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

subject_id = 134
session_id = 22
nwbfile_path = f"sub-{subject_id}/sub-{subject_id}_ses-{session_id}_behavior+ophys.nwb"

from dandi.dandiapi import DandiAPIClient

dandiset_id = "001170"

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

# The disk cache keeps blocks that have already been fetched, so repeated
# reads of the same part of the file do not go back over the network.
file = remfile.File(s3_url, disk_cache=remfile.DiskCache("nwb-cache"))
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(2022, 7, 21, 0, 0, tzinfo=tzoffset(None, -18000))

nwbfile.experiment_description

'Awake mice were head fixed on a running wheel and presented with odors. A meso-scale two-photon microscope was used to image glomerular activity in the olfactory bulb.'

nwbfile.session_description

'In this study, we explore odor-evoked activity representation in the olfactory bulb (OB) and how odor responses enable odor discrimination. Contrary to some previously cited theories that suggest a sparse representation, we hypothesize a more dense representation during odor presentation. A key question is how odors are reliably encoded in OB activity patterns, and how these patterns contribute to early odor processing. To address this problem, we recorded population level odor responses from the mouse OB with mesoscale two photon calcium imaging and applied machine learning techniques to suggest a model in which sparse coding is largely sufficient for olfaction, but redundant information may make odor coding more robust across different variables.'

nwbfile.subject

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

['two_photon_series_FOV1_channel1', 'two_photon_series_FOV2_channel1', 'two_photon_series_FOV3_channel1']

# 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["two_photon_microscope"]

nwbfile.imaging_planes["imaging_plane_channel1"]

nwbfile.processing["ophys"]

correlation_images_names = list(nwbfile.processing["ophys"]["correlation_images"].images.keys())
_ = [print(name) for name in correlation_images_names]

correlation_image_FOV1_channel1
correlation_image_FOV2_channel1
correlation_image_FOV3_channel1

fig, axs = plt.subplots(nrows=len(correlation_images_names), ncols=1, sharex=True, sharey=True)
for ci_index,ci_name in enumerate(correlation_images_names):    
    correlation_image = nwbfile.processing["ophys"]["correlation_images"].images[ci_name]
    axs[ci_index].imshow(correlation_image.data[:].T, cmap="binary")
    axs[ci_index].set_title(ci_name.replace("_"," "))
plt.show()

average_images_names = list(nwbfile.processing["ophys"]["average_images"].images.keys())
_ = [print(name) for name in average_images_names]

average_image_FOV1_channel1
average_image_FOV2_channel1
average_image_FOV3_channel1

fig, axs = plt.subplots(nrows=len(average_images_names), ncols=1, sharex=True, sharey=True)
for ai_index,ai_name in enumerate(average_images_names):    
    average_image = nwbfile.processing["ophys"]["average_images"].images[ai_name]
    axs[ai_index].imshow(average_image.data[:].T, cmap="binary")
    axs[ai_index].set_title(ai_name.replace("_"," "))
plt.show()

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

plane_segmentation_FOV1_channel1
plane_segmentation_FOV2_channel1
plane_segmentation_FOV3_channel1

ROI_index = 0

fig, axs = plt.subplots(nrows=len(plane_segmentation_names), ncols=1, sharex=True, sharey=True)
for ps_index,ps_name in enumerate(plane_segmentation_names):    
    average_image = nwbfile.processing["ophys"]["average_images"].images[average_images_names[ps_index]]
    axs[ps_index].imshow(average_image.data[:].T, cmap="binary")
    plane_segmentation = nwbfile.processing["ophys"]["image_segmentation"].plane_segmentations[ps_name]
    x_coordinates = [pix[0] for pix in plane_segmentation[ROI_index].pixel_mask[0]]
    y_coordinates = [pix[1] for pix in plane_segmentation[ROI_index].pixel_mask[0]]
    axs[ps_index].scatter(x_coordinates, y_coordinates, color="green")
    axs[ps_index].set_title(ps_name.replace("_"," "))
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]
fluorescence_traces = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[:1000, :10]
df = pd.DataFrame(fluorescence_traces)
df["time"] = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[:1000]
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.trials.to_dataframe()

# Prepare data for plotting
import numpy as np
ROI_index=0
start=0
stop=-1
fluorescence_data = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].data[start:stop, ROI_index]
fluorescence_timestamps = nwbfile.processing["ophys"]["fluorescence"][fluorescence_traces_name].timestamps[start:stop]

