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

from dandi.dandiapi import DandiAPIClient

client = DandiAPIClient.for_dandi_instance("dandi")

dandiset_id = "001038"
file_path = "sub-VGlut-A997/sub-VGlut-A997_ses-20200205-0001.nwb"

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

import h5py
from pynwb import NWBHDF5IO
import remfile

# We stream the file using remfile and open it with h5py and pynwb
file = remfile.File(s3_url)
h5_file = h5py.File(file, "r")

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

/opt/hostedtoolcache/Python/3.13.15/x64/lib/python3.13/site-packages/hdmf/spec/namespace.py:620: UserWarning: Ignoring the following cached namespace(s) because another version is already loaded:
core - cached version: 2.7.0, loaded version: 2.8.0
The loaded extension(s) may not be compatible with the cached extension(s) in the file. Please check the extension documentation and ignore this warning if these versions are compatible.
  self.warn_for_ignored_namespaces(ignored_namespaces)

nwbfile.subject

fluorescence_picoscope = nwbfile.acquisition["Fluorescence"]
fluorescence_picoscope

velocity_picoscope = nwbfile.acquisition["Velocity"]
velocity_picoscope

import pandas as pd

ttl_times = pd.merge(
    left=nwbfile.acquisition["TtlsTable"][:],
    right=nwbfile.acquisition["TtlTypesTable"][:],
    left_on="ttl_type",
    right_on="id",
)
ttl_times.head()

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = fluorescence_picoscope.data[10000:10500, 0]
timestamps = fluorescence_picoscope.get_timestamps()[10000:10500]

fig, ax = plt.subplots(figsize=(6, 2), dpi=300)
line_handle, = ax.plot(timestamps, data, color="green", linewidth=0.9, label="Fiber 1")

ttl_0 = ttl_times[ttl_times["ttl_type"] == 0]
violet_illumination_times = ttl_0[(ttl_0["timestamp"] > 2.5) & (ttl_0["timestamp"] <= 2.62)]
violet_handles = []
for illumination_onset in violet_illumination_times["timestamp"]:
    fill_end = illumination_onset + 0.005
    violet_handle = ax.fill_between(timestamps, max(data), where=(timestamps >= illumination_onset) & (timestamps <= fill_end), 
                                    color='purple', edgecolor='none', alpha=0.1, label="405 nm LED on")
    violet_handles.append(violet_handle)

ttl_1 = ttl_times[ttl_times["ttl_type"] == 1]
blue_illumination_times = ttl_1[(ttl_1["timestamp"] > 2.5) & (ttl_1["timestamp"] <= 2.62)]
blue_handles = []
for illumination_onset in blue_illumination_times["timestamp"]:
    fill_end = illumination_onset + 0.005
    blue_handle = ax.fill_between(timestamps, max(data), where=(timestamps >= illumination_onset) & (timestamps <= fill_end), 
                                  color='blue', edgecolor='none', alpha=0.1, label="470 nm LED on")
    blue_handles.append(blue_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, violet_handles[0], blue_handles[0]]
labels = ["Fiber 1", "405 nm LED on", "470 nm LED on"]
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()

fiber_photometry_response_series = nwbfile.acquisition["FiberPhotometryResponseSeries"]
fiber_photometry_response_series

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = fiber_photometry_response_series.data[1000:1500, 0]
timestamps = fiber_photometry_response_series.get_timestamps()[1000:1500]

control_signal = nwbfile.acquisition["FiberPhotometryResponseSeriesIsosbestic"]
control_data = control_signal.data[1000:1500, 0]

fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 3), dpi=300, sharex=True)

