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One Codex API - Python Client Library and CLI

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Command line interface (CLI) and Python client library for interacting with the One Codex v1 API (API docs).

MAINTAINERS: @clausmith, @boydgreenfield

Installation

This package provides 3 major pieces of functionality: (1) a core Python client library; (2) a simple CLI for interacting with the One Codex platform that uses that core library; and (3) optional extensions to the client library, which offers many features aimed at advanced users and provides functionality for use in interactive notebook environments (e.g., IPython notebooks).

Python 3.7 or later is required. Python 2 is no longer supported.

Basic installation

The CLI (and core Python library) may be simply installed using pip. To download a minimal installation (#1 and #2), simply run:

pip install onecodex

Installation with optional extensions

To also download the optional extensions to the client library, and all of their dependencies, run:

pip install 'onecodex[all]'

Using the CLI

Logging in

The CLI supports authentication using either your One Codex API key or your One Codex username and password. To log in using your username and password:

onecodex login

This command will save a credentials file at ~/.onecodex, which will then automatically be used for authentication the next time the CLI or Python client library are used (OS X/Linux only). You can clear this file and remove your API key from your machine with onecodex logout.

In a shared environment, we recommend directly using your One Codex API key, rather than logging in and storing it in a credentials file. To use API key authentication, simply pass your key as an argument to the onecodex command:

onecodex --api-key=YOUR_API_KEY samples

Your API key can be found on the One Codex settings page and should be 32 character string. You may also generate a new API key on the settings page in the web application. Note: Because your API key provides access to all of the samples and metadata in your account, you should immediately reset your key on the website if it is ever accidentally revealed or saved (e.g., checked into a GitHub repository).

Uploading files

The CLI supports uploading FASTA or FASTQ files (optionally gzip compressed) via the upload command.

onecodex upload bacterial_reads_file.fq.gz

Multiple files can be uploaded in a single command as well:

onecodex upload file1.fq.gz file2.fq.gz ...

You can also upload files using the Python client library:

uploaded_sample1 = ocx.Samples.upload("/path/to/file.fastq")

# Or upload a tuple of paired end files
uploaded_sample2 = ocx.Samples.upload(("/path/to/R1.fastq", "/path/to/R2.fastq"))

Which returns a Samples resource (as of 0.5.0). Samples can be associated with tags, metadata, and projects at upload timing using those respective keyword arguments:

# Note format must match the schema defined for our API, with arbitrary
# metadata allowed as a single-level dictionary in the `custom` field.
# See https://developer.onecodex.com/reference#the-metadata-resource for details.
metadata = {
    "platform": "Illumina NovaSeq 6000",
    "date_collected": "2019-04-14T00:51:54.832048+00:00",
    "external_sample_id": "my-lims-ID-or-similar",
    "custom": {
        "my-string-field": "A most interesting sample...",
        "my-boolean-field": True,
        "my-number-field-1": 1,
        "my-number-field-2": 2.0,
    }
}

Uploads can be made in parallel using Python threads (or multiple processes), e.g.:

import concurrent.futures
uploaded_samples = []

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
    futures = {executor.submit(ocx.Samples.upload, file) for file in LIST_OF_FILES}
    for future in concurrent.futures.as_completed(futures):
        try:
            uploaded_samples.append(future.result())
        except Exception as e:
            print("An execption occurred during your upload: {}".format(e))

Resources

The CLI supports retrieving your One Codex samples and analyses. The following resources may be queried:

  • Your samples (Samples)

  • Sample metadata (Metadata)

  • Analyses, which include several subtypes with additional functionality and fields:

    • Classifications, which are basic metagenomic classification results for your samples
    • Panels, which are in silico panels for particular genes or other functional markers (example on One Codex)
  • Jobs, which provide information on the name, version, and type of analysis which was performed for a given Analyses

Simply invoke the onecodex command, using one of the above resource names as a subcommand (all lowercase). For example:

# fetch all your samples
onecodex samples

# fetch a list of panels based on their ids
onecodex panels 0123456789abcdef 0987654321fdecba

Using the Python client library

Initialization

To load the API, use the following import:

from onecodex.api import Api

Instantiate an API client either by passing your API key or automatically fetching your credentials from ~/.onecodex if you've previously called onecodex login.

from onecodex.api import Api

# Instantiate a One Codex API object, will attempt to get credentials from ~/.onecodex
ocx = Api()

# Instantiate an API object, manually specifying an API key
ocx = Api(api_key="YOUR_API_KEY_HERE")

Resources

Resources are exposed as attributes on the API object. You can fetch a resource directly by its ID or you can fetch it using the query interface. Currently you can access resources using either get() or where(). If you need help finding the ID for a sample, its identifier is part of its url on our webpage: e.g. for an analysis at https://app.onecodex.com/analysis/public/1d9491c5c31345b6, the ID is 1d9491c5c31345b6. IDs are all short unique identifiers, consisting of 16 hexadecimal characters (0-9a-f).

sample_analysis = ocx.Classifications.get("1d9491c5c31345b6")   # Fetch an individual classification
sample_analysis.results()  # Returns classification results as JSON object
sample_analysis.table()    # Returns a pandas dataframe

In addition to methods on individual instances of a given resource (e.g., a Sample or an Analysis), the library also provides methods for aggregating sets of samples or analyses:

all_completed_analyses = ocx.Classifications.where(complete=True)
all_completed_analyses.to_otu()   # Returns classification results as JSON object
all_completed_analyses.to_df()    # Returns a pandas dataframe

Development

Environment Setup

Before developing, git and python version >=3.7 are needed. We recommend using pyenv for Python version management.

