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

from dandi.dandiapi import DandiAPIClient

dandiset_id = "001538"
client = DandiAPIClient()
dandiset = client.get_dandiset(dandiset_id, "0.260527.1302")
assets = dandiset.get_assets()
assets_list = list(assets)

from typing import Literal

def get_session_id(asset_path: str) -> str:
    """Extract session ID from DANDI asset path."""
    fname = asset_path.split('/')[-1]
    if '_ses-' not in fname:
        return ''
    return fname.split('_ses-', 1)[1].rsplit('_', 1)[0]

def get_modality(asset_path: str) -> str:
    """Extract modality suffix from DANDI asset path (icephys, icephys+ogen, ophys, etc.)."""
    fname = asset_path.split('/')[-1]
    if not fname.endswith('.nwb'):
        return ''
    return fname[:-len('.nwb')].rsplit('_', 1)[-1]

def parse_session_id(session_id: str) -> dict:
    """Parse a session_id string into its five component fields."""
    t = session_id.split('++')
    if len(t) < 5:
        return {}
    return {
        'cell_type': t[0],
        'state':     t[1],
        'pharm':     t[2],
        'genotype':  t[3],
        'date':      t[4],
    }

def get_cell_type(session_id: str) -> str:
    """Extract cell type (dSPN, iSPN, pan)."""
    if not session_id:
        return ""
    return session_id.split("++")[0]

def is_cell_type(session_id: str, cell_type: Literal["dSPN", "iSPN", "pan"]) -> bool:
    """Filter data by cell type."""
    return get_cell_type(session_id) == cell_type

def get_state(session_id: str) -> str:
    """Extract experimental state (CTRL, PD, OffState, OnState, OFF, ON)."""
    if not session_id:
        return ""
    return session_id.split("++")[1]

def is_state(session_id: str, state: Literal["CTRL", "LesionedControl", "OFF", "ON", "OffState", "OnState", "PD"]) -> bool:
    """Filter data by disease/treatment state."""
    return get_state(session_id) == state

def get_pharmacology(session_id: str) -> str:
    """Extract pharmacological treatment (none, D1RaSch, D2RaSul, M1RaOxoM, etc.)."""
    if not session_id:
        return ""
    return session_id.split("++")[2]

def is_pharmacology(session_id: str, pharmacology: Literal["none", "DMSO", "CNO", "D1RaSch", "D2RaSul", "M1RaOxoM", "M1RaThp", "M1RaTri"]) -> bool:
    """Filter data by pharmacological condition."""
    return get_pharmacology(session_id) == pharmacology

def get_genotype(session_id: str) -> str:
    """Extract animal genotype (WT, CDGIKO, M1RCRISPR, iSPNM1RKO)."""
    if not session_id:
        return ""
    return session_id.split("++")[3]

def is_genotype(session_id: str, genotype: Literal["CDGIKO", "M1RCRISPR", "WT", "iSPN", "iSPNM1RKO"]) -> bool:
    """Filter data by genotype."""
    return get_genotype(session_id) == genotype

def get_date(session_id: str) -> str:
    """Extract recording date (YYYYMMDD format)."""
    if not session_id:
        return ""
    return session_id.split("++")[4]

# Example: Parse metadata from a sample NWB file
nwb_assets = [a for a in assets_list if a.path.endswith('.nwb')]
sample_asset = nwb_assets[2]
session_id = get_session_id(sample_asset.path)
fields = parse_session_id(session_id)

print("Parsed metadata from sample asset:")
print(f"  Path:      {sample_asset.path}")
print(f"  Session:   {session_id}")
print(f"  Modality:  {get_modality(sample_asset.path)}")
print(f"  Cell type: {fields.get('cell_type')}")
print(f"  State:     {fields.get('state')}")
print(f"  Pharm:     {fields.get('pharm')}")
print(f"  Genotype:  {fields.get('genotype')}")
print(f"  Date:      {fields.get('date')}")

Parsed metadata from sample asset:
  Path:      sub-CDGI-mouse-1945/sub-CDGI-mouse-1945_ses-pan++ON++none++CDGIKO++20180223_image.nwb
  Session:   pan++ON++none++CDGIKO++20180223
  Modality:  image
  Cell type: pan
  State:     ON
  Pharm:     none
  Genotype:  CDGIKO
  Date:      20180223

# Filter for dSPN somatic excitability experiments (Figure 1E equivalent).
# In the published dandiset the experiment type is encoded in the modality suffix,
# not in the session_id.  Current-clamp somatic recordings use 'icephys' or
# 'icephys+ophys' modality combined with cell_type == 'dSPN'.

def is_dspn_icephys(asset_path: str) -> bool:
    """dSPN current-clamp files (somatic excitability)."""
    modality = get_modality(asset_path)
    cell_type = get_cell_type(get_session_id(asset_path))
    return modality in ('icephys', 'icephys+ophys') and cell_type == 'dSPN'

available_assets = [asset for asset in assets_list if is_dspn_icephys(asset.path)]

print(f"Found {len(available_assets)} dSPN current-clamp (somatic excitability) files:")
for i, asset in enumerate(available_assets[:3]):
    session_id = get_session_id(asset.path)
    fields = parse_session_id(session_id)
    print(f"  {i+1}. {asset.path}")
    print(f"     Session:   {session_id}")
    print(f"     Modality:  {get_modality(asset.path)}")
    print(f"     Cell type: {fields.get('cell_type')}")
    print(f"     State:     {fields.get('state')}")
    print(f"     Genotype:  {fields.get('genotype')}")
    print()

Found 18 dSPN current-clamp (somatic excitability) files:
  1. sub-Subject20160812LIDOFFtDTomato/sub-Subject20160812LIDOFFtDTomato_ses-dSPN++OffState++none++WT++20160812_icephys+ophys.nwb
     Session:   dSPN++OffState++none++WT++20160812
     Modality:  icephys+ophys
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

  2. sub-Subject20161111LIDOFFtDTomato/sub-Subject20161111LIDOFFtDTomato_ses-dSPN++OffState++none++WT++20161111_icephys+ophys.nwb
     Session:   dSPN++OffState++none++WT++20161111
     Modality:  icephys+ophys
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

  3. sub-Subject20161114LIDOFFtDTomato/sub-Subject20161114LIDOFFtDTomato_ses-dSPN++OffState++none++WT++20161114_icephys+ophys.nwb
     Session:   dSPN++OffState++none++WT++20161114
     Modality:  icephys+ophys
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

import h5py
import remfile
from pynwb import NWBHDF5IO

asset = available_assets[0]
s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
file_system = remfile.File(s3_url, disk_cache=remfile.DiskCache("nwb-cache"))
file = h5py.File(file_system, mode="r")

io = NWBHDF5IO(file=file)
nwbfile = io.read()

nwbfile

# The Current Clamp Responses are stored in acquisition. Let's check out the available keys
nwbfile.acquisition.keys()

dict_keys(['CurrentClampSeries_Cell3_Dendritic', 'CurrentClampSeries_Cell3_Somatic', 'ImageLineScanSource', 'LineScanCell3_Distal1_Ch1', 'LineScanCell3_Distal1_Ch2', 'LineScanCell3_Proximal1_Ch1', 'LineScanCell3_Proximal1_Ch2'])

import matplotlib.pyplot as plt
import numpy as np

rec_df = nwbfile.intracellular_recordings.to_dataframe()
soma_df = rec_df[rec_df[('intracellular_recordings', 'dendrite_type')] == 'Soma'].reset_index(drop=True)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

for ax, row_idx in [(ax1, 0), (ax2, 1)]:
    row = soma_df.iloc[row_idx]
    resp_ref = row[('responses', 'response')]
    ts = resp_ref.timeseries
    index_start = int(resp_ref.idx_start)
    count = int(resp_ref.count)
    voltage_mV = np.asarray(ts.data[index_start:index_start + count]) * 1000.0
    rate = float(ts.rate)
    t = np.arange(count) / rate
    current_pA = float(row[('intracellular_recordings', 'stimulus_current_pA')])

    ax.plot(t, voltage_mV)
    ax.set_xlabel("Time (s) aligned to sweep start")
    ax.set_ylabel("Voltage (mV)")
    ax.set_title(f"Sweep {row_idx + 1}: {current_pA:.0f} pA")

plt.tight_layout()
plt.show()

nwbfile.intracellular_recordings.to_dataframe()

