diff --git a/.github/workflows/conda-package-build.yml b/.github/workflows/conda-package-build.yml deleted file mode 100644 index 7cf8b7f..0000000 --- a/.github/workflows/conda-package-build.yml +++ /dev/null @@ -1,67 +0,0 @@ -name: build_publish_anaconda - -on: - push: - branches: [ master ] - pull_request: - branches: [ master ] - -jobs: - build-and-publish: - name: ${{ matrix.os }}, Python 3.${{ matrix.python-minor-version }} for conda deployment - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - max-parallel: 3 - matrix: - os: [ ubuntu-latest ] - python-minor-version: [9] - isMaster: - - ${{ github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/dev') }} - exclude: - - isMaster: false - os: ubuntu-latest - python-minor-version: 7 - - isMaster: false - os: ubuntu-latest - python-minor-version: 8 - - isMaster: false - os: macos-latest - python-minor-version: 7 - - isMaster: false - os: macos-latest - python-minor-version: 8 - - isMaster: false - os: macos-latest - python-minor-version: 9 - - isMaster: false - os: windows-latest - python-minor-version: 7 - - isMaster: false - os: windows-latest - python-minor-version: 8 - - isMaster: false - os: windows-latest - python-minor-version: 9 - - steps: - - name: Chekout - uses: actions/checkout@v3 - - name: Determine publish - uses: haya14busa/action-cond@v1 - id: publish - with: - cond: ${{ contains(github.ref, 'master') || startsWith(github.ref, 'refs/heads/v') }} - if_true: 'true' - if_false: 'false' - - name: Build and Publish - uses: openalea/action-build-publish-anaconda@v0.1.3 - with: - conda: conda - mamba: true - python: ${{ matrix.python-minor-version }} - numpy: '20.0' - channels: openalea3, conda-forge - token: ${{ secrets.ANACONDA_TOKEN }} - publish: ${{ steps.publish.outputs.value }} - label: main \ No newline at end of file diff --git a/.github/workflows/openalea_ci.yml b/.github/workflows/openalea_ci.yml new file mode 100644 index 0000000..3d610de --- /dev/null +++ b/.github/workflows/openalea_ci.yml @@ -0,0 +1,33 @@ +name: OpenAlea CI + +on: + push: + branches: + - main + - master + tags: + - 'v*' + pull_request: + types: + - opened + - synchronize + - reopened + release: + types: + - published + workflow_dispatch: + inputs: + check_before_tag: + description: "Run OpenAlea CI pre-tag build" + required: false + default: "true" + type: boolean + +run-name: > + ${{ github.event_name == 'workflow_dispatch' && 'OpenAlea CI pre-tag build' || 'OpenAlea CI' }} + +jobs: + build: + uses: openalea/action-build-publish-anaconda/.github/workflows/openalea_ci.yml@main + secrets: + anaconda_token: ${{ secrets.ANACONDA_TOKEN }} \ No newline at end of file diff --git a/conda/meta.yaml b/conda/meta.yaml index 97dd285..0c40341 100644 --- a/conda/meta.yaml +++ b/conda/meta.yaml @@ -1,40 +1,57 @@ -{% set data = load_setup_py_data() %} +{% set pyproject = load_file_data('../pyproject.toml', from_recipe_dir=True) %} +{% set name = pyproject.get('project').get('name') %} +{% set description = pyproject.get('project').get('description') %} +{% set version = environ.get('SETUPTOOLS_SCM_PRETEND_VERSION', "1.0.0.dev") %} +{% set license = pyproject.get('project').get('license') %} +{% set home = pyproject.get('project', {}).get('urls', {}).get('Homepage', '') %} +{% set build_deps = pyproject.get("build-system", {}).get("requires", []) %} +{% set deps = pyproject.get('project', {}).get('dependencies', []) %} +{% set conda_deps = pyproject.get('tool', {}).get('conda', {}).get('environment', {}).get('dependencies',[]) %} +{% set test_deps = pyproject.get('project', {}).get('optional-dependencies', {}).get('test',[]) %} + package: - name: openalea.dss - version: {{ data.get('version') }} + name: {{ name }} + version: {{ version }} source: path: .. build: noarch: python - preserve_egg_dir: True number: 0 - script: {{PYTHON}} setup.py install + preserve_egg_dir: True + script: + - {{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-build-isolation -vv requirements: - build: - - python {{PY_VER}} - - setuptools + host: + - python + {% for dep in build_deps %} + - {{ dep }} + {% endfor %} + run: - - python >=3.6 - - agroservices - - weatherdata - - matplotlib + - python + {% for dep in deps + conda_deps %} + - {{ dep }} + {% endfor %} test: requires: - - pytest + {% for dep in test_deps %} + - {{ dep }} + {% endfor %} imports: - - weatherdata + - openalea.dss source_files: - test/** - #commands: - # - cd test - # - pytest -v --ignore=test_weatherdata.py + about: - home: {{ data.get('url') }} - license: GPL-v3 - summary: {{ data.get('description') }} + home: {{ home }} + summary: {{ description }} + license: {{ license }} +extra: + recipe-maintainers: + - pradal \ No newline at end of file diff --git a/doc/requierements.txt b/doc/requierements.txt index 90e8eaa..9a5a6ca 100644 --- a/doc/requierements.txt +++ b/doc/requierements.txt @@ -1,5 +1,4 @@ python=3.8 -openalea.deploy agroservices requests (agroservices) appdirs (agroservices) diff --git a/example/DSS_demo_integration_dashboard.ipynb b/example/DSS_demo_integration_dashboard.ipynb new file mode 100644 index 0000000..f5e804a --- /dev/null +++ b/example/DSS_demo_integration_dashboard.ipynb @@ -0,0 +1,823 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# DSS demonstration\n", + "Access to notebook: https://github.com/openalea/DSS/blob/master/example/DSS_demonstration.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'openalea.dss'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenalea\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mweatherdata\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mipm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m WeatherDataHub\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenalea\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdss\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Manager\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mopenalea\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01magroservices\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mipm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m IPM \u001b[38;5;28;01mas\u001b[39;00m Hub\n", + "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'openalea.dss'" + ] + } + ], + "source": [ + "import pandas\n", + "pandas.set_option('display.max_colwidth', None)\n", + "pandas.set_option('display.max_row', 10)\n", + "import numpy as np\n", + "from openalea.weatherdata.ipm import WeatherDataHub\n", + "from openalea.dss import Manager\n", + "from openalea.agroservices.ipm import IPM as Hub\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acces to IPM DSS from OpenAlea python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Access to dss catalog " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Hub' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m h = \u001b[43mHub\u001b[49m()\n\u001b[32m 2\u001b[39m \u001b[38;5;28mdir\u001b[39m(h)\n", + "\u001b[31mNameError\u001b[39m: name 'Hub' is not defined" + ] + } + ], + "source": [ + "h = Hub()\n", + "dir(h)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Select dss, meta-informations " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 select dss and model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'IPM' object has no attribute 'get'", + "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 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m psitemp=\u001b[43mh\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m(dss=\u001b[33m\"\u001b[39m\u001b[33mno.nibio.vips\u001b[39m\u001b[33m\"\u001b[39m, model=\u001b[33m\"\u001b[39m\u001b[33mPSILARTEMP\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mAttributeError\u001b[39m: 'IPM' object has no attribute 'get'" + ] + } + ], + "source": [ + "psitemp=h.get(dss=\"no.nibio.vips\", model=\"PSILARTEMP\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 display meta-information of dss model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameiddescriptiontype_of_decisionpestscropsweather inputfield_observation inputoutputoutput_description
0Carrot fly flight modelPSILARTEMPTHE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae initial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\nShort-term tactical[PSILRO][DAUCS]1002NoneTMDD5C, THRESHOLD_1, THRESHOLD_2, THRESHOLD_3Accumulated day degrees, Threshold for start of flight period, Threshold for peak flight period, Threshold for end of 1st generation flight period
\n", + "
" + ], + "text/plain": [ + " name id \\\n", + "0 Carrot fly flight model PSILARTEMP \n", + "\n", + " description \\\n", + "0 THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae initial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n \n", + "\n", + " type_of_decision pests crops weather input \\\n", + "0 Short-term tactical [PSILRO] [DAUCS] 1002 \n", + "\n", + " field_observation input output \\\n", + "0 None TMDD5C, THRESHOLD_1, THRESHOLD_2, THRESHOLD_3 \n", + "\n", + " output_description \n", + "0 Accumulated day degrees, Threshold for start of flight period, Threshold for peak flight period, Threshold for end of 1st generation flight period " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psitemp.informations(\"dataframe\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 import IPM weatherdata or local OpenAlea ressouce from local IPM catalog" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ws=WeatherDataHub()\n", + "euw=ws.get_ressource(name='Euroweather seasonal gridded weather data and forecasts by IPM Decisions')\n", + "\n", + "weather=euw.data(parameters=[1002],latitude=[67.28],longitude=[14.37],\n", + " timeStart='2021-06-01',timeEnd=\"2021-08-20\",timeZone=\"Europe/Paris\",\n", + " display=\"json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Run model and vizualise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* run model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:      (time: 104)\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-04-14\n",
+       "Data variables:\n",
+       "    TMDD5C       (time) float64 0.0 0.0 0.0 0.0 0.0 ... 22.66 26.57 26.57 26.57\n",
+       "    THRESHOLD_1  (time) float64 260.0 260.0 260.0 260.0 ... 260.0 260.0 260.0\n",
+       "    THRESHOLD_2  (time) float64 360.0 360.0 360.0 360.0 ... 360.0 360.0 360.0\n",
+       "    THRESHOLD_3  (time) float64 560.0 560.0 560.0 560.0 ... 560.0 560.0 560.0\n",
+       "Attributes:\n",
+       "    name:             Carrot fly flight model\n",
+       "    id:               PSILARTEMP\n",
+       "    version:          1.0\n",
+       "    authors:          {'name': '', 'email': 'vips@nibio.no', 'organization': ...\n",
+       "    description:      THE PEST: The first generation of adult carrot fly emer...\n",
+       "    description_url:  https://www.vips-landbruk.no/forecasts/models/PSILARTEMP/
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 104)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-04-14\n", + "Data variables:\n", + " TMDD5C (time) float64 0.0 0.0 0.0 0.0 0.0 ... 22.66 26.57 26.57 26.57\n", + " THRESHOLD_1 (time) float64 260.0 260.0 260.0 260.0 ... 260.0 260.0 260.0\n", + " THRESHOLD_2 (time) float64 360.0 360.0 360.0 360.0 ... 360.0 360.0 360.0\n", + " THRESHOLD_3 (time) float64 560.0 560.0 560.0 560.0 ... 560.0 560.0 560.0\n", + "Attributes:\n", + " name: Carrot fly flight model\n", + " id: PSILARTEMP\n", + " version: 1.0\n", + " authors: {'name': '', 'email': 'vips@nibio.no', 'organization': ...\n", + " description: THE PEST: The first generation of adult carrot fly emer...\n", + " description_url: https://www.vips-landbruk.no/forecasts/models/PSILARTEMP/" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds=psitemp.run(weatherdata=weather)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* plot output" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "psitemp.plot(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "02ca071b7e78c344288e1079c8d0ea68009fe5a98fcc0669e2a2bbe220df75ce" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.13.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/example/DSS_demonstration.ipynb b/example/DSS_demonstration.ipynb index 519cb36..4c5d39a 100644 --- a/example/DSS_demonstration.ipynb +++ b/example/DSS_demonstration.ipynb @@ -5,23 +5,17 @@ "metadata": {}, "source": [ "# DSS demonstration\n", - "Access to notebook: https://github.com/H2020-IPM-openalea/DSS/blob/refactor/example/DSS_demonstration.ipynb" + "Access to notebook: https://github.com/openalea/DSS/blob/master/example/DSS_demonstration.ipynb" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "import pandas\n", - "pandas.set_option('display.max_colwidth', None)\n", - "pandas.set_option('display.max_row', 10)\n", - "import numpy as np\n", - "from weatherdata.ipm import WeatherDataHub\n", - "from openalea.dss import Hub\n", - "from docstring_parser import parse\n", - "import sys" + "from openalea.weatherdata.ipm import WeatherDataHub\n", + "from openalea.dss import Manager as ModelManager\n" ] }, { @@ -74,43 +68,43 @@ " \n", " \n", " 0\n", - " no.nibio.vips\n", - " PSILARTEMP\n", - " [PSILRO]\n", - " [DAUCS]\n", - " THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae \\ninitial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n\n", + " uk.WarwickHRI\n", + " LAMTEQ_WarwickHRI\n", + " [LAMTEQ]\n", + " [NARSS]\n", + " THE PEST: The Large Narcissus Fly (Merodon equestris) is a species of Hoverfly whose larvae feed on Narcissus (daffodils).  Large narcissus flies overwinter inside damaged bulbs as fully-grown larvae which move into the soil to form pupae in the spring. When they emerge, the adults lay their eggs in the soil close to narcissus bulbs. After they hatch, the larvae burrow through the soil and enter the bulbs via the basal plate. The larvae feed and grow inside the bulbs and destroy their centres.  There is only one generation each year.\\nTHE DECISION: This model predicts the timing of adult emergence, egg laying and egg hatching, enabling users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program.  The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching.  For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve.  This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant, the rates of development of a  population held at a constant temperature are normally distributed (Phelps et al, 1993).  The model uses soil temperatures  or air temperatures depending on the stage of development. As multiple cohorts progress simultaneously, adult emergence, egg  laying and/or egg hatching can occur at the same time. \\nTHE PARAMETERS: The Large Narcissus Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a  limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE:  Collier, R.H. & Finch, S. (1992). The effects of temperature on development of the large narcissus fly (Merodon equestris).  Annals of Applied Biology, 120, 383-390. Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993).   Monte Carlo simulation method for forecasting the timing of pest insect attacks.  Crop Protection 12, 335-342.\n", " \n", " \n", " 1\n", - " no.nibio.vips\n", - " DELIARADIC\n", - " [HYLERA]\n", - " [1BRSG]\n", - " THE PEST: Cabbage root fly larvae feed on the roots of brassicas, with damage being dependant on the crop type, growth stage and growing conditions. The cabbage root fly adults begin to lay eggs 5-7 days after emergence. Newly transplanted or recently emerged crops are most at risk as the root systems are less developed. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before subsequent larvae tunnel into the crop roots. \\nTHE MODEL: The model determins the start of egg laying as 160 degree-days (day-degrees) based on soil temperature (10 cm), over a base of temperature of 4 °C), OR based on the standard air temperature (2 m above the soil surface) at the same locations, egg laying starts at 210 degree days. \\nTHE PARAMETERS: The model uses Daily soil OR air temperature \\nSOURCE: NIBIO, Norway. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier due to higher soil temperature under the covers. This model should be used in combination with direct observations of eggs in the field. This is due to large variability and to get an idea of the severity of attack. The model only applies for cabbage fly, not turnip fly.\\n\n", + " uk.WarwickHRI\n", + " MELIAE_WarwickHRI\n", + " [MELIAE]\n", + " [BRSOB, BRSOK, BRSNN]\n", + " THE PEST: Adult pollen (bronzed-blossom) beetles (Meligethes aeneus/Brassicogethes aeneus) overwinter in the soil.   The beetles become active in early spring and fly to flowering brassica crops about a month later, where they feed on  the buds and flowers.  During feeding, females chew holes in the bases of the unopened flower buds and lay eggs in each hole.   After hatching, the larvae feed initially on pollen but later feed on unopened flowers and finally on newly-formed seed pods.  The fully-grown larvae drop to the soil where they pupate.  Adult (summer) beetles emerge 2-3 weeks later and the majority disperse to feed on other plants, including the florets of cauliflower crops, before they move to overwintering sites.   There is only one generation each year.\\nTHE DECISION: This model predicts the timing of spring emergence of adult beetles, egg laying and then the emergence of a new (summer)  generation of adults ready to disperse, followed by their dispersal.  This enables users to undertake targeted monitoring and/or  mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program.   The model simulates the development of cohorts of 500 individuals through spring emergence, egg laying and hatching, larval and pupal  development and emergence, followed by dispersal of the new generation of adult beetles. For each stage, the percentage development is  calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant,  the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993).  The model uses soil  temperatures or air temperatures depending on the stage of development. As multiple cohorts progress simultaneously, adult emergence/dispersal  and egg laying can occur at the same time.\\nTHE PARAMETERS: The Pollen Beetle forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations;  please select 'Edit Parameters' and select the most appropriate location for your farm.  The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993).  Monte Carlo simulation method for forecasting the timing of pest insect attacks.  Crop Protection 12, 335-342.\n", " \n", " \n", " 2\n", - " no.nibio.vips\n", - " MAMESTRABR\n", - " [BARABR]\n", - " [1BRSG]\n", - " The model for the warning system for cabbage moth was developed by Dr. Nina Svae Johansen. \\nIt is based on the minimum temperature threshold and the requirement for accumulated \\nday-degrees for the different stages of the cabbage moth [CITATION Joh96 \\t \\l 1044 ]. \\nThe accumulated degree-day model calculates forecasts for development of the cabbage moth \\nthrough the summer, generates warnings for the time when eggs and small larvae can be \\nregistered in the field and the best time for treatment [CITATION Joh97 \\t \\l 1044 ].