added tests stylesheet, deleted some hardcoded settings.

src/mpol/tests.mplstyle

@@ -0,0 +1,4 @@
+image.cmap: inferno
+figure.figsize: 7.1, 5.0
+figure.autolayout: True
+savefig.dpi: 200

test/conftest.py

@@ -8,6 +8,9 @@
 from mpol import coordinates, fourier, gridding, images, utils
 from mpol.__init__ import zenodo_record
 
+import matplotlib.pyplot as plt
+plt.style.use("mpol.tests")
+
 # private variables to this module
 _npz_path = files("mpol.data").joinpath("mock_data.npz")
 _nchan = 4

test/images_test.py

@@ -55,10 +55,8 @@ def test_imagecube_tofits(coords, tmp_path):
 def test_basecube_imagecube(coords, tmp_path):
     # create a mock cube that includes negative values
     nchan = 1
-    mean = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=-0.5)
-    std = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=0.5)
+    mean = torch.full((nchan, coords.npix, coords.npix), fill_value=-0.5)
+    std = torch.full((nchan, coords.npix, coords.npix), fill_value=0.5)
 
     # tensor
     base_cube = torch.normal(mean=mean, std=std)
@@ -83,7 +81,7 @@ def test_basecube_imagecube(coords, tmp_path):
     plt.colorbar(im, ax=ax[1])
     ax[1].set_title("mapped")
 
-    fig.savefig(tmp_path / "basecube_mapped.png", dpi=300)
+    fig.savefig(tmp_path / "basecube_mapped.png")
 
     # try passing through ImageLayer
     imagecube = images.ImageCube(coords=coords, nchan=nchan)
@@ -98,7 +96,7 @@ def test_basecube_imagecube(coords, tmp_path):
         origin="lower",
         interpolation="none",
     )
-    fig.savefig(tmp_path / "imagecube.png", dpi=300)
+    fig.savefig(tmp_path / "imagecube.png")
 
     plt.close("all")
 
@@ -108,10 +106,8 @@ def test_base_cube_conv_cube(coords, tmp_path):
 
     # create a mock cube that includes negative values
     nchan = 1
-    mean = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=-0.5)
-    std = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=0.5)
+    mean = torch.full((nchan, coords.npix, coords.npix), fill_value=-0.5)
+    std = torch.full((nchan, coords.npix, coords.npix), fill_value=0.5)
 
     # The HannConvCube expects to function on a pre-packed ImageCube,
     # so in order to get the plots looking correct on this test image,
@@ -141,7 +137,7 @@ def test_base_cube_conv_cube(coords, tmp_path):
     plt.colorbar(im, ax=ax[1])
     ax[1].set_title("convolved")
 
-    fig.savefig(tmp_path / "convcube.png", dpi=300)
+    fig.savefig(tmp_path / "convcube.png")
 
     plt.close("all")
 
@@ -151,10 +147,8 @@ def test_multi_chan_conv(coords, tmp_path):
     # and make sure that the HannConvCube works across channels
 
     nchan = 10
-    mean = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=-0.5)
-    std = torch.full(
-        (nchan, coords.npix, coords.npix), fill_value=0.5)
+    mean = torch.full((nchan, coords.npix, coords.npix), fill_value=-0.5)
+    std = torch.full((nchan, coords.npix, coords.npix), fill_value=0.5)
 
     # tensor
     test_cube = torch.normal(mean=mean, std=std)
@@ -180,16 +174,15 @@ def test_plot_test_img(packed_cube, coords, tmp_path):
 
     # put back to sky
     sky_cube = utils.packed_cube_to_sky_cube(packed_cube)
-    im = ax.imshow(
-        sky_cube[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-    )
+    im = ax.imshow(sky_cube[chan], extent=coords.img_ext, origin="lower")
     plt.colorbar(im)
-    fig.savefig(tmp_path / "sky_cube.png", dpi=300)
+    fig.savefig(tmp_path / "sky_cube.png")
 
     plt.close("all")
 
+
 def test_taper(coords, tmp_path):
-    for r in np.arange(0.0, 0.2, step=0.02):
+    for r in np.arange(0.0, 0.2, step=0.04):
         fig, ax = plt.subplots(ncols=1)
 
         taper_2D = images.uv_gaussian_taper(coords, r, r, 0.0)
@@ -205,37 +198,41 @@ def test_taper(coords, tmp_path):
         )
         plt.colorbar(im, ax=ax)
 
