Automatically analyze C. elegans feeding behavior. A couple of python scripts to analyze large stacks of image data in worm feeding.
tested on:
- python 2.7+
- numpy 1.9.2
- matplotlib 1.4+
- scipy 0.13.3
check_movie.py - main script with a basic matplotlib based GUI. Performs these tasks in one clean main script.
- Crop image (GUI based)
- Select region of interest (ROI) (GUI based)
- Manual mode: select ROI for each subprocess
- Automatic mode: Use template correlation to find ROI throughout movie.
- write batch submission script
WARP_parallel.py - divides work to cores for parallelization using python's multiprocessing module
warp.py - workhorse, the actual image analysis code. This can also be run separarely for simpler tasks. See below.
fluowarp.py - work in progress: track GCamp neuron activity.
check_movie essentially creates an automatic submission script.
Parameter details below.
positional arguments:
basename name/identifier for outputand scipts eg. yl0027
directory directory containing images
outdir directory for output
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
-mode MODE manual determination of bulb location or automatic.
-crop CROP Open crop dialog.
-nprocs NPROCS number of processes in parallelization
-script_dir SCRIPTDIR
directory where warp.py for image analysis is located
-account ACCOUNT midway account name for submission script header
-intrvl INTRVL Spacing between ROi detection.
-typ TYP image type by extension
-start START time stamp starting eg. frame 0 -> 0
-end END time stamp ending in frame number
-rotate ROT rotate image, binary
-chunk CHUNK spacing between drift corrections.
-roisize ROISIZE size [px] region of interest around bulb for image
analysis.
-size SIZE size of matching template (half width).
-entropybins BINS BINS BINS
histogram bins, arguments to numpy.linspace. (min,
max, nbin)
Running the script e.g. with these parameters:
python check_movie.py ser6 ../images/im_folder ../results/ -roisize 250 -entropybins 0.06 0.5 30 -typ bmpresults on this output file
#!/bin/sh
#SBATCH --account=ACCTNAME
#SBATCH --job-name=JOBNAME
#SBATCH --output=JOBNAME.out
#SBATCH --exclusive
#SBATCH --time=1:0:0
echo "start time: `date`"
python WARP_parallel.py -nprocs 16 -type bmp -basename ser6 -directory "../images/im_folder" -roi_file "../results/roi_ser6" -outdir "../results/" -cropx 5 65 -rotate False -chunk 60 -roisize 250 -entropybins 0.06 0.5 30.0 run this script using sbatch. Note the positional and optional arguments.
The warp.py file can be used as an independent image analysis library. If this is the case, import warp.py and call the function
warp_detector(params)
in your code. The argument is a dictionary of required parameters;
params = {'cropx': [0, -1],
'chunk': 60,
'roisize': 120,
'start': 0,
'end': 1801,
'rotate': False,
'directory': '../images/df0432/',
'y0': 469.84462300000001,
'x0': 69.5,
'type': 'png',
'basename': 'df0432',
'outdir': '../results/'}The parameters correspond to the list in the section on Usage. Additionally, x0 and y0 are the coordinates of the region of interest. The images are cropped to an area of +/- roisize around these coordinates after alignment.
