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@ -2,13 +2,12 @@
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Class for constructing and holding the basic FDTD operations and fields
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Class for constructing and holding the basic FDTD operations and fields
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"""
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"""
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from collections.abc import Callable, Sequence
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from typing import Callable, Type, Sequence
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from collections import OrderedDict
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import numpy
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import numpy
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from numpy.typing import NDArray
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from numpy.typing import NDArray
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from numpy import floating, complexfloating
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import jinja2
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import jinja2
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import warnings
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import warnings
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import logging
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import pyopencl
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import pyopencl
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import pyopencl.array
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import pyopencl.array
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@ -17,18 +16,13 @@ from pyopencl.elementwise import ElementwiseKernel
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from meanas.fdmath import vec
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from meanas.fdmath import vec
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logger = logging.getLogger(__name__)
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__author__ = 'Jan Petykiewicz'
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# Create jinja2 env on module load
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# Create jinja2 env on module load
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jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__.split('.')[0], 'kernels'))
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jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__.split('.')[0], 'kernels'))
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class FDTDError(Exception):
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""" Custom exception for opencl_fdtd """
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pass
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class Simulation:
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class Simulation:
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r"""
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r"""
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Constructs and holds the basic FDTD operations and related fields
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Constructs and holds the basic FDTD operations and related fields
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@ -70,7 +64,7 @@ class Simulation:
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dt: float
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dt: float
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inv_dxes: list[pyopencl.array.Array]
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inv_dxes: list[pyopencl.array.Array]
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arg_type: type
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arg_type: Type
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context: pyopencl.Context
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context: pyopencl.Context
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queue: pyopencl.CommandQueue
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queue: pyopencl.CommandQueue
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@ -91,7 +85,7 @@ class Simulation:
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initial_fields: dict[str, NDArray] | None = None,
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initial_fields: dict[str, NDArray] | None = None,
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context: pyopencl.Context | None = None,
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context: pyopencl.Context | None = None,
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queue: pyopencl.CommandQueue | None = None,
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queue: pyopencl.CommandQueue | None = None,
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float_type: type = numpy.float32,
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float_type: Type = numpy.float32,
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do_poynting: bool = True,
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do_poynting: bool = True,
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do_fieldsrc: bool = False,
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do_fieldsrc: bool = False,
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) -> None:
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) -> None:
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@ -140,7 +134,7 @@ class Simulation:
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if dxes is None:
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if dxes is None:
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dxes = 1.0
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dxes = 1.0
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if isinstance(dxes, float | int):
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if isinstance(dxes, (float, int)):
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uniform_dx = dxes
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uniform_dx = dxes
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min_dx = dxes
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min_dx = dxes
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else:
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else:
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@ -149,14 +143,13 @@ class Simulation:
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min_dx = min(min(dxn) for dxn in dxes[0] + dxes[1])
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min_dx = min(min(dxn) for dxn in dxes[0] + dxes[1])
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max_dt = min_dx * .99 / numpy.sqrt(3)
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max_dt = min_dx * .99 / numpy.sqrt(3)
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logger.info(f'{min_dx=}, {max_dt=}, {dt=}')
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if dt is None:
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if dt is None:
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self.dt = max_dt
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self.dt = max_dt
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elif dt > max_dt:
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elif dt > max_dt:
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warnings.warn(f'Warning: unstable dt: {dt}', stacklevel=2)
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warnings.warn(f'Warning: unstable dt: {dt}')
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elif dt <= 0:
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elif dt <= 0:
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raise FDTDError(f'Invalid dt: {dt}')
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raise Exception(f'Invalid dt: {dt}')
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else:
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else:
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self.dt = dt