-        fig.savefig(tmp_path / f"taper{r:.2f}.png", dpi=300)
+        fig.savefig(tmp_path / f"taper{r:.2f}.png")
 
     plt.close("all")
 
+
 def test_gaussian_kernel(coords, tmp_path):
     rs = np.array([0.02, 0.06, 0.10])
     nchan = 3
-    fig, ax = plt.subplots(nrows=len(rs), ncols=nchan, figsize=(10,10))
-    for i,r in enumerate(rs):
+    fig, ax = plt.subplots(nrows=len(rs), ncols=nchan, figsize=(10, 10))
+    for i, r in enumerate(rs):
         layer = images.GaussConvImage(coords, nchan=nchan, FWHM_maj=r, FWHM_min=0.5 * r)
         weight = layer.m.weight.detach().numpy()
         for j in range(nchan):
-            im = ax[i,j].imshow(weight[j,0], interpolation="none", origin="lower")
-            plt.colorbar(im, ax=ax[i,j])
+            im = ax[i, j].imshow(weight[j, 0], interpolation="none", origin="lower")
+            plt.colorbar(im, ax=ax[i, j])
 
-    fig.savefig(tmp_path / "filter.png", dpi=300)
+    fig.savefig(tmp_path / "filter.png")
     plt.close("all")
 
+
 def test_gaussian_kernel_rotate(coords, tmp_path):
     r = 0.04
-    Omegas = [0, 20, 40] # degrees
+    Omegas = [0, 20, 40]  # degrees
     nchan = 3
     fig, ax = plt.subplots(nrows=len(Omegas), ncols=nchan, figsize=(10, 10))
     for i, Omega in enumerate(Omegas):
-        layer = images.GaussConvImage(coords, nchan=nchan, FWHM_maj=r, FWHM_min=0.5 * r, Omega=Omega)
+        layer = images.GaussConvImage(
+            coords, nchan=nchan, FWHM_maj=r, FWHM_min=0.5 * r, Omega=Omega
+        )
         weight = layer.m.weight.detach().numpy()
         for j in range(nchan):
-            im = ax[i, j].imshow(weight[j, 0], interpolation="none",origin="lower")
+            im = ax[i, j].imshow(weight[j, 0], interpolation="none", origin="lower")
             plt.colorbar(im, ax=ax[i, j])
 
-    fig.savefig(tmp_path / "filter.png", dpi=300)
+    fig.savefig(tmp_path / "filter.png")
     plt.close("all")
 
 
@@ -245,71 +242,64 @@ def test_GaussConvImage(sky_cube, coords, tmp_path):
     nchan = sky_cube.size()[0]
 
     for r in np.arange(0.02, 0.11, step=0.04):
-
         layer = images.GaussConvImage(coords, nchan=nchan, FWHM_maj=r, FWHM_min=r)
 
         print("Kernel size", layer.m.weight.size())
 
         fig, ax = plt.subplots(ncols=2)
-
-        im = ax[0].imshow(
-            sky_cube[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+
+        im = ax[0].imshow(sky_cube[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(sky_cube[chan])
         ax[0].set_title(f"tot flux: {flux:.3f} Jy")
         plt.colorbar(im, ax=ax[0])
 
         c_sky = layer(sky_cube)
-        im = ax[1].imshow(
-            c_sky[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[1].imshow(c_sky[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(c_sky[chan])
         ax[1].set_title(f"tot flux: {flux:.3f} Jy")
 
         plt.colorbar(im, ax=ax[1])
-        fig.savefig(tmp_path / f"convolved_{r:.2f}.png", dpi=300)
+        fig.savefig(tmp_path / f"convolved_{r:.2f}.png")
 
     plt.close("all")
 
+
 def test_GaussConvImage_rotate(sky_cube, coords, tmp_path):
     # show only the first channel
     chan = 0
     nchan = sky_cube.size()[0]
 
     for Omega in [0, 30]:
-        layer = images.GaussConvImage(coords, nchan=nchan, FWHM_maj=0.10, FWHM_min=0.05, Omega=Omega)
+        layer = images.GaussConvImage(
+            coords, nchan=nchan, FWHM_maj=0.10, FWHM_min=0.05, Omega=Omega
+        )
 
         fig, ax = plt.subplots(ncols=2)
 
