Welcome to mplview’s documentation!

Contents:

mplview

A simple, embeddable Matplotlib-based image viewer.

• Free software: BSD 3-Clause
• Documentation: https://mplview.readthedocs.io.

Features

• TODO

Credits

This package was created with Cookiecutter and the nanshe-org/nanshe-cookiecutter project template.

Installation

Stable release

To install mplview, run this command in your terminal:

$ pip install mplview

This is the preferred method to install mplview, 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 mplview can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/jakirkham/mplview

Or download the tarball:

$ curl  -OL https://github.com/jakirkham/mplview/tarball/master

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

$ python setup.py install

Usage

To use mplview in a project:

import mplview

API

mplview package

Submodules

mplview.core module

class mplview.core.MatplotlibViewer(*args, **kwargs)

Bases: matplotlib.figure.Figure

Provides a way to interact with numpy arrays pulled from neuron images.

Wraps a Matplotlib figure instance.

color_range_update(vmin, vmax)

Handles an update to the vmin and vmax range based on the selection provided.

Parameters:

• the min value selected (vmin) –
• the max value selected (vmax) –

format_coord(x, y)

Include intensity when showing coordinates during mouseover.

Parameters:

• x (float) – cursor’s x position within the image.
• y (float) – cursor’s y position within the image.

Returns:

coordinates and intensity if it can be gotten.

Return type:

str

get_image(i=None)

Gets the current image or the image if it is a projection.

Parameters:i (int) – image to retrieve (defaults to selection). Returns:the current image. Return type:numpy.ndarray

set_images(new_neuron_images, cmap=<matplotlib.colors.LinearSegmentedColormap object>, use_matshow=False, vmin=None, vmax=None)

Sets the images to be viewed.

Parameters:new_neuron_images (numpy.ndarray) – array of images (first index is which image)

time_update()

Method to be called by the SequenceNavigator when the time changes. Updates image displayed.

class mplview.core.SequenceNavigator(fig, max_time, min_time=0, time_step=1, axcolor='lightgoldenrodyellow', hovercolor='0.975')

Bases: object

begin_time(event)

Sets time to min_time.

Parameters:Matplotlib event that caused the call to this callback. (event) –

disconnect(cid)

Disconnects the given cid from being notified of time updates.

Parameters:ID of callback to pull (cid) –

end_time(event)

Sets time to max_time.

Parameters:Matplotlib event that caused the call to this callback. (event) –

next_time(event)

Sets time to one time_step after.

Parameters:Matplotlib event that caused the call to this callback. (event) –

normalize_val(val)

Takes the time value and normalizes it to fit within the range. Then, makes sure it is a discrete number of steps from the min_time.

Parameters:float position from the slider bar to correct (val) – Returns:the normalized value. Return type:int

on_time_update(func)

Registers a callback function for notification when the time is updated.

Parameters:func (callable) – function call when the time is updated Returns:

a callback ID or cid to allow pulling the

callback when no longer necessary.

Return type:int

prev_time(event)

Sets time to one time_step prior.

Parameters:Matplotlib event that caused the call to this callback. (event) –

time_update(val)

Takes the time value and normalizes it within the range if it does not fit.

Parameters:float position from slider bar to move to (val) –

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/jakirkham/mplview/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

mplview could always use more documentation, whether as part of the official mplview 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/jakirkham/mplview/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 mplview for local development.

1. Fork the mplview repo on GitHub.

2. Clone your fork locally:

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

3. Install your local copy into an environment. Assuming you have conda installed, this is how you set up your fork for local development (on Windows drop source). Replace “<some version>” with the Python version used for testing.:

   $ conda create -n mplviewenv python="<some version>"
   $ source activate mplviewenv
   $ 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:

   $ flake8 mplview tests
   $ python setup.py test or py.test
   

   To get flake8, just conda install it into your environment.

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 2.7, 3.4, 3.5, and 3.6. Check https://travis-ci.org/jakirkham/mplview/pull_requests and make sure that the tests pass for all supported Python versions.

Tips

To run a subset of tests:

$ python -m unittest tests.test_mplview

Indices and tables

• Index
• Module Index
• Search Page