Update CCPi-Regularisation plugin for version 24.0.0

.github/workflows/build.yml

@@ -40,8 +40,8 @@ jobs:
     runs-on: [self-hosted, python, cuda]
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -80,10 +80,10 @@ jobs:
     strategy:
       matrix:
         include:
-        - python-version: 3.8
-          numpy-version: 1.21
         - python-version: '3.10'
-          numpy-version: 1.24
+          numpy-version: 1.23
+        - python-version: 3.12
+          numpy-version: 1.26
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -105,8 +105,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -132,7 +132,7 @@ jobs:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
     - uses: conda-incubator/setup-miniconda@v3
-      with: {python-version: '3.10'}
+      with: {python-version: 3.11}
     - name: install dependencies
       run: |
         cd docs

CHANGELOG.md

@@ -1,4 +1,5 @@
 * x.x.x
+  - Update to new CCPi-Regularisation toolkit v24.0.0. This is a backward incompatible release of the toolkit.
   - Set CMake Policy CMP0148 to OLD to avoid warnings in CMake 3.27
   - AcquisitionGeometry prints the first and last 10 angles, or all if there are 30 or less, rather than the first 20
   - Added a weight argument to the L1Norm function

README.md

@@ -36,7 +36,7 @@ where:
 
 - `astra-toolbox` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) projectors (GPLv3 license)
 - `tigre` (requires an NVIDIA GPU) enables support for [TIGRE](https://github.com/CERN/TIGRE) toolbox projectors (BSD license)
-- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/vais-ral/CCPi-Regularisation-Toolkit)
+- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit)
 - `tomophantom` can generate phantoms to use as test data [Tomophantom](https://github.com/dkazanc/TomoPhantom)
 
 ### Dependencies

Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py

@@ -18,9 +18,9 @@
 
 try:
     from ccpi.filters import regularisers
-    from ccpi.filters.cpu_regularisers import TV_ENERGY
+    from ccpi.filters.TV import TV_ENERGY
 except ImportError as exc:
-    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=20.04"`') from exc
+    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=24"`') from exc
 
 
 from cil.framework import DataOrder
@@ -229,14 +229,17 @@ def _fista_on_dual_rof(self, in_arr, tau):
 
         """
 
-        res , info = regularisers.FGP_TV(\
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_TV(\
               in_arr,\
               self.alpha * tau,\
               self.max_iteration,\
               self.tolerance,\
               self.methodTV,\
               self.nonnegativity,\
-              self.device)
+              infovector = info,
+              device = self.device)
 
         return res, info
 
@@ -315,14 +318,18 @@ def alpha1(self):
         return 1.
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.TGV(in_arr,
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.TGV(in_arr,
               self.alpha * tau,
               self.alpha1,
               self.alpha2,
               self.max_iteration,
               self.LipshitzConstant,
               self.tolerance,
-              self.device)
+              infovector = info,
+              device = self.device)
 
         # info: return number of iteration and reached tolerance
         # https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
@@ -400,7 +407,10 @@ def __call__(self,x):
         return np.nan
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.FGP_dTV(\
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_dTV(\
                 in_arr,\
                 self.reference,\
                 self.alpha * tau,\
@@ -409,7 +419,8 @@ def proximal_numpy(self, in_arr, tau):
                 self.eta,\
                 self.methodTV,\
                 self.nonnegativity,\
-                self.device)
+                infovector = info,
+                device = self.device)
         return res, info
 
     def convex_conjugate(self, x):
@@ -454,7 +465,7 @@ def __call__(self,x):
     def proximal_numpy(self, in_arr, tau):
         # remove any dimension of size 1
         in_arr = np.squeeze(in_arr)
-
+    
         res = regularisers.TNV(in_arr,
               self.alpha * tau,
               self.max_iteration,

Wrappers/Python/test/test_DataProcessor.py

@@ -572,21 +572,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -606,21 +606,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1343,21 +1343,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1375,21 +1375,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1770,7 +1770,7 @@ def test_process_acquisition_geometry(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_acquisition_geometry()
 
-        self.assertEquals(geometry_padded, self.ag_padded,
+        self.assertEqual(geometry_padded, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ag_padded))
 
 
@@ -1784,7 +1784,7 @@ def test_process_acquisition_geometry(self):
             0.,   90.,  180.,  270.,\
             360., 450.,  540., 630., 720.]
 
