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Jan Caron authoredRenames: datacollection --> dataset optimizer --> reconstruction scripts: interactive_setup is now implemented in an extended Spyder startup file. some other scripts are now the rest is NOT adapted, yet, which is the next task at hand furthermore scripts will be sorted and unused ones deleted in the next commit
Jan Caron authoredRenames: datacollection --> dataset optimizer --> reconstruction scripts: interactive_setup is now implemented in an extended Spyder startup file. some other scripts are now the rest is NOT adapted, yet, which is the next task at hand furthermore scripts will be sorted and unused ones deleted in the next commit
kernel_core.pyx 2.15 KiB
# -*- coding: utf-8 -*-
"""Numerical core routines for the :mod:`~.pyramid.kernel` module.
Provides helper functions to speed up kernel calculations in the :class:`~pyramid.kernel.Kernel`
class, by using C-speed for the for-loops and by omitting boundary and wraparound checks.
"""
import numpy as np
import math
cimport cython
cimport numpy as np
@cython.boundscheck(False)
@cython.wraparound(False)
def multiply_jacobi_core(
unsigned int v_dim, unsigned int u_dim,
double[:, :] u_phi, double[:, :] v_phi,
double[:] vector,
double[:] result):
'''Numerical core routine for the Jacobi matrix multiplication in the :class:`~.Kernel` class.
Parameters
----------
v_dim, u_dim : int
Dimensions of the projection along the two major axes.
v_phi, u_phi : :class:`~numpy.ndarray` (N=2)
Lookup tables for the pixel fields oriented in `u`- and `v`-direction.
vector : :class:`~numpy.ndarray` (N=1)
Input vector which should be multiplied by the Jacobi-matrix.
result : :class:`~numpy.ndarray` (N=1)
Vector in which the result of the multiplication should be stored.
Returns
-------
None
Notes
-----
The strategy involves iterating over the magnetization first and over the kernel in an inner
iteration loop.
'''
cdef unsigned int s, i, j, u_min, u_max, v_min, v_max, ri, u, v, size
size = u_dim * v_dim # Number of pixels
s = 0 # Current pixel (numbered consecutively)
# Go over all pixels:
for j in range(v_dim):
for i in range(u_dim):
u_min = (u_dim - 1) - i # u_max = u_min + u_dim
v_min = (v_dim - 1) - j # v_max = v_min + v_dim
ri = 0 # Current result component (numbered consecutively)
# Go over the current kernel cutout [v_min:v_max, u_min:u_max]:
for v in range(v_min, v_min + v_dim):
for u in range(u_min, u_min + u_dim):
result[ri] += vector[s] * u_phi[v, u]
result[ri] -= vector[s+size] * v_phi[v, u]
ri += 1
s += 1