import warnings
from datetime import datetime
from typing import Literal
import numpy as np
from pynwb import NWBFile
from pynwb.base import DynamicTable
from pynwb.device import Device
from .mock_ttl_signals import generate_mock_ttl_signal
from ...basedatainterface import BaseDataInterface
from ...basetemporalalignmentinterface import BaseTemporalAlignmentInterface
from ...datainterfaces import SpikeGLXNIDQInterface
from ...datainterfaces.ecephys.baserecordingextractorinterface import (
BaseRecordingExtractorInterface,
)
from ...datainterfaces.ecephys.basesortingextractorinterface import (
BaseSortingExtractorInterface,
)
from ...datainterfaces.ophys.baseimagingextractorinterface import (
BaseImagingExtractorInterface,
)
from ...datainterfaces.ophys.basesegmentationextractorinterface import (
BaseSegmentationExtractorInterface,
)
from ...tools.nwb_helpers import get_module
from ...utils import ArrayType, get_json_schema_from_method_signature
from ...utils.dict import DeepDict
[docs]
class MockInterface(BaseDataInterface):
"""
A mock interface for testing basic command passing without side effects.
"""
def __init__(self, verbose: bool = False, **source_data):
super().__init__(verbose=verbose, **source_data)
[docs]
def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict | None, **conversion_options):
return None
[docs]
class MockTimeSeriesInterface(BaseDataInterface):
"""
A mock TimeSeries interface for testing purposes.
This interface uses pynwb's mock_TimeSeries to create synthetic time series data
without only pynwb as a dependency.
"""
def __init__(
self,
*,
num_channels: int = 4,
sampling_frequency: float = 30_000.0,
duration: float = 1.0,
seed: int = 0,
verbose: bool = False,
metadata_key: str = "TimeSeries",
):
"""
Initialize a mock TimeSeries interface.
Parameters
----------
num_channels : int, optional
Number of channels to generate, by default 4.
sampling_frequency : float, optional
Sampling frequency in Hz, by default 30,000.0 Hz.
duration : float, optional
Duration of the data in seconds, by default 1.0.
seed : int, optional
Seed for the random number generator, by default 0.
verbose : bool, optional
Control verbosity, by default False.
metadata_key : str, optional
Key for the TimeSeries metadata in the metadata dictionary, by default "TimeSeries".
"""
self.num_channels = num_channels
self.sampling_frequency = sampling_frequency
self.duration = duration
self.seed = seed
self.metadata_key = metadata_key
super().__init__(verbose=verbose)
[docs]
def add_to_nwbfile(
self,
nwbfile: NWBFile,
metadata: dict | None = None,
):
"""
Add mock TimeSeries data to an NWB file.
Parameters
----------
nwbfile : NWBFile
The NWB file to which the TimeSeries data will be added.
metadata : dict, optional
Metadata dictionary. If None, uses default metadata.
"""
from pynwb.testing.mock.base import mock_TimeSeries
if metadata is None:
metadata = self.get_metadata()
# Generate mock data
rng = np.random.default_rng(self.seed)
num_samples = int(self.duration * self.sampling_frequency)
data = rng.standard_normal(size=(num_samples, self.num_channels)).astype("float32")
# Get TimeSeries kwargs from metadata
time_series_metadata = metadata.get("TimeSeries", {}).get(self.metadata_key, {})
tseries_kwargs = {
"name": time_series_metadata.get("name", "MockTimeSeries"),
"description": time_series_metadata.get("description", "Mock TimeSeries data"),
"unit": time_series_metadata.get("unit", "n.a."),
"data": data,
"starting_time": 0.0,
"rate": self.sampling_frequency,
}
# Apply any additional metadata
for key in ["comments", "conversion", "offset"]:
if key in time_series_metadata:
tseries_kwargs[key] = time_series_metadata[key]
time_series = mock_TimeSeries(**tseries_kwargs)
nwbfile.add_acquisition(time_series)
[docs]
class MockBehaviorEventInterface(BaseTemporalAlignmentInterface):
"""
A mock behavior event interface for testing purposes.
"""
[docs]
@classmethod
def get_source_schema(cls) -> dict:
source_schema = get_json_schema_from_method_signature(method=cls.__init__, exclude=["event_times"])
source_schema["additionalProperties"] = True
return source_schema
def __init__(self, event_times: ArrayType | None = None):
"""
Initialize the interface with event times for behavior.
