add half-precision to type_to_C

.gitignore

@@ -60,3 +60,6 @@ target/
 
 # PyCharm
 .idea/
+
+.mypy_cache/
+.pytest_cache/

README.md

@@ -6,10 +6,10 @@ electromagnetic solver implemented in Python and OpenCL.
 
 **Capabilities:**
 * Arbitrary distributions of the following:
-    * Dielectric constant (```epsilon```)
-    * Magnetic permeabilty (```mu```)
-    * Perfect electric conductor (```PEC```)
-    * Perfect magnetic conductor (```PMC```)
+    * Dielectric constant (`epsilon`)
+    * Magnetic permeabilty (`mu`)
+    * Perfect electric conductor (`PEC`)
+    * Perfect magnetic conductor (`PMC`)
 * Variable-sized rectangular grids
     * Stretched-coordinate PMLs (complex cell sizes allowed)
 
@@ -17,10 +17,10 @@ Currently, only periodic boundary conditions are included.
 PEC/PMC boundaries can be implemented by drawing PEC/PMC cells near the edges.
 Bloch boundary conditions are not included but wouldn't be very hard to add.
 
-The default solver ```opencl_fdfd.cg_solver(...)``` located in main.py
+The default solver `opencl_fdfd.cg_solver(...)` located in main.py
 implements the E-field wave operator directly (ie, as a list of OpenCL
 instructions rather than a matrix). Additionally, there is a slower
-(and slightly more versatile) solver in ```csr.py``` which attempts to solve
+(and slightly more versatile) solver in `csr.py` which attempts to solve
 an arbitrary sparse matrix in compressed sparse row (CSR) format using
 the same conjugate gradient method as the default solver. The CSR solver
 is significantly slower, but can be very useful for testing alternative
@@ -34,29 +34,29 @@ generalization to multiple GPUs should be pretty straightforward
 ## Installation
 
 **Dependencies:**
-* python 3 (written and tested with 3.5) 
+* python 3 (written and tested with 3.7)
 * numpy
 * pyopencl
 * jinja2
-* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools) (>=0.2)
+* [meanas](https://mpxd.net/code/jan/meanas) (>=0.5)
 
 
 Install with pip, via git:
 ```bash
-pip install git+https://mpxd.net/gogs/jan/opencl_fdfd.git@release
+pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release
 ```
 
 
 ## Use
 
-See the documentation for ```opencl_fdfd.cg_solver(...)```
+See the documentation for `opencl_fdfd.cg_solver(...)`
 (located in ```main.py```) for details about how to call the solver.
 The FDFD arguments are identical to those in
-```fdfd_tools.solvers.generic(...)```, and a few solver-specific
+`meanas.solvers.generic(...)`, and a few solver-specific
 arguments are available.
- 
+
 An alternate (slower) FDFD solver and a general gpu-based sparse matrix
-solver is available in ```csr.py```. These aren't particularly
+solver is available in `csr.py`. These aren't particularly
 well-optimized, and something like
 [MAGMA](http://icl.cs.utk.edu/magma/index.html) would probably be a
 better choice if you absolutely need to solve arbitrary sparse matrices

opencl_fdfd/LICENSE.md

@@ -0,0 +1 @@
+../LICENSE.md

opencl_fdfd/README.md

@@ -0,0 +1 @@
+../README.md

opencl_fdfd/__init__.py

@@ -31,7 +31,7 @@
 
 
   Dependencies:
-    - fdfd_tools    ( https://mpxd.net/gogs/jan/fdfd_tools )
+    - meanas    ( https://mpxd.net/code/jan/meanas )
     - numpy
     - pyopencl
     - jinja2
@@ -40,4 +40,5 @@
 from .main import cg_solver
 
 __author__ = 'Jan Petykiewicz'
-
+__version__ = '0.4'
+version = __version__

opencl_fdfd/csr.py

@@ -6,7 +6,7 @@ CSRMatrix sparse matrix representation.
 
 The FDFD solver (fdfd_cg_solver()) solves an FDFD problem by
 creating a sparse matrix representing the problem (using
-fdfd_tools) and then passing it to cg(), which performs a
+meanas) and then passing it to cg(), which performs a
 conjugate gradient solve.
 
 cg() is capable of solving arbitrary sparse matrices which
@@ -14,54 +14,66 @@ satisfy the constraints for the 'conjugate gradient' algorithm
 (positive definite, symmetric) and some that don't.
 """
 
-from typing import Dict, Any
+from typing import Dict, Any, Optional
 import time
+import logging
 
 import numpy
+from numpy.typing import NDArray, ArrayLike
 from numpy.linalg import norm
 import pyopencl
 import pyopencl.array
+import scipy
 