# Plot data
ax[0].plot(timestamps, data, color="green", linewidth=0.8, label='Data')

# Plot control data with offset
ax[1].plot(timestamps, control_data, color="grey", linewidth=0.5, label='Control Data (shifted)')

ax[0].spines['top'].set_visible(False)
ax[0].spines['right'].set_visible(False)
ax[0].spines['bottom'].set_visible(False)
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)

ax[0].legend(["Raw fluorescence Fiber 1"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})
ax[1].legend(["Isosbestic control"], frameon=False, bbox_to_anchor=(.96, 1), loc='upper left', prop={'size': 8})
ax[1].tick_params(axis='y', labelsize=8)
ax[0].tick_params(axis='y', labelsize=8)


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

nwbfile.lab_meta_data["FiberPhotometry"].fiber_photometry_table[:]

fiber_photometry_table_region = nwbfile.acquisition["FiberPhotometryResponseSeries"].fiber_photometry_table_region[:]
fiber_photometry_table_region

fiber_photometry_table_region["optical_fiber"][0]

fiber_photometry_table_region["indicator"][0]

fiber_photometry_table_region["excitation_source"][0]

fiber_photometry_table_region["photodetector"][0]

fiber_photometry_table_region["dichroic_mirror"][0]

fiber_photometry_table_region["emission_filter"][0]

fiber_photometry_table_region["excitation_filter"][0]

nwbfile.processing["ophys"]

df_over_f_traces = nwbfile.processing["ophys"]["DfOverFFiberPhotometryResponseSeries"]
df_over_f_traces

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = df_over_f_traces.data[0:3000, 0]
timestamps = df_over_f_traces.get_timestamps()[0:3000]

control_signal = nwbfile.processing["ophys"]["DfOverFFiberPhotometryResponseSeriesIsosbestic"]
control_data = control_signal.data[0:3000, 0]

fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8, 3), dpi=300, sharex=True)

# Plot data
ax[0].plot(timestamps, data, color="green", linewidth=0.5)

# Plot control data with offset
ax[1].plot(timestamps, control_data, color="grey", linewidth=0.1)

ax[0].spines['top'].set_visible(False)
ax[0].spines['right'].set_visible(False)
ax[0].spines['bottom'].set_visible(False)
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)

ax[0].legend(["∆F/F Fiber 1"], frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})
ax[1].legend(["Isosbestic control"], frameon=False, bbox_to_anchor=(.96, 1), loc='upper left', prop={'size': 8})
ax[1].tick_params(axis='y', labelsize=8)
ax[0].tick_params(axis='y', labelsize=8)


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

nwbfile.processing["behavior"]["Velocity"]

import pandas as pd

wheel_events = pd.merge(
    left=nwbfile.processing["behavior"]["WheelEvents"][:],
    right=nwbfile.processing["behavior"]["WheelEventTypes"][:],
    left_on="event_type",
    right_on="id",
)
wheel_events.head()

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = df_over_f_traces.data[:3000, 0]
timestamps = df_over_f_traces.get_timestamps()[:3000]

velocity_data = nwbfile.processing["behavior"]["Velocity"].data[:3000]

# Add events during selected time
accelerations = wheel_events[wheel_events["event_type"] == 0]
accelerations = accelerations[accelerations["timestamp"] <= 45]["timestamp"]

decelerations = wheel_events[wheel_events["event_type"] == 1]
decelerations = decelerations[decelerations["timestamp"] <= 45]["timestamp"]

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 2), dpi=300, sharex=True)

# Plot the main data
data_handle, = ax.plot(timestamps, data, color="green", linewidth=0.5, label="∆F/F from SNc")

# Add vertical lines for events
for time in accelerations:
    ax.axvline(time, color='grey', linewidth=0.1, linestyle="--")
for time in decelerations:
    ax.axvline(time, color='grey', linewidth=0.1, linestyle="--")

# Add scatter for acceleration and deceleration
arrowhead_size = 6  # Adjust this value to change the size of the arrowheads
accel_handle = ax.scatter(accelerations, [1.05] * len(accelerations), color='grey', marker='^', s=arrowhead_size, label='▲ Acceleration')
decel_handle = ax.scatter(decelerations, [1.05] * len(decelerations), marker='v', s=arrowhead_size, facecolor='none', edgecolor='grey', label='▽ Deceleration')

