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 |