-import fdfd_tools.solvers
+import meanas.fdfd.solvers
 
 from . import ops
 
 
-class CSRMatrix(object):
+__author__ = 'Jan Petykiewicz'
+
+logger = logging.getLogger(__name__)
+
+
+class CSRMatrix:
     """
     Matrix stored in Compressed Sparse Row format, in GPU RAM.
     """
-    row_ptr = None      # type: pyopencl.array.Array
-    col_ind = None      # type: pyopencl.array.Array
-    data = None         # type: pyopencl.array.Array
+    row_ptr: pyopencl.array.Array
+    col_ind: pyopencl.array.Array
+    data: pyopencl.array.Array
 
-    def __init__(self,
-                 queue: pyopencl.CommandQueue,
-                 m: 'scipy.sparse.csr_matrix'):
+    def __init__(
+            self,
+            queue: pyopencl.CommandQueue,
+            m: 'scipy.sparse.csr_matrix',
+            ) -> None:
         self.row_ptr = pyopencl.array.to_device(queue, m.indptr)
         self.col_ind = pyopencl.array.to_device(queue, m.indices)
         self.data = pyopencl.array.to_device(queue, m.data.astype(numpy.complex128))
 
 
-def cg(A: 'scipy.sparse.csr_matrix',
-       b: numpy.ndarray,
-       max_iters: int = 10000,
-       err_threshold: float = 1e-6,
-       context: pyopencl.Context = None,
-       queue: pyopencl.CommandQueue = None,
-       verbose: bool = False,
-       ) -> numpy.ndarray:
+def cg(
+        A: 'scipy.sparse.csr_matrix',
+        b: ArrayLike,
+        max_iters: int = 10000,
+        err_threshold: float = 1e-6,
+        context: Optional[pyopencl.Context] = None,
+        queue: Optional[pyopencl.CommandQueue] = None,
+        ) -> NDArray:
     """
     General conjugate-gradient solver for sparse matrices, where A @ x = b.
 
-    :param A: Matrix to solve (CSR format)
-    :param b: Right-hand side vector (dense ndarray)
-    :param max_iters: Maximum number of iterations
-    :param err_threshold: Error threshold for successful solve, relative to norm(b)
-    :param context: PyOpenCL context. Will be created if not given.
-    :param queue: PyOpenCL command queue. Will be created if not given.
-    :param verbose: Whether to print statistics to screen.
-    :return: Solution vector x; returned even if solve doesn't converge.
+    Args:
+        A: Matrix to solve (CSR format)
+        b: Right-hand side vector (dense ndarray)
+        max_iters: Maximum number of iterations
+        err_threshold: Error threshold for successful solve, relative to norm(b)
+        context: PyOpenCL context. Will be created if not given.
+        queue: PyOpenCL command queue. Will be created if not given.
+
+    Returns:
+        Solution vector x; returned even if solve doesn't converge.
     """
 
     start_time = time.perf_counter()
@@ -102,13 +114,11 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
     _, err2 = rhoerr_step(r, [])
     b_norm = numpy.sqrt(err2)
-    if verbose:
-        print('b_norm check: ', b_norm)
+    logging.debug(f'b_norm check: {b_norm}')
 
     success = False
     for k in range(max_iters):
-        if verbose:
-            print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
+        logging.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
 
         rho_prev = rho
         e = xr_step(x, p, r, v, alpha, [])
@@ -116,8 +126,7 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
         errs += [numpy.sqrt(err2) / b_norm]
 
-        if verbose:
-            print('err', errs[-1])
+        logging.debug(f'err {errs[-1]}')
 
         if errs[-1] < err_threshold:
             success = True
@@ -127,8 +136,8 @@ def cg(A: 'scipy.sparse.csr_matrix',
         e = a_step(v, m, p, e)
         alpha = rho / dot(p, v, e)
 
-        if verbose and k % 1000 == 0:
-            print(k)
+        if k % 1000 == 0:
+            logger.info(f'iteration {k}')
 
     '''
     Done solving
@@ -137,43 +146,50 @@ def cg(A: 'scipy.sparse.csr_matrix',
 
     x = x.get()
 
-    if verbose:
-        if success:
-            print('Success', end='')
-        else:
-            print('Failure', end=', ')
-        print(', {} iterations in {} sec: {} iterations/sec \
-                      '.format(k, time_elapsed, k / time_elapsed))
-        print('final error', errs[-1])
-        print('overhead {} sec'.format(start_time2 - start_time))
+    if success:
+        logging.info('Solve success')
+    else:
+        logging.warning('Solve failure')
+    logging.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
+    logging.debug(f'final error {errs[-1]}')
+    logging.debug(f'overhead {start_time2 - start_time} sec')
 
-        print('Final residual:', norm(A @ x - b) / norm(b))
+    residual = norm(A @ x - b) / norm(b)
+    logging.info(f'Final residual: {residual}')
     return x
 
 
-def fdfd_cg_solver(solver_opts: Dict[str, Any] = None,
-                   **fdfd_args
-                   ) -> numpy.ndarray:
+def fdfd_cg_solver(
+        solver_opts: Optional[Dict[str, Any]] = None,
+        **fdfd_args
+        ) -> NDArray:
     """
     Conjugate gradient FDFD solver using CSR sparse matrices, mainly for
      testing and development since it's much slower than the solver in main.py.
 
