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FISSA

FISSA (Fast Image Signal Separation Analysis) is a Python library for decontaminating somatic signals from two-photon calcium imaging data. It can read images in tiff format and ROIs in zips as exported by ImageJ; or operate with numpy arrays directly, which can be produced by importing files stored in other formats.

For details of the algorithm, please see our companion paper published in Scientific Reports. For the code used to generate the simulated data in the companion paper, see the SimCalc repository.

FISSA is compatible with both Python 2.7 and Python 3.5+. Using Python 3 is strongly encouraged, as Python 2 will no longer be maintained starting January 2020.

FISSA has been tested on Ubuntu 17.04 and on Windows Windows 10 with the Anaconda distribution.

Documentation, including the full API, is available online at https://fissa.readthedocs.io.

If you encounter a specific problem please open a new issue. For general discussion and help with installation or setup, please see the Gitter chat.

Usage

A general tutorial on the use of FISSA can be found at: https://rochefort-lab.github.io/fissa/examples/Basic%20usage.html

An example workflow with another Python toolbox (SIMA): https://rochefort-lab.github.io/fissa/examples/SIMA%20example.html

An example workflow importing data exported from a MATLAB toolbox (cNMF): https://rochefort-lab.github.io/fissa/examples/cNMF%20example.html

These notebooks can also be run on your own machine. To do so, you will need to download a copy of the repository, unzip it and browse to the examples directory. Then, start up a jupyter notebook server to run our notebooks. If you’re new to jupyter notebooks, an approachable tutorial can be found at https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook.

Installation

Installation on Windows

Basic prerequisites

Download and install, in the following order:

Installing FISSA

Open Anaconda Prompt.exe, which can be found through the Windows start menu or search, and type or copy-paste (by right clicking) the following:

conda install -c conda-forge shapely tifffile

Then, install FISSA by running the command

pip install fissa

You can check to see if FISSA is installed by running the command

python -c "import fissa; print(fissa.__version__)"

You will see your FISSA version number printed in the terminal.

If you want to use the interactive plotting from the notebooks, you should also install the HoloViews plotting toolbox, as follows

conda install -c ioam holoviews

See usage above for details on how to use FISSA.

Installation on Linux

Before installing FISSA, you will need to make sure you have all of its dependencies (and the dependencies of its dependencies) installed.

Here we will outline how to do all of these steps, assuming you already have both Python and pip installed. It is highly likely that your Linux distribution ships with these.

Dependencies of dependencies

These packages can be installed on Debian/Ubuntu with the following shell commands.

sudo apt-get update
sudo apt-get install gfortran libopenblas-dev liblapack-dev libatlas-dev libatlas-base-dev
sudo apt-get install libgeos-dev
sudo apt-get install libjpeg-dev
Installing FISSA

For normal usage of FISSA, you can install the latest release version on PyPI using pip:

pip install fissa

To also install fissa along with the dependencies required to run our sample notebooks (which include plots rendered with holoviews) you should run the following command:

pip install fissa['plotting']

You can check to see if FISSA is installed by running the command

python -c "import fissa; print(fissa.__version__)"

You will see your FISSA version number printed in the terminal.

Folder Structure

A clone of this repository will contain directories detailed below.

docs/

Contains the source for the documentation, which is available online at https://fissa.readthedocs.io. You can build a local copy of the documentation by running the command

make -C docs html

examples/

Contains example code. You can load the notebooks as .ipynb directly in GitHub, or on your system if you know how to use jupyter notebooks.

examples/exampleData/

Contains example data. It a zipfile with region of interests from ImageJ. It also contains three tiff stacks, which have been downsampled and cropped from full data from the Rochefort lab.

fissa/

Contains the toolbox.

fissa/tests/

Contains tests for the toolbox, which are run to ensure it will work as expected.

.ci/

Contains files necessary for deploying tests on continuous integration servers. Users should ignore this directory.

Citing FISSA

If you use FISSA for your research, please cite the following paper in any resulting publications:

S. W. Keemink, S. C. Lowe, J. M. P. Pakan, E. Dylda, M. C. W. van Rossum, and N. L. Rochefort. FISSA: A neuropil decontamination toolbox for calcium imaging signals, Scientific Reports, 8(1):3493, 2018. doi: 10.1038/s41598-018-21640-2.