-        self.assertEquals(geometry_padded, geometry_gold,
+        self.assertEqual(geometry_padded, geometry_gold,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, geometry_gold))
 
 
@@ -1795,7 +1795,7 @@ def test_process_acquisition_geometry_origin(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_acquisition_geometry()
 
-        self.assertEquals(geometry_padded, self.ag2_padded,
+        self.assertEqual(geometry_padded, self.ag2_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ag2_padded))
 
 
@@ -1807,7 +1807,7 @@ def test_process_image_geometry(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_image_geometry()
 
-        self.assertEquals(geometry_padded, self.ig_padded,
+        self.assertEqual(geometry_padded, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ig_padded))
 
 
@@ -1846,21 +1846,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, self.ag_padded,
+        self.assertEqual(geometry_out, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, self.ag_padded))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ag_padded,
+        self.assertEqual(data_out.geometry, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ag_padded))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ag_padded,
+        self.assertEqual(data_out.geometry, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ag_padded))
 
 
@@ -1880,21 +1880,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, self.ig_padded,
+        self.assertEqual(geometry_out, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, self.ig_padded))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ig_padded,
+        self.assertEqual(data_out.geometry, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ig_padded))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ig_padded,
+        self.assertEqual(data_out.geometry, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ig_padded))
 
 

Wrappers/Python/test/test_PluginsRegularisation.py

@@ -105,7 +105,7 @@ def test_functionality_FGP_TV(self):
         fcil = FGP_TV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 
@@ -118,7 +118,7 @@ def test_functionality_TGV(self):
         fcil = TGV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, 'cpu' )
+        outrgl = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, device='cpu' )
 
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
@@ -133,7 +133,7 @@ def test_functionality_FGP_dTV(self):
         fcil = FGP_dTV(ref)
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 

Wrappers/Python/test/test_algorithms.py

@@ -523,9 +523,9 @@ def test_PDHG_strongly_convex_gamma_g(self):
         pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau,
                     max_iteration=5, gamma_g=0.5)
         pdhg.run(1, verbose=0)
-        self.assertAlmostEquals(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_g * tau))
-        self.assertAlmostEquals(pdhg.tau, tau * pdhg.theta)
-        self.assertAlmostEquals(pdhg.sigma, sigma / pdhg.theta)
+        self.assertAlmostEqual(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_g * tau))
+        self.assertAlmostEqual(pdhg.tau, tau * pdhg.theta)
+        self.assertAlmostEqual(pdhg.sigma, sigma / pdhg.theta)
         pdhg.run(4, verbose=0)
         self.assertNotEqual(pdhg.sigma, sigma)
         self.assertNotEqual(pdhg.tau, tau)
@@ -557,9 +557,9 @@ def test_PDHG_strongly_convex_gamma_fcong(self):
         pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau,
                     max_iteration=5, gamma_fconj=0.5)
         pdhg.run(1, verbose=0)
-        self.assertEquals(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_fconj * sigma))
-        self.assertEquals(pdhg.tau, tau / pdhg.theta)
-        self.assertEquals(pdhg.sigma, sigma * pdhg.theta)
+        self.assertEqual(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_fconj * sigma))
+        self.assertEqual(pdhg.tau, tau / pdhg.theta)
+        self.assertEqual(pdhg.sigma, sigma * pdhg.theta)
         pdhg.run(4, verbose=0)
         self.assertNotEqual(pdhg.sigma, sigma)
         self.assertNotEqual(pdhg.tau, tau)