# install cell skipped during CI (deps preinstalled into system Python)

import plotly.io as pio
# Render plotly figures inline (works in Colab, Jupyter, and headless
# `nbconvert --execute`/`ipython`-script runners — by default, plotly
# would try to pop a browser window in headless contexts and crash).
pio.renderers.default = "iframe"

subject_id = "grabAM05"
session_id = "11252019-vis-stim"
nwbfile_path = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_behavior+image+ophys.nwb"

from dandi.dandiapi import DandiAPIClient

dandiset_id = "001172"

with DandiAPIClient() as client:
    asset = client.get_dandiset(dandiset_id, 'draft').get_asset_by_path(nwbfile_path)
    s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)

import h5py
import remfile

file = remfile.File(s3_url)
h5_file = h5py.File(file, "r")

from pynwb import NWBHDF5IO
io = NWBHDF5IO(file=h5_file, load_namespaces=True)
nwbfile = io.read()
nwbfile

nwbfile.session_start_time

datetime.datetime(2019, 11, 25, 13, 25, 17, tzinfo=tzoffset(None, -18000))

print(nwbfile.experiment_description)

Dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that higher arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic modulation and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

print(nwbfile.session_description)

Dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice. All imaging was performed during the second half of the light cycle in awake, behaving mice that were head-fixed so that they could freely run on a cylindrical wheel. Small (20Â° diameter) sinusoidal drifting gratings (2 Hz, 0.04 cycles per degree, 100% contrast) were generated using Psychtoolbox in MATLAB and presented on an LCD monitor at a distance of 20 cm. Stimuli were presented for 1-2 s. Air-puff stimuli were delivered using a thin metal tube aimed at the fur along the back and coupled to a solenoid valve (Clark Solutions) that delivered brief (200-ms) puffs of compressed air.

nwbfile.subject

photon_series_names = [photon_series_name for photon_series_name in nwbfile.acquisition.keys() if "OnePhotonSeries" in photon_series_name]
print(photon_series_names)

['OnePhotonSeriesBlueExcitationGreenChannel', 'OnePhotonSeriesGreenExcitationRedChannel', 'OnePhotonSeriesVioletExcitationGreenChannel']

# Visualize the imaging data.
from matplotlib import pyplot as plt
fig, axs = plt.subplots(nrows=len(photon_series_names), ncols=1, sharex=True, sharey=True)
for ps_index,ps_name in enumerate(photon_series_names):    
    photon_series = nwbfile.acquisition[ps_name]
    axs[ps_index].imshow(photon_series.data[70], cmap="binary")
    axs[ps_index].set_title(ps_name.replace("_"," "))
plt.show()

nwbfile.devices["OnePhotonMicroscope"]

imaging_planes_names = [imaging_planes_name for imaging_planes_name in nwbfile.imaging_planes.keys()]
print(imaging_planes_names)
nwbfile.imaging_planes[imaging_planes_names[0]]

['ImagingPlaneBlueExcitationGreenChannel', 'ImagingPlaneGreenExcitationRedChannel', 'ImagingPlaneVioletExcitationGreenChannel']

nwbfile.processing["ophys"]

dff_photon_series_names = [dff_photon_series_name for dff_photon_series_name in nwbfile.processing["ophys"].data_interfaces if "DFFOnePhotonSeries" in dff_photon_series_name]
print(dff_photon_series_names)

['DFFOnePhotonSeriesBlue', 'DFFOnePhotonSeriesGreen', 'DFFOnePhotonSeriesViolet']

import numpy as np
fig, axs = plt.subplots(nrows=len(dff_photon_series_names), ncols=1, sharex=True, sharey=True)
for ps_index,ps_name in enumerate(dff_photon_series_names):    
    dff_photon_series = nwbfile.processing["ophys"][ps_name]
    data = dff_photon_series.data[70]
    data[data == 0] = np.nan
    axs[ps_index].imshow(data, vmin=-0.01, cmap="binary")
    axs[ps_index].set_title(ps_name.replace("_"," "))
plt.show()

plane_segmentation_names = list(nwbfile.processing["ophys"]["ImageSegmentation"].plane_segmentations.keys())
_ = [print(name) for name in nwbfile.processing["ophys"]["ImageSegmentation"].plane_segmentations.keys()]

