spatiafi 1.8.0

Python library for interacting with the SpatiaFi API

Python SpatiaFi API

Python library for interacting with the SpatiaFi API.

Also included is gdal_auth which is a CLI tool to help with GCP authentication for GDAL.

Quickstart

Install the Package

pip install spatiafi

Get Authenticated Session

from spatiafi import get_session

session = get_session()
# The `session` object works just like `requests` but will automatically
# refresh the authentication token when it expires

params = {"item_id": "wildfire-risk-current-global-v1.0"}
url = "https://api.spatiafi.com/api/info"

response = session.get(url, params=params)

Using a proxy

If you're using the client in a constrained environment you may need to pass in proxies to the get_session function:

from spatiafi import get_session

proxies = {
    "http": "http://fake-proxy.example.com:443",
    "https": "http://fake-proxy.example.com:443",
}

session = get_session(proxies=proxies)

params = {"item_id": "wildfire-risk-current-global-v1.0"}
url = "https://api.spatiafi.com/api/info"

response = session.get(url, params=params)

Get help with gdal_auth

gdal_auth --help

---

GDAL Authentication

gdal_auth is a CLI tool to help with GCP authentication for GDAL.

This command can be used in three ways:

1. Set the environment variables in a file that can be sourced to set the environment variables:

   gdal_auth --file
   source /tmp/gdal_auth.env
   

2. Print a command that can be run to set the environment variables once:

   gdal_auth --line
   

   Running this command will output a line that you will need to copy and run. This will set the GDAL environment variables for anything run afterward in the same terminal session. (e.g. run the command and then in the command line run another command like qgis)

3. Print instructions for setting up aliases. These aliases allow gdal_* commands to be run as normal and authentication will be handled automatically:

   gdal_auth --alias
   

Note: for all options except --alias, the authentication will eventually expire. This is because the tokens generated by the Google Cloud SDK expire after 1 hour. The aliases will automatically refresh the authentication tokens when they expire.

---

Mini-Batch (Async Queue)

The AsyncQueue class is a helper class for running many (up to ~1 million) API queries in parallel. To use it, you must:

1. Create a task function
2. Create an AsyncQueue object
3. Enqueue tasks
4. Fetch results

tl;dr: See tests/test_async_queue.py for an example.

Create a AsyncQueue Task

A valid AsyncQueue task must:

• Be an async function
• Take a single argument
• Take an optional async session argument (if not provided, an async session will be created)
• Return a single, serializable object (a dict is recommended)

If your task function requires multiple arguments, you can:

• Use a wrapper function or closure (may not work on Windows or 'spawn' multiprocessing)
• Create a new function using functools.partial (as shown in tests/test_async_queue.py)
• Pass a tuple as the argument and unpack it in the task function e.g.

  async def task(args, session=None):
      arg1, arg2, arg3 = args
      ...
  
  with AsyncQueue(task) as async_queue:
      async_queue.enqueue((arg1, arg2, arg3))
  

Example Task Function

from spatiafi.async_queue import AsyncQueue
from spatiafi.session import get_async_session


async def get_point(point, session=None):
    """
    Get a point from the SpatiaFI API.
    """

    # Unpack the `point` tuple because we can only pass
    # a single argument to the task function.
    lon, lat = point

    # Create an async session if one is not provided.
    if session is None:
        session = await get_async_session()

    # Create the url.
    url = (
        "https://api.spatiafi.com/api/point/" + str(lon) + "," + str(lat)
    )

    params = {"item_id": "wildfire-risk-current-global-v1.0"}

    r = await session.get(url, params=params)

    # We want to raise for all errors except 400 (bad request)
    if not (r.status_code == 200 or r.status_code == 400):
        r.raise_for_status()

    return r.json()

Create an AsyncQueue and Enqueue Tasks

AsyncQueue takes a task function as an argument, and launches multiple instances of that task in parallel. The AsyncQueue.enqueue method takes a single argument is used to add tasks to the queue. The AsyncQueue.results property will return a list of results in the order they were enqueued.

When starting the AsyncQueue, it is highly recommended that you specify the number of workers/CPUs to use using the n_cores argument. The default is to use the minimum of 4 and the number of CPUs on the machine.

This queue is designed to be used with the with statement. Entering the with statement will start the subprocess and event loop. Exiting the with statement will wait for all tasks to finish and then stop the event loop and subprocess.

For example:

from spatiafi.async_queue import AsyncQueue

with AsyncQueue(get_point) as async_queue:
    for _, row in df.iterrows():
        async_queue.enqueue((row["lon"], row["lat"]))

results = async_queue.results

Alternatively, you can use the start and stop methods:

from spatiafi.async_queue import AsyncQueue

async_queue = AsyncQueue(get_point)
async_queue.start()

for _, row in df.iterrows():
    async_queue.enqueue((row["lon"], row["lat"]))

async_queue.stop()
results = async_queue.results

---

Development

Use a Virtual Environment

Development should be done in a virtual environment. It is recommended to use the virtual environment manager built into PyCharm. To create a new virtual environment:

• Open the project in PyCharm and select File > Settings > Project: spfi-api > Python Interpreter.
• In the top right corner of the window, click the gear icon and select Add Interpreter > Add Local Interpreter...

Mark src as a Source Root

In PyCharm, mark the src folder as a source root. This will allow you to import modules from the src folder without using relative imports. Right-click on the src folder and select Mark Directory as > Sources Root.

Bootstrap the Development Environment

Run ./scripts/bootstrap_dev.sh to install the package and development dependencies.

This will also set up access to our private PyPI server, generate the first requirements.txt (if required), and install pre-commit hooks.

Protip: This script can be run at any time if you're afraid you've messed up your environment.

Running the tests

Tests can be run locally via the scripts/test.sh script:

./scripts/test.sh

All additional arguments to that script will be passed to PyTest which allows you to do things such as run a single test:

./scripts/test.sh -k test_async_queue

Manage Dependencies in setup.cfg

Dependencies are managed in setup.cfg using the install_requires and extras_require sections.

To add a new dependency:

1. Install the package in the virtual environment with pip install <package_name> (Hint: use the terminal built in to PyCharm)
2. Run pip show <package_name> to get the package name and version
3. Add the package name and version to setup.cfg in the install_requires section. Use the compatible release syntax package_name ~=version.

DO NOT add the package to the requirements.txt file. This file is automatically generated by scripts/gen_requirements.sh.

If the dependency is only needed for development, add it to the dev section of extras_require in setup.cfg.

Building Docker Images Locally

tl;dr: run ./scripts/build_docker.sh.

We need to inject a GCP access token into the Docker build to access private PyPI packages. This requires using BuildKit (enabled by default in recent versions of Docker), and passing the token as a build argument.

pre-commit Hooks

This project uses pre-commit to run a series of checks before each git commit. To install the pre-commit hooks, run pre-commit install in the virtual environment. (This is done automatically by ./scripts/bootstrap_dev.sh)

To format all your code manually, run pre-commit run --all-files.

Note: If your code does not pass the pre-commit checks, automatic builds may fail.

Use pip-sync to Update Dependencies

To update local dependencies, run pip-sync in the virtual environment. This will make sure your virtual environment is in sync with the requirements.txt file, including uninstalling any packages that are not in the requirements.txt file.

Versions

The project uses semantic versioning.

Package versions are automatically generated from git tags. Create your first tag with git tag 0.1.0 and push it with git push --tags

Installation

tl;dr: ./scripts/install_package.sh

For development, it is recommended to install the package in editable mode with pip install -e .[dev].