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Class for constructing and holding the basic FDTD operations and fields
from typing import List, Dict, Callable
from collections import OrderedDict
import numpy
import jinja2
import warnings
import pyopencl
import pyopencl.array
from pyopencl.elementwise import ElementwiseKernel
from fdfd_tools import vec
__author__ = 'Jan Petykiewicz'
# Create jinja2 env on module load
jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
class Simulation(object):
Constructs and holds the basic FDTD operations and related fields
After constructing this object, call the (update_E, update_H, update_S) members
to perform FDTD updates on the stored (E, H, S) fields:
pmls = [{'axis': a, 'polarity': p} for a in 'xyz' for p in 'np']
sim = Simulation(grid.grids, do_poynting=True, pmls=pmls)
with open('sources.c', 'w') as f:
for t in range(max_t):
# Find the linear index for the center point, for Ey
ind = numpy.ravel_multi_index(tuple(grid.shape//2), dims=grid.shape, order='C') + \ * 1
# Perturb the field (i.e., add a soft current source)
sim.E[ind] += numpy.sin(omega * t * sim.dt)
event = sim.update_H([])
if sim.update_S:
event = sim.update_S([event])
with'saved_simulation', 'wb') as f:
dill.dump(fdfd_tools.unvec(sim.E.get(), grid.shape), f)
Code in the form
event2 = sim.update_H([event0, event1])
indicates that the update_H operation should be prepared immediately, but wait for
event0 and event1 to occur (i.e. previous operations to finish) before starting execution.
event2 can then be used to prepare further operations to be run after update_H.
E = None # type: pyopencl.array.Array
H = None # type: pyopencl.array.Array
S = None # type: pyopencl.array.Array
eps = None # type: pyopencl.array.Array
dt = None # type: float
inv_dxes = None # type: List[pyopencl.array.Array]
arg_type = None # type: numpy.float32 or numpy.float64
context = None # type: pyopencl.Context
queue = None # type: pyopencl.CommandQueue
update_E = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
update_H = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
update_S = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
update_J = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
sources = None # type: Dict[str, str]
def __init__(self,
epsilon: List[numpy.ndarray],
pmls: List[Dict[str, int or float]],
bloch_boundaries: List[Dict[str, int or float]] = (),
dxes: List[List[numpy.ndarray]] or float = None,
dt: float = None,
initial_fields: Dict[str, List[numpy.ndarray]] = None,
context: pyopencl.Context = None,
queue: pyopencl.CommandQueue = None,
float_type: numpy.float32 or numpy.float64 = numpy.float32,
do_poynting: bool = True,
do_fieldsrc: bool = False):
Initialize the simulation.
:param epsilon: List containing [eps_r,xx, eps_r,yy, eps_r,zz], where each element is a Yee-shifted ndarray
spanning the simulation domain. Relative epsilon is used.
:param pmls: List of dicts with keys:
'axis': One of 'x', 'y', 'z'.
'direction': One of 'n', 'p'.
'thickness': Number of layers, default 8.
'epsilon_eff': Effective epsilon to match to. Default 1.0.
'mu_eff': Effective mu to match to. Default 1.0.
'ln_R_per_layer': Desired (ln(R) / thickness) value. Default -1.6.
'm': Polynomial grading exponent. Default 3.5.
'ma': Exponent for alpha. Default 1.
:param bloch_boundaries: List of dicts with keys:
'axis': One of 'x', 'y', 'z'.
'real': Real part of bloch phase factor (i.e. real(exp(i * phase)))
'imag': Imaginary part of bloch phase factor (i.e. imag(exp(i * phase)))
:param dt: Time step. Default is min(dxes) * .99/sqrt(3).
:param initial_fields: Dict with optional keys ('E', 'H', 'F', 'G') containing initial values for the
specified fields (default is 0 everywhere). Fields have same format as epsilon.
:param context: pyOpenCL context. If not given, pyopencl.create_some_context(False) is called.
:param queue: pyOpenCL command queue. If not given, pyopencl.CommandQueue(context) is called.
:param float_type: numpy.float32 or numpy.float64. Default numpy.float32.
:param do_poynting: If true, enables calculation of the poynting vector, S.
Poynting vector calculation adds the following computational burdens:
* During update_H, ~6 extra additions/cell are performed in order to spatially
average E and temporally average H. These quantities are multiplied
(6 multiplications/cell) and then stored (6 writes/cell, cache-friendly).
* update_S performs a discrete cross product using the precalculated products
from update_H. This is not nice to the cache and similar to e.g. update_E
in complexity.