# Plot the velocity data on the background
velocity_handle, = ax.plot(timestamps, velocity_data, color="black", linewidth=0.1, zorder=1, label="Velocity")

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

# Add legends
handles = [data_handle, accel_handle, decel_handle, velocity_handle]
labels = ["∆F/F from SNc", "Acceleration", "Deceleration", "Velocity"]
ax.legend(handles, labels, frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 8})

# Adjust tick parameters
ax.tick_params(axis='y', labelsize=8)

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

events = nwbfile.processing["behavior"]["Events"][:]
events.head()

import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.lines as mlines

# Prepare data for plotting
data = df_over_f_traces.data[3000:5000, 0]
timestamps = df_over_f_traces.get_timestamps()[3000:5000]

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 2), dpi=300, sharex=True)

# Plot the main data
ax.plot(timestamps, data, color="green", linewidth=0.5)

licking_events = events[events["event_type"] == "Licking"]
licking_events = licking_events[(licking_events["start_time"] >= timestamps[0]) & (licking_events["stop_time"] < timestamps[-1])]

airpuff_events = events[events["event_type"] == "AirPuff"]
airpuff_events = airpuff_events[(airpuff_events["start_time"] >= timestamps[0]) & (airpuff_events["stop_time"] < timestamps[-1])]

for ind, row in licking_events.iterrows():
    ax.fill_between(timestamps, max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='blue', edgecolor='none', alpha=0.1)

for ind, row in airpuff_events.iterrows():
    ax.fill_between(timestamps, max(data), where=(timestamps >= row["start_time"]) & (timestamps <= row["stop_time"]), color='red', edgecolor='none', alpha=0.1)

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

# Create proxy lines for legend entries with corresponding colors and transparency
green_line = mlines.Line2D([], [], color='green', label='∆F/F from SNc', alpha=0.5)
blue_line = mlines.Line2D([], [], color='blue', label='Licking', alpha=0.1)
red_line = mlines.Line2D([], [], color='red', label='AirPuff', alpha=0.1)

# Set legends with proxy lines
ax.legend(handles=[green_line, blue_line, red_line], 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()

import pandas as pd

peak_events = pd.merge(
    left=nwbfile.analysis["PeakFluorescenceEvents"][:],
    right=nwbfile.analysis["PeakFluorescenceEventTypes"][:],
    left_on="event_type",
    right_on="id",
)
peak_events.tail()

import pandas as pd
from matplotlib import pyplot as plt

# Prepare data for plotting
data = df_over_f_traces.data[:3000, 0]
timestamps = df_over_f_traces.get_timestamps()[:3000]

# Add events during selected time
peak_event_times = peak_events[peak_events["event_type"] == 0]
peak_event_times = peak_event_times[peak_event_times["timestamp"] <= 45]["timestamp"]

fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 2), dpi=300, sharex=True)

# Plot the main data
data_handle, = ax.plot(timestamps, data, color="green", linewidth=0.5, label="∆F/F from SNc")

# Add vertical lines for events
for time in peak_event_times:
    ax.axvline(time, color='orange', linewidth=0.1, linestyle="--")

# Add scatter for peak events
arrowhead_size = 6  # Adjust this value to change the size of the arrowheads
peak_handle = ax.scatter(peak_event_times, [1.05] * len(peak_event_times), color='orange', marker='*', s=arrowhead_size, label='Peak fluorescence')

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

# Add legends
handles = [data_handle, peak_handle]
labels = ["∆F/F from SNc", "Peak fluorescence"]
ax.legend(handles, labels, frameon=False, bbox_to_anchor=(.95, 1), loc='upper left', prop={'size': 6})