Parameters
----------
event_times : list of floats, optional
The event times to set as timestamps for this interface.
The default is the array [1.2, 2.3, 3.4] to simulate a time series similar to the
MockSpikeGLXNIDQInterface.
"""
event_times = event_times or [1.2, 2.3, 3.4]
self.event_times = np.array(event_times)
self.original_event_times = np.array(event_times) # Make a copy of the initial loaded timestamps
[docs]
def get_original_timestamps(self) -> np.ndarray:
"""
Get the original event times before any alignment or transformation.
Returns
-------
np.ndarray
The original event times as a NumPy array.
"""
return self.original_event_times
[docs]
def get_timestamps(self) -> np.ndarray:
"""
Get the current (possibly aligned) event times.
Returns
-------
np.ndarray
The current event times as a NumPy array, possibly modified after alignment.
"""
return self.event_times
[docs]
def set_aligned_timestamps(self, aligned_timestamps: np.ndarray):
"""
Set the event times after alignment.
Parameters
----------
aligned_timestamps : np.ndarray
The aligned event timestamps to update the internal event times.
"""
self.event_times = aligned_timestamps
[docs]
def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict):
"""
Add the event times to an NWBFile as a DynamicTable.
Parameters
----------
nwbfile : NWBFile
The NWB file to which the event times will be added.
metadata : dict
Metadata to describe the event times in the NWB file.
Notes
-----
This method creates a DynamicTable to store event times and adds it to the NWBFile's acquisition.
"""
table = DynamicTable(name="BehaviorEvents", description="Times of various classified behaviors.")
table.add_column(name="event_time", description="Time of each event.")
for timestamp in self.get_timestamps():
table.add_row(event_time=timestamp)
nwbfile.add_acquisition(table)
[docs]
class MockSpikeGLXNIDQInterface(SpikeGLXNIDQInterface):
"""
A mock SpikeGLX interface for testing purposes.
"""
ExtractorName = "NumpyRecording"
[docs]
@classmethod
def get_source_schema(cls) -> dict:
source_schema = get_json_schema_from_method_signature(method=cls.__init__, exclude=["ttl_times"])
source_schema["additionalProperties"] = True
return source_schema
def __init__(
self, signal_duration: float = 7.0, ttl_times: list[list[float]] | None = None, ttl_duration: float = 1.0
):
"""
Define a mock SpikeGLXNIDQInterface by overriding the recording extractor to be a mock TTL signal.
Parameters
----------
signal_duration : float, default: 7.0
The number of seconds to simulate.
ttl_times : list of lists of floats, optional
The times within the `signal_duration` to trigger the TTL pulse for each channel.
The outer list is over channels, while each inner list is the set of TTL times for each specific channel.
The default generates 8 channels with periodic on/off cycle (which start in the 'off' state)
each of which is of length `ttl_duration` with a 0.1 second offset per channel.
ttl_duration : float, default: 1.0
How long the TTL pulses stays in the 'on' state when triggered, in seconds.
"""
from spikeinterface.extractors import NumpyRecording
self.has_analog_channels = True
self.has_digital_channels = False
if ttl_times is None:
# Begin in 'off' state
number_of_periods = int(np.ceil((signal_duration - ttl_duration) / (ttl_duration * 2)))
default_periodic_ttl_times = [ttl_duration * (1 + 2 * period) for period in range(number_of_periods)]
ttl_times = [[ttl_time + 0.1 * channel for ttl_time in default_periodic_ttl_times] for channel in range(8)]
number_of_channels = len(ttl_times)
channel_ids = [f"nidq#XA{channel_index}" for channel_index in range(number_of_channels)] # NIDQ channel IDs
channel_groups = ["NIDQChannelGroup"] * number_of_channels
self.analog_channel_ids = channel_ids
sampling_frequency = 25_000.0 # NIDQ sampling rate
number_of_frames = int(signal_duration * sampling_frequency)
traces = np.empty(shape=(number_of_frames, number_of_channels), dtype="int16")
for channel_index in range(number_of_channels):
traces[:, channel_index] = generate_mock_ttl_signal(
signal_duration=signal_duration,
ttl_times=ttl_times[channel_index],
ttl_duration=ttl_duration,
sampling_frequency_hz=sampling_frequency,
)
self.recording_extractor = NumpyRecording(
traces_list=traces, sampling_frequency=sampling_frequency, channel_ids=channel_ids
)
# NIDQ channel gains
self.recording_extractor.set_channel_gains(gains=[61.03515625] * self.recording_extractor.get_num_channels())
self.recording_extractor.set_property(key="group_name", values=channel_groups)
# Minimal meta so `get_metadata` works similarly to real NIDQ header
self.meta = {"acqMnMaXaDw": "0,0,8,1", "fileCreateTime": "2020-11-03T10:35:10", "niDev1ProductName": "PCI-6259"}
self.verbose = None
self.metadata_key = "SpikeGLXNIDQ"
self._analog_channel_groups = {
"nidq_analog": {
"channels": list(channel_ids),
}
}
self._digital_channel_groups = {}
[docs]
class MockRecordingInterface(BaseRecordingExtractorInterface):
"""An interface with a spikeinterface recording object for testing purposes."""
