feat: add test case for wavelet transform.

modopt/tests/test_opt.py

@@ -22,6 +22,12 @@
 except ImportError:
     SKLEARN_AVAILABLE = False
 
+PYWT_AVAILABLE = True
+try:
+    import pywt
+    import joblib
+except ImportError:
+    PYWT_AVAILABLE = False
 
 # Basic functions to be used as operators or as dummy functions
 func_identity = lambda x_val: x_val
@@ -156,7 +162,7 @@ def case_linear_identity(self):
 
         return linop, data_op, data_adj_op, res_op, res_adj_op
 
-    def case_linear_wavelet(self):
+    def case_linear_wavelet_convolve(self):
         """Case linear operator wavelet."""
         linop = linear.WaveletConvolve(
             filters=np.arange(8).reshape(2, 2, 2).astype(float)
@@ -168,6 +174,19 @@ def case_linear_wavelet(self):
 
         return linop, data_op, data_adj_op, res_op, res_adj_op
 
+    @pytest.mark.skipif(not PYWT_AVAILABLE, reason="PyWavelet not available.")
+    def case_linear_wavelet_transform(self):
+        linop = linear.WaveletTransform(
+            wavelet_name="haar",
+            shape=(8, 8),
+            level=2,
+        )
+        data_op = np.arange(64).reshape(8, 8).astype(float)
+        res_op, slices, shapes = pywt.ravel_coeffs(pywt.wavedecn(data_op, "haar", level=2))
+        data_adj_op = linop.op(data_op)
+        res_adj_op = pywt.waverecn(pywt.unravel_coeffs(data_adj_op, slices, shapes, "wavedecn"), "haar")
+        return linop, data_op, data_adj_op, res_op, res_adj_op
+
     @parametrize(weights=[[1.0, 1.0], None])
     def case_linear_combo(self, weights):
         """Case linear operator combo with weights."""