In [3]:
from clarityviz import atlasregiongraph
a = atlasregiongraph('Aut1367')
a.align_brain()
a.generate_atlas_region_graph('Aut1367/atlas/Aut1367_atlas.nii')
a.generate_atlas_region_plotly()
Step translation:
0. -0.289554412914
1. -0.303034329403
2. -0.317748535988
3. -0.327379343714
4. -0.337233442357
5. -0.34860303949
6. -0.359982255766
7. -0.366692599252
8. -0.376846251961
9. -0.385342100011
10. -0.398402432639
11. -0.405164213507
12. -0.409796559445
13. -0.412188172915
14. -0.414955647592
15. -0.414306881008
16. -0.41447168532
17. -0.414859495837
18. -0.415128678767
19. -0.415205192751
20. -0.415298835083
21. -0.415252711377
22. -0.415268344381
Step rigid:
0. -0.445312210881
1. -0.273723071978
2. -0.393397367633
3. -0.446751329687
4. -0.43659554463
5. -0.446586781203
6. -0.446365680887
7. -0.44780904369
8. -0.446354988249
9. -0.446972738411
10. -0.447664238431
Step affine:
0. -0.450158102992
1. -0.444424850764
2. -0.439583327748
3. -0.461739137663
4. -0.458080555043
5. -0.46295198052
6. -0.463988981766
7. -0.464744694696
8. -0.464715964305
9. -0.465698771231
10. -0.465690542763
11. -0.465781993942
12. -0.465733000539
13. -0.465917723221
14. -0.466092070503
15. -0.466160008315
Step 0: alpha=0.05, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -2.24079e+10, 9.45172, 0, -2.24079e+10 (97.26%), 1.100000e-03
1. -2.25549e+10, 31.103, 0, -2.25549e+10 (97.0906%), 1.210000e-03
2. -2.28177e+10, 72.6997, 0, -2.28177e+10 (96.7877%), 1.331000e-03
3. -2.34119e+10, 132.216, 0, -2.34119e+10 (96.103%), 1.464100e-03
4. -2.41812e+10, 226.582, 0, -2.41812e+10 (95.2165%), 1.610510e-03
5. -2.49788e+10, 357.407, 0, -2.49788e+10 (94.2974%), 1.771561e-03
6. -2.54253e+10, 514.078, 0, -2.54253e+10 (93.7828%), 1.948717e-03
7. -2.5554e+10, 711.333, 0, -2.5554e+10 (93.6345%), 2.143589e-03
8. -2.6033e+10, 949.846, 0, -2.6033e+10 (93.0825%), 2.357948e-03
9. -2.67758e+10, 1223.23, 0, -2.67758e+10 (92.2266%), 2.593742e-03
E = -2.67758e+10 (92.2266%)
Length = 31.1284
Time = 360.587s (6.00979m)
Step 1: alpha=0.02, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -2.82353e+10, 17.022, 0, -2.82353e+10 (98.246%), 1.100000e-03
1. -2.89413e+10, 62.2578, 0, -2.89413e+10 (97.3632%), 1.210000e-03
2. -2.96455e+10, 143.564, 0, -2.96455e+10 (96.4827%), 1.331000e-03
3. -3.02368e+10, 186.794, 0, -3.02368e+10 (95.7434%), 7.320500e-04
4. -3.04404e+10, 249.504, 0, -3.04404e+10 (95.4888%), 8.052550e-04
5. -3.08399e+10, 323.876, 0, -3.08399e+10 (94.9892%), 8.857805e-04
6. -3.11103e+10, 418.442, 0, -3.11103e+10 (94.6511%), 9.743586e-04
7. -3.14851e+10, 534.316, 0, -3.14851e+10 (94.1824%), 1.071794e-03
8. -3.20038e+10, 680.819, 0, -3.20038e+10 (93.5339%), 1.178974e-03
9. -3.20288e+10, 716.734, 0, -3.20288e+10 (93.5026%), 3.242178e-04
10. -3.22604e+10, 764.992, 0, -3.22604e+10 (93.213%), 3.566396e-04
11. -3.25004e+10, 818.883, 0, -3.25004e+10 (92.9129%), 3.923035e-04
12. -3.25748e+10, 881.264, 0, -3.25748e+10 (92.82%), 4.315339e-04
13. -3.26594e+10, 953.514, 0, -3.26595e+10 (92.7141%), 4.746873e-04