def _initialize_extractor(self, interface_kwargs: dict):
self.extractor_kwargs = interface_kwargs.copy()
self.extractor_kwargs.pop("verbose", None)
self.extractor_kwargs.pop("es_key", None)
extractor_class = self.get_extractor_class()
extractor_instance = extractor_class(**self.extractor_kwargs)
return extractor_instance
def __init__(
self,
*args,
num_channels: int = 4,
sampling_frequency: float = 30_000.0,
durations: tuple[float, ...] = (1.0,),
seed: int = 0,
verbose: bool = False,
es_key: str = "ElectricalSeries",
set_probe: bool = False,
):
# Handle deprecated positional arguments
if args:
parameter_names = [
"num_channels",
"sampling_frequency",
"durations",
"seed",
"verbose",
"es_key",
"set_probe",
]
# Number of positional parameters before *args in the signature (self is counted by Python in error messages)
num_positional_args_before_args = 1 # self
if len(args) > len(parameter_names):
raise TypeError(
f"MockRecordingInterface.__init__() takes at most {len(parameter_names) + num_positional_args_before_args} positional arguments "
f"but {len(args) + num_positional_args_before_args} were given. "
"Note: Positional arguments are deprecated and will be removed in June 2026 or after. Please use keyword arguments."
)
# Map positional args to keyword args, positional args take precedence
positional_values = dict(zip(parameter_names, args))
passed_as_positional = list(positional_values.keys())
warnings.warn(
f"Passing arguments positionally to MockRecordingInterface.__init__() is deprecated "
f"and will be removed in June 2026 or after. "
f"The following arguments were passed positionally: {passed_as_positional}. "
"Please use keyword arguments instead.",
FutureWarning,
stacklevel=2,
)
num_channels = positional_values.get("num_channels", num_channels)
sampling_frequency = positional_values.get("sampling_frequency", sampling_frequency)
durations = positional_values.get("durations", durations)
seed = positional_values.get("seed", seed)
verbose = positional_values.get("verbose", verbose)
es_key = positional_values.get("es_key", es_key)
set_probe = positional_values.get("set_probe", set_probe)
super().__init__(
num_channels=num_channels,
sampling_frequency=sampling_frequency,
durations=durations,
set_probe=set_probe,
seed=seed,
verbose=verbose,
es_key=es_key,
)
self.recording_extractor.set_channel_gains(gains=[1.0] * self.recording_extractor.get_num_channels())
self.recording_extractor.set_channel_offsets(offsets=[0.0] * self.recording_extractor.get_num_channels())
# If probe was set, customize contact IDs to use "e0", "e1", etc. format for testing
if set_probe and self.recording_extractor.has_probe():
probe = self.recording_extractor.get_probe()
contact_ids = [f"e{i}" for i in range(num_channels)]
probe.set_contact_ids(contact_ids)
self.recording_extractor = self.recording_extractor.set_probe(probe, group_mode="by_probe")
[docs]
class MockSortingInterface(BaseSortingExtractorInterface):
"""A mock sorting extractor interface for generating synthetic sorting data."""