-    Calls fdfd_tools.solvers.generic(**fdfd_args,
-                                     matrix_solver=opencl_fdfd.csr.cg,
-                                     matrix_solver_opts=solver_opts)
+    Calls meanas.fdfd.solvers.generic(
+        **fdfd_args,
+        matrix_solver=opencl_fdfd.csr.cg,
+        matrix_solver_opts=solver_opts,
+        )
 
-    :param solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
-        Default {}.
-    :param fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
-        Should include all of the arguments **except** matrix_solver and matrix_solver_opts
-    :return: E-field which solves the system.
+    Args:
+        solver_opts: Passed as matrix_solver_opts to fdfd_tools.solver.generic(...).
+            Default {}.
+        fdfd_args: Passed as **fdfd_args to fdfd_tools.solver.generic(...).
+            Should include all of the arguments **except** matrix_solver and matrix_solver_opts
+
+    Returns:
+        E-field which solves the system.
     """
 
     if solver_opts is None:
         solver_opts = dict()
 
-    x = fdfd_tools.solvers.generic(matrix_solver=cg,
-                                   matrix_solver_opts=solver_opts,
-                                   **fdfd_args)
+    x = meanas.fdfd.solvers.generic(
+        matrix_solver=cg,
+        matrix_solver_opts=solver_opts,
+        **fdfd_args,
+        )
 
     return x

opencl_fdfd/kernels/e2h.cl

@@ -31,7 +31,7 @@ __global char *pmc_z = pmc + ZZ;
 
 
 //Update H components; set them to 0 if PMC is enabled at that location.
-//Mu division and PMC conditional are only included if {{mu}} and {{pmc}} are true
+//Mu division and PMC conditional are only included if {mu} and {pmc} are true
 {% if pmc -%}
 if (pmc_x[i] != 0) {
     Hx[i] = zero;
@@ -42,9 +42,9 @@ if (pmc_x[i] != 0) {
     ctype Dyz = mul(sub(Ey[i + pz], Ey[i]), inv_dez[z]);
     ctype x_curl = sub(Dzy, Dyz);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hx[i] = mul(inv_mu_x[i], x_curl);
-    {%- else -%}
+    {%- else %}
     Hx[i] = x_curl;
     {%- endif %}
 }
@@ -59,9 +59,9 @@ if (pmc_y[i] != 0) {
     ctype Dzx = mul(sub(Ez[i + px], Ez[i]), inv_dex[x]);
     ctype y_curl = sub(Dxz, Dzx);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hy[i] = mul(inv_mu_y[i], y_curl);
-    {%- else -%}
+    {%- else %}
     Hy[i] = y_curl;
     {%- endif %}
 }
@@ -76,9 +76,9 @@ if (pmc_z[i] != 0) {
     ctype Dxy = mul(sub(Ex[i + py], Ex[i]), inv_dey[y]);
     ctype z_curl = sub(Dyx, Dxy);
 
-    {%- if mu -%}
+    {%- if mu %}
     Hz[i] = mul(inv_mu_z[i], z_curl);
-    {%- else -%}
+    {%- else %}
     Hz[i] = z_curl;
     {%- endif %}
 }

opencl_fdfd/main.py

@@ -6,66 +6,72 @@ operator implemented directly as OpenCL arithmetic (rather than as
 a matrix).
 """
 
-from typing import List
+from typing import List, Optional, cast
 import time
+import logging
 
 import numpy
+from numpy.typing import NDArray, ArrayLike
 from numpy.linalg import norm
 import pyopencl
 import pyopencl.array
 
-import fdfd_tools.operators
+import meanas.fdfd.operators
 
 from . import ops
 
+
 __author__ = 'Jan Petykiewicz'
 
+logger = logging.getLogger(__name__)
 
-def cg_solver(omega: complex,
-              dxes: List[List[numpy.ndarray]],
-              J: numpy.ndarray,
-              epsilon: numpy.ndarray,
-              mu: numpy.ndarray = None,
-              pec: numpy.ndarray = None,
-              pmc: numpy.ndarray = None,
-              adjoint: bool = False,
-              max_iters: int = 40000,
-              err_threshold: float = 1e-6,
-              context: pyopencl.Context = None,
-              verbose: bool = False,
-              ) -> numpy.ndarray:
+
+def cg_solver(
+        omega: complex,
+        dxes: List[List[NDArray]],
+        J: ArrayLike,
+        epsilon: ArrayLike,
+        mu: Optional[ArrayLike] = None,
+        pec: Optional[ArrayLike] = None,
+        pmc: Optional[ArrayLike] = None,
+        adjoint: bool = False,
+        max_iters: int = 40000,
+        err_threshold: float = 1e-6,
+        context: Optional[pyopencl.Context] = None,
+        ) -> NDArray:
     """
     OpenCL FDFD solver using the iterative conjugate gradient (cg) method
      and implementing the diagonalized E-field wave operator directly in
      OpenCL.
 
