dif_to_trk.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 19 15:39:41 2020

@author: Jacques Stout
Main handler for taking dwi files and extracting trk estimations
Part of the DTC pipeline
"""

import numpy as np
import os
from time import time
from dipy.io.image import save_nifti
from dipy.reconst.shm import CsaOdfModel
from dipy.direction import peaks_from_model
from dipy.tracking.local_tracking import LocalTracking
from dipy.direction import peaks
# We must import this explicitly, it is not imported by the top-level
# multiprocessing module.
from dipy.tracking import utils
from BIAC_tools import send_mail, isempty
from tract_save import save_trk_heavy_duty
from dipy.io.utils import create_tractogram_header
from dipy.io.streamline import load_trk
import tract_save
from tract_handler import get_trk_params, get_tract_params
import glob
from dipy.tracking.stopping_criterion import BinaryStoppingCriterion, ThresholdStoppingCriterion
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
#from dipy.denoise.localpca import mppca
#import dipy.tracking.life as life
#import dipy.reconst.dti as dti
#from dipy.reconst.dti import fractional_anisotropy

from dipy.io.image import load_nifti
import matplotlib.pyplot as plt

from tract_handler import target, prune_streamlines
import nibabel as nib
from dipy.tracking.streamline import Streamlines



def save_roisubset(trkfile, roislist, roisexcel, labelmask):
    #loads trk file, list of rois, the full correspondance of structure => label and the label mask, and saves the
    # tracts traversing each region

    trkdata = load_trk(trkfile, 'same')
    trkdata.to_vox()
    if hasattr(trkdata, 'space_attribute'):
        header = trkdata.space_attribute
    elif hasattr(trkdata, 'space_attributes'):
        header = trkdata.space_attributes
    trkstreamlines = trkdata.streamlines
    import pandas as pd
    df = pd.read_excel(roisexcel, sheet_name='Sheet1')
    df['Structure'] = df['Structure'].str.lower()

    for rois in roislist:

        labelslist = []
        for roi in rois:
            rslt_df = df.loc[df['Structure'] == roi.lower()]
            if rois[0].lower() == "wholebrain" or rois[0].lower() == "brain":
                labelslist = None
            else:
                labelslist = np.concatenate((labelslist, np.array(rslt_df.index2)))
        print(labelslist)
        if isempty(labelslist) and roi.lower() != "wholebrain" and roi.lower() != "brain":
            txt = "Warning: Unrecognized roi, will take whole brain as ROI. The roi specified was: " + roi
            print(txt)

        if isempty(labelslist):
            if labelmask is None:
                roimask = (fdwi_data[:, :, :, 0] > 0)
            else:
                roimask = np.where(labelmask == 0, False, True)
        else:
            if labelmask is None:
                raise ("File not found error: labels requested but labels file could not be found at "+dwipath+ " for subject " + subject)
            roimask = np.zeros(np.shape(labelmask),dtype=int)
            for label in labelslist:
                roimask = roimask + (labelmask == label)

        trkroipath = trkfile.replace(".trk", "_" + rois + ".trk")
        if not os.path.exists(trkroipath):
            affinetemp = np.eye(4)
            trkroistreamlines = target(trkstreamlines, affinetemp, roimask, include=True, strict="longstring")
            trkroistreamlines = Streamlines(trkroistreamlines)
            myheader = create_tractogram_header(trkroipath, *header)
            roi_sl = lambda: (s for s in trkroistreamlines)
            tract_save.save_trk_heavy_duty(trkroipath, streamlines=roi_sl,
                                           affine=header[0], header=myheader)


def QCSA_tractmake(data, affine, vox_size, gtab, mask, masktype, header, step_size, peak_processes, outpathtrk, subject='NA',
                   ratio=1, overwrite=False, get_params=False, doprune=False, figspath=None, verbose=None, sftp=None):
    # Compute odfs in Brain Mask
    t2 = time()
    if os.path.isfile(outpathtrk) and not overwrite:
        txt = "Subject already saved at "+outpathtrk
        print(txt)
        streamlines_generator = None
        params = None
        return outpathtrk, streamlines_generator, params

    csa_model = CsaOdfModel(gtab, 6)
    if peak_processes == 1:
        parallel = False
    else:
        parallel = True
    if verbose:
        send_mail("Starting calculation of Constant solid angle model for subject " + subject,subject="CSA model start")

    wholemask = np.where(mask == 0, False, True)
    print(f"There are {peak_processes} and {parallel} here")
    csa_peaks = peaks_from_model(model=csa_model,
                                 data=data,
                                 sphere=peaks.default_sphere,  # issue with complete sphere
                                 mask=wholemask,
                                 relative_peak_threshold=.5,
                                 min_separation_angle=25,
                                 parallel=parallel,
                                 nbr_processes=peak_processes)

