Ariel Rokem arokem

## fmri_tnsr.py
"""

Compute the TSNR of a bunch of nifti files and generate the equivalent nifti
SNR 3Ds.

Depends on nibabel, matplotlib and scipy

"""

import os

## dicom2nifti.py
"""

This script takes the raw dicoms as they come off the scanner,
converts to nifti and runs FSL motion correction.
Also makes a plot of the motion correction parameters.

Depends on matplotlib, FSL, dcm2nii

"""

## tensor.py
import warnings

import numpy as np
import scipy.linalg as la

class Tensor(object):
    """
    Represent a diffusion tensor.
    """

## simulation.py
import numpy as np

import tensor as ozt


# Global constants for this module:
AD = 1.5
RD = 0.5

# This converts b values from the data, so that it matches the units of ADC we

## spherical_harmonics
function Ymn = spherical_harmonics(degree, order, theta, phi)

% Compute the spherical harmonics of given degree and order at theta,phi
%
% Parameters
% ----------
% degree: int, the degree of the underlying Legendre polynomial
% order: int, the order of the r
%
% theta: bounded between 0 and pi

## sklearn_EN_example.py
import numpy as np
import sklearn.linear_model as lm

X = np.array([[ -2.18252949e-01,  -8.21949578e-02,  -4.64055457e-02,
         -1.78405908e-01,  -1.93863740e-01,   5.30667625e-02,
          1.83851107e-01,   1.23426449e-01,   1.97396315e-01,
         -2.12615837e-01,   7.06452283e-02,  -1.94509405e-01,
         -9.77929516e-02,   2.07135018e-01,  -3.40368338e-02,
          2.02970673e-01,  -2.28669466e-01,   4.17398420e-02,
          1.80163132e-01,   3.24254938e-02,  -2.41198452e-03,

## odf.py
from __future__ import division
from warnings import warn
import numpy as np
from .recspeed import local_maxima, remove_similar_vertices, _filter_peaks
from ..core.onetime import auto_attr
from dipy.core.sphere import unique_edges, unit_icosahedron, HemiSphere
#Classes OdfModel and OdfFit are using API ReconstModel and ReconstFit from .base

default_sphere = HemiSphere.from_sphere(unit_icosahedron.subdivide(3))

## t_mrdTensor.ipynb

      
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                arokem
                / t_mrdTensor.ipynb
            
            
              Created
              December 8, 2012 14:59
            
              
                t_mrdTensor
              
          
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## gist:4242011
{
 "metadata": {
  "name": "TensorModel"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

## gist:4287207
{
 "metadata": {
  "name": "std_bias"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
	"""

	Compute the TSNR of a bunch of nifti files and generate the equivalent nifti
	SNR 3Ds.

	Depends on nibabel, matplotlib and scipy

	"""

	import os
	"""

	This script takes the raw dicoms as they come off the scanner,
	converts to nifti and runs FSL motion correction.
	Also makes a plot of the motion correction parameters.

	Depends on matplotlib, FSL, dcm2nii

	"""
	import warnings

	import numpy as np
	import scipy.linalg as la

	class Tensor(object):
	"""
	Represent a diffusion tensor.
	"""
	import numpy as np

	import tensor as ozt


	# Global constants for this module:
	AD = 1.5
	RD = 0.5

	# This converts b values from the data, so that it matches the units of ADC we
	function Ymn = spherical_harmonics(degree, order, theta, phi)

	% Compute the spherical harmonics of given degree and order at theta,phi
	%
	% Parameters
	% ----------
	% degree: int, the degree of the underlying Legendre polynomial
	% order: int, the order of the r
	%
	% theta: bounded between 0 and pi
	import numpy as np
	import sklearn.linear_model as lm

	X = np.array([[ -2.18252949e-01, -8.21949578e-02, -4.64055457e-02,
	-1.78405908e-01, -1.93863740e-01, 5.30667625e-02,
	1.83851107e-01, 1.23426449e-01, 1.97396315e-01,
	-2.12615837e-01, 7.06452283e-02, -1.94509405e-01,
	-9.77929516e-02, 2.07135018e-01, -3.40368338e-02,
	2.02970673e-01, -2.28669466e-01, 4.17398420e-02,
	1.80163132e-01, 3.24254938e-02, -2.41198452e-03,
	from __future__ import division
	from warnings import warn
	import numpy as np
	from .recspeed import local_maxima, remove_similar_vertices, _filter_peaks
	from ..core.onetime import auto_attr
	from dipy.core.sphere import unique_edges, unit_icosahedron, HemiSphere
	#Classes OdfModel and OdfFit are using API ReconstModel and ReconstFit from .base

	default_sphere = HemiSphere.from_sphere(unit_icosahedron.subdivide(3))
	{
	"metadata": {
	"name": "TensorModel"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	{
	"metadata": {
	"name": "std_bias"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{