# TODO: Implement this class with the lazy generator once is merged
# https://github.com/SpikeInterface/spikeinterface/pull/2227
def _initialize_extractor(self, interface_kwargs: dict):
self.extractor_kwargs = interface_kwargs.copy()
self.extractor_kwargs.pop("verbose", None)
extractor_class = self.get_extractor_class()
extractor_instance = extractor_class(**self.extractor_kwargs)
return extractor_instance
def __init__(
self,
num_units: int = 4,
sampling_frequency: float = 30_000.0,
durations: tuple[float, ...] = (1.0,),
seed: int = 0,
verbose: bool = False,
):
"""
Parameters
----------
num_units : int, optional
Number of units to generate, by default 4.
sampling_frequency : float, optional
Sampling frequency of the generated data in Hz, by default 30,000.0 Hz.
durations : tuple of float, optional
Durations of the segments in seconds, by default (1.0,).
seed : int, optional
Seed for the random number generator, by default 0.
verbose : bool, optional
Control whether to display verbose messages during writing, by default True.
"""
super().__init__(
num_units=num_units,
sampling_frequency=sampling_frequency,
durations=durations,
seed=seed,
verbose=verbose,
)
# Sorting extractor to have string unit ids until is changed in SpikeInterface
# https://github.com/SpikeInterface/spikeinterface/pull/3588
string_unit_ids = [str(id) for id in self.sorting_extractor.unit_ids]
self.sorting_extractor = self.sorting_extractor.rename_units(new_unit_ids=string_unit_ids)
[docs]
class MockImagingInterface(BaseImagingExtractorInterface):
"""
A mock imaging interface for testing purposes.
"""
def _initialize_extractor(self, interface_kwargs: dict):
self.extractor_kwargs = interface_kwargs.copy()
self.extractor_kwargs.pop("verbose", None)
self.extractor_kwargs.pop("photon_series_type", None)
self.extractor_kwargs.pop("metadata_key", None)
extractor_class = self.get_extractor_class()
extractor_instance = extractor_class(**self.extractor_kwargs)
return extractor_instance
def __init__(
self,
num_samples: int = 30,
num_rows: int = 10,
num_columns: int = 10,
sampling_frequency: float = 30,
dtype: str = "uint16",
verbose: bool = False,
seed: int = 0,
photon_series_type: Literal["OnePhotonSeries", "TwoPhotonSeries"] = "TwoPhotonSeries",
metadata_key: str | None = None,
):
"""
Parameters
----------
num_samples : int, optional
The number of samples (frames) in the mock imaging data, by default 30.
num_rows : int, optional
The number of rows (height) in each frame of the mock imaging data, by default 10.
num_columns : int, optional
The number of columns (width) in each frame of the mock imaging data, by default 10.
sampling_frequency : float, optional
The sampling frequency of the mock imaging data in Hz, by default 30.
dtype : str, optional
The data type of the generated imaging data (e.g., 'uint16'), by default 'uint16'.
seed : int, optional
Random seed for reproducibility, by default 0.
photon_series_type : Literal["OnePhotonSeries", "TwoPhotonSeries"], optional
The type of photon series for the mock imaging data, either "OnePhotonSeries" or
"TwoPhotonSeries", by default "TwoPhotonSeries".
verbose : bool, default False
controls verbosity
"""
self.seed = seed
if metadata_key is None:
metadata_key = "mock_imaging"
super().__init__(
num_samples=num_samples,
num_rows=num_rows,
num_columns=num_columns,
sampling_frequency=sampling_frequency,
dtype=dtype,
verbose=verbose,
seed=seed,
metadata_key=metadata_key,
)
self.verbose = verbose
self.photon_series_type = photon_series_type
[docs]
def add_to_nwbfile(
self,
nwbfile: NWBFile,
metadata: dict | None = None,
*args,
photon_series_type: Literal["TwoPhotonSeries", "OnePhotonSeries"] = "TwoPhotonSeries",
photon_series_index: int = 0,
parent_container: Literal["acquisition", "processing/ophys"] = "acquisition",
stub_test: bool = False,
always_write_timestamps: bool = False,
iterator_type: str | None = "v2",
iterator_options: dict | None = None,
):
"""
Add imaging data to the NWB file.
This method demonstrates the *args pattern for deprecating positional arguments
while maintaining schema validation for keyword-only arguments.
Parameters
----------
nwbfile : NWBFile
The NWB file where the imaging data will be added.
metadata : dict, optional
Metadata for the NWBFile, by default None.
photon_series_type : {"TwoPhotonSeries", "OnePhotonSeries"}, optional
The type of photon series to be added, by default "TwoPhotonSeries".
photon_series_index : int, optional
The index of the photon series in the provided imaging data, by default 0.
parent_container : {"acquisition", "processing/ophys"}, optional
Specifies the parent container to which the photon series should be added.
stub_test : bool, optional
If True, only writes a small subset of frames for testing purposes, by default False.
always_write_timestamps : bool, optional
Whether to always write timestamps, by default False.
iterator_type : {"v2", None}, default: "v2"
The type of iterator for chunked data writing.
iterator_options : dict, optional
Options for controlling the iterative write process.
