Resolve some deprecation warnings (#36)

* Resolve some deprecation warnings

* Bump tested python versions

* Update docs requirements

* Loosen test threshold

* Resolve warnings

* Add requirement for example script

* Resolve some errors and warnings

* Update change summary

* Clean up

* Grammar fix

* Bug fix

* Fix some bugs due to changed sphinx functions

* Fix bug due to changed sphinx functions

* Bug fix

---------

Co-authored-by: Brendt Wohlberg <brendt@lanl.gov>

Author: bwohlberg

.github/workflows/pytest.yml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11", "3.12"]
+        python-version: ["3.12", "3.13"]
         os: [ubuntu-latest, macOS-latest, windows-latest]
 
     steps:

CHANGES.rst

@@ -8,6 +8,8 @@ Version 0.2.2   (unreleased)
 
 • Removed deprecated sporco.fista modules
 • Removed deprecation warning redirects for functions renamed in 0.2.0
+• Resolved a number of deprecation warnings
+• Fixed bugs in a number of example scripts
 
 
 Version 0.2.1   (2022-02-17)

docs/requirements.txt

@@ -1,5 +1,6 @@
 future
 scipy
+filetype
 matplotlib
 sphinx>=5.0.0
 numpydoc

docs/source/docntbk.py

@@ -8,6 +8,7 @@
 
 import os
 import os.path
+from pathlib import Path
 from glob import glob
 import re
 import pickle
@@ -68,11 +69,14 @@ def fetch_intersphinx_inventory(uri):
 
     # See https://stackoverflow.com/a/30981554
     class MockConfig(object):
+        intersphinx_cache_limit = None
         intersphinx_timeout = None
         tls_verify = False
+        tls_cacerts = None
+        user_agent = None
 
     class MockApp(object):
-        srcdir = ''
+        srcdir = Path('')
         config = MockConfig()
 
         def warn(self, msg):
@@ -126,6 +130,8 @@ def preprocess_script_string(str):
 
     # Remove header comment
     str = re.sub(r'^(#[^#\n]+\n){5}\n*', r'', str)
+    # Remove r from r""" ... """
+    str = re.sub('^r"""', '"""', str, flags=re.MULTILINE)
     # Insert notebook plotting configuration function
     str = re.sub(r'from sporco import plot', r'from sporco import plot'
                  '\nplot.config_notebook_plotting()',
@@ -500,7 +506,7 @@ def write_notebook_rst(txt, res, fnm, pth):
         # Partial path for current output image
         rpth = os.path.join(extfnm, rnew)
         # In RST text, replace old output name with the new one
-        txt = re.sub('\.\. image:: ' + r, '.. image:: ' + rpth, txt, re.M)
+        txt = re.sub(r'\.\. image:: ' + r, '.. image:: ' + rpth, txt, re.M)
         # Full path of the current output image
         fullrpth = os.path.join(pth, rpth)
         # Write the current output image to disk
@@ -818,7 +824,7 @@ def __init__(self, env, inv, baseurl):
         self.citeid = {}
         if not hasattr(env, 'bibtex_cache'):
             for cite in env.domaindata['cite']['citations']:
-                self.citenum[cite.key] = cite.label
+                self.citenum[cite.key] = cite.citation_id[2:]
                 self.citeid[cite.key] = cite.citation_id
 
 

examples/scripts/cdl/cbpdndl_grd.py

@@ -8,7 +8,7 @@
 Convolutional Dictionary Learning
 =================================
 