14. -3.26606e+10, 963.17, 0, -3.26606e+10 (92.7126%), 6.526950e-05
15. -3.2703e+10, 974.042, 0, -3.2703e+10 (92.6597%), 7.179645e-05
16. -3.27458e+10, 986.026, 0, -3.27458e+10 (92.6061%), 7.897610e-05
17. -3.27659e+10, 999.149, 0, -3.27659e+10 (92.581%), 8.687371e-05
18. -3.28095e+10, 1013.8, 0, -3.28095e+10 (92.5264%), 9.556108e-05
19. -3.28599e+10, 1030.06, 0, -3.28599e+10 (92.4635%), 1.051172e-04
E, E_velocity, E_rate, E_image (E_image %), LearningRate
20. -3.29014e+10, 1047.99, 0, -3.29014e+10 (92.4116%), 1.156289e-04
21. -3.29468e+10, 1067.91, 0, -3.29468e+10 (92.3548%), 1.271918e-04
22. -3.3002e+10, 1090.12, 0, -3.3002e+10 (92.2858%), 1.399110e-04
23. -3.30568e+10, 1115.18, 0, -3.30568e+10 (92.2173%), 1.539021e-04
24. -3.31205e+10, 1143.79, 0, -3.31205e+10 (92.1375%), 1.692923e-04
25. -3.31909e+10, 1175.37, 0, -3.31909e+10 (92.0495%), 1.862215e-04
26. -3.32138e+10, 1210.77, 0, -3.32138e+10 (92.0209%), 2.048437e-04
27. -3.33357e+10, 1248.52, 0, -3.33357e+10 (91.8685%), 2.253280e-04
28. -3.34565e+10, 1291.73, 0, -3.34565e+10 (91.7174%), 2.478608e-04
29. -3.36187e+10, 1339.43, 0, -3.36187e+10 (91.5146%), 2.726469e-04
30. -3.3692e+10, 1392.02, 0, -3.3692e+10 (91.423%), 2.999116e-04
31. -3.37102e+10, 1423.18, 0, -3.37102e+10 (91.4002%), 1.649514e-04
32. -3.37867e+10, 1457.22, 0, -3.37867e+10 (91.3046%), 1.814465e-04
33. -3.3799e+10, 1494.49, 0, -3.3799e+10 (91.2892%), 1.995912e-04
34. -3.39194e+10, 1536.55, 0, -3.39194e+10 (91.1386%), 2.195503e-04
35. -3.39583e+10, 1583.53, 0, -3.39583e+10 (91.09%), 2.415053e-04
36. -3.40949e+10, 1633.9, 0, -3.40949e+10 (90.9192%), 2.656558e-04
37. -3.4107e+10, 1661.24, 0, -3.4107e+10 (90.9041%), 1.461107e-04
38. -3.41778e+10, 1692.96, 0, -3.41778e+10 (90.8155%), 1.607218e-04
39. -3.41786e+10, 1701.4, 0, -3.41786e+10 (90.8145%), 4.419849e-05
E, E_velocity, E_rate, E_image (E_image %), LearningRate
40. -3.4179e+10, 1701.7, 0, -3.4179e+10 (90.8141%), 1.519323e-06
41. -3.4179e+10, 1701.78, 0, -3.41791e+10 (90.814%), 4.178139e-07
42. -3.41791e+10, 1701.82, 0, -3.41791e+10 (90.8139%), 2.297976e-07
43. -3.41791e+10, 1701.85, 0, -3.41791e+10 (90.8139%), 1.263887e-07
E = -3.41776e+10 (90.8158%)
Length = 39.9132
Time = 1196.06s (19.9344m)
Step 2: alpha=0.01, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -3.41576e+10, 54.646, 0, -3.41576e+10 (98.6143%), 1.100000e-03
1. -3.52912e+10, 50.6418, 0, -3.52912e+10 (97.0757%), 3.025000e-04
2. -3.5493e+10, 63.3073, 0, -3.5493e+10 (96.8017%), 3.327500e-04
3. -3.5656e+10, 69.5572, 0, -3.5656e+10 (96.5805%), 1.830125e-04
4. -3.59902e+10, 78.7728, 0, -3.59902e+10 (96.1268%), 2.013138e-04
E = -3.59902e+10 (96.1268%)
Length = 8.91541
Time = 242.616s (4.0436m)
clarityviz/atlasregiongraph.py:164: VisibleDeprecationWarning:
using a non-integer number instead of an integer will result in an error in the future
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-f34691650de0> in <module>()
3 a = atlasregiongraph('Aut1367')
4 a.align_brain()
----> 5 a.generate_atlas_region_graph('Aut1367/atlas/Aut1367_atlas.nii')
6 a.generate_atlas_region_plotly()
/root/clarityviz/clarityviz/atlasregiongraph.pyc in generate_atlas_region_graph(self, atlas_path)
162 locations = bright_points[:, 0:3]
163
--> 164 regions = [atlas_data[l[1], l[0], l[2]] for l in locations]
165
166 outfile = open(self._token + '/' + self._token + 'localeq.region.csv', 'w')
IndexError: index 595 is out of bounds for axis 0 with size 595
In [2]:
a.align_brain()
Step translation:
0. -0.289554412914
1. -0.303034329403
2. -0.317748535988
3. -0.327379343714
4. -0.337233442357
5. -0.34860303949
6. -0.359982255766
7. -0.366692599252
8. -0.376846251961
9. -0.385342100011
10. -0.398402432639
11. -0.405164213507
12. -0.409796559445
13. -0.412188172915
14. -0.414955647592
15. -0.414306881008
16. -0.41447168532
17. -0.414859495837
18. -0.415128678767
19. -0.415205192751
20. -0.415298835083
21. -0.415252711377
22. -0.415268344381
Step rigid:
0. -0.445312210881
1. -0.273723071978
2. -0.393397367633
3. -0.446751329687
4. -0.43659554463
5. -0.446586781203
6. -0.446365680887
7. -0.44780904369
8. -0.446354988249
9. -0.446972738411
10. -0.447664238431
Step affine:
0. -0.450158102992
1. -0.444424850764
2. -0.439583327748
3. -0.461739137663
4. -0.458080555043
5. -0.46295198052
6. -0.463988981766
7. -0.464744694696
8. -0.464715964305
9. -0.465698771231
10. -0.465690542763
11. -0.465781993942
12. -0.465733000539
13. -0.465917723221
14. -0.466092070503
15. -0.466160008314
Step 0: alpha=0.05, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -2.24079e+10, 9.45172, 0, -2.24079e+10 (97.26%), 1.100000e-03
1. -2.25549e+10, 31.103, 0, -2.25549e+10 (97.0906%), 1.210000e-03
2. -2.28177e+10, 72.6997, 0, -2.28177e+10 (96.7877%), 1.331000e-03
3. -2.34119e+10, 132.216, 0, -2.34119e+10 (96.103%), 1.464100e-03
4. -2.41812e+10, 226.582, 0, -2.41812e+10 (95.2165%), 1.610510e-03
5. -2.49788e+10, 357.407, 0, -2.49788e+10 (94.2974%), 1.771561e-03
6. -2.54253e+10, 514.078, 0, -2.54253e+10 (93.7828%), 1.948717e-03
7. -2.5554e+10, 711.333, 0, -2.5554e+10 (93.6345%), 2.143589e-03
8. -2.6033e+10, 949.846, 0, -2.6033e+10 (93.0825%), 2.357948e-03
9. -2.67758e+10, 1223.23, 0, -2.67758e+10 (92.2266%), 2.593742e-03
E = -2.67758e+10 (92.2266%)
Length = 31.1284
Time = 360.858s (6.01429m)
Step 1: alpha=0.02, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -2.82353e+10, 17.022, 0, -2.82353e+10 (98.246%), 1.100000e-03
1. -2.89413e+10, 62.2578, 0, -2.89413e+10 (97.3632%), 1.210000e-03
2. -2.96455e+10, 143.564, 0, -2.96455e+10 (96.4827%), 1.331000e-03
3. -3.02368e+10, 186.794, 0, -3.02368e+10 (95.7434%), 7.320500e-04
4. -3.04404e+10, 249.504, 0, -3.04404e+10 (95.4888%), 8.052550e-04
5. -3.08399e+10, 323.876, 0, -3.08399e+10 (94.9892%), 8.857805e-04
6. -3.11103e+10, 418.442, 0, -3.11103e+10 (94.6511%), 9.743586e-04
7. -3.14851e+10, 534.316, 0, -3.14851e+10 (94.1824%), 1.071794e-03
8. -3.20038e+10, 680.819, 0, -3.20038e+10 (93.5339%), 1.178974e-03
9. -3.20288e+10, 716.734, 0, -3.20288e+10 (93.5026%), 3.242178e-04
10. -3.22604e+10, 764.992, 0, -3.22604e+10 (93.213%), 3.566396e-04