"""
# Handle deprecated positional arguments
if args:
parameter_names = [
"photon_series_type",
"photon_series_index",
"parent_container",
"stub_test",
"always_write_timestamps",
"iterator_type",
"iterator_options",
]
num_positional_args_before_args = 2 # nwbfile, metadata
if len(args) > len(parameter_names):
raise TypeError(
f"add_to_nwbfile() takes at most {len(parameter_names) + num_positional_args_before_args} positional arguments but "
f"{len(args) + num_positional_args_before_args} were given. "
"Note: Positional arguments are deprecated and will be removed in June 2026 or after. Please use keyword arguments."
)
# Map positional args to keyword args, positional args take precedence
positional_values = dict(zip(parameter_names, args))
passed_as_positional = list(positional_values.keys())
warnings.warn(
f"Passing arguments positionally to add_to_nwbfile is deprecated "
f"and will be removed in June 2026 or after. "
f"The following arguments were passed positionally: {passed_as_positional}. "
"Please use keyword arguments instead.",
FutureWarning,
stacklevel=2,
)
photon_series_type = positional_values.get("photon_series_type", photon_series_type)
photon_series_index = positional_values.get("photon_series_index", photon_series_index)
parent_container = positional_values.get("parent_container", parent_container)
stub_test = positional_values.get("stub_test", stub_test)
always_write_timestamps = positional_values.get("always_write_timestamps", always_write_timestamps)
iterator_type = positional_values.get("iterator_type", iterator_type)
iterator_options = positional_values.get("iterator_options", iterator_options)
# Call parent implementation with keyword arguments
super().add_to_nwbfile(
nwbfile=nwbfile,
metadata=metadata,
photon_series_type=photon_series_type,
photon_series_index=photon_series_index,
parent_container=parent_container,
stub_test=stub_test,
always_write_timestamps=always_write_timestamps,
iterator_type=iterator_type,
iterator_options=iterator_options,
)
[docs]
class MockSegmentationInterface(BaseSegmentationExtractorInterface):
"""A mock segmentation interface for testing purposes."""
def _initialize_extractor(self, interface_kwargs: dict):
self.extractor_kwargs = interface_kwargs.copy()
self.extractor_kwargs.pop("verbose", None)
extractor_class = self.get_extractor_class()
extractor_instance = extractor_class(**self.extractor_kwargs)
return extractor_instance
def __init__(
self,
num_rois: int = 10,
num_samples: int = 30,
num_rows: int = 25,
num_columns: int = 25,
sampling_frequency: float = 30.0,
has_summary_images: bool = True,
has_raw_signal: bool = True,
has_dff_signal: bool = True,
has_deconvolved_signal: bool = True,
has_neuropil_signal: bool = True,
seed: int = 0,
verbose: bool = False,
metadata_key: str | None = None,
):
"""
Parameters
----------
num_rois : int, optional
number of regions of interest, by default 10.
num_samples : int, optional
number of samples (frames), by default 30.
num_rows : int, optional
number of rows in the hypothetical video from which the data was extracted, by default 25.
num_columns : int, optional
number of columns in the hypothetical video from which the data was extracted, by default 25.
sampling_frequency : float, optional
sampling frequency of the hypothetical video from which the data was extracted, by default 30.0.
has_summary_images : bool, optional
whether the dummy segmentation extractor has summary images or not (mean and correlation).
has_raw_signal : bool, optional
whether a raw fluorescence signal is desired in the object, by default True.
has_dff_signal : bool, optional
whether a relative (df/f) fluorescence signal is desired in the object, by default True.
has_deconvolved_signal : bool, optional
whether a deconvolved signal is desired in the object, by default True.
has_neuropil_signal : bool, optional
whether a neuropil signal is desired in the object, by default True.
seed: int, default 0
seed for the random number generator, by default 0
verbose : bool, optional
controls verbosity, by default False.
metadata_key : str, optional
Metadata key for this interface. When None, defaults to "mock_segmentation".
