Merge remote-tracking branch 'origin/unittest' into mpl3.9.1

Author: cvanelteren

baseline/test_colorbar.png

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+name: proplot
+channels:
+  - conda-forge
+dependencies:
+  - python >=3.6,
+  - ipykernel >=5.1
+  - matplotlib 3.4.3,
+  - numpy
+  - pytest
+  - pytest-mpl
+  - pandas
+

tests/baseline/test_inbounds_data.png renamed to baseline/test_inbounds_data.png

@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""
+Test 1D plotting overrides.
+"""
+import numpy as np
+import numpy.ma as ma
+import pandas as pd
+
+import proplot as pplt
+
+state = np.random.RandomState(51423)
+
+
+@pplt.tests.image_compare
+def test_auto_reverse():
+    """
+    Test enabled and disabled auto reverse.
+    """
+    x = np.arange(10)[::-1]
+    y = np.arange(10)
+    z = state.rand(10, 10)
+    fig, axs = pplt.subplots(ncols=2, nrows=3, share=0)
+    # axs[0].format(xreverse=False)  # should fail
+    axs[0].plot(x, y)
+    axs[1].format(xlim=(0, 9))  # manual override
+    axs[1].plot(x, y)
+    axs[2].plotx(x, y)
+    axs[3].format(ylim=(0, 9))  # manual override
+    axs[3].plotx(x, y)
+    axs[4].pcolor(x, y[::-1], z)
+    axs[5].format(xlim=(0, 9), ylim=(0, 9))  # manual override!
+    axs[5].pcolor(x, y[::-1], z)
+    fig.format(suptitle='Auto-reverse test', collabels=['reverse', 'fixed'])
+
+
+@pplt.tests.image_compare
+def test_cmap_cycles():
+    """
+    Test sampling of multiple continuous colormaps.
+    """
+    cycle = pplt.Cycle(
+        'Boreal', 'Grays', 'Fire', 'Glacial', 'yellow',
+        left=[0.4] * 5, right=[0.6] * 5,
+        samples=[3, 4, 5, 2, 1],
+    )
+    fig, ax = pplt.subplots()
+    data = state.rand(10, len(cycle)).cumsum(axis=1)
+    data = pd.DataFrame(data, columns=list('abcdefghijklmno'))
+    ax.plot(data, cycle=cycle, linewidth=2, legend='b')
+
+
+@pplt.tests.image_compare
+def test_column_iteration():
+    """
+    Test scatter column iteration.
+    """
+    fig, axs = pplt.subplots(ncols=2)
+    axs[0].plot(state.rand(5, 5), state.rand(5, 5), lw=5)
+    axs[1].scatter(
+        state.rand(5, 5), state.rand(5, 5), state.rand(5, 5), state.rand(5, 5)
+    )
+
+
+@pplt.tests.image_compare
+def test_bar_stack():
+    """
+    Test bar and area stacking.
+    """
+    # TODO: Add test here
+
+
+@pplt.tests.image_compare
+def test_bar_width():
+    """
+    Test relative and absolute widths.
+    """
+    fig, axs = pplt.subplots(ncols=3)
+    x = np.arange(10)
+    y = state.rand(10, 2)
+    for i, ax in enumerate(axs):
+        ax.bar(x * (2 * i + 1), y, width=0.8, absolute_width=i == 1)
+
+
+@pplt.tests.image_compare
+def test_bar_vectors():
+    """
+    Test vector arguments to bar plots.
+    """
+    fig, ax = pplt.subplots(refwidth=3, facecolor='orange0')
+    ax.bar(
+        np.arange(10),
+        np.arange(1, 11),
+        linewidth=3,
+        # facecolor=(np.repeat(0.1, 3) * np.arange(1, 11)[:, None]).tolist(),
+        edgecolor=[f'gray{i}' for i in range(9, -1, -1)],
+        facecolor=[f'gray{i}' for i in range(10)],
+        alpha=np.linspace(0.1, 1, 10),
+        hatch=[None, '//'] * 5,
+    )
+
+
+@pplt.tests.image_compare
+def test_boxplot_colors():
+    """
+    Test box colors and cycle colors.
+    """
+    fig = pplt.figure(share=False)
+    ax = fig.subplot(221)
+    box_data = state.uniform(-3, 3, size=(1000, 5))
+    violin_data = state.normal(0, 1, size=(1000, 5))
+    ax.box(box_data, fillcolor=['red', 'blue', 'green', 'orange', 'yellow'])
+    ax = fig.subplot(222)
