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#!/usr/bin/env python
"""
This example reproduces the ODFs in figure 6 and 7 from Paquette et al. 
"Optimal DSI reconstruction parameter recommendations: better ODFs and better connectivity".

The Optimal DSI (figure 7), is obtained using the parameters from the last
line of Tbl. 5 as the snr was estimated at 38 following an approach detailed 
in https://github.com/nipy/dipy/blob/master/doc/examples/snr_in_cc.py

Required python modules (with their respective reqs): 

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"""
Dirty example to display signal for individual shell of multi-shell as spherical function.
No background maps, just signal spheres.
Option to assume or not antipodal symmetry.
Assume the gradient table as at least 1 b0.
Uses so not very robust ad-hoc function to detect which sample belong to which shell.
"""

import numpy as np

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import numpy as np
import nibabel as nib


# shitty way to get 1 voxel of single-shell signal
from dipy.data import fetch_isbi2013_2shell, read_isbi2013_2shell
fetch_isbi2013_2shell()
img, gtab = read_isbi2013_2shell()
data = img.get_data()
del img

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import sys
import numpy as np
import nibabel as nib

import tractconverter as tc

from dipy.tracking.streamlinespeed import length, set_number_of_points

from dipy.tracking.vox2track import track_counts

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import argparse

import numpy as np

import nibabel as nib

from scipy.spatial.distance import pdist, squareform


DESCRIPTION = """

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import argparse

import numpy as np

import nibabel as nib

import sklearn.cluster as clu


DESCRIPTION = """

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from scipy.io import loadmat
import numpy as np
import sys

if __name__ == "__main__":
	print('input_ants_rigid_transform input_bvecs output_bvec')

	# Loading Ants transform
	trans = loadmat(sys.argv[1])
	matrice = trans['AffineTransform_double_3_3'][:9].reshape((3,3))

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#!/usr/bin/env python
import numpy as np
import nibabel as nib
import sys

def main(input, output, dx, dy, dz):
	img = nib.load(input)
	data = img.get_data()
	downsampling_factor = np.array([int(dx), int(dy), int(dz)])
	print('Input image is size {} {} {}'.format(data.shape[0], data.shape[1], data.shape[2]))

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import BeautifulSoup as bs
import requests
import pylab as pl


def countPubmed(searchterm, minyear, maxyear):
	quer = '%20'.join(searchterm.split(' '))
	N_res = []
	y = []
	for year in range(minyear, maxyear+1):

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#! /usr/bin/env python
# -*- coding: utf-8 -*-

# first draft of script to compute voxelwise bvec and bval after GNL
# work on the output of calc_grad_perc_dev
# calc_grad_perc_dev works on the fullwarp output of gradunwrap.py

import numpy as np
import nibabel as nib
import argparse