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# -*- coding: utf-8 -*-
# Copyright 2014 by Forschungszentrum Juelich GmbH
# Author: J. Caron
#
"""This module provides the abstract base class :class:`~.Projector` and concrete subclasses for
projections of vector and scalar fields."""
import abc
import itertools
import logging
import numpy as np
from numpy import pi
from scipy.sparse import coo_matrix, csr_matrix
from pyramid.fielddata import VectorData, ScalarData
from pyramid.quaternion import Quaternion
__all__ = ['RotTiltProjector', 'XTiltProjector', 'YTiltProjector', 'SimpleProjector']
class Projector(object, metaclass=abc.ABCMeta):
"""Abstract base class representing a projection function.
The :class:`~.Projector` class represents a projection function for a 3-dimensional
vector- or scalar field onto a 2-dimensional grid. :class:`~.Projector` is an abstract base
class and provides a unified interface which should be subclassed with a custom
:func:`__init__` function, which should call the parent :func:`__init__` method. Concrete
subclasses can be called as a function and take a `vector` as argument which contains the
3-dimensional field. The output is the projected field, given as a `vector`. Depending on the
length of the input and the given dimensions `dim` at construction time, vector or scalar
projection is choosen intelligently.
dim : tuple (N=3)
Dimensions (z, y, x) of the magnetization distribution.
Dimensions (v, u) of the projected grid.
size_3d : int
Number of voxels of the 3-dimensional grid.
size_2d : int
Number of pixels of the 2-dimensional projected grid.
weight : :class:`~scipy.sparse.csr_matrix` (N=2)
The weight matrix containing the weighting coefficients for the 3D to 2D mapping.
coeff : list (N=2)
List containing the six weighting coefficients describing the influence of the 3 components
of a 3-dimensional vector field on the 2 projected components.
_log = logging.getLogger(__name__ + '.Projector')
@abc.abstractmethod
def __init__(self, dim, dim_uv, weight, coeff):
self.weight = weight
self.coeff = coeff
self.size_2d, self.size_3d = weight.shape
self.n = 3 * np.prod(dim)
self.m = 2 * np.prod(dim_uv)
self._log.debug('Created ' + str(self))
return '%s(dim=%r, dim_uv=%r, weight=%r, coeff=%r)' % \
(self.__class__, self.dim, self.dim_uv, self.weight, self.coeff)
return 'Projector(dim=%s, dim_uv=%s, coeff=%s)' % (self.dim, self.dim_uv, self.coeff)
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def __call__(self, field_data):
if isinstance(field_data, VectorData):
field_empty = np.zeros((3, 1) + self.dim_uv, dtype=field_data.field.dtype)
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field_data_proj = VectorData(field_data.a, field_empty)
field_proj = self.jac_dot(field_data.field_vec).reshape((2,) + self.dim_uv)
field_data_proj.field[0:2, 0, ...] = field_proj
elif isinstance(field_data, ScalarData):
field_empty = np.zeros((1,) + self.dim_uv, dtype=field_data.field.dtype)
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field_data_proj = ScalarData(field_data.a, field_empty)
field_proj = self.jac_dot(field_data.field_vec).reshape(self.dim_uv)
field_data_proj.field[0, ...] = field_proj
else:
raise TypeError('Input is neither of type VectorData or ScalarData')
return field_data_proj
def _vector_field_projection(self, vector):
result = np.zeros(2 * self.size_2d, dtype=vector.dtype)
# Go over all possible component projections (z, y, x) to (u, v):
vec_x, vec_y, vec_z = np.split(vector, 3)
vec_x_weighted = self.weight.dot(vec_x)
vec_y_weighted = self.weight.dot(vec_y)
vec_z_weighted = self.weight.dot(vec_z)
slice_u = slice(0, self.size_2d)
slice_v = slice(self.size_2d, 2 * self.size_2d)
if self.coeff[0][0] != 0: # x to u
result[slice_u] += self.coeff[0][0] * vec_x_weighted
