Add docstrings

blech_clean_slate.py

@@ -4,6 +4,11 @@
 Info file and sorting table (csv) as kept
 """
 # Import stuff!
+"""
+Module for cleaning and resetting the Blech dataset.
+
+This module provides utilities to clean and reset data for analysis.
+"""
 import os
 import shutil
 import sys

blech_common_avg_reference.py

@@ -4,6 +4,11 @@
 
 # Import stuff!
 import tables
+"""
+Module for common average referencing in the Blech dataset.
+
+This module includes functions for applying common average reference techniques.
+"""
 import numpy as np
 import os
 import easygui

blech_exp_info.py

@@ -17,6 +17,11 @@
 X Misc Notes
 """
 
+"""
+Module for handling experimental information in the Blech dataset.
+
+This module includes functions to manage and retrieve experimental metadata.
+"""
 import json
 import numpy as np
 import os

blech_make_arrays.py

@@ -1,4 +1,9 @@
 # Import stuff!
+"""
+Module for creating arrays in the Blech dataset.
+
+This module includes functions to generate and manipulate data arrays.
+"""
 import numpy as np
 import tables
 import sys

blech_post_process.py

@@ -28,6 +28,11 @@
 import numpy as np
 import pylab as plt
 from sklearn.mixture import GaussianMixture
+"""
+Module for post-processing Blech dataset.
+
+This module provides functions for post-processing and analysis of data.
+"""
 import pandas as pd
 import matplotlib
 from glob import glob

blech_process.py

@@ -35,6 +35,11 @@
 import pylab as plt
 import json
 import sys
+"""
+Module for processing Blech dataset.
+
+This module includes functions for data processing and manipulation.
+"""
 import numpy as np
 import warnings
 

blech_units_characteristics.py

@@ -11,6 +11,11 @@
 - Dynamic population (ANOVA over time on PCA/other latents)
 """
 
+"""
+Module for analyzing unit characteristics in the Blech dataset.
+
+This module provides functions to compute and analyze characteristics of units.
+"""
 import numpy as np
 import tables
 import easygui

blech_units_plot.py

@@ -4,6 +4,11 @@
 import easygui
 import sys
 import os
+"""
+Module for plotting units in the Blech dataset.
+
+This module provides functions to visualize and analyze unit data.
+"""
 import matplotlib.pyplot as plt
 import shutil
 from tqdm import tqdm, trange

utils/infer_rnn_rates.py

@@ -1,113 +1,136 @@
 """
-Use Auto-regressive RNN to infer firing rates from a given data set.
+This module uses an Auto-regressive RNN to infer firing rates from a given data set.
+
+It includes functions for parsing arguments, loading configurations, setting up paths,
+and checking the existence of necessary directories. The main functionality involves
+processing neural spike data, training an RNN model, and visualizing the results.
 """
 
 import argparse
-parser = argparse.ArgumentParser(description = 'Infer firing rates using RNN')
-parser.add_argument('data_dir', help = 'Path to data directory')
-parser.add_argument('--override_config', action = 'store_true', 
-                    help = 'Override config file and use provided arguments'+\
-                            '(default: %(default)s)')
-parser.add_argument('--train_steps', type = int, default = 15000,
-                    help = 'Number of training steps (default: %(default)s)')
-# Hidden size of 8 was tested to be optimal across multiple datasets
-parser.add_argument('--hidden_size', type = int, default = 8,
-                    help = 'Hidden size of RNN (default: %(default)s)')
-parser.add_argument('--bin_size', type = int, default = 25,
-                    help = 'Bin size for binning spikes (default: %(default)s)')
-parser.add_argument('--train_test_split', type = float, default = 0.75,
-                    help = 'Fraction of data to use for training (default: %(default)s)')
-parser.add_argument('--no_pca', action = 'store_true', 
-                    help = 'Do not use PCA for preprocessing (default: %(default)s)')
-parser.add_argument('--retrain', action = 'store_true', 
-                    help = 'Force retraining of model. Will overwrite existing model'+\
-                            ' (default: %(default)s)')
-parser.add_argument('--time_lims', type = int, nargs = 2, default = [1500, 4500],
-                    help = 'Time limits inferred firing rates (default: %(default)s)')
-
+import argparse
 import json
-from pprint import pprint
 import os
-args = parser.parse_args()
-data_dir = args.data_dir
-script_path = os.path.abspath(__file__)
-blech_clust_path = os.path.dirname(os.path.dirname(script_path))
-
-if args.override_config:
-    print('Overriding config file\nUsing provided arguments\n')
-    train_steps = args.train_steps
-    hidden_size = args.hidden_size
-    bin_size = args.bin_size
-    train_test_split = args.train_test_split
-    use_pca = not args.no_pca
-    time_lims = args.time_lims
-else:
-    config_path = os.path.join(blech_clust_path, 'params', 'blechrnn_params.json')
-    if not os.path.exists(config_path):
-        raise FileNotFoundError(f'BlechRNN Config file not found @ {config_path}')
-    with open(config_path, 'r') as f:
-        config = json.load(f)
-    print('Using config file\n')
-    train_steps = config['train_steps']
-    hidden_size = config['hidden_size']
-    bin_size = config['bin_size']
-    train_test_split = config['train_test_split']
-    use_pca = config['use_pca']
-    time_lims = config['time_lims']
-
-params_dict = dict(
-        train_steps = train_steps,
-        hidden_size = hidden_size,
-        bin_size = bin_size,
-        train_test_split = train_test_split,
-        use_pca = use_pca,
-        time_lims = time_lims,
-        )
-pprint(params_dict)
-
-##############################
-
-# Check that blechRNN is on the Desktop, if so, add to path
 import sys
+
 import numpy as np
-from sklearn.preprocessing import StandardScaler
-from sklearn.decomposition import PCA
 import torch
 import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
 from scipy.stats import zscore
 import tables
+from pprint import pprint
 
