def test_precision_weight():
experiments = []
geomean = {}
geostder = {}
for num in range(4):
name = 'test_{}'.format(num)
experiments.append(name)
data1 = {
'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
'X': [x * (num + 1) for x in [5, 6, 7, 8, 9, 2, 4, 6, 8, 10]],
'Y': [x * (num + 1) for x in [6, 7, 8, 9, 10, 6, 8, 10, 12, 14]],
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}
df = pd.DataFrame(data=data1)
msds = msd.all_msds2(df)
msds.to_csv('msd_test_{}.csv'.format(num))
geomean[name], geostder[name] = msd.geomean_msdisp(name,
umppx=1,
fps=1,
upload=False)
slices, bins, bin_names = msd.binning(experiments, wells=1)
weights, w_holder = msd.precision_weight(experiments, geostder)
weights_t = np.array([8.3, 8.3, 8.3, 8.3])
npt.assert_equal(np.round(weights[weights.mask == False].data, 1),
weights_t)
def test_geomean_msdisp():
data1 = {
'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
'X': [5, 6, 7, 8, 9, 1, 2, 3, 4, 5],
'Y': [6, 7, 8, 9, 10, 2, 3, 4, 5, 6],
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}
geomean_t = np.array([2., 8., 18., 32.])
geostder_t = np.array([])
df = pd.DataFrame(data=data1)
msds = msd.all_msds2(df)
msds.to_csv('msd_test.csv')
geomean, geostder = msd.geomean_msdisp('test',
umppx=1,
fps=1,
upload=False)
npt.assert_equal(np.round(np.exp(geomean[geomean.mask == False].data), 1),
geomean_t)
npt.assert_equal(
np.round(np.exp(geostder[geostder.mask == False].data), 1), geostder_t)
# test 2
data1 = {
'Frame': [1, 2, 1, 2],
'Track_ID': [1, 1, 2, 2],
'X': [1, 2, 3, 4],
'Y': [1, 2, 3, 4],
'Quality': [10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10]
}
df = pd.DataFrame(data=data1)
msds = msd.all_msds2(df)
msds.to_csv('msd_test.csv')
geomean, geostder = msd.geomean_msdisp('test',
umppx=1,
fps=1,
upload=False)
npt.assert_equal(geomean, np.nan * np.ones(651))
npt.assert_equal(geostder, np.nan * np.ones(651))
# test 3
data1 = {
'Frame': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'Track_ID': [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
'X': [5, 6, 7, 8, 9, 2, 4, 6, 8, 10],
'Y': [6, 7, 8, 9, 10, 6, 8, 10, 12, 14],
'Quality': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
'SN_Ratio': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
'Mean_Intensity': [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
}
df = pd.DataFrame(data=data1)
geomean_t = np.array([4., 16., 36., 64.])
geostder_t = np.array([2., 2., 2., 2])
msds = msd.all_msds2(df)
msds.to_csv('msd_test.csv')
geomean, geostder = msd.geomean_msdisp('test',
umppx=1,
fps=1,
upload=False)
npt.assert_equal(np.round(np.exp(geomean[geomean.mask == False].data), 1),
geomean_t)
npt.assert_equal(
np.round(np.exp(geostder[geostder.mask == False].data), 1), geostder_t)
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):
#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))