For your convenience, the FISSA package ships with a copy of this citation in bibtex format, available at citation.bib.

License

Unless otherwise stated in individual files, all code is Copyright (c) 2015, Sander Keemink, Scott Lowe, and Nathalie Rochefort. All rights reserved.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

API Reference

fissa package

Submodules

fissa.ROI module

The functions below were adapted from the sima package http://www.losonczylab.org/sima version 1.3.0.

License

This file is Copyright (C) 2014 The Trustees of Columbia University in the City of New York.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.

fissa.ROI.poly2mask(polygons, im_size)[source]

Converts polygons to a sparse binary mask.

>>> from fissa.ROI import poly2mask
>>> poly1 = [[0,0], [0,1], [1,1], [1,0]]
>>> poly2 = [[0,1], [0,2], [2,2], [2,1]]
>>> mask = poly2mask([poly1, poly2], (3, 3))
>>> mask[0].todense()
matrix([[ True, False, False],
        [ True,  True, False],
        [False, False, False]], dtype=bool)
Parameters:
  • polygons (sequence of coordinates or sequence of Polygons) – A sequence of polygons where each is either a sequence of (x,y) or (x,y,z) coordinate pairs, an Nx2 or Nx3 numpy array, or a Polygon object.
  • im_size (tuple) – Final size of the resulting mask
Returns:

masks – A list of sparse binary masks of the points contained within the polygons, one mask per plane. Each mask is in linked list sparse matrix format.
Return type:

list of sparse matrices
fissa.core module

Main user interface for FISSA.

Authors:
class fissa.core.Experiment(images, rois, folder, nRegions=4, expansion=1, alpha=0.1, ncores_preparation=None, ncores_separation=None, method='nmf', lowmemory_mode=False, datahandler_custom=None)[source]

Bases: object

Does all the steps for FISSA.

__init__(images, rois, folder, nRegions=4, expansion=1, alpha=0.1, ncores_preparation=None, ncores_separation=None, method='nmf', lowmemory_mode=False, datahandler_custom=None)[source]

Initialisation. Set the parameters for your Fissa instance.

Parameters:
  • images (str or list) –

    The raw recording data. Should be one of:

    • the path to a directory containing TIFF files (string),
    • an explicit list of TIFF files (list of strings),
    • a list of array_like data already loaded into memory, each shaped (frames, y-coords, x-coords).

    Note that each TIFF/array is considered a single trial.

  • rois (str or list) –

    The roi definitions. Should be one of:

    • the path to a directory containing ImageJ ZIP files (string),
    • the path of a single ImageJ ZIP file (string),
    • a list of ImageJ ZIP files (list of strings),
    • a list of arrays, each encoding a ROI polygons,
    • a list of lists of binary arrays, each representing a ROI mask.

    This can either be a single roiset for all trials, or a different roiset for each trial.

  • folder (str) – Output path to a directory in which the extracted data will be stored.
  • nRegions (int, optional) – Number of neuropil regions to draw. Use a higher number for densely labelled tissue. Default is 4.
  • expansion (float, optional) – Expansion factor for the neuropil region, relative to the ROI area. Default is 1. The total neuropil area will be nRegions * expansion * area(ROI).
  • alpha (float, optional) – Sparsity regularizaton weight for NMF algorithm. Set to zero to remove regularization. Default is 0.1. (Not used for ICA method.)
  • ncores_preparation (int, optional (default: None)) – Sets the number of subprocesses to be used during the data preparation steps (ROI and subregions definitions, data extraction from tifs, etc.). If set to None (default), there will be as many subprocesses as there are threads or cores on the machine. Note that this behaviour can, especially for the data preparation step, be very memory-intensive.
  • ncores_separation (int, optional (default: None)) – Same as ncores_preparation, but for the separation step. Note that this step requires less memory per subprocess, and hence can often be set higher than ncores_preparation.
  • method ({'nmf', 'ica'}, optional) – Which blind source-separation method to use. Either ‘nmf’ for non-negative matrix factorization, or ‘ica’ for independent component analysis. Default (recommended) is ‘nmf’.
  • lowmemory_mode (bool, optional) – If True, FISSA will load TIFF files into memory frame-by-frame instead of holding the entire TIFF in memory at once. This option reduces the memory load, and may be necessary for very large inputs. Default is False.
  • datahandler_custom (object, optional) – A custom datahandler for handling ROIs and calcium data can be given here. See datahandler.py (the default handler) for an example.
calc_deltaf(freq, use_raw_f0=True, across_trials=True)[source]

Calculate deltaf/f0 for raw and result traces.

