allensdk.brain_observatory.ecephys.stimulus_analysis package¶
Submodules¶
- allensdk.brain_observatory.ecephys.stimulus_analysis.dot_motion module
- allensdk.brain_observatory.ecephys.stimulus_analysis.drifting_gratings module
DriftingGratingsDriftingGratings.METRICS_COLUMNSDriftingGratings.conditionwise_statistics_contrastDriftingGratings.contrastvalsDriftingGratings.known_stimulus_keys()DriftingGratings.make_star_plot()DriftingGratings.metricsDriftingGratings.nameDriftingGratings.null_conditionDriftingGratings.number_contrastDriftingGratings.number_oriDriftingGratings.number_tfDriftingGratings.orivalsDriftingGratings.plot_raster()DriftingGratings.plot_response_summary()DriftingGratings.stim_table_contrastDriftingGratings.stimulus_conditions_contrastDriftingGratings.tfvals
c50()f1_f0()modulation_index()
- allensdk.brain_observatory.ecephys.stimulus_analysis.flashes module
- allensdk.brain_observatory.ecephys.stimulus_analysis.natural_movies module
- allensdk.brain_observatory.ecephys.stimulus_analysis.natural_scenes module
- allensdk.brain_observatory.ecephys.stimulus_analysis.receptive_field_mapping module
ReceptiveFieldMappingReceptiveFieldMapping.METRICS_COLUMNSReceptiveFieldMapping.azimuthsReceptiveFieldMapping.elevationsReceptiveFieldMapping.get_receptive_field()ReceptiveFieldMapping.known_stimulus_keys()ReceptiveFieldMapping.metricsReceptiveFieldMapping.nameReceptiveFieldMapping.null_conditionReceptiveFieldMapping.number_azimuthsReceptiveFieldMapping.number_elevationsReceptiveFieldMapping.plot_raster()ReceptiveFieldMapping.plot_rf()ReceptiveFieldMapping.receptive_fields
convert_azimuth_to_degrees()convert_elevation_to_degrees()convert_pixel_area_to_degrees()convert_pixels_to_degrees()fit_2d_gaussian()gaussian_moments_2d()invert_rf()is_rf_inverted()rf_on_screen()threshold_rf()
- allensdk.brain_observatory.ecephys.stimulus_analysis.static_gratings module
StaticGratingsStaticGratings.METRICS_COLUMNSStaticGratings.known_stimulus_keys()StaticGratings.make_fan_plot()StaticGratings.metricsStaticGratings.nameStaticGratings.null_conditionStaticGratings.number_oriStaticGratings.number_phaseStaticGratings.number_sfStaticGratings.orivalsStaticGratings.phasevalsStaticGratings.plot_raster()StaticGratings.plot_response_summary()StaticGratings.sfvals
exp_function()fit_sf_tuning()gauss_function()get_sfdi()
- allensdk.brain_observatory.ecephys.stimulus_analysis.stimulus_analysis module
StimulusAnalysisStimulusAnalysis.METRICS_COLUMNSStimulusAnalysis.conditionwise_psthStimulusAnalysis.conditionwise_statisticsStimulusAnalysis.ecephys_sessionStimulusAnalysis.empty_metrics_table()StimulusAnalysis.get_intrinsic_timescale()StimulusAnalysis.known_spontaneous_keysStimulusAnalysis.known_stimulus_keys()StimulusAnalysis.metricsStimulusAnalysis.metrics_dtypesStimulusAnalysis.metrics_namesStimulusAnalysis.nameStimulusAnalysis.null_conditionStimulusAnalysis.plot_conditionwise_raster()StimulusAnalysis.plot_raster()StimulusAnalysis.presentationwise_spike_timesStimulusAnalysis.presentationwise_statisticsStimulusAnalysis.running_speedStimulusAnalysis.spikesStimulusAnalysis.stim_tableStimulusAnalysis.stim_table_spontaneousStimulusAnalysis.stimulus_conditionsStimulusAnalysis.total_presentationsStimulusAnalysis.trial_durationStimulusAnalysis.unit_countStimulusAnalysis.unit_ids
calculate_time_delayed_correlation()deg2rad()dsi()fano_factor()fit_exp()get_fr()lifetime_sparseness()osi()overall_firing_rate()reliability()running_modulation()
Module contents¶
- class allensdk.brain_observatory.ecephys.stimulus_analysis.DotMotion(ecephys_session, col_dir='Dir', col_speeds='Speed', trial_duration=1.0, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the dot motion stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) dm_analysis = DotMotion(session)
- or, alternatively, pass in the file path::
dm_analysis = DotMotion(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
dm_analysis = DotMotion(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- or a list of unit_ids:
dm_analysis = DotMotion(session, filter=[914580630, 914580280, 914580278])
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = dm_analysis.metrics()
- property METRICS_COLUMNS¶
- property directions¶
- property known_spontaneous_keys¶
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null stimulus (not used, so set to -1)
- property number_directions¶
- property number_speeds¶
- property speeds¶
- class allensdk.brain_observatory.ecephys.stimulus_analysis.DriftingGratings(ecephys_session, col_ori='orientation', col_tf='temporal_frequency', col_contrast='contrast', trial_duration=2.0, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the drifting gratings stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) dg_analysis = DriftingGratings(session)
- or, alternatively, pass in the file path::
dg_analysis = DriftingGratings(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
dg_analysis = DriftingGratings(session, filter={'location': 'probeC', 'structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = dg_analysis.metrics()
- property METRICS_COLUMNS¶
- property conditionwise_statistics_contrast¶
Conditionwise statistics for contrast stimulus
- property contrastvals¶
Array of grating temporal frequency conditions
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null (blank) stimulus
- property number_contrast¶
Number of grating temporal frequency conditions
- property number_ori¶
Number of grating orientation conditions
- property number_tf¶
