diff --git a/.github/workflows/automated-tests.yml b/.github/workflows/automated-tests.yml
index dcff57e..f634553 100644
--- a/.github/workflows/automated-tests.yml
+++ b/.github/workflows/automated-tests.yml
@@ -18,11 +18,11 @@ jobs:
API_CLIENT_SECRET: ${{ secrets.API_CLIENT_SECRET }}
API_USERNAME: ${{ secrets.API_USERNAME }}
API_PASSWORD: ${{ secrets.API_PASSWORD }}
- runs-on: ubuntu-latest
+ runs-on: ubuntu-22.04
strategy:
fail-fast: false
matrix:
- python-version: ["3.8"]
+ python-version: ["3.11"]
steps:
- uses: actions/checkout@v2
@@ -35,9 +35,9 @@ jobs:
sudo apt-add-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install gdal-bin libgdal-dev
- pip install gdal
+ pip install gdal==3.6.2
python -m pip install --upgrade pip
- python -m pip install flake8 pytest
+ python -m pip install flake8 pytest pip-system-certs
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt; fi
- name: Lint with flake8
@@ -48,4 +48,4 @@ jobs:
flake8 . --count --ignore=E501 --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Test with pytest
run: |
- make test
\ No newline at end of file
+ make test
diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml
index e51e1bb..15160dc 100644
--- a/.github/workflows/publish-package.yml
+++ b/.github/workflows/publish-package.yml
@@ -5,28 +5,28 @@ on:
tags:
- v**
-
jobs:
deploy:
- runs-on: ubuntu-latest
+ runs-on: ubuntu-22.04
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
with:
- python-version: '3.8'
+ python-version: '3.10'
- name: Install dependencies
run: |
sudo apt-add-repository ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install gdal-bin libgdal-dev
- pip install gdal
+ pip install gdal==3.6.2
python -m pip install --upgrade pip
pip install wheel
pip install -r requirements.txt
- pip install -r requirements-dev.txt
+ pip install -r requirements-dev.txt
+ pip install mkdocstrings-python
- name: Build package
run: make build
- name: Publish package
diff --git a/.github/workflows/test-publish-package.yml b/.github/workflows/test-publish-package.yml
new file mode 100644
index 0000000..0c68d12
--- /dev/null
+++ b/.github/workflows/test-publish-package.yml
@@ -0,0 +1,39 @@
+name: Publish package and docs test
+
+on:
+ push:
+ tags:
+ - test-v**
+
+jobs:
+ deploy:
+
+ runs-on: ubuntu-22.04
+
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python
+ uses: actions/setup-python@v3
+ with:
+ python-version: '3.10'
+ - name: Install dependencies
+ run: |
+ sudo apt-add-repository ppa:ubuntugis/ubuntugis-unstable
+ sudo apt-get update
+ sudo apt-get install gdal-bin libgdal-dev
+ pip install gdal==3.6.2
+ python -m pip install --upgrade pip
+ pip install wheel
+ pip install -r requirements.txt
+ pip install -r requirements-dev.txt
+ pip install mkdocstrings-python
+ - name: Build package
+ run: make build
+ - name: Publish package distributions to TestPyPI
+ uses: pypa/gh-action-pypi-publish@release/v1
+ with:
+ repository-url: https://test.pypi.org/legacy/
+ user: __token__
+ password: ${{ secrets.TEST_PYPI_API_TOKEN }}
+ - run: mkdocs gh-deploy --force
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
index 70ba153..71f16db 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3.9
+FROM python:3.10
WORKDIR /app
diff --git a/README.md b/README.md
index 30d138b..a6d4e3e 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@
-
+
@@ -21,11 +21,11 @@
Who we are
- Project description
+ Project description
·
- Report Bug
+ Report Bug
·
- Request Feature
+ Request Feature
@@ -36,7 +36,7 @@
[![Twitter][twitter-shield]][twitter-url]
[![Youtube][youtube-shield]][youtube-url]
[![languages][language-python-shiedl]][issues-url]
-[![CITest][CITest-shield]][CITest-url]
+
[![Issues][issues-shield]][issues-url]
[![MIT License][license-shield]][license-url]
@@ -81,7 +81,9 @@ EarthDaily Agro is the agricultural analysis division of EartDaily Analytics. Le
-Throught our platform, we make geospatial analytics easily accessible for you to be browsed or analyzed, within our cloud or within your own environment. We provide developers and data scientists both flexibility and extensibility with analytic ready data and digital agriculture ready development blocks. We empower your team to enrich your systems with information at the field, regional or continent level via our API or Apps.
+
+Throught our <geosys/> platform, we make geospatial analytics easily accessible for you to be browsed or analyzed, within our cloud or within your own environment. We provide developers and data scientists both flexibility and extensibility with analytic ready data and digital agriculture ready development blocks. We empower your team to enrich your systems with information at the field, regional or continent level via our API or Apps.
+
We have a team of experts around the world that understand local crops and ag industry, as well as advanced analytics to support your business.
@@ -100,7 +102,7 @@ The `geosyspy` python package aims to provide an easy and ready to use library a
* Analytic publication:
* Save and retrieve custom data in Analytics Fabrik
-See [Examples](examples.ipynb) notebook for more information
+See [documentation](https://earthdaily.github.io/GeosysPy/) and [Examples](examples.ipynb) notebook for more information
## Getting started
@@ -108,11 +110,20 @@ See [Examples](examples.ipynb) notebook for more information
Make sure you have valid credentials. If you need to get trial access, please register [here](https://earthdailyagro.com/geosys-api/#get-started).
-This package has been tested on Python 3.9.7.
+This package has been tested on Python 3.10.11.
### Installing
+#### Conda
+
+If you are using Conda, there are the steps to follow to create a virtual environment:
+
+```
+conda create --name demo
+conda activate demo
+```
+
#### For Linux / Mac OS
```
pip install geosyspy
@@ -147,7 +158,23 @@ API_USERNAME=
API_PASSWORD=
```
-3. Run the Jupyter notebook
+
+3. Install Jupyter notebook [Examples](examples.ipynb) dependencies
+
+```
+conda install --file requirements-notebook.txt
+```
+or
+```
+pip install -r requirements-notebook.txt
+```
+
+4. Set up the Jupyter Notebook kernel
+```
+python -m ipykernel install --user --name demo
+```
+
+5. Run the Jupyter notebook
### Run the package inside a Docker container
@@ -167,8 +194,8 @@ or, without .env file :
Then :
```python
->>> from geosyspy.geosys import Geosys
->>> from geosyspy.constants import Collection, Region, Env
+>>> from geosyspy import Geosys
+>>> from geosyspy.utils.constants import *
>>> import os
>>> client = Geosys(os.getenv('API_CLIENT_ID'), os.getenv('API_CLIENT_SECRET'), os.getenv('API_USERNAME'), os.getenv('API_PASSWORD'), Env.PREPROD, Region.NA)
@@ -181,8 +208,8 @@ Then :
Initialize client:
```python
-from geosyspy.geosys import Geosys
-from geosyspy.constants import Collection, Region, Env
+from geosyspy import Geosys
+from geosyspy.utils.constants import *
client = Geosys("API_CLIENT_ID", "API_CLIENT_SECRET", "API_USERNAME", "API_PASSWORD", Env.PREPROD, Region.NA)
@@ -196,9 +223,19 @@ polygon = "POLYGON((...))"
today = dt.date.today()
year_ago = dt.date.today() + relativedelta(months=-12)
-dataframe = client.get_time_series(polygon, year_ago, today, collection=Collection.MODIS, indicators=["NDVI"])
+dataframe = client.get_time_series(polygon, year_ago, today, collection=SatelliteImageryCollection.MODIS, indicators=["NDVI"])
```
+Use Geosyspy logger
+
+
+````python
+import logging
+
+geosys_logger = logging.getLogger('geosyspy')
+geosys_logger.setLevel(logging.DEBUG)
+````
+
See the Jupyter notebook [Examples](examples.ipynb) for a working example.
(back to top)
@@ -207,7 +244,7 @@ See the Jupyter notebook [Examples](examples.ipynb) for a working example.
## Resources
The following links will provide access to more information:
- [EarthDaily agro developer portal ](https://developer.geosys.com/)
--
+- [Pypi package](https://pypi.org/project/geosyspy/)
(back to top)
@@ -246,7 +283,7 @@ For any additonal information, please [email us](mailto:sales@earthdailyagro.com
[forks-url]: https://github.com/github_username/repo/network/members
[stars-shield]: https://img.shields.io/github/stars/qgis-plugin/repo.svg?style=plastic&logo=appveyor
[stars-url]: https://github.com/github_username/repo/stargazers
-[issues-shield]: https://img.shields.io/github/issues/GEOSYS/qgis-plugin/repo.svg?style=social
+[issues-shield]: https://img.shields.io/github/issues/GEOSYS/GeosysPy/repo.svg?style=social
[issues-url]: https://github.com/github_username/repo/issues
[license-shield]: https://img.shields.io/github/license/GEOSYS/qgis-plugin
[license-url]: https://www.gnu.org/licenses/gpl-3.0.en.html
@@ -260,5 +297,5 @@ For any additonal information, please [email us](mailto:sales@earthdailyagro.com
[language-python-url]: https://pypi.org/
[GitStars-shield]: https://img.shields.io/github/stars/GEOSYS?style=social
[GitStars-url]: https://img.shields.io/github/stars/GEOSYS?style=social
-[CITest-shield]: https://img.shields.io/github/workflow/status/GEOSYS/qgis-plugin/Continous%20Integration
-[CITest-url]: https://img.shields.io/github/workflow/status/GEOSYS/qgis-plugin/Continous%20Integration
+[CITest-shield]: https://img.shields.io/github/workflow/status/GEOSYS/GeosysPy/Continous%20Integration
+[CITest-url]: https://img.shields.io/github/workflow/status/GEOSYS/GeosysPy/Continous%20Integration
diff --git a/VERSION.txt b/VERSION.txt
index 8294c18..7dff5b8 100644
--- a/VERSION.txt
+++ b/VERSION.txt
@@ -1 +1 @@
-0.1.2
\ No newline at end of file
+0.2.1
\ No newline at end of file
diff --git a/docs/constants-reference.md b/docs/constants-reference.md
index e51416e..adf03c7 100644
--- a/docs/constants-reference.md
+++ b/docs/constants-reference.md
@@ -1,3 +1,3 @@
# Geosys
-::: geosyspy.constants
\ No newline at end of file
+::: geosyspy.utils.constants
\ No newline at end of file
diff --git a/docs/utils.md b/docs/utils.md
new file mode 100644
index 0000000..9e51d67
--- /dev/null
+++ b/docs/utils.md
@@ -0,0 +1,6 @@
+# Utils
+
+::: geosyspy.utils.constants
+::: geosyspy.utils.helper
+::: geosyspy.utils.http_client
+::: geosyspy.utils.oauth2_client
\ No newline at end of file
diff --git a/examples.ipynb b/examples.ipynb
index 045cd6e..89b5948 100644
--- a/examples.ipynb
+++ b/examples.ipynb
@@ -12,11 +12,10 @@
"import datetime as dt\n",
"from dateutil.relativedelta import relativedelta\n",
"import logging\n",
- "from geosyspy.constants import Collection, Region, Env\n",
+ "from geosyspy.utils.constants import *\n",
"\n",
- "\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.INFO)\n",
+ "logger = logging.getLogger('geosyspy')\n",
+ "logger.setLevel(logging.DEBUG)\n",
"\n",
"# read .env file\n",
"load_dotenv()\n",
@@ -26,11 +25,14 @@
"API_USERNAME = os.getenv('API_USERNAME')\n",
"API_PASSWORD = os.getenv('API_PASSWORD')\n",
"\n",
- "client = Geosys(API_CLIENT_ID, API_CLIENT_SECRET, API_USERNAME, API_PASSWORD, Env.PREPROD, Region.NA)\n",
+ "client = Geosys(API_CLIENT_ID, API_CLIENT_SECRET, API_USERNAME, API_PASSWORD, Env.PROD, Region.NA)\n",
"\n",
"polygon = \"POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))\"\n",
"today = dt.date.today()\n",
- "year_ago = dt.date.today() + relativedelta(months=-12)"
+ "year_ago = dt.date.today() + relativedelta(months=-12)\n",
+ "\n",
+ "# available crop codes of the connected user\n",
+ "crops = client.get_available_crops()\n"
]
},
{
@@ -40,7 +42,7 @@
"outputs": [],
"source": [
"# Get aggregated NDVI time series\n",
- "client.get_time_series(polygon, year_ago, today, collection=Collection.MODIS, indicators=[\"NDVI\"])"
+ "client.get_time_series(polygon, year_ago, today, collection=SatelliteImageryCollection.MODIS, indicators=[\"NDVI\"])"
]
},
{
@@ -50,7 +52,7 @@
"outputs": [],
"source": [
"# Get aggregated EVI time series\n",
- "client.get_time_series(polygon, year_ago, today, collection=Collection.MODIS, indicators=[\"EVI\"])"
+ "client.get_time_series(polygon, year_ago, today, collection=SatelliteImageryCollection.MODIS, indicators=[\"EVI\"])"
]
},
{
@@ -62,7 +64,7 @@
"# Get aggregated 'Forecast daily' time series\n",
"indicators = ['Precipitation', 'Temperature','Date']\n",
"point = \"POINT (0.0872845021171696 43.69457564315705)\"\n",
- "client.get_time_series(point, dt.date.today(), dt.date.today() + relativedelta(days=+5), collection=Collection.WEATHER_FORECAST_DAILY, indicators=indicators)"
+ "client.get_time_series(point, dt.date.today(), dt.date.today() + relativedelta(days=+5), collection=WeatherTypeCollection.WEATHER_FORECAST_DAILY, indicators=indicators)"
]
},
{
@@ -74,7 +76,7 @@
"# Get aggregated 'Forecast hourly' time series\n",
"indicators = ['Precipitation', 'Temperature']\n",
"point = \"POINT (0.0872845021171696 43.69457564315705)\"\n",
- "client.get_time_series(point, dt.date.today(), dt.date.today() + relativedelta(days=+2), collection=Collection.WEATHER_FORECAST_HOURLY, indicators=indicators)"
+ "client.get_time_series(point, dt.date.today(), dt.date.today() + relativedelta(days=+2), collection=WeatherTypeCollection.WEATHER_FORECAST_HOURLY, indicators=indicators)"
]
},
{
@@ -87,7 +89,7 @@
"indicators = ['Precipitation', 'Temperature']\n",
"start_date = dt.datetime.strptime(\"2022-01-01\", \"%Y-%m-%d\")\n",
"end_date = dt.datetime.strptime(\"2022-02-01\", \"%Y-%m-%d\")\n",
- "client.get_time_series(polygon, start_date, end_date, collection=Collection.WEATHER_HISTORICAL_DAILY, indicators=indicators)"
+ "client.get_time_series(polygon, start_date, end_date, collection=WeatherTypeCollection.WEATHER_HISTORICAL_DAILY, indicators=indicators)"
]
},
{
@@ -97,20 +99,111 @@
"outputs": [],
"source": [
"# Get satellite image time series for Modis NDVI\n",
- "client.get_satellite_image_time_series(polygon, year_ago, today, collections=[Collection.MODIS], indicators=[\"NDVI\"])"
+ "client.get_satellite_image_time_series(polygon, year_ago, today, collections=[SatelliteImageryCollection.MODIS], indicators=[\"NDVI\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "scrolled": false
+ },
"outputs": [],
"source": [
"# Get satellite image time series for LANDSAT_8 and SENTINEL_2 Reflectance\n",
- "time_series_xarr = client.get_satellite_image_time_series(polygon, year_ago, today, collections=[Collection.SENTINEL_2, Collection.LANDSAT_8], indicators=[\"Reflectance\"])\n",
+ "time_series_xarr = client.get_satellite_image_time_series(polygon, year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"Reflectance\"])\n",
"time_series_xarr\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "import xarray as xr\n",
+ "# Get satellite image time series for LANDSAT_8 and SENTINEL_2 NDVI \n",
+ "# list of available indicators: Reflectance, NDVI, EVI, GNDVI, NDWI, CVI, CVIn, LAI\n",
+ "ndvi_time_series_xarr = client.get_satellite_image_time_series(polygon, year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"ndvi\"])\n",
+ "\n",
+ "ndvi_time_series_xarr\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "lai_time_series_xarr = client.get_satellite_image_time_series(polygon, year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8], indicators=[\"NDWI\"])\n",
+ "lai_time_series_xarr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pip install zarr\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if ndvi_time_series_xarr.coords == lai_time_series_xarr.coords:\n",
+ " print(\"Les dimensions sont identiques.\")\n",
+ "else:\n",
+ " print(\"Les dimensions ne sont pas identiques.\")\n",
+ " print(ndvi_time_series_xarr.coords)\n",
+ " print(lai_time_series_xarr.coords)\n",
+ "\n",
+ "\n",
+ "datacube = xr.merge([ndvi_time_series_xarr, lai_time_series_xarr])\n",
+ "\n",
+ "datacube.to_zarr(\"result.zarr\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Display cumulative NDVI from last result\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "polygon_ndvi_ds = ndvi_time_series_xarr['ndvi'].sortby('time')\n",
+ "time_coords = polygon_ndvi_ds['time']\n",
+ "\n",
+ "# exclude Nan values to caluculate mean\n",
+ "masked_dataarray = polygon_ndvi_ds.where(~np.isnan(polygon_ndvi_ds))\n",
+ "\n",
+ "# NDVI mean calculattion\n",
+ "mean_ndvi = masked_dataarray.mean(dim=['x', 'y'])\n",
+ "\n",
+ "# Cumulative NDVI calculation \n",
+ "polygon_cumul_ndvi = mean_ndvi.cumsum(dim='time')\n",
+ "polygon_cumul_ndvi \n",
+ "\n",
+ "# build & display cumlative Ndvi graph\n",
+ "plt.plot(time_coords, polygon_cumul_ndvi)\n",
+ "plt.xlabel('Time')\n",
+ "plt.ylabel('NDVI Cumul')\n",
+ "plt.title('NVDI Cumul by date')\n",
+ "plt.show()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -128,7 +221,7 @@
"outputs": [],
"source": [
"# Get coverage for the polygon\n",
- "coverage_info_df, images_references = client.get_satellite_coverage_image_references(polygon, year_ago, today, collections=[Collection.SENTINEL_2, Collection.LANDSAT_8, Collection.LANDSAT_9])\n",
+ "coverage_info_df, images_references = client.get_satellite_coverage_image_references(polygon, year_ago, today, collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8, SatelliteImageryCollection.LANDSAT_9])\n",
"coverage_info_df"
]
},
@@ -148,7 +241,7 @@
"outputs": [],
"source": [
"# Download and save a specific image\n",
- "client.download_image(images_references[('2021-11-11', 'LANDSAT_8')])\n"
+ "client.download_image(images_references[('2023-05-03', 'SENTINEL_2')])\n"
]
},
{
@@ -189,7 +282,7 @@
"# Get time serie\n",
"start_date = dt.datetime.strptime(\"2018-12-30\", \"%Y-%m-%d\")\n",
"end_date = dt.datetime.strptime(\"2019-12-31\", \"%Y-%m-%d\")\n",
- "df = client.get_time_series(polygon, start_date, end_date, collection=Collection.MODIS, indicators=[\"NDVI\"])\n",
+ "df = client.get_time_series(polygon, start_date, end_date, collection=SatelliteImageryCollection.MODIS, indicators=[\"NDVI\"])\n",
"df.head()"
]
},
@@ -234,16 +327,334 @@
"end_date = dt.datetime.strptime(\"2022-04-01\", \"%Y-%m-%d\")\n",
"client.get_metrics(polygon, schema_id, start_date, end_date)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "start_date=\"2020-10-09\"\n",
+ "end_date=\"2022-10-09\"\n",
+ "sensors=[\"Sentinel_2\", \"Landsat_8\"]\n",
+ "denoiser=True\n",
+ "smoother=\"ww\"\n",
+ "eoc=True\n",
+ "aggregation=\"mean\"\n",
+ "index=\"ndvi\"\n",
+ "raw_data=True\n",
+ "str_polygon=\"POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))\"\n",
+ "\n",
+ "str_s3_path = client.get_mr_time_series(str_polygon, start_date, end_date, sensors, denoiser, smoother, eoc, aggregation, index, raw_data)\n",
+ "str_s3_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "str_polygon=\"POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))\"\n",
+ "\n",
+ "str_s3_path = client.get_mr_time_series(str_polygon)\n",
+ "str_s3_path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from notebook_utils import *\n",
+ "\n",
+ "# read MRTS outputs from AWS and plot result\n",
+ "dict_mrts = read_mrts_from_awsS3(str_s3_path)\n",
+ "plot_mrts_dict(dict_mrts)\n",
+ "\n",
+ "### others useful functions\n",
+ "# plot_mrts_from_aws(str_s3_path) : plot MRTS outputs from AWS\n",
+ "# download MRTS outputs from AWS and read outputs downloaded : download_s3_files(str_s3_path), read_mrts_data(str_downloaded_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## AGRIQUEST COMPUTATION"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate a panda data frame from Agriquest Weather Data:\n",
+ "# on all AMU of o block\n",
+ "# on a specific period\n",
+ "# for a specific weather indicator\n",
+ "aq_weather_data_frame = client.get_agriquest_weather_block_data(start_date=\"2022-06-01\",\n",
+ " end_date=\"2023-06-10\",\n",
+ " block_code=AgriquestBlocks.AMU_NORTH_AMERICA,\n",
+ " weather_type=AgriquestWeatherType.AVERAGE_TEMPERATURE)\n",
+ "\n",
+ "aq_weather_data_frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Generate a panda data frame from Agriquest NDVI Data:\n",
+ "# on all AMU of o block\n",
+ "# on a specific date\n",
+ "# for a specific commodity (ALL_VEGETATION or ALL_CROPS)\n",
+ "\n",
+ "aq_ndvi_data_frame = client.get_agriquest_ndvi_block_data(day_of_measure=\"2022-06-01\",\n",
+ " commodity_code=AgriquestCommodityCode.ALL_CROPS, \n",
+ " block_code=AgriquestBlocks.FRA_DEPARTEMENTS)\n",
+ "\n",
+ "aq_ndvi_data_frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ANALYTICS COMPUTATION"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Historical Harvest\n",
+ "harvest_historical_analytics = client.get_harvest_analytics(\n",
+ " season_duration=215,\n",
+ " season_start_day=1,\n",
+ " season_start_month=4,\n",
+ " crop=crops._2ND_CORN,\n",
+ " year=2021,\n",
+ " geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))\",\n",
+ " harvest_type=Harvest.HARVEST_HISTORICAL)\n",
+ "harvest_historical_analytics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Inseason Harvest\n",
+ "harvest_in_season_analytics = client.get_harvest_analytics(\n",
+ " season_duration=215,\n",
+ " season_start_day=1,\n",
+ " season_start_month=4,\n",
+ " crop=crops._2ND_CORN,\n",
+ " year=2021,\n",
+ " geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))\",\n",
+ " harvest_type=Harvest.HARVEST_IN_SEASON)\n",
+ "\n",
+ "harvest_in_season_analytics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Inseason Emergence\n",
+ "emergence_in_season_analytics = client.get_emergence_analytics(\n",
+ " season_duration=215,\n",
+ " season_start_day=1,\n",
+ " season_start_month=4,\n",
+ " crop=crops._2ND_CORN,\n",
+ " year=2021,\n",
+ " geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))\",\n",
+ " emergence_type=Emergence.EMERGENCE_IN_SEASON)\n",
+ "\n",
+ "emergence_in_season_analytics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Historical Emergence\n",
+ "emergence_historical_analytics = client.get_emergence_analytics(\n",
+ " season_duration=215,\n",
+ " season_start_day=1,\n",
+ " season_start_month=4,\n",
+ " crop=crops._2ND_CORN,\n",
+ " year=2021,\n",
+ " geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))\",\n",
+ " emergence_type=Emergence.EMERGENCE_HISTORICAL)\n",
+ "\n",
+ "emergence_historical_analytics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Emergence Delay\n",
+ "emergence_delay_analytics = client.get_emergence_analytics(\n",
+ " season_duration=215,\n",
+ " season_start_day=1,\n",
+ " season_start_month=4,\n",
+ " crop=crops.CORN,\n",
+ " year=2021,\n",
+ " geometry=\"POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))\",\n",
+ " emergence_type=Emergence.EMERGENCE_DELAY)\n",
+ "\n",
+ "emergence_delay_analytics\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Potential Score\n",
+ "potential_score_analytics = client.get_potential_score_analytics(\n",
+ " end_date = \"2022-03-06\",\n",
+ " nb_historical_years = 5,\n",
+ " season_duration = 200, \n",
+ " season_start_day = 1, \n",
+ " season_start_month = 10, \n",
+ " crop = crops.CORN,\n",
+ " sowing_date = \"2021-10-01\",\n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ ")\n",
+ "\n",
+ "potential_score_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Greenness\n",
+ "greenness_analytics = client.get_greenness_analytics(\n",
+ " start_date =\"2022-01-15\",\n",
+ " end_date = \"2022-05-31\", \n",
+ " crop = crops.CORN,\n",
+ " sowing_date = \"2022-01-15\",\n",
+ " # WKT\n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ " # GeoJson\n",
+ " #geometry='{\"type\": \"Polygon\",\"coordinates\": [[[ -54.260277780000003, -25.387777780000000 ],[ -54.260277780000003, -25.374444440000001 ],[ -54.259999999999998, -25.374166670000001 ],[ -54.259722220000000, -25.374444440000001 ],[ -54.259444440000003, -25.374444440000001 ],[ -54.258888890000001, -25.374722219999999 ],[ -54.258611110000004, -25.374722219999999 ],[ -54.258888890000001, -25.375000000000000 ],[ -54.258888890000001, -25.375555550000001 ],[ -54.258611110000004, -25.376111110000000 ],[ -54.258611110000004, -25.381944440000002 ],[ -54.258333329999999, -25.384166669999999 ],[ -54.256944439999998, -25.383611110000000 ],[ -54.256944439999998, -25.384166669999999 ],[ -54.257500000000000, -25.384166669999999 ],[ -54.257500000000000, -25.384444439999999 ],[ -54.257777779999998, -25.384166669999999 ],[ -54.258070160000003, -25.384158120000002 ],[ -54.258055560000003, -25.384444439999999 ],[ -54.258077300000004, -25.384722060000001 ],[ -54.257500000000000, -25.385277779999999 ],[ -54.256944439999998, -25.385000000000002 ],[ -54.256388890000004, -25.383611110000000 ],[ -54.254722219999998, -25.383055550000002 ],[ -54.254722219999998, -25.382500000000000 ],[ -54.254166670000004, -25.381944440000002 ],[ -54.254444440000000, -25.381666670000001 ],[ -54.254722219999998, -25.381666670000001 ],[ -54.254722219999998, -25.379444440000000 ],[ -54.252777780000002, -25.379444440000000 ],[ -54.252777780000002, -25.385833330000001 ],[ -54.254192230000001, -25.386153900000000 ],[ -54.253906700000002, -25.385892160000001 ],[ -54.253888889999999, -25.385000000000002 ],[ -54.254444440000000, -25.385555549999999 ],[ -54.254787100000001, -25.385820770000002 ],[ -54.254722219999998, -25.386111110000002 ],[ -54.260277780000003, -25.387777780000000 ]]]}',\n",
+ ")\n",
+ "\n",
+ "greenness_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Harvest Readiness\n",
+ "harvest_readiness_analytics = client.get_harvest_readiness_analytics(\n",
+ " start_date =\"2022-01-15\",\n",
+ " end_date = \"2022-05-31\", \n",
+ " crop = crops.CORN,\n",
+ " sowing_date = \"2022-01-15\",\n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ ")\n",
+ "\n",
+ "harvest_readiness_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - Planted area\n",
+ "planted_area_analytics = client.get_planted_area_analytics(\n",
+ " start_date =\"2022-01-15\",\n",
+ " end_date = \"2022-05-31\", \n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ ")\n",
+ "\n",
+ "planted_area_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - zarc\n",
+ "zarc_analytics = client.get_zarc_analytics(\n",
+ " start_date_emergence= \"2022-01-15\",\n",
+ " end_date_emergence = \"2022-05-31\",\n",
+ " nb_days_sowing_emergence = 20,\n",
+ " crop =crops.CORN, \n",
+ " soil_type = ZarcSoilType.NONE,\n",
+ " cycle = ZarcCycleType.NONE, \n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ ")\n",
+ "\n",
+ "zarc_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analytics processor - crop id\n",
+ "brazil_crop_id_analytics = client.get_brazil_crop_id_analytics(\n",
+ " start_date =\"2020-10-01\",\n",
+ " end_date = \"2021-05-31\", \n",
+ " season = CropIdSeason.SEASON_1,\n",
+ " geometry=\"POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))\"\n",
+ ")\n",
+ "\n",
+ "brazil_crop_id_analytics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# Display probability by crop value from last zarc analytics result\n",
+ "data_dict = eval(brazil_crop_id_analytics.values[0][1])\n",
+ "df_new = pd.DataFrame.from_records([data_dict])\n",
+ "df_new\n"
+ ]
}
],
"metadata": {
- "interpreter": {
- "hash": "0372a3937da5da54a3bfdd53fff2df272fc2d59026be775ab1768d4a942ca61a"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 ('agp')",
+ "display_name": "geosyspy_0.2.0",
"language": "python",
- "name": "python3"
+ "name": "geosyspy_0.2.0"
},
"language_info": {
"codemirror_mode": {
@@ -255,9 +666,13 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.11.4"
},
- "orig_nbformat": 4
+ "vscode": {
+ "interpreter": {
+ "hash": "343b2e8ed5eaa23bb5713879a0aa346059bcae91e4280657563d238a2165b641"
+ }
+ }
},
"nbformat": 4,
"nbformat_minor": 2
diff --git a/geosyspy/__init__.py b/geosyspy/__init__.py
index 198f2d9..856cc83 100644
--- a/geosyspy/__init__.py
+++ b/geosyspy/__init__.py
@@ -1 +1,4 @@
from .geosys import Geosys
+from .utils.logging_config import configure_logging
+
+configure_logging()
diff --git a/geosyspy/constants.py b/geosyspy/constants.py
deleted file mode 100644
index 991bc41..0000000
--- a/geosyspy/constants.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from enum import Enum
-
-
-class Collection(Enum):
- """
- Available imagery and weather collections
- """
- MODIS = "MODIS"
- SENTINEL_2 = "SENTINEL_2"
- LANDSAT_8 = "LANDSAT_8"
- LANDSAT_9 = "LANDSAT_9"
- WEATHER_FORECAST_DAILY = "WEATHER.FORECAST_DAILY"
- WEATHER_FORECAST_HOURLY = "WEATHER.FORECAST_HOURLY"
- WEATHER_HISTORICAL_DAILY = "WEATHER.HISTORICAL_DAILY"
-
-
-class Env(Enum):
- """
- Environment to target (PROD, PREPROD)
- """
- PROD = "prod"
- PREPROD = "preprod"
-
-
-class Region(Enum):
- """
- Region to target (NA, EU)
- """
- NA = "na"
- EU = "eu"
diff --git a/geosyspy/geosys.py b/geosyspy/geosys.py
index 052bc0a..ab15c2f 100644
--- a/geosyspy/geosys.py
+++ b/geosyspy/geosys.py
@@ -1,41 +1,22 @@
-from oauthlib.oauth2 import LegacyApplicationClient
-from requests_oauthlib import OAuth2Session
-from oauthlib.oauth2 import TokenExpiredError
-import json
-import re
-from urllib.parse import urljoin
-import pandas as pd
-from . import platforms
-import logging
import io
import zipfile
-from rasterio.io import MemoryFile
-from shapely import wkt
from pathlib import Path
-from . import image_reference
-import xarray as xr
-import rasterio
-import numpy as np
-from .constants import Collection
+import numpy as np
+import rasterio
+import xarray as xr
+from rasterio.io import MemoryFile
-def renew_access_token(func):
- """Decorator used to wrap the Geosys class's http methods.