The results can be accessed as self.deltaf_raw and self.deltaf_result. self.deltaf_raw is only the ROI trace instead of the traces across all subregions.

Parameters:
  • freq (float) – Imaging frequency, in Hz.
  • use_raw_f0 (bool, optional) – If True (default), use an f0 estimate from the raw ROI trace for both raw and result traces. If False, use individual f0 estimates for each of the traces.
  • across_trials (bool, optional) – If True, we estimate a single baseline f0 value across all trials. If False, each trial will have their own baseline f0, and df/f0 value will be relative to the trial-specific f0. Default is True.
save_to_matlab()[source]

Save the results to a matlab file.

Can be found in folder/matlab.mat.

This will give you a filename.mat file which if loaded in Matlab gives the following structs: ROIs, result, raw.

If df/f0 was calculated, these will also be stored as df_result and df_raw, which will have the same format as result and raw.

These can be interfaced with as follows, for cell 0, trial 0:

  • ROIs.cell0.trial0{1} polygon for the ROI
  • ROIs.cell0.trial0{2} polygon for first neuropil region
  • result.cell0.trial0(1,:) final extracted cell signal
  • result.cell0.trial0(2,:) contaminating signal
  • raw.cell0.trial0(1,:) raw measured celll signal
  • raw.cell0.trial0(2,:) raw signal from first neuropil region
separate(redo_prep=False, redo_sep=False)[source]

Separate all the trials with FISSA algorithm.

After running separate, data can be found as follows:

self.sep
Raw separation output, without being matched. Signal i for a specific cell and trial can be found as self.sep[cell][trial][i,:].
self.result
Final output, in order of presence in cell ROI. Signal i for a specific cell and trial can be found at self.result[cell][trial][i, :]. Note that the ordering is such that i = 0 is the signal most strongly present in the ROI, and subsequent entries are in diminishing order.
self.mixmat
The mixing matrix (how to go between self.separated and self.raw from the separation_prep() function).
self.info
Information about separation routine, iterations needed, etc.
Parameters:
  • redo_prep (bool, optional) – Whether to redo the preparation. Default is False. Note that if this is true, we set redo_sep = True as well.
  • redo_sep (bool, optional) – Whether to redo the separation. Default is False. Note that this parameter is ignored if redo_prep is set to True.
separation_prep(redo=False)[source]

Prepare and extract the data to be separated.

For each trial, performs the following steps:

  • Load in data as arrays
  • Load in ROIs as masks
  • Grow and seaparate ROIs to define neuropil regions
  • Using neuropil and original ROI regions, extract traces from data

After running this you can access the raw data (i.e. pre-separation) as self.raw and self.rois. self.raw is a list of arrays. self.raw[cell][trial] gives you the traces of a specific cell and trial, across cell and neuropil regions. self.roi_polys is a list of lists of arrays. self.roi_polys[cell][trial][region][0] gives you the polygon for the region for a specific cell, trial and region. region=0 is the cell, and region>0 gives the different neuropil regions. For separateable masks, it is possible multiple outlines are found, which can be accessed as self.roi_polys[cell][trial][region][i], where i is the outline index.

Parameters:redo (bool, optional) – If False, we load previously prepared data when possible. If True, we re-run the preparation, even if it has previously been run. Default is False.
fissa.core.extract_func(inputs)[source]

Extract data using multiprocessing.

Parameters:inputs (list) –

list of inputs

  1. image array
  2. the rois
  3. number of neuropil regions
  4. how much larger neuropil region should be then central ROI
Returns:
  • dict – Data across cells.
  • dict – Polygons for each ROI.
fissa.core.separate_func(inputs)[source]

Extraction function for multiprocessing.

