nwbwidgets package

Subpackages

• nwbwidgets.analysis package
  • Submodules
  • nwbwidgets.analysis.spikes module
  • Module contents
• nwbwidgets.controllers package
  • Submodules
  • nwbwidgets.controllers.group_and_sort_controllers module
  • nwbwidgets.controllers.misc module
  • nwbwidgets.controllers.time_window_controllers module
  • Module contents
• nwbwidgets.dashboards package
  • Submodules
  • nwbwidgets.dashboards.allen module
  • Module contents
• nwbwidgets.utils package
  • Submodules
  • nwbwidgets.utils.cmaps module
  • nwbwidgets.utils.dandi module
  • nwbwidgets.utils.dynamictable module
  • nwbwidgets.utils.functional module
  • nwbwidgets.utils.mpl module
  • nwbwidgets.utils.plotly module
  • nwbwidgets.utils.pynwb module
  • nwbwidgets.utils.testing module
  • nwbwidgets.utils.timeseries module
  • nwbwidgets.utils.units module
  • nwbwidgets.utils.widgets module
  • Module contents

Submodules

nwbwidgets.allen module

class nwbwidgets.allen.AllenRasterWidget(**kwargs: Any)

Bases: RasterWidget

Public constructor

make_group_and_sort(group_by=None, control_order=False)

class nwbwidgets.allen.AllenPSTHWidget(**kwargs: Any)

Bases: TimeIntervalsSelector

Creates a TimeInterval controller that controls InnerWidget.

Parameters

input_data (pynwb object) – Pynwb object (e.g. pynwb.misc.Units) belonging to a nwbfile that will be filtered by the TimeIntervalSelector controller.

InnerWidget

alias of PSTHWidget

class nwbwidgets.allen.AllenRasterGridWidget(**kwargs: Any)

Bases: TimeIntervalsSelector

Creates a TimeInterval controller that controls InnerWidget.

Parameters

input_data (pynwb object) – Pynwb object (e.g. pynwb.misc.Units) belonging to a nwbfile that will be filtered by the TimeIntervalSelector controller.

InnerWidget

alias of RasterGridWidget

class nwbwidgets.allen.AllenTuningCurveWidget(**kwargs: Any)

Bases: TimeIntervalsSelector

Creates a TimeInterval controller that controls InnerWidget.

Parameters

input_data (pynwb object) – Pynwb object (e.g. pynwb.misc.Units) belonging to a nwbfile that will be filtered by the TimeIntervalSelector controller.

InnerWidget

alias of TuningCurveWidget

class nwbwidgets.allen.AllenRasterGroupAndSortController(**kwargs: Any)

Bases: GroupAndSortController

Parameters

• dynamic_table (DynamicTable) – the table wrt which the grouping is performed

• group_by (str) – the column name from the dynamic table for which the grouping is performed

• window (None or bool,) –

• keep_rows (Iterable) – rows of dynamic table to consider in the grouping op

• control_limit (bool) – whether to control the limit of the displayed rows

• control_order (bool) – whether to control the order of the displayed rows based on other column

• groups (dict) – dict(column_name=column_values) to work with specific columns only

get_groups()

get_orderable_cols()

get_group_vals(by, rows_select=())

nwbwidgets.allen.allen_show_dynamic_table(node: DynamicTable, **kwargs) → Widget

nwbwidgets.allen.allen_show_electrodes(node: DynamicTable)

nwbwidgets.allen.load_allen_widgets()

nwbwidgets.base module

nwbwidgets.base.show_fields(node, **kwargs) → Widget

nwbwidgets.base.render_dataframe(dynamic_table: DynamicTable)

nwbwidgets.base.show_neurodata_base(node: NWBDataInterface, neurodata_vis_spec: dict) → Widget

Gets a pynwb object and returns a Vertical Box containing textual info and an expandable Accordion with it’s children.

nwbwidgets.base.dict2accordion(d: dict, neurodata_vis_spec: dict, **pass_kwargs) → Widget

nwbwidgets.base.lazy_tabs(in_dict: dict, node, style: ~typing.Union[~ipywidgets.widgets.widget_selectioncontainer.Accordion, ~ipywidgets.widgets.widget_selectioncontainer.Tab] = <class 'ipywidgets.widgets.widget_selectioncontainer.Tab'>) → Union[Accordion, Tab]

Creates a lazy tab object where multiple visualizations can be used for a single node and are generated on the fly

