Fix hardcoding of MAD in return_good_lfp_trial_inds in lfp_processing module

[kmaigler]

No description provided.

[abuzarmahmood]

Add a default value as the argument

[abuzarmahmood]

The issue involves addressing the hardcoding of the MAD (Median Absolute Deviation) threshold in the return_good_lfp_trial_inds function within the lfp_processing module of the blech_clust repository. The following suggestions and changes were made:

1. File to Modify:

   • File: /home/exouser/Desktop/blech_github_bot/src/repos/katzlabbrandeis/blech_clust/utils/ephys_data/lfp_processing.py
   • Description: This file contains the return_good_lfp_trial_inds function, which is responsible for returning a boolean array of good trials based on a MAD threshold. The function currently has a hardcoded deviation threshold value of 3.

2. Suggested Code Change:

   • Modify the return_good_lfp_trial_inds function to include a parameter for the deviation threshold, allowing users to specify a different value if needed. This will replace the hardcoded value and improve the function's usability.

3. Updated Code:

   def return_good_lfp_trial_inds(data, MAD_threshold=3):
       """
       Return boolean array of good trials (for all channels) based on MAD threshold
       Remove trials based on deviation from median LFP per trial
   
       Inputs:
           data : shape (n_channels, n_trials, n_timepoints)
           MAD_threshold : number of MADs to use as threshold for individual timepoints
   
       Outputs:
           good_trials_bool : boolean array of good trials
       """
       lfp_median = np.median(data, axis=1)
       lfp_MAD = MAD(data, axis=1)
       # Use total deviation per trial scaled by MAD to remove trial
       mean_trial_deviation = np.mean(
           np.abs(data - lfp_median[:, np.newaxis, :])/lfp_MAD[:, None], axis=2)
       deviation_median = np.median(mean_trial_deviation, axis=1)
       deviation_MAD = MAD(mean_trial_deviation, axis=1)
       fin_deviation_threshold = deviation_median + MAD_threshold * deviation_MAD
       # Remove trials with high deviation
       good_trials_bool = mean_trial_deviation < fin_deviation_threshold[:, np.newaxis]
       # Take only trials good for both regions
       good_trials_bool = np.all(good_trials_bool, axis=0)
       return good_trials_bool

4. Specific Changes:

   • Replace the hardcoded deviation_threshold = 3 with the MAD_threshold parameter in the calculation of fin_deviation_threshold.

This change allows the function to use a default MAD threshold of 3, but also provides flexibility for users to specify a different threshold if needed.

TERMINATE

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