Parameters:inputs (list) –

list of inputs

  1. Array with signals to separate
  2. Alpha input to npil.separate
  3. Method
  4. Current ROI number
Returns:
  • numpy.ndarray – The raw separated traces.
  • numpy.ndarray – The separated traces matched to the primary signal.
  • numpy.ndarray – Mixing matrix.
  • dict – Metadata for the convergence result.
fissa.datahandler module

FISSA functions to handle image and roi objects and return the right format.

If a custom version of this file is used (which can be defined at the declaration of the core FISSA Experiment class), it should have the same functions as here, with the same inputs and outputs.

Authors:
fissa.datahandler.extracttraces(data, masks)[source]

Extracts a temporal trace for each spatial mask.

Parameters:
  • data (array_like) – Data array as made by image2array. Should be shaped (frames, y, x).
  • masks (list of array_like) – List of binary arrays.
Returns:

Trace for each mask. Shaped (len(masks), n_frames).
Return type:

numpy.ndarray
fissa.datahandler.getmean(data)[source]

Determine the mean image across all frames.

Parameters:data (array_like) – Data array as made by image2array. Should be shaped (frames, y, x).
Returns:y by x array for the mean values
Return type:numpy.ndarray
fissa.datahandler.image2array(image)[source]

Loads a TIFF image from disk.

Parameters:image (str or array_like) – Either a path to a TIFF file, or array_like data.
Returns:A 3D array containing the data, with dimensions corresponding to (frames, y_coordinate, x_coordinate).
Return type:numpy.ndarray
fissa.datahandler.rois2masks(rois, data)[source]

Take the object rois and returns it as a list of binary masks.

Parameters:
  • rois (string or list of array_like) – Either a string with imagej roi zip location, list of arrays encoding polygons, or list of binary arrays representing masks
  • data (array) – Data array as made by image2array. Must be shaped (frames, y, x).
Returns:

List of binary arrays (i.e. masks)
Return type:

list
fissa.datahandler_framebyframe module

FISSA functions to handle image and roi objects and return the right format.

If a custom version of this file is used (which can be defined at the declaration of the core FISSA Experiment class), it should have the same functions as here, with the same inputs and outputs.

Authors:
fissa.datahandler_framebyframe.extracttraces(data, masks)[source]

Get the traces for each mask in masks from data.

Parameters:
  • data (PIL.Image) – An open PIL.Image handle to a multi-frame TIFF image.
  • masks (list of array_like) – List of binary arrays.
Returns:

Trace for each mask. Shaped (len(masks), n_frames).
Return type:

numpy.ndarray
fissa.datahandler_framebyframe.getmean(data)[source]

Determine the mean image across all frames.

Parameters:data (PIL.Image) – An open PIL.Image handle to a multi-frame TIFF image.
Returns:y-by-x array for the mean values.
Return type:numpy.ndarray
fissa.datahandler_framebyframe.image2array(image)[source]

Open a given image file as a PIL.Image instance.

Parameters:image (str or file) – A filename (string) of a TIFF image file, a pathlib.Path object, or a file object.
Returns:Handle from which frames can be loaded.
Return type:PIL.Image
fissa.datahandler_framebyframe.rois2masks(rois, data)[source]

Take the object ‘rois’ and returns it as a list of binary masks.

Parameters:
  • rois (str or list of array_like) – Either a string with imagej roi zip location, list of arrays encoding polygons, or list of binary arrays representing masks
  • data (PIL.Image) – An open PIL.Image handle to a multi-frame TIFF image.
Returns:

List of binary arrays (i.e. masks).
Return type:

list
fissa.deltaf module

Functions for computing correcting fluorescence signals for changes in baseline activity.

Authors:
  • Scott C Lowe
fissa.deltaf.findBaselineF0(rawF, fs, axis=0, keepdims=False)[source]

Find the baseline for a fluorescence imaging trace line.

The baseline, F0, is the 5th-percentile of the 1Hz lowpass filtered signal.