Parameters

• in_dict (dict) – keys are labels for tabs and values are functions

• node (NWBDataInterface) – instance of neurodata type to visualize

• style (ipywidgets.Tab or ipywidgets.Accordion, optional) – which way to present the data

Returns

tab

Return type

widget

class nwbwidgets.base.LazyTab(**kwargs: Any)

Bases: Tab

A lazy tab object where multiple visualizations can be used for a single node and are generated on the fly

Parameters

• func_dict (dict) – keys are labels for tabs and values are functions

• data (NWBDataInterface) – instance of neurodata type to visualize

nwbwidgets.base.lazy_show_over_data(list_, func_, labels=None, style: ~typing.Union[~ipywidgets.widgets.widget_selectioncontainer.Accordion, ~ipywidgets.widgets.widget_selectioncontainer.Tab] = <class 'ipywidgets.widgets.widget_selectioncontainer.Tab'>) → Union[Accordion, Tab]

Apply same function to list of data in lazy tabs or lazy accordion :param list_: :param func_: :param labels: :type labels: list of str :param style: :type style: widgets.Tab or widgets.Accordion

Returns

subtype Tab or Accordion

Return type

ipywidgets.Tab or ipywidgets.Accordion

nwbwidgets.base.nwb2widget(node, neurodata_vis_spec: dict, **pass_kwargs) → Widget

nwbwidgets.base.vis2widget(vis) → Widget

nwbwidgets.base.fig2widget(fig: Figure, **kwargs) → Widget

nwbwidgets.base.processing_module(node: ProcessingModule, neurodata_vis_spec: dict) → Widget

nwbwidgets.base.show_text_fields(node, exclude=('comments', 'interval'), **kwargs) → Widget

nwbwidgets.base.df2accordion(df: ~pandas.core.frame.DataFrame, by, func, style: ~typing.Union[~ipywidgets.widgets.widget_selectioncontainer.Accordion, ~ipywidgets.widgets.widget_selectioncontainer.Tab] = <class 'ipywidgets.widgets.widget_selectioncontainer.Accordion'>, detect_single=True) → Union[Accordion, Tab]

Visualize pandas.DataFrame with an ipywidgets.Accordion

Parameters

• df (pandas.DataFrame) –

• by (str) –

• func (visualization function) –

• style (ipywidgets.Tab or ipywidgets.Accordion, optional) –

• detect_single (bool) – If True, test if the dimension you are grouping by only has 1 unique value. If so, do not form an Accordion.

Return type

ipywigets.Accordion or ipywidgets.Tab

nwbwidgets.base.show_dset(dset: Dataset, **kwargs)

nwbwidgets.base.dataset_to_sheet(dset: Dataset)

nwbwidgets.base.show_dict(in_dict) → Widget

nwbwidgets.base.df_to_hover_text(df: DataFrame)

nwbwidgets.base.row_to_hover_text(row)

class nwbwidgets.base.TimeIntervalsSelector(**kwargs: Any)

Bases: VBox

Creates a TimeInterval controller that controls InnerWidget.

Parameters

input_data (pynwb object) – Pynwb object (e.g. pynwb.misc.Units) belonging to a nwbfile that will be filtered by the TimeIntervalSelector controller.

InnerWidget = None

stimulus_type_dd_callback(change)

nwbwidgets.base.show_multi_container_interface(node: MultiContainerInterface, neurodata_vis_spec=None)

nwbwidgets.behavior module

nwbwidgets.behavior.show_behavioral_events(beh_events: BehavioralEvents, neurodata_vis_spec: dict)

nwbwidgets.behavior.show_spatial_series(node: SpatialSeries, **kwargs)

nwbwidgets.behavior.route_spatial_series(spatial_series, **kwargs)

class nwbwidgets.behavior.SpatialSeriesTraceWidget(**kwargs: Any)

Bases: AbstractTraceWidget

Public constructor

abstract plot_data(data, units, tt)

abstract update_plot(data, tt)

set_out_fig()

class nwbwidgets.behavior.SpatialSeriesTraceWidget2D(**kwargs: Any)

Bases: SpatialSeriesTraceWidget

Public constructor

plot_data(data, units, tt)

update_plot(data, tt)

class nwbwidgets.behavior.SpatialSeriesTraceWidget3D(**kwargs: Any)