     All ndarray arguments should be 1D arrays. To linearize a list of 3 3D ndarrays,
-     either use fdfd_tools.vec() or numpy:
+     either use meanas.fdmath.vec() or numpy:
      f_1D = numpy.hstack(tuple((fi.flatten(order='F') for fi in [f_x, f_y, f_z])))
 
-    :param omega: Complex frequency to solve at.
-    :param dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
-    :param J: Electric current distribution (at E-field locations)
-    :param epsilon: Dielectric constant distribution (at E-field locations)
-    :param mu: Magnetic permeability distribution (at H-field locations)
-    :param pec: Perfect electric conductor distribution
-        (at E-field locations; non-zero value indicates PEC is present)
-    :param pmc: Perfect magnetic conductor distribution
-        (at H-field locations; non-zero value indicates PMC is present)
-    :param adjoint: If true, solves the adjoint problem.
-    :param max_iters: Maximum number of iterations. Default 40,000.
-    :param err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
-        Default 1e-6.
-    :param context: PyOpenCL context to run in. If not given, construct a new context.
-    :param verbose: If True, print progress to stdout. Default False.
-    :return: E-field which solves the system. Returned even if we did not converge.
-    """
+    Args:
+        omega: Complex frequency to solve at.
+        dxes: [[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]] (complex cell sizes)
+        J: Electric current distribution (at E-field locations)
+        epsilon: Dielectric constant distribution (at E-field locations)
+        mu: Magnetic permeability distribution (at H-field locations)
+        pec: Perfect electric conductor distribution
+            (at E-field locations; non-zero value indicates PEC is present)
+        pmc: Perfect magnetic conductor distribution
+            (at H-field locations; non-zero value indicates PMC is present)
+        adjoint: If true, solves the adjoint problem.
+        max_iters: Maximum number of iterations. Default 40,000.
+        err_threshold: If (r @ r.conj()) / norm(1j * omega * J) < err_threshold, success.
+            Default 1e-6.
+        context: PyOpenCL context to run in. If not given, construct a new context.
 
+    Returns:
+        E-field which solves the system. Returned even if we did not converge.
+    """
     start_time = time.perf_counter()
 
-    b = -1j * omega * J
+    shape = [dd.size for dd in dxes[0]]
 
-    shape = [d.size for d in dxes[0]]
+    b = -1j * omega * numpy.array(J, copy=False)
 
     '''
         ** In this comment, I use the following notation:
@@ -94,15 +100,16 @@ def cg_solver(omega: complex,
         We can accomplish all this simply by conjugating everything (except J) and
          reversing the order of L and R
     '''
+    epsilon = numpy.array(epsilon, copy=False)
     if adjoint:
         # Conjugate everything
-        dxes = [[numpy.conj(d) for d in dd] for dd in dxes]
+        dxes = [[numpy.conj(dd) for dd in dds] for dds in dxes]
         omega = numpy.conj(omega)
         epsilon = numpy.conj(epsilon)
         if mu is not None:
             mu = numpy.conj(mu)
 
-    L, R = fdfd_tools.operators.e_full_preconditioners(dxes)
+    L, R = meanas.fdfd.operators.e_full_preconditioners(dxes)
 
     if adjoint:
         b_preconditioned = R @ b
@@ -130,7 +137,7 @@ def cg_solver(omega: complex,
     rho = 1.0 + 0j
     errs = []
 
-    inv_dxes = [[load_field(1 / d) for d in dd] for dd in dxes]
+    inv_dxes = [[load_field(1 / numpy.array(dd, copy=False)) for dd in dds] for dds in dxes]
     oeps = load_field(-omega ** 2 * epsilon)
     Pl = load_field(L.diagonal())
     Pr = load_field(R.diagonal())
@@ -138,17 +145,18 @@ def cg_solver(omega: complex,
     if mu is None:
         invm = load_field(numpy.array([]))
     else:
-        invm = load_field(1 / mu)
+        invm = load_field(1 / numpy.array(mu, copy=False))
+        mu = numpy.array(mu, copy=False)
 
     if pec is None:
         gpec = load_field(numpy.array([]), dtype=numpy.int8)
     else:
-        gpec = load_field(pec.astype(bool), dtype=numpy.int8)
+        gpec = load_field(numpy.array(pec, dtype=bool, copy=False), dtype=numpy.int8)
 
     if pmc is None:
         gpmc = load_field(numpy.array([]), dtype=numpy.int8)
     else:
-        gpmc = load_field(pmc.astype(bool), dtype=numpy.int8)
+        gpmc = load_field(numpy.array(pmc, dtype=bool, copy=False), dtype=numpy.int8)
 
