/usr/lib/python2.7/dist-packages/dipy/core/gradients.py is in python-dipy 0.10.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | from __future__ import division, print_function, absolute_import
from ..utils.six import string_types
import numpy as np
from ..io import gradients as io
from .onetime import auto_attr
from .geometry import vector_norm
class GradientTable(object):
"""Diffusion gradient information
Parameters
----------
gradients : array_like (N, 3)
N diffusion gradients
b0_threshold : float
Gradients with b-value less than or equal to `b0_threshold` are
considered as b0s i.e. without diffusion weighting.
Attributes
----------
gradients : (N,3) ndarray
diffusion gradients
bvals : (N,) ndarray
The b-value, or magnitude, of each gradient direction.
qvals: (N,) ndarray
The q-value for each gradient direction. Needs big and small
delta.
bvecs : (N,3) ndarray
The direction, represented as a unit vector, of each gradient.
b0s_mask : (N,) ndarray
Boolean array indicating which gradients have no diffusion
weighting, ie b-value is close to 0.
b0_threshold : float
Gradients with b-value less than or equal to `b0_threshold` are
considered to not have diffusion weighting.
See Also
--------
gradient_table
"""
def __init__(self, gradients, big_delta=None, small_delta=None,
b0_threshold=0):
"""Constructor for GradientTable class"""
gradients = np.asarray(gradients)
if gradients.ndim != 2 or gradients.shape[1] != 3:
raise ValueError("gradients should be an (N, 3) array")
self.gradients = gradients
# Avoid nan gradients. Set these to 0 instead:
self.gradients = np.where(np.isnan(gradients), 0., gradients)
self.big_delta = big_delta
self.small_delta = small_delta
self.b0_threshold = b0_threshold
@auto_attr
def bvals(self):
return vector_norm(self.gradients)
@auto_attr
def qvals(self):
tau = self.big_delta - self.small_delta / 3.0
return np.sqrt(self.bvals / tau) / (2 * np.pi)
@auto_attr
def b0s_mask(self):
return self.bvals <= self.b0_threshold
@auto_attr
def bvecs(self):
# To get unit vectors we divide by bvals, where bvals is 0 we divide by
# 1 to avoid making nans
denom = self.bvals + (self.bvals == 0)
denom = denom.reshape((-1, 1))
return self.gradients / denom
@property
def info(self):
print('B-values shape (%d,)' % self.bvals.shape)
print(' min %f ' % self.bvals.min())
print(' max %f ' % self.bvals.max())
print('B-vectors shape (%d, %d)' % self.bvecs.shape)
print(' min %f ' % self.bvecs.min())
print(' max %f ' % self.bvecs.max())
def gradient_table_from_bvals_bvecs(bvals, bvecs, b0_threshold=0, atol=1e-2,
**kwargs):
"""Creates a GradientTable from a bvals array and a bvecs array
Parameters
----------
bvals : array_like (N,)
The b-value, or magnitude, of each gradient direction.
bvecs : array_like (N, 3)
The direction, represented as a unit vector, of each gradient.
b0_threshold : float
Gradients with b-value less than or equal to `bo_threshold` are
considered to not have diffusion weighting.
atol : float
Each vector in `bvecs` must be a unit vectors up to a tolerance of
`atol`.
Other Parameters
----------------
**kwargs : dict
Other keyword inputs are passed to GradientTable.
Returns
-------
gradients : GradientTable
A GradientTable with all the gradient information.
See Also
--------
GradientTable, gradient_table
"""
bvals = np.asarray(bvals, np.float)
bvecs = np.asarray(bvecs, np.float)
dwi_mask = bvals > b0_threshold
# check that bvals is (N,) array and bvecs is (N, 3) unit vectors
if bvals.ndim != 1 or bvecs.ndim != 2 or bvecs.shape[0] != bvals.shape[0]:
raise ValueError("bvals and bvecs should be (N,) and (N, 3) arrays "
"respectively, where N is the number of diffusion "
"gradients")
bvecs_close_to_1 = abs(vector_norm(bvecs) - 1) <= atol
if bvecs.shape[1] != 3 or not np.all(bvecs_close_to_1[dwi_mask]):
raise ValueError("bvecs should be (N, 3), a set of N unit vectors")
bvecs = np.where(bvecs_close_to_1[:, None], bvecs, 0)
bvals = bvals * bvecs_close_to_1
gradients = bvals[:, None] * bvecs
grad_table = GradientTable(gradients, b0_threshold=b0_threshold, **kwargs)
grad_table.bvals = bvals
grad_table.bvecs = bvecs
grad_table.b0s_mask = ~dwi_mask
return grad_table
def gradient_table(bvals, bvecs=None, big_delta=None, small_delta=None,
b0_threshold=0, atol=1e-2):
"""A general function for creating diffusion MR gradients.
It reads, loads and prepares scanner parameters like the b-values and
b-vectors so that they can be useful during the reconstruction process.
Parameters
----------
bvals : can be any of the four options
1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.
2. a path for the file which contains an array like the above (1).
3. an array of shape (N, 4) or (4, N). Then this parameter is
considered to be a b-table which contains both bvals and bvecs. In
this case the next parameter is skipped.
4. a path for the file which contains an array like the one at (3).
bvecs : can be any of two options
1. an array of shape (N, 3) or (3, N) with the b-vectors.
2. a path for the file which contains an array like the previous.
big_delta : float
acquisition timing duration (default None)
small_delta : float
acquisition timing duration (default None)
b0_threshold : float
All b-values with values less than or equal to `bo_threshold` are
considered as b0s i.e. without diffusion weighting.
atol : float
All b-vectors need to be unit vectors up to a tolerance.
Returns
-------
gradients : GradientTable
A GradientTable with all the gradient information.
Examples
--------
>>> from dipy.core.gradients import gradient_table
>>> bvals=1500*np.ones(7)
>>> bvals[0]=0
>>> sq2=np.sqrt(2)/2
>>> bvecs=np.array([[0, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... [sq2, sq2, 0],
... [sq2, 0, sq2],
... [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False
Notes
-----
1. Often b0s (b-values which correspond to images without diffusion
weighting) have 0 values however in some cases the scanner cannot
provide b0s of an exact 0 value and it gives a bit higher values
e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
2. We assume that the minimum number of b-values is 7.
3. B-vectors should be unit vectors.
"""
# If you provided strings with full paths, we go and load those from
# the files:
if isinstance(bvals, string_types):
bvals, _ = io.read_bvals_bvecs(bvals, None)
if isinstance(bvecs, string_types):
_, bvecs = io.read_bvals_bvecs(None, bvecs)
bvals = np.asarray(bvals)
# If bvecs is None we expect bvals to be an (N, 4) or (4, N) array.
if bvecs is None:
if bvals.shape[-1] == 4:
bvecs = bvals[:, 1:]
bvals = np.squeeze(bvals[:, 0])
elif bvals.shape[0] == 4:
bvecs = bvals[1:, :].T
bvals = np.squeeze(bvals[0, :])
else:
raise ValueError("input should be bvals and bvecs OR an (N, 4)"
" array containing both bvals and bvecs")
else:
bvecs = np.asarray(bvecs)
if (bvecs.shape[1] > bvecs.shape[0]) and bvecs.shape[0]>1:
bvecs = bvecs.T
return gradient_table_from_bvals_bvecs(bvals, bvecs, big_delta=big_delta,
small_delta=small_delta,
b0_threshold=b0_threshold,
atol=atol)
|