-blechRNN_path = os.path.join(os.path.expanduser('~'), 'Desktop', 'blechRNN')
-if os.path.exists(blechRNN_path):
-	sys.path.append(blechRNN_path)
-else:
-	raise FileNotFoundError('blechRNN not found on Desktop')
 from src.model import autoencoderRNN
 from src.train import train_model
-
-# script_path = '/home/abuzarmahmood/Desktop/blech_clust/utils/infer_rnn_rates.py'
-sys.path.append(blech_clust_path)
 from utils.ephys_data import ephys_data
 from utils.ephys_data import visualize as vz
 
-# mse loss performs better than poisson loss
-loss_name = 'mse'
+def parse_arguments():
+    """
+    Parse command-line arguments.
+
+    Returns:
+        argparse.Namespace: Parsed command-line arguments.
+    """
+    parser = argparse.ArgumentParser(description='Infer firing rates using RNN')
+    parser.add_argument('data_dir', help='Path to data directory')
+    parser.add_argument('--override_config', action='store_true', help='Override config file and use provided arguments (default: %(default)s)')
+    parser.add_argument('--train_steps', type=int, default=15000, help='Number of training steps (default: %(default)s)')
+    parser.add_argument('--hidden_size', type=int, default=8, help='Hidden size of RNN (default: %(default)s)')
+    parser.add_argument('--bin_size', type=int, default=25, help='Bin size for binning spikes (default: %(default)s)')
+    parser.add_argument('--train_test_split', type=float, default=0.75, help='Fraction of data to use for training (default: %(default)s)')
+    parser.add_argument('--no_pca', action='store_true', help='Do not use PCA for preprocessing (default: %(default)s)')
+    parser.add_argument('--retrain', action='store_true', help='Force retraining of model. Will overwrite existing model (default: %(default)s)')
+    parser.add_argument('--time_lims', type=int, nargs=2, default=[1500, 4500], help='Time limits inferred firing rates (default: %(default)s)')
+    return parser.parse_args()
+
+args = parse_arguments()
+data_dir = args.data_dir
+script_path = os.path.abspath(__file__)
+blech_clust_path = os.path.dirname(os.path.dirname(script_path))
 
-output_path = os.path.join(data_dir, 'rnn_output')
-artifacts_dir = os.path.join(output_path, 'artifacts')
-plots_dir = os.path.join(output_path, 'plots')
+def load_config(args, blech_clust_path):
+    """
+    Load configuration parameters from a JSON file or use command-line arguments.
+
+    Args:
+        args (argparse.Namespace): Parsed command-line arguments.
+        blech_clust_path (str): Path to the BlechRNN configuration directory.
+
+    Returns:
+        dict: Configuration parameters.
+    """
+    if args.override_config:
+        print('Overriding config file\nUsing provided arguments\n')
+        return {
+            'train_steps': args.train_steps,
+            'hidden_size': args.hidden_size,
+            'bin_size': args.bin_size,
+            'train_test_split': args.train_test_split,
+            'use_pca': not args.no_pca,
+            'time_lims': args.time_lims
+        }
+    else:
+        config_path = os.path.join(blech_clust_path, 'params', 'blechrnn_params.json')
+        if not os.path.exists(config_path):
+            raise FileNotFoundError(f'BlechRNN Config file not found @ {config_path}')
+        with open(config_path, 'r') as f:
+            config = json.load(f)
+        print('Using config file\n')
+        return config
+
+params_dict = load_config(args, blech_clust_path)
+pprint(params_dict)
 
