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‎examples/Basic usage - Function.html

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‎examples/Basic usage - Function.ipynb

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‎examples/Basic usage.html

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‎examples/Basic usage.ipynb

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‎examples/SIMA example.html

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‎examples/SIMA example.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using FISSA with SIMA\n",
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"\n",
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"[SIMA](http://www.losonczylab.org/sima/) is a toolbox for motion correction and cell detection.\n",
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"Here we illustrate how to create a workflow which uses SIMA to detect cells and FISSA to extract decontaminated signals from those cells.\n",
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"\n",
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"**Reference:**\n",
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"Kaifosh, P., Zaremba, J. D., Danielson, N. B., Losonczy, A. SIMA: Python software for analysis of dynamic fluorescence imaging data. *Frontiers in neuroinformatics*, **8**(80), 2014. doi: [10.3389/fninf.2014.00080](https://doi.org/10.3389/fninf.2014.00080).\n",
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"\n",
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"Please note that SIMA only supports Python 3.6 and below."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import packages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# FISSA toolbox\n",
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"import fissa\n",
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"\n",
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"# SIMA toolbox\n",
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"import sima\n",
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"import sima.segment\n",
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"\n",
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"# File operations\n",
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"import glob\n",
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"\n",
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"# For plotting our results, use numpy and matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Detecting cells with SIMA"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Setup data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define folder where tiffs are present\n",
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"tiff_folder = \"exampleData/20150529/\"\n",
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"\n",
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"# Find tiffs in folder\n",
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"tiffs = sorted(glob.glob(tiff_folder + \"/*.tif*\"))\n",
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"\n",
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"# define motion correction method\n",
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"mc_approach = sima.motion.DiscreteFourier2D()\n",
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"\n",
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"# Define SIMA dataset\n",
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"sequences = [sima.Sequence.create(\"TIFF\", tiff) for tiff in tiffs[:1]]\n",
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"try:\n",
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" dataset = sima.ImagingDataset(sequences, \"example.sima\")\n",
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"except Exception:\n",
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" dataset = sima.ImagingDataset.load(\"example.sima\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run SIMA segmentation algorithm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stica_approach = sima.segment.STICA(components=2)\n",
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"stica_approach.append(sima.segment.SparseROIsFromMasks())\n",
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"stica_approach.append(sima.segment.SmoothROIBoundaries())\n",
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"stica_approach.append(sima.segment.MergeOverlapping(threshold=0.5))\n",
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"rois = dataset.segment(stica_approach, \"auto_ROIs\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plot detected cells"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plotting lines surrounding each of the ROIs\n",
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"plt.figure(figsize=(7, 6))\n",
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"\n",
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"for roi in rois:\n",
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" # Plot border around cell\n",
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" plt.plot(roi.coords[0][:, 0], roi.coords[0][:, 1])\n",
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"\n",
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"# Invert the y-axis because image co-ordinates are labelled from top-left\n",
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"plt.gca().invert_yaxis()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Extract decontaminated signals with FISSA\n",
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"\n",
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"FISSA needs either ImageJ ROIs or numpy arrays as inputs for the ROIs. \n",
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"\n",
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"SIMA outputs ROIs as numpy arrays, and can be directly read into FISSA.\n",
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"\n",
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"A given roi is given as\n",
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"```python\n",
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"rois[i].coords[0][:, :2]\n",
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"```\n",
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"\n",
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"FISSA expects rois to be provided as a list of lists\n",
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"```python\n",
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"[[roiA1, roiA2, roiA3, ...]]\n",
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"```\n",
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"So some formatting will need to be done first."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rois_fissa = [roi.coords[0][:, :2] for roi in rois]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rois[0].coords[0][:, :2].shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can then run FISSA on the data using the ROIs supplied by SIMA having converted them to a FISSA-compatible format, `rois_fissa`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"output_folder = \"fissa_sima_example\"\n",
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"experiment = fissa.Experiment(tiff_folder, [rois_fissa], output_folder)\n",
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"experiment.separate()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plotting the results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fetch the colormap object for Cynthia Brewer's Paired color scheme\n",
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"cmap = plt.get_cmap(\"Paired\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Select which trial (TIFF index) to plot\n",
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"trial = 0\n",
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"\n",
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"# Plot the mean image and ROIs from the FISSA experiment\n",
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"plt.figure(figsize=(7, 7))\n",
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"plt.imshow(experiment.means[trial], cmap=\"gray\")\n",
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"\n",
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"for i_roi in range(len(experiment.roi_polys)):\n",
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" # Plot border around ROI\n",
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" for contour in experiment.roi_polys[i_roi, trial][0]:\n",
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" plt.plot(\n",
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" contour[:, 1],\n",
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" contour[:, 0],\n",
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" color=cmap((i_roi * 2 + 1) % cmap.N),\n",
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" )\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot all ROIs and trials\n",
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"\n",
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"# Get the number of ROIs and trials\n",
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"n_roi = experiment.result.shape[0]\n",
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"n_trial = experiment.result.shape[1]\n",
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"\n",
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"# Find the maximum signal intensities for each ROI\n",
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"roi_max_raw = [\n",
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" np.max([np.max(experiment.raw[i_roi, i_trial][0]) for i_trial in range(n_trial)])\n",
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" for i_roi in range(n_roi)\n",
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"]\n",
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"roi_max_result = [\n",
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" np.max([np.max(experiment.result[i_roi, i_trial][0]) for i_trial in range(n_trial)])\n",
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" for i_roi in range(n_roi)\n",
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"]\n",
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"roi_max = np.maximum(roi_max_raw, roi_max_result)\n",
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"\n",
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"# Plot our figure using subplot panels\n",
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"plt.figure(figsize=(16, 10))\n",
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"for i_roi in range(n_roi):\n",
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" for i_trial in range(n_trial):\n",
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" # Make subplot axes\n",
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" i_subplot = 1 + i_trial * n_roi + i_roi\n",
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" plt.subplot(n_trial, n_roi, i_subplot)\n",
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" # Plot the data\n",
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" plt.plot(\n",
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" experiment.raw[i_roi][i_trial][0, :],\n",
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" label=\"Raw (SIMA)\",\n",
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" color=cmap((i_roi * 2) % cmap.N),\n",
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" )\n",
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" plt.plot(\n",
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" experiment.result[i_roi][i_trial][0, :],\n",
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" label=\"FISSA\",\n",
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" color=cmap((i_roi * 2 + 1) % cmap.N),\n",
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" )\n",
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" # Labels and boiler plate\n",
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" plt.ylim([-0.05 * roi_max[i_roi], roi_max[i_roi] * 1.05])\n",
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" if i_roi == 0:\n",
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" plt.ylabel(\n",
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" \"Trial {}\\n\\nSignal intensity\\n(candela per unit area)\".format(\n",
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" i_trial + 1\n",
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" )\n",
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" )\n",
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" if i_trial == 0:\n",
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" plt.legend()\n",
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" plt.title(\"ROI {}\".format(i_roi))\n",
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" if i_trial == n_trial - 1:\n",
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" plt.xlabel(\"Time (frame number)\")\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The figure shows the raw signal from the ROI identified by SIMA (pale), and after decontaminating with FISSA (dark).\n",
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"The hues match the ROI locations drawn above.\n",
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"Each column shows the results from one of the ROIs detected by SIMA.\n",
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"Each row shows the results from one of the three trials."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python",
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"language": "python",
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"name": "python"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython",
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"version": "3.8.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎examples/Suite2p example.html

