Added a variety of tests

Author: cvanelteren

tests/baseline/test_inset_basic.png

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+import proplot as plt, numpy as np
+import pytest
+
+
+@pytest.mark.skip(reason="TODO")
+@pytest.mark.mpl_image_compare
+def test_geographic_single_projection():
+    fig = plt.figure(refwidth=3)
+    axs = fig.subplots(nrows=2, proj="robin", proj_kw={"lon_0": 180})
+    # proj = pplt.Proj('robin', lon_0=180)
+    # axs = pplt.subplots(nrows=2, proj=proj)  # equivalent to above
+    axs.format(
+        suptitle="Figure with single projection",
+        land=True,
+        latlines=30,
+        lonlines=60,
+    )
+    return fig
+
+
+@pytest.mark.skip(reason="TODO")
+@pytest.mark.mpl_image_compare
+def test_gegraphic_multiple_projections():
+    fig = plt.figure()
+    # Add projections
+    gs = plt.GridSpec(ncols=2, nrows=3, hratios=(1, 1, 1.4))
+    for i, proj in enumerate(("cyl", "hammer", "npstere")):
+        ax1 = fig.subplot(gs[i, 0], proj=proj, basemap=True)  # basemap
+        ax2 = fig.subplot(gs[i, 1], proj=proj)  # cartopy
+
+    # Format projections
+    fig.format(
+        land=True,
+        suptitle="Figure with several projections",
+        toplabels=("Basemap projections", "Cartopy projections"),
+        toplabelweight="normal",
+        latlines=30,
+        lonlines=60,
+        lonlabels="b",
+        latlabels="r",  # or lonlabels=True, labels=True, etc.
+    )
+    fig.subplotgrid[-2:].format(
+        latlines=20, lonlines=30
+    )  # dense gridlines for polar plots
+    plt.rc.reset()
+    return fig
+
+
+@pytest.mark.skip(reason="TODO")
+@pytest.mark.mpl_image_compare
+def test_drawing_in_projection():
+    # Fake data with unusual longitude seam location and without coverage over poles
+    offset = -40
+    lon = plt.arange(offset, 360 + offset - 1, 60)
+    lat = plt.arange(-60, 60 + 1, 30)
+    state = np.random.RandomState(51423)
+    data = state.rand(len(lat), len(lon))
+
+    # Plot data both without and with globe=True
+    for globe in (False, True):
+        string = "with" if globe else "without"
+        gs = plt.GridSpec(nrows=2, ncols=2)
+        fig = plt.figure(refwidth=2.5)
+        for i, ss in enumerate(gs):
+            ax = fig.subplot(ss, proj="kav7", basemap=(i % 2))
+            cmap = ("sunset", "sunrise")[i % 2]
+            if i > 1:
+                ax.pcolor(lon, lat, data, cmap=cmap, globe=globe, extend="both")
+            else:
+                m = ax.contourf(lon, lat, data, cmap=cmap, globe=globe, extend="both")
+                fig.colorbar(m, loc="b", span=i + 1, label="values", extendsize="1.7em")
+        fig.format(
+            suptitle=f"Geophysical data {string} global coverage",
+            toplabels=("Cartopy example", "Basemap example"),
+            leftlabels=("Filled contours", "Grid boxes"),
+            toplabelweight="normal",
+            leftlabelweight="normal",
+            coast=True,
+            lonlines=90,
+            abc="A.",
+            abcloc="ul",
+            abcborder=False,
+        )
+        return fig

tests/test_geographic.py

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+import proplot as pplt, numpy as np, pytest
+
+
+@pytest.mark.mpl_image_compare
+def test_inset_basic():
+    # Demonstrate that complex arrangements preserve
+    # spacing, aspect ratios, and axis sharing
+    gs = pplt.GridSpec(nrows=2, ncols=2)
+    fig = pplt.figure(refwidth=1.5, share=False)
+    for ss, side in zip(gs, "tlbr"):
+        ax = fig.add_subplot(ss)
+        px = ax.panel_axes(side, width="3em")
+    fig.format(
+        xlim=(0, 1),
+        ylim=(0, 1),
+        xlabel="xlabel",
+        ylabel="ylabel",
+        xticks=0.2,
+        yticks=0.2,
+        title="Title",
+        suptitle="Complex arrangement of panels",
+        toplabels=("Column 1", "Column 2"),
+        abc=True,
+        abcloc="ul",
+        titleloc="uc",
+        titleabove=False,
+    )
+    return fig

