bmtk.utils.brain_observatory package#

Subpackages#

Submodules#

bmtk.utils.brain_observatory.brain_observatory_cache module#

class bmtk.utils.brain_observatory.brain_observatory_cache.BrainObservatoryApi(base_uri=None, datacube_uri=None)[source]#

Bases: RmaTemplate

NWB_FILE_TYPE = 'NWBOphys'#

OPHYS_ANALYSIS_FILE_TYPE = 'OphysExperimentCellRoiMetricsFile'#

OPHYS_EVENTS_FILE_TYPE = 'ObservatoryEventsFile'#

rma_templates = {'brain_observatory_queries': [{'count': False, 'criteria_params': [], 'description': 'see name', 'model': 'IsiExperiment', 'name': 'list_isi_experiments', 'num_rows': 'all'}, {'count': False, 'criteria': '[id$in{{ isi_experiment_ids }}]', 'criteria_params': ['isi_experiment_ids'], 'description': 'see name', 'include': 'experiment_container(ophys_experiments,targeted_structure)', 'model': 'IsiExperiment', 'name': 'isi_experiment_by_ids', 'num_rows': 'all'}, {'count': False, 'criteria': '{% if ophys_experiment_ids is defined %}[id$in{{ ophys_experiment_ids }}]{%endif%}', 'criteria_params': ['ophys_experiment_ids'], 'description': 'see name', 'include': 'experiment_container,well_known_files(well_known_file_type),targeted_structure,specimen(donor(age,transgenic_lines))', 'model': 'OphysExperiment', 'name': 'ophys_experiment_by_ids', 'num_rows': 'all'}, {'count': False, 'criteria': '[attachable_id$eq{{ ophys_experiment_id }}],well_known_file_type[name$eqNWBOphys]', 'criteria_params': ['ophys_experiment_id'], 'description': 'see name', 'model': 'WellKnownFile', 'name': 'ophys_experiment_data', 'num_rows': 'all'}, {'count': False, 'criteria': '[attachable_id$eq{{ ophys_experiment_id }}],well_known_file_type[name$eqOphysExperimentCellRoiMetricsFile]', 'criteria_params': ['ophys_experiment_id'], 'description': 'see name', 'model': 'WellKnownFile', 'name': 'ophys_analysis_file', 'num_rows': 'all'}, {'count': False, 'criteria': '[attachable_id$eq{{ ophys_experiment_id }}],well_known_file_type[name$eqObservatoryEventsFile]', 'criteria_params': ['ophys_experiment_id'], 'description': 'see name', 'model': 'WellKnownFile', 'name': 'ophys_events_file', 'num_rows': 'all'}, {'count': False, 'criteria': '[api_class_name$eq{{ api_class_name }}]', 'criteria_params': ['api_class_name'], 'description': 'see name', 'model': 'ApiColumnDefinition', 'name': 'column_definitions', 'num_rows': 'all'}, {'count': False, 'description': 'see name', 'model': 'ApiColumnDefinition', 'name': 'column_definition_class_names', 'num_rows': 'all', 'only': ['api_class_name']}, {'count': False, 'criteria': '{% if stimulus_mapping_ids is defined %}[id$in{{ stimulus_mapping_ids }}]{%endif%}', 'criteria_params': ['stimulus_mapping_ids'], 'description': 'see name', 'model': 'ApiCamStimulusMapping', 'name': 'stimulus_mapping', 'num_rows': 'all'}, {'count': False, 'criteria': '{% if experiment_container_ids is defined %}[id$in{{ experiment_container_ids }}]{%endif%}', 'criteria_params': ['experiment_container_ids'], 'description': 'see name', 'include': 'ophys_experiments,isi_experiment,specimen(donor(conditions,age,transgenic_lines)),targeted_structure', 'model': 'ExperimentContainer', 'name': 'experiment_container', 'num_rows': 'all'}, {'count': False, 'criteria': '{% if experiment_container_metric_ids is defined %}[id$in{{ experiment_container_metric_ids }}]{%endif%}', 'criteria_params': ['experiment_container_metric_ids'], 'description': 'see name', 'model': 'ApiCamExperimentContainerMetric', 'name': 'experiment_container_metric', 'num_rows': 'all'}, {'criteria': '{% if cell_specimen_ids is defined %}[cell_specimen_id$in{{ cell_specimen_ids }}]{%endif%}', 'criteria_params': ['cell_specimen_ids'], 'description': 'see name', 'model': 'ApiCamCellMetric', 'name': 'cell_metric'}, {'count': False, 'criteria': '[id$eq{{ mapping_table_id }}],well_known_file_type[name$eqOphysCellSpecimenIdMapping]', 'criteria_params': ['mapping_table_id'], 'description': 'see name', 'model': 'WellKnownFile', 'name': 'cell_specimen_id_mapping_table', 'num_rows': 'all'}, {'count': False, 'criteria': '[attachable_id$eq{{ ophys_session_id }}],well_known_file_type[name$eqEyeDlcScreenMapping]', 'criteria_params': ['ophys_session_id'], 'description': 'h5 file containing mouse eye gaze mapped onto screen coordinates (as well as pupil and eye sizes)', 'model': 'WellKnownFile', 'name': 'eye_gaze_mapping_file', 'num_rows': 'all'}, {'count': False, 'criteria': 'well_known_file_type[name$eqEyeDlcScreenMapping]', 'description': 'Get a list of dictionaries for all eye mapping wkfs', 'model': 'WellKnownFile', 'name': 'all_eye_mapping_files', 'num_rows': 'all'}]}#