\\n\\nNote that the model is based on temperature, it is not related to the presence or \\nabsence of cabbage moth in the field. Thus, it is important to evaluate the situation in the field.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " uk.WarwickHRI\n", + " HYLERA_WarwickHRI\n", + " [HYLERA]\n", + " [BRSSS]\n", + " THE PEST: The cabbage root fly (Delia radicum) overwinters as a pupa, in diapause.  The first generation of adult flies emerges in the spring  and mated female flies lay their eggs in the soil close to the base of brassica plants.  After hatching, the larvae feed on the roots and may tunnel into them, causing damage.  These larvae form pupae, which lead to the emergence of a new generation of adults.  Depending on the local climate the number of cabbage root fly generations and their timing can differ.  When the weather is particularly hot, cabbage root fly pupae  may aestivate.  In some areas there is an additional biotype of cabbage root fly which has an extended pupal diapause and emerges later in the spring.  We call the two biotypes \"early emerging\" and \"late emerging\".  \\nTHE DECISION: The model predicts the timing of adult emergence and egg-laying throughout the year, enabling users to undertake targeted monitoring  and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program.   The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching. For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches  100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant,  the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993).  The model uses soil temperatures  or air temperatures depending on the stage of development. Within the model it is possible to specify the proportions of the early and late emerging biotypes  in the simulated population. As multiple cohorts progress simultaneously, adult emergence and egg laying can occur at the same time.\\nTHE PARAMETERS: The Cabbage Root Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Collier, R.H. & Finch, S. (1983).   Completion of diapause in field populations of the cabbage root fly (Delia radicum).  Entomologia experimentalis et applicata 34, 186 192. Collier, R.H. & Finch, S. (1983).   Effects of intensity and duration of low temperatures in regulating diapause development of the cabbage root fly (Delia radicum).   Entomologia experimentalis et applicata 34, 193 200. Collier, R. H. & Finch, S. (1986).  Accumulated temperatures for predicting cabbage root fly, Delia radicum (L.), (Diptera; Anthomyiidae) emergence in the spring.  Bulletin of Entomological Research 75, 395 404. Finch, S. & Collier, R.H. (1983).  Emergence of flies from overwintering populations of cabbage root fly pupae.  Ecological Entomology 8, 29 36. Finch, S. & Collier, R.H. (1983).  Emergence of flies from overwintering populations of cabbage root fly pupae.  Ecological Entomology 8, 29 36. Finch, S. & Collier, R. H. (1985).   Laboratory studies on aestivation in the cabbage root fly (Delia radicum).  Entomologia experimentalis et applicata 38, 137 143. Finch, S., Collier, R. H. & Skinner, G. (1986).  Local population differences in emergence of cabbage root flies from south west Lancashire; implications for pest forecasting and population divergence.  Ecological Entomology 11, 139 145. Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993).   Monte Carlo simulation method for forecasting the timing of pest insect attacks.  Crop Protection 12, 335-342.\n", " \n", " \n", " 3\n", - " no.nibio.vips\n", - " PSILAROBSE\n", + " uk.WarwickHRI\n", + " PSILRO_WarwickHRI\n", " [PSILRO]\n", - " [DAUCS]\n", - " The warning system model is based on weekly observations of adult carrot rust flies captured on yellow sticky traps. The model is based in its entirety on observations, with no input of weather data or weather forecasts. Traps are placed in the field edge and in the field and are examined for carrot rust flies weekly throughout the season. The number of adult carrot rust flies is registered in VIPS and is used in the warning system model. The observations are compared with the economic threshold levels and a warning is calculated. After organophosphates (which had a good effect against larvae) were removed from the market, they were replaced by pyrethroids that only work against the adult stage. Studies were carried out in 2005 and 2006 to adjust the larval-based thresholds to chemical control of adult flies. The experience from Norway and other countries indicated that the first treatment against carrot rust flies should be done as soon as possible after the first fly is observed on the traps. The threshold that is used in VIPS is therefore at the first observation of 1 fly.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " [DAUCS, PAVSA, APUGR, PARCR]\n", + " THE PEST: Carrot flies (Psila rosae/Chamaepsila rosae) overwinter in the soil either as diapausing pupae or as larvae.  Late-developing insects remain as larvae and continue to feed on carrot roots throughout the winter. The insects that  overwinter as larvae form pupae during the spring.  Adult flies subsequently emerge from both types of pupae.   The female flies lay eggs in the soil close to carrot plants and, after hatching, the larvae feed on the roots and  tunnel into them, causing damage.  These larvae form pupae, which lead to the emergence of a new generation of adults. Depending on the local climate the number of carrot fly generations and their timing can differ.  When the weather is  particularly hot, carrot fly pupae may aestivate.\\nTHE DECISION: The model predicts the timing of adult emergence and egg-laying throughout the year, enabling users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program.   The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching. For each stage,  the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated  over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is  incorporated by assuming that, at any instant, the rates of development of a population held at a constant temperature are normally  distributed (Phelps et al, 1993).  The model uses soil temperatures or air temperatures depending on the stage of development.  As multiple cohorts progress simultaneously, adult emergence and egg laying can occur at the same time.\\nTHE PARAMETERS: The Carrot Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters'  and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993).   Monte Carlo simulation method for forecasting the timing of pest insect attacks.  Crop Protection 12, 335-342. Collier, R.H., Elliott, M.S. & Finch, S. (1994).   Development of the overwintering stages of the carrot fly, Psila rosae, (Diptera:Psilidae).  Bulletin of Entomological Research 84, 469-476. Collier, R.H. & Finch, S. (1996).  Field and laboratory studies on the effects of temperature on the development of the carrot fly (Psila rosae F.).  Annals of Applied Biology 128, 1-11.\n", " \n", " \n", " 4\n", - " no.nibio.vips\n", - " DELIARFOBS\n", - " [HYLERA, HYLEFL]\n", - " [BRSOL, BRSOB, BRSOK]\n", - " The warning system model is based on weekly observations of oviposition by the \\nbrassica root flies [CITATION Bli991 \\l 1044 ]. The model is based in its entirety \\non observations, with no input of weather data or weather forecasts. The model does \\nnot distinguish between the cabbage maggot and the turnip fly. The observations consist \\nof collecting sand from the base of 10 plants and floating the eggs in water. The \\ncounts are registered in VIPS and the mean number of eggs is calculated. The observations \\nare compared to damage thresholds [CITATION Bli99 \\l 1044 ] and warnings are calculated.\\nThe damage thresholds are related to the plants developmental stage and tell how many eggs \\nthat can be on a plant before there will be a reduction in growth and yield. VIPS presents \\ntwo warning system models based on damage thresholds: one for newly planted cabbage and \\none for cabbage that has been in the field more than 4 weeks. The model can also be set up \\nas a private warning for the farmer’s own field, in which case the model will vary between \\nthe two different damage thresholds based on the age of the cabbage crop (starting at the \\ntime of planting).\\nThe warning system model is only valid for cabbage, cauliflower and broccoli. The damage \\nthreshold for cabbage, cauliflower and broccoli the first 4 weeks after planting is 14 eggs \\nper plant per week. After 4 weeks the damage threshold changes to 40 eggs per plant per \\nweek. Damage thresholds have not been determined for other crucifers.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " uk.WarwickHRI\n", + " it_horta_dss_tomato\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " ...\n", @@ -121,104 +115,91 @@ " ...\n", " \n", " \n", - " 27\n", - " com.ipmwise\n", - " ipmwise.no\n", - " [ANGAR, CONCA, SENVU, SENJA, SENVE, BIDTR, GALAP, ARTVU, EPIMO, APHAR, GNAUL, AVEFA, MYOAR, STEME, TUSFA, AMSCA, CHEAL, ARBTH, ANTAR, GAESS, LAPCO, BROMO, BROST, EROCI, DACGL, AETCY, AGSST, CERFO, FUMOF, MATIN, MATMT, MATCH, SOLTU, RAPRA, TRFRE, TRFPR, CENCY, GASSS, LYCAR, STAPA, BRSRS, MELNO, MENAR, ATRPA, TAROF, SOLNI, URTUR, URTDI, CHYSE, EQUAR, THLAR, POLLA, POLPE, POLCO, POLAV, MELAL, LOLPE, LOLMU, RANRE, POATR, POAPR, POAAN, BRANA, ALOMY, ALOGE, SINAR, RUMOB, RUMCR, SETVI, CONAR, SPRAR, HORVX, AVESA, TRZAX, SECCE, VIOAR, GERSS, SONSS, FESPR, FESRU, FESOV, FESAR, CIRAR, LAMSS, PAPRH, EPHPE, EPHHE, VLPMY, VERAR, VERPE, VERHE]\n", - " [3WHEC, 3BARC, 3OATC]\n", - " VIPS-Ugras 2.0 er en ny versjon av VIPS-ugras. IPM Consult har utviklet systemet og som på dansk heter IPMwise. \\nDet er tilpasset norske forhold av Norsk institutt for bioøkonomi (NIBIO) i samarbeid med Norsk Landbruksrådgiving (NLR). \\nDet er nye beregninger og tilpasninger, men det er mye det samme innholdet som gamle VIPS-Ugras. VIPS-Ugras 2.0 krever \\ninnlogging. Fordelen med innlogging er at systemet kan huske skiftene dine. Systemet er gratis i Norge. Du kan velge å \\nbruke ferdig innlagte effektkrav eller velge dine egne effektkrav. For en optimal plantevernløsning for de fleste forhold \\nanbefaler vi at du bruker systemets effektkrav \\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " 970\n", + " AHDB.OSR_disease_forecasts\n", + " RHYNSE\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 28\n", - " com.ipmwise\n", - " ipmwise.es\n", - " [ANGAR, CONCA, SENVU, SENJA, SENVE, BIDTR, GALAP, ARTVU, EPIMO, APHAR, GNAUL, AVEFA, MYOAR, STEME, TUSFA, AMSCA, CHEAL, ARBTH, ANTAR, GAESS, LAPCO, BROMO, BROST, EROCI, DACGL, AETCY, AGSST, CERFO, FUMOF, MATIN, MATMT, MATCH, SOLTU, RAPRA, TRFRE, TRFPR, CENCY, GASSS, LYCAR, STAPA, BRSRS, MELNO, MENAR, ATRPA, TAROF, SOLNI, URTUR, URTDI, CHYSE, EQUAR, THLAR, POLLA, POLPE, POLCO, POLAV, MELAL, LOLPE, LOLMU, RANRE, POATR, POAPR, POAAN, BRANA, ALOMY, ALOGE, SINAR, RUMOB, RUMCR, SETVI, CONAR, SPRAR, HORVX, AVESA, TRZAX, SECCE, VIOAR, GERSS, SONSS, FESPR, FESRU, FESOV, FESAR, CIRAR, LAMSS, PAPRH, EPHPE, EPHHE, VLPMY, VERAR, VERPE, VERHE, AVEST, BRODI, DIPER, KCHSC, LACSE, LOLRI, MATCH, PHABR, PHAMI, ...]\n", - " [3WHEC, 3BARC, 3TRIC, 3CERC, ZEAMX, GLXMA, TRZDS]\n", - " Adapta tu manejo de malas hierbas a tus necesidades como un agricultor profesional o un asesor de cultivos. \\nDecide por ti mismo el nivel de control de malas hierbas o utiliza el modulo basado en IPM del IPMwise para guiarte. \\nCaracteristicas principales: Las herramientas integradas en IPMWise pueden asistirte en planificar y decidir \\nlas mejores estrategias de control de malas hierbas en tus fincas. Puedes simular como afectan las diferentes\\ncondiciones de la parcela a la necesidad de control y a las herramientas disponibles. En caso de que no tengas \\nrecomendaciones especificas para controlar tus malas hierbas en un momento determinado, IPMWise te permite \\nsimular si en momentos anteriores o posteriores al actual es posible controlar tus infestaciones de malas hierbas. \\ncitation: null # Optional. Use the DOI as identifier (list)\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " 971\n", + " AHDB.OSR_disease_forecasts\n", + " PUCCHD\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 29\n", - " com.ipmwise\n", - " ipmwise.demo\n", - " [ACHMI, AETCY, AGSST, ALOGE, ALOMY, AMSCA, ANGAR, LYCAR, ANTAR, APHAR, ARBTH, ARTVU, ATRPA, AVEFA, AVESA, BIDTR, BRSRS, BRSNN, BROMO, BROST, CENCY, CERFO, MATMT, CHEAL, CHYSE, CIRAR, CONAR, CONCA, DACGL, AGRRE, EPIMO, EQUAR, EROCI, EPHHE, EPHPE, FESAR, FESOV, FESPR, FESRU, FUMOF, GAESS, GASSS, GALAP, GERMO, GNAUL, HORVX, LAMSS, LAPCO, LOLMU, LOLPE, MATCH, MENAR, MYOAR, PAPRH, POAAN, POAPR, POATR, POLAV, POLCO, POLLA, POLPE, MELAL, RANRE, RAPRA, RUMCR, RUMOB, SECCE, SENJA, SENVE, SENVU, SETVI, MELNO, SINAR, SOLNI, SOLTU, SONSS, SPRAR, STAPA, STEME, TAROF, THLAR, TRZAX, TRFPR, TRFRE, MATIN, TUSFA, URTDI, URTUR, VERAR, VERHE, VERPE, VIOAR, VLPMY]\n", - " [3WHEC, ZEAMX, 3BARC, 3RYEC, 3TRIC, 3OATC, 3BEEC, 3CERC, 3GRAC, 3LEGC]\n", - " Adapt weed management to your needs as a professional farmer or crop advisor. Decide yourself, the level of weed control \\nor let the built-in IPM principles of IPMwise guide you. The software contains useful tools to adapt the effort against weeds to the \\ncurrent weed population in a field and thus minimize the use of herbicides considerably. The software can be used to plan herbicide \\nefforts at both field level and as a general planning tool for crop advisors to find suitable tank mixtures and corresponding dose rates. \\nThe principles of the program are based on dose response data derived from efficacy reports. In Denmark, work is currently underway to \\nalso use the IPMwise decision support software in conjunction with artificial intelligence, automatic weed recognition and spray maps. \\nThis demo has been restricted concerning the number of crops and weeds compared to the full Danish version.\\ncitation: # Optional. Use the DOI as identifier (list)\\n- https://www.doi.org/10.1007/978-3-030-44402-0\\n- https://doi.org/10.1016/j.cropro.2014.06.012\\n- https://doi.org/10.1614/WT-D-13-00085.1\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE:\n", + " 972\n", + " AHDB.OSR_disease_forecasts\n", + " SlugWatch2023\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 30\n", - " dk.seges\n", - " SEPTORIAHU\n", - " [SEPTTR, LEPTNO, PYRNTR]\n", - " [3WHEC]\n", - " THE PEST: Leaf blotch diseases of wheat can be caused by septoria tritici blotch (Zymoseptoria tritici) and staganospora nodorum blotch (Parastagonospora nodorum), which are both favoured by wet conditions. \\nTHE DECISION: Fungicide treatments may need to be applied between stem extension and ear emergence, mainly to protect the upper leaves. \\nTHE MODEL: The humidity model estimates risk of septoria tritici blotch infections in winter wheat. Risk of attack is assumed after 20 hours with continuous wetness. A wet hour is defined as minimum 0,2 mm precipitation in an hour or minimum 85% relative humidity. \\nTHE PARAMETERS: Weather data from GS 31 are used. In addition, the dates of occurrence of growth stages 31 and 32 are entered. The model calculates the expected date for the other stages. This can be adjusted manually. Adding information on fungicide spraying dates is vital for the model. After spraying, the model assumes that the crop is protected for 10 days. The thresholds for number of wet hours and relative humidity can be adjusted manually. \\nSOURCE: Created by Aahus University and SEGES and released in Denmark in 2017. Tested in Lithuania, Norway, Sweden, Finland and Denmark in 2018 and 2019. \\nASSUMPTIONS: septoria tritici blotch is present and periods with high humidity create risk for a damaging epidemic\\n\n", + " 973\n", + " AHDB.OSR_disease_forecasts\n", + " SCLESC\n", + " [SCLESC]\n", + " [BRSNN]\n", + " Sclerotinia stem rot is usually the main disease to consider during the flowering stages of oilseed rape. There are 3 main risk factors: the presence of sclerotinia inoculum (spores), warm and humid conditions, and crops in flower.\\nIf spores are present and the crop is in flower, relative humidity must be above 80% and air temperatures above 7&deg;C for 23 continuous hours for the pathogen to infect the crop. This is what is predicted by the model.\n", " \n", " \n", - " 31\n", - " dk.seges\n", - " CPO\n", - " [SEPTTR, PUCCST, PUCCRE, ERYSGR, PSDCHE, PYRNTE, PUCCHD, RHYNSE]\n", - " [3WHEC, 3BARC, 3TRIC, 3OATC, 3RYEC]\n", - " The system has been developed by Aarhus University and SEGES and covers main diseases and pest in cereal crops.\\nThe models have been developed for guidance related to leaf and stem diseases in cereals including\\nwinter Wheat, spring barley, winter barley, spring oat, winter triticale and winter rye.\\nThe system also includes models for pest control in cereals.\\nThe models require information from field registration typically in the period from GS 30-31 to GS 65-71\\n(assessments of frequency of plants attacked) and for some diseases also precipitation is required\\n(days with more than 1 mm rain). Before getting information on need for treatments – information on crop,\\ncultivars susceptibility and growth stage (BBCH) is required.\\n\\nThe growers need to check the crops at weekly intervals or as a minimum follow the risk based on precipitation\\nor regional monitoring data for risk evaluation. In the Danish system fungicides and dose rates are\\nrecommended. In the IPM-decision system only a recommendation to treat or not to treat is given.\\n\\nThe models have been validated in Denmark and has also been tested in the Baltic and Nordic\\ncountries. It is expected to be valid in the northern part of Europe, given that the monitoring part of\\nthe system is followed.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: 1. Hansen, J.G., Secher, B.J., Jørgensen, LN. & Welling, B. 1994. Threshold for control of\\nSeptoria spp. in winter wheat based on precipitation and growth stage. Plant Pathology\\n43, 183-189.\\n\\n2. Secher, B.J., Jørgensen, L.N, Murali N.S. & Boll P.S. (1995). Field Validation of a\\nDecision Support System for Control of Pests and Diseases in Cereals in Denmark.\\nPesticide Science, 45, 195-199.\\n\\n3. Henriksen. K.E.; Jørgensen, L.N. & Nielsen, G.C.(2000) PC-Plant Protection –a tool to\\nreduce fungicide input in winter wheat, winter barley and spring barley in Denmark.\\nBrighton Crop Protection Conference. Pest and diseases. 835-840.\\n\\n4. Jørgensen, L.N. & Henriksen, K E. (2002) Control of diseases in different winter wheat\\nvarieties using thresholds and appropriate dosages. Petria vol 12 (1/2) 87-94\\n\\n5. Hagelskjær, L.& Jørgensen, LN, 2003 A web-based decision support system for\\nintegrated management of diesases and pest s in cereals. EPPO Bulletin 33, 467-471.\\n\\n6. Jørgensen, LN; Matzen, M, Nielsen, GC, Jalli, M; Ronis; Djule, A; Anderson, B; Ficke, A., Djule, A\\n(2020) Validation of risk models for control of leaf blotch diseases in wheat in the Nordic and\\nBaltic countries European Journal of Plante Pathology,. https://doi.org/10.1007/si 0658-020-02025-6\\n7. Jørgensen, LN; Matzen, N, Heick, T M, Madsen, HP; Kristjansen, H S; Kirkegaard, S;\\nAlmskou-Dahlgaard, A; Kristoffersen, R (2020). Control strategies in different cultivars.\\nApplied Crop Protection 2019.DCA rapport, 167, 58-71.\\nhttps://dcapub.au.dk/djfpublikation/index.asp?action=show&id=1325\\n\n", + " 974\n", + " AHDB.OSR_disease_forecasts\n", + " LEPTMA\n", + " [LEPTMA]\n", + " [BRSNN]\n", + " Temperature and rainfall information is used to simulate the development of Leptosphaeria maculans -- a key pathogen responsible for phoma leaf spot and phoma stem canker.\\nThe forecast predicts the date of the starting week when 10% of oilseed rape plants could potentially show symptoms of phoma leaf spot.\n", " \n", " \n", "\n", - "

