Python

Installation

• Individual package installation

• Enthough Canopy

• Miniconda (preferred)

IDE

• Pycharm

• Interactive debugging using iPython: http://www.scipy-lectures.org/advanced/debugging/#using-the-python-debugger

• iPython

Virtual Environments

1. Create a virtual environment for a project:

$ cd my_project_folder
$ virtualenv venv

Or to specify a Python version:

$ virtualenv venv -p <PATH_TO_PYTHON>

2. To begin using the virtual environment, it needs to be activated:

 
 $ source my_project/bin/activate

3. If you are done working in the virtual environment for the moment, you can deactivate it:

 $ deactivate

Packaging Code

Suggest workflow via Github Actions:

• on Pypi, create a Token for the “organization” with “global” scope

• on Github > Settings > Secrets > Add a secrets.

  • Warning: if you created the token at the organization level, you should create a secrets for the organization (not the repos)

• Create a Github action template for “publishing”

• Github uses twine to publish the package, everytime a release is created (does not work for draft releases)

• Edit the .yml file with the credentials for connecting to Pypi

  • Example: neuropoly/gifmaker

• To debug, you can create the package locally and verify its integrity:

  • Install twine: pip install twine

  • Build the distribution: python setup.py sdist bdist_wheel

  • Check integrity: twine check dist/*

Useful Packages

NeuroImaging

• Nipype

• Dipy - Diffusion imaging in Python

• NiLearn

Visualization

• PySurfer

• NiLearn

• The Connectome Viewer Toolkit

• Mayavi - 3D data visualization

• PyMVPA

• BrainVisa

Courses and Tutorials

• Learn Python with Socratica

• Excellent book on Python, git and Unix Shell, addressed to researchers

• Excellent notes on Python

• Matlab to Python(Numpy) cheat sheet

• Scipy lectures

• Presentation by Martin Luessi

• Commands

• 2D and 3D plotting using matlplotlib

Resources

• https://www.geeksforgeeks.org/command-line-scripts-python-packaging/

• Problem Solving with Algorithms and Data Structures using Python

Tips and Tricks

Nice Packages

- pytest-sugar: Nice visualization for pytestEdit

Matplotlib

Display 2d array:

from matplotlib.pylab import *
matshow(data2d, fignum=1, cmap=cm.gray), plt.colorbar(), show()

Interactive mode (can display several figures without freezing the process)

import matplotlib.pyplot as plt
plt.ion()
plt.plot([1.6, 2.7])
plt.draw()
# Turn interactive mode off
plt.ioff()

Using oriented-object approach (force backend)

from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.matshow(data2d, cmap='gray')
fig.savefig('myfig.png')