# soma_df was built in the previous plotting cell; reuse it here.
currents = soma_df[('intracellular_recordings', 'stimulus_current_pA')].values
print(f"{len(soma_df)} somatic sweeps, current range: {currents.min():.0f} to {currents.max():.0f} pA")

26 somatic sweeps, current range: -120 to 400 pA

index = min(20, len(soma_df) - 1)
row = soma_df.iloc[index]
current_in_pA = float(row[('intracellular_recordings', 'stimulus_current_pA')])

resp_ref = row[('responses', 'response')]
ts = resp_ref.timeseries
index_start = int(resp_ref.idx_start)
count = int(resp_ref.count)
voltage_mV = np.asarray(ts.data[index_start:index_start + count]) * 1000.0
rate = float(ts.rate)
aligned_times = np.arange(count) / rate

print(f"Sweep at {current_in_pA:.0f} pA: {count} samples at {rate:.0f} Hz ({count/rate*1000:.0f} ms)")

Sweep at 300 pA: 10000 samples at 10000 Hz (1000 ms)

plt.plot(aligned_times, voltage_mV)
plt.xlabel("Time (s) aligned to response start")
plt.ylabel("Voltage (mV)")
plt.title(f"Sweep at {current_in_pA:.0f} pA")
plt.show()

colors = plt.cm.autumn_r(np.linspace(0, 1, len(soma_df)))

for i, (_, row) in enumerate(soma_df.iterrows()):
    if i % 2 == 0:
        resp_ref = row[('responses', 'response')]
        ts = resp_ref.timeseries
        index_start = int(resp_ref.idx_start)
        count = int(resp_ref.count)
        voltage_mV = np.asarray(ts.data[index_start:index_start + count]) * 1000.0
        rate = float(ts.rate)
        t = np.arange(count) / rate
        current_pA_val = float(row[('intracellular_recordings', 'stimulus_current_pA')])
        plt.plot(t, voltage_mV, label=f"{current_pA_val:.0f} pA", color=colors[i])

plt.xlabel("Time (s) aligned to sweep start")
plt.ylabel("Voltage (mV)")
plt.title("Voltage Responses to Stimulus Currents")
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=7)
plt.tight_layout()
plt.show()

# Filter for dendritic excitability experiments: paired icephys + ophys recordings.
# These combine current clamp with two-photon line-scan imaging.

def is_dspn_icephys_ophys(asset_path: str) -> bool:
    """dSPN paired current-clamp + imaging files (dendritic excitability)."""
    return get_modality(asset_path) == 'icephys+ophys' and get_cell_type(get_session_id(asset_path)) == 'dSPN'

available_assets = [asset for asset in assets_list if is_dspn_icephys_ophys(asset.path)]

import h5py
import remfile
from pynwb import NWBHDF5IO

# Get one of the assets
asset = available_assets[0]
s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
file_system = remfile.File(s3_url, disk_cache=remfile.DiskCache("nwb-cache"))
file = h5py.File(file_system, mode="r")
io = NWBHDF5IO(file=file, load_namespaces=True)
nwbfile = io.read()
nwbfile

import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import butter, filtfilt

ophys_module = nwbfile.processing['ophys']
fluoresence_module = ophys_module.data_interfaces["Fluorescence"]

# In the published dataset ROI responses are consolidated per cell per location
# (not split by trial). Each series spans all trials concatenated.
distal_roi  = fluoresence_module["RoiResponseSeriesFluo4Cell3Distal1"]
proximal_roi = fluoresence_module["RoiResponseSeriesFluo4Cell3Proximal1"]

color_distal, color_proximal = "tab:blue", "tab:orange"

plt.figure(figsize=(10, 6))

for roi_response, color, label in [
    (distal_roi,   color_distal,   "Distal"),
    (proximal_roi, color_proximal, "Proximal"),
]:
    num_samples = roi_response.data.shape[0]
    sampling_rate = roi_response.rate
    aligned_timestamps = np.arange(num_samples) / sampling_rate
    b, a = butter(4, 10 / (0.5 * sampling_rate), btype="low")
    filtered_response = filtfilt(b, a, roi_response.data[:])
    plt.plot(aligned_timestamps, filtered_response, color=color, label=label)

plt.xlabel("Time (s) aligned to recording start")
plt.ylabel("Fluorescence (a.u.)")
plt.title("Distal vs Proximal ROI responses (low-pass 10 Hz)")
plt.legend()
plt.tight_layout()
plt.show()

import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from scipy.signal import butter, filtfilt

# Actual key names in the published dataset:
#   Line scan raw:  LineScanCell3_{Distal1|Proximal1}_Ch{1|2}
#   Source images:  ImageLineScanSource["Image{Alexa568|Fluo4}Cell3{Distal1|Proximal1}Trial{001..003}"]
#   ROI responses:  RoiResponseSeries{Fluo4|Alexa568}Cell3{Distal1|Proximal1}
line_scan_raw_data_name  = "LineScanCell3_Distal1_Ch1"
source_image_name        = "ImageAlexa568Cell3Distal1Trial001"
roi_response_series_name = "RoiResponseSeriesFluo4Cell3Distal1"

line_scan_data = nwbfile.acquisition[line_scan_raw_data_name]
source_images_container = nwbfile.acquisition['ImageLineScanSource']
source_image = source_images_container[source_image_name]
roi_response = fluoresence_module[roi_response_series_name]

# Get line scan coordinates from the ROI response series
pixel_mask = roi_response.rois[0]["pixel_mask"].iloc[0]

print(f"Extracted line scan coordinates:")
print(f"  X range: {pixel_mask['x'].min()} to {pixel_mask['x'].max()}")
print(f"  Y range: {pixel_mask['y'].min()} to {pixel_mask['y'].max()}")
print(f"  Number of pixels: {len(pixel_mask['x'])}")

alexa568_color = '#FF4500'
fluo4_color = '#32CD32'

alexa568_cmap = mcolors.LinearSegmentedColormap.from_list("alexa568", ["black", alexa568_color], N=256)
fluo4_cmap    = mcolors.LinearSegmentedColormap.from_list("fluo4",    ["white", fluo4_color],    N=256)

line_scan_raw_data = line_scan_data.data[:]
num_samples = line_scan_raw_data.shape[0]
num_pixels  = line_scan_raw_data.shape[1]
print(f"Line scan data shape: {line_scan_raw_data.shape}")

roi_response_data = roi_response.data[:]
roi_sampling_rate = roi_response.rate
print(f"ROI response shape: {roi_response_data.shape}")

cutoff_freq = 10
b_roi, a_roi = butter(1, cutoff_freq / (0.5 * roi_sampling_rate), btype="low")
roi_response_filtered = filtfilt(b_roi, a_roi, roi_response_data)

fig = plt.figure(figsize=(16, 10))
gs  = fig.add_gridspec(nrows=2, ncols=2, hspace=0.3, wspace=0.25)

ax_src = fig.add_subplot(gs[:, 0])
ax_src.imshow(source_image, cmap=alexa568_cmap, aspect="equal", origin="upper")
ax_src.set_title("Source Image (Alexa 568) with Line Scan Overlay")
ax_src.set_xlabel("X position (pixels)")
ax_src.set_ylabel("Y position (pixels)")
ax_src.plot(pixel_mask["x"], pixel_mask["y"], "yellow", linewidth=3, alpha=0.9, label="Line scan")
ax_src.legend(loc="best")