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

stimulus_table = nwbfile.trials.to_dataframe()
methyl_salicylate_stim = "Methyl Salicylate"
methyl_salicylate_stim_table = stimulus_table[stimulus_table["odorant"]==methyl_salicylate_stim]
methyl_salicylate_stim_handles = []
for (stimulus_start, stimulus_stop) in zip(methyl_salicylate_stim_table["start_time"], methyl_salicylate_stim_table["stop_time"]):
    methyl_salicylate_stim_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=methyl_salicylate_stim)
    methyl_salicylate_stim_handles.append(methyl_salicylate_stim_handle)

allyl_sulfide_stim = "Allyl Sulfide"
allyl_sulfide_stim_table = stimulus_table[stimulus_table["odorant"]==allyl_sulfide_stim]
allyl_sulfide_stim_handles = []
for (stimulus_start, stimulus_stop) in zip(allyl_sulfide_stim_table["start_time"], allyl_sulfide_stim_table["stop_time"]):
    allyl_sulfide_stim_handle = ax.fill_between(fluorescence_timestamps, np.nanmax(fluorescence_data), where=(fluorescence_timestamps >= stimulus_start) & (fluorescence_timestamps <= stimulus_stop),
                                    color='blue', edgecolor='none', alpha=0.9, label=allyl_sulfide_stim)
    allyl_sulfide_stim_handles.append(allyl_sulfide_stim_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, methyl_salicylate_stim_handles[0], allyl_sulfide_stim_handles[0]]
labels = ["ROI {ROI_index}".format(ROI_index=ROI_index), methyl_salicylate_stim, allyl_sulfide_stim]
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["treadmill_position"]

start=0
stop=-1
treadmill_data = nwbfile.acquisition["treadmill_position"].data[start:stop]
treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(treadmill_timestamps, treadmill_data, color="red", linewidth=0.5, label="treadmill position")

# 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"]["treadmill_velocity"]

data = nwbfile.processing["behavior"]["treadmill_velocity"].data[start:stop]
treadmill_timestamps = nwbfile.acquisition["treadmill_position"].timestamps[start:stop]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
ax.plot(fluorescence_timestamps, fluorescence_data, linewidth=0.5, label="fluorescence trace")
ax.plot(treadmill_timestamps, data*25, linewidth=0.5, label="treadmill velocity")

# 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	float64
Shape	(1044900,)
Array size	7.97 MiB
Chunk shape	(1044900,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	8359200
Compressed size (bytes)	449850
Compression ratio	18.58219406468823

Data type	float64
Shape	(1044900,)
Array size	7.97 MiB
Chunk shape	(1044900,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	8359200
Compressed size (bytes)	6151141
Compression ratio	1.3589673850753867

Data type	int16
Shape	(91000, 120, 433)
Array size	8.81 GiB
Chunk shape	(96, 120, 433)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	9456720000
Compressed size (bytes)	5400460181
Compression ratio	1.7510952183798723

Data type	float64
Shape	(91000,)
Array size	710.94 KiB
Chunk shape	(91000,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	728000
Compressed size (bytes)	556451
Compression ratio	1.3082912960889639

Data type	int32
Shape	(2,)
Array size	8.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	8
Compressed size (bytes)	8
Compression ratio	1.0

Installing requirements¶

Tutorial for Mesoscale Two-Photon Calcium Imaging of Population Level Odor Responses from the Mouse Olfactory Bulb.¶

Select the subject and session you want to load¶

Streaming an NWB file ¶

Reading an NWB file ¶

root (NWBFile)

Access subject ¶

subject (Subject)

Access TwoPhoton Imaging¶

Raw TwoPhoton Imaging ¶

two_photon_microscope (Device)

imaging_plane_channel1 (ImagingPlane)

Accessing the segmentation data¶

ophys (ProcessingModule)

Plot Correlation Images¶

Plot Average Images¶

Plot Image Segmentation¶

Visualize fluorescence traces¶

Access Odor stimuli ¶

Access Treadmill Traces ¶

treadmill_position (SpatialSeries)

treadmill_velocity (TimeSeries)

Data type	object
Shape	(3,)
Array size	24.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	24
Compressed size (bytes)	48
Compression ratio	0.5

