Load and plot image¶

This starter notebook does three things: load a file, get image data, and plot it with Matplotlib. Matplotlib is used only in documentation/notebook examples, not in the CloudScope runtime dependencies.

In [1]:

import matplotlib.pyplot as plt

from acqstore.sample_data import ensure_sample
from acqstore.acq_image import AcqImageList

import matplotlib.pyplot as plt from acqstore.sample_data import ensure_sample from acqstore.acq_image import AcqImageList

Load sample data¶

Use the registered velocity-sample-data sample for a reproducible first run. ensure_sample() downloads and caches the dataset on first use and returns a local folder path. Replace this with a local .oir, .czi, .tif, or .ome.zarr path when adapting the notebook.

In [2]:

sample_folder = ensure_sample('velocity-sample-data')
acq_list = AcqImageList(str(sample_folder))

acq = acq_list.get_files()[0]
print(acq.name)

header = acq.images.header
for k, v in header.as_dict().items():
    print(f"{k}: {v}")

sample_folder = ensure_sample('velocity-sample-data') acq_list = AcqImageList(str(sample_folder)) acq = acq_list.get_files()[0] print(acq.name) header = acq.images.header for k, v in header.as_dict().items(): print(f"{k}: {v}")

20251014_A98_0002.oir
path: /Users/cudmore/Library/Application Support/cloudscope/sample-data/velocity-sample-data-v1/velocity-sample-data/14d Saline/20251014/20251014_A98_0002.oir
shape: (30000, 20)
dims: ('Y', 'X')
sizes: {'Y': 30000, 'X': 20}
dtype: uint16
num_channels: 1
num_scenes: 1
physical_units: (0.0011339666666666667, 0.291)
physical_units_labels: ('seconds', 'um')
date: 20251014
time: 15:52:30
file_size: 5.6 MB

Get image data¶

This example asks the file loader for a single 2D plane.

In [3]:

channel_indices = acq.images.channel_indices
channel = channel_indices[0]
image = acq.images.get_slice_data(channel=channel, z=0, t=0)
print("channels:", channel_indices)
print(image.shape, image.dtype, image.min(), image.max())

channel_indices = acq.images.channel_indices channel = channel_indices[0] image = acq.images.get_slice_data(channel=channel, z=0, t=0) print("channels:", channel_indices) print(image.shape, image.dtype, image.min(), image.max())

channels: [0]
(30000, 20) uint16 36 1244

Plot with Matplotlib¶

In [4]:

# This is a line-scan kymograph (here 30000 time rows x 24 spatial columns).
# image.T puts time on the x axis and space on the y axis. Such a wide/short
# array is invisible with imshow's default aspect="equal" (square pixels), so we
# use aspect="auto" to fill the axes box.
plt.figure(figsize=(8, 5))
plt.imshow(image.T, cmap="gray", aspect="auto")
plt.title(f"{acq.name} — channel {channel}")
plt.xlabel("time (rows)")
plt.ylabel("space (columns)")
plt.colorbar(label="intensity");

# This is a line-scan kymograph (here 30000 time rows x 24 spatial columns). # image.T puts time on the x axis and space on the y axis. Such a wide/short # array is invisible with imshow's default aspect="equal" (square pixels), so we # use aspect="auto" to fill the axes box. plt.figure(figsize=(8, 5)) plt.imshow(image.T, cmap="gray", aspect="auto") plt.title(f"{acq.name} — channel {channel}") plt.xlabel("time (rows)") plt.ylabel("space (columns)") plt.colorbar(label="intensity");

In [ ]: