Merge pull request #1953 from AllenInstitute/1919-time-column-names

1919-time-column-names

Author: djkapner

allensdk/brain_observatory/behavior/eye_tracking_processing.py

@@ -231,7 +231,7 @@ def process_eye_tracking_data(eye_data: pd.DataFrame,
     cr_areas[likely_blinks] = np.nan
     eye_areas[likely_blinks] = np.nan
 
-    eye_data.insert(0, "time", frame_times)
+    eye_data.insert(0, "timestamps", frame_times)
     eye_data.insert(1, "cr_area", cr_areas)
     eye_data.insert(2, "eye_area", eye_areas)
     eye_data.insert(3, "pupil_area", pupil_areas)

allensdk/brain_observatory/behavior/rewards_processing.py

@@ -1,7 +1,6 @@
 from typing import Dict
 import numpy as np
 import pandas as pd
-from collections import defaultdict
 
 
 def get_rewards(data: Dict,
@@ -31,13 +30,14 @@ def get_rewards(data: Dict,
     trial_df = pd.DataFrame(data["items"]["behavior"]["trial_log"])
     rewards_dict = {"volume": [], "timestamps": [], "autorewarded": []}
     for idx, trial in trial_df.iterrows():
-        rewards = trial["rewards"]  # as i write this there can only ever be one reward per trial
+        rewards = trial["rewards"]
+        # as i write this there can only ever be one reward per trial
         if rewards:
             rewards_dict["volume"].append(rewards[0][0])
             rewards_dict["timestamps"].append(timestamps[rewards[0][2]])
             auto_rwrd = trial["trial_params"]["auto_reward"]
             rewards_dict["autorewarded"].append(auto_rwrd)
 
-    df = pd.DataFrame(rewards_dict).set_index("timestamps", drop=True)
+    df = pd.DataFrame(rewards_dict)
 
     return df

allensdk/brain_observatory/behavior/session_apis/data_io/behavior_nwb_api.py

@@ -224,7 +224,7 @@ def get_licks(self) -> np.ndarray:
             licks = lick_module.get_data_interface('licks')
 
             return pd.DataFrame({
-                'time': licks.timestamps[:],
+                'timestamps': licks.timestamps[:],
                 'frame': licks.data[:]
             })
         else:
@@ -238,11 +238,11 @@ def get_rewards(self) -> np.ndarray:
             volume = rewards.get_data_interface('volume').data[:]
             return pd.DataFrame({
                 'volume': volume, 'timestamps': time,
-                'autorewarded': autorewarded}).set_index('timestamps')
+                'autorewarded': autorewarded})
         else:
             return pd.DataFrame({
                 'volume': [], 'timestamps': [],
-                'autorewarded': []}).set_index('timestamps')
+                'autorewarded': []})
 
     def get_metadata(self) -> dict:
 

allensdk/brain_observatory/behavior/session_apis/data_io/behavior_ophys_nwb_api.py

@@ -233,7 +233,7 @@ def get_eye_tracking(self,
             eye_tracking_acquisition.corneal_reflection_tracking
 
         eye_tracking_dict = {
-            "time": eye_tracking.timestamps[:],
+            "timestamps": eye_tracking.timestamps[:],
             "cr_area": corneal_reflection_tracking.area_raw[:],
             "eye_area": eye_tracking.area_raw[:],
             "pupil_area": pupil_tracking.area_raw[:],
@@ -480,7 +480,7 @@ def add_eye_tracking_data_to_nwb(self, nwbfile: NWBFile,
             width=eye_tracking_df['eye_width'].values,
             height=eye_tracking_df['eye_height'].values,
             angle=eye_tracking_df['eye_phi'].values,
-            timestamps=eye_tracking_df['time'].values
+            timestamps=eye_tracking_df['timestamps'].values
         )
 
         pupil_tracking = EllipseSeries(

allensdk/brain_observatory/behavior/session_apis/data_transforms/behavior_data_transforms.py

@@ -110,7 +110,7 @@ def get_licks(self) -> pd.DataFrame:
                                   'range')
 
         lick_times = [stimulus_timestamps[frame] for frame in lick_frames]
-        return pd.DataFrame({"time": lick_times, "frame": lick_frames})
+        return pd.DataFrame({"timestamps": lick_times, "frame": lick_frames})
 
     @memoize
     def get_rewards(self) -> pd.DataFrame:

allensdk/brain_observatory/behavior/trials_processing.py

@@ -519,15 +519,14 @@ def get_trials_from_data_transform(input_transform) -> pd.DataFrame:
     monitor_delay = input_transform.get_monitor_delay()
     data = input_transform._behavior_stimulus_file()
 
-    assert rewards_df.index.name == 'timestamps'
     stimuli = data["items"]["behavior"]["stimuli"]
     trial_log = data["items"]["behavior"]["trial_log"]
 
     trial_bounds = get_trial_bounds(trial_log)
 
     all_trial_data = [None] * len(trial_log)
     lick_frames = licks_df.frame.values
-    reward_times = rewards_df.index.values
+    reward_times = rewards_df['timestamps'].values
 
     for idx, trial in enumerate(trial_log):
         # match each event in the trial log to the sync timestamps
@@ -628,7 +627,8 @@ def get_time(exp_data):
 def data_to_licks(data, time):
     lick_frames = data['items']['behavior']['lick_sensors'][0]['lick_events']
     lick_times = time[lick_frames]
-    return pd.DataFrame(data={"frame": lick_frames, 'time': lick_times})
+    return pd.DataFrame(data={"timestamps": lick_times,
+                              "frame": lick_frames})
 
 
 def get_mouse_id(exp_data):
@@ -926,12 +926,12 @@ def find_licks(reward_times, licks, window=3.5):
         return []
     else:
         reward_time = one(reward_times)
-        reward_lick_mask = ((licks['time'] > reward_time) &
-                            (licks['time'] < (reward_time + window)))
+        reward_lick_mask = ((licks['timestamps'] > reward_time) &
+                            (licks['timestamps'] < (reward_time + window)))
 
         tr_licks = licks[reward_lick_mask].copy()
-        tr_licks['time'] -= reward_time
-        return tr_licks['time'].values
+        tr_licks['timestamps'] -= reward_time
+        return tr_licks['timestamps'].values
 
 
 def calculate_reward_rate(response_latency=None,