32 rows × 5 columns

\n", + "

975 rows × 5 columns

\n", "" ], "text/plain": [ - " dss models \\\n", - "0 no.nibio.vips PSILARTEMP \n", - "1 no.nibio.vips DELIARADIC \n", - "2 no.nibio.vips MAMESTRABR \n", - "3 no.nibio.vips PSILAROBSE \n", - "4 no.nibio.vips DELIARFOBS \n", - ".. ... ... \n", - "27 com.ipmwise ipmwise.no \n", - "28 com.ipmwise ipmwise.es \n", - "29 com.ipmwise ipmwise.demo \n", - "30 dk.seges SEPTORIAHU \n", - "31 dk.seges CPO \n", - "\n", - " pests \\\n", - "0 [PSILRO] \n", - "1 [HYLERA] \n", - "2 [BARABR] \n", - "3 [PSILRO] \n", - "4 [HYLERA, HYLEFL] \n", - ".. ... \n", - "27 [ANGAR, CONCA, SENVU, SENJA, SENVE, BIDTR, GALAP, ARTVU, EPIMO, APHAR, GNAUL, AVEFA, MYOAR, STEME, TUSFA, AMSCA, CHEAL, ARBTH, ANTAR, GAESS, LAPCO, BROMO, BROST, EROCI, DACGL, AETCY, AGSST, CERFO, FUMOF, MATIN, MATMT, MATCH, SOLTU, RAPRA, TRFRE, TRFPR, CENCY, GASSS, LYCAR, STAPA, BRSRS, MELNO, MENAR, ATRPA, TAROF, SOLNI, URTUR, URTDI, CHYSE, EQUAR, THLAR, POLLA, POLPE, POLCO, POLAV, MELAL, LOLPE, LOLMU, RANRE, POATR, POAPR, POAAN, BRANA, ALOMY, ALOGE, SINAR, RUMOB, RUMCR, SETVI, CONAR, SPRAR, HORVX, AVESA, TRZAX, SECCE, VIOAR, GERSS, SONSS, FESPR, FESRU, FESOV, FESAR, CIRAR, LAMSS, PAPRH, EPHPE, EPHHE, VLPMY, VERAR, VERPE, VERHE] \n", - "28 [ANGAR, CONCA, SENVU, SENJA, SENVE, BIDTR, GALAP, ARTVU, EPIMO, APHAR, GNAUL, AVEFA, MYOAR, STEME, TUSFA, AMSCA, CHEAL, ARBTH, ANTAR, GAESS, LAPCO, BROMO, BROST, EROCI, DACGL, AETCY, AGSST, CERFO, FUMOF, MATIN, MATMT, MATCH, SOLTU, RAPRA, TRFRE, TRFPR, CENCY, GASSS, LYCAR, STAPA, BRSRS, MELNO, MENAR, ATRPA, TAROF, SOLNI, URTUR, URTDI, CHYSE, EQUAR, THLAR, POLLA, POLPE, POLCO, POLAV, MELAL, LOLPE, LOLMU, RANRE, POATR, POAPR, POAAN, BRANA, ALOMY, ALOGE, SINAR, RUMOB, RUMCR, SETVI, CONAR, SPRAR, HORVX, AVESA, TRZAX, SECCE, VIOAR, GERSS, SONSS, FESPR, FESRU, FESOV, FESAR, CIRAR, LAMSS, PAPRH, EPHPE, EPHHE, VLPMY, VERAR, VERPE, VERHE, AVEST, BRODI, DIPER, KCHSC, LACSE, LOLRI, MATCH, PHABR, PHAMI, ...] \n", - "29 [ACHMI, AETCY, AGSST, ALOGE, ALOMY, AMSCA, ANGAR, LYCAR, ANTAR, APHAR, ARBTH, ARTVU, ATRPA, AVEFA, AVESA, BIDTR, BRSRS, BRSNN, BROMO, BROST, CENCY, CERFO, MATMT, CHEAL, CHYSE, CIRAR, CONAR, CONCA, DACGL, AGRRE, EPIMO, EQUAR, EROCI, EPHHE, EPHPE, FESAR, FESOV, FESPR, FESRU, FUMOF, GAESS, GASSS, GALAP, GERMO, GNAUL, HORVX, LAMSS, LAPCO, LOLMU, LOLPE, MATCH, MENAR, MYOAR, PAPRH, POAAN, POAPR, POATR, POLAV, POLCO, POLLA, POLPE, MELAL, RANRE, RAPRA, RUMCR, RUMOB, SECCE, SENJA, SENVE, SENVU, SETVI, MELNO, SINAR, SOLNI, SOLTU, SONSS, SPRAR, STAPA, STEME, TAROF, THLAR, TRZAX, TRFPR, TRFRE, MATIN, TUSFA, URTDI, URTUR, VERAR, VERHE, VERPE, VIOAR, VLPMY] \n", - "30 [SEPTTR, LEPTNO, PYRNTR] \n", - "31 [SEPTTR, PUCCST, PUCCRE, ERYSGR, PSDCHE, PYRNTE, PUCCHD, RHYNSE] \n", - "\n", - " crops \\\n", - "0 [DAUCS] \n", - "1 [1BRSG] \n", - "2 [1BRSG] \n", - "3 [DAUCS] \n", - "4 [BRSOL, BRSOB, BRSOK] \n", - ".. ... \n", - "27 [3WHEC, 3BARC, 3OATC] \n", - "28 [3WHEC, 3BARC, 3TRIC, 3CERC, ZEAMX, GLXMA, TRZDS] \n", - "29 [3WHEC, ZEAMX, 3BARC, 3RYEC, 3TRIC, 3OATC, 3BEEC, 3CERC, 3GRAC, 3LEGC] \n", - "30 [3WHEC] \n", - "31 [3WHEC, 3BARC, 3TRIC, 3OATC, 3RYEC] \n", - "\n", - " description \n", - "0 THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae \\ninitial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n \n", - "1 THE PEST: Cabbage root fly larvae feed on the roots of brassicas, with damage being dependant on the crop type, growth stage and growing conditions. The cabbage root fly adults begin to lay eggs 5-7 days after emergence. Newly transplanted or recently emerged crops are most at risk as the root systems are less developed. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before subsequent larvae tunnel into the crop roots. \\nTHE MODEL: The model determins the start of egg laying as 160 degree-days (day-degrees) based on soil temperature (10 cm), over a base of temperature of 4 °C), OR based on the standard air temperature (2 m above the soil surface) at the same locations, egg laying starts at 210 degree days. \\nTHE PARAMETERS: The model uses Daily soil OR air temperature \\nSOURCE: NIBIO, Norway. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier due to higher soil temperature under the covers. This model should be used in combination with direct observations of eggs in the field. This is due to large variability and to get an idea of the severity of attack. The model only applies for cabbage fly, not turnip fly.\\n \n", - "2 The model for the warning system for cabbage moth was developed by Dr. Nina Svae Johansen. \\nIt is based on the minimum temperature threshold and the requirement for accumulated \\nday-degrees for the different stages of the cabbage moth [CITATION Joh96 \\t \\l 1044 ]. \\nThe accumulated degree-day model calculates forecasts for development of the cabbage moth \\nthrough the summer, generates warnings for the time when eggs and small larvae can be \\nregistered in the field and the best time for treatment [CITATION Joh97 \\t \\l 1044 ].\\n\\nNote that the model is based on temperature, it is not related to the presence or \\nabsence of cabbage moth in the field. Thus, it is important to evaluate the situation in the field.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - "3 The warning system model is based on weekly observations of adult carrot rust flies captured on yellow sticky traps. The model is based in its entirety on observations, with no input of weather data or weather forecasts. Traps are placed in the field edge and in the field and are examined for carrot rust flies weekly throughout the season. The number of adult carrot rust flies is registered in VIPS and is used in the warning system model. The observations are compared with the economic threshold levels and a warning is calculated. After organophosphates (which had a good effect against larvae) were removed from the market, they were replaced by pyrethroids that only work against the adult stage. Studies were carried out in 2005 and 2006 to adjust the larval-based thresholds to chemical control of adult flies. The experience from Norway and other countries indicated that the first treatment against carrot rust flies should be done as soon as possible after the first fly is observed on the traps. The threshold that is used in VIPS is therefore at the first observation of 1 fly.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - "4 The warning system model is based on weekly observations of oviposition by the \\nbrassica root flies [CITATION Bli991 \\l 1044 ]. The model is based in its entirety \\non observations, with no input of weather data or weather forecasts. The model does \\nnot distinguish between the cabbage maggot and the turnip fly. The observations consist \\nof collecting sand from the base of 10 plants and floating the eggs in water. The \\ncounts are registered in VIPS and the mean number of eggs is calculated. The observations \\nare compared to damage thresholds [CITATION Bli99 \\l 1044 ] and warnings are calculated.\\nThe damage thresholds are related to the plants developmental stage and tell how many eggs \\nthat can be on a plant before there will be a reduction in growth and yield. VIPS presents \\ntwo warning system models based on damage thresholds: one for newly planted cabbage and \\none for cabbage that has been in the field more than 4 weeks. The model can also be set up \\nas a private warning for the farmer’s own field, in which case the model will vary between \\nthe two different damage thresholds based on the age of the cabbage crop (starting at the \\ntime of planting).\\nThe warning system model is only valid for cabbage, cauliflower and broccoli. The damage \\nthreshold for cabbage, cauliflower and broccoli the first 4 weeks after planting is 14 eggs \\nper plant per week. After 4 weeks the damage threshold changes to 40 eggs per plant per \\nweek. Damage thresholds have not been determined for other crucifers.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - ".. ... \n", - "27 VIPS-Ugras 2.0 er en ny versjon av VIPS-ugras. IPM Consult har utviklet systemet og som på dansk heter IPMwise. \\nDet er tilpasset norske forhold av Norsk institutt for bioøkonomi (NIBIO) i samarbeid med Norsk Landbruksrådgiving (NLR). \\nDet er nye beregninger og tilpasninger, men det er mye det samme innholdet som gamle VIPS-Ugras. VIPS-Ugras 2.0 krever \\ninnlogging. Fordelen med innlogging er at systemet kan huske skiftene dine. Systemet er gratis i Norge. Du kan velge å \\nbruke ferdig innlagte effektkrav eller velge dine egne effektkrav. For en optimal plantevernløsning for de fleste forhold \\nanbefaler vi at du bruker systemets effektkrav \\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - "28 Adapta tu manejo de malas hierbas a tus necesidades como un agricultor profesional o un asesor de cultivos. \\nDecide por ti mismo el nivel de control de malas hierbas o utiliza el modulo basado en IPM del IPMwise para guiarte. \\nCaracteristicas principales: Las herramientas integradas en IPMWise pueden asistirte en planificar y decidir \\nlas mejores estrategias de control de malas hierbas en tus fincas. Puedes simular como afectan las diferentes\\ncondiciones de la parcela a la necesidad de control y a las herramientas disponibles. En caso de que no tengas \\nrecomendaciones especificas para controlar tus malas hierbas en un momento determinado, IPMWise te permite \\nsimular si en momentos anteriores o posteriores al actual es posible controlar tus infestaciones de malas hierbas. \\ncitation: null # Optional. Use the DOI as identifier (list)\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - "29 Adapt weed management to your needs as a professional farmer or crop advisor. Decide yourself, the level of weed control \\nor let the built-in IPM principles of IPMwise guide you. The software contains useful tools to adapt the effort against weeds to the \\ncurrent weed population in a field and thus minimize the use of herbicides considerably. The software can be used to plan herbicide \\nefforts at both field level and as a general planning tool for crop advisors to find suitable tank mixtures and corresponding dose rates. \\nThe principles of the program are based on dose response data derived from efficacy reports. In Denmark, work is currently underway to \\nalso use the IPMwise decision support software in conjunction with artificial intelligence, automatic weed recognition and spray maps. \\nThis demo has been restricted concerning the number of crops and weeds compared to the full Danish version.\\ncitation: # Optional. Use the DOI as identifier (list)\\n- https://www.doi.org/10.1007/978-3-030-44402-0\\n- https://doi.org/10.1016/j.cropro.2014.06.012\\n- https://doi.org/10.1614/WT-D-13-00085.1\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: \n", - "30 THE PEST: Leaf blotch diseases of wheat can be caused by septoria tritici blotch (Zymoseptoria tritici) and staganospora nodorum blotch (Parastagonospora nodorum), which are both favoured by wet conditions. \\nTHE DECISION: Fungicide treatments may need to be applied between stem extension and ear emergence, mainly to protect the upper leaves. \\nTHE MODEL: The humidity model estimates risk of septoria tritici blotch infections in winter wheat. Risk of attack is assumed after 20 hours with continuous wetness. A wet hour is defined as minimum 0,2 mm precipitation in an hour or minimum 85% relative humidity. \\nTHE PARAMETERS: Weather data from GS 31 are used. In addition, the dates of occurrence of growth stages 31 and 32 are entered. The model calculates the expected date for the other stages. This can be adjusted manually. Adding information on fungicide spraying dates is vital for the model. After spraying, the model assumes that the crop is protected for 10 days. The thresholds for number of wet hours and relative humidity can be adjusted manually. \\nSOURCE: Created by Aahus University and SEGES and released in Denmark in 2017. Tested in Lithuania, Norway, Sweden, Finland and Denmark in 2018 and 2019. \\nASSUMPTIONS: septoria tritici blotch is present and periods with high humidity create risk for a damaging epidemic\\n \n", - "31 The system has been developed by Aarhus University and SEGES and covers main diseases and pest in cereal crops.\\nThe models have been developed for guidance related to leaf and stem diseases in cereals including\\nwinter Wheat, spring barley, winter barley, spring oat, winter triticale and winter rye.\\nThe system also includes models for pest control in cereals.\\nThe models require information from field registration typically in the period from GS 30-31 to GS 65-71\\n(assessments of frequency of plants attacked) and for some diseases also precipitation is required\\n(days with more than 1 mm rain). Before getting information on need for treatments – information on crop,\\ncultivars susceptibility and growth stage (BBCH) is required.\\n\\nThe growers need to check the crops at weekly intervals or as a minimum follow the risk based on precipitation\\nor regional monitoring data for risk evaluation. In the Danish system fungicides and dose rates are\\nrecommended. In the IPM-decision system only a recommendation to treat or not to treat is given.\\n\\nThe models have been validated in Denmark and has also been tested in the Baltic and Nordic\\ncountries. It is expected to be valid in the northern part of Europe, given that the monitoring part of\\nthe system is followed.\\n\\nSOURCE: \\nASSUMPTIONS: \\nREFERENCE: 1. Hansen, J.G., Secher, B.J., Jørgensen, LN. & Welling, B. 1994. Threshold for control of\\nSeptoria spp. in winter wheat based on precipitation and growth stage. Plant Pathology\\n43, 183-189.\\n\\n2. Secher, B.J., Jørgensen, L.N, Murali N.S. & Boll P.S. (1995). Field Validation of a\\nDecision Support System for Control of Pests and Diseases in Cereals in Denmark.\\nPesticide Science, 45, 195-199.\\n\\n3. Henriksen. K.E.; Jørgensen, L.N. & Nielsen, G.C.(2000) PC-Plant Protection –a tool to\\nreduce fungicide input in winter wheat, winter barley and spring barley in Denmark.\\nBrighton Crop Protection Conference. Pest and diseases. 835-840.\\n\\n4. Jørgensen, L.N. & Henriksen, K E. (2002) Control of diseases in different winter wheat\\nvarieties using thresholds and appropriate dosages. Petria vol 12 (1/2) 87-94\\n\\n5. Hagelskjær, L.& Jørgensen, LN, 2003 A web-based decision support system for\\nintegrated management of diesases and pest s in cereals. EPPO Bulletin 33, 467-471.\\n\\n6. Jørgensen, LN; Matzen, M, Nielsen, GC, Jalli, M; Ronis; Djule, A; Anderson, B; Ficke, A., Djule, A\\n(2020) Validation of risk models for control of leaf blotch diseases in wheat in the Nordic and\\nBaltic countries European Journal of Plante Pathology,. https://doi.org/10.1007/si 0658-020-02025-6\\n7. Jørgensen, LN; Matzen, N, Heick, T M, Madsen, HP; Kristjansen, H S; Kirkegaard, S;\\nAlmskou-Dahlgaard, A; Kristoffersen, R (2020). Control strategies in different cultivars.\\nApplied Crop Protection 2019.DCA rapport, 167, 58-71.\\nhttps://dcapub.au.dk/djfpublikation/index.asp?action=show&id=1325\\n \n", - "\n", - "[32 rows x 5 columns]" + " dss models pests \\\n", + "0 uk.WarwickHRI LAMTEQ_WarwickHRI [LAMTEQ] \n", + "1 uk.WarwickHRI MELIAE_WarwickHRI [MELIAE] \n", + "2 uk.WarwickHRI HYLERA_WarwickHRI [HYLERA] \n", + "3 uk.WarwickHRI PSILRO_WarwickHRI [PSILRO] \n", + "4 uk.WarwickHRI it_horta_dss_tomato NaN \n", + ".. ... ... ... \n", + "970 AHDB.OSR_disease_forecasts RHYNSE NaN \n", + "971 AHDB.OSR_disease_forecasts PUCCHD NaN \n", + "972 AHDB.OSR_disease_forecasts SlugWatch2023 NaN \n", + "973 AHDB.OSR_disease_forecasts SCLESC [SCLESC] \n", + "974 AHDB.OSR_disease_forecasts LEPTMA [LEPTMA] \n", + "\n", + " crops \\\n", + "0 [NARSS] \n", + "1 [BRSOB, BRSOK, BRSNN] \n", + "2 [BRSSS] \n", + "3 [DAUCS, PAVSA, APUGR, PARCR] \n", + "4 NaN \n", + ".. ... \n", + "970 NaN \n", + "971 NaN \n", + "972 NaN \n", + "973 [BRSNN] \n", + "974 [BRSNN] \n", + "\n", + " description \n", + "0 THE PEST: The Large Narcissus Fly (Merodon equestris) is a species of Hoverfly whose larvae feed on Narcissus (daffodils). Large narcissus flies overwinter inside damaged bulbs as fully-grown larvae which move into the soil to form pupae in the spring. When they emerge, the adults lay their eggs in the soil close to narcissus bulbs. After they hatch, the larvae burrow through the soil and enter the bulbs via the basal plate. The larvae feed and grow inside the bulbs and destroy their centres. There is only one generation each year.\\nTHE DECISION: This model predicts the timing of adult emergence, egg laying and egg hatching, enabling users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program. The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching. For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant, the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993). The model uses soil temperatures or air temperatures depending on the stage of development. As multiple cohorts progress simultaneously, adult emergence, egg laying and/or egg hatching can occur at the same time. \\nTHE PARAMETERS: The Large Narcissus Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Collier, R.H. & Finch, S. (1992). The effects of temperature on development of the large narcissus fly (Merodon equestris). Annals of Applied Biology, 120, 383-390. Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993). Monte Carlo simulation method for forecasting the timing of pest insect attacks. Crop Protection 12, 335-342. \n", + "1 THE PEST: Adult pollen (bronzed-blossom) beetles (Meligethes aeneus/Brassicogethes aeneus) overwinter in the soil. The beetles become active in early spring and fly to flowering brassica crops about a month later, where they feed on the buds and flowers. During feeding, females chew holes in the bases of the unopened flower buds and lay eggs in each hole. After hatching, the larvae feed initially on pollen but later feed on unopened flowers and finally on newly-formed seed pods. The fully-grown larvae drop to the soil where they pupate. Adult (summer) beetles emerge 2-3 weeks later and the majority disperse to feed on other plants, including the florets of cauliflower crops, before they move to overwintering sites. There is only one generation each year.\\nTHE DECISION: This model predicts the timing of spring emergence of adult beetles, egg laying and then the emergence of a new (summer) generation of adults ready to disperse, followed by their dispersal. This enables users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program. The model simulates the development of cohorts of 500 individuals through spring emergence, egg laying and hatching, larval and pupal development and emergence, followed by dispersal of the new generation of adult beetles. For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant, the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993). The model uses soil temperatures or air temperatures depending on the stage of development. As multiple cohorts progress simultaneously, adult emergence/dispersal and egg laying can occur at the same time.\\nTHE PARAMETERS: The Pollen Beetle forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993). Monte Carlo simulation method for forecasting the timing of pest insect attacks. Crop Protection 12, 335-342. \n", + "2 THE PEST: The cabbage root fly (Delia radicum) overwinters as a pupa, in diapause. The first generation of adult flies emerges in the spring and mated female flies lay their eggs in the soil close to the base of brassica plants. After hatching, the larvae feed on the roots and may tunnel into them, causing damage. These larvae form pupae, which lead to the emergence of a new generation of adults. Depending on the local climate the number of cabbage root fly generations and their timing can differ. When the weather is particularly hot, cabbage root fly pupae may aestivate. In some areas there is an additional biotype of cabbage root fly which has an extended pupal diapause and emerges later in the spring. We call the two biotypes \"early emerging\" and \"late emerging\". \\nTHE DECISION: The model predicts the timing of adult emergence and egg-laying throughout the year, enabling users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program. The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching. For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant, the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993). The model uses soil temperatures or air temperatures depending on the stage of development. Within the model it is possible to specify the proportions of the early and late emerging biotypes in the simulated population. As multiple cohorts progress simultaneously, adult emergence and egg laying can occur at the same time.\\nTHE PARAMETERS: The Cabbage Root Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Collier, R.H. & Finch, S. (1983). Completion of diapause in field populations of the cabbage root fly (Delia radicum). Entomologia experimentalis et applicata 34, 186 192. Collier, R.H. & Finch, S. (1983). Effects of intensity and duration of low temperatures in regulating diapause development of the cabbage root fly (Delia radicum). Entomologia experimentalis et applicata 34, 193 200. Collier, R. H. & Finch, S. (1986). Accumulated temperatures for predicting cabbage root fly, Delia radicum (L.), (Diptera; Anthomyiidae) emergence in the spring. Bulletin of Entomological Research 75, 395 404. Finch, S. & Collier, R.H. (1983). Emergence of flies from overwintering populations of cabbage root fly pupae. Ecological Entomology 8, 29 36. Finch, S. & Collier, R.H. (1983). Emergence of flies from overwintering populations of cabbage root fly pupae. Ecological Entomology 8, 29 36. Finch, S. & Collier, R. H. (1985). Laboratory studies on aestivation in the cabbage root fly (Delia radicum). Entomologia experimentalis et applicata 38, 137 143. Finch, S., Collier, R. H. & Skinner, G. (1986). Local population differences in emergence of cabbage root flies from south west Lancashire; implications for pest forecasting and population divergence. Ecological Entomology 11, 139 145. Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993). Monte Carlo simulation method for forecasting the timing of pest insect attacks. Crop Protection 12, 335-342. \n", + "3 THE PEST: Carrot flies (Psila rosae/Chamaepsila rosae) overwinter in the soil either as diapausing pupae or as larvae. Late-developing insects remain as larvae and continue to feed on carrot roots throughout the winter. The insects that overwinter as larvae form pupae during the spring. Adult flies subsequently emerge from both types of pupae. The female flies lay eggs in the soil close to carrot plants and, after hatching, the larvae feed on the roots and tunnel into them, causing damage. These larvae form pupae, which lead to the emergence of a new generation of adults. Depending on the local climate the number of carrot fly generations and their timing can differ. When the weather is particularly hot, carrot fly pupae may aestivate.\\nTHE DECISION: The model predicts the timing of adult emergence and egg-laying throughout the year, enabling users to undertake targeted monitoring and/or mitigating actions to reduce the risk of damage to the crop.\\nTHE MODEL: A series of development rate equations form the basis of the simulation model and are linked together in a program. The model simulates the development of cohorts of 500 individuals through adult emergence, egg laying and hatching. For each stage, the percentage development is calculated each day by integrating the appropriate development rate curve. This percentage is accumulated over days until it reaches 100. At this point the individual moves to the next stage. Variability within the insect population is incorporated by assuming that, at any instant, the rates of development of a population held at a constant temperature are normally distributed (Phelps et al, 1993). The model uses soil temperatures or air temperatures depending on the stage of development. As multiple cohorts progress simultaneously, adult emergence and egg laying can occur at the same time.\\nTHE PARAMETERS: The Carrot Fly forecast requires hourly soil temperatures at a depth of approximately 6 cm and hourly air temperatures.\\nREGION: This DSS was adapted from work carried out in the UK\\nASSUMPTION: This model requires historic data to provide risk forecasts. At present, suitable historic data is only available for a limited number of locations; please select 'Edit Parameters' and select the most appropriate location for your farm. The start date for the model is 1st February, as this is often the coldest period in the year.\\nREFERENCE: Phelps, K., Collier, R.H., Reader, R.J. & Finch, S. (1993). Monte Carlo simulation method for forecasting the timing of pest insect attacks. Crop Protection 12, 335-342. Collier, R.H., Elliott, M.S. & Finch, S. (1994). Development of the overwintering stages of the carrot fly, Psila rosae, (Diptera:Psilidae). Bulletin of Entomological Research 84, 469-476. Collier, R.H. & Finch, S. (1996). Field and laboratory studies on the effects of temperature on the development of the carrot fly (Psila rosae F.). Annals of Applied Biology 128, 1-11. \n", + "4 NaN \n", + ".. ... \n", + "970 NaN \n", + "971 NaN \n", + "972 NaN \n", + "973 Sclerotinia stem rot is usually the main disease to consider during the flowering stages of oilseed rape. There are 3 main risk factors: the presence of sclerotinia inoculum (spores), warm and humid conditions, and crops in flower.\\nIf spores are present and the crop is in flower, relative humidity must be above 80% and air temperatures above 7°C for 23 continuous hours for the pathogen to infect the crop. This is what is predicted by the model. \n", + "974 Temperature and rainfall information is used to simulate the development of Leptosphaeria maculans -- a key pathogen responsible for phoma leaf spot and phoma stem canker.\\nThe forecast predicts the date of the starting week when 10% of oilseed rape plants could potentially show symptoms of phoma leaf spot. \n", + "\n", + "[975 rows x 5 columns]" ] }, "execution_count": 3, @@ -227,17 +208,10 @@ } ], "source": [ - "h = Hub()\n", + "h = ModelManager()\n", "h.display()\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Select dss, meta-informations " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -247,11 +221,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "psitemp=h.get(dss=\"no.nibio.vips\", model=\"PSILARTEMP\")" + "psitemp=h.get_model(\"no.nibio.vips\", \"PSILARTEMP\")" ] }, { @@ -263,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -302,9 +276,9 @@ " \n", " \n", " 0\n", - " Carrot rust fly temperature model\n", + " Carrot fly flight model\n", " PSILARTEMP\n", - " THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae \\ninitial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n\n", + " THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae initial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots.  \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C.  \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n\n", " Short-term tactical\n", " [PSILRO]\n", " [DAUCS]\n", @@ -318,11 +292,11 @@ "" ], "text/plain": [ - " name id \\\n", - "0 Carrot rust fly temperature model PSILARTEMP \n", + " name id \\\n", + "0 Carrot fly flight model PSILARTEMP \n", "\n", - " description \\\n", - "0 THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae \\ninitial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n \n", + " description \\\n", + "0 THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae initial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \\nTHE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \\nTHE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \\nTHE PARAMETERS: The model uses daily air temperature \\nSOURCE: Luke, Finland. \\nASSUMPTIONS: Be aware that in areas with field covers (plastic, single or double non-woven covers, etc.) with early crops the preceding season (either on the current field or neighboring fields), the flight period can start earlier than predicted due to higher soil temperature under the covers.\\nREFERENCE: Marjjula et al 2000\\n \n", "\n", " type_of_decision pests crops weather input \\\n", "0 Short-term tactical [PSILRO] [DAUCS] 1002 \n", @@ -334,13 +308,14 @@ "0 Accumulated day degrees, Threshold for start of flight period, Threshold for peak flight period, Threshold for end of 1st generation flight period " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "psitemp.informations(\"dataframe\")\n" + "psitemp.informations(\"dataframe\")\n", + "\n" ] }, { @@ -352,27 +327,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ws=WeatherDataHub()\n", - "slu=ws.get_ressource(name=\"SLU Lantmet service\")\n", + "slu=ws.get_ressource('se.slu.lantmet')\n", "\n", "weather=slu.data(parameters=[1002],latitude=[67.28],longitude=[14.37],\n", " timeStart='2021-06-01',timeEnd=\"2021-08-20\",timeZone=\"Europe/Paris\",\n", " display=\"json\")" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "#weather" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -389,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -410,37 +376,87 @@ "\n", "\n", "\n", - "
<xarray.Dataset>\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "
<xarray.Dataset> Size: 3kB\n",
        "Dimensions:      (time: 81)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 2021-05-30 2021-05-31 ... 2021-08-18\n",
+       "  * time         (time) datetime64[us] 648B 2021-05-31 2021-06-01 ... 2021-08-19\n",
        "Data variables:\n",
-       "    TMDD5C       (time) float64 0.67 1.87 8.15 17.6 ... 505.1 509.6 516.1 520.5\n",
-       "    THRESHOLD_1  (time) float64 260.0 260.0 260.0 260.0 ... 260.0 260.0 260.0\n",
-       "    THRESHOLD_2  (time) float64 360.0 360.0 360.0 360.0 ... 360.0 360.0 360.0\n",
-       "    THRESHOLD_3  (time) float64 560.0 560.0 560.0 560.0 ... 560.0 560.0 560.0\n",
+       "    TMDD5C       (time) float64 648B 8.73 16.16 25.07 ... 1.037e+03 1.047e+03\n",
+       "    THRESHOLD_1  (time) float64 648B 260.0 260.0 260.0 ... 260.0 260.0 260.0\n",
+       "    THRESHOLD_2  (time) float64 648B 360.0 360.0 360.0 ... 360.0 360.0 360.0\n",
+       "    THRESHOLD_3  (time) float64 648B 560.0 560.0 560.0 ... 560.0 560.0 560.0\n",
        "Attributes:\n",
-       "    name:             Carrot rust fly temperature model\n",
+       "    name:             Carrot fly flight model\n",
        "    id:               PSILARTEMP\n",
        "    version:          1.0\n",
-       "    authors:          {'name': 'Berit Nordskog', 'email': 'berit.nordskog@nib...\n",
+       "    authors:          {'name': '', 'email': 'vips@nibio.no', 'organization': ...\n",
        "    description:      THE PEST: The first generation of adult carrot fly emer...\n",
-       "    description_url:  https://www.vips-landbruk.no/forecasts/models/PSILARTEMP/
  • name :
    Carrot fly flight model
    id :
    PSILARTEMP
    version :
    1.0
    authors :
    {'name': '', 'email': 'vips@nibio.no', 'organization': 'NIBIO'}
    description :
    THE PEST: The first generation of adult carrot fly emerge from pupae in the soil in the spring, and lay eggs close to the base of vulnerable crops. Larvae initial feed at the surface, then tunnel into the tap root. Adults emerge mid-July and can lead to a second generation. \n", "THE DECISION: Treatments may need to be applied soon after adults arrive in the crop, before larvae tunnel into the crop roots. \n", "THE MODEL: The model determines the start of the flight period for the 1st generation of carrot rust fly based on accumuleted degree-days (260 day-degrees) over a base temperature of 5°C. \n", "THE PARAMETERS: The model uses daily air temperature \n", @@ -843,25 +1000,25 @@ "
    description_url :
    https://www.vips-landbruk.no/forecasts/models/PSILARTEMP/
    • " ], "text/plain": [ - "\n", + " Size: 3kB\n", "Dimensions: (time: 81)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2021-05-30 2021-05-31 ... 2021-08-18\n", + " * time (time) datetime64[us] 648B 2021-05-31 2021-06-01 ... 2021-08-19\n", "Data variables:\n", - " TMDD5C (time) float64 0.67 1.87 8.15 17.6 ... 505.1 509.6 516.1 520.5\n", - " THRESHOLD_1 (time) float64 260.0 260.0 260.0 260.0 ... 260.0 260.0 260.0\n", - " THRESHOLD_2 (time) float64 360.0 360.0 360.0 360.0 ... 360.0 360.0 360.0\n", - " THRESHOLD_3 (time) float64 560.0 560.0 560.0 560.0 ... 560.0 560.0 560.0\n", + " TMDD5C (time) float64 648B 8.73 16.16 25.07 ... 1.037e+03 1.047e+03\n", + " THRESHOLD_1 (time) float64 648B 260.0 260.0 260.0 ... 260.0 260.0 260.0\n", + " THRESHOLD_2 (time) float64 648B 360.0 360.0 360.0 ... 360.0 360.0 360.0\n", + " THRESHOLD_3 (time) float64 648B 560.0 560.0 560.0 ... 560.0 560.0 560.0\n", "Attributes:\n", - " name: Carrot rust fly temperature model\n", + " name: Carrot fly flight model\n", " id: PSILARTEMP\n", " version: 1.0\n", - " authors: {'name': 'Berit Nordskog', 'email': 'berit.nordskog@nib...\n", + " authors: {'name': '', 'email': 'vips@nibio.no', 'organization': ...\n", " description: THE PEST: The first generation of adult carrot fly emer...\n", " description_url: https://www.vips-landbruk.no/forecasts/models/PSILARTEMP/" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -880,19 +1037,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -900,63 +1055,21 @@ "psitemp.plot(ds)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example with field observation " - ] - }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "psiobs=h.get(dss=\"no.nibio.vips\", model=\"PSILAROBSE\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* import fieldObservation " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fieldobs=psiobs.df_reader_fieldObservation(path='C:/Users/mlabadie/Documents/GitHub/dss/example/psilarobs.csv',\n", - " longitude=11.025635, \n", - " latitude=59.715791, \n", - " timeZone=\"Europe/Paris\",\n", - " sep=\";\", dayfirst=True,\n", - " pestEPPOCode=\"SEPTAP\",\n", - " cropEPPOCode=\"APUGD\", \n", - " convert_name=None)\n", - "fieldobs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds=psiobs.run(fieldObservation=fieldobs,view=\"Json\")\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "psiobs.plot(ds)" + "import pandas\n", + "import matplotlib.pyplot as plt\n", + "import numpy\n", + "\n", + "pandas.set_option('display.max_rows',10)\n", + "pandas.set_option('display.max_colwidth',None)\n", + "\n", + "plt.rcParams['figure.dpi'] = 300 #dpi\n", + "plt.rcParams['savefig.dpi'] = 300" ] }, { @@ -986,9 +1099,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.13.