     '''
     Generate OpenCL kernels
@@ -171,12 +179,13 @@ def cg_solver(omega: complex,
 
     _, err2 = rhoerr_step(r, [])
     b_norm = numpy.sqrt(err2)
-    print('b_norm check: ', b_norm)
+    logging.debug(f'b_norm check: {b_norm}')
 
     success = False
     for k in range(max_iters):
-        if verbose:
-            print('[{:06d}] rho {:.4} alpha {:4.4}'.format(k, rho, alpha), end=' ')
+        do_print = (k % 100 == 0)
+        if do_print:
+            logger.debug(f'[{k:06d}] rho {rho:.4} alpha {alpha:4.4}')
 
         rho_prev = rho
         e = xr_step(x, p, r, v, alpha, [])
@@ -184,8 +193,8 @@ def cg_solver(omega: complex,
 
         errs += [numpy.sqrt(err2) / b_norm]
 
-        if verbose:
-            print('err', errs[-1])
+        if do_print:
+            logger.debug(f'err {errs[-1]}')
 
         if errs[-1] < err_threshold:
             success = True
@@ -196,7 +205,7 @@ def cg_solver(omega: complex,
         alpha = rho / dot(p, v, e)
 
         if k % 1000 == 0:
-            print(k)
+            logger.info(f'iteration {k}')
 
     '''
     Done solving
@@ -210,18 +219,19 @@ def cg_solver(omega: complex,
         x = (Pr * x).get()
 
     if success:
-        print('Success', end='')
+        logger.info('Solve success')
     else:
-        print('Failure', end=', ')
-    print(', {} iterations in {} sec: {} iterations/sec \
-                  '.format(k, time_elapsed, k / time_elapsed))
-    print('final error', errs[-1])
-    print('overhead {} sec'.format(start_time2 - start_time))
+        logger.warning('Solve failure')
+    logger.info(f'{k} iterations in {time_elapsed} sec: {k / time_elapsed} iterations/sec')
+    logger.debug(f'final error {errs[-1]}')
+    logger.debug(f'overhead {start_time2 - start_time} sec')
 
-    A0 = fdfd_tools.operators.e_full(omega, dxes, epsilon, mu).tocsr()
+    A0 = meanas.fdfd.operators.e_full(omega, dxes, epsilon, mu).tocsr()
     if adjoint:
         # Remember we conjugated all the contents of A earlier
         A0 = A0.T
-    print('Post-everything residual:', norm(A0 @ x - b) / norm(b))
+
+    residual = norm(A0 @ x - b) / norm(b)
+    logger.info(f'Post-everything residual: {residual}')
     return x
 

opencl_fdfd/ops.py

@@ -7,9 +7,11 @@ kernels for use by the other solvers.
 See kernels/ for any of the .cl files loaded in this file.
 """
 
-from typing import List, Callable
+from typing import List, Callable, Union, Type, Sequence, Optional, Tuple
+import logging
 
 import numpy
+from numpy.typing import NDArray, ArrayLike
 import jinja2
 
 import pyopencl
@@ -18,6 +20,8 @@ from pyopencl.elementwise import ElementwiseKernel
 from pyopencl.reduction import ReductionKernel
 
 
+logger = logging.getLogger(__name__)
+
 # Create jinja2 env on module load
 jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
 
@@ -25,14 +29,20 @@ jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
 operation = Callable[..., List[pyopencl.Event]]
 
 
-def type_to_C(float_type: numpy.float32 or numpy.float64) -> str:
+def type_to_C(
+        float_type: Type,
+        ) -> str:
     """
     Returns a string corresponding to the C equivalent of a numpy type.
 
-    :param float_type: numpy type: float32, float64, complex64, complex128
-    :return: string containing the corresponding C type (eg. 'double')
+    Args:
+        float_type: numpy type: float32, float64, complex64, complex128
+
+    Returns:
+        string containing the corresponding C type (eg. 'double')
     """
     types = {
+        numpy.float16: 'half',
         numpy.float32: 'float',
         numpy.float64: 'double',
         numpy.complex64: 'cfloat_t',
@@ -65,12 +75,13 @@ def ptrs(*args: str) -> List[str]:
     return [ctype + ' *' + s for s in args]
 
 
-def create_a(context: pyopencl.Context,
-             shape: numpy.ndarray,
-             mu: bool = False,
-             pec: bool = False,
-             pmc: bool = False,
-             ) -> operation:
+def create_a(
+        context: pyopencl.Context,
+        shape: ArrayLike,
+        mu: bool = False,
+        pec: bool = False,
+        pmc: bool = False,
+        ) -> operation:
     """
     Return a function which performs (A @ p), where A is the FDFD wave equation for E-field.
 