ParcellatedPlaneSegmentation

import numpy as np
from matplotlib.colors import ListedColormap
import random

photon_series = nwbfile.acquisition["OnePhotonSeriesBlueExcitationGreenChannel"]
plane_seg_name = plane_segmentation_names[0]
fig, ax = plt.subplots(nrows=1, ncols=1, sharex=True, sharey=True)   
plane_segmentation = nwbfile.processing["ophys"]["ImageSegmentation"].plane_segmentations[plane_seg_name]
for image_mask in plane_segmentation.image_mask:
    random_color = [random.random() for _ in range(3)] + [1]
    custom_cmap = ListedColormap([[0, 0, 0, 0], random_color])
    ax.imshow(image_mask.T, aspect="auto", cmap=custom_cmap)
ax.set_title(plane_seg_name)
plt.show()

import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

fluorescence_traces_names = [name for name in nwbfile.processing["ophys"]["Fluorescence"].roi_response_series.keys()]

fluorescence_traces_name = fluorescence_traces_names[0]
start_index = 0
stop_index = -1

fluorescence_rate = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].rate
fluorescence_starting_time = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].starting_time
start_time =fluorescence_starting_time + start_index*fluorescence_rate
stop_time = fluorescence_starting_time + stop_index*fluorescence_rate

fluorescence_traces = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].data[:, :10]

df = pd.DataFrame(fluorescence_traces)
df["time"] = np.linspace(start_time, stop_time, fluorescence_traces.shape[0])
df.set_index("time", inplace=True)
df.columns.name = 'ROIs'

import plotly.express as px

fig = px.line(df, facet_row="ROIs", facet_row_spacing=0.01)

# hide and lock down axes
fig.update_xaxes(visible=True, fixedrange=False)
fig.update_yaxes(visible=False, fixedrange=False)

# remove facet/subplot labels
fig.update_layout(annotations=[], overwrite=True)

# strip down the rest of the plot
fig.update_layout(
    showlegend=True,
    plot_bgcolor="white",
    margin=dict(t=10, l=10, b=10, r=10)
)

fig.show(config=dict(displayModeBar=True))

nwbfile.processing["behavior"]["EyeTracking"]

nwbfile.processing["behavior"]["PupilTracking"]

start_index = 0
stop_index = 2000

pupil_area_timestamps = nwbfile.processing["behavior"]["PupilTracking"]["pupil_area"].timestamps[start_index:stop_index]
pupil_area_signal = nwbfile.processing["behavior"]["PupilTracking"]["pupil_area"].data[start_index:stop_index]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(pupil_area_timestamps, pupil_area_signal, color="cyan", linewidth=0.5, label="Pupil Area")

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the 
ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

motion_svd_series = nwbfile.processing["behavior"]["MotionSVDSeriesWhiskers"]
motion_svd_series

motion_svd_masks = nwbfile.processing["behavior"]["MotionSVDMasksWhiskers"]
motion_svd_masks

n_components_to_visualize = 5
start_index = 0
stop_index = 100
fig, ax = plt.subplots(nrows=n_components_to_visualize, ncols=2, dpi=300)
for i in range(n_components_to_visualize):
    mask =np.array(motion_svd_masks[i].image_mask[i])
    ax[i, 0].imshow(mask, cmap="binary")
    ax[i, 0].set_axis_off()
    series = motion_svd_series.data[start_index:stop_index,i]
    timestamps = motion_svd_series.timestamps[start_index:stop_index]
    ax[i,1].plot(timestamps, series)

table_object_type = "VisualStimulus" # "VisualStimulus" "Airpuff" "WheelActivation"
table_object = nwbfile.intervals[table_object_type].to_dataframe()
table_object

start_index = 0
stop_index = 1000

fluorescence_rate = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].rate
fluorescence_starting_time = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].starting_time
start_time =fluorescence_starting_time + start_index/fluorescence_rate
stop_time = fluorescence_starting_time + stop_index/fluorescence_rate

