style and type fixes

fdtd.py

@@ -30,55 +30,64 @@ def perturbed_l3(a: float, radius: float, **kwargs) -> Pattern:
     """
     Generate a masque.Pattern object containing a perturbed L3 cavity.
 
-    :param a: Lattice constant.
-    :param radius: Hole radius, in units of a (lattice constant).
-    :param kwargs: Keyword arguments:
-        hole_dose, trench_dose, hole_layer, trench_layer: Shape properties for Pattern.
-                Defaults *_dose=1, hole_layer=0, trench_layer=1.
-        shifts_a, shifts_r: passed to pcgen.l3_shift; specifies lattice constant (1 -
-                multiplicative factor) and radius (multiplicative factor) for shifting
-                holes adjacent to the defect (same row). Defaults are 0.15 shift for
-                first hole, 0.075 shift for third hole, and no radius change.
+    Args:
+        a: Lattice constant.
+        radius: Hole radius, in units of a (lattice constant).
+        hole_dose: Dose for all holes. Default 1.
+        trench_dose: Dose for undercut trenches. Default 1.
+        hole_layer: Layer for holes. Default (0, 0).
+        trench_layer: Layer for undercut trenches. Default (1, 0).
+        shifts_a: passed to pcgen.l3_shift
+        shifts_r: passed to pcgen.l3_shift
         xy_size: [x, y] number of mirror periods in each direction; total size is
-                2 * n + 1 holes in each direction. Default [10, 10].
+                2 * n + 1 holes in each direction. Default (10, 10).
         perturbed_radius: radius of holes perturbed to form an upwards-driected beam
                 (multiplicative factor). Default 1.1.
         trench width: Width of the undercut trenches. Default 1.2e3.
-    :return: masque.Pattern object containing the L3 design
+
+    Returns:
+        `masque.Pattern` object containing the L3 design
     """
 
-    default_args = {'hole_dose':    1,
-                    'trench_dose':  1,
-                    'hole_layer':   0,
-                    'trench_layer': 1,
-                    'shifts_a':     (0.15, 0, 0.075),
-                    'shifts_r':     (1.0, 1.0, 1.0),
-                    'xy_size':      (10, 10),
-                    'perturbed_radius': 1.1,
-                    'trench_width': 1.2e3,
-                    }
+    default_args = {
+        'hole_dose':    1,
+        'trench_dose':  1,
+        'hole_layer':   0,
+        'trench_layer': 1,
+        'shifts_a':     (0.15, 0, 0.075),
+        'shifts_r':     (1.0, 1.0, 1.0),
+        'xy_size':      (10, 10),
+        'perturbed_radius': 1.1,
+        'trench_width': 1.2e3,
+        }
     kwargs = {**default_args, **kwargs}
 
-    xyr = pcgen.l3_shift_perturbed_defect(mirror_dims=kwargs['xy_size'],
-                                          perturbed_radius=kwargs['perturbed_radius'],
-                                          shifts_a=kwargs['shifts_a'],
-                                          shifts_r=kwargs['shifts_r'])
+    xyr = pcgen.l3_shift_perturbed_defect(
+        mirror_dims=kwargs['xy_size'],
+        perturbed_radius=kwargs['perturbed_radius'],
+        shifts_a=kwargs['shifts_a'],
+        shifts_r=kwargs['shifts_r'],
+        )
     xyr *= a
     xyr[:, 2] *= radius
 
     pat = Pattern()
-    pat.name = 'L3p-a{:g}r{:g}rp{:g}'.format(a, radius, kwargs['perturbed_radius'])
+    pat.name = f'L3p-a{a:g}r{radius:g}rp{kwargs["perturbed_radius"]:g}'
     pat.shapes += [shapes.Circle(radius=r, offset=(x, y),
                                  dose=kwargs['hole_dose'],
                                  layer=kwargs['hole_layer'])
                    for x, y, r in xyr]
 
     maxes = numpy.max(numpy.fabs(xyr), axis=0)
-    pat.shapes += [shapes.Polygon.rectangle(
-        lx=(2 * maxes[0]), ly=kwargs['trench_width'],
-        offset=(0, s * (maxes[1] + a + kwargs['trench_width'] / 2)),
-        dose=kwargs['trench_dose'], layer=kwargs['trench_layer'])
-                   for s in (-1, 1)]
+    pat.shapes += [
+        shapes.Polygon.rectangle(
+            lx=(2 * maxes[0]),
+            ly=kwargs['trench_width'],
+            offset=(0, s * (maxes[1] + a + kwargs['trench_width'] / 2)),
+            dose=kwargs['trench_dose'],
+            layer=kwargs['trench_layer'],
+            )
+        for s in (-1, 1)]
     return pat
 