save_ophys_experiment_data(ophys_experiment_id, file_name)[source]#

class bmtk.utils.brain_observatory.brain_observatory_cache.BrainObservatoryCache(cache=True, manifest_file=None, base_uri=None, api=None)[source]#

Bases: Cache

ANALYSIS_DATA_KEY = 'ANALYSIS_DATA'#

CELL_SPECIMENS_KEY = 'CELL_SPECIMENS'#

EVENTS_DATA_KEY = 'EVENTS_DATA'#

EXPERIMENTS_KEY = 'EXPERIMENTS'#

EXPERIMENT_CONTAINERS_KEY = 'EXPERIMENT_CONTAINERS'#

EXPERIMENT_DATA_KEY = 'EXPERIMENT_DATA'#

EYE_GAZE_DATA_KEY = 'EYE_GAZE_DATA'#

MANIFEST_VERSION = '1.3'#

STIMULUS_MAPPINGS_KEY = 'STIMULUS_MAPPINGS'#

build_manifest(file_name)[source]#

Construct a manifest for this Cache class and save it in a file.

Parameters:

file_name: string: File location to save the manifest.

get_ophys_experiment_data(ophys_experiment_id, file_name=None)[source]#

Download the NWB file for an ophys_experiment (if it hasn’t already been downloaded) and return a data accessor object.

Parameters:

file_name: string: File name to save/read the data set. If file_name is None, the file_name will be pulled out of the manifest. If caching is disabled, no file will be saved. Default is None.
ophys_experiment_id: integer: id of the ophys_experiment to retrieve

Returns:

BrainObservatoryNwbDataSet

class bmtk.utils.brain_observatory.brain_observatory_cache.BrainObservatoryNwbDataSet(nwb_file)[source]#

Bases: object

FILE_METADATA_MAPPING = {'age': 'general/subject/age', 'device_string': 'general/devices/2-photon microscope', 'excitation_lambda': 'general/optophysiology/imaging_plane_1/excitation_lambda', 'experiment_container_id': 'general/experiment_container_id', 'fov': 'general/fov', 'generated_by': 'general/generated_by', 'genotype': 'general/subject/genotype', 'imaging_depth': 'general/optophysiology/imaging_plane_1/imaging depth', 'indicator': 'general/optophysiology/imaging_plane_1/indicator', 'ophys_experiment_id': 'general/session_id', 'session_start_time': 'session_start_time', 'session_type': 'general/session_type', 'sex': 'general/subject/sex', 'specimen_name': 'general/specimen_name', 'targeted_structure': 'general/optophysiology/imaging_plane_1/location'}#

