ndx-depth-moseq NWB扩展

ndx-depth-moseq是一种用于在NWB中存储depth-moseq输出（一种自动运动序列算法）的标准化格式。目前，此扩展仅支持depth-moseq-extract的输出，但将根据需要扩展以涵盖其他类型的depth-moseq输出。

此扩展包括3种新的神经数据类型

• DepthImageSeries，它是为深度视频（具有恒定参考深度）简单扩展了pynwb.image.ImageSeries的。
• MoSeqExtractParameterGroup，它存储了depth-moseq-extract算法的所有各种参数。
• MoSeqExtractGroup，它存储了所有相关的depth-moseq输出，包括DepthImageSeries、MoSeqExtractParameterGroup，以及各种原生神经数据类型，如Position。

安装

pip install ndx-depth-moseq

用法

"""Example of usage with mock data."""
from datetime import datetime
from pytz import timezone
import numpy as np
from pynwb.image import GrayscaleImage, ImageMaskSeries
from pynwb import NWBFile, TimeSeries
from pynwb.behavior import (
    CompassDirection,
    Position,
    SpatialSeries,
)
from ndx_depth_moseq import DepthImageSeries, MoSeqExtractGroup, MoSeqExtractParameterGroup

# Define mock data (this will be replaced with the actual data) 
version = "0.1.0"
num_frames = 10
num_rows = 512
num_cols = 424
processed_depth_video = np.zeros((num_frames, num_rows, num_cols))
loglikelihood_video = np.zeros((num_frames, num_rows, num_cols))
timestamps = np.arange(num_frames)
background = np.zeros((num_rows, num_cols))
is_flipped = np.zeros(num_frames, dtype=bool)
roi = np.zeros((num_rows, num_cols))
true_depth = 1.0
kinematic_var_names = ['centroid_x_mm', 'centroid_y_mm', 'height_ave_mm', 'angle', 'velocity_2d_mm', 'velocity_3d_mm', 'velocity_theta', 'length_mm', 'width_mm', 'area_px', 'width_px', 'length_px']
kinematic_vars = {k: np.zeros(num_frames) for k in kinematic_var_names}
kinematic_vars['length_px'] += 1
kinematic_vars['width_px'] += 1
parameters = {
    'angle_hampel_sig': np.array([3], dtype=np.int64)[0],
    'angle_hampel_span': np.array([5], dtype=np.int64)[0],
    'bg_roi_depth_range_min': np.array([0], dtype=np.int64)[0],
    'bg_roi_depth_range_max': np.array([1000], dtype=np.int64)[0],
    'bg_roi_dilate_x': np.array([10], dtype=np.int64)[0],
    'bg_roi_dilate_y': np.array([10], dtype=np.int64)[0],
    'bg_roi_fill_holes': True,
    'bg_roi_gradient_filter': True,
    'bg_roi_gradient_kernel': np.array([5], dtype=np.int64)[0],
    'bg_roi_gradient_threshold': np.array([10], dtype=np.int64)[0],
    'bg_roi_index': np.array([0], dtype=np.int64)[0],
    'bg_roi_shape': 'ellipse',
    'bg_roi_weight_area': np.array([0.5], dtype=np.float64)[0],
    'bg_roi_weight_extent': np.array([0.5], dtype=np.float64)[0],
    'bg_roi_weight_dist': np.array([0.5], dtype=np.float64)[0],
    'cable_filter_iters': np.array([5], dtype=np.int64)[0],
    'cable_filter_shape': 'ellipse',
    'cable_filter_size_x': np.array([5], dtype=np.int64)[0],
    'cable_filter_size_y': np.array([5], dtype=np.int64)[0],
    'centroid_hampel_sig': np.array([3], dtype=np.int64)[0],
    'centroid_hampel_span': np.array([5], dtype=np.int64)[0],
    'chunk_overlap': np.array([0], dtype=np.int64)[0],
    'chunk_size': np.array([100], dtype=np.int64)[0],
    'compress': False,
    'compress_chunk_size': np.array([100], dtype=np.int64)[0],
    'compress_threads': np.array([1], dtype=np.int64)[0],
    'config_file': 'config.yaml',
    'crop_size_width': np.array([512], dtype=np.int64)[0],
    'crop_size_height': np.array([424], dtype=np.int64)[0],
    'flip_classifier': 'flip_classifier.pkl',
    'flip_classifier_smoothing': np.array([5], dtype=np.int64)[0],
    'fps': np.array([30], dtype=np.int64)[0],
    'frame_dtype': 'uint16',
    'frame_trim_beginning': np.array([0], dtype=np.int64)[0],
    'frame_trim_end': np.array([0], dtype=np.int64)[0],
    'max_height': np.array([1000], dtype=np.int64)[0],
    'min_height': np.array([0], dtype=np.int64)[0],
    'model_smoothing_clips_x': np.array([5], dtype=np.int64)[0],
    'model_smoothing_clips_y': np.array([5], dtype=np.int64)[0],
    'spatial_filter_size': np.array([5], dtype=np.int64)[0],
    'tail_filter_iters': np.array([5], dtype=np.int64)[0],
    'tail_filter_shape': 'ellipse',
    'tail_filter_size_x': np.array([5], dtype=np.int64)[0],
    'tail_filter_size_y': np.array([5], dtype=np.int64)[0],
    'temporal_filter_size': np.array([5], dtype=np.int64)[0],
    'tracking_model_init': 'mean',
    'tracking_model_ll_clip': np.array([5], dtype=np.int64)[0],
    'tracking_model_ll_threshold': np.array([5], dtype=np.int64)[0],
    'tracking_model_mask_threshold': np.array([5], dtype=np.int64)[0],
    'tracking_model_segment': True,
    'use_plane_bground': True,
    'use_tracking_model': True,
    'write_movie': False,
}

