n", + " warm_spell_duration_index,\n", + ")\n", + "from earthkit.climate.utils.percentile import calculate_percentile_doy\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.rcParams[\"figure.figsize\"] = (8, 5)" + ] + }, + { + "cell_type": "markdown", + "id": "d1552f095264a74f", + "metadata": {}, + "source": [ + "## Loading CMIP6 data\n", + "\n", + "We’ll use *daily gridded data* from the ACCESS-CM2 model for maximum (`tasmax`) and minimum (`tasmin`) temperature, for both historical and SSP585 future scenarios.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5c6692d99197d4af", + "metadata": {}, + "outputs": [], + "source": [ + "# Load temperature\n", + "tasmin_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_ACCESS-CM2_historical_reference.nc\",\n", + ")\n", + "tasmin_ssp585 = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_ACCESS-CM2_ssp585_far_future.nc\",\n", + ")\n", + "\n", + "tasmax_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_ACCESS-CM2_historical_reference.nc\",\n", + ")\n", + "tasmax_ssp585 = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_ACCESS-CM2_ssp585_far_future.nc\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c4c8b8fc1f899c6a", + "metadata": {}, + "source": [ + "## Inspect and visualize the raw data\n", + "\n", + "Before computing indices, let’s plot a few example grids to see how the raw variables look.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "51a81669f2d9f6fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define domain (Southern Spain)\n", + "domain = [-7.5, 0, 35.8, 40.2]\n", + "\n", + "# Create the figure with 2x3 maps\n", + "figure = ekp.Figure(\n", + " crs=ccrs.NearsidePerspective(central_longitude=-3.75, central_latitude=38.0),\n", + " rows=2,\n", + " columns=2,\n", + " size=(15, 10)\n", + ")\n", + "\n", + "# Define variables and their datasets\n", + "variables = {\n", + " \"tasmin\": (tasmin_hist, tasmin_ssp585, \"celsius\"),\n", + " \"tasmax\": (tasmax_hist, tasmax_ssp585, \"celsius\"),\n", + "}\n", + "\n", + "# HISTORICAL (row 0)\n", + "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", + " hist_clim = hist.to_xarray().mean(\"time\")\n", + " cmap = \"autumn\"\n", + " style = ekp.styles.Style(colors=cmap, units=units)\n", + " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", + " map_plot.quickplot(hist_clim, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.legend(location=\"right\")\n", + " map_plot.title(f\"{var} Climatology (Historical)\")\n", + "\n", + "# SSP585 (row 1)\n", + "for col, (var, (hist, ssp, units)) in enumerate(variables.items()):\n", + " ssp_clim = ssp.to_xarray().mean(\"time\")\n", + " cmap = \"autumn_r\"\n", + " style = ekp.styles.Style(colors=cmap, units=units)\n", + " map_plot = figure.add_map(row=1, column=col, domain=domain)\n", + " map_plot.quickplot(ssp_clim, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.legend(location=\"right\")\n", + " map_plot.title(f\"{var} Climatology (SSP585)\")\n", + "\n", + "# Final layout\n", + "figure.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4d1696013a521f57", + "metadata": {}, + "source": [ + "## Compute Daily Temperature Range (DTR)\n", + "\n", + "**DTR** is simply the difference between the daily maximum and minimum temperature. We'll compute it for the SSP585 scenario.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "764958ce770ccb1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'dtr' (time: 30, lat: 88, lon: 150)> Size: 2MB\n",
+       "dask.array<where, shape=(30, 88, 150), dtype=float32, chunksize=(3, 40, 40), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n",
+       "  * lat      (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n",
+       "  * lon      (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n",
+       "    height   float64 8B 2.0\n",
+       "Attributes:\n",
+       "    units:           K\n",
+       "    units_metadata:  temperature: difference\n",
+       "    cell_methods:     time range within days time: mean over days\n",
+       "    history:         [2026-03-11 14:57:12] dtr: DTR(tasmin=tasmin, tasmax=tas...\n",
+       "    standard_name:   air_temperature\n",
+       "    long_name:       Mean diurnal temperature range\n",
+       "    description:     Annual mean diurnal temperature range.
" + ], + "text/plain": [ + " Size: 2MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n", + " * lat (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n", + " * lon (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n", + " height float64 8B 2.0\n", + "Attributes:\n", + " units: K\n", + " units_metadata: temperature: difference\n", + " cell_methods: time range within days time: mean over days\n", + " history: [2026-03-11 14:57:12] dtr: DTR(tasmin=tasmin, tasmax=tas...\n", + " standard_name: air_temperature\n", + " long_name: Mean diurnal temperature range\n", + " description: Annual mean diurnal temperature range." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dtr = daily_temperature_range(tasmax=tasmax_ssp585.to_xarray(), tasmin=tasmin_ssp585.to_xarray())\n", + "dtr" + ] + }, + { + "cell_type": "markdown", + "id": "4391fa1aae1f6c40", + "metadata": {}, + "source": [ + "## Compute Warm Spell Duration Index (WSDI)\n", + "\n", + "**WSDI** identifies periods of at least 6 consecutive days where the daily maximum temperature exceeds the 90th percentile of the historical reference period (calculated per day of year).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "180da1d03bf711ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'warm_spell_duration_index' (time: 30, lat: 88, lon: 150)> Size: 3MB\n",
+       "dask.array<where, shape=(30, 88, 150), dtype=float64, chunksize=(1, 14, 15), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n",
+       "  * lat      (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n",
+       "  * lon      (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n",
+       "    height   float64 8B 2.0\n",
+       "Attributes:\n",
+       "    units:          days\n",
+       "    cell_methods:    time: sum over days\n",
+       "    history:        [2026-03-11 14:57:23] warm_spell_duration_index: WARM_SPE...\n",
+       "    standard_name:  number_of_days_with_air_temperature_above_threshold\n",
+       "    long_name:      Number of days with at least 6 consecutive days where the...\n",
+       "    description:    Annual number of days with at least 6 consecutive days wh...
" + ], + "text/plain": [ + " Size: 3MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n", + " * lat (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n", + " * lon (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n", + " height float64 8B 2.0\n", + "Attributes:\n", + " units: days\n", + " cell_methods: time: sum over days\n", + " history: [2026-03-11 14:57:23] warm_spell_duration_index: WARM_SPE...\n", + " standard_name: number_of_days_with_air_temperature_above_threshold\n", + " long_name: Number of days with at least 6 consecutive days where the...\n", + " description: Annual number of days with at least 6 consecutive days wh..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# WSDI (using historical baseline)\n", + "# Calculate 90th percentile from historical data\n", + "tasmax_per = calculate_percentile_doy(tasmax_hist.to_xarray(), variable=\"tasmax\", percentile=90)\n", + "\n", + "# Merge percentile with target dataset\n", + "ds_merged = tasmax_ssp585.to_xarray().merge(tasmax_per)\n", + "\n", + "wsdi = warm_spell_duration_index(ds=ds_merged)\n", + "\n", + "wsdi" + ] + }, + { + "cell_type": "markdown", + "id": "03ee226871c56f6", + "metadata": {}, + "source": [ + "## Compute Heating Degree Days (HDD)\n", + "\n", + "**HDD** is the annual cumulative sum of temperature degrees for daily mean temperatures below a threshold (commonly 17.0°C). This version uses an approximation based on Tmin and Tmax.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "84d79e5631853e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'heating_degree_days_approximation' (time: 30, lat: 88,\n",
+       "                                                       lon: 150)> Size: 2MB\n",
+       "dask.array<where, shape=(30, 88, 150), dtype=float32, chunksize=(3, 40, 40), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n",
+       "  * lat      (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n",
+       "  * lon      (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n",
+       "    height   float64 8B 2.0\n",
+       "Attributes:\n",
+       "    units:           K days\n",
+       "    units_metadata:  temperature: difference\n",
+       "    cell_methods:     time: sum over days\n",
+       "    history:         [2026-03-11 14:57:25] heating_degree_days_approximation:...\n",
+       "    standard_name:   integral_of_air_temperature_deficit_wrt_time\n",
+       "    long_name:       Cumulative sum of temperature degrees for daily temperat...\n",
+       "    description:     Annual cumulative heating degree days (temperature below...
" + ], + "text/plain": [ + " Size: 2MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2071-01-01 2072-01-01 ... 2100-01-01\n", + " * lat (lat) float64 704B 35.82 35.87 35.92 35.97 ... 40.07 40.12 40.17\n", + " * lon (lon) float64 1kB -7.475 -7.425 -7.375 ... -0.125 -0.075 -0.025\n", + " height float64 8B 2.0\n", + "Attributes:\n", + " units: K days\n", + " units_metadata: temperature: difference\n", + " cell_methods: time: sum over days\n", + " history: [2026-03-11 14:57:25] heating_degree_days_approximation:...\n", + " standard_name: integral_of_air_temperature_deficit_wrt_time\n", + " long_name: Cumulative sum of temperature degrees for daily temperat...\n", + " description: Annual cumulative heating degree days (temperature below..." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tas_da = (tasmax_ssp585.to_xarray()[\"tasmax\"] + tasmin_ssp585.to_xarray()[\"tasmin\"]) / 2\n", + "\n", + "hdd = heating_degree_days_approximation(\n", + " tasmax_ssp585.to_xarray().tasmax,\n", + " tasmin=tasmin_ssp585.to_xarray().tasmin,\n", + " tas=tas_da,\n", + ")\n", + "hdd" + ] + }, + { + "cell_type": "markdown", + "id": "4b68e986be47ca28", + "metadata": {}, + "source": [ + "## Visualization of Temperature Indices\n", + "\n", + "Finally, let's visualize the climatologies of these temperature indices across our domain.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "224647310d69f06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define domain (Southern Spain)\n", + "domain = [-7.5, 0, 35.8, 40.2]\n", + "\n", + "# Create the figure with 2x3 maps\n", + "figure = ekp.Figure(\n", + " crs=ccrs.NearsidePerspective(central_longitude=-3.75, central_latitude=38.0),\n", + " rows=1,\n", + " columns=3,\n", + " size=(15, 6)\n", + ")\n", + "\n", + "# Define indices and corresponding datasets\n", + "indices = {\"DTR\": dtr, \"WSDI\": wsdi, \"HDD\": hdd}\n", + "cmaps = {\"DTR\": \"autumn_r\", \"WSDI\": \"autumn_r\", \"HDD\": \"autumn_r\"}\n", + "units = {\"DTR\": \"K\", \"WSDI\": \"days\", \"HDD\": \"K days\"}\n", + "\n", + "# PLOT: Each index climatology\n", + "for col, (name, index_obj) in enumerate(indices.items()):\n", + " ds = index_obj.mean(\"time\")\n", + " cmap = cmaps[name]\n", + "\n", + " style = ekp.styles.Style(colors=cmap, units=units[name])\n", + " map_plot = figure.add_map(row=0, column=col, domain=domain)\n", + " map_plot.quickplot(ds, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.title(f\"{name} Climatology (SSP585)\")\n", + " map_plot.legend(location=\"right\")\n", + "\n", + "figure.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 01af53b659a76b0611054ea1b0a08a6a81efb195 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 16:41:55 +0100 Subject: [PATCH 38/47] feat: heatwave evolution notebook --- docs/notebooks/heatwave_evolution.ipynb | 465 +++++++++++------------- 1 file changed, 214 insertions(+), 251 deletions(-) diff --git a/docs/notebooks/heatwave_evolution.ipynb b/docs/notebooks/heatwave_evolution.ipynb index c3f6ce7..0db734f 100644 --- a/docs/notebooks/heatwave_evolution.ipynb +++ b/docs/notebooks/heatwave_evolution.ipynb @@ -1,254 +1,217 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use Case: Heatwave Evolution Analysis\n", - "\n", - "This notebook demonstrates how to use `earthkit-climate` to analyze whether heatwaves are becoming more frequent and longer over time. We will compute heatwave indices from daily minimum (`tasmin`) and maximum (`tasmax`) temperature data and compare different decades.\n", - "\n", - "We'll use:\n", - "- **`heat_wave_frequency`**: Number of heatwaves in a given period.\n", - "- **`heat_wave_max_length`**: Maximum length of a heatwave in a given period.\n", - "\n", - "A heatwave is defined as a period where both the daily **minimum** temperature exceeds a threshold (e.g., 22°C) **and** the daily **maximum** temperature exceeds another threshold (e.g., 30°C) for a minimum number of consecutive days.\n", - "\n", - "The analysis will involve loading CMIP6 data, computing indices, aggregating results by decade, and visualizing spatial anomalies." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "46d7c451", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import earthkit.data as ekd\n", - "import earthkit.plots as ekp\n", - "import numpy\n", - "\n", - "from earthkit.climate.indicators.temperature import (\n", - " heat_wave_frequency,\n", - " heat_wave_max_length,\n", - ")\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "7fec24a5", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "We will load historical daily minimum (`tasmin`) and maximum (`tasmax`) temperature data from the ACCESS-CM2 CMIP6 model." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7aec26b6", - "metadata": {}, - "outputs": [], - "source": [ - "tasmax_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - ")\n", - "ds_tasmax = tasmax_hist.to_xarray()\n", - "\n", - "tasmin_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", - ")\n", - "ds_tasmin = tasmin_hist.to_xarray()" - ] - }, - { - "cell_type": "markdown", - "id": "90561dd6", - "metadata": {}, - "source": [ - "## Computing Heatwave Indices\n", - "\n", - "We define a heatwave as a period of at least 5 consecutive days where:\n", - "- The daily **minimum** temperature exceeds **22°C** (`thresh_tasmin`)\n", - "- The daily **maximum** temperature exceeds **30°C** (`thresh_tasmax`)\n", - "\n", - "Both conditions must be satisfied simultaneously." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ba4943e3", - "metadata": {}, - "outputs": [], - "source": [ - "thresh_tasmin = \"22.0 degC\"\n", - "thresh_tasmax = \"30 degC\"\n", - "window = 5\n", - "\n", - "# Compute annual heatwave frequency\n", - "hwf = heat_wave_frequency(\n", - " ds_tasmin.tasmin,\n", - " ds_tasmax.tasmax,\n", - " thresh_tasmin=thresh_tasmin,\n", - " thresh_tasmax=thresh_tasmax,\n", - " window=window,\n", - " freq=\"YS\"\n", - ")\n", - "\n", - "# Compute annual maximum heatwave length\n", - "hwl = heat_wave_max_length(\n", - " ds_tasmin.tasmin,\n", - " ds_tasmax.tasmax,\n", - " thresh_tasmin=thresh_tasmin,\n", - " thresh_tasmax=thresh_tasmax,\n", - " window=window,\n", - " freq=\"YS\"\n", - ")\n", - "\n", - "# xclim sets units=\"1\" for dimensionless indices; replace with \"dimensionless\"\n", - "if hwf.attrs.get(\"units\") == \"1\":\n", - " hwf.attrs[\"units\"] = \"dimensionless\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "d8a3fa64", - "metadata": {}, - "source": [ - "## Decadal Aggregation and Anomalies\n", - "\n", - "We compare the 1980s (1980-1989) with the 2000s (2000-2009) to see how heatwaves have evolved." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "053a9f9d", - "metadata": {}, - "outputs": [], - "source": [ - "hwf_1980s = hwf.sel(time=slice(\"1980\", \"1989\")).mean(dim=\"time\", keep_attrs=True)\n", - "hwf_2000s = hwf.sel(time=slice(\"2000\", \"2009\")).mean(dim=\"time\", keep_attrs=True)\n", - "hwf_anomaly = hwf_2000s - hwf_1980s\n", - "hwf_anomaly.attrs.update(hwf.attrs)\n", - "\n", - "hwl_1980s = hwl.sel(time=slice(\"1980\", \"1989\")).mean(dim=\"time\", keep_attrs=True)\n", - "hwl_2000s = hwl.sel(time=slice(\"2000\", \"2009\")).mean(dim=\"time\", keep_attrs=True)\n", - "hwl_anomaly = hwl_2000s - hwl_1980s\n", - "hwl_anomaly.attrs.update(hwl.attrs)" - ] - }, - { - "cell_type": "markdown", - "id": "650559e9", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "Let's visualize the anomalies in heatwave frequency and length." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c5ad5fa", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "No points given", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 21\u001b[39m\n\u001b[32m 19\u001b[39m map_plot = figure.add_map(row=\u001b[32m0\u001b[39m, column=col)\n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# Use quickplot via Map object\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m21\u001b[39m \u001b[43mmap_plot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mquickplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 23\u001b[39m map_plot.coastlines()\n\u001b[32m 24\u001b[39m map_plot.gridlines()\n", - 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"\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/components/extractors.py:455\u001b[39m, in \u001b[36mextract_plottables_3D\u001b[39m\u001b[34m(subplot, method_name, args, x, y, z, style, no_style, units, xunits, yunits, every, source_units, extract_domain, auto_style, regrid, metadata, **kwargs)\u001b[39m\n\u001b[32m 453\u001b[39m \u001b[38;5;66;03m# Step 11: Create the plot with or without interpolation\u001b[39;00m\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_style:\n\u001b[32m--> \u001b[39m\u001b[32m455\u001b[39m mappable = \u001b[43mplot_with_interpolation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 456\u001b[39m \u001b[43m \u001b[49m\u001b[43msubplot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 457\u001b[39m \u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 458\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 459\u001b[39m \u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 460\u001b[39m \u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 461\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 462\u001b[39m \u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 463\u001b[39m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 464\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 465\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 466\u001b[39m warnings.warn(\u001b[33m\"\u001b[39m\u001b[33mStyle not set - using raw matplotlib method.\u001b[39m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/components/extractors.py:1019\u001b[39m, in \u001b[36mplot_with_interpolation\u001b[39m\u001b[34m(subplot, style, method_name, x_values, y_values, z_values, source_crs, kwargs)\u001b[39m\n\u001b[32m 1016\u001b[39m interpolate = Interpolate(**interpolate)\n\u001b[32m 1018\u001b[39m \u001b[38;5;66;03m# Apply interpolation\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1019\u001b[39m x_values, y_values, z_values = \u001b[43minterpolate\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1020\u001b[39m \u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1021\u001b[39m \u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1022\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1023\u001b[39m \u001b[43m \u001b[49m\u001b[43msource_crs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msource_crs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1024\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_crs\u001b[49m\u001b[43m=\u001b[49m\u001b[43msubplot\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1025\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1027\u001b[39m \u001b[38;5;66;03m# Handle transform settings after interpolation\u001b[39;00m\n\u001b[32m 1028\u001b[39m _ = kwargs.pop(\u001b[33m\"\u001b[39m\u001b[33mtransform_first\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/resample.py:250\u001b[39m, in \u001b[36mInterpolate.apply\u001b[39m\u001b[34m(self, x, y, z, source_crs, target_crs)\u001b[39m\n\u001b[32m 247\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 248\u001b[39m target_x, target_y = x, y\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgrids\u001b[49m\u001b[43m.\u001b[49m\u001b[43minterpolate_unstructured\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 251\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_x\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 253\u001b[39m \u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 254\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtarget_shape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 255\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_resolution\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtarget_resolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 256\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 257\u001b[39m \u001b[43m \u001b[49m\u001b[43mdistance_threshold\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdistance_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/earthkit/plots/geo/grids.py:275\u001b[39m, in \u001b[36minterpolate_unstructured\u001b[39m\u001b[34m(x, y, z, target_shape, target_resolution, method, distance_threshold)\u001b[39m\n\u001b[32m 272\u001b[39m z_filtered = z[mask]\n\u001b[32m 274\u001b[39m \u001b[38;5;66;03m# Interpolate the filtered data onto the structured grid\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m275\u001b[39m grid_z = \u001b[43mgriddata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 276\u001b[39m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcolumn_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_filtered\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_filtered\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 277\u001b[39m \u001b[43m \u001b[49m\u001b[43mz_filtered\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 278\u001b[39m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrid_y\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 279\u001b[39m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 280\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 282\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m np.isnan(grid_z).any() \u001b[38;5;129;01mand\u001b[39;00m is_global(x, y, np.max(np.diff(np.unique(y))) * \u001b[32m2\u001b[39m):\n\u001b[32m 283\u001b[39m warnings.warn(\n\u001b[32m 284\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mInterpolation produced NaN values in the global output grid, reinterpolating with `nearest`.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 285\u001b[39m )\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/predictia-projects/git/c3s-indices/earthkit-climate/.pixi/envs/dev/lib/python3.12/site-packages/scipy/interpolate/_ndgriddata.py:320\u001b[39m, in \u001b[36mgriddata\u001b[39m\u001b[34m(points, values, xi, method, fill_value, rescale)\u001b[39m\n\u001b[32m 318\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[32m 319\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m method == \u001b[33m'\u001b[39m\u001b[33mlinear\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m320\u001b[39m ip = \u001b[43mLinearNDInterpolator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 321\u001b[39m \u001b[43m \u001b[49m\u001b[43mrescale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrescale\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 322\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m ip(xi)\n\u001b[32m 323\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m method == \u001b[33m'\u001b[39m\u001b[33mcubic\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ndim == \u001b[32m2\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:306\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.LinearNDInterpolator.__init__\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:97\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.NDInterpolatorBase.__init__\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mscipy/interpolate/_interpnd.pyx:310\u001b[39m, in \u001b[36mscipy.interpolate._interpnd.LinearNDInterpolator._calculate_triangulation\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_qhull.pyx:1889\u001b[39m, in \u001b[36mscipy.spatial._qhull.Delaunay.__init__\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mscipy/spatial/_qhull.pyx:280\u001b[39m, in \u001b[36mscipy.spatial._qhull._Qhull.__init__\u001b[39m\u001b[34m()\u001b[39m\n", - "\u001b[31mValueError\u001b[39m: No points given" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "252bb4a7", + "metadata": {}, + "source": [ + "# Use Case: Heatwave Evolution (Historical vs SSP585)\n", + "\n", + "This notebook demonstrates how to use `earthkit-climate` to analyze how summer heatwaves evolve under climate change. We compare a recent historical decade (2005-2014) with a future decade under the SSP585 scenario (2091-2100).\n", + "\n", + "We'll use:\n", + "- **`heat_wave_frequency`**: Number of heatwaves per year.\n", + "- **`heat_wave_total_length`**: Total number of days in a heatwave per year.\n", + "\n", + "A heatwave is defined as at least 3 consecutive days during summer (JJA) where both daily Tmin > 22°C and Tmax > 30°C." + ] + }, + { + "cell_type": "markdown", + "id": "f272caea", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "46d7c451", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import numpy\n", + "import xarray\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " heat_wave_frequency,\n", + " heat_wave_total_length,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "7fec24a5", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "We load both historical and SSP585 daily temperature data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7aec26b6", + "metadata": {}, + "outputs": [], + "source": [ + "def load_scenario(scenario):\n", + " filename_map = {\n", + " \"historical\": \"ACCESS-CM2_historical_reference.nc\",\n", + " \"ssp585\": \"ACCESS-CM2_ssp585_far_future.nc\"\n", + " }\n", + " tasmax_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_{filename_map[scenario]}\"\n", + " tasmin_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_{filename_map[scenario]}\"\n", + " return ekd.from_source(\"url\", tasmin_url).to_xarray(), ekd.from_source(\"url\", tasmax_url).to_xarray()\n", + "\n", + "ds_tasmin_hist, ds_tasmax_hist = load_scenario(\"historical\")\n", + "ds_tasmin_ssp, ds_tasmax_ssp = load_scenario(\"ssp585\")" + ] + }, + { + "cell_type": "markdown", + "id": "90561dd6", + "metadata": {}, + "source": [ + "## Computing Heatwave Indices\n", + "\n", + "We define a heatwave as a period of at least 3 consecutive days during summer (JJA) where:\n", + "- The daily **minimum** temperature exceeds **22°C** (`thresh_tasmin`)\n", + "- The daily **maximum** temperature exceeds **30°C** (`thresh_tasmax`)\n", + "\n", + "Both conditions must be satisfied simultaneously.\n", + "\n", + "*Note: These thresholds are adapted to the sample dataset to ensure non-zero results for demonstration. In a real-world study, these should be chosen based on the local climatology.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba4943e3", + "metadata": {}, + "outputs": [], + "source": [ + "thresh_tasmin = \"22.0 degC\"\n", + "thresh_tasmax = \"30.0 degC\"\n", + "window = 3\n", + "\n", + "def compute_indices(ds_tasmin: xarray.Dataset, ds_tasmax: xarray.Dataset):\n", + " \"\"\"Compute heatwave frequency and total length.\"\"\"\n", + " hw_args = dict(thresh_tasmin=thresh_tasmin, thresh_tasmax=thresh_tasmax, window=window, freq=\"YS\",)\n", + " hwf = heat_wave_frequency(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", + " hwt = heat_wave_total_length(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", + " for da in [hwf, hwt]:\n", + " if da.attrs.get(\"units\") == \"1\": da.attrs[\"units\"] = \"dimensionless\"\n", + " return hwf, hwt\n", + "\n", + "hwf_hist, hwt_hist = compute_indices(ds_tasmin_hist, ds_tasmax_hist)\n", + "hwf_ssp, hwt_ssp = compute_indices(ds_tasmin_ssp, ds_tasmax_ssp)" + ] + }, + { + "cell_type": "markdown", + "id": "d8a3fa64", + "metadata": {}, + "source": [ + "## Decadal Aggregation and Comparison\n", + "\n", + "We calculate the average for the last decade of the historical period (2005-2014) and the future period (2091-2100)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "053a9f9d", + "metadata": {}, + "outputs": [], + "source": [ + "hwf_hist_mean = hwf_hist.mean(dim=\"time\", keep_attrs=True)\n", + "hwt_hist_mean = hwt_hist.mean(dim=\"time\", keep_attrs=True)\n", + "\n", + "hwf_ssp_mean = hwf_ssp.mean(dim=\"time\", keep_attrs=True)\n", + "hwt_ssp_mean = hwt_ssp.mean(dim=\"time\", keep_attrs=True)\n", + "\n", + "hwf_anomaly = hwf_ssp_mean - hwf_hist_mean\n", + "hwf_anomaly.attrs.update(hwf_hist.attrs)\n", + "hwt_anomaly = hwt_ssp_mean - hwt_hist_mean\n", + "hwt_anomaly.attrs.update(hwt_hist.attrs)" + ] + }, + { + "cell_type": "markdown", + "id": "650559e9", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "Let's visualize the anomalies in heatwave frequency and length." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5ad5fa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = ekp.Figure(rows=2, columns=3, size=(18, 10))\n", + "plot_matrix = [\n", + " [(\"HWF (Hist: 1971-2000)\", hwf_hist_mean, \"YlOrRd\"), (\"HWF (SSP585: 2071-2100)\", hwf_ssp_mean, \"YlOrRd\"), (\"HWF Anomaly\", hwf_anomaly, \"RdBu_r\")],\n", + " [(\"HWT (Hist: 1971-2000)\", hwt_hist_mean, \"YlOrRd\"), (\"HWT (SSP585: 2071-2100)\", hwt_ssp_mean, \"YlOrRd\"), (\"HWT Anomaly\", hwt_anomaly, \"RdBu_r\")]\n", + "]\n", + "for row_idx, row_plots in enumerate(plot_matrix):\n", + " for col_idx, (name, data, cmap) in enumerate(row_plots):\n", + " map_plot = figure.add_map(row=row_idx, column=col_idx)\n", + " valid_data = data.values[~numpy.isnan(data.values)]\n", + " if valid_data.size == 0:\n", + " map_plot.title(f\"{name} (No data)\")\n", + " elif numpy.all(valid_data == valid_data[0]):\n", + " style = ekp.styles.Style(colors=cmap, levels=[valid_data[0] - 1, valid_data[0] + 1])\n", + " map_plot.quickplot(data, style=style)\n", + " else:\n", + " style = ekp.styles.Style(colors=cmap)\n", + " map_plot.quickplot(data, style=style)\n", + " map_plot.coastlines(); map_plot.gridlines(); map_plot.title(name); map_plot.legend(location=\"right\")\n", + "figure.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figure = ekp.Figure(\n", - " rows=1, columns=2, size=(14, 5)\n", - ")\n", - "\n", - "indices = {\n", - " \"HWF Anomaly\": hwf_anomaly,\n", - " \"HWL Anomaly\": hwl_anomaly,\n", - "}\n", - "\n", - "cmaps = {\n", - " \"HWF Anomaly\": \"RdBu_r\",\n", - " \"HWL Anomaly\": \"RdBu_r\",\n", - "}\n", - "\n", - "for col, (name, data) in enumerate(indices.items()):\n", - " # Explicitly set units for the style to avoid inference issues\n", - " style = ekp.styles.Style(colors=cmaps[name])\n", - "\n", - " map_plot = figure.add_map(row=0, column=col)\n", - " # Use quickplot via Map object\n", - " map_plot.quickplot(data, style=style)\n", - "\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.title(f\"{name} (2000s minus 1980s)\")\n", - " map_plot.legend(location=\"right\")\n", - "\n", - "figure.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } From 104026f79ecd3b9c66857747c7b151e1ac7d4a8b Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 16:42:45 +0100 Subject: [PATCH 39/47] fix: notebook --- docs/notebooks/heatwave_evolution.ipynb | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/docs/notebooks/heatwave_evolution.ipynb b/docs/notebooks/heatwave_evolution.ipynb index 0db734f..8391fb4 100644 --- a/docs/notebooks/heatwave_evolution.ipynb +++ b/docs/notebooks/heatwave_evolution.ipynb @@ -155,13 +155,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "7c5ad5fa", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -191,6 +191,24 @@ " map_plot.coastlines(); map_plot.gridlines(); map_plot.title(name); map_plot.legend(location=\"right\")\n", "figure.show()" ] + }, + { + "cell_type": "markdown", + "id": "a6a6f464", + "metadata": {}, + "source": [ + "### Analysis of Heat Wave Evolution: Historical vs. Future Projections (SSP5-8.5)\n", + "\n", + "This section evaluates the intensification of heat waves over the southern Iberian Peninsula by comparing the **Historical baseline (1971-2000)** with the **long-term future (2071-2100)** under the high-emission **SSP5-8.5** scenario.\n", + "\n", + "#### Key Observations\n", + "* **Heat Wave Frequency (HWF):** The top row illustrates a drastic shift. While the historical period (left) shows heat waves as rare events restricted to small coastal pockets, the future projection (center) indicates a widespread increase. The **HWF Anomaly** (right) confirms that the frequency will rise by up to **8 events per period**, with the highest impact in the **Guadalquivir Valley and southwestern regions**.\n", + "* **Heat Wave Total Days (HWT):** The bottom row reveals an even more critical trend in duration. From a baseline of near-zero days, the end-of-century scenario projects over **120 to 140 cumulative days** of heat wave conditions. \n", + "* **Spatial Patterns:** The **HWT Anomaly** highlights a \"hotspot\" along the **Mediterranean coast and the southern interior**. This suggests that while inland areas see more frequent events, coastal and southern regions will face significantly longer-lasting heat stress, likely due to the compounding effect of high nighttime temperatures.\n", + "\n", + "#### Climate Implication\n", + "The transition from the historical \"yellow\" maps to the future \"deep red\" maps signifies a regime shift: what were once extreme, outlier events are projected to become a dominant feature of the summer climate by 2100." + ] } ], "metadata": { From f8baf7359fc6200d22cc2c34e941f5b4e4822543 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Wed, 11 Mar 2026 17:09:38 +0100 Subject: [PATCH 40/47] feat: Update tropical nights notebook. --- .../tropical_nights_cooling_demand.ipynb | 58 +++++-------------- 1 file changed, 15 insertions(+), 43 deletions(-) diff --git a/docs/notebooks/tropical_nights_cooling_demand.ipynb b/docs/notebooks/tropical_nights_cooling_demand.ipynb index eef5da7..56779b6 100644 --- a/docs/notebooks/tropical_nights_cooling_demand.ipynb +++ b/docs/notebooks/tropical_nights_cooling_demand.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "11c39356", "metadata": {}, "outputs": [], @@ -57,7 +57,7 @@ "source": [ "tasmin_hist = ekd.from_source(\n", " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_gridded_day_CMIP6_ACCESS-CM2_r1i1p1f1_deepESD_day_historical.nc\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_ACCESS-CM2_historical_reference.nc\",\n", ")\n", "ds_tasmin = tasmin_hist.to_xarray()" ] @@ -125,36 +125,19 @@ "execution_count": 10, "id": "28c4e7f1", "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "module 'earthkit.plots' has no attribute 'plot'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m fig, (ax1, ax2) = plt.subplots(\u001b[32m1\u001b[39m, \u001b[32m2\u001b[39m, subplot_kw={\u001b[33m'\u001b[39m\u001b[33mprojection\u001b[39m\u001b[33m'\u001b[39m: ccrs.PlateCarree()})\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[43mekp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m(tn_trend, ax=ax1, title=\u001b[33m\"\u001b[39m\u001b[33mTrend in Tropical Nights (days/decade)\u001b[39m\u001b[33m\"\u001b[39m, cmap=\u001b[33m\"\u001b[39m\u001b[33mYlOrRd\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m ekp.plot(cdd_trend, ax=ax2, title=\u001b[33m\"\u001b[39m\u001b[33mTrend in Cooling Degree Days (K days/decade)\u001b[39m\u001b[33m\"\u001b[39m, cmap=\u001b[33m\"\u001b[39m\u001b[33mYlOrRd\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 6\u001b[39m plt.show()\n", - "\u001b[31mAttributeError\u001b[39m: module 'earthkit.plots' has no attribute 'plot'" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw={'projection': ccrs.PlateCarree()})\n", + "figure = ekp.Figure(rows=1, columns=2, size=(18, 8))\n", "\n", - "ekp.plot(tn_trend, ax=ax1, title=\"Trend in Tropical Nights (days/decade)\", cmap=\"YlOrRd\")\n", - "ekp.plot(cdd_trend, ax=ax2, title=\"Trend in Cooling Degree Days (K days/decade)\", cmap=\"YlOrRd\")\n", + "ax1 = figure.add_map(row=0, column=0)\n", + "ax1.quickplot(tn_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", + "ax1.coastlines(); ax1.gridlines(); ax1.title(\"Trend in Tropical Nights (days/decade)\")\n", "\n", - "plt.show()" + "ax2 = figure.add_map(row=0, column=1)\n", + "ax2.quickplot(cdd_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", + "ax2.coastlines(); ax2.gridlines(); ax2.title(\"Trend in Cooling Degree Days (K days/decade)\")\n", + "\n", + "figure.show()" ] }, { @@ -170,18 +153,7 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Aggregating spatially for a simple correlation plot\n", "tn_mean = tn.mean(dim=(\"lat\", \"lon\"))\n", @@ -198,7 +170,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "dev", "language": "python", "name": "python3" }, @@ -212,7 +184,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.12" } }, "nbformat": 4, From fd8533da7e021e937d24a8fc46ffdc32474f2097 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Thu, 12 Mar 2026 10:09:08 +0100 Subject: [PATCH 41/47] docs: Add new frost days notebook and refresh existing examples, including dependency updates. --- docs/notebooks/frost_days_pyrenees.ipynb | 310 + docs/notebooks/heatwave_evolution.ipynb | 9 +- .../tropical_nights_cooling_demand.ipynb | 61 +- pixi.lock | 7506 ++++++++++++++--- pixi.toml | 1 + 5 files changed, 6897 insertions(+), 990 deletions(-) create mode 100644 docs/notebooks/frost_days_pyrenees.ipynb diff --git a/docs/notebooks/frost_days_pyrenees.ipynb b/docs/notebooks/frost_days_pyrenees.ipynb new file mode 100644 index 0000000..2b27cb8 --- /dev/null +++ b/docs/notebooks/frost_days_pyrenees.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro", + "metadata": {}, + "source": [ + "# Climate Change in the Pyrenees: Decrease in Frost Days (1980 vs 2023)\n", + "\n", + "This notebook demonstrates how to analyze the impact of climate change in a specific region using **ERA5 reanalysis data** in its native GRIB format. Using the `earthkit-climate` package, we will compute the **Frost Days (FD)** index for the **Aragonese Pyrenees** and compare two distinct periods (1980 and 2023) to visualize the \"cold retreat\".\n", + "\n", + "## Prerequisites & Data Access\n", + "\n", + "To run this notebook, you must have an account on the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu/). \n", + "\n", + "Because `earthkit` retrieves ERA5 data via the `cdsapi`, you need to store your credentials in a `.cdsapirc` file located in your home directory:\n", + "\n", + "* **Linux/macOS:** `~/.cdsapirc`\n", + "* **Windows:** `%USERPROFILE%\\.cdsapirc`\n", + "\n", + "The file must contain your specific URL and API Key:\n", + "```text\n", + "url: https://cds.climate.copernicus.eu/api/\n", + "key: YOUR_API_KEY\n", + "```\n", + "\n", + "### What is the Frost Days (FD) Index?\n", + "The Frost Days index is defined as the number of days in a year where the daily minimum temperature ($T_{min}$) falls below 0°C. It is a key indicator for agriculture, hydrology, and biodiversity in high-mountain ecosystems." + ] + }, + { + "cell_type": "markdown", + "id": "requirements", + "metadata": {}, + "source": [ + "## 1. Setup and Study Area\n", + "\n", + "First, we import the necessary libraries and define our coordinates for the Aragonese Pyrenees (Bounding Box)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "imports", + "metadata": {}, + "outputs": [], + "source": [ + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import numpy as np\n", + "\n", + "from earthkit.climate.indicators.temperature import frost_days" + ] + }, + { + "cell_type": "markdown", + "id": "download_info", + "metadata": {}, + "source": [ + "## 2. Data Acquisition (ERA5)\n", + "\n", + "We use the Copernicus Climate Data Store (CDS) API to download hourly 2m temperature data for the peak frost months (January-March and October-December) for both 1980 and 2023." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "download_code", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requesting data from CDS... (this might take a few minutes)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-03-12 09:20:42,593 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "2026-03-12 09:20:42,593 INFO Request ID is c850a818-30c1-4661-939f-0b79c9a0ad5f\n", + "INFO:ecmwf.datastores.legacy_client:Request ID is c850a818-30c1-4661-939f-0b79c9a0ad5f\n", + "2026-03-12 09:20:42,684 INFO status has been updated to accepted\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", + "2026-03-12 09:20:56,336 INFO status has been updated to running\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", + "2026-03-12 09:35:05,093 INFO status has been updated to successful\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", + "2026-03-12 09:35:06,969 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "2026-03-12 09:35:06,970 INFO Request ID is 6953fc2e-57f6-4984-8861-25c397a97d8a\n", + "INFO:ecmwf.datastores.legacy_client:Request ID is 6953fc2e-57f6-4984-8861-25c397a97d8a\n", + "2026-03-12 09:35:07,038 INFO status has been updated to accepted\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", + "2026-03-12 09:35:20,646 INFO status has been updated to running\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", + "2026-03-12 09:43:27,692 INFO status has been updated to successful\n", + "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data successfully loaded into earthkit objects!\n" + ] + } + ], + "source": [ + "# 1. Define the study area [North, West, South, East]\n", + "# Coordinates for the Aragonese Pyrenees\n", + "PYRENEES_AREA = [43, -1, 42, 0.8]\n", + "\n", + "print(\"Requesting data from CDS... (this might take a few minutes)\")\n", + "\n", + "# 2. Retrieve and open 1980 data (The Baseline)\n", + "data_1980 = ekd.from_source(\n", + " \"cds\",\n", + " \"reanalysis-era5-single-levels\",\n", + " variable=\"2m_temperature\",\n", + " product_type=\"reanalysis\",\n", + " year=\"1980\",\n", + " month=[f\"{i:02d}\" for i in range(1, 13)],\n", + " day=[f\"{i:02d}\" for i in range(1, 32)],\n", + " time=[f\"{i:02d}:00\" for i in range(24)],\n", + " area=PYRENEES_AREA,\n", + " format=\"grib\"\n", + ")\n", + "\n", + "# 4. Retrieve and open 2023 data (The Comparison)\n", + "data_2023 = ekd.from_source(\n", + " \"cds\",\n", + " \"reanalysis-era5-single-levels\",\n", + " variable=\"2m_temperature\",\n", + " product_type=\"reanalysis\",\n", + " year=\"2023\",\n", + " month=[f\"{i:02d}\" for i in range(1, 13)],\n", + " day=[f\"{i:02d}\" for i in range(1, 32)],\n", + " time=[f\"{i:02d}:00\" for i in range(24)],\n", + " area=PYRENEES_AREA,\n", + " format=\"grib\"\n", + ")\n", + "\n", + "print(\"Data successfully loaded into earthkit objects!\")" + ] + }, + { + "cell_type": "markdown", + "id": "processing_info", + "metadata": {}, + "source": [ + "## 3. Processing with Earthkit & Xarray\n", + "\n", + "We now process the hourly temperature data in Kelvin to obtain the annual count of Frost Days." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "processing_code", + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Load data and rename variables\n", + "# '2t' is the ERA5 shorthand for 2m temperature\n", + "ds_80 = data_1980.to_xarray().rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", + "ds_23 = data_2023.to_xarray().rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", + "\n", + "# 2. Set essential CF-compliant attributes\n", + "# We keep the data in Kelvin (K) as it's the CF standard\n", + "for ds in [ds_80, ds_23]:\n", + " ds['tas'].attrs.update({\n", + " 'standard_name': 'air_temperature',\n", + " 'units': 'K',\n", + " 'cell_methods': 'time: point' # Instantaneous hourly values\n", + " })\n", + "\n", + "# 3. Calculate Daily Minimum Temperature (Tmin)\n", + "# Resampling often \"cleans\" attributes, so we re-apply them to the result\n", + "tmin_80 = ds_80['tas'].resample(time='1D').min()\n", + "tmin_23 = ds_23['tas'].resample(time='1D').min()\n", + "\n", + "for tmin in [tmin_80, tmin_23]:\n", + " tmin.attrs['units'] = 'K'\n", + " tmin.attrs['standard_name'] = 'air_temperature'\n", + " tmin.attrs['cell_methods'] = 'time: minimum' # Now it represents the daily min\n", + "\n", + "# 4. Compute Frost Days Index\n", + "# frost_days automatically detects 'K' and uses 273.15 as the freezing threshold\n", + "fd_80 = frost_days(tmin_80).squeeze()\n", + "fd_23 = frost_days(tmin_23).squeeze()\n", + "\n", + "# 5. Calculate the difference (loss of frost days)\n", + "# A negative value indicates fewer frost days in 2023 compared to 1980\n", + "diff_fd = fd_23 - fd_80" + ] + }, + { + "cell_type": "markdown", + "id": "visualize_info", + "metadata": {}, + "source": [ + "## 4. Visualizing the \"Cold Retreat\"\n", + "\n", + "We'll plot the difference between the two periods to visualize where the decrease in Frost Days is most significant." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "visualize_code", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the Aragonese Pyrenees bounding box [min_lon, max_lon, min_lat, max_lat]\n", + "# Adjust these slightly if your specific dataset has different borders\n", + "ARAGON_EXTENT = [-1.0, 0.8, 42.0, 42.9]\n", + "\n", + "plot_matrix = [\n", + " [\n", + " (\"Frost Days (1980)\", fd_80, \"Blues_r\"),\n", + " (\"Frost Days (2023)\", fd_23, \"Blues_r\"),\n", + " (\"Change in Frost Days\", diff_fd, \"RdBu\") # _r flips it so red = decrease/warming\n", + " ]\n", + "]\n", + "\n", + "figure = ekp.Figure(rows=1, columns=3, size=(18, 7))\n", + "\n", + "for row_idx, row_plots in enumerate(plot_matrix):\n", + " for col_idx, (name, data, cmap) in enumerate(row_plots):\n", + " # Create map and immediately set the spatial focus\n", + " map_plot = figure.add_map(row=row_idx, column=col_idx, domain=ARAGON_EXTENT)\n", + "\n", + " # Validate data within the domain\n", + " valid_data = data.values[~np.isnan(data.values)]\n", + "\n", + " if valid_data.size == 0:\n", + " map_plot.title(f\"{name} (No data in domain)\")\n", + " continue\n", + "\n", + " # Logic to center the anomaly colorbar at 0\n", + " if \"Change\" in name or \"Anomaly\" in name:\n", + " # Find the maximum absolute deviation from zero\n", + " max_val = np.nanmax(np.abs(valid_data))\n", + " # Create symmetric levels (e.g., from -40 to +40)\n", + " style = ekp.styles.Style(\n", + " colors=cmap,\n", + " levels=np.linspace(-max_val, max_val, 11)\n", + " )\n", + " elif np.all(valid_data == valid_data[0]):\n", + " style = ekp.styles.Style(colors=cmap, levels=[valid_data[0] - 1, valid_data[0] + 1])\n", + " else:\n", + " style = ekp.styles.Style(colors=cmap)\n", + "\n", + " # Plotting & Layers\n", + " map_plot.quickplot(data, style=style)\n", + "\n", + " # Adding high-resolution features for a small domain\n", + " map_plot.coastlines(resolution='10m', color=\"black\", linewidth=1)\n", + " map_plot.borders(resolution='10m', linestyle=\"-\") # Helpful for France/Spain border\n", + "\n", + " # Gridlines with labels restricted to the Pyrenees box\n", + " map_plot.gridlines(draw_labels=True, dms=True)\n", + "\n", + " map_plot.title(name, fontsize=12)\n", + " map_plot.legend(location=\"right\", label=\"Days\")\n", + "\n", + "# Add a super-title for the entire figure\n", + "figure.title(\"Frost Day Evolution: Aragonese Pyrenees (1980 vs 2023)\")\n", + "figure.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/notebooks/heatwave_evolution.ipynb b/docs/notebooks/heatwave_evolution.ipynb index 8391fb4..aeceb1a 100644 --- a/docs/notebooks/heatwave_evolution.ipynb +++ b/docs/notebooks/heatwave_evolution.ipynb @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "7c5ad5fa", "metadata": {}, "outputs": [ @@ -188,7 +188,10 @@ " else:\n", " style = ekp.styles.Style(colors=cmap)\n", " map_plot.quickplot(data, style=style)\n", - " map_plot.coastlines(); map_plot.gridlines(); map_plot.title(name); map_plot.legend(location=\"right\")\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.title(name)\n", + " map_plot.legend(location=\"right\")\n", "figure.show()" ] }, @@ -227,7 +230,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/notebooks/tropical_nights_cooling_demand.ipynb b/docs/notebooks/tropical_nights_cooling_demand.ipynb index 56779b6..e8833cb 100644 --- a/docs/notebooks/tropical_nights_cooling_demand.ipynb +++ b/docs/notebooks/tropical_nights_cooling_demand.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "11c39356", "metadata": {}, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "e3a9b72d", "metadata": {}, "outputs": [], @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "59afd49a", "metadata": {}, "outputs": [], @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "6b05e705", "metadata": {}, "outputs": [], @@ -122,20 +122,40 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "28c4e7f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "figure = ekp.Figure(rows=1, columns=2, size=(18, 8))\n", "\n", "ax1 = figure.add_map(row=0, column=0)\n", "ax1.quickplot(tn_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", - "ax1.coastlines(); ax1.gridlines(); ax1.title(\"Trend in Tropical Nights (days/decade)\")\n", + "ax1.coastlines()\n", + "ax1.gridlines()\n", + "ax1.title(\"Trend in Tropical Nights (days/decade)\")\n", + "ax1.legend(location=\"right\")\n", + "\n", "\n", "ax2 = figure.add_map(row=0, column=1)\n", "ax2.quickplot(cdd_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", - "ax2.coastlines(); ax2.gridlines(); ax2.title(\"Trend in Cooling Degree Days (K days/decade)\")\n", + "ax2.coastlines()\n", + "ax2.gridlines()\n", + "ax2.title(\"Trend in Cooling Degree Days (K days/decade)\")\n", + "\n", + "ax2.legend(location=\"right\")\n", + "\n", "\n", "figure.show()" ] @@ -151,9 +171,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Aggregating spatially for a simple correlation plot\n", "tn_mean = tn.mean(dim=(\"lat\", \"lon\"))\n", @@ -166,6 +197,14 @@ "plt.grid(True)\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "id": "a5e29adb", + "metadata": {}, + "source": [ + "The results confirm a sustained anthropogenic warming trend across the southern Iberian Peninsula, with an increase of up to 8 tropical nights per decade in coastal areas and the Guadalquivir Valley. The strong linear correlation observed in the scatter plot demonstrates that rising nighttime minimum temperatures are a critical driver of energy demand: as nights lose their capacity to naturally cool the environment, the Cooling Degree Days (CDD) index increases proportionally. This scenario suggests that, if current trends persist, the region will face not only public health challenges due to thermal stress but also growing pressure on electrical infrastructure and household economic sustainability due to a heightened reliance on air conditioning." + ] } ], "metadata": { @@ -184,7 +223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/pixi.lock b/pixi.lock index c0a9442..9c90167 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,6 +9,7 @@ environments: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.3-hef928c7_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda - 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accessible-pygments >=0.0.5 + - beautifulsoup4 + - pygments >=2.7 + - python >=3.10 + - sphinx >=7.0,<10.0 + - sphinx-basic-ng + license: MIT + license_family: MIT + purls: + - pkg:pypi/furo?source=hash-mapping + size: 83092 + timestamp: 1772974091117 - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda sha256: 6c33bf0c4d8f418546ba9c250db4e4221040936aef8956353bc764d4877bc39a md5: d411fc29e338efb48c5fd4576d71d881 @@ -8101,6 +8149,22 @@ packages: - pkg:pypi/jsonschema?source=hash-mapping size: 81688 timestamp: 1755595646123 +- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + sha256: db973a37d75db8e19b5f44bbbdaead0c68dde745407f281e2a7fe4db74ec51d7 + md5: ada41c863af263cc4c5fcbaff7c3e4dc + depends: + - attrs >=22.2.0 + - jsonschema-specifications >=2023.3.6 + - python >=3.10 + - referencing >=0.28.4 + - rpds-py >=0.25.0 + - python + license: MIT + license_family: MIT + purls: + - pkg:pypi/jsonschema?source=compressed-mapping + size: 82356 + timestamp: 1767839954256 - 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"sphinx-rtd-theme" + "furo" ] [tool.coverage.run] From 69fa718df0690d1a5c4124181b19685d78f46238 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 16 Mar 2026 10:46:08 +0100 Subject: [PATCH 43/47] docs: Add a data demonstration disclaimer note to all introductory notebooks. --- docs/notebooks/frost_days_pyrenees.ipynb | 68 +-- docs/notebooks/heatwave_evolution.ipynb | 475 +++++++++--------- .../intro_precipitation_indices.ipynb | 7 +- .../notebooks/intro_temperature_indices.ipynb | 5 +- .../tropical_nights_cooling_demand.ipynb | 464 ++++++++--------- 5 files changed, 521 insertions(+), 498 deletions(-) diff --git a/docs/notebooks/frost_days_pyrenees.ipynb b/docs/notebooks/frost_days_pyrenees.ipynb index 2b27cb8..f09d179 100644 --- a/docs/notebooks/frost_days_pyrenees.ipynb +++ b/docs/notebooks/frost_days_pyrenees.ipynb @@ -9,20 +9,17 @@ "\n", "This notebook demonstrates how to analyze the impact of climate change in a specific region using **ERA5 reanalysis data** in its native GRIB format. Using the `earthkit-climate` package, we will compute the **Frost Days (FD)** index for the **Aragonese Pyrenees** and compare two distinct periods (1980 and 2023) to visualize the \"cold retreat\".\n", "\n", - "## Prerequisites & Data Access\n", + "> [!NOTE]\n", + "> The data shown in this repository is for **demonstration purposes only**. To obtain the full datasets for professional analysis, please use the official channels.\n", "\n", - "To run this notebook, you must have an account on the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu/). \n", + "## Prerequisites & Data Access\n", "\n", - "Because `earthkit` retrieves ERA5 data via the `cdsapi`, you need to store your credentials in a `.cdsapirc` file located in your home directory:\n", + "To run this notebook, you must have an account on the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu/).\n", "\n", - "* **Linux/macOS:** `~/.cdsapirc`\n", - "* **Windows:** `%USERPROFILE%\\.cdsapirc`\n", + "### CDS API Configuration\n", + "Because `earthkit` retrieves ERA5 data via the `cdsapi`, you need to configure your API access. Instead of manual local configuration, we recommend following the **official guide** to ensure your setup is always up to date:\n", "\n", - "The file must contain your specific URL and API Key:\n", - "```text\n", - "url: https://cds.climate.copernicus.eu/api/\n", - "key: YOUR_API_KEY\n", - "```\n", + "* **[Official CDS API How-to Guide](https://cds.climate.copernicus.eu/how-to-api)**\n", "\n", "### What is the Frost Days (FD) Index?\n", "The Frost Days index is defined as the number of days in a year where the daily minimum temperature ($T_{min}$) falls below 0°C. It is a key indicator for agriculture, hydrology, and biodiversity in high-mountain ecosystems." @@ -40,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "imports", "metadata": {}, "outputs": [], @@ -59,12 +56,16 @@ "source": [ "## 2. Data Acquisition (ERA5)\n", "\n", - "We use the Copernicus Climate Data Store (CDS) API to download hourly 2m temperature data for the peak frost months (January-March and October-December) for both 1980 and 2023." + "We use the Copernicus Climate Data Store (CDS) API to download hourly 2m temperature data. You can find the official dataset entry here:\n", + "\n", + "* **[ERA5 single levels reanalysis](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview)**\n", + "\n", + "We download data for the peak frost months (January-March and October-December) for both 1980 and 2023 for our study area." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "id": "download_code", "metadata": {}, "outputs": [ @@ -79,27 +80,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-03-12 09:20:42,593 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "2026-03-16 09:40:26,809 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "2026-03-12 09:20:42,593 INFO Request ID is c850a818-30c1-4661-939f-0b79c9a0ad5f\n", - "INFO:ecmwf.datastores.legacy_client:Request ID is c850a818-30c1-4661-939f-0b79c9a0ad5f\n", - "2026-03-12 09:20:42,684 INFO status has been updated to accepted\n", + "2026-03-16 09:40:26,810 INFO Request ID is d42bf3be-fc3c-4437-ac14-635da5d82c40\n", + "INFO:ecmwf.datastores.legacy_client:Request ID is d42bf3be-fc3c-4437-ac14-635da5d82c40\n", + "2026-03-16 09:40:26,894 INFO status has been updated to accepted\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", - "2026-03-12 09:20:56,336 INFO status has been updated to running\n", + "2026-03-16 09:41:16,815 INFO status has been updated to running\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", - "2026-03-12 09:35:05,093 INFO status has been updated to successful\n", + "2026-03-16 09:54:49,160 INFO status has been updated to successful\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", - "2026-03-12 09:35:06,969 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", + "2026-03-16 09:54:51,063 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "2026-03-12 09:35:06,970 INFO Request ID is 6953fc2e-57f6-4984-8861-25c397a97d8a\n", - "INFO:ecmwf.datastores.legacy_client:Request ID is 6953fc2e-57f6-4984-8861-25c397a97d8a\n", - "2026-03-12 09:35:07,038 INFO status has been updated to accepted\n", + "2026-03-16 09:54:51,064 INFO Request ID is 08c9b937-ab48-4cb6-90cd-5c3f4b9fb292\n", + "INFO:ecmwf.datastores.legacy_client:Request ID is 08c9b937-ab48-4cb6-90cd-5c3f4b9fb292\n", + "2026-03-16 09:54:51,159 INFO status has been updated to accepted\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", - "2026-03-12 09:35:20,646 INFO status has been updated to running\n", + "2026-03-16 09:56:06,790 INFO status has been updated to running\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", - "2026-03-12 09:43:27,692 INFO status has been updated to successful\n", + "2026-03-16 10:05:11,723 INFO status has been updated to successful\n", "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", - " \r" + " " ] }, { @@ -108,6 +109,13 @@ "text": [ "Data successfully loaded into earthkit objects!