# Adjust tick parameters
ax.tick_params(axis='y', labelsize=6)

plt.xlabel('Time (s)', fontsize=6)
plt.tick_params(axis='x', labelsize=6)
plt.tight_layout()
plt.show()

from IPython.display import IFrame

url = "https://neurosift.app/?p=/nwb&url=https://api.dandiarchive.org/api/assets/9994f42b-fdbb-4938-a3a3-6add744bf169/download/&dandisetId=001038&dandisetVersion=draft&tab=neurodata-items:neurodata-item:/processing/ophys/DfOverFFiberPhotometryResponseSeries|FiberPhotometryResponseSeries@neurodata-item:/processing/ophys/DfOverFFiberPhotometryResponseSeriesIsosbestic|FiberPhotometryResponseSeries@neurodata-item:/processing/behavior/Velocity|TimeSeries"
IFrame(url, width=1200, height=1000)

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Chunk shape	(2500000,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	27044056
Compressed size (bytes)	8128605
Compression ratio	3.327023025476081

Data type	float64
Shape	(169024, 2)
Array size	2.58 MiB
Chunk shape	(169024, 2)
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Compression opts	4
Uncompressed size (bytes)	2704384
Compressed size (bytes)	1137393
Compression ratio	2.377704100517587

	location	indicator	optical_fiber	excitation_source	photodetector	dichroic_mirror	fiber_depth_in_mm	baseline_fluorescence	normalized_fluorescence	recording_target_type	signal_to_noise_ratio	cross_correlation_with_acceleration	coordinates	emission_filter	excitation_filter
id
0	SNc	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber1 abc.OpticalFiber at 0x139836832922464\nFields:\n core_diameter_in_um: 200.0\n description: The optical fiber used to record the GCaMP fluorescence from the SNc brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource470 abc.ExcitationSource at 0x139836830370064\nFields:\n description: Blue excitation light (470 nm LED, Thorlabs, M70F3) and violet excitation light (for the isosbestic control)\nwere coupled into the optic fiber such that a power of 0.75 mW was delivered to the fiber tip.\nThen, 470 nm and 405 nm excitation were alternated at 100 Hz using a waveform generator,\neach filtered with a corresponding filter.\n\n excitation_wavelength_in_nm: 470.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M70F3\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	3.6	1.041767	0.686421	cell bodies in SNc	18.898114	0.067393	[nan, nan, nan]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter470 abc.BandOpticalFilter at 0x139836830371024\nFields:\n bandwidth_in_nm: 30.0\n center_wavelength_in_nm: 472.0\n description: The band-pass filter used to isolate the 470 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF02-472/30-25\n
1	TS	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber2 abc.OpticalFiber at 0x139836830370384\nFields:\n core_diameter_in_um: 200.0\n description: The second optical fiber used to record the GCaMP fluorescence from the TS brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource470 abc.ExcitationSource at 0x139836830370064\nFields:\n description: Blue excitation light (470 nm LED, Thorlabs, M70F3) and violet excitation light (for the isosbestic control)\nwere coupled into the optic fiber such that a power of 0.75 mW was delivered to the fiber tip.\nThen, 470 nm and 405 nm excitation were alternated at 100 Hz using a waveform generator,\neach filtered with a corresponding filter.\n\n excitation_wavelength_in_nm: 470.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M70F3\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	1.0	1.050157	0.053596	axons in striatum	0.000000	0.014802	[3.0748749730962577, -0.58, 2.1599445426572172]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter470 abc.BandOpticalFilter at 0x139836830371024\nFields:\n bandwidth_in_nm: 30.0\n center_wavelength_in_nm: 472.0\n description: The band-pass filter used to isolate the 470 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF02-472/30-25\n
2	SNc	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber1 abc.OpticalFiber at 0x139836832922464\nFields:\n core_diameter_in_um: 200.0\n description: The optical fiber used to record the GCaMP fluorescence from the SNc brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource405 abc.ExcitationSource at 0x139836832922128\nFields:\n description: Violet LED (405 nm, Thorlabs, M405FP1) for the isosbestic control.\n excitation_wavelength_in_nm: 405.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M405FP1\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	3.6	1.041767	0.686421	cell bodies in SNc	18.898114	0.067393	[nan, nan, nan]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter405 abc.BandOpticalFilter at 0x139836830370704\nFields:\n bandwidth_in_nm: 15.0\n center_wavelength_in_nm: 406.0\n description: The band-pass filter used to isolate the 405 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-406/15-25\n
3	TS	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber2 abc.OpticalFiber at 0x139836830370384\nFields:\n core_diameter_in_um: 200.0\n description: The second optical fiber used to record the GCaMP fluorescence from the TS brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource405 abc.ExcitationSource at 0x139836832922128\nFields:\n description: Violet LED (405 nm, Thorlabs, M405FP1) for the isosbestic control.\n excitation_wavelength_in_nm: 405.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M405FP1\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	1.0	1.050157	0.053596	axons in striatum	0.000000	0.014802	[3.0748749730962577, -0.58, 2.1599445426572172]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter405 abc.BandOpticalFilter at 0x139836830370704\nFields:\n bandwidth_in_nm: 15.0\n center_wavelength_in_nm: 406.0\n description: The band-pass filter used to isolate the 405 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-406/15-25\n

