ref: numpy int deprecation warnings, typing, move sinogram generator …

…in tests to separate file

Thanks Stefan Zanko @SZanko

radontea/_alg_bpj.py

@@ -107,8 +107,8 @@ def backproject(sinogram: np.ndarray, angles: np.ndarray,
         pad = 0
         order = ln
 
-    padl = np.int32(np.ceil(pad / 2))
-    padr = np.int32(pad - padl)
+    padl = int(np.ceil(pad / 2))
+    padr = int(pad - padl)
 
     if padval is None:
         if verbose > 0:

radontea/_threed.py

@@ -21,7 +21,8 @@ def do_work(in_queue, out_list, count, max_count):
 
 def volume_recon(func2d: Callable, sinogram: np.ndarray = None,
                  angles: np.ndarray = None,
-                 count=None, max_count=None, ncpus=None, **kwargs):
+                 count=None, max_count=None, ncpus=None,
+                 **kwargs) -> np.ndarray:
     """Slice-wise 3D inversion of the Radon transform
 
     Computes the slice-wise 3D inverse of the Radon transform using

tests/sinogram.py

@@ -0,0 +1,23 @@
+"""Creates a sinogram for testing purposes"""
+import numpy as np
+
+
+def create_test_sino(A, N):
+    """
+    Creates test sinogram.
+    """
+    resar = np.zeros((A, N), dtype=float)
+    angles = np.linspace(0, np.pi, A, endpoint=False)
+    x = np.linspace(-N/2, N/2, N, endpoint=True)
+    dev = np.sqrt(N/2)
+    off = N/7
+    for ii in range(A):
+        # Gaussian distribution sinogram
+        x0 = np.cos(angles[ii])*off
+        y = np.exp(-(x-x0)**2/dev**2)
+        resar[ii] = y
+
+    # some normalization, so that max of Radon-inversion
+    # is about 1.
+    resar = 2 * dev * resar / resar.max()
+    return resar, angles

tests/test_alg_art.py

@@ -3,31 +3,11 @@
 import numpy as np
 
 import radontea
-
-
-def create_test_sino(A, N):
-    """
-    Creates test sinogram.
-    """
-    resar = np.zeros((A, N), dtype=float)
-    angles = np.linspace(0, np.pi, A, endpoint=False)
-    x = np.linspace(-N/2, N/2, N, endpoint=True)
-    dev = np.sqrt(N/2)
-    off = N/7
-    for ii in range(A):
-        # Gaussian distribution sinogram
-        x0 = np.cos(angles[ii])*off
-        y = np.exp(-(x-x0)**2/dev**2)
-        resar[ii] = y
-
-    # some normalization, so that max of Radon-inversion
-    # is about 1.
-    resar = 2 * dev * resar / resar.max()
-    return resar, angles
+import sinogram
 
 
 def test_2d_art():
-    sino, angles = create_test_sino(A=100, N=100)
+    sino, angles = sinogram.create_test_sino(A=100, N=100)
     r = radontea.art(sino, angles)
 
     # np.savetxt('outfile.txt', np.array(r).flatten().view(float), fmt="%.8f")

tests/test_alg_bpj.py

@@ -4,30 +4,11 @@
 
 import radontea
 
-
-def create_test_sino(A, N):
-    """
-    Creates test sinogram.
-    """
-    resar = np.zeros((A, N), dtype=float)
-    angles = np.linspace(0, np.pi, A, endpoint=False)
-    x = np.linspace(-N/2, N/2, N, endpoint=True)
-    dev = np.sqrt(N/2)
-    off = N/7
-    for ii in range(A):
-        # Gaussian distribution sinogram
-        x0 = np.cos(angles[ii])*off
-        y = np.exp(-(x-x0)**2/dev**2)
-        resar[ii] = y
-
-    # some normalization, so that max of Radon-inversion
-    # is about 1.
-    resar = 2 * dev * resar / resar.max()
-    return resar, angles
+import sinogram
 
 
 def test_2d_backproject():
-    sino, angles = create_test_sino(A=100, N=100)
+    sino, angles = sinogram.create_test_sino(A=100, N=100)
     r = radontea.backproject(sino, angles, padding=True)
 