 
@@ -226,7 +235,8 @@ def main():
 #                                                                                            pml_thickness+m:-pml_thickness-m, :].sum() * dx * dx * dx
 
         if t % 100 == 0:
-            logger.info('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
+            avg = (t + 1) / (time.perf_counter() - start)
+            logger.info(f'iteration {t}: average {avg} iterations per sec')
             sys.stdout.flush()
 
     with lzma.open('saved_simulation', 'wb') as f:

opencl_fdtd/simulation.py

@@ -2,9 +2,10 @@
 Class for constructing and holding the basic FDTD operations and fields
 """
 
-from typing import List, Dict, Callable
+from typing import List, Dict, Callable, Type, Union, Optional, Sequence
 from collections import OrderedDict
 import numpy
+from numpy.typing import NDArray
 import jinja2
 import warnings
 
@@ -31,8 +32,8 @@ class Simulation(object):
 
         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', 'wt') as f:
-            f.write(repr(sim.sources))
+        with open('sources.c', 'w') as f:
+            f.write(f'{sim.sources}')
 
         for t in range(max_t):
             sim.update_E([]).wait()
@@ -56,38 +57,38 @@ class Simulation(object):
      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]
+    E: pyopencl.array.Array
+    H: pyopencl.array.Array
+    S: pyopencl.array.Array
+    eps: pyopencl.array.Array
+    dt: float
+    inv_dxes: List[pyopencl.array.Array]
 
-    arg_type = None     # type: numpy.float32 or numpy.float64
+    arg_type: Type
 
-    context = None      # type: pyopencl.Context
-    queue = None        # type: pyopencl.CommandQueue
+    context: pyopencl.Context
+    queue: 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]
+    update_E: Callable[[List[pyopencl.Event]], pyopencl.Event]
+    update_H: Callable[[List[pyopencl.Event]], pyopencl.Event]
+    update_S: Callable[[List[pyopencl.Event]], pyopencl.Event]
+    update_J: Callable[[List[pyopencl.Event]], pyopencl.Event]
+    sources: 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_poynting_halves: bool = False,
-                 do_fieldsrc: bool = False,
-                 ) -> None:
+    def __init__(
+            self,
+            epsilon: NDArray,
+            pmls: Sequence[Dict[str, float]],
+            bloch_boundaries: Sequence[Dict[str, float]] = (),
+            dxes: Union[List[List[NDArray]], float, None] = None,
+            dt: Optional[float] = None,
+            initial_fields: Optional[Dict[str, NDArray]] = None,
+            context: Optional[pyopencl.Context] = None,
+            queue: Optional[pyopencl.CommandQueue] = None,
+            float_type: Type = numpy.float32,
+            do_poynting: bool = True,
+            do_fieldsrc: bool = False,
+            ) -> None:
         """
         Initialize the simulation.
 
@@ -113,14 +114,13 @@ class Simulation(object):
             context: pyOpenCL context. If not given, pyopencl.create_some_context(False) is called.
             queue: pyOpenCL command queue. If not given, pyopencl.CommandQueue(context) is called.
             float_type: numpy.float32 or numpy.float64. Default numpy.float32.
-            do_poynting: If true, enables calculation of the poynting vector, S.
+            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 temporally
-                    sum H. The results are then multiplied by E (6 multiplications/cell) and
+                * During update_H, 12 extra additions/cell are performed in order to temporally
+                    sum E and H. The results are then multiplied by E (6 multiplications/cell) and
                     then stored (6 writes/cell, cache-friendly). The E-field components are
                     reused from the H-field update and do not require additional H
                 * GPU memory requirements increase by 50% (for storing S)
-            do_poynting_halves: TODO DOCUMENT
         """
         if initial_fields is None:
             initial_fields = {}
@@ -147,9 +147,9 @@ class Simulation(object):
         if dt is None:
             self.dt = max_dt
         elif dt > max_dt:
-            warnings.warn('Warning: unstable dt: {}'.format(dt))
+            warnings.warn(f'Warning: unstable dt: {dt}')
         elif dt <= 0:
-            raise Exception('Invalid dt: {}'.format(dt))
+            raise Exception(f'Invalid dt: {dt}')
         else:
             self.dt = dt
 
