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Add test coverage for plot_translations, plot_nanomap, and plot_nanomap_with_images

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
allevitan
2026-03-24 16:01:45 +01:00
co-authored by Claude Sonnet 4.6
parent 25c7b66c7c
commit 29e9fddd72
1 changed files with 92 additions and 7 deletions
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tests/tools/test_plotting.py
+92 -7
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@@ -13,13 +13,22 @@ def test_plot_amplitude(show_plot):
plotting.plot_amplitude(im, basis=np.array([[0, -1], [-1, 0], [0, 0]]), title='Test Amplitude')
if show_plot:
plt.show()
# Test with numpy array
im = scipy.datasets.ascent().astype(np.complex128)
plt.close('all')
# Test with numpy array and an extra dimension
im = np.stack([scipy.datasets.ascent().astype(np.complex128)]*3, axis=0)
plotting.plot_amplitude(im, title='Test Amplitude')
if show_plot:
plt.show()
plt.close('all')
# Test with pytorch tensor and two extra dimensions
im = t.as_tensor(np.stack([im]*5, axis=0))
plotting.plot_amplitude(im, title='Test Amplitude',
additional_axis_labels=['Hi','There'])
if show_plot:
plt.show()
plt.close('all')
def test_plot_phase(show_plot):
# Test with tensor
@@ -27,12 +36,14 @@ def test_plot_phase(show_plot):
plotting.plot_phase(im, title='Test Phase')
if show_plot:
plt.show()
plt.close('all')
# Test with numpy array
im = initializers.gaussian([512, 512], [200, 200], amplitude=100, curvature=[.1, .1]).numpy()
plotting.plot_phase(im, title='Test Phase', basis=np.array([[0, -1], [-1, 0], [0, 0]]))
if show_plot:
plt.show()
plt.close('all')
def test_plot_colorized(show_plot):
@@ -42,9 +53,83 @@ def test_plot_colorized(show_plot):
plotting.plot_colorized(im, title='Test Colorize', basis=np.array([[0, -1], [-1, 0], [0, 0]]))
if show_plot:
plt.show()
plt.close('all')
# Test with numpy array
# Test with numpy array and hsv
im = im.numpy()
plotting.plot_colorized(im, title='Test Colorize')
plotting.plot_colorized(im, title='Test Colorize', use_cmocean=False)
if show_plot:
plt.show()
plt.close('all')
def test_plot_translations(show_plot):
rng = np.random.default_rng(0)
trans_np = rng.uniform(-5e-6, 5e-6, (20, 2))
trans_t = t.as_tensor(trans_np)
# numpy, defaults
plotting.plot_translations(trans_np)
if show_plot:
plt.show()
plt.close('all')
# torch tensor and reuse figure
fig = plotting.plot_translations(trans_t)
plotting.plot_translations(trans_np, lines=False, color='red', label='scan', fig=fig, clear_fig=False)
if show_plot:
plt.show()
plt.close('all')
def test_plot_nanomap(show_plot):
rng = np.random.default_rng(0)
trans_np = rng.uniform(-5e-6, 5e-6, (20, 2))
values_np = np.random.default_rng(1).uniform(0, 1, 20)
trans_t = t.as_tensor(trans_np)
values_t = t.as_tensor(values_np)
# numpy, defaults
plotting.plot_nanomap(trans_np, values_np)
if show_plot:
plt.show()
plt.close('all')
# torch tensors
plotting.plot_nanomap(trans_t, values_t, units='nm', cmap_label='Intensity', convention='sample')
if show_plot:
plt.show()
plt.close('all')
def test_plot_nanomap_with_images(show_plot):
rng = np.random.default_rng(0)
trans_np = rng.uniform(-5e-6, 5e-6, (20, 2))
values_np = np.random.default_rng(1).uniform(0, 1, 20)
# plot_nanomap_with_images requires tensor translations
trans_t = t.as_tensor(trans_np)
values_t = t.as_tensor(values_np)
def get_image_2d(i):
return np.random.default_rng(i).uniform(0, 1, (32, 32))
def get_image_3d(i):
return np.random.default_rng(i).uniform(0, 1, (4, 32, 32))
# basic call, no values
plotting.plot_nanomap_with_images(trans_np, get_image_2d)
if show_plot:
plt.show()
plt.close('all')
# with explicit values
plotting.plot_nanomap_with_images(trans_t, get_image_2d, values=values_np)
if show_plot:
plt.show()
plt.close('all')
# 3D image stack
fig = plt.figure(figsize=(11,7))
plotting.plot_nanomap_with_images(trans_np, get_image_3d, values=values_t, fig=fig)
if show_plot:
plt.show()
plt.close('all')