Initialize mpl pytests

Author: cvanelteren

tests/baseline/test_colorbar.png

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+import proplot as plt, numpy as np, pytest
+from matplotlib.testing import setup
+
+
+@pytest.fixture()
+def setup_mpl():
+    setup()
+    plt.clf()
+
+
+@pytest.mark.mpl_image_compare
+def test_standardized_inputs_1d():
+    N = 5
+    state = np.random.RandomState(51423)
+    with plt.rc.context({"axes.prop_cycle": plt.Cycle("Grays", N=N, left=0.3)}):
+        # Sample data
+        x = np.linspace(-5, 5, N)
+        y = state.rand(N, 5)
+        fig = plt.figure(
+            share=False, suptitle="Standardized input demonstration", figsize=(5, 5)
+        )
+
+        # Plot by passing both x and y coordinates
+        ax = fig.subplot(121, title="Manual x coordinates")
+        ax.area(x, -1 * y / N, stack=True)
+        ax.bar(x, y, linewidth=0, alpha=1, width=0.8)
+        ax.plot(x, y + 1, linewidth=2)
+        ax.scatter(x, y + 2, marker="s", markersize=5**2)
+
+        # Plot by passing just y coordinates
+        # Default x coordinates are inferred from DataFrame,
+        # inferred from DataArray, or set to np.arange(0, y.shape[0])
+        ax = fig.subplot(122, title="Auto x coordinates")
+        ax.area(-1 * y / N, stack=True)
+        ax.bar(y, linewidth=0, alpha=1)
+        ax.plot(y + 1, linewidth=2)
+        ax.scatter(y + 2, marker="s", markersize=5**2)
+        fig.format(xlabel="xlabel", ylabel="ylabel")
+    return fig
+
+
+if __name__ == "__main__":
+    for func in [
+        test_standardized_inputs_1d,
+    ]:
+        func().savefig(f"./tests/baseline/{func.__name__}.png")

tests/baseline/test_inbounds_data.png

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+import pytest
+
+import proplot as plt, numpy as np
+from matplotlib.testing import setup
+
+
+@pytest.fixture()
+def setup_mpl():
+    setup()
+    plt.clf()
+
+
+@pytest.mark.mpl_image_compare
+def test_standardized_input():
+    # Sample data
+    state = np.random.RandomState(51423)
+    x = y = np.array([-10, -5, 0, 5, 10])
+    xedges = plt.edges(x)
+    yedges = plt.edges(y)
+    data = state.rand(y.size, x.size)  # "center" coordinates
+    lim = (np.min(xedges), np.max(xedges))
+
+    with plt.rc.context({"cmap": "Grays", "cmap.levels": 21}):
+        # Figure
+        fig = plt.figure(refwidth=2.3, share=False)
+        axs = fig.subplots(ncols=2, nrows=2)
+        axs.format(
+            xlabel="xlabel",
+            ylabel="ylabel",
+            xlim=lim,
+            ylim=lim,
+            xlocator=5,
+            ylocator=5,
+            suptitle="Standardized input demonstration",
+            toplabels=("Coordinate centers", "Coordinate edges"),
+        )
+
+        # Plot using both centers and edges as coordinates
+        axs[0].pcolormesh(x, y, data)
+        axs[1].pcolormesh(xedges, yedges, data)
+        axs[2].contourf(x, y, data)
+        axs[3].contourf(xedges, yedges, data)
+    fig.show()
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_inbounds_data():
+    # Sample data
+    cmap = "turku_r"
+    state = np.random.RandomState(51423)
+    N = 80
+    x = y = np.arange(N + 1)
+    data = 10 + (state.normal(0, 3, size=(N, N))).cumsum(axis=0).cumsum(axis=1)
+    xlim = ylim = (0, 25)
+
+    # Plot the data
+    fig, axs = plt.subplots(
+        [[0, 1, 1, 0], [2, 2, 3, 3]],
+        wratios=(1.3, 1, 1, 1.3),
+        span=False,
+        refwidth=2.2,
+    )
+    axs[0].fill_between(
+        xlim,
+        *ylim,
+        zorder=3,
+        edgecolor="red",
+        facecolor=plt.set_alpha("red", 0.2),
+    )
+    for i, ax in enumerate(axs):
+        inbounds = i == 1
+        title = f"Restricted lims inbounds={inbounds}"
+        title += " (default)" if inbounds else ""
+        ax.format(
+            xlim=(None if i == 0 else xlim),
+            ylim=(None if i == 0 else ylim),
+            title=("Default axis limits" if i == 0 else title),
+        )
+        ax.pcolor(x, y, data, cmap=cmap, inbounds=inbounds)
+    fig.format(
+        xlabel="xlabel",
+        ylabel="ylabel",
+        suptitle="Default vmin/vmax restricted to in-bounds data",
+    )
+    fig.show()
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_colorbar():
+    # Sample data
+    state = np.random.RandomState(51423)
+    data = 10 + state.normal(0, 1, size=(33, 33)).cumsum(axis=0).cumsum(axis=1)
+
+    # Figure
+    fig, axs = plt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], ref=3, refwidth=2.3)
+    axs.format(yformatter="none", suptitle="Discrete vs. smooth colormap levels")
+
+    # Pcolor
+    axs[0].pcolor(data, cmap="viridis", colorbar="l")
+    axs[0].set_title("Pcolor plot\ndiscrete=True (default)")
+    axs[1].pcolor(data, discrete=False, cmap="viridis", colorbar="r")
+    axs[1].set_title("Pcolor plot\ndiscrete=False")
+
+    # Imshow
+    m = axs[2].imshow(data, cmap="oslo", colorbar="b")
+    axs[2].format(title="Imshow plot\ndiscrete=False (default)", yformatter="auto")
+    fig.show()
+    return fig
+
+
+if __name__ == "__main__":
+    for func in [
+        test_standardized_input,
+        test_inbounds_data,
+        test_colorbar,
+    ]:
+        fig = func()
+        fig.savefig(f"./tests/baseline/{func.__name__}.png")
+    plt.show(block=1)