ax_raw = fig.add_subplot(gs[0, 1])
vmin = np.percentile(line_scan_raw_data, 2)
vmax = np.percentile(line_scan_raw_data, 98)
im_raw = ax_raw.imshow(
    line_scan_raw_data.T, aspect="auto", cmap=fluo4_cmap, origin="lower",
    extent=[0, num_samples, 0, num_pixels],
    vmin=vmin, vmax=vmax,
)
ax_raw.set_title("Line Scan Raw Data (Fluo-4)")
ax_raw.set_xlabel("Line Scans (samples)")
ax_raw.set_ylabel("Position along line (pixels)")
ax_raw.set_xlim(0, num_samples)

cax_raw = inset_axes(ax_raw, width="3%", height="50%", loc="upper right",
                     bbox_to_anchor=(-0.15, -0.15, 1, 1),
                     bbox_transform=ax_raw.transAxes, borderpad=0)
fig.colorbar(im_raw, cax=cax_raw, label="Ca²⁺ fluorescence")

ax_roi = fig.add_subplot(gs[1, 1])
ax_roi.plot(roi_response_data, color=fluo4_color, linewidth=1, alpha=0.6, label="Raw")
ax_roi.plot(roi_response_filtered, color='darkgreen', linewidth=2,
            label=f"Filtered ({cutoff_freq} Hz, 1st order)")
ax_roi.set_title("ROI Response Time Series (Fluo-4)")
ax_roi.set_xlabel("Line Scans (samples)")
ax_roi.set_ylabel("Ca²⁺ fluorescence (a.u.)")
ax_roi.legend()
ax_roi.set_xlim(0, num_samples)

plt.tight_layout()
plt.show()

Extracted line scan coordinates:
  X range: 88 to 106
  Y range: 73 to 73
  Number of pixels: 19
Line scan data shape: (7500, 19)
ROI response shape: (7500,)

/tmp/ipykernel_2219/1009501177.py:86: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
  plt.tight_layout()

# Filter for optogenetic oEPSC experiments (Figure 2G-H equivalent).
# These use combined icephys + optogenetics modality with dSPN cell type.

def is_dspn_oepsc(asset_path: str) -> bool:
    """dSPN optogenetic oEPSC files."""
    return get_modality(asset_path) == 'icephys+ogen' and get_cell_type(get_session_id(asset_path)) == 'dSPN'

available_oepsc_assets = [asset for asset in assets_list if is_dspn_oepsc(asset.path)]

print(f"Found {len(available_oepsc_assets)} dSPN oEPSC files:")
for i, asset in enumerate(available_oepsc_assets[:3]):
    session_id = get_session_id(asset.path)
    fields = parse_session_id(session_id)
    print(f"  {i+1}. {asset.path}")
    print(f"     Session:   {session_id}")
    print(f"     Cell type: {fields.get('cell_type')}")
    print(f"     State:     {fields.get('state')}")
    print(f"     Genotype:  {fields.get('genotype')}")
    print()

Found 7 dSPN oEPSC files:
  1. sub-dSPN-mouse-dSPN++OffState++none++WT++20230706/sub-dSPN-mouse-dSPN++OffState++none++WT++20230706_ses-dSPN++OffState++none++WT++20230706_icephys+ogen.nwb
     Session:   dSPN++OffState++none++WT++20230706
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

  2. sub-dSPN-mouse-dSPN++OffState++none++WT++20230713/sub-dSPN-mouse-dSPN++OffState++none++WT++20230713_ses-dSPN++OffState++none++WT++20230713_icephys+ogen.nwb
     Session:   dSPN++OffState++none++WT++20230713
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

  3. sub-dSPN-mouse-dSPN++OffState++none++WT++20230817/sub-dSPN-mouse-dSPN++OffState++none++WT++20230817_ses-dSPN++OffState++none++WT++20230817_icephys+ogen.nwb
     Session:   dSPN++OffState++none++WT++20230817
     Cell type: dSPN
     State:     OffState
     Genotype:  WT

import h5py
import remfile
from pynwb import NWBHDF5IO

asset = available_oepsc_assets[0]
s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
file_system = remfile.File(s3_url, disk_cache=remfile.DiskCache("nwb-cache"))
file = h5py.File(file_system, mode="r")

io = NWBHDF5IO(file=file)
nwbfile = io.read()
nwbfile

print("Available acquisition keys:")
acquisition_keys = list(nwbfile.acquisition.keys())
print(acquisition_keys)

print(f"\nFound {len([k for k in acquisition_keys if 'VoltageClamp' in k])} VoltageClampSeries in acquisition module")
print("These contain the raw current recordings from voltage clamp experiments")

Available acquisition keys:
['VoltageClampSeriesSweep001', 'VoltageClampSeriesSweep002', 'VoltageClampSeriesSweep003', 'VoltageClampSeriesSweep004', 'VoltageClampSeriesSweep005', 'VoltageClampSeriesSweep006', 'VoltageClampSeriesSweep007', 'VoltageClampSeriesSweep008', 'VoltageClampSeriesSweep009', 'VoltageClampSeriesSweep010']

Found 10 VoltageClampSeries in acquisition module
These contain the raw current recordings from voltage clamp experiments

# Access the optogenetic epochs table
optogenetics_table_df = nwbfile.intervals["optogenetic_epochs_table"].to_dataframe()
optogenetics_table_df.head()

import matplotlib.pyplot as plt
import numpy as np

# Get optogenetic timing information
optogenetics_table_df = nwbfile.intervals["optogenetic_epochs_table"].to_dataframe()
stimulation_entries_df = optogenetics_table_df[optogenetics_table_df["stimulation_on"] == True]
detection_entries_df = optogenetics_table_df[optogenetics_table_df["stage_name"] == "detection"]

# Create subplots side by side
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

for trial_index, (trial_name, ax, title) in enumerate([(1, ax1, "VoltageClampSeriesSweep001"), (7, ax2, "VoltageClampSeriesSweep007")]):
    # Get voltage clamp response
    series_name = f"VoltageClampSeriesSweep{trial_index + 1:03d}"
    voltage_clamp_response = nwbfile.acquisition[series_name]
    timestamps_in_seconds = voltage_clamp_response.get_timestamps()
    data_in_amperes = voltage_clamp_response.get_data_in_units()
    data_in_pico_amperes = data_in_amperes * 1e12
    
    # Get timing info for this trial
    stimulation_info = stimulation_entries_df.iloc[trial_index]
    detection_info = detection_entries_df.iloc[trial_index]
    
    stimulation_start_ms = stimulation_info["start_time"] * 1000
    stimulation_stop_ms = stimulation_info["stop_time"] * 1000
    detection_start_ms = detection_info["start_time"] * 1000
    detection_stop_ms = detection_info["stop_time"] * 1000
    
    # Show data up to 50ms after detection window ends
    timestamps_in_milliseconds = timestamps_in_seconds * 1000
    starting_time = timestamps_in_milliseconds[0]
    end_time = detection_stop_ms + 50
    mask = (timestamps_in_milliseconds >= starting_time) & (timestamps_in_milliseconds <= end_time)
    timestamps_filtered = timestamps_in_milliseconds[mask]
    data_filtered = data_in_pico_amperes[mask]
    
    # Plot the data
    ax.plot(timestamps_filtered, data_filtered, 'b-', linewidth=1)
    ax.axvspan(stimulation_start_ms, stimulation_stop_ms, color="cyan", alpha=0.8, label="Stimulation")
    ax.axvspan(detection_start_ms, detection_stop_ms, color="gray", alpha=0.15, label="Detection")
    
    ax.set_xlabel("Time (ms)")
    ax.set_ylabel("Current (pA)")
    ax.set_title(title)
    
    if trial_index == 0:  # Add legend only to first subplot
        ax.legend()

plt.tight_layout()
plt.show()

print("Stimulation and detection windows:")
print(f"Trial 1 - Stimulation: {stimulation_entries_df.iloc[0]['start_time']*1000:.1f}-{stimulation_entries_df.iloc[0]['stop_time']*1000:.1f} ms")
print(f"Trial 1 - Detection: {detection_entries_df.iloc[0]['start_time']*1000:.1f}-{detection_entries_df.iloc[0]['stop_time']*1000:.1f} ms")

