Use Case

This will take you though a use case of working with raw image data in TIFF format as well as post processed data using Caiman ROI extraction software.

Download a TIF file and a extract pipeline output .mat file from here.

Create an Imaging Extractor Object:

from notebook.services.config import ConfigManager
cm = ConfigManager().update('notebook', {'limit_output': 1000})
import roiextractors
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
img_ext = roiextractors.TiffImagingExtractor(r'source\demoMovie.tif' sampling_frequency=100)

img_ext.get_series(start_sample=10, end_sample=11)

Output:

array([[[1050,  676,  652, ..., 1523, 1406, 1627],
        [ 898,  908,  864, ...,  990, 1016, 1058],
        [ 899,  875,  827, ..., 1100, 1122, 1147],
        ...,
        [ 664,  957, 1040, ...,  833,  828, 1075],
        [ 893,  899,  858, ...,  807,  899,  981],
        [ 691,  653,  812, ...,  708,  715,  779]]], dtype=uint16)

img_ext.get_frame_shape()

Output:

(60, 80)

img_ext.get_num_samples()

Output:

2000

img_ext.get_sampling_frequency()

Output:

100.0

img_ext.get_num_channels()

Output:

1

vid_fra = img_ext.get_video(start_frame=0,end_frame=1)
plt.imshow(vid_fra)
plt.show()

Output:

Create a SegmentationExtractor Object

seg_ext = roiextractors.CaimanSegmentationExtractor(r'source\caiman.hdf5')

# will output a list of ids of all accepted rois
seg_ext.get_accepted_list()

Output:

[0,1,2,....71]

seg_ext.get_num_samples()

Output:

1000

seg_ext.get_roi_locations(roi_ids=[2])

Output:

array([[ 4],
   [43]])

plt.plot(seg_ext.get_sampling_frequency()*np.arange(seg_ext.get_num_samples()),seg_ext.get_traces(roi_ids=[2], name='dff').squeeze())
plt.show()

Output:

plt.imshow(seg_ext.get_roi_image_masks(roi_ids=[5]).squeeze())
plt.show()

Output:

seg_ext.get_frame_shape()

Output:

(128, 128)

seg_ext.get_num_rois()

Output:

72