-
- This decorator wraps the geosys http methods (get,post...) and checks
- wether the used token is still valid or not. If not, it fetches a new token and
- uses it to make another request.
-
- """
-
- def wrapper(self, *args, **kwargs):
- try:
- return func(self, *args, **kwargs)
- except TokenExpiredError:
- self._Geosys__refresh_token()
- return func(self, *args, **kwargs)
-
- return wrapper
+from geosyspy import image_reference
+from geosyspy.services.agriquest_service import *
+from geosyspy.services.analytics_fabric_service import *
+from geosyspy.services.analytics_processor_service import *
+from geosyspy.services.gis_service import *
+from geosyspy.services.map_product_service import *
+from geosyspy.services.master_data_management_service import *
+from geosyspy.services.vegetation_time_series_service import *
+from geosyspy.services.weather_service import *
+from geosyspy.utils.geosys_platform_urls import *
class Geosys:
@@ -44,212 +25,51 @@ class Geosys:
`client = Geosys(api_client_id, api_client_secret, api_username, api_password, env, region)`
Parameters:
- str_api_client_id (str): The client id
- str_api_client_secret (str): The client secret
- str_api_username (str): The api username
- str_api_password (str): The api user password
- enum_env (enum): 'Env.PROD' or 'Env.PREPROD'
- enum_region (enum): 'Region.NA' or 'Region.EU'
- str_priority_queue (str): 'realtime' or 'bulk'
+ enum_env: 'Env.PROD' or 'Env.PREPROD'
+ enum_region: 'Region.NA'
+ priority_queue: 'realtime' or 'bulk'
"""
- def __init__(
- self,
- str_api_client_id,
- str_api_client_secret,
- str_api_username,
- str_api_password,
- enum_env,
- enum_region,
- str_priority_queue="realtime",
- ):
- """Initializes a Geosys instance with the required credentials
- to connect to the GEOSYS API.
- """
- self.region = enum_region
- self.env = enum_env
- self.str_id_server_url = platforms.IDENTITY_URLS[enum_region.value][enum_env.value]
- self.base_url = platforms.GEOSYS_API_URLS[enum_region.value][enum_env.value]
- self.master_data_management_endpoint = "master-data-management/v6/seasonfields"
- self.vts_endpoint = "vegetation-time-series/v1/season-fields"
- self.vts_by_pixel_endpoint = "vegetation-time-series/v1/season-fields/pixels"
- self.flm_catalog_imagery = (
- "field-level-maps/v4/season-fields/{}/catalog-imagery"
- )
- self.flm_coverage = "field-level-maps/v4/season-fields/{}/coverage"
- self.weather_endpoint = "Weather/v1/weather"
- self.analytics_fabric_endpoint = "analytics/metrics"
- self.analytics_fabric_schema_endpoint = "analytics/schemas"
- self.str_api_client_id = str_api_client_id
- self.str_api_client_secret = str_api_client_secret
- self.str_api_username = str_api_username
- self.str_api_password = str_api_password
- self.token = None
- self.priority_queue = str_priority_queue
- self.priority_headers = {"bulk": "Geosys_API_Bulk", "realtime": ""}
- self.list_collection_lr = [Collection.MODIS]
- self.list_collection_mr = [Collection.LANDSAT_8, Collection.LANDSAT_9, Collection.SENTINEL_2]
- self.list_collection_weather = [
- Collection.WEATHER_FORECAST_DAILY,
- Collection.WEATHER_FORECAST_HOURLY,
- Collection.WEATHER_HISTORICAL_DAILY,
- ]
- self.__authenticate()
-
- def __authenticate(self):
- """Authenticates the client to the API.
-
- This method connects the user to the API which generates a token that
- will be valid for one hour. A refresh token is also generated, which
- makes it possible for the http methods wrappers to get a new token
- once the previous one is no more valid through the renew_access_token
- decorator. This method is only run once when a Geosys object is instantiated.
-
- """
-
- try:
- oauth = OAuth2Session(
- client=LegacyApplicationClient(client_id=self.str_api_client_id)
- )
- self.token = oauth.fetch_token(
- token_url=self.str_id_server_url,
- username=self.str_api_username,
- password=self.str_api_password,
- client_id=self.str_api_client_id,
- client_secret=self.str_api_client_secret,
- )
- self.token["refresh_token"] = oauth.cookies["refresh_token"]
- logging.info("Authenticated")
- except Exception as e:
- logging.error(e)
-
- def __refresh_token(self):
- """Fetches a new token."""
-
- client = OAuth2Session(self.str_api_client_id, token=self.token)
- self.token = client.refresh_token(
- self.str_id_server_url,
- client_id=self.str_api_client_id,
- client_secret=self.str_api_client_secret,
- )
-
- def __get_matched_str_from_pattern(self, pattern, text):
- """Returns the first occurence of the matched pattern in text.
-
- Args:
- pattern : A string representing the regex pattern to look for.
- text : The text to look into.
-
- Returns:
- A string representing the first occurence in text of the pattern.
-
- """
- p = re.compile(pattern)
- return p.findall(text)[0]
-
- @renew_access_token
- def __get(self, url_endpoint, headers={}):
- """Gets the url_endpopint.
-
- Args:
- url_endpoint : A string representing the url to get.
-
- Returns:
- A response object.
- """
- client = OAuth2Session(self.str_api_client_id, token=self.token)
- return client.get(url_endpoint, headers=headers)
-
- @renew_access_token
- def __post(self, url_endpoint, payload, headers={}):
- """Posts payload to the url_endpoint.
-
- Args:
- url_endpoint : A string representing the url to post paylaod to.
- payload : A python dict representing the payload.
-
- Returns:
- A response object.
- """
- client = OAuth2Session(self.str_api_client_id, token=self.token)
- return client.post(url_endpoint, json=payload, headers=headers)
-
- @renew_access_token
- def __patch(self, url_endpoint, payload):
- """Patchs payload to the url_endpoint.
-
- Args:
- url_endpoint : A string representing the url to patch paylaod to.
- payload : A python dict representing the payload.
-
- Returns:
- A response object.
- """
- client = OAuth2Session(self.str_api_client_id, token=self.token)
- return client.patch(url_endpoint, json=payload)
-
- def __create_season_field_id(self, polygon):
- """Posts the payload below to the master data management endpoint.
-
- This method returns a season field id. The season field id is required
- to request other APIs endpoints.
-
- Args:
- polygon : A string representing a polygon.
-
- Returns:
- A response object.
-
- """
- payload = {
- "Geometry": polygon,
- "Crop": {"Id": "CORN"},
- "SowingDate": "2022-01-01",
- }
- str_mdm_url = urljoin(self.base_url, self.master_data_management_endpoint)
-
- return self.__post(str_mdm_url, payload)
-
- def __extract_season_field_id(self, polygon):
- """Extracts the season field id from the response object.
-
- Args:
- polygon : A string representing a polygon.
-
- Returns:
- A string rerpesenting the season field id.
-
- Raises:
- ValueError: The response status code is not as expected.
- """
-
- response = self.__create_season_field_id(polygon)
- dict_response = response.json()
-
- if (
- response.status_code == 400
- and "sowingDate" in dict_response["errors"]["body"]
- ):
- pattern = r"\sId:\s(\w+),"
- str_text = dict_response["errors"]["body"]["sowingDate"][0]["message"]
- return self.__get_matched_str_from_pattern(pattern, str_text)
-
- elif response.status_code == 201:
- return dict_response["id"]
- else:
- raise ValueError(
- f"Cannot handle HTTP response : {str(response.status_code)} : {str(response.json())}"
- )
-
- def get_time_series(self, polygon, start_date, end_date, collection, indicators):
+ def __init__(self,
+ client_id: str = None,
+ client_secret: str = None,
+ username: str = None,
+ password: str = None,
+ enum_env: Env = Env.PROD,
+ enum_region: Region = Region.NA,
+ priority_queue: str = "realtime",
+ bearer_token: str = None
+ ):
+ self.logger = logging.getLogger(__name__)
+ self.region: str = enum_region.value
+ self.env: str = enum_env.value
+ self.base_url: str = GEOSYS_API_URLS[enum_region.value][enum_env.value]
+ self.gis_url: str = GIS_API_URLS[enum_region.value][enum_env.value]
+ self.priority_queue: str = priority_queue
+ self.http_client: HttpClient = HttpClient(client_id, client_secret, username, password, enum_env.value,
+ enum_region.value, bearer_token)
+ self.__master_data_management_service = MasterDataManagementService(self.base_url, self.http_client)
+ self.__analytics_fabric_service = AnalyticsFabricService(self.base_url, self.http_client)
+ self.__analytics_processor_service = AnalyticsProcessorService(self.base_url, self.http_client)
+ self.__agriquest_service = AgriquestService(self.base_url, self.http_client)
+ self.__weather_service = WeatherService(self.base_url, self.http_client)
+ self.__gis_service = GisService(self.gis_url, self.http_client)
+ self.__vts_service = VegetationTimeSeriesService(self.base_url, self.http_client)
+ self.__map_product_service = MapProductService(self.base_url, self.http_client, self.priority_queue)
+
+ def get_time_series(self, polygon: str,
+ start_date: datetime,
+ end_date: datetime,
+ collection: enumerate,
+ indicators: [str]) -> pd.DataFrame:
"""Retrieve a time series of the indicator for the aggregated polygon on the collection targeted.
Args:
- polygon (str): The polygon
- start_date (datetime): The start date of the time series
- end_date (datetime): The end date of the time series
- collection (enum): The collection targeted
- indicators (str list): The indicators to retrieve on the collection
+ polygon : The polygon
+ start_date : The start date of the time series
+ end_date : The end date of the time series
+ collection : The collection targeted
+ indicators : The indicators to retrieve on the collection
Returns:
(dataframe): A pandas dataframe for the time series
@@ -257,277 +77,134 @@ def get_time_series(self, polygon, start_date, end_date, collection, indicators)
Raises:
ValueError: The collection doesn't exist
"""
-
- if collection in self.list_collection_weather:
- return self.__get_weather(
+ if collection in WeatherTypeCollection:
+ return self.__weather_service.get_weather(
polygon,
start_date,
end_date,
- collection.value.split(".").pop(),
+ collection,
indicators,
)
- elif collection in self.list_collection_lr:
- return self.__get_modis_time_series(
- polygon, start_date, end_date, indicators[0]
+ elif collection in LR_SATELLITE_COLLECTION:
+ # extract seasonfield id from geometry
+ season_field_id: str = self.__master_data_management_service.extract_season_field_id(polygon)
+ return self.__vts_service.get_modis_time_series(
+ season_field_id, start_date, end_date, indicators[0]
)
else:
raise ValueError(f"{collection} collection doesn't exist")
- def get_satellite_image_time_series(
- self, polygon, start_date, end_date, collections, indicators
- ):
- """Retrieve a pixel-by-pxel time series of the indicator on the collection targeted.
+ def get_satellite_image_time_series(self, polygon: str,
+ start_date: datetime,
+ end_date: datetime,
+ collections: list[SatelliteImageryCollection],
+ indicators: [str]
+ ):
+ """Retrieve a pixel-by-pixel time series of the indicator on the collection targeted.
Args:
- polygon (str): The polygon
- start_date (datetime): The start date of the time series
- end_date (datetime): The end date of the time series
- collections (enum list): The collections targeted
- indicators (str list): The indicators to retrieve on the collections
+ polygon : The polygon
+ start_date : The start date of the time series
+ end_date : The end date of the time series
+ collections : The Satellite Imagery Collection targeted
+ indicators : The indicators to retrieve on the collections
Returns:
- ('dataframe or xarray'): Either a pandas dataframe or an xarray for the time series
+ ('dataframe or xarray'): Either a pandas dataframe or a xarray for the time series
"""
if not collections:
raise ValueError(
- "The argument collections is empty. It must be a list of constants.Collection objects"
+ "The argument collections is empty. It must be a list of SatelliteImageryCollection objects"
)
- elif all([isinstance(elem, Collection) for elem in collections]):
- if set(collections).issubset(set(self.list_collection_lr)):
- return self.__get_time_series_by_pixel(
- polygon, start_date, end_date, indicators[0]
+ elif all([isinstance(elem, SatelliteImageryCollection) for elem in collections]):
+ # extract seasonfield id from geometry
+ season_field_id: str = self.__master_data_management_service.extract_season_field_id(polygon)
+
+ if set(collections).issubset(set(LR_SATELLITE_COLLECTION)):
+ return self.__vts_service.get_time_series_by_pixel(
+ season_field_id, start_date, end_date, indicators[0]
)
- elif set(collections).issubset(set(self.list_collection_mr)):
+ elif set(collections).issubset(set(MR_SATELLITE_COLLECTION)):
return self.__get_images_as_dataset(
- polygon, start_date, end_date, collections
+ season_field_id, start_date, end_date, collections, indicators[0]
)
else:
raise TypeError(
- f"Argument collections must be a list of constants.Collection objects"
+ f"Argument collections must be a list of SatelliteImageryCollection objects"
)
- def __get_modis_time_series(self, polygon, start_date, end_date, indicator):
- """Returns a pandas DataFrame.
-
- This method returns a time series of 'indicator' within the range
- 'start_date' -> 'end_date' as a pandas DataFrame :
-
- | index | value |
-
- date | indicator | 1 |
- __________________________________
- start-date | indicator | 2 |
-
- ... | ... | ... |
-
- end-date | indicator | 8 |
-
- Args:
- polygon : A string representing a polygon.
- start_date : A datetime object representing the start date of the date interval the user wants to filter on.
- end_date : A datetime object representing the final date of the date interval the user wants to filter on.
- indicator : A string representing the indicator whose time series the user wants.
-
- Returns:
- df : A Pandas DataFrame containing two columns : index and value, and an index called 'date'.
-
- """
- logging.info("Calling APIs for aggregated time series")
- str_season_field_id = self.__extract_season_field_id(polygon)
- str_start_date = start_date.strftime("%Y-%m-%d")
- str_end_date = end_date.strftime("%Y-%m-%d")
- parameters = f"/values?$offset=0&$limit=9999&$count=false&SeasonField.Id={str_season_field_id}&index={indicator}&$filter=Date >= '{str_start_date}' and Date <= '{str_end_date}'"
- str_vts_url = urljoin(self.base_url, self.vts_endpoint + parameters)
-
- response = self.__get(str_vts_url)
-
- if response.status_code == 200:
- dict_response = response.json()
- df = pd.read_json(json.dumps(dict_response))
- df.set_index("date", inplace=True)
- return df
- else:
- logging.info(response.status_code)
-
- def __get_time_series_by_pixel(self, polygon, start_date, end_date, indicator):
- """Returns a pandas DataFrame.
-
- This method returns a time series of 'indicator' by pixel within the range 'start_date' -> 'end_date'
- as well as the pixel's coordinates X,Y in the MODIS's sinusoidal projection as a pandas DataFrame :
-
-
-
- | index | value | pixel.id | X | Y |
- _______________________________________|
- date | indicator | | | | |
- ___________________________________________________|
-
- start-date | indicator | 2 | 1 | | |
-
- ... | ... | ... | ... | | |
-
- end-date | indicator | 8 | 1000 | | |
-
- Args:
- polygon : A string representing a polygon.
- start_date : A datetime object representing the start date of the date interval the user wants to filter on.
- end_date : A datetime object representing the final date of the date interval the user wants to filter on.
- indicator : A string representing the indicator whose time series the user wants.
-
- Returns:
- df : A Pandas DataFrame containing five columns : index, value, pixel.id, X, Y and an index called 'date'.
-
- """
-
- logging.info("Calling APIs for time series by the pixel")
- str_season_field_id = self.__extract_season_field_id(polygon)
- str_start_date = start_date.strftime("%Y-%m-%d")
- str_end_date = end_date.strftime("%Y-%m-%d")
- parameters = f"/values?$offset=0&$limit=9999&$count=false&SeasonField.Id={str_season_field_id}&index={indicator}&$filter=Date >= '{str_start_date}' and Date <= '{str_end_date}'"
- str_vts_url = urljoin(self.base_url, self.vts_by_pixel_endpoint + parameters)
- # PSX/PSY : size in meters of one pixel
- # MODIS_GRID_LENGTH : theoretical length of the modis grid in meters
- # MOIS_GRID_HEIGHT : theoretical height of the modis grid in meters
- PSX = 231.65635826
- PSY = -231.65635826
- MODIS_GRID_LENGTH = 4800 * PSX * 36
- MODIS_GRID_HEIGHT = 4800 * PSY * 18
-
- response = self.__get(str_vts_url)
-
- if response.status_code == 200:
- df = pd.json_normalize(response.json())
- df.set_index("date", inplace=True)
-
- # Extracts h, v, i and j from the pixel dataframe
- logging.info("Computing X and Y coordinates per pixel... ")
- df["h"] = df["pixel.id"].str.extract(r"h(.*)v").astype(int)
- df["v"] = df["pixel.id"].str.extract(r"v(.*)i").astype(int)
- df["i"] = df["pixel.id"].str.extract(r"i(.*)j").astype(int)
- df["j"] = df["pixel.id"].str.extract(r"j(.*)$").astype(int)
-
- # XUL/YUL : The coordinates of the top left corner of the tile h,v's top left pixel
- # X/Y : the coordinates of the top left corner of the i,j pixel
- df["XUL"] = (df["h"] + 1) * 4800 * PSX - MODIS_GRID_LENGTH / 2
- df["YUL"] = (df["v"] + 1) * 4800 * PSY + MODIS_GRID_HEIGHT / 2
- df["X"] = df["i"] * PSX + df["XUL"]
- df["Y"] = df["j"] * PSY + df["YUL"]
- logging.info("Done ! ")
- return df[["index", "value", "pixel.id", "X", "Y"]]
- else:
- logging.info(response.status_code)
-
- def get_satellite_coverage_image_references(
- self, polygon, start_date, end_date, collections=[Collection.SENTINEL_2, Collection.LANDSAT_8]
- ):
+ def get_satellite_coverage_image_references(self, polygon: str,
+ start_date: datetime,
+ end_date: datetime,
+ collections: list[SatelliteImageryCollection] = [
+ SatelliteImageryCollection.SENTINEL_2,
+ SatelliteImageryCollection.LANDSAT_8]
+ ) -> tuple:
"""Retrieves a list of images that covers a polygon on a specific date range.
- The return is a tuple: a dataframe with all the images covering the polygon, and
- an dictionary images_references. Key= a tuple (image_date, image_sensor).
+ The return is a tuple: a dataframe with all the images covering the polygon, and
+ a dictionary images_references. Key= a tuple (image_date, image_sensor).
Value = an object image_reference, to use with the method `download_image()`
Args:
- polygon (str): The polygon
- start_date (datetime): The start date of the time series
- end_date (datetime): The end date of the time series
- sensors (str list): The sensors to check the coverage on
+ polygon: The polygon
+ start_date: The start date of the time series
+ end_date: The end date of the time series
+ collections: The sensors to check the coverage on
Returns:
(tuple): images list and image references for downloading
"""
+ # extract seasonfield id from geometry
+ season_field_id: str = self.__master_data_management_service.extract_season_field_id(polygon)
- df = self.__get_satellite_coverage(polygon, start_date, end_date, collections)
+ df = self.__map_product_service.get_satellite_coverage(season_field_id, start_date, end_date, "", collections)
images_references = {}
-
- for i, image in df.iterrows():
- images_references[
- (image["image.date"], image["image.sensor"])
- ] = image_reference.ImageReference(
- image["image.id"],
- image["image.date"],
- image["image.sensor"],
- image["seasonField.id"],
- )
+ if df is not None:
+ for i, image in df.iterrows():
+ images_references[
+ (image["image.date"], image["image.sensor"])
+ ] = image_reference.ImageReference(
+ image["image.id"],
+ image["image.date"],
+ image["image.sensor"],
+ image["seasonField.id"],
+ )
return df, images_references
- def __get_satellite_coverage(
- self, polygon, start_date, end_date, sensors=[Collection.SENTINEL_2, Collection.LANDSAT_8]
- ):
- logging.info("Calling APIs for coverage")
- str_season_field_id = self.__extract_season_field_id(polygon)
- str_start_date = start_date.strftime("%Y-%m-%d")
- str_end_date = end_date.strftime("%Y-%m-%d")
- sensors = [elem.value for elem in sensors]
- parameters = f"?maps.type=INSEASON_NDVI&Image.Sensor=$in:{'|'.join(sensors)}&CoverageType=CLEAR&$limit=9999&$filter=Image.Date >= '{str_start_date}' and Image.Date <= '{str_end_date}'"
-
- str_flm_url = urljoin(
- self.base_url,
- self.flm_catalog_imagery.format(str_season_field_id) + parameters,
- )
- response = self.__get(
- str_flm_url,
- {"X-Geosys-Task-Code": self.priority_headers[self.priority_queue]},
- )
-
- if response.status_code == 200:
- df = pd.json_normalize(response.json())
- if df.empty:
- return df
- else:
- return df[
- [
- "coverageType",
- "image.id",
- "image.availableBands",
- "image.sensor",
- "image.soilMaterial",
- "image.spatialResolution",
- "image.weather",
- "image.date",
- "seasonField.id",
- ]
- ]
- else:
- logging.info(response.status_code)
-
- def __get_zipped_tiff(self, field_id, image_id):
- parameters = f"/{image_id}/reflectance-map/TOC/image.tiff.zip"
- download_tiff_url = urljoin(
- self.base_url, self.flm_coverage.format(field_id) + parameters
- )
-
- response_zipped_tiff = self.__get(
- download_tiff_url,
- {"X-Geosys-Task-Code": self.priority_headers[self.priority_queue]},
- )
- return response_zipped_tiff
-
- def download_image(self, image_reference, str_path=""):
+ def download_image(self, image_reference,
+ path: str = ""):
"""Downloads a satellite image locally
Args:
image_reference (ImageReference): An ImageReference object representing the image to download
- str_path (str): the path to download the image to
+ path (str): the path to download the image to
"""
- response_zipped_tiff = self.__get_zipped_tiff(
+
+ response_zipped_tiff = self.__map_product_service.get_zipped_tiff(
image_reference.season_field_id, image_reference.image_id
)
- if str_path == "":
- str_path = Path.cwd() / f"image_{image_reference.image_id}_tiff.zip"
- with open(str_path, "wb") as f:
- logging.info(f"writing to {str_path}")
+ if path == "":
+ path = Path.cwd() / f"image_{image_reference.image_id}_tiff.zip"
+ with open(path, "wb") as f:
+ self.logger.info(f"writing to {path}")
f.write(response_zipped_tiff.content)
- def __get_images_as_dataset(self, polygon, start_date, end_date, collections):
+ def __get_images_as_dataset(self, polygon: str,
+ start_date: datetime,
+ end_date: datetime,
+ collections: list[SatelliteImageryCollection],
+ indicator: str) -> 'np.ndarray[Any , np.dtype[np.float64]]':
"""Returns all the 'sensors_list' images covering 'polygon' between
- 'start_date' and 'end_date' as an xarray dataset.
+ 'start_date' and 'end_date' as a xarray dataset.
Args:
polygon : A string representing the polygon that the images will be covering.
start_date : The date from which the method will start looking for images.
- end_date : The date at which the methd will stop looking images.
- sensors_list : A list of the sensors' names as strings.
+ end_date : The date at which the method will stop looking images.
+ collections : A list of Satellite Imagery Collection.
+ indicator : A string representing the indicator whose time series the user wants.
Returns:
The image's numpy array.
@@ -553,8 +230,8 @@ def get_coordinates_by_pixel(raster):
# Selects the covering images in the provided date range
# and sorts them by resolution, from the highest to the lowest.
# Keeps only the first image if two are found on the same date.
- df_coverage = self.__get_satellite_coverage(
- polygon, start_date, end_date, collections
+ df_coverage = self.__map_product_service.get_satellite_coverage(
+ polygon, start_date, end_date, indicator, collections
)
# Return empty dataset if no coverage on the polygon between start_date, end_date
@@ -572,11 +249,15 @@ def get_coordinates_by_pixel(raster):
# for each image id and some additional data (bands, sensor...)
dict_archives = {}
for i, row in df_coverage.iterrows():
+ if indicator.upper() != "REFLECTANCE":
+ bands = [indicator]
+ else:
+ bands = row["image.availableBands"]
dict_archives[row["image.id"]] = {
- "byte_archive": self.__get_zipped_tiff(
- row["seasonField.id"], row["image.id"]
+ "byte_archive": self.__map_product_service.get_zipped_tiff(
+ row["seasonField.id"], row["image.id"], indicator
).content,
- "bands": row["image.availableBands"],
+ "bands": bands,
"date": row["image.date"],
"sensor": row["image.sensor"],
}
@@ -590,42 +271,39 @@ def get_coordinates_by_pixel(raster):
first_img_id = df_coverage.iloc[0]["image.id"]
for img_id, dict_data in dict_archives.items():
with zipfile.ZipFile(io.BytesIO(dict_data["byte_archive"]), "r") as archive:
- list_files = archive.namelist()
- for file in list_files:
- list_words = file.split(".")
