def download_and_calc_MSDs(prefix):
import diff_classifier.aws as aws
import diff_classifier.utils as ut
import diff_classifier.msd as msd
import diff_classifier.features as ft
import os
import os.path as op
import numpy as np
import numpy.ma as ma
import pandas as pd
remote_folder = "01_18_Experiment/{}".format(prefix.split('_')[0])
local_folder = os.getcwd()
ires = 512
for row in range(0, 4):
for col in range(0, 4):
filename = "Traj_{}_{}_{}.csv".format(prefix, row, col)
to_download = remote_folder+'/'+filename
local_name = local_folder+'/'+filename
aws.download_s3(to_download, local_name)
if row==0 and col==0:
merged = msd.all_msds(ut.csv_to_pd(local_name))
else:
to_add = ut.csv_to_pd(local_name)
to_add['X'] = to_add['X'] + ires*row
to_add['Y'] = to_add['Y'] + ires*col
to_add['Track_ID'] = to_add['Track_ID'] + max(merged['Track_ID'])
merged.append(msd.all_msds(to_add))
print('Successfully downloaded and calculated MSDs for {}_{}_{}'.format(prefix, row, col))
merged.to_csv('MSD_{}.csv'.format(prefix))
print('Saved MSDs as MSD_{}.csv'.format(prefix))
merged_ft = ft.calculate_features(merged)
merged_ft.to_csv('features_{}.csv'.format(prefix))
print('Saved features as features_{}.csv'.format(prefix))
def test_all_msds():
d = {
'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5],
'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6]
}
df = pd.DataFrame(data=d)
di = {
'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'Track_ID': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2],
'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5],
'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6],
'MSDs': [float(i) for i in [0, 2, 8, 18, 32, 0, 2, 8, 18, 32]],
'Gauss': [0, 0.25, 0.25, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25]
}
cols = ['Frame', 'Track_ID', 'X', 'Y', 'MSDs', 'Gauss']
dfi = pd.DataFrame(data=di)[cols]
pdt.assert_frame_equal(dfi, all_msds(df))