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/example/dss_demo.ipynb b/example/dss_demo.ipynb index f1dfc9b..b0de8bb 100644 --- a/example/dss_demo.ipynb +++ b/example/dss_demo.ipynb @@ -16,14 +16,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas\n", "#pandas.set_option('display.max_colwidth', 50)\n", "import numpy as np\n", - "from weatherdata.ipm import WeatherDataHub\n", + "from openalea.weatherdata.ipm import WeatherDataHub\n", "from openalea.dss import Hub\n", "from openalea.dss.datadir import datadir\n" ] @@ -829,7 +829,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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    " ] @@ -1744,7 +1744,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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    " ] @@ -1809,7 +1809,7 @@ "outputs": [ { "data": { - "image/png": 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4MQYOHIhhw4YhOTkZs2bNKnD5bdiwYXBwcMDTTz8NX19fJCYmYsaMGVCpVLo+LMaMqzBubm549dVXsXDhQgQEBOD5558v1XM++ugjTJo0Ca+++ir69++P5ORkfPzxx7C3t8eUKVP0jlcoFJg9ezYePXqEpk2b4ujRo/j000/RrVs33VD5yZMn4+bNm+jQoQOqV6+OlJQUzJ8/X6//1pgxY7Bhwwa0bt0aY8eORYMGDaBWq3H9+nXs3r0bUVFRaN68eanq/frrr2PdunUYNWoUqlevjo4dO+rt79GjBzZu3IiRI0fipZdewo0bNzBt2jT4+vri0qVLesfWr18fBw4cwLZt2+Dr6wsXF5dCW8dsbGwwa9YsvPLKK+jRoweGDx+OzMxMfPHFF0hJScHMmTNLFXtFKO3vzYcffoitW7eiffv2mDx5MhwdHfHtt98WmAogMDAQn3zyCT744ANcvXoVXbt2RZUqVXDnzh38/vvvcHJywscffyxFVcmSSdo9muhfMTExYvDgwaJGjRpCoVAIJycn8dRTT4nJkyfrDZ8+evSoaNmypXB0dBReXl5i6NCh4s8//xQARHR0tO64wYMHCycnp0LPBUC8/fbbhe47fPiwaN++vXBychIODg6iRYsWYtu2bQWOmzdvnggKChJyubzAufPTjhi5e/duoftXrlwp6tatK+zt7UVYWJhYt25dgdFYK1euFO3atRNVq1YVCoVC+Pn5iT59+uiGP5clLkNGY2kdOHBAABAzZ84sstzCfPfdd6JBgwZCoVAIlUolevXqpTeEWIj/3rOzZ8+Ktm3bCgcHB+Hu7i7eeust8ejRI91x27dvF926dRPVqlUTCoVCeHt7i+eee05vOLYQQjx69Eh8+OGHIiQkRHfe+vXri7Fjx+qNCCru8yCEZmSUv7+/ACA++OCDQo+ZOXOmCAwMFEqlUtStW1csXbq00NcxJiZGPP3008LR0VEA0I3EK2zouRBCbN68WTRv3lzY29sLJycn0aFDB/Hbb7/pHVPU5ys6OrrAkO7CtGnTRtSrV6/A9oCAANG9e/cC2wt7vUr7e/Pbb7/phq/7+PiI9957TyxZsqTQODdv3izatWsnXF1dhVKpFAEBAeKll14Se/fuLVB3opLIhMjTRkpEVIyoqCgsXLgQN27cMFqLERGRqfEyFhGV6Pjx44iLi8OCBQswfPhwJjpEZFHYskNEJZLJZHB0dMRzzz2H6Ohoq1+dnIisC1t2iKhE/J+IiCwZh54TERGRVWOyQ0RERFaNyQ4RERFZNSY7REREZNWY7BAREZFVY7JDREREVo3JDhEREVk1JjtERERk1ZjsEBERkVVjskNERERWjckOERERWTUmO0RERGTVmOwQERGRVWOyQ0RERFaNyQ4RERFZNSY7REREZNWY7BAREZFVY7JDREREVo3JDhEREVk1JjtERERk1ZjsEBERkVVjskNERERWjckOERERWTUmO0RERGTVmOwQERGRVWOyQ0RERFaNyQ4RERFZNSY7REREZNWY7BAREZFVY7JDREREVo3JDhEREVk1W6kDMDW1Wo3bt2/DxcUFMplM6nCIiIioFIQQePjwIfz8/GBjU762GatPdm7fvg1/f3+pwyAiIqIyuHHjBqpXr16uMqw+2XFxcQGgebFcXV0ljoaIiIhKIy0tDf7+/rrv8fKw+mRHe+nK1dWVyQ4REZGFMUYXFHZQJiIiIqvGZIeIiIisGpMdIiIismpW32eHiKybEAI5OTnIzc2VOhQiMoBcLoetrW2FTAvDZIeILFZWVhYSEhLw+PFjqUMhojJwdHSEr68vFAqFSc/DZIeILJJarUZ8fDzkcjn8/PygUCg4cSiRhRBCICsrC3fv3kV8fDxq165d7okDi8Nkh4gsUlZWFtRqNfz9/eHo6Ch1OERkIAcHB9jZ2eGff/5BVlYW7O3tTXYudlAmIotmyv8Gici0Kur3l38liIiIyKox2SEiIiKrxmSHiIiIrBqTHSKiCiKTyYq9DRkypMBxzs7OaNiwIVasWFFomatXr4ZcLseIESMK7Dtw4IBeWV5eXujWrRvOnDlT6pgvX76M1157DdWrV4dSqURQUBD69++PU6dO6R23fft2tG3bFi4uLnB0dETTpk0LxKyNJyUlpcB5GjVqhKlTp+oeBwYGQiaT4fjx43rHjRkzBm3bttU7pqib9rjC5H9tCrutWLGi2JgDAwMxb968AjHLZDI4ODggNDQUX3zxBYQQumOuXbtW5Pm0dc3NzcWMGTMQGhoKBwcHuLu7o0WLFoiOjtaVM2TIEPTu3bvIeuWNNzc3F3PnzkWDBg1gb28PNzc3dOvWDb/99pvec1esWAE3N7ciX7MhQ4boYrWzs0PVqlXRqVMnLF++HGq1usjnmQMmO0RkPI/uAscXAU8eSB2JWUpISNDd5s2bB1dXV71t8+fP1x0bHR2NhIQEnDlzBn379sVrr72GX375pUCZy5cvx4QJE7B27doi5xu6ePEiEhISsGPHDjx48ABdu3ZFampqifGeOnUKERERiIuLw+LFi3H+/Hls2rQJoaGhiIqK0h339ddfo1evXmjVqhVOnDiBs2fPol+/fhgxYgTGjx9fhldKw97eHu+//36R+0+ePKl77TZs2KBX14SEBGzcuLHI57Zq1Urvte/Tpw+6du2qt61v374Gx/zJJ58gISEBsbGxGD9+PCZNmoQlS5YUOG7v3r1650pISEBERAQAYOrUqZg3bx6mTZuG8+fPY//+/Rg2bBgePDD890oIgX79+uGTTz7Bu+++i9jYWBw8eBD+/v5o27YtNm/ebFB52tfo2rVr+Pnnn9GuXTuMHj0aPXr0QE5OjsHxVRQOPSci4zkyBzi+QJPstJtYoacWQuBJtjSzKDvYyUs1x4+Pj4/uvkqlgkwm09uWl5ubm27fpEmTMHv2bOzevRtdunTRHXPt2jUcPXoUGzZswP79+/HTTz/h1VdfLVCWt7e3rrzZs2fjmWeewfHjx/XKyk8IgSFDhqB27do4fPiw3qiZRo0aYfTo0QCAGzduICoqCmPGjMH06dN1x0RFRUGhUODdd9/Fyy+/jObNm5f4+uQ3fPhwLFy4EDt37sRzzz1XYL+Xl5fuvru7u15dS6JQKPReewcHB2RmZhb5fpSWi4uLroyhQ4di4cKF2L17N4YPH653nIeHR5Hn2rZtG0aOHImXX35Zt61hw4ZliufHH3/ETz/9hK1bt+L555/XbV+yZAmSk5MxdOhQdOrUCU5OTqUqT6lU6uKuVq0aGjdujBYtWqBDhw5YsWIFhg4dWqY4TY3JDhEZT+Jf+j8r0JPsXIRNLtjyURHOf9IFjgrT/DnNzc3Fhg0bcP/+fdjZ2entW758Obp37w6VSoWBAwdi2bJlhSY7eTk4OAAAsrOziz0uJiYG586dw+rVqwsdHqxNKH766SdkZ2cX2oIzfPhwTJo0CWvWrClTshMYGIgRI0Zg4sSJ6Nq1q0VNMyCEwMGDBxEbG4vatWsb9FwfHx/s27cPI0eO1EvoymL16tWoU6eOXqKjFRUVhY0bN2LPnj2FXhIrrfbt26Nhw4bYuHGj2SY7lvPJISLzd/eC/k8qs/79+8PZ2RlKpRJ9+/aFu7u73heJWq3GihUrMHDgQABAv379cOzYMVy+fLnIMpOTk/Hxxx/DxcUFzZo1K/b8ly5dAgCEhoYWe1xcXBxUKhV8fX0L7FMoFKhZsybi4uKKLaM4H374IeLj47Fq1aoyl2EM1atXh7Ozs97t+vXrBY57//33de9bu3btIITAu+++W+C4Vq1aFShPu77bnDlzcPfuXfj4+KBBgwYYMWIEfv755wJlbN++vUAZ3bp10zsmLi4OdevWLbRO2u3leX+0QkNDce3atXKXYyps2SEi40hPBtLvau4/iAeyMwA7082Imp+DnRznPyn6soypz21sc+fORceOHXHjxg2MGzcOY8eORXBwsG7/7t27kZ6ervty8/T0ROfOnbF8+XK9y0mA5osaANLT01G7dm2sX78e3t7exZ5f26m2vEtwCCHKVYaXlxfGjx+PyZMnl6kPjbEcPnwYLi4uetsK6wD93nvvYciQIbh79y4++OADtG/fHq1atSpw3Lp16wokIXK55nMUFhaGv//+G3/88QeOHDmCQ4cO4fnnn8eQIUPw3Xff6Y5v164dFi5cqFfGiRMndAlwaRljmZXyvs+mxmSHiIzj3sX/7gs1kHwZ8AmvsNPLZDKTXUqSgo+PD4KDgxEcHIz169fjqaeeQpMmTRAWFgZAcwnr/v37ektlqNVqnD59GtOmTdN9cQKaL2pXV1d4eXnB1dW1VOevU6cOACA2NhaNGjUq9rjU1FTcvn0bfn5+evuysrJw9epVtG/fHgB0505NTS3QryYlJQUqlarQc4wbNw4LFizAggULShW7KQQFBRWI2da24OfN09NT975t2LABwcHBaNGiBTp27Kh3nL+/v17ymp+NjQ2aNm2Kpk2bYuzYsfjhhx8waNAgfPDBBwgKCgIAODk5FSjj5s2beo/r1KmD8+fPF3qO2NhYADD4MltRZWnjMke8jEVExpH/0hUvZRlNcHAwIiMjMXGiptN3cnIytmzZgrVr1yImJkbv9ujRowKXPIKCglCrVq1SJzqAphNyWFgYZs+eXeiwYu3Q5sjISNja2mL27NkFjlm0aBHS09PRv39/ANAt9njy5Em94xISEnDr1i2EhIQUGouzszM++ugjfPbZZ0hLSyt1HaRWpUoVvPPOOxg/frze8POy0Ca56enpBj2vX79+uHTpErZt21Zg3+zZs+Hh4YFOnTqVK7Z9+/bhr7/+QmRkZLnKMSXr+TeIiKR192Lxj6lcoqKi0LBhQ5w6dQpHjhyBh4cHXn755QKddnv06IFly5ahR48e5TqfTCZDdHQ0OnbsiNatW2PSpEkIDQ3Fo0ePsG3bNuzevRsHDx5EjRo1MGvWLIwfPx729vYYNGgQ7OzssGXLFkyaNAlRUVG6zskuLi4YPnw4oqKiYGtri4YNG+L27dv44IMPULduXXTu3LnIeN58803MnTu3zJ2dpfL222/j888/x4YNG/DSSy/pticnJyMxMVHvWDc3N9jb2+Oll17C008/jVatWsHHxwfx8fGYOHEi6tSpU2Ifqvz69euH9evXY/Dgwfjiiy/QoUMHpKWl4dtvv8XWrVuxfv16vZFYubm5iImJ0StDoVDokq3MzEwkJiYiNzcXd+7cwa5duzBjxgz06NGjxM7xUmKyQ0TGoW3J8Q4Dks6zZcfI6tevj44dO2Ly5Mm4efMmXnjhhUJHJ0VGRqJv3764c+dOuc/ZrFkznDp1Cp999hmGDRuGe/fuwdfXF61atdKbTG/s2LGoVasWvvzyS8yfPx+5ubmoV68eFi5ciNdee02vzLlz58LX1xeTJk3CtWvX4O3tjXbt2mHt2rWFXhbSsrOzw7Rp0zBgwIBy16sieXl5YdCgQZg6dSpefPFF3fb8l7UAYM2aNejXrx+6dOmCNWvWYMaMGUhNTYWPjw/at2+PqVOnFvsaFUYmk+HHH3/E/PnzMXfuXLz99ttQKpVo2bIl9u/fj2eeeUbv+EePHuGpp57S2xYQEKDrfLxr1y74+vrC1tYWVapUQcOGDfHVV19h8ODBZj1aTibK27Zm5tLS0qBSqZCammpQEy4RGWh2XeDhbaDtRODADMArFHj7hMlOl5GRgfj4eAQFBcHevuI6QhOR8RT3e2zM72/zTcOIyHJkpGoSHQCo21PzM/kykFv8XC5ERBWByQ4Rld/df+fpcPEDvOsCCmdAnQPcvyptXFSkw4cPF5ijJe/NGqxatarI+tWrV0/q8KgCsc8OEZWftn+OVwggk2l+3vpDs92r8BE2JK0mTZoU6IhqbXr27FlkZ+b8s1GTdWOyQ0Tlp0t2Qv/7eesPjsgyYw4ODsXO82INXFxcCkwESJUTL2MRUflpkxptK472J0dkEZEZYLJDROWnS3ZC9X+yZYeIzACTHSIqn8xHQOq/CyLmb9m5dwnIzZEmLiKifzHZIaLyuffvSCwnb8DRXXNfVQOwdQByM4GUf6SLjYgITHaIqLzy99cBABsbwKvOv/vZb4eIpMVkh4jKJ/9ILC1dvx0mO0QkLSY7RFQ+hbXs5H3MTso6Mpms2NuQIUMKHOfs7IyGDRtixYoVhZa5evVqyOVyjBgxosC+AwcO6JXl5eWFbt264cyZM6WO+fLly3jttddQvXp1KJVKBAUFoX///jh16pTecdu3b0fbtm3h4uICR0dHNG3atEDM2ni0K6bn1ahRI0ydOlX3ODAwEDKZDMePH9c7bsyYMWjbtq3eMUXdtMeV5PTp03j55ZdRtWpV2Nvbo06dOhg2bBji4jSXaK9du6ZXrkqlQosWLQpdSfzJkyeYMmUKQkJCoFQq4enpiZdeegnnzp3TO27q1Km68uRyOfz9/TF06FDcvXu3VDEXVee1a9cC0H/vbWxsoFKp8NRTT2HChAlISEgoUF5aWho++ugj1KtXDw4ODvDw8EDTpk0xa9YsPHjwQHdc27ZtCz1vYZ8/c8Jkh4jKhy07pZaQkKC7zZs3D66urnrb5s+frzs2OjoaCQkJOHPmDPr27YvXXnsNv/zyS4Eyly9fjgkTJmDt2rV4/Phxoee9ePEiEhISsGPHDjx48ABdu3ZFampqifGeOnUKERERiIuLw+LFi3H+/Hls2rQJoaGhiIqK0h339ddfo1evXmjVqhVOnDiBs2fPol+/fhgxYgTGjx9fhldKw97eHu+//36R+0+ePKl77TZs2KBX14SEBGzcuLHEc2zfvh0tWrRAZmYmVq1ahdjYWHz//fdQqVT46KOP9I7du3cvEhIScOLECTRr1gyRkZH4+++/dfszMzPRsWNHLF++HNOmTUNcXBx27tyJ3NxcNG/evEDiVq9ePSQkJOD69etYuHAhtm3bZtDK4drPSN5b79699Y65ePEibt++jZMnT+L999/H3r17ER4ejr/++kt3zP3799GiRQtER0dj/PjxOHHiBH777TdMmTIFMTExWL16tV6Zw4YNK3DeWbNmlTpuSQgrl5qaKgCI1NRUqUMhsj5Zj4WYohJiiqsQD5P09927rNk+raoQublGP/WTJ0/E+fPnxZMnTzQb1GohMh9Jc1OrDY4/OjpaqFSqQvcBEJs2bdLb5u7uLsaNG6e3LT4+Xjg4OIiUlBTRvHlzsXLlSr39+/fvFwDEgwcPdNuOHDkiAIhdu3YVG59arRb16tUTERERIreQ909b5vXr14WdnV2B2IQQ4quvvhIAxPHjx4uMR6thw4ZiypQpuscBAQFi9OjRQqFQiB07dui2jx49WrRp06bA84sruyjp6enC09NT9O7du9D92rLi4+MFAHH69GndvrS0NAFAfPXVV7ptM2fOFDKZTMTExOiVk5ubK5o0aSLCwsKE+t/PypQpU0TDhg31jvv000+FjY2NePz4cYmxF/YZyauo1+Px48ciJCREPP3007ptw4cPF05OTuLmzZuFlqXO8/lu06aNGD16dInxlVaB3+M8jPn9zRmUiajs7l0CIAAHd8DJU3+fWwAgVwI5TzRD06sEmjaW7MfAdD/TnqMok24DCieTFJ2bm4sNGzbg/v37BZY4WL58Obp37w6VSoWBAwdi2bJlJbYMODg4AACys4tfpDUmJgbnzp3D6tWrYWNT8CKAm5sbAOCnn35CdnZ2oS04w4cPx6RJk7BmzZoil20oTmBgIEaMGIGJEyeia9euhcZRHr/88gvu3buHCRMmFLpfW8f8srOzsXTpUgD6y06sXr0anTp1QsOGDfWOt7GxwdixY/HKK6/gzJkzaNSoUaHlOjg4QK1WIyfHdNM1ODg4YMSIERg7diySkpLg6emJdevWYeDAgahWrVqhz5HJZCaLp6LwMhYRlV3eyQTz/0GU2wKetfWPo1Lr378/nJ2doVQq0bdvX7i7u2Po0KG6/Wq1GitWrMDAgQMBAP369cOxY8dw+fLlIstMTk7Gxx9/DBcXFzRr1qzY81+6dAkAEBoaWuxxcXFxUKlU8PX1LbBPoVCgZs2aur4vZfHhhx8iPj4eq1atKnMZRSltHbVatWoFZ2dn2NvbIyoqCoGBgejTp49uf1xcHOrWrVvoc7Xbi3otLly4gIULF6JZs2alXuJC+xnJe7t6teTFd7X1vXbtGu7evYuUlBSEhOj3uYuIiNCV2b9/f719CxYsKHDelStXlipmqbBlh4jKLu8CoIXxCgHu/K05rk4X08Zi56hpYZGCnaPRi5w7dy46duyIGzduYNy4cRg7dqzeWla7d+9Geno6unXrBgDw9PRE586dsXz5ckyfPl2vrOrVqwMA0tPTUbt2baxfvx7e3t7Fnl8IAaD8/9ULIcpVhpeXF8aPH4/Jkyejb9++5YolP20dS2vdunUIDQ1FXFwcxowZg0WLFsHd3d2gc+V9Lf766y84OzsjNzcXmZmZaNu2LZYsWVLqeLSfkbz8/f3LFEv+92jTpk3IysrC+++/jydPnujte+WVV/DBBx/obSvp8yQ1JjtEVHZFdU7WqshlI2Qyk11KkoKPjw+Cg4MRHByM9evX46mnnkKTJk0QFhYGQHMJ6/79+3B0/C/RUqvVOH36NKZNmwa5XK7bfvjwYbi6usLLywuurq6lOn+dOpp5kmJjY4u87KI9LjU1Fbdv34afn/5lxKysLFy9ehXt27cHAN25U1NTC1wiSklJgUqlKvQc48aNw4IFC7BgwYJSxV5a2jpeuHABLVu2LPF4f39/1K5dG7Vr14azszMiIyNx/vx53Rd9nTp1cP78+UKfe+GC5neldu3aum0hISHYunUr5HI5/Pz8oFQqDYpf+xkxVGxsLADNZUIPDw+4ubnp4tOqUaMGAM1iqvlHz6lUKotbRJaXsYio7Ioadq7FBUGNIjg4GJGRkZg4cSIAzeWoLVu2YO3atYiJidG7PXr0CD///LPe84OCglCrVq1SJzqAZih4WFgYZs+eDbVaXWC/9gswMjIStra2mD17doFjFi1ahPT0dN1lkNq1a8PGxgYnT57UOy4hIQG3bt0qcClFy9nZGR999BE+++wzpKWllboOJencuTM8PT2LHElU2BB5rTZt2iA8PByfffaZblu/fv2wd+/eAkP71Wo15s6di7CwML3+PAqFAsHBwQgKCjI40SmrJ0+eYMmSJWjdujW8vLxgY2ODPn364IcffsCtW7cqJAYpSJrszJgxA02bNoWLiwu8vb3Ru3dvXLyo/x+gEAJTp06Fn58fHBwc0LZt2wLzFRCRBHIygfv/9g8oTcuOgZcMSF9UVBS2bduGU6dO4fvvv4eHhwdefvllhIeH624NGjRAjx49sGzZsnKfTyaTITo6GnFxcWjdujV27tyJq1ev4uzZs/jss8/Qq1cvAJoWgFmzZmHevHn44IMPcOHCBVy5cgVz5szBhAkTEBUVpeuc7OLiguHDhyMqKgqbN29GfHw8fvvtN/Tv3x9169ZF586di4znzTffhEqlwpo1a8pdNy0nJyd899132LFjB3r27Im9e/fi2rVrOHXqFCZMmFDi3DFRUVFYvHixLkkYO3YsmjVrhueffx7r16/H9evXcfLkSURGRiI2NhbLli0zamfflJQUJCYm6t3S09P1jklKSkJiYiIuXbqEtWvX4umnn8a9e/ewcOFC3THTp09HtWrV0Lx5cyxfvhxnz57FlStXsGnTJhw7dkyvlRAAHj9+XOC8eefiMUvlHs9VDl26dBHR0dHi77//FjExMaJ79+6iRo0a4tGjR7pjZs6cKVxcXMSGDRvEX3/9Jfr27St8fX1FWlpaqc7BoedEJpJ4TjO0fLp/0UOvc7KE+Nhdc1zKDaOevrghq5bA0KHnQgjRqVMn0a1bN1G/fn0xcuTIQp+7YcMGYWtrKxITE8s0HDu/ixcvildffVX4+fkJhUIhAgICRP/+/cWff/6pd9yWLVvEs88+K5ycnIS9vb2IiIgQy5cvL1BeRkaG+OSTT0TdunWFg4ODCAgIEEOGDBEJCQl6xwUEBIi5c+fqbVu9erUAYLSh51onT54UL774ovDy8hJKpVIEBweLN998U1y6dEkIUfjQcyE0Q7JDQkLEW2+9pduWnp4uPvzwQxEcHCzs7OyEu7u7iIyMFH/99Zfecwsbem4IAIXeZsyYIYT47/UAIGQymXBxcRENGzYU7733XoHXWgghUlJSxMSJE0VoaKhQKpXCwcFBNGjQQHz00UciOTlZd1ybNm0KPW+XLl3KVI+KGnouE8J8/t26e/cuvL29cfDgQbRu3RpCCPj5+WHMmDG6iaUyMzNRtWpVfP755xg+fHiJZaalpUGlUiE1NdWgJlwiKsHfG4GfXgOqNwOG7in6uG+bay5jDdwABHcs+jgDZWRkID4+HkFBQbC3tzdauURUcYr7PTbm97dZ9dnRzuip7d0eHx+PxMREvaZNpVKJNm3a4OjRo4WWkZmZibS0NL0bEZlASf11tLhsBBFJzGySHSEExo0bh2eeeQbh4eEAgMTERABA1apV9Y6tWrWqbl9+M2bMgEql0t1KMwyPiMqgpJFYWlw2wiwdPny4wFwpeW/WYNWqVUXWr169elKHV6Tp06cXGbd2qgEyjNkMPR81ahTOnj2LI0eOFNiXv0OXKGbehokTJ2LcuHG6x2lpaUx4iEwh74SCxWHLjllq0qQJYmJipA7DpHr27FnkzM35Z6M2JyNGjNCbrDAv7QzYZBizSHbeeecdbN26FYcOHdJNfgVo5hAANC08eWfnTEpKKtDao6VUKitsCB9RpZWbDST/O1NviZex8rTsCFFwpmWShIODg8XNlWIoFxeXUs9GbE7c3d1LPVkhlY6kl7GEEBg1ahQ2btyIffv2ISgoSG9/UFAQfHx8sGfPf50fs7KycPDgQbRq1aqiwyUirfvxgDobUDgDqurFH+sRDMhsgIxU4NGdiomPiCgPSVt23n77baxevRpbtmyBi4uLrh+OSqWCg4MDZDIZxowZg+nTp+tmrZw+fTocHR0xYMAAKUMnqty0/W8865TcUmOrBNxralqC7l4AXHxMHx8RUR6SJjvaSY3atm2rtz06OhpDhgwBAEyYMAFPnjzByJEj8eDBAzRv3hy7d++2yKZJIqtR2v46Wl6h/yY7F4GabU0WFhFRYSRNdkozxY9MJsPUqVMxdepU0wdERKVT0gKg+XmFABe2c0QWEUnCbIaeE5EFKUvLTt7nERFVICY7RGQYdS5wL05z35CWHYAtO0QkCSY7RGSYB9eA3EzA1gFwq1G653jUBiADHicD6fdMGZ1Zk8lkxd60fRXzbnN2dkbDhg2xYsWKQstcvXo15HJ5oYtWHjhwQK8sLy8vdOvWrcCq3MW5fPkyXnvtNVSvXh1KpRJBQUHo378/Tp06pXfc9u3b0bZtW7i4uMDR0RFNmzYtELM2nsJWE2/UqJFed4XAwEDIZDIcP35c77gxY8bo+nlqjynqlr8/aFFOnz6Nl19+GVWrVoW9vT3q1KmDYcOGIS5Ok9Rfu3ZNr1yVSoUWLVpg27ZtBcp68uQJpkyZgpCQECiVSnh6euKll14qsID11KlTdeXJ5XL4+/tj6NChuHv3bqlilslk2Lx5s95je3t7/PPPP3rH9e7dW/e5AjRTtwwfPhw1atSAUqmEj48PunTpgmPHjumOCQwMxLx58/Qea2N1cHBAYGAg+vTpg3379umdK//rlPeW/32saEx2iMgw2ktRnrUBG3nxx2opHIEqAf8+v/K27iQkJOhu8+bNg6urq962+fPn646Njo5GQkICzpw5g759++K1117DL7/8UqDM5cuXY8KECVi7di0eP35c6HkvXryIhIQE7NixAw8ePEDXrl11y/MU59SpU4iIiEBcXBwWL16M8+fPY9OmTQgNDUVUVJTuuK+//hq9evVCq1atcOLECZw9exb9+vXDiBEjMH78+DK8Uhr29va6dRELc/LkSd1rt2HDBr26JiQkYOPGjSWeY/v27WjRogUyMzOxatUqxMbG4vvvv4dKpcJHH32kd+zevXuRkJCAEydOoFmzZoiMjMTff/+t25+ZmYmOHTti+fLlmDZtGuLi4rBz507k5uaiefPmBb7w69Wrh4SEBFy/fh0LFy7Etm3b8OqrrxryEumRyWSYPHlyscdERkbizJkzWLlyJeLi4rB161a0bdsW9+/fL/Z5n3zyCRISEnDx4kX873//g5ubGzp27IjPPvuswLHa1ynvLSIiosz1MopyLyVq5rjqOZGRHZqtWcX8p6GGPW9VH83zfl9qlDAq26rn7u7uYty4cXrb4uPjhYODg0hJSRHNmzcXK1eu1Ntf2ErgR44cEQDErl27io1PrVaLevXqiYiICJGbm1tgv7bM69evCzs7uwKxCSHEV199JQCI48ePFxmPVsOGDcWUKVN0jwMCAsTo0aOFQqEQO3bs0G0fPXq00VY9T09PF56enqJ3796F7teWVdiq52lpaQKA+Oqrr3TbZs6cKWQymYiJidErJzc3VzRp0kSEhYUJtVothCh81fNPP/1U2NjYiMePH5cYe/7PCADx3nvvCRsbG3H27Fnd9l69eonBgwfr6gNAHDhwoNiy8684X9gK9EIIMXnyZGFjYyMuXLgghCh6dfjiVNSq52zZISLDlHYB0PxMvGyEEAKPsx9LchOlGFlaVrm5ufjxxx9x//79AkscLF++HN27d4dKpcLAgQOxbNmyEsvTLjeQnZ1d7HExMTE4d+4coqKiYGNT8KvCzc0NAPDTTz8hOzu70Bac4cOHw9nZGWvWrCkxrsIEBgZixIgRmDhxItRqdZnKKM4vv/yCe/fuYcKECYXu19Yxv+zsbCxduhSA/rITq1evRqdOndCwYUO9421sbDB27FicP3++2EuIDg4OUKvVyMnJMbAmGq1atUKPHj0wceLEQvdr19favHkzMjMzy3SOvEaPHg0hBLZs2VLuskzNLJaLICILUtoFQPMz8YKgT3KeoPnqwtdBMrUTA07A0c7RqGX2798fcrkcGRkZyM3Nhbu7O4YOHarbr1arsWLFCnz99dcAgH79+mHcuHG4fPlykctAJCcn4+OPP4aLiwuaNWtW7PkvXboEAAgNLf59jouLg0ql0lvSR0uhUKBmzZq6vi9l8eGHHyI6OhqrVq3CoEGDylxOYUpbR61WrVrBxsYGT548gVqt1vVd0YqLi0O7du0KfW7dunV1xzRq1KjA/gsXLmDhwoVo1qxZueaRmzFjBho0aIDDhw/j2Wef1dtna2uLFStWYNiwYVi0aBEaN26MNm3aoF+/fmjQoIHB53J3d4e3tzeuXbumt137OuWVmpoKubyUl71NgC07RFR6anWekViGJjtcENQQc+fORUxMDPbs2YNGjRph7ty5eknM7t27kZ6erlsF29PTE507d8by5csLlFW9enU4OzvD09MTsbGxWL9+Pby9vYs9v7a1qqhFl0tLFLNwc2l4eXlh/PjxmDx5MrKyssoVS36GtsitW7cOp0+fxtatWxEcHIzvvvuu1GtYFfZ6/vXXX3B2doaDgwPCwsLg7++PVatWGRRTfmFhYXj11VeL7OsUGRmJ27dvY+vWrejSpQsOHDiAxo0bF9kBviSFvb/r1q1DTEyM3k3KRAdgyw4RGSL1BpD9GJArgCqBhj3Xs47m56M7wOP7gKNxFzp0sHXAiQEnjFqmIec2Nh8fHwQHByM4OBjr16/HU089hSZNmiAsLAyA5hLW/fv34ej4X4uSWq3G6dOnMW3aNL0vl8OHD8PV1RVeXl5wdXUt1fnr1NG8X7GxsYW2ROQ9LjU1Fbdv34afn5/evqysLFy9ehXt27cHAN25U1NTC1wiSklJgUqlKvQc48aNw4IFC7BgwYJSxV5a2jpeuHABLVu2LPF4f39/3dJFzs7OiIyMxPnz53WJY506dXD+/PlCn3vhgqZFs3bt2rptISEh2Lp1K+RyOfz8/Iy2iPXHH3+MOnXq6I3Wysve3h6dOnVCp06dMHnyZAwdOhRTpkzRG7VVGsnJybh7926BdS39/f3NbpFZtuwQUelpW2U8agNyA/9XUroAKn/N/Xtlv6xRFJlMBkc7R0lu5W39KElwcDAiIyN1fTGSk5OxZcsWrF27tsB/0I8ePcLPP/+s9/ygoCDUqlWr1IkOoBkKHhYWhtmzZxfaX0Y7fDwyMhK2traYPXt2gWMWLVqE9PR09O/fH4Dmi97GxgYnT57UOy4hIQG3bt1CSEjh/cCcnZ3x0Ucf4bPPPkNaWlqp61CSzp07w9PTE7NmzSp0f2FD5LXatGmD8PBwvdFI/fr1w969ewv0y1Gr1Zg7dy7CwsL0+vMoFAoEBwcjKCjIaIkOoEk2Ro0ahUmTJiE3N7fE48PCwpCenm7weebPnw8bGxv07t27DFFWLCY7RFR6hi4TkR8nFyyzqKgobNu2DadOncL3338PDw8PvPzyywgPD9fdGjRogB49epSqo3JJZDIZoqOjERcXh9atW2Pnzp24evUqzp49i88++wy9evUCANSoUQOzZs3CvHnz8MEHH+DChQu4cuUK5syZgwkTJiAqKgrNm2v6Urm4uGD48OGIiorC5s2bER8fj99++w39+/dH3bp10blz5yLjefPNN6FSqcrc2bkwTk5O+O6777Bjxw707NkTe/fuxbVr13Dq1ClMmDCh0LmL8oqKisLixYtx69YtAMDYsWPRrFkzPP/881i/fj2uX7+OkydPIjIyErGxsVi2bJnJE2OtiRMn4vbt29i7d69uW3JyMtq3b48ffvgBZ8+eRXx8PNavX49Zs2bp3s+iPHz4EImJibhx4wYOHTqEN998E59++ik+++yzAq04ycnJSExM1LtlZGSYpJ6lxWSHiErP0GUi8uOyEWVWv359dOzYEZMnT8by5cvxwgsvFDpKKjIyEtu3b8edO3fKfc5mzZrh1KlTqFWrFoYNG4a6deuiZ8+eOHfunN6kc2PHjsWmTZtw+PBhNGnSBOHh4Vi9ejUWLlyIL7/8Uq/MuXPnYujQoZg0aRLq1auHV155BUFBQdi9ezdsbYtuLbSzs8O0adOM/qXZq1cvHD16FHZ2dhgwYABCQ0PRv39/pKam4tNPPy32uT169EBgYKCudcfe3h779u3D4MGDMWnSJAQHB6Nr166Qy+U4fvw4WrRoYdTYi+Pu7o73339f7/VydnZG8+bNMXfuXLRu3Rrh4eH46KOPMGzYMHzzzTfFljd58mT4+voiODgYgwYNQmpqKn799ddC+wZ17NgRvr6+ereiLqlVFJkw5ZhJM5CWlgaVSoXU1FSDmnCJqBBLOwC3TgEvrwTq9Tb8+X/+D9j6DlCrPTBoU7lCycjIQHx8PIKCgmBvb1+usohIGsX9Hhvz+5stO0RUOkKwZYeILBKTHSIqnbTbQNZDwMYWcK9ZtjK0I7LSbgEZxutoSoY7fPiwbpK5wm7WYNWqVUXWr169elKHV6Tp06cXGbd2qgEyDIeeE1HpaDsVu9cCbBVlK8PBDXDxBR4maEZkVW9itPDIME2aNEFMTIzUYZhUz549dZ2j88s/G7U5GTFihN5khXlpZ8AmwzDZIaLSKesyEfl5hWiSnbsXmOxIyMHBwezmQjE2FxeXcs1GLBV3d/dST1ZIpcPLWERUOmVdJiI/Iy8bYeVjLIisWkX9/jLZIaLSMWbLTt7yykh7GeLx48fli4eIJKP9/TX1ZUVexiKikglhdi07crkcbm5uSEpKAgA4Opp+JmMiMg4hBB4/foykpCS4ubmZfO0sJjtEVLJHSUBGCiCzATzK2c9Dm+ykXAey0gGFU5mL8vHxAQBdwkNElsXNzU33e2xKTHaIqGTaVpgqQYBdOSfwc3QHnLyA9LuaEVl+T5W5KJlMBl9fX3h7eyM7O7t8cRFRhbKzs6uw1dCZ7BBRyco7mWB+XqGaZOfuxXIlO1pyubzC/mgSkeVhB2UiKll5FwDNjwuCElEFYrJDRCUzRctO3nKJiEyIyQ4RlYwtO0RkwZjsEFHx0u8Bj+8BkP23tlV5aVt2HlwDsp8Yp0wioiIw2SGi4mkvNbnVABSOxinTyQtwqAIINZB82ThlEhEVgckOERXPWJMJ5iWTsd8OEVUYJjtEVDxjLRORH/vtEFEFYbJDRMUzRctO3vKY7BCRiTHZIaLiGXvYuZaRFgQlIioJkx0iKtqTB8CjRM19LyONxNLSJk/JV4CcLOOWTUSUB5MdIira3TjNT9fqgNLFuGW7+AJKV0DkAvevGLdsIqI8mOwQUdGMPZlgXjIZOykTUYVgskNERTNVfx0t9tshogrAZIeIimbKlh2AI7KIqEIw2SGioplqjh0tT7bsEJHpMdkhosJlpAFpNzX3jbUmVn7aJOreJSA3xzTnIKJKj8kOERXu3iXNT+eqgKO7ac6h8gfsHAF1NvAg3jTnIKJKj8kOERXO1P11AMDG5r9WI/bbISITYbJDRIUz1TIR+bGTMhGZGJMdIiqcqTsna3H4ORGZGJMdIiocW3aIyEow2SGigrLSgZTrmvsmT3byjMhS55r2XERUKTHZIaKC7l0CIABHD8DJ07TnqhIIyJVATgaQ8o9pz0VElRKTHSIqyNTLRORlI88zIov9dojI+JjsEFFBFTHsPC8uCEpEJsRkh4gKqsiWnbznYcsOEZkAkx0iKogtO0RkRZjsEJG+7Iz/lm6o8JadOECtrphzElGlwWSHiPQlXwaEGrBXadbFqgjuQYCNHZCd/t/io0RERsJkh4j05Z1MUCarmHPK7QCP4H/Pz347RGRcTHaISF9FLRORH/vtEJGJMNkhIn0VtUxEflw2gohMhMkOEemTvGWHl7GIyLiY7BDRf3KygPtXNPcla9m5CAhRsecmIqvGZIeI/nP/KqDOARTOgGu1ij23Ry1AJgcy04CHCRV7biKyakx2iOg/eScTrKiRWFq2SsC9pn4cRERGwGSHiP5T0ctE5Md+O0RkAkx2iOg/Fb1MRH4ckUVEJsBkh4j+I3nLDhcEJSLjY7JDRBq5OUDyJc19yVp2/j1vUixHZBGR0TDZISKNB9eA3CzA1gFQ1ZAmBs/aAGRARgqQfleaGIjI6jDZISINXX+dOoCNRH8a7ByAKoH68RARlZOkyc6hQ4fw/PPPw8/PDzKZDJs3b9bbP2TIEMhkMr1bixYtpAmWyNpJtUxEfuy3Q0RGJmmyk56ejoYNG+Kbb74p8piuXbsiISFBd9u5c2cFRkhUiUi1TER+XBCUiIzMVsqTd+vWDd26dSv2GKVSCR8fnwqKiKgSY8sOEVkps++zc+DAAXh7e6NOnToYNmwYkpKSij0+MzMTaWlpejciKoE6F7gXp7kvebLDlh0iMi6zTna6deuGVatWYd++fZg9ezZOnjyJ9u3bIzMzs8jnzJgxAyqVSnfz9/evwIiJLFTKdSAnA5ArAbcAaWPxrKP5mX4XSE+WNhYisgpmnez07dsX3bt3R3h4OJ5//nn8/PPPiIuLw44dO4p8zsSJE5Gamqq73bhxowIjJrJQ2ktGnrUBuaRXtwGl839D3+/xUhYRlZ/Ef9UM4+vri4CAAFy6dKnIY5RKJZRKZQVGRWQFpF4mIj+vECD1uiaugFZSR0NEFs6sW3byS05Oxo0bN+Dr6yt1KETWReplIvLjgqBEZESStuw8evQIly9f1j2Oj49HTEwM3N3d4e7ujqlTpyIyMhK+vr64du0aJk2aBE9PT7zwwgsSRk1khe6ZybBzLY7IIiIjkjTZOXXqFNq1a6d7PG7cOADA4MGDsXDhQvz111/43//+h5SUFPj6+qJdu3ZYt24dXFxcpAqZyPoIYYYtO0x2iMh4JE122rZtC1HMYn+//PJLBUZDVEml3QKyHgE2toB7Tamj0fD6d0TWw9tARipgr5I2HiKyaBbVZ4eITEDbOdkjGJDbSRuLlr0KcPHT3L8bJ20sRGTxmOwQVXbmskxEfpxckIiMhMkOUWVnLstE5Kfrt8Nkh4jKh8kOUWVn9i077KRMROXDZIeoMhPCAlp2mOwQUfmUO9lJS0vD5s2bERsba4x4iKgiPbqjGe0ks9F0UDYn2pad1OtA5iNpYyEii2ZwstOnTx988803AIAnT56gSZMm6NOnDxo0aIANGzYYPUAiMiFtq457TcDWzJZZcXQHnLw19+9xRBYRlZ3Byc6hQ4fw7LPPAgA2bdoEIQRSUlLw1Vdf4dNPPzV6gERkQuY2mWB+7LdDREZgcLKTmpoKd3d3AMCuXbsQGRkJR0dHdO/evdgFOonIDJnbAqD5cUQWERmBwcmOv78/jh07hvT0dOzatQudO3cGADx48AD29vZGD5CITIgtO0RUCRi8XMSYMWPwyiuvwNnZGQEBAWjbti0AzeWt+vXrGzs+IjIltuwQUSVgcLIzcuRINGvWDDdu3ECnTp1gY6NpHKpZsyb77BBZkvR7wONkADLAo7bU0RROm+w8uAZkPwHsHCQNh4gsU5kWAm3SpAmaNGmit6179+5GCYiIKoi2taRKAKBwlDaWojh5Ag7uwJP7wL1LgG8DqSMiIgtkcLIzbty4QrfLZDLY29sjODgYvXr10nViJiIzZa6TCeYlk2niu35U02+HyQ4RlYHByc7p06fx559/Ijc3FyEhIRBC4NKlS5DL5QgNDcWCBQsQFRWFI0eOICwszBQxE5ExmOsyEfl5hfyb7LDfDhGVjcGjsXr16oWOHTvi9u3b+OOPP/Dnn3/i1q1b6NSpE/r3749bt26hdevWGDt2rCniJSJjsYSWHYCdlImo3AxOdr744gtMmzYNrq6uum2urq6YOnUqZs2aBUdHR0yePBl//PGHUQMlIiOzpJYdgMPPiajMyjSpYFJSUoHtd+/eRVpaGgDAzc0NWVlZ5Y+OiEzj8X3NulgA4FlH2lhKom3ZuX8VyMmUNhYiskhluoz1+uuvY9OmTbh58yZu3bqFTZs24Y033kDv3r0BAL///jvq1DHzP6BElZl2rSmVP6B0kTaWkrj4AEoVIHKB5CtSR0NEFsjgDsqLFy/G2LFj0a9fP+Tk5GgKsbXF4MGDMXfuXABAaGgovvvuO+NGSkTGY+6TCeYlk2nivPm7Ju6qHPhARIYxONlxdnbG0qVLMXfuXFy9ehVCCNSqVQvOzs66Yxo1amTMGInI2Mx9mYj8dMkO++0QkeHKNKkgoEl6GjTgnBdEFsmSWnYAjsgionIxONlJT0/HzJkz8euvvyIpKQlqtVpv/9WrV40WHBGZiMW17GiTHbbsEJHhDE52hg4dioMHD2LQoEHw9fWFTCYzRVxEZCoZaUDaLc19cx+