+    ax.violin(violin_data, fillcolor=['gray1', 'gray7'], hatches=[None, '//'], means=True, barstds=2)  # noqa: E501
+    ax = fig.subplot(223)
+    ax.boxh(box_data, cycle='pastel2')
+    ax = fig.subplot(224)
+    ax.violinh(violin_data, cycle='pastel1')
+
+
+@pplt.tests.image_compare
+def test_boxplot_vectors():
+    """
+    Test vector property arguments.
+    """
+    coords = (0.5, 1, 2)
+    counts = (10, 20, 100)
+    labels = ['foo', 'bar', 'baz']
+    datas = []
+    for count in counts:
+        data = state.rand(count)
+        datas.append(data)
+    datas = np.array(datas, dtype=object)
+    fig, ax = pplt.subplot(refwidth=3)
+    ax.boxplot(
+        coords,
+        datas,
+        lw=2,
+        notch=False,
+        whis=(10, 90),
+        cycle='538',
+        fillalpha=[0.5, 0.5, 1],
+        hatch=[None, '//', '**'],
+        boxlw=[2, 1, 1],
+    )
+    ax.format(xticklabels=labels)
+
+
+@pplt.tests.image_compare
+def test_histogram_types():
+    """
+    Test the different histogram types using basic keywords.
+    """
+    fig, axs = pplt.subplots(ncols=2, nrows=2, share=False)
+    data = state.normal(size=(100, 5))
+    data += np.arange(5)
+    kws = ({'stack': 0}, {'stack': 1}, {'fill': 0}, {'fill': 1, 'alpha': 0.5})
+    for ax, kw in zip(axs, kws):
+        ax.hist(data, ec='k', **kw)
+
+
+@pplt.tests.image_compare
+def test_invalid_plot():
+    """
+    Test lines with missing or invalid values.
+    """
+    fig, axs = pplt.subplots(ncols=2)
+    data = state.normal(size=(100, 5))
+    for j in range(5):
+        data[:, j] = np.sort(data[:, j])
+        data[:19 * (j + 1), j] = np.nan
+        # data[:20, :] = np.nan
+    data_masked = ma.masked_invalid(data)  # should be same result
+    for ax, dat in zip(axs, (data, data_masked)):
+        ax.plot(dat, means=True, shade=True)
+
+
+@pplt.tests.image_compare
+def test_invalid_dist():
+    """
+    Test distributions with missing or invalid data.
+    """
+    fig, axs = pplt.subplots(ncols=2, nrows=2)
+    data = state.normal(size=(100, 5))
+    for i in range(5):  # test uneven numbers of invalid values
+        data[:10 * (i + 1), :] = np.nan
+    data_masked = ma.masked_invalid(data)  # should be same result
+    for ax, dat in zip(axs[:2], (data, data_masked)):
+        ax.violin(dat, means=True)
+    for ax, dat in zip(axs[2:], (data, data_masked)):
+        ax.box(dat, fill=True, means=True)
+
+
+@pplt.tests.image_compare
+def test_pie_charts():
+    """
+    Test basic pie plots. No examples in user guide right now.
+    """
+    pplt.rc.inlinefmt = 'svg'
+    labels = ['foo', 'bar', 'baz', 'biff', 'buzz']
+    array = np.arange(1, 6)
+    data = pd.Series(array, index=labels)
+    fig = pplt.figure()
+    ax = fig.subplot(121)
+    ax.pie(array, edgefix=True, labels=labels, ec='k', cycle='reds')
+    ax = fig.subplot(122)
+    ax.pie(data, ec='k', cycle='blues')
+
+
+@pplt.tests.image_compare
+def test_parametric_labels():
+    """
+    Test passing strings as parametric 'color values'. This is likely
+    a common use case.
+    """
+    pplt.rc.inlinefmt = 'svg'
+    fig, ax = pplt.subplots()
+    ax.parametric(
+        state.rand(5), c=list('abcde'), lw=20, colorbar='b', cmap_kw={'left': 0.2}
+    )
+
+
+@pplt.tests.image_compare
+def test_parametric_colors():
+    """
+    Test color input arguments. Should be able to make monochromatic
+    plots for case where we want `line` without sticky x/y edges.
+    """
+    fig, axs = pplt.subplots(ncols=2, nrows=2)
+    colors = (
+        [(0, 1, 1), (0, 1, 0), (1, 0, 0), (0, 0, 1), (1, 1, 0)],