MOTION_CORRECTION_DATASETS = ['MotionCorrection/2p_image_series/xy_translations', 'MotionCorrection/2p_image_series/xy_translation']#

PIPELINE_DATASET = 'brain_observatory_pipeline'#

STIMULUS_TABLE_TYPES = {'abstract_feature_series': ['drifting_gratings', 'static_gratings'], 'indexed_time_series': ['natural_scenes', 'locally_sparse_noise', 'locally_sparse_noise_4deg', 'locally_sparse_noise_8deg'], 'repeated_indexed_time_series': ['natural_movie_one', 'natural_movie_two', 'natural_movie_three']}#

SUPPORTED_PIPELINE_VERSION = '3.0'#

get_cell_specimen_ids()[source]#

Returns an array of cell IDs for all cells in the file

Returns:

cell specimen IDs: list

get_cell_specimen_indices(cell_specimen_ids)[source]#

Given a list of cell specimen ids, return their index based on their order in this file.

Parameters:

cell_specimen_ids: list of cell specimen ids

get_corrected_fluorescence_traces(cell_specimen_ids=None)[source]#

Returns an array of demixed and neuropil-corrected fluorescence traces for all ROIs and the timestamps for each datapoint

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return traces for. If this is None (default) then all are returned

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
traces: 2D numpy array: Corrected fluorescence traces for each cell

get_demixed_traces(cell_specimen_ids=None)[source]#

Returns an array of demixed fluorescence traces for all ROIs and the timestamps for each datapoint

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return traces for. If this is None (default) then all are returned

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
traces: 2D numpy array: Demixed fluorescence traces for each cell

get_dff_traces(cell_specimen_ids=None)[source]#

Returns an array of dF/F traces for all ROIs and the timestamps for each datapoint

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return data for. If this is None (default) then all are returned

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
dF/F: 2D numpy array: dF/F values for each cell

get_fluorescence_timestamps()[source]#: Returns an array of timestamps in seconds for the fluorescence traces

get_fluorescence_traces(cell_specimen_ids=None)[source]#

Returns an array of fluorescence traces for all ROI and the timestamps for each datapoint

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return traces for. If this is None (default) then all are returned

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
traces: 2D numpy array: Fluorescence traces for each cell

get_locally_sparse_noise_stimulus_template(stimulus, mask_off_screen=True)[source]#

Return an array of the stimulus template for the specified stimulus.

Parameters:

stimulus: string

Which locally sparse noise stimulus to retrieve. Must be one of:: stimulus_info.LOCALLY_SPARSE_NOISE stimulus_info.LOCALLY_SPARSE_NOISE_4DEG stimulus_info.LOCALLY_SPARSE_NOISE_8DEG

mask_off_screen: boolean

Set off-screen regions of the stimulus to LocallySparseNoise.LSN_OFF_SCREEN.

Returns:

tuple: (template, off-screen mask)

get_max_projection()[source]#

Returns the maximum projection image for the 2P movie.

Returns:

max projection: np.ndarray

get_metadata()[source]#

Returns a dictionary of meta data associated with each experiment, including Cre line, specimen number, visual area imaged, imaging depth

Returns:

metadata: dictionary

get_motion_correction()[source]#: Returns a Panda DataFrame containing the x- and y- translation of each image used for image alignment

get_neuropil_r(cell_specimen_ids=None)[source]#

Returns a scalar value of r for neuropil correction of flourescence traces

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return traces for. If this is None (default) then results for all are returned

Returns:

r: 1D numpy array, len(r)=len(cell_specimen_ids): Scalar for neuropil subtraction for each cell

get_neuropil_traces(cell_specimen_ids=None)[source]#

Returns an array of neuropil fluorescence traces for all ROIs and the timestamps for each datapoint

Parameters:

cell_specimen_ids: list or array (optional): List of cell IDs to return traces for. If this is None (default) then all are returned

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
traces: 2D numpy array: Neuropil fluorescence traces for each cell

get_pupil_location(as_spherical=True)[source]#

Returns the x, y pupil location.

