# coding: utf-8

# # Ferret DWI tractography

# In[1]:

get_ipython().magic(u'pylab')

# ## Imports & Plotting function

# In[2]:

from __future__ import division, print_function, absolute_import

import numpy as np

import nibabel as nib

from dipy.reconst.peaks import peaks_from_model, PeaksAndMetrics

from dipy.core.sphere import Sphere

from dipy.data import get_sphere

from dipy.io.gradients import read_bvals_bvecs

from dipy.core.gradients import gradient_table

from dipy.tracking import utils

from common import load_nifti, save_trk, save_peaks, load_peaks, save_trk_old_style

from dipy.viz import actor, window, fvtk

#from ipdb import set_trace

def show_results(streamlines, vol, affine, world_coords=True, opacity=0.6):

del show_m

# ### Simple viewers

# In[3]:

def simple_viewer(streamlines, vol, affine):

window.show(renderer)

# In[4]:

def show_gradients(gtab):

window.show(renderer)

# ## Tracking set of functions

# The following set of code allows to do the tracktography of Diffusion Weighted Images.

#

# The inputs necessary are:

# * a nifti file of the raw data

# * its associated bvals and bvecs

# * (optional) a mask in nifti

#

# Output:

# * FA, evecs & rgb nifti files

# * .trk file containing the streamlines

# * (annexes) image (png) of the tensor using elipsoids

#

# Parameters:

# * own mask or masking based on the DWI data (median_otsu)

# * type of model used. Here TensorModel (DTI). Can also be ConstrainedSphericalDeconvModel (CSD) or others

# * type of fiting of the model. Here (by default) Weighted Least Square (WLS). Can be Ordinary Least Square (OLS) or others

# * seed density. To increase the number of streamlines

# * threshold of the streamline length filter

# ### Input file paths and loading

# In[31]:

dname = '/Volumes/Samsung_T1/dti/Dipy/P32_F16/'

fdwi = dname + '2dseq.src.gz.nii.gz' #nii from 2dseq in dsi_studio

data, affine = load_nifti(fdwi)

fbval = dname + 'bvals' #from dsi_studio

fbvec = dname + 'bvecs'

bvals, bvecs = read_bvals_bvecs(fbval, fbvec)

gtab = gradient_table(bvals, bvecs, b0_threshold=50)

fmask = None #dname + 'mask_extern.nii' #None if wish to get an automated mask from dwi data

# In[65]:

#checks for gtab

#show_gradients(gtab)

np.sum(gtab.b0s_mask)

print(gtab.info)

plot(np.sort(gtab.bvals))

# ### Hand-made mask or autogenerated mask from the DWI data

# In[32]:

if fmask is None:

from dipy.segment.mask import median_otsu

b0_mask, mask = median_otsu(data) # TODO: check parameters to improve the mask

else:

mask, mask_affine = load_nifti(fmask)

mask = np.squeeze(mask) #fix mask dimensions

# ### DTI Model computation and fitting (further for CSD--not tested)

# In[33]:

# compute DTI model

from dipy.reconst.dti import TensorModel

tenmodel = TensorModel(gtab)#, fit_method='OLS') #, min_signal=5000)

# In[34]:

# fit the dti model

tenfit = tenmodel.fit(data, mask=mask)

# ### DWI indicators computation and saving in nifti files (FA, first eigen vector, rgb tensor)

# In[35]:

# save fa

ffa = dname + 'tensor_fa.nii.gz'

fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)

nib.save(fa_img, ffa)

# In[10]:

# save first eigen vector

evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)

nib.save(evecs_img, dname+'tensor_evecs.nii.gz')

# In[32]:

# compute and save rgb

from dipy.reconst.dti import color_fa

RGB = color_fa(tenfit.fa, tenfit.evecs)

nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine), dname+'tensor_rgb.nii.gz')

# In[ ]:

# In[36]:

sh_order = 8 #TODO: check what that does

if data.shape[-1] < 15:

raise ValueError('You need at least 15 unique DWI volumes to '

'compute fiber ODFs. You currently have: {0}'