Bases: SpatialSeriesTraceWidget

Public constructor

plot_data(data, units, tt)

update_plot(data, tt)

nwbwidgets.behavior.trial_align_spatial_series(spatial_series, trials=None)

nwbwidgets.brains module

nwbwidgets.brains.make_cylinder_mesh(radius, height, sections=32, position=(0, 0, 0), direction=(1, 0, 0), **kwargs)

nwbwidgets.brains.make_cylinders(positions, directions, radius=1, height=1, sections=32, name='cylinders', **kwargs)

class nwbwidgets.brains.HumanElectrodesPlotlyWidget(**kwargs: Any)

Bases: VBox

Public constructor

static find_normals(points, k=3)

show_electrodes(electrodes: DynamicTable, color_by)

plot_human_brain(left_opacity=1.0, right_opacity=1.0)

observe_left_opacity(change)

observe_right_opacity(change)

color_electrode_by(change)

nwbwidgets.dynamictablesummary module

class nwbwidgets.dynamictablesummary.DynamicTableSummaryWidget(**kwargs: Any)

Bases: VBox

Public constructor

max_selection(change)

reset_dropdown()

plot_hist_bar(col_names_display, nbins, show_labels)

nwbwidgets.ecephys module

nwbwidgets.ecephys.show_spectrogram(nwbobj: TimeSeries, channel=0, **kwargs)

class nwbwidgets.ecephys.ElectrodeGroupsWidget(**kwargs: Any)

Bases: ValueWidget, HBox

Public constructor

nwbwidgets.ecephys.show_electrodes(electrodes_table)

nwbwidgets.ecephys.show_ccf(electrodes_table=None, **kwargs)

nwbwidgets.ecephys.show_spike_event_series(ses: SpikeEventSeries, **kwargs)

class nwbwidgets.ecephys.ElectricalSeriesWidget(**kwargs: Any)

Bases: BaseGroupedTraceWidget

Parameters

• time_series (TimeSeries) –

• dynamic_table_region_name (str, optional) –

• foreign_time_window_controller (StartAndDurationController, optional) –

• foreign_group_and_sort_controller (GroupAndSortController, optional) –

• mpl_plotter (function) – Choose function to use when creating figures

• kwargs –

nwbwidgets.ephys_viz_interface module

import ipywidgets as widgets import pynwb from .view import default_neurodata_vis_spec import spikeextractors as se from pynwb.ecephys import LFP

ephys_viz_neurodata_vis_spec = dict(default_neurodata_vis_spec)

def _set_spec():

ephys_viz_neurodata_vis_spec[pynwb.ecephys.LFP] = show_lfp

def show_lfp(node: LFP, **kwargs):

import spikeextractors as se import ephys_viz as ev try:

recording = LFPRecordingExtractor(lfp_node=node)

except:

return widgets.Text(‘Problem creating LFPRecordingExtractor’)

return ev.TimeseriesView(

recording=recording, initial_y_scale_factor=5

).show(render=False)

class LFPRecordingExtractor(se.RecordingExtractor):

def __init__(self, lfp_node: LFP):

super().__init__() lfp = list(lfp_node.electrical_series.values())[0] self._samplerate = lfp.rate self._data = lfp.data self._num_channels = self._data.shape[1] self._num_timepoints = self._data.shape[0]

def get_channel_ids(self):

return list(range(self._num_channels))

def get_num_frames(self):

return self._num_timepoints

def get_sampling_frequency(self):

return self._samplerate

def get_traces(self, channel_ids=None, start_frame=None, end_frame=None):

if start_frame is None:

start_frame = 0

if end_frame is None:

end_frame = self.get_num_frames()

if channel_ids is None:

channel_ids = self.get_channel_ids()

return self._data[start_frame:end_frame, :][:, channel_ids].T

_set_spec()

nwbwidgets.file module

nwbwidgets.file.show_nwbfile(nwbfile: NWBFile, neurodata_vis_spec: dict) → Widget

Gets a pynwb object and returns a Vertical Box containing textual info and an expandable Accordion with it’s children.

nwbwidgets.icephys module

nwbwidgets.icephys.show_single_sweep_sequence(sweep_sequence, axs=None, title=None, **kwargs) → Figure

Show a single rep of a single stimulus sequence

Parameters

• sweep_sequence –

• axs ([matplotlib.pyplot.Axes, matplotlib.pyplot.Axes], optional) –

• title (str, optional) –

• kwargs (dict) – passed to show_indexed_timeseries_mpl

Return type

matplotlib.pyplot.Figure

nwbwidgets.icephys.show_sweep_sequence_reps(stim_df: DataFrame, **kwargs) → Figure

