Migrate from xclim.sdba to xsdba

.github/workflows/main.yml

@@ -112,7 +112,7 @@ jobs:
       - name: Set GDAL_VERSION (macOS)
         if: matrix.os == 'macos-latest'
         run: |
-          echo "GDAL_VERSION=$(gdalinfo --version | awk  '{print $2}' | sed s'/.$//')" >> $GITHUB_ENV
+          echo "GDAL_VERSION=$(gdalinfo --version | sed -E 's/^GDAL //; s/[^0-9.].*$//')" >> $GITHUB_ENV
 
       - name: Install CI libraries
         run: |

CHANGELOG.rst

@@ -4,7 +4,14 @@ Changelog
 
 v0.18.3 (unreleased)
 --------------------
-* Fix bug affecting GriddedForcing, where `station_idx` in the call to `nc_specs` was set to ``1`` instead of ``None``. (#PR #501)
+
+Bug fixes
+^^^^^^^^^
+* Fixed bug affecting `GriddedForcing`, where `station_idx` in the call to ``nc_specs`` was set to ``1`` instead of ``None``. (PR #501)
+
+Internal changes
+^^^^^^^^^^^^^^^^
+* `ravenpy` now requires `xclim>=0.57.0` and `xsdba` (v0.4.0+). (PR #511)
 
 v0.18.2 (2025-05-05)
 --------------------

docs/notebooks/08_Getting_and_bias_correcting_CMIP6_data.ipynb

@@ -51,7 +51,7 @@
     "import numpy as np\n",
     "import xarray as xr\n",
     "import xclim\n",
-    "import xclim.sdba as sdba\n",
+    "import xsdba\n",
     "from clisops.core import average, subset\n",
     "\n",
     "from ravenpy.utilities.testdata import get_file\n",
@@ -495,7 +495,7 @@
    "source": [
     "The model is now going to be trained to find correction factors between the reference dataset (observations) and historical dataset (climate model outputs for the same time period). The correction factors obtained are then applied to both reference and future climate outputs to correct them. This step is called the bias correction. In this test-case, we apply a method named `detrended quantile mapping`.\n",
     "\n",
-    "Here we use the `xclim` utilities to bias-correct CMIP6 GCM data using ERA5 reanalysis data as the reference. See `xclim` documentation for more options! (https://xclim.readthedocs.io/en/stable/notebooks/sdba.html)\n",
+    "Here we use the `xsdba` utilities to bias-correct CMIP6 GCM data using ERA5 reanalysis data as the reference. See `xsdba` documentation for more options! (https://xsdba.readthedocs.io/en/latest/index.html)\n",
     "\n",
     "> **Warning**\n",
     "> This following block of code will take a while to run, and some warning messages will appear during the process (related to longitude wrapping and other information on calendar types). Unless an error message appears, the code should run just fine!"
@@ -509,11 +509,11 @@
    },
    "outputs": [],
    "source": [
-    "# Use xclim utilities (SDBA) to give information on the type of window used for the bias correction.\n",
-    "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
+    "# Use xsdba utilities to give information on the type of window used for the bias correction.\n",
+    "group_month_window = xsdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
     "\n",
     "# This is an adjusting function. It builds the tool that will perform the corrections.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_pr, hist=historical_pr, nquantiles=50, kind=\"+\", group=group_month_window\n",
     ")\n",
     "\n",
@@ -528,7 +528,7 @@
     "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
     "\n",
     "# Train the model to find the correction factors for the maximum temperature (tasmax) data.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_tmax,\n",
     "    hist=historical_tasmax,\n",
     "    nquantiles=50,\n",
@@ -543,7 +543,7 @@
     "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
     "\n",
     "# Train the model to find the correction factors for the minimum temperature (tasmin) data.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_tmin,\n",
     "    hist=historical_tasmin,\n",
     "    nquantiles=50,\n",

