typo fix, get corpus for each trial

README.md

@@ -32,7 +32,7 @@ The data used in this repository (which can be downloaded from [Dryad](https://d
 - `t15_copyTask.pkl`: This file contains the online Copy Task results required for generating Figure 2.
 - `t15_personalUse.pkl`: This file contains the Conversation Mode data required for generating Figure 4.
 - `t15_copyTask_neuralData.zip`: This dataset contains the neural data for the Copy Task.
-    - There are more than 11,300 sentences from 45 sessions spanning 20 months. Each trial of data includes: 
+    - There are 10,948 sentences from 45 sessions spanning 20 months. Each trial of data includes: 
         - The session date, block number, and trial number
         - 512 neural features (2 features [-4.5 RMS threshold crossings and spike band power] per electrode, 256 electrodes), binned at 20 ms resolution. The data were recorded from the speech motor cortex via four high-density microelectrode arrays (64 electrodes each). The 512 features are ordered as follows in all data files: 
             - 0-64: ventral 6v threshold crossings

model_training/evaluate_model.py

@@ -21,6 +21,8 @@ parser.add_argument('--data_dir', type=str, default='../data/hdf5_data_final',
 parser.add_argument('--eval_type', type=str, default='test', choices=['val', 'test'],
                     help='Evaluation type: "val" for validation set, "test" for test set. '
                          'If "test", ground truth is not available.')
+parser.add_argument('--csv_path', type=str, default='../data/t15_copyTaskData_description.csv',
+                    help='Path to the CSV file with metadata about the dataset (relative to the current working directory).')
 parser.add_argument('--gpu_number', type=int, default=1,
                     help='GPU number to use for RNN model inference. Set to -1 to use CPU.')
 args = parser.parse_args()
@@ -33,6 +35,9 @@ data_dir = args.data_dir
 # define evaluation type
 eval_type = args.eval_type  # can be 'val' or 'test'. if 'test', ground truth is not available
 
+# load csv file
+b2txt_csv_df = pd.read_csv(args.csv_path)
+
 # load model args
 model_args = OmegaConf.load(os.path.join(model_path, 'checkpoint/args.yaml'))
 
@@ -85,7 +90,7 @@ for session in model_args['dataset']['sessions']:
     if f'data_{eval_type}.hdf5' in files:
         eval_file = os.path.join(data_dir, session, f'data_{eval_type}.hdf5')
 
-        data = load_h5py_file(eval_file)
+        data = load_h5py_file(eval_file, b2txt_csv_df)
         test_data[session] = data
 
         total_test_trials += len(test_data[session]["neural_features"])

model_training/evaluate_model_helpers.py

@@ -26,7 +26,7 @@ def _extract_transcription(input):
         trans += chr(input[c])
     return trans
 
-def load_h5py_file(file_path):
+def load_h5py_file(file_path, b2txt_csv_df):
     data = {
         'neural_features': [],
         'n_time_steps': [],
@@ -36,7 +36,8 @@ def load_h5py_file(file_path):
         'sentence_label': [],
         'session': [],
         'block_num': [],
-        'trial_num': []
+        'trial_num': [],
+        'corpus': [],
     }
     # Open the hdf5 file for that day
     with h5py.File(file_path, 'r') as f:
@@ -57,6 +58,11 @@ def load_h5py_file(file_path):
             block_num = g.attrs['block_num']
             trial_num = g.attrs['trial_num']
 
+            # match this trial up with the csv to get the corpus name
+            year, month, day = session.split('.')[1:]
+            date = f'{year}-{month}-{day}'
+            row = b2txt_csv_df[(b2txt_csv_df['Date'] == date) & (b2txt_csv_df['Block number'] == block_num)]
+            corpus_name = row['Corpus'].values[0]
 
             data['neural_features'].append(neural_features)
             data['n_time_steps'].append(n_time_steps)
@@ -67,6 +73,7 @@ def load_h5py_file(file_path):
             data['session'].append(session)
             data['block_num'].append(block_num)
             data['trial_num'].append(trial_num)
+            data['corpus'].append(corpus_name)
     return data
 
 def rearrange_speech_logits_pt(logits):