def test_outlierfunction():
# Test outlierfunction
# Test data
x = np.random.normal(size=(1000))
expected = np.where(x>2)[0]
actual = np.ravel(outlier(x, 2))
# Did you forget to return the value?
if actual is None:
raise RuntimeError("function returned None")
assert_allclose(expected, actual)
def test_outlierfunction():
# Test outlierfunction
# Test data
x = np.random.normal(size=(1000))
expected = np.where(x > 2)[0]
actual = np.ravel(outlier(x, 2))
# Did you forget to return the value?
if actual is None:
raise RuntimeError("function returned None")
assert_allclose(expected, actual)
import numpy as np
from outlierfunction import outlier
for i in range(1,10):
for j in range(1,4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath='ds005/sub00'+`i`+'/BOLD/task001_run00'+`j`+'/QA/'
# dvars path and name, call function to get outliers
dvarsfile=txtpath+'dvars.txt'
dvarssave=txtpath+'dvars_outlier_sub'+`i`+'run'+`j`+'.txt'
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
dvars_outlier = outlier(dvarsfile,dvarssave, 0.5)
# fd path and name, call function to get outliers
fdfile=txtpath+'fd.txt'
fdsave=txtpath+'fd_outlier_sub'+`i`+'run'+`j`+'.txt'
# common boundary for fd is 0.2 - 0.5
# paper used 0.5
fd_outlier = outlier(fdfile, fdsave, 0.5)
for i in range(10,17):
for j in range(1,4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath='ds005/sub0'+`i`+'/BOLD/task001_run00'+`j`+'/QA/'
# dvars path and name, call function to get outliers
dvarsfile=txtpath+'dvars.txt'
dvarssave=txtpath+'dvars_outlier_sub'+`i`+'run'+`j`+'.txt'
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
#This script calculates the outliers for subjects runs based on fd and dvars
dvars_out = {}
fd_out = {}
for i in range(1,10):
for j in range(1,4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath='../../data/ds005/sub00'+`i`+'/BOLD/task001_run00'+`j`+'/QA/'
# dvars path and name, call function to get outliers
dvarsfile=txtpath+'dvars.txt'
dvars_data = np.loadtxt(dvarsfile)
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
dvars_outlier = outlier(dvars_data, 0.5)
dvars_out['sub'+`i`+'run'+`j`] = dvars_outlier[0].tolist()
# fd path and name, call function to get outliers
fdfile=txtpath+'fd.txt'
fd_data = np.loadtxt(fdfile)
# common boundary for fd is 0.2 - 0.5
# paper used 0.5
fd_outlier = outlier(fd_data, 0.5)
fd_out['sub'+`i`+'run'+`j`] = fd_outlier[0].tolist()
for i in range(10,17):
for j in range(1,4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath='../../data/ds005/sub0'+`i`+'/BOLD/task001_run00'+`j`+'/QA/'
# dvars path and name, call function to get outliers
from outlierfunction import outlier
dvars_out = {}
fd_out = {}
for i in range(1, 10):
for j in range(1, 4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath = '../../data/ds005/sub00' + ` i ` + '/BOLD/task001_run00' + ` j ` + '/QA/'
# dvars path and name, call function to get outliers
dvarsfile = txtpath + 'dvars.txt'
dvars_data = np.loadtxt(dvarsfile)
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
dvars_outlier = outlier(dvars_data, 0.5)
dvars_out['sub' + ` i ` + 'run' + ` j `] = dvars_outlier[0].tolist()
# fd path and name, call function to get outliers
fdfile = txtpath + 'fd.txt'
fd_data = np.loadtxt(fdfile)
# common boundary for fd is 0.2 - 0.5
# paper used 0.5
fd_outlier = outlier(fd_data, 0.5)
fd_out['sub' + ` i ` + 'run' + ` j `] = fd_outlier[0].tolist()
for i in range(10, 17):
for j in range(1, 4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath = '../../data/ds005/sub0' + ` i ` + '/BOLD/task001_run00' + ` j ` + '/QA/'
# dvars path and name, call function to get outliers
import numpy as np
from outlierfunction import outlier
for i in range(1, 10):
for j in range(1, 4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath = 'ds005/sub00' + ` i ` + '/BOLD/task001_run00' + ` j ` + '/QA/'
# dvars path and name, call function to get outliers
dvarsfile = txtpath + 'dvars.txt'
dvarssave = txtpath + 'dvars_outlier_sub' + ` i ` + 'run' + ` j ` + '.txt'
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
dvars_outlier = outlier(dvarsfile, dvarssave, 0.5)
# fd path and name, call function to get outliers
fdfile = txtpath + 'fd.txt'
fdsave = txtpath + 'fd_outlier_sub' + ` i ` + 'run' + ` j ` + '.txt'
# common boundary for fd is 0.2 - 0.5
# paper used 0.5
fd_outlier = outlier(fdfile, fdsave, 0.5)
for i in range(10, 17):
for j in range(1, 4):
# set general path for reaching dvars and fd files
# also path for saving files
txtpath = 'ds005/sub0' + ` i ` + '/BOLD/task001_run00' + ` j ` + '/QA/'
# dvars path and name, call function to get outliers
dvarsfile = txtpath + 'dvars.txt'
dvarssave = txtpath + 'dvars_outlier_sub' + ` i ` + 'run' + ` j ` + '.txt'
# common boundary for dvars is 0.3 - 0.5
# paper used 0.5