- if list_words[-1] == "tif":
- img_in_bytes = archive.read(file)
- with MemoryFile(img_in_bytes) as memfile:
- with memfile.open() as raster:
- dict_coords = get_coordinates_by_pixel(raster)
- xarr = xr.DataArray(
- raster.read(masked=True),
- dims=["band", "y", "x"],
- coords={
- "band": dict_data["bands"],
- "y": dict_coords["y"],
- "x": dict_coords["x"],
- "time": dict_data["date"],
- },
+ images_in_bytes = [archive.read(file) for file in archive.namelist() if file.endswith('.tif')]
+ for image in images_in_bytes:
+ with MemoryFile(image) as memfile:
+ with memfile.open() as raster:
+ dict_coords = get_coordinates_by_pixel(raster)
+ xarr = xr.DataArray(
+ raster.read(masked=True),
+ dims=["band", "y", "x"],
+ coords={
+ "band": dict_data["bands"],
+ "y": dict_coords["y"],
+ "x": dict_coords["x"],
+ "time": dict_data["date"],
+ },
+ )
+
+ if img_id == first_img_id:
+ len_y = len(dict_coords["y"])
+ len_x = len(dict_coords["x"])
+ print(
+ f"The highest resolution's image grid size is {(len_x, len_y)}"
)
-
- if img_id == first_img_id:
- len_y = len(dict_coords["y"])
- len_x = len(dict_coords["x"])
- print(
- f"The highest resolution's image grid size is {(len_x, len_y)}"
- )
- else:
- logging.info(
- f"interpolating {img_id} to {first_img_id}'s grid"
- )
- xarr = xarr.interp(
- x=list_xarr[0].coords["x"].data,
- y=list_xarr[0].coords["y"].data,
- method="linear",
- )
- list_xarr.append(xarr)
- list_crs.append(raster.crs.to_string())
+ else:
+ self.logger.info(
+ f"interpolating {img_id} to {first_img_id}'s grid"
+ )
+ xarr = xarr.interp(
+ x=list_xarr[0].coords["x"].data,
+ y=list_xarr[0].coords["y"].data,
+ method="linear",
+ )
+ list_xarr.append(xarr)
+ list_crs.append(raster.crs.to_string())
# Adds the img's raster's crs to the initial dataframe
df_coverage["crs"] = list_crs
@@ -633,10 +311,10 @@ def get_coordinates_by_pixel(raster):
# Concatenates all the DataArrays in list_xarr in order
# to create one final DataArray with an additional dimension
# 'time'. This final DataArray is then transformed into
- # an xarray Dataset containing one data variable "reflectance".
+ # a xarray Dataset containing one data variable "reflectance".
final_xarr = xr.concat(list_xarr, "time")
- dataset = xr.Dataset(data_vars={"reflectance": final_xarr})
+ dataset = xr.Dataset(data_vars={indicator.lower(): final_xarr})
# Adds additional metadata to the dataset.
dataset = dataset.assign_coords(
@@ -658,127 +336,48 @@ def get_coordinates_by_pixel(raster):
)
return dataset
- def __get_weather(self, polygon, start_date, end_date, weather_type, fields):
- """Returns the weather data as a pandas dataframe.
+ ###########################################
+ # ANALYTICS FABRIC #
+ ###########################################
- Args:
- polygon : A string representing a polygon.
- start_date : A datetime object representing the start date of the date interval the user wants to filter on.
- end_date : A datetime object representing the final date of the date interval the user wants to filter on.
- weather_type : A string representing the collection ["HISTORICAL_DAILY", "FORECAST_DAILY", "FORECAST_HOURLY"]
- fields : An array of strings representings the fields to select (eg: Precipitation, Temperature)
-
- Returns:
- The image's numpy array.
-
- """
-
- allowed_weather_types = [
- "HISTORICAL_DAILY",
- "FORECAST_DAILY",
- "FORECAST_HOURLY",
- ]
- if weather_type not in allowed_weather_types:
- raise ValueError(f"weather_type should be either {allowed_weather_types}")
-
- if "Date" not in fields:
- fields.append("Date")
-
- str_start_date = start_date.strftime("%Y-%m-%d")
- str_end_date = end_date.strftime("%Y-%m-%d")
- polygon_wkt = wkt.loads(polygon)
- str_weather_fields = ",".join(fields)
- parameters = f"?%24offset=0&%24limit=9999&%24count=false&Location={polygon_wkt.centroid.wkt}&Date=%24between%3A{str_start_date}T00%3A00%3A00.0000000Z%7C{str_end_date}T00%3A00%3A00.0000000Z&Provider=GLOBAL1&WeatherType={weather_type}&$fields={str_weather_fields}"
- str_weather_url = urljoin(self.base_url, self.weather_endpoint + parameters)
-
- response = self.__get(str_weather_url)
-
- if response.status_code == 200:
- df = pd.json_normalize(response.json())
- if df.empty:
- return df
- else:
- df.set_index("date", inplace=True)
- df["Location"] = polygon_wkt.centroid.wkt
- return df.sort_index()
- else:
- logging.error(response.status_code)
- raise ValueError(response.content)
-
- def create_schema_id(self, schema_id, schema):
+ def create_schema_id(self, schema_id: str,
+ schema: dict):
"""Create a schema in Analytics Fabrics
Args:
- schema_id (str): The schema id to create
- schema (dict): Dict representing the schema {'property_name': 'property_type'}
+ schema_id: The schema id to create
+ schema: Dict representing the schema {'property_name': 'property_type'}
Returns:
A http response object.
"""
- properties = []
- for prop_name, datatype in schema.items():
- prop = {
- "Name": prop_name,
- "Datatype": datatype,
- "UnitCategory": None,
- "IsPartOfKey": False,
- "IsOptional": False,
- }
- properties.append(prop)
-
- payload = {
- "Id": schema_id,
- "Properties": properties,
- "Metadata": {"OnAggregationCompleted": "Off"},
- }
- str_af_url = urljoin(
- self.base_url,
- self.analytics_fabric_schema_endpoint,
- )
- response = self.__post(str_af_url, payload)
- if response.status_code == 201:
- return response.content
- else:
- logging.info(response.status_code)
+ return self.__analytics_fabric_service.create_schema_id(schema_id=schema_id, schema=schema)
- def get_metrics(self, polygon, schema_id, start_date, end_date):
+ def get_metrics(self, polygon: str,
+ schema_id: str,
+ start_date: datetime,
+ end_date: datetime):
"""Returns metrics from Analytics Fabrics in a pandas dataframe.
Args:
- polygon (str): A string representing a polygon.
- start_date (datetime): A datetime object representing the start date of the date interval the user wants to filter on.
- end_date (datetime): A datetime object representing the final date of the date interval the user wants to filter on.
- schema_id (str): A string representing a schema existing in Analytics Fabrics
+ polygon : A string representing a polygon.
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ schema_id : A string representing a schema existing in Analytics Fabrics
Returns:
df : A Pandas DataFrame containing severals columns with metrics
"""
- season_field_id = self.__extract_season_field_id(polygon)
- logging.info("Calling APIs for metrics")
- str_start_date = start_date.strftime("%Y-%m-%d")
- str_end_date = end_date.strftime("%Y-%m-%d")
- parameters = f'?%24limit=9999&Timestamp=$between:{str_start_date}|{str_end_date}&$filter=Entity.ExternalTypedIds.Contains("SeasonField:{season_field_id}@LEGACY_ID_{self.region.value.upper()}")&$filter=Schema.Id=={schema_id}'
- str_af_url = urljoin(
- self.base_url,
- self.analytics_fabric_endpoint + parameters,
- )
- response = self.__get(str_af_url)
-
- if response.status_code == 200:
- df = pd.json_normalize(response.json())
- df.drop("Entity.TypedId", inplace=True, axis=1)
- df.rename(
- columns={"Timestamp": "date"},
- inplace=True,
- )
- df = df.sort_values(by="date")
- df.set_index("date", inplace=True)
- return df
- else:
- logging.info(response.status_code)
- def push_metrics(self, polygon, schema_id, values):
+ season_field_id: str = self.__master_data_management_service.extract_season_field_id(polygon)
+ season_field_unique_id: str = self.__master_data_management_service.get_season_field_unique_id(season_field_id)
+
+ return self.__analytics_fabric_service.get_metrics(season_field_unique_id, schema_id, start_date, end_date)
+
+ def push_metrics(self, polygon: str,
+ schema_id: str,
+ values: dict):
"""Push metrics in Analytics Fabrics
Args:
@@ -789,24 +388,551 @@ def push_metrics(self, polygon, schema_id, values):
Returns:
A response object.
"""
- season_field_id = self.__extract_season_field_id(polygon)
- payload = []
- for value in values:
- prop = {
- "Entity": {
- "TypedId": f"SeasonField:{season_field_id}@LEGACY_ID_{self.region.value.upper()}"
- },
- "Schema": {"Id": schema_id, "Version": 1},
- }
- prop = dict(prop, **value)
- payload.append(prop)
+ season_field_id: str = self.__master_data_management_service.extract_season_field_id(polygon)
+
+ return self.__analytics_fabric_service.push_metrics(season_field_id, schema_id, values)
+
+ ###########################################
+ # MASTER DATA MANAGEMENT #
+ ###########################################
+
+ def get_available_crops(self):
+ """Build the list of available crop codes for the connected user in an enum
+
+ Returns:
+ crop_enum: an Enum containing all available crop codes of the connected user
+ """
+ # get crop code list
+ result = self.__master_data_management_service.get_available_crops_code()
+
+ # build an enum with all available crop codes for the connected user
+ crop_enum = Enum('CropEnum',
+ {crop['code'] if not crop['code'][0].isdigit() else '_' + crop['code']: crop['code'] for crop
+ in result})
+
+ return crop_enum
+
+ def get_available_permissions(self):
+ """Build the list of available permissions codes for the connected user in an enum
+
+ Returns:
+ permissions: a string array containing all available permissions of the connected user
+ """
+ # get crop code list
+ result = self.__master_data_management_service.get_permission_codes()
+
+ # build a string array with all available permission codes for the connected user
+ permissions = result["permissions"]
+
+ return permissions
+
+ ###########################################
+ # AGRIQUEST #
+ ###########################################
+ def get_agriquest_weather_block_data(self,
+ start_date: str,
+ end_date: str,
+ block_code: AgriquestBlocks,
+ weather_type: AgriquestWeatherType
+ ):
+ """Retrieve data on all AMU of an AgriquestBlock for the specified weather indicator.
+
+ Args:
+ start_date (str): The start date to retrieve data (format: 'YYYY-MM-dd')
+ end_date (str): The end date to retrieve data (format: 'YYYY-MM-dd')
+ block_code (AgriquestBlocks): The AgriquestBlock name (Enum)
+ weather_type (AgriquestWeatherType) : The Agriquest weather indicator to retrieve (Enum)
+
+ Returns:
+ result ('dataframe'): pandas dataframe
+ """
+ # date convert
+ start_datetime = datetime.strptime(start_date, "%Y-%m-%d").date()
+ end_datetime = datetime.strptime(end_date, "%Y-%m-%d").date()
+
+ # check if the block is dedicated to France
+ isFrance = self.__agriquest_service.is_block_for_france(block_code)
+
+ # build the weather indicator list
+ weather_indicators = self.__agriquest_service.weather_indicators_builder(start_datetime, end_datetime, isFrance)
+
+ # call the weather endpoint to retrieve data
+ result = self.__agriquest_service.get_agriquest_block_weather_data(start_date=start_date, end_date=end_date,
+ block_code=block_code,
+ indicator_list=weather_indicators,
+ weather_type=weather_type)
+
+ return result
+
+ def get_agriquest_ndvi_block_data(self,
+ day_of_measure: str,
+ block_code: AgriquestBlocks,
+ commodity_code: AgriquestCommodityCode
+ ):
+ """Retrieve data on all AMU of an AgriquestBlock for NDVI index
+
+ Args:
+ day_of_measure (str) : The date of measure (format: 'YYYY-MM-dd')
+ block_code (AgriquestBlocks) : The AgriquestBlock name (Enum)
+ commodity_code (AgriquestCommodityCode) : The commodity code (Enum)
+ Returns:
+ result ('dataframe'): pandas dataframe result
+ """
+
+ # call the weather endpoint to retrieve data, indicator of NDVI = 1
+ result = self.__agriquest_service.get_agriquest_block_ndvi_data(date=day_of_measure, block_code=block_code,
+ commodity=commodity_code, indicator_list=[1])
+
+ return result
+
+ ###########################################
+ # ANALYTICS PROCESSOR #
+ ###########################################
+
+ def get_mr_time_series(self,
+ polygon,
+ start_date: str = "2010-01-01",
+ end_date=None,
+ list_sensors=["micasense", "sequoia", "m4c", "sentinel_2",
+ "landsat_8", "landsat_9", "cbers4", "kazstsat",
+ "alsat_1b", "huanjing_2", "deimos", "gaofen_1", "gaofen_6",
+ "resourcesat2", "dmc_2", "landsat_5", "landsat_7",
+ "spot", "rapideye_3a", "rapideye_1b"],
+ denoiser: bool = True,
+ smoother: str = "ww",
+ eoc: bool = True,
+ aggregation: str = "mean",
+ index: str = "ndvi",
+ raw_data: bool = False
+ ):
+
+ """Retrieve mr time series on the collection targeted.
+
+ Args:
+ start_date : The start date of the time series
+ end_date : The end date of the time series
+ list_sensors : The Satellite Imagery Collection targeted
+ denoiser : A boolean value indicating whether a denoising operation should be applied or not.
+ smoother : The type or name of the smoothing technique or algorithm to be used.
+ eoc : A boolean value indicating whether the "end of curve" detection should be performed.
+ func : The type or name of the function to be applied to the data.
+ index : The type or name of the index used for data manipulation or referencing
+ raw_data : A boolean value indicating whether the data is in its raw/unprocessed form.
+ polygon : A string representing a polygon.
+
+ Returns:
+ string : s3 bucket path
+ """
+ task_id = self.__analytics_processor_service.launch_mr_time_series_processor(
+ start_date=start_date,
+ end_date=end_date,
+ polygon=polygon,
+ raw_data=raw_data,
+ denoiser=denoiser,
+ smoother=smoother,
+ aggregation=aggregation,
+ list_sensors=list_sensors,
+ index=index,
+ eoc=eoc,
+
- str_af_url = urljoin(
- self.base_url,
- self.analytics_fabric_endpoint,
)
- response = self.__patch(str_af_url, payload)
- if response.status_code == 200:
- return response.status_code
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ return self.__analytics_processor_service.get_s3_path_from_task_and_processor(task_id, processor_name="mrts")
+
+ def get_harvest_analytics(self,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ crop: Enum,
+ year: int,
+ geometry: str,
+ harvest_type: Harvest):
+ """launch a harvest analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (Enum): the geosys crop code,
+ year (int): the year value,
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+ harvest_type (Harvest): the type of Harvest analytics (INSEASON/HISTORICAL)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_harvest_processor(
+ season_duration=season_duration,
+ season_start_day=season_start_day,
+ season_start_month=season_start_month,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ crop=crop.value,
+ year=year,
+ harvest_type=harvest_type
+ )
+
+ self.logger.info(f"Task Id: {task_id}")
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ if harvest_type == Harvest.HARVEST_IN_SEASON:
+ schema = "INSEASON_HARVEST"
+ else:
+ schema = "HISTORICAL_HARVEST"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_emergence_analytics(self,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ crop: Enum,
+ year: int,
+ geometry: str,
+ emergence_type: Emergence):
+ """launch an emergence analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (Enum): the crop code,
+ year (int): the year value,
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+ emergence_type (Emergence): the type of Emergence analytics (INSEASON/HISTORICAL/DELAY)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_emergence_processor(
+ season_duration=season_duration,
+ season_start_day=season_start_day,
+ season_start_month=season_start_month,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ crop=crop.value,
+ year=year,
+ emergence_type=emergence_type
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ if emergence_type == Emergence.EMERGENCE_IN_SEASON:
+ schema = "INSEASON_EMERGENCE"
+ elif emergence_type == Emergence.EMERGENCE_HISTORICAL:
+ schema = "HISTORICAL_EMERGENCE"
else:
- logging.info(response.status_code)
+ schema = "EMERGENCE_DELAY"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_brazil_crop_id_analytics(self,
+ start_date: str,
+ end_date: str,
+ season: CropIdSeason,
+ geometry: str):
+ """launch a brazil-in-season-crop-id analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ season (CropIdSeason): the season name,
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_brazil_in_season_crop_id_processor(
+ start_date=start_date,
+ end_date=end_date,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ season=season.value
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ schema = "CROP_IDENTIFICATION"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_potential_score_analytics(self,
+ end_date: str,
+ nb_historical_years: int,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ sowing_date: str,
+ crop: Enum,
+ geometry: str):
+ """launch a potential score analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (Enum): the crop code,
+ end_date (str): end date used to calculate potential score
+ sowing_date (str): sowing date of the filed used to calculate potential score
+ nb_historical_years (int): number of historical years data to calculate potential score
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_potential_score_processor(
+ end_date=end_date,
+ nb_historical_years=nb_historical_years,
+ sowing_date=sowing_date,
+ season_duration=season_duration,
+ season_start_day=season_start_day,
+ season_start_month=season_start_month,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ crop=crop.value
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ schema = "POTENTIAL_SCORE"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_greenness_analytics(self,
+ start_date: str,
+ end_date: str,
+ sowing_date: str,
+ crop: Enum,
+ geometry: str):
+ """launch a greenness analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ sowing_date(str): sowing date of the field used to calculate potential score
+ crop (Enum): the crop code,
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_greenness_processor(
+ start_date=start_date,
+ end_date=end_date,
+ sowing_date=sowing_date,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ crop=crop.value
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ schema = "GREENNESS"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_harvest_readiness_analytics(self,
+ start_date: str,
+ end_date: str,
+ sowing_date: str,
+ crop: Enum,
+ geometry: str):
+ """launch a harvest readiness analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ sowing_date(str): sowing date of the field used to calculate potential score
+ crop (Enum): the crop code,
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON)
+
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_harvest_readiness_processor(
+ start_date=start_date,
+ end_date=end_date,
+ sowing_date=sowing_date,
+ seasonfield_id=sf_unique_id,
+ geometry=geometry,
+ crop=crop.value
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ schema = "HARVEST_READINESS"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+ def get_planted_area_analytics(self,
+ start_date: str,
+ end_date: str,
+ geometry: str):
+ """launch a planted area analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_planted_area_processor(start_date, end_date, sf_unique_id)
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ schema = "PLANTED_AREA"
+
+ # if task successfully completed, get latests metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
+
+
+ def get_zarc_analytics(self,
+ start_date_emergence: str,
+ end_date_emergence: str,
+ nb_days_sowing_emergence: int,
+ crop: Enum,
+ soil_type: ZarcSoilType,
+ cycle: ZarcCycleType,
+ geometry: str):
+ """launch a zarc analytics processor and get the metrics in a panda dataframe object
+
+ Args:
+ start_date_emergence (str) : the emergence start date used for the request (format YYYY-MM-dd)
+ end_date_emergence (str) : the emergence end date used for the request (format YYYY-MM-dd)
+ nb_days_sowing_emergence (int): the number of days for sowing emergence
+ crop (Enum): the zarc crop code,
+ soil_type (ZarcSoilType): the zarc soil type (1/2/3),
+ cycle (ZarcCycleType): the zarc cycle type (1/2/3),
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+ Returns:
+ A Pandas DataFrame containing several columns with metrics
+ """
+ # validate and convert the geometry to WKT
+ geometry = Helper.convert_to_wkt(geometry)
+
+ if geometry is None:
+ raise ValueError("The geometry is not a valid WKT of GeoJson")
+
+ # get municipio id from geometry
+ municipio_id = self.__gis_service.get_municipio_id_from_geometry(geometry)
+
+ if municipio_id == 0:
+ raise ValueError(f"No municipio id found for this geometry")
+
+ # Create seasonfield from geometry and extract uniqueId
+ sfd_public_id = self.__master_data_management_service.extract_season_field_id(geometry)
+ sf_unique_id = self.__master_data_management_service.get_season_field_unique_id(sfd_public_id)
+
+ task_id = self.__analytics_processor_service.launch_zarc_processor(
+ start_date_emergence=start_date_emergence,
+ end_date_emergence=end_date_emergence,
+ crop=crop.value,
+ cycle=cycle.value,
+ soil_type=soil_type.value,
+ municipio=municipio_id,
+ nb_days_sowing_emergence=nb_days_sowing_emergence,
+ seasonfield_id=sf_unique_id
+ )
+
+ # check the task status to continue or not the process
+ self.__analytics_processor_service.wait_and_check_task_status(task_id)
+
+ # Analytics Schema
+ schema = "ZARC"
+
+ # if task successfully completed, get metrics from analytics fabric
+ return self.__analytics_fabric_service.get_lastest_metrics(sf_unique_id, schema)
diff --git a/geosyspy/services/__init__.py b/geosyspy/services/__init__.py
new file mode 100644
index 0000000..c36f219
--- /dev/null
+++ b/geosyspy/services/__init__.py
@@ -0,0 +1,3 @@
+__all__ = ["agriquest_service", "analytics_processor_service", "analytics_fabric_service",
+ "master_data_management_service", "weather_service", "vegetation_time_series_service",
+ "map_product_service"]
diff --git a/geosyspy/services/agriquest_service.py b/geosyspy/services/agriquest_service.py
new file mode 100644
index 0000000..843cfda
--- /dev/null
+++ b/geosyspy/services/agriquest_service.py
@@ -0,0 +1,159 @@
+import json
+import logging
+import pandas as pd
+from datetime import datetime, timedelta
+from urllib.parse import urljoin
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+
+
+class AgriquestService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ def weather_indicators_builder(self, start_date, end_date, isFrance):
+ """build weather indicators list from 2 dates
+
+ Args:
+ start_date (datetime) : the start date used for the request
+ end_date (datetime) : the end date used for the request
+ isFrance (boolean) : resuqest mad for France or not
+
+ Returns:
+ A list of int (weather indicators)
+ - 2 : ENS observed data
+ - 3 : Arome Weather Observed data (Meteo France, France only)
+ - 4 : ECMWF Weather Forecast Data
+ - 5 : GFS Weather Forecast Data
+
+ """
+
+ today = datetime.now().date()
+ result = []
+
+ # Forecast indicators
+ if end_date > today:
+ result.extend([4, 5])
+
+ if start_date < today:
+ if not isFrance:
+ result.append(2)
+ else:
+ result.append(3)
+
+ return result
+
+ def is_block_for_france(self, block_code: AgriquestBlocks):
+ """
+ method to check if the services block code is dedicated to France
+
+ Args:
+ block_code (AgriquestBlocks): the block code to check
+ Returns:
+ boolean
+ """
+ if (block_code.value in [item.value for item in AgriquestFranceBlockCode]):
+ return True
+ return False
+
+ def get_agriquest_block_weather_data(self,
+ start_date: str,
+ end_date: str,
+ block_code: AgriquestBlocks,
+ indicator_list: [int],
+ weather_type: AgriquestWeatherType
+ ):
+ """
+ method to call Weather AgriQuest Api and build a panda DataFrame
+
+ Args:
+ start_date (str),
+ end_date (str),
+ block_code (AgriquestBlocks): the AQ block to check ,
+ indicator_list ([int]): list of weather indicator types,
+ weather_type(AgriquestWeatherType): type of weather data to retrieve
+ Returns:
+ Panda DataFrame representing the value corresponding to the weather_type of each AMU of the block,
+ on the specified period provided in parameter.
+ """
+
+ payload = {
+ "analyticName": weather_type.name,
+ "commodityId": AgriquestCommodityCode.ALL_VEGETATION.value,
+ "startDate": start_date,
+ "endDate": end_date,
+ "idPixelType": 1,
+ "idBlock": block_code.value,
+ "indicatorTypeIds": indicator_list
+ }
+ parameters: str = f"/{weather_type.value}/export-map/year-of-interest"
+ aq_url: str = urljoin(self.base_url, GeosysApiEndpoints.AGRIQUEST_ENDPOINT.value + parameters)
+ response = self.http_client.post(aq_url, payload)
+ if response.status_code == 200:
+ dict_response = response.json()
+ df = pd.read_json(json.dumps(dict_response))
+
+ # Ignore first line
+ df = df.iloc[1:]
+
+ # first line as column header
+ df.columns = df.iloc[0]
+
+ # Ignore first line after using columns header
+ df = df.iloc[1:]
+
+ df = df.rename(columns={"Name": "AMU"})
+ return df
+ else:
+ self.logger.info(response.status_code)
+
+
+ def get_agriquest_block_ndvi_data(self,
+ date: str,
+ block_code: AgriquestBlocks,
+ commodity: AgriquestCommodityCode,
+ indicator_list: [int]
+ ):
+ """
+ method to call year-of-interest AgriQuest Api and build a panda DataFrame
+
+ Args:
+ date (str),
+ block_code (AgriquestBlocks): the AQ block to check ,
+ indicator_list ([int]): list of indicator types
+ Returns:
+ Panda DataFrame representing the value corresponding to NDVI index of each AMU of the block,
+ on the specified date (dayOfMeasure) provided in parameter.
+ """
+
+ payload = {
+ "analyticName": "NDVI",
+ "commodityId": commodity.value,
+ "dayOfMeasure": date,
+ "idPixelType": 1,
+ "idBlock": block_code.value,
+ "indicatorTypeIds": indicator_list
+ }
+ parameters: str = f"/vegetation-vigor-index/export-map/year-of-interest"
+ aq_url: str = urljoin(self.base_url, GeosysApiEndpoints.AGRIQUEST_ENDPOINT.value + parameters)
+ response = self.http_client.post(aq_url, payload)
+ if response.status_code == 200:
+ dict_response = response.json()
+ df = pd.read_json(json.dumps(dict_response))
+
+ # Ignore first line
+ df = df.iloc[1:]
+
+ # first line as column header
+ df.columns = df.iloc[0]
+
+ # Ignore first line after using columns header
+ df = df.iloc[1:]
+
+ df = df.rename(columns={"Name": "AMU", "Value": "NDVI"})
+ return df
+ else:
+ self.logger.info(response.status_code)
diff --git a/geosyspy/services/analytics_fabric_service.py b/geosyspy/services/analytics_fabric_service.py
new file mode 100644
index 0000000..e68a069
--- /dev/null
+++ b/geosyspy/services/analytics_fabric_service.py
@@ -0,0 +1,221 @@
+import logging
+import pandas as pd
+from datetime import datetime
+from urllib.parse import urljoin
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+from typing import Optional
+
+
+class AnalyticsFabricService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+
+ @staticmethod
+ def build_timestamp_query_parameters(start_date: Optional[datetime] = None,
+ end_date: Optional[datetime] = None):
+ """ Build Timestamp parameter to provide in AF api calls
+ Args:
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ Returns:
+ A string query params to include in api call
+ """
+
+ if start_date is None and end_date is None:
+ return ""
+ if start_date is not None and end_date is None:
+ return f'&Timestamp=$gte:{start_date}'
+ elif start_date is None and end_date is not None:
+ return f'&Timestamp:$lte:{end_date}'
+ else:
+ return f'&Timestamp=$between:{start_date}|{end_date}'
+
+ def create_schema_id(self, schema_id: str,
+ schema: dict):
+ """Create a schema in Analytics Fabrics
+
+ Args:
+ schema_id: The schema id to create
+ schema: Dict representing the schema {'property_name': 'property_type'}
+
+ Returns:
+ A http response object.