Show data from multiple reps of the same stimulus type

Parameters

• stim_df (pandas.DataFrame) –

• kwargs (dict) – passed to show_single_sweep_sequence

Return type

matplotlib.pyplot.Figure

nwbwidgets.icephys.show_sweep_sequences(node: ~ndx_icephys_meta.icephys.SweepSequences, *args, style: ~typing.Union[~ipywidgets.widgets.widget_selectioncontainer.Accordion, ~ipywidgets.widgets.widget_selectioncontainer.Tab] = <class 'ipywidgets.widgets.widget_selectioncontainer.Accordion'>, **kwargs) → Union[Accordion, Tab]

Visualize the sweep sequences table with a lazy accordion of sweep sequence repetitions

Parameters

• node (SweepSequences) –

• style (widgets.Accordion or widgets.Tabs) –

Return type

widgets.Accordion or widgets.Tabs

nwbwidgets.icephys.show_sequential_recordings(nwbfile, elec_name, sequence_id=0)

class nwbwidgets.icephys.IVCurveWidget(**kwargs: Any)

Bases: VBox

Public constructor

update_electrode(change)

update_stimulus(change)

update_figure()

nwbwidgets.image module

class nwbwidgets.image.ImageSeriesWidget(**kwargs: Any)

Bases: VBox

Widget showing ImageSeries.

Public constructor

time_to_index(time)

set_controls(**kwargs)

get_frame(idx)

set_out_fig()

nwbwidgets.image.show_image_series(image_series: ImageSeries, neurodata_vis_spec: dict)

nwbwidgets.image.show_grayscale_image_series(image_series: ImageSeries, neurodata_vis_spec: dict)

nwbwidgets.image.show_index_series(index_series, neurodata_vis_spec: dict)

nwbwidgets.image.show_grayscale_image(grayscale_image: GrayscaleImage, neurodata_vis_spec=None)

nwbwidgets.image.show_rbga_image(rgb_image: RGBImage, neurodata_vis_spec=None)

nwbwidgets.misc module

nwbwidgets.misc.show_annotations(annotations: AnnotationSeries, **kwargs)

nwbwidgets.misc.show_session_raster(units: Units, time_window=None, units_window=None, show_obs_intervals=True, order=None, group_inds=None, labels=None, show_legend=True, progress_bar=None)

Parameters

• units (pynwb.misc.Units) –

• time_window ([int, int]) –

• units_window ([int, int]) –

• show_obs_intervals (bool) –

• order (array-like, optional) –

• group_inds (array-like, optional) –

• labels (array-like, optional) –

• show_legend (bool) – default = True Does not show legend if color_by is None or ‘id’.

• progress_bar (FloatProgress, optional) –

Return type

matplotlib.pyplot.Figure

class nwbwidgets.misc.RasterWidget(**kwargs: Any)

Bases: HBox

Public constructor

make_group_and_sort(group_by=None, control_order=True)

nwbwidgets.misc.show_decomposition_series(node, **kwargs)

nwbwidgets.misc.show_decomposition_traces(node: DecompositionSeries)

class nwbwidgets.misc.PSTHWidget(**kwargs: Any)

Bases: VBox

Public constructor

get_trials()

make_group_and_sort(window=None, control_order=False)

update(index: int, start_labels: tuple = ('start_time',), start: float = 0.0, end: float = 1.0, order=None, group_inds=None, labels=None, sigma_in_secs=0.05, ntt: int = 1000, progress_bar=None, figsize=(12, 7), nbins=30, plot_type='histogram', align_line_color=(0.7, 0.7, 0.7))

Parameters

• index (int) – Index of unit

• start_label (str, optional) – Trial column name to align on

• start (float) – Start time for calculation before or after (negative or positive) the reference point (aligned to).

• end (float) – End time for calculation before or after (negative or positive) the reference point (aligned to).