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Compression	gzip
Compression opts	4
Uncompressed size (bytes)	2704384
Compressed size (bytes)	1134038
Compression ratio	2.3847384302818777

	location	indicator	optical_fiber	excitation_source	photodetector	dichroic_mirror	fiber_depth_in_mm	baseline_fluorescence	normalized_fluorescence	recording_target_type	signal_to_noise_ratio	cross_correlation_with_acceleration	coordinates	emission_filter	excitation_filter
id
0	SNc	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber1 abc.OpticalFiber at 0x139836832922464\nFields:\n core_diameter_in_um: 200.0\n description: The optical fiber used to record the GCaMP fluorescence from the SNc brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource470 abc.ExcitationSource at 0x139836830370064\nFields:\n description: Blue excitation light (470 nm LED, Thorlabs, M70F3) and violet excitation light (for the isosbestic control)\nwere coupled into the optic fiber such that a power of 0.75 mW was delivered to the fiber tip.\nThen, 470 nm and 405 nm excitation were alternated at 100 Hz using a waveform generator,\neach filtered with a corresponding filter.\n\n excitation_wavelength_in_nm: 470.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M70F3\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	3.6	1.041767	0.686421	cell bodies in SNc	18.898114	0.067393	[nan, nan, nan]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter470 abc.BandOpticalFilter at 0x139836830371024\nFields:\n bandwidth_in_nm: 30.0\n center_wavelength_in_nm: 472.0\n description: The band-pass filter used to isolate the 470 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF02-472/30-25\n
1	TS	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber2 abc.OpticalFiber at 0x139836830370384\nFields:\n core_diameter_in_um: 200.0\n description: The second optical fiber used to record the GCaMP fluorescence from the TS brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource470 abc.ExcitationSource at 0x139836830370064\nFields:\n description: Blue excitation light (470 nm LED, Thorlabs, M70F3) and violet excitation light (for the isosbestic control)\nwere coupled into the optic fiber such that a power of 0.75 mW was delivered to the fiber tip.\nThen, 470 nm and 405 nm excitation were alternated at 100 Hz using a waveform generator,\neach filtered with a corresponding filter.\n\n excitation_wavelength_in_nm: 470.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M70F3\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	1.0	1.050157	0.053596	axons in striatum	0.000000	0.014802	[3.0748749730962577, -0.58, 2.1599445426572172]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter470 abc.BandOpticalFilter at 0x139836830371024\nFields:\n bandwidth_in_nm: 30.0\n center_wavelength_in_nm: 472.0\n description: The band-pass filter used to isolate the 470 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF02-472/30-25\n
