This library provides a wrapper to the Evolve App Server's API, allowing users of the Evolve SDK to authenticate with the Evolve App Server and upload studies.
from geojson import FeatureCollection
from zepben.eas import EasClient, StudyInput, StudyResultInput, GeoJsonOverlayInput, ResultSectionInput, SectionType, Mutation
eas_client = EasClient(
host="<host>",
port=1234,
access_token="<access_token>",
asynchronous=False,
)
eas_client.mutation(
Mutation.add_studies(
[
StudyInput(
name="<study name>",
description="<study description>",
tags=["<tag>", "<tag2>"],
results=[
StudyResultInput(
name="<result_name>",
geoJsonOverlay=GeoJsonOverlayInput(
data=FeatureCollection(...),
styles=["style1"]
),
sections=[
ResultSectionInput(
type=SectionType.TABLE,
name="<table name>",
description="<table description>",
columns=[
{"key": "<column 1 key>", "name": "<column 1 name>"},
{"key": "<column 2 key>", "name": "<column 2 name>"},
],
data=[
{"<column 1 key>": "<column 1 row 1 value>", "<column 2 key>": "<column 2 row 1 value>"},
{"<column 1 key>": "<column 1 row 2 value>", "<column 2 key>": "<column 2 row 2 value>"}
]
)
]
)
],
styles=[
{
"id": "style1",
# other Mapbox GL JS style properties
}
]
)
]
)
)
eas_client.close()The EasClient can operate in async mode if specified, like so:
from aiohttp import ClientSession
from geojson import FeatureCollection
from zepben.eas import EasClient, StudyInput, StudyResultInput, GeoJsonOverlayInput, ResultSectionInput, SectionType, Mutation
async def upload():
eas_client = EasClient(
host="<host>",
port=1234,
access_token="<access_token>",
asynchronous=True, # returns all methods as plain async methods
)
await eas_client.mutation(
Mutation.add_studies(
[
StudyInput(
name="<study name>",
description="<study description>",
tags=["<tag>", "<tag2>"],
results=[
StudyResultInput(
name="<result_name>",
geoJsonOverlay=GeoJsonOverlayInput(
data=FeatureCollection(...),
styles=["style1"]
),
sections=[
ResultSectionInput(
type=SectionType.TABLE,
name="<table name>",
description="<table description>",
columns=[
{"key": "<column 1 key>", "name": "<column 1 name>"},
{"key": "<column 2 key>", "name": "<column 2 name>"},
],
data=[
{"<column 1 key>": "<column 1 row 1 value>", "<column 2 key>": "<column 2 row 1 value>"},
{"<column 1 key>": "<column 1 row 2 value>", "<column 2 key>": "<column 2 row 2 value>"}
]
)
]
)
],
styles=[
{
"id": "style1",
# other Mapbox GL JS style properties
}
]
)
]
)
)
await eas_client.close()Most of the objects passed into requests are similar. The new EasClient is fully type hinted and self documenting.
For example.
from zepben.eas import EasClient, WorkPackageInput, HcExecutorConfigInput, FeederConfigsInput, FeederConfigInput
client = EasClient(host='host', port=1234)
client.get_work_package_cost_estimation(
WorkPackageInput(
feederConfigs=FeederConfigsInput(
configs=[
FeederConfigInput(
feeder='myFeeder',
years=[2024, 2025],
scenarios=['scenario1']
)
]
)
)
)Hovering over any kwarg or looking at any class definition will show all possible parameters, and their expected types.
Legacy convenience methods can be enabled by passing enable_legacy_methods to __init__ of EasClient. eg:
from zepben.eas import EasClient
client = EasClient(enable_legacy_methods=True)This will enable all deprecated and opt_in methods on the class, they are disabled by default.
To regenerate the graphql client you will need to install zepben.eas with eas-codegen optional dependencies:
pip install -e ".[eas-codegen]"With these installed and EAS running locally on port 7654, you can then generate the client:
ariadne-codegen