Parameters:

as_sphericalbool: Whether to return the location as spherical (default) or not. If true, the result is altitude and azimuth in degrees, otherwise it is x, y in centimeters. (0,0) is the center of the monitor.

Returns:

(timestamps, location): Timestamps is an (Nx1) array of timestamps in seconds. Location is an (Nx2) array of spatial location.

get_pupil_size()[source]#

Returns the pupil area in pixels.

Returns:

(timestamps, areas): Timestamps is an (Nx1) array of timestamps in seconds. Areas is an (Nx1) array of pupil areas in pixels.

get_roi_ids()[source]#

Returns an array of IDs for all ROIs in the file

Returns:

ROI IDs: list

get_roi_mask(cell_specimen_ids=None)[source]#

Returns an array of all the ROI masks

Parameters:

cell specimen IDs: list or array (optional): List of cell IDs to return traces for. If this is None (default) then all are returned

Returns:

List of ROI_Mask objects

get_roi_mask_array(cell_specimen_ids=None)[source]#

Return a numpy array containing all of the ROI masks for requested cells. If cell_specimen_ids is omitted, return all masks.

Parameters:

cell_specimen_ids: list: List of cell specimen ids. Default None.

Returns:

np.ndarray: NxWxH array, where N is number of cells

get_running_speed()[source]#: Returns the mouse running speed in cm/s

get_session_type()[source]#

Returns the type of experimental session, presently one of the following: three_session_A, three_session_B, three_session_C

Returns:

session type: string

get_stimulus(frame_ind)[source]#

get_stimulus_epoch_table()[source]#

Returns a pandas dataframe that summarizes the stimulus epoch duration for each acquisition time index in the experiment

Parameters:

None

Returns:

timestamps: 2D numpy array: Timestamp for each fluorescence sample
traces: 2D numpy array: Fluorescence traces for each cell

get_stimulus_table(stimulus_name)[source]#

Return a stimulus table given a stimulus name

Notes

For more information, see: http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf

get_stimulus_template(stimulus_name)[source]#

Return an array of the stimulus template for the specified stimulus.

Parameters:

stimulus_name: string: Must be one of the strings returned by list_stimuli().

Returns:

stimulus table: pd.DataFrame

list_stimuli()[source]#

Return a list of the stimuli presented in the experiment.

Returns:

stimuli: list of strings

property number_of_cells#: Number of cells in the experiment

save_analysis_arrays(*datasets)[source]#

save_analysis_dataframes(*tables)[source]#

property stimulus_search#

exception bmtk.utils.brain_observatory.brain_observatory_cache.NoEyeTrackingException[source]#: Bases: Exception

bmtk.utils.brain_observatory.cache module#

class bmtk.utils.brain_observatory.cache.Cache(manifest=None, cache=True, version=None, **kwargs)[source]#

Bases: object

add_manifest_paths(manifest_builder)[source]#

build_manifest(file_name)[source]#

get_cache_path(file_name, manifest_key, *args)[source]#

load_manifest(file_name, version=None)[source]#

bmtk.utils.brain_observatory.cache.get_default_manifest_file(cache_name)[source]#

bmtk.utils.brain_observatory.manifest module#

class bmtk.utils.brain_observatory.manifest.Manifest(config=None, relative_base_dir='.', version=None)[source]#

Bases: object

DIR = 'dir'#

DIRNAME = 'dir_name'#

FILE = 'file'#

VERSION = 'manifest_version'#

add_file(file_key, file_name, dir_key=None, path_format=None)[source]#

Insert a new file entry.

