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Merging multiple files into a single file for downstream analysis

Steinmetz Dataset

We will be using a subset of the Steinmetz dataset (Nature 2019) which contains 3 Neuropixels recordings of 400-700 neurons each from across the mouse brain during a visual behavior task. Briefly, the task was a decision-making task where the mouse had to decide whether the contrast of a left stimulus is higher or the contrast of the right stimulus, and reported their decision by turning a wheel such that the stimulus with the higher contrast was moved to the center (e.g. if the right stimulus had a higher contrast, they moved the wheel to the left).

Download the data

For a more detailed decsription (maybe while the data is being downloaded?) you can watch this short (~8min) YouTube video

Task: Merge single-trial variables into a single CSV file

The goal is to create a CSV file that contains all the variables that have a single value per trial. These variables are basically task-related and performance-related variables that are stored in the task and performance folder for each session:

  • Each column of the CSV file should be single variable and each row is a single trial.
  • Additionally we would also want to have the metadata (the info in the metadata.json) in this CSV file (info such as session_date, mouse_name, stim_onset, etc.)

Focusing on a single session: We will be focusing on a single recording session (e.g. subject "Lederberg" and session "20171205").

Here are the steps for this task:

  1. create a CLI/script, called merge_task_data.py that given the path to the task data, combines all files into a CSV file called task_data.csv
  2. create a CLI/script that given the path to the performance data, combines all the files into a CSV file called performance_data.csv
  3. create a CLI/script, called merge_single_trial_data.py, that takes the path to task_data.csv and performance_data.csv and combines them together into a single CSV file called single_trial_data.csv

This is a group project, so let's do it together (while someone shares the screen). And feel free to explore the data as you like (e.g. in a Jupyter Notebook), and use all the tools (Google, ChatGPT, etc.) you have access to.

Things to keep in mind

  • Let's stay organized:
    • scripts in scripts folder
    • CLIs in clis folder
    • processed data (i.e. resulting CSV files) in the steinmetz/processed folder
  • Let's keep track and document our development by making commits (and pushing to GitHub)