https://github.com/int-brain-lab/ibl-photometry

A collection of methods and tools for experimental photometry data
https://github.com/int-brain-lab/ibl-photometry

Last synced: about 1 month ago
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A collection of methods and tools for experimental photometry data

• Host: GitHub
• URL: https://github.com/int-brain-lab/ibl-photometry
• Owner: int-brain-lab
• License: mit
• Created: 2023-10-23T15:24:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-18T19:37:47.000Z (2 months ago)
• Last Synced: 2024-10-19T12:25:24.869Z (2 months ago)
• Language: Python
• Size: 41.7 MB
• Stars: 0
• Watchers: 25
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# photometry-tools

A collection of methods and tools for experimental photometry data

## Note about the preferred formats

A good practice is to keep the raw photometry data in a dataframe with columns:

- times

- raw_isosbestic

- raw_calcium

- (optional) calcium

The preferred interchange format is the parquet format (`.pqt`), which is a binary format that is fast to read and write, compressed and keeps typing information.

You can easily convert a dataframe to parquet `pd.to_parquet('my_file.pqt')` and read it back `pd.read_parquet('my_file.pqt')`.

cf. example [here](./src/examples/csv_preprocessing.py)