Parameters:

file_keystring: Reference to the entry.
file_namestring: Subtitutions of the %s, %d style allowed.
dir_keystring: Reference to the parent directory entry.
path_formatstring, optional: File type for further parsing.

add_path(key, path, path_type='dir', absolute=True, path_format=None, parent_key=None)[source]#

Insert a new entry.

Parameters:

keystring: Identifier for referencing the entry.
pathstring: Specification for a path using %s, %d style substitution.
path_typestring enumeration: ‘dir’ (default) or ‘file’
absoluteboolean: Is the spec relative to the process current directory.
path_formatstring, optional: Indicate a known file type for further parsing.
parent_keystring: Refer to another entry.

get_path(path_key, *args)[source]#

Retrieve an entry with substitutions.

Parameters:

path_keystring: Refer to the entry to retrieve.
argsany types, optional: arguments to be substituted into the path spec for %s, %d, etc.

Returns:

string: Path with parent structure and substitutions applied.

load_config(config, version=None)[source]#

Load paths into the manifest from an Allen SDK config section.

Parameters:

configConfig: Manifest section of an Allen SDK config.

classmethod safe_make_parent_dirs(file_name)[source]#

Create a parent directories for file.

Parameters:

file_namestring

Returns:

leftmoststring: most rootward directory created

classmethod safe_mkdir(directory)[source]#

Create path if not already there.

Parameters:

directorystring: create it if it doesn’t exist

Returns:

leftmoststring: most rootward directory created

class bmtk.utils.brain_observatory.manifest.ManifestBuilder[source]#

Bases: object

add_path(key, spec, typename='dir', parent_key=None, format=None)[source]#

df_columns = ['key', 'parent_key', 'spec', 'type', 'format']#

get_config()[source]#

set_version(value)[source]#

write_json_file(path, overwrite=False)[source]#

write_json_string()[source]#

exception bmtk.utils.brain_observatory.manifest.ManifestVersionError(message, version, found_version)[source]#

Bases: Exception

property outdated#

bmtk.utils.brain_observatory.rma_engine module#

class bmtk.utils.brain_observatory.rma_engine.HttpEngine(scheme: str, host: str, timeout: float = 1200, chunksize: int = 10240, **kwargs)[source]#

Bases: object

stream(route)[source]#

Makes an http request and returns an iterator over the response.

Parameters:

route: the http route (under this object’s host) to request against.

class bmtk.utils.brain_observatory.rma_engine.RmaEngine(scheme, host, rma_prefix: str = 'api/v2/data', rma_format: str = 'json', page_size: int = 5000, **kwargs)[source]#

Bases: HttpEngine

add_page_params(url, start, count=None)[source]#

property format_query_string#

get_rma(query: str)[source]#

Makes a paging rma query

Parameters:

query: The RMA query parameters

get_rma_list(query)[source]#

get_rma_tabular(query, try_infer_dtypes=True)[source]#

bmtk.utils.brain_observatory.rma_template module#

class bmtk.utils.brain_observatory.rma_template.Api(api_base_url_string=None)[source]#

Bases: object

cleanup_truncated_file(file_path)[source]#

Helper for removing files.

Parameters:

file_pathstring: Absolute path including the file name to remove.

construct_well_known_file_download_url(well_known_file_id)[source]#

Join data api endpoint and id.