ROI_index=22
fluorescence_data = nwbfile.processing["ophys"]["Fluorescence"][fluorescence_traces_name].data[:, ROI_index]
fluorescence_timestamps = np.linspace(start_time, stop_time, fluorescence_data.shape[0])

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
line_handle, = ax.plot(fluorescence_timestamps, fluorescence_data, color="green", linewidth=0.5, label=f"ROI {ROI_index}".format(ROI_index=ROI_index))

handles = []
for (stimulus_start, stimulus_stop) in zip(table_object["start_time"], table_object["stop_time"]):
    handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop),color='yellow', edgecolor='none', alpha=0.9, label=table_object_type)
    handles.append(handle)

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the legend with the appropriate handles
handles = [line_handle, handles[0]]
labels = [f"ROI {ROI_index}".format(ROI_index=ROI_index), table_object_type]
ax.legend(handles, labels, frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

nwbfile.acquisition["WheelSignal"]

start_index = 0
stop_index = 200000

wheel_signal_rate = nwbfile.acquisition["WheelSignal"].rate
wheel_signal_starting_time = nwbfile.acquisition["WheelSignal"].starting_time
start_time = wheel_signal_starting_time + start_index/wheel_signal_rate
stop_time = wheel_signal_starting_time + stop_index/wheel_signal_rate

wheel_signal = nwbfile.acquisition["WheelSignal"].data[start_index:stop_index]
wheel_signal_timestamps = np.linspace(start_time, stop_time, wheel_signal.shape[0])

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(wheel_signal_timestamps, wheel_signal, color="red", linewidth=0.5, label="Wheel Signal")

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the 
ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

nwbfile.processing["behavior"]["WheelSpeed"]

start_index = 0
stop_index = 200000

wheel_speed_rate = nwbfile.processing["behavior"]["WheelSpeed"].rate
wheel_speed_starting_time = nwbfile.processing["behavior"]["WheelSpeed"].starting_time
start_time = wheel_speed_starting_time + start_index/wheel_speed_rate
stop_time = wheel_speed_starting_time + stop_index/wheel_speed_rate

wheel_speed = nwbfile.processing["behavior"]["WheelSpeed"].data[start_index:stop_index]
wheel_speed_timestamps = np.linspace(start_time, stop_time, wheel_speed.shape[0])

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(wheel_speed_timestamps, wheel_speed, color="red", linewidth=0.5, label="Wheel Speed")

# Hide top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create the 
ax.legend(frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

ax.tick_params(axis='y', labelsize=8)
plt.xlabel('Time (s)', fontsize=8)
plt.tick_params(axis='x', labelsize=8)
plt.show()

Data type	uint16
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Uncompressed size (bytes)	11289493504
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Data type	int32
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Compressed size (bytes)	8
Compression ratio	1.0

Data type	uint16
Shape	(21533, 512, 512)
Array size	10.51 GiB
Chunk shape	(19, 512, 512)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	11289493504
Compressed size (bytes)	8178093603
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Data type	int32
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Compressed size (bytes)	8
Compression ratio	1.0

Data type	uint16
Shape	(21533, 512, 512)
Array size	10.51 GiB
Chunk shape	(19, 512, 512)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	11289493504
Compressed size (bytes)	7950318523
Compression ratio	1.4200051823508557

Installing requirements¶

Tutorial for Dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice.¶

Select the subject and session you want to load¶

Streaming an NWB file ¶

Reading an NWB file ¶

root (NWBFile)

Access subject ¶

subject (Subject)

