| Total Complexity | 0 |
| Total Lines | 25 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | #!/usr/bin/env python |
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| 2 | |||
| 3 | import sys |
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| 4 | import diff_classifier.knotlets as kn |
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| 5 | |||
| 6 | to_track = [] |
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| 7 | result_futures = {} |
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| 8 | |||
| 9 | remote_folder = '10_05_18_coverage' #Folder in AWS S3 containing files to be analyzed |
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| 10 | bucket = 'evanepst.data' |
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| 11 | vids = 10 |
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| 12 | pups = [2, 3] |
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| 13 | types = ['0_10xs', '0_15xs', '0_20xs', '0_25xs', '0_40xs', '0_50xs', '0_60xs', '0_75xs', '1xs', 'PSCOOH'] |
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| 14 | for typ in types: |
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| 15 | for num in range(1, vids+1): |
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| 16 | #to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num)) |
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| 17 | to_track.append('5mM_{}_XY{}'.format(typ, '%02d' % num)) |
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| 18 | |||
| 19 | #to_track = [ '100x_0_4_0_6_gel_0_6_bulk_vid_5', |
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| 20 | # '100x_0_4_1_2_gel_0_4_bulk_vid_3'] |
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| 21 | |||
| 22 | for prefix in to_track[int(sys.argv[1]):int(sys.argv[2])]: |
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| 23 | kn.assemble_msds(prefix, remote_folder, bucket=bucket) |
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| 24 | print('Successfully output msds for {}'.format(prefix)) |
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| 25 | |||
| 27 |