Stimulation and detection windows:
Trial 1 - Stimulation: 20.0-20.3 ms
Trial 1 - Detection: 60.0-420.0 ms

import matplotlib.pyplot as plt
import numpy as np

# Control parameters for event detection
detection_window_shift_ms = 100  # Shift detection window start by this many ms
event_merge_distance_ms = 1.0    # Merge events within this distance

def merge_nearby_events(event_times, event_amplitudes, merge_distance_ms):
    """Merge events within merge_distance_ms, keeping maximum amplitude"""
    if len(event_times) == 0:
        return event_times, event_amplitudes
    
    times = np.array(event_times)
    amplitudes = np.array(event_amplitudes)
    sorted_indices = np.argsort(times)
    times = times[sorted_indices]
    amplitudes = amplitudes[sorted_indices]
    
    merged_times = []
    merged_amplitudes = []
    
    i = 0
    while i < len(times):
        current_time = times[i]
        current_amp = amplitudes[i]
        
        # Find all events within merge_distance_ms
        j = i + 1
        max_amp = current_amp
        max_amp_time = current_time
        
        while j < len(times) and (times[j] - current_time) <= merge_distance_ms:
            if amplitudes[j] > max_amp:
                max_amp = amplitudes[j]
                max_amp_time = times[j]
            j += 1
        
        merged_times.append(max_amp_time)
        merged_amplitudes.append(max_amp)
        i = j
    
    return merged_times, merged_amplitudes

# Demonstrate event detection on one sweep
trial_index = 0
series_name = f"VoltageClampSeriesSweep{trial_index + 1:03d}"
voltage_clamp_response = nwbfile.acquisition[series_name]
timestamps_in_seconds = voltage_clamp_response.get_timestamps()
data_in_amperes = voltage_clamp_response.get_data_in_units()
data_in_pico_amperes = data_in_amperes * 1e12

# Get timing information
stimulation_info = stimulation_entries_df.iloc[trial_index]
detection_info = detection_entries_df.iloc[trial_index]

detection_start_ms_original = detection_info["start_time"] * 1000
detection_stop_ms = detection_info["stop_time"] * 1000
detection_start_ms_shifted = detection_start_ms_original + detection_window_shift_ms

# Filter data to shifted detection window
timestamps_in_milliseconds = timestamps_in_seconds * 1000
detection_mask = (timestamps_in_milliseconds >= detection_start_ms_shifted) & (timestamps_in_milliseconds <= detection_stop_ms)
detection_data = data_in_pico_amperes[detection_mask]
detection_timestamps = timestamps_in_milliseconds[detection_mask]

if len(detection_data) > 0:
    # Calculate MAD-based standard deviation
    noise_median = np.median(detection_data)
    mad = np.median(np.abs(detection_data - noise_median))
    mad_std = mad * 1.4826  # MAD to standard deviation conversion
    
    event_threshold_positive = noise_median + 5 * mad_std
    event_threshold_negative = noise_median - 5 * mad_std
    
    # Find events
    positive_event_indices = np.where(detection_data > event_threshold_positive)[0]
    negative_event_indices = np.where(detection_data < event_threshold_negative)[0]
    
    # Calculate amplitudes and merge nearby events
    positive_event_times_raw = detection_timestamps[positive_event_indices] if len(positive_event_indices) > 0 else []
    negative_event_times_raw = detection_timestamps[negative_event_indices] if len(negative_event_indices) > 0 else []
    
    positive_event_amplitudes_raw = detection_data[positive_event_indices] - noise_median if len(positive_event_indices) > 0 else []
    negative_event_amplitudes_raw = noise_median - detection_data[negative_event_indices] if len(negative_event_indices) > 0 else []
    
    positive_event_times_merged, positive_event_amplitudes_merged = merge_nearby_events(
        positive_event_times_raw, positive_event_amplitudes_raw, event_merge_distance_ms)
    negative_event_times_merged, negative_event_amplitudes_merged = merge_nearby_events(
        negative_event_times_raw, negative_event_amplitudes_raw, event_merge_distance_ms)
    
    print(f"Event Detection Results for {series_name}:")
    print(f"  Noise median: {noise_median:.2f} pA")
    print(f"  MAD-based std: {mad_std:.2f} pA")
    print(f"  Positive threshold: {event_threshold_positive:.2f} pA")
    print(f"  Negative threshold: {event_threshold_negative:.2f} pA")
    print(f"  Raw events: +{len(positive_event_amplitudes_raw)}, -{len(negative_event_amplitudes_raw)}")
    print(f"  Merged events: +{len(positive_event_amplitudes_merged)}, -{len(negative_event_amplitudes_merged)}")
    
    if len(positive_event_amplitudes_merged) > 0:
        print(f"  Positive event amplitudes: {[f'{amp:.1f}' for amp in positive_event_amplitudes_merged]} pA")
    if len(negative_event_amplitudes_merged) > 0:
        print(f"  Negative event amplitudes: {[f'{amp:.1f}' for amp in negative_event_amplitudes_merged]} pA")
        
else:
    print(f"No data in detection window for {series_name}")

Event Detection Results for VoltageClampSeriesSweep001:
  Noise median: -17.52 pA
  MAD-based std: 1.27 pA
  Positive threshold: -11.18 pA
  Negative threshold: -23.85 pA
  Raw events: +0, -5
  Merged events: +0, -1
  Negative event amplitudes: ['7.5'] pA

import matplotlib.pyplot as plt
import numpy as np

# Get all acquisition keys for voltage clamp series
acquisition_keys = [key for key in nwbfile.acquisition.keys() if key.startswith("VoltageClampSeries")]
num_sweeps = len(acquisition_keys)

# Calculate grid dimensions
cols = 3
rows = (num_sweeps + cols - 1) // cols

# Create subplots grid
fig, axes = plt.subplots(rows, cols, figsize=(15, 5*rows))
axes = axes.flatten() if num_sweeps > 1 else [axes]

# Initialize summary statistics
all_positive_amplitudes = []
all_negative_amplitudes = []
total_events = 0

# Process each sweep
for i, sweep_key in enumerate(acquisition_keys):
    trial_number = int(sweep_key.split('Sweep')[-1])
    
    # Get voltage clamp response
    voltage_clamp_response = nwbfile.acquisition[sweep_key]
    timestamps_in_seconds = voltage_clamp_response.get_timestamps()
    data_in_amperes = voltage_clamp_response.get_data_in_units()
    data_in_pico_amperes = data_in_amperes * 1e12
    
    # Get timing information for this trial
    if trial_number <= len(stimulation_entries_df) and trial_number <= len(detection_entries_df):
        stimulation_info = stimulation_entries_df.iloc[trial_number - 1]
        detection_info = detection_entries_df.iloc[trial_number - 1]
        
        stimulation_start_ms = stimulation_info["start_time"] * 1000
        stimulation_stop_ms = stimulation_info["stop_time"] * 1000
        detection_start_ms_original = detection_info["start_time"] * 1000
        detection_stop_ms = detection_info["stop_time"] * 1000
        detection_start_ms_shifted = detection_start_ms_original + detection_window_shift_ms
        
        # Show data up to 50ms after detection window ends
        timestamps_in_milliseconds = timestamps_in_seconds * 1000
        starting_time = timestamps_in_milliseconds[0]
        end_time = detection_stop_ms + 50
        mask = (timestamps_in_milliseconds >= starting_time) & (timestamps_in_milliseconds <= end_time)
        timestamps_filtered = timestamps_in_milliseconds[mask]
        data_filtered = data_in_pico_amperes[mask]
        
