Python implementation of the Gaussian peak detection described in Segré et al. Nature Methods (2008).
- numpy
- scipy
- scikit-image
- pandas
from peak_detection import detect_peaks
from tifffile import TiffFile
fname = 'sample.tif'
detection_parameters = {'w_s': 10,
'peak_radius': 4.,
'threshold': 60.,
'max_peaks': 10
}
sample = TiffFile(fname)
peaks = detect_peaks(sample.asarray(), shape_label=('t', 'z', 'x', 'y'), parallel=True, **detection_parameters)2013-06-28 19:52:38:INFO:peak_detection.detection: Parallel mode enabled: 5 cores will be used to process 9 stacks
2013-06-28 19:52:40:INFO:peak_detection.detection: Detection done for stack number 3: 4 peaks detected (1/9 - 11%)
2013-06-28 19:52:40:INFO:peak_detection.detection: Detection done for stack number 2: 3 peaks detected (2/9 - 22%)
2013-06-28 19:52:40:INFO:peak_detection.detection: Detection done for stack number 4: 5 peaks detected (3/9 - 33%)
2013-06-28 19:52:40:INFO:peak_detection.detection: Detection done for stack number 1: 2 peaks detected (4/9 - 44%)
2013-06-28 19:52:41:INFO:peak_detection.detection: Detection done for stack number 0: 1 peaks detected (5/9 - 55%)
2013-06-28 19:52:41:INFO:peak_detection.detection: Detection done for stack number 5: 6 peaks detected (6/9 - 66%)
2013-06-28 19:52:42:INFO:peak_detection.detection: Detection done for stack number 6: 7 peaks detected (7/9 - 77%)
2013-06-28 19:52:42:INFO:peak_detection.detection: Detection done for stack number 7: 8 peaks detected (8/9 - 88%)
2013-06-28 19:52:42:INFO:peak_detection.detection: Detection done for stack number 8: 9 peaks detected (9/9 - 100%)
2013-06-28 19:52:42:INFO:peak_detection.detection: Reordering stacks
2013-06-28 19:52:42:INFO:peak_detection.detection: Add original shape to DataFrame as columns. Shape = (3, 3, 54, 209)
2013-06-28 19:52:42:INFO:peak_detection.detection: Detection is done
2013-06-28 19:52:42:INFO:peak_detection.detection: 45 peaks detected in 9 stacks
print(peaks) x y w I t z
stacks id
0 0 19.022877 28.102197 2.9038 195.396065 0 0
1 0 19.022877 28.102197 2.9038 195.396065 0 1
1 25.022877 189.102197 2.9038 195.396065 0 1
2 0 14.022877 133.102197 2.9038 195.396065 0 2
1 29.022877 44.102197 2.9038 195.396065 0 2
2 29.022877 97.102197 2.9038 195.396065 0 2
3 0 19.022877 28.102197 2.9038 195.396065 1 0
1 24.022877 132.102197 2.9038 195.396065 1 0
2 24.022877 178.102197 2.9038 195.396065 1 0
3 29.022877 79.102197 2.9038 195.396065 1 0
4 0 19.022877 27.102197 2.9038 195.396065 1 1
1 26.022877 181.102197 2.9038 195.396065 1 1
2 28.022877 80.102197 2.9038 195.396065 1 1
3 28.022877 128.102197 2.9038 195.396065 1 1
4 45.022877 43.102197 2.9038 195.396065 1 1
5 0 15.022877 147.102197 2.9038 195.396065 1 2
1 17.022877 55.102197 2.9038 195.396065 1 2
2 18.022877 88.102197 2.9038 195.396065 1 2
3 27.022877 22.102197 2.9038 195.396065 1 2
4 35.022877 122.102197 2.9038 195.396065 1 2
5 38.022877 66.102197 2.9038 195.396065 1 2
6 0 14.022877 131.102197 2.9038 195.396065 2 0
1 15.022877 75.102197 2.9038 195.396065 2 0
2 32.022877 39.102197 2.9038 195.396065 2 0
3 34.022877 99.102197 2.9038 195.396065 2 0
4 36.022877 67.102197 2.9038 195.396065 2 0
5 36.022877 157.102197 2.9038 195.396065 2 0
6 37.022877 125.102197 2.9038 195.396065 2 0
7 0 14.022877 131.102197 2.9038 195.396065 2 1
1 15.022877 75.102197 2.9038 195.396065 2 1
2 16.022877 176.102197 2.9038 195.396065 2 1
3 32.022877 39.102197 2.9038 195.396065 2 1
4 34.022877 99.102197 2.9038 195.396065 2 1
5 36.022877 67.102197 2.9038 195.396065 2 1
6 36.022877 157.102197 2.9038 195.396065 2 1
7 37.022877 125.102197 2.9038 195.396065 2 1
8 0 14.022877 8.102197 2.9038 195.396065 2 2
1 14.022877 131.102197 2.9038 195.396065 2 2
2 15.022877 75.102197 2.9038 195.396065 2 2
3 16.022877 176.102197 2.9038 195.396065 2 2
4 32.022877 39.102197 2.9038 195.396065 2 2
5 34.022877 99.102197 2.9038 195.396065 2 2
6 36.022877 67.102197 2.9038 195.396065 2 2
7 36.022877 157.102197 2.9038 195.396065 2 2
8 37.022877 125.102197 2.9038 195.396065 2 2