Data type	float32
Shape	(182000, 17)
Array size	11.80 MiB
Chunk shape	(160575, 15)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	12376000
Compressed size (bytes)	10756953
Compression ratio	1.15051167370537

	pixel_mask
id
0	[[150, 83, 1.0], [151, 83, 1.0], [152, 83, 1.0], [153, 83, 1.0], [154, 83, 1.0], [155, 83, 1.0], [156, 83, 1.0], [157, 83, 1.0], [158, 83, 1.0], [159, 83, 1.0], [160, 83, 1.0], [161, 83, 1.0], [149, 84, 1.0], [150, 84, 1.0], [151, 84, 1.0], [152, 84, 1.0], [153, 84, 1.0], [154, 84, 1.0], [155, 84, 1.0], [156, 84, 1.0], [157, 84, 1.0], [158, 84, 1.0], [159, 84, 1.0], [160, 84, 1.0], [161, 84, 1.0], [162, 84, 1.0], [163, 84, 1.0], [147, 85, 1.0], [148, 85, 1.0], [149, 85, 1.0], [150, 85, 1.0], [151, 85, 1.0], [152, 85, 1.0], [153, 85, 1.0], [154, 85, 1.0], [155, 85, 1.0], [156, 85, 1.0], [157, 85, 1.0], [158, 85, 1.0], [159, 85, 1.0], [160, 85, 1.0], [161, 85, 1.0], [162, 85, 1.0], [163, 85, 1.0], [164, 85, 1.0], [147, 86, 1.0], [148, 86, 1.0], [149, 86, 1.0], [150, 86, 1.0], [151, 86, 1.0], [152, 86, 1.0], [153, 86, 1.0], [154, 86, 1.0], [155, 86, 1.0], [156, 86, 1.0], [157, 86, 1.0], [158, 86, 1.0], [159, 86, 1.0], [160, 86, 1.0], [161, 86, 1.0], [162, 86, 1.0], [163, 86, 1.0], [164, 86, 1.0], [146, 87, 1.0], [147, 87, 1.0], [148, 87, 1.0], [149, 87, 1.0], [150, 87, 1.0], [151, 87, 1.0], [152, 87, 1.0], [153, 87, 1.0], [154, 87, 1.0], [155, 87, 1.0], [156, 87, 1.0], [157, 87, 1.0], [158, 87, 1.0], [159, 87, 1.0], [160, 87, 1.0], [161, 87, 1.0], [162, 87, 1.0], [163, 87, 1.0], [164, 87, 1.0], [147, 88, 1.0], [148, 88, 1.0], [149, 88, 1.0], [150, 88, 1.0], [151, 88, 1.0], [152, 88, 1.0], [153, 88, 1.0], [154, 88, 1.0], [155, 88, 1.0], [156, 88, 1.0], [157, 88, 1.0], [158, 88, 1.0], [159, 88, 1.0], [160, 88, 1.0], [161, 88, 1.0], [162, 88, 1.0], [163, 88, 1.0], [148, 89, 1.0], ...]
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2	[[145, 100, 1.0], [146, 100, 1.0], [147, 100, 1.0], [143, 101, 1.0], [144, 101, 1.0], [145, 101, 1.0], [146, 101, 1.0], [147, 101, 1.0], [148, 101, 1.0], [149, 101, 1.0], [142, 102, 1.0], [143, 102, 1.0], [144, 102, 1.0], [145, 102, 1.0], [146, 102, 1.0], [147, 102, 1.0], [148, 102, 1.0], [149, 102, 1.0], [150, 102, 1.0], [141, 103, 1.0], [142, 103, 1.0], [143, 103, 1.0], [144, 103, 1.0], [145, 103, 1.0], [146, 103, 1.0], [147, 103, 1.0], [148, 103, 1.0], [149, 103, 1.0], [150, 103, 1.0], [151, 103, 1.0], [140, 104, 1.0], [141, 104, 1.0], [142, 104, 1.0], [143, 104, 1.0], [144, 104, 1.0], [145, 104, 1.0], [146, 104, 1.0], [147, 104, 1.0], [148, 104, 1.0], [149, 104, 1.0], [150, 104, 1.0], [151, 104, 1.0], [152, 104, 1.0], [140, 105, 1.0], [141, 105, 1.0], [142, 105, 1.0], [143, 105, 1.0], [144, 105, 1.0], [145, 105, 1.0], [146, 105, 