+        ['b', 'r', 'g', 'm', 'c', 'y'],
+        'black',
+        (0.5, 0.5, 0.5),
+    )
+    for ax, color in zip(axs, colors):
+        ax.parametric(
+            state.rand(5), state.rand(5),
+            linewidth=2, label='label', color=color, colorbar='b', legend='b'
+        )
+
+
+@pplt.tests.image_compare
+def test_scatter_args():
+    """
+    Test diverse scatter keyword parsing and RGB scaling.
+    """
+    x, y = state.randn(50), state.randn(50)
+    data = state.rand(50, 3)
+    fig, axs = pplt.subplots(ncols=4, share=0)
+    ax = axs[0]
+    ax.scatter(x, y, s=80, fc='none', edgecolors='r')
+    ax = axs[1]
+    ax.scatter(data, c=data, cmap='reds')  # column iteration
+    ax = axs[2]
+    with pplt.tests.warns(pplt.internals.ProplotWarning) as record:
+        ax.scatter(data[:, 0], c=data, cmap='reds')  # actual colors
+    assert len(record) == 1
+    ax = axs[3]
+    ax.scatter(data, mean=True, shadestd=1, barstd=0.5)  # distribution
+    ax.format(xlim=(-0.1, 2.1))
+
+
+@pplt.tests.image_compare
+def test_scatter_inbounds():
+    """
+    Test in-bounds scatter plots.
+    """
+    fig, axs = pplt.subplots(ncols=2, share=False)
+    N = 100
+    fig.format(xlim=(0, 20))
+    for i, ax in enumerate(axs):
+        c = ax.scatter(np.arange(N), np.arange(N), c=np.arange(N), inbounds=bool(i))
+        ax.colorbar(c, loc='b')
+
+
+@pplt.tests.image_compare
+def test_scatter_alpha():
+    """
+    Test behavior with multiple alpha values.
+    """
+    fig, ax = pplt.subplots()
+    data = state.rand(10)
+    alpha = np.linspace(0.1, 1, data.size)
+    with pplt.tests.warns(pplt.internals.ProplotWarning) as record:
+        ax.scatter(data, alpha=alpha)
+    assert len(record) == 1
+    with pplt.tests.warns(pplt.internals.ProplotWarning) as record:
+        ax.scatter(data + 1, c=np.arange(data.size), cmap='BuRd', alpha=alpha)
+    assert len(record) == 1
+    ax.scatter(data + 2, color='k', alpha=alpha)
+    ax.scatter(data + 3, color=[f'red{i}' for i in range(data.size)], alpha=alpha)
+
+
+@pplt.tests.image_compare
+def test_scatter_cycle():
+    """
+    Test scatter property cycling.
+    """
+    fig, ax = pplt.subplots()
+    cycle = pplt.Cycle(
+        '538',
+        marker=['X', 'o', 's', 'd'],
+        sizes=[20, 100],
+        edgecolors=['r', 'k']
+    )
+    ax.scatter(
+        state.rand(10, 4),
+        state.rand(10, 4),
+        cycle=cycle,
+        area_size=False,
+    )
+
+
+@pplt.tests.image_compare
+def test_scatter_sizes():
+    """
+    Test marker size scaling.
+    """
+    # Compare setting size to input size
+    size = 20
+    with pplt.rc.context({'lines.markersize': size}):
+        fig = pplt.figure()
+        ax = fig.subplot(121, margin=0.15)
+        for i in range(3):
+            kw = {'absolute_size': i == 2}
+            if i == 1:
+                kw['smin'] = 0
+                kw['smax'] = size ** 2  # should be same as relying on lines.markersize
+            ax.scatter(np.arange(5), [0.25 * (1 + i)] * 5, size ** 2, **kw)
+    # Test various size arguments
+    ax = fig.subplot(122, margin=0.15)
+    data = state.rand(5) * 500
+    ax.scatter(
+        np.arange(5), [0.25] * 5,
+        c='blue7', sizes=[5, 10, 15, 20, 25], area_size=False, absolute_size=True,
+    )
+    ax.scatter(
+        np.arange(5), [0.50] * 5, c='red7', sizes=data, absolute_size=True
+    )
+    ax.scatter(
+        np.arange(5), [0.75] * 5, c='red7', sizes=data, absolute_size=False
+    )
+    for i, d in enumerate(data):
+        ax.text(i, 0.5, format(d, '.0f'), va='center', ha='center')