update deps

opencl_fdtd/__init__.py

@@ -1,4 +1,7 @@
-from .simulation import Simulation, type_to_C
+from .simulation import (
+    Simulation as Simulation,
+    type_to_C as type_to_C,
+    )
 
 __author__ = 'Jan Petykiewicz'
 __version__ = '0.4'

opencl_fdtd/simulation.py

@@ -2,12 +2,13 @@
 Class for constructing and holding the basic FDTD operations and fields
 """
 
-from typing import Callable, Type, Sequence
+from collections.abc import Callable, Sequence
-from collections import OrderedDict
 import numpy
 from numpy.typing import NDArray
+from numpy import floating, complexfloating
 import jinja2
 import warnings
+import logging
 
 import pyopencl
 import pyopencl.array
@@ -16,13 +17,18 @@ from pyopencl.elementwise import ElementwiseKernel
 from meanas.fdmath import vec
 
 
-__author__ = 'Jan Petykiewicz'
+logger = logging.getLogger(__name__)
 
 
 # Create jinja2 env on module load
 jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__.split('.')[0], 'kernels'))
 
 
+class FDTDError(Exception):
+    """ Custom exception for opencl_fdtd """
+    pass
+
+
 class Simulation:
     r"""
     Constructs and holds the basic FDTD operations and related fields
@@ -64,7 +70,7 @@ class Simulation:
     dt: float
     inv_dxes: list[pyopencl.array.Array]
 
-    arg_type: Type
+    arg_type: type
 
     context: pyopencl.Context
     queue: pyopencl.CommandQueue
@@ -85,7 +91,7 @@ class Simulation:
             initial_fields: dict[str, NDArray] | None = None,
             context: pyopencl.Context | None = None,
             queue: pyopencl.CommandQueue | None = None,
-            float_type: Type = numpy.float32,
+            float_type: type = numpy.float32,
             do_poynting: bool = True,
             do_fieldsrc: bool = False,
             ) -> None:
@@ -134,7 +140,7 @@ class Simulation:
         if dxes is None:
             dxes = 1.0
 
-        if isinstance(dxes, (float, int)):
+        if isinstance(dxes, float | int):
             uniform_dx = dxes
             min_dx = dxes
         else:
@@ -143,13 +149,14 @@ class Simulation:
             min_dx = min(min(dxn) for dxn in dxes[0] + dxes[1])
 
         max_dt = min_dx * .99 / numpy.sqrt(3)
+        logger.info(f'{min_dx=}, {max_dt=}, {dt=}')
 
         if dt is None:
             self.dt = max_dt
         elif dt > max_dt:
-            warnings.warn(f'Warning: unstable dt: {dt}')
+            warnings.warn(f'Warning: unstable dt: {dt}', stacklevel=2)
         elif dt <= 0:
-            raise Exception(f'Invalid dt: {dt}')
+            raise FDTDError(f'Invalid dt: {dt}')
         else:
             self.dt = dt
 
@@ -173,28 +180,31 @@ class Simulation:
         def ptr(arg: str) -> str:
             return ctype + ' *' + arg
 
-        base_fields = OrderedDict()
+        base_fields = {
-        base_fields[ptr('E')] = self.E
+            ptr('E'): self.E,
-        base_fields[ptr('H')] = self.H
+            ptr('H'): self.H,
-        base_fields[ctype + ' dt'] = self.dt
+            ctype + ' dt': self.dt,
+            }
         if uniform_dx is 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):
+            for name, field in zip(inv_dx_names, self.inv_dxes, strict=True):
                 base_fields[ptr(name)] = field
 
-        eps_field = OrderedDict()
+        eps_field = {ptr('eps'): self.eps}
-        eps_field[ptr('eps')] = self.eps
 
         if bloch_boundaries:
-            base_fields[ptr('F')] = self.F
+            base_fields |= {
-            base_fields[ptr('G')] = self.G
+                ptr('F'): self.F,
+                ptr('G'): self.G,
+                }
 
-            bloch_fields = OrderedDict()
+            bloch_fields = {
-            bloch_fields[ptr('E')] = self.F
+                ptr('E'): self.F,
-            bloch_fields[ptr('H')] = self.G
+                ptr('H'): self.G,
-            bloch_fields[ctype + ' dt'] = self.dt
+                ctype + ' dt': self.dt,
-            bloch_fields[ptr('F')] = self.E
+                ptr('F'): self.E,
-            bloch_fields[ptr('G')] = self.H
+                ptr('G'): self.H,
+                }
 
         common_source = jinja_env.get_template('common.cl').render(
             ftype=ctype,
@@ -216,8 +226,8 @@ class Simulation:
         if bloch_boundaries:
             bloch_args = jinja_args.copy()
             bloch_args['do_poynting'] = False
-            bloch_args['bloch'] = [
+            bloch_args['bloch'] = [{
-                {'axis': b['axis'],
+                    'axis': b['axis'],
                     'real': b['imag'],
                     'imag': b['real'],
                     }
@@ -227,7 +237,7 @@ class Simulation:
             self.sources['F'] = F_source
             self.sources['G'] = G_source
 
-        S_fields = OrderedDict()
+        S_fields = {}
         if do_poynting:
             self.S = pyopencl.array.zeros_like(self.E)
             S_fields[ptr('S')] = self.S
@@ -237,7 +247,7 @@ class Simulation:
             S_fields[ptr('S0')] = self.S0
             S_fields[ptr('S1')] = self.S1
 
-        J_fields = OrderedDict()
+        J_fields = {}
         if do_fieldsrc:
             J_source = jinja_env.get_template('update_j.cl').render(**jinja_args)
             self.sources['J'] = J_source
@@ -247,37 +257,36 @@ class Simulation:
             J_fields[ptr('Jr')] = self.Jr
             J_fields[ptr('Ji')] = self.Ji
 
-        '''
+        #
-        PML
+        # PML
-        '''
+        #
         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
+        # 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 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 = base_fields | J_fields
-            [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):
+    def _create_pmls(self, pmls: Sequence[dict[str, float]]) -> tuple[dict[str, pyopencl.array.Array], ...]:
         ctype = type_to_C(self.arg_type)
 
         def ptr(arg: str) -> str:
             return ctype + ' *' + arg
 
-        pml_e_fields = OrderedDict()
+        pml_e_fields = {}
-        pml_h_fields = OrderedDict()
+        pml_h_fields = {}
         for pml in pmls:
             a = 'xyz'.find(pml['axis'])
 
@@ -285,7 +294,9 @@ class Simulation:
             kappa_max = numpy.sqrt(pml['mu_eff'] * pml['epsilon_eff'])
             alpha_max = pml['cfs_alpha']
 
-            def par(x):
+            print(sigma_max, kappa_max, alpha_max, pml['thickness'], self.dt)
+
+            def par(x, pml=pml, sigma_max=sigma_max, kappa_max=kappa_max, alpha_max=alpha_max):     # noqa: ANN001, ANN202
                 scaling = (x / pml['thickness']) ** pml['m']
                 sigma = scaling * sigma_max
                 kappa = 1 + scaling * (kappa_max - 1)
@@ -301,8 +312,13 @@ class Simulation:
             elif pml['polarity'] == 'n':
                 xh -= 0.5
 
+            logger.debug(f'{pml=}')
+            logger.debug(f'{xe=}')
+            logger.debug(f'{xh=}')
+            logger.debug(f'{par(xe)=}')
+            logger.debug(f'{par(xh)=}')
             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)):
+            for name_e, name_h, pe, ph in zip(pml_p_names[0], pml_p_names[1], par(xe), par(xh), strict=True):
                 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)
 
@@ -313,13 +329,13 @@ class Simulation:
             psi_shape = list(self.shape)
             psi_shape[a] = pml['thickness']
 
-            for ne, nh in zip(*psi_names):
+            for ne, nh in zip(*psi_names, strict=True):
                 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) -> Callable[..., pyopencl.Event]:
+    def _create_operation(self, source: str, args_fields: Sequence[dict[str, pyopencl.array.Array]]) -> Callable[..., pyopencl.Event]:
-        args = OrderedDict()
+        args = {}
         for d in args_fields:
             args.update(d)
         update = ElementwiseKernel(
@@ -334,38 +350,30 @@ class Simulation:
             context: pyopencl.Context | None = None,
             queue: pyopencl.CommandQueue | None = None,
             ) -> None:
-        if context is None:
+        self.context = context or pyopencl.create_some_context()
-            self.context = pyopencl.create_some_context()
+        self.queue = queue or pyopencl.CommandQueue(self.context)
-        else:
-            self.context = context
-
-        if queue is None:
-            self.queue = pyopencl.CommandQueue(self.context)
-        else:
-            self.queue = queue
 