1.0], [147, 105, 1.0], [148, 105, 1.0], [149, 105, 1.0], [150, 105, 1.0], [151, 105, 1.0], [152, 105, 1.0], [139, 106, 1.0], [140, 106, 1.0], [141, 106, 1.0], [142, 106, 1.0], [143, 106, 1.0], [144, 106, 1.0], [145, 106, 1.0], [146, 106, 1.0], [147, 106, 1.0], [148, 106, 1.0], [149, 106, 1.0], [150, 106, 1.0], [151, 106, 1.0], [152, 106, 1.0], [139, 107, 1.0], [140, 107, 1.0], [141, 107, 1.0], [142, 107, 1.0], [143, 107, 1.0], [144, 107, 1.0], [145, 107, 1.0], [146, 107, 1.0], [147, 107, 1.0], [148, 107, 1.0], [149, 107, 1.0], [150, 107, 1.0], [151, 107, 1.0], [152, 107, 1.0], [139, 108, 1.0], [140, 108, 1.0], [141, 108, 1.0], [142, 108, 1.0], [143, 108, 1.0], [144, 108, 1.0], [145, 108, 1.0], [146, 108, 1.0], [147, 108, 1.0], [148, 108, 1.0], [149, 108, 1.0], [150, 108, 1.0], [151, 108, 1.0], [152, 108, 1.0], [140, 109, 1.0], [141, 109, 1.0], ...]
3	[[138, 90, 1.0], [139, 90, 1.0], [140, 90, 1.0], [137, 91, 1.0], [138, 91, 1.0], [139, 91, 1.0], [140, 91, 1.0], [141, 91, 1.0], [136, 92, 1.0], [137, 92, 1.0], [138, 92, 1.0], [139, 92, 1.0], [140, 92, 1.0], [141, 92, 1.0], [142, 92, 1.0], [143, 92, 1.0], [135, 93, 1.0], [136, 93, 1.0], [137, 93, 1.0], [138, 93, 1.0], [139, 93, 1.0], [140, 93, 1.0], [141, 93, 1.0], [142, 93, 1.0], [143, 93, 1.0], [144, 93, 1.0], [134, 94, 1.0], [135, 94, 1.0], [136, 94, 1.0], [137, 94, 1.0], [138, 94, 1.0], [139, 94, 1.0], [140, 94, 1.0], [141, 94, 1.0], [142, 94, 1.0], [143, 94, 1.0], [144, 94, 1.0], [134, 95, 1.0], [135, 95, 1.0], [136, 95, 1.0], [137, 95, 1.0], [138, 95, 1.0], [139, 95, 1.0], [140, 95, 1.0], [141, 95, 1.0], [142, 95, 1.0], [143, 95, 1.0], [144, 95, 1.0], [145, 95, 1.0], [134, 96, 1.0], [135, 96, 1.0], [136, 96, 1.0], [137, 96, 1.0], [138, 96, 1.0], [139, 96, 1.0], [140, 96, 1.0], [141, 96, 1.0], [142, 96, 1.0], [143, 96, 1.0], [144, 96, 1.0], [135, 97, 1.0], [136, 97, 1.0], [137, 97, 1.0], [138, 97, 1.0], [139, 97, 1.0], [140, 97, 1.0], [141, 97, 1.0], [142, 97, 1.0], [143, 97, 1.0], [144, 97, 1.0], [135, 98, 1.0], [136, 98, 1.0], [137, 98, 1.0], [138, 98, 1.0], [139, 98, 1.0], [140, 98, 1.0], [141, 98, 1.0], [142, 98, 1.0], [143, 98, 1.0], [144, 98, 1.0], [136, 99, 1.0], [137, 99, 1.0], [138, 99, 1.0], [139, 99, 1.0], [140, 99, 1.0], [141, 99, 1.0], [142, 99, 1.0], [143, 99, 1.0], [137, 100, 1.0], [138, 100, 1.0], [139, 100, 1.0], [140, 100, 1.0], [141, 100, 1.0], [142, 100, 1.0], [138, 101, 1.0], [139, 101, 1.0], [140, 101, 1.0]]

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	start_time	stop_time	odorant	concentration	solution_date
id
0	59.2888	60.2928	Methyl Salicylate	0.1	2022-05-17
1	60.2966	61.2995	Methyl Salicylate	0.1	2022-05-17
2	61.3034	62.3067	Methyl Salicylate	0.1	2022-05-17
3	62.3106	63.3130	Methyl Salicylate	0.1	2022-05-17