+ """
+ properties = []
+ for prop_name, datatype in schema.items():
+ prop = {
+ "Name": prop_name,
+ "Datatype": datatype,
+ "UnitCategory": None,
+ "IsPartOfKey": False,
+ "IsOptional": False,
+ }
+ properties.append(prop)
+
+ payload = {
+ "Id": schema_id,
+ "Properties": properties,
+ "Metadata": {"OnAggregationCompleted": "Off"},
+ }
+ af_url: str = urljoin(
+ self.base_url,
+ GeosysApiEndpoints.ANALYTICS_FABRIC_SCHEMA_ENDPOINT.value,
+ )
+ response = self.http_client.post(af_url, payload)
+ dict_response = response.json()
+ if response.status_code == 201 :
+ return response.content
+ elif response.status_code == 400 and "This schema already exists." in str(dict_response["Errors"]["Body"]["Id"]):
+ self.logger.info(f"The schema {schema_id} already exists.")
+ else:
+ self.logger.info(response.status_code)
+
+ def get_metrics(self, season_field_id: str,
+ schema_id: str,
+ start_date: Optional[datetime] = None,
+ end_date: Optional[datetime] = None):
+ """Returns metrics from Analytics Fabrics in a pandas dataframe.
+ Filters on date:
+ if start_date is None: <= end_date
+ if end_date is None: >= start_date
+ if start_date & end_date not None: between start_date & end_date
+ if start_date & end_date both None: no filter on dates
+
+
+ Args:
+ season_field_id : A string representing the seasonfield id
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ schema_id : A string representing a schema existing in Analytics Fabrics
+
+ Returns:
+ df : A Pandas DataFrame containing several columns with metrics
+
+ """
+ self.logger.info("Calling APIs for metrics")
+ if start_date is not None:
+ start_date: str = start_date.strftime("%Y-%m-%d")
+ if end_date is not None:
+ end_date: str = end_date.strftime("%Y-%m-%d")
+
+ timestamp_params = self.build_timestamp_query_parameters(start_date, end_date);
+ parameters: str = f'?%24filter=Entity.TypedId==\'SeasonField:{season_field_id}\'' \
+ f'{timestamp_params}' \
+ f'&Schema.Id={schema_id}' \
+ f'&%24limit=None'
+
+ af_url: str = urljoin(
+ self.base_url,
+ GeosysApiEndpoints.ANALYTICS_FABRIC_ENDPOINT.value + parameters,
+ )
+ response = self.http_client.get(af_url)
+
+ if response.status_code == 200:
+ df = pd.json_normalize(response.json())
+ if df.empty:
+ if start_date is not None and end_date is not None:
+ date_msg =f"between:{start_date} and {end_date} "
+ elif start_date is None and end_date is not None:
+ date_msg =f"<= {end_date} "
+ elif start_date is not None and end_date is None:
+ date_msg = f">= {start_date} "
+ self.logger.info(f"No metrics found in Analytic Fabric with "
+ f"SchemaId: {schema_id}, "
+ f"SeasonField:{season_field_id} "
+ f"{date_msg} ")
+ return df
+ df.drop("Entity.TypedId", inplace=True, axis=1)
+ df.rename(
+ columns={"Timestamp": "date"},
+ inplace=True,
+ )
+ df = df.sort_values(by="date")
+ df.set_index("date", inplace=True)
+ return df
+ else:
+ self.logger.error("Issue in get_metrics. Status Code: "+str(response.status_code) +
+ " Error:" + str(response.json()))
+
+ def get_lastest_metrics(self, season_field_id: str,
+ schema_id: str):
+ """Returns latest metrics from Analytics Fabrics in a pandas dataframe.
+
+ Args:
+ season_field_id : A string representing the seasonfield id
+ schema_id : A string representing a schema existing in Analytics Fabrics
+
+ Returns:
+ df : A Pandas DataFrame containing several columns with metrics
+
+ """
+ self.logger.info("Calling APIs for Latest metrics")
+
+ parameters: str = f'?%24filter=Entity.TypedId==\'SeasonField:{season_field_id}\'' \
+ f'&Schema.Id={schema_id}' \
+ f'&%24limit=1' \
+ f'&$sort=-Timestamp'
+
+ af_url: str = urljoin(
+ self.base_url,
+ GeosysApiEndpoints.ANALYTICS_FABRIC_LATEST_ENDPOINT.value + parameters,
+ )
+ response = self.http_client.get(af_url)
+
+ if response.status_code == 200:
+ df = pd.json_normalize(response.json())
+ if df.empty:
+ self.logger.info(f"No Latest metrics found in Analytic Fabric with "
+ f"SchemaId: {schema_id}, "
+ f"SeasonField:{season_field_id} ")
+ return df
+ df.drop("Entity.TypedId", inplace=True, axis=1)
+ df.rename(
+ columns={"Timestamp": "date"},
+ inplace=True,
+ )
+ df = df.sort_values(by="date")
+ df.set_index("date", inplace=True)
+ return df
+ else:
+ self.logger.error("Issue in get_latests_metrics. Status Code: "+str(response.status_code)
+ + " Error:" + str(response.json()))
+
+ def push_metrics(self, season_field_id: str,
+ schema_id: str,
+ values: dict):
+ """Push metrics in Analytics Fabrics
+
+ Args:
+ season_field_id : A string representing the seasonFieldId.
+ schema_id : The schema on which to save
+ values : Dict representing values to push
+
+ Returns:
+ A response object.
+ """
+ payload = []
+ for value in values:
+ prop = {
+ "Entity": {
+ "TypedId": f"SeasonField:{season_field_id}@LEGACY_ID_NA"
+ },
+ "Schema": {"Id": schema_id, "Version": 1},
+ }
+ prop = dict(prop, **value)
+ payload.append(prop)
+
+ af_url: str = urljoin(
+ self.base_url,
+ GeosysApiEndpoints.ANALYTICS_FABRIC_ENDPOINT.value,
+ )
+ response = self.http_client.patch(af_url, payload)
+ if response.status_code == 200:
+ return response.status_code
+ else:
+ self.logger.error("Issue in push_metrics. Status Code: "+str(response.status_code)
+ + " Error:" + str(response.json()))
diff --git a/geosyspy/services/analytics_processor_service.py b/geosyspy/services/analytics_processor_service.py
new file mode 100644
index 0000000..c255ab1
--- /dev/null
+++ b/geosyspy/services/analytics_processor_service.py
@@ -0,0 +1,563 @@
+import json
+import logging
+from urllib.parse import urljoin
+from geosyspy.utils.constants import *
+from geosyspy.services.service_constants import *
+from geosyspy.utils.http_client import *
+import retrying
+import datetime
+
+
+class AnalyticsProcessorService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ @retrying.retry(wait_exponential_multiplier=1000, wait_exponential_max=10000, stop_max_attempt_number=50, retry_on_exception=lambda exc: isinstance(exc, KeyError))
+ def wait_and_check_task_status(self, task_id: str):
+ """Check task status until it is ended for a specific analytics processor run
+
+ Args:
+ task_id (str) : A string representing a task id
+
+ Returns:
+ task_status (str): the status of the task
+
+ """
+
+ events_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.PROCESSOR_EVENTS_ENDPOINT.value + "/" + task_id)
+
+ while True:
+ response = self.http_client.get(events_endpoint)
+ if response.ok:
+ dict_resp = json.loads(response.content)
+ task_status = dict_resp["status"]
+ else:
+ self.logger.info(response.status_code)
+ return "Failed"
+
+ if task_status == "Ended":
+ break # the task is completed.
+ elif task_status == "Running":
+ self.logger.info("Retry -- Task still running")
+ raise KeyError("Task still running") # raise exception to retry
+ else:
+ raise Exception(f"Task Status: {task_status}, Content: {response.content}" )
+
+ return task_status
+
+ def get_s3_path_from_task_and_processor(self, task_id: str,
+ processor_name: str):
+ """Returns S3 path related to task_id
+
+ Args:
+ task_id : A string representing a task id
+ processor_name: the processor name
+ Returns:
+ path : uri
+
+ """
+
+ events_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.PROCESSOR_EVENTS_ENDPOINT.value + "/" + task_id)
+
+ response = self.http_client.get(events_endpoint)
+ if response.ok:
+ dict_resp = json.loads(response.content)
+ customer_code: str = dict_resp["customerCode"].lower().replace("_", "-")
+ user_id: str = dict_resp["userId"]
+ task_id = dict_resp["taskId"]
+ return f"s3://geosys-{customer_code}/{user_id}/{processor_name}/{task_id}"
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_mr_time_series_processor(self, polygon,
+ start_date: str,
+ end_date,
+ list_sensors,
+ denoiser: bool,
+ smoother: str,
+ eoc: bool,
+ aggregation: str,
+ index: str,
+ raw_data: bool):
+ """launch a MRTS analytics processor and get the task id in result
+
+ Args:
+ start_date : The start date of the time series
+ end_date : The end date of the time series
+ list_sensors : The Satellite Imagery Collection targeted
+ denoiser : A boolean value indicating whether a denoising operation should be applied or not.
+ smoother : The type or name of the smoothing technique or algorithm to be used.
+ eoc : A boolean value indicating whether the "end of curve" detection should be performed.
+ func : The type or name of the function to be applied to the data.
+ index : The type or name of the index used for data manipulation or referencing
+ raw_data : A boolean value indicating whether the data is in its raw/unprocessed form.
+ polygon : A string representing a polygon.
+
+ Returns:
+ taskId (str)
+ """
+
+ if end_date is None:
+ end_date = datetime.datetime.today().strftime("%Y-%m-%d")
+ payload = {
+ "parametersProfile": {
+ "code": ProcessorConfiguration.MRTS.value['profile'],
+ "version": 1
+ },
+ "parameters": {
+ "start_date": start_date,
+ "end_date": end_date,
+ "sensors": list_sensors,
+ "denoiser": denoiser,
+ "smoother": smoother,
+ "eoc": eoc,
+ "aggregation": aggregation,
+ "index": index,
+ "raw_data": raw_data
+ },
+ "data": [
+ {"wkt": polygon}
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(
+ ProcessorConfiguration.MRTS.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_planted_area_processor(self,
+ start_date: str,
+ end_date: str,
+ seasonfield_id: str):
+ """launch a planted area analytics processor and get the task id in result
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+
+ Returns:
+ taskId (str)
+ """
+
+ payload = {
+ "parametersProfileCode": ProcessorConfiguration.PLANTED_AREA.value['profile'],
+ "data":
+ [
+ {
+ "id": seasonfield_id + "@ID",
+ "start_date": start_date,
+ "end_date": end_date
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration.PLANTED_AREA.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_harvest_processor(self,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ crop: str,
+ year: int,
+ seasonfield_id: str,
+ geometry: str,
+ harvest_type: Harvest):
+ """launch a harvest analytics processor and get the task id in result
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (str): the geosys crop code,
+ year (int): the year value,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calcultate the analytic (WKT or GeoJSON),
+ harvest_type (Harvest): the type of Harvest analytics (INSEASON/HISTORICAL)
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration[harvest_type.name].value['profile'],
+ "parameters": {
+ "season_duration": season_duration,
+ "season_start_day": season_start_day,
+ "season_start_month": season_start_month
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "crop": crop,
+ "year": year,
+ "geom": geometry
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration[harvest_type.name].value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_emergence_processor(self,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ crop: str,
+ year: int,
+ seasonfield_id: str,
+ geometry: str,
+ emergence_type: Emergence):
+ """launch an emergence analytics processor and get the task id in result
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (str): the geosys crop code,
+ year (int): the year value,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+ emergence_type (Emergence): the type of Emergence analytics (INSEASON/HISTORICAL/DELAY)
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration[emergence_type.name].value['profile'],
+ "parameters": {
+ "season_duration": season_duration,
+ "season_start_day": season_start_day,
+ "season_start_month": season_start_month
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "crop": crop,
+ "year": year,
+ "geom": geometry
+ }
+ ]
+ }
+
+ if emergence_type == Emergence.EMERGENCE_DELAY:
+ payload["parameters"]["emergence_delay"] = True
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format( ProcessorConfiguration[emergence_type.name].value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_potential_score_processor(self,
+ season_duration: int,
+ season_start_day: int,
+ season_start_month: int,
+ crop: str,
+ end_date: str,
+ sowing_date: str,
+ nb_historical_years: int,
+ seasonfield_id: str,
+ geometry: str):
+ """launch a potential score analytics processor and get the task id in result
+
+ Args:
+ season_duration (int): the duration of the season in days,
+ season_start_day (int): the start day value (1 - 31),
+ season_start_month (int): the start month value (1 - 12),
+ crop (str): the geosys crop code,
+ end_date (str): end date used to calculate potential score
+ sowing_date (str): sowing date of the filed used to calculate potential score
+ nb_historical_years (int): number of historical years data to calculate potential score
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON)
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration.POTENTIAL_SCORE.value['profile'],
+ "parameters": {
+ "end_date" : end_date,
+ "nb_historical_years" : nb_historical_years,
+ "season_duration": season_duration,
+ "season_start_day": season_start_day,
+ "season_start_month": season_start_month
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "crop": crop,
+ "sowing_date": sowing_date,
+ "geom": geometry
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration.POTENTIAL_SCORE.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_brazil_in_season_crop_id_processor(self,
+ start_date: str,
+ end_date: str,
+ season: str,
+ seasonfield_id: str,
+ geometry: str):
+ """launch a brazil-in-season-crop-id analytics processor and get the task id in result
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+
+ season (str): the season name,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration.BRAZIL_IN_SEASON_CROP_ID.value['profile'],
+ "parameters": {
+ "start_date": start_date,
+ "end_date": end_date,
+ "season": season
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "geom": geometry
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration.BRAZIL_IN_SEASON_CROP_ID.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_greenness_processor(self,
+ start_date: str,
+ end_date: str,
+ sowing_date: str,
+ crop: str,
+ seasonfield_id: str,
+ geometry: str):
+ """launch a greenness analytics processor and get the task id in result
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ sowing_date(str): sowing date of the field used to calculate potential score
+ crop (str): the EDA crop code,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration.GREENNESS.value['profile'],
+ "parameters": {
+ "start_date": start_date,
+ "end_date": end_date,
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "crop": crop,
+ "sowing_date": sowing_date,
+ "geom": geometry
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration.GREENNESS.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+
+ def launch_harvest_readiness_processor(self,
+ start_date: str,
+ end_date: str,
+ sowing_date: str,
+ crop: str,
+ seasonfield_id: str,
+ geometry: str):
+ """launch a harvest readiness analytics processor and get the task id in result
+
+ Args:
+ start_date (str) : the start date used for the request (format YYYY-MM-dd)
+ end_date (str) : the end date used for the request (format YYYY-MM-dd)
+ sowing_date(str): sowing date of the field used to calculate potential score
+ crop (str): the EDA crop code,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+ geometry (str): the geometry to calculate the analytic (WKT or GeoJSON),
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration.HARVEST_READINESS.value['profile'],
+ "parameters": {
+ "start_date": start_date,
+ "end_date": end_date,
+ },
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "crop": crop,
+ "sowing_date": sowing_date,
+ "geom": geometry
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(ProcessorConfiguration.HARVEST_READINESS.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
+ def launch_zarc_processor(self,
+ start_date_emergence: str,
+ end_date_emergence: str,
+ nb_days_sowing_emergence: int,
+ crop: str,
+ municipio: int,
+ soil_type: str,
+ cycle: str,
+ seasonfield_id:str):
+ """launch a zarc analytics processor and get the task id in result
+
+ Args:
+ start_date_emergence (str) : the emergence start date used for the request (format YYYY-MM-dd)
+ end_date_emergence (str) : the emergence end date used for the request (format YYYY-MM-dd)
+ nb_days_sowing_emergence (int): the number of days for sowing emergence
+ crop (str): the zarc crop code,
+ municipio (int): the municipio id,
+ soil_type (str): the zarc soil type,
+ cycle (str): the zarc cycle value,
+ seasonfield_id (sfd) : seasonField geosys uniqueId
+
+ Returns:
+ taskId (str)
+ """
+
+ # build payload for api call
+ payload = {
+ "parametersProfile": ProcessorConfiguration.ZARC.value['profile'],
+ "data":
+ [
+ {
+ "id": "SeasonField:" + seasonfield_id + "@ID",
+ "start_date_emergence": start_date_emergence,
+ "end_date_emergence": end_date_emergence,
+ "crop_zarc": crop,
+ "municipio_zarc": municipio,
+ "nb_days_sowing_emergence": nb_days_sowing_emergence,
+ "soil_type_zarc": soil_type,
+ "cycle_zarc": cycle
+ }
+ ]
+ }
+
+ processor_endpoint: str = urljoin(self.base_url,
+ GeosysApiEndpoints.LAUNCH_PROCESSOR_ENDPOINT.value.format(
+ ProcessorConfiguration.ZARC.value['api_processor_path']))
+
+ response = self.http_client.post(processor_endpoint, payload)
+
+ if response.ok:
+ task_id = json.loads(response.content)["taskId"]
+ return task_id
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(response.content)
+
diff --git a/geosyspy/services/gis_service.py b/geosyspy/services/gis_service.py
new file mode 100644
index 0000000..2cf9a11
--- /dev/null
+++ b/geosyspy/services/gis_service.py
@@ -0,0 +1,50 @@
+from urllib.parse import urljoin
+import logging
+from geosyspy.utils.http_client import *
+
+
+class GisService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ def get_municipio_id_from_geometry(self,
+ geometry:str
+ ):
+ """
+ method to call Gis Api to retrieve Municipio id from a geometry
+
+ Args:
+ geometry (str): the geometry (WKT or GeosJson) to retrieve the municipio Id
+ Returns:
+ the internal id of the municipio
+ """
+
+ payload = {
+ "properties": ["id"],
+ "features": [geometry]
+ }
+ parameters: str = f"/layerservices/api/v1/layers/BRAZIL_MUNICIPIOS/intersect"
+ gis_url: str = urljoin(self.base_url, parameters)
+ response = self.http_client.post(gis_url, payload)
+ if response.status_code == 200:
+ dict_response = response.json()
+
+ try:
+ # extract & return municipio id from response
+ municipio_id = dict_response[0][0]["properties"]["id"]
+ if isinstance(municipio_id, int):
+ return municipio_id
+ else:
+ self.logger.warning("No municipio id found for this geometry")
+ return 0
+ except:
+ return 0
+
+ else:
+ self.logger.info(response.status_code)
+ raise ValueError(
+ f"No municipio id found for this geometry"
+ )
\ No newline at end of file
diff --git a/geosyspy/services/map_product_service.py b/geosyspy/services/map_product_service.py
new file mode 100644
index 0000000..f7058b8
--- /dev/null
+++ b/geosyspy/services/map_product_service.py
@@ -0,0 +1,89 @@
+import json
+import logging
+import pandas as pd
+from datetime import datetime
+from urllib.parse import urljoin
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+from requests import HTTPError
+
+
+class MapProductService:
+
+ def __init__(self, base_url: str, http_client: HttpClient, priority_queue: str):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.priority_queue: str = priority_queue
+ self.logger = logging.getLogger(__name__)
+
+ def get_satellite_coverage(self, season_field_id: str,
+ start_date: datetime,
+ end_date: datetime,
+ indicator,
+ sensors_collection: list[SatelliteImageryCollection] = [
+ SatelliteImageryCollection.SENTINEL_2,
+ SatelliteImageryCollection.LANDSAT_8]
+ ):
+
+ self.logger.info("Calling APIs for coverage")
+ start_date: str = start_date.strftime("%Y-%m-%d")
+ end_date: str = end_date.strftime("%Y-%m-%d")
+ sensors: list[str] = [elem.value for elem in sensors_collection]
+
+ if indicator == "" or indicator.upper() == "REFLECTANCE" or indicator.upper() == "NDVI":
+ mapType = "INSEASON_NDVI"
+ else:
+ mapType = f"INSEASON_{indicator.upper()}"
+
+ parameters = f"?maps.type={mapType}&Image.Sensor=$in:{'|'.join(sensors)}&CoverageType=CLEAR&$limit=9999&$filter=Image.Date >= '{start_date}' and Image.Date <= '{end_date}'"
+
+ flm_url: str = urljoin(
+ self.base_url,
+ GeosysApiEndpoints.FLM_CATALOG_IMAGERY.value.format(season_field_id) + parameters,
+ )
+ response = self.http_client.get(
+ flm_url,
+ {"X-Geosys-Task-Code": PRIORITY_HEADERS[self.priority_queue]},
+ )
+
+ if response.status_code == 200:
+ df = pd.json_normalize(response.json())
+ if df.empty:
+ return df
+ else:
+ return df[
+ [
+ "coverageType",
+ "maps",
+ "image.id",
+ "image.availableBands",
+ "image.sensor",
+ "image.soilMaterial",
+ "image.spatialResolution",
+ "image.weather",
+ "image.date",
+ "seasonField.id",
+ ]
+ ]
+ else:
+ self.logger.info(response.status_code)
+
+ def get_zipped_tiff(self, field_id: str,
+ image_id: str,
+ indicator: str = ""):
+ parameters = f"/{image_id}/reflectance-map/TOC/image.tiff.zip"
+
+ if indicator != "" and indicator.upper() != "REFLECTANCE":
+ parameters = f"/{image_id}/base-reference-map/INSEASON_{indicator.upper()}/image.tiff.zip?resolution=Sensor"
+
+ download_tiff_url: str = urljoin(
+ self.base_url, GeosysApiEndpoints.FLM_COVERAGE.value.format(field_id) + parameters
+ )
+
+ response_zipped_tiff = self.http_client.get(
+ download_tiff_url,
+ {"X-Geosys-Task-Code": PRIORITY_HEADERS[self.priority_queue]},
+ )
+ if response_zipped_tiff.status_code != 200:
+ raise HTTPError("Unable to download tiff.zip file. Server error: " + str(response_zipped_tiff.status_code))
+ return response_zipped_tiff
\ No newline at end of file
diff --git a/geosyspy/services/master_data_management_service.py b/geosyspy/services/master_data_management_service.py
new file mode 100644
index 0000000..5a02793
--- /dev/null
+++ b/geosyspy/services/master_data_management_service.py
@@ -0,0 +1,145 @@
+from urllib.parse import urljoin
+import logging
+
+from geosyspy.utils.constants import *
+from geosyspy.utils.helper import Helper
+from geosyspy.utils.http_client import *
+from datetime import datetime
+
+
+class MasterDataManagementService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ def create_season_field_id(self, polygon: str) -> object:
+ """Posts the payload below to the master data management endpoint.
+
+ This method returns a season field id. The season field id is required
+ to request other APIs endpoints.
+
+ Args:
+ polygon : A string representing a polygon.
+
+ Returns:
+ A response object.
+
+ """
+
+ year = datetime.now().year
+ payload = {
+ "Geometry": polygon,
+ "Crop": {"Id": "CORN"},
+ "SowingDate": f"{year}-01-01",
+ }
+ mdm_url: str = urljoin(self.base_url, GeosysApiEndpoints.MASTER_DATA_MANAGEMENT_ENDPOINT.value+"/seasonfields")
+ return self.http_client.post(mdm_url, payload)
+
+ def extract_season_field_id(self, polygon: str) -> str:
+ """Extracts the season field id from the response object.
+
+ Args:
+ polygon : A string representing a polygon.
+
+ Returns:
+ A string representing the season field id.
+
+ Raises:
+ ValueError: The response status code is not as expected.
+ """
+
+ response = self.create_season_field_id(polygon)
+ dict_response = response.json()
+
+ if response.status_code == 400 and "sowingDate" in dict_response["errors"]["body"]:
+
+ text: str = dict_response["errors"]["body"]["sowingDate"][0]["message"]
+ return Helper.get_matched_str_from_pattern(SEASON_FIELD_ID_REGEX, text)
+
+ elif response.status_code == 201:
+ return dict_response["id"]
+ else:
+ raise ValueError(
+ f"Cannot handle HTTP response : {str(response.status_code)} : {str(response.json())}"
+ )
+
+ def get_season_field_unique_id(self, season_field_id: str) -> str:
+ """Extracts the season field unique id from the response object.
+
+ Args:
+ season_field_id : A string representing the seasonfield legacy na id
+
+ Returns:
+ A string representing the season field unique id.
+
+ Raises:
+ ValueError: The response status code is not as expected.
+ """
+
+ mdm_url: str = urljoin(self.base_url, GeosysApiEndpoints.MASTER_DATA_MANAGEMENT_ENDPOINT.value + f"/seasonfields/{season_field_id}?$fields=externalids")
+
+ response = self.http_client.get(mdm_url)
+
+ dict_response = response.json()
+
+ # extract unique id from response:
+ if response.status_code == 200:
+ return dict_response["externalIds"]["id"]
+ else:
+ raise ValueError(
+ f"Cannot handle HTTP response : {str(response.status_code)} : {str(response.json())}"
+ )
+
+
+ def get_available_crops_code(self) -> [str]:
+ """Extracts the list of available crops for the connected user
+
+ Args:
+
+ Returns:
+ A list of string representing the available crop codes
+
+ Raises:
+ ValueError: The response status code is not as expected.
+ """
+ mdm_url: str = urljoin(self.base_url, GeosysApiEndpoints.MASTER_DATA_MANAGEMENT_ENDPOINT.value + f"/crops?$fields=code&$limit=none")
+
+ response = self.http_client.get(mdm_url)
+
+ dict_response = response.json()
+
+ if response.status_code == 200:
+ return dict_response
+ else:
+ raise ValueError(
+ f"Cannot handle HTTP response : {str(response.status_code)} : {str(response.json())}"
+ )
+
+
+ def get_permission_codes(self) -> [str]:
+ """Extracts the list of available permissions for the connected user
+
+ Args:
+
+ Returns:
+ A list of string representing the available permissions codes
+
+ Raises:
+ ValueError: The response status code is not as expected.
+ """
+ mdm_url: str = urljoin(self.base_url, GeosysApiEndpoints.MASTER_DATA_MANAGEMENT_ENDPOINT.value + f"/profile?$fields=permissions&$limit=none")
+
+ response = self.http_client.get(mdm_url)
+
+ dict_response = response.json()
+
+ if response.status_code == 200:
+ return dict_response
+ else:
+ raise ValueError(
+ f"Cannot handle HTTP response : {str(response.status_code)} : {str(response.json())}"
+ )
+
+
diff --git a/geosyspy/services/service_constants.py b/geosyspy/services/service_constants.py
new file mode 100644
index 0000000..8e8b9c9
--- /dev/null
+++ b/geosyspy/services/service_constants.py
@@ -0,0 +1,20 @@
+from enum import Enum
+
+
+class ProcessorConfiguration(Enum):
+ """
+ Available different Geosys Api Processor key path & profile values
+ """
+ PLANTED_AREA = {"profile":"planted_area_default", "api_processor_path":"planted_area"}
+ POTENTIAL_SCORE = {"profile":"potential_score_default", "api_processor_path":"potential_score"}
+ BRAZIL_IN_SEASON_CROP_ID = {"profile":"brazil_in_season_crop_id_default", "api_processor_path":"brazil_in_season_crop_id"}
+ GREENNESS = {"profile":"greenness_default", "api_processor_path":"greenness"}
+ HARVEST_READINESS = {"profile":"harvest_readiness_default", "api_processor_path":"harvest_readiness"}
+ ZARC = {"profile":"zarc_default", "api_processor_path":"zarc"}
+ EMERGENCE_IN_SEASON = {"profile":"inseason-emergence_default", "api_processor_path":"emergence-date"}
+ EMERGENCE_HISTORICAL = {"profile":"historical-emergence_default", "api_processor_path":"historical-emergence"}
+ EMERGENCE_DELAY = {"profile": "inseason-emergence_default", "api_processor_path": "emergence-delay"}
+ HARVEST_IN_SEASON = {"profile":"inseason-harvest_default", "api_processor_path":"inseason-harvest"}
+ HARVEST_HISTORICAL = {"profile":"historical-harvest_default", "api_processor_path":"historical-harvest"}
+ MRTS = {"profile":"mrts_default", "api_processor_path":"mrts"}
+
diff --git a/geosyspy/services/vegetation_time_series_service.py b/geosyspy/services/vegetation_time_series_service.py
new file mode 100644
index 0000000..35ad06c
--- /dev/null
+++ b/geosyspy/services/vegetation_time_series_service.py
@@ -0,0 +1,131 @@
+import json
+import logging
+import pandas as pd
+from datetime import datetime
+from urllib.parse import urljoin
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+
+
+class VegetationTimeSeriesService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ def get_modis_time_series(self, season_field_id:str,
+ start_date:datetime,
+ end_date:datetime,
+ indicator:str):
+ """Returns a pandas DataFrame.