• order –

• group_inds –

• labels –

• sigma_in_secs (float, optional) – standard deviation of gaussian kernel

• ntt – Number of time points to use for smooth curve

• progress_bar –

• figsize (tuple, optional) –

Return type

matplotlib.Figure

nwbwidgets.misc.show_histogram(data, ax: Axes, start: float, end: float, group_inds=None, nbins: int = 30)

nwbwidgets.misc.show_psth_smoothed(data, ax, start: float, end: float, group_inds=None, sigma_in_secs: float = 0.05, ntt: int = 1000)

nwbwidgets.misc.plot_grouped_events(data, window, group_inds=None, colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], ax=None, labels=None, show_legend=True, offset=0, unobserved_intervals_list=None, progress_bar=None, figsize=(8, 6), fontsize=12)

Parameters

• data (array-like) –

• window (array-like [float, float]) – Time in seconds

• group_inds (array-like dtype=int, optional) –

• colors (array-like, optional) –

• ax (plt.Axes, optional) –

• labels (array-like dtype=str, optional) –

• show_legend (bool, optional) –

• offset (number, optional) –

• unobserved_intervals_list (array-like, optional) –

• progress_bar (FloatProgress, optional) –

• figsize (tuple, optional) –

• fontsize (int, optional) –

nwbwidgets.misc.plot_unobserved_intervals(unobserved_intervals_list, ax, offset=0, color=(0.85, 0.85, 0.85))

nwbwidgets.misc.show_psth_raster(data, start=-0.5, end=2.0, group_inds=None, labels=None, ax=None, show_legend=True, align_line_color=(0.7, 0.7, 0.7), progress_bar: Optional[FloatProgress] = None, fontsize=12) → Axes

Parameters

• data (array-like) –

• start (float) – Start time for calculation before or after (negative or positive) the reference point (aligned to).

• end (float) – End time for calculation before or after (negative or positive) the reference point (aligned to).

• group_inds (array-like, optional) –

• labels (array-like, optional) –

• ax (plt.Axes, optional) –

• show_legend (bool, optional) –

• align_line_color (array-like, optional) – [R, G, B] (0-1) Default = [0.7, 0.7, 0.7]

• progress_bar (FloatProgress, optional) –

• fontsize (int, optional) –

Return type

plt.Axes

nwbwidgets.misc.raster_grid(units: Units, time_intervals: TimeIntervals, index, start, end, rows_label=None, cols_label=None, trials_select=None, align_by='start_time') → Figure

Parameters

• units (pynwb.misc.Units) –

• time_intervals (pynwb.epoch.TimeIntervals) –

• index (int) –

• start (float) – Start time for calculation before or after (negative or positive) the reference point (aligned to).

• end (float) – End time for calculation before or after (negative or positive) the reference point (aligned to).

• rows_label (str, optional) –

• cols_label (str, optional) –

• trials_select (np.array(dtype=bool), optional) –

• align_by (str, optional) –

Return type

plt.Figure

nwbwidgets.misc.plot_grouped_events_plotly(data, window=None, group_inds=None, colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], labels=None, show_legend=True, unobserved_intervals_list=None, progress_bar=None, fig=None, **kwargs)

class nwbwidgets.misc.RasterWidgetPlotly(**kwargs: Any)

Bases: HBox

Public constructor

toggle_legend(change)

update_fig(change)

nwbwidgets.misc.show_session_raster_plotly(units: Units, fig, time_window=None, order=None, progress_bar=None, **kwargs)

Parameters

• units (pynwb.misc.Units) –

• time_window ([int, int]) –

• show_obs_intervals (bool) –

• order (array-like, optional) –

• group_inds (array-like, optional) –

• labels (array-like, optional) –

• show_legend (bool) – default = True Does not show legend if color_by is None or ‘id’.

• progress_bar (FloatProgress, optional) –

Return type

go.FigureWidget

class nwbwidgets.misc.UnitsAndTrialsControllerWidget(**kwargs: Any)

Bases: VBox

Creates a UnitsAndTrials controller that controls InnerWidget.

Parameters

• units (pynwb.misc.Units object) –

• trials (pynwb.epoch.TimeIntervals object) –

• unit_index (int) –

InnerWidget = None

get_trials()

get_groups(trials)

rows_callback(change)

Gets triggered when self.rows_controller changes. Updates other dropdown options.

class nwbwidgets.misc.RasterGridWidget(**kwargs: Any)

Bases: VBox

Public constructor

class nwbwidgets.misc.TuningCurveWidget(**kwargs: Any)

Bases: VBox

Public constructor

class nwbwidgets.misc.TuningCurveExtendedWidget(**kwargs: Any)

Bases: VBox

Public constructor

nwbwidgets.misc.draw_tuning_curve(units: Units, time_intervals: TimeIntervals, index, start, end, rows_label=None, cols_label=None, align_by='start_time') → Figure

nwbwidgets.misc.draw_tuning_curve_1d(units: Units, time_intervals: TimeIntervals, index, start, end, rows_label=None, align_by='start_time') → Figure

nwbwidgets.misc.draw_tuning_curve_2d(units: Units, time_intervals: TimeIntervals, index, start, end, rows_label=None, cols_label=None, align_by='start_time') → Figure

nwbwidgets.misc.sort_mixed_type_list(x)

Returns the indexes for a sorted list of mixed types

nwbwidgets.ophys module

class nwbwidgets.ophys.TwoPhotonSeriesWidget(**kwargs: Any)

Bases: VBox

Widget showing Image stack recorded over time from 2-photon microscope.

