whether the b0s were combined first or not,
how the roi was transformed from standard space,
and all the rois
'''
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
print 'Combination: {} {} {} {} {} {}'.format(incl_excl, n_b0s, b0_order, sep_av, transform, roi_name)
results_file, results_dir = wrangle_text_files(data_dir, incl_excl, n_b0s,
b0_order, sep_av, transform,
roi_name, subs, locs, scans)
data = read_in_data(results_file)
plot_data(data, results_dir, roi_name, colors, shapes)
# Now answer specific questions that you care about
Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, colors, shapes)
# How does everything change with the different number of B0s?
Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list, colors, shapes)
'''
# 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):
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)
# Create deviations
RandomA = self.y_test.sample(1000)
RandomB = self.y_test.sample(1000)
deviations = abs(RandomA.values - RandomB.values)
mad = np.mean((deviations))
print("Mean Absolute Deviations: {}".format(mad))
# plot deviations
plt.hist(deviations, 50, density=True, range=(0, 1000),
Label='Absolute Deviations from data', alpha=0.4)
plt.vlines(mad, 0, 1, linewidth=4, color='k',
Label='Mean Absolute Deviations')
plt.gca().set_ylim([0, max(fig[0]) * 1.1])
plt.legend()
if __name__ == "__main__":
# %% ## Example Usage of the code.
df = read_in_data(
r'https://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip')
example_pipe = Pipeline(config)
example_pipe.create_test_training_sets(df)
example_pipe.hyper_parameter_search()
example_pipe.build_pipeline()
example_pipe.assess_model()
# Not implemented functionatliy.
# example_pipe.pickle_pipeline()
# example_pipe.retrain_model(df)
'''
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
print 'Combination: {} {} {} {} {} {}'.format(incl_excl, n_b0s,
b0_order, sep_av,
transform, roi_name)
results_file, results_dir = wrangle_text_files(data_dir, incl_excl,
n_b0s, b0_order, sep_av,
transform, roi_name,
subs, locs, scans)
data = read_in_data(results_file)
plot_data(data, results_dir, roi_name, colors, shapes)
# Now answer specific questions that you care about
Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list,
colors, shapes)
# How does everything change with the different number of B0s?
Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list,
ec_b0_list, colors, shapes)
'''
# 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):
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 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)