-if not os.path.exists(output_path):
-	os.mkdir(output_path)
-if not os.path.exists(artifacts_dir):
-	os.mkdir(artifacts_dir)
-if not os.path.exists(plots_dir):
-	os.mkdir(plots_dir)
+# Extract parameters from the config
+hidden_size = params_dict['hidden_size']
+time_lims = params_dict['time_lims']
+bin_size = params_dict['bin_size']
+use_pca = params_dict['use_pca']
+train_test_split = params_dict['train_test_split']
+train_steps = params_dict['train_steps']
+
+def setup_paths(data_dir):
+    """
+    Set up directories for output, artifacts, and plots.
+
+    Args:
+        data_dir (str): Path to the data directory.
+
+    Returns:
+        tuple: Paths for output, artifacts, and plots directories.
+    """
+    output_path = os.path.join(data_dir, 'rnn_output')
+    artifacts_dir = os.path.join(output_path, 'artifacts')
+    plots_dir = os.path.join(output_path, 'plots')
+
+    for path in [output_path, artifacts_dir, plots_dir]:
+        if not os.path.exists(path):
+            os.mkdir(path)
+
+    return output_path, artifacts_dir, plots_dir
+
+def check_blechRNN_path():
+    """
+    Check if the blechRNN directory exists on the Desktop and append it to sys.path.
+
+    Raises:
+        FileNotFoundError: If the blechRNN directory is not found.
+    """
+    blechRNN_path = os.path.join(os.path.expanduser('~'), 'Desktop', 'blechRNN')
+    if os.path.exists(blechRNN_path):
+        sys.path.append(blechRNN_path)
+    else:
+        raise FileNotFoundError('blechRNN not found on Desktop')
 
+check_blechRNN_path()
+sys.path.append(blech_clust_path)
 
+loss_name = 'mse'
+output_path, artifacts_dir, plots_dir = setup_paths(data_dir)
 
 print(f'Processing data from {data_dir}')
-
 data = ephys_data.ephys_data(data_dir)
 data.get_spikes()
 
@@ -128,7 +151,6 @@
     model_name = f'taste_{taste_ind}_hidden_{hidden_size}_loss_{loss_name}'
     model_save_path = os.path.join(artifacts_dir, f'{model_name}.pt')
 
-    # taste_spikes = np.concatenate(spike_array)
     # Cut taste_spikes to time limits
     # Shape: (trials, neurons, time)
     taste_spikes = taste_spikes[..., time_lims[0]:time_lims[1]]
@@ -161,7 +183,6 @@
 
     # Perform standard scaling
     scaler = StandardScaler()
-    # scaler = MinMaxScaler()
     inputs_long = scaler.fit_transform(inputs_long)
 
     if use_pca: 
@@ -171,9 +192,6 @@
         inputs_pca = pca_obj.fit_transform(inputs_long)
         n_components = inputs_pca.shape[-1]
 
-        # # Scale the PCA outputs
-        # pca_scaler = StandardScaler()
-        # inputs_pca = pca_scaler.fit_transform(inputs_pca)
 
         inputs_trial_pca = inputs_pca.reshape(inputs.shape[0], -1, n_components)
 
@@ -301,8 +319,6 @@
                 )
         # Save artifacts and plots
         torch.save(net, model_save_path)
-        # np.save(loss_path, loss)
-        # np.save(cross_val_loss_path, cross_val_loss)
         with open(loss_path, 'w') as f:
             json.dump(loss, f)
         with open(cross_val_loss_path, 'w') as f:
@@ -349,12 +365,6 @@
 
     # If pca was performed, first reverse PCA, then reverse pca standard scaling
     if use_pca: 
-        # # Reverse NMF scaling
-        # pred_firing_long = nmf_scaler.inverse_transform(pred_firing_long)
-        # pred_firing_long = pca_scaler.inverse_transform(pred_firing_long)
-
-        # Reverse NMF transform
-        # pred_firing_long = nmf_obj.inverse_transform(pred_firing_long)
         pred_firing_long = pca_obj.inverse_transform(pred_firing_long)
 
     # Reverse standard scaling