2	SNc	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber1 abc.OpticalFiber at 0x139836832922464\nFields:\n core_diameter_in_um: 200.0\n description: The optical fiber used to record the GCaMP fluorescence from the SNc brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource405 abc.ExcitationSource at 0x139836832922128\nFields:\n description: Violet LED (405 nm, Thorlabs, M405FP1) for the isosbestic control.\n excitation_wavelength_in_nm: 405.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M405FP1\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	3.6	1.041767	0.686421	cell bodies in SNc	18.898114	0.067393	[nan, nan, nan]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter405 abc.BandOpticalFilter at 0x139836830370704\nFields:\n bandwidth_in_nm: 15.0\n center_wavelength_in_nm: 406.0\n description: The band-pass filter used to isolate the 405 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-406/15-25\n
3	TS	GCaMP6f abc.Indicator at 0x139836832919440\nFields:\n injection_coordinates_in_mm: [-3.25 1.55 -3.8 ]\n injection_location: SNc\n label: AAV-GCaMP6f\n	Fiber2 abc.OpticalFiber at 0x139836830370384\nFields:\n core_diameter_in_um: 200.0\n description: The second optical fiber used to record the GCaMP fluorescence from the TS brain region.\n manufacturer: Doric\n model: MFP_200/230/900-0.57_1.5m_FC-FLT_LAF\n numerical_aperture: 0.57\n	ExcitationSource405 abc.ExcitationSource at 0x139836832922128\nFields:\n description: Violet LED (405 nm, Thorlabs, M405FP1) for the isosbestic control.\n excitation_wavelength_in_nm: 405.0\n illumination_type: LED\n manufacturer: Thorlabs\n model: M405FP1\n	PhotodetectorPMT abc.Photodetector at 0x139836832922800\nFields:\n description: Hamamatsu GaAsP photomultiplier tube (signal amplified using Stanford Research Systems SR570 preamplifier).\n detected_wavelength_in_nm: 520.0\n detector_type: PMT\n manufacturer: Hamamatsu Photonics\n model: H10770PA-40\n	DichroicMirror abc.DichroicMirror at 0x139836832921456\nFields:\n cut_on_wavelength_in_nm: 505.0\n description: The dichroic mirror used to separate green fluorescence from the excitation light.\n manufacturer: Chroma Tech Corp\n model: T505lpxr\n transmission_band_in_nm: [505. 750.]\n	1.0	1.050157	0.053596	axons in striatum	0.000000	0.014802	[3.0748749730962577, -0.58, 2.1599445426572172]	EmissionFilter abc.BandOpticalFilter at 0x139836832921792\nFields:\n bandwidth_in_nm: 50.0\n center_wavelength_in_nm: 540.0\n description: The band-pass filter used to further filter the green fluorescence after it was separated from the excitation light by a dichroic mirror.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-540/50-25\n	OpticalFilter405 abc.BandOpticalFilter at 0x139836830370704\nFields:\n bandwidth_in_nm: 15.0\n center_wavelength_in_nm: 406.0\n description: The band-pass filter used to isolate the 405 nm excitation light.\n filter_type: Bandpass\n manufacturer: Semrock\n model: FF01-406/15-25\n

Installing requirements¶

Azcorra 2023 demo¶

Streaming an NWB file ¶

root (NWBFile)

Access Subject Metadata ¶

subject (Subject)

Access fiber photometry data ¶

Picoscope signals¶

Fluorescence (TimeSeries)

Velocity (TimeSeries)

Picoscope TTL signals¶

Fiber photometry traces¶

FiberPhotometryResponseSeries

Fiber photometry metadata¶

Fiber1 (OpticalFiber)

GCaMP6f (Indicator)

ExcitationSource470 (ExcitationSource)

PhotodetectorPMT (Photodetector)

DichroicMirror

EmissionFilter (BandOpticalFilter)

OpticalFilter470 (BandOpticalFilter)

Access processed fiber photometry data ¶

ophys (ProcessingModule)