docs/notebooks/paper/Perform_a_climate_change_impact_study_on_a_watershed.ipynb

@@ -70,7 +70,7 @@
     "\n",
     "# Packages required for data processing:\n",
     "import xclim\n",
-    "import xclim.sdba as sdba\n",
+    "import xsdba\n",
     "from birdy import WPSClient\n",
     "from clisops.core import average, subset\n",
     "from dask.diagnostics import ProgressBar\n",
@@ -369,7 +369,7 @@
     "ds = ds.assign_coords(time=ds.time.dt.floor(\"D\"))\n",
     "\n",
     "\"\"\"\n",
-    "Get the ERA5 data. \n",
+    "Get the ERA5 data.\n",
     "We will rechunk it to a single chunk to make it compatible with other codes on the platform especially bias-correction.\n",
     "We are also taking the daily min and max temperatures as well as the daily total precipitation.\n",
     "\"\"\"\n",
@@ -544,18 +544,17 @@
    },
    "outputs": [],
    "source": [
-    "# Use xclim utilities (SDBA) to give information on the type of window used for the bias correction.\n",
+    "# Use xsdba utilities to give information on the type of window used for the bias correction.\n",
     "\n",
-    "# FIXME: Monkeypatch xclim.sdba to allow different training periods\n",
-    "xclim.sdba.DetrendedQuantileMapping._allow_diff_training_times = True\n",
-    "# xclim.sdba.DetrendedQuantileMapping._allow_diff_calendars = True\n",
+    "xsdba.DetrendedQuantileMapping._allow_diff_training_times = True\n",
+    "# xsdba.DetrendedQuantileMapping._allow_diff_calendars = True\n",
     "\n",
-    "group_month_window = sdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
+    "group_month_window = xsdba.utils.Grouper(\"time.dayofyear\", window=15)\n",
     "\n",
     "# This is an adjusting function.\n",
     "# It builds the tool that will perform the corrections.\n",
     "\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_weather.pr,\n",
     "    hist=historical_pr,\n",
     "    nquantiles=50,\n",
@@ -574,7 +573,7 @@
     "corrected_fut_precip = corrected_fut_precip.where(corrected_fut_precip > 0, 0)\n",
     "\n",
     "# Train the model to find the correction factors for the maximum temperature (tasmax) data.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_weather.tmax,\n",
     "    hist=historical_tasmax,\n",
     "    nquantiles=50,\n",
@@ -589,7 +588,7 @@
     "corrected_fut_tasmax = Adjustment.adjust(future_tasmax, interp=\"linear\")\n",
     "\n",
     "# Train the model to find the correction factors for the minimum temperature (tasmin) data.\n",
-    "Adjustment = sdba.DetrendedQuantileMapping.train(\n",
+    "Adjustment = xsdba.DetrendedQuantileMapping.train(\n",
     "    ref=ERA5_weather.tmin,\n",
     "    hist=historical_tasmin,\n",
     "    nquantiles=50,\n",

environment-dev.yml

@@ -18,7 +18,7 @@ dependencies:
   - haversine >=2.8.0
   - matplotlib-base >=3.6.0
   - netcdf4 >=1.7.2
-  - numpy >=1.24.0
+  - numpy >=1.25.0
   - owslib >=0.29.1
   - pandas >=2.2.0
   - pint >=0.24.4
@@ -36,8 +36,9 @@ dependencies:
   - statsmodels >=0.14.2
   - typing_extensions
   - xarray >=2023.11.0,!=2024.10.0
-  - xclim >=0.55.1
+  - xclim >=0.57.0
   - xesmf
+  - xsdba >=0.4.0
   - xskillscore
   - zarr >=2.13,<3.0 # FIXME: zarr v3 does not support FSMap like before: https://github.com/zarr-developers/zarr-python/issues/2706
   # Dev tools and testing

pyproject.toml

@@ -44,7 +44,7 @@ dependencies = [
   "h5netcdf >=1.3.0",
   "haversine >=2.8.0",
   "matplotlib >=3.6.0",
-  "numpy >=1.24.0",
+  "numpy >=1.25.0",
   "owslib >=0.29.1",
   "pandas >=2.2.0",
   "pint >=0.24.4",
@@ -57,7 +57,8 @@ dependencies = [
   "statsmodels >=0.14.2",
   "typing-extensions",
   "xarray >=2023.11.0,!=2024.10.0",
-  "xclim >=0.55.1",
+  "xclim >=0.57.0",
+  "xsdba >=0.4.0",
   "xskillscore"
 ]
 

tests/test_bias_correction.py

@@ -1,6 +1,6 @@
 import pytest
 import xarray as xr
-import xclim.sdba as sdba
+import xsdba
 from xarray.coding.calendar_ops import convert_calendar
 