@@ -91,12 +102,15 @@ def create_a(context: pyopencl.Context,
 
      and returns a list of pyopencl.Event.
 
-    :param context: PyOpenCL context
-    :param shape: Dimensions of the E-field
-    :param mu: False iff (mu == 1) everywhere
-    :param pec: False iff no PEC anywhere
-    :param pmc: False iff no PMC anywhere
-    :return: Function for computing (A @ p)
+    Args:
+        context: PyOpenCL context
+        shape: Dimensions of the E-field
+        mu: False iff (mu == 1) everywhere
+        pec: False iff no PEC anywhere
+        pmc: False iff no PMC anywhere
+
+    Returns:
+        Function for computing (A @ p)
     """
 
     common_source = jinja_env.get_template('common.cl').render(shape=shape)
@@ -110,41 +124,68 @@ def create_a(context: pyopencl.Context,
     Convert p to initial E (ie, apply right preconditioner and PEC)
     '''
     p2e_source = jinja_env.get_template('p2e.cl').render(pec=pec)
-    P2E_kernel = ElementwiseKernel(context,
-                                   name='P2E',
-                                   preamble=preamble,
-                                   operation=p2e_source,
-                                   arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg))
+    P2E_kernel = ElementwiseKernel(
+        context,
+        name='P2E',
+        preamble=preamble,
+        operation=p2e_source,
+        arguments=', '.join(ptrs('E', 'p', 'Pr') + pec_arg),
+        )
 
     '''
     Calculate intermediate H from intermediate E
     '''
-    e2h_source = jinja_env.get_template('e2h.cl').render(mu=mu,
-                                                         pmc=pmc,
-                                                         common_cl=common_source)
-    E2H_kernel = ElementwiseKernel(context,
-                                   name='E2H',
-                                   preamble=preamble,
-                                   operation=e2h_source,
-                                   arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des))
+    e2h_source = jinja_env.get_template('e2h.cl').render(
+        mu=mu,
+        pmc=pmc,
+        common_cl=common_source,
+        )
+    E2H_kernel = ElementwiseKernel(
+        context,
+        name='E2H',
+        preamble=preamble,
+        operation=e2h_source,
+        arguments=', '.join(ptrs('E', 'H', 'inv_mu') + pmc_arg + des),
+        )
 
     '''
     Calculate final E (including left preconditioner)
     '''
-    h2e_source = jinja_env.get_template('h2e.cl').render(pec=pec,
-                                                         common_cl=common_source)
-    H2E_kernel = ElementwiseKernel(context,
-                                   name='H2E',
-                                   preamble=preamble,
-                                   operation=h2e_source,
-                                   arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs))
+    h2e_source = jinja_env.get_template('h2e.cl').render(
+        pec=pec,
+        common_cl=common_source,
+        )
+    H2E_kernel = ElementwiseKernel(
+        context,
+        name='H2E',
+        preamble=preamble,
+        operation=h2e_source,
+        arguments=', '.join(ptrs('E', 'H', 'oeps', 'Pl') + pec_arg + dhs),
+        )
 
-    def spmv(E, H, p, idxes, oeps, inv_mu, pec, pmc, Pl, Pr, e):
+    def spmv(
+            E: pyopencl.array.Array,
+            H: pyopencl.array.Array,
+            p: pyopencl.array.Array,
+            idxes: Sequence[Sequence[pyopencl.array.Array]],
+            oeps: pyopencl.array.Array,
+            inv_mu: Optional[pyopencl.array.Array],
+            pec: Optional[pyopencl.array.Array],
+            pmc: Optional[pyopencl.array.Array],
+            Pl: pyopencl.array.Array,
+            Pr: pyopencl.array.Array,
+            e: List[pyopencl.Event],
+            ) -> List[pyopencl.Event]:
         e2 = P2E_kernel(E, p, Pr, pec, wait_for=e)
         e2 = E2H_kernel(E, H, inv_mu, pmc, *idxes[0], wait_for=[e2])
         e2 = H2E_kernel(E, H, oeps, Pl, pec, *idxes[1], wait_for=[e2])
         return [e2]
 
+    logger.debug(f'Preamble: \n{preamble}')
+    logger.debug(f'p2e: \n{p2e_source}')
+    logger.debug(f'e2h: \n{e2h_source}')
+    logger.debug(f'h2e: \n{h2e_source}')
+
     return spmv
 