        # Calculate noise statistics and detect events
        detection_mask = (timestamps_filtered >= detection_start_ms_shifted) & (timestamps_filtered <= detection_stop_ms)
        detection_data = data_filtered[detection_mask]
        detection_timestamps = timestamps_filtered[detection_mask]
        
        if len(detection_data) > 0:
            # MAD-based noise estimation
            noise_median = np.median(detection_data)
            mad = np.median(np.abs(detection_data - noise_median))
            mad_std = mad * 1.4826
            
            event_threshold_positive = noise_median + 5 * mad_std
            event_threshold_negative = noise_median - 5 * mad_std
            
            # Find and merge events
            positive_event_indices = np.where(detection_data > event_threshold_positive)[0]
            negative_event_indices = np.where(detection_data < event_threshold_negative)[0]
            
            positive_event_times_raw = detection_timestamps[positive_event_indices] if len(positive_event_indices) > 0 else []
            negative_event_times_raw = detection_timestamps[negative_event_indices] if len(negative_event_indices) > 0 else []
            
            positive_event_amplitudes_raw = detection_data[positive_event_indices] - noise_median if len(positive_event_indices) > 0 else []
            negative_event_amplitudes_raw = noise_median - detection_data[negative_event_indices] if len(negative_event_indices) > 0 else []
            
            # Merge nearby events
            positive_event_times_merged, positive_event_amplitudes_merged = merge_nearby_events(
                positive_event_times_raw, positive_event_amplitudes_raw, event_merge_distance_ms)
            negative_event_times_merged, negative_event_amplitudes_merged = merge_nearby_events(
                negative_event_times_raw, negative_event_amplitudes_raw, event_merge_distance_ms)
            
            # Collect amplitudes for summary
            all_positive_amplitudes.extend(positive_event_amplitudes_merged)
            all_negative_amplitudes.extend(negative_event_amplitudes_merged)
            
            sweep_events = len(positive_event_amplitudes_merged) + len(negative_event_amplitudes_merged)
            total_events += sweep_events
            
            # Plot
            ax = axes[i]
            ax.plot(timestamps_filtered, data_filtered, 'b-', linewidth=1)
            ax.axvspan(stimulation_start_ms, stimulation_stop_ms, color="cyan", alpha=0.8, label="Stimulation")
            ax.axvspan(detection_start_ms_original, detection_stop_ms, color="gray", alpha=0.05, label="Original Detection")
            ax.axvspan(detection_start_ms_shifted, detection_stop_ms, color="lightblue", alpha=0.15, label="Shifted Detection")
            ax.axhline(y=noise_median, color='black', linestyle='-', alpha=0.5, linewidth=1)
            ax.axhline(y=event_threshold_positive, color='red', linestyle='--', alpha=0.7, linewidth=1)
            ax.axhline(y=event_threshold_negative, color='red', linestyle='--', alpha=0.7, linewidth=1)
            
            # Get plot limits for event markers
            y_min, y_max = ax.get_ylim()
            
            # Add event markers
            for event_time in positive_event_times_merged:
                ax.plot([event_time, event_time], [event_threshold_positive, y_max], 
                        color='red', alpha=0.8, linewidth=2)
            
            for event_time in negative_event_times_merged:
                ax.plot([event_time, event_time], [y_min, event_threshold_negative], 
                        color='red', alpha=0.8, linewidth=2)
            
            ax.set_xlabel("Time (ms)")
            ax.set_ylabel("Current (pA)")
            ax.set_title(f"{sweep_key} (Events: {sweep_events})")
            
            # Add legend only to first subplot
            if i == 0:
                ax.legend(loc='upper right', fontsize=8)

# Hide empty subplots
for i in range(num_sweeps, len(axes)):
    axes[i].set_visible(False)

plt.tight_layout()
plt.show()

# Print comprehensive summary
print(f"\n=== COMPREHENSIVE oEPSC ANALYSIS SUMMARY ===")
print(f"Session: {get_session_id(asset.path)}")
print(f"Total sweeps analyzed: {num_sweeps}")
print(f"Total events detected: {total_events}")
print(f"Positive events: {len(all_positive_amplitudes)}")
print(f"Negative events: {len(all_negative_amplitudes)}")

if len(all_positive_amplitudes) > 0:
    print(f"\nPositive event statistics:")
    print(f"  Mean amplitude: {np.mean(all_positive_amplitudes):.2f} ± {np.std(all_positive_amplitudes):.2f} pA")
    print(f"  Median amplitude: {np.median(all_positive_amplitudes):.2f} pA")
    print(f"  Range: {np.min(all_positive_amplitudes):.1f} to {np.max(all_positive_amplitudes):.1f} pA")

if len(all_negative_amplitudes) > 0:
    print(f"\nNegative event statistics:")
    print(f"  Mean amplitude: {np.mean(all_negative_amplitudes):.2f} ± {np.std(all_negative_amplitudes):.2f} pA")
    print(f"  Median amplitude: {np.median(all_negative_amplitudes):.2f} pA")
    print(f"  Range: {np.min(all_negative_amplitudes):.1f} to {np.max(all_negative_amplitudes):.1f} pA")

print(f"\nAnalysis parameters:")
print(f"  Detection window shift: {detection_window_shift_ms} ms")
print(f"  Event merge distance: {event_merge_distance_ms} ms")
print(f"  Event threshold: ±5 x MAD-based standard deviation")

=== COMPREHENSIVE oEPSC ANALYSIS SUMMARY ===
Session: dSPN++OffState++none++WT++20230706
Total sweeps analyzed: 10
Total events detected: 75
Positive events: 0
Negative events: 75

Negative event statistics:
  Mean amplitude: 14.32 ± 5.97 pA
  Median amplitude: 12.94 pA
  Range: 6.9 to 33.6 pA

Analysis parameters:
  Detection window shift: 100 ms
  Event merge distance: 1.0 ms
  Event threshold: ±5 x MAD-based standard deviation

# Filter for acetylcholine biosensor experiments (Figure 5F equivalent).
# These use pure ophys modality with pan cell type (GRABACh3.0 pan-striatal expression).

def is_achfp(asset_path: str) -> bool:
    """Acetylcholine biosensor (GRABACh3.0) files."""
    return get_modality(asset_path) == 'ophys' and get_cell_type(get_session_id(asset_path)) == 'pan'

available_oepsc_assets = [asset for asset in assets_list if is_achfp(asset.path)]

print(f"Found {len(available_oepsc_assets)} acetylcholine biosensor files:")
for i, asset in enumerate(available_oepsc_assets[:]):
    session_id = get_session_id(asset.path)
    fields = parse_session_id(session_id)
    print(f"  {i+1}. {asset.path}")
    print(f"     Session:   {session_id}")
    print(f"     Cell type: {fields.get('cell_type')}")
    print(f"     State:     {fields.get('state')}")
    print(f"     Genotype:  {fields.get('genotype')}")
    print()

Found 11 acetylcholine biosensor files:
  1. sub-SubjectRecordedAt20240402/sub-SubjectRecordedAt20240402_ses-pan++CTRL++none++WT++20240402_ophys.nwb
     Session:   pan++CTRL++none++WT++20240402
     Cell type: pan
     State:     CTRL
     Genotype:  WT

  2. sub-SubjectRecordedAt20240405/sub-SubjectRecordedAt20240405_ses-pan++CTRL++none++WT++20240405_ophys.nwb
     Session:   pan++CTRL++none++WT++20240405
     Cell type: pan
     State:     CTRL
     Genotype:  WT

  3. sub-SubjectRecordedAt20240410/sub-SubjectRecordedAt20240410_ses-pan++CTRL++none++WT++20240410_ophys.nwb
     Session:   pan++CTRL++none++WT++20240410
     Cell type: pan
     State:     CTRL
     Genotype:  WT

  4. sub-SubjectRecordedAt20240412/sub-SubjectRecordedAt20240412_ses-pan++PD++none++WT++20240412_ophys.nwb
     Session:   pan++PD++none++WT++20240412
     Cell type: pan
     State:     PD
     Genotype:  WT