DfOverFFiberPhotometryResponseSeries (FiberPhotometryResponseSeries)

Access processed behavior ¶

Velocity (TimeSeries)

Access derived events¶

Wheel events¶

Stimuli events¶

Fluorescence peaks¶

View NWB ¶

	event_name	event_type_description	pulse_value	duration
id
0	Ch405	The times when the 405 nm LED was on.	1	0.005
1	Ch470	The times when the 470 nm LED was on.	1	0.005

	event_name	event_type_description
id
0	peaksG	Large transient peaks for Fiber 1 fluorescence
1	peaksGRun	Large transient peaks occurring during running periods for Fiber 1 fluorescence

	timestamp	event_type	peak_fluorescence
id
0	14.09	0	0.068825
1	16.97	0	0.235765
2	18.69	0	0.075402
3	20.71	0	0.119960

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

	start_time	stop_time	event_type	tags	timeseries
id
0	13.91	201.82	MovOnOff	[]	[(55642, 751678, Velocity pynwb.base.TimeSeries at 0x139836829980368\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (6761014,), type "<f4">\n description: Velocity from rotary encoder collected at 4000 Hz by Picoscope.\n offset: 0.0\n rate: 4000.2000543686745\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n unit: Volts\n)]
1	28.59	28.67	Reward	[long]	[(114365, 320, Reward pynwb.base.TimeSeries at 0x139836834804272\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (6761014,), type "<f4">\n description: The raw voltage trace from the reward delivery trigger collected at 4000 Hz by Picoscope.\n offset: 0.0\n rate: 4000.2000543686745\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n unit: Volts\n)]
2	28.85	32.24	Licking	[]	[(115405, 13561, Licking pynwb.base.TimeSeries at 0x139836836246416\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (6761014,), type "<f4">\n description: The raw voltage trace from the licking sensor collected at 4000 Hz by Picoscope.\n offset: 0.0\n rate: 4000.2000543686745\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n unit: Volts\n)]
3	32.42	32.52	Licking	[]	[(129686, 400, Licking pynwb.base.TimeSeries at 0x139836836246416\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (6761014,), type "<f4">\n description: The raw voltage trace from the licking sensor collected at 4000 Hz by Picoscope.\n offset: 0.0\n rate: 4000.2000543686745\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n unit: Volts\n)]

	event_name	event_type_description
id
0	Acceleration onset	The times of the Acceleration onset event.
1	Deceleration onset	The times of the Deceleration onset event.

	timestamp	ttl_type	event_name	event_type_description	pulse_value	duration
0	0.001000	1	Ch470	The times when the 470 nm LED was on.	1	0.005
1	0.006000	0	Ch405	The times when the 405 nm LED was on.	1	0.005
2	0.010999	1	Ch470	The times when the 470 nm LED was on.	1	0.005
3	0.015999	0	Ch405	The times when the 405 nm LED was on.	1	0.005
4	0.020999	1	Ch470	The times when the 470 nm LED was on.	1	0.005

	timestamp	event_type	event_name	event_type_description
0	13.95	0	Acceleration onset	The times of the Acceleration onset event.
1	16.93	0	Acceleration onset	The times of the Acceleration onset event.
2	21.87	0	Acceleration onset	The times of the Acceleration onset event.
3	27.46	1	Deceleration onset	The times of the Deceleration onset event.
4	27.83	0	Acceleration onset	The times of the Acceleration onset event.

	timestamp	event_type	peak_fluorescence	event_name	event_type_description
1101	1683.64	0	0.099074	peaksG	Large transient peaks for Fiber 1 fluorescence
1102	1685.67	0	0.058068	peaksG	Large transient peaks for Fiber 1 fluorescence
1103	1685.67	1	0.058068	peaksGRun	Large transient peaks occurring during running...
1104	1689.86	0	0.113421	peaksG	Large transient peaks for Fiber 1 fluorescence
1105	1689.86	1	0.113421	peaksGRun	Large transient peaks occurring during running...