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self.dt = dt
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@ -180,31 +173,28 @@ class Simulation:
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def ptr(arg: str) -> str:
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def ptr(arg: str) -> str:
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return ctype + ' *' + arg
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return ctype + ' *' + arg
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base_fields = {
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base_fields = OrderedDict()
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ptr('E'): self.E,
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base_fields[ptr('E')] = self.E
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ptr('H'): self.H,
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base_fields[ptr('H')] = self.H
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ctype + ' dt': self.dt,
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base_fields[ctype + ' dt'] = self.dt
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}
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if uniform_dx is False:
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if uniform_dx is False:
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inv_dx_names = ['inv_d' + eh + r for eh in 'eh' for r in 'xyz']
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inv_dx_names = ['inv_d' + eh + r for eh in 'eh' for r in 'xyz']
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for name, field in zip(inv_dx_names, self.inv_dxes, strict=True):
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for name, field in zip(inv_dx_names, self.inv_dxes):
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base_fields[ptr(name)] = field
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base_fields[ptr(name)] = field
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eps_field = {ptr('eps'): self.eps}
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eps_field = OrderedDict()
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eps_field[ptr('eps')] = self.eps
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if bloch_boundaries:
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if bloch_boundaries:
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base_fields |= {
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base_fields[ptr('F')] = self.F
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ptr('F'): self.F,
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base_fields[ptr('G')] = self.G
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ptr('G'): self.G,
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}
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bloch_fields = {
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bloch_fields = OrderedDict()
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ptr('E'): self.F,
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bloch_fields[ptr('E')] = self.F
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ptr('H'): self.G,
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bloch_fields[ptr('H')] = self.G
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ctype + ' dt': self.dt,
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bloch_fields[ctype + ' dt'] = self.dt
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ptr('F'): self.E,
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bloch_fields[ptr('F')] = self.E
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ptr('G'): self.H,
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bloch_fields[ptr('G')] = self.H
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}
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common_source = jinja_env.get_template('common.cl').render(
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common_source = jinja_env.get_template('common.cl').render(
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ftype=ctype,
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ftype=ctype,
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@ -226,18 +216,18 @@ class Simulation:
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if bloch_boundaries:
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if bloch_boundaries:
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bloch_args = jinja_args.copy()
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bloch_args = jinja_args.copy()
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bloch_args['do_poynting'] = False
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bloch_args['do_poynting'] = False
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bloch_args['bloch'] = [{
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bloch_args['bloch'] = [
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'axis': b['axis'],
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{'axis': b['axis'],
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'real': b['imag'],
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'real': b['imag'],
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'imag': b['real'],
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'imag': b['real'],
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}
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}
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for b in bloch_boundaries]
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for b in bloch_boundaries]
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F_source = jinja_env.get_template('update_e.cl').render(**bloch_args)
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F_source = jinja_env.get_template('update_e.cl').render(**bloch_args)
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G_source = jinja_env.get_template('update_h.cl').render(**bloch_args)
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G_source = jinja_env.get_template('update_h.cl').render(**bloch_args)
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self.sources['F'] = F_source
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self.sources['F'] = F_source
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self.sources['G'] = G_source
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self.sources['G'] = G_source
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S_fields = {}
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S_fields = OrderedDict()
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if do_poynting:
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if do_poynting:
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self.S = pyopencl.array.zeros_like(self.E)
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self.S = pyopencl.array.zeros_like(self.E)
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S_fields[ptr('S')] = self.S
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S_fields[ptr('S')] = self.S
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@ -247,7 +237,7 @@ class Simulation:
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S_fields[ptr('S0')] = self.S0
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S_fields[ptr('S0')] = self.S0
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S_fields[ptr('S1')] = self.S1
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S_fields[ptr('S1')] = self.S1
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J_fields = {}
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J_fields = OrderedDict()
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if do_fieldsrc:
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if do_fieldsrc:
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J_source = jinja_env.get_template('update_j.cl').render(**jinja_args)
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J_source = jinja_env.get_template('update_j.cl').render(**jinja_args)
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self.sources['J'] = J_source
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self.sources['J'] = J_source
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@ -257,36 +247,37 @@ class Simulation:
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J_fields[ptr('Jr')] = self.Jr
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J_fields[ptr('Jr')] = self.Jr
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J_fields[ptr('Ji')] = self.Ji
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J_fields[ptr('Ji')] = self.Ji
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#
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'''
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# PML
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PML
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#
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'''
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pml_e_fields, pml_h_fields = self._create_pmls(pmls)
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pml_e_fields, pml_h_fields = self._create_pmls(pmls)
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if bloch_boundaries:
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if bloch_boundaries:
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pml_f_fields, pml_g_fields = self._create_pmls(pmls)
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pml_f_fields, pml_g_fields = self._create_pmls(pmls)
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#
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'''
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# Create operations
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Create operations
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#
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'''
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self.update_E = self._create_operation(E_source, (base_fields, eps_field, pml_e_fields))
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self.update_E = self._create_operation(E_source, (base_fields, eps_field, pml_e_fields))
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self.update_H = self._create_operation(H_source, (base_fields, pml_h_fields, S_fields))
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self.update_H = self._create_operation(H_source, (base_fields, pml_h_fields, S_fields))
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if bloch_boundaries:
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if bloch_boundaries:
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self.update_F = self._create_operation(F_source, (bloch_fields, eps_field, pml_f_fields))
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self.update_F = self._create_operation(F_source, (bloch_fields, eps_field, pml_f_fields))
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self.update_G = self._create_operation(G_source, (bloch_fields, pml_g_fields))
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self.update_G = self._create_operation(G_source, (bloch_fields, pml_g_fields))
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if do_fieldsrc:
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if do_fieldsrc:
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args = base_fields | J_fields
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args = OrderedDict()
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[args.update(d) for d in (base_fields, J_fields)]
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var_args = [ctype + ' ' + v for v in 'cs'] + ['uint ' + r + m for r in 'xyz' for m in ('min', 'max')]
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var_args = [ctype + ' ' + v for v in 'cs'] + ['uint ' + r + m for r in 'xyz' for m in ('min', 'max')]
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update = ElementwiseKernel(self.context, operation=J_source,
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update = ElementwiseKernel(self.context, operation=J_source,
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arguments=', '.join(list(args.keys()) + var_args))
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arguments=', '.join(list(args.keys()) + var_args))
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self.update_J = lambda e, *a: update(*args.values(), *a, wait_for=e)
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self.update_J = lambda e, *a: update(*args.values(), *a, wait_for=e)
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def _create_pmls(self, pmls: Sequence[dict[str, float]]) -> tuple[dict[str, pyopencl.array.Array], ...]:
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def _create_pmls(self, pmls):
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ctype = type_to_C(self.arg_type)
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ctype = type_to_C(self.arg_type)
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def ptr(arg: str) -> str:
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def ptr(arg: str) -> str:
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return ctype + ' *' + arg
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return ctype + ' *' + arg
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pml_e_fields = {}
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pml_e_fields = OrderedDict()
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pml_h_fields = {}
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pml_h_fields = OrderedDict()
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for pml in pmls:
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for pml in pmls:
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a = 'xyz'.find(pml['axis'])
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a = 'xyz'.find(pml['axis'])
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@ -294,9 +285,7 @@ class Simulation:
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kappa_max = numpy.sqrt(pml['mu_eff'] * pml['epsilon_eff'])
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kappa_max = numpy.sqrt(pml['mu_eff'] * pml['epsilon_eff'])
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alpha_max = pml['cfs_alpha']
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alpha_max = pml['cfs_alpha']
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print(sigma_max, kappa_max, alpha_max, pml['thickness'], self.dt)
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def par(x):
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def par(x, pml=pml, sigma_max=sigma_max, kappa_max=kappa_max, alpha_max=alpha_max): # noqa: ANN001, ANN202
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scaling = (x / pml['thickness']) ** pml['m']
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scaling = (x / pml['thickness']) ** pml['m']
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sigma = scaling * sigma_max
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sigma = scaling * sigma_max
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kappa = 1 + scaling * (kappa_max - 1)
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kappa = 1 + scaling * (kappa_max - 1)
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@ -312,13 +301,8 @@ class Simulation:
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elif pml['polarity'] == 'n':
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elif pml['polarity'] == 'n':
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xh -= 0.5
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xh -= 0.5
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logger.debug(f'{pml=}')
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logger.debug(f'{xe=}')
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logger.debug(f'{xh=}')
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logger.debug(f'{par(xe)=}')
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logger.debug(f'{par(xh)=}')
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pml_p_names = [['p' + pml['axis'] + i + eh + pml['polarity'] for i in '012'] for eh in 'eh']
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pml_p_names = [['p' + pml['axis'] + i + eh + pml['polarity'] for i in '012'] for eh in 'eh']
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for name_e, name_h, pe, ph in zip(pml_p_names[0], pml_p_names[1], par(xe), par(xh), strict=True):
