# pyfakefs [![PyPI version](https://badge.fury.io/py/pyfakefs.svg)](https://badge.fury.io/py/pyfakefs) [![Python version](https://img.shields.io/pypi/pyversions/pyfakefs.svg)](https://img.shields.io/pypi/pyversions/pyfakefs.svg) ![Testsuite](https://github.com/pytest-dev/pyfakefs/workflows/Testsuite/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pytest-pyfakefs/badge/?version=latest)](https://pytest-pyfakefs.readthedocs.io/en/latest/?badge=latest) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/pytest-dev/pyfakefs/main.svg)](https://results.pre-commit.ci/latest/github/pytest-dev/pyfakefs/main)

pyfakefs implements a fake file system that mocks the Python file system modules.
Using pyfakefs, your tests operate on a fake file system in memory without
touching the real disk. The software under test requires no modification to
work with pyfakefs.

Pyfakefs creates a new empty in-memory file system at each test start, which replaces
the real filesystem during the test. Think of pyfakefs as making a per-test temporary
directory, except for an entire file system.

There are several means to achieve this: by using
the `fs` fixture if running pytest, by using `fake_filesystem_unittest.TestCase` as a
base class if using unittest, by using a `fake_filesystem_unittest.Patcher` instance
as a context manager, or by using the `patchfs` decorator.

pyfakefs works with current versions of Linux, Windows and macOS.

## Documentation

This document provides a general overview for pyfakefs.  There is more:

* The documentation at **Read the Docs**:
  * The [Release documentation](https://pytest-pyfakefs.readthedocs.io/en/stable)
    contains usage documentation for pyfakefs and a description of the
    most relevant classes, methods and functions for the last version
    released on PyPI
  * The [Development documentation](https://pytest-pyfakefs.readthedocs.io/en/latest)
    contains the same documentation for the current main branch
  * The [Release 3.7 documentation](https://pytest-pyfakefs.readthedocs.io/en/v3.7.2/)
    contains usage documentation for the last version of pyfakefs
    supporting Python 2.7
* The [Release Notes](https://github.com/pytest-dev/pyfakefs/blob/main/CHANGES.md)
  show a list of changes in the latest versions

## Usage
The simplest method to use pyfakefs is using the `fs` fixture with `pytest`.
Refer to the
[usage documentation](https://pytest-pyfakefs.readthedocs.io/en/latest/usage.html)
for information on other test scenarios, test customization and
using convenience functions.

## Features
Apart from automatically mocking most file-system functions, pyfakefs
provides some additional features:
- mapping files and directories from the real file system into the fake filesystem
- configuration and tracking of the file system size
- pause and resume of patching to be able to use the real file system inside a
  test step
- (limited) emulation of other OSes (Linux, macOS or Windows)
- configuration to behave as if running as a non-root user while running
  under root

## Compatibility
pyfakefs works with CPython 3.7 and above, on Linux, Windows and macOS, and
with PyPy3.

pyfakefs works with [pytest](http://doc.pytest.org) version 3.0.0 or above,
though a current version is recommended.

pyfakefs will not work with Python libraries that use C libraries to access the
file system. This is because pyfakefs cannot patch the underlying C libraries'
file access functions--the C libraries will always access the real file
system. Refer to the
[documentation](https://pytest-pyfakefs.readthedocs.io/en/latest/intro.html#limitations)
for more information about the limitations of pyfakefs.

## Development

### Continuous integration

pyfakefs is currently automatically tested on Linux, macOS and Windows, with
Python 3.7 to 3.12, and with PyPy3 on Linux, using
[GitHub Actions](https://github.com/pytest-dev/pyfakefs/actions).

### Running pyfakefs unit tests

#### On the command line
pyfakefs unit tests can be run using `pytest` (all tests) or `unittest`
(all tests except `pytest`-specific ones):

```bash
$ cd pyfakefs/
$ export PYTHONPATH=$PWD

$ python -m pytest pyfakefs
$ python -m pyfakefs.tests.all_tests
```

Similar scripts are called by `tox` and Github Actions. `tox` can be used to
run tests locally against supported python versions:

```bash
$ tox
```

#### In a Docker container

The `Dockerfile` at the repository root will run the tests on the latest
Ubuntu version.  Build the container:
```bash
cd pyfakefs/
docker build -t pyfakefs .
```
Run the unit tests in the container:
```bash
docker run -t pyfakefs
```

### Contributing to pyfakefs

We always welcome contributions to the library. Check out the
[Contributing Guide](https://github.com/pytest-dev/pyfakefs/blob/main/CONTRIBUTING.md)
for more information.

## History
pyfakefs.py was initially developed at Google by Mike Bland as a modest fake
implementation of core Python modules.  It was introduced to all of Google
in September 2006. Since then, it has been enhanced to extend its
functionality and usefulness.  At last count, pyfakefs was used in over 20,000
Python tests at Google.