if self.coeff[0][1] != 0: # y to u
result[slice_u] += self.coeff[0][1] * vec_y_weighted
if self.coeff[0][2] != 0: # z to u
result[slice_u] += self.coeff[0][2] * vec_z_weighted
if self.coeff[1][0] != 0: # x to v
result[slice_v] += self.coeff[1][0] * vec_x_weighted
if self.coeff[1][1] != 0: # y to v
result[slice_v] += self.coeff[1][1] * vec_y_weighted
if self.coeff[1][2] != 0: # z to v
result[slice_v] += self.coeff[1][2] * vec_z_weighted
return result
def _vector_field_projection_T(self, vector):
result = np.zeros(3 * self.size_3d)
# Go over all possible component projections (u, v) to (z, y, x):
vec_u, vec_v = np.split(vector, 2)
vec_u_weighted = self.weight.T.dot(vec_u)
vec_v_weighted = self.weight.T.dot(vec_v)
slice_x = slice(0, self.size_3d)
slice_y = slice(self.size_3d, 2 * self.size_3d)
slice_z = slice(2 * self.size_3d, 3 * self.size_3d)
if self.coeff[0][0] != 0: # u to x
result[slice_x] += self.coeff[0][0] * vec_u_weighted
if self.coeff[0][1] != 0: # u to y
result[slice_y] += self.coeff[0][1] * vec_u_weighted
if self.coeff[0][2] != 0: # u to z
result[slice_z] += self.coeff[0][2] * vec_u_weighted
if self.coeff[1][0] != 0: # v to x
result[slice_x] += self.coeff[1][0] * vec_v_weighted
if self.coeff[1][1] != 0: # v to y
result[slice_y] += self.coeff[1][1] * vec_v_weighted
if self.coeff[1][2] != 0: # v to z
result[slice_z] += self.coeff[1][2] * vec_v_weighted
return result
def _scalar_field_projection(self, vector):
self._log.debug('Calling _scalar_field_projection')
return np.array(self.weight.dot(vector))
def _scalar_field_projection_T(self, vector):
self._log.debug('Calling _scalar_field_projection_T')
return np.array(self.weight.T.dot(vector))
def jac_dot(self, vector):
"""Multiply a `vector` with the jacobi matrix of this :class:`~.Projector` object.
Parameters
----------
vector : :class:`~numpy.ndarray` (N=1)
Vector containing the field which should be projected. Must have the same or 3 times
the size of `size_3d` of the projector for scalar and vector projection, respectively.
Returns
-------
proj_vector : :class:`~numpy.ndarray` (N=1)
Vector containing the projected field of the 2-dimensional grid. The length is
always`size_2d`.
"""
if len(vector) == 3 * self.size_3d: # mode == 'vector'
return self._vector_field_projection(vector)
elif len(vector) == self.size_3d: # mode == 'scalar'
return self._scalar_field_projection(vector)
else:
raise AssertionError('Vector size has to be suited either for '
'vector- or scalar-field-projection!')
def jac_T_dot(self, vector):
"""Multiply a `vector` with the transp. jacobi matrix of this :class:`~.Projector` object.
Parameters
----------
vector : :class:`~numpy.ndarray` (N=1)
Vector containing the field which should be projected. Must have the same or 2 times
the size of `size_2d` of the projector for scalar and vector projection, respectively.
Returns
-------
proj_vector : :class:`~numpy.ndarray` (N=1)
Vector containing the multiplication of the input with the transposed jacobi matrix
of the :class:`~.Projector` object.
"""
if len(vector) == 2 * self.size_2d: # mode == 'vector'
return self._vector_field_projection_T(vector)
elif len(vector) == self.size_2d: # mode == 'scalar'
return self._scalar_field_projection_T(vector)
else:
raise AssertionError('Vector size has to be suited either for '
"""Get specific information about the projector as a string.
Parameters
----------
verbose: boolean, optional
If this is true, the text looks prettier (maybe using latex). Default is False for the
use in file names and such.
Returns
-------
info : string
Information about the projector as a string, e.g. for the use in plot titles.
class RotTiltProjector(Projector):
"""Class representing a projection function with a rotation around z followed by tilt around x.