@@ -216,10 +216,12 @@ class Simulation(object):
         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]
+            bloch_args['bloch'] = [
+                {'axis': b['axis'],
+                 'real': b['imag'],
+                 'imag': b['real'],
+                }
+                for b in bloch_boundaries]
             F_source = jinja_env.get_template('update_e.cl').render(**bloch_args)
             G_source = jinja_env.get_template('update_h.cl').render(**bloch_args)
             self.sources['F'] = F_source
@@ -316,15 +318,22 @@ class Simulation(object):
                 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):
+    def _create_operation(self, source, args_fields) -> Callable[..., pyopencl.Event]:
         args = OrderedDict()
-        [args.update(d) for d in args_fields]
-        update = ElementwiseKernel(self.context, operation=source,
-                                   arguments=', '.join(args.keys()))
+        for d in args_fields:
+            args.update(d)
+        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):
+    def _create_context(
+            self,
+            context: Optional[pyopencl.Context] = None,
+            queue: Optional[pyopencl.CommandQueue] = None,
+            ) -> None:
         if context is None:
             self.context = pyopencl.create_some_context()
         else:
@@ -335,16 +344,16 @@ class Simulation(object):
         else:
             self.queue = queue
 
-    def _create_eps(self, epsilon: List[numpy.ndarray]):
+    def _create_eps(self, epsilon: NDArray) -> pyopencl.array.Array:
         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))
+            raise Exception(f'Epsilon shape mismatch. Expected {self.shape}, got {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):
+    def _create_field(self, initial_value: Optional[NDArray] = None) -> pyopencl.array.Array:
         if initial_value is None:
             return pyopencl.array.zeros_like(self.eps)
         else:
@@ -355,23 +364,30 @@ class Simulation(object):
             return pyopencl.array.to_device(self.queue, vec(initial_value).astype(self.arg_type))
 
 
-def type_to_C(float_type: numpy.dtype) -> str:
+def type_to_C(
+        float_type: 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')
+    Args:
+        float_type: e.g. numpy.float32
+
+    Returns:
+        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'
-    else:
-        raise Exception('Unsupported type')
-    return arg_type
+    types = {
+        numpy.float16: 'half',
+        numpy.float32: 'float',
+        numpy.float64: 'double',
+        numpy.complex64: 'cfloat_t',
+        numpy.complex128: 'cdouble_t',
+    }
+    if float_type not in types:
+        raise Exception(f'Unsupported type: {float_type}')
+
+    return types[float_type]
 
 #            def par(x):
 #                scaling = ((x / (pml['thickness'])) ** pml['m'])

opencl_fdtd/test_simulation.py

@@ -2,7 +2,7 @@ import unittest
 import numpy
 
 from opencl_fdtd import Simulation
-from fdfd_tools import fdtd
+from meanas import fdtd
 
 
 class BasicTests():
@@ -25,16 +25,18 @@ class BasicTests():
         dxes = self.dxes if self.dxes is not None else tuple(tuple(numpy.ones(s) for s in e0.shape[1:]) for _ in range(2))
         dV = numpy.prod(numpy.meshgrid(*dxes[0], indexing='ij'), axis=0)
         u0 = self.j_mag * self.j_mag / self.epsilon[self.src_mask] * dV[mask]
-        args = {'dxes': self.dxes,
-                'epsilon': self.epsilon}
+        args = {
+            'dxes': self.dxes,
+            'epsilon': self.epsilon,
+            }
 
