openepi-client

A python client for accessing data from OpenEPI. Can be installed from PyPI on https://pypi.org/project/openepi-client/

Table of Contents

• Weather Data
  • Sync usage
  • Async usage
• Geocoding
  • Sync usage
  • Async usage
• Flood predictions
  • Sync usage
  • Async usage
• Deforestation
  • Sync usage
  • Async usage
• Soil Information
  • Sync usage
  • Async usage
• Crop Health Information
  • Sync usage
  • Sync usage

Weather

Sync usage

from openepi_client import GeoLocation
from openepi_client.weather import WeatherClient

# Getting the sunrise and sunset times for a location
sunrise_sunset = WeatherClient.get_sunrise(geolocation=GeoLocation(lat=51.5074, lon=0.1278))

# Getting the weather forecast for a location
forecast = WeatherClient.get_location_forecast(geolocation=GeoLocation(lat=51.5074, lon=0.1278, alt=0))

Async usage

from openepi_client import GeoLocation
from openepi_client.weather import AsyncWeatherClient

# Getting the sunrise and sunset times for a location
sunrise_sunset = await AsyncWeatherClient.get_sunrise(geolocation=GeoLocation(lat=51.5074, lon=0.1278))

# Getting the weather forecast for a location
forecast = await AsyncWeatherClient.get_location_forecast(geolocation=GeoLocation(lat=51.5074, lon=0.1278, alt=0))

Geocoding

Sync usage

from openepi_client import GeoLocation
from openepi_client.geocoding import GeocodeClient

# Searching for the coordinates to a named place
feature_collection = GeocodeClient.geocode(q="Kigali, Rwanda")

# Geocode with priority to a lat and lon
feature_collection = GeocodeClient.geocode(q="Kigali, Rwanda", geolocation=GeoLocation(lat=51.5074, lon=0.1278))

# Reverse geocode
feature_collection = GeocodeClient.reverse_geocode(geolocation=GeoLocation(lat=51.5074, lon=0.1278))

Async usage

from openepi_client import GeoLocation
from openepi_client.geocoding import AsyncGeocodeClient

# Searching for coordinates for a location
feature_collection = await AsyncGeocodeClient.geocode(q="Kigali, Rwanda")

# Geocode with priority to a lat and lon
feature_collection = await AsyncGeocodeClient.geocode(q="Kigali, Rwanda", geolocation=GeoLocation(lat=51.5074, lon=0.1278))

# Reverse geocode
feature_collection = await AsyncGeocodeClient.reverse_geocode(geolocation=GeoLocation(lat=51.5074, lon=0.1278))

Flood

Sync usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.flood import FloodClient
from datetime import date, timedelta

# Get the return period thresholds for a given geolocation
thresholds = FloodClient.get_threshold(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get the return period thresholds for a given bounding box
thresholds = FloodClient.get_threshold(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

# Get a summary flood forecast for a given coordinate
summary = FloodClient.get_summary(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get a summary flood forecast for a given coordinate and include neighboring cells
summary = FloodClient.get_summary(geolocation=GeoLocation(lat=-3.422, lon=30.075), include_neighbors=True)

# Get a summary flood forecast for a given bounding box
summary = FloodClient.get_summary(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

# Get a detailed flood forecast for a given coordinate
detailed = FloodClient.get_detailed(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get a detailed flood forecast for a given coordinate and timeframe (inclusive bounds)
start_date = date.today()
end_date = start_date + timedelta(days=4)
detailed = FloodClient.get_detailed(geolocation=GeoLocation(lat=-3.422, lon=30.075), start_date=start_date, end_date=end_date)

# Get a detailed flood forecast for a given bounding box
detailed = FloodClient.get_detailed(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

Async usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.flood import AsyncFloodClient
from datetime import date, timedelta

# Get the return period thresholds for a given geolocation
thresholds = await AsyncFloodClient.get_threshold(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get the return period thresholds for a given bounding box
thresholds = await AsyncFloodClient.get_threshold(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

# Get a summary flood forecast for a given coordinate
summary = await AsyncFloodClient.get_summary(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get a summary flood forecast for a given coordinate and include neighboring cells
summary = await AsyncFloodClient.get_summary(geolocation=GeoLocation(lat=-3.422, lon=30.075), include_neighbors=True)

# Get a summary flood forecast for a given bounding box
summary = await AsyncFloodClient.get_summary(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

# Get a detailed flood forecast for a given coordinate
detailed = await AsyncFloodClient.get_detailed(geolocation=GeoLocation(lat=-3.422, lon=30.075))

# Get a detailed flood forecast for a given coordinate and timeframe (inclusive bounds)
start_date = date.today()
end_date = start_date + timedelta(days=4)
detailed = await AsyncFloodClient.get_detailed(geolocation=GeoLocation(lat=-3.422, lon=30.075), start_date=start_date, end_date=end_date)

# Get a detailed flood forecast for a given bounding box
detailed = await AsyncFloodClient.get_detailed(bounding_box=BoundingBox(min_lat=4.764412, min_lon=22.0, max_lat=5.015732, max_lon=23.05))

Deforestation

Sync usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.deforestation import DeforestationClient

# Get the yearly forest cover loss within a river basin for a given geolocation
forest_loss = DeforestationClient.get_basin(geolocation=GeoLocation(lat=5.175, lon=37.124))