     def _create_eps(self, epsilon: NDArray) -> pyopencl.array.Array:
         if len(epsilon) != 3:
-            raise Exception('Epsilon must be a list with length of 3')
+            raise FDTDError('Epsilon must be a list with length of 3')
-        if not all((e.shape == epsilon[0].shape for e in epsilon[1:])):
+        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])
+            raise FDTDError('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(f'Epsilon shape mismatch. Expected {self.shape}, got {epsilon[0].shape}')
+            raise FDTDError(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: NDArray | None = None) -> pyopencl.array.Array:
         if initial_value is None:
             return pyopencl.array.zeros_like(self.eps)
-        else:
         if len(initial_value) != 3:
-                Exception('Initial field value must be a list of length 3')
+            raise FDTDError('Initial field value must be a list of length 3')
-            if not all((f.shape == self.shape for f in initial_value)):
+        if not all(f.shape == self.shape for f in initial_value):
-                Exception('Initial field list elements must have same shape as epsilon elements')
+            raise FDTDError('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: Type,
+        float_type: type,
         ) -> str:
     """
     Returns a string corresponding to the C equivalent of a numpy type.
@@ -385,7 +393,7 @@ def type_to_C(
         numpy.complex128: 'cdouble_t',
     }
     if float_type not in types:
-        raise Exception(f'Unsupported type: {float_type}')
+        raise FDTDError(f'Unsupported type: {float_type}')
 
     return types[float_type]
 

opencl_fdtd/test_simulation.py

@@ -323,8 +323,9 @@ class JdotE_3DUniformDX(unittest.TestCase):
         self.epsilon = numpy.full(shape, 4, dtype=float)
         self.epsilon[self.src_mask] = 2
 
-        e = numpy.random.randint(-128, 128 + 1, size=shape).astype(float)
+        rng = numpy.random.default_rng()
-        h = numpy.random.randint(-128, 128 + 1, size=shape).astype(float)
+        e = rng.integers(-128, 128 + 1, size=shape, dtype=float)
+        h = rng.integers(-128, 128 + 1, size=shape, dtype=float)
         self.es = [e]
         self.hs = [h]
         self.ss = []

pyproject.toml

@@ -32,10 +32,10 @@ classifiers = [
     "License :: OSI Approved :: GNU Affero General Public License v3",
     "Topic :: Scientific/Engineering",
     ]
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 dynamic = ["version"]
 dependencies = [
-    "numpy~=1.21",
+    "numpy>=1.26",
     "pyopencl",
     "jinja2",
     "meanas>=0.3",
@@ -44,3 +44,50 @@ dependencies = [
 [tool.hatch.version]
 path = "opencl_fdtd/__init__.py"
 
+
+[tool.ruff]
+exclude = [
+    ".git",
+    "dist",
+    ]
+line-length = 145
+indent-width = 4
+lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+lint.select = [
+    "NPY", "E", "F", "W", "B", "ANN", "UP", "SLOT", "SIM", "LOG",
+    "C4", "ISC", "PIE", "PT", "RET", "TCH", "PTH", "INT",
+    "ARG", "PL", "R", "TRY",
+    "G010", "G101", "G201", "G202",
+    "Q002", "Q003", "Q004",
+    ]
+lint.ignore = [
+    #"ANN001",   # No annotation
+    "ANN002",   # *args
+    "ANN003",   # **kwargs
+    "ANN401",   # Any
+    "ANN101",   # self: Self
+    "SIM108",   # single-line if / else assignment
+    "RET504",   # x=y+z; return x
+    "PIE790",   # unnecessary pass
+    "ISC003",   # non-implicit string concatenation
+    "C408",     # dict(x=y) instead of {'x': y}
+    "PLR09",    # Too many xxx
+    "PLR2004",  # magic number
+    "PLC0414",  # import x as x
+    "TRY003",   # Long exception message
+    ]
+
+
+[[tool.mypy.overrides]]
+module = [
+    "scipy",
+    "scipy.optimize",
+    "scipy.linalg",
+    "scipy.sparse",
+    "scipy.sparse.linalg",
+    "pyopencl",
+    "pyopencl.array",
+    "pyopencl.elementwise",
+    "pyopencl.reduction",
+    ]
+ignore_missing_imports = true