Number of grating temporal frequency conditions
- property orivals¶
Array of grating orientation conditions
- property stim_table_contrast¶
- property stimulus_conditions_contrast¶
Stimulus conditions for contrast stimulus
- property tfvals¶
Array of grating temporal frequency conditions
- class allensdk.brain_observatory.ecephys.stimulus_analysis.Flashes(ecephys_session, col_color='color', trial_duration=0.25, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the full-field flash stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) fl_analysis = Flashes(session)
- or, alternatively, pass in the file path::
fl_analysis = Flashes(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
fl_analysis = Flashes(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = fl_analysis.metrics()
- property METRICS_COLUMNS¶
- property colors¶
Array of ‘color’ conditions (black vs. white flash)
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null stimulus (not used, so set to -1)
- property number_colors¶
Number of ‘color’ conditions (black vs. white flash)
- class allensdk.brain_observatory.ecephys.stimulus_analysis.NaturalMovies(ecephys_session, trial_duration=None, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the natural movies stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) nm_analysis = NaturalMovies(session)
- or, alternatively, pass in the file path::
nm_analysis = Flashes(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
nm_analysis = NaturalMovies(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = nm_analysis.metrics()
TODO: Need to find a default trial_duration otherwise class will fail
- property METRICS_COLUMNS¶
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
- class allensdk.brain_observatory.ecephys.stimulus_analysis.NaturalScenes(ecephys_session, col_image='frame', trial_duration=0.25, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the natural scenes stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) ns_analysis = NaturalScenes(session)
- or, alternatively, pass in the file path::
ns_analysis = NaturalScenes(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
ns_analysis = NaturalScenes(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = ns_analysis.metrics()
- property METRICS_COLUMNS¶
- property frames¶
- property images¶
Array of iamge labels
- property images_nonblank¶
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null (blank) stimulus
- property number_images¶
Number of images shown
- property number_nonblank¶
Number of images shown (excluding blank condition)
- class allensdk.brain_observatory.ecephys.stimulus_analysis.ReceptiveFieldMapping(ecephys_session, col_pos_x='x_position', col_pos_y='y_position', trial_duration=0.25, minimum_spike_count=10.0, mask_threshold=0.5, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the receptive field mapping stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) rf_analysis = ReceptiveFieldMapping(session)
- or, alternatively, pass in the file path::
rf_analysis = ReceptiveFieldMapping(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
rf_analysis = ReceptiveFieldMapping( session, filter={ 'location': 'probeC', 'ecephys_structure_acronym': 'VISp' } )
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = rf_analysis.metrics()
- property METRICS_COLUMNS¶
- property azimuths¶
Array of stimulus azimuths
- property elevations¶
Array of stimulus elevations
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null stimulus (not used, so set to -1)
- property number_azimuths¶
Number of stimulus azimuths
- property number_elevations¶
Number of stimulus elevations
- property receptive_fields¶
Spatial receptive fields for N units (9 x 9 x N matrix of responses)
- class allensdk.brain_observatory.ecephys.stimulus_analysis.StaticGratings(ecephys_session, col_ori='orientation', col_sf='spatial_frequency', col_phase='phase', trial_duration=0.25, **kwargs)[source]¶
Bases:
StimulusAnalysisA class for computing single-unit metrics from the static gratings stimulus of an ecephys session NWB file.
- To use, pass in a EcephysSession object::
session = EcephysSession.from_nwb_path(‘/path/to/my.nwb’) sg_analysis = StaticGratings(session)
- or, alternatively, pass in the file path::
sg_analysis = StaticGratings(‘/path/to/my.nwb’)
You can also pass in a unit filter dictionary which will only select units with certain properties. For example to get only those units which are on probe C and found in the VISp area:
sg_analysis = StaticGratings(session, filter={'location': 'probeC', 'ecephys_structure_acronym': 'VISp'})
- To get a table of the individual unit metrics ranked by unit ID::
metrics_table_df = sg_analysis.metrics()
- property METRICS_COLUMNS¶
- classmethod known_stimulus_keys()[source]¶
Used for discovering the correct stimulus_name key for a given StimulusAnalysis subclass (when stimulus_key is not explicity set). Should return a list of “stimulus_name” strings.
- property metrics¶
Returns a pandas DataFrame of the stimulus response metrics for each unit.
- property name¶
Return the stimulus name.
- property null_condition¶
Stimulus condition ID for null (blank) stimulus
- property number_ori¶
Number of grating orientation conditions
- property number_phase¶
Number of grating phase conditions
- property number_sf¶
Number of grating orientation conditions
- property orivals¶
Array of grating orientation conditions
- property phasevals¶
Array of grating phase conditions
- property sfvals¶
Array of grating spatial frequency conditions