+
+ This method returns a time series of 'indicator' within the range
+ 'start_date' -> 'end_date' as a pandas DataFrame :
+
+ | index | value |
+
+ date | indicator | 1 |
+ __________________________________
+ start-date | indicator | 2 |
+
+ ... | ... | ... |
+
+ end-date | indicator | 8 |
+
+ Args:
+ season_field_id : A string representing the season_field_id.
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ indicator : A string representing the indicator whose time series the user wants.
+
+ Returns:
+ df : A Pandas DataFrame containing two columns : index and value, and an index called 'date'.
+
+ """
+
+ self.logger.info("Calling APIs for aggregated time series")
+ start_date: str = start_date.strftime("%Y-%m-%d")
+ end_date: str = end_date.strftime("%Y-%m-%d")
+ parameters: str = f"/values?$offset=0&$limit=None&$count=false&SeasonField.Id={season_field_id}&index={indicator}&$filter=Date >= '{start_date}' and Date <= '{end_date}'"
+ vts_url: str = urljoin(self.base_url, GeosysApiEndpoints.VTS_ENDPOINT.value + parameters)
+ response = self.http_client.get(vts_url)
+
+ if response.status_code == 200:
+ dict_response = response.json()
+ df = pd.read_json(json.dumps(dict_response))
+ df.set_index("date", inplace=True)
+ return df
+ else:
+ self.logger.info(response.status_code)
+
+
+ def get_time_series_by_pixel(self, season_field_id: str,
+ start_date: datetime,
+ end_date: datetime,
+ indicator: str) -> pd.DataFrame:
+ """Returns a pandas DataFrame.
+
+ This method returns a time series of 'indicator' by pixel within the range 'start_date' -> 'end_date'
+ as well as the pixel's coordinates X,Y in the MODIS's sinusoidal projection as a pandas DataFrame :
+
+
+
+ | index | value | pixel.id | X | Y |
+ _______________________________________|
+ date | indicator | | | | |
+ ___________________________________________________|
+
+ start-date | indicator | 2 | 1 | | |
+
+ ... | ... | ... | ... | | |
+
+ end-date | indicator | 8 | 1000 | | |
+
+ Args:
+ polygon : A string representing a polygon.
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ indicator : A string representing the indicator whose time series the user wants.
+
+ Returns:
+ df : A Pandas DataFrame containing five columns : index, value, pixel.id, X, Y and an index called 'date'.
+
+ """
+
+ self.logger.info("Calling APIs for time series by the pixel")
+ start_date: str = start_date.strftime("%Y-%m-%d")
+ end_date: str = end_date.strftime("%Y-%m-%d")
+ parameters: str = f"/values?$offset=0&$limit=None&$count=false&SeasonField.Id={season_field_id}&index={indicator}&$filter=Date >= '{start_date}' and Date <= '{end_date}'"
+ vts_url: str = urljoin(self.base_url, GeosysApiEndpoints.VTS_BY_PIXEL_ENDPOINT.value + parameters)
+ # PSX/PSY : size in meters of one pixel
+ # MODIS_GRID_LENGTH : theoretical length of the modis grid in meters
+ # MODIS_GRID_HEIGHT : theoretical height of the modis grid in meters
+ PSX = 231.65635826
+ PSY = -231.65635826
+ MODIS_GRID_LENGTH = 4800 * PSX * 36
+ MODIS_GRID_HEIGHT = 4800 * PSY * 18
+
+ response = self.http_client.get(vts_url)
+
+ if response.status_code == 200:
+ df = pd.json_normalize(response.json())
+ df.set_index("date", inplace=True)
+
+ # Extracts h, v, i and j from the pixel dataframe
+ self.logger.info("Computing X and Y coordinates per pixel... ")
+ df["h"] = df["pixel.id"].str.extract(r"h(.*)v").astype(int)
+ df["v"] = df["pixel.id"].str.extract(r"v(.*)i").astype(int)
+ df["i"] = df["pixel.id"].str.extract(r"i(.*)j").astype(int)
+ df["j"] = df["pixel.id"].str.extract(r"j(.*)$").astype(int)
+
+ # XUL/YUL : The coordinates of the top left corner of the tile h,v's top left pixel
+ # X/Y : the coordinates of the top left corner of the i,j pixel
+ df["XUL"] = (df["h"] + 1) * 4800 * PSX - MODIS_GRID_LENGTH / 2
+ df["YUL"] = (df["v"] + 1) * 4800 * PSY + MODIS_GRID_HEIGHT / 2
+ df["X"] = df["i"] * PSX + df["XUL"]
+ df["Y"] = df["j"] * PSY + df["YUL"]
+ self.logger.info("Done ! ")
+ return df[["index", "value", "pixel.id", "X", "Y"]]
+ else:
+ self.logger.info(response.status_code)
\ No newline at end of file
diff --git a/geosyspy/services/weather_service.py b/geosyspy/services/weather_service.py
new file mode 100644
index 0000000..6ef23bb
--- /dev/null
+++ b/geosyspy/services/weather_service.py
@@ -0,0 +1,63 @@
+import logging
+import pandas as pd
+from datetime import datetime
+from urllib.parse import urljoin
+
+from shapely import wkt
+
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+
+
+class WeatherService:
+
+ def __init__(self, base_url: str, http_client: HttpClient):
+ self.base_url: str = base_url
+ self.http_client: HttpClient = http_client
+ self.logger = logging.getLogger(__name__)
+
+ def get_weather(self, polygon: str,
+ start_date: datetime,
+ end_date: datetime,
+ weather_type: WeatherTypeCollection,
+ fields: [str]):
+ """Returns the weather data as a pandas dataframe.
+
+ Args:
+ polygon : A string representing a polygon.
+ start_date : A datetime object representing the start date of the date interval the user wants to filter on.
+ end_date : A datetime object representing the final date of the date interval the user wants to filter on.
+ weather_type : A list representing the weather type collection ["HISTORICAL_DAILY", "FORECAST_DAILY", "FORECAST_HOURLY"]
+ fields : A list of strings representing the fields to select (eg: Precipitation, Temperature)
+
+ Returns:
+ The image's numpy array.
+
+ """
+
+ if weather_type not in WeatherTypeCollection:
+ raise ValueError(f"weather_type should be either {[item.value for item in WeatherTypeCollection]}")
+ weather_type = weather_type.value
+ if "Date" not in fields:
+ fields.append("Date")
+
+ start_date: str = start_date.strftime("%Y-%m-%d")
+ end_date: str = end_date.strftime("%Y-%m-%d")
+ polygon_wkt = wkt.loads(polygon)
+ weather_fields: str = ",".join(fields)
+ parameters: str = f"?%24offset=0&%24limit=None&%24count=false&Location={polygon_wkt.centroid.wkt}&Date=%24between%3A{start_date}T00%3A00%3A00.0000000Z%7C{end_date}T00%3A00%3A00.0000000Z&Provider=GLOBAL1&WeatherType={weather_type}&$fields={weather_fields}"
+ weather_url: str = urljoin(self.base_url, GeosysApiEndpoints.WEATHER_ENDPOINT.value + parameters)
+
+ response = self.http_client.get(weather_url)
+
+ if response.status_code == 200:
+ df = pd.json_normalize(response.json())
+ if df.empty:
+ return df
+ else:
+ df.set_index("date", inplace=True)
+ df["Location"] = polygon_wkt.centroid.wkt
+ return df.sort_index()
+ else:
+ self.logger.error(response.status_code)
+ raise ValueError(response.content)
\ No newline at end of file
diff --git a/geosyspy/utils/__init__.py b/geosyspy/utils/__init__.py
new file mode 100644
index 0000000..b33d820
--- /dev/null
+++ b/geosyspy/utils/__init__.py
@@ -0,0 +1 @@
+__all__ = ["constants", "geosys_platform_urls", "oauth2_client", "http_client", "helper", "jwt_validator"]
diff --git a/geosyspy/utils/constants.py b/geosyspy/utils/constants.py
new file mode 100644
index 0000000..a5f1883
--- /dev/null
+++ b/geosyspy/utils/constants.py
@@ -0,0 +1,166 @@
+from enum import Enum
+
+
+class SatelliteImageryCollection(Enum):
+ """
+ Available imagery collections
+ """
+ MODIS = "MODIS"
+ SENTINEL_2 = "SENTINEL_2"
+ LANDSAT_8 = "LANDSAT_8"
+ LANDSAT_9 = "LANDSAT_9"
+
+
+class WeatherTypeCollection(Enum):
+ """
+ Available weather collections
+ """
+ WEATHER_FORECAST_DAILY = "FORECAST_DAILY"
+ WEATHER_FORECAST_HOURLY = "FORECAST_HOURLY"
+ WEATHER_HISTORICAL_DAILY = "HISTORICAL_DAILY"
+
+
+class Env(Enum):
+ """
+ Environment to target (PROD, PREPROD)
+ """
+ PROD = "prod"
+ PREPROD = "preprod"
+
+
+class Region(Enum):
+ """
+ Region to target (NA)
+ """
+ NA = "na"
+
+
+class Harvest(Enum):
+ """
+ Type of Harvest query used for Harvest analytics processor
+ """
+ HARVEST_IN_SEASON = "IN_SEASON"
+ HARVEST_HISTORICAL = "HISTORICAL"
+
+class Emergence(Enum):
+ """
+ Type of Emergence query used for Emergence analytics processor
+ """
+ EMERGENCE_IN_SEASON = "IN_SEASON"
+ EMERGENCE_HISTORICAL = "HISTORICAL"
+ EMERGENCE_DELAY = "DELAY"
+
+
+class AgriquestCommodityCode(Enum):
+ """
+ Available AgriQuest Commodity values
+ """
+ ALL_VEGETATION = 33
+ ALL_CROPS = 35
+
+
+class AgriquestFranceBlockCode(Enum):
+ """
+ Available AgriQuest Block codes dedicated to France
+ """
+ FRA_CANTONS = 216
+ FRA_COMMUNES = 135
+ FRA_DEPARTEMENTS = 226
+
+
+class AgriquestBlocks(Enum):
+ """
+ Available AgriQuest Block codes
+ """
+ FIRST_LEVEL = 129
+ AMU_AUSTRALIA_LEVEL_1 = 205
+ AMU_AUSTRALIA_LEVEL_2 = 206
+ AMU_CHINA = 202
+ AMU_EUROPE_RUSSIA = 197
+ AMU_INDIA = 204
+ AMU_MEXICO = 212
+ AMU_NORTH_AMERICA = 207
+ AMU_SOUTH_AFRICA = 213
+ BM_REGIONS = 139
+ CAR = 140
+ COUNTY = 141
+ FRA_CANTONS = 216
+ FRA_COMMUNES = 135
+ FRA_DEPARTEMENTS = 226
+ MESOREGION = 131
+ NORTH_AFRICA_AMU = 125
+ RAION = 127
+ SERBIA = 132
+ SOUTH_AMERICA_MUNICIPIOS_2020 = 267
+ SOUTH_AMERICA_AMU = 115
+ SPAIN_COMARCAS = 136
+ US_ASD = 130
+ WESTERN_AFRICA_AMU = 122
+
+
+class AgriquestWeatherType(Enum):
+ """
+ Available AgriQuest Weather types
+ """
+ CUMULATIVE_PRECIPITATION = "cumulative-precipitation"
+ MIN_TEMPERATURE = "min-temperature"
+ AVERAGE_TEMPERATURE = "average-temperature"
+ MAX_TEMPERATURE = "max-temperature"
+ MAX_WIND_SPEED = "max-wind-speed"
+ RELATIVE_HUMIDITY = "relative-humidity"
+ SNOW_DEPTH = "snow-depth"
+ SOIL_MOISTURE = "soil-moisture"
+ SOLAR_RADIATION = "solar-radiation"
+
+class ZarcSoilType(Enum):
+ """
+ Available Soil Type values for analytics processor Zarc
+ """
+ SOIL_TYPE_1 = "1"
+ SOIL_TYPE_2 = "2"
+ SOIL_TYPE_3 = "3"
+ NONE = None
+
+class CropIdSeason(Enum):
+ """
+ Available season values for analytics processor Zarc
+ """
+ SEASON_1="SEASON_1"
+ SEASON_2="SEASON_2"
+
+
+class ZarcCycleType(Enum):
+ """
+ Available season values for analytics processor Zarc
+ """
+ CYCLE_TYPE_1 = "1"
+ CYCLE_TYPE_2 = "2"
+ CYCLE_TYPE_3 = "3"
+ NONE = None
+
+
+class GeosysApiEndpoints(Enum):
+ """
+ Available Geosys APIs Endpoints
+ """
+ MASTER_DATA_MANAGEMENT_ENDPOINT = "master-data-management/v6"
+ VTS_ENDPOINT = "vegetation-time-series/v1/season-fields"
+ VTS_BY_PIXEL_ENDPOINT = "vegetation-time-series/v1/season-fields/pixels"
+ FLM_CATALOG_IMAGERY = "field-level-maps/v4/season-fields/{}/catalog-imagery"
+ FLM_COVERAGE = "field-level-maps/v4/season-fields/{}/coverage"
+ WEATHER_ENDPOINT = "Weather/v1/weather"
+ ANALYTICS_FABRIC_ENDPOINT = "analytics/metrics"
+ ANALYTICS_FABRIC_LATEST_ENDPOINT = "analytics/metrics-latest"
+ ANALYTICS_FABRIC_SCHEMA_ENDPOINT = "analytics/schemas"
+ AGRIQUEST_ENDPOINT = "agriquest/Geosys.Agriquest.CropMonitoring.WebApi/v0/api"
+ # Analytics processor
+ PROCESSOR_EVENTS_ENDPOINT = "analytics-pipeline/v1/processors/events"
+ LAUNCH_PROCESSOR_ENDPOINT = "analytics-pipeline/v1/processors/{}/launch"
+
+
+LR_SATELLITE_COLLECTION = [SatelliteImageryCollection.MODIS]
+MR_SATELLITE_COLLECTION = [SatelliteImageryCollection.LANDSAT_8, SatelliteImageryCollection.LANDSAT_9,
+ SatelliteImageryCollection.SENTINEL_2]
+
+PRIORITY_HEADERS = {"bulk": "Geosys_API_Bulk", "realtime": ""}
+SEASON_FIELD_ID_REGEX = r"\sId:\s(\w+),"
diff --git a/geosyspy/platforms.py b/geosyspy/utils/geosys_platform_urls.py
similarity index 52%
rename from geosyspy/platforms.py
rename to geosyspy/utils/geosys_platform_urls.py
index 98b03c8..45db580 100644
--- a/geosyspy/platforms.py
+++ b/geosyspy/utils/geosys_platform_urls.py
@@ -1,7 +1,6 @@
"""
This file contains python dictionaries used to store the Geosys' APIs
-URLs for the preprod/prod environments for both of north america
-and Europe.
+URLs for the preprod/prod environments.
"""
@@ -9,19 +8,17 @@
'na': {
'preprod': 'https://identity.preprod.geosys-na.com/v2.1/connect/token',
'prod': 'https://identity.geosys-na.com/v2.1/connect/token'
- },
- 'eu': {
- 'preprod': 'https://identity.preprod.geosys-na.com/v2.1/connect/token',
- 'prod': 'https://identity.geosys-eu.com/v2.1/connect/token'
}
}
GEOSYS_API_URLS = {
'na': {
'preprod': 'https://api-pp.geosys-na.net',
'prod': 'https://api.geosys-na.net'
- },
- 'eu': {
- 'preprod': 'https://api-pp.geosys-na.net',
- 'prod': 'https://api.geosys-eu.net'
+ }
+}
+GIS_API_URLS = {
+ 'na': {
+ 'preprod': 'https://gis-services-pp.geosys.com',
+ 'prod': 'https://gis-services.geosys.com'
}
}
diff --git a/geosyspy/utils/helper.py b/geosyspy/utils/helper.py
new file mode 100644
index 0000000..0b18482
--- /dev/null
+++ b/geosyspy/utils/helper.py
@@ -0,0 +1,68 @@
+import re
+import json
+from shapely import wkt
+from shapely.geometry import shape
+
+
+class Helper:
+
+ @staticmethod
+ def get_matched_str_from_pattern(pattern: str,
+ text: str) -> str:
+ """Returns the first occurence of the matched pattern in text.
+
+ Args:
+ pattern : A string representing the regex pattern to look for.
+ text : The text to look into.
+
+ Returns:
+ A string representing the first occurence in text of the pattern.
+
+ """
+ p = re.compile(pattern)
+ return p.findall(text)[0]
+
+ @staticmethod
+ def convert_to_wkt(geometry):
+ """ convert a geometry (WKT or geoJson) to WKT
+ Args:
+ geometry : A string representing the geometry (WKT or geoJson)
+
+ Returns:
+ a valid WKT
+
+ """
+
+ try:
+ # check if the geometry is a valid WKT
+ if Helper.is_valid_wkt(geometry):
+ # return the wkt
+ return geometry
+ except:
+ try:
+ # check if the geometry is a valid geoJson
+ geojson_data = json.loads(geometry)
+ geom = shape(geojson_data)
+ geometry = geom.wkt
+
+ return geometry
+
+ except ValueError:
+ # geometry is not a valid geoJson
+ return None
+
+ @staticmethod
+ def is_valid_wkt(geometry):
+ """ check if the geometry is a valid WKT
+ Args:
+ geometry : A string representing the geometry
+
+ Returns:
+ boolean (True/False)
+
+ """
+ try:
+ wkt.loads(geometry)
+ return True
+ except ValueError:
+ return False
\ No newline at end of file
diff --git a/geosyspy/utils/http_client.py b/geosyspy/utils/http_client.py
new file mode 100644
index 0000000..8cb6cc8
--- /dev/null
+++ b/geosyspy/utils/http_client.py
@@ -0,0 +1,89 @@
+from . import oauth2_client
+from oauthlib.oauth2 import TokenExpiredError
+from requests_oauthlib import OAuth2Session
+
+
+def renew_access_token(func):
+ """Decorator used to wrap the Geosys class's http methods.
+
+ This decorator wraps the geosys http methods (get,post...) and checks
+ whether the used token is still valid or not. If not, it fetches a new token and
+ uses it to make another request.
+
+ """
+
+ def wrapper(self, *args, **kwargs):
+ try:
+ return func(self, *args, **kwargs)
+ except TokenExpiredError:
+ self.access_token = self.__client_oauth.get_refresh_token()
+ return func(self, *args, **kwargs)
+
+ return wrapper
+
+
+class HttpClient:
+
+ def __init__(
+ self,
+ client_id: str,
+ client_secret: str,
+ username: str,
+ password: str,
+ enum_env: str,
+ enum_region: str,
+ bearer_token: str = None
+ ):
+ self.__client_oauth = oauth2_client.Oauth2Api(
+ client_id=client_id,
+ client_secret=client_secret,
+ password=password,
+ username=username,
+ enum_env=enum_env,
+ enum_region=enum_region,
+ bearer_token=bearer_token
+ )
+ self.access_token = self.__client_oauth.token
+
+ self.__client = OAuth2Session(self.__client_oauth.client_id, token=self.__client_oauth.token)
+
+ @renew_access_token
+ def get(self, url_endpoint: str, headers={}):
+ """Gets the url_endpopint.
+
+ Args:
+ url_endpoint : A string representing the url to get.
+
+ Returns:
+ A response object.
+ """
+ return self.__client.get(url_endpoint, headers=headers)
+
+ @renew_access_token
+ def post(self, url_endpoint: str, payload: dict, headers={}):
+ """Posts payload to the url_endpoint.
+
+ Args:
+ url_endpoint : A string representing the url to post paylaod to.
+ payload : A python dict representing the payload.
+
+ Returns:
+ A response object.
+ """
+ return self.__client.post(url_endpoint, json=payload, headers=headers)
+
+ @renew_access_token
+ def patch(self, url_endpoint: str, payload: dict):
+ """Patchs payload to the url_endpoint.
+
+ Args:
+ url_endpoint : A string representing the url to patch paylaod to.
+ payload : A python dict representing the payload.
+
+ Returns:
+ A response object.
+ """
+ return self.__client.patch(url_endpoint, json=payload)
+
+ def get_access_token(self):
+ return self.access_token
diff --git a/geosyspy/utils/jwt_validator.py b/geosyspy/utils/jwt_validator.py
new file mode 100644
index 0000000..ca910ad
--- /dev/null
+++ b/geosyspy/utils/jwt_validator.py
@@ -0,0 +1,80 @@
+import jwt
+import base64
+import json
+from datetime import datetime, timezone
+from cryptography import x509
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import serialization
+import logging
+
+
+logger = logging.getLogger(__name__)
+
+
+def check_token_validity(token: str, certificate_key: str, algorithms=['RS256']) -> bool:
+ """
+ Check the validity of a JWT token.
+
+ Args:
+ token (str): The JWT token to check.
+ certificate_key (str): The certificate key in PEM format.
+ algorithms (list, optional): The encryption algorithms to use.
+ Default is ['RS256'].
+
+ Returns:
+ bool: True if the token is valid, False otherwise.
+ """
+ try:
+ cert_bytes = certificate_key.encode()
+ cert = x509.load_pem_x509_certificate(cert_bytes, default_backend())
+ public_key = cert.public_key()
+
+ # extract public key in PEM format
+ public_key = public_key.public_bytes(
+ encoding=serialization.Encoding.PEM,
+ format=serialization.PublicFormat.SubjectPublicKeyInfo
+ ).decode()
+ aud = __get_audience_from_token(token)
+ decoded_token = jwt.decode(token, public_key, algorithms=algorithms, audience=aud)
+ expiration_timestamp = decoded_token['exp']
+ expiration_datetime = datetime.fromtimestamp(expiration_timestamp, tz=timezone.utc)
+
+ if expiration_datetime > datetime.now(timezone.utc):
+ return True
+ else:
+ return False
+ except jwt.ExpiredSignatureError:
+ logger.error("Expired Token")
+ return False
+ except jwt.InvalidTokenError as e:
+ logger.error("Invalid Token." + str(e))
+ return False
+ except Exception as e:
+ logger.error("Invalid Token." + str(e))
+ return False
+
+
+def __get_audience_from_token(token: str) -> str:
+ """
+ Get the audience from a JWT token.
+
+ Args:
+ token (str): The JWT token.
+
+ Returns:
+ str: The audience value.
+ """
+ # parse the token (header, payload, signature)
+ header, payload, signature = token.split('.')
+
+ # add missing data
+ padding = '=' * (4 - len(payload) % 4)
+ payload += padding
+
+ # payload decoding & serialization
+ decoded_payload = base64.urlsafe_b64decode(payload.encode('utf-8'))
+ payload_json = decoded_payload.decode('utf-8')
+
+ payload_dict = json.loads(payload_json)
+ audience = payload_dict.get('aud')
+ return audience
diff --git a/geosyspy/utils/logging_config.py b/geosyspy/utils/logging_config.py
new file mode 100644
index 0000000..bd66ef3
--- /dev/null
+++ b/geosyspy/utils/logging_config.py
@@ -0,0 +1,23 @@
+# geosyspy/logging_config.py
+
+import logging
+
+
+def configure_logging(level=logging.INFO, handlers=None):
+ """
+ Configure logs for geosyspy.
+ Args:
+ level (int): log level
+ handlers ([logging.handler]): logger handlers
+ """
+ logger = logging.getLogger('geosyspy')
+ if handlers is None:
+ handlers = [logging.StreamHandler()] # By default, use a console handler
+
+ for handler in handlers:
+ handler.setLevel(level)
+ handler.setFormatter(logging.Formatter(
+ "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+ ))
+ logger.addHandler(handler)
+
diff --git a/geosyspy/utils/oauth2_client.py b/geosyspy/utils/oauth2_client.py
new file mode 100644
index 0000000..25c4573
--- /dev/null
+++ b/geosyspy/utils/oauth2_client.py
@@ -0,0 +1,68 @@
+from oauthlib.oauth2 import LegacyApplicationClient
+from requests_oauthlib import OAuth2Session
+from . import geosys_platform_urls
+import logging
+
+
+class Oauth2Api:
+ def __init__(
+ self,
+ client_id: str,
+ client_secret: str,
+ username: str,
+ password: str,
+ enum_env: str,
+ enum_region: str,
+ bearer_token: str = None
+ ):
+ """Initializes a Geosys instance with the required credentials
+ to connect to the GEOSYS API.
+ """
+ self.logger = logging.getLogger(__name__)
+ self.client_id = client_id
+ self.server_url = geosys_platform_urls.IDENTITY_URLS[enum_region][enum_env]
+ self.client_secret = client_secret
+ self.token = None
+ self.username = username
+ self.password = password
+
+ if bearer_token:
+ self.token = {"access_token": bearer_token}
+ else:
+ self.__authenticate()
+
+ def __authenticate(self):
+ """Authenticates the http_client to the API.
+
+ This method connects the user to the API which generates a token that
+ will be valid for one hour. A refresh token is also generated, which
+ makes it possible for the http methods wrappers to get a new token
+ once the previous one is no more valid through the renew_access_token
+ decorator. This method is only run once when a Geosys object is instantiated.
+
+ """
+
+ try:
+ oauth = OAuth2Session(
+ client=LegacyApplicationClient(client_id=self.client_id)
+ )
+ self.token = oauth.fetch_token(
+ token_url=self.server_url,
+ username=self.username,
+ password=self.password,
+ client_id=self.client_id,
+ client_secret=self.client_secret,
+ )
+ self.token["refresh_token"] = oauth.cookies["refresh_token"]
+ self.logger.info("Authenticated")
+ except Exception as e:
+ logging.error(e)
+
+ def get_refresh_token(self):
+ """Fetches a new token."""
+ client = OAuth2Session(self.client_id, token=self.token)
+ return client.refresh_token(
+ self.server_url,
+ client_id=self.client_id,
+ client_secret=self.client_secret,
+ )
diff --git a/install.md b/install.md
index 99d992d..f13a6b9 100644
--- a/install.md
+++ b/install.md
@@ -6,13 +6,19 @@ Geosyspy requires **rasterio** to work and rasterio in turn requires **GDAL**. Y
* [Rasterio](https://www.lfd.uci.edu/~gohlke/pythonlibs/#rasterio)
-For instance, if you have a **python 3.9 distribution** installed on a **64 bits Windows machine**, you will need to find and download the files of which the names contain **cp39** (which means CPython 3.9 which comes with the Python3.9 distribution) and **amd64** (which means a machine running on a 64 bits cpu).
+For instance, if you have a **python 3.10 distribution** installed on a **64 bits Windows machine**, you will need to find and download the files of which the names contain **cp39** (which means CPython 3.9 which comes with the Python3.9 distribution) and **amd64** (which means a machine running on a 64 bits cpu).
