Utilities to look at widefield data and align with the allen reference map.
Project description
wfield - tools to analysis widefield data
This is a python package for visualizing and analysing data collected with a widefield macroscope.
Use the graphical interface to launch analysis on NeuroCAAS
These tools are for:
- Motion correction
- Data reduction
- Hemodynamic correction
- Matching to the Allen CCF
- Visualize raw/reduced data and ROIs
Use cases and instructions here
A dataset that can be used to demo some of the functionality of this repository will be made available in the demoRec
folder.
File format conventions
-
raw frame data, when stored in binary files (uint16)
the filename must end with:_NCHANNELS_H_W_DTYPE.dat
Example: "frames_2_540_640_uint16.dat" H and W are the dimensions of a single frame.
These files can be opened with the commandwfield open_raw <filename>
-
denoised/decomposed (locally processed) data are stored as
npy
arrays
U.npy
are the spatial components(H, W, NCOMPONENTS)
SVT.npy
are the temporal components(NCOMPONENTS, NFRAMES)
-
SVTcorr.npy
is the hemodynamic corrected temporal components(NCOMPONENTS, NFRAMES)
-
info.json
has information about the dataset like theframe_rate
or then_channels
NeuroCAAS results folder
U_atlas.npy
are the spatial components transformed to a common atlas reference frame.
(H, W, NCOMPONENTS)reduced_spatial.npy
are the spatial components from the PMD(H*W,NCOMPONENTS)
The H and W of the matrix are in theconfig.yaml
file (or useU_atlas
)SVTcorr.npy
are the hemodynamic corrected temporal components(NCOMPONENTS, NFRAMES)
reduced_temporal.npy
are the temporal components for the first channel `(NCOMPONENTS, NFRAMES)- LocaNMF returns a matlab file with components in spatial components in the
A
variable and temporal components in theC
variable.
Installation (from pip)
pip install wfield
Installation (using Anaconda)
To install start by getting Anaconda or Miniconda. Install also git. Run the following commands from a terminal window (Anaconda prompt or git Bash on Windows, the Mac OS terminal). Check the notes below for common issues.
-
Go to the folder where you want to install and clone the repository:
git clone https://github.com/jcouto/wfield.git
. This creates a directory; go inside that directory:cd wfield
. Alternatively you can download the repository as a [zip file] (https://github.com/churchlandlab/wfield/archive/master.zip). -
Use anaconda to install all dependencies:
conda env create -f env.yml
the file env.yml is inside thewfield
directory. -
Enter the environment
conda activate wfield
and install wfield using the commandpython setup.py install
followed bypython setup.py references
-
You will need to run
conda activate wfield
to activate the environment before running the software every time you start a terminal. -
You are all set.
Typewfield -h
to see the available commands.
Go here for instructions on how to use NeuroCAAS.
Note: Some reference files used to match to the Allen Common Coordinate Framework are copied from the folder references to $HOME/.wfield
during installation.
Note for Mac users:
git
when you try the instructions you will be asked to install git, if that fails you can runconda install git
to install using anaconda.
Note for Windows users:
- Get a terminal like git bash [optional]
Runconda init bash
to activate conda ongit bash
- When you install Anaconda, set the option to install as system python (this makes that it is visible from the terminal without having to run the Anaconda Prompt).
Note for developers: In some cases you may want to make changes to the software, if you need this run python setup.py develop
(you can not move the folder after this - the installation will point to that directory).
The software was tested on Windows, Linux and MacOS Catalina. Installation takes less than 5 minutes on a standard computer with fast access to internet and a previous anaconda installation.
Tutorial
Instructions to use with NeuroCAAS here.
There is a command-line inteface to run computations from the command line. Do wfield -h
to learn more.
Notebook examples are here.
Look at this one to load decomposed data and extract ROIs.
Copyright (C) 2020 Joao Couto - jpcouto@gmail.com
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/.
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