Make bm3d dependency optional

MANIFEST.in

@@ -1,13 +1,14 @@
 include MANIFEST.in
 include setup.py
-include setup.cfg
 include README.rst
 include LICENSE
 include CHANGES.rst
 include requirements.txt
+include dev_requirements.txt
+include examples/scriptcheck.sh
+include docs/docs_requirements.txt
 
 recursive-include scico *.py
 recursive-include scico/data *.png *.npz
 recursive-include docs Makefile *.py *.ipynb *.rst *.bib *.css *.svg *.ico
-recursive-include examples Makefile *.txt *.rst *.py
-include examples/pytojnb examples/scriptcheck
+recursive-include examples *_requirements.txt *.txt *.rst *.py

examples/examples_requirements.txt

@@ -1,6 +1,5 @@
 astra-toolbox
-cached_property
 colour_demosaicing
-xdesign
+xdesign>=0.5.5
 ray[tune]
 hyperopt

scico/denoiser.py

@@ -12,7 +12,12 @@
 
 from jax.experimental import host_callback as hcb
 
-import bm3d as tunibm3d
+try:
+    import bm3d as tunibm3d
+except ImportError:
+    have_bm3d = False
+else:
+    have_bm3d = True
 
 import scico.numpy as snp
 from scico._flax import DnCNNNet, load_weights
@@ -38,19 +43,21 @@ def bm3d(x: JaxArray, sigma: float, is_rgb: bool = False):
     Returns:
         Denoised output.
     """
+    if not have_bm3d:
+        raise RuntimeError("Package bm3d is required for use of this function.")
 
     if is_rgb is True:
         bm3d_eval = tunibm3d.bm3d_rgb
     else:
         bm3d_eval = tunibm3d.bm3d
 
     if np.iscomplexobj(x):
-        raise TypeError(f"BM3D requries real-valued inputs, got {x.dtype}")
+        raise TypeError(f"BM3D requires real-valued inputs, got {x.dtype}")
 
     # Support arrays with more than three axes when the additional axes are singletons.
     x_in_shape = x.shape
 
-    if x.ndim < 2:
+    if isinstance(x.ndim, tuple) or x.ndim < 2:
         raise ValueError(
             "BM3D requires two dimensional (M, N) or three dimensional (M, N, C)"
             f" inputs; got ndim = {x.ndim}"
@@ -62,7 +69,7 @@ def bm3d(x: JaxArray, sigma: float, is_rgb: bool = False):
     #       updated; this presumes 'np' profile (bs=8)
     if np.min(x.shape[:2]) < 8:
         raise ValueError(
-            f"Two leading dimensions of input cannot be smaller than block size "
+            "Two leading dimensions of input cannot be smaller than block size "
             f"(8); got image size = {x.shape}"
         )
 
@@ -94,7 +101,6 @@ class DnCNN(FlaxMap):
 
     def __init__(self, variant: str = "6M"):
         """
-
         Note that all DnCNN models are trained for single-channel image
         input. Multi-channel input is supported via independent denoising
         of each channel.
@@ -109,7 +115,7 @@ def __init__(self, variant: str = "6M"):
                 with respect to data in the range [0, 1].
         """
         if variant not in ["6L", "6M", "6H", "17L", "17M", "17H"]:
-            raise RuntimeError(f"Invalid value of parameter variant: {variant}")
+            raise ValueError(f"Invalid value of parameter variant: {variant}")
         if variant[0] == "6":
             nlayer = 6
         else:
@@ -130,7 +136,7 @@ def __call__(self, x: JaxArray) -> JaxArray:
         if np.iscomplexobj(x):
             raise TypeError(f"DnCNN requries real-valued inputs, got {x.dtype}")
 
-        if x.ndim < 2:
+        if isinstance(x.ndim, tuple) or x.ndim < 2:
             raise ValueError(
                 "DnCNN requires two dimensional (M, N) or three dimensional (M, N, C)"
                 f" inputs; got ndim = {x.ndim}"

scico/linop/abel.py

@@ -65,10 +65,10 @@ def inverse(self, y: JaxArray) -> JaxArray:
         """Perform inverse Abel transform.
 
         Args:
-            y: Input image (assumed to be a result of an Abel transform)
+            y: Input image (assumed to be a result of an Abel transform).
 
         Returns:
-            Output of inverse Abel transform
+            Output of inverse Abel transform.
         """
         return _pyabel_transform(y, direction="inverse", proj_mat_quad=self.proj_mat_quad).astype(
             self.input_dtype

scico/ray/tune.py

@@ -24,8 +24,6 @@
 from ray.tune.suggest.hyperopt import HyperOptSearch
 from ray.tune.trial import Trial
 
-__author__ = """Brendt Wohlberg <[email protected]>"""
-
 
 class _CustomReporter(TuneReporterBase):
     """Custom status reporter for :mod:`ray.tune`."""

scico/test/test_denoiser.py

@@ -4,10 +4,11 @@
 
 import pytest
 
-from scico.denoiser import DnCNN, bm3d
+from scico.denoiser import DnCNN, bm3d, have_bm3d
 from scico.random import randn
 
 
+@pytest.mark.skipif(not have_bm3d, reason="bm3d package not installed")
 class TestBM3D:
     def setup(self):
         key = None
@@ -36,16 +37,16 @@ def test_bad_inputs(self):
         x, key = randn((32,), key=None, dtype=np.float32)
         with pytest.raises(ValueError):
             bm3d(x, 1.0)
-
-        x, key = randn((12, 12, 4, 3), key=None, dtype=np.float32)
+        x, key = randn((12, 12, 4, 3), key=key, dtype=np.float32)
         with pytest.raises(ValueError):
             bm3d(x, 1.0)
-
-        x_b, key = randn(((2, 3), (3, 4, 5)), key=None, dtype=np.float32)
+        x, key = randn(((2, 3), (3, 4, 5)), key=key, dtype=np.float32)
         with pytest.raises(ValueError):
             bm3d(x, 1.0)
-
-        z, key = randn((32, 32), key=None, dtype=np.complex64)
+        x, key = randn((5, 9), key=key, dtype=np.float32)
+        with pytest.raises(ValueError):
+            bm3d(x, 1.0)
+        z, key = randn((32, 32), key=key, dtype=np.complex64)
         with pytest.raises(TypeError):
             bm3d(z, 1.0)
 
@@ -71,19 +72,24 @@ def test_multi_channel(self):
         assert no_jit.dtype == np.float32
         assert jitted.dtype == np.float32
 
+    def test_init(self):
+        dncnn = DnCNN(variant="6L")
+        x = dncnn(self.x_sngchn)
+        dncnn = DnCNN(variant="17H")
+        x = dncnn(self.x_mltchn)
+        with pytest.raises(ValueError):
+            dncnn = DnCNN(variant="3A")
+
     def test_bad_inputs(self):
         x, key = randn((32,), key=None, dtype=np.float32)
         with pytest.raises(ValueError):
             self.dncnn(x)
-
         x, key = randn((12, 12, 4, 3), key=None, dtype=np.float32)
         with pytest.raises(ValueError):
             self.dncnn(x)
-
-        x_b, key = randn(((2, 3), (3, 4, 5)), key=None, dtype=np.float32)
+        x, key = randn(((2, 3), (3, 4, 5)), key=None, dtype=np.float32)
         with pytest.raises(ValueError):
             self.dncnn(x)
-
         z, key = randn((32, 32), key=None, dtype=np.complex64)
         with pytest.raises(TypeError):
             self.dncnn(z)

scico/test/test_functional.py

@@ -15,6 +15,7 @@
 import scico.numpy as snp
 from scico import denoiser, functional
 from scico.blockarray import BlockArray
+from scico.denoiser import have_bm3d
 from scico.random import randn
 
 NO_BLOCK_ARRAY = [functional.L21Norm, functional.NuclearNorm]
@@ -286,6 +287,7 @@ def foo(c):
     np.testing.assert_allclose(non_pmap, pmapped)
 
 
+@pytest.mark.skipif(not have_bm3d, reason="bm3d package not installed")
 class TestBM3D:
     def setup(self):
         key = None

setup.py

@@ -35,7 +35,7 @@ def get_init_variable_value(var):
 """
 
 # Set install_requires from requirements.txt file
-with open(os.path.join("requirements.txt")) as f:
+with open("requirements.txt") as f:
     lines = f.readlines()
 install_requires = [line.strip() for line in lines]
 
@@ -59,8 +59,21 @@ def get_init_variable_value(var):
         f"requirements.txt ({req_jax_ver}) do not match"
     )
 
-tests_require = ["pytest", "pytest-runner"]
 python_requires = ">=3.8"
+tests_require = ["pytest", "pytest-runner"]
+
+extra_require_files = [
+    "dev_requirements.txt",
+    os.path.join("docs", "docs_requirements.txt"),
+    os.path.join("examples", "examples_requirements.txt"),
+    os.path.join("examples", "notebooks_requirements.txt"),
+]
+extras_require = {"tests": tests_require}
+for require_file in extra_require_files:
+    extras_label = os.path.basename(require_file).partition("_")[0]
+    with open(require_file) as f:
+        lines = f.readlines()
+    extras_require[extras_label] = [line.strip() for line in lines if line[0:2] != "-r"]
 
 
 setup(
@@ -69,7 +82,18 @@ def get_init_variable_value(var):
     description="Scientific Computational Imaging COde: A Python "
     "package for scientific imaging problems",
     long_description=longdesc,
-    keywords=["Computational Imaging", "Inverse Problems", "Optimization", "ADMM", "PGM"],
+    keywords=[
+        "Computational Imaging",
+        "Scientific Imaging",
+        "Inverse Problems",
+        "Plug-and-Play Priors",
+        "Total Variation",
+        "Optimization",
+        "ADMM",
+        "Linearized ADMM",
+        "PDHG",
+        "PGM",
+    ],
     platforms="Any",
     license="BSD",
     url="https://github.com/lanl/scico",
@@ -81,7 +105,7 @@ def get_init_variable_value(var):
     python_requires=python_requires,
     tests_require=tests_require,
     install_requires=install_requires,
-    extras_require={"tests": tests_require},
+    extras_require=extras_require,
     classifiers=[
         "License :: OSI Approved :: BSD License",
         "Development Status :: 3 - Alpha",