Merge branch 'rc/2.13.3'

allensdk/__init__.py

@@ -35,7 +35,7 @@
 #
 import logging
 
-__version__ = '2.13.2'
+__version__ = '2.13.3'
 
 
 try:

allensdk/brain_observatory/behavior/data_objects/eye_tracking/eye_tracking_table.py

@@ -188,9 +188,9 @@ def from_nwb(cls, nwbfile: NWBFile,
             dilation_frames=dilation_frames)
 
         eye_tracking_data["likely_blink"] = likely_blinks
-        eye_tracking_data.at[likely_blinks, "eye_area"] = np.nan
-        eye_tracking_data.at[likely_blinks, "pupil_area"] = np.nan
-        eye_tracking_data.at[likely_blinks, "cr_area"] = np.nan
+        eye_tracking_data.loc[likely_blinks, "eye_area"] = np.nan
+        eye_tracking_data.loc[likely_blinks, "pupil_area"] = np.nan
+        eye_tracking_data.loc[likely_blinks, "cr_area"] = np.nan
 
         return EyeTrackingTable(eye_tracking=eye_tracking_data)
 

allensdk/brain_observatory/behavior/stimulus_processing.py

@@ -283,13 +283,21 @@ def get_stimulus_metadata(pkl) -> pd.DataFrame:
         stimulus_index_df = pd.DataFrame(columns=[
             'image_name', 'image_category', 'image_set', 'phase',
             'spatial_frequency', 'image_index'])
+        stimulus_index_df = stimulus_index_df.astype({
+            'image_name': str,
+            'image_category': str,
+            'image_set': str,
+            'phase': float,
+            'spatial_frequency': float,
+            'image_index': int
+        })
 
     # get the grating metadata will be empty if gratings are absent
     grating_df = get_gratings_metadata(stimuli,
                                        start_idx=len(stimulus_index_df))
-    stimulus_index_df = stimulus_index_df.append(grating_df,
-                                                 ignore_index=True,
-                                                 sort=False)
+    stimulus_index_df = pd.concat([stimulus_index_df, grating_df],
+                                  ignore_index=True,
+                                  sort=False)
 
     # Add an entry for omitted stimuli
     omitted_df = pd.DataFrame({'image_category': ['omitted'],
@@ -299,8 +307,9 @@ def get_stimulus_metadata(pkl) -> pd.DataFrame:
                                'phase': np.NaN,
                                'spatial_frequency': np.NaN,
                                'image_index': len(stimulus_index_df)})
-    stimulus_index_df = stimulus_index_df.append(omitted_df, ignore_index=True,
-                                                 sort=False)
+    stimulus_index_df = pd.concat([stimulus_index_df, omitted_df],
+                                  ignore_index=True,
+                                  sort=False)
     stimulus_index_df.set_index(['image_index'], inplace=True, drop=True)
     return stimulus_index_df
 

allensdk/brain_observatory/multi_stimulus_running_speed/README.md

@@ -0,0 +1,29 @@
+Extract running speed
+=====================
+Calculates an average running speed for the subject on each stimulus frame.
+
+
+Running
+-------
+```
+python -m allensdk.brain_observatory.multi_stimulus_running_speed --input_json <path to input json> --output_json <path to output json>
+```
+See the schema file for detailed information about input json contents.
+
+
+Input data
+----------
+- Mapping stimulus pickle : Contains information about the mapping stimuli that were 
+presented in this experiment.
+- Behavior stimulus pickle : Contains information about the behavior stimuli that were 
+presented in this experiment.
+- Replay stimulus pickle : Contains information about the replay stimuli that were 
+presented in this experiment.
+- Sync h5 : Contains information about the times at which each frame was presented.
+
+
+Output data
+-----------
+- Running speeds h5 : Contains two tables. These are:
+  - running_speed : rows are intervals. Columns list frame times, frame indexes, mean velocities, and the net rotations from which those velocities are calculated. Known artifacts are removed, but the data are otherwise unfiltered.
+  - raw_data : rows are samples. Columns list acquisition times, signal and supply voltages, and net rotations since the last timestamp.

allensdk/brain_observatory/multi_stimulus_running_speed/__main__.py

@@ -0,0 +1,8 @@
+from allensdk.brain_observatory.\
+    multi_stimulus_running_speed.multi_stimulus_running_speed import (
+        MultiStimulusRunningSpeed
+    )
+
+if __name__ == "__main__":
+    multi_stimulus_running_speed = MultiStimulusRunningSpeed()
+    multi_stimulus_running_speed.process()

allensdk/brain_observatory/multi_stimulus_running_speed/_schemas.py

@@ -0,0 +1,60 @@
+import argschema
+from argschema.fields import Nested
+
+
+class MultiStimulusRunningSpeedInputParameters(argschema.ArgSchema):
+    output_path = argschema.fields.OutputFile(
+        required=True,
+        description="The location to write the output file"
+    )
+
+    mapping_pkl_path = argschema.fields.InputFile(
+        required=True,
+        help="path to pkl file containing raw stimulus information",
+    )
+    behavior_pkl_path = argschema.fields.InputFile(
+        required=True,
+        help="path to pkl file containing raw stimulus information",
+    )
+    replay_pkl_path = argschema.fields.InputFile(
+        required=True,
+        help="path to pkl file containing raw stimulus information",
+    )
+    sync_h5_path = argschema.fields.InputFile(
+        required=True,
+        help="path to h5 file containing synchronization information",
+    )
+
+    use_lowpass_filter = argschema.fields.Bool(
+        required=True,
+        default=True,
+        description=(
+            "apply a low pass filter to the running speed results"
+            )
+        )
+
+    zscore_threshold = argschema.fields.Float(
+        required=True,
+        default=10.0,
+        description=(
+            "The threshold to use for removing outlier "
+            "running speeds which might be noise and not true signal"
+        )
+    )
+
+
+class MultiStimulusRunningSpeedOutputSchema(argschema.schemas.DefaultSchema):
+    input_parameters = Nested(
+        MultiStimulusRunningSpeedInputParameters,
+        description=("Input parameters the module was run with"),
+        required=True,
+    )
+
+
+class MultiStimulusRunningSpeedOutputParameters(
+    MultiStimulusRunningSpeedOutputSchema
+):
+    output_path = argschema.fields.OutputFile(
+        required=True,
+        help="Filtered running speed hdf5 output file."
+    )