# Create the NWB file
nwbfile = NWBFile(
    session_description="session_description",
    identifier="identifier",
    session_start_time=datetime.now(timezone("US/Pacific")),
)

# Add Imaging Data
kinect = nwbfile.create_device(name="kinect", manufacturer="Microsoft", description="Microsoft Kinect 2")
flipped_series = TimeSeries(
    name="flipped_series",
    data=is_flipped,
    unit="a.u.",
    timestamps=timestamps,
    description="Boolean array indicating whether the image was flipped left/right",
)
processed_depth_video = DepthImageSeries(
    name="processed_depth_video",
    data=processed_depth_video,
    unit="millimeters",
    format="raw",
    timestamps=flipped_series.timestamps,
    description="3D array of depth frames (nframes x w x h, in mm)",
    distant_depth=true_depth,
    device=kinect,
)
loglikelihood_video = ImageMaskSeries(
    name="loglikelihood_video",
    data=loglikelihood_video,
    masked_imageseries=processed_depth_video,
    unit="a.u.",
    format="raw",
    timestamps=flipped_series.timestamps,
    description="Log-likelihood values from the tracking model (nframes x w x h)",
    device=kinect,
)
background = GrayscaleImage(
    name="background",
    data=background,
    description="Computed background image.",
)
roi = GrayscaleImage(
    name="roi",
    data=roi,
    description="Computed region of interest.",
)

# Add Position Data
position_data = np.vstack(
    (kinematic_vars["centroid_x_mm"], kinematic_vars["centroid_y_mm"], kinematic_vars["height_ave_mm"])
).T
position_series = SpatialSeries(
    name="position",
    description="Position (x, y, height) in an open field.",
    data=position_data,
    timestamps=flipped_series.timestamps,
    reference_frame="top left",
    unit="mm",
)
position = Position(spatial_series=position_series, name="position")

# Add Compass Direction Data
heading_2d_series = SpatialSeries(
    name="heading_2d",
    description="Head orientation.",
    data=kinematic_vars["angle"],
    timestamps=flipped_series.timestamps,
    reference_frame="top left",
    unit="radians",
)
heading_2d = CompassDirection(spatial_series=heading_2d_series, name="heading_2d")

# Add speed/velocity data
speed_2d = TimeSeries(
    name="speed_2d",
    description="2D speed (mm / frame), note that missing frames are not accounted for",
    data=kinematic_vars["velocity_2d_mm"],
    timestamps=flipped_series.timestamps,
    unit="mm/frame",
)
speed_3d = TimeSeries(
    name="speed_3d",
    description="3D speed (mm / frame), note that missing frames are not accounted for",
    data=kinematic_vars["velocity_3d_mm"],
    timestamps=flipped_series.timestamps,
    unit="mm/frame",
)
angular_velocity_2d = TimeSeries(
    name="angular_velocity_2d",
    description="Angular component of velocity (arctan(vel_x, vel_y))",
    data=kinematic_vars["velocity_theta"],
    timestamps=flipped_series.timestamps,
    unit="radians/frame",
)

# Add length/width/area data
length = TimeSeries(
    name="length",
    description="Length of mouse (mm)",
    data=kinematic_vars["length_mm"],
    timestamps=flipped_series.timestamps,
    unit="mm",
)
width = TimeSeries(
    name="width",
    description="Width of mouse (mm)",
    data=kinematic_vars["width_mm"],
    timestamps=flipped_series.timestamps,
    unit="mm",
)
width_px_to_mm = kinematic_vars["width_mm"] / kinematic_vars["width_px"]
length_px_to_mm = kinematic_vars["length_mm"] / kinematic_vars["length_px"]
area_px_to_mm2 = width_px_to_mm * length_px_to_mm
area_mm2 = kinematic_vars["area_px"] * area_px_to_mm2
area = TimeSeries(
    name="area",
    description="Pixel-wise area of mouse (mm^2)",
    data=area_mm2,
    timestamps=flipped_series.timestamps,
    unit="mm^2",
)

# Add Parameters
parameters = MoSeqExtractParameterGroup(name="parameters", **parameters)

# Add MoseqExtractGroup
moseq_extract_group = MoSeqExtractGroup(
    name="moseq_extract_group",
    version=version,
    parameters=parameters,
    background=background,
    processed_depth_video=processed_depth_video,
    loglikelihood_video=loglikelihood_video,
    roi=roi,
    flipped_series=flipped_series,
    depth_camera=kinect,
    position=position,
    heading_2d=heading_2d,
    speed_2d=speed_2d,
    speed_3d=speed_3d,
    angular_velocity_2d=angular_velocity_2d,
    length=length,
    width=width,
    area=area,
)
# Add data into a behavioral processing module
behavior_module = nwbfile.create_processing_module(
    name="behavior",
    description="Processed behavioral data from MoSeq",
)
behavior_module.add(moseq_extract_group)

此扩展是使用ndx-template创建的。