https://github.com/fasttrackorg/fastanalysis

FastAnalysis is a python module for analysing result from FastTrack tracking software.
https://github.com/fasttrackorg/fastanalysis

Last synced: 16 days ago
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FastAnalysis is a python module for analysing result from FastTrack tracking software.

• Host: GitHub
• URL: https://github.com/fasttrackorg/fastanalysis
• Owner: FastTrackOrg
• License: mit
• Created: 2020-06-27T13:46:32.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-06-10T15:57:12.000Z (over 2 years ago)
• Last Synced: 2024-12-06T18:46:42.358Z (about 1 month ago)
• Language: Python
• Homepage:
• Size: 2.33 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FastAnalysis

 FastAnalysis is a python module for analysing result from [FastTrack](http://www.fasttrack.sh) tracking software.

 

 ## Project info

 ![Linux tests](https://github.com/FastTrackOrg/FastAnalysis/workflows/Linux%20tests/badge.svg) ![Macos tests](https://github.com/FastTrackOrg/FastAnalysis/workflows/Macos%20tests/badge.svg) ![Windows tests](https://github.com/FastTrackOrg/FastAnalysis/workflows/Windows%20tests/badge.svg)

 

 ## Installation

 To install the module:

### From PyPi

  ```

  pip install fastanalysis

  ```

### From source

 ```

 git clone https://github.com/FastTrackOrg/FastAnalysis.git

 cd FastAnalysis

 pip install .

 ```

 

 ## Development

 The project is under active development and do not have public API documentation, a more or less complete API documentation can be generated using Sphinx.

 

 ## Documentation

 

 ### Retrieve data from a tracking.txt file

 A tracking.txt file can be loaded and several method are available to select objects and frames:

 ```

 import fastanalysis as fa

 

 tracking = fa.Load("tracking.txt")

 fa.isObjectsInFrame(0, 100) # Check if object with id 0 is in the 100th frame

 fa.isObjectsInFrame([0, 1], 100) # Check if object with id 0 and 1 is in the 100th frame

 a = tracking.getObjectsInFrames(0, 100) # Select data of the object with id 0 in the 100th frame

 b = tracking.getObjectsInFrames([0,1], [100,101]) # Select data of the object with id 0 and 1 in the 100th and 101th frames

 c = tracking.getObjects(0) # Select data of the object with id 0 for all the frames

 d = tracking.getObjects([0,1]) # Select data of the object with id 0 and 1 for all the frames

 e = tracking.getFrame(100) # Select data for all the objects in the 100th frame

 f = tracking.getFrame([100,101]) # Select data for all the objects in the 100th and 101th frames

 ```       

 

 ### Export data

 ```

 import fastanalysis as fa

 

 tracking = fa.Load("tracking.txt")

 keys = fa.keys() # List export features

 fa.export("output.xlsx", delimiter='\t', keys=["xHead", "yHead", "imageNumber", "id"], ids=None, indexes=None, format="excel") # Export a list of features for all the frames and all the objects in an excel file.

 fa.export("output.txt", delimiter='\t', keys=None, ids=None, indexes=None, format="excel") # Export all thefeatures for all the frames and all the objects in an csv file.

 ```

 Two format are supported: csv and excel.

 

 ### Plot metrics

 ```

 import fastanalysis as fa

 import matplotlib.pyplot as plt

 

 tracking = fa.Load("tracking.txt")

 plotObj = fa.Plot(tracking)

 plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=True) # Plot velocity distribution for objects 1 and 0 in 2 subplots

 plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=False) # Plot velocity distribution for objects 1 and 0 in 1 plot

 plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=True) # Plot velocity distribution for objects 1 and 0 pooled in 1 distribution

 ```