"""
if metadata_key is None:
metadata_key = "mock_segmentation"
super().__init__(
num_rois=num_rois,
num_samples=num_samples,
num_rows=num_rows,
num_columns=num_columns,
sampling_frequency=sampling_frequency,
has_summary_images=has_summary_images,
has_raw_signal=has_raw_signal,
has_dff_signal=has_dff_signal,
has_deconvolved_signal=has_deconvolved_signal,
has_neuropil_signal=has_neuropil_signal,
verbose=verbose,
seed=seed,
metadata_key=metadata_key,
)
[docs]
class MockPoseEstimationInterface(BaseTemporalAlignmentInterface):
"""
A mock pose estimation interface for testing purposes.
"""
display_name = "Mock Pose Estimation"
keywords = (
"behavior",
"pose estimation",
"mock",
)
associated_suffixes = []
info = "Mock interface for pose estimation data testing."
[docs]
@classmethod
def get_source_schema(cls) -> dict:
source_schema = get_json_schema_from_method_signature(method=cls.__init__, exclude=["timestamps", "confidence"])
source_schema["additionalProperties"] = True
return source_schema
def __init__(
self,
num_samples: int = 1000,
num_nodes: int = 3,
seed: int = 0,
verbose: bool = False,
metadata_key: str = "MockPoseEstimation",
pose_estimation_metadata_key: str | None = None,
):
"""
Initialize a mock pose estimation interface.
Parameters
----------
num_samples : int, optional
Number of samples to generate, by default 1000.
num_nodes : int, optional
Number of nodes/body parts to track, by default 3.
seed : int, optional
Random seed for reproducible data generation, by default 0.
verbose : bool, optional
Control verbosity, by default False.
metadata_key : str, default: "MockPoseEstimation"
Metadata key for this interface.
pose_estimation_metadata_key : str, optional
Deprecated. Renamed to ``metadata_key``; passing it forwards the value to
``metadata_key`` and will be removed on or after December 2026.
"""
if pose_estimation_metadata_key is not None:
warnings.warn(
"The 'pose_estimation_metadata_key' argument has been renamed to 'metadata_key' and "
"will be removed on or after December 2026. Please use 'metadata_key' instead.",
DeprecationWarning,
stacklevel=2,
)
metadata_key = pose_estimation_metadata_key
self.num_samples = num_samples
self.num_nodes = num_nodes
self.metadata_key = metadata_key
self.seed = seed
self.verbose = verbose
# Set metadata defaults
self.scorer = "MockScorer"
self.source_software = "MockSourceSoftware"
# Generate random nodes and edges
orbital_body_parts = [
"head",
"neck",
"left_shoulder",
"right_shoulder",
"chest",
"left_elbow",
"right_elbow",
"left_wrist",
"right_wrist",
"pelvis",
]
# Use orbital body parts if we have enough, otherwise generate generic nodes
if num_nodes <= len(orbital_body_parts):
self.nodes = orbital_body_parts[:num_nodes]
else:
self.nodes = orbital_body_parts + [f"node_{i}" for i in range(len(orbital_body_parts), num_nodes)]
# Generate random edges (connect some nodes randomly)
np.random.seed(seed) # For reproducible edge generation
num_edges = min(num_nodes - 1, max(1, num_nodes // 2)) # Reasonable number of edges
possible_edges = [(i, j) for i in range(num_nodes) for j in range(i + 1, num_nodes)]
selected_edges = np.random.choice(len(possible_edges), size=num_edges, replace=False)
self.edges = np.array([possible_edges[i] for i in selected_edges], dtype="uint8")
# Generate timestamps (private attributes)
self._original_timestamps = np.linspace(0.0, float(num_samples) / 30.0, num_samples)
self._timestamps = np.copy(self._original_timestamps)
# Generate pose estimation data
self.pose_data = self._generate_pose_data()
super().__init__(verbose=verbose)
# Import ndx_pose to ensure it's available
import ndx_pose # noqa: F401
def _generate_pose_data(self) -> np.ndarray:
"""Generate pose estimation data with center following Lissajous trajectory and nodes fixed on circle."""