# Get yearly forest cover loss for all river basins within the given bounding box
forest_loss = DeforestationClient.get_basin(bounding_box=BoundingBox(min_lat=30.909622, min_lon=28.850951, max_lat=-1.041395, max_lon=-2.840114))

Async usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.deforestation import AsyncDeforestationClient

# Get the yearly forest cover loss within a river basin for a given geolocation
forest_loss = await AsyncDeforestationClient.get_basin(geolocation=GeoLocation(lat=5.175, lon=37.124))

# Get yearly forest cover loss for all river basins within the given bounding box
forest_loss = await AsyncDeforestationClient.get_basin(bounding_box=BoundingBox(min_lat=30.909622, min_lon=28.850951, max_lat=-1.041395, max_lon=-2.840114))

Soil

Sync usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.soil import SoilClient

# Get the most probable soil type at the queried location
# and the probability of the top 4 most probable soil types
soil_type = SoilClient.get_soil_type(
  geolocation=GeoLocation(lat=60.1, lon=9.58),
  top_k=4
)

# Get the mean and the 0.05 quantile of the soil 
# properties at the queried location and depths       
properties = ["clay", "silt"]
depths = ["0-5cm", "5-15cm"]
values = ["mean", "Q0.05"]
soil_property = SoilClient.get_soil_property(
  geolocation=GeoLocation(lat=60.1, lon=9.58), 
  depths=depths, 
  properties=properties, 
  values=values
)

# Get a summary of the soil types in the queried bounding box, 
# represented by a mapping of each soil type to the number 
# of occurrences in the bounding box
soil_type_summary = SoilClient.get_soil_type_summary(
  bounding_box=BoundingBox(
    min_lat=60.1,
    max_lat=60.12,
    min_lon=9.58,
    max_lon=9.6,
  )
)

Async usage

from openepi_client import GeoLocation, BoundingBox
from openepi_client.soil import AsyncSoilClient

# Get the most probable soil type at the queried location
# and the probability of the top 4 most probable soil types
soil_type = await AsyncSoilClient.get_soil_type(
  geolocation=GeoLocation(lat=60.1, lon=9.58),
  top_k=4
)

# Get the mean and the 0.05 quantile of the soil 
# properties at the queried location and depths       
properties = ["clay", "silt"]
depths = ["0-5cm", "5-15cm"]
values = ["mean", "Q0.05"]
soil_property = await AsyncSoilClient.get_soil_property(
  geolocation=GeoLocation(lat=60.1, lon=9.58), 
  depths=depths, 
  properties=properties, 
  values=values
)

# Get a summary of the soil types in the queried bounding box, 
# represented by a mapping of each soil type to the number 
# of occurrences in the bounding box
soil_type_summary = await AsyncSoilClient.get_soil_type_summary(
  bounding_box=BoundingBox(
    min_lat=60.1,
    max_lat=60.12,
    min_lon=9.58,
    max_lon=9.6,
  )
)

Crop Health

Sync usage

import os
from openepi_client.crop_health import CropHealthClient

image_path = os.path.abspath("cocoa.jpg")

# Get the predicted health of the crop pictured in cocoa.jpg with the binary model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = CropHealthClient.get_binary_prediction(image_data)

# Get the predicted health of the crop pictured in cocoa.jpg with the single-HLT model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = CropHealthClient.get_singleHLT_prediction(image_data)

# Get the predicted health of the crop pictured in cocoa.jpg with the multi-HLT model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = CropHealthClient.get_multiHLT_prediction(image_data)

Async usage

import os
from openepi_client.crop_health import AsyncCropHealthClient

image_path = os.path.abspath("cocoa.jpg")

# Get the predicted health of the crop pictured in cocoa.jpg with the binary model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = await AsyncCropHealthClient.get_binary_prediction(image_data)

# Get the predicted health of the crop pictured in cocoa.jpg with the single-HLT model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = await AsyncCropHealthClient.get_singleHLT_prediction(image_data)

# Get the predicted health of the crop pictured in cocoa.jpg with the multi-HLT model.
with open(image_path, "rb") as f:
  image_data = f.read()
  health = await AsyncCropHealthClient.get_multiHLT_prediction(image_data)

Updating the client

The following commands are used to update the client types. The commands are run from the root of the project.

poetry run datamodel-codegen --url https://api.openepi.io/weather/openapi.json --output openepi_client/weather/_weather_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"
poetry run datamodel-codegen --url https://api.openepi.io/geocoding/openapi.json --output openepi_client/geocoding/_geocoding_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"
poetry run datamodel-codegen --url https://api.openepi.io/flood/openapi.json --output openepi_client/flood/_flood_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"
poetry run datamodel-codegen --url https://api.openepi.io/deforestation/openapi.json --output openepi_client/deforestation/_deforestation_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"
poetry run datamodel-codegen --url https://api.openepi.io/soil/openapi.json --output openepi_client/soil/_soil_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"
poetry run datamodel-codegen --url https://api.openepi.io/crop-health/openapi.json --output openepi_client/crop_health/_crop_health_types.py --enum-field-as-literal all --output-model-type pydantic_v2.BaseModel --input-file-type "openapi"