Parameters:

well_known_file_idinteger or string representing an integer: well known file id

Returns:

string: the well-known-file download url for the current api api server

bmtk.utils.brain_observatory.stimulus_info module#

class bmtk.utils.brain_observatory.stimulus_info.BinaryIntervalSearchTree(search_list)[source]#

Bases: object

add(input_list, tmp=None)[source]#

static from_df(input_df)[source]#

search(fi, tmp=None)[source]#

class bmtk.utils.brain_observatory.stimulus_info.BrainObservatoryMonitor(experiment_geometry=None)[source]#

Bases: Monitor

http://help.brain-map.org/display/observatory/Documentation?preview=/10616846/10813485/VisualCoding_VisualStimuli.pdf # noqa: E501 https://www.cnet.com/products/asus-pa248q/specs/

grating_to_screen(phase, spatial_frequency, orientation, **kwargs)[source]#

lsn_image_to_screen(img, **kwargs)[source]#

pixels_to_visual_degrees(n, **kwargs)[source]#

visual_degrees_to_pixels(vd, **kwargs)[source]#

warp_image(img, **kwargs)[source]#

class bmtk.utils.brain_observatory.stimulus_info.ExperimentGeometry(distance, mon_height_cm, mon_width_cm, mon_res, eyepoint)[source]#

Bases: object

generate_warp_coordinates()[source]#

property warp_coordinates#

class bmtk.utils.brain_observatory.stimulus_info.Monitor(n_pixels_r, n_pixels_c, panel_size, spatial_unit)[source]#

Bases: object

property aspect_ratio#

get_mask()[source]#

grating_to_screen(phase, spatial_frequency, orientation, distance_from_monitor, p2p_amp=256, baseline=127, translation=(0, 0))[source]#

property height#

lsn_image_to_screen(img, stimulus_type, origin='lower', background_color=127, translation=(0, 0))[source]#

map_stimulus(source_stimulus_coordinate, source_stimulus_type, target_stimulus_type)[source]#

property mask#

natural_movie_image_to_screen(img, origin='lower', translation=(0, 0))[source]#

natural_scene_image_to_screen(img, origin='lower', translation=(0, 0))[source]#

property panel_size#

property pixel_size#

pixels_to_visual_degrees(n, distance_from_monitor, small_angle_approximation=True)[source]#

set_spatial_unit(new_unit)[source]#

show_image(img, ax=None, show=True, mask=False, warp=False, origin='lower')[source]#

spatial_frequency_to_pix_per_cycle(spatial_frequency, distance_from_monitor)[source]#

visual_degrees_to_pixels(vd, distance_from_monitor, small_angle_approximation=True)[source]#

property width#

class bmtk.utils.brain_observatory.stimulus_info.StimulusSearch(nwb_dataset)[source]#

Bases: object

search(fi)[source]#

bmtk.utils.brain_observatory.stimulus_info.all_stimuli()[source]#: Return a list of all stimuli in the data set

bmtk.utils.brain_observatory.stimulus_info.get_spatial_grating(height=None, aspect_ratio=None, ori=None, pix_per_cycle=None, phase=None, p2p_amp=2, baseline=0)[source]#

bmtk.utils.brain_observatory.stimulus_info.get_spatio_temporal_grating(t, temporal_frequency=None, **kwargs)[source]#

bmtk.utils.brain_observatory.stimulus_info.lsn_coordinate_to_monitor_coordinate(lsn_coordinate, monitor_shape, stimulus_type)[source]#

bmtk.utils.brain_observatory.stimulus_info.make_display_mask(display_shape=(1920, 1200))[source]#: Build a display-shaped mask that indicates which pixels are on screen after warping the stimulus.

bmtk.utils.brain_observatory.stimulus_info.map_monitor_coordinate_to_stimulus_coordinate(monitor_coordinate, monitor_shape, stimulus_type)[source]#

bmtk.utils.brain_observatory.stimulus_info.map_monitor_coordinate_to_template_coordinate(monitor_coord, monitor_shape, template_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.map_stimulus(source_stimulus_coordinate, source_stimulus_type, target_stimulus_type, monitor_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.map_stimulus_coordinate_to_monitor_coordinate(template_coordinate, monitor_shape, stimulus_type)[source]#

bmtk.utils.brain_observatory.stimulus_info.map_template_coordinate_to_monitor_coordinate(template_coord, monitor_shape, template_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.mask_stimulus_template(template_display_coords, template_shape, display_mask=None, threshold=1.0)[source]#

Build a mask for a stimulus template of a given shape and display coordinates that indicates which part of the template is on screen after warping.