-If you want to install GDAL 3.4.2, rasterio 1.2.10 and geosyspy on a 64 bits machine running Python3.9, below are the commands (to run in a folder with the downloaded wheels) :
+If you want to install GDAL 3.4.3, rasterio 1.2.10 and geosyspy on a 64 bits machine running Python3.10, below are the commands (to run in a folder with the downloaded wheels) :
pip install -U pip
pip install -U setuptools
- pip install GDAL-3.4.2-cp39-cp39-win_amd64
- pip install rasterio-1.2.10-cp39-cp39-win_amd64
+ pip install GDAL‑3.4.3‑cp310‑cp310‑win_amd64.whl
+ pip install rasterio‑1.2.10‑cp310‑cp310‑win_amd64.whl
pip install geosyspy
+
+
+If you're using Conda, there are the steps to follow :
+
+ conda install pip
+ pip install geosyspy
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index b9e33db..3a194e0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -9,4 +9,5 @@ plugins:
nav:
- Home: index.md
- Geosys class: geosys-reference.md
- - Constants: constants-reference.md
\ No newline at end of file
+ - Constants: constants-reference.md
+ - Utils: utils.md
\ No newline at end of file
diff --git a/notebook_utils.py b/notebook_utils.py
new file mode 100644
index 0000000..0605cbe
--- /dev/null
+++ b/notebook_utils.py
@@ -0,0 +1,209 @@
+import boto3
+import os
+from urllib.parse import urlparse
+from io import StringIO
+import glob
+import json
+import pandas as pd
+import matplotlib.pyplot as plt
+
+def read_mrts_data(str_path):
+ """ Read MRTS outputs dowloaded from AWS
+ return dict containing path on files and dict containing data
+
+ Args:
+ str_path (_string_): path containing MRTS files
+
+ Returns:
+ data_files: data_files = {
+ "metadata": metadata_file path,
+ "raw_df" : raw data path,
+ "df" : df denoised path,
+ "timeserie" : ts path
+ }
+ data_files: dict_data = {
+ "metadata" : metadata in json,
+ "raw_df" : dataframe containing raw data,
+ "df" : dataframe containing data denoised if no smmother,
+ "ts" : dataframe containing timeserie denoised if smmother
+ }
+
+ """
+ path_data = os.path.join(str_path.split('/')[-3],str_path.split('/')[-2],str_path.split('/')[-1])
+
+ metadata_file = glob.glob(os.path.join(path_data,"*.json"))[0]
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+
+ str_index = metadata['parameters']['index']
+ str_func = metadata['parameters']['aggregation']
+ str_smoother = metadata['parameters']['smoother']
+ raw_data = metadata['parameters']['raw_data']
+
+ data_files = {
+ "metadata": metadata_file,
+ "raw_df" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-Raw-2*.csv"))[0] if raw_data else None,
+ "df" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-2*.csv"))[0] if str_smoother == "false" else None,
+ "timeserie" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-2*.csv"))[0] if str_smoother != "false" else None
+ }
+ dict_data = {
+ "metadata" : metadata,
+ "raw_df" : pd.read_csv(data_files['raw_df'], sep=",") if raw_data else None,
+ "df" : pd.read_csv(data_files['df'], sep=",") if str_smoother == "false" else None,
+ "ts" : pd.read_csv(data_files['timeserie'], sep=",") if str_smoother != "false" else None
+ }
+
+ return data_files, dict_data
+
+def plot_mrts_dict(dict_data):
+ """ Plot MRTS outputs from the dict generated by the function read_mrts_data_from_awsS3
+ or the function read_mrts_data
+
+ Args:
+ dict_data (_dictionnaire_): dict_data = {
+ "metadata" : metadata in json,
+ "raw_df" : dataframe containing raw data,
+ "df" : dataframe containing data denoised if no smmother,
+ "ts" : dataframe containing timeserie denoised if smmother
+ }
+ """
+ fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,6))
+ metadata = dict_data['metadata']
+ index = metadata['parameters']['index']
+
+ if index != 'reflectances':
+ if dict_data['raw_df'] is not None:
+ raw_df = dict_data['raw_df']
+ raw_df['date'] = pd.to_datetime(raw_df['date'], format='%Y-%m-%d')
+ raw_df = raw_df.set_index('date')
+ plt.plot(raw_df[index] ,'.', markersize=12, label=index)
+ plt.plot(raw_df[index].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12, label='Denoised')
+ if dict_data['df'] is not None:
+ df = dict_data['df']
+ df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
+ df = df.set_index('date')
+ plt.plot(df['vegetation_index'] ,'.', markersize=12, label=index)
+ if dict_data['ts'] is not None:
+ df_smooth = dict_data['ts']
+ df_smooth['date'] = pd.to_datetime(df_smooth['date'], format='%Y-%m-%d')
+ df_smooth = df_smooth.set_index('date')
+ plt.plot(df_smooth, linewidth=2, label='Smooth ' + index)
+ else:
+ if dict_data['raw_df'] is not None:
+ raw_df = dict_data['raw_df']
+ raw_df['date'] = pd.to_datetime(raw_df['date'], format='%Y-%m-%d')
+ raw_df = raw_df.set_index('date')
+ plt.plot(raw_df['nir'] ,'.', markersize=12, label='nir')
+ plt.plot(raw_df['nir'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12, label='Denoised')
+ plt.plot(raw_df['red'] ,'.', markersize=12, label='red')
+ plt.plot(raw_df['red'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
+ plt.plot(raw_df['green'] ,'.', markersize=12, label='green')
+ plt.plot(raw_df['green'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
+ plt.plot(raw_df['blue'] ,'.', markersize=12, label='blue')
+ plt.plot(raw_df['blue'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
+ if dict_data['df'] is not None:
+ df = dict_data['df']
+ df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
+ df = df.set_index('date')
+ plt.plot(df['nir'] ,'.', markersize=12, label=index)
+ plt.plot(df['red'] ,'.', markersize=12, label=index)
+ plt.plot(df['green'] ,'.', markersize=12, label=index)
+ plt.plot(df['blue'] ,'.', markersize=12, label=index)
+ if dict_data['ts'] is not None:
+ df_smooth = dict_data['ts']
+ df_smooth['date'] = pd.to_datetime(df_smooth['date'], format='%Y-%m-%d')
+ df_smooth = df_smooth.set_index('date')
+ plt.plot(df_smooth['nir'], linewidth=2, label='Smooth ' + 'nir')
+ plt.plot(df_smooth['red'], linewidth=2, label='Smooth ' + 'red')
+ plt.plot(df_smooth['green'], linewidth=2, label='Smooth ' + 'green')
+ plt.plot(df_smooth['blue'], linewidth=2, label='Smooth ' + 'blue')
+
+ if index == 'ndvi':
+ plt.ylim((0,1))
+ elif index == 'evi':
+ plt.ylim((0,1))
+ elif index == 'cvin':
+ plt.ylim((0,1))
+ elif index == 'gndvi':
+ plt.ylim((0,1))
+ elif index == 'ndwi':
+ plt.ylim((0,1))
+ elif index == 'reflectances':
+ plt.ylim((0,1))
+ elif index == 's2rep':
+ plt.ylim((710,730))
+ ax.set_ylabel(index)
+ plt.legend()
+ plt.grid(True)
+ plt.show()
+
+def read_mrts_from_awsS3(str_s3_path):
+ """ Read MRTS outputs direclty from S3 AWS
+ return dict containing path on files and dict containing data
+
+ Args:
+ str_s3_path (_string_): URI S3 (aws)
+
+ Returns:
+ data_files: dict_data = {
+ "metadata" : metadata in json,
+ "raw_df" : dataframe containing raw data,
+ "df" : dataframe containing data denoised if no smmother,
+ "ts" : dataframe containing timeserie denoised if smmother
+ }
+
+ """
+ s3 = boto3.resource('s3')
+ parsed_url = urlparse(str_s3_path)
+ bucket_name = parsed_url.netloc
+ directory_name = parsed_url.path.lstrip('/')
+ bucket = s3.Bucket(bucket_name)
+ s3 = boto3.client('s3')
+ for obj in bucket.objects.filter(Prefix=directory_name):
+ response = s3.get_object(Bucket=bucket_name, Key=obj.key)
+ file_content = response['Body'].read().decode('utf-8')
+ if obj.key.split('/')[-1].split('.')[-1] == 'json' :
+ # Utilisez la méthode `get_object` pour récupérer l'objet du fichier
+ metadata = json.load(StringIO(file_content))
+ elif obj.key.split('/')[-1].split('.')[-1] == 'csv' :
+ if 'Raw' in obj.key.split('/')[-1] :
+ csv_data = StringIO(file_content)
+ raw_df = pd.read_csv(csv_data, sep=",")
+ else :
+ csv_data = StringIO(file_content)
+ df = pd.read_csv(csv_data, sep=",")
+
+ str_smoother = metadata['parameters']['smoother']
+ raw_data = metadata['parameters']['raw_data']
+
+ dict_data = {
+ "metadata" : metadata,
+ "raw_df" : raw_df if raw_data else None,
+ "df" : df if str_smoother == "false" else None,
+ "ts" : df if str_smoother != "false" else None
+ }
+ return dict_data
+
+def plot_mrts_from_aws(str_s3_path):
+ """ Plot MRTS outputs stored on S3 AWS
+ Args:
+ str_s3_path (_string_): URI S3 (aws)
+ """
+ dict_data = read_mrts_from_awsS3(str_s3_path)
+ plot_mrts_dict(dict_data)
+
+def download_s3_files(str_s3_path):
+ """download files stored on aws S3 bucket
+
+ Args:
+ str_s3_path (_string_): URI S3 (aws)
+ """
+ s3 = boto3.resource('s3')
+ parsed_url = urlparse(str_s3_path)
+ bucket_name = parsed_url.netloc
+ directory_name = parsed_url.path.lstrip('/')
+ bucket = s3.Bucket(bucket_name)
+ for obj in bucket.objects.filter(Prefix=directory_name):
+ if not os.path.exists(os.path.dirname(obj.key)):
+ os.makedirs(os.path.dirname(obj.key))
+ bucket.download_file(obj.key, obj.key)
\ No newline at end of file
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 37093c8..6bb5d57 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -4,4 +4,13 @@ flake8
black
twine
mkdocs
-mkdocstrings
\ No newline at end of file
+mkdocstrings-python
+requests-mock
+pandas
+shapely
+xarray
+requests
+requests-oauthlib
+oauthlib
+matplotlib
+boto3
\ No newline at end of file
diff --git a/requirements-notebook.txt b/requirements-notebook.txt
new file mode 100644
index 0000000..60377bd
--- /dev/null
+++ b/requirements-notebook.txt
@@ -0,0 +1,4 @@
+ipykernel
+python-dotenv
+matplotlib
+boto3
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 15823cf..7aad9db 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,11 @@ requests
requests-oauthlib
oauthlib
scipy
-pandas==1.3.5
+pandas
shapely
rasterio
-xarray
\ No newline at end of file
+xarray
+retrying
+pip-system-certs
+PyJWT
+cryptography
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 224a779..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
index d9495bb..0a1ea51 100644
--- a/setup.py
+++ b/setup.py
@@ -4,7 +4,7 @@
import pathlib
# Third party imports
-from setuptools import setup
+from setuptools import setup, find_packages
# The directory containing this file
HERE = pathlib.Path(__file__).resolve().parent
@@ -22,8 +22,8 @@
long_description=README,
long_description_content_type="text/markdown",
author="Geosys",
- packages=["geosyspy"],
+ packages=find_packages(),
include_package_data=True,
data_files=[('', ['VERSION.txt'])],
- install_requires=["requests", "requests-oauthlib", "oauthlib", "scipy", "pandas==1.3.5", "shapely", "rasterio", "xarray"]
-)
+ install_requires=["requests", "requests-oauthlib", "oauthlib", "scipy", "pandas", "shapely", "rasterio", "xarray", "retrying"]
+)
\ No newline at end of file
diff --git a/test_geosyspy/test_geosys.py b/test_geosyspy/test_geosys.py
deleted file mode 100644
index 271b67b..0000000
--- a/test_geosyspy/test_geosys.py
+++ /dev/null
@@ -1,188 +0,0 @@
-from datetime import datetime
-from geosyspy import Geosys
-from dotenv import load_dotenv
-import os
-import datetime as dt
-import numpy as np
-from geosyspy.constants import Collection, Env, Region
-# read .env file
-load_dotenv()
-
-API_CLIENT_ID = os.getenv("API_CLIENT_ID")
-API_CLIENT_SECRET = os.getenv("API_CLIENT_SECRET")
-API_USERNAME = os.getenv("API_USERNAME")
-API_PASSWORD = os.getenv("API_PASSWORD")
-
-
-class TestGeosys:
-
- # polygon with two pixels : mh11v4i225j4612, mh11v4i226j4612
- polygon = "POLYGON((-91.29152885756007 40.39177489815265,-91.28403789132507 40.391776131485386,-91.28386736508233 40.389390758655935,-91.29143832829979 40.38874592864832,-91.29152885756007 40.39177489815265))"
- client = Geosys(
- API_CLIENT_ID, API_CLIENT_SECRET, API_USERNAME, API_PASSWORD, Env.PREPROD, Region.NA
- )
-
- def test_authenticate(self):
- assert set(
- [
- "access_token",
- "expires_in",
- "token_type",
- "scope",
- "expires_at",
- "refresh_token",
- ]
- ).issubset(set(self.client.token.keys()))
-
- def test_get_time_series_modis_ndvi(self):
-
- start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
- end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
-
- df = self.client.get_time_series(
- self.polygon, start_date, end_date, Collection.MODIS, ["NDVI"]
- )
-
- assert df.index.name == "date"
- assert "value" in df.columns
- assert "index" in df.columns
- assert len(df.index) == 7
- date_range = list(map(lambda x: x.strftime("%Y-%m-%d"), df.index))
- assert set(
- [
- "2020-01-01",
- "2020-01-02",
- "2020-01-03",
- "2020-01-04",
- "2020-01-05",
- "2020-01-06",
- "2020-01-07",
- ]
- ).issubset(set(date_range))
-
- def test_get_satellite_image_time_series_modis_ndvi(self):
-
- start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
- end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
-
- df = self.client.get_satellite_image_time_series(
- self.polygon, start_date, end_date, [Collection.MODIS], ["NDVI"]
- )
- assert df.index.name == "date"
- assert set(["value", "index", "pixel.id"]).issubset(set(df.columns))
- assert np.all((df["index"].values == "NDVI"))
- assert len(df.index) == 14
-
- assert set(
- [
- "2020-01-01",
- "2020-01-02",
- "2020-01-03",
- "2020-01-04",
- "2020-01-05",
- "2020-01-06",
- "2020-01-07",
- ]
- ).issubset(set(df.index))
-
- assert set(["mh11v4i225j4612", "mh11v4i226j4612"]).issubset(set(df["pixel.id"]))
-
- def test_get_satellite_coverage_image_references(self):
- start_date = dt.datetime.strptime("2021-01-01", "%Y-%m-%d")
- end_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
- info, images_references = self.client.get_satellite_coverage_image_references(
- self.polygon, start_date, end_date, [Collection.SENTINEL_2]
- )
-
- assert set(
- [
- "coverageType",
- "image.id",
- "image.availableBands",
- "image.sensor",
- "image.soilMaterial",
- "image.spatialResolution",
- "image.weather",
- "image.date",
- "seasonField.id",
- ]
- ).issubset(set(info.columns))
-
- assert len(info) == len(images_references)
- for i, image_info in info.iterrows():
- assert (
- image_info["image.date"],
- image_info["image.sensor"],
- ) in images_references
-
- def get_time_series_weather_historical_daily(self):
-
- start_date = dt.datetime.strptime("2021-01-01", "%Y-%m-%d")
- end_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
- indicators = [
- "Precipitation",
- "Temperature.Ground",
- "Temperature.Standard",
- "Temperature.StandardMax",
- "Date",
- ]
-
- df = self.client.get_time_series(
- self.polygon,
- start_date,
- end_date,
- Collection.WEATHER_HISTORICAL_DAILY,
- indicators,
- )
-
- assert set(
- [
- "precipitation.cumulative",
- "precipitation.probabilities",
- "temperature.ground",
- "temperature.standard",
- "temperature.standardMax",
- ]
- ).issubset(set(df.columns))
- assert df.index.name == "date"
-
- # def test_get_metrics(self):
-
- # lai_radar_polygon = "POLYGON((-52.72591542 -18.7395779,-52.72604885 -18.73951122,-52.72603114 -18.73908689,-52.71556835 -18.72490316,-52.71391916 -18.72612966,-52.71362802 -18.72623726,-52.71086473 -18.72804231,-52.72083542 -18.74173696,-52.72118937 -18.74159174,-52.72139229 -18.7418552,-52.72600257 -18.73969719,-52.72591542 -18.7395779))"
- # schema_id = "LAI_RADAR"
- # start_date = dt.datetime.strptime("2022-01-24", "%Y-%m-%d")
- # end_date = dt.datetime.strptime("2022-01-30", "%Y-%m-%d")
- # df = self.client.get_metrics(lai_radar_polygon, schema_id, start_date, end_date)
-
- # assert set(
- # [
- # "Values.RVI",
- # "Values.LAI",
- # "Schema.Id",
- # ]
- # ).issubset(set(df.columns))
- # assert set(
- # [
- # "2022-01-24T00:00:00Z",
- # "2022-01-25T00:00:00Z",
- # "2022-01-26T00:00:00Z",
- # "2022-01-27T00:00:00Z",
- # "2022-01-28T00:00:00Z",
- # "2022-01-29T00:00:00Z",
- # "2022-01-30T00:00:00Z",
- # ]
- # ).issubset(set(df.index))
- # assert df.index.name == "date"
-
- def test_get_satellite_image_time_series(self):
- start_date = dt.datetime.strptime("2021-03-01", "%Y-%m-%d")
- end_date = dt.datetime.strptime("2021-04-30", "%Y-%m-%d")
- dataset = self.client.get_satellite_image_time_series(
- self.polygon,
- start_date,
- end_date,
- collections=[Collection.SENTINEL_2, Collection.LANDSAT_8],
- indicators=["Reflectance"],
- )
-
- assert dict(dataset.dims) == {'band': 5, 'y': 36, 'x': 66, 'time': 7}
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/resources/Refletance_map_mock.tiff.zip b/tests/resources/Refletance_map_mock.tiff.zip
new file mode 100644
index 0000000..5569e48
Binary files /dev/null and b/tests/resources/Refletance_map_mock.tiff.zip differ
diff --git a/tests/resources/agriquest_ndvi_data_mock_http_response b/tests/resources/agriquest_ndvi_data_mock_http_response
new file mode 100644
index 0000000..4cdb3a2
--- /dev/null
+++ b/tests/resources/agriquest_ndvi_data_mock_http_response
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\ No newline at end of file
diff --git a/tests/resources/agriquest_weather_data_mock_http_response b/tests/resources/agriquest_weather_data_mock_http_response
new file mode 100644
index 0000000..3898118
--- /dev/null
+++ b/tests/resources/agriquest_weather_data_mock_http_response
@@ -0,0 +1,887 @@
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+ "28.6195500306417"
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+ "34.8128479838253"
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+ "04/28/23",
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+ "05/01/22",
+ "04/28/23",
+ "32.3800877451751"
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+ [
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+ "47.4256125476177",
+ "2175143.7400741",
+ "05/01/22",
+ "04/28/23",
+ "24.7421998862483"
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+ "1288319.80636205",
+ "05/01/22",
+ "04/28/23",
+ "28.480545367237"
+ ],
+ [
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+ "1772274.89458084",
+ "05/01/22",
+ "04/28/23",
+ "22.9959174979676"
+ ],
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+ "1.42787764424347",
+ "47.6168806402028",
+ "1586911.92108844",
+ "05/01/22",
+ "04/28/23",
+ "23.0566039814967"
+ ],
+ [
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+ "29.3524490262803"
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+ [
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+ "47.2585727533426",
+ "1521610.81748589",
+ "05/01/22",
+ "04/28/23",
+ "23.065008256469"
+ ],
+ [
+ "Charente-Maritime",
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+ "1706214.93628663",
+ "05/01/22",
+ "04/28/23",
+ "26.9427166321046"
+ ],
+ [
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+ "150982.763104368",
+ "05/01/22",
+ "04/28/23",
+ "37.5780827562508"
+ ],
+ [
+ "Drôme",
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+ "1620699.42648868",
+ "05/01/22",
+ "04/28/23",
+ "20.5253372036335"
+ ],
+ [
+ "Hautes-Alpes",
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+ "44.6639654244265",
+ "1407954.93119105",
+ "05/01/22",
+ "04/28/23",
+ "17.3042443986164"
+ ],
+ [
+ "Meuse",
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+ "04/28/23",
+ "33.9526682594784"
+ ],
+ [
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+ "4.16144800827812",
+ "48.3046341121795",
+ "1489469.56371084",
+ "05/01/22",
+ "04/28/23",
+ "27.5259841359792"
+ ],
+ [
+ "Haute-Marne",
+ "5.22556487495728",
+ "48.1104685218163",
+ "1545931.99648744",
+ "05/01/22",
+ "04/28/23",
+ "30.5967082711895"
+ ],
+ [
+ "Vosges",
+ "6.38013942569127",
+ "48.1962735724646",
+ "1457357.5355833",
+ "05/01/22",
+ "04/28/23",
+ "33.9888824139515"
+ ],
+ [
+ "Vaucluse",
+ "5.17732891514692",
+ "44.0071764888125",
+ "883917.120446257",
+ "05/01/22",
+ "04/28/23",
+ "15.3068505115942"
+ ],
+ [
+ "Lozère",
+ "3.49970355907195",
+ "44.5175366438888",
+ "1278784.5276781",
+ "05/01/22",
+ "04/28/23",
+ "19.1396941214673"
+ ],
+ [
+ "Alpes-de-Haute-Provence",
+ "6.24465390599831",
+ "44.1061172758651",
+ "1728081.03437926",
+ "05/01/22",
+ "04/28/23",
+ "17.9122390952382"
+ ],
+ [
+ "Morbihan",
+ "-2.81218268011372",
+ "47.8475865265542",
+ "1693893.7322425",
+ "05/01/22",
+ "04/28/23",
+ "35.1499926342328"
+ ],
+ [
+ "Doubs",
+ "6.36272222421339",
+ "47.1659635940096",
+ "1298913.913808",
+ "05/01/22",
+ "04/28/23",
+ "42.6370720934297"
+ ],
+ [
+ "Jura",
+ "5.69737730693424",
+ "46.7293599950923",
+ "1247558.25175067",
+ "05/01/22",
+ "04/28/23",
+ "43.8595728381763"
+ ],
+ [
+ "Ardèche",
+ "4.42558206099433",
+ "44.7527714532297",
+ "1375537.99479293",
+ "05/01/22",
+ "04/28/23",
+ "21.5507470271628"
+ ],
+ [
+ "Gard",
+ "4.17986125357777",
+ "43.9936012526349",
+ "1451959.77356846",
+ "05/01/22",
+ "04/28/23",
+ "16.4377360612547"
+ ]
+]
\ No newline at end of file
diff --git a/tests/resources/create_schema_mock_http_response b/tests/resources/create_schema_mock_http_response
new file mode 100644
index 0000000..0a428f4
--- /dev/null
+++ b/tests/resources/create_schema_mock_http_response
@@ -0,0 +1,3 @@
+{
+ "Id": "NEW_SCHEMA"
+}
\ No newline at end of file
diff --git a/tests/resources/gis_layer_municipio_data_mock_http_response b/tests/resources/gis_layer_municipio_data_mock_http_response
new file mode 100644
index 0000000..ac4beee
--- /dev/null
+++ b/tests/resources/gis_layer_municipio_data_mock_http_response
@@ -0,0 +1,13 @@
+[
+ [
+ {
+ "layer": "BRAZIL_MUNICIPIOS",
+ "geometry_acreage": 21.99,
+ "status_overlap": true,
+ "intersection_percent": 100,
+ "properties": {
+ "id": 121935
+ }
+ }
+ ]
+]
\ No newline at end of file
diff --git a/tests/resources/launch_processor_data_mock_http_response b/tests/resources/launch_processor_data_mock_http_response
new file mode 100644
index 0000000..644ff2a
--- /dev/null
+++ b/tests/resources/launch_processor_data_mock_http_response
@@ -0,0 +1 @@
+{"taskId":"cb58faaf8a5640e4913d16bfde3f5bbf"}
\ No newline at end of file
diff --git a/tests/resources/master_data_management_get_unique_id_mock_http_response b/tests/resources/master_data_management_get_unique_id_mock_http_response
new file mode 100644
index 0000000..7a1b09e
--- /dev/null
+++ b/tests/resources/master_data_management_get_unique_id_mock_http_response
@@ -0,0 +1,6 @@
+{
+ "externalIds": {
+ "id": "4XcGhZvA1OjpO3gUwYM61e",
+ "legacY_ID_NA": "y3v9pw1"
+ }
+}
\ No newline at end of file
diff --git a/tests/resources/master_data_management_post_extract_id_mock_http_response b/tests/resources/master_data_management_post_extract_id_mock_http_response
new file mode 100644
index 0000000..be897c9
--- /dev/null
+++ b/tests/resources/master_data_management_post_extract_id_mock_http_response
@@ -0,0 +1,3 @@
+{
+ "id":"ajqxm3v"
+}
\ No newline at end of file
diff --git a/tests/resources/metrics_harvest_mock_http_response b/tests/resources/metrics_harvest_mock_http_response
new file mode 100644
index 0000000..b6ebca5
--- /dev/null
+++ b/tests/resources/metrics_harvest_mock_http_response
@@ -0,0 +1,20 @@
+[
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-06-15T12:24:03.137Z",
+ "Values": {
+ "harvest_year_1": "2020-10-02",
+ "harvest_year_2": "2019-07-05",
+ "harvest_year_3": "2018-07-19",
+ "harvest_year_4": "2017-07-26",
+ "harvest_year_5": "2016-09-22",
+ "historical_harvest_average": "08-14"
+ },
+ "Entity": {
+ "TypedId": "SeasonField:4VRIYlBhJvPqy1kPAx9q6R"
+ },
+ "Schema": {
+ "Id": "HISTORICAL_HARVEST"
+ }
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/metrics_lai_radar_mock_http_response b/tests/resources/metrics_lai_radar_mock_http_response
new file mode 100644
index 0000000..1867fe5
--- /dev/null
+++ b/tests/resources/metrics_lai_radar_mock_http_response
@@ -0,0 +1,1696 @@
+[
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-05-02T00:00:00Z",
+ "Values": {
+ "RVI": 0.15941725764057468,
+ "LAI": 1.3936802459822675
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-05-01T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.4742092909729507
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-30T00:00:00Z",
+ "Values": {
+ "RVI": 0.20025300425178277,
+ "LAI": 1.547938092300813
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-29T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.6148850281900256
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-28T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.675105233313104
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-27T00:00:00Z",
+ "Values": {
+ "RVI": 0.163803978582527,
+ "LAI": 1.728670248614269
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-26T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.775647671132812
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-25T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.8160808038264487
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-24T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.8500123909300485
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-23T00:00:00Z",
+ "Values": {
+ "RVI": 0.23978602348356506,
+ "LAI": 1.877508353314367
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-22T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.8986815238447778
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-21T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.9137153827399933
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-20T00:00:00Z",
+ "Values": {
+ "RVI": 0.16276198115867938,
+ "LAI": 1.9228118030494594
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-19T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.9261387961307512
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-18T00:00:00Z",
+ "Values": {
+ "RVI": 0.15181116915616638,
+ "LAI": 1.9237782571269597
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-17T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.9157390609296034
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-16T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.9019701581415422
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-15T00:00:00Z",
+ "Values": {
+ "RVI": 0.2525996103580754,
+ "LAI": 1.8824585689914337
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-14T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.857327377248192
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
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+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-13T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.8269337241354457
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-12T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.7918685479068601
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-11T00:00:00Z",
+ "Values": {
+ "RVI": 0.2532434886205629,
+ "LAI": 1.7529563234214616
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-10T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.7112548017189537
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-09T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 1.668054749595026
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-08T00:00:00Z",
+ "Values": {
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+ "LAI": 1.6247673409571053
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+ "Entity": {
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+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-07T00:00:00Z",
+ "Values": {
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+ "LAI": 1.5828115481801042
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
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+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-06T00:00:00Z",
+ "Values": {
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+ "LAI": 1.5435015334621693
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+ "Entity": {
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+ {
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+ "Values": {
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+ {
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+ "Values": {
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+ "Entity": {
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+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-03T00:00:00Z",
+ "Values": {
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+ "LAI": 1.4520070872756565
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+ {
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+ "Timestamp": "2023-04-02T00:00:00Z",
+ "Values": {
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+ "Entity": {
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+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-04-01T00:00:00Z",
+ "Values": {
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+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
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+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-03-31T00:00:00Z",
+ "Values": {
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+ "LAI": 1.412689792010038
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+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
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+ "Schema": {