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for name_e, name_h, pe, ph in zip(pml_p_names[0], pml_p_names[1], par(xe), par(xh)):
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pml_e_fields[ptr(name_e)] = pyopencl.array.to_device(self.queue, pe)
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pml_e_fields[ptr(name_e)] = pyopencl.array.to_device(self.queue, pe)
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pml_h_fields[ptr(name_h)] = pyopencl.array.to_device(self.queue, ph)
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pml_h_fields[ptr(name_h)] = pyopencl.array.to_device(self.queue, ph)
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@ -329,13 +313,13 @@ class Simulation:
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psi_shape = list(self.shape)
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psi_shape = list(self.shape)
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psi_shape[a] = pml['thickness']
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psi_shape[a] = pml['thickness']
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for ne, nh in zip(*psi_names, strict=True):
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for ne, nh in zip(*psi_names):
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pml_e_fields[ptr(ne)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
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pml_e_fields[ptr(ne)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
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pml_h_fields[ptr(nh)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
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pml_h_fields[ptr(nh)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
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return pml_e_fields, pml_h_fields
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return pml_e_fields, pml_h_fields
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def _create_operation(self, source: str, args_fields: Sequence[dict[str, pyopencl.array.Array]]) -> Callable[..., pyopencl.Event]:
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def _create_operation(self, source, args_fields) -> Callable[..., pyopencl.Event]:
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args = {}
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args = OrderedDict()
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for d in args_fields:
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for d in args_fields:
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args.update(d)
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args.update(d)
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update = ElementwiseKernel(
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update = ElementwiseKernel(
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@ -350,30 +334,38 @@ class Simulation:
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context: pyopencl.Context | None = None,
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context: pyopencl.Context | None = None,
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queue: pyopencl.CommandQueue | None = None,
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queue: pyopencl.CommandQueue | None = None,
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) -> None:
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) -> None:
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self.context = context or pyopencl.create_some_context()
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if context is None:
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self.queue = queue or pyopencl.CommandQueue(self.context)
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self.context = pyopencl.create_some_context()
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else:
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self.context = context
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if queue is None:
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self.queue = pyopencl.CommandQueue(self.context)
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else:
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self.queue = queue
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def _create_eps(self, epsilon: NDArray) -> pyopencl.array.Array:
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|
def _create_eps(self, epsilon: NDArray) -> pyopencl.array.Array:
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|
if len(epsilon) != 3:
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|
if len(epsilon) != 3:
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|
raise FDTDError('Epsilon must be a list with length of 3')
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|
raise Exception('Epsilon must be a list with length of 3')
|
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|
|
if not all(e.shape == epsilon[0].shape for e in epsilon[1:]):
|
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|
if not all((e.shape == epsilon[0].shape for e in epsilon[1:])):
|
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|
|
raise FDTDError('All epsilon grids must have the same shape. Shapes are {}', [e.shape for e in epsilon])
|
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|
|
raise Exception('All epsilon grids must have the same shape. Shapes are {}', [e.shape for e in epsilon])
|
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|
|
if not epsilon[0].shape == self.shape:
|
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|
|
if not epsilon[0].shape == self.shape:
|
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|
|
raise FDTDError(f'Epsilon shape mismatch. Expected {self.shape}, got {epsilon[0].shape}')
|
|
|
|
raise Exception(f'Epsilon shape mismatch. Expected {self.shape}, got {epsilon[0].shape}')
|
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|
|
self.eps = pyopencl.array.to_device(self.queue, vec(epsilon).astype(self.arg_type))
|
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|
|
self.eps = pyopencl.array.to_device(self.queue, vec(epsilon).astype(self.arg_type))
|
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|
|
def _create_field(self, initial_value: NDArray | None = None) -> pyopencl.array.Array:
|
|
|
|
def _create_field(self, initial_value: NDArray | None = None) -> pyopencl.array.Array:
|
|
|
|
if initial_value is None:
|
|
|
|
if initial_value is None:
|
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|
|
return pyopencl.array.zeros_like(self.eps)
|
|
|
|
return pyopencl.array.zeros_like(self.eps)
|
|
|
|
if len(initial_value) != 3:
|
|
|
|
else:
|
|
|
|
raise FDTDError('Initial field value must be a list of length 3')
|
|
|
|
if len(initial_value) != 3:
|
|
|
|
if not all(f.shape == self.shape for f in initial_value):
|
|
|
|
Exception('Initial field value must be a list of length 3')
|
|
|
|
raise FDTDError('Initial field list elements must have same shape as epsilon elements')
|
|
|
|
if not all((f.shape == self.shape for f in initial_value)):
|
|
|
|
return pyopencl.array.to_device(self.queue, vec(initial_value).astype(self.arg_type))
|
|
|
|
Exception('Initial field list elements must have same shape as epsilon elements')
|
|
|
|
|
|
|
|
return pyopencl.array.to_device(self.queue, vec(initial_value).astype(self.arg_type))
|
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|
|
def type_to_C(
|
|
|
|
def type_to_C(
|
|
|
|
float_type: type,
|
|
|
|
float_type: Type,
|
|
|
|
) -> str:
|
|
|
|
) -> str:
|
|
|
|
"""
|
|
|
|
"""
|
|
|
|
Returns a string corresponding to the C equivalent of a numpy type.
|
|
|
|
Returns a string corresponding to the C equivalent of a numpy type.
|
|
|
@ -393,7 +385,7 @@ def type_to_C(
|
|
|
|
numpy.complex128: 'cdouble_t',
|
|
|
|
numpy.complex128: 'cdouble_t',
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if float_type not in types:
|
|
|
|
if float_type not in types:
|
|
|
|
raise FDTDError(f'Unsupported type: {float_type}')
|
|
|
|
raise Exception(f'Unsupported type: {float_type}')
|
|
|
|
|
|
|
|
|
|
|
|
return types[float_type]
|
|
|
|
return types[float_type]
|
|
|
|
|
|
|
|
|
|
|
|