    duration = time() - t2
    if verbose:
        print(subject + ' CSA duration %.3f' % (duration,))

    t3 = time()


    if verbose:
        send_mail('Computing classifier for local tracking for subject ' + subject +
                  ',it has been ' + str(round(duration)) + 'seconds since the start of tractmaker',subject="Seed computation" )

        print('Computing classifier for local tracking for subject ' + subject)

    if masktype == "FA":
        #tensor_model = dti.TensorModel(gtab)
        #tenfit = tensor_model.fit(data, mask=labels > 0)
        #FA = fractional_anisotropy(tenfit.evals)
        FA_threshold = 0.05
        classifier = ThresholdStoppingCriterion(mask, FA_threshold)

        if figspath is not None:
            fig = plt.figure()
            mask_fa = mask.copy()
            mask_fa[mask_fa < FA_threshold] = 0
            plt.xticks([])
            plt.yticks([])
            plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower',
                       interpolation='nearest')
            fig.tight_layout()
            fig.savefig(figspath + 'threshold_fa.png')
    else:
        classifier = BinaryStoppingCriterion(wholemask)

    # generates about 2 seeds per voxel
    # seeds = utils.random_seeds_from_mask(fa > .2, seeds_count=2,
    #                                      affine=np.eye(4))

    # generates about 2 million streamlines
    # seeds = utils.seeds_from_mask(fa > .2, density=1,
    #                              affine=np.eye(4))

    if verbose:
        print('Computing seeds')
    seeds = utils.seeds_from_mask(wholemask, density=1,
                                  affine=np.eye(4))

    #streamlines_generator = local_tracking.local_tracker(csa_peaks,classifier,seeds,affine=np.eye(4),step_size=step_size)
    if verbose:
        print('Computing the local tracking')
        duration = time() - t2
        send_mail('Start of the local tracking ' + ',it has been ' + str(round(duration)) +
                  'seconds since the start of tractmaker', subject="Seed computation")

    #stepsize = 2 #(by default)
    stringstep = str(step_size)
    stringstep = stringstep.replace(".", "_")
    if verbose:
        print("stringstep is "+stringstep)

    streamlines_generator = LocalTracking(csa_peaks, classifier,
                                          seeds, affine=np.eye(4), step_size=step_size)

    if verbose:
        duration = time() - t2
        txt = 'About to save streamlines at ' + outpathtrk + ',it has been ' + str(round(duration)) + \
              'seconds since the start of tractmaker',
        send_mail(txt,subject="Tract saving" )

    cutoff = 2
    if doprune:
        streamlines_generator = prune_streamlines(list(streamlines_generator), data[:, :, :, 0], cutoff=cutoff,
                                                  verbose=verbose)
        myheader = create_tractogram_header(outpathtrk, *header)
        sg = lambda: (s for i, s in enumerate(streamlines_generator) if i % ratio == 0)
        save_trk_heavy_duty(outpathtrk, streamlines=sg,
                            affine=affine, header=myheader,
                            shape=mask.shape, vox_size=vox_size, sftp=sftp)
    else:
        sg = lambda: (s for i, s in enumerate(streamlines_generator) if i % ratio == 0)
        myheader = create_tractogram_header(outpathtrk, *header)
        save_trk_heavy_duty(outpathtrk, streamlines=sg,
                            affine=affine, header=myheader,
                            shape=mask.shape, vox_size=vox_size, sftp=sftp)
    if verbose:
        duration = time() - t2
        txt = "Tract files were saved at "+outpathtrk + ',it has been ' + str(round(duration)) + \
              'seconds since the start of tractmaker'
        print(txt)
        send_mail(txt,subject="Tract saving" )

    # save everything - will generate a 20+ GBytes of data - hard to manipulate

    # possibly add parameter in csv file or other to decide whether to save large tractogram file
    # outpathfile=outpath+subject+"bmCSA_detr"+stringstep+".trk"
    # myheader=create_tractogram_header(outpathfile,*get_reference_info(fdwi))
    duration3 = time() - t2
    if verbose:
        print(duration3)
        print(subject + ' Tracking duration %.3f' % (duration3,))
        send_mail("Finished file save at "+outpathtrk+" with tracking duration of " + str(duration3) + "seconds",
                  subject="file save update" )

    if get_params:
        numtracts, minlength, maxlength, meanlength, stdlength = get_trk_params(streamlines_generator, verbose)
        params = [numtracts, minlength, maxlength, meanlength, stdlength]
        if verbose:
            print("For subject " + str(subject) + " the number of tracts is " + str(numtracts) + ", the minimum length is " +
                  str(minlength) + ", the maximum length is " + str(maxlength) + ", the mean length is " +
                  str(meanlength) + ", the std is " + str(stdlength))
    else:
        params = None

    return outpathtrk, streamlines_generator, params