Simplify BlockArray implementation

docs/source/notes.rst

@@ -185,7 +185,7 @@ When evaluating the gradient of ``f``  at 0, :func:`scico.grad` returns ``nan``:
    >>> import scico
    >>> import scico.numpy as snp
    >>> f = lambda x: snp.linalg.norm(x)**2
-   >>> scico.grad(f)(snp.zeros(2)) #
+   >>> scico.grad(f)(snp.zeros(2, dtype=snp.float32)) #
    DeviceArray([nan, nan], dtype=float32)
 
 This can be fixed by defining the squared :math:`\ell_2` norm directly as
@@ -194,7 +194,7 @@ This can be fixed by defining the squared :math:`\ell_2` norm directly as
 ::
 
    >>> g = lambda x: snp.sum(x**2)
-   >>> scico.grad(g)(snp.zeros(2))
+   >>> scico.grad(g)(snp.zeros(2, dtype=snp.float32))
    DeviceArray([0., 0.], dtype=float32)
 
 An alternative is to define a `custom derivative rule <https://jax.readthedocs.io/en/latest/notebooks/Custom_derivative_rules_for_Python_code.html#enforcing-a-differentiation-convention>`_ to enforce a particular derivative convention at a point.

examples/scripts/denoise_tv_iso_pgm.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/Usr/bin/env python
 # -*- coding: utf-8 -*-
 # This file is part of the SCICO package. Details of the copyright
 # and user license can be found in the 'LICENSE.txt' file distributed
@@ -39,8 +39,8 @@
 import scico.numpy as snp
 import scico.random
 from scico import functional, linop, loss, operator, plot
-from scico.array import ensure_on_device
-from scico.blockarray import BlockArray
+from scico.numpy import BlockArray
+from scico.numpy.util import ensure_on_device
 from scico.optimize.pgm import AcceleratedPGM, RobustLineSearchStepSize
 from scico.typing import JaxArray
 from scico.util import device_info
@@ -96,6 +96,7 @@ def __init__(
         super().__init__(y=y, A=A, scale=1.0)
         self.lmbda = lmbda
 
+    @jax.jit
     def __call__(self, x: Union[JaxArray, BlockArray]) -> float:
 
         xint = self.y - self.lmbda * self.A(x)
@@ -117,14 +118,15 @@ class IsoProjector(functional.Functional):
     def __call__(self, x: Union[JaxArray, BlockArray]) -> float:
         return 0.0
 
+    @jax.jit
     def prox(self, v: JaxArray, lam: float, **kwargs) -> JaxArray:
         norm_v_ptp = jnp.sqrt(jnp.sum(jnp.abs(v) ** 2, axis=0))
 
         x_out = v / jnp.maximum(jnp.ones(v.shape), norm_v_ptp)
         out1 = v[0, :, -1] / jnp.maximum(jnp.ones(v[0, :, -1].shape), jnp.abs(v[0, :, -1]))
-        x_out_1 = jax.ops.index_update(x_out, jax.ops.index[0, :, -1], out1)
+        x_out = x_out.at[0, :, -1].set(out1)
         out2 = v[1, -1, :] / jnp.maximum(jnp.ones(v[1, -1, :].shape), jnp.abs(v[1, -1, :]))
-        x_out = jax.ops.index_update(x_out_1, jax.ops.index[1, -1, :], out2)
+        x_out = x_out.at[1, -1, :].set(out2)
 
         return x_out
 

examples/scripts/sparsecode_poisson_pgm.py

@@ -22,7 +22,7 @@
 $I(\mathbf{x}^{(0)} \geq 0)$ is the non-negative indicator.
 
 This example also demonstrates the application of
-[blockarray.BlockArray](../_autosummary/scico.blockarray.rst#scico.blockarray.BlockArray),
+[blockarray.BlockArray](../_autosummary/scico.numpy.rst#scico.numpy.BlockArray),
 [functional.SeparableFunctional](../_autosummary/scico.functional.rst#scico.functional.SeparableFunctional),
 and
 [functional.ZeroFunctional](../_autosummary/scico.functional.rst#scico.functional.ZeroFunctional)
@@ -40,7 +40,7 @@
 import scico.numpy as snp
 import scico.random
 from scico import functional, loss, plot
-from scico.blockarray import BlockArray
+from scico.numpy import BlockArray
 from scico.operator import Operator
 from scico.optimize.pgm import (
     AcceleratedPGM,

scico/_flax.py

@@ -17,7 +17,7 @@
 from flax.core import Scope  # noqa
 from flax.linen.module import _Sentinel  # noqa
 
-from scico.blockarray import BlockArray
+from scico.numpy import BlockArray
 from scico.typing import JaxArray
 
 # The imports of Scope and _Sentinel (above) and the definition of Module

scico/_generic_operators.py

@@ -23,8 +23,8 @@
 
 import scico.numpy as snp
 from scico._autograd import linear_adjoint
-from scico.array import is_complex_dtype, is_nested
-from scico.blockarray import BlockArray, block_sizes
+from scico.numpy import BlockArray
+from scico.numpy.util import is_complex_dtype, is_nested, shape_to_size
 from scico.typing import BlockShape, DType, JaxArray, Shape
 
 
@@ -152,8 +152,8 @@ def __init__(
         # Determine the shape of the "vectorized" operator (as an element of ℝ^{n × m}
         # If the function returns a BlockArray we need to compute the size of each block,
         # then sum.
-        self.input_size = int(np.sum(block_sizes(self.input_shape)))
-        self.output_size = int(np.sum(block_sizes(self.output_shape)))
+        self.input_size = shape_to_size(self.input_shape)
+        self.output_size = shape_to_size(self.output_shape)
 
         self.shape = (self.output_shape, self.input_shape)
         self.matrix_shape = (self.output_size, self.input_size)
@@ -320,8 +320,8 @@ def freeze(self, argnum: int, val: Union[JaxArray, BlockArray]) -> Operator:
         def concat_args(args):
             # Creates a blockarray with args and the frozen value in the correct place
             # Eg if this operator takes a blockarray with two blocks, then
-            # concat_args(args) = BlockArray.array([val, args]) if argnum = 0
-            # concat_args(args) = BlockArray.array([args, val]) if argnum = 1
+            # concat_args(args) = snp.blockarray([val, args]) if argnum = 0
+            # concat_args(args) = snp.blockarray([args, val]) if argnum = 1
 
             if isinstance(args, (DeviceArray, np.ndarray)):
                 # In the case that the original operator takes a blcokarray with two
@@ -336,7 +336,7 @@ def concat_args(args):
                     arg_list.append(args[i - 1])
                 else:
                     arg_list.append(val)
-            return BlockArray.array(arg_list)
+            return snp.blockarray(arg_list)
 
         return Operator(
             input_shape=input_shape,