compare figures instead of images (#236)

* compare figures instead of images

this is more robust to changes in mpl-styling

* more test updates

* update gitignore for new tests

.gitignore

@@ -136,3 +136,6 @@ _build
 autoapi
 
 _version.py
+
+# mpl testing
+result_images/

tests/_util.py

@@ -0,0 +1,18 @@
+__all__ = [
+    "set_param_values",
+]
+
+
+def set_param_values(controls, params):
+    """
+    A bit sketch because it set the index for ipywidgets
+    and direct value for mpl sliders. big TODO there.
+    """
+    for p, v in params.items():
+        slider = controls.controls[p]
+        if "Box" in str(slider.__class__):
+            for obj in slider.children:
+                if "Slider" in str(obj.__class__):
+                    obj.value = v
+        else:
+            slider.set_val(v)

tests/test_generic.py

@@ -2,19 +2,11 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-import pytest
-from matplotlib import __version__ as mpl_version
-from packaging import version
 
 from mpl_interactions.generic import heatmap_slicer, image_segmenter
 
-if version.parse(mpl_version) >= version.parse("3.3"):
-    mplsuffix = ""
-else:
-    mplsuffix = 32
 
-
-@pytest.mark.mpl_image_compare(style="default", filename=f"test_heatmap_slicer{mplsuffix}.png")
+# just smoketests here. hadn't set image comparison styling properly
 def test_heatmap_slicer():
     x = np.linspace(0, np.pi, 100)
     y = np.linspace(0, 10, 200)
@@ -31,10 +23,8 @@ def test_heatmap_slicer():
         interaction_type="move",
         cmap="plasma",
     )
-    return fig
 
 
-@pytest.mark.mpl_image_compare(style="default")
 def test_image_segmentation():
     image = plt.imread(
         "https://github.com/matplotlib/matplotlib/raw/v3.3.0/lib/matplotlib/mpl-data/sample_data/ada.png"

tests/test_pyplot.py

@@ -1,12 +1,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
-import pytest
 from matplotlib import __version__ as mpl_version
+from matplotlib.testing.decorators import check_figures_equal
 from packaging import version
 
 import mpl_interactions.ipyplot as iplt
-from mpl_interactions.pyplot import interactive_hist, interactive_plot
-from mpl_interactions.utils import figure
+from mpl_interactions.pyplot import interactive_plot
+
+from ._util import set_param_values
 
 np.random.seed(1111111121)
 
@@ -17,23 +18,11 @@ def f_hist(loc, scale):
     return np.random.randn(1000) * scale + loc
 
 
-@pytest.mark.mpl_image_compare(style="default")
-def test_hist_plot():
-    fig = figure(2)
-    _ = interactive_hist(f_hist, density=True, loc=(5.5, 100), scale=(10, 15))
-    fig.tight_layout()
-    return fig
-
-
-@pytest.mark.mpl_image_compare(style="default")
-def test_hist_controls():
-    if not mpl_gr_32:
-        pytest.skip("wonky font differences")
-    fig, ax = plt.subplots()
-    controls = interactive_hist(
-        f_hist, density=True, loc=(5.5, 100), scale=(10, 15), slider_formats="{:.1f}"
-    )
-    return controls.control_figures[0]
+# # smoke test
+# def test_hist_plot(fig_test, fig_ref):
+#     fig, ax =
+#     test_ax = fig_test.add_subplot()
+#     _ = interactive_hist(f_hist, density=True, loc=(5.5, 100), scale=(10, 15), ax=test_ax)
 
 
 def f1(x, tau, beta):
@@ -44,30 +33,6 @@ def f2(x, tau, beta):
     return np.sin(x * beta) * x * tau
 