The :class:`~.XTiltProjector` class represents a projection function for a 3-dimensional
vector- or scalar field onto a 2-dimensional grid, which is a concrete subclass of
:class:`~.Projector`.
Attributes
----------
dim : tuple (N=3)
Dimensions (z, y, x) of the magnetization distribution.
rotation : float
Angle in `rad` describing the rotation around the z-axis before the tilt is happening.
tilt : float
Angle in `rad` describing the tilt of the beam direction relative to the x-axis.
dim_uv : tuple (N=2), optional
Dimensions (v, u) of the projection. If not set defaults to the (y, x)-dimensions.
subcount : int (optional)
Number of subpixels along one axis. This is used to create the lookup table which uses
a discrete subgrid to estimate the impact point of a voxel onto a pixel and the weight on
all surrounding pixels. Default is 11 (odd numbers provide a symmetric center).
sparsity : float
Measures the sparsity of the weighting (not the complete one!), 1 means completely sparse!
_log = logging.getLogger(__name__ + '.RotTiltProjector')
def __init__(self, dim, rotation, tilt, dim_uv=None, subcount=11):
self._log.debug('Calling __init__')
self.rotation = rotation
self.tilt = tilt
# Determine dimensions:
dim_z, dim_y, dim_x = dim
center = (dim_z / 2., dim_y / 2., dim_x / 2.)
if dim_uv is None:
dim_v = max(dim_x, dim_y) # first rotate around z-axis (take x and y into account)
dim_u = max(dim_v, dim_z) # then tilt around x-axis (now z matters, too)
dim_uv = (dim_v, dim_u)
dim_v, dim_u = dim_uv
# Creating coordinate list of all voxels:
voxels = list(itertools.product(range(dim_z), range(dim_y), range(dim_x)))
# Calculate vectors to voxels relative to rotation center:
voxel_vecs = (np.asarray(voxels) + 0.5 - np.asarray(center)).T
# Create tilt, rotation and combined quaternion, careful: Quaternion(w,x,y,z), not (z,y,x):
quat_x = Quaternion.from_axisangle((1, 0, 0), tilt) # Tilt around x-axis
quat_z = Quaternion.from_axisangle((0, 0, 1), rotation) # Rotate around z-axis
quat = quat_x * quat_z # Combined quaternion (first rotate around z, then tilt around x)
# Calculate impact positions on the projected pixel coordinate grid (flip because quat.):
impacts = np.flipud(quat.matrix[:2, :].dot(np.flipud(voxel_vecs))) # only care for x/y
impacts[1, :] += dim_u / 2. # Shift back to normal indices
impacts[0, :] += dim_v / 2. # Shift back to normal indices
# Calculate equivalence radius:
# Prepare weight matrix calculation:
rows = [] # 2D projection
columns = [] # 3D distribution
data = [] # weights
# Create 4D lookup table (1&2: which neighbour weight?, 3&4: which subpixel is hit?)
weight_lookup = self._create_weight_lookup(subcount, R)
# Go over all voxels:
for i, voxel in enumerate(voxels):
column_index = voxel[0] * dim_y * dim_x + voxel[1] * dim_x + voxel[2]
remainder, impact = np.modf(impacts[:, i]) # split index of impact and remainder!
sub_pixel = (remainder * subcount).astype(dtype=np.int) # sub_pixel inside impact px.
# Go over all influenced pixels (impact and neighbours, indices are [0, 1, 2]!):
for px_ind in list(itertools.product(range(3), range(3))):
# Pixel indices influenced by the impact (px_ind-1 to center them around impact):
pixel = (impact + np.array(px_ind) - 1).astype(dtype=np.int)
# Check if pixel is out of bound:
if 0 <= pixel[0] < dim_uv[0] and 0 <= pixel[1] < dim_uv[1]:
# Lookup weight in 4-dimensional lookup table!
weight = weight_lookup[px_ind[0], px_ind[1], sub_pixel[0], sub_pixel[1]]
# Only write into sparse matrix if weight is not zero:
if weight != 0.:
row_index = pixel[0] * dim_u + pixel[1]
columns.append(column_index)
rows.append(row_index)
data.append(weight)
# Calculate weight matrix and coefficients for jacobi matrix:
shape = (np.prod(dim_uv), np.prod(dim))
self.sparsity = 1. - len(data) / np.prod(shape, dtype=np.float)
weights = csr_matrix(coo_matrix((data, (rows, columns)), shape=shape))
# Calculate coefficients by rotating unity matrix (unit vectors, (x,y,z)):
coeff = quat.matrix[:2, :].dot(np.eye(3))
super().__init__(dim, dim_uv, weights, coeff)
self._log.debug('Created ' + str(self))
@staticmethod
def _create_weight_lookup(subcount, R):
s = subcount
Rz = R * s # Radius in subgrid units
dim_zoom = (3 * s, 3 * s) # Dimensions of the subgrid, (3, 3) because of neighbour count!
cent_zoom = (np.asarray(dim_zoom) / 2.).astype(dtype=np.int) # Center of the subgrid
y, x = np.indices(dim_zoom)
y -= cent_zoom[0]
x -= cent_zoom[1]
# Calculate projected thickness of an equivalence sphere (normed!):
d = np.where(np.hypot(x, y) <= Rz, Rz ** 2 - x ** 2 - y ** 2, 0)
d /= d.sum()
# Create lookup table (4D):
lookup = np.zeros((3, 3, s, s))
# Go over all 9 pixels (center and neighbours):
for pixel in list(itertools.product(range(3), range(3))):
pixel_lb = np.array(pixel) * s # Convert to subgrid, hit bottom left of the pixel!
# Go over all subpixels in the center that can be hit:
for sub_pixel in list(itertools.product(range(s), range(s))):
shift = np.array(sub_pixel) - np.array((s // 2, s // 2)) # relative to center!
lb = pixel_lb - shift # Shift summing zone according to hit subpixel!
# Make sure, that the summing zone is in bounds (otherwise correct accordingly):
lb = np.where(lb >= 0, lb, [0, 0])
tr = np.where(lb < 3 * s, lb + np.array((s, s)), [3 * s, 3 * s])
# Calculate weight by summing over the summing zone:
weight = d[lb[0]:tr[0], lb[1]:tr[1]].sum()
lookup[pixel[0], pixel[1], sub_pixel[0], sub_pixel[1]] = weight
return lookup
def get_info(self, verbose=False):
"""Get specific information about the projector as a string.
Parameters
----------
verbose: boolean, optional
If this is true, the text looks prettier (maybe using latex). Default is False for the
use in file names and such.
Returns
-------
info : string
Information about the projector as a string, e.g. for the use in plot titles.
"""
theta_ang = int(np.round(self.rotation * 180 / pi))
phi_ang = int(np.round(self.tilt * 180 / pi))
if verbose:
return u'$\\theta = {:d}$°, $\phi = {:d}$°'.format(theta_ang, phi_ang)
return u'theta={:d}_phi={:d}°'.format(theta_ang, phi_ang)
class XTiltProjector(Projector):
"""Class representing a projection function with a tilt around the x-axis.
The :class:`~.XTiltProjector` class represents a projection function for a 3-dimensional
vector- or scalar field onto a 2-dimensional grid, which is a concrete subclass of
:class:`~.Projector`.
Attributes
----------
dim : tuple (N=3)
Dimensions (z, y, x) of the magnetization distribution.
tilt : float
Angle in `rad` describing the tilt of the beam direction relative to the x-axis.
dim_uv : tuple (N=2), optional
Dimensions (v, u) of the projection. If not set defaults to the (y, x)-dimensions.
_log = logging.getLogger(__name__ + '.XTiltProjector')
def __init__(self, dim, tilt, dim_uv=None):
self.tilt = tilt
# Set starting variables:
# length along projection (proj, z), perpendicular (perp, y) and rotation (rot, x) axis:
dim_proj, dim_perp, dim_rot = dim
if dim_uv is None:
dim_uv = (max(dim_perp, dim_proj), dim_rot) # x-y-plane
dim_v, dim_u = dim_uv # y, x
assert dim_v >= dim_perp and dim_u >= dim_rot, 'Projected dimensions are too small!'