Google released pyfakefs to the public in 2011 as Google Code project
[pyfakefs](http://code.google.com/p/pyfakefs/):
* Fork
  [jmcgeheeiv-pyfakefs](http://code.google.com/p/jmcgeheeiv-pyfakefs/) added
  [direct support for unittest and doctest](../../wiki/Automatically-find-and-patch-file-functions-and-modules)
* Fork
  [shiffdane-jmcgeheeiv-pyfakefs](http://code.google.com/p/shiffdane-jmcgeheeiv-pyfakefs/)
  added further corrections

After the [shutdown of Google Code](http://google-opensource.blogspot.com/2015/03/farewell-to-google-code.html)
was announced, [John McGehee](https://github.com/jmcgeheeiv) merged all three Google Code projects together
[here on GitHub](https://github.com/pytest-dev/pyfakefs) where an enthusiastic community actively supports, maintains
and extends pyfakefs. In 2022, the repository has been transferred to
[pytest-dev](https://github.com/pytest-dev) to ensure continuous maintenance.


# GaNDLF

The **G**ener**a**lly **N**uanced **D**eep **L**earning **F**ramework for segmentation, regression and classification.

## Why use this?

- Supports multiple
  - Deep Learning model architectures
  - Data dimensions (2D/3D)
  - Channels/images/sequences
  - Prediction classes
  - Domain modalities (i.e., Radiology Scans and Digitized Histopathology Tissue Sections)
  - Problem types (segmentation, regression, classification)
  - Multi-GPU (on same machine) training
- Built-in
  - Nested cross-validation (and related combined statistics)
  - Support for parallel HPC-based computing
  - Support for training check-pointing
  - Support for [Automatic mixed precision](https://pytorch.org/blog/accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision/)
- Robust data augmentation, courtesy of [TorchIO](https://github.com/fepegar/torchio/)
- Handles imbalanced classes (e.g., very small tumor in large organ)
- Leverages robust open source software
- No need to write any code to generate robust models

## Citation

Please cite the following article for GaNDLF ([full paper](https://www.nature.com/articles/s44172-023-00066-3)):

```bib
@article{pati2023gandlf,
    author={Pati, Sarthak and Thakur, Siddhesh P. and Hamamc{\i}, {\.{I}}brahim Ethem and Baid, Ujjwal and Baheti, Bhakti and Bhalerao, Megh and G{\"u}ley, Orhun and Mouchtaris, Sofia and Lang, David and Thermos, Spyridon and Gotkowski, Karol and Gonz{\'a}lez, Camila and Grenko, Caleb and Getka, Alexander and Edwards, Brandon and Sheller, Micah and Wu, Junwen and Karkada, Deepthi and Panchumarthy, Ravi and Ahluwalia, Vinayak and Zou, Chunrui and Bashyam, Vishnu and Li, Yuemeng and Haghighi, Babak and Chitalia, Rhea and Abousamra, Shahira and Kurc, Tahsin M. and Gastounioti, Aimilia and Er, Sezgin and Bergman, Mark and Saltz, Joel H. and Fan, Yong and Shah, Prashant and Mukhopadhyay, Anirban and Tsaftaris, Sotirios A. and Menze, Bjoern and Davatzikos, Christos and Kontos, Despina and Karargyris, Alexandros and Umeton, Renato and Mattson, Peter and Bakas, Spyridon},
    title={GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows},
    journal={Communications Engineering},
    year={2023},
    month={May},
    day={16},
    volume={2},
    number={1},
    pages={23},
    abstract={Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows.},
    issn={2731-3395},
    doi={10.1038/s44172-023-00066-3},
    url={https://doi.org/10.1038/s44172-023-00066-3}
}
```

## Documentation

GaNDLF has extensive documentation and it is arranged in the following manner:

- [Home](https://mlcommons.github.io/GaNDLF/)
- [Installation](https://mlcommons.github.io/GaNDLF/setup)
- [Usage](https://mlcommons.github.io/GaNDLF/usage)
- [Extension](https://mlcommons.github.io/GaNDLF/extending)
- [Frequently Asked Questions](https://mlcommons.github.io/GaNDLF/faq)
- [Acknowledgements](https://mlcommons.github.io/GaNDLF/acknowledgements)

## Contributing

Please see the [contributing guide](./CONTRIBUTING.md) for more information.

### Weekly Meeting

The GaNDLF development team hosts a weekly meeting to discuss feature additions, issues, and general future directions. If you are interested to join, please send us an email!

## Disclaimer
- The software has been designed for research purposes only and has neither been reviewed nor approved for clinical use by the Food and Drug Administration (FDA) or by any other federal/state agency.
- This code (excluding dependent libraries) is governed by [the Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) provided in the [LICENSE file](./LICENSE) unless otherwise specified.

## Contact
For more information or any support, please post on the [Discussions](https://github.com/mlcommons/GaNDLF/discussions) section.