  5. sub-SubjectRecordedAt20240416/sub-SubjectRecordedAt20240416_ses-pan++PD++none++WT++20240416_ophys.nwb
     Session:   pan++PD++none++WT++20240416
     Cell type: pan
     State:     PD
     Genotype:  WT

  6. sub-SubjectRecordedAt20240509/sub-SubjectRecordedAt20240509_ses-pan++PD++none++WT++20240509_ophys.nwb
     Session:   pan++PD++none++WT++20240509
     Cell type: pan
     State:     PD
     Genotype:  WT

  7. sub-SubjectRecordedAt20240524/sub-SubjectRecordedAt20240524_ses-pan++OFF++none++WT++20240524_ophys.nwb
     Session:   pan++OFF++none++WT++20240524
     Cell type: pan
     State:     OFF
     Genotype:  WT

  8. sub-SubjectRecordedAt20240529/sub-SubjectRecordedAt20240529_ses-pan++OFF++none++WT++20240529_ophys.nwb
     Session:   pan++OFF++none++WT++20240529
     Cell type: pan
     State:     OFF
     Genotype:  WT

  9. sub-SubjectRecordedAt20240531/sub-SubjectRecordedAt20240531_ses-pan++OFF++none++WT++20240531_ophys.nwb
     Session:   pan++OFF++none++WT++20240531
     Cell type: pan
     State:     OFF
     Genotype:  WT

  10. sub-SubjectRecordedAt20240603/sub-SubjectRecordedAt20240603_ses-pan++OFF++none++WT++20240603_ophys.nwb
     Session:   pan++OFF++none++WT++20240603
     Cell type: pan
     State:     OFF
     Genotype:  WT

  11. sub-SubjectRecordedAt20240604/sub-SubjectRecordedAt20240604_ses-pan++OFF++none++WT++20240604_ophys.nwb
     Session:   pan++OFF++none++WT++20240604
     Cell type: pan
     State:     OFF
     Genotype:  WT

import h5py
import remfile
from pynwb import NWBHDF5IO

asset = available_oepsc_assets[0]
s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)
file_system = remfile.File(s3_url, disk_cache=remfile.DiskCache("nwb-cache"))
file = h5py.File(file_system, mode="r")

io = NWBHDF5IO(file=file)
nwbfile = io.read()
nwbfile

trials_df = nwbfile.trials.to_dataframe()
trials_df

fluorescence_module = nwbfile.processing["ophys"]["Fluorescence"]
fluorescence_module

import matplotlib.pyplot as plt

trials_df = nwbfile.trials.to_dataframe()
trial_index = 10
trial_row = trials_df.iloc[trial_index]
stimulus_start_time = trial_row["stimulus_start_time"]
treatment = trial_row["treatment"]
roi_response_series_name = trial_row["roi_series_name"]
stimulation = trial_row["stimulation"]

roi_response_series = fluorescence_module[roi_response_series_name]


timestamps = roi_response_series.get_timestamps()
data = roi_response_series.data[:]

plt.plot(timestamps, data)
plt.axvline(stimulus_start_time, color='r', linestyle='--')
plt.title(f"{treatment} - {stimulation}")
plt.ylabel("Fluorescence (a.u.)")
plt.xlabel("Time (s)")

Text(0.5, 0, 'Time (s)')

import numpy as np

# pick 6 random trials that have a stimulus_start_time
valid_idx = trials_df[trials_df["stimulus_start_time"].notna()].index.values
rng = np.random.default_rng(42)
chosen = rng.choice(valid_idx, size=6, replace=False)

fig, axes = plt.subplots(2, 3, figsize=(15, 8), sharex=False, sharey=True)
axes = axes.ravel()

# compute global y-limits across the chosen series (skip missing)
y_mins, y_maxs = [], []
for tid in chosen:
    row = trials_df.loc[tid]
    roi_name = row["roi_series_name"]
    series = fluorescence_module[roi_name]

    y = series.data[:]
    y_mins.append(np.nanmin(y))
    y_maxs.append(np.nanmax(y))

ymin = float(np.min(y_mins))
ymax = float(np.max(y_maxs))
pad = 0.05 * (ymax - ymin) if ymax > ymin else 1.0
ymin -= pad
ymax += pad

for ax, tid in zip(axes, chosen):
    row = trials_df.loc[tid]
    roi_name = row["roi_series_name"]
    stim_t = float(row["stimulus_start_time"])
    series = fluorescence_module[roi_name]

    ts = series.get_timestamps()
    y = series.data[:]
    rel_ts = ts - stim_t

    ax.plot(rel_ts, y, color="C0", lw=1)
    ax.axvline(0.0, color="r", linestyle="--", lw=1)
    ax.set_title(f"Observation {tid}: {row['treatment']} — {row['stimulation']}", fontsize=10)
    ax.set_xlabel("Time from stimulus (s)")
    ax.set_ylabel("Fluorescence (a.u.)")
    ax.set_ylim(ymin, ymax)

# overall layout
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.show()

Data type	float32
Shape	(240000,)
Array size	937.50 KiB
Chunk shape	(240000,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	960000
Compressed size (bytes)	285418
Compression ratio	3.3634879369906594

Data type	float32
Shape	(260000,)
Array size	1015.62 KiB
Chunk shape	(260000,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	1040000
Compressed size (bytes)	329622
Compression ratio	3.1551292086086486

Data type	uint16
Shape	(7500, 19)
Array size	278.32 KiB
Chunk shape	(7500, 19)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	285000
Compressed size (bytes)	224718
Compression ratio	1.2682562144554508

Data type	uint16
Shape	(7500, 19)
Array size	278.32 KiB
Chunk shape	(7500, 19)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	285000
Compressed size (bytes)	182657
Compression ratio	1.56030154880459

Data type	uint16
Shape	(7500, 20)
Array size	292.97 KiB
Chunk shape	(7500, 20)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	300000
Compressed size (bytes)	244955
Compression ratio	1.2247147435243209

Installing requirements¶

State-dependent modulation of spiny projection neurons controlslevodopa-induced dyskinesia in a mouse model of Parkinson’s disease¶

Fetching data¶

Current Clamp Data Access and Organization¶

NWB Data Structure for Current Clamp Experiments¶

Streaming NWB Files from DANDI¶

root (NWBFile)

Exploring the Acquisition Module¶

Accessing Individual Sweeps via the IntracellularRecordingsTable¶

Systematic Data Access via Intracellular Recordings Table¶

Accessing Individual Sweep Data Through the Table¶

Systematic Access to All Sweeps¶

Dendritic Excitability Experiments - Line Scans¶

NWB Organization for Two-Photon Imaging Data¶

Filtering for Dendritic Excitability Data¶

root (NWBFile)

Streaming a Dendritic Excitability NWB File¶

Accessing ROI Response Data from the Ophys Module¶

Line Scans and Fluoresence¶

Comprehensive Line Scan Visualization: Linking Images, Raw Data, and ROI Responses¶

Optogenetic Experiments - oEPSC Analysis¶

NWB Organization for Optogenetic Data¶

Filtering for Optogenetic Data from Figure 2¶

Streaming an NWB file with Optogenetic stimulation¶

root (NWBFile)

Optogenetic Epochs Table¶

Visualizing Voltage Clamp Responses with Stimulation Windows¶

Event Detection and Analysis¶

MAD-Based Noise Estimation for Robust Event Detection¶

Comprehensive Event Analysis Grid¶

Acetylcholine Biosensor Experiments - GRABACh3.0 Imaging¶

NWB Organization for Biosensor Data¶

Filtering for Acetylcholine Biosensor Data from Figure 5¶

Streaming Acetylcholine Biosensor Data¶

root (NWBFile)