tests/test_inset.py

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+#!/usr/bin/env python3
+# import proplot as pplt
+import numpy as np, pandas as pd, proplot as pplt
+import pytest
+
+
+@pytest.mark.mpl_image_compare
+def test_statistical_boxplot():
+    # Sample data
+    N = 500
+    state = np.random.RandomState(51423)
+    data1 = state.normal(size=(N, 5)) + 2 * (state.rand(N, 5) - 0.5) * np.arange(5)
+    data1 = pd.DataFrame(data1, columns=pd.Index(list("abcde"), name="label"))
+    data2 = state.rand(100, 7)
+    data2 = pd.DataFrame(data2, columns=pd.Index(list("abcdefg"), name="label"))
+
+    # Figure
+    fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], span=False)
+    axs.format(abc="A.", titleloc="l", grid=False, suptitle="Boxes and violins demo")
+
+    # Box plots
+    ax = axs[0]
+    obj1 = ax.box(data1, means=True, marker="x", meancolor="r", fillcolor="gray4")
+    ax.format(title="Box plots")
+
+    # Violin plots
+    ax = axs[1]
+    obj2 = ax.violin(data1, fillcolor="gray6", means=True, points=100)
+    ax.format(title="Violin plots")
+
+    # Boxes with different colors
+    ax = axs[2]
+    ax.boxh(data2, cycle="pastel2")
+    ax.format(title="Multiple colors", ymargin=0.15)
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_panel_dist():
+    # Sample data
+    N = 500
+    state = np.random.RandomState(51423)
+    x = state.normal(size=(N,))
+    y = state.normal(size=(N,))
+    bins = pplt.arange(-3, 3, 0.25)
+
+    # Histogram with marginal distributions
+    fig, axs = pplt.subplots(ncols=2, refwidth=2.3)
+    axs.format(
+        abc="A.",
+        abcloc="l",
+        titleabove=True,
+        ylabel="y axis",
+        suptitle="Histograms with marginal distributions",
+    )
+    colors = ("indigo9", "red9")
+    titles = ("Group 1", "Group 2")
+    for ax, which, color, title in zip(axs, "lr", colors, titles):
+        ax.hist2d(
+            x,
+            y,
+            bins,
+            vmin=0,
+            vmax=10,
+            levels=50,
+            cmap=color,
+            colorbar="b",
+            colorbar_kw={"label": "count"},
+        )
+        color = pplt.scale_luminance(color, 1.5)  # histogram colors
+        px = ax.panel(which, space=0)
+        px.histh(y, bins, color=color, fill=True, ec="k")
+        px.format(grid=False, xlocator=[], xreverse=(which == "l"))
+        px = ax.panel("t", space=0)
+        px.hist(x, bins, color=color, fill=True, ec="k")
+        px.format(grid=False, ylocator=[], title=title, titleloc="l")
+    return fig

tests/test_statistical_plotting.py

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+import proplot as pplt, pytest
+
+
+@pytest.mark.mpl_image_compare
+def test_stress_test_subplots():
+    fig = pplt.figure(space=0, refwidth=0.7)
+    axs = fig.subplots(nrows=8, ncols=8)
+    axs.format(
+        abc=True,
+        abcloc="ur",
+        xlabel="x axis",
+        ylabel="y axis",
+        xticks=[],
+        yticks=[],
+        suptitle="A-b-c label stress test",
+    )
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_subplot_variable_spacing():
+    # Stress test of the tight layout algorithm
+    # Add large labels along the edge of one subplot
+    descrip = "variable"
+    equal = False
+    fig, axs = pplt.subplots(
+        nrows=3, ncols=3, refwidth=1.1, share=False, equal=bool(equal)
+    )
+    axs[1].format(xlabel="xlabel\nxlabel", ylabel="ylabel\nylabel\nylabel\nylabel")
+    axs.format(
+        grid=False,
+        toplabels=("Column 1", "Column 2", "Column 3"),
+        leftlabels=("Row 1", "Row 2", "Row 3"),
+        suptitle=f"Tight layout with {descrip} row-column spacing",
+    )
+    return fig
+
+
+@pytest.mark.mpl_image_compare
+def test_subplot_equal_spacing():
+    # Stress test of the tight layout algorithm
+    # Add large labels along the edge of one subplot
+    descrip = "equal"
+    equal = True
+    fig, axs = pplt.subplots(
+        nrows=3, ncols=3, refwidth=1.1, share=False, equal=bool(equal)
+    )
+    axs[1].format(xlabel="xlabel\nxlabel", ylabel="ylabel\nylabel\nylabel\nylabel")
+    axs.format(
+        grid=False,
+        toplabels=("Column 1", "Column 2", "Column 3"),
+        leftlabels=("Row 1", "Row 2", "Row 3"),
+        suptitle=f"Tight layout with {descrip} row-column spacing",
+    )
+    return fig