+ "Id": "LAI_RADAR"
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+ },
+ {
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+ {
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+ {
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+ {
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+ {
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+ "Values": {
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+ "Entity": {
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+ {
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+ "Entity": {
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+ },
+ {
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+ "Timestamp": "2023-03-06T00:00:00Z",
+ "Values": {
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+ "Entity": {
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+ {
+ "KeyValues": {},
+ "Timestamp": "2023-03-05T00:00:00Z",
+ "Values": {
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+ "LAI": 1.464520768573524
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+ "Entity": {
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+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-01-03T00:00:00Z",
+ "Values": {
+ "RVI": "NaN",
+ "LAI": 2.500203222509851
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ },
+ {
+ "KeyValues": {},
+ "Timestamp": "2023-01-02T00:00:00Z",
+ "Values": {
+ "RVI": 0.21924885147869452,
+ "LAI": 2.5396446264619383
+ },
+ "Entity": {
+ "TypedId": "SeasonField:7Yo8NgtnijnFTztESJEEfm"
+ },
+ "Schema": {
+ "Id": "LAI_RADAR"
+ }
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/processor_event_data_mock_http_response b/tests/resources/processor_event_data_mock_http_response
new file mode 100644
index 0000000..274a3c5
--- /dev/null
+++ b/tests/resources/processor_event_data_mock_http_response
@@ -0,0 +1,72 @@
+{
+ "events": [
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:00.678Z",
+ "source": "Analytics.geosys-analyticsprocessors-webapi",
+ "message": "Processor called",
+ "eventData": {
+ "TaskId": "4d0980e07b7245d49419ff5ec87fff09",
+ "Error": null
+ }
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:00.731Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Processor started"
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:03.087Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Computation finished"
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:03.124Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Format results for the callback"
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:03.153Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Callback called"
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:03.446Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Results succesfully sent to Analytics Fabric",
+ "eventData": {
+ "error": ""
+ }
+ },
+ {
+ "level": "Information",
+ "date": "2023-06-15T12:24:03.473Z",
+ "source": "AnalyticsProcessor.Inseason Harvest",
+ "message": "Processor ended"
+ }
+ ],
+ "id": "4d0980e07b7245d49419ff5ec87fff09",
+ "taskId": "4d0980e07b7245d49419ff5ec87fff09",
+ "processorId": "historical-harvest",
+ "userId": "2tKecZgMyEP6EkddLxa1gV",
+ "userLogin": "ETotain_US",
+ "customerCode": "GEOSYS_US",
+ "parametersProfile": {
+ "code": "historical_harvest_default"
+ },
+ "parameters": {
+ "season_duration": 215,
+ "season_start_day": 1,
+ "season_start_month": 4,
+ "historical": true
+ },
+ "status": "Ended",
+ "createdAt": "2023-06-15T12:24:00.439Z",
+ "modifiedAt": "2023-06-15T12:24:03.474Z",
+ "duration": 3
+}
\ No newline at end of file
diff --git a/tests/resources/satellite_coverage_image_references_mock_http_response b/tests/resources/satellite_coverage_image_references_mock_http_response
new file mode 100644
index 0000000..e98cd04
--- /dev/null
+++ b/tests/resources/satellite_coverage_image_references_mock_http_response
@@ -0,0 +1,64 @@
+[
+ {
+ "coverageType": "CLEAR",
+ "image": {
+ "id": "IKc73hpUQ6w8nSGtmog9A7iGsm9eKYT2SN42mZg0JWO",
+ "availableBands": [
+ "Nir",
+ "Red",
+ "Blue",
+ "Green"
+ ],
+ "sensor": "LANDSAT_8",
+ "soilMaterial": "BARE",
+ "spatialResolution": 15.0,
+ "weather": "COLD",
+ "zenithAngleInDegree": 0.0,
+ "date": "2022-11-30"
+ },
+ "maps": [
+ {
+ "type": "INSEASON_NDVI"
+ }
+ ],
+ "seasonField": {
+ "id": "d1l2nqm",
+ "customerExternalId": null,
+ "externalIds": {
+ "id": "3Ec2mBmZRLcrLfChu9ZlJz",
+ "legacY_ID_NA": "d1l2nqm"
+ }
+ }
+ },
+ {
+ "coverageType": "CLEAR",
+ "image": {
+ "id": "IKc73hpUQ6uPitMqTFtBQGpp6rcDKT21cEHm34LPQc0",
+ "availableBands": [
+ "Nir",
+ "Red",
+ "Blue",
+ "Green"
+ ],
+ "sensor": "LANDSAT_9",
+ "soilMaterial": "VEGETATION",
+ "spatialResolution": 15.0,
+ "weather": "WET",
+ "zenithAngleInDegree": 0.0,
+ "date": "2022-05-11"
+ },
+ "maps": [
+ {
+ "type": "INSEASON_NDVI"
+ }
+ ],
+ "seasonField": {
+ "id": "d1l2nqm",
+ "customerExternalId": null,
+ "externalIds": {
+ "id": "3Ec2mBmZRLcrLfChu9ZlJz",
+ "legacY_ID_NA": "d1l2nqm"
+ }
+ }
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/satellite_image_time_series_landsat8_mock_http_response b/tests/resources/satellite_image_time_series_landsat8_mock_http_response
new file mode 100644
index 0000000..e98cd04
--- /dev/null
+++ b/tests/resources/satellite_image_time_series_landsat8_mock_http_response
@@ -0,0 +1,64 @@
+[
+ {
+ "coverageType": "CLEAR",
+ "image": {
+ "id": "IKc73hpUQ6w8nSGtmog9A7iGsm9eKYT2SN42mZg0JWO",
+ "availableBands": [
+ "Nir",
+ "Red",
+ "Blue",
+ "Green"
+ ],
+ "sensor": "LANDSAT_8",
+ "soilMaterial": "BARE",
+ "spatialResolution": 15.0,
+ "weather": "COLD",
+ "zenithAngleInDegree": 0.0,
+ "date": "2022-11-30"
+ },
+ "maps": [
+ {
+ "type": "INSEASON_NDVI"
+ }
+ ],
+ "seasonField": {
+ "id": "d1l2nqm",
+ "customerExternalId": null,
+ "externalIds": {
+ "id": "3Ec2mBmZRLcrLfChu9ZlJz",
+ "legacY_ID_NA": "d1l2nqm"
+ }
+ }
+ },
+ {
+ "coverageType": "CLEAR",
+ "image": {
+ "id": "IKc73hpUQ6uPitMqTFtBQGpp6rcDKT21cEHm34LPQc0",
+ "availableBands": [
+ "Nir",
+ "Red",
+ "Blue",
+ "Green"
+ ],
+ "sensor": "LANDSAT_9",
+ "soilMaterial": "VEGETATION",
+ "spatialResolution": 15.0,
+ "weather": "WET",
+ "zenithAngleInDegree": 0.0,
+ "date": "2022-05-11"
+ },
+ "maps": [
+ {
+ "type": "INSEASON_NDVI"
+ }
+ ],
+ "seasonField": {
+ "id": "d1l2nqm",
+ "customerExternalId": null,
+ "externalIds": {
+ "id": "3Ec2mBmZRLcrLfChu9ZlJz",
+ "legacY_ID_NA": "d1l2nqm"
+ }
+ }
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/satellite_image_time_series_modis_ndvi_mock_http_response b/tests/resources/satellite_image_time_series_modis_ndvi_mock_http_response
new file mode 100644
index 0000000..afbf8c3
--- /dev/null
+++ b/tests/resources/satellite_image_time_series_modis_ndvi_mock_http_response
@@ -0,0 +1,128 @@
+[
+ {
+ "index": "NDVI",
+ "value": 0.18119788,
+ "date": "2020-01-01",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.19871489,
+ "date": "2020-01-01",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.19149646,
+ "date": "2020-01-02",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.20834241,
+ "date": "2020-01-02",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.20124013,
+ "date": "2020-01-03",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.2174293,
+ "date": "2020-01-03",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.21034396,
+ "date": "2020-01-04",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.22589585,
+ "date": "2020-01-04",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.21874636,
+ "date": "2020-01-05",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.23368648,
+ "date": "2020-01-05",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.22640644,
+ "date": "2020-01-06",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.24076674,
+ "date": "2020-01-06",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.23330155,
+ "date": "2020-01-07",
+ "pixel": {
+ "id": "mh11v4i225j4612",
+ "coverageRatio": 0.95
+ }
+ },
+ {
+ "index": "NDVI",
+ "value": 0.24712055,
+ "date": "2020-01-07",
+ "pixel": {
+ "id": "mh11v4i226j4612",
+ "coverageRatio": 1.0
+ }
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/time_series_modis_ndvi_mock_http_response b/tests/resources/time_series_modis_ndvi_mock_http_response
new file mode 100644
index 0000000..36688b1
--- /dev/null
+++ b/tests/resources/time_series_modis_ndvi_mock_http_response
@@ -0,0 +1,37 @@
+[
+ {
+ "index": "NDVI",
+ "value": 0.19018096,
+ "date": "2020-01-01"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.20013541,
+ "date": "2020-01-02"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.20954226,
+ "date": "2020-01-03"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.21831929,
+ "date": "2020-01-04"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.22640796,
+ "date": "2020-01-05"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.2337707,
+ "date": "2020-01-06"
+ },
+ {
+ "index": "NDVI",
+ "value": 0.24038822,
+ "date": "2020-01-07"
+ }
+ ]
\ No newline at end of file
diff --git a/tests/resources/time_series_weather_forecast_daily_mock_http_response b/tests/resources/time_series_weather_forecast_daily_mock_http_response
new file mode 100644
index 0000000..3a11f13
--- /dev/null
+++ b/tests/resources/time_series_weather_forecast_daily_mock_http_response
@@ -0,0 +1,80 @@
+[
+ {
+ "precipitation": {
+ "cumulative": 0.0,
+ "probabilities": 10.0
+ },
+ "temperature": {
+ "standard": 67.20799998623355,
+ "standardMax": 78.97999997681599
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-04-28T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.610236220472441,
+ "probabilities": 79.0
+ },
+ "temperature": {
+ "standard": 62.455999990035174,
+ "standardMax": 68.35999998531203
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-04-29T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.30708661417322836,
+ "probabilities": 70.0
+ },
+ "temperature": {
+ "standard": 58.927999992857615,
+ "standardMax": 65.11999998790401
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-04-30T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.11811023622047245,
+ "probabilities": 70.0
+ },
+ "temperature": {
+ "standard": 57.18199999425436,
+ "standardMax": 64.30999998855202
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-05-01T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.007874015748031496,
+ "probabilities": 13.9
+ },
+ "temperature": {
+ "standard": 57.361999994110406,
+ "standardMax": 67.54999998595994
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-05-02T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0,
+ "probabilities": 0.0
+ },
+ "temperature": {
+ "standard": 60.583999991532735,
+ "standardMax": 71.68999998264796
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2023-05-03T00:00:00Z",
+ "weatherType": "FORECAST_DAILY"
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/time_series_weather_historical_daily_mock_http_response b/tests/resources/time_series_weather_historical_daily_mock_http_response
new file mode 100644
index 0000000..7eb9fd1
--- /dev/null
+++ b/tests/resources/time_series_weather_historical_daily_mock_http_response
@@ -0,0 +1,4394 @@
+[
+ {
+ "precipitation": {
+ "cumulative": 0.027559055118110236
+ },
+ "temperature": {
+ "standard": 39.12800000869761,
+ "standardMax": 43.394000005284816
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-01T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.011811023622047244
+ },
+ "temperature": {
+ "standard": 34.934000012052735,
+ "standardMax": 38.89400000888482
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-02T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.05905511811023623
+ },
+ "temperature": {
+ "standard": 37.220000010224005,
+ "standardMax": 46.580000002735986
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-03T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.027559055118110236
+ },
+ "temperature": {
+ "standard": 36.57200001074238,
+ "standardMax": 40.42400000766076
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-04T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.003937007874015748
+ },
+ "temperature": {
+ "standard": 35.7620000113904,
+ "standardMax": 37.004000010396794
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-05T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 36.248000011001565,
+ "standardMax": 42.0620000063504
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-06T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 30.326000015739233,
+ "standardMax": 39.47000000842401
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-07T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 29.426000016459234,
+ "standardMax": 38.534000009172736
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-08T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.003937007874015748
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+ "temperature": {
+ "standard": 31.08200001513436,
+ "standardMax": 36.15800001107355
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-09T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
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+ "temperature": {
+ "standard": 32.360000014112025,
+ "standardMax": 38.10200000951842
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-10T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
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+ "temperature": {
+ "standard": 27.626000017899234,
+ "standardMax": 34.23200001261436
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-11T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 34.646000012283196,
+ "standardMax": 46.1120000031104
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-12T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.015748031496062992
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+ "temperature": {
+ "standard": 46.50800000279355,
+ "standardMax": 51.745999998603196
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-13T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.043307086614173235
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+ "temperature": {
+ "standard": 46.40000000287994,
+ "standardMax": 49.712000000230404
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-14T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.03937007874015748
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+ "temperature": {
+ "standard": 45.80600000335517,
+ "standardMax": 49.58600000033122
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-15T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 37.9040000096768,
+ "standardMax": 45.626000003499236
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-16T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
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+ "temperature": {
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+ "standardMax": 49.74800000020157
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-17T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
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+ "temperature": {
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+ "standardMax": 49.568000000345634
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+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-01-18T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
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+ "date": "2021-12-27T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
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+ },
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+ "standardMax": 59.179999992655986
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+ "date": "2021-12-30T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 52.35799999811355,
+ "standardMax": 62.77999998977599
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2021-12-31T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0
+ },
+ "temperature": {
+ "standard": 51.15199999907842,
+ "standardMax": 63.28399998937274
+ },
+ "location": "POINT (0.0872845021171696 43.69457564315705)",
+ "date": "2022-01-01T00:00:00Z",
+ "weatherType": "HISTORICAL_DAILY"
+ }
+]
\ No newline at end of file
diff --git a/tests/resources/weather_data_mock_http_response b/tests/resources/weather_data_mock_http_response
new file mode 100644
index 0000000..8679d99
--- /dev/null
+++ b/tests/resources/weather_data_mock_http_response
@@ -0,0 +1,62 @@
+[
+ {
+ "precipitation": {
+ "cumulative": 0.22834645669291338,
+ "probabilities": 72.0
+ },
+ "temperature": {
+ "standard": 71.47399998282076
+ },
+ "date": "2023-06-19T00:00:00Z"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.3937007874015748,
+ "probabilities": 70.0
+ },
+ "temperature": {
+ "standard": 70.1959999838432
+ },
+ "date": "2023-06-20T00:00:00Z"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.9645669291338583,
+ "probabilities": 70.0
+ },
+ "temperature": {
+ "standard": 68.25199998539843
+ },
+ "date": "2023-06-21T00:00:00Z"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.37401574803149609,
+ "probabilities": 70.0
+ },
+ "temperature": {
+ "standard": 67.02799998637761
+ },
+ "date": "2023-06-22T00:00:00Z"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0,
+ "probabilities": 10.0
+ },
+ "temperature": {
+ "standard": 67.54999998595994
+ },
+ "date": "2023-06-23T00:00:00Z"
+ },
+ {
+ "precipitation": {
+ "cumulative": 0.0,
+ "probabilities": 0.0
+ },
+ "temperature": {
+ "standard": 70.8619999833104
+ },
+ "date": "2023-06-24T00:00:00Z"
+ }
+]
\ No newline at end of file
diff --git a/tests/test_helper.py b/tests/test_helper.py
new file mode 100644
index 0000000..56b7c2a
--- /dev/null
+++ b/tests/test_helper.py
@@ -0,0 +1,46 @@
+import logging
+import requests_mock
+import requests
+import os
+
+
+def load_data_from_textfile(file_name):
+ # valid path for all os system
+ file_path = os.path.join(os.path.dirname(__file__), f"resources/{file_name}").replace("\\", "/")
+ if os.path.isfile(file_path):
+ with open(file_path, 'r') as data:
+ return data.read()
+ else:
+ logging.error(f"Unable to read file: {file_name}")
+
+
+def load_binary_data_from_zipfile(file_name):
+ # valid path for all os system
+ file_path = os.path.join(os.path.dirname(__file__), f"resources/{file_name}").replace("\\", "/")
+ if os.path.isfile(file_path):
+ with open(file_path, 'rb') as data:
+ return data.read()
+ else:
+ logging.error(f"Unable to read zip file: {file_name}")
+
+
+def mock_http_response_text_content(method, content=None, status_code=200):
+ if method == "GET":
+ with requests_mock.Mocker() as m:
+ m.get('http://geosys.com', text=content)
+ return requests.get('http://geosys.com')
+ if method == "POST":
+ with requests_mock.Mocker() as m:
+ m.post('http://geosys.com', text=content, status_code= status_code)
+ return requests.post('http://geosys.com' )
+ if method == "PATCH":
+ with requests_mock.Mocker() as m:
+ m.patch('http://geosys.com', text=content)
+ return requests.patch('http://geosys.com')
+
+
+def mock_http_response_binary_content(method, binary_content=None):
+ if method == "GET":
+ with requests_mock.Mocker() as m:
+ m.get(url='http://geosys.com', content=binary_content)
+ return requests.get(url='http://geosys.com')
diff --git a/tests/test_int_geosys.py b/tests/test_int_geosys.py
new file mode 100644
index 0000000..35dc0a7
--- /dev/null
+++ b/tests/test_int_geosys.py
@@ -0,0 +1,307 @@
+from datetime import datetime
+import pytest
+from dateutil.relativedelta import relativedelta
+
+from geosyspy import Geosys
+from dotenv import load_dotenv
+import os
+import datetime as dt
+import numpy as np
+from geosyspy.utils.constants import *
+
+# read .env file
+load_dotenv()
+
+API_CLIENT_ID = os.getenv("API_CLIENT_ID")
+API_CLIENT_SECRET = os.getenv("API_CLIENT_SECRET")
+API_USERNAME = os.getenv("API_USERNAME")
+API_PASSWORD = os.getenv("API_PASSWORD")
+
+POLYGON = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestGeosys:
+ client = Geosys(API_CLIENT_ID,
+ API_CLIENT_SECRET,
+ API_USERNAME,
+ API_PASSWORD,
+ Env.PREPROD,
+ Region.NA
+ )
+
+ prod_client = Geosys(API_CLIENT_ID,
+ API_CLIENT_SECRET,
+ API_USERNAME,
+ API_PASSWORD,
+ Env.PROD,
+ Region.NA
+ )
+ # get list of available crops
+ crops = prod_client.get_available_crops()
+
+ def test_authenticate(self):
+ credentials = self.client.http_client.get_access_token();
+ assert {"access_token", "expires_in", "token_type", "scope", "expires_at",
+ "refresh_token"}.issubset(set(credentials.keys()))
+ assert credentials['access_token'] is not None
+ assert credentials['refresh_token'] is not None
+ assert credentials['expires_at'] > datetime.today().timestamp()
+
+ def test_get_time_series_modis_ndvi(self):
+ start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
+
+ df = self.client.get_time_series(
+ POLYGON, start_date, end_date, SatelliteImageryCollection.MODIS, ["NDVI"]
+ )
+
+ assert df.index.name == "date"
+ assert "value" in df.columns
+ assert "index" in df.columns
+ assert len(df.index) == 7
+ date_range = list(map(lambda x: x.strftime("%Y-%m-%d"), df.index))
+ assert {"2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05", "2020-01-06",
+ "2020-01-07"}.issubset(set(date_range))
+
+ def test_get_satellite_image_time_series_modis_ndvi(self):
+ start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
+ POLYGON = "POLYGON((-91.29152885756007 40.39177489815265,-91.28403789132507 40.391776131485386,-91.28386736508233 40.389390758655935,-91.29143832829979 40.38874592864832,-91.29152885756007 40.39177489815265))"
+ df = self.client.get_satellite_image_time_series(
+ POLYGON, start_date, end_date, [SatelliteImageryCollection.MODIS], ["NDVI"]
+ )
+ assert df.index.name == "date"
+ assert {"value", "index", "pixel.id"}.issubset(set(df.columns))
+ assert np.all((df["index"].values == "NDVI"))
+ assert len(df.index) == 14
+
+ assert {"2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05", "2020-01-06",
+ "2020-01-07"}.issubset(set(df.index))
+
+ assert {"mh11v4i225j4612", "mh11v4i226j4612"}.issubset(set(df["pixel.id"]))
+
+ def test_get_satellite_coverage_image_references(self):
+ end_date = dt.date.today()
+ start_date = dt.date.today() + relativedelta(months=-12)
+ info, images_references = self.client.get_satellite_coverage_image_references(
+ POLYGON, start_date, end_date,
+ collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8,
+ SatelliteImageryCollection.LANDSAT_9])
+
+ assert {"coverageType", "image.id", "image.availableBands", "image.sensor", "image.soilMaterial",
+ "image.spatialResolution", "image.weather", "image.date", "seasonField.id"}.issubset(set(info.columns))
+
+ assert len(info) == len(images_references)
+ for i, image_info in info.iterrows():
+ assert (
+ image_info["image.date"],
+ image_info["image.sensor"],
+ ) in images_references
+
+ def get_time_series_weather_historical_daily(self):
+ start_date = dt.datetime.strptime("2021-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
+ indicators = [
+ "Precipitation",
+ "Temperature.Ground",
+ "Temperature.Standard",
+ "Temperature.StandardMax",
+ "Date",
+ ]
+
+ df = self.client.get_time_series(
+ POLYGON,
+ start_date,
+ end_date,
+ WeatherTypeCollection.WEATHER_HISTORICAL_DAILY,
+ indicators,
+ )
+
+ assert {"precipitation.cumulative", "precipitation.probabilities", "temperature.ground", "temperature.standard",
+ "temperature.standardMax"}.issubset(set(df.columns))
+ assert df.index.name == "date"
+
+ # def test_get_metrics(self):
+ #
+ # lai_radar_polygon = "POLYGON((-52.72591542 -18.7395779,-52.72604885 -18.73951122,-52.72603114 -18.73908689,-52.71556835 -18.72490316,-52.71391916 -18.72612966,-52.71362802 -18.72623726,-52.71086473 -18.72804231,-52.72083542 -18.74173696,-52.72118937 -18.74159174,-52.72139229 -18.7418552,-52.72600257 -18.73969719,-52.72591542 -18.7395779))"
+ # schema_id = "LAI_RADAR"
+ # start_date = dt.datetime.strptime("2022-01-24", "%Y-%m-%d")
+ # end_date = dt.datetime.strptime("2022-01-30", "%Y-%m-%d")
+ # df = self.client.get_metrics(lai_radar_polygon, schema_id, start_date, end_date)
+ #
+ # assert set(
+ # [
+ # "Values.RVI",
+ # "Values.LAI",
+ # "Schema.Id",
+ # ]
+ # ).issubset(set(df.columns))
+ # assert set(
+ # [
+ # "2022-01-24T00:00:00Z",
+ # "2022-01-25T00:00:00Z",
+ # "2022-01-26T00:00:00Z",
+ # "2022-01-27T00:00:00Z",
+ # "2022-01-28T00:00:00Z",
+ # "2022-01-29T00:00:00Z",
+ # "2022-01-30T00:00:00Z",
+ # ]
+ # ).issubset(set(df.index))
+ # assert df.index.name == "date"
+
+ def test_get_satellite_image_time_series(self):
+ start_date = dt.datetime.strptime("2022-05-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2023-04-28", "%Y-%m-%d")
+ dataset = self.client.get_satellite_image_time_series(
+ POLYGON,
+ start_date,
+ end_date,
+ collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8],
+ indicators=["Reflectance"],
+ )
+ assert dict(dataset.dims) == {'band': 4, 'y': 51, 'x': 48, 'time': 1}
+
+ def test_get_agriquest_weather_time_series(self):
+ start_date = "2022-05-01"
+ end_date = "2023-04-28"
+ dataset = self.client.get_agriquest_weather_block_data(
+ start_date=start_date,
+ end_date=end_date,
+ block_code=AgriquestBlocks.FRA_DEPARTEMENTS,
+ weather_type=AgriquestWeatherType.CUMULATIVE_PRECIPITATION
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert len(dataset["AMU"]) == 97
+
+ def test_get_agriquest_ndvi_time_series(self):
+ date = "2023-06-05"
+ dataset = self.client.get_agriquest_ndvi_block_data(
+ day_of_measure=date,
+ commodity_code=AgriquestCommodityCode.ALL_VEGETATION,
+ block_code=AgriquestBlocks.AMU_NORTH_AMERICA,
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert dataset.keys()[-1] == "NDVI"
+
+ def test_get_harvest_analytics(self):
+ dataset = self.prod_client.get_harvest_analytics(
+ season_duration=215,
+ season_start_day=1,
+ season_start_month=4,
+ crop=self.crops._2ND_CORN,
+ year=2021,
+ geometry="POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))",
+ harvest_type=Harvest.HARVEST_HISTORICAL)
+
+ assert dataset.keys()[0] == 'Values.harvest_year_1'
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'HISTORICAL_HARVEST'
+
+ def test_get_emergence_analytics(self):
+ dataset = self.prod_client.get_emergence_analytics(
+ season_duration=215,
+ season_start_day=1,
+ season_start_month=4,
+ crop=self.crops._2ND_CORN,
+ year=2021,
+ geometry="POLYGON ((-56.785919346530768 -21.208154463301554 , -56.79078750820733 -21.206043784434833 , -56.790973809206818 -21.206069651656232 , -56.791373799079636 -21.197107091323097 , -56.785129186971687 -21.196010916846863 , -56.781397554331065 -21.19535575112814 , -56.777108478217059 -21.202038412606473 , -56.778435977920665 -21.211398619037478 , -56.785919346530768 -21.208154463301554))",
+ emergence_type=Emergence.EMERGENCE_IN_SEASON)
+
+ assert dataset.keys()[0] == 'Values.EmergenceDate'
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'INSEASON_EMERGENCE'
+
+ def test_get_potential_score_analytics(self):
+ dataset = self.prod_client.get_potential_score_analytics(
+ end_date="2022-03-06",
+ nb_historical_years=5,
+ season_duration=200,
+ season_start_day=1,
+ season_start_month=10,
+ crop=self.crops.CORN,
+ sowing_date="2021-10-01",
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+ assert dataset.keys()[0] == 'Values.historical_potential_score'
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'POTENTIAL_SCORE'
+
+ def test_get_greenness_analytics(self):
+ dataset = self.prod_client.get_greenness_analytics(
+ start_date="2022-01-15",
+ end_date="2022-05-31",
+ crop=self.crops.CORN,
+ sowing_date="2022-01-15",
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+ assert dataset.keys()[0] == "Values.peak_found"
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'GREENNESS'
+
+ def test_get_harvest_readinesss_analytics(self):
+ dataset = self.prod_client.get_harvest_readiness_analytics(
+ start_date="2022-01-15",
+ end_date="2022-05-31",
+ crop=self.crops.CORN,
+ sowing_date="2022-01-15",
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+ assert dataset.keys()[0] == "Values.date"
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'HARVEST_READINESS'
+
+ def test_get_planted_area_analytics(self):
+ dataset = self.prod_client.get_planted_area_analytics(
+ start_date="2022-01-15",
+ end_date="2022-05-31",
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+ assert dataset.keys()[0] == "Values.planted_area"
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'PLANTED_AREA'
+
+ def test_get_brazil_crop_id_analytics(self):
+ dataset = self.prod_client.get_brazil_crop_id_analytics(
+ start_date="2020-10-01",
+ end_date="2021-05-31",
+ season=CropIdSeason.SEASON_1,
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+
+ assert dataset.keys()[0] == "Values.crop_code"
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'CROP_IDENTIFICATION'
+
+ @pytest.mark.skip(reason="soucis SSL dans github")
