ccurtis7 / diff_classifier

notebooks/geoaverage.py

#!/usr/bin/env python

import turtle
import sys
import diff_classifier.knotlets as kn
import numpy as np
import diff_classifier.aws as aws
import diff_classifier.msd as msd

folder = '09_26_18_tissue_study'
bucket = 'hpontes.data'
#experiment = 'test' #Used for naming purposes. Should exclude XY and well information

#vids = 2
to_track = []
frames = 651
fps = 100.02
umppx = 0.07

vids = 5
covers = ['10K', '1K', '5K', 'COOH']
slices = [4, 5, 6]
for cover in covers:
    for slic in slices:
        for num in range(1, vids+1):

The variable range does not seem to be defined.

            #to_track.append('100x_0_4_1_2_gel_{}_bulk_vid_{}'.format(vis, num))
            to_track.append('{}_tissue_S{}_XY{}'.format(cover, slic, num))

geomean = {}
gSEM = {}
for sample_name in to_track[int(sys.argv[1]):int(sys.argv[2])]:
    # Users can toggle between using pre-calculated geomean files and calculating new values by commenting out the relevant
    # lines of code within the for loop.
    #aws.download_s3('{}/geomean_{}.csv'.format(folder, sample_name), 'geomean_{}.csv'.format(sample_name), bucket_name=bucket)
    #aws.download_s3('{}/geoSEM_{}.csv'.format(folder, sample_name), 'geoSEM_{}.csv'.format(sample_name), bucket_name=bucket)
    #geomean[sample_name] = np.genfromtxt('geomean_{}.csv'.format(sample_name))
    #gSEM[sample_name] = np.genfromtxt('geoSEM_{}.csv'.format(sample_name))

    aws.download_s3('{}/msd_{}.csv'.format(folder, sample_name), 'msd_{}.csv'.format(sample_name), bucket_name=bucket)
    geomean[sample_name], gSEM[sample_name] = msd.geomean_msdisp(sample_name, umppx=umppx, fps=fps,
                                                                 remote_folder=folder, bucket=bucket)
    print('Done with {}'.format(sample_name))