Public constructor

nwbwidgets.ophys.show_df_over_f(df_over_f: DfOverF, neurodata_vis_spec: dict)

nwbwidgets.ophys.show_image_segmentation(img_seg: ImageSegmentation, neurodata_vis_spec: dict)

nwbwidgets.ophys.show_plane_segmentation_3d_voxel(plane_seg: PlaneSegmentation)

nwbwidgets.ophys.show_plane_segmentation_3d_mask(plane_seg: PlaneSegmentation)

class nwbwidgets.ophys.PlaneSegmentation2DWidget(**kwargs: Any)

Bases: VBox

Public constructor

on_button_click(b)

update_fig(color_by)

show_plane_segmentation_2d(color_wheel: list = ['red', 'blue', 'green', 'black', 'magenta', 'yellow'], color_by: Optional[str] = None, threshold: float = 0.01, fig: Optional[Figure] = None, width: int = 600, ref_image=None)

Parameters

• plane_seg (PlaneSegmentation) –

• color_wheel (list, optional) –

• color_by (str, optional) –

• threshold (float, optional) –

• fig (plotly.graph_objects.Figure, optional) –

• width (int, optional) – width of image in pixels. Height is automatically determined to be proportional

• ref_image (image, optional) –

compute_outline(i, threshold)

nwbwidgets.ophys.route_plane_segmentation(plane_seg: PlaneSegmentation, neurodata_vis_spec: dict)

nwbwidgets.ophys.show_grayscale_volume(vol: GrayscaleVolume, neurodata_vis_spec: dict)

class nwbwidgets.ophys.RoiResponseSeriesWidget(**kwargs: Any)

Bases: BaseGroupedTraceWidget

Parameters

• time_series (TimeSeries) –

• dynamic_table_region_name (str, optional) –

• foreign_time_window_controller (StartAndDurationController, optional) –

• foreign_group_and_sort_controller (GroupAndSortController, optional) –

• mpl_plotter (function) – Choose function to use when creating figures

• kwargs –

nwbwidgets.panel module

class nwbwidgets.panel.Panel(**kwargs: Any)

Bases: VBox

NWB widgets Panel for visualization of NWB files.

Parameters

• stream_mode (str, optional) – Either “fsspec” or “ros3”. Defaults to “fsspec”.

• cache_path (str, optional) – The path to cached data if streaming with “fsspec”. If left as None, a directory “nwb-cache” is created under the current working directory. Defaults to None.

• enable_dandi_source (bool, optional) – Enable DANDI source option. Defaults to True.

• enable_s3_source (bool, optional) – Enable S3 source option. Defaults to True.

• enable_local_source (bool, optional) – Enable local source option. Defaults to True.

updated_source(args=None)

Update Panel components depending on chosen source.

create_components_dandi_source(args=None)

Create widgets components for DANDI option

create_components_s3_source()

Create widgets components for S3 option

create_components_local_dir_source()

Create widgets components for Loca dir option

create_components_local_file_source()

Create widgets components for Local file option

list_dandiset_files_dropdown(args=None)

Populate dropdown with all files and text area with summary

list_local_dir_files(args=None)

List NWB files in local dir

stream_dandiset_file(args=None)

Stream NWB file from DANDI

stream_s3_file(args=None)

Stream NWB file from S3 url

load_local_dir_file(args=None)

Load local NWB file

load_local_file(args=None)

Load local NWB file

nwbwidgets.spectrum module

nwbwidgets.spectrum.show_spectrum(node: Spectrum, **kwargs) → Widget

nwbwidgets.spectrum.plot_spectrum_figure(spectrum, channel_nos, frequency_nos)