JpaVtgUq+DORmA3I7aeMhIoticLLz888/Y8eOHXj66adNEQ8RmZp2JJaLL+DgJmkopaaqDiicgaxHwP14wMtCkjQiMgsGDz2vUqUK170ismSW1l8H0IzI0rZCsd8OERnI4GRn2rRpmDx5Mh4/fmyKeIjI1CxlmYj82G+HiMrI4MtYs2fPxpUrV1C1alUEBgbCzk7/2vmff/5ptOCIyAQsZZmI/HTLRrBlh4gMY3Cyo50lmYgsFFt2iKiSMTjZmTJliiniIKKKkJUOpFzX3Le4ZOfflp17cYA6F7CRSxsPEVkMg/vsEJEF047EcvICHC1soIFbDcDWAcjNBB5ckzoaIrIgpWrZcXd3R1xcHDw9PVGlSpVi59a5f/++0YIjIiOztMkE87KRA561gcSzmnp41JI6IiKyEKVKdubOnQsXFxfdfU4kSGShLHHYeV5eof8mOxeA0OekjoaILESpkp3Bgwfr7g8ZMsRUsRCRqVlyyw6QZ0QWOykTUekZ3GdHLpcjKSmpwPbk5GTI5ewwSGTWrKFlB+DwcyIyiMHJjhCi0O2ZmZlQKBTlDoiITCT7yX8dey22ZeffuO/FAfkWISYiKkqph55/9dVXAACZTIbvvvsOzs7Oun25ubk4dOgQQkMt9A8oUWWQfBkQasChimY0liWqEgjIFUD2YyD1BlAlQOqIiMgClDrZmTt3LgBNy86iRYv0LlkpFAoEBgZi0aJFxo+QiIwjb38dSx1kILcFPGoDSec09WGyQ0SlUOpkJz4+HgDQrl07bNy4EVWqVDFZUERkApbeX0fLK+TfZOcCUKez1NEQkQUweAbl/fv3myIOIjI1S10mIj8uG0FEBjI42cnNzcWKFSvw66+/IikpCep8nQT37dtntOCIyIgsdQHQ/LggKBEZyOBkZ/To0VixYgW6d++O8PBwTjBIZAlysoDkK5r71tSyI4Tl9j8iogpjcLKzdu1a/Pjjj3juOc5eSmQx7l8BRC6gdAVcfKWOpnzcawI2tkDWQyDtNqCqJnVERGTmDJ5nR6FQIDg42BSxEJGp5O2cbOktIbYKwP3fdbF4KYuISsHgZCcqKgrz588vcnJBIjJD1tJfR4vLRhCRAQy+jHXkyBHs378fP//8M+rVqwc7Ozu9/Rs3bjRacERkJNYyEkvLKxSI3cqWHSIqFYOTHTc3N7zwwgumiIWITMXSFwDNjy07RGQAg5Od6OhoU8RBRKaSmwPcu6S571lH2liMJe/wc47IIqISGNxnh4gszIN4QJ0N2DkCKn+pozEOj2BAZgNkpACPkqSOhojMnMEtO0FBQcXOrXP16tVyBURERqbt1+JZB7Cxkv9v7Bw0i4Lev6qpn0tVqSMiIjNmcLIzZswYvcfZ2dk4ffo0du3ahffee89YcRGRsVhb52Qtr9B/k52LQM02UkdDRGasTDMoF+bbb7/FqVOnyh0QERmZtQ071/IKAS7u5IgsIiqR0dq0u3Xrhg0bNhirOCIyFmtu2QE4IouISmS0ZOenn36Cu7u7sYojImNQ5/43EssaW3YAtuwQUYkMvoz11FNP6XVQFkIgMTERd+/exYIFC4waHBGVU8o/QE4GIFdqOvRaE+0w+sf3gPR7gJOntPEQkdkyONnp3bu33mMbGxt4eXmhbdu2CA21smZyIkunvcTjWQewkUsbi7EpnAC3GkDKdU09mewQUREMTnamTJliijiIyBTyLgBqjbxC/012LgCBT0sdDRGZKYOTHQDIzc3F5s2bERsbC5lMhrCwMPTs2RNyuZX950hk6axtmYj8vEKAS7vZSZmIimVwsnP58mU899xzuHXrFkJCQiCEQFxcHPz9/bFjxw7UqlXLFHESUVlUhpYdgJ2UiahYBo/Gevfdd1GrVi3cuHEDf/75J06fPo3r168jKCgI7777riliJKKyUKuBu3Ga+1bbssPh50RUMoNbdg4ePIjjx4/rDTP38PDAzJkz8fTTvGZOZDbSbgLZ6YCNHeAeJHU0pqEdkfUoEXjyAHCoIm08RGSWDG7ZUSqVePjwYYHtjx49gkKhMEpQRGQE2tYOj2BAbidtLKZi7wq4VtPc17ZiERHlY3Cy06NHD7z55ps4ceIEhBAQQuD48eMYMWIEevbsaYoYiagsrL2/jhYnFySiEhic7Hz11VeoVasWWrZsCXt7e9jb2+Ppp59GcHAw5s+fb4oYiagsrHWZiPzYb4eISmBwnx03Nzds2bIFly9fRmxsLIQQCAsLQ3BwsCniI6KystYFQPNjyw4RlaBM8+wAQHBwMBMcInMlhPXPsaPFlh0iKoHBl7FeeuklzJw5s8D2L774Ai+//LJBZR06dAjPP/88/Pz8IJPJsHnzZr39QghMnToVfn5+cHBwQNu2bXHu3DlDQyaqfB4mAJlpgEwOeFj53FfaEVlpN4GMNGljISKzZHCyc/DgQXTv3r3A9q5du+LQoUMGlZWeno6GDRvim2++KXT/rFmzMGfOHHzzzTc4efIkfHx80KlTp0JHgxFRHtpLOu41AVultLGYmqM74FxVc1+7wjsRUR4GX8Yqaoi5nZ0d0tIM+6+qW7du6NatW6H7hBCYN28ePvjgA7z44osAgJUrV6Jq1apYvXo1hg8fbmjoRJVHZemvo+UVAjy6o0nyqkdIHQ0RmRmDW3bCw8Oxbt26AtvXrl2LsLAwowQFAPHx8UhMTETnzp1125RKJdq0aYOjR48W+bzMzEykpaXp3YgqncoyEkuLy0YQUTEMbtn56KOPEBkZiStXrqB9+/YAgF9//RVr1qzB+vXrjRZYYmIiAKBq1ap626tWrYp//vmnyOfNmDEDH3/8sdHiILJIlaVzspZuRBY7KRNRQQa37PTs2RObN2/G5cuXMXLkSERFReHmzZvYu3cvevfubfQAZTKZ3mMhRIFteU2cOBGpqam6240bN4weE5FZEwJIitXcrzSXsdiyQ0RFK9PQ8+7duxfaSdmYfHx8AGhaeHx9fXXbk5KSCrT25KVUKqFUWnmHTKLipN8FMlIAyADP2lJHUzG0yU7KdSArHVA4SRsPEZkVg1t2KkpQUBB8fHywZ88e3basrCwcPHgQrVq1kjAyIjOnbd2oEgjYOUgaSoVx8gQcPQAIjsgiogLKPKmgMTx69AiXL1/WPY6Pj0dMTAzc3d1Ro0YNjBkzBtOnT0ft2rVRu3ZtTJ8+HY6OjhgwYICEUROZucrWX0fLKxT45zdN/f0aSR0NEZkRSZOdU6dOoV27drrH48aNAwAMHjwYK1aswIQJE/DkyROMHDkSDx48QPPmzbF79264uLhIFTKR+assC4Dm5xXyb7LDfjtEpE/SZKdt27YQQhS5XyaTYerUqZg6dWrFBUVk6Spzyw7AEVlEVEC5+uwIIYpNVohIApW5ZQdgyw4RFVCmZGfZsmUIDw+Hvb097O3tER4eju+++87YsRGRodKTNaOxgP/WjKostC07D+KB7AxpYyEis1KmSQXnzp2Ld955By1btgQAHDt2DGPHjsW1a9fw6aefGj1IIiqle/9ewlHVAJTO0sZS0ZyrAvYqICMVSL4M+IRLHRERmQmDk52FCxdi6dKl6N+/v25bz5490aBBA7zzzjtMdoikVFkvYQGATKZp3blxQvM6MNkhon8ZfBkrNzcXTZo0KbA9IiICOTk5RgmKiMqosi0Amh+XjSCiQhic7AwcOBALFy4ssH3JkiV45ZVXjBIUEZVRZVsAND8uG0FEhSjT0PNly5Zh9+7daNGiBQDg+PHjuHHjBl599VXdXDkAMGfOHONESUSlU1mHnWuxZYeICmFwsvP333+jcePGAIArV64AALy8vODl5YW///5bd1xxi3USkQk8SQEeJmjue1WykVha2iTv/hUgJwuwVUgbDxGZBYOTnf3795siDiIqr3txmp8ufppRSZWRazVA4QxkPQLuXwW8K2kLFxHpMduFQInIQJV5JJaWTMbJBYmogDL12Tl58iTWr1+P69evIysrS2/fxo0bjRIYERmosvfX0fIKBW79wX47RKRjcMvO2rVr8fTTT+P8+fPYtGkTsrOzcf78eezbtw8qVSVtOicyB2zZ0WDLDhHlY3CyM336dMydOxfbt2+HQqHA/PnzERsbiz59+qBGjRqmiJGISoMtOxpcEJSI8jE42bly5Qq6d+8OAFAqlUhPT4dMJsPYsWOxZMkSowdIRKWQ+RBIvaG5z5Ydzc/kS0AuJzolojIkO+7u7nj48CEAoFq1arrh5ikpKXj8+LFxoyOi0tGOxHLyBhzdpY1FaqoagK0DkJsFPLgmdTREZAYMTnaeffZZ7NmzBwDQp08fjB49GsOGDUP//v3RoUMHowdIRKVQ2ZeJyMvG5r95hthvh4hQhtFY33zzDTIyMgAAEydOhJ2dHY4cOYIXX3wRH330kdEDJKJSqOzLROTnFQoknNG8LnV7SB0NEUnMoGQnJycH27ZtQ5cuXQAANjY2mDBhAiZMmGCS4IiolNiyo4/LRhBRHgZdxrK1tcVbb72FzMxMU8VDRGXBlh19XBCUiPIwuM9O8+bNcfr0aVPEQkRlkfUYePCP5j6THQ3t63AvDlDnShsLEUnO4D47I0eORFRUFG7evImIiAg4OTnp7W/QoIHRgiOiUki+BEAADu6Ak6fU0ZgHtwBArgRyMoCU64B7kNQREZGESp3svP7665g3bx769u0LAHj33Xd1+2QyGYQQkMlkyM3lf1FEFSrvZIIymbSxmAu5LeBZG7jzt+b1YbJDVKmVOtlZuXIlZs6cifj4eFPGQ0SG4jIRhfMK+TfZuQCEdJU6GiKSUKmTHSEEACAgIMBkwRBRGXCZiMLl7bdDRJWaQR2UZWwiJzI/HHZeOC4ISkT/MqiDcp06dUpMeO7fv1+ugIjIADmZwP2rmvts2dGXd0FQIdifiagSMyjZ+fjjj6FSqUwVCxEZKvkKIHIBpQpw8ZE6GvPiXhOwsQWyHgFptwBVdakjIiKJGJTs9OvXD97e3qaKhYgMlbdzMlsu9MntAI9gzWt09wKTHaJKrNR9dthfh8gMsb9O8bhsBBHBgGRHOxqLiMwIl4koHpeNICIYcBlLrVabMg4iKgsOOy8eW3aICGVYG4uIzERuNpB8WXOfl7EKl7dlh63TRJUWkx0iS3U/HlBnAwpndr4tikcwILMBMlKBR3ekjoaIJMJkh8hSafuheNbhSKyi2Co1Q9AB9tshqsSY7BBZKvbXKZ28kwsSUaXEZIfIUnEB0NLhshFElR6THSJLxZad0mHLDlGlx2SHyBKpc/9bzZstO8Vjyw5Rpcdkh8gSPbgG5GYCtg6AWw2pozFvHrUByIDHyUD6PamjISIJMNkhskTaSzKetQEbubSxmDuFI1AlQHOfrTtElRKTHSJLxGUiDMNlI4gqNSY7RJaIC4AahstGEFVqTHaILBFbdgzDlh2iSo3JDpGlUavzjMRislMqbNkhqtSY7BBZmtQbQPZjQK4AqgRKHY1l8Kyj+fnoDvD4vrSxEFGFY7JDZGm0rRMetQG5rbSxWAqlC6Dy19zXtooRUaXBZIfI0nCZiLLh5IJElRaTHSJLw2UiyobLRhBVWkx2iCwNW3bKhi07RJUWkx0iSyIEW3bKii07RJUWkx0iS5J2G8h6CNjYAu41pY7GsmhHZKXdAjLSpI2FiCoUkx0iS6K9BONeC7BVSBuLpXFwA1x8Nfc5IouoUmGyQ2RJuExE+bDfDlGlxGSHyJJwmYjy4bIRRJUSkx0iS8KWnfLhshFElRKTHSJLIQRbdsqLLTtElRKTHSJL8SgJyEgBZDaAR7DU0Vgmz39bdlKuA1np0sZCRBWGyQ6RpdC2RlQJBOzsJQ3FYjl5AI6emvsckUVUaTDZIbIUnEzQODi5IFGlw2SHyFJwmQjj4PBzokqHyQ6RpWDLjnGwZYeo0mGyQ2Qp2LJjHGzZIap0mOwQWYL0e8Dje5r72jWeqGy0LTsPrgHZTyQNhYgqBpMdIkugveTiVgNQOEkbi6Vz9gbs3QChBpIvSx0NEVUAJjtEloCTCRqPTMZ+O0SVjFknO1OnToVMJtO7+fj4SB0WUcXjMhHGxX47RJWKrdQBlKRevXrYu3ev7rFcLpcwGiKJsGXHuLhsBFGlYvbJjq2tLVtziDjs3Li4IChRpWLWl7EA4NKlS/Dz80NQUBD69euHq1evFnt8ZmYm0tLS9G5EFu3JA+BRouY+R2IZhzZpTL4C5GRJGwsRmZxZJzvNmzfH//73P/zyyy9YunQpEhMT0apVKyQnJxf5nBkzZkClUulu/v7+FRgxkQnc/XcNJ9dqgL2rtLFYC1c/QOECiFzg/hWpoyEiEzPrZKdbt26IjIxE/fr10bFjR+zYsQMAsHLlyiKfM3HiRKSmpupuN27cqKhwiUyDkwkan0zGTspElYjZ99nJy8nJCfXr18elS5eKPEapVEKpVFZgVEQmxv46puEVCtw6xX47RJWAWbfs5JeZmYnY2Fj4+vpKHQpRxWHLjmmwZYeo0jDrZGf8+PE4ePAg4uPjceLECbz00ktIS0vD4MGDpQ6NqOKwZcc0OLEgUaVh1pexbt68if79++PevXvw8vJCixYtcPz4cQQEBEgdGlHFyEgD0m5q7nMklnFpW3buXQJycwC5Wf85JKJyMOvf7rVr10odApG07v3bP825KuDoLm0s1kblD9g5AtmPgQfxgGdtqSMiIhMx68tYRJUe++uYjo3Nf61l7LdDZNWY7BCZMy4TYVpcNoKoUmCyQ2TOuACoaXHZCKJKgckOkTljy45psWWHqFJgskNkrrLSgZTrmvtMdkwj74gsda60sRCRyTDZITJX9y4BEICjB+DkKXU01qlKICBXAjkZQMo/UkdDRCbCZIfIXHEyQdOzkecZkcV+O0TWiskOkbnisPOKwWUjiKwekx0ic8WWnYrBZSOIrB6THSJzxZadisGWHSKrx2SHyBxlZ2iWMADYsmNqupadOECtljYWIjIJJjtE5ij5MiDUgL1Ksy4WmY57EGBjB2Sn/7foKhFZFSY7ROYo72SCMpm0sVg7uR3gEay5z347RFaJyQ6ROeIyERWL/XaIrBqTHSJzxGUiKhaXjSCyakx2iMwRW3YqFhcEJbJqTHaIzE1OFnD/iuY+W3YqRt65doSQNhYiMjomO0Tm5v5VQJ0DKJwB12pSR1M5eNQCZHIgMw14mCB1NERkZEx2iMxN3skEORKrYtgqAfeamvvst0NkdZjsEJkbLhMhDfbbIbJaTHaIzA2XiZAGR2QRWS0mO0Tmhi070uCCoERWi8kOkTnJzQGSL2nus2WnYmlf76RYjsgisjJMdojMyYNrQG4WYOsAqGpIHU3l4lkbgAzISAHS70odDREZEZMdInOi669TB7Dhr2eFsnMAqgRq7rPfDpFV4V9TInPCZSKkxX47RFaJyQ6ROeEyEdLigqBEVonJDpE5YcuOtNiyQ2SVmOwQmQt1LnAvTnOfyY402LJDZJWY7BCZi5TrQE4GIFcCbgFSR1M5edbR/Ey/C6QnSxsLERkNkx0ic6G9dOJZG5DbShtLZaV0/m/I/z1eyiKyFkx2iMwFl4kwD7yURWR1mOwQmQsuE2EeuCAokdVhskNkLtiyYx64ICiR1WGyQ2QOhGDLjrng8HMiq8Nkh8gcpN4EstMBG1vAvabU0VRuXv+OyHqYADxJkTQUIjIOJjtE5kDbiuARDMjtpI2lsrNXAS5+mvvaeY+IyKIx2SEyB1bUXydbnS11COXHEVlEVoXJDpE5sIJlIv5J+wdj949F0x+a4v1D7+PWo1tSh1R27LdDZFU4cxmRObDgBUAfZDzAojOL8OPFH5EjcgAAO+N3Yu8/e/FK2CsYWn8oXBWuEkdpILbsEFkVtuwQSc1CR2Jl5mZi+d/L8dzG57D6wmrkiBy0rt4a89vNR3Of5shSZyH672h039gdq2JXITvXgi5vsWWHyKqwZYdIag8TgcxUQGaj6aBs5tRCjZ/jf8b8P+cjIT0BABDqHoqoJlFo4dsCANDOvx0O3zqMOafm4ErqFcz8fSZWx67G2Iix6FCjA2QymZRVKJm2ZSf1BpD5EFC6SBsPEZULkx0iqWkvlbjXBGyV0sZSgpOJJzH71GycSz4HAKjqWBXvNn4XPWr2gI3sv4ZimUyG1tVbo5VfK2y6vAnfnv4W1x9ex9gDY/GU91MY32Q8Gng1kKoaJXN0B5y8gfQkzYisahFSR0RE5cBkh0hqFnAJKz41HnP/mIv9N/YDABxtHTG0/lAMDBsIB1uHIp9na2OLl+u8jOeCnkP039FYeW4lTiedxis7X0HXwK4Y3Xg0qrtUr6hqGMYrRJPs3L3IZIfIwrHPDpHUzHjYefKTZHx6/FO8sOUF7L+xH3KZHH1D+mLHizswrMGwYhOdvJzsnDDqqVHY/sJ29A7uDRlk2HVtF3pu7okvT36J1MxUE9ekDLhsBJHVYLJDJDUzbNnJyMnAd399h+6bumPdxXXIFblo698WG3ttxIctPoSng2eZyq3qVBXTnp6G9c+vRwvfFshWZ2Pl+ZV4buNz+N+5/yErN8vINSkHLghKZDV4GYtISkIAd2M1982gZUct1NhxdQe+Ov0VEtMTAQBhHmEY32Q8mvo0Ndp5QtxDsKTTEvx2+zfMPjUbl1Mu44tTX2DNhTUYEzEGnQM6S9+JmS07RFaDyQ6RlNLvAU8eAJABHrUlDeVEwgnMPjUbsfc1yZevky/ebfwungt6Tq/zsbHIZDI8U+0ZtPRtiS1XtuDr01/j5qObGH9wPBp6NcT4JuPRyLuR0c9batpk58E/QNZjQOEoXSxEVC5MdoikpG01qBIg2ZfplZQrmPPHHBy6eQgA4GznjKH1h+KVuq/A3tbe5OeX28jxYu0X0TWwK1aeW4noc9E4c/cMBv08CJ0COmFM4zGo4VrD5HEU4OQJOLgDT+4DyZcA34YVHwMRGQX77BBJScJlIu49uYdPjn2CF7e+iEM3D8FWZosBoQOw48UdeKP+GxWS6OTlaOeItxq9hR0v7EBk7UjYyGyw55896LWlFz7//XOkZKRUaDyQyTi5IJGVYLJDJCXtqtoV2F/nSc4TLD6zGN03dsf6uPVQCzU61OiATb02YWLziXC3d6+wWArj5eiFqa2m4qfnf8Iz1Z5BjjoHP8T+gOc2PocVf6+o2E7M7KRMZBV4GYtIShXYspOrzsW2q9vw9Z9fI+lJEgAg3CMc45uOR0RV85tHpnaV2ljYcSGO3j6K2admI+5BHGb/MRtrL67F6Maj0TWwq+k7MbOTMpFVqDTJzuOsHNhm5UgdBpEeh6SLkAHIcAuG2oSfzxMJx/FVzBxcTrkEQNP5eGTDd9GxRmfYyGzw2Ix/Nxp5NsOKzqux89p2LD77LW49uoUJhyZg5bn/4Z1GY/GUd2OTndumSjDsAaiTLiDDjF8jImtkzL9LMiGEMFppZigtLQ0qlQr+Y36EjZKjKch8qPAIZ+zfBADUy1iGdJRugj5D2CgTofTeCVtnzeUykWuPzHvtkf2gJSDsjH4+k5NlQeF+GArPg5DZaC5nZafVQ+bdrhBZXkY/nTce4Hf7t5EjbBCWGY0sWOBrRmSh1JmPcWNeH6SmpsLV1bVcZVWalh0icxMsuwUAuCk8jZ7oyGzToPDcAzu3U5DJBISQI/tBC2Teaw/kOhn1XBVKKJCV3AHZKc2g8NoLO7ffYed6DrYusch+0AJZ9zpAGLF+SXBDmnCEq+wxAmWJiBP+RiubiCpOpWnZSbibXO7MkMiY5Kf/B+XPY5FbswMy+/1olDKf5DzBqtj/4fvYFcjIzQAAtPfviJEN34G/iwTDt03sauoVfBMzH7/dPgwAcLJzxpCw19E3ZACUcuMsqqpc2RXyWyeR2XsZcsN6G6VMIipZWloafL082LJjCEeFLRwVlaa6ZAkeaPrPyKvWLfdnM1ediy1XtuCb09/g7pO7AIAGXg3wXpP3pJ2Yz8TCvUKwqNMCvQkRvz3zFTZeXm+8CRG9Q4FbJ6F8cAng3xCiCpNjxN83/uYSScVIC4D+dus3fHnqS1xOuQwAqO5c3XyWXKggzX2bY22PtdhxdQfm/zkfCekJmHh4Ir4//335l7rgiCwii8dkh0gq5VwA9OL9i5h9ajaOJRwDALgqXDG8wXD0C+0HhVxhrCgtho3MBs/Xeh6dAjrhh9gf8N1f3+F88nm8/svraOvfFmMjxqKmqqbhBXNiQSKLx2SHSAoZaUCapoMyPOsY9NQ76XfwTcw32HJ5CwQEbG00Mx+/2eBNqJQqEwRrWext7TG0/lC8EPwCFp5ZiJ/ifsKBGwdw+OZhvFTnJbzV8C14OHiUvkBty1vyZSA3G5BzRBaRpeEMykRS0M6c7OILOLiV6inp2en45vQ36LGpBzZf3gwBgS6BXbC191a81/Q9Jjr5eDh44MMWH2Jjr41o698WuSIX6y6uQ/dN3fHdX98hIyejdAWpqgMKZ0CdDdyPN23QRGQSTHaIpGBAf50cdQ5+vPgjum/sjsVnFyMjNwNPeT+FH577AV+2+RL+LhwOXZyaqpr4uv3XWN5lOcI8wpCenY75f85Hj009sPXKVqiFuvgCZLL/Wt/Yb4fIIvEyFpEUSrFMhBACh28dxuxTs3E19SoAoIZLDYyNGIsONTpUms7HxtLUpynWdF+Dn+N/1nVi/uDIB/jh/A+IahKF5r7Ni36yVyhw+0/22yGyUEx2iKSg65xceMtObHIsZp+ajROJJwAAKqUKbzV8C33q9IEd+4yUmY3MBt1rdkfHgI5YFbsKS88uRez9WAzdPRStq7fGuIhxqOVWq+ATdQuCsmWHyBIx2SGSQhEtO4npifj69NfYdmUbBATsbOwwsO5ADG0wFK4KToppLEq5Eq+Hv44Xgl/AojOL8OPFH3Ho5iEcuXUEkbUjMbLRSHg6eP73BI7IIrJoFtFnZ8GCBQgKCoK9vT0iIiJw+PBhqUMiKrusdCDluub+v1+ij7Ie6fUjERB4Lug5bHthG8Y1GcdEx0Sq2FfBxOYTsanXJnSo0QFqocb6uPV4buNzWHRmER5nP9YcqG3ZuRcHqHOlC5iIysTsk51169ZhzJgx+OCDD3D69Gk8++yz6NatG65fvy51aERlox2J5eiJbHsXrL2wVjdCKDM3ExFVI7Cm+xp83vpzVHOuJm2slUSgKhDz2s3Dyq4rUd+zPp7kPMG3Md/i+U3PY9OlTch1rQbY2gO5mcCDa1KHS0QGMvu1sZo3b47GjRtj4cKFum1169ZF7969MWPGjBKfr10byxhraxAZxZm1EJuG40BAY8xxc8a1tGsAgEDXQIyNGIt2/u3Y+VhCQgj8cu0XzPtzHm490syFVKdKHUQl3kSr2xeAfmuA0OckjpLI+hnz+9usk52srCw4Ojpi/fr1eOGFF3TbR48ejZiYGBw8eLDEMrQv1uff94K9Izt2khlIu4XTGXdxysEeAFBFWQVvNXoLL9V5CXY2/Iyai6zcLKy5sAaLzy7Gw6yHAIBWj5+gnqMv4OIjcXRE1i/jcTbeH7TF+hcCvXfvHnJzc1G1alW97VWrVkViYmKhz8nMzERmZqbucVpaGgBgxaM4yHPlpguWqLRkABzsoZDJMajeELxR/w24KFykjoryUcgVGFxvMHrV6oXFZxdjbexqHHV0wFGkAA9TpA6PyOrlPjFe/zizTna08jfpCyGKbOafMWMGPv744wLb+9rXgL0D/2sm8+CkcEFkm2nwrVLIMGcyK272bni/2fsYENQDm45Mw5PsdKlDIqoUMkQ2piLWKGVZ3WWswlp2/P392WeHiIjIghizz45Zj8ZSKBSIiIjAnj179Lbv2bMHrVq1KvQ5SqUSrq6uejciIiKqvMz+Mta4ceMwaNAgNGnSBC1btsSSJUtw/fp1jBgxQurQiIiIyAKYfbLTt29fJCcn45NPPkFCQgLCw8Oxc+dOBAQESB0aERERWQCz7rNjDJxnh4iIyPJUmj47REREROXFZIeIiIisGpMdIiIismpMdoiIiMiqMdkhIiIiq8Zkh4iIiKwakx0iIiKyakx2iIiIyKox2SEiIiKrZvbLRZSXdoLotLQ0iSMhIiKi0tJ+bxtjoQerT3aSk5MBAP7+/hJHQkRERIZKTk6GSqUqVxlWn+y4u7sDAK5fv17uF8scNW3aFCdPnpQ6DJNg3SyTNdcNsO76sW6WyVrrlpqaiho1aui+x8vD6pMdGxtNtySVSmWVC4HK5XKrrBfAulkqa64bYN31Y90skzXXDfjve7xcZRghDpLQ22+/LXUIJsO6WSZrrhtg3fVj3SyTNdfNWGTCGD1/zJgxl4gnIiKiimHM72+rb9lRKpWYMmUKlEql1KEQERFRKRnz+9vqW3aIiIiocrP6lh0iIiKq3JjsEBERkVVjsmMhFixYgKCgINjb2yMiIgKHDx8GAGRnZ+P9999H/fr14eTkBD8/P7z66qu4ffu2xBGXXlF1A4CpU6ciNDQUTk5OqFKlCjp27IgTJ05IGK1hiqtbXsOHD4dMJsO8efMqNsByKK5uQ4YMgUwm07u1aNFCwmgNV9J7Fxsbi549e0KlUsHFxQUtWrTA9evXJYrWMMXVLf/7pr198cUXEkZcesXV7dGjRxg1ahSqV68OBwcH1K1bFwsXLpQwWsMUV7c7d+5gyJAh8PPzg6OjI7p27YpLly5JGK2ZEWT21q5dK+zs7MTSpUvF+fPnxejRo4WTk5P4559/REpKiujYsaNYt26duHDhgjh27Jho3ry5iIiIkDrsUimubkIIsWrVKrFnzx5x5coV8ffff4s33nhDuLq6iqSkJIkjL1lJddPatGmTaNiwofDz8xNz586VJlgDlVS3wYMHi65du4qEhATdLTk5WeKoS6+k+l2+fFm4u7uL9957T/z555/iypUrYvv27eLOnTsSR16ykuqW9z1LSEgQy5cvFzKZTFy5ckXiyEtWUt2GDh0qatWqJfbv3y/i4+PF4sWLhVwuF5s3b5Y48pIVVze1Wi1atGghnn32WfH777+LCxcuiDfffFPUqFFDPHr0SOrQzYJVJTvffvutCAwMFEqlUjRu3FgcOnRIt2/Dhg2ic+fOwsPDQwAQp0+fli5QAzVr1kyMGDFCb1toaKj4v//7v0KP//333wWAAl+q5sjQuqWmpgoAYu/evRURXrmUpm43b94U1apVE3///bcICAiwmGSnpLoNHjxY9OrVS4LIjKOk+vXt21cMHDhQitDKzdDfuV69eon27dtXRGjlVlLd6tWrJz755BO9/Y0bNxYffvhhhcVYVsXV7eLFiwKA+Pvvv3X7cnJyhLu7u1i6dGlFh2qwgwcPih49eghfX18BQGzatElvv1qtFlOmTBG+vr7C3t5etGnTRq+upWE1l7HWrVuHMWPG4IMPPsDp06fx7LPPolu3brpm5fT0dDz99NOYOXOmxJEaJisrC3/88Qc6d+6st71z5844evRooc9JTU2FTCaDm5tbBURYdobWLSsrC0uWLIFKpULDhg0rKswyKU3d1Go1Bg0ahPfeew/16tWTIswyKe37duDAAXh7e6NOnToYNmwYkpKSKjrUMimpfmq1Gjt27ECdOnXQpUsXeHt7o3nz5ti8ebM0ARvA0N+5O3fuYMeOHXjjjTcqKsQyK03dnnnmGWzduhW3bt2CEAL79+9HXFwcunTpIkXIpVZS3TIzMwEA9vb2un1yuRwKhQJHjhyp0FjLIj09HQ0bNsQ333xT6P5Zs2Zhzpw5+Oabb3Dy5En4+PigU6dOePjwYanPYTXJzpw5c/DGG29g6NChqFu3LubNmwd/f3/d9dhBgwZh8uTJ6Nixo8SRGubevXvIzc1F1apV9bZXrVoViYmJBY7PyMjA//3f/2HAgAFmP4liaeu2fft2ODs7w97eHnPnzsWePXvg6elZ0eEapDR1+/zzz2Fra4t3331XihDLrDR169atG1atWoV9+/Zh9uzZOHnyJNq3b6/7o2zOSqpfUlISHj16hJkzZ6Jr167YvXs3XnjhBbz44os4ePCgRFGXjqF/T1auXAkXFxe8+OKLFRVimZWmbl999RXCwsJQvXp1KBQKdO3aFQsWLMAzzzwjRcilVlLdQkNDERAQgIkTJ+LBgwfIysrCzJkzkZiYiISEBImiLr1u3brh008/LfRzJoTAvHnz8MEHH+DFF19EeHg4Vq5cicePH2P16tWlPodVrI2lzXr/7//+T297ca0flkYmk+k9FkIU2JadnY1+/fpBrVZjwYIFFRleuZRUt3bt2iEmJgb37t3D0qVL0adPH5w4cQLe3t4VHarBiqrbH3/8gfnz5+PPP/8scIylKO5969u3r257eHg4mjRpgoCAAOzYscMivjiBouunVqsBAL169cLYsWMBAI0aNcLRo0exaNEitGnTpsJjNVRp/p4AwPLly/HKK6/otRiYu+Lq9tVXX+H48ePYunUrAgICcOjQIYwcORK+vr4W8Y9wUXWzs7PDhg0b8MYbb8Dd3R1yuRwdO3ZEt27dJIrUeOLj45GYmKjXqqVUKtGmTRscPXoUw4cPL1U5VtGyY+h/K5bE09MTcrm8QD2SkpL06pudnY0+ffogPj4ee/bsMftWHaD0dXNyckJwcDBatGiBZcuWwdbWFsuWLavocA1SUt0OHz6MpKQk1KhRA7a2trC1tcU///yDqKgoBAYGShN0KZX2fcvL19cXAQEBFjE6pKT6eXp6wtbWFmFhYXr769ata/ajsQx57w4fPoyLFy9i6NChFRlimZVUtydPnmDSpEmYM2cOnn/+eTRo0ACjRo1C37598eWXX0oUdemU5n2LiIhATEwMUlJSkJCQgF27diE5ORlBQUFShGw02jqX9/vdKpIdrdL+t2JJFAoFIiIisGfPHr3te/bsQatWrQD8l+hcunQJe/fuhYeHhxShGqw0dSuMEMLsL4eUVLdBgwbh7NmziImJ0d38/Pzw3nvv4ZdffpEo6tIpy/uWnJyMGzduwNfXtyJCLJeS6qdQKNC0aVNcvHhRb39cXBwCAgIqMlSDGfLeLVu2DBEREWbfP06rpLplZ2cjOzu7wAracrlc11pnrgx531QqFby8vHDp0iWcOnUKvXr1qshQTabc3+/l60NtHjIzM4VcLhcbN27U2/7uu++K1q1b622Lj4+3uNFY2iGHy5YtE+fPnxdjxowRTk5O4tq1ayI7O1v07NlTVK9eXcTExOgNGc3MzJQ69BIVV7dHjx6JiRMnimPHjolr166JP/74Q7zxxhtCqVQa3BNfCsXVrTCWNBqruLo9fPhQREVFiaNHj4r4+Hixf/9+0bJlS1GtWjWRlpYmdeilUtJ7t3HjRmFnZyeWLFkiLl26JL7++mshl8vF4cOHJY68ZKX5XKampgpHR0excOFCCSM1XEl1a9OmjahXr57Yv3+/uHr1qoiOjhb29vZiwYIFEkdespLq9uOPP4r9+/eLK1euiM2bN4uAgADx4osvShy14ZBvNNaVK1cEAPHnn3/qHdezZ0/x6quvlr5cYwUotWbNmom33npLb1vdunULDKe0xGRHCM2w+oCAAKFQKETjxo3FwYMHhRD/1aew2/79+6UNupSKqtuTJ0/ECy+8IPz8/IRCoRC+vr6iZ8+e4vfff5c44tIrqm6FsaRkR4ii6/b48WPRuXNn4eXlJezs7ESNGjXE4MGDxfXr1yWO2DAlvXfLli0TwcHBwt7eXjRs2NAi5mrRKqluixcvFg4ODiIlJUWiCMuuuLolJCSIIUOGCD8/P2Fvby9CQkLE7NmzhVqtljDi0iuubvPnzxfVq1fX/c59+OGHFvEPb375kx21Wi18fHzE559/rtuWmZkpVCqVWLRoUanLtZqFQNetW4dBgwZh0aJFaNmyJZYsWYKlS5fi3LlzCAgIwP3793H9+nXcvn0b3bt3x9q1axESEgIfHx/4+PhIHT4REVGl9OjRI1y+fBkA8NRTT2HOnDlo164d3N3dUaNGDXz++eeYMWMGoqOjUbt2bUyfPh0HDhzAxYsX4eLiUrqTGC8fk15xWW90dHShrR9TpkyRLmAiIqJKbv/+/YV+Pw8ePFgI8d+kgj4+PkKpVIrWrVuLv/76y6BzWE3LDhEREVFhrGo0FhEREVF+THaIiIjIqjHZISIiIqvGZIeIiIisGpMdIiIismpMdoiIiMiqWVyyM2TIEMhkMowYMaLAvpEjR0Imk2HIkCEVHxgRERGZJYtLdgDA398fa9euxZMnT3TbMjIysGbNGtSoUUPCyIiIiMjcWGSy07hxY9SoUQMbN27Ubdu4cSP8/f3x1FNP6bbt2rULzzzzDNzc3ODh4YEePXrgypUruv3t27fHqFGj9MpOTk6GUqnEvn37TF8RIiIiMjmLTHYA4LXXXkN0dLTu8fLly/H666/rHZOeno5x48bh5MmT+PXXX2FjY4MXXngBarUaADB06FCsXr0amZmZuuesWrUKfn5+aNeuXcVUhIiIiEzKYpOdQYMG4ciRI7h27Rr++ecf/Pbbbxg4cKDeMZGRkXjxxRdRu3ZtNGrUCMuWLcNff/2F8+fP6/bLZDJs2bJF95zo6GhdvyAiIiKyfBab7Hh6eqJ79+5YuXIloqOj0b17d3h6euodc+XKFQwYMAA1a9aEq6srgoKCAADXr18HACiVSgwcOBDLly8HAMTExODMmTPs4ExERGRFbKUOoDxef/11XZ+bb7/9tsD+559/Hv7+/li6dCn8/PygVqsRHh6OrKws3TFDhw5Fo0aNcPPmTSxfvhwdOnRAQEBAhdWBiIiITMuik52uXbvqEpcuXbro7UtOTkZsbCwWL16MZ599FgBw5MiRAmXUr18fTZo0wdKlS7F69Wp8/fXXpg+ciIiIKoxFJztyuRyxsbG6+3lVqVIFHh4eWLJkCXx9fXH9+nX83//9X6HlDB06FKNGjYKjoyNeeOEFk8dNREREFcdi++xoubq6wtXVtcB2GxsbrF27Fn/88QfCw8MxduxYfPHFF4WW0b9/f9ja2mLAgAGwt7c3dchERERUgWRCCCF1EFK7ceMGAgMDcfLkSTRu3FjqcIiIiMiIKnWyk52djYSEBPzf//2fbvg6ERERWReLv4xVHr/99hsCAgLwxx9/YNGiRVKHQ0RERCZQqVt2iIiIyPpV6pYdIiIisn5MdoiIiMiqmX2yM2PGDDRt2hQuLi7w9vZG7969cfHiRb1jhBCYOnUq/Pz84ODggLZt2+LcuXO6/ffv38c777yDkJAQODo6okaNGnj33XeRmpqqV86DBw8waNAgqFQqqFQqDBo0CCkpKRVRTSIiIjIRs092Dh48iLfffhvHjx/Hnj17kJOTg86dOyM9PV13zKxZszBnzhx88803OHnyJHx8fNCpUyc8fPgQAHD79m3cvn0bX375Jf766y+sWLECu3btwhtvvKF3rgEDBiAmJga7du3Crl27EBMTg0GDBlVofYmIiMi4LK6D8t27d+Ht7Y2DBw+idevWEELAz88PY8aMwfvvvw8AyMzMRNWqVfH5559j+PDhhZazfv16DBw4EOnp6bC1tUVsbCzCwsJw/PhxNG/eHABw/PhxtGzZEhcuXEBISEiF1ZGIiIiMx+xbdvLTXnpyd3cHAMTHxyMxMRGdO3fWHaNUKtGmTRscPXq02HJcXV1ha6tZMePYsWNQqVS6RAcAWrRoAZVKVWw5REREZN4sKtkRQmDcuHF45plnEB4eDgBITEwEAFStWlXv2KpVq+r25ZecnIxp06bptfokJibC29u7wLHe3t5FlkNERETmz6IWAh01ahTOnj1b6OrlMplM77EQosA2AEhLS0P37t0RFhaGKVOmFFtGceUQERGRZbCYlp133nkHW7duxf79+1G9enXddh8fHwAo0PqSlJRUoLXn4cOH6Nq1K5ydnbFp0ybY2dnplXPnzp0C5717926BcoiIiMhymH2yI4TAqFGjsHHjRuzbtw9BQUF6+4OCguDj44M9e/botmVlZeHgwYNo1aqVbltaWho6d+4MhUKBrVu3FljdvGXLlkhNTcXvv/+u23bixAmkpqbqlUNERESWxexHY40cORKrV6/Gli1b9EZEqVQqODg4AAA+//xzzJgxA9HR0ahduzamT5+OAwcO4OLFi3BxccHDhw/RqVMnPH78GJs2bYKTk5OuHC8vL8jlcgBAt27dcPv2bSxevBgA8OabbyIgIADbtm2rwBoTERGRMZl9slNUf5no6GgMGTIEgKb15+OPP8bixYvx4MEDNG/eHN9++62uE/OBAwfQrl27QsuJj49HYGAgAM3kg++++y62bt0KAOjZsye++eYbuLm5GbVOREREVHHMPtkhIiIiKg+z77NDREREVB5MdoiIiMiqMdkhIiIiq8Zkh4iIiKwakx0iIiKyakx2iIiIyKox2SEiIiKrxmSHiMzSgQMHIJPJkJKSInUoRGThOKkgEZmFtm3bolGjRpg3bx4AzRp39+/fR9WqVYucSZ2IqDRspQ6AiKgwCoUCPj4+UodBRFaAl7GISHJDhgzBwYMHMX/+fMhkMshkMqxYsULvMtaKFSvg5uaG7du3IyQkBI6OjnjppZeQnp6OlStXIjAwEFWqVME777yD3NxcXdlZWVmYMGECqlWrBicnJzRv3hwHDhyQpqJEJAm27BCR5ObPn4+4uDiEh4fjk08+AQCcO3euwHGPHz/GV199hbVr1+Lhw4d48cUX8eKLL8LNzQ07d+7E1atXERkZiWeeeQZ9+/YFALz22mu4du0a1q5dCz8/P2zatAldu3bFX3/9hdq1a1doPYlIGkx2iEhyKpUKCoUCjo6OuktXFy5cKHBcdnY2Fi5ciFq1agEAXnrpJXz//fe4c+cOnJ2dERYWhnbt2mH//v3o27cvrly5gjVr1uDmzZvw8/MDAIwfPx67du1CdHQ0pk+fXnGVJCLJMNkhIovh6OioS3QAoGrVqggMDISzs7PetqSkJADAn3/+CSEE6tSpo1dOZmYmPDw8KiZoIpIckx0ishh2dnZ6j2UyWaHb1Go1AECtVkMul+OPP/6AXC7XOy5vgkRE1o3JDhGZBYVCodex2Bieeuop5ObmIikpCc8++6xRyyYiy8HRWERkFgIDA3HixAlcu3YN9+7d07XOlEedOnXwyiuv4NVXX8XGjRsRHx+PkydP4vPPP8fOnTuNEDURWQImO0RkFsaPHw+5XI6wsDB4eXnh+vXrRik3Ojoar776KqKiohASEoKePXvixIkT8Pf3N0r5RGT+OIMyERERWTW27BAREZFVY7JDREREVo3JDhEREVk1JjtERERk1ZjsEBERkVVjskNERERWjckOERERWTUmO0RERGTVmOwQERGRVWOyQ0RERFaNyQ4RERFZNSY7REREZNX+H8Qg/2eP+sNpAAAAAElFTkSuQmCC\n", + "image/png": 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", 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\n", + "image/png": 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", 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    " ] diff --git a/example/ipm_dss.ipynb b/example/ipm_dss.ipynb index adbe7ca..a98f320 100644 --- a/example/ipm_dss.ipynb +++ b/example/ipm_dss.ipynb @@ -16,12 +16,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "#from openalea.dss.ipm_DSS import DSSHub,DSSdata\n", - "from openalea.dss.ipm_DSS import Hub\n", + "from openalea.dss import Manager as Hub\n", + "\n", "import pandas\n" ] }, @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -58,400 +58,158 @@ " \n", " \n", " \n", - " Modelid\n", - " DSSid\n", - " name\n", + " dss\n", + " models\n", + " pests\n", + " crops\n", " description\n", - " endpoint\n", " \n", " \n", " \n", " \n", " 0\n", - " no.nibio.vips\n", - " PSILARTEMP\n", - " Carrot rust fly temperature model\n", - " {'other': 'The warning system model «Carrot ru...