pydaisi 0.2.7

A Python interface for the Daisi Platform

Simples steps for using the PyDaisi SDK

Preliminary tasks

Install with PIP:

• pip install pydaisi

(Optional) Set your personal access token:

Create your personal access token

• https://app.daisi.io/settings/personal-access-tokens

Set it in the environment:

export DAISI_ACCESS_TOKEN=a1b2c3d4e5f67890abcdef124567890

or in a .env file:

DAISI_ACCESS_TOKEN=a1b2c3d4e5f67890abcdef124567890

Using PyDaisi

Normal calls

You can call the Daisi function, it will run until complete, and the result will be available in the value attribute when it has returned.

from pydaisi import Daisi

# instantiate a Daisi object
daisi = Daisi("my-pebble-tutorial")
# call a Daisi function. You can also use positional parameters: daisi.median("London")
temp = daisi.median(city="London")
print(f"Median temperature in London was: {temp.value}")
print(f"Mean temperature in Amsterdam was: {daisi.mean('Amsterdam')}.value")

Async calls

You can also use Python asyncio functions to create and dispatch many calls asynchronously.

from pydaisi import Daisi
import asyncio

async def callmany()
    calls = []
    # sets up a call, but does not execute
    with Daisi("my-pebble-tutorial") as daisi:
        calls.append(daisi.defer.mean("Paris").fetch_result())
        calls.append(daisi.defer.mean("London").fetch_result())
        calls.append(daisi.defer.mean("Amsterdam").fetch_result())
        await asyncio.gather(calls)

Parallel Execution

You may also use helper functions to execute many calls from your synchronous code

from pydaisi import Daisi

with Daisi('my_daisi_name`) as my_daisi:
    calls = []
    calls.append(my_daisi.endpoint1_(**kwargs))
    calls.append(my_daisi.endpoint2_(**kwargs))
    calls.append(my_daisi.endpoint3_(**kwargs))
    print(Daisi.parallel_run(*calls))