Plot power vs frequencies and/or phase vs frequencies. :param spectrum: :type spectrum: Spectrum :param channel_nos: Input from the channel range slider widget: (channel_no start, channel_no end) :type channel_nos: tuple :param frequency_nos: Input from frequency range slider widget: (freq start, freq end) :type frequency_nos: tuple

nwbwidgets.spectrum.check_spectrum(node)

nwbwidgets.timeseries module

nwbwidgets.timeseries.show_ts_fields(node)

nwbwidgets.timeseries.show_timeseries_mpl(time_series: TimeSeries, time_window=None, ax=None, zero_start=False, xlabel=None, ylabel=None, title=None, figsize=None, **kwargs)

Parameters

• time_series (TimeSeries) –

• time_window ([int int]) –

• ax (plt.Axes) –

• zero_start (bool) –

• xlabel (str) –

• ylabel (str) –

• title (str) –

• figsize (tuple, optional) –

• kwargs –

nwbwidgets.timeseries.show_indexed_timeseries_mpl(node: TimeSeries, istart=0, istop=None, ax=None, zero_start=False, xlabel='time (s)', ylabel=None, title=None, figsize=None, neurodata_vis_spec=None, **kwargs)

nwbwidgets.timeseries.show_indexed_timeseries_plotly(timeseries: TimeSeries, istart: int = 0, istop: Optional[int] = None, time_window: Optional[list] = None, trace_range: Optional[list] = None, offsets=None, fig: Optional[FigureWidget] = None, col=None, row=None, zero_start=False, scatter_kwargs: Optional[dict] = None, figure_kwargs: Optional[dict] = None)

nwbwidgets.timeseries.plot_traces(timeseries: TimeSeries, time_window=None, trace_window=None, title: Optional[str] = None, ylabel: str = 'traces', **kwargs)

Parameters

• timeseries (TimeSeries) –

• time_window ([float, float], optional) – Start time and end time in seconds.

• trace_window ([int int], optional) – Index range of traces to view

• title (str, optional) –

• ylabel (str, optional) –

nwbwidgets.timeseries.show_timeseries(node, **kwargs)

class nwbwidgets.timeseries.AbstractTraceWidget(**kwargs: Any)

Bases: VBox

Public constructor

abstract set_out_fig()

set_controls(**kwargs)

set_children()

class nwbwidgets.timeseries.SingleTracePlotlyWidget(**kwargs: Any)

Bases: AbstractTraceWidget

Public constructor

set_out_fig()

class nwbwidgets.timeseries.SeparateTracesPlotlyWidget(**kwargs: Any)

Bases: AbstractTraceWidget

Public constructor

set_out_fig()

nwbwidgets.timeseries.plot_grouped_traces(time_series: TimeSeries, time_window=None, order=None, ax=None, figsize=(8, 7), group_inds=None, labels=None, colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], show_legend=True, dynamic_table_region_name=None, window=None, **kwargs)

nwbwidgets.timeseries.plot_grouped_traces_plotly(time_series: TimeSeries, time_window, order, group_inds=None, labels=None, colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], fig=None, **kwargs)

class nwbwidgets.timeseries.BaseGroupedTraceWidget(**kwargs: Any)

Bases: HBox

Parameters

• time_series (TimeSeries) –

• dynamic_table_region_name (str, optional) –

• foreign_time_window_controller (StartAndDurationController, optional) –

• foreign_group_and_sort_controller (GroupAndSortController, optional) –

• mpl_plotter (function) – Choose function to use when creating figures

• kwargs –

class nwbwidgets.timeseries.MultiTimeSeriesWidget(**kwargs: Any)

Bases: VBox

Parameters

• time_series_list (list of TimeSeries) –

• widget_class_list (list of classes, optional) –

• constrain_time_range (bool, optional) – Default is False

class nwbwidgets.timeseries.AlignMultiTraceTimeSeriesByTrialsAbstract(**kwargs: Any)

Bases: VBox

Public constructor

get_trials()

make_group_and_sort(window=None, control_order=False, control_limit=False)

plot_group(group_inds, data_trialized, time_trialized, fig, order)

class nwbwidgets.timeseries.AlignMultiTraceTimeSeriesByTrialsConstant(**kwargs: Any)

Bases: AlignMultiTraceTimeSeriesByTrialsAbstract

Public constructor

align_data(start_label, before, after, index=None)

update(index: int, start_label: str = 'start_time', before: float = 0.0, after: float = 1.0, order=None, group_inds=None, labels=None, align_to_zero=False, sem=False, fig: Optional[FigureWidget] = None)

class nwbwidgets.timeseries.AlignMultiTraceTimeSeriesByTrialsVariable(**kwargs: Any)