\n", - " https://coremanager.vips.nibio.no/models/PSILA...\n", + " uk.WarwickHRI\n", + " LAMTEQ_WarwickHRI\n", + " [LAMTEQ]\n", + " [NARSS]\n", + " THE PEST: The Large Narcissus Fly (Merodon equ...\n", " \n", " \n", " 1\n", - " no.nibio.vips\n", - " DELIARADIC\n", - " Cabbage fly flight period temperature model\n", - " {'other': 'The model determines the flight per...\n", - " https://coremanager.vips.nibio.no/models/DELIA...\n", + " uk.WarwickHRI\n", + " MELIAE_WarwickHRI\n", + " [MELIAE]\n", + " [BRSOB, BRSOK, BRSNN]\n", + " THE PEST: Adult pollen (bronzed-blossom) beetl...\n", " \n", " \n", " 2\n", - " no.nibio.vips\n", - " MAMESTRABR\n", - " Cabbage moth model\n", - " {'other': 'The model for the warning system fo...\n", - " https://coremanager.vips.nibio.no/models/MAMES...\n", + " uk.WarwickHRI\n", + " HYLERA_WarwickHRI\n", + " [HYLERA]\n", + " [BRSSS]\n", + " THE PEST: The cabbage root fly (Delia radicum)...\n", " \n", " \n", " 3\n", - " no.nibio.vips\n", - " PSILAROBSE\n", - " Carrot rust fly observation model\n", - " {'other': 'The warning system model is based o...\n", - " https://coremanager.vips.nibio.no/models/PSILA...\n", + " uk.WarwickHRI\n", + " PSILRO_WarwickHRI\n", + " [PSILRO]\n", + " [DAUCS, PAVSA, APUGR, PARCR]\n", + " THE PEST: Carrot flies (Psila rosae/Chamaepsil...\n", " \n", " \n", " 4\n", - " no.nibio.vips\n", - " DELIARFOBS\n", - " Cabbage root fly and turnip fly observation model\n", - " {'other': 'The warning system model is based o...\n", - " https://coremanager.vips.nibio.no/models/DELIA...\n", - " \n", - " \n", - " 5\n", - " no.nibio.vips\n", - " NAERSTADMO\n", - " Nærstad model\n", - " {'other': 'The model is based on several years...\n", - " https://coremanager.vips.nibio.no/models/NAERS...\n", - " \n", - " \n", - " 6\n", - " no.nibio.vips\n", - " ALTERNARIA\n", - " Alternaria TOMCAST\n", - " {'other': 'TOMCAST is based on a model that wa...\n", - " https://coremanager.vips.nibio.no/models/ALTER...\n", - " \n", - " \n", - " 7\n", - " no.nibio.vips\n", - " NEGPROGMOD\n", - " Negative prognosis\n", - " {'other': 'From a specific date (50% germinati...\n", - " https://coremanager.vips.nibio.no/models/NEGPR...\n", - " \n", - " \n", - " 8\n", - " no.nibio.vips\n", - " SEPAPIICOL\n", - " Septoria apiicola model\n", - " {'other': 'This model is based on a calculatio...\n", - " https://coremanager.vips.nibio.no/models/SEPAP...\n", - " \n", - " \n", - " 9\n", - " no.nibio.vips\n", - " BREMIALACT\n", - " Bremia lactucae (Downy mildew of lettuce) model\n", - " {'other': 'TODO\n", - "', 'created_by': '', 'age': ''...\n", - " https://coremanager.vips.nibio.no/models/run/ipmd\n", - " \n", - " \n", - " 10\n", - " adas.datamanipulation\n", - " CIBSEsingleday\n", - " CIBSE hourly temperature for a single day\n", - " {'other': 'This model calculates hourly temper...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", - " \n", - " \n", - " 11\n", - " adas.datamanipulation\n", - " CIBSEmultipledays\n", - " CIBSE hourly temperature for multiple days\n", - " {'other': 'This model calculates hourly temper...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", - " \n", - " \n", - " 12\n", - " adas.datamanipulation\n", - " Sin14R-1singleday\n", - " Sin14R-1 hourly temperature for a single day\n", - " {'other': 'This model calculates hourly temper...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", - " \n", - " \n", - " 13\n", - " adas.datamanipulation\n", - " Sin14R-1multipledays\n", - " Sin14R-1 hourly temperature for multiple days\n", - " {'other': 'This model calculates hourly temper...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", - " \n", - " \n", - " 14\n", - " adas.datamanipulation\n", - " Hourly_RH\n", - " Hourly Relative Humidiy\n", - " {'other': 'This model simulates hourly realtiv...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", - " \n", - " \n", - " 15\n", - " adas.datamanipulation\n", - " LeafWetnessDuration_RH\n", - " Leaf Wetness Duration_RH\n", - " {'other': 'This is a simple relative humidity ...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", + " uk.WarwickHRI\n", + " it_horta_dss_tomato\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 16\n", - " adas.datamanipulation\n", - " LeafWetnessDuration\n", - " Leaf Wetness Duration\n", - " {'other': '\n", - "This is a simple relative humidity...\n", - " http://web1.adas.co.uk/adas_datamanipulation/a...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 17\n", - " gr.gaiasense.ipm\n", - " PLASVI\n", - " Downy mildew of grapevine\n", - " {'other': 'The warning system model «Downy mil...\n", - " https://gaiasense.neuropublic.gr/np_dws/ws/dis...\n", + " 970\n", + " AHDB.OSR_disease_forecasts\n", + " RHYNSE\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 18\n", - " adas.dss\n", - " DASGPA\n", - " Cutworm Model\n", - " {'other': 'This model assesses Cutworm risk in...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", + " 971\n", + " AHDB.OSR_disease_forecasts\n", + " PUCCHD\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 19\n", - " adas.dss\n", - " SITDMO\n", - " Orange Wheat Blossom Midge Model\n", - " {'other': 'This is a simple Phase Indication D...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", + " 972\n", + " AHDB.OSR_disease_forecasts\n", + " SlugWatch2023\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", - " 20\n", - " adas.dss\n", - " MELIAE\n", - " Pollen Beetle Model\n", - " {'other': 'This model is based on the work of ...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", + " 973\n", + " AHDB.OSR_disease_forecasts\n", + " SCLESC\n", + " [SCLESC]\n", + " [BRSNN]\n", + " Sclerotinia stem rot is usually the main disea...\n", " \n", " \n", - " 21\n", - " adas.dss\n", - " HAPDMA\n", - " Saddle Gall Midge Model\n", - " {'other': 'A simple DSS which predicts the dat...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", - " \n", - " \n", - " 22\n", - " adas.dss\n", - " CARPPO\n", - " Codling Moth Model\n", - " {'other': 'This is a simple risk based DSS for...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", - " \n", - " \n", - " 23\n", - " adas.dss\n", - " RHOPPA\n", - " TSUM Model\n", - " {'other': 'This is a simple risk DSS for predi...\n", - " https://app-rsk-adas-dss-dev-001.azurewebsites...\n", - " \n", - " \n", - " 24\n", - " com.ipmwise\n", - " ipmwise.dk\n", - " IPMwise Denmark\n", - " {'other': 'Dansk: Tilpas ukrudtsbekæmpelsen ti...\n", - " http://www.ipmwise.dk/\n", - " \n", - " \n", - " 25\n", - " com.ipmwise\n", - " ipmwise.no\n", - " VIPS-ugras Norway\n", - " {'other': 'VIPS-Ugras 2.0 er en ny versjon av ...\n", - " http://vipsugras.ipmwise.com\n", - " \n", - " \n", - " 26\n", - " com.ipmwise\n", - " ipmwise.es\n", - " IPMwise Spain\n", - " {'other': 'Adapta tu manejo de malas hierbas a...\n", - " http://www.ipmwise.es\n", - " \n", - " \n", - " 27\n", - " com.ipmwise\n", - " ipmwise.demo\n", - " IPMwise demo version\n", - " {'other': 'Adapt weed management to your needs...\n", - " http://demo.ipmwise.com/\n", - " \n", - " \n", - " 28\n", - " dk.seges\n", - " SEPTORIAHU\n", - " Septoria Humidity Model\n", - " {'other': 'The humidity model is a decision su...\n", - " https://coremanager.vips.nibio.no/models/SEPTO...\n", - " \n", - " \n", - " 29\n", - " dk.seges\n", - " CPO\n", - " CPO model (diseases and pest)\n", - " {'other': 'The system has been developed by Aa...\n", - " https://devtest-plantevaernonline.vfltest.dk/c...\n", + " 974\n", + " AHDB.OSR_disease_forecasts\n", + " LEPTMA\n", + " [LEPTMA]\n", + " [BRSNN]\n", + " Temperature and rainfall information is used t...\n", " \n", " \n", "\n", + "

    975 rows × 5 columns

    \n", "" ], "text/plain": [ - " Modelid DSSid \\\n", - "0 no.nibio.vips PSILARTEMP \n", - "1 no.nibio.vips DELIARADIC \n", - "2 no.nibio.vips MAMESTRABR \n", - "3 no.nibio.vips PSILAROBSE \n", - "4 no.nibio.vips DELIARFOBS \n", - "5 no.nibio.vips NAERSTADMO \n", - "6 no.nibio.vips ALTERNARIA \n", - "7 no.nibio.vips NEGPROGMOD \n", - "8 no.nibio.vips SEPAPIICOL \n", - "9 no.nibio.vips BREMIALACT \n", - "10 adas.datamanipulation CIBSEsingleday \n", - "11 adas.datamanipulation CIBSEmultipledays \n", - "12 adas.datamanipulation Sin14R-1singleday \n", - "13 adas.datamanipulation Sin14R-1multipledays \n", - "14 adas.datamanipulation Hourly_RH \n", - "15 adas.datamanipulation LeafWetnessDuration_RH \n", - "16 adas.datamanipulation LeafWetnessDuration \n", - "17 gr.gaiasense.ipm PLASVI \n", - "18 adas.dss DASGPA \n", - "19 adas.dss SITDMO \n", - "20 adas.dss MELIAE \n", - "21 adas.dss HAPDMA \n", - "22 adas.dss CARPPO \n", - "23 adas.dss RHOPPA \n", - "24 com.ipmwise ipmwise.dk \n", - "25 com.ipmwise ipmwise.no \n", - "26 com.ipmwise ipmwise.es \n", - "27 com.ipmwise ipmwise.demo \n", - "28 dk.seges SEPTORIAHU \n", - "29 dk.seges CPO \n", + " dss models pests \\\n", + "0 uk.WarwickHRI LAMTEQ_WarwickHRI [LAMTEQ] \n", + "1 uk.WarwickHRI MELIAE_WarwickHRI [MELIAE] \n", + "2 uk.WarwickHRI HYLERA_WarwickHRI [HYLERA] \n", + "3 uk.WarwickHRI PSILRO_WarwickHRI [PSILRO] \n", + "4 uk.WarwickHRI it_horta_dss_tomato NaN \n", + ".. ... ... ... \n", + "970 AHDB.OSR_disease_forecasts RHYNSE NaN \n", + "971 AHDB.OSR_disease_forecasts PUCCHD NaN \n", + "972 AHDB.OSR_disease_forecasts SlugWatch2023 NaN \n", + "973 AHDB.OSR_disease_forecasts SCLESC [SCLESC] \n", + "974 AHDB.OSR_disease_forecasts LEPTMA [LEPTMA] \n", "\n", - " name \\\n", - "0 Carrot rust fly temperature model \n", - "1 Cabbage fly flight period temperature model \n", - "2 Cabbage moth model \n", - "3 Carrot rust fly observation model \n", - "4 Cabbage root fly and turnip fly observation model \n", - "5 Nærstad model \n", - "6 Alternaria TOMCAST \n", - "7 Negative prognosis \n", - "8 Septoria apiicola model \n", - "9 Bremia lactucae (Downy mildew of lettuce) model \n", - "10 CIBSE hourly temperature for a single day \n", - "11 CIBSE hourly temperature for multiple days \n", - "12 Sin14R-1 hourly temperature for a single day \n", - "13 Sin14R-1 hourly temperature for multiple days \n", - "14 Hourly Relative Humidiy \n", - "15 Leaf Wetness Duration_RH \n", - "16 Leaf Wetness Duration \n", - "17 Downy mildew of grapevine \n", - "18 Cutworm Model \n", - "19 Orange Wheat Blossom Midge Model \n", - "20 Pollen Beetle Model \n", - "21 Saddle Gall Midge Model \n", - "22 Codling Moth Model \n", - "23 TSUM Model \n", - "24 IPMwise Denmark \n", - "25 VIPS-ugras Norway \n", - "26 IPMwise Spain \n", - "27 IPMwise demo version \n", - "28 Septoria Humidity Model \n", - "29 CPO model (diseases and pest) \n", + " crops \\\n", + "0 [NARSS] \n", + "1 [BRSOB, BRSOK, BRSNN] \n", + "2 [BRSSS] \n", + "3 [DAUCS, PAVSA, APUGR, PARCR] \n", + "4 NaN \n", + ".. ... \n", + "970 NaN \n", + "971 NaN \n", + "972 NaN \n", + "973 [BRSNN] \n", + "974 [BRSNN] \n", "\n", - " description \\\n", - "0 {'other': 'The warning system model «Carrot ru... \n", - "1 {'other': 'The model determines the flight per... \n", - "2 {'other': 'The model for the warning system fo... \n", - "3 {'other': 'The warning system model is based o... \n", - "4 {'other': 'The warning system model is based o... \n", - "5 {'other': 'The model is based on several years... \n", - "6 {'other': 'TOMCAST is based on a model that wa... \n", - "7 {'other': 'From a specific date (50% germinati... \n", - "8 {'other': 'This model is based on a calculatio... \n", - "9 {'other': 'TODO\n", - "', 'created_by': '', 'age': ''... \n", - "10 {'other': 'This model calculates hourly temper... \n", - "11 {'other': 'This model calculates hourly temper... \n", - "12 {'other': 'This model calculates hourly temper... \n", - "13 {'other': 'This model calculates hourly temper... \n", - "14 {'other': 'This model simulates hourly realtiv... \n", - "15 {'other': 'This is a simple relative humidity ... \n", - "16 {'other': '\n", - "This is a simple relative humidity... \n", - "17 {'other': 'The warning system model «Downy mil... \n", - "18 {'other': 'This model assesses Cutworm risk in... \n", - "19 {'other': 'This is a simple Phase Indication D... \n", - "20 {'other': 'This model is based on the work of ... \n", - "21 {'other': 'A simple DSS which predicts the dat... \n", - "22 {'other': 'This is a simple risk based DSS for... \n", - "23 {'other': 'This is a simple risk DSS for predi... \n", - "24 {'other': 'Dansk: Tilpas ukrudtsbekæmpelsen ti... \n", - "25 {'other': 'VIPS-Ugras 2.0 er en ny versjon av ... \n", - "26 {'other': 'Adapta tu manejo de malas hierbas a... \n", - "27 {'other': 'Adapt weed management to your needs... \n", - "28 {'other': 'The humidity model is a decision su... \n", - "29 {'other': 'The system has been developed by Aa... \n", + " description \n", + "0 THE PEST: The Large Narcissus Fly (Merodon equ... \n", + "1 THE PEST: Adult pollen (bronzed-blossom) beetl... \n", + "2 THE PEST: The cabbage root fly (Delia radicum)... \n", + "3 THE PEST: Carrot flies (Psila rosae/Chamaepsil... \n", + "4 NaN \n", + ".. ... \n", + "970 NaN \n", + "971 NaN \n", + "972 NaN \n", + "973 Sclerotinia stem rot is usually the main disea... \n", + "974 Temperature and rainfall information is used t... \n", "\n", - " endpoint \n", - "0 https://coremanager.vips.nibio.no/models/PSILA... \n", - "1 https://coremanager.vips.nibio.no/models/DELIA... \n", - "2 https://coremanager.vips.nibio.no/models/MAMES... \n", - "3 https://coremanager.vips.nibio.no/models/PSILA... \n", - "4 https://coremanager.vips.nibio.no/models/DELIA... \n", - "5 https://coremanager.vips.nibio.no/models/NAERS... \n", - "6 https://coremanager.vips.nibio.no/models/ALTER... \n", - "7 https://coremanager.vips.nibio.no/models/NEGPR... \n", - "8 https://coremanager.vips.nibio.no/models/SEPAP... \n", - "9 https://coremanager.vips.nibio.no/models/run/ipmd \n", - "10 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "11 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "12 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "13 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "14 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "15 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "16 http://web1.adas.co.uk/adas_datamanipulation/a... \n", - "17 https://gaiasense.neuropublic.gr/np_dws/ws/dis... \n", - "18 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "19 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "20 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "21 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "22 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "23 https://app-rsk-adas-dss-dev-001.azurewebsites... \n", - "24 http://www.ipmwise.dk/ \n", - "25 http://vipsugras.ipmwise.com \n", - "26 http://www.ipmwise.es \n", - "27 http://demo.ipmwise.com/ \n", - "28 https://coremanager.vips.nibio.no/models/SEPTO... \n", - "29 https://devtest-plantevaernonline.vfltest.dk/c... " + "[975 rows x 5 columns]" ] }, - "execution_count": 2, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hub = Hub()\n", - "hub.list_dss(ViewDataFrame=True)" + "hub.display()" ] }, { @@ -463,24 +221,56 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "PSI=hub.get_dss(ModelId='no.nibio.vips', DSSId='PSILARTEMP') " + "PSI=hub.get_model('no.nibio.vips', 'PSILARTEMP') " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Invalid frequency: 86400S. Failed to parse with error message: ValueError(\"Invalid frequency: S. Failed to parse with error message: KeyError('S'). Did you mean s?