# Creating coordinate list of all voxels (for one slice):
voxels = list(itertools.product(range(dim_proj), range(dim_perp))) # z-y-plane
# Calculate positions along the projected pixel coordinate system:
center = (dim_proj / 2., dim_perp / 2.)
positions = self._get_position(voxels, center, tilt, dim_v)
# Calculate weight-matrix:
r = 1 / np.sqrt(np.pi) # radius of the voxel circle
rho = 0.5 / r
row = []
col = []
data = []
# one slice:
for i, voxel in enumerate(voxels):
impacts = self._get_impact(positions[i], r, dim_v) # impact along projected y-axis
voxel_index = voxel[0] * dim_rot * dim_perp + voxel[1] * dim_rot # 0: z, 1: y
impact_index = impact * dim_u + (dim_u - dim_rot) // 2
distance = np.abs(impact + 0.5 - positions[i])
col.append(voxel_index)
row.append(impact_index)
data.append(self._get_weight(delta, rho))
# All other slices (along x):
columns = col
rows = row
for s in np.arange(1, dim_rot):
columns = np.hstack((np.array(columns), np.array(col) + s))
rows = np.hstack((np.array(rows), np.array(row) + s))
# Calculate weight matrix and coefficients for jacobi matrix:
shape = (np.prod(dim_uv), np.prod(dim))
self.sparsity = 1. - len(data) / np.prod(shape, dtype=np.float)
weight = csr_matrix(coo_matrix((np.tile(data, dim_rot), (rows, columns)), shape=shape))
coeff = [[1, 0, 0], [0, np.cos(tilt), np.sin(tilt)]]
self._log.debug('Created ' + str(self))
@staticmethod
def _get_position(points, center, tilt, size):
point_vecs = np.asarray(points) + 0.5 - np.asarray(center) # vectors pointing to points
direc_vec = np.array((np.cos(tilt), -np.sin(tilt))) # vector pointing along projection
distances = np.cross(direc_vec, point_vecs) # here (special case): divisor is one!
distances += size / 2. # Shift to the center of the projection
return distances
@staticmethod
def _get_impact(pos, r, size):
return [x for x in np.arange(np.floor(pos - r), np.floor(pos + r) + 1, dtype=int)
if 0 <= x < size]
@staticmethod
def _get_weight(delta, rho): # use circles to represent the voxels
lo, up = delta - rho, delta + rho
# Upper boundary:
if up >= 1:
w_up = 0.5
else:
w_up = (up * np.sqrt(1 - up ** 2) + np.arctan(up / np.sqrt(1 - up ** 2))) / pi
# Lower boundary:
if lo <= -1:
w_lo = -0.5
else:
w_lo = (lo * np.sqrt(1 - lo ** 2) + np.arctan(lo / np.sqrt(1 - lo ** 2))) / pi
return w_up - w_lo
"""Get specific information about the projector as a string.
Parameters
----------
verbose: boolean, optional
If this is true, the text looks prettier (maybe using latex). Default is False for the
use in file names and such.
Returns
-------
info : string
Information about the projector as a string, e.g. for the use in plot titles.
return u'x-tilt: $\phi = {:d}$°'.format(int(np.round(self.tilt * 180 / pi)))
return u'xtilt_phi={:d}°'.format(int(np.round(self.tilt * 180 / pi)))
class YTiltProjector(Projector):
"""Class representing a projection function with a tilt around the y-axis.
The :class:`~.YTiltProjector` class represents a projection function for a 3-dimensional
vector- or scalar field onto a 2-dimensional grid, which is a concrete subclass of
:class:`~.Projector`.
Attributes
----------
dim : tuple (N=3)
Dimensions (z, y, x) of the magnetization distribution.
tilt : float
Angle in `rad` describing the tilt of the beam direction relative to the y-axis.
dim_uv : tuple (N=2), optional
Dimensions (v, u) of the projection. If not set defaults to the (y, x)-dimensions.