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<!DOCTYPE HTML>
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<html lang='en'><head><meta charset='utf-8'>
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<meta http-equiv='refresh' content='0;url=https://rochefort-lab.github.io/fissa-suite2p-example/Suite2p%20example.html' />
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<link rel='canonical' href='https://rochefort-lab.github.io/fissa-suite2p-example/Suite2p%20example.html' />
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</head>
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<body><p>The page been moved to <a href='https://rochefort-lab.github.io/fissa-suite2p-example/Suite2p%20example.html'>https://rochefort-lab.github.io/fissa-suite2p-example/Suite2p%20example.html</a></p></body>
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</html>

‎examples/basic_usage.py

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#!/usr/bin/env python
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"""
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Basic FISSA usage example.
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This file contains a step-by-step example workflow for using the FISSA toolbox
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with a class-based/object-oriented interface.
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An example notebook is provided here:
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https://github.com/rochefort-lab/fissa/blob/master/examples/Basic%20usage.ipynb
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"""
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import fissa
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# Define the data to extract
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rois = "exampleData/20150429.zip"
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images = "exampleData/20150529"
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# Define the name of the experiment extraction location
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output_dir = "fissa_example"
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# Make sure you use a different output path for each experiment you run.
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# Instantiate a fissa experiment object
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experiment = fissa.Experiment(images, rois, output_dir)
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# Run the FISSA separation algorithm
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experiment.separate()
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# Export to a .mat file which can be opened with MATLAB (optional)
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experiment.save_to_matlab()

‎examples/basic_usage_func.py

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#!/usr/bin/env python
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"""
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Basic FISSA usage example.
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This file contains a step-by-step example workflow for using the FISSA toolbox
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with a function-based interface.
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An example notebook is provided here:
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https://github.com/rochefort-lab/fissa/blob/master/examples/Basic%20usage%20-%20Function.ipynb
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"""
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import fissa
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# Define the data to extract
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rois = "exampleData/20150429.zip"
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images = "exampleData/20150529"
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# Define the name of the experiment extraction location
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# Make sure you use a different output path for each experiment you run.
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output_dir = "fissa_example"
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# Run FISSA on this data
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result = fissa.run_fissa(images, rois, output_dir, export_to_matfile=True)

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