\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] } ], "source": [ @@ -160,15 +168,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "id": "processing_code", "metadata": {}, "outputs": [], "source": [ "# 1. Load data and rename variables\n", "# '2t' is the ERA5 shorthand for 2m temperature\n", - "ds_80 = data_1980.to_xarray().rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", - "ds_23 = data_2023.to_xarray().rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", + "ds_80 = data_1980.to_xarray(time_dim_mode=\"forecast\").rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", + "ds_23 = data_2023.to_xarray(time_dim_mode=\"forecast\").rename({'2t': 'tas', 'forecast_reference_time': 'time'})\n", "\n", "# 2. Set essential CF-compliant attributes\n", "# We keep the data in Kelvin (K) as it's the CF standard\n", @@ -211,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "id": "visualize_code", "metadata": {}, "outputs": [ diff --git a/docs/notebooks/heatwave_evolution.ipynb b/docs/notebooks/heatwave_evolution.ipynb index aeceb1a..1b51cc9 100644 --- a/docs/notebooks/heatwave_evolution.ipynb +++ b/docs/notebooks/heatwave_evolution.ipynb @@ -1,238 +1,241 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "252bb4a7", - "metadata": {}, - "source": [ - "# Use Case: Heatwave Evolution (Historical vs SSP585)\n", - "\n", - "This notebook demonstrates how to use `earthkit-climate` to analyze how summer heatwaves evolve under climate change. We compare a recent historical decade (2005-2014) with a future decade under the SSP585 scenario (2091-2100).\n", - "\n", - "We'll use:\n", - "- **`heat_wave_frequency`**: Number of heatwaves per year.\n", - "- **`heat_wave_total_length`**: Total number of days in a heatwave per year.\n", - "\n", - "A heatwave is defined as at least 3 consecutive days during summer (JJA) where both daily Tmin > 22°C and Tmax > 30°C." - ] - }, - { - "cell_type": "markdown", - "id": "f272caea", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "46d7c451", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import earthkit.data as ekd\n", - "import earthkit.plots as ekp\n", - "import numpy\n", - "import xarray\n", - "\n", - "from earthkit.climate.indicators.temperature import (\n", - " heat_wave_frequency,\n", - " heat_wave_total_length,\n", - ")\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "7fec24a5", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "We load both historical and SSP585 daily temperature data." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7aec26b6", - "metadata": {}, - "outputs": [], - "source": [ - "def load_scenario(scenario):\n", - " filename_map = {\n", - " \"historical\": \"ACCESS-CM2_historical_reference.nc\",\n", - " \"ssp585\": \"ACCESS-CM2_ssp585_far_future.nc\"\n", - " }\n", - " tasmax_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_{filename_map[scenario]}\"\n", - " tasmin_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_{filename_map[scenario]}\"\n", - " return ekd.from_source(\"url\", tasmin_url).to_xarray(), ekd.from_source(\"url\", tasmax_url).to_xarray()\n", - "\n", - "ds_tasmin_hist, ds_tasmax_hist = load_scenario(\"historical\")\n", - "ds_tasmin_ssp, ds_tasmax_ssp = load_scenario(\"ssp585\")" - ] - }, - { - "cell_type": "markdown", - "id": "90561dd6", - "metadata": {}, - "source": [ - "## Computing Heatwave Indices\n", - "\n", - "We define a heatwave as a period of at least 3 consecutive days during summer (JJA) where:\n", - "- The daily **minimum** temperature exceeds **22°C** (`thresh_tasmin`)\n", - "- The daily **maximum** temperature exceeds **30°C** (`thresh_tasmax`)\n", - "\n", - "Both conditions must be satisfied simultaneously.\n", - "\n", - "*Note: These thresholds are adapted to the sample dataset to ensure non-zero results for demonstration. In a real-world study, these should be chosen based on the local climatology.*\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ba4943e3", - "metadata": {}, - "outputs": [], - "source": [ - "thresh_tasmin = \"22.0 degC\"\n", - "thresh_tasmax = \"30.0 degC\"\n", - "window = 3\n", - "\n", - "def compute_indices(ds_tasmin: xarray.Dataset, ds_tasmax: xarray.Dataset):\n", - " \"\"\"Compute heatwave frequency and total length.\"\"\"\n", - " hw_args = dict(thresh_tasmin=thresh_tasmin, thresh_tasmax=thresh_tasmax, window=window, freq=\"YS\",)\n", - " hwf = heat_wave_frequency(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", - " hwt = heat_wave_total_length(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", - " for da in [hwf, hwt]:\n", - " if da.attrs.get(\"units\") == \"1\": da.attrs[\"units\"] = \"dimensionless\"\n", - " return hwf, hwt\n", - "\n", - "hwf_hist, hwt_hist = compute_indices(ds_tasmin_hist, ds_tasmax_hist)\n", - "hwf_ssp, hwt_ssp = compute_indices(ds_tasmin_ssp, ds_tasmax_ssp)" - ] - }, - { - "cell_type": "markdown", - "id": "d8a3fa64", - "metadata": {}, - "source": [ - "## Decadal Aggregation and Comparison\n", - "\n", - "We calculate the average for the last decade of the historical period (2005-2014) and the future period (2091-2100)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "053a9f9d", - "metadata": {}, - "outputs": [], - "source": [ - "hwf_hist_mean = hwf_hist.mean(dim=\"time\", keep_attrs=True)\n", - "hwt_hist_mean = hwt_hist.mean(dim=\"time\", keep_attrs=True)\n", - "\n", - "hwf_ssp_mean = hwf_ssp.mean(dim=\"time\", keep_attrs=True)\n", - "hwt_ssp_mean = hwt_ssp.mean(dim=\"time\", keep_attrs=True)\n", - "\n", - "hwf_anomaly = hwf_ssp_mean - hwf_hist_mean\n", - "hwf_anomaly.attrs.update(hwf_hist.attrs)\n", - "hwt_anomaly = hwt_ssp_mean - hwt_hist_mean\n", - "hwt_anomaly.attrs.update(hwt_hist.attrs)" - ] - }, - { - "cell_type": "markdown", - "id": "650559e9", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "Let's visualize the anomalies in heatwave frequency and length." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c5ad5fa", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figure = ekp.Figure(rows=2, columns=3, size=(18, 10))\n", - "plot_matrix = [\n", - " [(\"HWF (Hist: 1971-2000)\", hwf_hist_mean, \"YlOrRd\"), (\"HWF (SSP585: 2071-2100)\", hwf_ssp_mean, \"YlOrRd\"), (\"HWF Anomaly\", hwf_anomaly, \"RdBu_r\")],\n", - " [(\"HWT (Hist: 1971-2000)\", hwt_hist_mean, \"YlOrRd\"), (\"HWT (SSP585: 2071-2100)\", hwt_ssp_mean, \"YlOrRd\"), (\"HWT Anomaly\", hwt_anomaly, \"RdBu_r\")]\n", - "]\n", - "for row_idx, row_plots in enumerate(plot_matrix):\n", - " for col_idx, (name, data, cmap) in enumerate(row_plots):\n", - " map_plot = figure.add_map(row=row_idx, column=col_idx)\n", - " valid_data = data.values[~numpy.isnan(data.values)]\n", - " if valid_data.size == 0:\n", - " map_plot.title(f\"{name} (No data)\")\n", - " elif numpy.all(valid_data == valid_data[0]):\n", - " style = ekp.styles.Style(colors=cmap, levels=[valid_data[0] - 1, valid_data[0] + 1])\n", - " map_plot.quickplot(data, style=style)\n", - " else:\n", - " style = ekp.styles.Style(colors=cmap)\n", - " map_plot.quickplot(data, style=style)\n", - " map_plot.coastlines()\n", - " map_plot.gridlines()\n", - " map_plot.title(name)\n", - " map_plot.legend(location=\"right\")\n", - "figure.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a6a6f464", - "metadata": {}, - "source": [ - "### Analysis of Heat Wave Evolution: Historical vs. Future Projections (SSP5-8.5)\n", - "\n", - "This section evaluates the intensification of heat waves over the southern Iberian Peninsula by comparing the **Historical baseline (1971-2000)** with the **long-term future (2071-2100)** under the high-emission **SSP5-8.5** scenario.\n", - "\n", - "#### Key Observations\n", - "* **Heat Wave Frequency (HWF):** The top row illustrates a drastic shift. While the historical period (left) shows heat waves as rare events restricted to small coastal pockets, the future projection (center) indicates a widespread increase. The **HWF Anomaly** (right) confirms that the frequency will rise by up to **8 events per period**, with the highest impact in the **Guadalquivir Valley and southwestern regions**.\n", - "* **Heat Wave Total Days (HWT):** The bottom row reveals an even more critical trend in duration. From a baseline of near-zero days, the end-of-century scenario projects over **120 to 140 cumulative days** of heat wave conditions. \n", - "* **Spatial Patterns:** The **HWT Anomaly** highlights a \"hotspot\" along the **Mediterranean coast and the southern interior**. This suggests that while inland areas see more frequent events, coastal and southern regions will face significantly longer-lasting heat stress, likely due to the compounding effect of high nighttime temperatures.\n", - "\n", - "#### Climate Implication\n", - "The transition from the historical \"yellow\" maps to the future \"deep red\" maps signifies a regime shift: what were once extreme, outlier events are projected to become a dominant feature of the summer climate by 2100." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "cells": [ + { + "cell_type": "markdown", + "id": "252bb4a7", + "metadata": {}, + "source": [ + "# Use Case: Heatwave Evolution (Historical vs SSP585)\n", + "\n", + "> [!NOTE]\n", + "> The data shown in this repository is for **demonstration purposes only**. To obtain the full datasets for professional analysis, please use the official channels.\n", + "\n", + "This notebook demonstrates how to use `earthkit-climate` to analyze how summer heatwaves evolve under climate change. We compare a recent historical decade (2005-2014) with a future decade under the SSP585 scenario (2091-2100).\n", + "\n", + "We'll use:\n", + "- **`heat_wave_frequency`**: Number of heatwaves per year.\n", + "- **`heat_wave_total_length`**: Total number of days in a heatwave per year.\n", + "\n", + "A heatwave is defined as at least 3 consecutive days during summer (JJA) where both daily Tmin > 22°C and Tmax > 30°C." + ] + }, + { + "cell_type": "markdown", + "id": "f272caea", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "46d7c451", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import numpy\n", + "import xarray\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " heat_wave_frequency,\n", + " heat_wave_total_length,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "7fec24a5", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "We load both historical and SSP585 daily temperature data." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7aec26b6", + "metadata": {}, + "outputs": [], + "source": [ + "def load_scenario(scenario):\n", + " filename_map = {\n", + " \"historical\": \"ACCESS-CM2_historical_reference.nc\",\n", + " \"ssp585\": \"ACCESS-CM2_ssp585_far_future.nc\"\n", + " }\n", + " tasmax_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmax_{filename_map[scenario]}\"\n", + " tasmin_url = f\"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_{filename_map[scenario]}\"\n", + " return ekd.from_source(\"url\", tasmin_url).to_xarray(), ekd.from_source(\"url\", tasmax_url).to_xarray()\n", + "\n", + "ds_tasmin_hist, ds_tasmax_hist = load_scenario(\"historical\")\n", + "ds_tasmin_ssp, ds_tasmax_ssp = load_scenario(\"ssp585\")" + ] + }, + { + "cell_type": "markdown", + "id": "90561dd6", + "metadata": {}, + "source": [ + "## Computing Heatwave Indices\n", + "\n", + "We define a heatwave as a period of at least 3 consecutive days during summer (JJA) where:\n", + "- The daily **minimum** temperature exceeds **22°C** (`thresh_tasmin`)\n", + "- The daily **maximum** temperature exceeds **30°C** (`thresh_tasmax`)\n", + "\n", + "Both conditions must be satisfied simultaneously.\n", + "\n", + "*Note: These thresholds are adapted to the sample dataset to ensure non-zero results for demonstration. In a real-world study, these should be chosen based on the local climatology.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ba4943e3", + "metadata": {}, + "outputs": [], + "source": [ + "thresh_tasmin = \"22.0 degC\"\n", + "thresh_tasmax = \"30.0 degC\"\n", + "window = 3\n", + "\n", + "def compute_indices(ds_tasmin: xarray.Dataset, ds_tasmax: xarray.Dataset):\n", + " \"\"\"Compute heatwave frequency and total length.\"\"\"\n", + " hw_args = dict(thresh_tasmin=thresh_tasmin, thresh_tasmax=thresh_tasmax, window=window, freq=\"YS\",)\n", + " hwf = heat_wave_frequency(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", + " hwt = heat_wave_total_length(ds_tasmin.tasmin, ds_tasmax.tasmax, **hw_args)\n", + " for da in [hwf, hwt]:\n", + " if da.attrs.get(\"units\") == \"1\": da.attrs[\"units\"] = \"dimensionless\"\n", + " return hwf, hwt\n", + "\n", + "hwf_hist, hwt_hist = compute_indices(ds_tasmin_hist, ds_tasmax_hist)\n", + "hwf_ssp, hwt_ssp = compute_indices(ds_tasmin_ssp, ds_tasmax_ssp)" + ] + }, + { + "cell_type": "markdown", + "id": "d8a3fa64", + "metadata": {}, + "source": [ + "## Decadal Aggregation and Comparison\n", + "\n", + "We calculate the average for the last decade of the historical period (2005-2014) and the future period (2091-2100)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "053a9f9d", + "metadata": {}, + "outputs": [], + "source": [ + "hwf_hist_mean = hwf_hist.mean(dim=\"time\", keep_attrs=True)\n", + "hwt_hist_mean = hwt_hist.mean(dim=\"time\", keep_attrs=True)\n", + "\n", + "hwf_ssp_mean = hwf_ssp.mean(dim=\"time\", keep_attrs=True)\n", + "hwt_ssp_mean = hwt_ssp.mean(dim=\"time\", keep_attrs=True)\n", + "\n", + "hwf_anomaly = hwf_ssp_mean - hwf_hist_mean\n", + "hwf_anomaly.attrs.update(hwf_hist.attrs)\n", + "hwt_anomaly = hwt_ssp_mean - hwt_hist_mean\n", + "hwt_anomaly.attrs.update(hwt_hist.attrs)" + ] + }, + { + "cell_type": "markdown", + "id": "650559e9", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "Let's visualize the anomalies in heatwave frequency and length." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5ad5fa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = ekp.Figure(rows=2, columns=3, size=(18, 10))\n", + "plot_matrix = [\n", + " [(\"HWF (Hist: 1971-2000)\", hwf_hist_mean, \"YlOrRd\"), (\"HWF (SSP585: 2071-2100)\", hwf_ssp_mean, \"YlOrRd\"), (\"HWF Anomaly\", hwf_anomaly, \"RdBu_r\")],\n", + " [(\"HWT (Hist: 1971-2000)\", hwt_hist_mean, \"YlOrRd\"), (\"HWT (SSP585: 2071-2100)\", hwt_ssp_mean, \"YlOrRd\"), (\"HWT Anomaly\", hwt_anomaly, \"RdBu_r\")]\n", + "]\n", + "for row_idx, row_plots in enumerate(plot_matrix):\n", + " for col_idx, (name, data, cmap) in enumerate(row_plots):\n", + " map_plot = figure.add_map(row=row_idx, column=col_idx)\n", + " valid_data = data.values[~numpy.isnan(data.values)]\n", + " if valid_data.size == 0:\n", + " map_plot.title(f\"{name} (No data)\")\n", + " elif numpy.all(valid_data == valid_data[0]):\n", + " style = ekp.styles.Style(colors=cmap, levels=[valid_data[0] - 1, valid_data[0] + 1])\n", + " map_plot.quickplot(data, style=style)\n", + " else:\n", + " style = ekp.styles.Style(colors=cmap)\n", + " map_plot.quickplot(data, style=style)\n", + " map_plot.coastlines()\n", + " map_plot.gridlines()\n", + " map_plot.title(name)\n", + " map_plot.legend(location=\"right\")\n", + "figure.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a6a6f464", + "metadata": {}, + "source": [ + "### Analysis of Heat Wave Evolution: Historical vs. Future Projections (SSP5-8.5)\n", + "\n", + "This section evaluates the intensification of heat waves over the southern Iberian Peninsula by comparing the **Historical baseline (1971-2000)** with the **long-term future (2071-2100)** under the high-emission **SSP5-8.5** scenario.\n", + "\n", + "#### Key Observations\n", + "* **Heat Wave Frequency (HWF):** The top row illustrates a drastic shift. While the historical period (left) shows heat waves as rare events restricted to small coastal pockets, the future projection (center) indicates a widespread increase. The **HWF Anomaly** (right) confirms that the frequency will rise by up to **8 events per period**, with the highest impact in the **Guadalquivir Valley and southwestern regions**.\n", + "* **Heat Wave Total Days (HWT):** The bottom row reveals an even more critical trend in duration. From a baseline of near-zero days, the end-of-century scenario projects over **120 to 140 cumulative days** of heat wave conditions. \n", + "* **Spatial Patterns:** The **HWT Anomaly** highlights a \"hotspot\" along the **Mediterranean coast and the southern interior**. This suggests that while inland areas see more frequent events, coastal and southern regions will face significantly longer-lasting heat stress, likely due to the compounding effect of high nighttime temperatures.\n", + "\n", + "#### Climate Implication\n", + "The transition from the historical \"yellow\" maps to the future \"deep red\" maps signifies a regime shift: what were once extreme, outlier events are projected to become a dominant feature of the summer climate by 2100." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/notebooks/intro_precipitation_indices.ipynb b/docs/notebooks/intro_precipitation_indices.ipynb index b053001..d3915a4 100644 --- a/docs/notebooks/intro_precipitation_indices.ipynb +++ b/docs/notebooks/intro_precipitation_indices.ipynb @@ -7,6 +7,9 @@ "source": [ "# Introduction to Precipitation-based Climate Indices\n", "\n", + "> [!NOTE]\n", + "> The data shown in this repository is for **demonstration purposes only**. To obtain the full datasets for professional analysis, please use the official channels.\n", + "\n", "This notebook demonstrates how to compute and visualize **precipitation-based climate indices** from CMIP6 datasets using the `earthkit-climate` and `xclim` packages.\n", "\n", "We'll use:\n", @@ -14,7 +17,7 @@ " - *SDII*: Simple Daily Intensity Index (average precipitation on wet days)\n", " - *CWD*: Consecutive Wet Days (max number of wet days in a row)\n", "\n", - "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios.\n" + "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios." ] }, { @@ -1609,7 +1612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.12.13" } }, "nbformat": 4, diff --git a/docs/notebooks/intro_temperature_indices.ipynb b/docs/notebooks/intro_temperature_indices.ipynb index 766f7be..0b68af5 100644 --- a/docs/notebooks/intro_temperature_indices.ipynb +++ b/docs/notebooks/intro_temperature_indices.ipynb @@ -7,6 +7,9 @@ "source": [ "# Introduction to Temperature-based Climate Indices\n", "\n", + "> [!NOTE]\n", + "> The data shown in this repository is for **demonstration purposes only**. To obtain the full datasets for professional analysis, please use the official channels.\n", + "\n", "This notebook demonstrates how to compute and visualize **temperature-based climate indices** from CMIP6 datasets using the `earthkit-climate` and `xclim` packages.\n", "\n", "We'll use:\n", @@ -15,7 +18,7 @@ " - *WSDI*: Warm Spell Duration Index (≥6 consecutive days above 90th percentile)\n", " - *HDD*: Heating Degree Days (based on temperature below threshold)\n", "\n", - "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios.\n" + "We’ll load **ACCESS-CM2 CMIP6 data** for both *historical* and *SSP585* scenarios." ] }, { diff --git a/docs/notebooks/tropical_nights_cooling_demand.ipynb b/docs/notebooks/tropical_nights_cooling_demand.ipynb index e8833cb..fe90be1 100644 --- a/docs/notebooks/tropical_nights_cooling_demand.ipynb +++ b/docs/notebooks/tropical_nights_cooling_demand.ipynb @@ -1,231 +1,237 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use Case: Tropical Nights and Cooling Demand Analysis\n", - "\n", - "This notebook explores how increasing temperatures affect nighttime heat stress and the demand for cooling. We use `earthkit-climate` to compute tropical nights and cooling degree days, analyzing their trends and correlation.\n", - "\n", - "We'll use:\n", - "- **`tropical_nights`**: Number of days where $T_{min} > 20^{\\circ}C$.\n", - "- **`cooling_degree_days`**: Cumulative degrees above a comfort threshold ($18^{\\circ}C$).\n", - "\n", - "The workflow includes loading temperature data, computing indices, performing trend analysis, and visualizing the relationship between the two indicators." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "11c39356", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "\n", - "import cartopy.crs as ccrs\n", - "import earthkit.data as ekd\n", - "import earthkit.plots as ekp\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from earthkit.climate.indicators.temperature import (\n", - " cooling_degree_days,\n", - " tropical_nights,\n", - ")\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "plt.rcParams[\"figure.figsize\"] = (12, 6)" - ] - }, - { - "cell_type": "markdown", - "id": "37803e37", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "We load historical daily minimum temperature (`tasmin`) from CMIP6 simulations. We will use `tasmin` also as a proxy for `tas` to calculate cooling degree days if `tas` is not available, or just focus on minimum temperature trends if preferred. Here we load `tasmin` to compute tropical nights." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e3a9b72d", - "metadata": {}, - "outputs": [], - "source": [ - "tasmin_hist = ekd.from_source(\n", - " \"url\",\n", - " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_ACCESS-CM2_historical_reference.nc\",\n", - ")\n", - "ds_tasmin = tasmin_hist.to_xarray()" - ] - }, - { - "cell_type": "markdown", - "id": "b6d9669e", - "metadata": {}, - "source": [ - "## Computing Indices\n", - "\n", - "We compute the indices on an annual basis. For demonstration, we'll use `tasmin` for both, noting that `cooling_degree_days` usually uses `tas`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "59afd49a", - "metadata": {}, - "outputs": [], - "source": [ - "# Compute annual tropical nights\n", - "tn = tropical_nights(ds_tasmin.tasmin, thresh=\"293.15 K\", freq=\"YS\")\n", - "\n", - "# Compute annual cooling degree days (using tasmin for this example)\n", - "cdd = cooling_degree_days(ds_tasmin.tasmin, thresh=\"291.15 K\", freq=\"YS\")" - ] - }, - { - "cell_type": "markdown", - "id": "48782406", - "metadata": {}, - "source": [ - "## Trend Analysis\n", - "\n", - "We estimate the trend for both indicators over the available period." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6b05e705", - "metadata": {}, - "outputs": [], - "source": [ - "# Simple linear trend using xarray's polyfit\n", - "tn_trend = tn.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10\n", - "cdd_trend = cdd.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10" - ] - }, - { - "cell_type": "markdown", - "id": "17759260", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "### Trend Maps\n", - "\n", - "We visualize the spatial patterns of trends in tropical nights and cooling degree days." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "28c4e7f1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figure = ekp.Figure(rows=1, columns=2, size=(18, 8))\n", - "\n", - "ax1 = figure.add_map(row=0, column=0)\n", - "ax1.quickplot(tn_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", - "ax1.coastlines()\n", - "ax1.gridlines()\n", - "ax1.title(\"Trend in Tropical Nights (days/decade)\")\n", - "ax1.legend(location=\"right\")\n", - "\n", - "\n", - "ax2 = figure.add_map(row=0, column=1)\n", - "ax2.quickplot(cdd_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", - "ax2.coastlines()\n", - "ax2.gridlines()\n", - "ax2.title(\"Trend in Cooling Degree Days (K days/decade)\")\n", - "\n", - "ax2.legend(location=\"right\")\n", - "\n", - "\n", - "figure.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Correlation Analysis\n", - "\n", - "Finally, we analyze the relationship between tropical nights and cooling demand by plotting their correlation over time." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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htHMoAIg8AnIAYaNMrleOrWBJmVMFqoHl4zqRUPCnW1GZm6KoNE+ZEWUoFVzpZEhjMN98882g2ucFcAVnXfcCvMCAYU8nswo0FaCqpFNlrzqhUwCn372sS6joYoay8QqCS6JgIfCChk4sVZ6qbIoCch17UYayKDr+xQXkurCiDJ6yosrEan8qe1XAonHCCkxU1q+gRby2Kjtd1OsRtUcnpIFjYj0KNPaktIGFToQVMKmcWmOeS7qo4c1Qr5Nbr53BKm27Qs173r05rvqsFnVRTJUNgfQcuhU1WZ4XUCq40edBF3NU4aCsrN6PgROzlWWSRgX4uiljqAti+p7Rd0Hgha898T4LKjH2hr4U/Cyo/xXw67tHnyG1WxeevDkXRN9vyoxqnLwuGOiClT53et3FVW+orbpQpkBXmXgFbQrQSptJDuxn77srGOqfPX2XBH5fFsym6zjr8+F9X2q7osYvq226KKeLg6UNyEt6vV57yvKa9/Q+8PZZlv+fStt+XZi47LLLXLWV+l0Xa3VcvAqj0u6nYJsB+A8BOYCwURYp8GRe5bLKLmmiIS+LoeyNgrai1t4uroxSJ7DKtnjl4TJ37tyg2+dlNRRwBWa/VQpblgyDstG66URL5Zu6GKFAQevbljWYK4qCK538BnsCposEysYpOyle5kyZ4qJmny9p/wr2tS+VXyp48MpiFdyqfF1tVACisbziTaCmgKa4jJ3u1wWAota+DpwwKhRU0aD3ocZZ7imjpvehgojAia38pDyOq55DwalX7l3wMdFFM33OApeV0mclWMokqt9UDq/JFQP7VBfoFOxriEEwk1p5bVQpsjcBYSDv86HvBF3A0k0TdqkqRuO6dRy9SfR0McqrFNBwAZVPa0iMqnqKmqhS32faXuP+PcqUB0sXT4Ido+1lX1UFUhSVPOu7TBcYdBHQ+77UZ9+j16nvI+/7Uttpm4IU9BdXWVIW3v8PpblwWlqqitIFWm94Q1n+fyotfXfqPaQKC5WzB/6/GQzvAncojwOA8sWkbgDKjU5KdfKmcZ8elXQqQNRJoYJ376aTjOJOeJRlKbhebVETeunkr6SJz1ReqW28kunAWccl8CRpT1Sa7Y3XVpZDgbkm81EZrIK5vRE4aZVoHKIC3sDy1dLQsVdG0svYq/RS2S2dFAYee/WFtvFODL2LCV45ZWCWXJPU6WTeK+fWpHAaF6+Mkh4P7GevvDLwuRTEecGOAiGVxCpIC9xGbQnHcnWaFV77DpwcqShqgzKd3mRMouC8LCfA3rEs+L7U/XuapK+syuO4qn81REUBZ+BzKLjxnkOvTxcGApcYK+pzu6djoUntVInizRkRyCv19So7vAsoezq2yk4qoNTwi8D2K6jXa9AFJQWmKkPXZ0hUbqzx/HrNKhcXzQ+hbLjos6Xx9JpnQd8B+pwVRZ+rgjP+72mCwqIccsgh7vtGr8GjPt/TkB5dSNBnuOBEYjqWmlFdVRQ6jhpeou+2gt+XGvOs1+B9X+qnytu9FQW816jvCu1D/RcK+v9B/0/o+zBQURcDSkNt1ne/+tj7nJbl/6fS8r5PA/teFy2C/V7RRU+1J3BCzb05DgDKHxlyAOVGJzC9e/d2k/7op7IQmnlX5XoqH1Y5s7IrynbrZFsTTgWOQ/Qo6NNEUVpOSUGFZgT3Tso0Jto7wVE2TtlanVTrxLhgEK9AQSdfytqqvFrlpsowqSRVExAV9dwlBT3K/OmkXiekKiPU+GQ9996O5dMJoUqsVdKqMbiaBV4ZKh2HkmjcqzepnE7ydEwVFHgTuGkcoiaF0kRbOvlWubFO3pUNU0Cj4yJe9YDG0uo4ekudKeDWBFIKZrwl2FS2ru01vjFw5mVdPFD7VeauWaB1MUAnjHpunaRrPgAF9TqOurih94ROgvV6ldlVNltj4kNJ2S+NO1YVQ0n0OtV+TYinDKxOgJXtVODjXWgoLe9Y6t8ryNekWN79GkesoEXH0JuUMBTK47hqnLkyvfocKwDVZ0fvW5Vd63kV9On9qmOoVQ30XtNQB72nJDBo3NOx0OdJF/f0OdDFKl38Ul8qqFQpufbtZXC9463vEh0HXYQrioJnXaDxLqppvL0CJu8zo4y8Ji1UgKs5MPQalelVEOR9dkSffW2r/WgYi76P1CYF9cWtba+hBDpO+t7R69U+lXkveFz2RHNf6Ll0kUD9oO87fT73VFqtvtEFNH1eVUKt97omJdP3l8qpVTUg2p/eK5rgTBfr9LlQJl8Tn2kYgheQ9+3b1wXy+m7X9vo+1uvR517HLlR0YUdLhKkSSRdC9D7QhYzAZQhLom3V7+ojZavVRr0/ApclK8v/T6Wlqgi9lzVEQsdJ/aSLg7p4pjYFcxz0fvRWT9B3ioaUBDN5HoDIIiAHUK4UAOrEXePAddLqzbysk2uVuyqTpSBWZZ7FnewooNSJlAJGb7kkLWmkEyQFoV6pqk4kNcGTxpVqffCCAbno3+mkXW3SNvpdyxjppDIYGs+uNulkXCfBOnHT2FGd5O8tBa860VLQqgBEJ3Ka4X1PZfA6OddNFDzoAobKIwODEp1oKruiduumdntruns06ZuCI/WTTqq9gFzBqDJmBS8MKMDQ8lwFZ8lW+3URRI+pSkIl8dq3N+u6Xo9KZNWvOobeUAJVGgSeJIeSgggvmCyOsoO62KITZ028pRJ3legrIxnsWFhdhNKM4zqhV1m1F5ArsFCWVWXLCoxCGZCXx3HVBRwFAArWvKXI9PnV+98r5dZPjfv13msKcDV2Wu9JBYCe0hwLfY/ou0OvR98j+h7QsdX9utjn0YUI9ZX6VzOhFxeQy1lnneW+IxQYK1Oq16T3h16TPtu6KdjT3wp+FJzr/ax2ehUieu/ropO+S7QPvT80v4JeZ3GZYQV82pcCYAX3uuilCxf67OrCyZ5WDvBo//oe1dh2tUmfeV1w3FNwp/e33te6OKKZ0TUkQBcQ9Pn1ltHz6P2i/eqiqo6pvpf03Rd4UUdBpgJLvef0/asLG+prXQAqaR3yslBf6/Wp3boIoO9KTcynY7enTLy3tKUuGKgf9f+F5gIIrOAoy/9PpaXjqGOk7xa9z3UsFfTrwlJpJ6j06LOsz5z+D1S/6Dvau3gCIPol5EZqthkAwB55y7GVNuuD8FDJsTeGVlRRoQCz4FwGAMqPLmToglPgXAi6CKDgvGApOwBEKzLkAACUQKXlygIqm60MqP5WeatKS4uaNR5A+VB1xbfffuuy4qpc0ThwTaKpyh8A8Asy5AAQxciQRweVkarkVuOeVbqssb4qx9ZPAJGhcnWVx2sMvIZiaAI6zYugWyhXtgCAcCIgBwAAAAAgArh8CAAAAABABBCQAwAAAAAQAQTkAAAAAABEQFKsL4eh9TS1xmTgupIAAAAAAIRDdna2m3iyYcOGVqVKlfgNyBWMf/XVV5FuBgAAAAAgzhx55JF24IEHxm9Arsy4dyBq1qxpfpCbm2tZWVmWnJxsCQkJkW4O9oD+8g/6yl/oL/+gr/yF/vIP+spf6C//yC2HeGvLli0uMezFo3EbkHtl6grG69SpY355g1SsWNGqV69OQO4D9Jd/0Ff+Qn/5B33lL/SXf9BX/kJ/+UduOcZbpRk2zaRuAAAAAABEQExnyAEAAAAA2JPsnBxbsvRv27h5h9WpVdXatG5gFRLDn78mIAcAAAAAxK35C1bb5KkZlrkxK+++enWSLb1vqh2V1iSsz03JOgAAAAAgboPx8RPn5AvGRX/rfj0eTgTkAAAAAIC4LFOfPDWjxG0mT8tw24ULATkAAAAAIO4sWfp3ocx4QZkbstx24UJADgAAAACIOxs37wjpdmVBQA4AAAAAiDt1alUN6XZlQUAOAAAAAIg7bVo3cLOpl6Re3WS3XbgQkAMAAAAA4k6FxES3tFlJ0vukhnU9cgJyAAAAAEBcOiqtiQ0b1KlQplyZcd0f7nXIk8K6dwAAAAAAothRaU0srUMjN5u6JnDTmHGVqYczM+4hIAcAAAAAxLUKiYnWNqVhuT8vJesAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAQTkAAAAAADEa0C+bds2GzFihJ188snWrVs3e+655/IeW7JkiQ0YMMA6duxoPXr0sHfffTeibQUAAAAAIGYC8gceeMCWLVtmTzzxhA0fPtwmT55sM2bMsF27dtkNN9xgKSkp9tJLL1n//v1tzJgxblsAAAAAAPwsyaLA3LlzXeDdunVrd+vSpYt99tlnVrt2bduyZYsNHTrUKleubE2bNrVZs2bZzJkzrVWrVpFuNgAAAAAA/s6Q//vvvy7g9lSpUsXdt3LlSmvcuHG+x1q2bGmrVq2KUEsBAAAAAIihDPlJJ51kr776qh1xxBG2YcMG+/DDD+3666+3tWvXWvXq1fNtW7NmTVuxYkVQ+8/NzXU3P/Da6Zf2xjv6yz/oK3+hv/yDvvIX+ss/6Ct/ob/8I7cc4q1g9h0VAbmC7z59+rjAPCcnx/1U2fqTTz5Z5PbaJhhZWVlWsWJF85Pt27dHugkIAv3lH/SVv9Bf/kFf+Qv95R/0lb/QX/6xPYzxluJPXwXkd911lzVv3tzuv/9+27hxo40dO9Zee+01S0xMLPLqQlJScM1OTk4ulGmPVnq9enNUq1bNEhISIt0c7AH95R/0lb/QX/5BX/kL/eUf9JW/0F/+kVsO8ZYmJ/dNQP7rr7/a7Nmz7f3337d99tnH3XfVVVfZY489Zv369XOTugXaunVr0MG1DrTfgls/tjme0V/+QV/5C/3lH/SVv9Bf/kFf+Qv95R8JYYy3gtlvxCd1864eVKpUKd+kbjt37nRZ8zVr1rh1yj3Lly+3Fi1aRKStAAAAAACESsQDci1lppnUVa6u2dN/+OEHe/bZZ+2EE06w1NRUt/TZuHHj3GNvvPGGLVy40Dp37hzpZgMAAAAA4O+AXOPBH3zwQZcFv/jii9165O3atXM/lTWfMGGCW/6sd+/e9sILL9ioUaOsWbNmkW42AAAAAAB7JeJjyL0s+SOPPFLkYykpKTZlypRybxMAAAAAADGdIQcAAAAAIB4RkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABAvM6yDgAAAADILzsnx5Ys/ds2bt5hdWpVtTatG1iFRHKqsYSAHAAAAACizPwFq23y1AzL3JiVd1+9OsmW3jfVjkprEtG2IXS4vAIAAAAAURaMj584J18wLvpb9+txxAYCcgAAAACIojJ1ZcZLMnlahtsO/kdADgAAAABRQmPGC2bGC8rckOW2g/8RkAMAAABAlNAEbqHcDtGNgBwAAAAAooRmUw/ldohuBOQAAAAAECW0tJlmUy9JvbrJbjv4HwE5AAAAAEQJrTOupc1Kkt4nlfXIYwQBOQAAAABEEa0zPmxQp0KZcmXGdT/rkMeOpEg3AAAAAACQn4LutA6N3GzqmsBNY8ZVpq4MOmIHATkAAAAARCEF321TGka6GQgjLq8AAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAQTkAAAAAABEAAE5AAAAAAARQEAOAAAAAEAEEJADAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAQTkAAAAAABEAAE5AAAAAAARQEAOAAAAAEAEEJADAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAUkWQU8++aQ9/fTTRT723nvv2caNG+2ee+6xn3/+2Ro0aGADBw60M888s9zbCQAAAABATAXk/fv3t/POOy/ffR999JG9+eabVrduXbvkkkvsuOOOs9GjR9vXX39tY8aMsVatWrkbAAAAAAB+FtGAPDk52d0KBuTnnHOOZWRk2JYtW2zo0KFWuXJla9q0qc2aNctmzpxJQA4AAAAA8L2oGkO+YsUKW7x4sXXt2tVWrlxpjRs3dsG4p2XLlrZq1aqIthEAAAAAgIhmyHfs2GGbN2+2SpUqWe3atS0xce9j+9dff91OPvlkq1mzpm3fvt2qV6+e73Hdr6A9WLm5ue7mB147/dLeeEd/+Qd95S/0l3/QV/5Cf/kHfeUv9Jd/5JZDvBXMvksdkOfk5Ngbb7xhr776qn355Zf2xx9/5D2moLxt27Z2xhln2MUXX2wtWrQoU4D//vvv20MPPeT+zs7OLrYdwcrKyrKKFSuan+iCBPyD/vIP+spf6C//oK/8hf7yD/rKX+gv/9gexnhL8WdIA/JPP/3UrrzySktKSrKzzjrL+vTpY82bN7caNWrYP//8Y+vXr7clS5bY7NmzLS0tzfr162fjxo2zKlWqlLohH3zwgZtJvX379u5vZdyLurKgNgRL49QLZtujlV6z3hzVqlWzhISESDcHe0B/+Qd95S/0l3/QV/5Cf/kHfeUv9Jd/5JZDvLVr165Sb7vH6HbKlCl233332SOPPGKdO3cudrvjjz/eLUu2YcMGt1SZ/lYgX3DStuJMnz7dzj777Ly/9e80qVugrVu3limw1oH2W3DrxzbHM/rLP+grf6G//IO+8hf6yz/oK3+hv/wjIYzxVjD73WNA3rBhQ/viiy+sVq1apdqhlisbP368ffLJJy5VX5qA/IcffnBjwwPXGFcGfs2aNbZt27a8IHz58uV5GXQAAAAAAPxsjzOxnX766YWC8bffftu++uor9/t3331n1113nT3++OP5Ssw1OVv9+vVL1QiNTT/ppJPc5HCe1NRU97dK3zWzurZZuHBhiVl6AAAAAAD8Iuip0SdMmGB9+/Z1k7pt2rTJBd5Lly61e++9126++eagG6Cy9A8//NCtPR5IE8XpubT8We/eve2FF16wUaNGWbNmzYJ+DgAAAAAAok3QM6RNnDjRpk2bZj169LBJkya5EvKZM2fa3LlzrVevXm68eTC0lNn//ve/Ih9LSUlxY9gBAAAAALB4z5BnZmbaYYcd5n7XMmXnnXee+33//fe3jRs3hr6FAAAAAADEoKAD8o4dO9qdd97psuQfffSRWwZN07orc86EawAAAAAAhKlkXZO3qTT99ddft7Fjx7rM+C233OJKy2fMmBHs7gAAAAAAiEtBB+QtWrSwBQsW5Ltv0KBBdvvtt7vF1QEAAAAAQJhK1jX7uZYi8xxwwAEE4wAAAAAAhDMg79Spkz3//PPWvHlzt1a4ytaXLVsW7G4AAAAAAIhrQQfk999/v33//ff266+/2sCBA23evHmWlpbmZl7XZG8//vhjeFoKAAAAAEA8B+Sexo0b24knnuhK2Fu2bGk//PCDW4/85JNPtqOPPtoWL14c2pYCAAAAABDPAfmXX35pN998s7Vp08batm1r7777rl100UW2du1a++KLL2zNmjV21FFHWc+ePcPTYgAAAAAA4nGW9WOPPdaOOOIIV66uoLtRo0b5Hq9UqZJdccUVNmvWrFC2EwAAAACA+A7If/nlF2vatGmJ2xx88MH2008/7U27AAAAAACIaUEH5ArGP/74Y/v6669t586deff/888/tnnzZnvsscdC3UYAAAAAAGJO0AH5vffea6NHj7YOHTrY/Pnz3dJnSUlJbmx5enp6eFoJAAAAAEC8T+qmDPjrr79uc+fOtapVq9q0adPc7/fcc094WggAAAAAQAwKOiDfsGGDGyMuderUsfXr17vfe/XqZW+99VboWwgAAAAAQAwKOiA//vjjbdy4cW7MeFpamj355JO2fft2mz59utWsWTM8rQQAAAAAIN7HkD/44IN2xhlnuLXHx4wZY507d3aBeOXKle3FF18MTysBAAAAAIj3gLxNmzZu6bOcnByrUKGCrVixwi1x1rhxY6tfv354WgkAAAAAQLwH5JKQkOCCcVFmvH379qFuFwAAAAAAMa1UAXliYqILwksjOzt7b9sEAAAAFH2umZNjS5b+bRs377A6tapam9YNrEJi0NMiAYB/AvLZs2fn/T5jxgz7/PPP3fhxL0u+a9cuu+OOO6x79+7haykAAADi2vwFq23y1AzL3JiVd1+9OsmW3jfVjkprEtG2AUDYAvITTjgh7/dBgwbZ1KlTrV27dvm2qVevnvXv399uvfXWMjUEAAAAKCkYHz9xTqH7FZzr/mGDOhGUA/CdoOt7NKHb7t27C92vSd5WrlwZqnYBAAAAeWXqyoyXZPK0DLcdAMR0QK5lzi677DKbM2eObdu2zXbs2GFffPGFDRw40D0GAAAAhJLGjAeWqRclc0OW2w4AYjognzx5sh1yyCF28sknW61atax69ep20kkn2aGHHuoeAwAAAEJJE7iFcjsA8O2yZ7Vr17YpU6bYxIkTXfm6Zl9v3ry51ahRIzwtBAAAQFzTbOqh3A4AfL0OudSsWdMOP/zw0LYGAAAAKEBLm2k29ZLK1uvVTXbbAYCfsGgjAAAAoprWGdfSZiVJ75PKeuQAfIeAHAAAAFFP64xraTNlygtmxlnyDEBMl6xv2rTJjR3fk/fff9+6du0ainYBAAAAhYLytA6N3GzqmsBNY8ZVpq4MOgD4Uam+vdLS0uyHH34o9nEtf6al0Hr06BHKtgEAAAD5KPhum9LQjju6qftJMA4g5gPyjh07uttrr71W6LFPP/3ULXn20Ucf2axZs8LRRgAAAAAA4jMgf/HFF+3OO++0vn372i233GK5ubn277//2pAhQ+zUU0+1448/3hYtWuTWIwcAAAAAACFc9mzo0KHWrl07u/DCC+3rr7+23377zdavX2+vvvqqnXvuuaXdDQAAAAAACHaWdWXDFYz/9ddftmzZMnv99dcJxgEAAAAACFeGfMqUKfn+vvLKK23kyJHWs2dPGzNmjFWpUiXvsQEDBpSlHQAAAAAAxJVSBeQKvguqXr26+zl27Ni8+xISEgjIAQAAAAAIVUC+cuXK0mwGAAAAAABCNYZc2fG3337bgrFjxw67/PLLbdWqVUH9OwAAAAAA4sUeA/KLLrrIbrvtNrvsssvsl19+KXHbv//+2yZNmmRt27a1li1bWtOmTUPZVgAAAAAA4qdkvXnz5jZ//ny77777LDU11fbdd1878sgjbZ999rEaNWrYpk2bbN26dS5YX7BggR133HE2bdo0O/roo8vnFQAAAAAAEKtjyKtVq2ajR492mfIPP/zQvvjiC1u+fLlt3rzZKleu7ILzPn362IsvvmgHHXRQ+FsNAAAAAEA8BOQeBd/du3d3NwAAAAAAEMYx5AAAAAAAIPQIyAEAAAAAiPaSdQAAAES37JwcW7L0b9u4eYfVqVXV2rRuYBUSycEAQMwE5Lt373ZrjWuW9dzcXPv222/dEmd169YNfQsBAABQKvMXrLbJUzMsc2NW3n316iRbet9UOyqtCUcRAKJM0JdLtbRZo0aN3IzqCsZPO+00S0tLc/fNnDkzPK0EAADAHoPx8RPn5AvGRX/rfj0OAPB5QD548GA799xzbcCAAS4A//nnn+23336zu+66y26++ebwtBIAAAAllqkrM16SydMy3HYAAB8H5N99953ddNNNVr16dXvnnXesf//+tt9++9n5559vS5cuDU8rAQAAUCyNGS+YGS8oc0OWLft5PUcRAPwckB9wwAFuzLjGkL/11lvWtWvXvEB9n332CUcbAQAAUAJN4FYam7b8w3EEAD8H5HfccYddeOGFtv/++1urVq3smGOOsalTp7pM+bXXXlvmhmzZssXGjx9v3bp1syFDhuTdv2TJElce37FjR+vRo4e9++67ZX4OAACAWKTZ1Eujds0qYW8LACCMs6z369fPDjvsMDd2vEuXLu4+zbb+xBNPWO/eva0s/vnnH7vyyiutUqVKNnLkSBfoy65du+yGG26w4447zkaPHm1ff/21jRkzxj3ubQMAABDvtLSZZlMvqWy9Xt1ka9Wyvu3IKrm0HQAQxQH5k08+6SZ1O/TQQ/PuU+Z6b7zyyiu2YcMGmz59uiUnJ+fdn5GR4TLnQ4cOtcqVK7ul1WbNmuUmkyMgBwAA+D9aZ1xLm2k29eKk90llPXIA8HvJ+qhRo1y5+oknnmiPPvqo/f7773vdCAXYyq4HBuOycuVKa9y4sQvGPS1btrRVq1bt9XMCAADEEq0zPmxQJ5cpL5gZ1/2sQw4AMZAh/+OPP9xa5B988IFNmzbNZa+POOIIu+CCC1zm/MADDwxqf7t377YVK1a4TLjGoStTrvHiw4YNs+3bt7vZ3APVrFnTbR8MrZeumx947fRLe+Md/eUf9JW/0F/+QV9FlyNTG1tq+wPcbOqawE1jxlWmrgx64PkQ5xnRj77yF/rLP3LL4XswmH0HHZBLWlqau2mCt++//96uueYaN9ZbQXSHDh3ssssus4EDB1pCQsIe97Vp0ybLzs62zz//3O0jKSnJ7r77bpswYYLVr1+/yH+TE+QamllZWVaxYkXzE12MgH/QX/5BX/kL/eUf9FV0aXxAsrtJUWPG6S//oK/8hf7yj+1hjLcUf4Y1INfM51ry7O2333YTrbVr187uuecelyH/6aefbMSIEfbll1/a888/X6oMuVcK37ZtW/f7ddddZ7fddpulp6cXeXVBQXswVApfMNMerfR69eaoVq1aqS5oILLoL/+gr/yF/vIPv/XVgm/X2H9eXWgbNv3/ZcLq1q5q/XoebmntG1us81t/xTP6yl/oL//ILYfvQU1OHraAXJOpqWRcwXPPnj1typQpblx34OP16tVzM7CXJiCvW7euOxCB48SbNWtmO3fudFltlbIH2rp1a9DBtfbvt/90/NjmeEZ/+Qd95S/0l3/4oa/mL1hd5KRnv/+51e5/ZE5cjbMu2F/ZOTm2ZOnfbj1zLaGmWdtV5o7I88NnC/8f/eUfCWH8bAWz36AD8j59+livXr0sJSWl2G1Utv7RRx+Van9a6kzjzlX6ftBBB7n71q5d6wL01q1b21NPPWXbtm3LC8KXL19u7du3D7bZAAAgjingnDw1o8RtJk/LsLQOjeIuENWFCh2bwCXTNDGcZm2PlwsUAOCrWdYVjP/555+2evXqvNuyZcvsk08+cdso/a+J2YJZ23zSpEk2f/58W7x4sU2cONG6detmqampVrt2bRs3bpybWf2NN96whQsXWufOnYNtNgAAiGPK/pa0Rrdkbshy28UTr2qg4LHR37pfjwMAwifoDPlXX31l5513XpHLnalcXQF1sM4++2z766+/3Lhxpfe1pNqQIUNc9lyTu40dO9Yti9agQQN3QUAl7QAAAKWlUuxQbhcLqBoAAB8G5JpRXQG0Jl5T6biWP9t3333d/QrUy0qzsutWkLLxGqcOAABQVhoXHcrt4q1qoG1Kw3JrFwDEk6BL1n/88Ue3vJkmcqtataqblE2/q6x8/Pjx4WklAADAXtAkZRoXXZJ6dZPddvGCqgEA8GFA3qRJEzeOW5o3b+7Gfcu///5rmZmZoW8hAADAXtJEbZqkrCTpfVLjakI3qgYAIPKC/l9n+PDh1rdvX5cpv/rqq23w4MF2wQUX2DnnnGPnn39+eFoJAACwlzRjuJY2K5gpV2Y8npY881A1AAA+HEOenp5uTZs2tfr169tFF11kderUsY8//thuueUWu/LKK8PTSgAAgBBQ0K2lzVhz+/9XDRS1Nnu8Vg0AQNQH5HLSSSfl/d6jRw93AwAA8AMFmExSlr9qoNA65HWTXTAeb1UDABDVAbnGjr/yyiu2dOlS27x5s9WqVcstddarVy834zoAAAD8haoBAIicUtcgaZmzI444wjIyMuyAAw6wTp062X777efWJdf9ehwAAAD+rRo47uim7idl6gAQRRnyxx57zK03/v3331ubNm0KPf7NN9+4tcn1mCZ6AwAAAAAAIciQT5o0yR5++OEig3Hp0KGDW4Nc2wEAgNiTnZNjPy5eZ3PnrXI/9TcAACiHDPmyZcvs8MMPL3Gbjh072s8//7yXzQEAANFm/oLVhSf9qpPsZug+MrVxRNsGAEDMZ8h3795tFStWLHGbSpUq2c6dO0PVLgAAECXBuJbFCgzGRX/r/gXfrolY2wAAiIsMeW5urqWlpVmFChVKDNoBAEDsUFm6MuMl+c9rC+2uW///cqgAACDEAfnkyZOD2CUAAIgFS5b+XSgzXtCGjTvKrT0AAMRlQH7RRReFvyUAACCqbNxMsA0AQFSsQw4AAOJLnVpVI90EAABiWqky5AAAIP60ad3AzaZeUtl63ToE7QXH3avUX9UFuqChY1ghkfwHAKBoBOQAAKBICiS1tJlmUy9OvwtKXhY1npS0PNxRaU0i2jYAQHQq0yXbLVu22Msvv2z33Xefbdq0yd33ww8/hLptAAAgwhRIDhvUyQWWgerVTXb3p7VnHfLSLA+nxwEA2OsM+bx58+zMM8+0/fff3xYvXmwXXHCBW6O8S5cuNnLkSLv88suD3SUAAIjyoDytQ6MiS7G1NGq8K83ycJOnZbhjSPk6AGCvMuRDhgyxESNG2KJFi6xKlSruvmrVqtnzzz/vMuYAACD2KJBsm9LQjju6qftJYBnc8nCZG7LcdgAA7FVA/uOPP1r37t0L3X/ggQfa2rVrg90dAABAXCwPxzJyAIC9DsgPOeQQe//99/P+TkhIcD8nTZpkhx56aLC7AwAAiIvl4VhGDgCw12PIH374YevcubN99NFHtnPnTrv77rtd1vz777+39957L9jdAQAAxPzycJoET9sBALBXGfIjjzzSzajetm1bF5ivW7fOOnXqZD/99JOdcMIJwe4OAAAgJpaHK0l6n1TG3QMAQrMOeaNGjeyuu+5ywXj9+vWtQoUKZdkNAABATC0PV2gd8rrJLhhnHXIAQEgC8q1bt7qZ1qdNm2a7du2yZcuWWcOGDe2kk06yF154wVJSUoLdJQAAQEwvDwcAQFGC/h/i2muvdbOpz507N9+yZz169LCrr7462N0BAADEDJaHAwCENSDXxG2a2C01NTVvhnXp16+fffXVV8HuDgAAAACAuBR0QF65cmXLzMwsdH9GRobVrl07VO0CAAAAACCmBR2Qa/x4enq6vf322+5vjSHXGuRXXHGFDRw4MBxtBAAAAAAg5gQ9qduwYcOsYsWKbiz59u3brWvXrtagQQMbPny4ewwAAAAAAIQhIM/KyrLBgwe7m1e6Xq9evWB3AwAAAABAXAuqZD03N9cF35plXfQ7wTgAAAAAAGEOyDWr+sknn2yffPJJGZ4KAAAAAACUuWT977//tssuu8yeeeYZS0oq/M8J1gEAAAAACENA3q1bN3cDAAAAAADlGJCPHDlyL54OAAAAAACUKSAfPXp0sY8lJiZakyZN7IwzznBLoQEAUFrZOTm2ZOnftnHzDqtbu6o1PiCZgwcAAGJa0AH5l19+abNmzbJjjjnGKlSo4O7bvXu3ffHFF9ahQwe3FNr1119vM2bMsCOOOCIcbQYAxJj5C1bb5KkZlrkxy/1dpUqSPT6uqy34do0d0aFJpJsHAAAQ+VnWZb/99rNHHnnEPv/8c5s9e7a7zZkzx913yCGH2C+//GKXX365DRkyJDwtBgDEXDA+fuKcvGA80CNPfekeBwAAiEVBB+T//e9/XUl6QV27drV33nnH/a6AfNGiRaFpIQAgpsvUlRkvyeRpGW47AAAAi/eAPDk52aZPn17o/jfffNOqVavmfl+9erU1bNgwNC0EAMQsjRkvKjMeKHNDltsOAADA4n0M+d13320XX3yxvfvuu27MuNYi/+abb+zTTz+1KVOm2M6dO+26666za6+9NjwtBgDEDE3gFsrtAAAAYjog79evn6WmptrTTz9ty5cvdxO7KTB//PHHrWXLlm6bV155xQ4++OBwtBcAEEPq1Koa0u0AAABiOiCXlJQUe+CBB2zdunVWv379vNnWPQTjAIDSaNO6gdWrk1xi2Xq9usluOwAAAIv3MeRbt261Sy+91KpWrWoHHHCA/frrr7Z9+3Y78sgjbfHixeFpJQAgJlVITLT0vqklbpPeJ9VtBwAAEGuCPsPR2PC1a9fa3LlzrUqVKu4+TebWo0cPu/rqq8PRRgBADDsqrYkNG9TJZcoLum5gR/c4AABALAq6ZP29995z6463adPGEhIS8o0tHzt2bKjbBwCIAwq60zo0crOpawK3urX/b8x4WvvGkW4aAABA9ATklStXtszMzEL3Z2RkWO3atUPVLgBAnFFZetuU/1syMzc317Zt2xbpJgEAAERXyfqQIUMsPT3d3n77bff3smXLbNKkSXbFFVfYwIEDw9FGAAAAAABiTtAZ8mHDhlnFihXdWHJN5ta1a1dr0KCBDR8+3D0WrDVr1tg555yT775KlSrZF198YUuWLLF77rnHfv75Z/ccCvjPPPPMoJ8DAAAAAICYWPZs8ODB7uaVrterV6/MDdA+atSoYa+99lrefRqbvmvXLrvhhhvsuOOOs9GjR9vXX39tY8aMsVatWrkbAAAAAABxFZAvXbrU3TZv3my1atWy1q1b71VAvmHDBqtbt65bzzzQvHnzbMuWLTZ06FA3br1p06Y2a9YsmzlzJgE5AAAAACB+AnKVkGtZs0WLFln16tVdEL1+/XrLysqyQw891J544gk75phjyhSQFxXQr1y50ho3buyCcU/Lli1t1apVQT8HAAAAAAC+DMgXLFhgXbp0sUGDBtn7779v+++/f95jv/76q02YMME6d+5sn332maWmpgZdsv7HH39Yr169XNa9ffv2dtNNN7nx6Qr8A9WsWdNWrFhhwdJsvbr5gddOv7Q33tFf/kFf+Qv95R/0lb/QX/5BX/kL/eUfueUQbwWz71IF5CNHjrRbbrnF3Qo68MAD7ZFHHnHB84gRI9w65cHQv2/Xrp317NnTsrOzbfz48XbbbbfZYYcdVuT2OTk5Fixl8TURnZ/oggT8g/7yD/rKX+gv/6Cv/IX+8g/6yl/oL//YHsZ4S/FnSANyjedW0F2S/v37u+XPgqXMu24eBfX9+vVzmfKiriwkJQU/D11ycnKhbHu00mvWm6NatWpucjtEN/rLP+grf6G//IO+8hf6yz/oK3+hv/wjtxziLU1QXlqlim43bdrkZkIvicaUq+R8bzVr1sz9VEZbk7oF2rp1a5kCax1ovwW3fmxzPKO//IO+8hf6yz/oK3+hv/yDvvIX+ss/EsIYbwWz36TSXkVQKXlJwfC2bdusLC655BLr06ePnXrqqXnrkotmXdfv2q/3vMuXL3eZcwAAAAAA/K5UAfnxxx/v1gEvzXbB6tixoz3++OPWoEEDVzYwbtw4NzGcJonT/fo7PT3dMjIybOHChTZ8+PCgnwMAAAAAAF8G5J9++mnYGqBge8eOHS7Q3rlzpwvG9XulSpXc7O1jx4613r17u4B91KhReSXtAAAAAAD4WVLEG5CUZNddd527FZSSkmJTpkyJSLsAAAAAAAinxLDuHQAAAAAARGeGHAAAlK/snBxbsvRv27h5h9WpVdXatG5gFRK5Rg8AQHkjIAcAII7MX7DaJk/NsMyNWXn31auTbOl9U+2otCYRbRsAAPEmqSzLlBUnMTHRmjRpYhdccIEb/w0AAKIrGB8/cU6h+xWc6/5hgzoRlAMAUI6Crk/Lzs62V155xXbv3u3WJ9dt165d7r7ff//dPvnkE7dW+IwZM8LTYgAAUKYydWXGSzJ5WobbDgAARGmGfMuWLfbcc89Zr1698t3frVs3e/nll+3999+3hx9+2G699Vbr0qVLKNsKAADKSGPGA8vUi5K5Ictt1zalIccZAIBozJDPnj3b0tLSCt1/xBFH5K1XftZZZ9myZctC00IAALDXNIFbKLcDAAARCMgbNmxoTzzxRKH7J02aZPvuu6/7fdGiRdasWbMQNA8AAISCZlMP5XYAACACJeuPPvqonX/++fbmm2/a4YcfbklJSfbtt9/aunXrbPr06bZz504bNmyY3XnnnSFoHgAAwWNZr8K0tJlmUy+pbL1e3WS3HQAAiNKAvHPnzrZq1Sr7z3/+YytWrLAKFSrYiSeeaL1797batWu7bebNm2d169YNR3sBACgRy3oVTeuMa2mzomZZ96T3SWU9cgAAon0dcgXb1113XYmPAwBQ3ljWq2RaZ1xLmxVah7xusgvGWYccAIAoD8g3bNhgDzzwgH333Xe2ffv2Qo9r2TMAAKJ1Wa+0Do3iOgusoFvHQLOpawI3jRlXmXo8HxMAAHwTkPfp08d++eUXO/fcc61qVSZ+AQBEB5b1Kj0F3yxtBgCADwPyuXPn2hdffGHt2rULT4sAACgDlvUCAAB+E3R9WosWLdxM6gAARBOW9QIAADGfIb/sssusX79+9uCDD1q1atUKPX788ceHqm0AAJQay3oBAICYD8gHDx7sfnbr1q3QYwkJCZadnR2algEAEASW9QIAADFfsp6Tk1PsjWAcABANy3rVq5Oc734t66X7WdYLAAD4LkOuMeOVKlUKf2sAANhLLOsFAABiKiBv3ry5W+pMQXliYqIrTS8OWXIAQKSxrBcAAIiZgPz111/Py5B/8sknJQbkAAAAAAAgRAH50Ucfnff7iSeeWJp/AgAAAAAA9jYgHz16tJXWiBEjSr0tAAAAAADxqlQB+ezZs0u1M5WyE5ADACIhOyfHliz92zZu3mF1alV165JrLDkAAEBcBOQAAP+JhUB2/oLVNnlqhmVuzMq7T0ufpfdNZakzAADg74C8qGXQpk2bZj/++KP9888/1q5dO+vVq5fVrFkz9C0EAIRNLASyeg3jJ84pdL9ek+5n/XEAABCtgk6B/PTTT9ayZUu7/fbb3VJoa9eudWPMU1JSbMmSJeFpJQAgbIFsYDAeGMjqcWXPf1y8zubOW+V+6u9oovbogkJJJk/LiLp2AwAAlClDfs0119jJJ59sTz31lFWsWDFv7fErr7zSBg0aZB999BFHFgCiXGkC2Sef/8qe+88C27BpR9Rmz1VqX/CCQkGZG7Lcdm1TGpZbuwAAAMKSIZ83b57dfPPNecG4VKhQwYYNG2ZffvllsLsDAERpILt127/5gvGC2fNooHHvodwOAAAgqgPyRo0a2cKFCwvd/+2331qDBg1C1S4AQBjtbYAaLWXgmoQulNsBAABEdcn6LbfcYpdddpl99913dvTRR1tSUpLLjD/yyCM2bty48LQSABBSexugRksZuGaEVxl9Sdn+enWT3XYAAAC+D8gvueQSN5v6gw8+6ILwypUrW9u2bd2s6926dQtPKwEA5R7I+qEMXMuzaUx7UbOse9L7pPpuGTcAABAfynSGcv7559v//vc/27Ztm2VmZtrnn39OMA4APuIFsnsjWsrANcGcljbTBYaCmXGWPAMAADG3Dvnff/9tjz32WL51yC+//HJr2rRp6FsIAAhrIFtwHfLSiLYycL2WtA6NXBm9Mve6WKD2kRkHAAAxlSFXZrxFixb2wQcfWJMmTeyggw6yjz/+2A455BA3AzsAwD8UyD424SzreXa7oP5dNJaBqz0a037c0U3dz2hrHwAAwF5nyAcPHuzWIh87dmy++0eMGGFDhgwhKAcAH/r4s+WlzowrGI+WdcgBAADiKiBXmfpLL71U6P7+/fvb+PHjQ9UuAEAUrUkuF/XuYGec1tplnrXkGeXhAAAA5RyQt2zZ0mbNmuV+BtJ9Bx544F42BwBQ3ko7W3rtWlVdMD5/wepC4841oZomiSNzDgAAEMaA/J577nGzrM+ZM8c6duyYtw7566+/bi+//HKwuwMARFhpZ0vXdgrGi1piTMG57mdWcwAAgNILesabM8880y1zlpiYaE899ZQ9++yzlpCQYPPnz7ezzjor2N0BAKJkTfI9jR1v1bKey4yXZPK0DFfODgAAgDAte3bkkUfa1KlTy/JPAQBRuiZ5UZlvjyZyW/Zz5h7HmmduyHJjyzXLOQAAAEKUIf/mm2/swgsvtHvvvTff/Q899JD16tXLPQ4A8Pea5AUz5cqMe2XopR1rXtrtAAAA4l2pMuSffPKJdenSxXr06OHGjQc64ogj3NrkxxxzjM2cOdNOOOGEcLUVABBGCrrTOjQqdvb0YMaaAwAAIEQB+e233+4mcxs2bFihx4499lh3e+CBB9x2muwNAOBPCr6LKzf3xpqXVLaujLq2AwAAQIhK1n/44Qc777zzStzm7LPPtkWLFpVmdwAAH481L4nGmnsZdQAAAJSsVGdN9erVs99++63Ebf766y+rWpUyRQCI97HmAAAACGHJenp6ul199dX22muvWevWrQs9/vPPP9s111xjAwYMKOXTAgBidaw5AAAAQjyGfPXq1XbwwQdbhw4drGnTplajRg3bvn27rVy50s2w3rt3b7v77rtL+bQAgPKm9cFDFUSXNNYcAAAAIQzIExMT7ZlnnrEhQ4bYBx98YL/++qsLxuvUqWOHH364PfXUU+4nACA6zV+w2iZPzcg3IZvKzjUmnDJzAACAKA7IPYcccoi7AQD8FYyPn1h4BQwF57qfsd8AAACRwYA/AIjhEvXvf/zTJk2eX+J2k6dluG0BAAAQxwH5pEmTLC0tzX7//Xf395IlS9xEcR07drQePXrYu+++G+kmAoBvsuLX3PC2jR73sW3bvrPEbTM3ZLmx5QAAAIjTgHzt2rX24osv5v29a9cuu+GGGywlJcVeeukl69+/v40ZM8aWLVsW0XYCgF9K1APHi++JJnoDAABAnAbk48aNs1NOOSXv74yMDNuyZYsNHTrUzep+wQUX2GGHHWYzZ86MaDsBIJqp9FyTtwVLs64DAAAgCgNyzbJeoUKFUt3K4tNPP7VffvnFrXfu0XJqjRs3tsqVK+fd17JlS1u1alWZngMA4oFKz4PJjEu9usluCTQAAABE4Szrs2fPDlsD/vnnH3vggQdceXpg8K1l1apXr55v25o1a9qKFSuCfo7c3Fx38wOvnX5pb7yjv/wjXvpq89Z/rEqVoBbQsEv7pVpiQkJUHZt46a9YQF/5C/3lH/SVv9Bf/pFbDucYwey7VGdtJ5xwgoXLc88950rSTzzxxLzJ3CQ7O7vI7XPKMBNwVlaWVaxY0fxEFyTgH/SXf8R6Xx2aUtceH9c16H+3bds2i0ax3l+xhL7yF/rLP+grf6G//GN7GM8xFH+WVlJZdj527Fj7+uuvbefOnfky3bt377avvvqq1PtavXq1vfLKK/kmcwssky/qykJSUtBNtuTk5ELZ9mil16w3R7Vq1SwhISHSzcEe0F/+ES99pTHkN9z2nm3YVPwkbdWTK9rVl3e0lFb7WIXEqJlKJC77KxbQV/5Cf/kHfeUv9Jd/5JbDOYYmKC+toKPbq666ygXd5513npuI7corr7QaNWq4JcsGDx4c1L6mTp1qO3bssL59+7q/vQC8Z8+ebjy5JnULtHXr1jIF1jrQfjuh82Ob4xn95R+x3ldJFSpYn/MPd7OsF2fQZR3t0JT9yrVdZRXr/RVL6Ct/ob/8g77yF/rLPxLCeI4RzH6DDsi1FvhHH31k7du3t0cffdQGDRrkJlvr0KGDPfHEE0Ht64orrrB+/frl/f3XX3+5+x5++GHLzMy0NWvWuDJKLwhfvny5e14AQPGOSmtiwwZ1crOtB07wpsnb0vukuscBAAAQecHXf5tZpUqV3M99993XlZ0rIFegPH/+/KD2U7duXXfzeLO077fffnbooYda7dq1XRZe2XItg7Zw4UIbPnx4WZoMAHFFQXdah0Zu1nWtMa5lzTSTerSWqAMAAMSjoAPyc845x5Wpv/HGG9a5c2e744473PhxlZ+npKSErGEK+idMmODGq/fu3dsaNGhgo0aNsmbNmoXsOQAglin4bpvSMNLNAAAAQKgC8gcffNCVqau8fMyYMXbJJZdYnz59XJb8+eeft72x//7724IFC/L+VoA/ZcqUvdonAAAAAAAxEZBrArcXXngh7+/p06eHuk0AAAAAAMS8oAPyzz//vMTHjz/++L1pDwAAAAAAcSHogPzEE08sdmp3rfetpckAAAAAAECIA/KcnJxC9+3evduuvvpqa9y4cbC7AwAAAAAgLoVk/ZukpCS76aab7Omnnw7F7gAAAAAAiHllWoe8KMuXL7esrKxQ7Q4A9ig7J4d1tgEAABA/AflJJ53kxosHUiD+7bff2uDBg0PZNgAo1vwFq23y1AzL3Pj/LwTWq5Ns6X1T7ai0Jhw5AAAAxMekbloKbezYsS5YB4DyCMbHT5xT6H4F57p/2KBOBOUAAACIvYB85MiR4WkJAJSyTF2Z8ZJMnpZhaR0aWYXEkEyTAQAAAERHQL5z506bMmWKfffdd7Z9+/ZCjz/33HOhahsAFLJk6d/5ytSLkrkhy23XNqUhRxAAAACxE5APGDDAPvroIzv11FOtatWq4WkVABRj4+YdId0OAAAA8E1A/v7779uMGTPsmGOOCU+LAKAEdWpVDel2AAAAQKQEPcCyYcOGVqtWrfC0BgD2oE3rBm429ZLUq5vstgMAAABiKiC/9dZbbeDAgbZ48WJbvXp1oRsAhJMmatPSZiVJ75PKhG4AAACIvZL1Sy+91P1s27ZtvvXIc3Nz3d/Z2dmhbSEAFKB1xrW0WaF1yOsmu2CcdcgBAAAQkwH5ypUrw9MSAAiCgm4tbabZ1DWBm8aMq0ydpc4AAAAQswH5gQceGJ6WAAhqLW4C0f8rX2dpMwAAAMRVhvyOO+4odh3yX375JVRtA1CE+QtWFy7VrpPsxlVTqg0AAADEcEDep08fN1b8mmuuYR1yIALB+PiJcwrdr+Bc92tcNUE5AAAAEKMB+aJFi+zbb7+1Vq1ahadFAIotU1dmvCSTp2W4cdWMowYAAABicNmz9u3b2++//x6e1gAolsaMB5apFyVzQ5bbDgAAAEAMZsjPOOMM69Wrl40aNcqqVatW6PEBAwaEqm0AAmgm8VBuBwAAAMBnAfkzzzxjycnJdv/99xd6TGPLCciB8NCyXqHcDgAAAEBksQ454BNaY1uzqZdUtl6vbrLbDgAAAEAMjiEvbim0sWPH2mGHHRaK3QEogiZq09JmJUnvk8qEbgAAAECsB+Sa2O3BBx+0o446yg466CCbOnWqnXfeeaFtHYB8tKSZljZTprxgZpwlzwAAAIAYLllfv369vfbaa/byyy/b//73P0tJSbElS5bYjBkz7LTTTgtfKwHkC8q1tJlmU9cEbhozrjJ1ljoDAAAAYjAgnzx5sgvCP/30U2vXrp2de+659vTTT7u1yKtWrWoHHnhg+FsKII+C77YpDTkiAAAAQKwH5AMHDnQ/r7vuOrv++uutUaNG4W4XAAAAAAAxrVQB+W+//WavvPKKuzVr1sw6dOjgxoufc845bqkzAPErOyeH8nkAAAAgXAH5PvvsY9dee627rVmzxpWvKzi/+eabXUD+/PPP2yWXXOImdwMQP+YvWG2Tp2bkW4pNE85pNniNdQcAAAAQwlnWGzdubDfeeKMtWLDAli1bZqNGjbL//ve/bjx5+/btg90dAB8H4+Mnzim0Lrr+1v16HAAAAECY1iFXRvyOO+6w77//3hYtWmRnnnnm3uwOgI/K1JUZL8nkaRluu1ii1/Pj4nU2d94q9zPWXh8AAACieNmzkhxyyCHuBiD2acm1gpnxgjI3ZLntYmU2eMrzAQAAEFUZcgDxSeufh3K7aEd5PgAAAMKBgBxA0OrUqhrS7aJZvJbnAwAAIMoD8nXr1lkOJ6FA3I1/btO6gZtNvST16ia77eKpPB8AAAAI6xjyrVu32pAhQ2zatGm2a9cuN9N6w4YN7aSTTrIXXnjBUlJSgt0lAJ+Nf66QmOj+rWZTL056n1S3nd/FW3k+AAAAyk/QZ8tai3zt2rU2d+5cq1KliruvWrVq1qNHD7v66qvD0UYAUTj+WYH8sEGdCmXKlRnX/bGyDnk8lecDAAAgyjPk7733ns2ZM8fatGljCQkJeff369fPxo4dG+r2ASiH8c9pHRqVKZutoFv/VuXayhArKFWZeixkxguW55dUth4r5fkAAAAoX0GfNVeuXNkyMzML3Z+RkWG1a9cOVbsA+GT8s4JvLW123NFN3c9YCsYDy/NLEivl+QAAAChfQZ9Bavx4enq6vf322+5vjSGfNGmSXXHFFTZw4MBwtBFAGTH+OTTipTwfAAAAUV6yPmzYMKtYsaIbS759+3br2rWrNWjQwIYPH+4eAxA9GP8cOvFQng8AAIAoD8hl8ODB7uaVrterVy/U7QIQAox/Di2vPB8AAACISED++eefF/tYYmKiNWnSxN0ARF48LU8GAAAAxHxAfu6559rGjRstNzc33/2BM64fe+yx9uqrr9q+++4bmlYC2Ovxz4XWIa+b7IJxxj8DAAAAPgnINVZcE7k9+eSTLiMuu3fvtiuvvNJatGhhvXv3tquuusquu+46F5QDiDzGPwMAAAAxEJA/9thj9vHHH+cF424nSUl2yy232EknneR+3n///e53ANGD8c8AAABAdAl64OjmzZvtxx9/LHT/Tz/9ZFu3bv3/O2ZMKgAAAAAAocuQX3rppXbxxRe79cjbt2/vsuPffPONPfDAA24d8uzsbLvjjjvswgsvDHbXAAAAAADEjaAD8nHjxlnLli3tqaeesgkTJliFChXc37o/PT3dbdOlSxfr169fqfe5bt069+8XLFhgNWrUsPPOO88F/bJkyRK755577Oeff3brnSvoP/PMM4NtNgAAAAAA/g7INZv6FVdc4W7F0QRvpZWTk2PDhg1zM7I///zz9ueff9ptt93m/j7llFPshhtusOOOO85Gjx5tX3/9tY0ZM8ZatWrlbgAAAAAAxE1ALpplXWXqO3fuzLvvn3/+sczMTDepWzBWr15tixcvtgcffNDq169vTZs2tc6dO9ucOXOsdu3atmXLFhs6dKhVrlzZPTZr1iybOXMmATkAAAAAwNeCDsiVxb788sutYcOGLpu93377uTXJ//jjD+vevXvQDWjcuLHNnj3blap7VAavAH/lypXucQXjHpXHr1q1KujnAQAAAADA1wH5XXfdZc8++6wNGDDABdGfffaZNW/e3GWxK1asGHQDFHwHBuMaM/7BBx+4ieGWL19u1atXz7d9zZo1bcWKFUE9hy4Y6OYHXjv90t54R3/5B33lL/SXf9BX/kJ/+Qd95S/0l3/klkO8Fcy+gw7If//9dzvxxBPzgmMtgybXXHONHXvssXbfffdZWWht8xEjRti///7rMu16jqVLlxY77jwYWVlZZbpYEEnbt2+PdBMQBPrLP+grf6G//IO+8hf6yz/oK3+hv/xjexjjLcWfYQvItdTZiy++6MaKH3roofbqq6+6+zThmrLdZdWxY0ebOnWqy4rff//99vrrr7u1zIu6uqCl1oKRnJxcKNMerfR69eaoVq2am0AP0Y3+8g/6yl/oL/+gr/yF/vIP+spf6C//yC2HeGvXrl3hC8jHjx9vZ5xxhp122mk2cuRIO/300+2ZZ56xTZs22UMPPWRlpaBZk7bptnbtWps+fbp169bNTeoWaOvWrUEH1zrQfgtu/djmeEZ/+Qd95S/0l3/QV/5Cf/kHfeUv9Jd/JIQx3gpmv0llyWT/+uuvrgRcQbTKyufNm2ctWrSwdu3aBbs7l1nXGuSvvPJKXsOVGddEbhqbvmbNGtu2bVteEK4MujLyAAAAAAD4WWIwG2vsdq9evdxPBeOiWdbPOeecMgXjovXEN27caA888IALvhcsWODK4LUGeWpqqlv6TAG7ZlZ/4403bOHChW5ZNAAAAAAA/CyoDLky1xkZGW45sjp16oSkAbVq1bJHHnnErUPet29fN1Fcjx49rHfv3m5M+oQJE2zs2LHu7wYNGtioUaOsWbNmIXluAAAAAAAiJeiSdc1+ftFFF9n1119f5ORqWg4tWCkpKfbUU08V+9iUKVOC3icAAAAAADEVkGt5MhkzZkyhxzQGvCwBOQAAAAAA8SbogFzl6gAAAAAAoBwndQsMyu+99167+uqrbf369e6+9957by+bAgAAAABA/Ag6IH/33Xft0EMPtS+++MKeffZZt064Fla/6aab7O677w5PK4EolZ2T435++fVq+3Hxury/AQAAACDkAfltt91mzzzzjL3zzjtWqVIld1+1atVccP7EE08EuzvAt+YvWG033PZ/lSFPPDfPRt33kV1zw9vufgAAAAAIeUD+yy+/2FFHHVXk8mUbNmwIdneALynoHj9xjm3YtCPf/Zkbs9z9BOUAAAAAQh6QH3300fbcc8/lm1l99+7ddtdddxUZqAOxRmXpk6dmlLjN5GkZlK8DAAAACO0s648//rideuqpbiz5v//+6yZ2++mnn1xQPmPGjGB3B/jOkqV/u0x4STI3ZLnt2qY0LLd2AQAAAIjxgLxly5YuAH/ppZfsu+++c/d1797d+vfvbzVq1AhHG4GosnHzjpBuBwAAACA+BR2Qa3mz008/3S677LLwtAiIcnVqVQ3pdgAAAADiU9BjyPv27Wv169d3GXHNtK6ydSCetGndwOrVSS5xm3p1k912AAAAABCygHz9+vX2xhtvuKB86NCh1qBBA+vdu7dNnz7dduygRBexNXmb1hafO29VvjXGKyQmWnrf1BL/bXqfVLcdAAAAAISsZD0pKclOOeUUd3vwwQfdePKRI0fa+eefb8nJybZt27ZgdwlEHS1bppnUAydvU1ZcgfhRaU3cbdigTjbt9YWFMuMKxvU4AAAAAIQ0IBfNqP7pp5/aW2+95crWs7Ky7JJLLrGePXuWZXdAVK4xXpC3xrgCcS8oT21/gO3IyrKrLjnaatWo4srUyYwDAAAACEtA3qdPH/vggw9cpvyss86yp59+2i2DVqFChWB3Bfh2jfG0Do1c4O0F3x2PaGIJCQnl1EoAAAAAcRmQV61a1V5++WVXsq6gHIglrDEOAAAAoLwEPevUs88+65Y9CwzGVbKudcmVMQf8jDXGAQAAAJSXMk8DvXPnTnvzzTetV69ets8++9g111zjZl4H/Iw1xgEAAACUl6BqzrOzs+3DDz90Jetvv/22Gze+efNme+aZZ6xfv36UsCNm1hgPnF29INYYBwAAAFBuGfLZs2fbFVdcYfvuu6+lp6dblSpV7NVXX7U///zTKlasaMcccwzBOGICa4wDAAAAiKoMuSZwUzZ88ODBNmbMGDexGxCrvDXGC61DzhrjAAAAAMo7IJ8/f74rU3/ttdfsqaeesjPOOMPOO+8869q1ayjbAkRVUK6lzTTruiZ609hy1hgHAAAAUO4l60cccYRNmDDBfv31V3vvvfesQYMGLluun7t27bJZs2bZ9u3bQ9owIBJrkP+4eJ3NnbfK/ZS2KQ3tuKObup/emuMAAAAAEApBLyTeqVMnd3vkkUfs448/tldeecVGjBhhN954o1sOTTOvA34zf8HqwiXqdZItvW+qy5YDAAAAQKiVOeWXmJhop512mpthfd26dS4wr169emhbB5RTMD5+4pxCM6vrb92vxwEAAAAg1EJSg5uUlGTdu3e3F198MRS7A8q1TF2Z8ZJMnpbhtgMAAACAUGJQLOKaJm0rac1xydyQ5bYDAAAAgFAiIEdc0wzqodwOAAAAAEqLgBxxTcuZhXI7AAAAACgtAnLENa0trtnUS1KvbrLbDgAAAABCiYAccU1ri2tps5Kk90llDXIAAAAAIUdAjpDSbOQ/Ll5nc+etcj+Lmp28NNuUJ60zPmxQp0KZcmXGdT/rkAMAAAAIh6Sw7BVxSet1awmxwFnLFeQqA+0FtaXZJhL03GkdGrnZ1DWBm8aMq0xdGXQAAAAACAcCcoSEAu3xE+cUul+Bt+5Xpln2tE0kg3IF321TGkbs+QEAAADEFwJy7DWVnCvrXZLnpi7Y434mT8twWWqy0gAAAADiAfW42Gsq8w4sQS/Kho073K0kmRuy3L4AAAAAIB4QkGOvacx1NO4LAAAAAKIZATn2miZAi8Z9AQAAAEA0IyDHXtNs5AWXDCuobp2q7lYSLTOmfQEAAABAPCAgx17TJGxatqwkl/RNc7eSpPdJZUI3AAAAAHGDgBwhoeXKtGxZwUy5st7ecmal2QYAAAAA4gXLniFkFFBr2TLNlK7J2TQeXCXogcuYlWYbAAAAAIgHBOQIKQXWbVMa7vU2AAAAABDrSEsCAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABAvAbkf/zxh91www128skn2xlnnGEPPPCA7dy50z22ZMkSGzBggHXs2NF69Ohh7777bqSbCwAAAACA/wPy3bt32+DBg61SpUr27LPP2ujRo23mzJk2efJk27VrlwvUU1JS7KWXXrL+/fvbmDFjbNmyZZFuNgAAAAAAeyXJImzRokW2evVqe+GFF6xq1arWrFkzu/DCC+3jjz+2ww47zLZs2WJDhw61ypUrW9OmTW3WrFkuYG/VqlWkmw4AAAAAgH8z5Pvvv7+NHz/eBeMeBd9JSUm2cuVKa9y4sfvb07JlS1u1alWEWgsAAAAAQIxkyPfdd1938+Tk5NgHH3xgp512mm3fvt2qV6+eb/uaNWvaihUrgnqO3Nxcd/MDr51+aW+8o7/8g77yF/rLP+grf6G//IO+8hf6yz9yyyHeCmbfEQ/IC3ruuedcmXrPnj1dGXtRFLQHIysryypWrGh+oosR8A/6yz/oK3+hv/yDvvIX+ss/6Ct/ob/8Y3sY4y3Fn74MyD/77DM3mdvTTz9tVapUscTExCKvLqicPRjJycmFMu3RSq9Xb45q1apZQkJCpJuDPaC//IO+8hf6yz/oK3+hv/yDvvIX+ss/cssh3tLk5L4LyH/++We744477Pbbb7eDDz44L5BWtjzQ1q1bgw6udaD9Ftz6sc3xjP7yD/rKX+gv/6Cv/IX+8g/6yl/oL/9ICGO8Fcx+Iz6pm2RmZtr1119vvXv3duuQe5o3b25r1qyxbdu25d23fPlya9GiRYRaCgAAAABAaEQ8IN+5c6fdeOON1qRJE7vgggts/fr1ebfU1FSrXbu2jRs3zs2s/sYbb9jChQutc+fOkW42AAAAAAB7JeIl699//71bi1y6dOmS77FJkybZhAkTbOzYsS573qBBAxs1apRbqxwAAAAAAD+LeECuLPiCBQtK3GbKlCnl1h4AAAAAAOKiZB0AAAAAgHhEQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAQTkAAAAAABEAAE5AAAAAAARQEAOAAAAAEAEEJADAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5AAAAAAARAABOQAAAAAAEUBADgAAAABABBCQAwAAAAAQAQTkAAAAAABEAAE5AAAAAAARQEAeJbJzcuzHxevsy69X5/0NAAAAAIhdSZFuAMzmL1htk6dmWObGLKtSJckeH9fVbrjtPetz/uF2VFoTDhEAAAAAxCAy5FEQjI+fOMcF44E2bNrh7tfjAAAAAIDYQ0AeQSpLV2a8JJOnZVC+DgAAAAAxiIA8gpYs/btQZrygzA1ZbjsAAAAAQGwhII+gjZt3hHQ7AAAAAIB/EJBHUJ1aVUO6HQAAAADAPwjII6hN6wZWr05yidvUq5vstgMAAAAAxBYC8giqkJho6X1TS9wmvU+q2w4AAAAAEFuI9CJM64wPG9SpUKa8bp2q7n7WIQcAAACA2JQU6Qbg/4LytA6N3Gzqm7f+4w7JhLu6WVKFChweAAAAAIhRBORRQmXpbVMaWm5urm3bto0ydQAAAACIcZSsAwAAAAAQAQTkAAAAAABEAAE5AAAAAAARwBjyPcjOyXGTrW3cvMPq1Krq1gRnGTIAAAAAwN4iIC/B/AWrbfLUDMvcmJV3n5Yn09rhLEcGAAAAANgblKyXEIyPnzgnXzAu+lv363EAAAAAAMqKgLyYMnVlxksyeVqG2w4AAAAAgLIgIC+CxowXzIwXlLkhy20HAAAAAEBZEJAXQRO4hXI7AAAAAAAKIiAvgmZTD+V2AAAAAAAUREBeBC1tptnUS1KvbrLbDgAAAACAsiAgL4LWGdfSZiVJ75PKeuQAAAAAgDIjIC+G1hkfNqhToUy5MuO6n3XIAQAAAAB7I2mv/nWMU9Cd1qGRm01dE7hpzLjK1JVBBwAAAABgbxCQ74GC77YpDffqIAMAAAAAUBCpXgAAAAAAIiCmM+TZ2dnu55YtW8wvcnNzLSsry3bt2mUJCQmRbg72gP7yD/rKX+gv/6Cv/IX+8g/6yl/oL//ILYd4y4s/vXg0bgPy7du3u59fffVVpJsCAAAAAIgj27dvt/r165e4TUKuLhHEqH/++cfWrVtn1apVswoVKkS6OQAAAACAGJedne2C8YYNG1qVKlXiNyAHAAAAACBaMakbAAAAAAARQEAOAAAAAEAEEJADAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAAAAAEAEE5FHkjz/+sBtuuMFOPvlkO+OMM+yBBx6wnTt3RrpZ2INJkyZZWlqa/f777xyrKLZlyxYbP368devWzYYMGRLp5qAE27ZtsxEjRrjvQvXXc889x/GKIuvXr7ennnrKLrnkknz3L1myxAYMGGAdO3a0Hj162LvvvhuxNqLkvsrIyLCLL77YOnXqZOeee669/fbbHLIo7q9AAwcOtO7du5druxBcX61cudJuvPFGO+WUU+z555/n8EVxf61cudKuuOIKO/74461nz542Z86ciLQvKSLPikJ2795tgwcPthYtWtizzz7r3ji33367VatWzb1REJ3Wrl1rL774YqSbgT34559/7Morr7RKlSrZyJEjrVWrVhyzKKaLkcuWLbMnnnjC1q1bZ7fddpvtv//+1qVLl0g3Le7dfffd9t///tdq1KhhVapUyTseu3btcheUjzvuOBs9erR9/fXXNmbMGPdZ4/MWXX2l/7euu+46u/zyy10fKTjXtvqMHXHEERFqLYrrr0Dvv/++ffvtt7bvvvtywKK0rxTgpaen2wknnGCPPfaYNWnSJGLtRMn9pdhr2LBh1qZNG3cuP3v2bBs+fLi9+uqr1qhRIytPZMijxKJFi2z16tUuK9SsWTP3n+KFF14YsSs1KJ1x48a5K6CIbq+88opt2LDBHn/8cTvyyCOtdu3akW4SSjB37lx3QtO6dWt31VqB+GeffcYxiwJ169Z1GR8FdIEU1KkKZejQoda0aVO74IIL7LDDDrOZM2dGrK3xrri++uCDD9xnSxnyxo0b29lnn+2+F/W5Q/T1V2Dl0COPPOIqKBG9ffXggw9a+/bt7c4773SBXnJyckTaiD3315o1a+zXX3+166+/3g488ED3naiLXfPmzbPyRkAeJXRlWuW0VatWzbuvcuXKlpREEUO0+vTTT+2XX35xgQOim4KC3r178x+jT/z777/u+8+jK9q6D5F31VVXuZPMgpQVUnAX2G8tW7a0VatWlXMLsae+Upm6LpwE4nwjevvL8+STT7pAjyqG6O2rzZs32/z58+3SSy+NSLsQXH955xWBWfNInW8QkEcJXZFRqZ8nJyfHXcXWGEpEZwm0ympVohl4Aoroo5KkFStWuOxd//793Zjku+66y/UhotNJJ53kSsa2b9/urmB/+OGH1rlz50g3CyVQX1WvXj3ffTVr1nT3I7roxPSQQw7J+1tD5L766ivON6LY8uXLXckt859Et59//tkSEhJcUK65GXT7z3/+E+lmoRi6aKzS9BdeeMGdK6oqWRlzDTcobwTkUUqTGCmA0AQDiM7+UVnmiSeeGOmmYA82bdpk2dnZ9vnnn9s111zjyshUXjthwgSOXZRS+Zj+U1Rgfs4551i7du0YPx7l9Bkrii4uI3qpfzSvhrLmbdu2jXRzUIz77rvPldM2bNiQYxTFMjMzXWD3008/ufkZLrvsMjcXyowZMyLdNBShQoUKbs6TKVOm2DHHHOPOPa699tpyHz8uBORRSGMlJ0+ebGPHji12Yg9Ejsb6a0yyJoJA9NN/jjJq1Cg7+uij3Yz4Gkf03nvvESxEKVUwNG/e3H0PPvTQQ+7k5rXXXot0s1CCxMREy83NLXQ/w66i28MPP2y//fab3XLLLZFuCoqh1QoU6PXt25djFOU0uaUqg3T+rgtcXbt2dReV33nnnUg3DcXMy3DHHXe4z5Ymdbv11lvdykmLFy+28sYA5Sgsd9GbQzOsH3zwwZFuDoowdepU27FjR95/jt5JqKoZtJxCScuVIDKTeaiELHBogSZO1JKCyp7rcUQPZcY106lmE95nn33yxn9ptlpNFIbopImLVNUVaOvWrYXK2BE9tNSZbrrwpdmHEZ00GZWWVfWGMKoaRYGfqho0yZvGlSM61KtXz51vaEUXj6opVcKO6PPhhx+6i8neZG+a7PL777935/lKDJQnAvIooiugKpfQ5FPMohm9tAxdv3798v7+66+/3H3KNBx00EERbRsK03+Mmj1TX7Je/2jZHwXozLYefXSiKYEnNKoU0gUURC9VNGi8vzIOXhCuca8EC9Hpm2++sfvvv99NJqsLlIhejz76aF6ll3zyySeuSk+TvDVo0CCibUN+WrpY34G6sKzzDlEFCkMNotPOnTutYsWK+e7T+UYk5j6hZD2K3hQ33nijW69QWSBNsuLdvBNURAdlVDWbsHfbb7/93P36WatWrUg3D0XQBRSVIekqtUqRJk6c6CZ305VRRBdlE/S5UrCgGbp/+OEHe/bZZyMyyQpKLzU11V3g0lKQ6rc33njDFi5cyGR8UUgXJHW+oUkuNalR4PkGoo/OLQLPOXQOorGv+p1hjdFFVV2agFTjklXxqiGo06dPt+7du0e6aSiChjHq+1BjyPVT/aXqvEicb5AhjxLK3mktctGau4EUSGjcK4Cy0Tq7qmS47bbbXDmZJuNjttropDHHWsdVqxhoEiNVMqi/ilubF9FBFQ2aKFFjJ1Xlpcyd5m0g+xp9dMKp5ZmeeeYZdwu0YMGCiLULiAXDhw+3e+65xy2Jq4snl19+uZ1++umRbhaKSQDoIrIm3nvqqafckIOBAwe6hE15S8gtahYWAAAAAAAQVtRrAgAAAAAQAQTkAAAAAABEAAE5AAAAAAARQEAOAAAAAEAEEJADAAAAABABBOQAAAAAAEQAATkAAAAAABFAQA4AAAAAQAQQkAMAot7XX39tCQkJ9vHHH1ssWbdunSUlJdmzzz5r0eLTTz91x7oour+428UXXxyW9rz66qt22GGH7fV+nn/+eWvatGmxj6kfFi5cWOgxvTYdExk/frx17959r58PAAAPATkAIOq9/PLLlpiY6H7GEgWbubm5vnldf/zxR96tY8eOdsMNN+T9/fDDD4flOXv27GlfffWVhVt2drZdc801rj+Ko9f7xhtvhOT5Vq1a5YJ9/QQAxC8CcgBAVFOA9Nprr9ngwYNt+vTptmvXrrAEY5GgQPy6665zGdi//vrLot2+++6bd6tUqZJVr1497+9atWqF7bhWrlzZwk2vRZUYymwXRwG0XjcAAKFCQA4AiGpz5861DRs22JgxY1xZ8Ycffpj32CWXXGK9e/fO+1vBugLD//73v+7vn3/+2U499VQXbB166KF594vKiS+99FJr0aKFXXjhhe6+L7/80o455hirWrWqNW/e3CZPnpy3fU5Ojg0bNsxq165tTZo0sfvvv9/q1auXV87slTQfcMABVr9+fbvooots8+bNxb6uNWvW2Lx58+z66693Jdm66BBI7bvrrrvs2GOPteTkZDv66KNtxYoV+bKrzz33nB100EFWo0YN69Wrl/3zzz/u8VGjRtmJJ55YbOn1li1b7LLLLrO6deu624ABA2zbtm0WCnoeZZp1HIYPH+7uW7JkiesHvQ69rieffDJvewXAhx9+uN1444223377uWM6evToYku/V65caV26dHF9qvsDM/O///67XXDBBe547LPPPjZkyJBSXxTQ86ov1OZNmzYVuU3B46r3YqtWraxmzZp29tlnu+NYsHT/zjvvdO+HBg0a2N133523n2bNmrnf9VN/y4IFC9z7r1q1am6/L774YqnaDgDwLwJyAEBUUxb5jDPOcEHKWWedla+8W+XM77//vu3cudP9/fnnn7uMeufOnV2Aecopp9iRRx7pxgbfcsstLmj1glpRQKz9TZo0yQVhCvR0++mnn2zEiBF2+eWXu2BSnn76aXvmmWdckK6x7MuWLXMXCjwKDLWfl156ybVDwaECwpJeV4cOHVxwf+655xZZtq59Kjj95ptvXKB7xx135HtcbVLVwAcffGAfffSRTZkypVTHdNCgQbZ48WIXoM+aNcuVhI8dO9ZC5YcffrCZM2fa7bff7oJ/9YMCz4yMDPd6FPi+9957edt/99137kLK//73P7v33nvdhYh33nmn0H7//fdf17e6iKA2P/LII+45vIsZurCiizIKbFVaPm3aNHfRorTU51WqVLHbbrttj9tmZmbaeeedZ926dXOvS8/9+uuv59vm119/td9++83mz5/vLuCo//R+0oUdrwxfP/W3LhyceeaZdsIJJ7jjd9NNN1l6errbNwAghuUCABCldu/enduwYcPcadOmub9nzJiRW6NGjdwdO3a4v3fu3Jlbt27d3JkzZ7q/Bw8enNunTx/3+9NPP53brl27fPs788wzc++++273+4EHHui28Wzbti33yy+/dM/p2X///XOnTp3qfm/fvn3uiBEj8rVN/43Onj3b/d2oUaPc6dOn5z2+YMGC3MqVK+fm5OQU+do6dOiQe88997jflyxZkpuQkJC7Zs2avMfVvkcffTTv78cffzz34IMPdr+vXLnSPff333+f93jPnj1zr776avf7yJEjc0844YR8zxfY1u+++y73jz/+yHvs1ltvzT3ttNPc79qmNKcH2r+epyD921mzZuX9/eSTT+YecMABubt27cq7b9iwYbmdOnVyv0+ePDm3cePG+fbRv3//3LPPPjvvcR0Leeutt1z/q6889913X+7o0aPd71999VXuhg0b8h7Te+Hyyy8vtJ+CAh977bXXchMTE3O/+eabQsct8Lg+/PDDuU2bNs3Nzs7O20+/fv1yL7roorx96r0b+H7aZ599cl999dV8faifsn79evf3nDlz8rZ/4YUXcn/88cdiegAAEAvIkAMAotYnn3xiGzdudFlIOfnkk13ZupddrVixoisVfuutt9zfKkk///zz3e/KiivTrdJm7zZjxgyXtfRoXx5l4FWW3rVrV9t///3d9pqszMu+L1++3GW0PRUqVMj7ff369bZ27Vrr169f3nMdf/zxLqP7559/Fnpd2pey3sqMS+vWre3ggw+2V155Jd922o9HZfTbt28P6vHiNG7c2JVPe+XW48aNy3udoRB4XJX9TktLy3dfp06d7Mcff8z7WxP2BVJVwy+//FJov8ou61iprzzKJHuVAxrLrqoEDTdQ2bomzQv2den9o/L6q6++usQJ3tSHKrUPbHvge0KUbQ+8r6Q+8krmVaGhyo8XXnjBtUXvCwBA7CIgBwBELZVxqwRZ44EV3CjIUml5YHm3xgyrvFkBnpYRU0DjUQCvwNy7qUxbY9GLohLz008/3Y3n1v60vQJzjwKrwKCyKAoAvedSIKox7Bo7XJDK2kXPpdelm9pWXrOta+y8JjCbOHGi+3nllVeG7bk0IVvB46YLH95496Ls3r27yGBYE6oVtySbaEiDLo5oWIFKvXv06FGmNj/66KPugklJ5e6leT8E64EHHnDPqwsS6ps2bdq4uQYAALErtP+TAAAQIgrE33zzTXvooYfcuGGPJl7TpGEaI64MsbKZO3bssJEjR7pMurKQkpKSYm+//bYbo+3NjO39m6JoPLm201hfT2BQ2LJlSxcsedl6BY0eb9IuZd+9x73nKypoUybcGyPsWb16tbsgoDHummhub+jCRVZWVt7fgW0VjTefOnVq3nHV9uHStm1bN55bbfCOxRdffOEm2QsM0ANpDLgmqytIAerSpUvda9MEcd5FEI1TV7XBt99+6zLL3r51oaMsVDmgJc5uvvnmYrfR+0EVGXqPeBcJAl/jnhS8sPD999+797vGsWtM+dChQ/OqJvQ3ACA2kSEHAEQllZcr8FLQqkDMu6ksXFlXBduiAEhl6wr6vHJ16d+/vwuQNOu1StcVcCvzGDjTeiAFwcq+K3utcmQFRMqae5ncK664wh588EGXPdfjBbPKCppUOq2JvTQTuCYmKzjTuWjCLmXzlaUOfF0KjhWAhSJL3r59execarI3ZelVfl0wmFRArrJwTQqnyehKyljvjT59+riAWxdRVHKu4/v444/nm/BOWWAdL7VHE+cpyNYs8AXpGGn29oEDB7qKAr0+vTZdhNHs97owouy4jv+zzz7r3hNlfV2aLM4L+oui2f21VJ1mh9f7Qf2mY1laderUcUG53k96n+miiIYRaPiAZtHXpHi6XyX6AIDYRUAOAIhKygwqACuYvfXGjRecbV1BmcZ/ezQ2WkG9ZrnW2G9lUDUTdmAGO5DKx5WNV2ZUS0+pJLl79+5uNm3RhQEF0Qr0FWh7y1Z5Y4gVkGv28muvvdYF1pq9vKg1rdVuZXCVhS1IryMUAblK9bVue9++fe2kk05yQWxgcKl2Kfht166dPfbYY24t9OLKxPeW+kWBs4JxjbnWDOY6zt5Sc6L2aYk4HXdVDihLHNiXHvWJLsRoXL4uOugiiS6C6HWqHzTbugJZ9eW7777rLgIEVgoEQ8dL7SwpoFbAr4Ba7y/N6K55AwqOhy+O3p/qI83+7y3tprkQ/vOf/7ggXBd8dFFA70EAQOxK0Mx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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Aggregating spatially for a simple correlation plot\n", - "tn_mean = tn.mean(dim=(\"lat\", \"lon\"))\n", - "cdd_mean = cdd.mean(dim=(\"lat\", \"lon\"))\n", - "\n", - "plt.scatter(tn_mean, cdd_mean)\n", - "plt.xlabel(\"Average Annual Tropical Nights\")\n", - "plt.ylabel(\"Average Annual Cooling Degree Days (K days)\")\n", - "plt.title(\"Relationship Between Nighttime Heat Stress and Cooling Demand\")\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a5e29adb", - "metadata": {}, - "source": [ - "The results confirm a sustained anthropogenic warming trend across the southern Iberian Peninsula, with an increase of up to 8 tropical nights per decade in coastal areas and the Guadalquivir Valley. The strong linear correlation observed in the scatter plot demonstrates that rising nighttime minimum temperatures are a critical driver of energy demand: as nights lose their capacity to naturally cool the environment, the Cooling Degree Days (CDD) index increases proportionally. This scenario suggests that, if current trends persist, the region will face not only public health challenges due to thermal stress but also growing pressure on electrical infrastructure and household economic sustainability due to a heightened reliance on air conditioning." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "cells": [ + { + "cell_type": "markdown", + "id": "ad24e99b", + "metadata": {}, + "source": [ + "# Use Case: Tropical Nights and Cooling Demand Analysis\n", + "\n", + "> [!NOTE]\n", + "> The data shown in this repository is for **demonstration purposes only**. To obtain the full datasets for professional analysis, please use the official channels.\n", + "\n", + "This notebook explores how increasing temperatures affect nighttime heat stress and the demand for cooling. We use `earthkit-climate` to compute tropical nights and cooling degree days, analyzing their trends and correlation.\n", + "\n", + "We'll use:\n", + "- **`tropical_nights`**: Number of days where $T_{min} > 20^{\\circ}C$.\n", + "- **`cooling_degree_days`**: Cumulative degrees above a comfort threshold ($18^{\\circ}C$).\n", + "\n", + "The workflow includes loading temperature data, computing indices, performing trend analysis, and visualizing the relationship between the two indicators." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "11c39356", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "import cartopy.crs as ccrs\n", + "import earthkit.data as ekd\n", + "import earthkit.plots as ekp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from earthkit.climate.indicators.temperature import (\n", + " cooling_degree_days,\n", + " tropical_nights,\n", + ")\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.rcParams[\"figure.figsize\"] = (12, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "37803e37", + "metadata": {}, + "source": [ + "## Loading Data\n", + "\n", + "We load historical daily minimum temperature (`tasmin`) from CMIP6 simulations. We will use `tasmin` also as a proxy for `tas` to calculate cooling degree days if `tas` is not available, or just focus on minimum temperature trends if preferred. Here we load `tasmin` to compute tropical nights." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e3a9b72d", + "metadata": {}, + "outputs": [], + "source": [ + "tasmin_hist = ekd.from_source(\n", + " \"url\",\n", + " \"https://sites.ecmwf.int/repository/earthkit-climate/tasmin_ACCESS-CM2_historical_reference.nc\",\n", + ")\n", + "ds_tasmin = tasmin_hist.to_xarray()" + ] + }, + { + "cell_type": "markdown", + "id": "b6d9669e", + "metadata": {}, + "source": [ + "## Computing Indices\n", + "\n", + "We compute the indices on an annual basis. For demonstration, we'll use `tasmin` for both, noting that `cooling_degree_days` usually uses `tas`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "59afd49a", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute annual tropical nights\n", + "tn = tropical_nights(ds_tasmin.tasmin, thresh=\"293.15 K\", freq=\"YS\")\n", + "\n", + "# Compute annual cooling degree days (using tasmin for this example)\n", + "cdd = cooling_degree_days(ds_tasmin.tasmin, thresh=\"291.15 K\", freq=\"YS\")" + ] + }, + { + "cell_type": "markdown", + "id": "48782406", + "metadata": {}, + "source": [ + "## Trend Analysis\n", + "\n", + "We estimate the trend for both indicators over the available period." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b05e705", + "metadata": {}, + "outputs": [], + "source": [ + "# Simple linear trend using xarray's polyfit\n", + "tn_trend = tn.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10\n", + "cdd_trend = cdd.polyfit(dim='time', deg=1).polyfit_coefficients.sel(degree=1) * 1e9 * 60 * 60 * 24 * 365 * 10" + ] + }, + { + "cell_type": "markdown", + "id": "17759260", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "### Trend Maps\n", + "\n", + "We visualize the spatial patterns of trends in tropical nights and cooling degree days." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "28c4e7f1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure = ekp.Figure(rows=1, columns=2, size=(18, 8))\n", + "\n", + "ax1 = figure.add_map(row=0, column=0)\n", + "ax1.quickplot(tn_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", + "ax1.coastlines()\n", + "ax1.gridlines()\n", + "ax1.title(\"Trend in Tropical Nights (days/decade)\")\n", + "ax1.legend(location=\"right\")\n", + "\n", + "\n", + "ax2 = figure.add_map(row=0, column=1)\n", + "ax2.quickplot(cdd_trend, style=ekp.styles.Style(colors=\"YlOrRd\"))\n", + "ax2.coastlines()\n", + "ax2.gridlines()\n", + "ax2.title(\"Trend in Cooling Degree Days (K days/decade)\")\n", + "\n", + "ax2.legend(location=\"right\")\n", + "\n", + "\n", + "figure.show()" + ] + }, + { + "cell_type": "markdown", + "id": "431f89a3", + "metadata": {}, + "source": [ + "### Correlation Analysis\n", + "\n", + "Finally, we analyze the relationship between tropical nights and cooling demand by plotting their correlation over time." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c56322b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Aggregating spatially for a simple correlation plot\n", + "tn_mean = tn.mean(dim=(\"lat\", \"lon\"))\n", + "cdd_mean = cdd.mean(dim=(\"lat\", \"lon\"))\n", + "\n", + "plt.scatter(tn_mean, cdd_mean)\n", + "plt.xlabel(\"Average Annual Tropical Nights\")\n", + "plt.ylabel(\"Average Annual Cooling Degree Days (K days)\")\n", + "plt.title(\"Relationship Between Nighttime Heat Stress and Cooling Demand\")\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a5e29adb", + "metadata": {}, + "source": [ + "The results confirm a sustained anthropogenic warming trend across the southern Iberian Peninsula, with an increase of up to 8 tropical nights per decade in coastal areas and the Guadalquivir Valley. The strong linear correlation observed in the scatter plot demonstrates that rising nighttime minimum temperatures are a critical driver of energy demand: as nights lose their capacity to naturally cool the environment, the Cooling Degree Days (CDD) index increases proportionally. This scenario suggests that, if current trends persist, the region will face not only public health challenges due to thermal stress but also growing pressure on electrical infrastructure and household economic sustainability due to a heightened reliance on air conditioning." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } From fb640eabe45247c902ad939c682ca3e9cd3ea921 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 16 Mar 2026 13:29:35 +0100 Subject: [PATCH 44/47] feat: Add earthkit_transforms, geopandas, and pyogrio dependencies. --- pixi.lock | 110 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ pixi.toml | 3 ++ 2 files changed, 113 insertions(+) diff --git a/pixi.lock b/pixi.lock index 1813c92..ff0d2d9 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1212,17 +1212,20 @@ environments: - 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certifi + - numpy + - packaging + - cython>=3.1 ; extra == 'dev' + - pytest ; extra == 'test' + - pytest-cov ; extra == 'test' + - pytest-benchmark ; extra == 'benchmark' + - geopandas ; extra == 'geopandas' + requires_python: '>=3.10' +- pypi: https://files.pythonhosted.org/packages/ad/e0/656b6536549d41b5aec57e0deca1f269b4f17532f0636836f587e581603a/pyogrio-0.12.1-cp312-cp312-macosx_12_0_arm64.whl + name: pyogrio + version: 0.12.1 + sha256: 7a0d5ca39184030aec4cde30f4258f75b227a854530d2659babc8189d76e657d + requires_dist: + - certifi + - numpy + - packaging + - cython>=3.1 ; extra == 'dev' + - pytest ; extra == 'test' + - pytest-cov ; extra == 'test' + - pytest-benchmark ; extra == 'benchmark' + - geopandas ; extra == 'geopandas' + requires_python: '>=3.10' - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda sha256: 417fba4783e528ee732afa82999300859b065dc59927344b4859c64aae7182de md5: 3687cc0b82a8b4c17e1f0eb7e47163d5 diff --git a/pixi.toml b/pixi.toml index baca93e..be9be11 100644 --- a/pixi.toml +++ b/pixi.toml @@ -24,6 +24,9 @@ pytest-mock = "*" ruff = "*" nbformat = ">=5.10.4,<6" +[feature.dev.pypi-dependencies] +earthkit-transforms = ">=0.5.3.1, <0.6" + [feature.docs.dependencies] nbsphinx = ">=0.9.8,<0.10" pandoc = "*" From cb539d016eec7e87fc61ae4a0556476275d7df97 Mon Sep 17 00:00:00 2001 From: cuadradot Date: Mon, 16 Mar 2026 13:31:52 +0100 Subject: [PATCH 45/47] refactor: Replace `resample().min()` with `ekt.temporal.daily_reduce()` for daily minimum temperature calculation and import `earthkit.transforms`. --- docs/notebooks/frost_days_pyrenees.ipynb | 53 ++++-------------------- 1 file changed, 7 insertions(+), 46 deletions(-) diff --git a/docs/notebooks/frost_days_pyrenees.ipynb b/docs/notebooks/frost_days_pyrenees.ipynb index f09d179..f98eaf5 100644 --- a/docs/notebooks/frost_days_pyrenees.ipynb +++ b/docs/notebooks/frost_days_pyrenees.ipynb @@ -37,13 +37,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "imports", "metadata": {}, "outputs": [], "source": [ "import earthkit.data as ekd\n", "import earthkit.plots as ekp\n", + "import earhtki.transforms as ekt\n", "import numpy as np\n", "\n", "from earthkit.climate.indicators.temperature import frost_days" @@ -65,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "download_code", "metadata": {}, "outputs": [ @@ -73,49 +74,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requesting data from CDS... (this might take a few minutes)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-16 09:40:26,809 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "2026-03-16 09:40:26,810 INFO Request ID is d42bf3be-fc3c-4437-ac14-635da5d82c40\n", - "INFO:ecmwf.datastores.legacy_client:Request ID is d42bf3be-fc3c-4437-ac14-635da5d82c40\n", - "2026-03-16 09:40:26,894 INFO status has been updated to accepted\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", - "2026-03-16 09:41:16,815 INFO status has been updated to running\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", - "2026-03-16 09:54:49,160 INFO status has been updated to successful\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", - "2026-03-16 09:54:51,063 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "INFO:ecmwf.datastores.legacy_client:[2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview)\n", - "2026-03-16 09:54:51,064 INFO Request ID is 08c9b937-ab48-4cb6-90cd-5c3f4b9fb292\n", - "INFO:ecmwf.datastores.legacy_client:Request ID is 08c9b937-ab48-4cb6-90cd-5c3f4b9fb292\n", - "2026-03-16 09:54:51,159 INFO status has been updated to accepted\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to accepted\n", - "2026-03-16 09:56:06,790 INFO status has been updated to running\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to running\n", - "2026-03-16 10:05:11,723 INFO status has been updated to successful\n", - "INFO:ecmwf.datastores.legacy_client:status has been updated to successful\n", - " " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Requesting data from CDS... (this might take a few minutes)\n", "Data successfully loaded into earthkit objects!\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] } ], "source": [ @@ -168,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "processing_code", "metadata": {}, "outputs": [], @@ -189,8 +150,8 @@ "\n", "# 3. Calculate Daily Minimum Temperature (Tmin)\n", "# Resampling often \"cleans\" attributes, so we re-apply them to the result\n", - "tmin_80 = ds_80['tas'].resample(time='1D').min()\n", - "tmin_23 = ds_23['tas'].resample(time='1D').min()\n", + "tmin_80 = ekt.temporal.daily_reduce(ds_80[\"tas\"], how=\"min\")\n", + "tmin_23 = ekt.temporal.daily_reduce(ds_23[\"tas\"], how=\"min\")\n", "\n", "for tmin in [tmin_80, tmin_23]:\n", " tmin.attrs['units'] = 'K'\n", From 59a4c6daf8e5c7ba76e8c9cda39d3ec841632dcb Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 17 Mar 2026 15:10:35 +0100 Subject: [PATCH 46/47] Fix: temperature indicators --- pixi.lock | 2004 ++----- .../climate/indicators/temperature.py | 5227 ++++++++++++++++- tests/unit/indicators/test_temperature.py | 2 +- 3 files changed, 5828 insertions(+), 1405 deletions(-) diff --git a/pixi.lock b/pixi.lock index 28ffe36..832336c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -8,10 +8,7 @@ environments: packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - 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Developed specifically to aid in determining plant suitability of + geographic regions. The Australian National Botanical Gardens (ANBG) classification + scheme divides categories into 5-degree Celsius zones, starting from -15 degrees Celsius + and ending at 20 degrees Celsius. + + **Units:** + + - hz: dimensionless + + This function wraps `xclim.indicators.atmos.australian_hardiness_zones + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum temperature. + window : int + The length of the averaging window, in years. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.australian_hardiness_zones( + tasmin=tasmin, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.biologically_effective_degree_days) +def biologically_effective_degree_days( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '10 degC', + method: Literal['gladstones', 'icclim', 'jones', 'smoothed', 'stepwise'] = 'gladstones', + cap_value: float = 1.0, + low_dtr: Any = '10 degC', + high_dtr: Any = '13 degC', + max_daily_degree_days: Any = '9 degC', + start_date: str | str = '04-01', + end_date: str | str = '11-01', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Biologically effective degree days. + + Considers daily minimum and maximum temperature with a given base threshold between 1 + April and 31 October, with a maximum daily value for cumulative degree days (typically + 9°C), and integrates modification coefficients for latitudes between 40°N and 50°N as + well as for swings in daily temperature range. Metric originally published in Gladstones + (1992). + + **Units:** + + - bedd: K days + + This function wraps `xclim.indicators.atmos.biologically_effective_degree_days + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None and method is not "icclim", a CF-conformant "latitude" + field must be available within the passed DataArray. + thresh_tasmin : Any + The minimum temperature threshold. + method : Literal['gladstones', 'icclim', 'jones', 'smoothed', 'stepwise'] + The formula to use for the daily temperature range and latitude coefficient. The + "gladstones" method uses a temperature range adjustment and a latitude coefficient + based on :cite:t:`gladstones_wine_2011`. End_date should be "11-01" for the Northern + Hemisphere. The "huglin" method uses a temperature range adjustment and a stepwise + latitude coefficient for values between 40° and 50° based on + :cite:t:`huglin_nouveau_1978`. End_date should be "11-01" for the Northern + Hemisphere. The "icclim" method does not implement daily temperature range and nor a + latitude coefficient based on :cite:t:`project_team_eca&d_algorithm_2013`. End date + should be "10-01" for the Northern Hemisphere. The "interpolated" method uses a + temperature range adjustment and a smoothed curve latitude coefficient for values + between 40° and 50° based on :cite:t:`huglin_nouveau_1978`. The "jones" method uses + a temperature range adjustment and integrates axial tilt, latitude, and day-of-year + based on :cite:t:`hall_spatial_2010`. End_date should be "11-01" for the Northern + Hemisphere. + cap_value : float + The value to use for the latitude coefficient for latitudes north of 50°N or south + of 50°S. Only applicable for methods "huglin" and "interpolated". + low_dtr : Any + The lower bound for daily temperature range adjustment. + high_dtr : Any + The higher bound for daily temperature range adjustment. + max_daily_degree_days : Any + The maximum number of biologically effective degrees days that can be summed daily. + start_date : str | str + The hemisphere-based start date to consider (north = April, south = October). + end_date : str | str + The hemisphere-based start date to consider (north = October, south = April). This + date is non-inclusive. + freq : str + Resampling frequency (For Southern Hemisphere, should be "YS-JUL"). + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.biologically_effective_degree_days( + tasmin=tasmin, + tasmax=tasmax, + lat=lat, + thresh_tasmin=thresh_tasmin, + method=method, + cap_value=cap_value, + low_dtr=low_dtr, + high_dtr=high_dtr, + max_daily_degree_days=max_daily_degree_days, + start_date=start_date, + end_date=end_date, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_days) +def cold_spell_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '-10 degC', + window: int = 5, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Cold spell days. + + The number of days that are part of a cold spell. A cold spell is defined as a minimum + number of consecutive days with mean daily temperature below a given threshold. + + **Units:** + + - cold_spell_days: days + + This function wraps `xclim.indicators.atmos.cold_spell_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature below which a cold spell begins. + window : int + Minimum number of days with temperature below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_days( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_duration_index) +def cold_spell_duration_index( + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, + *, + window: int = 6, + freq: str = 'YS', + resample_before_rl: bool = True, + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Cold spell duration index (csdi). + + Number of days part of a percentile-defined cold spell. A cold spell occurs when the + daily minimum temperature is below a given percentile for a given number of consecutive + days. + + **Units:** + + - csdi_{window}: days + + This function wraps `xclim.indicators.atmos.cold_spell_duration_index + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmin_per : xarray.DataArray | str + The nth percentile of daily minimum temperature with `dayofyear` coordinate. + window : int + Minimum number of days with temperature below threshold to qualify as a cold spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Keep + bootstrap to `False` when there is no common period, as bootstrapping is + computationally expensive, and it might provide the wrong results. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_duration_index( + tasmin=tasmin, + tasmin_per=tasmin_per, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_frequency) +def cold_spell_frequency( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '-10 degC', + window: int = 5, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Cold spell frequency. + + The frequency of cold periods of `N` days or more, during which the temperature over a + given time window of days is below a given threshold. + + **Units:** + + - cold_spell_frequency: dimensionless + + This function wraps `xclim.indicators.atmos.cold_spell_frequency + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature below which a cold spell begins. + window : int + Minimum number of days with temperature below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_frequency( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_max_length) +def cold_spell_max_length( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '-10 degC', + window: int = 1, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Cold spell maximum length. + + The maximum length of a cold period of `N` days or more, during which the temperature + over a given time window of days is below a given threshold. + + **Units:** + + - cold_spell_max_length: days + + This function wraps `xclim.indicators.atmos.cold_spell_max_length + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + The temperature threshold needed to trigger a cold spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_max_length( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cold_spell_total_length) +def cold_spell_total_length( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '-10 degC', + window: int = 3, + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Cold spell total length. + + The total length of cold periods of `N` days or more, during which the temperature over + a given time window of days is below a given threshold. + + **Units:** + + - cold_spell_total_length: days + + This function wraps `xclim.indicators.atmos.cold_spell_total_length + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + The temperature threshold needed to trigger a cold spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a cold + spell. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cold_spell_total_length( + tas=tas, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.consecutive_frost_days) +def consecutive_frost_days( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS-JUL', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Consecutive frost days. + + Maximum number of consecutive days where the daily minimum temperature is below 0°C + + **Units:** + + - consecutive_frost_days: days + + This function wraps `xclim.indicators.atmos.consecutive_frost_days + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.consecutive_frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_frost_free_days) +def maximum_consecutive_frost_free_days( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Maximum consecutive frost free days. + + Maximum number of consecutive frost-free days: where the daily minimum temperature is + above or equal to 0°C + + **Units:** + + - consecutive_frost_free_days: days + + This function wraps `xclim.indicators.atmos.maximum_consecutive_frost_free_days + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_frost_free_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cool_night_index) +def cool_night_index( + tasmin: xarray.DataArray | str = 'tasmin', + lat: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, + *, + freq: Literal['YS', 'YS-JAN'] = 'YS', + **kwargs: Any, +) -> Any: + """ + Cool night index. + + A night coolness variable which takes into account the mean minimum night temperatures + during the month when ripening usually occurs beyond the ripening period. + + **Units:** + + - cool_night_index: degC + + This function wraps `xclim.indicators.atmos.cool_night_index + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + lat : xarray.DataArray | str | None + Latitude coordinate as an array, float or string. If None, a CF-conformant + "latitude" field must be available within the passed DataArray. + freq : Literal['YS', 'YS-JAN'] + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cool_night_index( + tasmin=tasmin, + lat=lat, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cooling_degree_days) +def cooling_degree_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '18.0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Cooling degree days. + + The cumulative degree days for days when the mean daily temperature is above a given + threshold and buildings must be air conditioned. + + **Units:** + + - cooling_degree_days: K days + + This function wraps `xclim.indicators.atmos.cooling_degree_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Temperature threshold above which air is cooled. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cooling_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.cooling_degree_days_approximation) +def cooling_degree_days_approximation( + tasmax: xarray.DataArray | str = 'tasmax', + tasmin: xarray.DataArray | str = 'tasmin', + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '18.0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Cooling degree days approximation. + + The cumulative degree days for days when temperatures are above a given threshold and + buildings must be air conditioned. This method integrates mean, minimum, and maximum + temperatures, accounting for asymmetry in the distributions of temperatures throughout + the diurnal cycle. + + **Units:** + + - cooling_degree_days_approximation: K days + + This function wraps `xclim.indicators.atmos.cooling_degree_days_approximation + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Temperature threshold above which air is cooled. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.cooling_degree_days_approximation( + tasmax=tasmax, + tasmin=tasmin, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.corn_heat_units) +def corn_heat_units( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '4.44 degC', + thresh_tasmax: Any = '10 degC', + **kwargs: Any, +) -> Any: + """ + Corn heat units. + + A temperature-based index used to estimate the development of corn crops. Corn growth + occurs when the daily minimum and maximum temperatures exceed given thresholds. + + **Units:** + + - chu: dimensionless + + This function wraps `xclim.indicators.atmos.corn_heat_units + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed for corn growth. + thresh_tasmax : Any + The maximum temperature threshold needed for corn growth. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.corn_heat_units( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.chill_portions) +def chill_portions( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Chill portions. + + Chill portions are a measure to estimate the bud breaking potential of different crops. + The constants and functions are taken from Luedeling et al. (2009) which formalises the + method described in Fishman et al. (1987). The model computes the accumulation of cold + temperatures in a two-step process. First, cold temperatures contribute to an + intermediate product that is transformed to a chill portion once it exceeds a certain + concentration. The intermediate product can be broken down at higher temperatures but + the final product is stable even at higher temperature. Thus the dynamic model is more + accurate than other chill models like the Chilling hours or Utah model, especially in + moderate climates like Israel, California or Spain. + + **Units:** + + - cp: dimensionless + + This function wraps `xclim.indicators.atmos.chill_portions + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Hourly temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.chill_portions( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.chill_units) +def chill_units( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + positive_only: bool = False, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Chill units. + + Chill units are a measure to estimate the bud breaking potential of different crop based + on Richardson et al. (1974). The Utah model assigns a weight to each hour depending on + the temperature recognising that high temperatures can actual decrease, the potential + for bud breaking. Providing `positive_only=True` will ignore days with negative chill + units. + + **Units:** + + - cu: dimensionless + + This function wraps `xclim.indicators.atmos.chill_units + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Hourly temperature. + positive_only : bool + If `True`, only positive daily chill units are aggregated. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.chill_units( + tas=tas, + positive_only=positive_only, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.degree_days_exceedance_date) +def degree_days_exceedance_date( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + sum_thresh: Any = '25 K days', + op: Literal['>', 'gt', '<', 'lt', '>=', 'ge', '<=', 'le'] = '>', + after_date: str | None = None, + never_reached: str | int | None = None, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Degree day exceedance date. + + The day of the year when the sum of degree days exceeds a threshold, occurring after a + given date. Degree days are calculated above or below a given temperature threshold. + + **Units:** + + - degree_days_exceedance_date: dimensionless + + This function wraps `xclim.indicators.atmos.degree_days_exceedance_date + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base degree-days evaluation. + sum_thresh : Any + Threshold of the degree days sum. + op : Literal['>', 'gt', '<', 'lt', '>=', 'ge', '<=', 'le'] + If equivalent to '>', degree days are computed as `tas - thresh` and if equivalent + to '<', they are computed as `thresh - tas`. + after_date : str | None + Date at which to start the cumulative sum. In "MM-DD" format, defaults to the start + of the sampling period. + never_reached : str | int | None + What to do when `sum_thresh` is never exceeded. If an int, the value to assign as a + day-of-year. If a string, must be in "MM-DD" format, the day-of-year of that date is + assigned. Default (None) assigns "NaN". + freq : str + Resampling frequency. If `after_date` is given, `freq` should be annual. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.degree_days_exceedance_date( + tas=tas, + thresh=thresh, + sum_thresh=sum_thresh, + op=op, + after_date=after_date, + never_reached=never_reached, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_freezethaw_cycles) +def daily_freezethaw_cycles( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Daily freeze-thaw cycles. + + The number of days with a freeze-thaw cycle. A freeze-thaw cycle is defined as a day + where maximum daily temperature is above a given threshold and minimum daily temperature + is at or below a given threshold, usually 0°C for both. + + **Units:** + + - dlyfrzthw: days + + This function wraps `xclim.indicators.atmos.daily_freezethaw_cycles + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.daily_freezethaw_cycles( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_temperature_range) def daily_temperature_range( - ds: conversions.EarthkitData | xarray.Dataset, + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Mean of daily temperature range. + + The average difference between the daily maximum and minimum temperatures. + + **Units:** + + - dtr: K + + This function wraps `xclim.indicators.atmos.daily_temperature_range + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.daily_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.max_daily_temperature_range) +def max_daily_temperature_range( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Maximum of daily temperature range. + + The maximum difference between the daily maximum and minimum temperatures. + + **Units:** + + - dtrmax: K + + This function wraps `xclim.indicators.atmos.max_daily_temperature_range + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.max_daily_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.daily_temperature_range_variability) +def daily_temperature_range_variability( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Variability of daily temperature range. + + The average day-to-day variation in daily temperature range. + + **Units:** + + - dtrvar: K + + This function wraps `xclim.indicators.atmos.daily_temperature_range_variability + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.daily_temperature_range_variability( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.extreme_temperature_range) +def extreme_temperature_range( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Extreme temperature range. + + The maximum of the maximum temperature minus the minimum of the minimum temperature. + + **Units:** + + - etr: K + + This function wraps `xclim.indicators.atmos.extreme_temperature_range + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.extreme_temperature_range( + tasmin=tasmin, + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.fire_season) +def fire_season( + tas: xarray.DataArray | str = 'tas', + snd: xarray.DataArray | str | None = None, + ds: xarray.Dataset | Any = None, + *, + method: str = 'WF93', + freq: str | None = None, + temp_start_thresh: Any = '12 degC', + temp_end_thresh: Any = '5 degC', + temp_condition_days: int = 3, + snow_condition_days: int = 3, + snow_thresh: Any = '0.01 m', + **kwargs: Any, +) -> Any: + """ + Fire season mask. + + Binary mask of the active fire season, defined by conditions on consecutive daily + temperatures and, optionally, snow depths. + + **Units:** + + - fire_season: dimensionless + + This function wraps `xclim.indicators.atmos.fire_season + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Daily surface temperature, cffdrs recommends using maximum daily temperature. + snd : xarray.DataArray | str | None + Snow depth, used with method == 'LA08'. + method : str + Which method to use. "LA08" and "GFWED" need the snow depth. + freq : str | None + If given only the longest fire season for each period defined by this frequency, + Every "seasons" are returned if None, including the short shoulder seasons. + temp_start_thresh : Any + Minimal temperature needed to start the season. Must be scalar. + temp_end_thresh : Any + Maximal temperature needed to end the season. Must be scalar. + temp_condition_days : int + Number of days with temperature above or below the thresholds to trigger a start or + an end of the fire season. + snow_condition_days : int + Parameters for the fire season determination. See :py:func:`fire_season`. + Temperature is in degC, snow in m. The `snow_thresh` parameters is also used when + `dry_start` is set to "GFWED". + snow_thresh : Any + Minimal snow depth level to end a fire season, only used with method "LA08". Must be + scalar. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.fire_season( + tas=tas, + snd=snd, + method=method, + freq=freq, + temp_start_thresh=temp_start_thresh, + temp_end_thresh=temp_end_thresh, + temp_condition_days=temp_condition_days, + snow_condition_days=snow_condition_days, + snow_thresh=snow_thresh, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tg_above) +def first_day_tg_above( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + **Units:** + + - first_day_tg_above: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tg_above + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tg_above( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tg_below) +def first_day_tg_below( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + **Units:** + + - first_day_tg_below: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tg_below + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tg_below( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tn_above) +def first_day_tn_above( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + **Units:** + + - first_day_tn_above: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tn_above + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tn_above( + tasmin=tasmin, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tn_below) +def first_day_tn_below( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + **Units:** + + - first_day_tn_below: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tn_below + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tn_below( + tasmin=tasmin, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tx_above) +def first_day_tx_above( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures superior to a given temperature threshold. + + Returns first day of period where temperature is superior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: January 1st). + + **Units:** + + - first_day_tx_above: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tx_above + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tx_above( + tasmax=tasmax, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.first_day_tx_below) +def first_day_tx_below( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + after_date: str = '07-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + First day of temperatures inferior to a given temperature threshold. + + Returns first day of period where temperature is inferior to a threshold over a given + number of days (default: 1), limited to a starting calendar date (default: July 1st). + + **Units:** + + - first_day_tx_below: dimensionless + + This function wraps `xclim.indicators.atmos.first_day_tx_below + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum surface temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.first_day_tx_below( + tasmax=tasmax, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_frequency) +def freezethaw_spell_frequency( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Freeze-thaw spell frequency. + + Frequency of daily freeze-thaw spells. A freeze-thaw spell is defined as a number of + consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a given threshold, usually 0°C for both. + + **Units:** + + - freezethaw_spell_frequency: days + + This function wraps `xclim.indicators.atmos.freezethaw_spell_frequency + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_frequency( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_max_length) +def freezethaw_spell_max_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + op_tasmin: Literal['<', '<=', 'lt', 'le'] = '<=', + op_tasmax: Literal['>', '>=', 'gt', 'ge'] = '>', + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Maximal length of freeze-thaw spells. + + Maximal length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number + of consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a threshold, usually 0°C for both. + + **Units:** + + - freezethaw_spell_max_length: days + + This function wraps `xclim.indicators.atmos.freezethaw_spell_max_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + op_tasmin : Literal['<', '<=', 'lt', 'le'] + Comparison operation for tasmin. Default: "<=". + op_tasmax : Literal['>', '>=', 'gt', 'ge'] + Comparison operation for tasmax. Default: ">". + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_max_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + op_tasmin=op_tasmin, + op_tasmax=op_tasmax, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezethaw_spell_mean_length) +def freezethaw_spell_mean_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '0 degC', + thresh_tasmax: Any = '0 degC', + window: int = 1, + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Freeze-thaw spell mean length. + + Average length of daily freeze-thaw spells. A freeze-thaw spell is defined as a number + of consecutive days where maximum daily temperatures are above a given threshold and + minimum daily temperatures are at or below a given threshold, usually 0°C for both. + + **Units:** + + - freezethaw_spell_mean_length: days + + This function wraps `xclim.indicators.atmos.freezethaw_spell_mean_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The temperature threshold needed to trigger a freeze event. + thresh_tasmax : Any + The temperature threshold needed to trigger a thaw event. + window : int + The minimal length of spells to be included in the statistics. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.freezethaw_spell_mean_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freezing_degree_days) +def freezing_degree_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Freezing degree days. + + The cumulative degree days for days when the average temperature is below a given + threshold, typically 0°C. + + **Units:** + + - freezing_degree_days: K days + + This function wraps `xclim.indicators.atmos.freezing_degree_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.freezing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.freshet_start) +def freshet_start( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + after_date: str = '01-01', + window: int = 5, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Day of year of spring freshet start. + + Day of year of the spring freshet start, defined as the first day when the temperature + exceeds a certain threshold for a given number of consecutive days. + + **Units:** + + - freshet_start: dimensionless + + This function wraps `xclim.indicators.atmos.freshet_start + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + after_date : str + Date of the year after which to look for the first event. Should have the format + '%m-%d'. + window : int + Minimum number of days with temperature above the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.freshet_start( + tas=tas, + thresh=thresh, + op=op, + after_date=after_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_days) +def frost_days( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Frost days. + + Number of days where the daily minimum temperature is below a given threshold. + + **Units:** + + - frost_days: days + + This function wraps `xclim.indicators.atmos.frost_days + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_end) +def frost_free_season_end( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Frost free season end. + + First day when the temperature is below a given threshold for a given number of + consecutive days after a median calendar date. + + **Units:** + + - frost_free_season_end: dimensionless + + This function wraps `xclim.indicators.atmos.frost_free_season_end + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date what must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_end( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_length) +def frost_free_season_length( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Frost free season length. + + Duration of the frost free season, defined as the period when the minimum daily + temperature is above 0°C without a freezing window of `N` days, with freezing occurring + after a median calendar date. + + **Units:** + + - frost_free_season_length: days + + This function wraps `xclim.indicators.atmos.frost_free_season_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date what must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_length( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_season_start) +def frost_free_season_start( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + window: int = 5, + mid_date: str | None = '07-01', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Frost free season start. + + First day when minimum daily temperature exceeds a given threshold for a given number of + consecutive days + + **Units:** + + - frost_free_season_start: dimensionless + + This function wraps `xclim.indicators.atmos.frost_free_season_start + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above/under the threshold to start/end the + season. + mid_date : str | None + A date that must be included in the season. `None` removes that constraint. + op : Literal['>', 'gt', '>=', 'ge'] + How to compare tasmin and the threshold. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_season_start( + tasmin=tasmin, + thresh=thresh, + window=window, + mid_date=mid_date, + op=op, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_free_spell_max_length) +def frost_free_spell_max_length( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0.0 degC', + window: int = 1, + freq: str = 'YS-JUL', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Frost free spell maximum length. + + The maximum length of a frost free period of `N` days or more, during which the minimum + temperature over a given time window of days is above a given threshold. + + **Units:** + + - frost_free_spell_max_length: days + + This function wraps `xclim.indicators.atmos.frost_free_spell_max_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + The temperature threshold needed to trigger a frost-free spell. + window : int + Minimum number of days with temperatures above thresholds to qualify as a frost-free + day. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_free_spell_max_length( + tasmin=tasmin, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.frost_season_length) +def frost_season_length( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + window: int = 5, + mid_date: str | None = '01-01', + thresh: Any = '0 degC', + freq: str = 'YS-JUL', + op: Literal['<', 'lt', '<=', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Frost season length. + + Duration of the freezing season, defined as the period when the daily minimum + temperature is below 0°C without a thawing window of days, with the thaw occurring after + a median calendar date. + + **Units:** + + - frost_season_length: days + + This function wraps `xclim.indicators.atmos.frost_season_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + window : int + Minimum number of days with temperature below threshold to mark the beginning and + end of frost season. + mid_date : str | None + The date must be included in the season. It is the earliest the end of the season + can be. ``None`` removes that constraint. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.frost_season_length( + tasmin=tasmin, + window=window, + mid_date=mid_date, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_degree_days) +def growing_degree_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '4.0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Growing degree days. + + The cumulative degree days for days when the average temperature is above a given + threshold. + + **Units:** + + - growing_degree_days: K days + + This function wraps `xclim.indicators.atmos.growing_degree_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.growing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_end) +def growing_season_end( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '5.0 degC', + mid_date: str | None = '07-01', + window: int = 5, + freq: str = 'YS', + op: Literal['>', '>=', 'lt', 'le'] = '>=', + **kwargs: Any, +) -> Any: + """ + Growing season end. + + The first day when the temperature is below a certain threshold for a certain number of + consecutive days after a given calendar date. + + **Units:** + + - growing_season_end: dimensionless + + This function wraps `xclim.indicators.atmos.growing_season_end + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + mid_date : str | None + Date of the year after which to look for the end of the season. Should have the + format '%m-%d'. ``None`` removes that constraint. + window : int + Minimum number of days with temperature below threshold needed for evaluation. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'lt', 'le'] + Comparison operation. Default: ">". Note that this comparison is what defines the + season. The end of the season happens when the condition is NOT met for `window` + consecutive days. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_end( + tas=tas, + thresh=thresh, + mid_date=mid_date, + window=window, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_length) +def growing_season_length( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '5.0 degC', + window: int = 6, + mid_date: str | None = '07-01', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + **kwargs: Any, +) -> Any: + """ + Growing season length. + + Number of days between the first occurrence of a series of days with a daily average + temperature above a threshold and the first occurrence of a series of days with a daily + average temperature below that same threshold, occurring after a given calendar date. + + **Units:** + + - growing_season_length: days + + This function wraps `xclim.indicators.atmos.growing_season_length + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + window : int + Minimum number of days with temperature above the threshold to mark the beginning + and end of growing season. + mid_date : str | None + Date of the year before which the season must start and after which it can end. + Should have the format '%m-%d'. Setting `None` removes that constraint. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_length( + tas=tas, + thresh=thresh, + window=window, + mid_date=mid_date, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.growing_season_start) +def growing_season_start( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '5.0 degC', + mid_date: str | None = '07-01', + window: int = 5, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', + **kwargs: Any, +) -> Any: + """ + Growing season start. + + The first day when the temperature exceeds a certain threshold for a given number of + consecutive days. + + **Units:** + + - growing_season_start: dimensionless + + This function wraps `xclim.indicators.atmos.growing_season_start + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + mid_date : str | None + Date of the year before which the season must start. Should have the format '%m-%d'. + ``None`` removes that constraint. + window : int + Minimum number of days with temperature above threshold needed for evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">=". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.growing_season_start( + tas=tas, + thresh=thresh, + mid_date=mid_date, + window=window, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_frequency) +def heat_spell_frequency( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', + **kwargs: Any, +) -> Any: + """ + Heat spell frequency. + + Number of heat spells. A heat spell occurs when rolling averages of daily minimum and + maximumtemperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_spell_frequency: dimensionless + + This function wraps `xclim.indicators.atmos.heat_spell_frequency + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_frequency( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_max_length) +def heat_spell_max_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', + **kwargs: Any, +) -> Any: + """ + Heat spell maximum length. + + The longest heat spell of a period. A heat spell occurs when rolling averages of daily + minimum and maximum temperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_spell_max_length: days + + This function wraps `xclim.indicators.atmos.heat_spell_max_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_max_length( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_spell_total_length) +def heat_spell_total_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + window: int = 3, + win_reducer: Literal['min', 'max', 'sum', 'mean'] = 'mean', + freq: str = 'YS', + min_gap: int = 1, + resample_before_rl: bool = True, + thresh_tasmin: Any = '20 °C', + thresh_tasmax: Any = '33 °C', + **kwargs: Any, +) -> Any: + """ + Heat spell total length. + + Total length of heat spells. A heat spell occurs when rolling averages of daily minimum + and maximum temperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_spell_total_length: days + + This function wraps `xclim.indicators.atmos.heat_spell_total_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum surface temperature. + tasmax : xarray.DataArray | str + Maximum surface temperature. + window : int + Minimum length of a spell. + win_reducer : Literal['min', 'max', 'sum', 'mean'] + Reduction along the spell length to compute the spell value. Note that this does not + matter when `window` is 1. + freq : str + Resampling frequency. + min_gap : int + The shortest possible gap between two spells. Spells closer than this are merged by + assigning the gap steps to the merged spell. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + thresh_tasmin : Any + Threshold for tasmin + thresh_tasmax : Any + Threshold for tasmax + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_spell_total_length( + tasmin=tasmin, + tasmax=tasmax, + window=window, + win_reducer=win_reducer, + freq=freq, + min_gap=min_gap, + resample_before_rl=resample_before_rl, + ds=ds, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_frequency) +def heat_wave_frequency( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Heat wave frequency. + + Number of heat waves. A heat wave occurs when daily minimum and maximum temperatures + exceed given thresholds for a number of days. + + **Units:** + + - heat_wave_frequency: dimensionless + + This function wraps `xclim.indicators.atmos.heat_wave_frequency + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_frequency( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_index) +def heat_wave_index( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25 degC', + window: int = 5, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Heat wave index. + + Number of days that constitute heatwave events. A heat wave occurs when daily minimum + and maximum temperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_wave_index: days + + This function wraps `xclim.indicators.atmos.heat_wave_index + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_index( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_max_length) +def heat_wave_max_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Heat wave maximum length. + + Maximal duration of heat waves. A heat wave occurs when daily minimum and maximum + temperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_wave_max_length: days + + This function wraps `xclim.indicators.atmos.heat_wave_max_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_max_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heat_wave_total_length) +def heat_wave_total_length( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '22.0 degC', + thresh_tasmax: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Heat wave total length. + + Total length of heat waves. A heat wave occurs when daily minimum and maximum + temperatures exceed given thresholds for a number of days. + + **Units:** + + - heat_wave_total_length: days + + This function wraps `xclim.indicators.atmos.heat_wave_total_length + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + The minimum temperature threshold needed to trigger a heatwave event. + thresh_tasmax : Any + The maximum temperature threshold needed to trigger a heatwave event. + window : int + Minimum number of days with temperatures above thresholds to qualify as a heatwave. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heat_wave_total_length( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heating_degree_days) +def heating_degree_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '17.0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Heating degree days. + + The cumulative degree days for days when the mean daily temperature is below a given + threshold and buildings must be heated. + + **Units:** + + - heating_degree_days: K days + + This function wraps `xclim.indicators.atmos.heating_degree_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heating_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.heating_degree_days_approximation) +def heating_degree_days_approximation( + tasmax: xarray.DataArray | str = 'tasmax', + tasmin: xarray.DataArray | str = 'tasmin', + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '17.0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Heating degree days approximation. + + The cumulative degree days for days where temperatures are below a given threshold and + buildings must be heated. This method integrates mean, minimum, and maximum + temperatures, accounting for asymmetry in the distributions of temperatures throughout + the diurnal cycle. + + **Units:** + + - heating_degree_days_approximation: K days + + This function wraps `xclim.indicators.atmos.heating_degree_days_approximation + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmin : xarray.DataArray | str + Minimum daily temperature. + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.heating_degree_days_approximation( + tasmax=tasmax, + tasmin=tasmin, + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_days) +def hot_days( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Hot days. + + Number of days where the daily maximum temperature is above a given threshold. + + **Units:** + + - hot_days: days + + This function wraps `xclim.indicators.atmos.hot_days + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.hot_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_frequency) +def hot_spell_frequency( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Hot spell frequency. + + The frequency of hot periods of `N` days or more, during which the temperature over a + given time window of days is above a given threshold. + + **Units:** + + - hot_spell_frequency: dimensionless + + This function wraps `xclim.indicators.atmos.hot_spell_frequency + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature below which a hot spell begins. + window : int + Minimum number of days with temperature above the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_frequency( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_max_length) +def hot_spell_max_length( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '30 degC', + window: int = 1, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Hot spell maximum length. + + The maximum length of a hot period of `N` days or more, during which the temperature + over a given time window of days is above a given threshold. + + **Units:** + + - hot_spell_max_length: days + + This function wraps `xclim.indicators.atmos.hot_spell_max_length + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below thresholds to qualify as a hot spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_max_length( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_max_magnitude) +def hot_spell_max_magnitude( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25.0 degC', + window: int = 3, + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Hot spell maximum magnitude. + + Magnitude of the most intensive heat wave per {freq}. A heat wave occurs when daily + maximum temperatures exceed given thresholds for a number of days. + + **Units:** + + - hot_spell_max_magnitude: K d + + This function wraps `xclim.indicators.atmos.hot_spell_max_magnitude + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to designate a heatwave. + window : int + Minimum number of days with temperature above the threshold to qualify as a + heatwave. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_max_magnitude( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.hot_spell_total_length) +def hot_spell_total_length( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '30 degC', + window: int = 3, + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Hot spell total length. + + The total length of hot periods of `N` days or more, during which the temperature over a + given time window of days is above a given threshold. + + **Units:** + + - hot_spell_total_length: days + + This function wraps `xclim.indicators.atmos.hot_spell_total_length + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + The temperature threshold needed to trigger a hot spell. + window : int + Minimum number of days with temperatures below the threshold to qualify as a hot + spell. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.hot_spell_total_length( + tasmax=tasmax, + thresh=thresh, + window=window, + freq=freq, + op=op, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.huglin_index) +def huglin_index( + tas: xarray.DataArray | str = 'tas', + tasmax: xarray.DataArray | str = 'tasmax', + lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '10 degC', + method: str = 'jones', + cap_value: float = 1.0, + start_date: str | str = '04-01', + end_date: str | str = '10-01', + freq: Literal['YS', 'YS-JAN', 'YS-JUL'] = 'YS', + **kwargs: Any, +) -> Any: + """ + Huglin heliothermal index. + + Heat-summation index for agroclimatic suitability estimation, developed specifically for + viticulture. Considers daily minimum and maximum temperature with a given base + threshold, typically between 1 April and 30September, and integrates a day-length + coefficient calculation for higher latitudes. Metric originally published in Huglin + (1978). Day-length coefficient based on Hall & Jones (2010). + + **Units:** + + - hi: dimensionless + + This function wraps `xclim.indicators.atmos.huglin_index + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None, a CF-conformant "latitude" field must be available + within the passed DataArray. + thresh : Any + The temperature threshold. + method : str + The formula to use for the latitude coefficient calculation. The "huglin" method + uses a stepwise latitude coefficient for values between 40° and 50° based on + :cite:t:`huglin_nouveau_1978`. The "interpolated" method uses a smoothed curve + latitude coefficient for values based on the intervals set in + :cite:t:`huglin_nouveau_1978`. The "jones" method integrates axial tilt, latitude, + and day-of-year based on :cite:t:`hall_spatial_2010`. The "icclim" method is + deprecated but is identical to method "huglin". + cap_value : float + The value to use for the latitude coefficient when latitude is above 50°N or below + 50°S. Only applicable for methods "huglin", "icclim", and "interpolated" (default: + 1.0). + start_date : str | str + The hemisphere-based start date to consider (north = April, south = October). + end_date : str | str + The hemisphere-based start date to consider (north = October, south = April). This + date is non-inclusive. + freq : Literal['YS', 'YS-JAN', 'YS-JUL'] + Resampling frequency (default: "YS"; For Southern Hemisphere, should be "YS-JUL"). + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.huglin_index( + tas=tas, + tasmax=tasmax, + lat=lat, + thresh=thresh, + method=method, + cap_value=cap_value, + start_date=start_date, + end_date=end_date, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.ice_days) +def ice_days( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Ice days. + + Number of days where the daily maximum temperature is below 0°C + + **Units:** + + - ice_days: days + + This function wraps `xclim.indicators.atmos.ice_days + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.ice_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.last_spring_frost) +def last_spring_frost( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + op: Literal['<', 'lt', '<=', 'le'] = '<', + before_date: str = '07-01', + window: int = 1, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Last spring frost. + + The last day when minimum temperature is below a given threshold for a certain number of + days, limited by a final calendar date. + + **Units:** + + - last_spring_frost: dimensionless + + This function wraps `xclim.indicators.atmos.last_spring_frost + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + before_date : str + Date of the year before which to look for the final frost event. Should have the + format '%m-%d'. + window : int + Minimum number of days with temperature below the threshold needed for evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.last_spring_frost( + tasmin=tasmin, + thresh=thresh, + op=op, + before_date=before_date, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.late_frost_days) +def late_frost_days( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Late frost days. + + Number of days where the daily minimum temperature is below a given threshold between a + givenstart date and a given end date. + + **Units:** + + - late_frost_days: days + + This function wraps `xclim.indicators.atmos.late_frost_days + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Freezing temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.late_frost_days( + tasmin=tasmin, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.latitude_temperature_index) +def latitude_temperature_index( + tas: xarray.DataArray | str = 'tas', + lat: xarray.DataArray | str = 'lat', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Latitude temperature index. + + A climate indice based on mean temperature of the warmest month and a latitude-based + coefficient to account for longer day-length favouring growing conditions. Developed + specifically for viticulture. Mean temperature of warmest month multiplied by the + difference of latitude factor coefficient minus latitude. Metric originally published in + Jackson, D. I., & Cherry, N. J. (1988). + + **Units:** + + - lti: dimensionless + + This function wraps `xclim.indicators.atmos.latitude_temperature_index + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + lat : xarray.DataArray | str + Latitude coordinate. If None, a CF-conformant "latitude" field must be available + within the passed DataArray. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.latitude_temperature_index( + tas=tas, + lat=lat, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.maximum_consecutive_warm_days) +def maximum_consecutive_warm_days( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25 degC', + freq: str = 'YS', + resample_before_rl: bool = True, + **kwargs: Any, +) -> Any: + """ + Maximum consecutive warm days. + + Maximum number of consecutive days where the maximum daily temperature exceeds a certain + threshold. + + **Units:** + + - maximum_consecutive_warm_days: days + + This function wraps `xclim.indicators.atmos.maximum_consecutive_warm_days + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Max daily temperature. + thresh : Any + Threshold temperature. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.maximum_consecutive_warm_days( + tasmax=tasmax, + thresh=thresh, + freq=freq, + resample_before_rl=resample_before_rl, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg10p) +def tg10p( + tas: xarray.DataArray | str = 'tas', + tas_per: xarray.DataArray | str = 'tas_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '<', + **kwargs: Any, +) -> Any: + """ + Days with mean temperature below the 10th percentile. + + Number of days with mean temperature below the 10th percentile. + + **Units:** + + - tg10p: days + + This function wraps `xclim.indicators.atmos.tg10p + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + tas_per : xarray.DataArray | str + 10th percentile of daily mean temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg10p( + tas=tas, + tas_per=tas_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg90p) +def tg90p( + tas: xarray.DataArray | str = 'tas', + tas_per: xarray.DataArray | str = 'tas_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', + **kwargs: Any, +) -> Any: + """ + Days with mean temperature above the 90th percentile. + + Number of days with mean temperature above the 90th percentile. + + **Units:** + + - tg90p: days + + This function wraps `xclim.indicators.atmos.tg90p + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + tas_per : xarray.DataArray | str + 90th percentile of daily mean temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg90p( + tas=tas, + tas_per=tas_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_days_above) +def tg_days_above( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '>', + **kwargs: Any, +) -> Any: + """ + Number of days with mean temperature above a given threshold. + + The number of days with mean temperature above a given threshold. + + **Units:** + + - tg_days_above: days + + This function wraps `xclim.indicators.atmos.tg_days_above + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg_days_above( + tas=tas, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_days_below) +def tg_days_below( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Number of days with mean temperature below a given threshold. + + The number of days with mean temperature below a given threshold. + + **Units:** + + - tg_days_below: days + + This function wraps `xclim.indicators.atmos.tg_days_below + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg_days_below( + tas=tas, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_max) +def tg_max( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Maximum of mean temperature. + + Maximum of daily mean temperature. + + **Units:** + + - tg_max: K + + This function wraps `xclim.indicators.atmos.tg_max + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg_max( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_mean) +def tg_mean( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Mean temperature. + + Mean of daily mean temperature. + + **Units:** + + - tg_mean: K + + This function wraps `xclim.indicators.atmos.tg_mean + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg_mean( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tg_min) +def tg_min( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Minimum of mean temperature. + + Minimum of daily mean temperature. + + **Units:** + + - tg_min: K + + This function wraps `xclim.indicators.atmos.tg_min + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tg_min( + tas=tas, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.thawing_degree_days) +def thawing_degree_days( + tas: xarray.DataArray | str = 'tas', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '0 degC', + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Thawing degree days. + + The cumulative degree days for days when the average temperature is above a given + threshold, typically 0°C. + + **Units:** + + - thawing_degree_days: K days + + This function wraps `xclim.indicators.atmos.thawing_degree_days + `_. + + Parameters + ---------- + tas : xarray.DataArray | str + Mean daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.thawing_degree_days( + tas=tas, + thresh=thresh, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn10p) +def tn10p( + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Days with minimum temperature below the 10th percentile. + + Number of days with minimum temperature below the 10th percentile. + + **Units:** + + - tn10p: days + + This function wraps `xclim.indicators.atmos.tn10p + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Mean daily temperature. + tasmin_per : xarray.DataArray | str + 10th percentile of daily minimum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn10p( + tasmin=tasmin, + tasmin_per=tasmin_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn90p) +def tn90p( + tasmin: xarray.DataArray | str = 'tasmin', + tasmin_per: xarray.DataArray | str = 'tasmin_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['>', '>=', 'gt', 'ge'] = '>', + **kwargs: Any, +) -> Any: + """ + Days with minimum temperature above the 90th percentile. + + Number of days with minimum temperature above the 90th percentile. + + **Units:** + + - tn90p: days + + This function wraps `xclim.indicators.atmos.tn90p + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmin_per : xarray.DataArray | str + 90th percentile of daily minimum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn90p( + tasmin=tasmin, + tasmin_per=tasmin_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_days_above) +def tn_days_above( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '20.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', **kwargs: Any, ) -> Any: """ - Compute the daily temperature range (DTR) using the xclim indices module. + Number of days with minimum temperature above a given threshold. + + The number of days with minimum temperature above a given threshold. + + **Units:** + + - tn_days_above: days + + This function wraps `xclim.indicators.atmos.tn_days_above + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Input data containing maximum and minimum daily temperature values. + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indices.daily_temperature_range`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed daily temperature range converted back to an Earthkit-compatible type. + Any + The computed index. + """ + return xclim.indicators.atmos.tn_days_above( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_days_below) +def tn_days_below( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '-10.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', + **kwargs: Any, +) -> Any: """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.daily_temperature_range) - return wrapper(ds, **kwargs) + Number of days with minimum temperature below a given threshold. + The number of days with minimum temperature below a given threshold. -def heating_degree_days( - tas: xarray.DataArray | str = "tas", + **Units:** + + - tn_days_below: days + + This function wraps `xclim.indicators.atmos.tn_days_below + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn_days_below( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_max) +def tn_max( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Maximum of minimum temperature. + + Maximum of daily minimum temperature. + + **Units:** + + - tn_max: K + + This function wraps `xclim.indicators.atmos.tn_max + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn_max( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_mean) +def tn_mean( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Mean of minimum temperature. + + Mean of daily minimum temperature. + + **Units:** + + - tn_mean: K + + This function wraps `xclim.indicators.atmos.tn_mean + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn_mean( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tn_min) +def tn_min( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Minimum temperature. + + Minimum of daily minimum temperature. + + **Units:** + + - tn_min: K + + This function wraps `xclim.indicators.atmos.tn_min + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tn_min( + tasmin=tasmin, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tropical_nights) +def tropical_nights( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '20.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + **kwargs: Any, +) -> Any: + """ + Tropical nights. + + Number of days where minimum temperature is above a given threshold. + + **Units:** + + - tropical_nights: days + + This function wraps `xclim.indicators.atmos.tropical_nights + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tropical_nights( + tasmin=tasmin, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx10p) +def tx10p( + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Days with maximum temperature below the 10th percentile. + + Number of days with maximum temperature below the 10th percentile. + + **Units:** + + - tx10p: days + + This function wraps `xclim.indicators.atmos.tx10p + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + 10th percentile of daily maximum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx10p( + tasmax=tasmax, + tasmax_per=tasmax_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx90p) +def tx90p( + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + bootstrap: bool = False, + op: Literal['<', '<=', 'lt', 'le'] = '>', + **kwargs: Any, +) -> Any: + """ + Days with maximum temperature above the 90th percentile. + + Number of days with maximum temperature above the 90th percentile. + + **Units:** + + - tx90p: days + + This function wraps `xclim.indicators.atmos.tx90p + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + 90th percentile of daily maximum temperature. + freq : str + Resampling frequency. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['<', '<=', 'lt', 'le'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx90p( + tasmax=tasmax, + tasmax_per=tasmax_per, + freq=freq, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_days_above) +def tx_days_above( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25.0 degC', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>', + **kwargs: Any, +) -> Any: + """ + Number of days with maximum temperature above a given threshold. + + The number of days with maximum temperature above a given threshold. + + **Units:** + + - tx_days_above: days + + This function wraps `xclim.indicators.atmos.tx_days_above + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', 'gt', '>=', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx_days_above( + tasmax=tasmax, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_days_below) +def tx_days_below( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh: Any = '25.0 degC', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', + **kwargs: Any, +) -> Any: + """ + Number of days with maximum temperature below a given threshold. + + The number of days with maximum temperature below a given threshold. + + **Units:** + + - tx_days_below: days + + This function wraps `xclim.indicators.atmos.tx_days_below + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh : Any + Threshold temperature on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['<', 'lt', '<=', 'le'] + Comparison operation. Default: "<". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx_days_below( + tasmax=tasmax, + thresh=thresh, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_max) +def tx_max( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Maximum temperature. + + Maximum of daily maximum temperature. + + **Units:** + + - tx_max: K + + This function wraps `xclim.indicators.atmos.tx_max + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx_max( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_mean) +def tx_mean( + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + freq: str = 'YS', + **kwargs: Any, +) -> Any: + """ + Mean of maximum temperature. + + Mean of daily maximum temperature. + + **Units:** + + - tx_mean: K + + This function wraps `xclim.indicators.atmos.tx_mean + `_. + + Parameters + ---------- + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx_mean( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_min) +def tx_min( + tasmax: xarray.DataArray | str = 'tasmax', ds: xarray.Dataset | Any = None, *, - thresh: Any = "17.0 degC", - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ - Compute the Heating Degree Days (HDD) using the approximation method - from the xclim indicators module. + Minimum of maximum temperature. - This version uses both daily maximum and minimum temperatures, following - the approach used in :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + Minimum of daily maximum temperature. + + **Units:** + + - tx_min: K + + This function wraps `xclim.indicators.atmos.tx_min + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Daily maximum, minimum and mean temperature data. + tasmax : xarray.DataArray | str + Maximum daily temperature. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any - Additional keyword arguments forwarded to - :func:`xclim.indicators.atmos.heating_degree_days_approximation`. + Additional keyword arguments. + + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.tx_min( + tasmax=tasmax, + freq=freq, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.tx_tn_days_above) +def tx_tn_days_above( + tasmin: xarray.DataArray | str = 'tasmin', + tasmax: xarray.DataArray | str = 'tasmax', + ds: xarray.Dataset | Any = None, + *, + thresh_tasmin: Any = '22 degC', + thresh_tasmax: Any = '30 degC', + freq: str = 'YS', + op: Literal['>', '>=', 'gt', 'ge'] = '>', + **kwargs: Any, +) -> Any: + """ + Number of days with daily minimum and maximum temperatures exceeding thresholds. + + Number of days with daily maximum and minimum temperatures above given thresholds. + + **Units:** + + - tx_tn_days_above: days - Common arguments include: + This function wraps `xclim.indicators.atmos.tx_tn_days_above + `_. - - `thresh` : str, default "18.0 degC" - Base temperature threshold for heating. - - `freq` : str, default "YS" - Frequency for accumulation (e.g., "YS" = yearly sum). + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum daily temperature. + tasmax : xarray.DataArray | str + Maximum daily temperature. + thresh_tasmin : Any + Threshold temperature for tasmin on which to base evaluation. + thresh_tasmax : Any + Threshold temperature for tasmax on which to base evaluation. + freq : str + Resampling frequency. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed Heating Degree Days (HDD) converted back to an Earthkit-compatible type. + Any + The computed index. + """ + return xclim.indicators.atmos.tx_tn_days_above( + tasmin=tasmin, + tasmax=tasmax, + thresh_tasmin=thresh_tasmin, + thresh_tasmax=thresh_tasmax, + freq=freq, + op=op, + ds=ds, + **kwargs, + ) + +@format_handler() +# @metadata_handler(xclim.indicators.atmos.usda_hardiness_zones) +def usda_hardiness_zones( + tasmin: xarray.DataArray | str = 'tasmin', + ds: xarray.Dataset | Any = None, + *, + window: int = 30, + freq: str = 'YS', + **kwargs: Any, +) -> Any: """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.heating_degree_days) - return wrapper(ds, **kwargs) + Usda hardiness zones. + + A climate indice based on a multi-year rolling average of the annual minimum + temperature. Developed specifically to aid in determining plant suitability of + geographic regions. The USDA classificationscheme divides categories into 10 degree + Fahrenheit zones, with 5-degree Fahrenheit half-zones, starting from -65 degrees + Fahrenheit and ending at 65 degrees Fahrenheit. + + **Units:** + + - hz: dimensionless + + This function wraps `xclim.indicators.atmos.usda_hardiness_zones + `_. + + Parameters + ---------- + tasmin : xarray.DataArray | str + Minimum temperature. + window : int + The length of the averaging window, in years. + freq : str + Resampling frequency. + ds : xarray.Dataset | Any + Input dataset. + **kwargs : Any + Additional keyword arguments. + Returns + ------- + Any + The computed index. + """ + return xclim.indicators.atmos.usda_hardiness_zones( + tasmin=tasmin, + window=window, + freq=freq, + ds=ds, + **kwargs, + ) +@format_handler() +# @metadata_handler(xclim.indicators.atmos.warm_spell_duration_index) def warm_spell_duration_index( - tasmax: xarray.DataArray | str = "tasmax", - tasmax_per: xarray.DataArray | str = "tasmax_per", + tasmax: xarray.DataArray | str = 'tasmax', + tasmax_per: xarray.DataArray | str = 'tasmax_per', ds: xarray.Dataset | Any = None, *, window: int = 6, - freq: str = "YS", + freq: str = 'YS', resample_before_rl: bool = True, bootstrap: bool = False, - op: Literal[">", ">=", "gt", "ge"] = ">", + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, ) -> Any: """ - Compute the Warm Spell Duration Index (WSDI) using the xclim indices module. - The 90th percentile threshold must be pre-calculated and included in the input dataset `ds` - as a variable named `{variable}_per` (e.g., `tasmax_per`). + Warm spell duration index. + + Number of days part of a percentile-defined warm spell. A warm spell occurs when the + maximum daily temperature is above a given percentile for a given number of consecutive + days. + + **Units:** + + - warm_spell_duration_index: days + + This function wraps `xclim.indicators.atmos.warm_spell_duration_index + `_. Parameters ---------- - ds : conversions.EarthkitData | xarray.Dataset - Daily maximum temperature data for the target period, including the pre-calculated percentile. + tasmax : xarray.DataArray | str + Maximum daily temperature. + tasmax_per : xarray.DataArray | str + Percentile(s) of daily maximum temperature. + window : int + Minimum number of days with temperature above threshold to qualify as a warm spell. + freq : str + Resampling frequency. + resample_before_rl : bool + Determines if the resampling should take place before or after the run length + encoding (or a similar algorithm) is applied to runs. + bootstrap : bool + Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. + Bootstrapping is only useful when the percentiles are computed on a part of the + studied sample. This period, common to percentiles and the sample must be + bootstrapped to avoid inhomogeneities with the rest of the time series. Do not + enable bootstrap when there is no common period, otherwise it will provide the wrong + results. Note that bootstrapping is computationally expensive. + op : Literal['>', '>=', 'gt', 'ge'] + Comparison operation. Default: ">". + ds : xarray.Dataset | Any + Input dataset. **kwargs : Any - Additional arguments forwarded to :func:`xclim.indicators.atmos.warm_spell_duration_index`. + Additional keyword arguments. Returns ------- - conversions.EarthkitData - The computed WSDI index as an Earthkit-compatible field. + Any + The computed index. """ - # Create wrapper inside the function - wrapper = wrap_xclim_indicator(xclim.indicators.atmos.warm_spell_duration_index) + return xclim.indicators.atmos.warm_spell_duration_index( + tasmax=tasmax, + tasmax_per=tasmax_per, + window=window, + freq=freq, + resample_before_rl=resample_before_rl, + bootstrap=bootstrap, + op=op, + ds=ds, + **kwargs, + ) - return wrapper(earthkit_input=ds, **kwargs) diff --git a/tests/unit/indicators/test_temperature.py b/tests/unit/indicators/test_temperature.py index efa3361..a3e5ff6 100644 --- a/tests/unit/indicators/test_temperature.py +++ b/tests/unit/indicators/test_temperature.py @@ -127,7 +127,7 @@ def test_temperature_indicator( ds_in = dummy_temp_ds # Call the earthkit function - earthkit_fn(ds_in, **kwargs) + earthkit_fn(ds=ds_in, **kwargs) # Verify wrapped function called with the dataset and arguments mock_fn.assert_called_once() From 3417a320ff7c5ce184bf617cebbebe71cebad36c Mon Sep 17 00:00:00 2001 From: cuadradot Date: Tue, 17 Mar 2026 15:59:39 +0100 Subject: [PATCH 47/47] refactor: Add blank lines after `@format_handler()` decorators for improved readability. --- .../climate/indicators/precipitation.py | 384 +++++++++--------- .../climate/indicators/temperature.py | 89 +++- tools/xclim_wrappers_generator.py | 5 +- 3 files changed, 282 insertions(+), 196 deletions(-) diff --git a/src/earthkit/climate/indicators/precipitation.py b/src/earthkit/climate/indicators/precipitation.py index 82e03cd..b064b7a 100644 --- a/src/earthkit/climate/indicators/precipitation.py +++ b/src/earthkit/climate/indicators/precipitation.py @@ -8,8 +8,6 @@ """Precipitation indices.""" -"""Precipitation indices.""" - from typing import Any, Literal import xarray @@ -22,7 +20,7 @@ @format_handler() # @metadata_handler(xclim.indicators.atmos.antecedent_precipitation_index) def antecedent_precipitation_index( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, window: int = 7, @@ -72,11 +70,11 @@ def antecedent_precipitation_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_dry_days) def maximum_consecutive_dry_days( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -127,11 +125,11 @@ def maximum_consecutive_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cffwis_indices) def cffwis_indices( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - sfcWind: xarray.DataArray | str = "sfcWind", - hurs: xarray.DataArray | str = "hurs", - lat: xarray.DataArray | str = "lat", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + sfcWind: xarray.DataArray | str = 'sfcWind', + hurs: xarray.DataArray | str = 'hurs', + lat: xarray.DataArray | str = 'lat', snd: xarray.DataArray | str | None = None, ffmc0: xarray.DataArray | str | None = None, dmc0: xarray.DataArray | str | None = None, @@ -233,13 +231,13 @@ def cffwis_indices( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_and_dry_days) def cold_and_dry_days( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - tas_per: xarray.DataArray | str = "tas_per", - pr_per: xarray.DataArray | str = "pr_per", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -291,13 +289,13 @@ def cold_and_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_and_wet_days) def cold_and_wet_days( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - tas_per: xarray.DataArray | str = "tas_per", - pr_per: xarray.DataArray | str = "pr_per", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -349,11 +347,11 @@ def cold_and_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_wet_days) def maximum_consecutive_wet_days( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -404,14 +402,14 @@ def maximum_consecutive_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_over_precip_doy_thresh) def days_over_precip_doy_thresh( - pr: xarray.DataArray | str = "pr", - pr_per: xarray.DataArray | str = "pr_per", + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', bootstrap: bool = False, - op: Literal[">", ">=", "gt", "ge"] = ">", + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, ) -> Any: """ @@ -472,14 +470,14 @@ def days_over_precip_doy_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_over_precip_thresh) def days_over_precip_thresh( - pr: xarray.DataArray | str = "pr", - pr_per: xarray.DataArray | str = "pr_per", + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', bootstrap: bool = False, - op: Literal[">", ">=", "gt", "ge"] = ">", + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, ) -> Any: """ @@ -540,12 +538,12 @@ def days_over_precip_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.days_with_snow) def days_with_snow( - prsn: xarray.DataArray | str = "prsn", + prsn: xarray.DataArray | str = 'prsn', ds: xarray.Dataset | Any = None, *, - low: Any = "0 kg m-2 s-1", - high: Any = "1E6 kg m-2 s-1", - freq: str = "YS-JUL", + low: Any = '0 kg m-2 s-1', + high: Any = '1E6 kg m-2 s-1', + freq: str = 'YS-JUL', **kwargs: Any, ) -> Any: """ @@ -593,9 +591,9 @@ def days_with_snow( @format_handler() # @metadata_handler(xclim.indicators.atmos.drought_code) def drought_code( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - lat: xarray.DataArray | str = "lat", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + lat: xarray.DataArray | str = 'lat', snd: xarray.DataArray | str | None = None, dc0: xarray.DataArray | str | None = None, season_mask: xarray.DataArray | str | None = None, @@ -677,11 +675,11 @@ def drought_code( @format_handler() # @metadata_handler(xclim.indicators.atmos.griffiths_drought_factor) def griffiths_drought_factor( - pr: xarray.DataArray | str = "pr", - smd: xarray.DataArray | str = "smd", + pr: xarray.DataArray | str = 'pr', + smd: xarray.DataArray | str = 'smd', ds: xarray.Dataset | Any = None, *, - limiting_func: str = "xlim", + limiting_func: str = 'xlim', **kwargs: Any, ) -> Any: """ @@ -731,10 +729,10 @@ def griffiths_drought_factor( @format_handler() # @metadata_handler(xclim.indicators.atmos.duff_moisture_code) def duff_moisture_code( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - hurs: xarray.DataArray | str = "hurs", - lat: xarray.DataArray | str = "lat", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + hurs: xarray.DataArray | str = 'hurs', + lat: xarray.DataArray | str = 'lat', snd: xarray.DataArray | str | None = None, dmc0: xarray.DataArray | str | None = None, season_mask: xarray.DataArray | str | None = None, @@ -815,12 +813,12 @@ def duff_moisture_code( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_days) def dry_days( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0.2 mm/d", - freq: str = "YS", - op: Literal["<", "lt", "<=", "le"] = "<", + thresh: Any = '0.2 mm/d', + freq: str = 'YS', + op: Literal['<', 'lt', '<=', 'le'] = '<', **kwargs: Any, ) -> Any: """ @@ -868,14 +866,14 @@ def dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_frequency) def dry_spell_frequency( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 3, - freq: str = "YS", + freq: str = 'YS', resample_before_rl: bool = True, - op: Literal["sum", "max", "min", "mean"] = "sum", + op: Literal['sum', 'max', 'min', 'mean'] = 'sum', **kwargs: Any, ) -> Any: """ @@ -936,13 +934,13 @@ def dry_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_max_length) def dry_spell_max_length( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 1, - op: Literal["max", "sum"] = "sum", - freq: str = "YS", + op: Literal['max', 'sum'] = 'sum', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -999,13 +997,13 @@ def dry_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.dry_spell_total_length) def dry_spell_total_length( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 3, - op: Literal["sum", "max", "min", "mean"] = "sum", - freq: str = "YS", + op: Literal['sum', 'max', 'min', 'mean'] = 'sum', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -1066,13 +1064,13 @@ def dry_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.dryness_index) def dryness_index( - pr: xarray.DataArray | str = "pr", - evspsblpot: xarray.DataArray | str = "evspsblpot", + pr: xarray.DataArray | str = 'pr', + evspsblpot: xarray.DataArray | str = 'evspsblpot', lat: xarray.DataArray | str | None = None, ds: xarray.Dataset | Any = None, *, - wo: Any = "200 mm", - freq: Literal["YS", "YS-JAN"] = "YS", + wo: Any = '200 mm', + freq: Literal['YS', 'YS-JAN'] = 'YS', **kwargs: Any, ) -> Any: """ @@ -1126,10 +1124,10 @@ def dryness_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.mcarthur_forest_fire_danger_index) def mcarthur_forest_fire_danger_index( - drought_factor: xarray.DataArray | str = "drought_factor", - tasmax: xarray.DataArray | str = "tasmax", - hurs: xarray.DataArray | str = "hurs", - sfcWind: xarray.DataArray | str = "sfcWind", + drought_factor: xarray.DataArray | str = 'drought_factor', + tasmax: xarray.DataArray | str = 'tasmax', + hurs: xarray.DataArray | str = 'hurs', + sfcWind: xarray.DataArray | str = 'sfcWind', ds: xarray.Dataset | Any = None, **kwargs: Any, ) -> Any: @@ -1187,11 +1185,11 @@ def mcarthur_forest_fire_danger_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.first_snowfall) def first_snowfall( - prsn: xarray.DataArray | str = "prsn", + prsn: xarray.DataArray | str = 'prsn', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS-JUL", + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, ) -> Any: """ @@ -1238,14 +1236,14 @@ def first_snowfall( @format_handler() # @metadata_handler(xclim.indicators.atmos.fraction_over_precip_doy_thresh) def fraction_over_precip_doy_thresh( - pr: xarray.DataArray | str = "pr", - pr_per: xarray.DataArray | str = "pr_per", + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', bootstrap: bool = False, - op: Literal[">", ">=", "gt", "ge"] = ">", + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, ) -> Any: """ @@ -1307,14 +1305,14 @@ def fraction_over_precip_doy_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.fraction_over_precip_thresh) def fraction_over_precip_thresh( - pr: xarray.DataArray | str = "pr", - pr_per: xarray.DataArray | str = "pr_per", + pr: xarray.DataArray | str = 'pr', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', bootstrap: bool = False, - op: Literal[">", ">=", "gt", "ge"] = ">", + op: Literal['>', '>=', 'gt', 'ge'] = '>', **kwargs: Any, ) -> Any: """ @@ -1376,13 +1374,13 @@ def fraction_over_precip_thresh( @format_handler() # @metadata_handler(xclim.indicators.atmos.high_precip_low_temp) def high_precip_low_temp( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - pr_thresh: Any = "0.4 mm/d", - tas_thresh: Any = "-0.2 degC", - freq: str = "YS", + pr_thresh: Any = '0.4 mm/d', + tas_thresh: Any = '-0.2 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1434,9 +1432,9 @@ def high_precip_low_temp( @format_handler() # @metadata_handler(xclim.indicators.atmos.keetch_byram_drought_index) def keetch_byram_drought_index( - pr: xarray.DataArray | str = "pr", - tasmax: xarray.DataArray | str = "tasmax", - pr_annual: xarray.DataArray | str = "pr_annual", + pr: xarray.DataArray | str = 'pr', + tasmax: xarray.DataArray | str = 'tasmax', + pr_annual: xarray.DataArray | str = 'pr_annual', kbdi0: xarray.DataArray | str | None = None, ds: xarray.Dataset | Any = None, **kwargs: Any, @@ -1491,11 +1489,11 @@ def keetch_byram_drought_index( @format_handler() # @metadata_handler(xclim.indicators.atmos.last_snowfall) def last_snowfall( - prsn: xarray.DataArray | str = "prsn", + prsn: xarray.DataArray | str = 'prsn', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS-JUL", + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, ) -> Any: """ @@ -1542,12 +1540,12 @@ def last_snowfall( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_ratio) def liquid_precip_ratio( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "QS-DEC", + thresh: Any = '0 degC', + freq: str = 'QS-DEC', **kwargs: Any, ) -> Any: """ @@ -1597,12 +1595,12 @@ def liquid_precip_ratio( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_average) def liquid_precip_average( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "YS", + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1651,12 +1649,12 @@ def liquid_precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.liquid_precip_accumulation) def liquid_precip_accumulation( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "YS", + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1705,11 +1703,11 @@ def liquid_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_n_day_precipitation_amount) def max_n_day_precipitation_amount( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, window: int = 1, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1754,11 +1752,11 @@ def max_n_day_precipitation_amount( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_pr_intensity) def max_pr_intensity( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, window: int = 1, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1803,11 +1801,11 @@ def max_pr_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.precip_average) def precip_average( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "YS", + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1855,10 +1853,10 @@ def precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.precip_accumulation) def precip_accumulation( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1903,13 +1901,13 @@ def precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.rain_on_frozen_ground_days) def rain_on_frozen_ground_days( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/d", + thresh: Any = '1 mm/d', window: int = 7, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -1963,23 +1961,23 @@ def rain_on_frozen_ground_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.rain_season) def rain_season( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh_wet_start: Any = "25.0 mm", + thresh_wet_start: Any = '25.0 mm', window_wet_start: int = 3, window_not_dry_start: int = 30, - thresh_dry_start: Any = "1.0 mm", + thresh_dry_start: Any = '1.0 mm', window_dry_start: int = 7, - method_dry_start: str = "per_day", - date_min_start: str = "05-01", - date_max_start: str = "12-31", - thresh_dry_end: Any = "0.0 mm", + method_dry_start: str = 'per_day', + date_min_start: str = '05-01', + date_max_start: str = '12-31', + thresh_dry_end: Any = '0.0 mm', window_dry_end: int = 20, - method_dry_end: str = "per_day", - date_min_end: str = "09-01", - date_max_end: str = "12-31", - freq: Any = "YS-JAN", + method_dry_end: str = 'per_day', + date_min_end: str = '09-01', + date_max_end: str = '12-31', + freq: Any = 'YS-JAN', **kwargs: Any, ) -> Any: """ @@ -2082,13 +2080,13 @@ def rain_season( @format_handler() # @metadata_handler(xclim.indicators.atmos.rprctot) def rprctot( - pr: xarray.DataArray | str = "pr", - prc: xarray.DataArray | str = "prc", + pr: xarray.DataArray | str = 'pr', + prc: xarray.DataArray | str = 'prc', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm/day", - freq: str = "YS", - op: Literal[">", "gt", ">=", "ge"] = ">=", + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, ) -> Any: """ @@ -2140,10 +2138,10 @@ def rprctot( @format_handler() # @metadata_handler(xclim.indicators.atmos.max_1day_precipitation_amount) def max_1day_precipitation_amount( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2185,12 +2183,12 @@ def max_1day_precipitation_amount( @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_pr_intensity) def daily_pr_intensity( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", - op: Literal[">", "gt", ">=", "ge"] = ">=", + thresh: Any = '1 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, ) -> Any: """ @@ -2238,11 +2236,11 @@ def daily_pr_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.snowfall_frequency) def snowfall_frequency( - prsn: xarray.DataArray | str = "prsn", + prsn: xarray.DataArray | str = 'prsn', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS-JUL", + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, ) -> Any: """ @@ -2289,11 +2287,11 @@ def snowfall_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.snowfall_intensity) def snowfall_intensity( - prsn: xarray.DataArray | str = "prsn", + prsn: xarray.DataArray | str = 'prsn', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS-JUL", + thresh: Any = '1 mm/day', + freq: str = 'YS-JUL', **kwargs: Any, ) -> Any: """ @@ -2340,12 +2338,12 @@ def snowfall_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.solid_precip_average) def solid_precip_average( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "YS", + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2394,12 +2392,12 @@ def solid_precip_average( @format_handler() # @metadata_handler(xclim.indicators.atmos.solid_precip_accumulation) def solid_precip_accumulation( - pr: xarray.DataArray | str = "pr", - tas: xarray.DataArray | str = "tas", + pr: xarray.DataArray | str = 'pr', + tas: xarray.DataArray | str = 'tas', ds: xarray.Dataset | Any = None, *, - thresh: Any = "0 degC", - freq: str = "YS", + thresh: Any = '0 degC', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2448,13 +2446,13 @@ def solid_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_and_dry_days) def warm_and_dry_days( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - tas_per: xarray.DataArray | str = "tas_per", - pr_per: xarray.DataArray | str = "pr_per", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2506,13 +2504,13 @@ def warm_and_dry_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_and_wet_days) def warm_and_wet_days( - tas: xarray.DataArray | str = "tas", - pr: xarray.DataArray | str = "pr", - tas_per: xarray.DataArray | str = "tas_per", - pr_per: xarray.DataArray | str = "pr_per", + tas: xarray.DataArray | str = 'tas', + pr: xarray.DataArray | str = 'pr', + tas_per: xarray.DataArray | str = 'tas_per', + pr_per: xarray.DataArray | str = 'pr_per', ds: xarray.Dataset | Any = None, *, - freq: str = "YS", + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2564,11 +2562,11 @@ def warm_and_wet_days( @format_handler() # @metadata_handler(xclim.indicators.atmos.water_cycle_intensity) def water_cycle_intensity( - pr: xarray.DataArray | str = "pr", - evspsbl: xarray.DataArray | str = "evspsbl", + pr: xarray.DataArray | str = 'pr', + evspsbl: xarray.DataArray | str = 'evspsbl', ds: xarray.Dataset | Any = None, *, - freq: Any = "YS", + freq: Any = 'YS', **kwargs: Any, ) -> Any: """ @@ -2613,11 +2611,11 @@ def water_cycle_intensity( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_precip_accumulation) def wet_precip_accumulation( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1 mm/day", - freq: str = "YS", + thresh: Any = '1 mm/day', + freq: str = 'YS', **kwargs: Any, ) -> Any: """ @@ -2663,14 +2661,14 @@ def wet_precip_accumulation( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_frequency) def wet_spell_frequency( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 3, - freq: str = "YS", + freq: str = 'YS', resample_before_rl: bool = True, - op: Literal["sum", "min", "max", "mean"] = "sum", + op: Literal['sum', 'min', 'max', 'mean'] = 'sum', **kwargs: Any, ) -> Any: """ @@ -2732,13 +2730,13 @@ def wet_spell_frequency( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_max_length) def wet_spell_max_length( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 1, - op: Literal["min", "sum", "max", "mean"] = "sum", - freq: str = "YS", + op: Literal['min', 'sum', 'max', 'mean'] = 'sum', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -2799,13 +2797,13 @@ def wet_spell_max_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.wet_spell_total_length) def wet_spell_total_length( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm", + thresh: Any = '1.0 mm', window: int = 3, - op: Literal["min", "sum", "max", "mean"] = "sum", - freq: str = "YS", + op: Literal['min', 'sum', 'max', 'mean'] = 'sum', + freq: str = 'YS', resample_before_rl: bool = True, **kwargs: Any, ) -> Any: @@ -2866,12 +2864,12 @@ def wet_spell_total_length( @format_handler() # @metadata_handler(xclim.indicators.atmos.wetdays) def wetdays( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm/day", - freq: str = "YS", - op: Literal[">", "gt", ">=", "ge"] = ">=", + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, ) -> Any: """ @@ -2919,12 +2917,12 @@ def wetdays( @format_handler() # @metadata_handler(xclim.indicators.atmos.wetdays_prop) def wetdays_prop( - pr: xarray.DataArray | str = "pr", + pr: xarray.DataArray | str = 'pr', ds: xarray.Dataset | Any = None, *, - thresh: Any = "1.0 mm/day", - freq: str = "YS", - op: Literal[">", "gt", ">=", "ge"] = ">=", + thresh: Any = '1.0 mm/day', + freq: str = 'YS', + op: Literal['>', 'gt', '>=', 'ge'] = '>=', **kwargs: Any, ) -> Any: """ diff --git a/src/earthkit/climate/indicators/temperature.py b/src/earthkit/climate/indicators/temperature.py index a82d0d2..44d1197 100644 --- a/src/earthkit/climate/indicators/temperature.py +++ b/src/earthkit/climate/indicators/temperature.py @@ -69,6 +69,7 @@ def australian_hardiness_zones( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.biologically_effective_degree_days) def biologically_effective_degree_days( @@ -173,6 +174,7 @@ def biologically_effective_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_days) def cold_spell_days( @@ -236,6 +238,7 @@ def cold_spell_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_duration_index) def cold_spell_duration_index( @@ -308,6 +311,7 @@ def cold_spell_duration_index( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_frequency) def cold_spell_frequency( @@ -370,6 +374,7 @@ def cold_spell_frequency( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_max_length) def cold_spell_max_length( @@ -433,6 +438,7 @@ def cold_spell_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cold_spell_total_length) def cold_spell_total_length( @@ -496,6 +502,7 @@ def cold_spell_total_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.consecutive_frost_days) def consecutive_frost_days( @@ -549,6 +556,7 @@ def consecutive_frost_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_frost_free_days) def maximum_consecutive_frost_free_days( @@ -603,6 +611,7 @@ def maximum_consecutive_frost_free_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cool_night_index) def cool_night_index( @@ -653,6 +662,7 @@ def cool_night_index( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cooling_degree_days) def cooling_degree_days( @@ -702,6 +712,7 @@ def cooling_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.cooling_degree_days_approximation) def cooling_degree_days_approximation( @@ -761,6 +772,7 @@ def cooling_degree_days_approximation( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.corn_heat_units) def corn_heat_units( @@ -814,6 +826,7 @@ def corn_heat_units( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.chill_portions) def chill_portions( @@ -866,6 +879,7 @@ def chill_portions( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.chill_units) def chill_units( @@ -918,6 +932,7 @@ def chill_units( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.degree_days_exceedance_date) def degree_days_exceedance_date( @@ -987,6 +1002,7 @@ def degree_days_exceedance_date( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_freezethaw_cycles) def daily_freezethaw_cycles( @@ -1058,6 +1074,7 @@ def daily_freezethaw_cycles( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_temperature_range) def daily_temperature_range( @@ -1106,6 +1123,7 @@ def daily_temperature_range( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.max_daily_temperature_range) def max_daily_temperature_range( @@ -1154,6 +1172,7 @@ def max_daily_temperature_range( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.daily_temperature_range_variability) def daily_temperature_range_variability( @@ -1202,6 +1221,7 @@ def daily_temperature_range_variability( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.extreme_temperature_range) def extreme_temperature_range( @@ -1250,6 +1270,7 @@ def extreme_temperature_range( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.fire_season) def fire_season( @@ -1328,6 +1349,7 @@ def fire_season( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tg_above) def first_day_tg_above( @@ -1390,6 +1412,7 @@ def first_day_tg_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tg_below) def first_day_tg_below( @@ -1452,6 +1475,7 @@ def first_day_tg_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tn_above) def first_day_tn_above( @@ -1514,6 +1538,7 @@ def first_day_tn_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tn_below) def first_day_tn_below( @@ -1576,6 +1601,7 @@ def first_day_tn_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tx_above) def first_day_tx_above( @@ -1638,6 +1664,7 @@ def first_day_tx_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.first_day_tx_below) def first_day_tx_below( @@ -1700,6 +1727,7 @@ def first_day_tx_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_frequency) def freezethaw_spell_frequency( @@ -1775,6 +1803,7 @@ def freezethaw_spell_frequency( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_max_length) def freezethaw_spell_max_length( @@ -1850,6 +1879,7 @@ def freezethaw_spell_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.freezethaw_spell_mean_length) def freezethaw_spell_mean_length( @@ -1917,6 +1947,7 @@ def freezethaw_spell_mean_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.freezing_degree_days) def freezing_degree_days( @@ -1966,6 +1997,7 @@ def freezing_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.freshet_start) def freshet_start( @@ -2028,6 +2060,7 @@ def freshet_start( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_days) def frost_days( @@ -2076,6 +2109,7 @@ def frost_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_end) def frost_free_season_end( @@ -2138,6 +2172,7 @@ def frost_free_season_end( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_length) def frost_free_season_length( @@ -2201,6 +2236,7 @@ def frost_free_season_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_season_start) def frost_free_season_start( @@ -2263,6 +2299,7 @@ def frost_free_season_start( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_free_spell_max_length) def frost_free_spell_max_length( @@ -2326,6 +2363,7 @@ def frost_free_spell_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.frost_season_length) def frost_season_length( @@ -2390,6 +2428,7 @@ def frost_season_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_degree_days) def growing_degree_days( @@ -2439,6 +2478,7 @@ def growing_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_end) def growing_season_end( @@ -2503,6 +2543,7 @@ def growing_season_end( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_length) def growing_season_length( @@ -2567,6 +2608,7 @@ def growing_season_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.growing_season_start) def growing_season_start( @@ -2629,6 +2671,7 @@ def growing_season_start( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_frequency) def heat_spell_frequency( @@ -2705,6 +2748,7 @@ def heat_spell_frequency( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_max_length) def heat_spell_max_length( @@ -2781,6 +2825,7 @@ def heat_spell_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_spell_total_length) def heat_spell_total_length( @@ -2857,6 +2902,7 @@ def heat_spell_total_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_frequency) def heat_wave_frequency( @@ -2927,6 +2973,7 @@ def heat_wave_frequency( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_index) def heat_wave_index( @@ -2990,6 +3037,7 @@ def heat_wave_index( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_max_length) def heat_wave_max_length( @@ -3060,6 +3108,7 @@ def heat_wave_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heat_wave_total_length) def heat_wave_total_length( @@ -3130,6 +3179,7 @@ def heat_wave_total_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heating_degree_days) def heating_degree_days( @@ -3179,6 +3229,7 @@ def heating_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.heating_degree_days_approximation) def heating_degree_days_approximation( @@ -3238,6 +3289,7 @@ def heating_degree_days_approximation( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_days) def hot_days( @@ -3286,6 +3338,7 @@ def hot_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_frequency) def hot_spell_frequency( @@ -3348,6 +3401,7 @@ def hot_spell_frequency( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_max_length) def hot_spell_max_length( @@ -3410,6 +3464,7 @@ def hot_spell_max_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_max_magnitude) def hot_spell_max_magnitude( @@ -3469,6 +3524,7 @@ def hot_spell_max_magnitude( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.hot_spell_total_length) def hot_spell_total_length( @@ -3532,6 +3588,7 @@ def hot_spell_total_length( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.huglin_index) def huglin_index( @@ -3618,6 +3675,7 @@ def huglin_index( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.ice_days) def ice_days( @@ -3666,6 +3724,7 @@ def ice_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.last_spring_frost) def last_spring_frost( @@ -3728,6 +3787,7 @@ def last_spring_frost( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.late_frost_days) def late_frost_days( @@ -3777,6 +3837,7 @@ def late_frost_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.latitude_temperature_index) def latitude_temperature_index( @@ -3830,6 +3891,7 @@ def latitude_temperature_index( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.maximum_consecutive_warm_days) def maximum_consecutive_warm_days( @@ -3884,6 +3946,7 @@ def maximum_consecutive_warm_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg10p) def tg10p( @@ -3945,6 +4008,7 @@ def tg10p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg90p) def tg90p( @@ -4006,6 +4070,7 @@ def tg90p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_days_above) def tg_days_above( @@ -4058,6 +4123,7 @@ def tg_days_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_days_below) def tg_days_below( @@ -4110,6 +4176,7 @@ def tg_days_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_max) def tg_max( @@ -4154,6 +4221,7 @@ def tg_max( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_mean) def tg_mean( @@ -4198,6 +4266,7 @@ def tg_mean( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tg_min) def tg_min( @@ -4242,6 +4311,7 @@ def tg_min( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.thawing_degree_days) def thawing_degree_days( @@ -4291,6 +4361,7 @@ def thawing_degree_days( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn10p) def tn10p( @@ -4352,6 +4423,7 @@ def tn10p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn90p) def tn90p( @@ -4413,6 +4485,7 @@ def tn90p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_days_above) def tn_days_above( @@ -4465,6 +4538,7 @@ def tn_days_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_days_below) def tn_days_below( @@ -4517,6 +4591,7 @@ def tn_days_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_max) def tn_max( @@ -4561,6 +4636,7 @@ def tn_max( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_mean) def tn_mean( @@ -4605,6 +4681,7 @@ def tn_mean( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tn_min) def tn_min( @@ -4649,6 +4726,7 @@ def tn_min( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tropical_nights) def tropical_nights( @@ -4701,6 +4779,7 @@ def tropical_nights( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx10p) def tx10p( @@ -4762,6 +4841,7 @@ def tx10p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx90p) def tx90p( @@ -4823,6 +4903,7 @@ def tx90p( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_days_above) def tx_days_above( @@ -4875,6 +4956,7 @@ def tx_days_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_days_below) def tx_days_below( @@ -4927,6 +5009,7 @@ def tx_days_below( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_max) def tx_max( @@ -4971,6 +5054,7 @@ def tx_max( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_mean) def tx_mean( @@ -5015,6 +5099,7 @@ def tx_mean( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_min) def tx_min( @@ -5059,6 +5144,7 @@ def tx_min( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.tx_tn_days_above) def tx_tn_days_above( @@ -5119,6 +5205,7 @@ def tx_tn_days_above( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.usda_hardiness_zones) def usda_hardiness_zones( @@ -5171,6 +5258,7 @@ def usda_hardiness_zones( **kwargs, ) + @format_handler() # @metadata_handler(xclim.indicators.atmos.warm_spell_duration_index) def warm_spell_duration_index( @@ -5242,4 +5330,3 @@ def warm_spell_duration_index( ds=ds, **kwargs, ) - diff --git a/tools/xclim_wrappers_generator.py b/tools/xclim_wrappers_generator.py index 5061b21..dfc2a9c 100644 --- a/tools/xclim_wrappers_generator.py +++ b/tools/xclim_wrappers_generator.py @@ -333,10 +333,11 @@ def generate_module_content(category: str, indicators: List[Any]) -> str: xclim_obj_ref=xclim_obj_ref, docstring=indented_doc, ) - functions_code.append(code) - return MODULE_TEMPLATE.format(category_title=category.capitalize(), functions_code="".join(functions_code)) + return MODULE_TEMPLATE.format( + category_title=category.capitalize(), functions_code="\n".join(functions_code) + ).rstrip() + "\n" def main():