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Metric Value
eloc 19
dl 0
loc 29
rs 10
c 0
b 0
f 0
wmc 0
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#!/usr/bin/env python
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import sys
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import diff_classifier.knotlets as kn
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to_track = []
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result_futures = {}
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remote_folder = '1_7_19_P01_region_dependent_MPT' #Folder in AWS S3 containing files to be analyzed
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bucket = 'mckenna.data'
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vids = 5
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inflams = ['PAM']
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hemis = ['contra', 'ipsi']
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regions = ['cc', 'cortex']
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for inflam in inflams:
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    for hemi in hemis:
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        for region in regions:
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            for num in range(1, vids+1):
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                #to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num))
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                to_track.append('{}_{}_{}_vid_{}'.format(inflam, hemi, region, '%01d' % num))
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#to_track = [ '100x_0_4_0_6_gel_0_6_bulk_vid_5',
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# 	     '100x_0_4_1_2_gel_0_4_bulk_vid_3']
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for prefix in to_track[int(sys.argv[1]):int(sys.argv[2])]:
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    kn.split(prefix, remote_folder, bucket=bucket)
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    print('Successfully output subimages for {}'.format(prefix))
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#kn.assemble_msds(sys.argv[1], remote_folder, bucket=bucket)
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