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Analysis code for 2p imaging for 3d vision project

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v1_depth_map

Analysis code for v1 depth map project.

Installation

  1. Create an empty environment conda create --name <env_name>
  2. Activate the environment by conda activate <env_name>.
  3. Install pip conda install pip.
  4. Install this package by pip install ..

Setting up for offline uses of database

  1. Follow installation instructures for https://github.com/znamlab/flexiznam.git.
  2. Set up config for the flexiznam package by flexiznam config. The config file should be found at ~/.flexiznam/config.yml.
  3. Make sure that the path under project_paths points to the path you have downloaded the data. e.g.:
project_paths:
    hey2_3d-vision_foodres_20220101:
        processed: your_data_path
        raw: your_data_path
  1. Turn on the offline mode by adding the following to your config:
offline_mode: true
offline_yaml: offline_database.json

Figure plotting

  1. Run the notebooks under ./v1_depth_map/figures to plot the corresponding figures.
  2. When first running the figure notebooks, change reload to True to reload data.

Batch analysis

  1. Run the bash script in each folder under ./v1_depth_map/batch_analysis to conduct corresponding analysis for all sessions.
  2. Remember to change the path in the bash script for #SBATCH --output= and cd to your local path to this repo.
  3. Remember to change the conda environment name to your own environment.

Precompute data

  1. To precompute data for plotting figures, run the corresponding bash script.

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Analysis code for 2p imaging for 3d vision project

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