def main():
patient_number = "N011618"
noddi_data = noddistudy.NoddiData(patient_number)
# data_full = noddi_data.get_full()
data_odi = noddi_data.get_odi()
# data_fiso = noddi_data.get_fiso()
data_odi = data_odi.transpose(2,0,1)[:,:,:,None]
data_odi = np.concatenate((data_odi,data_odi,data_odi),
axis=3)
data_odi = tf.convert_to_tensor(data_odi)
perceptual_loss(data_odi,data_odi)
directions = [128, 64, 32, 24, 16, 8]
### MAIN LOOP ###
for num_directions in directions:
print("Generating 1D data for %i directions" % num_directions)
subsampling_indices = subsampling.gensamples(num_directions)
num_cases = len(patient_database)
print("\n")
mm = 0
for patient_number in sorted(patient_database.keys()):
noddi_data = noddistudy.NoddiData(patient_number)
if (noddi_data.get_type() == "test"
or noddi_data.get_type() == "duplicate"):
continue
print("Currently reading: %s as %s data" %
(patient_number, noddi_data.get_type()))
data_full = noddi_data.get_full()[:, :, 2:(-1 - 3), :]
data_odi = noddi_data.get_odi()[:, :, 2:(-1 - 3)]
data_fiso = noddi_data.get_fiso()[:, :, 2:(-1 - 3)]
data_ficvf = noddi_data.get_ficvf()[:, :, 2:(-1 - 3)]
data_gfa = noddi_data.get_gfa()[:, :, 2:(-1 - 3)]
data_subsampled = data_full[:, :, :, subsampling_indices]
def gensamples(num_samples,
patient_number="N011618",
shuffle=False,
verbose=False,
plot=False,
random_seed=500):
"""Sampling pattern generator for the directions in DSI study.
Parameters
----------
num_samples: int
Number of directions to be sampled
patient_number: str
String of the patient number used in the path
shuffle: Bool
Whether or not to suffle the sampling list
verbose: Bool
If the sampling is printed out when called
plot: Bool
If a normalized test example is printed
Returns
-------
subsampling: array_like
1D array of the sampling indices
"""
path_to_bvals = ("/v/raid1b/khodgson/MRIdata/DTI/CNC_Imris/"
"Stroke_patients/Stroke_DSI_Processing/Data/Bvals")
patient_number = "N011618"
filename_bvals = "%s/%s_bvals.txt" % (path_to_bvals, patient_number)
with open(filename_bvals) as bvalue_file:
line = []
for bvalue in bvalue_file.readlines()[0].split():
line.append(int(float(bvalue)))
bvalues = np.array(line)
path_to_bvecs = ("/v/raid1b/khodgson/MRIdata/DTI/CNC_Imris/"
"Stroke_patients/Stroke_DSI_Processing/Data/Bvecs")
patient_number = "N011618"
filename_bvecs = "%s/%s_original_bvecs.txt" % (path_to_bvecs,
patient_number)
directions = np.zeros((bvalues.shape[0], 3))
with open(filename_bvecs) as bvecs_file:
lines = bvecs_file.readlines()
for ii in range(len(lines)):
for jj in range(bvalues.shape[0]):
directions[jj, ii] = bvalues[jj] * float(lines[ii].split()[jj])
# now divide the directions into shells
shell1_divide = 1000
shell2_divide = 2000
bvalue_dict = {}
bvalue_dict["b0"] = []
bvalue_dict["shell1"] = []
bvalue_dict["shell2"] = []
bvalue_dict["shell3"] = []
for ii in range(bvalues.shape[0]):
if bvalues[ii] == 0:
bvalue_dict["b0"].append(ii)
elif (0 < bvalues[ii]) and (bvalues[ii] <= shell1_divide):
bvalue_dict["shell1"].append(ii)
elif (shell1_divide < bvalues[ii]) and (bvalues[ii] <= shell2_divide):
bvalue_dict["shell2"].append(ii)
elif (shell2_divide < bvalues[ii]):
bvalue_dict["shell3"].append(ii)
if random_seed < 500:
num_b0 = round(num_samples * len(bvalue_dict["b0"]) / bvalues.shape[0])
num_shell1 = round(num_samples * len(bvalue_dict["shell1"]) /
bvalues.shape[0])
num_shell2 = round(num_samples * len(bvalue_dict["shell2"]) /
bvalues.shape[0])
num_shell3 = num_samples - (num_b0 + num_shell1 + num_shell2)
np.random.seed(seed=random_seed)
b0 = np.random.choice(bvalue_dict["b0"], num_b0, replace=False)
shell1 = np.random.choice(bvalue_dict["shell1"],
num_shell1,
replace=False)
shell2 = np.random.choice(bvalue_dict["shell2"],
num_shell2,
replace=False)
shell3 = np.random.choice(bvalue_dict["shell3"],
num_shell3,
replace=False)
subsampling = np.concatenate((b0, shell1, shell2, shell3), axis=0)
else:
num_b0 = round(num_samples * len(bvalue_dict["b0"]) / bvalues.shape[0])
num_shell1 = round(num_samples * len(bvalue_dict["shell1"]) /
bvalues.shape[0])
num_shell2 = num_samples - (num_b0 + num_shell1)
np.random.seed(seed=random_seed)
b0 = np.random.choice(bvalue_dict["b0"], num_b0, replace=False)
shell1 = np.random.choice(bvalue_dict["shell1"],
num_shell1,
replace=False)
shell2 = np.random.choice(bvalue_dict["shell2"],
num_shell2,
replace=False)
subsampling = np.concatenate((b0, shell1, shell2), axis=0)
directions_used = np.zeros((num_samples, 3))
directions_total = np.zeros((directions.shape[0] - num_samples, 3))
jj = 0
kk = 0
for ii in range(directions.shape[0]):
if ii in subsampling:
directions_used[jj, :] = directions[ii, :]
jj += 1
else:
directions_total[kk, :] = directions[ii, :]
kk += 1
assert jj == num_samples
if shuffle is True:
np.random.shuffle(subsampling)
if verbose is True:
print("b0 sampling:")
print(b0)
print("shell1 sampling:")
print(shell1)
print("shell2 sampling:")
print(shell2)
print("shell3 sampling:")
print(shell3)
print("num_samples")
print(subsampling)
if plot is True:
study = noddistudy.NoddiData(patient_number)
data_full = study.get_full()
data = data_full[:, :, :, subsampling]
maxs = np.amax(data.reshape(-1, num_samples), axis=0).squeeze()
print(maxs)
data /= maxs[None, None, None, :]
plt.figure()
display.Render(data[:, :, 25, :])
plt.show()
return directions_used, directions_total
from matplotlib import pyplot as plt
import json
import os
import sys
import time
from utils import display
from utils import readhdf5
sys.path.append("/home/mirl/egibbons/noddi")
from noddi_utils import noddistudy
metadata_name = "/home/mirl/egibbons/noddi/noddi_utils/noddi_metadata.json"
with open(metadata_name) as metadata:
database = json.load(metadata)
print(database.keys())
for patient_name in database.keys():
study = noddistudy.NoddiData(patient_name)
data = study.get_full()
plt.figure()
display.Render(data[:, :, 25, :])
plt.show()
# list_names = noddistudy.NoddiData()