 
@@ -159,8 +200,11 @@ def create_xr_step(context: pyopencl.Context) -> operation:
     after waiting for all in the list e
     and returns a list of pyopencl.Event
 
-    :param context: PyOpenCL context
-    :return: Function for performing x and r updates
+    Args:
+        context: PyOpenCL context
+
+    Returns:
+        Function for performing x and r updates
     """
     update_xr_source = '''
     x[i] = add(x[i], mul(alpha, p[i]));
@@ -169,19 +213,28 @@ def create_xr_step(context: pyopencl.Context) -> operation:
 
     xr_args = ', '.join(ptrs('x', 'p', 'r', 'v') + [ctype + ' alpha'])
 
-    xr_kernel = ElementwiseKernel(context,
-                                  name='XR',
-                                  preamble=preamble,
-                                  operation=update_xr_source,
-                                  arguments=xr_args)
+    xr_kernel = ElementwiseKernel(
+        context,
+        name='XR',
+        preamble=preamble,
+        operation=update_xr_source,
+        arguments=xr_args,
+        )
 
-    def xr_update(x, p, r, v, alpha, e):
+    def xr_update(
+            x: pyopencl.array.Array,
+            p: pyopencl.array.Array,
+            r: pyopencl.array.Array,
+            v: pyopencl.array.Array,
+            alpha: complex,
+            e: List[pyopencl.Event],
+            ) -> List[pyopencl.Event]:
         return [xr_kernel(x, p, r, v, alpha, wait_for=e)]
 
     return xr_update
 
 
-def create_rhoerr_step(context: pyopencl.Context) -> operation:
+def create_rhoerr_step(context: pyopencl.Context) -> Callable[..., Tuple[complex, complex]]:
     """
     Return a function
      ri_update(r, e)
@@ -192,8 +245,11 @@ def create_rhoerr_step(context: pyopencl.Context) -> operation:
     after waiting for all pyopencl.Event in the list e
     and returns a list of pyopencl.Event
 
-    :param context: PyOpenCL context
-    :return: Function for performing x and r updates
+    Args:
+        context: PyOpenCL context
+
+    Returns:
+        Function for performing x and r updates
     """
 
     update_ri_source = '''
@@ -205,16 +261,18 @@ def create_rhoerr_step(context: pyopencl.Context) -> operation:
     # Use a vector type (double3) to make the reduction simpler
     ri_dtype = pyopencl.array.vec.double3
 
-    ri_kernel = ReductionKernel(context,
-                                name='RHOERR',
-                                preamble=preamble,
-                                dtype_out=ri_dtype,
-                                neutral='(double3)(0.0, 0.0, 0.0)',
-                                map_expr=update_ri_source,
-                                reduce_expr='a+b',
-                                arguments=ctype + ' *r')
+    ri_kernel = ReductionKernel(
+        context,
+        name='RHOERR',
+        preamble=preamble,
+        dtype_out=ri_dtype,
+        neutral='(double3)(0.0, 0.0, 0.0)',
+        map_expr=update_ri_source,
+        reduce_expr='a+b',
+        arguments=ctype + ' *r',
+        )
 
-    def ri_update(r, e):
+    def ri_update(r: pyopencl.array.Array, e: List[pyopencl.Event]) -> Tuple[complex, complex]:
         g = ri_kernel(r, wait_for=e).astype(ri_dtype).get()
         rr, ri, ii = [g[q] for q in 'xyz']
         rho = rr + 2j * ri - ii
@@ -234,48 +292,66 @@ def create_p_step(context: pyopencl.Context) -> operation:
     after waiting for all pyopencl.Event in the list e
     and returns a list of pyopencl.Event
 
-    :param context: PyOpenCL context
-    :return: Function for performing the p update
+    Args:
+        context: PyOpenCL context
+
+    Returns:
+        Function for performing the p update
     """
     update_p_source = '''
     p[i] = add(r[i], mul(beta, p[i]));
     '''
     p_args = ptrs('p', 'r') + [ctype + ' beta']
 
-    p_kernel = ElementwiseKernel(context,
-                                 name='P',
-                                 preamble=preamble,
-                                 operation=update_p_source,
-                                 arguments=', '.join(p_args))
+    p_kernel = ElementwiseKernel(
+        context,
+        name='P',
+        preamble=preamble,
+        operation=update_p_source,
+        arguments=', '.join(p_args),
+        )
 