Bases: AlignMultiTraceTimeSeriesByTrialsAbstract

Public constructor

align_data(start_label, before, after, index=None)

update(index: int, start_label: str = 'start_time', before: float = 0.0, after: float = 1.0, order=None, group_inds=None, labels=None, align_to_zero=False, fig: Optional[FigureWidget] = None)

nwbwidgets.version module

nwbwidgets.view module

nwbwidgets.view.show_dynamic_table(node, **kwargs) → Widget

nwbwidgets.view.nwb2widget(node, neurodata_vis_spec={<class 'pynwb.file.NWBFile'>: <function show_nwbfile>, <class 'ndx_icephys_meta.icephys.SweepSequences'>: <function show_sweep_sequences>, <class 'pynwb.behavior.BehavioralEvents'>: <function show_behavioral_events>, <class 'pynwb.misc.Units'>: OrderedDict([('Summary', <class 'nwbwidgets.dynamictablesummary.DynamicTableSummaryWidget'>), ('Session Raster', <class 'nwbwidgets.misc.RasterWidget'>), ('Grouped PSTH', <class 'nwbwidgets.misc.PSTHWidget'>), ('Raster Grid', <class 'nwbwidgets.misc.RasterGridWidget'>), ('Tuning Curves', <class 'nwbwidgets.misc.TuningCurveWidget'>), ('Combined', <class 'nwbwidgets.misc.TuningCurveExtendedWidget'>), ('table', <function show_dynamic_table>)]), <class 'pynwb.misc.DecompositionSeries'>: <function show_decomposition_series>, <class 'pynwb.file.Subject'>: <function show_fields>, <class 'pynwb.ecephys.SpikeEventSeries'>: <function show_spike_event_series>, <class 'pynwb.ophys.ImageSegmentation'>: <function show_image_segmentation>, <class 'pynwb.ophys.TwoPhotonSeries'>: <class 'nwbwidgets.ophys.TwoPhotonSeriesWidget'>, <class 'abc.GrayscaleVolume'>: <function show_grayscale_volume>, <class 'pynwb.ophys.PlaneSegmentation'>: <function route_plane_segmentation>, <class 'pynwb.ophys.DfOverF'>: <function show_df_over_f>, <class 'pynwb.ophys.RoiResponseSeries'>: <class 'nwbwidgets.ophys.RoiResponseSeriesWidget'>, <class 'pynwb.misc.AnnotationSeries'>: OrderedDict([('text', <function show_text_fields>), ('times', <function show_annotations>)]), <class 'hdmf.utils.LabelledDict'>: <function dict2accordion>, <class 'pynwb.base.ProcessingModule'>: <function processing_module>, <class 'hdmf.common.table.DynamicTable'>: {'Summary': <class 'nwbwidgets.dynamictablesummary.DynamicTableSummaryWidget'>, 'table': <function show_dynamic_table>}, <class 'pynwb.ecephys.ElectricalSeries'>: <class 'nwbwidgets.ecephys.ElectricalSeriesWidget'>, <class 'pynwb.behavior.SpatialSeries'>: <function route_spatial_series>, <class 'pynwb.image.GrayscaleImage'>: <function show_grayscale_image>, <class 'pynwb.image.RGBImage'>: <function show_rbga_image>, <class 'pynwb.image.RGBAImage'>: <function show_rbga_image>, <class 'pynwb.base.Image'>: <function show_rbga_image>, <class 'pynwb.image.ImageSeries'>: <class 'nwbwidgets.image.ImageSeriesWidget'>, <class 'pynwb.image.IndexSeries'>: <function show_index_series>, <class 'pynwb.base.TimeSeries'>: <function show_timeseries>, <class 'pynwb.core.MultiContainerInterface'>: <function show_multi_container_interface>, <class 'pynwb.core.NWBContainer'>: <function show_neurodata_base>, <class 'pynwb.core.NWBDataInterface'>: <function show_neurodata_base>, <class 'h5py._hl.dataset.Dataset'>: <function show_dset>, <class 'zarr.core.Array'>: <function show_dset>, <class 'abc.Spectrum'>: <function show_spectrum>, <class 'pynwb.icephys.SequentialRecordingsTable'>: {'Summary': <class 'nwbwidgets.dynamictablesummary.DynamicTableSummaryWidget'>, 'table': <function show_dynamic_table>, 'I-V Analysis': <class 'nwbwidgets.icephys.IVCurveWidget'>}})

Module contents