         # Make sure initial energy and E dot J are correct
         energy0 = fdtd.energy_estep(h0=h0, e1=e0, h2=self.hs[1], **args)
         e_dot_j_0 = fdtd.delta_energy_j(j0=(e0 - 0) * self.epsilon, e1=e0, dxes=self.dxes)
         self.assertTrue(numpy.allclose(energy0[mask], u0))
-        self.assertFalse(energy0[~mask].any(), msg='energy0: {}'.format(energy0))
+        self.assertFalse(energy0[~mask].any(), msg=f'{energy0=}')
         self.assertTrue(numpy.allclose(e_dot_j_0[mask], u0))
-        self.assertFalse(e_dot_j_0[~mask].any(), msg='e_dot_j_0: {}'.format(e_dot_j_0))
+        self.assertFalse(e_dot_j_0[~mask].any(), msg=f'{e_dot_j_0=}')
 
 
     def test_energy_conservation(self):
@@ -47,22 +49,25 @@ class BasicTests():
             with self.subTest(i=ii):
                 u_hstep = fdtd.energy_hstep(e0=self.es[ii-1], h1=self.hs[ii], e2=self.es[ii], **args)
                 u_estep = fdtd.energy_estep(h0=self.hs[ii], e1=self.es[ii], h2=self.hs[ii + 1], **args)
-                self.assertTrue(numpy.allclose(u_hstep.sum(), u0), msg='u_hstep: {}\n{}'.format(u_hstep.sum(), numpy.rollaxis(u_hstep, -1)))
-                self.assertTrue(numpy.allclose(u_estep.sum(), u0), msg='u_estep: {}\n{}'.format(u_estep.sum(), numpy.rollaxis(u_estep, -1)))
+                self.assertTrue(numpy.allclose(u_hstep.sum(), u0), msg=f'u_hstep: {u_hstep.sum()}\n{numpy.moveaxis(u_hstep, -1, 0)}')
+                self.assertTrue(numpy.allclose(u_estep.sum(), u0), msg=f'u_estep: {u_estep.sum()}\n{numpy.moveaxis(u_estep, -1, 0)}')
 
 
     def test_poynting(self):
         for ii in range(1, 3):
             with self.subTest(i=ii):
                 s = fdtd.poynting(e=self.es[ii], h=self.hs[ii+1] + self.hs[ii])
+                sf = numpy.moveaxis(s, -1, 0)
+                ss = numpy.moveaxis(self.ss[ii], -1, 0)
                 self.assertTrue(numpy.allclose(s, self.ss[ii], rtol=1e-4),
-                                msg='From ExH:\n{}\nFrom sim.S:\n{}'.format(numpy.rollaxis(s, -1),
-                                                                            numpy.rollaxis(self.ss[ii], -1)))
+                                msg=f'From ExH:\n{sf}\nFrom sim.S:\n{ss}')
 
 
     def test_poynting_divergence(self):
-        args = {'dxes': self.dxes,
-                'epsilon': self.epsilon}
+        args = {
+            'dxes': self.dxes,
+            'epsilon': self.epsilon,
+            }
 
         dxes = self.dxes if self.dxes is not None else tuple(tuple(numpy.ones(s) for s in self.epsilon.shape[1:]) for _ in range(2))
         dV = numpy.prod(numpy.meshgrid(*dxes[0], indexing='ij'), axis=0)
@@ -75,9 +80,11 @@ class BasicTests():
 
                 du_half_h2e = u_estep - u_hstep
                 div_s_h2e = self.dt * fdtd.poynting_divergence(e=self.es[ii], h=self.hs[ii], dxes=self.dxes) * dV
+
+                du_half_h2e_f = numpy.moveaxis(du_half_h2e, -1, 0)
+                div_s_h2e_f = -numpy.moveaxis(div_s_h2e, -1, 0)
                 self.assertTrue(numpy.allclose(du_half_h2e, -div_s_h2e, rtol=1e-4),
-                                msg='du_half_h2e\n{}\ndiv_s_h2e\n{}'.format(numpy.rollaxis(du_half_h2e, -1),
-                                                                           -numpy.rollaxis(div_s_h2e, -1)))
+                                msg=f'du_half_h2e\n{du_half_h2e_f}\ndiv_s_h2e\n{div_s_h2e_f}')
 
