Partial commit

Author: lukelbd

Diff for: proplot/tests/test_1dplots.py

@@ -0,0 +1,288 @@
+#!/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
+
+# ## Cmap cycles
+#
+# Test sampling of multiple continuous colormaps.
+
+# %% tags=[]
+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 = np.random.rand(10, len(cycle)).cumsum(axis=1)
+data = pd.DataFrame(data, columns=list('abcdefghijklmno'))
+ax.plot(data, cycle=cycle, linewidth=2, legend='b')
+
+# %% [markdown]
+# ## Auto reverse
+#
+# Test enabled and disabled auto reverse.
+
+# %%
+x = np.arange(10)[::-1]
+y = np.arange(10)
+z = np.random.rand(10, 10)
+fig, axs = pplt.subplots(ncols=2, nrows=2, share=False)
+# 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].pcolor(x, y[::-1], z)
+axs[3].format(xlim=(0, 9), ylim=(0, 9))  # manual override!
+axs[3].pcolor(x, y[::-1], z)
+
+
+# ## Invalid values
+#
+# Test distributions with missing or invalid values.
+
+fig, axs = pplt.subplots(ncols=2)
+data = np.random.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)
+
+fig, axs = pplt.subplots(ncols=2, nrows=2)
+data = np.random.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)
+
+# ## Histogram types
+#
+# Test the different histogram types using basic keywords.
+
+fig, axs = pplt.subplots(ncols=2, nrows=2, share=False)
+data = np.random.normal(size=(100, 5))
+data += np.arange(5)
+kws = ({'stack': False}, {'stack': True}, {'fill': False}, {'fill': True, 'alpha': 0.5})
+for ax, kw in zip(axs, kws):
+    ax.hist(data, ec='k', **kw)
+
+
+# ## 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=i)
+    ax.colorbar(c, loc='b')
+
+# ## Scatter columns
+#
+# Test scatter column iteration and property cycling. Note we cannot
+# retrieve metadata from `s` and `c`.
+#
+
+fig, ax = pplt.subplots()
+cycle = pplt.Cycle(
+    '538', marker=['X', 'o', 's', 'd'], sizes=[50, 100], edgecolors=['r', 'k']
+)
+ax.scatter(np.random.rand(10, 4), np.random.rand(10, 4), cycle=cycle)
+
+fig, axs = pplt.subplots(ncols=2)
+axs[0].plot(np.random.rand(5, 5), np.random.rand(5, 5), lw=5)
+axs[1].scatter(np.random.rand(5, 5), np.random.rand(5, 5), s=np.random.rand(5, 5) * 300)
+
+# ## Scatter colors
+
+# Test diverse scatter keyword parsing.
+
+
+x = np.random.randn(60)
+y = np.random.randn(60)
+
+fig, axs = pplt.subplots()
+axs.scatter(x, y, s=80, fc='none', edgecolors='r')
+
+# Test RGB color scaling.
+
+fig, axs = pplt.subplots(ncols=3)
+data = np.random.rand(50, 3)
+ax = axs[0]
+ax.scatter(data, c=data, cmap='reds')
+ax = axs[1]
+ax.scatter(data[:, 0], c=data, cmap='reds', )  # cycle='foo')  # should warn
+ax = axs[2]
+ax.scatter(data, mean=True, shadestd=1, barstd=0.5)
+ax.format(xlim=(-0.1, 2.1))
+
+# ## Scatter sizes
+
+# Test marker size scaling.
+
+fig = pplt.figure()
+ax = fig.subplot(margin=0.15)
+data = np.random.rand(5) * 500
+ax.scatter(
+    np.arange(5),
+    [0.25] * 5,
+    c='blue7',
+    sizes=['5pt', '10pt', '15pt', '20pt', '25pt']
+)
+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=True
+)
+for i, d in enumerate(data):
+    ax.text(i, 0.5, format(d, '.0f'), va='center', ha='center')
+
+fig, axs = pplt.subplots(ncols=3)
+pplt.rc.reset()
+pplt.rc['lines.markersize'] = 20
+N = 50
+x = np.random.rand(N)
+y = np.random.rand(N)
+for i, ax in enumerate(axs):
+    kw = {'absolute_size': i == 2}
+    if i == 1:
+        kw['smax'] = 20 ** 2  # should be same as relying on lines.markersize
+    ax.scatter(x, y, x * y, **kw)
+
+# ## Bar width
+
+# Test relative and absolute widths.
+
+fig, axs = pplt.subplots(ncols=3)
+x = np.arange(10)
+y = np.random.rand(10, 2)
+for i, ax in enumerate(axs):
+    ax.bar(x * (2 * i + 1), y, width=0.8, absolute_width=i == 1)
+
+# ## 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')
+
+# ## Box violin plots
+#
+# Test new default behavior of passing cycles to box/violin commands.
+
+fig = pplt.figure()
+ax = fig.subplot(121)
+ax.box(
+    np.random.uniform(-3, 3, size=(1000, 5)),
+    # cycle='blues_r',
+    fillcolor=['red', 'blue', 'green', 'orange', 'yellow'],
+    # ec='face',
+)
+ax = fig.subplot(122)
+ax.violin(
+    np.random.normal(0, 1, size=(1000, 5)),
+    # cycle='greys',
+    fillcolor=['gray1', 'gray7'],
+    means=True,
+    barstds=2,
+)
+
+
+# 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')
+print(obj1)
+ax.format(title='Box plots')
+
+# Violin plots
+ax = axs[1]
+obj2 = ax.violin(data1, fillcolor='gray6', means=True, points=100)
+print(obj2)
+ax.format(title='Violin plots')
+
+# Boxes with different colors
+ax = axs[2]
+ax.format(title='Multiple colors', ymargin=0.15)
+ax.boxh(data2, cycle='pastel2')
+
+# ## Parametric labels
+#
+# Test passing strings as parametric 'color values'. Likely a common use case.
+
+pplt.rc.inlinefmt = 'svg'
+fig, ax = pplt.subplots()
+c = ax.parametric(
+    np.random.rand(5), c=list('abcde'), lw=20, colorbar='b', cmap_kw={'left': 0.2}
+)
+
+# ## Parametric labels
+#
+# Test parametric plots with string labels.
+
+fig, ax = pplt.subplots()
+ax.parametric(
+    np.random.rand(5), c=list('abcde'), lw=5, colorbar='b', cmap_kw={'left': 0.2}
+)
+
+# ## Parametric color input
+#
+# Test color input arguments. Should be able to make monochromatic
+# plots for case where we want `line` without sticky x/y edges.
+
+# %matplotlib inline
+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),
+    # [(0.5, 0.5, 0.5)],
+)
+for ax, color in zip(axs, colors):
+    ax.parametric(
+        np.random.rand(5), np.random.rand(5),
+        linewidth=2, label='label', color=color, colorbar='b', legend='b'
+    )