Accessing Fluorescence Time Series Data¶

Fluorescence

Data type	object
Shape	(11,)
Array size	88.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	88
Compressed size (bytes)	176
Compression ratio	0.5

	pixel_mask
id
0	[[88, 73, 1.0], [89, 73, 1.0], [90, 73, 1.0], [91, 73, 1.0], [92, 73, 1.0], [93, 73, 1.0], [94, 73, 1.0], [95, 73, 1.0], [96, 73, 1.0], [97, 73, 1.0], [98, 73, 1.0], [99, 73, 1.0], [100, 73, 1.0], [101, 73, 1.0], [102, 73, 1.0], [103, 73, 1.0], [104, 73, 1.0], [105, 73, 1.0], [106, 73, 1.0]]
1	[[88, 73, 1.0], [89, 73, 1.0], [90, 73, 1.0], [91, 73, 1.0], [92, 73, 1.0], [93, 73, 1.0], [94, 73, 1.0], [95, 73, 1.0], [96, 73, 1.0], [97, 73, 1.0], [98, 73, 1.0], [99, 73, 1.0], [100, 73, 1.0], [101, 73, 1.0], [102, 73, 1.0], [103, 73, 1.0], [104, 73, 1.0], [105, 73, 1.0], [106, 73, 1.0]]
2	[[60, 71, 1.0], [61, 71, 1.0], [62, 71, 1.0], [63, 71, 1.0], [64, 71, 1.0], [65, 71, 1.0], [66, 71, 1.0], [67, 71, 1.0], [68, 71, 1.0], [69, 71, 1.0], [70, 71, 1.0], [71, 71, 1.0], [72, 71, 1.0], [73, 71, 1.0], [74, 71, 1.0], [75, 71, 1.0], [76, 71, 1.0], [77, 71, 1.0], [78, 71, 1.0], [79, 71, 1.0]]
3	[[60, 71, 1.0], [61, 71, 1.0], [62, 71, 1.0], [63, 71, 1.0], [64, 71, 1.0], [65, 71, 1.0], [66, 71, 1.0], [67, 71, 1.0], [68, 71, 1.0], [69, 71, 1.0], [70, 71, 1.0], [71, 71, 1.0], [72, 71, 1.0], [73, 71, 1.0], [74, 71, 1.0], [75, 71, 1.0], [76, 71, 1.0], [77, 71, 1.0], [78, 71, 1.0], [79, 71, 1.0]]

	cell_id	condition_subfolder	dendrite_type	dendrite_distance_um	stimulus_current_pA	sweep_start_time_s	irt_row	simultaneous_row	sequential_row	repetition_row	experimental_condition_row	line_scan_ch1	line_scan_ch2	roi_response_ch1	roi_response_ch2
id
0	3	LID off-state	Soma	0.0	-120.0	0.0	0	0	0	0	0	(None, None, None)	(None, None, None)	(None, None, None)	(None, None, None)
1	3	LID off-state	Soma	0.0	-100.0	5.0	1	1	0	0	0	(None, None, None)	(None, None, None)	(None, None, None)	(None, None, None)
2	3	LID off-state	Soma	0.0	-80.0	10.0	2	2	0	0	0	(None, None, None)	(None, None, None)	(None, None, None)	(None, None, None)
3	3	LID off-state	Soma	0.0	-60.0	16.0	3	3	0	0	0	(None, None, None)	(None, None, None)	(None, None, None)	(None, None, None)

	electrode
id
0	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n
1	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n
2	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n
3	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n

	stimulus
id
0	(0, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
1	(10000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
2	(20000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
3	(30000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)

	intracellular_recordings							electrodes		stimuli		responses
	cell_id	dendrite_type	dendrite_distance_um	sweep_start_time	stimulus_current_pA	stimulus_protocol_name	condition_subfolder	id	electrode	id	stimulus	id	response
(intracellular_recordings, id)
0	3	Soma	0.0	0.0	-120.0	Step 1_-120 pA	LID off-state	0	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n	0	(0, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)	0	(0, 10000, CurrentClampSeries_Cell3_Somatic pynwb.icephys.CurrentClampSeries at 0x140138888765328\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Concatenated current-clamp recordings for Cell 3 soma_excitability. 26 sweeps merged end-to-end (Pattern B). Per-sweep wall-clock offsets stored in IntracellularRecordingsTable.sweep_start_time.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: volts\n)
1	3	Soma	0.0	5.0	-100.0	Step 2_-100 pA	LID off-state	1	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n	1	(10000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)	1	(10000, 10000, CurrentClampSeries_Cell3_Somatic pynwb.icephys.CurrentClampSeries at 0x140138888765328\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Concatenated current-clamp recordings for Cell 3 soma_excitability. 26 sweeps merged end-to-end (Pattern B). Per-sweep wall-clock offsets stored in IntracellularRecordingsTable.sweep_start_time.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: volts\n)
2	3	Soma	0.0	10.0	-80.0	Step 3_-80 pA	LID off-state	2	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n	2	(20000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)	2	(20000, 10000, CurrentClampSeries_Cell3_Somatic pynwb.icephys.CurrentClampSeries at 0x140138888765328\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Concatenated current-clamp recordings for Cell 3 soma_excitability. 26 sweeps merged end-to-end (Pattern B). Per-sweep wall-clock offsets stored in IntracellularRecordingsTable.sweep_start_time.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: volts\n)
3	3	Soma	0.0	16.0	-60.0	Step 4_-60 pA	LID off-state	3	Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n	3	(30000, 10000, CurrentClampSeries_Cell3_Somatic_Stimulus pynwb.icephys.CurrentClampStimulusSeries at 0x140138882073616\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Commanded current waveform paired with CurrentClampSeries_Cell3_Somatic. Synthesized from each sweep's Bruker VoltageOutput XML (step amplitude × pulse width); the per-sweep amplitude is also exposed via the IntracellularRecordingsTable.stimulus_current_pA column.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)	3	(30000, 10000, CurrentClampSeries_Cell3_Somatic pynwb.icephys.CurrentClampSeries at 0x140138888765328\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (260000,), type "<f4">\n description: Concatenated current-clamp recordings for Cell 3 soma_excitability. 26 sweeps merged end-to-end (Pattern B). Per-sweep wall-clock offsets stored in IntracellularRecordingsTable.sweep_start_time.\n electrode: Cell3_Soma pynwb.icephys.IntracellularElectrode at 0x140138888765008\nFields:\n cell_id: Cell3\n description: Patch-clamp electrode at the soma of Cell 3, recorded as part of the mouse-day icephys merge.\n device: MultiClamp700B pynwb.device.Device at 0x140138887174464\nFields:\n description: Molecular Devices MultiClamp 700B patch-clamp amplifier.\n\n location: Caudoputamen\n\n gain: 1.0\n offset: 0.0\n rate: 10000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: volts\n)

	simultaneous_recordings	stimulus_type
id
0	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]	F-I_curve
1	[26, 27, 28]	dendritic_trio
2	[29, 30, 31]	dendritic_trio

	sequential_recordings	cell_id	protocol	condition_subfolder
id
0	[0]	3	soma_fi_curve	LID off-state
1	[1]	3	dend_prox1	LID off-state
2	[2]	3	dend_dist1	LID off-state

