110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
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"""
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Math functions for finite difference simulations
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Basic discrete calculus etc.
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"""
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from typing import List, Callable, Tuple, Dict
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import numpy
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from .. import field_t, field_updater
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def deriv_forward(dx_e: List[numpy.ndarray] = None) -> field_updater:
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"""
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Utility operators for taking discretized derivatives (backward variant).
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Args:
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dx_e: Lists of cell sizes for all axes
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`[[dx_0, dx_1, ...], [dy_0, dy_1, ...], ...]`.
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Returns:
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List of functions for taking forward derivatives along each axis.
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"""
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if dx_e:
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derivs = [lambda f: (numpy.roll(f, -1, axis=0) - f) / dx_e[0][:, None, None],
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lambda f: (numpy.roll(f, -1, axis=1) - f) / dx_e[1][None, :, None],
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lambda f: (numpy.roll(f, -1, axis=2) - f) / dx_e[2][None, None, :]]
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else:
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derivs = [lambda f: numpy.roll(f, -1, axis=0) - f,
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lambda f: numpy.roll(f, -1, axis=1) - f,
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lambda f: numpy.roll(f, -1, axis=2) - f]
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return derivs
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def deriv_back(dx_h: List[numpy.ndarray] = None) -> field_updater:
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"""
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Utility operators for taking discretized derivatives (forward variant).
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Args:
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dx_h: Lists of cell sizes for all axes
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`[[dx_0, dx_1, ...], [dy_0, dy_1, ...], ...]`.
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Returns:
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List of functions for taking forward derivatives along each axis.
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"""
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if dx_h:
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derivs = [lambda f: (f - numpy.roll(f, 1, axis=0)) / dx_h[0][:, None, None],
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lambda f: (f - numpy.roll(f, 1, axis=1)) / dx_h[1][None, :, None],
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lambda f: (f - numpy.roll(f, 1, axis=2)) / dx_h[2][None, None, :]]
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else:
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derivs = [lambda f: f - numpy.roll(f, 1, axis=0),
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lambda f: f - numpy.roll(f, 1, axis=1),
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lambda f: f - numpy.roll(f, 1, axis=2)]
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return derivs
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def curl_forward(dx_e: List[numpy.ndarray] = None) -> field_updater:
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"""
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Curl operator for use with the E field.
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Args:
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dx_e: Lists of cell sizes for all axes
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`[[dx_0, dx_1, ...], [dy_0, dy_1, ...], ...]`.
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Returns:
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Function `f` for taking the discrete forward curl of a field,
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`f(E)` -> curlE \\( = \\nabla_f \\times E \\)
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"""
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Dx, Dy, Dz = deriv_forward(dx_e)
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def ce_fun(e: field_t) -> field_t:
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output = numpy.empty_like(e)
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output[0] = Dy(e[2])
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output[1] = Dz(e[0])
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output[2] = Dx(e[1])
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output[0] -= Dz(e[1])
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output[1] -= Dx(e[2])
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output[2] -= Dy(e[0])
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return output
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return ce_fun
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def curl_back(dx_h: List[numpy.ndarray] = None) -> field_updater:
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"""
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Create a function which takes the backward curl of a field.
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Args:
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dx_h: Lists of cell sizes for all axes
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`[[dx_0, dx_1, ...], [dy_0, dy_1, ...], ...]`.
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Returns:
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Function `f` for taking the discrete backward curl of a field,
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`f(H)` -> curlH \\( = \\nabla_b \\times H \\)
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"""
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Dx, Dy, Dz = deriv_back(dx_h)
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def ch_fun(h: field_t) -> field_t:
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output = numpy.empty_like(h)
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output[0] = Dy(h[2])
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output[1] = Dz(h[0])
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output[2] = Dx(h[1])
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output[0] -= Dz(h[1])
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output[1] -= Dx(h[2])
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output[2] -= Dy(h[0])
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return output
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return ch_fun
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