Parameters:

template_display_coords: list: list of (x,y) display coordinates
template_shape: tuple: (width,height) of the display template
display_mask: np.ndarray: boolean 2D mask indicating which display coordinates are on screen after warping.
threshold: float: Fraction of pixels associated with a template display coordinate that should remain on screen to count as belonging to the mask.

Returns:

tuple: (template mask, pixel fraction)

bmtk.utils.brain_observatory.stimulus_info.monitor_coordinate_to_lsn_coordinate(monitor_coordinate, monitor_shape, stimulus_type)[source]#

bmtk.utils.brain_observatory.stimulus_info.monitor_coordinate_to_natural_movie_coordinate(monitor_coordinate, monitor_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.natural_movie_coordinate_to_monitor_coordinate(natural_movie_coordinate, monitor_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.natural_scene_coordinate_to_monitor_coordinate(natural_scene_coordinate, monitor_shape)[source]#

bmtk.utils.brain_observatory.stimulus_info.rotate(X, Y, theta)[source]#

bmtk.utils.brain_observatory.stimulus_info.sessions_with_stimulus(stimulus)[source]#: Return the names of the sessions that contain a given stimulus.

bmtk.utils.brain_observatory.stimulus_info.stimuli_in_session(session, allow_unknown=True)[source]#

Return a list what stimuli are available in a given session.

Parameters:

session: string

Must be one of: [: stimulus_info.THREE_SESSION_A, stimulus_info.THREE_SESSION_B, stimulus_info.THREE_SESSION_C, stimulus_info.THREE_SESSION_C2 ]

bmtk.utils.brain_observatory.stimulus_info.translate_image_and_fill(img, translation=(0, 0))[source]#

bmtk.utils.brain_observatory.stimulus_info.warp_stimulus_coords(vertices, distance=15.0, mon_height_cm=32.5, mon_width_cm=51.0, mon_res=(1920, 1200), eyepoint=(0.5, 0.5))[source]#

For a list of screen vertices, provides a corresponding list of texture coordinates.

Parameters:

vertices: numpy.ndarray: [[x0,y0], [x1,y1], …] A set of vertices to convert to texture positions.
distance: float: distance from the monitor in cm.
mon_height_cm: float: monitor height in cm
mon_width_cm: float: monitor width in cm
mon_res: tuple: monitor resolution (x,y)
eyepoint: tuple

Returns:

np.ndarray: x,y coordinates shaped like the input that describe what pixel coordinates are displayed an the input coordinates after warping the stimulus.

bmtk.utils.brain_observatory.utils module#

class bmtk.utils.brain_observatory.utils.FakeTqdm(*args, **kwargs)[source]#

Bases: object

update(*args, **kwargs)[source]#

bmtk.utils.brain_observatory.utils.file_hash_from_path(file_path)[source]#

Return the hexadecimal file hash for a file

Parameters:

file_path: Union[str, Path]: path to a file

Returns:

str:: The file hash (Blake2b; hexadecimal) of the file

bmtk.utils.brain_observatory.utils.infer_column_types(dataframe)[source]#

bmtk.utils.brain_observatory.utils.json_handler(obj)[source]#: Used by write_json convert a few non-standard types to things that the json package can handle.

bmtk.utils.brain_observatory.utils.write_from_stream(path: str, stream)[source]#

bmtk.utils.brain_observatory package#

Subpackages#

Submodules#

bmtk.utils.brain_observatory.brain_observatory_cache module#

bmtk.utils.brain_observatory.cache module#

bmtk.utils.brain_observatory.manifest module#

bmtk.utils.brain_observatory.rma_engine module#

bmtk.utils.brain_observatory.rma_template module#

bmtk.utils.brain_observatory.stimulus_info module#

bmtk.utils.brain_observatory.utils module#

Module contents#

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