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echo "usage: png2pdfslides.sh FOLDER_WITH_PNG CROP_WIDTH by CROP_HEIGHT CROP_OFFSETX CROP_OFFSETX"
echo "output: merged.pdf in FOLDER_WITH_PNG"
echo " "

# This script converts PNG screenshots into a compiled PDF using file name ordering (presumably corresponding to timestamp if consecutive screenshots)
# 1) Goes into folder $MAINF
# 2) Crops an $WIDTH by $HEIGHT region for each PNG starting with a $OFFSETX pixels offset from the left and a OFFSETY pixels offset from the top
# 3) Converts each croped PNG to JPEG2000 for lossless PDF embedding
# 4) Converts each JPEG2000 to PDF
# 5) Merges all the PDFs together into one merged.pdf

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# Crossing tissues sensitivity estimation
import numpy as np

# D1, D2, D3, the tissues diffusivities
# assumes D1 >= D2=D3


# comment and uncomment as needed
# um^2 / ms
MD = 0.14

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

import numpy as np
import nibabel as nib
import argparse


DESCRIPTION = """
Compute mean b-value from voxelwise btable (output of compute_spatial_btable.py).

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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

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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
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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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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import argparse

import numpy as np

import nibabel as nib

import sklearn.cluster as clu


DESCRIPTION = """

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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 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