     # np.savetxt('outfile.txt', np.array(r).flatten().view(float), fmt="%.8f")
@@ -41,7 +22,7 @@ def test_3d_backproject():
     M = 30
     N = 100
 
-    sino, angles = create_test_sino(A=100, N=100)
+    sino, angles = sinogram.create_test_sino(A=100, N=100)
     sino3d = np.zeros((A, M, N), dtype=float)
     for ii in range(M):
         sino3d[:, ii, :] = sino

tests/test_alg_fmp.py

@@ -4,30 +4,11 @@
 
 import radontea
 
-
-def create_test_sino(A, N):
-    """
-    Creates test sinogram.
-    """
-    resar = np.zeros((A, N), dtype=float)
-    angles = np.linspace(0, np.pi, A, endpoint=False)
-    x = np.linspace(-N/2, N/2, N, endpoint=True)
-    dev = np.sqrt(N/2)
-    off = N/7
-    for ii in range(A):
-        # Gaussian distribution sinogram
-        x0 = np.cos(angles[ii])*off
-        y = np.exp(-(x-x0)**2/dev**2)
-        resar[ii] = y
-
-    # some normalization, so that max of Radon-inversion
-    # is about 1.
-    resar = 2 * dev * resar / resar.max()
-    return resar, angles
+import sinogram
 
 
 def test_2d_fmp():
-    sino, angles = create_test_sino(A=100, N=101)
+    sino, angles = sinogram.create_test_sino(A=100, N=101)
     r = radontea.fourier_map(sino, angles)
 
     # np.savetxt('outfile.txt', np.array(r).flatten().view(float), fmt="%.8f")

tests/test_alg_int.py

@@ -3,31 +3,11 @@
 import numpy as np
 
 import radontea
-
-
-def create_test_sino(A, N):
-    """
-    Creates test sinogram.
-    """
-    resar = np.zeros((A, N), dtype=float)
-    angles = np.linspace(0, np.pi, A, endpoint=False)
-    x = np.linspace(-N/2, N/2, N, endpoint=True)
-    dev = np.sqrt(N/2)
-    off = N/7
-    for ii in range(A):
-        # Gaussian distribution sinogram
-        x0 = np.cos(angles[ii])*off
-        y = np.exp(-(x-x0)**2/dev**2)
-        resar[ii] = y
-
-    # some normalization, so that max of Radon-inversion
-    # is about 1.
-    resar = 2 * dev * resar / resar.max()
-    return resar, angles
+import sinogram
 
 
 def test_2d_int():
-    sino, angles = create_test_sino(A=20, N=30)
+    sino, angles = sinogram.create_test_sino(A=20, N=30)
     r = radontea.integrate(sino, angles)
 
     # np.savetxt('outfile.txt', np.array(r).flatten().view(float), fmt="%.8f")

tests/test_alg_sart.py

@@ -4,30 +4,11 @@
 
 import radontea
 
-
-def create_test_sino(A, N):
-    """
-    Creates test sinogram.
-    """
-    resar = np.zeros((A, N), dtype=float)
-    angles = np.linspace(0, np.pi, A, endpoint=False)
-    x = np.linspace(-N/2, N/2, N, endpoint=True)
-    dev = np.sqrt(N/2)
-    off = N/7
-    for ii in range(A):
-        # Gaussian distribution sinogram
-        x0 = np.cos(angles[ii])*off
-        y = np.exp(-(x-x0)**2/dev**2)
-        resar[ii] = y
-
-    # some normalization, so that max of Radon-inversion
-    # is about 1.
-    resar = 2 * dev * resar / resar.max()
-    return resar, angles
+import sinogram
 
 
 def test_2d_sart():
-    sino, angles = create_test_sino(A=100, N=100)
+    sino, angles = sinogram.create_test_sino(A=100, N=100)
     r = radontea.sart(sino, angles)
 
     # np.savetxt('outfile.txt', np.array(r).flatten().view(float), fmt="%.8f")