Access OnePhoton Imaging¶

Raw OnePhoton Imaging ¶

OnePhotonMicroscope (Device)

ImagingPlaneBlueExcitationGreenChannel (ImagingPlane)

Accessing the segmentation data¶

ophys (ProcessingModule)

Access DF/F one photon series¶

Plot Image Segmentation: Functional Parcellation¶

Visualize fluorescence traces¶

Access Processed Behavior ¶

EyeTracking

PupilTracking

MotionSVDSeriesWhiskers (MotionSVDSeries)

MotionSVDMasksWhiskers (MotionSVDMasks)

Access Visual Stimulus, Wheel Activation or Airpuff tables ¶

Access Wheel Signal and Wheel Speed ¶

WheelSignal (TimeSeries)

WheelSpeed (TimeSeries)

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Compression ratio	undefined

Data type	float64
Shape	(21533,)
Array size	168.23 KiB
Chunk shape	(21533,)
Compression	gzip
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Data type	object
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	event_name	event_type_description	pulse_value
id
0	TTLSignalBlueLED	The onset times of the TTLSignalBlueLED event.	1
1	TTLSignalVioletLED	The onset times of the TTLSignalVioletLED event.	1
2	TTLSignalGreenLED	The onset times of the TTLSignalGreenLED event.	1
3	TTLSignalPupilCamera	The onset times of the TTLSignalPupilCamera event.	1

Data type	float32
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Chunk shape	(21533, 2)
Compression	gzip
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Uncompressed size (bytes)	172264
Compressed size (bytes)	143268
Compression ratio	1.2023899265711813

	start_time	stop_time	contrast	orientation	stimulus_frequency	spatial_frequency	stimulus_size	screen_coordinates
id
0	34.1448	36.1544	5	0	2	0.04	2	[20.0, 667.97, 317.97, 932.03]
1	41.1948	43.1876	20	0	2	0.04	2	[20.0, 667.97, 317.97, 932.03]
2	48.2280	50.2210	50	0	2	0.04	2	[20.0, 667.97, 317.97, 932.03]
3	55.2612	57.2542	20	0	2	0.04	2	[20.0, 667.97, 317.97, 932.03]

	timestamp	ttl_type
id
0	10.0512	0
1	10.1512	0
2	10.2512	0
3	10.3512	0

	image_mask
id
0	[[-3.513492e-06, -2.670329e-06, -2.9400137e-06, -1.8760653e-06, 3.9393107e-08, 4.1557726e-07, -9.8129085e-08, -1.2985523e-06, -3.2443597e-07, 4.881574e-07, 1.585716e-07, 8.48521e-07, -2.7109184e-06, -4.08644e-06, -3.7728048e-06, -4.9545206e-06, 4.9307193e-08, 7.257241e-06, -4.5909053e-07, 1.3213542e-05, 8.727532e-06, -1.299239e-06, -7.467419e-06, -6.0563484e-06, -2.016722e-05, -2.6120944e-05, -2.4555473e-05, -2.8879731e-05, -3.571971e-05, -2.8082319e-05, -2.1386204e-05, -2.4582398e-05, -2.296203e-05, -3.6001755e-05, -2.6749865e-05, -1.44063915e-05, -1.34587235e-05, -3.2903502e-06, -2.1117257e-06, 2.3623072e-05, -2.1052516e-05, -3.8539438e-05, -3.1344716e-05, -3.1237418e-05, -2.0183732e-05, -1.8325996e-05, -2.2361359e-05, -2.0679674e-05, -3.2817094e-05, -1.8276867e-05, -2.4564866e-05, -5.1742586e-06, -9.319039e-06, -2.9726583e-05, -3.0922543e-05, -2.208007e-05, -2.7723741e-05, -2.8097784e-05, -1.1310319e-05, -2.483358e-05, -2.9106175e-05, -1.2679544e-05, -9.710045e-06, -1.1928818e-05, 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