\")", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6213\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets._get_offset\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mKeyError\u001b[39m: 'S'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6344\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets.to_offset\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6219\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets._get_offset\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6137\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets.raise_invalid_freq\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: Invalid frequency: S. Failed to parse with error message: KeyError('S'). Did you mean s?", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[16]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mhub\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPSI\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mtime_start\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m2021-01-01\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mtime_end\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m2021-12-31\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m#PSI.input_DSS_weather_model_json(TimeStart=\"2021-01-01\", \u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# TimeEnd=\"2021-12-31\",\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[38;5;66;03m# weatherDataService='Finnish Meteorological Institute measured data',\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m# parameters=[1002],\u001b[39;00m\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# stationId=[101104])\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/devlp/git/DSS/src/openalea/dss/manager.py:80\u001b[39m, in \u001b[36mManager.run_model\u001b[39m\u001b[34m(self, model, time_start, time_end, weather_data_source, field_observations)\u001b[39m\n\u001b[32m 78\u001b[39m parameters = [item[\u001b[33m'\u001b[39m\u001b[33mparameter_code\u001b[39m\u001b[33m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m model._model[\u001b[33m'\u001b[39m\u001b[33minput\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mweather_parameters\u001b[39m\u001b[33m'\u001b[39m]]\n\u001b[32m 79\u001b[39m interval = model._model[\u001b[33m'\u001b[39m\u001b[33minput\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mweather_parameters\u001b[39m\u001b[33m'\u001b[39m][\u001b[32m0\u001b[39m][\u001b[33m'\u001b[39m\u001b[33minterval\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m weather_data = \u001b[43mweather_data_source\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeStart\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtime_start\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeEnd\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtime_end\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m=\u001b[49m\u001b[43minterval\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 81\u001b[39m field_data = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 82\u001b[39m input_data = agro_fakers.input_data(model._model, weather_data, field_data)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/devlp/git/DSS/src/openalea/dss/fakers.py:26\u001b[39m, in \u001b[36mWeatherDataSource.data\u001b[39m\u001b[34m(self, parameters, stationId, timeStart, timeEnd, timeZone, altitude, longitude, latitude, interval, length)\u001b[39m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdata\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[32m 10\u001b[39m parameters=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 11\u001b[39m stationId =\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m (...)\u001b[39m\u001b[32m 18\u001b[39m interval = \u001b[32m3600\u001b[39m,\n\u001b[32m 19\u001b[39m length=\u001b[32m3\u001b[39m):\n\u001b[32m 20\u001b[39m weather_data = agro_fakers.weather_data(parameters=parameters,\n\u001b[32m 21\u001b[39m interval=interval,\n\u001b[32m 22\u001b[39m longitude=longitude,\n\u001b[32m 23\u001b[39m latitude=latitude,\n\u001b[32m 24\u001b[39m altitude=altitude,\n\u001b[32m 25\u001b[39m length=length)\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mweather_data_as_xarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweather_data\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/devlp/git/weatherdata/src/openalea/weatherdata/converters.py:8\u001b[39m, in \u001b[36mweather_data_as_xarray\u001b[39m\u001b[34m(weather_data)\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mweather_data_as_xarray\u001b[39m(weather_data):\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m times = \u001b[43mpandas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdate_range\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweather_data\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtimeStart\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[43m=\u001b[49m\u001b[43mweather_data\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtimeEnd\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 10\u001b[39m \u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mweather_data\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43minterval\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mS\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtime\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 13\u001b[39m datas = [np.array(weather_data[\u001b[33m'\u001b[39m\u001b[33mlocationWeatherData\u001b[39m\u001b[33m'\u001b[39m][\u001b[32m0\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m'\u001b[39m]).astype(\u001b[33m\"\u001b[39m\u001b[33mfloat\u001b[39m\u001b[33m\"\u001b[39m)]\n\u001b[32m 15\u001b[39m dats = [[data[:, i].reshape(data.shape[\u001b[32m0\u001b[39m], \u001b[32m1\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(data.shape[\u001b[32m1\u001b[39m])] \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m datas]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/envs/ipmdss/lib/python3.14/site-packages/pandas/core/indexes/datetimes.py:1442\u001b[39m, in \u001b[36mdate_range\u001b[39m\u001b[34m(start, end, periods, freq, tz, normalize, name, inclusive, unit, **kwargs)\u001b[39m\n\u001b[32m 1440\u001b[39m freq = \u001b[33m\"\u001b[39m\u001b[33mD\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1441\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m freq \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1442\u001b[39m freq = \u001b[43mto_offset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1444\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m NaT \u001b[38;5;129;01mor\u001b[39;00m end \u001b[38;5;129;01mis\u001b[39;00m NaT:\n\u001b[32m 1445\u001b[39m \u001b[38;5;66;03m# This check needs to come before the `unit = start.unit` line below\u001b[39;00m\n\u001b[32m 1446\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNeither `start` nor `end` can be NaT\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6229\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets.to_offset\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6352\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets.to_offset\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/tslibs/offsets.pyx:6137\u001b[39m, in \u001b[36mpandas._libs.tslibs.offsets.raise_invalid_freq\u001b[39m\u001b[34m()\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: Invalid frequency: 86400S. Failed to parse with error message: ValueError(\"Invalid frequency: S. Failed to parse with error message: KeyError('S'). Did you mean s?\")" + ] + } + ], "source": [ - "PSI.input_DSS_weather_model_json(TimeStart=\"2021-01-01\", \n", - " TimeEnd=\"2021-12-31\",\n", - " weatherDataService='Finnish Meteorological Institute measured data',\n", - " parameters=[1002],\n", - " stationId=[101104])" + "hub.run_model(PSI,\n", + " time_start=\"2021-01-01\",\n", + " time_end=\"2021-12-31\")\n", + "#PSI.input_DSS_weather_model_json(TimeStart=\"2021-01-01\", \n", + "# TimeEnd=\"2021-12-31\",\n", + "# weatherDataService='Finnish Meteorological Institute measured data',\n", + "# parameters=[1002],\n", + "# stationId=[101104])" ] }, { @@ -576,9 +366,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.13.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/example/psilarobs.csv b/example/psilarobs.csv index 2a063fb..558df50 100644 --- a/example/psilarobs.csv +++ b/example/psilarobs.csv @@ -1,14 +1,14 @@ time;trapCountCropEdge;trapCountCropInside -01/06/2020;; -02/06/2020;; -03/06/2020;; -04/06/2020;; -05/06/2020;; -06/06/2020;; -07/06/2020;; -08/06/2020;; -09/06/2020;; -10/06/2020;; -11/06/2020;; +01/06/2020;22;2 +02/06/2020;7;1 +03/06/2020;2;5 +04/06/2020;5;3 +05/06/2020;0;0 +06/06/2020;0;0 +07/06/2020;0;0 +08/06/2020;20;1 +09/06/2020;15;2 +10/06/2020;17;2 +11/06/2020;10;2 12/06/2020;22;2 -13/06/2020;; +13/06/2020;22;2 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c8961e4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,131 @@ +[build-system] +requires = [ + "setuptools", + "setuptools_scm", +] +build-backend = "setuptools.build_meta" + +# where your source lies if you followed src layout +[tool.setuptools.packages.find] +where = ["src"] +namespaces = true + +[tool.setuptools] +include-package-data = false # force explicit declaration of data (disable automatic inclusion) + +[tool.setuptools.package-data] +"openalea.dss" = ["data/*"] +"*" = ['*.txt', '*.png', '*.csv', '*.json'] + +# enable dynamic version based on git tags +[tool.setuptools_scm] +# Format version to ease alignment with conda/meta.yaml tag-based versioning +fallback_version = "2.1.0" +version_scheme = "guess-next-dev" +local_scheme = "no-local-version" + + +[project] +name = "openalea.dss" +authors = [ + { name = "Christian Fournier"}, + { name = "Christophe Pradal"}, + { name = "Marc Labadie"}, +] + +description = 'Management of IPM DSS from Python, that transforms DSS outputs into efficient Python data structure ' +readme = "README.md" +license = "CECILL-C" +license-files = ["LICENSE"] +requires-python = ">=3.10" +dynamic = ["version"] +classifiers = [ + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", +] +keywords = ["OpenAlea", "DSS", "weather", "IPM"] + +# you can list here all dependencies that are pip-instalable, and that have a name identical to the one used by conda (to allow reuse of this list in meta.yaml) +# If conda name is different, please do not declare the pip name, and declare conda name in the next section +dependencies = [ + "fastapi", + "pydantic", + "pandas", + "xarray", + "numpy", +] + + +[project.entry-points."wralea"] + +plgov="openalea.ipmdss_wralea.pl_gov_edwin" +nibio="openalea.ipmdss_wralea.no_nibio_vips" +wur="openalea.ipmdss_wralea.nl_wur_IWMPRAISE" +adas="openalea.ipmdss_wralea.adas_datamanipulation" +ipmwise="openalea.ipmdss_wralea.com_ipmwise" +dk="openalea.ipmdss_wralea.dk_au_agro" +slug="openalea.ipmdss_wralea.slugstatus_farming_co_uk" +best4soil="openalea.ipmdss_wralea.Best4Soil_Support_Tools" +ahdb="openalea.ipmdss_wralea.AHDB_OSR_disease_forecasts" +wur2="openalea.ipmdss_wralea.nl_wur_LateBlight" +isip="openalea.ipmdss_wralea.de_ISIP" +seges="openalea.ipmdss_wralea.dk_seges" +adas2="openalea.ipmdss_wralea.adas_dss" +horta="openalea.ipmdss_wralea.it_horta_dss" +gaia="openalea.ipmdss_wralea.gr_gaiasense_ipm" +warwick="openalea.ipmdss_wralea.uk_WarwickHRI" +dss="openalea.ipmdss_wralea" + +[tool.conda.environment] +channels = [ + "openalea3", + "conda-forge" +] +dependencies = [ + "openalea.agroservices", + "openalea.weatherdata", + "matplotlib-base", + "fastapi", + "pydantic", + "pandas", + "xarray", + "numpy", + "scipy" +] + +[project.optional-dependencies] +test = [ + "pytest", + "nbmake", +] +dev = [ + "pytest >=6", + "pytest-cov >=3", +] +doc = [ + "sphinx-autobuild", + "pydata-sphinx-theme", + "myst-parser", + "sphinx-favicon", + "ipykernel", + "sphinx-copybutton", + "ipython_genutils", + "nbsphinx", +] + +[project.urls] +Repository = "https://github.com/openalea/DSS" +Homepage = "https://ipmdss.readthedocs.io" +"Bug Tracker" = "https://github.com/openalea/DSS/issues" +Discussions = "https://github.com/openalea/DSS/discussions" +Changelog = "https://github.com/openalea/DSS/releases" + diff --git a/setup.py b/setup.py deleted file mode 100644 index b0402c7..0000000 --- a/setup.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- python -*- -# -# Copyright INRIA - CIRAD - INRAe -# -# File author(s): -# -# File contributor(s): -# -# Distributed under the Cecill-C License. -# See accompanying file LICENSE.txt or copy at -# http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.html -# -# OpenAlea WebSite : http://github.com/openalea/weatherdata -# -# ============================================================================== - -# ============================================================================== -from setuptools import setup, find_packages -# ============================================================================== - - -pkg_root_dir = 'src' -packages = [pkg for pkg in find_packages(pkg_root_dir)] -top_pkgs = [pkg for pkg in packages if len(pkg.split('.')) <= 2] -package_dir = dict([('', pkg_root_dir)] + - [(pkg, pkg_root_dir + "/" + pkg.replace('.', '/')) - for pkg in top_pkgs]) - -_version = {} -with open("src/openalea/dss/version.py") as fp: - exec(fp.read(), _version) -version = _version['version'] - -description = 'IPM DSS Python interface' -long_description = 'Management of IPM DSS from python, and transform DSS outputs in a native and efficient Python data structure ' - -wraleas= """ -openalea.ipmdss_wralea.pl_gov_edwin -openalea.ipmdss_wralea.no_nibio_vips -openalea.ipmdss_wralea.nl_wur_IWMPRAISE -openalea.ipmdss_wralea.adas_datamanipulation -openalea.ipmdss_wralea.com_ipmwise -openalea.ipmdss_wralea.dk_au_agro -openalea.ipmdss_wralea.slugstatus_farming_co_uk -openalea.ipmdss_wralea.Best4Soil_Support_Tools -openalea.ipmdss_wralea.AHDB_OSR_disease_forecasts -openalea.ipmdss_wralea.nl_wur_LateBlight -openalea.ipmdss_wralea.de_ISIP -openalea.ipmdss_wralea.dk_seges -openalea.ipmdss_wralea.adas_dss -openalea.ipmdss_wralea.it_horta_dss -openalea.ipmdss_wralea.gr_gaiasense_ipm -openalea.ipmdss_wralea.uk_WarwickHRI -""".split() - -entries = ["dss = openalea.ipmdss_wralea"] -entries.extend(["%s = %s"%(k.split('.')[-1], k) for k in wraleas]) - -setup( - name="openalea.dss", - version=version, - description=description, - long_description=long_description, - - author="* Christian Fournier\n" - "* Marc Labadie\n" - "* Christophe Pradal\n", - - maintainer="", - maintainer_email="", - - url="https://github.com/H2020-IPM-openalea/DSS", - license="Cecill-C", - keywords='openalea, DSS, weather', - - # package installation - packages=packages, - package_dir=package_dir, - zip_safe=False, - - # See MANIFEST.in - include_package_data=True, - entry_points = { - "wralea": entries}, - ) diff --git a/src/openalea/__init__.py b/src/openalea/__init__.py deleted file mode 100644 index de40ea7..0000000 --- a/src/openalea/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__import__('pkg_resources').declare_namespace(__name__) diff --git a/src/openalea/dss/datamanipulation.py b/src/openalea/dss/datamanipulation.py index 3c9a0da..f920656 100644 --- a/src/openalea/dss/datamanipulation.py +++ b/src/openalea/dss/datamanipulation.py @@ -1,5 +1,21 @@ import pandas import numpy + + +def angle_between_vectors(v1, v2): + """Calcule l'angle entre deux vecteurs en radians""" + v1 = numpy.array(v1) + v2 = numpy.array(v2) + + # Produit scalaire / (norme v1 * norme v2) + cos_angle = numpy.dot(v1, v2) / (numpy.linalg.norm(v1) * numpy.linalg.norm(v2)) + + # Clamp pour éviter les erreurs d'arrondi + cos_angle = numpy.clip(cos_angle, -1.0, 1.0) + + angle_rad = numpy.arccos(cos_angle) + return angle_rad + """ Short meteorological models """ def linear_degree_days(varname,data, start_date=None, base_temp=0., max_temp=35.): df = data[varname].copy() @@ -254,13 +270,12 @@ def wind_speed_on_leaf(wind_speed=0., leaf_height=0., canopy_height=0., lai=0., wind_speed_on_leaf: float Wind speed on the given leaf """ - from math import exp - from openalea.plantgl import all as pgl + from math import exp, degrees eta = canopy_height * ( (cd*lai/canopy_height)/(2*lc**2) )**(1./3) if is_in_rows: - angle = degrees(pgl.angle(row_direction, wind_direction)) + angle = numpy.degrees(angle_between_vectors(row_direction, wind_direction)) reduction = param_reduc * (1-angle/90) return wind_speed * exp(max(0., eta-reduction)*((leaf_height/canopy_height)-1)) else: diff --git a/src/openalea/dss/fakers.py b/src/openalea/dss/fakers.py index ee6a69c..c8515fc 100644 --- a/src/openalea/dss/fakers.py +++ b/src/openalea/dss/fakers.py @@ -1,9 +1,9 @@ """Faker classes""" -import agroservices.ipm.fakers as agro_fakers -from weatherdata.converters import weather_data_as_xarray +from openalea.agroservices.ipm import fakers as agro_fakers +from openalea.weatherdata.converters import weather_data_as_xarray -class WeatherDataSource(object): +class WeatherDataSource: def data(self, diff --git a/src/openalea/dss/ipm_DSS.py b/src/openalea/dss/ipm_DSS.py index 0d36949..82be78c 100644 --- a/src/openalea/dss/ipm_DSS.py +++ b/src/openalea/dss/ipm_DSS.py @@ -20,7 +20,7 @@ def __call__(self, *arg, **kwarg): instance.__class__ = _ instance.__call__.__func__.__doc__ = doc -class DSS(object): +class DSS: def __init__(self, name, meta, models, manager): self.name = name self.meta = meta @@ -71,7 +71,9 @@ def _model_call(*args, **kwargs): return model else: raise ValueError('Model ' + model_name + ' not found in ' + self.name) -class Model(object): + + +class Model: """ Model Class derived from Hub. It allows to displays informations and run model and plot output Parameters @@ -349,8 +351,8 @@ def input(weatherdata=None, fieldObservation=None): ], "fieldObservationQuantifications": [ - {"trapCountCropEdge":int(fieldObservation.trapCountCropEdge.dropna().values), - "trapCountCropInside":int(fieldObservation.trapCountCropInside.dropna().values) + {"trapCountCropEdge":sum(fieldObservation.trapCountCropEdge.dropna().values), + "trapCountCropInside":sum(fieldObservation.trapCountCropInside.dropna().values) } ] } @@ -359,7 +361,7 @@ def input(weatherdata=None, fieldObservation=None): field_observation_input={ "modelId":self.model, "configParameters": - {"timeZone": fieldObservation.index.tz._tzname, + {"timeZone": fieldObservation.index.tz.key, "startDateCalculation":np.datetime_as_string(pandas.Timestamp.to_datetime64(fieldObservation.index[0]), unit='D'), "endDateCalculation":np.datetime_as_string(pandas.Timestamp.to_datetime64(fieldObservation.index[-1]), unit='D') } diff --git a/src/openalea/dss/manager.py b/src/openalea/dss/manager.py index 77328c7..11ae966 100644 --- a/src/openalea/dss/manager.py +++ b/src/openalea/dss/manager.py @@ -1,8 +1,8 @@ """Mother class managing DSS catalog""" import pandas -from agroservices.ipm.ipm import IPM -import agroservices.ipm.fakers as agro_fakers +from openalea.agroservices.ipm.ipm import IPM +import openalea.agroservices.ipm.fakers as agro_fakers from openalea.dss.ipm_DSS import DSS import openalea.dss.fakers as fakers diff --git a/src/openalea/fastapi/main.py b/src/openalea/fastapi/main.py index c65c001..6048668 100644 --- a/src/openalea/fastapi/main.py +++ b/src/openalea/fastapi/main.py @@ -4,8 +4,8 @@ from json_to_pydantic import json_to_pydantic,jsontext -from agroservices.ipm.ipm import IPM -import agroservices.ipm.fakers as fakers +from openalea.agroservices.ipm.ipm import IPM +import openalea.agroservices.ipm.fakers as fakers ipm = IPM() model = ipm.get_model(DSSId='no.nibio.vips',ModelId='PSILARTEMP') input_data = fakers.input_data(model) diff --git a/src/openalea/ipmdss_wralea/__wralea__.py b/src/openalea/ipmdss_wralea/__wralea__.py index 212a429..d7f6aab 100644 --- a/src/openalea/ipmdss_wralea/__wralea__.py +++ b/src/openalea/ipmdss_wralea/__wralea__.py @@ -4,9 +4,9 @@ __editable__ = True __description__ = 'DSS package containing list of DSS model' __license__ = 'CeCILL-C' -__url__ = 'https://github.com/H2020-IPM-openalea/DSS' +__url__ = 'https://github.com/openalea/DSS' __alias__ = [] -__version__ = '1.0' +__version__ = '1.1.0' __authors__ = 'Marc LABADIE, Christophe Pradal, Christian Fournier, Corinne Robert' __institutes__ = 'CIRAD/INRAE' __icon__ = 'icon.png' diff --git a/test/test_model.py b/test/test_model.py index d01ffdc..3c0e656 100644 --- a/test/test_model.py +++ b/test/test_model.py @@ -3,7 +3,7 @@ from openalea.dss import Manager -from weatherdata.ipm import WeatherDataHub +from openalea.weatherdata.ipm import WeatherDataHub import xarray as xr h= Manager()