-        im = ax[0].imshow(
-            sky_cube[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[0].imshow(sky_cube[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(sky_cube[chan])
         ax[0].set_title(f"tot flux: {flux:.3f} Jy")
         plt.colorbar(im, ax=ax[0])
 
         c_sky = layer(sky_cube)
-        im = ax[1].imshow(
-            c_sky[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[1].imshow(c_sky[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(c_sky[chan])
         ax[1].set_title(f"tot flux: {flux:.3f} Jy")
 
         plt.colorbar(im, ax=ax[1])
-        fig.savefig(tmp_path / f"convolved_{Omega:.0f}_deg.png", dpi=300)
+        fig.savefig(tmp_path / f"convolved_{Omega:.0f}_deg.png")
 
     plt.close("all")
 
+
 def test_GaussFourier(packed_cube, coords, tmp_path):
     chan = 0
 
     for FWHM in np.linspace(0.02, 0.5, num=10):
         fig, ax = plt.subplots(ncols=2)
         # put back to sky
         sky_cube = utils.packed_cube_to_sky_cube(packed_cube)
-        im = ax[0].imshow(
-            sky_cube[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[0].imshow(sky_cube[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(sky_cube[chan])
         ax[0].set_title(f"tot flux: {flux:.3f} Jy")
         plt.colorbar(im, ax=ax[0])
@@ -329,38 +319,35 @@ def test_GaussFourier(packed_cube, coords, tmp_path):
         ax[1].set_title(f"tot flux: {flux:.3f} Jy")
 
         plt.colorbar(im, ax=ax[1])
-        fig.savefig(tmp_path / "convolved_FWHM_{:.2f}.png".format(FWHM), dpi=300)
+        fig.savefig(tmp_path / "convolved_FWHM_{:.2f}.png".format(FWHM))
 
     plt.close("all")
 
+
 def test_GaussFourier_rotate(packed_cube, coords, tmp_path):
     chan = 0
 
     sky_cube = utils.packed_cube_to_sky_cube(packed_cube)
 
-    for Omega in [0, 20, 40]:
+    for Omega in [0, 30]:
         layer = images.GaussConvFourier(
-            coords, FWHM_maj=0.16, FWHM_min=0.06, Omega=Omega
+            coords, FWHM_maj=0.10, FWHM_min=0.05, Omega=Omega
         )
 
         fig, ax = plt.subplots(ncols=2)
 
-        im = ax[0].imshow(
-            sky_cube[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[0].imshow(sky_cube[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(sky_cube[chan])
         ax[0].set_title(f"tot flux: {flux:.3f} Jy")
         plt.colorbar(im, ax=ax[0])
 
         c_sky = layer(sky_cube)
-        im = ax[1].imshow(
-            c_sky[chan], extent=coords.img_ext, origin="lower", cmap="inferno"
-        )
+        im = ax[1].imshow(c_sky[chan], extent=coords.img_ext, origin="lower")
         flux = coords.cell_size**2 * torch.sum(c_sky[chan])
         ax[1].set_title(f"tot flux: {flux:.3f} Jy")
 
         plt.colorbar(im, ax=ax[1])
-        fig.savefig(tmp_path / f"convolved_{Omega:.2f}.png", dpi=300)
+        fig.savefig(tmp_path / f"convolved_{Omega:.0f}_deg.png")
 
     plt.close("all")
 
@@ -370,14 +357,12 @@ def test_GaussFourier_point(coords, tmp_path):
 
     # create an image with a point source in the center
     sky_cube = torch.zeros((1, coords.npix, coords.npix))
-    cpix = coords.npix//2
-    sky_cube[0,cpix,cpix] = 1.0
+    cpix = coords.npix // 2
+    sky_cube[0, cpix, cpix] = 1.0
 
     fig, ax = plt.subplots(ncols=2, sharex=True, sharey=True)
     # put back to sky
-    im = ax[0].imshow(
-        sky_cube[0], extent=coords.img_ext, origin="lower", cmap="inferno"
-    )
+    im = ax[0].imshow(sky_cube[0], extent=coords.img_ext, origin="lower")
     flux = coords.cell_size**2 * torch.sum(sky_cube[0])
     ax[0].set_title(f"tot flux: {flux:.3f} Jy")
     plt.colorbar(im, ax=ax[0])
@@ -401,6 +386,6 @@ def test_GaussFourier_point(coords, tmp_path):
     ax[1].set_ylim(-r, r)
 
     plt.colorbar(im, ax=ax[1])
-    fig.savefig(tmp_path / "point_source_FWHM_{:.2f}.png".format(FWHM), dpi=300)
+    fig.savefig(tmp_path / "point_source_FWHM_{:.2f}.png".format(FWHM))
 
     plt.close("all")