To download the client library from GitHub:

git clone https://github.com/onecodex/onecodex.git
cd onecodex/

To set up the project, first create a virtual environment and then install dependencies:

# If you are on a M1 Macbook, run the line below, adjusting the version as needed
export HDF5_DIR=/opt/homebrew/Cellar/hdf5/1.12.1_1/

virtualenv venv
source venv/bin/activate
pip install -e '.[all,testing,reports]'  # -e specifies development mode so that the package doesn't have to be reinstalled after code edits

Test are run via the Makefile. Note this may take awhile at first because of installing dependencies:

make lint
make test

We use pre-commit for automated linting using black, flake8, and various whitespace and newline formatters during development.

Writing Unit Tests

We use pytest as our unit testing framework. Tests should be able to run without an internet connection, and One Codex API calls must be mocked. We use responses to mock API responses.

Tip: Any API calls that do not have a matching mock will raise an error. You can figure out which API calls need to be mocked by writing a test, running it, and inspecting the error message to see which route(s) are missing.

Fixtures

These pytest fixtures may be helpful when writing unit tests:

  • ocx: this is a mocked Api object that uses the api/v1 One Codex API schema.
  • ocx_experimental: this is a mocked Api object that uses the api/v1_experimental One Codex API schema. Use this to test experimental features.
  • api_data: this mocks some API data for v1 and v1_experimental APIs.

Mocking API Data

API data, including schemas, are stored in tests/data/api/:

tests/data/api
├── v1  # the API version
│   ├── ...
│   ├── analyses
│   │   └── index.json  # payload for accessing GET::api/v1/analyses. Will also be used to mock each resource instance, e.g. GET::api/v1/analyses/<uuid>
│   ├── classifications
│   │   ├── 0f4ee4ecb3a3412f
│   │   │   └── results
│   │   │       └── index.json  # payload for accessing GET::api/v1/classifications/0f4ee4ecb3a3412f/results
│   │   └── index.json  # payload for accessing GET::api/v1/classifications. Instance routes are also auto-mocked
│   ├── ...
│   ├── schema
│   │   ├── index.json  # payload for accessing GET::api/v1/schema
│   │   └── index_all.json  # payload for accessing GET::api/v1/schema?expand=all
│   └── ...
└── v1_experimental
    └── ...

The directory structure mirrors the One Codex API. For example:

  • The payload for API route api/v1/classifications is stored at tests/data/api/v1/classifications/index.json.
  • API route api/v1/classifications/0f4ee4ecb3a3412f/results has its payload stored at tests/data/api/v1/classifications/0f4ee4ecb3a3412f/results/index.json.
  • For the v1_experimental API, store things under api/v1_experimental/.

This idea can be extended to arbitrary nesting/depths within the API.

Note: If the payload is large, you can gzip it and name it index.json.gz.

A resource's instance list payload (e.g. api/v1/analyses gives you a list of analyses) is used to auto-mock each resource instance (e.g. api/v1/analyses/<uuid>). You don't need to create an index.json for each instance.

Mocking API Schemas

API schemas work similarly to regular API data, but with a couple of special rules:

  • Each API schema directory must have index.json and index_all.json. index.json contains the payload for e.g. api/v1/schema, and index_all.json contains the payload for e.g. api/v1/schema?expand=all.
  • If the schema requires it, you can optionally define resource-specific schemas by including <resource-name>.json in the schema directory (e.g. assemblies.json for api/v1_experimental/assemblies/schema).

conftest.py

API data is loaded in tests/conftest.py. If you need to mock API calls in a way that's not supported by this framework, you can add custom mocked calls in conftest.py.

Things that are not supported by mocking in tests/data/api/:

  • Non-GET requests (e.g. DELETE)
  • Query parameters (with the exception of schema?expand=all)

Jupyter Notebook Custom Exporters

We also package custom Jupyter notebook nbconvert exporters. These can be tested with the following snippets and the provided example.ipynb report.

Our OneCodexHTMLExporter:

jupyter nbconvert --execute --to onecodex_html --ExecutePreprocessor.timeout=-1 --output="$ONE_CODEX_REPORT_FILENAME" --output-dir="." notebook_examples/example.ipynb && open example.html

And using the OneCodexPDFExporter:

jupyter nbconvert --execute --to onecodex_pdf --ExecutePreprocessor.timeout=-1 --output="$ONE_CODEX_REPORT_FILENAME" --output-dir="." notebook_examples/example.ipynb && open example.pdf

Note these require the installation of several npm packages for headless rendering: npm install vega-lite vega-cli canvas. See altair-saver for more details.