added plot_utils and imshow_two routine.

test/images_test.py

@@ -4,22 +4,22 @@
 import torch
 from astropy.io import fits
 from mpol import coordinates, images, plot, utils
+from plot_utils import imshow_two
 
-
-def test_single_chan():
+def test_instantiate_single_chan():
     coords = coordinates.GridCoords(cell_size=0.015, npix=800)
     im = images.ImageCube(coords=coords)
     assert im.nchan == 1
 
 
-def test_basecube_grad():
+def test_basecube_apply_grad():
     coords = coordinates.GridCoords(cell_size=0.015, npix=800)
     bcube = images.BaseCube(coords=coords)
     loss = torch.sum(bcube())
     loss.backward()
 
 
-def test_imagecube_grad(coords):
+def test_imagecube_apply_grad(coords):
     bcube = images.BaseCube(coords=coords)
     # try passing through ImageLayer
     imagecube = images.ImageCube(coords=coords)
@@ -67,21 +67,7 @@ def test_basecube_imagecube(coords, tmp_path):
     # the default softplus function should map everything to positive values
     output = basecube()
 
-    fig, ax = plt.subplots(ncols=2, nrows=1)
-
-    im = ax[0].imshow(
-        np.squeeze(base_cube.detach().numpy()), origin="lower", interpolation="none"
-    )
-    plt.colorbar(im, ax=ax[0])
-    ax[0].set_title("input")
-
-    im = ax[1].imshow(
-        np.squeeze(output.detach().numpy()), origin="lower", interpolation="none"
-    )
-    plt.colorbar(im, ax=ax[1])
-    ax[1].set_title("mapped")
-
-    fig.savefig(tmp_path / "basecube_mapped.png")
+    imshow_two(tmp_path / "basecube_mapped.png", [base_cube, output], titles=["input", "mapped"])
 
     # try passing through ImageLayer
     imagecube = images.ImageCube(coords=coords, nchan=nchan)

test/plot_utils.py

@@ -0,0 +1,76 @@
+import matplotlib.pyplot as plt
+import torch
+import numpy as np
+
+
+def imshow_two(path, imgs, **kwargs):
+    """Plot two images side by side, with scalebars.
+    
+    imgs is a list
+    Parameters
+    ----------
+    path : string
+        path and filename to save figure
+    imgs : list
+        length-2 list of images to plot. Arguments are designed to be very permissive. If the image is a PyTorch tensor, the routine converts it to numpy, and then numpy.squeeze is called.
+    titles: list
+        if provided, list of strings corresponding to title for each subplot.
+    
+        
+    Returns
+    -------
+    None
+    """
+
+    xx = 7.1  # in
+    rmargin = 0.8
+    lmargin = 0.8
+    tmargin = 0.3
+    bmargin = 0.5
+    middle_sep = 1.2
+    ax_width = (xx - rmargin - lmargin - middle_sep) / 2
+    ax_height = ax_width
+    cax_width = 0.1
+    cax_sep = 0.15
+    cax_height = ax_height
+    yy = bmargin + ax_height + tmargin
+
+    fig = plt.figure(figsize=(xx, yy))
+
+    ax = []
+    cax = []
+    for i in [0, 1]:
+        ax.append(
+            fig.add_axes(
+                [
+                    (lmargin + i * (ax_width + middle_sep)) / xx,
+                    bmargin / yy,
+                    ax_width / xx,
+                    ax_height / yy,
+                ]
+            )
+        )
+        cax.append(
+            fig.add_axes(
+                (
+                    [
+                        (lmargin + (i + 1) * ax_width + i * middle_sep + cax_sep) / xx,
+                        bmargin / yy,
+                        cax_width / xx,
+                        cax_height / yy,
+                    ]
+                )
+            )
+        )
+
+        img = imgs[i]
+        img = img.detach().numpy() if torch.is_tensor(img) else img
+
+        im = ax[i].imshow(np.squeeze(img), origin="lower", interpolation="none")
+        plt.colorbar(im, cax=cax[i])
+
+        if "titles" in kwargs:
+            ax[i].set_title(kwargs["titles"][i])
+
+
+    fig.savefig(path)