_log = logging.getLogger(__name__ + '.YTiltProjector')
def __init__(self, dim, tilt, dim_uv=None):
# Set starting variables:
# length along projection (proj, z), rotation (rot, y) and perpendicular (perp, x) axis:
dim_proj, dim_rot, dim_perp = dim
if dim_uv is None:
dim_uv = (dim_rot, max(dim_perp, dim_proj)) # x-y-plane
dim_v, dim_u = dim_uv # y, x
assert dim_v >= dim_rot and dim_u >= dim_perp, 'Projected dimensions are too small!'
# Creating coordinate list of all voxels (for one slice):
voxels = list(itertools.product(range(dim_proj), range(dim_perp))) # z-x-plane
# Calculate positions along the projected pixel coordinate system:
center = (dim_proj / 2., dim_perp / 2.)
positions = self._get_position(voxels, center, tilt, dim_u)
# Calculate weight-matrix:
r = 1 / np.sqrt(np.pi) # radius of the voxel circle
row = []
col = []
data = []
# one slice:
for i, voxel in enumerate(voxels):
impacts = self._get_impact(positions[i], r, dim_u) # impact along projected x-axis
voxel_index = voxel[0] * dim_perp * dim_rot + voxel[1] # 0: z, 1: x
for impact in impacts:
impact_index = impact + (dim_v - dim_rot) // 2 * dim_u
distance = np.abs(impact + 0.5 - positions[i])
delta = distance / r
col.append(voxel_index)
row.append(impact_index)
data.append(self._get_weight(delta, rho))
# All other slices (along y):
columns = col
rows = row
for s in np.arange(1, dim_rot):
columns = np.hstack((np.array(columns), np.array(col) + s * dim_perp))
rows = np.hstack((np.array(rows), np.array(row) + s * dim_u))
# Calculate weight matrix and coefficients for jacobi matrix:
shape = (np.prod(dim_uv), np.prod(dim))
self.sparsity = 1. - len(data) / np.prod(shape, dtype=np.float)
weight = csr_matrix(coo_matrix((np.tile(data, dim_rot), (rows, columns)), shape=shape))
coeff = [[np.cos(tilt), 0, np.sin(tilt)], [0, 1, 0]]
self._log.debug('Created ' + str(self))
@staticmethod
def _get_position(points, center, tilt, size):
point_vecs = np.asarray(points) + 0.5 - np.asarray(center) # vectors pointing to points
direc_vec = np.array((np.cos(tilt), -np.sin(tilt))) # vector pointing along projection
distances = np.cross(direc_vec, point_vecs) # here (special case): divisor is one!
distances += size / 2. # Shift to the center of the projection
return distances
@staticmethod
def _get_impact(pos, r, size):
return [x for x in np.arange(np.floor(pos - r), np.floor(pos + r) + 1, dtype=int)
if 0 <= x < size]
@staticmethod
def _get_weight(delta, rho): # use circles to represent the voxels
lo, up = delta - rho, delta + rho
# Upper boundary:
if up >= 1:
w_up = 0.5
else:
w_up = (up * np.sqrt(1 - up ** 2) + np.arctan(up / np.sqrt(1 - up ** 2))) / pi
# Lower boundary:
if lo <= -1:
w_lo = -0.5
else:
w_lo = (lo * np.sqrt(1 - lo ** 2) + np.arctan(lo / np.sqrt(1 - lo ** 2))) / pi
return w_up - w_lo
"""Get specific information about the projector as a string.
Parameters
----------
verbose: boolean, optional
If this is true, the text looks prettier (maybe using latex). Default is False for the
use in file names and such.
Returns
-------
info : string
Information about the projector as a string, e.g. for the use in plot titles.
return u'y-tilt: $\phi = {:d}$°'.format(int(np.round(self.tilt * 180 / pi)))
return u'ytilt_phi={:d}°'.format(int(np.round(self.tilt * 180 / pi)))
class SimpleProjector(Projector):
"""Class representing a projection function along one of the major axes.
The :class:`~.SimpleProjector` class represents a projection function for a 3-dimensional
vector- or scalar field onto a 2-dimensional grid, which is a concrete subclass of
:class:`~.Projector`.