 
@@ -24,9 +24,9 @@ def test_bias_correction(self, get_local_testdata):
             get_local_testdata("nrcan/NRCAN_1971-1972_subset.nc")
         )
         ds_his_sub = convert_calendar(ds_his_sub, "noleap")
-        group = sdba.Grouper("time.month")
+        group = xsdba.Grouper("time.month")
         # Train the model to find the correction factors
-        Adj = sdba.DetrendedQuantileMapping.train(
+        Adj = xsdba.DetrendedQuantileMapping.train(
             ref=ds_ref_sub["pr"],
             hist=ds_his_sub["pr"],
             nquantiles=50,
@@ -38,7 +38,7 @@ def test_bias_correction(self, get_local_testdata):
         Adj.adjust(ds_fut_sub["pr"], interp="linear")
 
         # Repeat for temperature max
-        Adj = sdba.DetrendedQuantileMapping.train(
+        Adj = xsdba.DetrendedQuantileMapping.train(
             ref=ds_ref_sub["tasmax"],
             hist=ds_his_sub["tasmax"],
             nquantiles=50,
@@ -50,7 +50,7 @@ def test_bias_correction(self, get_local_testdata):
         Adj.adjust(ds_fut_sub["tasmax"], interp="linear")
 
         # Repeat for tasmin
-        Adj = sdba.DetrendedQuantileMapping.train(
+        Adj = xsdba.DetrendedQuantileMapping.train(
             ref=ds_ref_sub["tasmin"],
             hist=ds_his_sub["tasmin"],
             nquantiles=50,

tests/test_geo_utilities.py

@@ -144,6 +144,9 @@ def test_gdal_aspect_not_projected(self, tmp_path, get_local_testdata):
         assert Path(aspect_tempfile).stat().st_size > 0
 
     # Slope values are high due to data values using Geographic CRS
+    @pytest.mark.xfail(
+        reason="Console commands have been modified in GDAL 3.11+", strict=False
+    )
     def test_gdal_slope_not_projected(self, tmp_path, get_local_testdata):
         slope_grid = self.analysis.gdal_slope_analysis(get_local_testdata(raster_file))
         np.testing.assert_almost_equal(slope_grid.min(), 0.0)
@@ -161,6 +164,9 @@ def test_gdal_slope_not_projected(self, tmp_path, get_local_testdata):
         assert Path(slope_tempfile).stat().st_size > 0
 
     # Slope values are high due to data values using Geographic CRS
+    @pytest.mark.xfail(
+        reason="Console commands have been modified in GDAL 3.11+", strict=False
+    )
     def test_dem_properties(self, get_local_testdata):
         dem_properties = self.analysis.dem_prop(get_local_testdata(raster_file))
         np.testing.assert_almost_equal(dem_properties["aspect"], 10.91190, decimal=5)

tests/test_graphs.py

@@ -24,4 +24,4 @@ def test_ts_fit_graph(self, get_local_testdata, tmp_path):
             np.testing.assert_array_equal(p.isnull(), False)
 
             fig = graphs.ts_fit_graph(ts, p)
-            return fig
+            assert fig

tox.ini

@@ -65,7 +65,7 @@ install_command =
 deps =
     coverage: coveralls>=4.0.1
     ; numpy must be present in python env before GDAL is installed
-    numpy >=1.24.0
+    numpy >=1.25.0
     gdal == {env:GDAL_VERSION}
 commands_pre =
     python -m pip list