-    def p_update(p, r, beta, e):
+    def p_update(
+            p: pyopencl.array.Array,
+            r: pyopencl.array.Array,
+            beta: complex,
+            e: List[pyopencl.Event]) -> List[pyopencl.Event]:
         return [p_kernel(p, r, beta, wait_for=e)]
 
     return p_update
 
 
-def create_dot(context: pyopencl.Context) -> operation:
+def create_dot(context: pyopencl.Context) -> Callable[..., complex]:
     """
     Return a function for performing the dot product
      p @ v
     with the signature
-     dot(p, v, e) -> float
+     dot(p, v, e) -> complex
 
-    :param context: PyOpenCL context
-    :return: Function for performing the dot product
+    Args:
+        context: PyOpenCL context
+
+    Returns:
+        Function for performing the dot product
     """
     dot_dtype = numpy.complex128
 
-    dot_kernel = ReductionKernel(context,
-                                 name='dot',
-                                 preamble=preamble,
-                                 dtype_out=dot_dtype,
-                                 neutral='zero',
-                                 map_expr='mul(p[i], v[i])',
-                                 reduce_expr='add(a, b)',
-                                 arguments=ptrs('p', 'v'))
+    dot_kernel = ReductionKernel(
+        context,
+        name='dot',
+        preamble=preamble,
+        dtype_out=dot_dtype,
+        neutral='zero',
+        map_expr='mul(p[i], v[i])',
+        reduce_expr='add(a, b)',
+        arguments=ptrs('p', 'v'),
+        )
 
-    def dot(p, v, e):
+    def dot(
+            p: pyopencl.array.Array,
+            v: pyopencl.array.Array,
+            e: List[pyopencl.Event],
+            ) -> complex:
         g = dot_kernel(p, v, wait_for=e)
         return g.get()
 
@@ -296,8 +372,11 @@ def create_a_csr(context: pyopencl.Context) -> operation:
     The function waits on all the pyopencl.Event in e before running, and returns
      a list of pyopencl.Event.
 
-    :param context: PyOpenCL context
-    :return: Function for sparse (M @ v) operation where M is in CSR format
+    Args:
+        context: PyOpenCL context
+
+    Returns:
+        Function for sparse (M @ v) operation where M is in CSR format
     """
     spmv_source = '''
     int start = m_row_ptr[i];
@@ -318,13 +397,20 @@ def create_a_csr(context: pyopencl.Context) -> operation:
     m_args = 'int *m_row_ptr, int *m_col_ind, ' + ctype + ' *m_data'
     v_in_args = ctype + ' *v_in'
 
-    spmv_kernel = ElementwiseKernel(context,
-                                    name='csr_spmv',
-                                    preamble=preamble,
-                                    operation=spmv_source,
-                                    arguments=', '.join((v_out_args, m_args, v_in_args)))
+    spmv_kernel = ElementwiseKernel(
+        context,
+        name='csr_spmv',
+        preamble=preamble,
+        operation=spmv_source,
+        arguments=', '.join((v_out_args, m_args, v_in_args)),
+        )
 
-    def spmv(v_out, m, v_in, e):
+    def spmv(
+            v_out,
+            m,
+            v_in,
+            e: List[pyopencl.Event],
+            ) -> List[pyopencl.Event]:
         return [spmv_kernel(v_out, m.row_ptr, m.col_ind, m.data, v_in, wait_for=e)]
 
     return spmv

pyproject.toml

@@ -0,0 +1,48 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "opencl_fdfd"
+description = "OpenCL FDFD solver"
+readme = "README.md"
+license = { file = "LICENSE.md" }
+authors = [
+    { name="Jan Petykiewicz", email="jan@mpxd.net" },
+    ]
+homepage = "https://mpxd.net/code/jan/opencl_fdfd"
+repository = "https://mpxd.net/code/jan/opencl_fdfd"
+keywords = [
+    "FDFD",
+    "finite",
+    "difference",
+    "frequency",
+    "domain",
+    "simulation",
+    "optics",
+    "electromagnetic",
+    "dielectric",
+    "PML",
+    "solver",
+    "FDTD",
+    ]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Manufacturing",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: GNU Affero General Public License v3",
+    "Topic :: Scientific/Engineering",
+    ]
+requires-python = ">=3.8"
+dynamic = ["version"]
+dependencies = [
+    "numpy~=1.21",
+    "pyopencl",
+    "jinja2",
+    "meanas>=0.5",
+    ]
+
+[tool.hatch.version]
+path = "opencl_fdfd/__init__.py"

setup.py

@@ -1,21 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import setup, find_packages
-
-setup(name='opencl_fdfd',
-      version='0.3',
-      description='Opencl FDFD solver',
-      author='Jan Petykiewicz',
-      author_email='anewusername@gmail.com',
-      url='https://mpxd.net/gogs/jan/opencl_fdfd',
-      packages=find_packages(),
-      install_requires=[
-            'numpy',
-            'pyopencl',
-            'jinja2',
-            'fdfd_tools>=0.3',
-      ],
-      extras_require={
-      },
-      )
-