11. -3.25004e+10, 818.883, 0, -3.25004e+10 (92.9129%), 3.923035e-04
12. -3.25748e+10, 881.264, 0, -3.25748e+10 (92.82%), 4.315339e-04
13. -3.26594e+10, 953.514, 0, -3.26595e+10 (92.7141%), 4.746873e-04
14. -3.26606e+10, 963.17, 0, -3.26606e+10 (92.7126%), 6.526950e-05
15. -3.2703e+10, 974.042, 0, -3.2703e+10 (92.6597%), 7.179645e-05
16. -3.27458e+10, 986.026, 0, -3.27458e+10 (92.6061%), 7.897610e-05
17. -3.27659e+10, 999.149, 0, -3.27659e+10 (92.581%), 8.687371e-05
18. -3.28095e+10, 1013.8, 0, -3.28095e+10 (92.5264%), 9.556108e-05
19. -3.28599e+10, 1030.06, 0, -3.28599e+10 (92.4635%), 1.051172e-04
E, E_velocity, E_rate, E_image (E_image %), LearningRate
20. -3.29014e+10, 1047.99, 0, -3.29014e+10 (92.4116%), 1.156289e-04
21. -3.29468e+10, 1067.91, 0, -3.29468e+10 (92.3548%), 1.271918e-04
22. -3.3002e+10, 1090.12, 0, -3.3002e+10 (92.2858%), 1.399110e-04
23. -3.30568e+10, 1115.18, 0, -3.30568e+10 (92.2173%), 1.539021e-04
24. -3.31205e+10, 1143.79, 0, -3.31205e+10 (92.1375%), 1.692923e-04
25. -3.31909e+10, 1175.37, 0, -3.31909e+10 (92.0495%), 1.862215e-04
26. -3.32138e+10, 1210.77, 0, -3.32138e+10 (92.0209%), 2.048437e-04
27. -3.33357e+10, 1248.52, 0, -3.33357e+10 (91.8685%), 2.253280e-04
28. -3.34565e+10, 1291.73, 0, -3.34565e+10 (91.7174%), 2.478608e-04
29. -3.36187e+10, 1339.43, 0, -3.36187e+10 (91.5146%), 2.726469e-04
30. -3.3692e+10, 1392.02, 0, -3.3692e+10 (91.423%), 2.999116e-04
31. -3.37102e+10, 1423.18, 0, -3.37102e+10 (91.4002%), 1.649514e-04
32. -3.37867e+10, 1457.22, 0, -3.37867e+10 (91.3046%), 1.814465e-04
33. -3.3799e+10, 1494.49, 0, -3.3799e+10 (91.2892%), 1.995912e-04
34. -3.39194e+10, 1536.55, 0, -3.39194e+10 (91.1386%), 2.195503e-04
35. -3.39583e+10, 1583.53, 0, -3.39583e+10 (91.09%), 2.415053e-04
36. -3.40949e+10, 1633.9, 0, -3.40949e+10 (90.9192%), 2.656558e-04
37. -3.4107e+10, 1661.24, 0, -3.4107e+10 (90.9041%), 1.461107e-04
38. -3.41778e+10, 1692.96, 0, -3.41778e+10 (90.8155%), 1.607218e-04
39. -3.41786e+10, 1701.4, 0, -3.41786e+10 (90.8145%), 4.419849e-05
E, E_velocity, E_rate, E_image (E_image %), LearningRate
40. -3.4179e+10, 1701.7, 0, -3.4179e+10 (90.8141%), 1.519323e-06
41. -3.4179e+10, 1701.78, 0, -3.41791e+10 (90.814%), 4.178139e-07
42. -3.41791e+10, 1701.82, 0, -3.41791e+10 (90.8139%), 2.297976e-07
43. -3.41791e+10, 1701.85, 0, -3.41791e+10 (90.8139%), 1.263887e-07
E = -3.41776e+10 (90.8158%)
Length = 39.9132
Time = 1196.46s (19.941m)
Step 2: alpha=0.01, beta=0.05, scale=1.0
E, E_velocity, E_rate, E_image (E_image %), LearningRate
0. -3.41576e+10, 54.646, 0, -3.41576e+10 (98.6143%), 1.100000e-03
1. -3.52912e+10, 50.6418, 0, -3.52912e+10 (97.0757%), 3.025000e-04
2. -3.5493e+10, 63.3073, 0, -3.5493e+10 (96.8017%), 3.327500e-04
3. -3.5656e+10, 69.5572, 0, -3.5656e+10 (96.5805%), 1.830125e-04
4. -3.59902e+10, 78.7728, 0, -3.59902e+10 (96.1268%), 2.013138e-04
E = -3.59902e+10 (96.1268%)
Length = 8.91541
Time = 241.124s (4.01873m)
In [ ]:
In [ ]:
Content source: NeuroDataDesign/seelviz
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