Parameters:
  • rawF (array_like) – Raw fluorescence signal.
  • fs (float) – Sampling frequency of rawF, in Hz.
  • axis (int, optional) – Dimension which contains the time series. Default is 0.
  • keepdims (bool, optional) – Whether to preserve the dimensionality of the input. Default is False.
Returns:

baselineF0 – The baseline fluorescence of each recording, as an array.
Return type:

numpy.ndarray

Note

In typical usage, the input rawF is expected to be sized (numROI, numTimePoints, numRecs) and the output will then be sized (numROI, 1, numRecs) if keepdims is True.

fissa.neuropil module

Functions for removal of neuropil from calcium signals.

Authors:
Created:
2015-05-15
fissa.neuropil.lowPassFilter(F, fs=40, nfilt=40, fw_base=10, axis=0)[source]

Low pass filters a fluorescence imaging trace line.

Parameters:
  • F (array_like) – Fluorescence signal.
  • fs (float, optional) – Sampling frequency of F, in Hz. Default is 40.
  • nfilt (int, optional) – Number of taps to use in FIR filter. Default is 40.
  • fw_base (float, optional) – Cut-off frequency for lowpass filter, in Hz. Default is 10.
  • axis (int, optional) – Along which axis to apply low pass filtering. Default is 0.
Returns:

Low pass filtered signal with the same shape as F.
Return type:

numpy.ndarray
fissa.neuropil.separate(S, sep_method='nmf', n=None, maxiter=10000, tol=0.0001, random_state=892, maxtries=10, W0=None, H0=None, alpha=0.1)[source]

For the signals in S, finds the independent signals underlying it, using ica or nmf.

Parameters:
  • S (array_like) – 2-d array containing mixed input signals. Each column of S should be a different signal, and each row an observation of the signals. For S[i,j], j = each signal, i = signal content. The first column, j = 0, is considered the primary signal and the one for which we will try to extract a decontaminated equivalent.
  • sep_method ({'ica','nmf'}) –

    Which source separation method to use, either ICA or NMF.

    • ’ica’: Independent Component Analysis
    • ’nmf’: Non-negative Matrix Factorization
  • n (int, optional) – How many components to estimate. If None (default), use PCA to estimate how many components would explain at least 99% of the variance and adopt this value for n.
  • maxiter (int, optional) – Number of maximally allowed iterations. Default is 500.
  • tol (float, optional) – Error tolerance for termination. Default is 1e-5.
  • random_state (int, optional) – Initial state for the random number generator. Set to None to use the numpy.random default. Default seed is 892.
  • maxtries (int, optional) – Maximum number of tries before algorithm should terminate. Default is 10.
  • W0 (array_like, optional) – Optional starting condition for W in NMF algorithm. (Ignored when using the ICA method.)
  • H0 (array_like, optional) – Optional starting condition for H in NMF algorithm. (Ignored when using the ICA method.)
  • alpha (float, optional) – Sparsity regularizaton weight for NMF algorithm. Set to zero to remove regularization. Default is 0.1. (Ignored when using the ICA method.)
Returns:

  • numpy.ndarray – The raw separated traces.
  • numpy.ndarray – The separated traces matched to the primary signal, in order of matching quality (see Methods below).
  • numpy.ndarray – Mixing matrix.
  • dict – Metadata for the convergence result, with keys:
    • ’random_state’: seed for ICA initiation
    • ’iterations’: number of iterations needed for convergence
    • ’max_iterations’: maximum number of iterations allowed
    • ’converged’: whether the algorithm converged or not (bool)

Notes

Concept by Scott Lowe and Sander Keemink. Normalize the columns in estimated mixing matrix A so that sum(column)=1. This results in a relative score of how strongly each separated signal is represented in each ROI signal.

fissa.readimagejrois module

Based on code originally written by Luis Pedro Coelho <luis@luispedro.org>, 2012, available at https://gist.github.com/luispedro/3437255, distributed under the MIT License.

Modified
fissa.readimagejrois.read_imagej_roi_zip(filename)[source]

Reads an ImageJ ROI zip set and parses each ROI individually

Parameters:filename (str) – Path to the ImageJ ROis zip file
Returns:roi_list – List of the parsed ImageJ ROIs
Return type:list
fissa.readimagejrois.read_roi(roi_obj)[source]

Parses an individual ImageJ ROI

This is based on the Java implementation: http://rsbweb.nih.gov/ij/developer/source/ij/io/RoiDecoder.java.html

Parameters:

roi_obj (file object) – File object containing a single ImageJ ROI
Returns:

Returns a parsed ROI object, a dictionary with either a ‘polygons’ or a ‘mask’ field.
Return type:

dict
Raises:
  • IOError – If there is an error reading the roi file object
  • ValueError – If unable to parse ROI
fissa.roitools module

Functions used for ROI manipulation.

