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

from dandi.dandiapi import DandiAPIClient


client = DandiAPIClient.for_dandi_instance("dandi")

dandiset_id = "000727"
dandiset = client.get_dandiset(dandiset_id)
asset_list = [asset for asset in dandiset.get_assets() if "nwb" in asset.path]
# The paths are:
[asset.path for asset in asset_list]

['sub-fly-098/sub-fly-098_ses-20200627130208_behavior+image+ophys.nwb',
 'sub-fly-097/sub-fly-097_ses-20200627113329_behavior+image+ophys.nwb',
 'sub-fly-089/sub-fly-089_ses-20200618081653_behavior+image+ophys.nwb',
 'sub-fly-094/sub-fly-094_ses-20200620122048_behavior+image+ophys.nwb',
 'sub-fly-087/sub-fly-087_ses-20200228161226_behavior+image+ophys.nwb',
 'sub-fly-100/sub-fly-100_ses-20200702083849_behavior+image+ophys.nwb',
 'sub-fly-099/sub-fly-099_ses-20200627142647_behavior+image+ophys.nwb',
 'sub-fly-101/sub-fly-101_ses-20200702100612_behavior+image+ophys.nwb',
 'sub-fly-105/sub-fly-105_ses-20200704142706_behavior+image+ophys.nwb']

import remfile
from h5py import File
from pynwb import NWBHDF5IO

asset = asset_list[0]
# Here we build the url to download the file
url = f"https://api.dandiarchive.org/api/dandisets/{asset.dandiset_id}/versions/{asset.version_id}/assets/{asset.identifier}/download"


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

io = NWBHDF5IO(file=h5_file)
nwbfile = io.read()
nwbfile

two_photon_series_anatomical_red = nwbfile.acquisition["TwoPhotonSeriesAnatomicalRed"]
two_photon_series_anatomical_red

two_photon_series_anatomical_red.data.shape

(100, 1024, 512, 241)

# Let's visualize the average value of one of the planes
plane_index = 100

mean_plane = two_photon_series_anatomical_red.data[..., plane_index].mean(axis=0)
# Note that taking the mean across the axis=0 is the time average
mean_plane.shape

(1024, 512)

import matplotlib.pyplot as plt


fig, ax = plt.subplots(figsize=(8, 6))

# The sizes of the image are stored in the imaging_plane of the TwoPhotonSeriesAnatomicalRed
imaging_plane = two_photon_series_anatomical_red.imaging_plane
grid_spacing_data = imaging_plane.grid_spacing[:]

height, width = mean_plane.shape
x, y = grid_spacing_data[:2]
extent = [0,  height * y, width * x, 0]



img = ax.imshow(mean_plane.T, cmap='gist_heat', extent=extent)

two_photon_series_description = two_photon_series_anatomical_red.description
title = f'{two_photon_series_description} - Mean Fluoresence at plane {plane_index}'
ax.set_title(title)

unit = imaging_plane.origin_coords_unit
ax.set_xlabel(f'X ({unit})')
ax.set_ylabel(f'Y ({unit})')

plt.show()

fictrac_container = nwbfile.processing["behavior"]["FicTrac"]
fictrac_container

import matplotlib.pyplot as plt


spatial_series = fictrac_container["SpatialSeriesPosition"]
data_in_units = spatial_series.get_data_in_units()
unit = spatial_series.unit
timestamps = spatial_series.get_timestamps()

fig, ax = plt.subplots(figsize=(12, 10))
sc = ax.scatter(data_in_units[:, 0], data_in_units[:, 1], c=timestamps, cmap='Blues')
ax.set_xlabel(f"X ({unit})")  
ax.set_ylabel(f"Y ({unit})") 

plt.colorbar(mappable=sc, ax=ax, label='Time (seconds)')

description = spatial_series.description 
ax.set_title(description)

# Add a annotation at 0, 0 that says start
# Adding a red point at (0, 0)
ax.plot(0, 0, 'ro')  # 'ro' specifies red color and circle marker

# Adding the text annotation "start" near the red point
# You can adjust the 'x' and 'y' arguments in ax.text() to move the text to the desired position
ax.text(-0.001, -0.001, 'Start', color='red', ha='right', va="bottom")
        
plt.show()

Data type	uint16
Shape	(100, 1024, 512, 241)
Array size	23.54 GiB
Chunk shape	(9, 1024, 512, 1)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	25270681600
Compressed size (bytes)	18928061030
Compression ratio	1.3350908769761083

Data type	int64
Shape	(3,)
Array size	24.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	24
Compressed size (bytes)	24
Compression ratio	1.0

Data type	float64
Shape	(3,)
Array size	24.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	24
Compressed size (bytes)	24
Compression ratio	1.0

Data type	float64
Shape	(3,)
Array size	24.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	24
Compressed size (bytes)	24
Compression ratio	1.0

Data type	uint16
Shape	(100, 1024, 512, 241)
Array size	23.54 GiB
Chunk shape	(9, 1024, 512, 1)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	25270681600
Compressed size (bytes)	17578556868
Compression ratio	1.4375856783785672

Installing requirements¶

How to use the data in this dandiset¶

root (NWBFile)

Looking at the anatomical data¶

TwoPhotonSeriesAnatomicalRed (TwoPhotonSeries)

Looking at the FicTrac SpatialSeries data¶

FicTrac (Position)

Data type	uint8
Shape	(0, 0, 0)
Array size	0.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	0
Compressed size (bytes)	0
Compression ratio	undefined

Data type	object
Shape	(1,)
Array size	8.00 bytes
Chunk shape	None
Compression	None
Compression opts	None
Uncompressed size (bytes)	8
Compressed size (bytes)	16
Compression ratio	0.5

Data type	float32
Shape	(3384, 314, 146, 91)
Array size	52.59 GiB
Chunk shape	(54, 314, 146, 1)
Compression	gzip
Compression opts	4
Uncompressed size (bytes)	56469538944
Compressed size (bytes)	16556548743
Compression ratio	3.4107071359225727