def collapse_data(data_dir, incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
"""
collapse_data reads in files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
n_b0s_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#------------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
#==========================================================================
print ' Collapsing data: B0 orders by ec volume'
# Find all the results files in all the b0_order folders
for incl_excl, n_b0s, sep_av, transform, roi_name in it.product(incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = np.genfromtxt(file, dtype=None, delimiter=' ', names=True)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'ALL_ORDERS', sep_av, transform)
return data_allorders, results_allorders_dir
def Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the volume that you register to affect the measurement
when you use all the data"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the choice of eddy correct volume affect the measurements?'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(6))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'ALL_ORDERS', sep_av, transform)
# Now plot the data
plot_data(data_allorders, results_allorders_dir, roi_name, colors, shapes)
scans = [ 'DTI_2A', 'DTI_2B' ]
incl_excl_list = [ 'EXCL_VOL31' ]
n_b0s_list = [ '6' ]
ec_b0_list = [ '00', '14', '27', '41', '54', '68' ]
#b0_order = [ '00_14_27_41_54_68' ]
sep_av_list = [ 'SEPARATE' ]
transform_list = [ 'MNI_DIFF_FA_DIRECT', 'MNI_DIFF_VIA_HIGHRES_LIN', 'MNI_DIFF_VIA_HIGHRES_NL_BBR' ]
roi_list = [ 'lcing', 'rcing', 'wholebrain', 'bodycc' ]
data_allorders = None
colors, shapes = set_colors_shapes()
#==============================================================================
#==============================================================================
# And now get going:
b0_orders = get_b0_orders(1)
for incl_excl, sep_av, b0_order in it.product(incl_excl_list, sep_av_list, b0_orders):
'''
Loop through the combinations of include/exclude volume 31,
whether the b0s were combined first or not,
and the ways of ordering just 1 b0 (6)
'''
run_registrations(data_dir, incl_excl, 1, b0_order, sep_av, subs, locs, scans)
for incl_excl, sep_av, n_b0s in it.product(incl_excl_list, sep_av_list, n_b0s_list):
'''
Loop through the combinations of include/exclude volume 31,
whether the b0s were combined first or not,
and the number of b0s that were included
'''
ec_b0_list = ['00', '14', '27', '41', '54', '68']
#b0_order = [ '00_14_27_41_54_68' ]
sep_av_list = ['SEPARATE']
transform_list = [
'MNI_DIFF_FA_DIRECT', 'MNI_DIFF_VIA_HIGHRES_LIN',
'MNI_DIFF_VIA_HIGHRES_NL_BBR'
]
roi_list = ['lcing', 'rcing', 'wholebrain', 'bodycc']
data_allorders = None
colors, shapes = set_colors_shapes()
#==============================================================================
#==============================================================================
# And now get going:
b0_orders = get_b0_orders(1)
for incl_excl, sep_av, b0_order in it.product(incl_excl_list, sep_av_list,
b0_orders):
'''
Loop through the combinations of include/exclude volume 31,
whether the b0s were combined first or not,
and the ways of ordering just 1 b0 (6)
'''
run_registrations(data_dir, incl_excl, 1, b0_order, sep_av, subs, locs,
scans)
for incl_excl, sep_av, n_b0s in it.product(incl_excl_list, sep_av_list,
n_b0s_list):
'''
Loop through the combinations of include/exclude volume 31,
def Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list,
ec_b0_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the number of B0s you include change your measurement?"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the number of B0s change your measurement'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name, ec_b0 in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1, 7):
b0_orders = get_b0_orders(np.int(n_b0s))
b0_orders = [order for order in b0_orders if order[:2] == ec_b0]
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order),
sep_av, transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order, 'n_b0s': n_b0s}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(
data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS',
incl_excl, 'ALL_B0S',
'B0_{}'.format(ec_b0),
sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir,
roi_name, colors, shapes)
# Now do the same thing, but with REALLY all the B0s
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1, 7):
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order),
sep_av, transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order, 'n_b0s': n_b0s}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(
data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS',
incl_excl, 'ALL_B0S',
'ALL_B0S', sep_av,
transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir,
roi_name, colors, shapes)
def collapse_data(data_dir, incl_excl_list, n_b0s_list, sep_av_list,
transform_list, roi_list):
"""
collapse_data reads in files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
n_b0s_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#------------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
#==========================================================================
print ' Collapsing data: B0 orders by ec volume'
# Find all the results files in all the b0_order folders
for incl_excl, n_b0s, sep_av, transform, roi_name in it.product(
incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av,
transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = np.genfromtxt(file,
dtype=None,
delimiter=' ',
names=True)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS',
incl_excl,
'B0S_{}'.format(n_b0s),
'ALL_ORDERS', sep_av,
transform)
return data_allorders, results_allorders_dir
def Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list,
roi_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the volume that you register to affect the measurement
when you use all the data"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the choice of eddy correct volume affect the measurements?'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(6))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'B0_ORDER_{}'.format(b0_order), sep_av,
transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_6', 'ALL_ORDERS', sep_av,
transform)
# Now plot the data
plot_data(data_allorders, results_allorders_dir, roi_name, colors,
shapes)
def Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the number of B0s you include change your measurement?"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the number of B0s change your measurement'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name, ec_b0 in it.product(incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1,7):
b0_orders = get_b0_orders(np.int(n_b0s))
b0_orders = [ order for order in b0_orders if order[:2] == ec_b0 ]
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = { 'b0_order': b0_order, 'n_b0s' : n_b0s }
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'ALL_B0S',
'B0_{}'.format(ec_b0), sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir, roi_name, colors, shapes)
# Now do the same thing, but with REALLY all the B0s
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1,7):
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = { 'b0_order': b0_order, 'n_b0s' : n_b0s }
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'ALL_B0S',
'ALL_B0S', sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir, roi_name, colors, shapes)