* GPU memory requirements are approximately doubled, since S and the intermediate
products must be stored.
if initial_fields is None:
initial_fields = {}
self.shape = epsilon[0].shape
self.arg_type = float_type
self.sources = {}
self._create_context(context, queue)
if dxes is None:
dxes = 1.0
if isinstance(dxes, (float, int)):
uniform_dx = dxes
min_dx = dxes
uniform_dx = False
self.inv_dxes = [self._create_field(1 / dxn) for dxn in dxes[0] + dxes[1]]
min_dx = min(min(dxn) for dxn in dxes[0] + dxes[1])
max_dt = min_dx * .99 / numpy.sqrt(3)
if dt is None:
self.dt = max_dt
elif dt > max_dt:
warnings.warn('Warning: unstable dt: {}'.format(dt))
elif dt <= 0:
raise Exception('Invalid dt: {}'.format(dt))
self.dt = dt
self.E = self._create_field(initial_fields.get('E', None))
self.H = self._create_field(initial_fields.get('H', None))
if bloch_boundaries:
self.F = self._create_field(initial_fields.get('F', None))
self.G = self._create_field(initial_fields.get('G', None))
for pml in pmls:
pml.setdefault('thickness', 8)
pml.setdefault('epsilon_eff', 1.0)
pml.setdefault('mu_eff', 1.0)
pml.setdefault('ln_R_per_layer', -1.6)
pml.setdefault('m', 3.5)
pml.setdefault('cfs_alpha', 0)
pml.setdefault('ma', 1)
ctype = type_to_C(self.arg_type)
def ptr(arg: str) -> str:
return ctype + ' * restrict ' + arg
base_fields = OrderedDict()
base_fields[ptr('E')] = self.E
base_fields[ptr('H')] = self.H
base_fields[ctype + ' dt'] = self.dt
if uniform_dx == False:
inv_dx_names = ['inv_d' + eh + r for eh in 'eh' for r in 'xyz']
for name, field in zip(inv_dx_names, self.inv_dxes):
base_fields[ptr(name)] = field
eps_field = OrderedDict()
eps_field[ptr('eps')] = self.eps
if bloch_boundaries:
base_fields[ptr('F')] = self.F
base_fields[ptr('G')] = self.G
bloch_fields = OrderedDict()
bloch_fields[ptr('E')] = self.F
bloch_fields[ptr('H')] = self.G
bloch_fields[ctype + ' dt'] = self.dt
bloch_fields[ptr('F')] = self.E
bloch_fields[ptr('G')] = self.H
common_source = jinja_env.get_template('').render(
jinja_args = {
'common_header': common_source,
'pmls': pmls,
'do_poynting': do_poynting,
'bloch': bloch_boundaries,
'uniform_dx': uniform_dx,
E_source = jinja_env.get_template('').render(**jinja_args)
H_source = jinja_env.get_template('').render(**jinja_args)
self.sources['E'] = E_source
self.sources['H'] = H_source
if bloch_boundaries:
bloch_args = jinja_args.copy()
bloch_args['do_poynting'] = False
bloch_args['bloch'] = [{'axis': b['axis'],
'real': b['imag'],
'imag': b['real']}
for b in bloch_boundaries]
F_source = jinja_env.get_template('').render(**bloch_args)
G_source = jinja_env.get_template('').render(**bloch_args)
self.sources['F'] = F_source
self.sources['G'] = G_source
S_fields = OrderedDict()
if do_poynting:
S_source = jinja_env.get_template('').render(**jinja_args)
self.sources['S'] = S_source
self.oS = pyopencl.array.zeros(self.queue, self.E.shape + (2,), dtype=self.arg_type)
self.S = pyopencl.array.zeros_like(self.E)
S_fields[ptr('oS')] = self.oS
S_fields[ptr('S')] = self.S
J_fields = OrderedDict()
if do_fieldsrc:
J_source = jinja_env.get_template('').render(**jinja_args)
self.sources['J'] = J_source
self.Ji = pyopencl.array.zeros_like(self.E)
self.Jr = pyopencl.array.zeros_like(self.E)
J_fields[ptr('Jr')] = self.Jr
J_fields[ptr('Ji')] = self.Ji
pml_e_fields, pml_h_fields = self._create_pmls(pmls)
if bloch_boundaries:
pml_f_fields, pml_g_fields = self._create_pmls(pmls)
Create operations
self.update_E = self._create_operation(E_source, (base_fields, eps_field, pml_e_fields))
self.update_H = self._create_operation(H_source, (base_fields, pml_h_fields, S_fields))
if do_poynting:
self.update_S = self._create_operation(S_source, (base_fields, S_fields))
if bloch_boundaries:
self.update_F = self._create_operation(F_source, (bloch_fields, eps_field, pml_f_fields))
self.update_G = self._create_operation(G_source, (bloch_fields, pml_g_fields))
