On file types and sizes

CaImAn is designed to perform analysis on datasets saved over a single or multiple files. However maximum efficiency is achieved when each dataset is saved as a sequence of files of medium size (1-2GBs). Please note the following:

• If you're using TIFF files make sure that the files are saved in multipage format. This is particularly important as multipage TIFF files can be indexed and individual frames can be read without loading the entire file in memory. On the contrary single page TIFFs would load the entire file before reading an individual frame. This can cause significant problems in CaImAn in terms of speed and memory consumption, as a lot of the parallelization (e.g. during motion correction) happens by passing the path to a file to multiple processes each of which will only read and process a small part of it. Bear in mind that TIFF files of size 4GB or larger saved through ImageJ/FIJI are automatically save in single page format and should be avoided. If you have such a file you can split into multiple shorter files through ImageJ/FIJI or through CaImAn using the following script

import numpy as np
import caiman as cm
fname = ''  # path to file
m = cm.load(fname)  # load the file
T = m.shape(0)  # total number of frames for the file
L = 1000  # length of each individual file
fileparts = fname.split(".")
for t in np.arange(0,T,L):
   m[t:t+L].save((".").join(fileparts[:-1]) + '_' + str(t//L) + '.' + fileparts[-1])

HDF5/H5 files in general do not suffer from this problem.

• Single frame files should be avoided. The reason is that several functions, e.g. motion correction, memory mapping, are designed to work on small sets of frames and in general assume that each file has more than 1 frames. If your data is saved as a series of single frame files, you should convert them in a single (or multiple) files. You can do this by using the following script:

import os
import glob
import caiman as cm
fld = ''  # path to folder where the data is located
fls = glob.glob(os.path.join(fld,'*.tif'))  #  change tif to the extension you need
fls.sort()  # make sure your files are sorted alphanumerically
m = cm.load_movie_chain(fls)
m.save(os.path.join(fld,'data.tif'))

If the number of frames is too big, you can split into multiple files as explained above. Make sure that your files are sorted alphanumerically before combining them. This can be tricky if your files are initially saved as 'file1.tif, file2.tif, ..., file10.tif`. In this case you can consult this page.