# Fixed to 2D for now
shape = (self.num_samples, self.num_nodes, 2)
# Generate Lissajous trajectory for the center
time_points = np.linspace(0, 4 * np.pi, self.num_samples)
center_x = 320 + 80 * np.sin(1.2 * time_points) # Center follows Lissajous
center_y = 240 + 60 * np.sin(1.7 * time_points + np.pi / 3)
# Generate data for all nodes
data = np.zeros(shape)
circle_radius = 50 # Radius of circle around center
for node_index in range(self.num_nodes):
# Position each node equally spaced around a circle relative to center
angle = 2 * np.pi * node_index / self.num_nodes
# Fixed position on circle relative to center (no oscillations)
offset_x = circle_radius * np.cos(angle)
offset_y = circle_radius * np.sin(angle)
# Final position: center + fixed circle position
data[:, node_index, 0] = center_x + offset_x
data[:, node_index, 1] = center_y + offset_y
return data
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def get_original_timestamps(self) -> np.ndarray:
"""Get the original timestamps before any alignment."""
return self._original_timestamps
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def get_timestamps(self) -> np.ndarray:
"""Get the current (possibly aligned) timestamps."""
return self._timestamps
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def set_aligned_timestamps(self, aligned_timestamps: np.ndarray):
"""Set aligned timestamps."""
self._timestamps = aligned_timestamps
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def add_to_nwbfile(self, nwbfile: NWBFile, metadata: dict | None = None, **conversion_options):
"""Add mock pose estimation data to NWBFile using ndx-pose, reading names from metadata."""
from ndx_pose import PoseEstimation, PoseEstimationSeries, Skeleton, Skeletons
if metadata is None:
metadata = self.get_metadata()
pose_metadata = metadata["Behavior"]["Pose"]
container_entry = pose_metadata["PoseEstimations"][self.metadata_key]
behavior_module = get_module(nwbfile, "behavior")
# Lazy device creation: only write a device when the container references one. ndx-pose
# makes ``devices`` optional, so an absent ``device_metadata_key`` means "no device", not a
# fabricated placeholder. Reuse an existing Device with the same name when present, which
# lets multiple interfaces share a device by pointing at the same ``device_metadata_key``.
device = None
device_metadata_key = container_entry.get("device_metadata_key")
if device_metadata_key is not None:
device_entry = metadata["Devices"][device_metadata_key]
device_name = device_entry["name"]
if device_name in nwbfile.devices:
device = nwbfile.devices[device_name]
else:
device = Device(name=device_name, description=device_entry.get("description", ""))
nwbfile.add_device(device)
# Lazy skeleton creation: only write a skeleton when the container references one. Reuse an
# existing Skeleton with the same name when present.
skeleton = None
skeleton_metadata_key = container_entry.get("skeleton_metadata_key")
if skeleton_metadata_key is not None:
skeleton_entry = pose_metadata["Skeletons"][skeleton_metadata_key]
skeleton_name = skeleton_entry["name"]
existing_skeletons = (
behavior_module["Skeletons"].skeletons if "Skeletons" in behavior_module.data_interfaces else {}
)
if skeleton_name in existing_skeletons:
skeleton = existing_skeletons[skeleton_name]
else:
skeleton = Skeleton(name=skeleton_name, nodes=skeleton_entry["nodes"], edges=self.edges)
pose_estimation_series_metadata = container_entry["PoseEstimationSeries"]
pose_estimation_series = []
for index, node_name in enumerate(self.nodes):
series_metadata = pose_estimation_series_metadata[node_name]
series = PoseEstimationSeries(
name=series_metadata["name"],
description=series_metadata["description"],
data=self.pose_data[:, index, :],
unit=series_metadata["unit"],
reference_frame=series_metadata["reference_frame"],
timestamps=self.get_timestamps(),
confidence=np.ones(self.num_samples),
confidence_definition=series_metadata["confidence_definition"],
)
pose_estimation_series.append(series)
pose_estimation = PoseEstimation(
name=container_entry["name"],
description=container_entry["description"],
pose_estimation_series=pose_estimation_series,
skeleton=skeleton,
devices=[device] if device is not None else None,
scorer=container_entry["scorer"],
source_software=container_entry["source_software"],
dimensions=(
np.array(container_entry["dimensions"], dtype="uint16")
if container_entry.get("dimensions") is not None
else None
),
original_videos=container_entry.get("original_videos"),
labeled_videos=container_entry.get("labeled_videos"),
)
behavior_module.add(pose_estimation)
if skeleton is not None:
if "Skeletons" not in behavior_module.data_interfaces:
skeletons = Skeletons(skeletons=[skeleton])
behavior_module.add(skeletons)
elif skeleton.name not in behavior_module["Skeletons"].skeletons:
behavior_module["Skeletons"].add_skeletons(skeleton)