Command line interface - mps-analyze

If you have an imaging file you might want to run a number of different analysis operations on it. The mps-analyze script takes as input an imaging file and output a folder with figures and spreadsheets.

Say that we have a file file.nd2. Then we can analyze it using the command

mps-analyze file.nd2

or

python -m mps analyze file.nd2

You can also list all the possible option using the command

mps-analyze --help

The script will display some info in the terminal

2022-06-01 15:31:34,135 - mps.scripts.analyze - INFO - Run analysis script
2022-06-01 15:31:34,138 - mps.load - INFO - Load nd2 file /Users/henriknf/file.nd2
2022-06-01 15:31:34,309 - mps.load - INFO - Loaded 591 frames in 0.17 seconds
2022-06-01 15:31:37,174 - mps.scripts.analyze - INFO - Finished analyzing MPS data. Data stored folder 'file'.
Total elapsed time: 3.04 seconds

and a new folder called file contains the following files

file
├── EAD_analysis.png
├── README.txt
├── apd_analysis.png
├── average.png
├── average_pacing.png
├── background.png
├── chopped_data.csv
├── chopped_data.png
├── chopped_data_aligned.png
├── data.npy
├── data.txt
├── metadata.json
├── original.png
├── original_pacing.png
├── settings.json
├── sliced_filtered.png
├── trace.png
└── unchopped_data.csv

The file called README.txt contains a description of all the files in the folder. For easy reference we also provide this description below

• apd_analysis.png Figure showing plots of action potential durations and corrected actions potential durations (using the friderica correction formula). Top panel shows bars of the APD for the different beats where the * indicated if the APD for that beat is significantly different. The middle panel show the APD for each each plotted as a line as well as a linear fit of the APD80 and cAPD80 line. The intention behind this panel is to see it there is any correlation between the APD and the beat number The lower panel shows where the cut is made to compute the different APDs

• average_pacing.png These are the average trace with pacing

• average.png These are the average of the traces in chopped_data

• background.png This plots the original trace and the background that we subtract in the corrected trace.

• chopped_data_aligned.png All beats plotted on with the time starting at zero

• chopped_data.csv A csv file with the chopped data

• chopped_data.png Left panel: All the beats that we were able to extract from the corrected trace Right panel: The intersection of all beats where APD80 and APD30 are within 1 standard deviation of the mean.

• data.npy A file containing all the results. You can load the results in python as follows

  >> import numpy as np
  >> data = np.load('data.npy', allow_pickle=True).item()
  

  data is now a python dictionary.

• data.txt This contains a short summary of analysis.

• EAD_analysis.png A plot of the beats containing EADs

• frequency_analysis.png A plot of the frequencies of the different beats computed as the time between peaks.

• metadata.json Metadata stored inside the raw data

• original_pacing.png This is the the raw trace obtained after averaging the frames in the stack without any background correction or filtering together with the pacing amplitude.

• original.png This is the the raw trace obtained after averaging the frames in the stack without any background correction or filtering

• sliced_filtered.png Original trace where we have performed slicing and filtering

• trace.png The is the corrected version of the original trace where we have performed a background correction and filtering

• unchopped_data.csv A csv file with all the unchopped data (original and corrected)