Authors:
fissa.roitools.find_roi_edge(mask)[source]

Finds the outline of a mask, using the find_contour function from skimage.measure.

Parameters:mask (array_like) – the mask, a binary array
Returns:outline – Array with coordinates of pixels in the outline of the mask
Return type:list of (n,2)-ndarrays
fissa.roitools.get_mask_com(mask)[source]

Get the center of mass for a boolean mask.

Parameters:mask (array_like) – A two-dimensional boolean-mask.
Returns:
  • float – Center of mass along first dimension.
  • float – Center of mass along second dimension.
fissa.roitools.get_npil_mask(mask, totalexpansion=4)[source]

Given the masks for a ROI, find the surrounding neuropil.

Parameters:
  • mask (array_like) – The reference ROI mask to expand the neuropil from. The array should contain only boolean values.
  • expansion (float, optional) – How much larger to make the neuropil total area than mask area.
Returns:

A boolean numpy.ndarray mask, where the region surrounding the input is now True and the region of the input mask is False.
Return type:

numpy.ndarray

Notes

Our implementation is as follows:

  • On even iterations (where indexing begins at zero), expand the mask in each of the 4 cardinal directions.
  • On odd numbered iterations, expand the mask in each of the 4 diagonal directions.

This procedure generates a neuropil whose shape is similar to the shape of the input ROI mask.

Note

For fixed number of iterations, squarer input masks will have larger output neuropil masks.

fissa.roitools.getmasks(rois, shpe)[source]

Get the masks for the specified rois.

Parameters:
  • rois (list) – list of roi coordinates. Each roi coordinate should be a 2d-array or equivalent list. I.e.: roi = [[0,0], [0,1], [1,1], [1,0]] or roi = np.array([[0,0], [0,1], [1,1], [1,0]]) I.e. a n by 2 array, where n is the number of coordinates. If a 2 by n array is given, this will be transposed.
  • shpe (array/list) – shape of underlying image [width,height]
Returns:

List of masks for each roi in the rois list
Return type:

list of numpy.ndarray
fissa.roitools.getmasks_npil(cellMask, nNpil=4, expansion=1)[source]

Generate neuropil masks using the get_npil_mask function.

Parameters:
  • cellMask (array_like) – the cell mask (boolean 2d arrays)
  • nNpil (int) – number of neuropil subregions
  • expansion (float) – How much larger to make neuropil subregion area than in cellMask
Returns:

Returns a list with soma + neuropil masks (boolean 2d arrays)
Return type:

list
fissa.roitools.readrois(roiset)[source]

read the imagej rois in the zipfile roiset, and make sure that the third dimension (i.e. frame number) is always zero.

Parameters:roiset (str) – folder to a zip file with rois
Returns:Returns the rois as polygons
Return type:list
fissa.roitools.shift_2d_array(a, shift=1, axis=None)[source]

Shifts an entire array in the direction of axis by the amount shift, without refilling the array.

Parameters:
  • a (array_like) – Input array.
  • shift (int, optional) – How much to shift array by. Default is 1.
  • axis (int, optional) – The axis along which elements are shifted. By default, the array is flattened before shifting, after which the original shape is restored.
Returns:

Array with the same shape as a, but shifted appropriately.
Return type:

numpy.ndarray
fissa.roitools.split_npil(mask, centre, num_slices, adaptive_num=False)[source]

Splits a mask into a number of approximately equal slices by area around the center of the mask.

Parameters:
  • mask (array_like) – Mask as a 2d boolean array.
  • centre (tuple) – The center co-ordinates around which the mask will be split.
  • num_slices (int) – The number of slices into which the mask will be divided.
  • adaptive_num (bool, optional) – If True, the num_slices input is treated as the number of slices to use if the ROI is surrounded by valid pixels, and automatically reduces the number of slices if it is on the boundary of the sampled region.
Returns:

A list with num_slices many masks, each of which is a 2d boolean numpy array.
Return type:

list

Contributing code

Reporting Issues

If you encounter a problem when implementing or using FISSA, we want to hear about it!