if do_fieldsrc:
args = OrderedDict()
[args.update(d) for d in (base_fields, J_fields)]
var_args = [ctype + ' ' + v for v in 'cs'] + ['uint ' + r + m for r in 'xyz' for m in ('min', 'max')]
update = ElementwiseKernel(self.context, operation=J_source,
arguments=', '.join(list(args.keys()) + var_args))
self.update_J = lambda e, *a: update(*args.values(), *a, wait_for=e)
def _create_pmls(self, pmls):
ctype = type_to_C(self.arg_type)
def ptr(arg: str) -> str:
return ctype + ' *' + arg
pml_e_fields = OrderedDict()
pml_h_fields = OrderedDict()
for pml in pmls:
a = 'xyz'.find(pml['axis'])
sigma_max = -pml['ln_R_per_layer'] / 2 * (pml['m'] + 1)
kappa_max = numpy.sqrt(pml['mu_eff'] * pml['epsilon_eff'])
alpha_max = pml['cfs_alpha']
def par(x):
scaling = ((x / (pml['thickness'])) ** pml['m'])
sigma = scaling * sigma_max
kappa = 1 + scaling * (kappa_max - 1)
alpha = ((1 - x / pml['thickness']) ** pml['ma']) * alpha_max
p0 = numpy.exp(-(sigma / kappa + alpha) * self.dt)
p1 = sigma / (sigma + kappa * alpha) * (p0 - 1)
p2 = 1/kappa
return p0, p1, p2
xe, xh = (numpy.arange(1, pml['thickness'] + 1, dtype=self.arg_type)[::-1] for _ in range(2))
if pml['polarity'] == 'p':
xe -= 0.5
elif pml['polarity'] == 'n':
xh -= 0.5
pml_p_names = [['p' + pml['axis'] + i + eh + pml['polarity'] for i in '012'] for eh in 'eh']
for name_e, name_h, pe, ph in zip(pml_p_names[0], pml_p_names[1], par(xe), par(xh)):
pml_e_fields[ptr(name_e)] = pyopencl.array.to_device(self.queue, pe)
pml_h_fields[ptr(name_h)] = pyopencl.array.to_device(self.queue, ph)
uv = 'xyz'.replace(pml['axis'], '')
psi_base = 'Psi_' + pml['axis'] + pml['polarity'] + '_'
psi_names = [[psi_base + eh + c for c in uv] for eh in 'EH']
psi_shape = list(self.shape)
psi_shape[a] = pml['thickness']
for ne, nh in zip(*psi_names):
pml_e_fields[ptr(ne)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
pml_h_fields[ptr(nh)] = pyopencl.array.zeros(self.queue, tuple(psi_shape), dtype=self.arg_type)
return pml_e_fields, pml_h_fields
def _create_operation(self, source, args_fields):
args = OrderedDict()
[args.update(d) for d in args_fields]
update = ElementwiseKernel(self.context, operation=source,
arguments=', '.join(args.keys()))
return lambda e: update(*args.values(), wait_for=e)
def _create_context(self, context: pyopencl.Context = None,
queue: pyopencl.CommandQueue = None):
if context is None:
self.context = pyopencl.create_some_context()
self.context = context
if queue is None:
self.queue = pyopencl.CommandQueue(self.context)
self.queue = queue
def _create_eps(self, epsilon: List[numpy.ndarray]):
if len(epsilon) != 3:
raise Exception('Epsilon must be a list with length of 3')
if not all((e.shape == epsilon[0].shape for e in epsilon[1:])):
raise Exception('All epsilon grids must have the same shape. Shapes are {}', [e.shape for e in epsilon])
if not epsilon[0].shape == self.shape:
raise Exception('Epsilon shape mismatch. Expected {}, got {}'.format(self.shape, epsilon[0].shape))
self.eps = pyopencl.array.to_device(self.queue, vec(epsilon).astype(self.arg_type))
def _create_field(self, initial_value: List[numpy.ndarray] = None):
if initial_value is None:
return pyopencl.array.zeros_like(self.eps)
if len(initial_value) != 3:
Exception('Initial field value must be a list of length 3')
if not all((f.shape == self.shape for f in initial_value)):
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))
def type_to_C(float_type) -> str:
Returns a string corresponding to the C equivalent of a numpy type.
Only works for float16, float32, float64.
:param float_type: e.g. numpy.float32
:return: string containing the corresponding C type (eg. 'double')
if float_type == numpy.float16:
arg_type = 'half'
elif float_type == numpy.float32:
arg_type = 'float'
elif float_type == numpy.float64:
arg_type = 'double'
raise Exception('Unsupported type')
return arg_type