-This example demonstrating the use of :class:`.dictlrn.DictLearn` to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are :class:`.cbpdn.ConvBPDNGradReg` and :func:`.admm.ccmod.ConvCnstrMOD` respectively, so the resulting dictionary learning algorithm is not equivalent to :class:`.dictlrn.cbpdndl.ConvBPDNDictLearn`. Sparse coding with a CBPDN variant that includes a gradient regularization term on one of the coefficient maps :cite:`wohlberg-2016-convolutional2` enables CDL without the need for the usual lowpass/highpass filtering as a pre-processing of the training images.
+This example demonstrates the use of :class:`.dictlrn.DictLearn` to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are :class:`.cbpdn.ConvBPDNGradReg` and :func:`.admm.ccmod.ConvCnstrMOD` respectively, so the resulting dictionary learning algorithm is not equivalent to :class:`.dictlrn.cbpdndl.ConvBPDNDictLearn`. Sparse coding with a CBPDN variant that includes a gradient regularization term on one of the coefficient maps :cite:`wohlberg-2016-convolutional2` enables CDL without the need for the usual lowpass/highpass filtering as a pre-processing of the training images.
 """
 
 
@@ -53,15 +53,15 @@
 cri = cnvrep.CDU_ConvRepIndexing(D0.shape, S)
 
 
-"""
+r"""
 Set up weights for the $\ell_1$ norm to disable regularization of the coefficient map corresponding to the impulse filter.
 """
 
 wl1 = np.ones((1,)*4 + (D0.shape[2:]), dtype=np.float32)
 wl1[..., 0] = 0.0
 
 
-"""
+r"""
 Set of weights for the $\ell_2$ norm of the gradient to disable regularization of all coefficient maps except for the one corresponding to the impulse filter.
 """
 

examples/scripts/cdl/cbpdndl_jnt_clr.py

@@ -8,7 +8,7 @@
 Convolutional Dictionary Learning
 =================================
 
-This example demonstrating the use of :class:`.dictlrn.DictLearn` to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are :class:`.cbpdn.ConvBPDNJoint` and :func:`.admm.ccmod.ConvCnstrMOD` respectively, so the resulting dictionary learning algorithm is not equivalent to :class:`.dictlrn.cbpdndl.ConvBPDNDictLearn`. The example uses colour input images and a greyscale dictionary :cite:`wohlberg-2016-convolutional`.
+This example demonstrates the use of :class:`.dictlrn.DictLearn` to construct a dictionary learning algorithm with the flexibility of choosing the sparse coding and dictionary update classes. In this case they are :class:`.cbpdn.ConvBPDNJoint` and :func:`.admm.ccmod.ConvCnstrMOD` respectively, so the resulting dictionary learning algorithm is not equivalent to :class:`.dictlrn.cbpdndl.ConvBPDNDictLearn`. The example uses colour input images and a greyscale dictionary :cite:`wohlberg-2016-convolutional`.
 """
 
 

examples/scripts/cdl/cbpdndl_md_clr.py

@@ -53,7 +53,7 @@
 Sw = W * S
 
 
-"""
+r"""
 $\ell_2$-TV denoising with a spatial mask as a non-linear lowpass filter.
 """
 

examples/scripts/cdl/cbpdndl_md_gry.py

@@ -53,7 +53,7 @@
 Sw = W * S
 
 
-"""
+r"""
 $\ell_2$-TV denoising with a spatial mask as a non-linear lowpass filter.
 """
 

examples/scripts/cdl/cbpdndl_video.py

@@ -34,7 +34,7 @@
 frmlst = []
 for i, frm in enumerate(reader):
     if i >= 250:
-        frm = zoom(signal.rgb2gray(frm.astype(np.float32)/255.0), 0.25)
+        frm = zoom(signal.rgb2gray(np.array(frm, dtype=np.float32)/255.0), 0.25)
         frmlst.append(frm[20:-20, 70:-70])
 vid = np.stack(frmlst, axis=2)
 

examples/scripts/csc/cbpdn_ams_clr.py

@@ -63,7 +63,7 @@
 imgwp = spad(imgw)
 
 
-"""
+r"""
 $\ell_2$-TV denoising with a spatial mask as a non-linear lowpass filter. The highpass component is the difference between the test image and the lowpass component, multiplied by the mask for faster convergence of the convolutional sparse coding (see :cite:`wohlberg-2017-convolutional3`).
 """