 
-@pytest.mark.mpl_image_compare(style="default")
-def test_mixed_types():
-    if not mpl_gr_32:
-        pytest.skip("wonky font differences")
-
-    def foo(x, **kwargs):
-        return x
-
-    x = np.linspace(0, 1)
-    a = np.linspace(0, 10)
-    b = (0, 10, 15)
-    # set order is determined in part by PYTHONHASHSEED
-    # but there doesn't seem to be an easy way to set this for pytest
-    # so the unordered set will change its order from test to test :/
-    # c = {'this', 'set will be', 'unordered'}
-    d = {("this", "set will be", "ordered")}
-    e = 0  # this will not get a slider
-
-    # can't test ipywidgets yet
-    # no mpl widgets for booleans
-    # f = widgets.Checkbox(value=True, description='A checkbox!!')
-    return interactive_plot(x, foo, a=a, b=b, d=d, e=e, display=False).control_figures[0]
-
-
 def f1(x, tau, beta):
     return np.sin(x * tau) * x * beta
 
@@ -81,7 +46,6 @@ def f2(x, tau, beta):
 beta = (1, 2)
 
 
-@pytest.mark.mpl_image_compare(style="default")
 def test_multiple_functions():
     fig, ax = plt.subplots()
     controls = interactive_plot(x, f1, tau=tau, beta=beta, label="f1")
@@ -90,35 +54,48 @@ def test_multiple_functions():
     return fig
 
 
-@pytest.mark.mpl_image_compare(style="default")
-def test_styling():
-    fig, ax = plt.subplots()
-    controls = interactive_plot(
-        x,
-        f1,
-        beta=beta,
-        tau=tau,
-        label="f1",
-    )
-    iplt.title("the value of tau is: {tau:.2f}", controls=controls["tau"])
+@check_figures_equal(extensions=["png"])
+def test_plot(fig_test, fig_ref):
+    test_ax = fig_test.add_subplot()
+
+    # TODO: fix the horrible ylim scaling
+    # get it outside of the plot command
+    ylims = (-10, 10)
+    controls = interactive_plot(x, f1, beta=beta, tau=tau, label="f1", ax=test_ax, ylim=ylims)
     interactive_plot(
-        x,
-        f2,
-        controls=controls,
-        label="custom label!",
-        linestyle="--",
+        x, f2, controls=controls, label="custom label!", linestyle="--", ax=test_ax, ylim=ylims
     )
-    plt.legend()
-    return fig
+    set_param_values(controls, {"beta": 5, "tau": 4})
+    test_ax.legend()
 
+    ref_ax = fig_ref.add_subplot()
+    ref_ax.plot(x, f1(x, **controls.params), label="f1")
+    ref_ax.plot(x, f2(x, **controls.params), linestyle="--", label="custom label!")
+    ref_ax.legend()
+    ref_ax.set_ylim(ylims)
+    for fig in controls.control_figures:
+        plt.close(fig)
 
-def test_imshow_scalars():
+
+@check_figures_equal(extensions=["png"])
+def test_imshow_scalars(fig_test, fig_ref):
     # and by proxy all the scalar handling
-    def mask(min_distance):
-        return np.random.randn(10, 10)
 
-    fig, ax = plt.subplots()
-    iplt.imshow(mask, min_distance=(1, 10), alpha=(0, 1))
+    mask_arr = np.random.randn(10, 10) * 10
+
+    def mask(min_distance):
+        new_arr = np.copy(mask_arr)
+        new_arr[mask_arr < min_distance] = 0
+        return new_arr
+
+    test_ax = fig_test.add_subplot()
+    ctrls = iplt.imshow(mask, min_distance=(1, 10), alpha=(0, 1), ax=test_ax)
+    set_param_values(ctrls, {"min_distance": 5.5, "alpha": 0.75})
+    ref_ax = fig_ref.add_subplot()
+    print(ctrls.params)
+    ref_ax.imshow(mask(ctrls.params["min_distance"]), alpha=ctrls.params["alpha"])
+    for fig in ctrls.control_figures:
+        plt.close(fig)
 
 
 def test_title():