In [1]:
#!/usr/bin/env python
# Copyright 2016 NeuroData (http://neurodata.io)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# qa_tensor.py
# Created by Vikram Chandrashekhar.
# Edited by Greg Kiar.
# Email: Greg Kiar @ gkiar@jhu.edu
from dipy.reconst.dti import fractional_anisotropy, color_fa
from argparse import ArgumentParser
from scipy import ndimage
import os
import re
import numpy as np
import nibabel as nb
import sys
import matplotlib
matplotlib.use('Agg') # very important above pyplot import
import matplotlib.pyplot as plt
def tensor2fa(tensors, tensor_name, dti, derivdir, qcdir):
'''
outdir: location of output directory.
fname: name of output fa map file. default is none (name created based on
input file)
'''
dti_data = nb.load(dti)
affine = dti_data.get_affine()
dti_data = dti_data.get_data()
# create FA map
FA = fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
# generate the RGB FA map
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tensors.evecs)
fname = os.path.split(tensor_name)[1].split(".")[0] + '_fa_rgb.nii.gz'
fa = nb.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nb.save(fa, derivdir + fname)
fa_pngs(fa, fname, qcdir)
def fa_pngs(data, fname, outdir):
'''
data: fa map
'''
im = data.get_data()
fig = plot_rgb(im)
fname = os.path.split(fname)[1].split(".")[0] + '.png'
plt.savefig(outdir + fname, format='png')
def plot_rgb(im):
plt.rcParams.update({'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large'})
if im.shape == (182, 218, 182):
x = [78, 90, 100]
y = [82, 107, 142]
z = [88, 103, 107]
else:
shap = im.shape
x = [int(shap[0]*0.35), int(shap[0]*0.51), int(shap[0]*0.65)]
y = [int(shap[1]*0.35), int(shap[1]*0.51), int(shap[1]*0.65)]
z = [int(shap[2]*0.35), int(shap[2]*0.51), int(shap[2]*0.65)]
coords = (x, y, z)
labs = ['Sagittal Slice (YZ fixed)',
'Coronal Slice (XZ fixed)',
'Axial Slice (XY fixed)']
var = ['X', 'Y', 'Z']
idx = 0
for i, coord in enumerate(coords):
for pos in coord:
idx += 1
ax = plt.subplot(3, 3, idx)
ax.set_title(var[i] + " = " + str(pos))
if i == 0:
image = ndimage.rotate(im[pos, :, :], 90)
elif i == 1:
image = ndimage.rotate(im[:, pos, :], 90)
else:
image = im[:, :, pos]
if idx % 3 == 1:
ax.set_ylabel(labs[i])
ax.yaxis.set_ticks([0, image.shape[0]/2, image.shape[0] - 1])
ax.xaxis.set_ticks([0, image.shape[1]/2, image.shape[1] - 1])
plt.imshow(image)
fig = plt.gcf()
fig.set_size_inches(12.5, 10.5, forward=True)
return fig
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from dipy.reconst.dti import fractional_anisotropy, color_fa
from argparse import ArgumentParser
from scipy import ndimage
import os
import re
import numpy as np
import nibabel as nb
import sys
import matplotlib
matplotlib.use('Agg') # very important above pyplot import
import matplotlib.pyplot as plt
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img = nb.load('../../../../../Desktop/result/dogsig1_gausig2.3/v100_ch0_tensorfsl_dogsig1_gausig2.3.nii')
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img.shape
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data = img.get_data()
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from dipy.segment.mask import median_otsu
maskdata, mask = median_otsu(data, 3, 1)
print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
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import dipy.reconst.dti as dti
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img3 = nb.load('../../../../../Desktop/result/v100_ch0_gv_dogsig1.nii')
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img3.shape
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data3 = img3.get_data()
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data3_slice1 = data3[:, :, 0, 0]
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print data3_slice1.shape
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data3_slice1_eigvec = data3[:, :, 0, :]
print data3_slice1_eigvec.shape
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FA = fractional_anisotropy(data3_slice1_eigvec)
##????
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FA[np.isnan(FA)] = 0
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print FA.shape
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abc = np.ones((5000, 5000))
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FA.shape
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v.shape[-2:]
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if (FA.shape != v.shape) or ((3, 3) != v.shape[-2:]):
print 'help'
if (FA.shape != v.shape):
print 'yes'
if ((3,3) != v.shape[-2:]):
print 'oh no'
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RGB = color_fa(FA, v)
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from numpy import linalg as LA
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w, v = LA.eigh(data3_slice1)
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print v.shape
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print w.shape
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img2 = nb.load('../../../../../Desktop/result/dogsig1_gausig2.3/v100_ch0_tensordtk_dogsig1_gausig2.3_tensor.nii')
img2.shape
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from_lower_triangular(img)