+ def test_get_zarc_analytics(self):
+ dataset = self.client.get_zarc_analytics(
+ start_date_emergence="2022-01-15",
+ end_date_emergence="2022-05-31",
+ nb_days_sowing_emergence=20,
+ crop=self.crops.CORN,
+ soil_type=ZarcSoilType.NONE,
+ cycle=ZarcCycleType.NONE,
+ geometry="POLYGON ((-54.26027778 -25.38777778, -54.26027778 -25.37444444, -54.26 -25.37416667, -54.25972222 -25.37444444, -54.25944444 -25.37444444, -54.25888889 -25.37472222, -54.258611110000004 -25.37472222, -54.25888889 -25.375, -54.25888889 -25.37555555, -54.258611110000004 -25.37611111, -54.258611110000004 -25.38194444, -54.25833333 -25.38416667, -54.25694444 -25.38361111, -54.25694444 -25.38416667, -54.2575 -25.38416667, -54.2575 -25.38444444, -54.25777778 -25.38416667, -54.25807016 -25.384158120000002, -54.25805556 -25.38444444, -54.258077300000004 -25.38472206, -54.2575 -25.38527778, -54.25694444 -25.385, -54.256388890000004 -25.38361111, -54.25472222 -25.38305555, -54.25472222 -25.3825, -54.254166670000004 -25.38194444, -54.25444444 -25.38166667, -54.25472222 -25.38166667, -54.25472222 -25.37944444, -54.25277778 -25.37944444, -54.25277778 -25.38583333, -54.25419223 -25.3861539, -54.2539067 -25.38589216, -54.25388889 -25.385, -54.25444444 -25.38555555, -54.2547871 -25.385820770000002, -54.25472222 -25.38611111, -54.26027778 -25.38777778))"
+ )
+
+ assert dataset.keys()[0] == "Values.emergence_date"
+ assert dataset.keys()[-1] == 'Schema.Id'
+ assert dataset.values[0][-1] == 'ZARC'
+
+
+ def test_get_mr_time_series(self):
+ result:str = self.client.get_mr_time_series(
+ start_date="2020-10-09",
+ end_date="2022-10-09",
+ list_sensors=["Sentinel_2", "Landsat_8"],
+ denoiser=True,
+ smoother="ww",
+ eoc=True,
+ aggregation="mean",
+ index="ndvi",
+ raw_data=True,
+ polygon="POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))"
+ )
+
+ assert result.startswith('s3://geosys-geosys-us/2tKecZgMyEP6EkddLxa1gV')
+ assert '/mrts/' in result
\ No newline at end of file
diff --git a/tests/test_unit_agriquest_service.py b/tests/test_unit_agriquest_service.py
new file mode 100644
index 0000000..ad6e839
--- /dev/null
+++ b/tests/test_unit_agriquest_service.py
@@ -0,0 +1,70 @@
+import datetime
+import json
+from unittest.mock import patch
+
+from geosyspy.services.agriquest_service import AgriquestService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+from geosyspy.utils.constants import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestAgriquestService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = AgriquestService(base_url=url, http_client=http_client)
+
+ def test_is_block_for_france(self):
+ is_france_block = self.service.is_block_for_france(block_code=AgriquestBlocks.FRA_DEPARTEMENTS)
+ assert is_france_block == True
+
+ is_france_block = self.service.is_block_for_france(block_code=AgriquestBlocks.AMU_NORTH_AMERICA)
+ assert is_france_block == False
+
+ def test_weather_indicators_builder(self):
+ start_date = datetime.datetime.now() - datetime.timedelta(days=1)
+ end_date = start_date + datetime.timedelta(days=7, hours=3, minutes=30)
+
+ indicators = self.service.weather_indicators_builder(start_date=start_date.date(), end_date = end_date.date(), isFrance = True)
+ assert set(indicators) == set([3,4,5])
+
+ indicators = self.service.weather_indicators_builder(start_date=start_date.date(), end_date=end_date.date(),
+ isFrance=False)
+ assert set(indicators) == set([2, 4, 5])
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_agriquest_weather_block_data(self, get_response):
+ get_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "agriquest_weather_data_mock_http_response"))
+ start_date = "2022-05-01"
+ end_date = "2023-04-28"
+
+ dataset = self.service.get_agriquest_block_weather_data(
+ start_date=start_date,
+ end_date=end_date,
+ block_code=AgriquestBlocks.FRA_DEPARTEMENTS,
+ weather_type=AgriquestWeatherType.CUMULATIVE_PRECIPITATION,
+ indicator_list=[3,4,5]
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert len(dataset["AMU"]) == 97
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_agriquest_ndvi_block_data(self, get_response):
+ get_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "agriquest_ndvi_data_mock_http_response"))
+ date = "2023-06-05"
+ dataset = self.service.get_agriquest_block_ndvi_data(
+ date=date,
+ commodity=AgriquestCommodityCode.ALL_VEGETATION,
+ block_code=AgriquestBlocks.AMU_NORTH_AMERICA,
+ indicator_list=[1]
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert dataset.keys()[-1] == "NDVI"
diff --git a/tests/test_unit_analytics_fabric_service.py b/tests/test_unit_analytics_fabric_service.py
new file mode 100644
index 0000000..50e0fc6
--- /dev/null
+++ b/tests/test_unit_analytics_fabric_service.py
@@ -0,0 +1,88 @@
+import datetime
+import json
+from unittest.mock import patch
+
+from geosyspy.services.analytics_fabric_service import AnalyticsFabricService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestAnalyticsFabricService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = AnalyticsFabricService(base_url=url, http_client=http_client)
+
+ def test_get_build_timestamp_query_parameters(self):
+ start_date = datetime.datetime.now()
+ end_date = start_date + datetime.timedelta(days=7, hours=3, minutes=30)
+
+ timestamp = self.service.build_timestamp_query_parameters(end_date=end_date)
+ assert timestamp == f'&Timestamp:$lte:{end_date}'
+
+ timestamp = self.service.build_timestamp_query_parameters(start_date=start_date)
+ assert timestamp == f'&Timestamp=$gte:{start_date}'
+
+ timestamp = self.service.build_timestamp_query_parameters(start_date=start_date, end_date=end_date)
+ assert timestamp == f'&Timestamp=$between:{start_date}|{end_date}'
+
+ timestamp = self.service.build_timestamp_query_parameters()
+ assert timestamp == ''
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_metrics(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "metrics_harvest_mock_http_response"))
+ start_date = datetime.datetime.now()
+ end_date = start_date + datetime.timedelta(days=7, hours=3, minutes=30)
+
+ metrics = self.service.get_metrics(start_date=start_date, end_date=end_date, season_field_id= 'seasonfieldFakeId', schema_id='HISTORICAL_HARVEST')
+
+ assert metrics['Schema.Id'][0] == 'HISTORICAL_HARVEST'
+ assert metrics['Values.harvest_year_1'][0] == '2020-10-02'
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_latest_metrics(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "metrics_harvest_mock_http_response"))
+
+ metrics = self.service.get_lastest_metrics(season_field_id= 'seasonfieldFakeId', schema_id='HISTORICAL_HARVEST')
+
+ assert metrics['Schema.Id'][0] == 'HISTORICAL_HARVEST'
+ assert metrics['Values.harvest_year_1'][0] == '2020-10-02'
+
+ @patch('geosyspy.utils.http_client.HttpClient.patch')
+ def test_push_metrics(self, patch_response):
+ patch_response.return_value = mock_http_response_text_content("PATCH", load_data_from_textfile(
+ "metrics_harvest_mock_http_response"))
+ data = [{
+ "Timestamp": "2022-01-01",
+ "Values": {
+ "NDVI": 0.5
+ }
+ }]
+
+ result = self.service.push_metrics(season_field_id='seasonfieldFakeId', schema_id='HISTORICAL_HARVEST', values=data)
+
+ assert result == 200
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_create_schema(self, get_response):
+ get_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "create_schema_mock_http_response"), status_code=201)
+ schema ={
+ "Timestamp": '2021-01-01',
+ "Values": {
+ "NDVI": 0.5
+ }
+ }
+ response = self.service.create_schema_id(schema_id='NEW_SCHEMA', schema = schema)
+ data = json.loads(response.decode('utf-8'))
+ assert data['Id'] == "NEW_SCHEMA"
+
diff --git a/tests/test_unit_analytics_processor_service.py b/tests/test_unit_analytics_processor_service.py
new file mode 100644
index 0000000..5a46bb6
--- /dev/null
+++ b/tests/test_unit_analytics_processor_service.py
@@ -0,0 +1,162 @@
+from unittest.mock import patch
+
+from geosyspy.services.analytics_processor_service import AnalyticsProcessorService
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestAnalyticsProcessorService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = AnalyticsProcessorService(base_url=url, http_client=http_client)
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_task_status(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "processor_event_data_mock_http_response"))
+
+ task_status = self.service.wait_and_check_task_status("task_id")
+ assert task_status == "Ended"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_planted_area_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_planted_area_processor(start_date='2020-01-01', end_date='2021-01-01', seasonfield_id= 'seasonfieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_zarc_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_zarc_processor(start_date_emergence ='2020-01-01',
+ municipio=123,
+ soil_type=ZarcSoilType.SOIL_TYPE_1.value,
+ nb_days_sowing_emergence=50,
+ crop="Corn",
+ end_date_emergence='2021-01-01',
+ cycle= ZarcCycleType.CYCLE_TYPE_1.value,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_greenness_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_greenness_processor(start_date='2020-01-01',
+ crop="Corn",
+ end_date='2021-01-01',
+ sowing_date='2020-01-01',
+ geometry=geometry,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_emergence_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_emergence_processor(season_start_day='2020-01-01',
+ crop="Corn",
+ year=2021,
+ emergence_type=Emergence.EMERGENCE_IN_SEASON,
+ season_duration=110,
+ season_start_month=10,
+ geometry=geometry,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_harvest_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_harvest_processor(season_start_day='2020-01-01',
+ year=2021,
+ crop ='Corn',
+ season_duration=110,
+ season_start_month=10,
+ harvest_type=Harvest.HARVEST_HISTORICAL,
+ geometry=geometry,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_harvest_readiness_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_harvest_readiness_processor(start_date='2020-01-01',
+ end_date='2020-01-01',
+ sowing_date='2020-01-01',
+ crop ='Corn',
+ geometry=geometry,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_brazil_in_season_crop_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_brazil_in_season_crop_id_processor(start_date='2020-01-01',
+ end_date='2020-01-01',
+ geometry=geometry,
+ season=2021,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_launch_potential_score_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_potential_score_processor(end_date='2021-01-01',
+ season_start_month=10,
+ season_duration=120,
+ season_start_day=30,
+ sowing_date='2020-01-01',
+ crop='Corn',
+ nb_historical_years=5,
+ geometry=geometry,
+ seasonfield_id='seasonFieldFakeId')
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_mr_time_series_processor(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "launch_processor_data_mock_http_response"))
+
+ task_id = self.service.launch_mr_time_series_processor(start_date="2020-10-09",
+ end_date="2022-10-09",
+ list_sensors=["Sentinel_2", "Landsat_8"],
+ denoiser=True,
+ smoother="ww",
+ eoc=True,
+ aggregation="mean",
+ index="ndvi",
+ raw_data=True,
+ polygon="POLYGON ((-0.49881816 46.27330504, -0.49231649 46.27320122, -0.49611449 46.26983426, -0.49821735 46.27094671, -0.49881816 46.27330504))")
+
+ assert task_id == "cb58faaf8a5640e4913d16bfde3f5bbf"
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_s3_path_from_task_and_processor(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "processor_event_data_mock_http_response"))
+
+ s3_path = self.service.get_s3_path_from_task_and_processor(task_id="4d0980e07b7245d49419ff5ec87fff09",
+ processor_name="mrts")
+
+ assert s3_path == "s3://geosys-geosys-us/2tKecZgMyEP6EkddLxa1gV/mrts/4d0980e07b7245d49419ff5ec87fff09"
\ No newline at end of file
diff --git a/tests/test_unit_geosys.py b/tests/test_unit_geosys.py
new file mode 100644
index 0000000..2ab2f87
--- /dev/null
+++ b/tests/test_unit_geosys.py
@@ -0,0 +1,163 @@
+from datetime import datetime
+from unittest.mock import patch
+from geosyspy import Geosys
+from dotenv import load_dotenv
+import datetime as dt
+import numpy as np
+from geosyspy.utils.constants import *
+from tests.test_helper import *
+
+# read .env file
+load_dotenv()
+
+API_CLIENT_ID = os.getenv("API_CLIENT_ID")
+API_CLIENT_SECRET = os.getenv("API_CLIENT_SECRET")
+API_USERNAME = os.getenv("API_USERNAME")
+API_PASSWORD = os.getenv("API_PASSWORD")
+
+# polygon with two pixels : mh11v4i225j4612, mh11v4i226j4612
+POLYGON = "POLYGON((-91.29152885756007 40.39177489815265,-91.28403789132507 40.391776131485386,-91.28386736508233 " \
+ "40.389390758655935,-91.29143832829979 40.38874592864832,-91.29152885756007 40.39177489815265))"
+
+class TestGeosys:
+ client = Geosys(API_CLIENT_ID,
+ API_CLIENT_SECRET,
+ API_USERNAME,
+ API_PASSWORD,
+ Env.PREPROD,
+ Region.NA
+ )
+
+ def test_authenticate(self):
+ credentials = self.client.http_client.get_access_token();
+ assert {"access_token", "expires_in", "token_type", "scope", "expires_at",
+ "refresh_token"}.issubset(set(credentials.keys()))
+ assert credentials['access_token'] is not None
+ assert credentials['refresh_token'] is not None
+ assert credentials['expires_at'] > datetime.today().timestamp()
+
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_time_series_weather_historical_daily(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "time_series_weather_historical_daily_mock_http_response"))
+ start_date = dt.datetime.strptime("2021-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
+ indicators = [
+
+ "Precipitation",
+ "Temperature",
+ "Temperature.Ground",
+ "Temperature.Standard",
+ "Temperature.StandardMax",
+ "Location",
+ "Date",
+ ]
+
+ df = self.client.get_time_series(
+ POLYGON,
+ start_date,
+ end_date,
+ WeatherTypeCollection.WEATHER_HISTORICAL_DAILY,
+ indicators,
+ )
+
+ assert {"precipitation.cumulative", "temperature.standard", "temperature.standardMax",
+ "Location"}.issubset(set(df.columns))
+ assert df.index.name == "date"
+ assert df["weatherType"].iloc[1] == "HISTORICAL_DAILY"
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_metrics(self, get_response):
+
+ fake_master_data_management_response = mock_http_response_text_content("GET", load_data_from_textfile(
+ "master_data_management_get_unique_id_mock_http_response"))
+ fake_analytics_fabric_response = mock_http_response_text_content("GET", load_data_from_textfile(
+ "metrics_lai_radar_mock_http_response"))
+ get_response.side_effect= [fake_master_data_management_response, fake_analytics_fabric_response]
+
+
+ lai_radar_polygon = "POLYGON((-52.72591542 -18.7395779,-52.72604885 -18.73951122,-52.72603114 -18.73908689,-52.71556835 -18.72490316,-52.71391916 -18.72612966,-52.71362802 -18.72623726,-52.71086473 -18.72804231,-52.72083542 -18.74173696,-52.72118937 -18.74159174,-52.72139229 -18.7418552,-52.72600257 -18.73969719,-52.72591542 -18.7395779))"
+ schema_id = "LAI_RADAR"
+ start_date = dt.datetime.strptime("2023-01-02", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2023-05-02", "%Y-%m-%d")
+ df = self.client.get_metrics(lai_radar_polygon, schema_id, start_date, end_date)
+
+ assert {"Values.RVI", "Values.LAI", "Schema.Id"}.issubset(set(df.columns))
+ assert {"2023-01-02T00:00:00Z", "2023-01-03T00:00:00Z", "2023-01-14T00:00:00Z", "2023-02-25T00:00:00Z",
+ "2023-03-26T00:00:00Z", "2023-04-27T00:00:00Z", "2023-05-02T00:00:00Z"}.issubset(set(df.index))
+ assert df.index.name == "date"
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_time_series_weather_forecast_daily(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "time_series_weather_forecast_daily_mock_http_response"))
+ start_date = dt.datetime.strptime("2021-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
+ indicators = [
+ "Precipitation",
+ "Temperature.Standard",
+ "Temperature.StandardMax",
+ "Date",
+ "Location",
+ "WeatherType"
+ ]
+
+ df = self.client.get_time_series(
+ POLYGON,
+ start_date,
+ end_date,
+ WeatherTypeCollection.WEATHER_HISTORICAL_DAILY,
+ indicators,
+ )
+
+ assert {'weatherType', 'precipitation.cumulative', 'precipitation.probabilities', 'temperature.standard',
+ 'temperature.standardMax', 'Location'}.issubset(set(df.columns))
+ assert df.index.name == "date"
+ assert df["weatherType"].iloc[1] == "FORECAST_DAILY"
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_satellite_image_time_series(self, get_response):
+ fake_get_tiff_zip_response = mock_http_response_binary_content("GET", load_binary_data_from_zipfile("Refletance_map_mock.tiff.zip"))
+ fake_image_time_series_response = mock_http_response_text_content("GET", load_data_from_textfile(
+ "satellite_image_time_series_landsat8_mock_http_response"))
+ get_response.side_effect= [fake_image_time_series_response, fake_get_tiff_zip_response, fake_get_tiff_zip_response]
+ start_date = dt.datetime.strptime("2022-05-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2023-04-28", "%Y-%m-%d")
+ dataset = self.client.get_satellite_image_time_series(
+ POLYGON,
+ start_date,
+ end_date,
+ collections=[SatelliteImageryCollection.SENTINEL_2, SatelliteImageryCollection.LANDSAT_8],
+ indicators=["Reflectance"],
+ )
+ assert dict(dataset.dims) == {'band': 4, 'y': 80, 'x': 81, 'time': 2}
+
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_agriquest_weather_block_data(self, get_response):
+ get_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "agriquest_weather_data_mock_http_response"))
+ start_date = "2022-05-01"
+ end_date = "2023-04-28"
+ dataset = self.client.get_agriquest_weather_block_data(
+ start_date=start_date,
+ end_date=end_date,
+ block_code=AgriquestBlocks.FRA_DEPARTEMENTS,
+ weather_type=AgriquestWeatherType.CUMULATIVE_PRECIPITATION
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert len(dataset["AMU"]) == 97
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_agriquest_ndvi_block_data(self, get_response):
+ get_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "agriquest_ndvi_data_mock_http_response"))
+ date = "2023-06-05"
+ dataset = self.client.get_agriquest_ndvi_block_data(
+ day_of_measure=date,
+ commodity_code=AgriquestCommodityCode.ALL_VEGETATION,
+ block_code=AgriquestBlocks.AMU_NORTH_AMERICA,
+ )
+ assert dataset.keys()[0] == "AMU"
+ assert dataset.keys()[-1] == "NDVI"
diff --git a/tests/test_unit_gis_service.py b/tests/test_unit_gis_service.py
new file mode 100644
index 0000000..22217a2
--- /dev/null
+++ b/tests/test_unit_gis_service.py
@@ -0,0 +1,32 @@
+from datetime import datetime
+from unittest.mock import patch
+from geosyspy import Geosys
+from dotenv import load_dotenv
+import datetime as dt
+import numpy as np
+
+from geosyspy.services.gis_service import GisService
+from geosyspy.utils.constants import *
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestGisService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = GisService(base_url=url, http_client=http_client)
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_get_municipio_id_from_geometry(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "gis_layer_municipio_data_mock_http_response"))
+
+ municipio_id = self.service.get_municipio_id_from_geometry(geometry=geometry)
+ assert municipio_id == 121935
\ No newline at end of file
diff --git a/tests/test_unit_http_client.py b/tests/test_unit_http_client.py
new file mode 100644
index 0000000..6a77880
--- /dev/null
+++ b/tests/test_unit_http_client.py
@@ -0,0 +1,56 @@
+from unittest.mock import patch
+from geosyspy.utils.http_client import *
+
+
+
+@patch('geosyspy.utils.http_client.HttpClient.get')
+def test_get_request_from_api_should_success(get_response):
+ get_response.return_value = "HTTP 200 OK"
+ client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+
+ response = client.get(url_endpoint="http://geosys.com", headers={})
+ assert client.get.call_count == 1
+ assert response == "HTTP 200 OK"
+
+
+@patch('geosyspy.utils.http_client.HttpClient.post')
+def test_post_request_should_success(post_response):
+ post_response.return_value = "HTTP 201 OK"
+ client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ payload = {
+ "Geometry": "polygon",
+ "Crop": {"Id": "CORN"},
+ "SowingDate": "2022-01-01",
+ }
+ response = client.post(url_endpoint="http://geosys.com", payload=payload, headers={})
+ assert client.post.call_count == 1
+ assert response == "HTTP 201 OK"
+
+
+@patch('geosyspy.utils.http_client.HttpClient.patch')
+def test_patch_request_should_success(patch_response):
+ patch_response.return_value = "HTTP 200 OK"
+ client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ payload = {
+ "Geometry": "new polygon",
+ "Crop": {"Id": "CORN"},
+ "SowingDate": "2022-01-01",
+ }
+ response = client.patch(url_endpoint="http://geosys.com", payload=payload)
+ assert client.patch.call_count == 1
+ assert response == "HTTP 200 OK"
diff --git a/tests/test_unit_map_product_service.py b/tests/test_unit_map_product_service.py
new file mode 100644
index 0000000..357d3bd
--- /dev/null
+++ b/tests/test_unit_map_product_service.py
@@ -0,0 +1,37 @@
+import datetime as dt
+import numpy as np
+from unittest.mock import patch
+
+from geosyspy.services.map_product_service import MapProductService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+from geosyspy.utils.constants import *
+
+
+class TestMapProductService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ priority_queue = "realtime"
+
+ service = MapProductService(base_url=url, http_client=http_client,priority_queue=priority_queue)
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_satellite_coverage(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "satellite_coverage_image_references_mock_http_response"))
+ start_date = dt.datetime.strptime("2022-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2023-01-01", "%Y-%m-%d")
+ info = self.service.get_satellite_coverage(
+ "fakeSeasonFieldId", start_date, end_date, "NDVI", [SatelliteImageryCollection.SENTINEL_2]
+ )
+
+ assert {"coverageType", "image.id", "image.availableBands", "image.sensor", "image.soilMaterial",
+ "image.spatialResolution", "image.weather", "image.date", "seasonField.id"}.issubset(set(info.columns))
+
+
+
diff --git a/tests/test_unit_master_data_management_service.py b/tests/test_unit_master_data_management_service.py
new file mode 100644
index 0000000..43e5c3b
--- /dev/null
+++ b/tests/test_unit_master_data_management_service.py
@@ -0,0 +1,42 @@
+from unittest.mock import patch
+
+from geosyspy.services.master_data_management_service import MasterDataManagementService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+
+class TestMasterDataManagementService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = MasterDataManagementService(base_url=url, http_client=http_client)
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_create_season_field_id(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "master_data_management_post_extract_id_mock_http_response"))
+
+ response = self.service.create_season_field_id(polygon=geometry)
+ assert response.status_code == 200
+
+ @patch('geosyspy.utils.http_client.HttpClient.post')
+ def test_extract_season_field_id(self, post_response):
+ post_response.return_value = mock_http_response_text_content("POST", load_data_from_textfile(
+ "master_data_management_post_extract_id_mock_http_response"),status_code=201)
+
+ response = self.service.extract_season_field_id(polygon=geometry)
+ assert response == "ajqxm3v"
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_extract_season_field_id(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "master_data_management_get_unique_id_mock_http_response"), status_code=201)
+
+ response = self.service.get_season_field_unique_id(season_field_id="fakeSeasonFieldId")
+ assert response == "4XcGhZvA1OjpO3gUwYM61e"
\ No newline at end of file
diff --git a/tests/test_unit_oauth2_client.py b/tests/test_unit_oauth2_client.py
new file mode 100644
index 0000000..4530a5b
--- /dev/null
+++ b/tests/test_unit_oauth2_client.py
@@ -0,0 +1,42 @@
+from unittest.mock import patch, call
+from geosyspy.utils.oauth2_client import Oauth2Api
+
+
+def get_oauth():
+ return Oauth2Api("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+
+
+def test_oauth2_should_initialize():
+ oauth = get_oauth()
+ assert oauth.client_id == "client_id_123"
+ assert oauth.client_secret == "client_secret_123456"
+ assert oauth.username == "username_123"
+ assert oauth.password == "password_123"
+ assert oauth.server_url != ""
+ assert oauth.token is None
+
+@patch("geosyspy.utils.oauth2_client.OAuth2Session")
+def test_oauth2_get_token_should_work(OAuth2Session):
+ OAuth2Session.return_value = OAuth2Session
+ oauth = get_oauth()
+ OAuth2Session.refresh_token.return_value = "628x9x0xx447xx4x421x517x4x474x33x2065x4x1xx523xxxxx6x7x20"
+ oauth.get_refresh_token()
+ assert OAuth2Session.refresh_token.call_count == 1
+ OAuth2Session.refresh_token.assert_has_calls(
+ [
+ call(
+ oauth.server_url,
+ client_id=oauth.client_id,
+ client_secret=oauth.client_secret
+ )
+ ]
+ )
+
+
+
+
diff --git a/tests/test_unit_vegetation_time_series_service.py b/tests/test_unit_vegetation_time_series_service.py
new file mode 100644
index 0000000..7dd276f
--- /dev/null
+++ b/tests/test_unit_vegetation_time_series_service.py
@@ -0,0 +1,58 @@
+import datetime as dt
+import numpy as np
+from unittest.mock import patch
+
+from geosyspy.services.vegetation_time_series_service import VegetationTimeSeriesService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+
+
+class TestVegetationTimeSeriesService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = VegetationTimeSeriesService(base_url=url, http_client=http_client)
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_time_series_modis_ndvi(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "time_series_modis_ndvi_mock_http_response"))
+ start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
+ df = self.service.get_modis_time_series(
+ "fakeSeasonFieldId", start_date, end_date, ["NDVI"]
+ )
+
+ assert df.index.name == "date"
+ assert "value" in df.columns
+ assert "index" in df.columns
+ assert len(df.index) == 7
+ date_range = list(map(lambda x: x.strftime("%Y-%m-%d"), df.index))
+ assert {"2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05", "2020-01-06",
+ "2020-01-07"}.issubset(set(date_range))
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_satellite_image_time_series_modis_ndvi(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "satellite_image_time_series_modis_ndvi_mock_http_response"))
+ start_date = dt.datetime.strptime("2020-01-01", "%Y-%m-%d")
+ end_date = dt.datetime.strptime("2020-01-07", "%Y-%m-%d")
+
+ df = self.service.get_time_series_by_pixel(
+ "fakeSeasonFieldId", start_date, end_date, ["NDVI"]
+ )
+ assert df.index.name == "date"
+ assert {"value", "index", "pixel.id"}.issubset(set(df.columns))
+ assert np.all((df["index"].values == "NDVI"))
+ assert len(df.index) == 14
+
+ assert {"2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05", "2020-01-06",
+ "2020-01-07"}.issubset(set(df.index))
+
+ assert {"mh11v4i225j4612", "mh11v4i226j4612"}.issubset(set(df["pixel.id"]))
+
+
diff --git a/tests/test_unit_weather_service.py b/tests/test_unit_weather_service.py
new file mode 100644
index 0000000..5240d3e
--- /dev/null
+++ b/tests/test_unit_weather_service.py
@@ -0,0 +1,35 @@
+from unittest.mock import patch
+import datetime
+from geosyspy.services.weather_service import WeatherService
+from geosyspy.utils.http_client import *
+from tests.test_helper import *
+from geosyspy.utils.constants import *
+
+geometry = "POLYGON((-91.17523978603823 40.29787117039518,-91.17577285022956 40.29199489606421,-91.167613719932 40.29199489606421,-91.1673028670095 40.29867040193312,-91.17523978603823 40.29787117039518))"
+
+class TestWeatherService:
+ url = "https://testurl.com"
+ http_client = HttpClient("client_id_123",
+ "client_secret_123456",
+ "username_123",
+ "password_123",
+ "preprod",
+ "na")
+ service = WeatherService(base_url=url, http_client=http_client)
+
+ @patch('geosyspy.utils.http_client.HttpClient.get')
+ def test_get_weather(self, get_response):
+ get_response.return_value = mock_http_response_text_content("GET", load_data_from_textfile(
+ "weather_data_mock_http_response"))
+ start_date = datetime.datetime.now()
+ end_date = start_date + datetime.timedelta(days=7, hours=3, minutes=30)
+
+ data = self.service.get_weather(polygon=geometry, start_date=start_date, end_date=end_date,
+ weather_type=WeatherTypeCollection.WEATHER_FORECAST_DAILY,
+ fields=['precipitation','temperature'])
+
+ assert data.index.__len__() == 6
+ assert data['precipitation.cumulative'][0] == 0.22834645669291338
+ assert data['temperature.standard'][0] == 71.47399998282076
+
+