Layout cleanup

blech_make_arrays.py

@@ -211,7 +211,7 @@ def create_emg_trials_for_digin(
             match_ind = np.where(
                     np.abs(taste_info_frame['start'] - this_start) < match_tol
                     )[0]
-            assert len(match_ind) == 1, 'Exact match not found'
+            assert len(match_ind) == 1, f'Exact match not found between taste and laser signals given tolerance of {(match_tol)/sampling_rate} sec'
             match_trials_ind.append(match_ind[0])
         match_trials = taste_info_frame.iloc[match_trials_ind]['abs_trial_num'].values
         laser_info_frame['abs_trial_num'] = match_trials
@@ -479,4 +479,4 @@ def create_emg_trials_for_digin(
     hf5.close()
 
     # Write successful execution to log
-    this_pipeline_check.write_to_log(script_path, 'completed')
+    this_pipeline_check.write_to_log(script_path, 'completed')

params/_templates/sorting_params_template.json

@@ -53,6 +53,10 @@
         2000,
         5000
     ],
+    "responsiveness_pre_post_durations": [
+        1000,
+        2000
+    ], 
     "pal_iden_calc_params": {
         "window_size": 250,
         "step_size": 25

params/dependency_graph.json

@@ -7,10 +7,8 @@
             "blech_process.py": "blech_common_avg_reference.py",
             "blech_post_process.py": "blech_process.py",
             "blech_units_plot.py": "blech_post_process.py",
-            "blech_make_psth.py": "blech_make_arrays.py",
             "emg/emg_filter.py": "blech_make_arrays.py",
-            "blech_palatability_identity_setup.py": "blech_make_psth.py",
-            "blech_overlay_psth.py": "blech_palatability_identity_setup.py",
+            "blech_unit_characteristics.py": "blech_make_arrays.py",
             "utils/qa_utils/unit_similarity.py": "blech_post_process.py",
             "utils/qa_utils/drift_check.py": "blech_post_process.py"
         },

pipeline_testing/test_data_handling/download_test_data.sh

@@ -1,3 +1,5 @@
+# Google Drive link : https://drive.google.com/file/d/1EcpUIqp81h3J89-6dEEueeULqBlKW5a7/view?usp=sharing
+
 SCRIPT_PATH=$(realpath ${BASH_SOURCE[0]})
 # Find path to neuRecommend as parent directory of test.sh
 DIR_PATH=$(dirname ${SCRIPT_PATH})

utils/cluster_stability.py

@@ -22,18 +22,19 @@
 import seaborn as sns
 import matplotlib
 
-def load_electrode_data(electrode_num): 
+def load_electrode_data(data_dir, electrode_num): 
     """
     Load data from disk
     """
 
     loading_paths = [\
-        f'./spike_waveforms/electrode{electrode_num:02}/spike_waveforms.npy',
-        f'./spike_times/electrode{electrode_num:02}/spike_times.npy',
-        f'./spike_waveforms/electrode{electrode_num:02}/pca_waveforms.npy',
-        f'./spike_waveforms/electrode{electrode_num:02}/energy.npy',
-        f'./spike_waveforms/electrode{electrode_num:02}/spike_amplitudes.npy',
+        f'spike_waveforms/electrode{electrode_num:02}/spike_waveforms.npy',
+        f'spike_times/electrode{electrode_num:02}/spike_times.npy',
+        f'spike_waveforms/electrode{electrode_num:02}/pca_waveforms.npy',
+        f'spike_waveforms/electrode{electrode_num:02}/energy.npy',
+        f'spike_waveforms/electrode{electrode_num:02}/spike_amplitudes.npy',
         ]
+    loading_paths = [os.path.join(data_dir, x) for x in loading_paths]
 
     loaded_dat = [np.load(x) for x in loading_paths]
 
@@ -78,7 +79,7 @@ def return_clustering_solutions(electrode_num):
 	pca_slices,
 	energy,
 	amplitudes,
-) = load_electrode_data(electrode_num) 
+) = load_electrode_data(data_dir, electrode_num) 
 
 # Load predictions for all clustering solutions
 predictions = [load_cluster_predictions(electrode_num, x) \