Gitter

To get help resolving implementation difficulties, or similar one-off problems, please ask for help on our gitter channel.

Reporting Bugs and Issues

If you experience a bug, please report it by opening a new issue. When reporting issues, please include code that reproduces the issue and whenever possible, an image that demonstrates the issue. The best reproductions are self-contained scripts with minimal dependencies.

Make sure you mention the following things:

  • What did you do?
  • What did you expect to happen?
  • What actually happened?
  • What versions of FISSA and Python are you using, and on which operating system?

Feature requests

If you have a new feature or enhancement to an existing feature you would like to see implemented, please check the list of existing issues and if you can’t find it make a new issue to request it. If you do find it in the list, you can post a comment saying +1 (or :+1: if you are a fan of emoticons) to indicate your support for this feature.

Documentation

We are glad to accept any sort of documentation: function docstrings, tutorials, Jupyter notebooks demonstrating implementation details, etc.

reStructuredText documents and notebooks live in the source code repository under the doc/ directory.

Docstrings

Documentation for classes and functions should follow the format prescribed for numpy.

A complete example of this is available here.

How to contribute

The preferred way to contribute to FISSA is to fork the main repository on GitHub.

  1. Fork the project repository. Click on the ‘Fork’ button near the top of the page. This creates a copy of the code under your account on the GitHub server.

  2. Clone this copy to your local disk:

    $ git clone git@github.com:YourUserName/fissa.git
$ cd fissa

  3. Create a branch to hold and track your changes

    $ git checkout -b my-feature

    and start making changes.

  4. Work on this copy on your computer using Git to do the version control. When you’re done editing, do:

    $ git add modified_files
$ git commit

    to record your changes in Git, writing a commit message following the specifications below, then push them to GitHub with:

    $ git push -u origin my-feature

  5. Finally, go to the web page of your fork of the FISSA repo, and click ‘Pull request’ to issue a pull request and send your changes to the maintainers for review. This will also send a notification email to the committers.

If any of the above seems like magic to you, then look up the Git documentation on the web.

It is recommended to check that your contribution complies with the following rules before submitting a pull request.

  • All public functions and methods should have informative docstrings, with sample usage included in the doctest format when appropriate.

  • All unit tests pass. Check with (from the top level source folder):

    $ pip install pytest
$ py.test

  • Code with good unit test coverage (at least 90%, ideally 100%). Check with

    $ pip install pytest pytest-cov
$ py.test --cov=fissa --cov-config .coveragerc

    and look at the value in the ‘Cover’ column for any files you have added or amended.

    If the coverage value is too low, you can inspect which lines are or are not being tested by generating a html report. After opening this, you can navigate to the appropriate module and see lines which were not covered, or were only partially covered. If necessary, you can do this as follows:

    $ py.test --cov=fissa --cov-config .coveragerc --cov-report html --cov-report term-missing
$ sensible-browser ./htmlcov/index.html

  • No pyflakes warnings. Check with:

    $ pip install pyflakes
$ pyflakes path/to/module.py

  • No PEP8 warnings. Check with:

    $ pip install pep8
$ pep8 path/to/module.py

    AutoPEP8 can help you fix some of the easier PEP8 errors.

    $ pip install autopep8
$ autopep8 -i -a -a path/to/module.py

    Note that using the -i flag will modify your existing file in-place, so be sure to save any changes made in your editor beforehand.

These tests can be collectively performed in one line with:

$ pip install -r requirements-dev.txt
$ py.test --flake8 --cov=fissa --cov-config .coveragerc --cov-report html --cov-report term

Commit messages

Commit messages should be clear, precise and stand-alone. Lines should not exceed 72 characters.

It is useful to indicate the nature of your commits with a commit flag, as described in the numpy development guide.