Attributes
----------
dim : tuple (N=3)
Dimensions (z, y, x) of the magnetization distribution.
axis : {'z', 'y', 'x'}, optional
Main axis along which the magnetic distribution is projected (given as a string). Defaults
to the z-axis.
dim_uv : tuple (N=2), optional
Dimensions (v, u) of the projection. If not set it uses the 3D default dimensions.
_log = logging.getLogger(__name__ + '.SimpleProjector')
AXIS_DICT = {'z': (0, 1, 2), 'y': (1, 0, 2), 'x': (2, 1, 0)} # (0:z, 1:y, 2:x) -> (proj, v, u)
# coordinate switch for 'x': u, v --> z, y (not y, z!)!
def __init__(self, dim, axis='z', dim_uv=None):
assert axis in {'z', 'y', 'x'}, 'Projection axis has to be x, y or z (given as a string)!'
self.axis = axis
proj, v, u = self.AXIS_DICT[axis]
dim_proj, dim_v, dim_u = dim[proj], dim[v], dim[u]
dim_z, dim_y, dim_x = dim
size_2d = dim_u * dim_v
size_3d = np.prod(dim)
data = np.repeat(1, size_3d) # size_3d ones in the matrix (each voxel is projected)
indptr = np.arange(0, size_3d + 1, dim_proj) # each row has dim_proj 1-entries
if axis == 'z':
coeff = [[1, 0, 0], [0, 1, 0]]
indices = np.array([np.arange(row, size_3d, size_2d)
for row in range(size_2d)]).reshape(-1)
elif axis == 'y':
coeff = [[1, 0, 0], [0, 0, 1]]
indices = np.array(
[np.arange(row % dim_x, dim_x * dim_y, dim_x) + row // dim_x * dim_x * dim_y
for row in range(size_2d)]).reshape(-1)
elif axis == 'x':
coeff = [[0, 0, 1], [0, 1, 0]] # Caution, coordinate switch: u, v --> z, y (not y, z!)
indices = np.array(
[np.arange(dim_x) + (row % dim_z) * dim_x * dim_y + row // dim_z * dim_x
for row in range(size_2d)]).reshape(-1)
else:
raise ValueError('{} is not a valid axis parameter (use x, y or z)!'.format(axis))
if dim_uv is not None:
indptr = list(indptr) # convert to use insert() and append()
Jan Caron
committed
# Calculate padding:
d_v = (np.floor((dim_uv[0] - dim_v) / 2).astype(int),
np.ceil((dim_uv[0] - dim_v) / 2).astype(int))
d_u = (np.floor((dim_uv[1] - dim_u) / 2).astype(int),
np.ceil((dim_uv[1] - dim_u) / 2).astype(int))
indptr.extend([indptr[-1]] * d_v[1] * dim_uv[1]) # add empty lines at the end
for i in np.arange(dim_v, 0, -1): # all slices in between
up, lo = i * dim_u, (i - 1) * dim_u # upper / lower slice end
indptr[up:up] = [indptr[up]] * d_u[1] # end of the slice
indptr[lo:lo] = [indptr[lo]] * d_u[0] # start of the slice
indptr = [0] * d_v[0] * dim_uv[1] + indptr # insert empty rows at the beginning
else: # Make sure dim_uv is defined (used for the assertion)
dim_uv = dim_v, dim_u
assert dim_uv[0] >= dim_v and dim_uv[1] >= dim_u, 'Projected dimensions are too small!'
# Create weight-matrix:
shape = (np.prod(dim_uv), np.prod(dim))
self.sparsity = 1. - len(data) / np.prod(shape, dtype=np.float)
weight = csr_matrix((data, indices, indptr), shape=shape)
self._log.debug('Created ' + str(self))
"""Get specific information about the projector as a string.
Parameters
----------
verbose: boolean, optional
If this is true, the text looks prettier (maybe using latex). Default is False for the
use in file names and such.
Returns
-------
info : string
Information about the projector as a string, e.g. for the use in plot titles.