Data type	float64
Shape	(20001,)
Array size	156.26 KiB
Chunk shape	(20001,)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	160008
Compressed size (bytes)	32586
Compression ratio	4.910329589394219

	start_time	stop_time	stimulation_on	pulse_length_in_ms	period_in_ms	number_pulses_per_pulse_train	number_trains	intertrain_interval_in_ms	power_in_mW	wavelength_in_nm	optogenetic_sites	stage_name
id
0	0.00	0.0200	False	NaN	NaN	-1	-1	NaN	0.0	470.0	[0]	pre_stimulation
1	0.02	0.0203	True	0.3	NaN	1	1	NaN	NaN	470.0	[0]	stimulation
2	0.06	0.4200	False	NaN	NaN	-1	-1	NaN	0.0	470.0	[0]	detection
3	0.42	1.0000	False	NaN	NaN	-1	-1	NaN	0.0	470.0	[0]	post_detection

	effector	excitation_source
id
0	ChR2_effector abc.Effector at 0x140138503251216\nFields:\n description: Excitatory channelrhodopsin expressed via AAV viral injection\n label: hChR2(H134R)-EYFP\n manufacturer: Addgene\n viral_vector_injection: M1_cortex_injection abc.ViralVectorInjection at 0x140138503250544\nFields:\n ap_in_mm: 1.15\n description: Stereotaxic injection of AAV5-hSyn-hChR2(H134R)-EYFP into M1 motor cortex ipsilateral to 6-OHDA lesion for corticostriatal terminal labeling. Coordinates: AP +1.15mm, ML -1.60mm, DV -1.55mm relative to Bregma. Volume: 0.15 µL. Expression time: 4 weeks. Coordinates: AP 1.15mm, ML -1.6mm, DV -1.55mm relative to Bregma at the cortical surface. Injection volume: 0.15µL.\n dv_in_mm: -1.55\n hemisphere: ipsilateral to 6-OHDA lesion\n location: Primary motor area\n ml_in_mm: -1.6\n pitch_in_deg: 0.0\n reference: Bregma at the cortical surface\n roll_in_deg: 0.0\n viral_vector: AAV5-hSyn-hChR2-H134R-EYFP abc.ViralVector at 0x140138503249200\nFields:\n construct_name: AAV5-hSyn-hChR2(H134R)-EYFP\n description: Adeno-associated virus serotype 5 expressing channelrhodopsin-2 (H134R variant) fused to enhanced yellow fluorescent protein under the human synapsin promoter. Used for optogenetic activation of corticostriatal terminals. Addgene #26973. Construct: AAV5-hSyn-hChR2(H134R)-EYFP from Addgene. Titer: 1e+13 vg/ml.\n manufacturer: Addgene\n titer_in_vg_per_ml: 10000000000000.0\n\n volume_in_uL: 0.15\n yaw_in_deg: 0.0\n\n	LED470nmSource abc.ExcitationSource at 0x140138503252560\nFields:\n description: Blue LED excitation source for ChR2 activation. Wavelength (470 nm) specified per-epoch in OptogeneticEpochsTable.\n exposure_time_in_s: 0.0003\n model: LED470nmModel abc.ExcitationSourceModel at 0x140138503252224\nFields:\n description: Blue LED model for 470nm optogenetic stimulation\n excitation_mode: one-photon\n manufacturer: Unknown\n source_type: LED\n wavelength_range_in_nm: [455. 485.]\n\n power_in_W: 0.001\n

	electrode
id
0	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n
1	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n
2	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n
3	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n

	response
id
0	(0, 20001, VoltageClampSeriesSweep001 pynwb.icephys.VoltageClampSeries at 0x140138503247856\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 001 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 20000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
1	(0, 20001, VoltageClampSeriesSweep002 pynwb.icephys.VoltageClampSeries at 0x140138500868048\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 002 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 20000.000000046613\n resolution: -1.0\n starting_time: 14.868482\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
2	(0, 20001, VoltageClampSeriesSweep003 pynwb.icephys.VoltageClampSeries at 0x140138500868368\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 003 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 19999.99999933607\n resolution: -1.0\n starting_time: 28.686085\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
3	(0, 20001, VoltageClampSeriesSweep004 pynwb.icephys.VoltageClampSeries at 0x140138503776736\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 004 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 19999.99999933607\n resolution: -1.0\n starting_time: 57.157828\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)

	intracellular_recordings						electrodes		stimuli		responses
	led_intensity	sweep_number	cell_number	session_letter	dendrite_type	dendrite_distance_um	id	electrode	id	stimulus	id	response
(intracellular_recordings, id)
0	100b	001	3	c	Soma	0	0	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n	0	(None, None, None)	0	(0, 20001, VoltageClampSeriesSweep001 pynwb.icephys.VoltageClampSeries at 0x140138503247856\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 001 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 20000.0\n resolution: -1.0\n starting_time: 0.0\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
1	100b	002	3	c	Soma	0	1	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n	1	(None, None, None)	1	(0, 20001, VoltageClampSeriesSweep002 pynwb.icephys.VoltageClampSeries at 0x140138500868048\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 002 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 20000.000000046613\n resolution: -1.0\n starting_time: 14.868482\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
2	100b	003	3	c	Soma	0	2	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n	2	(None, None, None)	2	(0, 20001, VoltageClampSeriesSweep003 pynwb.icephys.VoltageClampSeries at 0x140138500868368\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 003 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 19999.99999933607\n resolution: -1.0\n starting_time: 28.686085\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)
3	100b	004	3	c	Soma	0	3	IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n	3	(None, None, None)	3	(0, 20001, VoltageClampSeriesSweep004 pynwb.icephys.VoltageClampSeries at 0x140138503776736\nFields:\n comments: no comments\n conversion: 1.0\n data: <HDF5 dataset "data": shape (20001,), type "<f8">\n description: Voltage clamp recording from dSPN - LID off-state - Session c, Cell 3 - LED intensity 100b, Sweep 004 - Sr²⁺-oEPSC protocol at -70 mV holding potential\n electrode: IntracellularElectrode pynwb.icephys.IntracellularElectrode at 0x140138503907776\nFields:\n cell_id: Cell3\n description: Whole-cell patch clamp electrode recording from dSPN in the dorsolateral striatum - LID off-state - Session c - Sr²⁺-oEPSC protocol with LED 100b intensity\n device: Multiclamp700B Ch1 pynwb.device.Device at 0x140138503907232\nFields:\n description: Prairie View intracellular recording device: Multiclamp700B Ch1\n\n location: Caudoputamen\n slice: Sagittal slices (280-μm thick) were sectioned using a vibratome (Leica VT1200).\nAfter cutting, slices were incubated at 34 °C for 30 min in ACSF containing\n(in mM): 124 NaCl, 3 KCl, 1 NaH2PO4, 2.0 CaCl2, 1.0 MgCl2, 26 NaHCO3\nand 13.89 glucose, after which they were stored at room temperature until\nrecording. External solutions were oxygenated with carbogen (95%CO2/5%O2)\nat all time.\n\n\n offset: 0.0\n rate: 19999.99999933607\n resolution: -1.0\n starting_time: 57.157828\n starting_time_unit: seconds\n stimulus_description: N/A\n unit: amperes\n)

	simultaneous_recordings	stimulus_type
id
0	[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]	Sr2+_oEPSC_optogenetic_protocol

	start_time	stop_time	stimulation_on	pulse_length_in_ms	period_in_ms	number_pulses_per_pulse_train	number_trains	intertrain_interval_in_ms	power_in_mW	wavelength_in_nm	optogenetic_sites	stage_name
id
0	0.00	0.020000	False	NaN	NaN	-1	-1	NaN	0.0	470.0	e...	pre_stimulation
1	0.02	0.020300	True	0.3	NaN	1	1	NaN	NaN	470.0	e...	stimulation
2	0.06	0.420000	False	NaN	NaN	-1	-1	NaN	0.0	470.0	e...	detection
3	0.42	1.000000	False	NaN	NaN	-1	-1	NaN	0.0	470.0	e...	post_detection
4	1.00	14.868482	False	NaN	NaN	-1	-1	NaN	0.0	470.0	e...	inter_sweep_interval

Data type	int64
Shape	(2,)
Array size	16.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	16
Compressed size (bytes)	16
Compression ratio	1.0

	start_time	stop_time	treatment	stimulation	repetition_number	stimulus_start_time	roi_series_name
id
0	0.071	22.231084	control	burst_stimulation	1	10.071	RoiResponseSeriesGRABChControlBurstStimulationRepetition001
1	31.083	53.842006	control	burst_stimulation	2	41.083	RoiResponseSeriesGRABChControlBurstStimulationRepetition002
2	75.077	96.837804	control	single_pulse	1	85.077	RoiResponseSeriesGRABChControlSinglePulseRepetition001
3	104.082	126.641365	control	single_pulse	2	114.082	RoiResponseSeriesGRABChControlSinglePulseRepetition002