You can use these flags at the start of your commit messages:

API: an (incompatible) API change
BLD: change related to building the package
BUG: bug fix
CI: change continuous integration build
DEP: deprecate something, or remove a deprecated object
DEV: development tool or utility
DOC: documentation; only change/add/remove docstrings, markdown or comments
ENH: enhancement; add a new feature without removing existing features
MAINT: maintenance commit (refactoring, typos, etc.); no functional change
REV: revert an earlier commit
RF: refactoring
STY: style fix (whitespace, PEP8)
TST: addition or modification of tests
REL: related to releases

Notes

This document was based on the contribution guidelines for sklearn, numpy and Pillow.

Changelog

All notable changes to this project will be documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Categories for changes are: Added, Changed, Deprecated, Removed, Fixed, Security.

Version 0.6.3

Release date: 2020-04-03. Full commit changelog on github.

Fixed

  • Specify a maximum version for the panel dependency of holoviews on Python <3.6, which allows us to continue supporting Python 3.5, otherwise dependencies fail to install. (#101)
  • Save deltaf to MATLAB compatible output. (#70)
  • Wipe downstream data stored in the experiment object if upstream data changes, so data that is present is always consistent with each other. (#93)
  • Prevent slashes in paths from doubling up if the input path has a trailing slash. (#71)
  • Documentation updates. (#91, #88, #97, #89)

Version 0.6.2

Release date: 2020-03-11. Full commit changelog on github.

Fixed

  • Specify a maximum version for tifffile dependency on Python <3.6, which allows us to continue supporting Python 2.7 and 3.5, which otherwise fail to import dependencies correctly. (#87)
  • Documentation fixes and updates. (#64, #65, #67, #76, #77, #78, #79, #92)

Version 0.6.1

Release date: 2019-03-11. Full commit changelog on github.

Fixed

  • Allow deltaf.findBaselineF0 to run with fewer than 90 samples, by reducing the pad-length if necessary. (#62)
  • Basic usage notebook wasn’t supplying the correct datahandler_custom argument for the custom datahandler (it was using datahandler instead, which is incorrect; this was silently ignored previously but will now trigger an error). (#62)
  • Use ncores_preparation for perparation step, not ncores_separation. (#59)
  • Only use ncores_separation for separation step, not all cores. (#59)
  • Allow both byte strings and unicode strings to be arguments of functions which require strings. Previously, byte strings were required on Python 2.7 and unicode strings on Python 3. (#60)

Version 0.6.0

Release date: 2019-02-26. Full commit changelog on github.

Added

  • Python 3 compatibility. (#33)
  • Documentation generation, with Sphinx, Sphinx-autodoc, and Napoleon. (#38)

Version 0.5.3

Release date: 2019-02-18. Full commit changelog on github.

Fixed

  • Fix f0 detection with low sampling rates. (#27)

Version 0.5.2

Release date: 2018-03-07. Full commit changelog on github.

Changed

  • The default alpha value was changed from 0.2 to 0.1. (#20)

Version 0.5.1

Release date: 2018-01-10. Full commit changelog on github.

Added

  • Possibility to define custom datahandler script for other formats
  • Added low memory mode option to load larger tiffs frame-by-frame (#14)
  • Added option to use ICA instead of NMF (not recommended, but is a lot faster).
  • Added the option for users to define a custom data and ROI loading script. (#13)

Fixed

  • Fixed custom datahandler usage. (#14)
  • Documentation fixes. (#12)

Version 0.5.0

Release date: 2017-10-05

Initial release

Credits

Development

  • Sander Keemink : Conception, Overall development
  • Scott Lowe : Overall development

Supervision

  • Nathalie Rochefort
  • Mark van Rossum

Testing

  • Janelle Pakan
  • Evelyn Dylda

External Code

Funding

  • SK: DTC & Eurospin
  • Wellcome Trust (Sir Henry Dale Fellowship), EU funding (Career Integration Grant)
  • JP: EU intra-European Fellowship
  • Centre for Integrative Physiology, University of Edinburgh

Citation

S. W. Keemink, S. C. Lowe, J. M. P. Pakan, E. Dylda, M. C. W. van Rossum, and N. L. Rochefort. FISSA: A neuropil decontamination toolbox for calcium imaging signals, Scientific Reports, 8(1):3493, 2018. DOI:10.1038/s41598-018-21640-2.

Indices and tables