                 if u_eprev is None:
                     u_eprev = u_estep
@@ -86,15 +93,18 @@ class BasicTests():
                 # previous half-step
                 du_half_e2h = u_hstep - u_eprev
                 div_s_e2h = self.dt * fdtd.poynting_divergence(e=self.es[ii-1], h=self.hs[ii], dxes=self.dxes) * dV
+                du_half_e2h_f = numpy.moveaxis(du_half_e2h, -1, 0)
+                div_s_e2h_f = -numpy.moveaxis(div_s_e2h, -1, 0)
                 self.assertTrue(numpy.allclose(du_half_e2h, -div_s_e2h, rtol=1e-4),
-                                msg='du_half_e2h\n{}\ndiv_s_e2h\n{}'.format(numpy.rollaxis(du_half_e2h, -1),
-                                                                           -numpy.rollaxis(div_s_e2h, -1)))
+                                msg=f'du_half_e2h\n{du_half_e2h_f}\ndiv_s_e2h\n{div_s_e2h_f}')
                 u_eprev = u_estep
 
 
     def test_poynting_planes(self):
-        args = {'dxes': self.dxes,
-                'epsilon': self.epsilon}
+        args = {
+            'dxes': self.dxes,
+            'epsilon': self.epsilon,
+            }
         dxes = self.dxes if self.dxes is not None else tuple(tuple(numpy.ones(s) for s in self.epsilon.shape[1:]) for _ in range(2))
         dV = numpy.prod(numpy.meshgrid(*dxes[0], indexing='ij'), axis=0)
 
@@ -118,8 +128,9 @@ class BasicTests():
                 planes = [s_h2e[px].sum(), -s_h2e[mx].sum(),
                           s_h2e[py].sum(), -s_h2e[my].sum(),
                           s_h2e[pz].sum(), -s_h2e[mz].sum()]
+                du = (u_estep - u_hstep)[self.src_mask[1]]
                 self.assertTrue(numpy.allclose(sum(planes), (u_estep - u_hstep)[self.src_mask[1]]),
-                                msg='planes: {} (sum: {})\n du:\n {}'.format(planes, sum(planes), (u_estep - u_hstep)[self.src_mask[1]]))
+                                msg=f'planes: {planes} (sum: {sum(planes)})\n du:\n {du}')
 
                 if u_eprev is None:
                     u_eprev = u_estep
@@ -132,15 +143,14 @@ class BasicTests():
                 planes = [s_e2h[px].sum(), -s_e2h[mx].sum(),
                           s_e2h[py].sum(), -s_e2h[my].sum(),
                           s_e2h[pz].sum(), -s_e2h[mz].sum()]
+                du = (u_hstep - u_eprev)[self.src_mask[1]]
                 self.assertTrue(numpy.allclose(sum(planes), (u_hstep - u_eprev)[self.src_mask[1]]),
-                                msg='planes: {} (sum: {})\n du:\n {}'.format(planes, sum(planes), (u_hstep - u_eprev)[self.src_mask[1]]))
+                                msg=f'planes: {du} (sum: {sum(planes)})\n du:\n {du}')
 
                 # previous half-step
                 u_eprev = u_estep
 
 
-
-
 class Basic2DNoDXOnlyVacuum(unittest.TestCase, BasicTests):
     def setUp(self):
         shape = [3, 5, 5, 1]
@@ -348,8 +358,10 @@ class JdotE_3DUniformDX(unittest.TestCase):
         e1 = self.es[2]
         j0 = numpy.zeros_like(e0)
         j0[self.src_mask] = self.j_mag
-        args = {'dxes': self.dxes,
-                'epsilon': self.epsilon}
+        args = {
+            'dxes': self.dxes,
+            'epsilon': self.epsilon,
+            }
         e2h = fdtd.maxwell_h(dt=self.dt, dxes=self.dxes)
 
         #ee = j0 * (2 * e0 - j0)
@@ -365,4 +377,5 @@ class JdotE_3DUniformDX(unittest.TestCase):
         u_hstep = fdtd.energy_hstep(e0=self.es[0], h1=self.hs[1], e2=self.es[1], **args)
         u_estep = fdtd.energy_estep(h0=self.hs[-2], e1=self.es[-2], h2=self.hs[-1], **args)
         #breakpoint()
-        self.assertTrue(numpy.allclose(u0.sum(), (u_estep - u_hstep).sum()), msg='{} != {}'.format(u0.sum(), (u_estep - u_hstep).sum()))
+        du = (u_estep - u_hstep).sum()
+        self.assertTrue(numpy.allclose(u0.sum(), (u_estep - u_hstep).sum()), msg=f'{u0.sum()} != {du}')