Welcome to adnipy’s documentation!

adnipy

Process ADNI study data with adnipy.

Adnipy is a python package designed for working with the ADNI database. It also offers some handy tools for file operations.

• Free software: MIT license
• Documentation: https://adnipy.readthedocs.io

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Installation

Stable release

To install adnipy, run this command in your terminal:

$ pip install adnipy

This is the preferred method to install adnipy, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources

The sources for adnipy can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/mcsitter/adnipy

Or download the tarball:

$ curl  -OL https://github.com/mcsitter/adnipy/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

Usage

To use adnipy in a project:

import adnipy

adnipy

adnipy package

Submodules

adnipy.adni module

Pandas dataframe extension for ADNI.

class adnipy.adni.ADNI(pandas_dataframe)

Bases: object

Dataframe deals with ADNI data.

This class presents methods, which are designed to work with data from the ADNI database.

DATES = ['Acq Date', 'Downloaded', 'EXAMDATE', 'EXAMDATE_bl', 'update_stamp', 'USERDATE', 'update_stamp', 'USERDATE', 'USERDATE2', 'SCANDATE', 'TAUTRANDT', 'update_stamp', 'USERDATE', 'USERDATE2', 'SCANDATE', 'TRANDATE', 'update_stamp']

INDEX = ['Subject ID', 'Image ID']

MAPPER = {'ASSAYTIME': 'TAUTIME', 'Acq Date': 'SCANDATE', 'Image': 'Image ID', 'Image Data ID': 'Image ID', 'PTID': 'Subject ID', 'Subject': 'Subject ID'}

drop_dynamic()

Remove images which are dynamic.

Drops all rows, in which the Description contains ‘Dynamic’.

Returns:All images that are not dynamic. Return type:pd.DataFrame

groups(grouped_mci=True)

Create a dataframe for each group and save it to a csv file.

Parameters:grouped_mci (bool, default True) – If true, ‘LMCI’ and ‘EMCI’ are treated like ‘MCI’. However, the original values will stills be in the output. Returns:Dictionnairy with a dataframe for each group. Return type:dict

longitudinal()

Keep only longitudinal data.

This requires an ‘RID’ or ‘Subject ID’ column in the dataframe. Do not use if multiple images are present for a single timepoint.

Parameters:images (pd.DataFrame) – This dataframe will be modified. Returns:A dataframe with only longitudinal data. Return type:pd.DataFrame

See also

drop_dynamic()

rid()

Add a roster ID column.

Will not work if ‘RID’ is already present or ‘Subject ID’ is missing.

Returns:Dataframe with a ‘RID’ column. Return type:pd.DataFrame

Examples

>>> subjects = {"Subject ID": ["100_S_1000", "101_S_1001"]}
>>> collection = pd.DataFrame(subjects)
>>> collection
   Subject ID
0  100_S_1000
1  101_S_1001
>>> collection.adni.rid()
   Subject ID   RID
0  100_S_1000  1000
1  101_S_1001  1001

standard_column_names()

Rename dataframe columns to module standard.

This function helps when working with multiple dataframes, since the same data can have different names. It will also call rid() on the dataframe.

Returns:This will have standardized columns names. Return type:pd.DataFrame

See also

rid()

Examples

>>> subjects = pd.DataFrame({"Subject": ["101_S_1001", "102_S_1002"]})
>>> subjects
      Subject
0  101_S_1001
1  102_S_1002
>>> subjects.adni.standard_column_names()
"VISCODE2" not included.
   Subject ID   RID
0  101_S_1001  1001
1  102_S_1002  1002

>>> images = pd.DataFrame({"Image": [100001, 100002]})
>>> images
    Image
0  100001
1  100002
>>> images.adni.standard_column_names()
"VISCODE2" not included.
   Image ID
0    100001
1    100002

standard_dates()

Change type of date columns to datetime.

Returns:Dates will have the appropriate dtype. Return type:pd.DataFrame

standard_index(index=None)

Process dataframes into a standardized format.

The output is easy to read. Applying functions the the output may not work as expected.

Parameters:index (list of str, default None) – These columns will be the new index. Returns:An easy to read dataframe for humans. Return type:pd.DataFrame

timepoints(second='first')

Extract timepoints from a dataframe.

Parameters:second ({'first' or 'last'}, default 'first') – ‘last’ to have the latest, ‘first’ to have the earliest values for timepoint 2.

adnipy.adnipy module

Process ADNI study data with adnipy.

adnipy.adnipy.get_matching_images(left, right)

Match different scan types based on closest date.

The columns ‘Subject ID’ and ‘SCANDATE’ are required.

Parameters:

• left (pd.DataFrame) – Dataframe containing the tau scans.
• right (pd.DataFrame) – Dataframe containing the mri scans.

Returns:

For each timepoint there is a match from both inputs.

Return type:

pd.DataFrame

adnipy.adnipy.read_csv(file)

Return a csv file as a pandas.DataFrame.

Recognizes missing values used in the ADNI database.

Parameters:file (str, pathlib.Path) – The path to the .csv file. Returns:Returns the file as a dataframe. Return type:pd.DataFrame

See also

standard_column_names(), standard_dates(), standard_index()

adnipy.adnipy.timedelta(old, new)

Get timedelta between timepoints.

Parameters:

• old (pd.DataFrame) – This is the older dataframe.
• new (pd.DataFrame) – This is the newer dataframe.

Returns:

The content will be timedelta values. Look into numpy for more options.

Return type:

pd.Series

adnipy.data module

Process data created in Matlab.

adnipy.data.image_id_from_filename(filename)

Extract image ID of single ADNI .nii filename.

Images from the ADNI database have a specific formatting. Using regular expressions the image ID can be extracted from filenames.

Parameters:filename (str) – It must contain the Image ID at the end. Returns:Image as a integer. Return type:numpy.int64

Examples

>>> image_id_from_filename("*_I123456.nii")
123456

Module contents

Process ADNI study data with adnipy.

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at https://github.com/mcsitter/adnipy/issues.

If you are reporting a bug, please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation

adnipy could always use more documentation, whether as part of the official adnipy docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at https://github.com/mcsitter/adnipy/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here’s how to set up adnipy for local development.

1. Fork the adnipy repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/adnipy.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv adnipy
   $ cd adnipy/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

   $ flake8 adnipy tests
   $ python setup.py test or py.test
   $ tox
   

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 3.5, 3.6, 3.7 and 3.8. Check https://travis-ci.org/mcsitter/adnipy/pull_requests and make sure that the tests pass for all supported Python versions.

Tips

To run a subset of tests:

$ $ py.test tests.test_adnipy

Deploying

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:

$ bumpversion patch # possible: major / minor / patch
$ git push
$ git push --tags

Travis will then deploy to PyPI if tests pass.

Credits

Development Lead

• Maximilian Cosmo Sitter <msitter@smail.uni-koeln.de>

Contributors

None yet. Why not be the first?

History

0.0.1 (2019-09-05)

• First release on GitHub.
• First release on PyPI.

0.1.0 (2019-10-25)

• Improved documentation.
• Added pandas dataframe class extension for ADNI

Indices and tables

• Index
• Module Index
• Search Page