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Utilities

Separate Data from Different Sessions

Separates data into sessions based on frame information and optionally bins the data.

Syntax:

S = separate_sessions(data, F, bin, sf)

Description:

This function separates data into sessions based on provided frame information (F). If F is not provided, the function prompts the user to select a file containing frame data. The data can be optionally binned using the specified bin size (bin) and sampling frequency (sf).

Inputs:

  • data: Matrix of data to be separated into sessions.

  • F (optional): Frame information used to define intervals for separating sessions. If not provided, the user will be prompted to select a file.

  • bin (optional): Bin size for binning the data. If set to 0, no binning is applied. Default is 0 if not specified.

  • sf (optional): Sampling frequency used when binning the data. Default is 1 if not specified.

Outputs:

  • S: Cell array containing separated session data.

Example Usage:

% Separate spike data with default bin size and sampling frequency (no binning)
S=separate_sessions(neuron.S, neuron.CaliAli_opt.F);

% Separate spike data with 1s bin considering Sampling frequency of 10.
S=separate_sessions(neuron.S, neuron.CaliAli_opt.F,1,10);

% Separate raw Calcim traces data with default bin size and sampling frequency (no binning)
S=separate_sessions(neuron.C_raw, neuron.CaliAli_opt.F);

Simulate Calcium Imaging Videos

Use the open-source Simulate_Ca_Imaging_video toolbox to generate synthetic datasets that mimic one-photon miniscope recordings. With a single configuration file you can specify:

  • Field of view dimensions (spatial resolution) and frame count to match your target memory footprint.
  • Number of neurons, soma size distribution, firing statistics, and neuropil background.
  • Motion trajectories (rigid or drifting), intermittent occlusions, and shot/Poisson noise.
  • Acquisition parameters such as frame rate and signal-to-noise ratio.

Exported videos share the same layout as CaliAli demo sessions, making them ideal for benchmarking new parameter presets, testing automatic batch sizing, or teaching the workflow without sharing animal data. Refer to the repository README for installation instructions and example scenarios (low_memory, high_density, drift_only, etc.).

Other Functions

Save Workspace

save_workspace(neuron);

Updating Paths for Video and MAT Files

If you've changed the location of the videos and files generated during the analysis, you'll need to run the following function and select the new 'source_extraction' folder.

 neuron=update_folder_path(neuron);
save_workspace(neuron);

Plot Neuron Contours 

%% To visualize neurons contours:
neuron.Coor=[]  

%% Plot over PNR image:
   neuron.show_contours(0.9, [], neuron.PNR, 0);  %PNR

%% Plot over correlation image:
   neuron.show_contours(0.6, [], neuron.Cn,0);   %CORR

%% Plot over PNR.Corr image:
  neuron.show_contours(0.6, [], neuron.Cn.*neuron.PNR,0); %PNR*CORR

%% Plot over neuron footprints:
 A=neuron.A;A=full(A./max(A,[],1)); A=reshape(max(A,[],2),[size(neuron.Cn,1),size(neuron.Cn,2)]);
 neuron.show_contours(0.6, [], A, 0);