Skip to main content

Utility functions to write LivingPark notebooks.

Project description

LivingPark utils

A collection of utility functions to write LivingPark notebooks.

Usage examples:

import livingpark_utils
from livingpark_utils import download
from livingpark_utils.clinical import moca2mmse
from livingpark_utils.dataset import ppmi

utils = livingpark_utils.LivingParkUtils()
downloader = download.ppmi.Downloader(utils.study_files_dir)

utils.notebook_init()
utils.get_study_files(["Demographics.csv"], default=downloader)
utils.get_T1_nifti_files(
    cohort, default=downloader
)  # `cohort` is of type: pd.DataFrame

ppmi.find_nifti_file_in_cache(x["PATNO"], x["EVENT_ID"], x["Description"])
ppmi.disease_duration()

moca2mmse(2)

Exclude subjects from a cohort without leaking patient information.

from livingpark_utils.ignore import (
    insert_hash,
    remove_ignored,
)

# Assuming a cohort definition defined as `cohort`.
cohort = insert_hash(cohort, columns=["PATNO", "EVENT_ID", "Description"])
remove_ignored(cohort, ignore_file=".ppmiignore")

Usage to execute utility notebooks:

from livingpark_utils.scripts import run

run.mri_metadata()
run.pd_status()

Note: Optionally use the %%capture cell magic to further hide notebook outputs.

CLI commands

Download T1 nifti files using a cohort definition file.

$ get_T1_nifti_files <cohort_file> --downloader (ppmi) [--symlink=<bool>]
[--force=<bool>] [--timeout=<int>]

The cohort_file is a csv file created beforehand. Respectively to the chosen downloader, it must have the following columns:

  • PPMI: PATNO, EVENT_ID, and Description.

Troubleshooting

Permission issues on Windows

We use symbolic links when creating the folder for cached data. Unfortunately, by default, Windows does not authorize users to create symbolic links. To fix this issue on your machine, please follow the guide from this blog post.

Contributing guidelines

We welcome contributions of any kind in the form of Pull-Request to this repository. See also LivingPark contributing guidelines.

Make sure to:

  • Use Python type annotations
  • Include Python docstrings using numpydoc format for all functions
  • Format docstrings
  • Run psf/black on the files you modify
  • Run pre-commit run --all before committing, this will be checked in your PR

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

livingpark_utils-0.9.tar.gz (49.0 kB view details)

Uploaded Source

Built Distribution

livingpark_utils-0.9-py3-none-any.whl (59.7 kB view details)

Uploaded Python 3

File details

Details for the file livingpark_utils-0.9.tar.gz.

File metadata

  • Download URL: livingpark_utils-0.9.tar.gz
  • Upload date:
  • Size: 49.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for livingpark_utils-0.9.tar.gz
Algorithm Hash digest
SHA256 e0aad8213081ee130f77b359c74d3ef90212f653b0aab495f0cb236f7fcef826
MD5 f90c205a3758521ad124eaae80554fdf
BLAKE2b-256 cd6c79c4b990b81713faac1f3d0f57bb7271f254d318abe6caf8e9721d6a09b6

See more details on using hashes here.

Provenance

File details

Details for the file livingpark_utils-0.9-py3-none-any.whl.

File metadata

File hashes

Hashes for livingpark_utils-0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 8006ef606be7fc40e5b209bd16e1f73e91105b60d169c8d862853b8775ca3cf0
MD5 3da3e66ce1f2b6a3617c17802f6e1c81
BLAKE2b-256 2b4d6488ee14b0d78ee9b2230616b4c40c63d5b62172d44c5675e1825b1815e4

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page