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 = '1_7_19_P01_region_dependent_MPT' #Folder in AWS S3 containing files to be analyzed |
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10 | bucket = 'mckenna.data' |
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11 | vids = 5 |
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12 | inflams = ['PAM'] |
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13 | hemis = ['contra', 'ipsi'] |
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14 | regions = ['cc', 'cortex'] |
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15 | |||
16 | for inflam in inflams: |
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17 | for hemi in hemis: |
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18 | for region in regions: |
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19 | for num in range(1, vids+1): |
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20 | #to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num)) |
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21 | to_track.append('{}_{}_{}_vid_{}'.format(inflam, hemi, region, '%01d' % num)) |
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22 | |||
23 | #to_track = [ '100x_0_4_0_6_gel_0_6_bulk_vid_5', |
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24 | # '100x_0_4_1_2_gel_0_4_bulk_vid_3'] |
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25 | |||
26 | for prefix in to_track[int(sys.argv[1]):int(sys.argv[2])]: |
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27 | kn.split(prefix, remote_folder, bucket=bucket) |
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28 | print('Successfully output subimages for {}'.format(prefix)) |
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29 | |||
30 | #kn.assemble_msds(sys.argv[1], remote_folder, bucket=bucket) |
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31 |