' DWI volumes.'.format(data.shape[-1]))

elif data.shape[-1] < 30:

sh_order = 6

# In[37]:

# compute the response equation ?

from dipy.reconst.csdeconv import auto_response

response, ratio = auto_response(gtab, data)

response = list(response)

# In[35]:

# for CSD

#from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel

#model = ConstrainedSphericalDeconvModel(gtab, response,

#sh_order=sh_order)

# In[ ]:

# In[38]:

peaks_sphere = get_sphere('symmetric362')

#TODO: check what that does

peaks_csd = peaks_from_model(model=tenmodel,

parallel=False)

# In[39]:

peaks_csd.affine = affine

fpeaks = dname + 'peaks.npz'

save_peaks(fpeaks, peaks_csd)

# In[40]:

from dipy.io.trackvis import save_trk

from dipy.tracking import utils

from dipy.tracking.local import (ThresholdTissueClassifier,

LocalTracking)

stopping_thr = 0.25 #0.25

pam = load_peaks(fpeaks)

#ffa = dname + 'tensor_fa_nomask.nii.gz'

fa, fa_affine = load_nifti(ffa)

classifier = ThresholdTissueClassifier(fa,

stopping_thr)

# In[41]:

# seeds

seed_density = 1

seed_mask = fa > 0.4 #0.4 #TODO: check this parameter

seeds = utils.seeds_from_mask(

seed_mask,

density=seed_density,

affine=affine)

# In[ ]:

# In[42]:

#TODO: check what that does

#if use_sh:

# detmax_dg = \

# DeterministicMaximumDirectionGetter.from_shcoeff(

# pam.shm_coeff,

# max_angle=30.,

# sphere=pam.sphere)

#

# streamlines = \

# LocalTracking(detmax_dg, classifier, seeds, affine,

# step_size=.5)

#

#else:

# tractography, if affine then in world coordinates

streamlines = LocalTracking(pam, classifier,

seeds, affine=affine, step_size=.5)

# Compute streamlines and store as a list.

streamlines = list(streamlines)

#ftractogram = dname + 'tractogram.trk'

#save .trk

#save_trk_old_style(ftractogram, streamlines, affine, fa.shape)

#render

show_results(streamlines, fa, fa_affine)

# In[18]:

# threshold on streamline length

from dipy.tracking.utils import length

lengths = list(length(streamlines))

new_streamlines = [ s for s, l in zip(streamlines, lengths) if l > 2. ] #3.5

# In[19]:

# info length streamlines

print(len(streamlines))

print(len(new_streamlines))

print(max(length(streamlines)))

print(min(length(streamlines)))

print(max(length(new_streamlines)))

print(min(length(new_streamlines)))

# In[20]:

# show new tracto

new_streamlines = list(new_streamlines)

new_lengths = list(length(new_streamlines))

fnew_tractogram = dname + 'filteredtractogram.trk'

save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape)

show_results(new_streamlines, fa, fa_affine, opacity=0.6)

# In[161]:

# In[43]:

import matplotlib.pyplot as plt

fig_hist, ax = plt.subplots(1)

ax.hist(lengths, color='burlywood')

ax.set_xlabel('Length')

ax.set_ylabel('Count')

plt.show()

plt.legend()

#plt.savefig('length_histogram.png')

# In[ ]:

# ## Annexes

# In[ ]:

# In[30]:

print('Computing tensor ellipsoids in a part of the splenium of the CC')

from dipy.data import get_sphere

sphere = get_sphere('symmetric724')

from dipy.viz import fvtk

ren = fvtk.ren()

evals = tenfit.evals[13:43, 44:74, 28:29]

evecs = tenfit.evecs[13:43, 44:74, 28:29]

# In[31]:

cfa = RGB[13:43, 44:74, 28:29]

cfa /= cfa.max()

fvtk.add(ren, fvtk.tensor(evals, evecs, cfa, sphere))

print('Saving illustration as tensor_ellipsoids.png')

fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))

# In[ ]:

# In[27]:

# first endpoint of the first streamline

streamlines[0][0]

# In[28]:

# other endpoint of the first streamline

streamlines[0][-1]

# In[ ]: