From 6e7f1d75a273e0705c3e9a529405d26a12726376 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Sat, 2 Mar 2024 18:21:26 +0100 Subject: [PATCH 1/8] Initialize mpl pytests --- tests/baseline/test_colorbar.png | Bin 0 -> 329974 bytes tests/baseline/test_inbounds_data.png | Bin 0 -> 336928 bytes tests/baseline/test_standardized_input.png | Bin 0 -> 425869 bytes .../baseline/test_standardized_inputs_1d.png | Bin 0 -> 35151 bytes tests/test_1d.py | 46 +++++++ tests/test_imshow.py | 121 ++++++++++++++++++ 6 files changed, 167 insertions(+) create mode 100644 tests/baseline/test_colorbar.png create mode 100644 tests/baseline/test_inbounds_data.png create mode 100644 tests/baseline/test_standardized_input.png create mode 100644 tests/baseline/test_standardized_inputs_1d.png create mode 100644 tests/test_1d.py create mode 100644 tests/test_imshow.py diff --git a/tests/baseline/test_colorbar.png b/tests/baseline/test_colorbar.png new file mode 100644 index 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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") diff --git a/tests/test_imshow.py b/tests/test_imshow.py new file mode 100644 index 000000000..51012651b --- /dev/null +++ b/tests/test_imshow.py @@ -0,0 +1,121 @@ +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) From 2e32de181dd94f919c45e23a011bea79406df629 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Sun, 3 Mar 2024 12:29:20 +0100 Subject: [PATCH 2/8] Expaned unittest --- tests/baseline/test_colorbar.png | Bin 329974 -> 96206 bytes tests/baseline/test_inbounds_data.png | Bin 336928 -> 43116 bytes tests/baseline/test_standardized_input.png | Bin 425869 -> 61213 bytes .../baseline/test_standardized_inputs_1d.png | Bin 35151 -> 47315 bytes tests/test_1d.py | 13 ------------- tests/test_imshow.py | 11 ----------- 6 files changed, 24 deletions(-) diff --git a/tests/baseline/test_colorbar.png b/tests/baseline/test_colorbar.png index 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index 50c6bfd16..f3a54d170 100644 --- a/tests/test_1d.py +++ b/tests/test_1d.py @@ -2,12 +2,6 @@ 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 @@ -37,10 +31,3 @@ def test_standardized_inputs_1d(): 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") diff --git a/tests/test_imshow.py b/tests/test_imshow.py index 51012651b..0fcc623a9 100644 --- a/tests/test_imshow.py +++ b/tests/test_imshow.py @@ -108,14 +108,3 @@ def test_colorbar(): 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) From 17e1283f001ecb4a026c2aea22790fb533f0b557 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Wed, 24 Jul 2024 22:01:32 +0200 Subject: [PATCH 3/8] Added micromamba environment for reproducible builds --- baseline/test_colorbar.png | Bin 0 -> 104900 bytes baseline/test_inbounds_data.png | Bin 0 -> 43116 bytes baseline/test_standardized_input.png | Bin 0 -> 64594 bytes baseline/test_standardized_inputs_1d.png | Bin 0 -> 47315 bytes env.yml | 11 +++++++++++ 5 files changed, 11 insertions(+) create mode 100644 baseline/test_colorbar.png create mode 100644 baseline/test_inbounds_data.png create mode 100644 baseline/test_standardized_input.png create mode 100644 baseline/test_standardized_inputs_1d.png create mode 100644 env.yml diff --git a/baseline/test_colorbar.png b/baseline/test_colorbar.png new file mode 100644 index 0000000000000000000000000000000000000000..bc3044515679696829d77825af1b60c8e568188a GIT binary 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matplotlib 3.4.3, + - numpy + - pytest + - pytest-mpl + From 60ec01be23601eafdc7b8a367adae8579d2d3c83 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Wed, 24 Jul 2024 22:11:48 +0200 Subject: [PATCH 4/8] Added pandas to env --- env.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/env.yml b/env.yml index 995afb3b6..f07020d9c 100644 --- a/env.yml +++ b/env.yml @@ -8,4 +8,5 @@ dependencies: - numpy - pytest - pytest-mpl + - pandas From 9d1f659fcda3a2f4f6bc27a7b5ec247baa87923e Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Wed, 24 Jul 2024 22:12:05 +0200 Subject: [PATCH 5/8] Added a variety of tests --- tests/baseline/test_inset_basic.png | Bin 0 -> 39285 bytes tests/test_geographic.py | 84 ++++++++++++++++++++++++++++ tests/test_inset.py | 28 ++++++++++ tests/test_statistical_plotting.py | 77 +++++++++++++++++++++++++ tests/test_subplots.py | 55 ++++++++++++++++++ 5 files changed, 244 insertions(+) create mode 100644 tests/baseline/test_inset_basic.png create mode 100644 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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 diff --git a/tests/test_inset.py b/tests/test_inset.py new file mode 100644 index 000000000..5a4c45a97 --- /dev/null +++ b/tests/test_inset.py @@ -0,0 +1,28 @@ +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 diff --git a/tests/test_statistical_plotting.py b/tests/test_statistical_plotting.py new file mode 100644 index 000000000..620cd4ac0 --- /dev/null +++ b/tests/test_statistical_plotting.py @@ -0,0 +1,77 @@ +#!/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 diff --git a/tests/test_subplots.py b/tests/test_subplots.py new file mode 100644 index 000000000..d31024cf5 --- /dev/null +++ b/tests/test_subplots.py @@ -0,0 +1,55 @@ +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 From f235e30ad6b581c84cbe6fc634a405ca9f876735 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Wed, 24 Jul 2024 22:13:22 +0200 Subject: [PATCH 6/8] Freezing baseline --- tests/baseline/test_colorbar.png | Bin 96206 -> 97578 bytes tests/baseline/test_inbounds_data.png | Bin 43116 -> 40861 bytes tests/baseline/test_standardized_input.png | Bin 61213 -> 34865 bytes .../baseline/test_standardized_inputs_1d.png | Bin 47315 -> 45287 bytes 4 files changed, 0 insertions(+), 0 deletions(-) diff --git a/tests/baseline/test_colorbar.png b/tests/baseline/test_colorbar.png index 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z1?tZWS9SXI#a$P92v=%@OE+Of#jMeR^VS0X%!20B8p0fcyT*SRg>ZJum2&E2DX$&jKTFtDsjv_GFfcOjGgmwyAFI99{zrIwj793LSiCNA4M|b zliCBLPzAzh72QeA6{G?z^5$GFW%VGSZ?bK&5EE8w2X}jsadDRxNVTAvCRw?WGiFbN1le3I6V;# z`xJB$q**e+Xae{g>vDpChz2d(-{9?iVU_2)bvhCjjlNH(raqSZitvi=G#Csg5Y(f+ zb){(*p}p`{(T}KO3+CsZZ}2E)VbbHa>&|pjVB0fy|EU^(=BHP5LSH0>aID28JC|!A ziHWr%`*;QEF_fX9C1o`yj+-lHOrLGsKeF`rLp)Qjvki;LJ!CCV2v?KMH22fE6@H@W zRjJkW)|kjhCq^HlZZOe5V0c7IiR4aqw@7RdsicfH!=TzRWJQWLb%w3ir*6rAAm@DE zHUHV4pGyi220QFQXQ^=7AD^L%SIaXOtz?#cHDcvCO_AsQy%7to)+^PBOvLmsiDx-I zFRnFUt-h>iCa*JW*_nQf*sCxMF3B)WE~S6Q7x4w{Z}eDMdp#*_i*ZXBz@frLby6dZ zlP~Zs*(7ftD+^L6)<|li?If69_4n?5BntYuogubH`{_eNTLyZLo}|zwp?@;7vTo7H zio z3)n)ckrQNoeB8CyuB{ZQc>+hqhZ8gYY*|Yl>{8M7Hfw{OlwvpzA)yKJ# zOpfrE9kCibW_RGBR1Yojtj_YZz0{o4Ql From 7c815bff156d93da89e78d5c7dc749a3ef489665 Mon Sep 17 00:00:00 2001 From: cvanelteren Date: Wed, 24 Jul 2024 22:57:31 +0200 Subject: [PATCH 7/8] Added Luke's tests --- proplot/tests/test_1dplots.py | 344 ++++++++++++++++++++++++++++++ proplot/tests/test_2dplots.py | 322 ++++++++++++++++++++++++++++ proplot/tests/test_axes.py | 128 +++++++++++ proplot/tests/test_colorbar.py | 228 ++++++++++++++++++++ proplot/tests/test_docs.py | 78 +++++++ proplot/tests/test_format.py | 271 +++++++++++++++++++++++ proplot/tests/test_integration.py | 139 ++++++++++++ proplot/tests/test_legend.py | 160 ++++++++++++++ proplot/tests/test_projections.py | 139 ++++++++++++ proplot/tests/test_subplots.py | 168 +++++++++++++++ 10 files changed, 1977 insertions(+) create mode 100644 proplot/tests/test_1dplots.py create mode 100644 proplot/tests/test_2dplots.py create mode 100644 proplot/tests/test_axes.py create mode 100644 proplot/tests/test_colorbar.py create mode 100644 proplot/tests/test_docs.py create mode 100644 proplot/tests/test_format.py create mode 100644 proplot/tests/test_integration.py create mode 100644 proplot/tests/test_legend.py create mode 100644 proplot/tests/test_projections.py create mode 100644 proplot/tests/test_subplots.py diff --git a/proplot/tests/test_1dplots.py b/proplot/tests/test_1dplots.py new file mode 100644 index 000000000..aa7ab327d --- /dev/null +++ b/proplot/tests/test_1dplots.py @@ -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') diff --git a/proplot/tests/test_2dplots.py b/proplot/tests/test_2dplots.py new file mode 100644 index 000000000..8e8fee55b --- /dev/null +++ b/proplot/tests/test_2dplots.py @@ -0,0 +1,322 @@ +#!/usr/bin/env python3 +""" +Test 2D plotting overrides. +""" +import numpy as np +import pytest +import xarray as xr + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_colormap_vcenter(): + """ + Test colormap vcenter. + """ + fig, axs = pplt.subplots(ncols=3) + data = 10 * state.rand(10, 10) - 3 + axs[0].pcolor(data, vcenter=0) + axs[1].pcolor(data, vcenter=1) + axs[2].pcolor(data, vcenter=2) + + +@pplt.tests.image_compare +def test_auto_diverging(): + """ + Test that auto diverging works. + """ + # Test with basic data + fig = pplt.figure() + # fig.format(collabels=('Auto sequential', 'Auto diverging'), suptitle='Default') + ax = fig.subplot(121) + ax.pcolor(state.rand(10, 10) * 5, colorbar='b') + ax = fig.subplot(122) + ax.pcolor(state.rand(10, 10) * 5 - 3.5, colorbar='b') + fig.format(toplabels=('Sequential', 'Diverging')) + + # Test with explicit vcenter + fig, axs = pplt.subplots(ncols=3) + data = 5 * state.rand(10, 10) + axs[0].pcolor(data, vcenter=0, colorbar='b') # otherwise should be disabled + axs[1].pcolor(data, vcenter=1.5, colorbar='b') + axs[2].pcolor(data, vcenter=4, colorbar='b', symmetric=True) + + # Test when cmap input disables auto diverging. + fig, axs = pplt.subplots(ncols=2, nrows=2, refwidth=2) + cmap = pplt.Colormap(('red7', 'red3', 'red1', 'blue1', 'blue3', 'blue7'), listmode='discrete') # noqa: E501 + data1 = 10 * state.rand(10, 10) + data2 = data1 - 2 + for i, cmap in enumerate(('RdBu_r', cmap)): + for j, data in enumerate((data1, data2)): + cmap = pplt.Colormap(pplt.Colormap(cmap)) + axs[i, j].pcolormesh(data, cmap=cmap, colorbar='b') + + fig, axs = pplt.subplots(ncols=3) + data = state.rand(5, 5) * 10 - 5 + for i, ax in enumerate(axs[:2]): + ax.pcolor(data, sequential=bool(i), colorbar='b') + axs[2].pcolor(data, diverging=False, colorbar='b') # should have same effect + + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) * 10 + 2 + for ax, norm in zip(axs, (None, 'div')): + ax.pcolor(data, norm=norm, colorbar='b') + + +@pplt.tests.image_compare +def test_colormap_mode(): + """ + Test auto extending, auto discrete. Should issue warnings. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2, share=False) + axs[0].pcolor(state.rand(5, 5) % 0.3, extend='both', cyclic=True, colorbar='b') + axs[1].pcolor(state.rand(5, 5), sequential=True, diverging=True, colorbar='b') + axs[2].pcolor(state.rand(5, 5), discrete=False, qualitative=True, colorbar='b') + pplt.rc['cmap.discrete'] = False # should be ignored below + axs[3].contourf(state.rand(5, 5), colorbar='b') + + +@pplt.tests.image_compare +def test_contour_labels(): + """ + Test contour labels. We use a separate `contour` object when adding labels to + filled contours or else weird stuff happens (see below). We could just modify + filled contour edges when not adding labels but that would be inconsistent with + behavior when labels are active. + """ + data = state.rand(5, 5) * 10 - 5 + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.contourf( + data, edgecolor='k', linewidth=1.5, + labels=True, labels_kw={'color': 'k', 'size': 'large'} + ) + ax = axs[1] + m = ax.contourf(data) + ax.clabel(m, colors='black', fontsize='large') # looks fine without this + for o in m.collections: + o.set_linewidth(1.5) + o.set_edgecolor('k') + + +@pplt.tests.image_compare +def test_contour_negative(): + """ + Ensure `cmap.monochrome` properly assigned. + """ + fig = pplt.figure(share=False) + ax = fig.subplot(131) + data = state.rand(10, 10) * 10 - 5 + ax.contour(data, color='k') + ax = fig.subplot(132) + ax.tricontour(*(state.rand(3, 20) * 10 - 5), color='k') + ax = fig.subplot(133) + ax.contour(data, cmap=['black']) # fails but that's ok + + +@pplt.tests.image_compare +def test_contour_single(): + """ + Test whether single contour works. + """ + da = xr.DataArray( + np.array( + [ + [0, 1, 2], + [3, 4, 5], + [6, 7, 8], + [9, 10, 11] + ] + ), + dims=['y', 'x'] + ) + fig, ax = pplt.subplots() + ax.contour(da, levels=[5.0], color='r') + + +@pplt.tests.image_compare +def test_edge_fix(): + """ + Test edge fix applied to 1D plotting utilities. + """ + # Test basic application + # TODO: This should make no difference for PNG plots? + pplt.rc.edgefix = 1 + fig, axs = pplt.subplots(ncols=2, share=False) + axs.format(grid=False) + axs[0].bar(state.rand(10,) * 10 - 5, width=1, negpos=True) + axs[1].area(state.rand(5, 3), stack=True) + + # Test whether ignored for transparent colorbars + data = state.rand(10, 10) + cmap = 'magma' + fig, axs = pplt.subplots(nrows=3, ncols=2, refwidth=2.5, share=False) + for i, iaxs in enumerate((axs[:2], axs[2:4])): + if i == 0: + cmap = pplt.Colormap('magma', alpha=0.5) + alpha = None + iaxs.format(title='Colormap alpha') + else: + cmap = 'magma' + alpha = 0.5 + iaxs.format(title='Single alpha') + iaxs[0].contourf(data, cmap=cmap, colorbar='b', alpha=alpha) + iaxs[1].pcolormesh(data, cmap=cmap, colorbar='b', alpha=alpha) + axs[4].bar(data[:3, :3], alpha=0.5) + axs[5].area(data[:3, :3], alpha=0.5, stack=True) + + +@pplt.tests.image_compare +def test_flow_functions(): + """ + These are seldom used and missing from documentation. Be careful + not to break anything basic. + """ + fig, ax = pplt.subplots() + for _ in range(2): + ax.streamplot(state.rand(10, 10), 5 * state.rand(10, 10), label='label') + + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.quiver( + state.rand(10, 10), 5 * state.rand(10, 10), c=state.rand(10, 10), + label='label' + ) + ax = axs[1] + ax.quiver(state.rand(10), state.rand(10), label='single') + + +@pplt.tests.image_compare +def test_gray_adjustment(): + """ + Test gray adjustments when creating segmented colormaps. + """ + fig, ax = pplt.subplots() + data = state.rand(5, 5) * 10 - 5 + cmap = pplt.Colormap(['blue', 'grey3', 'red']) + ax.pcolor(data, cmap=cmap, colorbar='b') + + +@pplt.tests.image_compare +def test_ignore_message(): + """ + Test various ignored argument warnings. + """ + warning = pplt.internals.ProplotWarning + fig, axs = pplt.subplots(ncols=2, nrows=2) + with pytest.warns(warning): + axs[0].contour( + state.rand(5, 5) * 10, levels=pplt.arange(10), symmetric=True + ) + with pytest.warns(warning): + axs[1].contourf( + state.rand(10, 10), levels=np.linspace(0, 1, 10), + locator=5, locator_kw={} + ) + with pytest.warns(warning): + axs[2].contourf( + state.rand(10, 10), + levels=pplt.arange(0, 1, 0.2), + vmin=0, + vmax=2, + locator=3, + colorbar='b' + ) + with pytest.warns(warning): + axs[3].hexbin( + state.rand(1000), + state.rand(1000), + levels=pplt.arange(0, 20), + gridsize=10, + locator=2, + colorbar='b', + cmap='blues', + ) + + +@pplt.tests.image_compare +def test_levels_with_vmin_vmax(): + """ + Make sure `vmin` and `vmax` go into level generation algorithm. + """ + # Sample data + state = np.random.RandomState(51423) + x = y = np.array([-10, -5, 0, 5, 10]) + data = state.rand(y.size, x.size) + + # Figure + fig = pplt.figure(refwidth=2.3, share=False) + axs = fig.subplots() + m = axs.pcolormesh(x, y, data, vmax=1.35123) + axs.colorbar([m], loc='r') + + +@pplt.tests.image_compare +def test_level_restriction(): + """ + Test `negative`, `positive`, and `symmetric` with and without discrete. + """ + fig, axs = pplt.subplots(ncols=3, nrows=2) + data = 20 * state.rand(10, 10) - 5 + keys = ('negative', 'positive', 'symmetric') + for i, grp in enumerate((axs[:3], axs[3:])): + for j, ax in enumerate(grp): + kw = {keys[j]: True, 'discrete': bool(1 - i)} + ax.pcolor(data, **kw, colorbar='b') + + +@pplt.tests.image_compare +def test_qualitative_colormaps(): + """ + Test both `colors` and `cmap` input and ensure extend setting is used for + extreme only if unset. + """ + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) + colors = pplt.get_colors('set3') + for ax, extend in zip(axs, ('both', 'neither')): + ax.pcolor( + data, extend=extend, + colors=colors, colorbar='b' + ) + + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) + cmap = pplt.Colormap('set3') + cmap.set_under('black') # does not overwrite + for ax, extend in zip(axs, ('both', 'neither')): + ax.pcolor( + data, extend=extend, cmap=cmap, colorbar='b' + ) + + +@pplt.tests.image_compare +def test_segmented_norm(): + """ + Test segmented norm with non-discrete levels. + """ + fig, ax = pplt.subplots() + ax.pcolor( + state.rand(5, 5) * 10, + discrete=False, + norm='segmented', + norm_kw={'levels': [0, 2, 10]}, + colorbar='b' + ) + + +@pplt.tests.image_compare +def test_triangular_functions(): + """ + Test triangular functions. Here there is no remotely sensible way to infer + coordinates so we skip standardize function. + """ + fig, ax = pplt.subplots() + N = 30 + y = state.rand(N) * 20 + x = state.rand(N) * 50 + da = xr.DataArray(state.rand(N), dims=('x',), coords={'x': x, 'y': ('x', y)}) + ax.tricontour(da.x, da.y, da, labels=True) diff --git a/proplot/tests/test_axes.py b/proplot/tests/test_axes.py new file mode 100644 index 000000000..6252d9c1c --- /dev/null +++ b/proplot/tests/test_axes.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +""" +Test twin, inset, and panel axes. +""" +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_inset_colors(): + """ + Test color application for zoom boxes. + """ + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes( + (0.5, 0.5, 0.3, 0.3), zoom=True, zoom_kw={'color': 'r', 'fc': 'r', 'ec': 'b'} + ) # zoom_kw={'alpha': 1}) + # ix = ax.inset_axes((40, 40, 20, 20), zoom=True, transform='data') + ix.format(xlim=(10, 20), ylim=(10, 20), grid=False) + + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes( + (0.3, 0.5, 0.5, 0.3), zoom=True, + zoom_kw={'lw': 3, 'ec': 'red9', 'a': 1, 'c': pplt.set_alpha('red4', 0.5)} + ) + ix.format(xlim=(10, 20), ylim=(10, 20)) + + +@pplt.tests.image_compare +def test_inset_zoom_update(): + """ + Test automatic limit adjusment with successive changes. Without the extra + lines in `draw()` and `get_tight_bbox()` this fails. + """ + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes((40, 40, 20, 20), zoom=True, transform='data') + ix.format(xlim=(10, 20), ylim=(10, 20), grid=False) + + ix.format(xlim=(10, 20), ylim=(10, 30)) + fig.show() + + ax.format(ylim=(0, 300)) + fig.show() + + +@pplt.tests.image_compare +def test_panels_with_sharing(): + """ + Previously the below text would hide the second y label. + """ + fig, axs = pplt.subplots(ncols=2, share=False, refwidth=1.5) + axs.panel('left') + fig.format(ylabel='ylabel', xlabel='xlabel') + + +@pplt.tests.image_compare +def test_panels_without_sharing(): + """ + What should happen if `share=False` but figure-wide sharing enabled? + Strange use case but behavior appears "correct." + """ + fig, axs = pplt.subplots(ncols=2, share=True, refwidth=1.5, includepanels=False) + axs.panel('left', share=False) + fig.format(ylabel='ylabel', xlabel='xlabel') + + fig, axs = pplt.subplots(ncols=2, refwidth=1.5, includepanels=True) + for _ in range(3): + p = axs[0].panel('l', space=0) + p.format(xlabel='label') + fig.format(xlabel='xlabel') + + +@pplt.tests.image_compare +@pplt.tests.image_compare +def test_panels_suplabels(): + """ + Test label sharing for `includepanels=True`. + """ + for b in range(3): + for ncols in range(1, 3): + fig, axs = pplt.subplots(ncols=ncols, refwidth=1.5, includepanels=(b == 2)) + if b: + for _ in range(3): + axs[0].panel('l') + axs.format(xlabel='xlabel\nxlabel\nxlabel', ylabel='ylabel', suptitle='sup') + + +def test_twin_axes(): + """ + Adjust twin axis positions. Should allow easily switching the location. + """ + # Test basic twin creation and tick, spine, label location changes + fig = pplt.figure() + ax = fig.subplot() + ax.format( + ycolor='blue', ylabel='orig', ylabelcolor='blue9', + yspineloc='l', labelweight='bold', xlabel='xlabel', + xtickloc='t', xlabelloc='b', + ) + ax.alty( + loc='r', color='r', labelcolor='red9', label='other', labelweight='bold' + ) + + # Simple example but doesn't quite work. Figure out how to specify left vs. right + # spines for 'offset' locations... maybe needs another keyword. + fig, ax = pplt.subplots() + ax.format(ymax=10, ylabel='Reference') + ax.alty(color='green', label='Green', max=8) + ax.alty(color='red', label='Red', max=15, loc=('axes', -0.2)) + ax.alty(color='blue', label='Blue', max=5, loc=('axes', 1.2), ticklabeldir='out') + + # A worked example from Riley Brady + # Uses auto-adjusting limits + fig, ax = pplt.subplots() + axs = [ax, ax.twinx(), ax.twinx()] + axs[-1].spines['right'].set_position(('axes', 1.2)) + colors = ('Green', 'Red', 'Blue') + for ax, color in zip(axs, colors): + data = state.random(1) * state.random(10) + ax.plot(data, marker='o', linestyle='none', color=color) + ax.format(ylabel='%s Thing' % color, ycolor=color) + axs[0].format(xlabel='xlabel') diff --git a/proplot/tests/test_colorbar.py b/proplot/tests/test_colorbar.py new file mode 100644 index 000000000..0476d2888 --- /dev/null +++ b/proplot/tests/test_colorbar.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python3 +""" +Test colorbars. +""" +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_outer_align(): + """ + Test various align options. + """ + fig, ax = pplt.subplots() + ax.plot(np.empty((0, 4)), labels=list('abcd')) + ax.legend(loc='bottom', align='right', ncol=2) + ax.legend(loc='left', align='bottom', ncol=1) + ax.colorbar('magma', loc='r', align='top', shrink=0.5, label='label', extend='both') + ax.colorbar( + 'magma', loc='top', ticklen=0, tickloc='bottom', align='left', + shrink=0.5, label='Title', extend='both', labelloc='top', labelweight='bold' + ) + # ax.colorbar( + # 'magma', loc='top', align='left', shrink=0.5, label='label', extend='both' + # ) + ax.colorbar('magma', loc='right', extend='both', label='test extensions') + fig.suptitle('Align demo') + + +@pplt.tests.image_compare +def test_colorbar_ticks(): + """ + Test ticks modification. + """ + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.colorbar( + 'magma', loc='bottom', ticklen=10, linewidth=3, tickminor=True + ) + ax = axs[1] + ax.colorbar( + 'magma', loc='bottom', ticklen=10, linewidth=3, tickwidth=1.5, tickminor=True + ) + + +@pplt.tests.image_compare +def test_discrete_ticks(): + """ + Test `DiscreteLocator`. + """ + levels = pplt.arange(0, 2, 0.1) + data = state.rand(5, 5) * 2 + for j, width in enumerate((0.8, 1.2, 2)): + fig, axs = pplt.subplots(share=False, ncols=2, nrows=2, refwidth=width) + for i, ax in enumerate(axs): + cmd = ax.contourf if i // 2 == 0 else ax.pcolormesh + m = cmd(data, levels=levels, extend='both') + ax.colorbar(m, loc='t' if i // 2 == 0 else 'b') + ax.colorbar(m, loc='l' if i % 2 == 0 else 'r') + fig.save(f'~/Downloads/tmp{j}.png', dpi=300) + + +@pplt.tests.image_compare +def test_discrete_vs_fixed(): + """ + Test `DiscreteLocator` for numeric on-the-fly + mappable ticks and `FixedLocator` otherwise. + """ + fig, axs = pplt.subplots(ncols=2, nrows=3, refwidth=1.3, share=False) + axs[0].plot(state.rand(10, 5), labels=list('xyzpq'), colorbar='b') # fixed + axs[1].plot(state.rand(10, 5), labels=np.arange(5), colorbar='b') # discrete + axs[2].contourf( + state.rand(10, 10), + colorbar='b', colorbar_kw={'ticklabels': list('xyzpq')} # fixed + ) + axs[3].contourf(state.rand(10, 10), colorbar='b') # discrete + axs[4].pcolormesh( + state.rand(10, 10) * 20, colorbar='b', levels=[0, 2, 4, 6, 8, 10, 15, 20] + ) # fixed + axs[5].pcolormesh( + state.rand(10, 10) * 20, colorbar='b', levels=pplt.arange(0, 20, 2) + ) # discrete + + +@pplt.tests.image_compare +def test_uneven_levels(): + """ + Test even and uneven levels with discrete cmap. Ensure minor ticks are disabled. + """ + N = 20 + state = np.random.RandomState(51423) + data = np.cumsum(state.rand(N, N), axis=1) * 12 + colors = [ + 'white', + 'indigo1', 'indigo3', 'indigo5', 'indigo7', 'indigo9', + 'yellow1', 'yellow3', 'yellow5', 'yellow7', 'yellow9', + 'violet1', 'violet3' + ] + levels_even = pplt.arange(1, 12, 1) + levels_uneven = [1., 1.25, 1.5, 2., 2.5, 3., 3.75, 4.5, 6., 7.5, 9., 12.] + fig, axs = pplt.subplots(ncols=2, refwidth=3.) + axs[0].pcolor( + data, levels=levels_uneven, colors=colors, colorbar='r', extend='both' + ) + axs[1].pcolor( + data, levels=levels_even, colors=colors, colorbar='r', extend='both' + ) + + +@pplt.tests.image_compare +def test_on_the_fly_mappable(): + """ + Test on-the-fly mappable generation. + """ + fig, axs = pplt.subplots(ncols=2, nrows=3, space=3) + axs.format(aspect=0.5) + axs[0].colorbar('magma', vmin=None, vmax=100, values=[0, 1, 2, 3, 4], loc='bottom') + axs[1].colorbar('magma', vmin=None, vmax=100, loc='bottom') + axs[2].colorbar('colorblind', vmin=None, vmax=None, values=[0, 1, 2], loc='bottom') + axs[3].colorbar('colorblind', vmin=None, vmax=None, loc='bottom') + axs[4].colorbar(['r', 'b', 'g', 'k', 'w'], values=[0, 1, 2], loc='b') + axs[5].colorbar(['r', 'b', 'g', 'k', 'w'], loc='bottom') + + # Passing labels to plot function. + fig, ax = pplt.subplots() + ax.scatter(state.rand(10, 4), labels=['foo', 'bar', 'baz', 'xyz'], colorbar='b') + + # Passing string value lists. This helps complete the analogy with legend 'labels'. + fig, ax = pplt.subplots() + hs = ax.line(state.rand(20, 5)) + ax.colorbar(hs, loc='b', values=['abc', 'def', 'ghi', 'pqr', 'xyz']) + + +@pplt.tests.image_compare +def test_inset_colorbars(): + """ + Test basic functionality. + """ + # Simple example + fig, ax = pplt.subplots() + ax.colorbar('magma', loc='ul') + + # Colorbars from lines + fig = pplt.figure(share=False, refwidth=2) + ax = fig.subplot(121) + state = np.random.RandomState(51423) + data = 1 + (state.rand(12, 10) - 0.45).cumsum(axis=0) + cycle = pplt.Cycle('algae') + hs = ax.line( + data, lw=4, cycle=cycle, colorbar='lr', + colorbar_kw={'length': '8em', 'label': 'line colorbar'} + ) + ax.colorbar( + hs, loc='t', values=np.arange(0, 10), + label='line colorbar', ticks=2 + ) + + # Colorbars from a mappable + ax = fig.subplot(122) + m = ax.contourf( + data.T, extend='both', cmap='algae', + levels=pplt.arange(0, 3, 0.5) + ) + fig.colorbar( + m, loc='r', length=1, # length is relative + label='interior ticks', tickloc='left' + ) + ax.colorbar( + m, loc='ul', length=6, # length is em widths + label='inset colorbar', tickminor=True, alpha=0.5, + ) + fig.format( + suptitle='Colorbar formatting demo', + xlabel='xlabel', ylabel='ylabel', titleabove=False + ) + + +@pplt.tests.image_compare +def test_segmented_norm_center(): + """ + Test various align options. + """ + fig, ax = pplt.subplots() + cmap = pplt.Colormap('NegPos', cut=0.1) + data = state.rand(10, 10) * 10 - 2 + levels = [-4, -3, -2, -1, 0, 1, 2, 4, 8, 16, 32, 64, 128] + norm = pplt.SegmentedNorm(levels, vcenter=0, fair=1) + ax.pcolormesh(data, levels=levels, norm=norm, cmap=cmap, colorbar='b') + + +@pplt.tests.image_compare +def test_segmented_norm_ticks(): + """ + Ensure segmented norm ticks show up in center when `values` are passed. + """ + fig, ax = pplt.subplots() + data = state.rand(10, 10) * 10 + values = (1, 5, 5.5, 6, 10) + ax.contourf( + data, + values=values, + colorbar='ll', + colorbar_kw={'tickminor': True, 'minorlocator': np.arange(-20, 20, 0.5)}, + ) + + +@pplt.tests.image_compare +def test_reversed_levels(): + """ + Test negative levels with a discrete norm and segmented norm. + """ + # %% + fig, axs = pplt.subplots(ncols=4, nrows=2, refwidth=1.8) + data = state.rand(20, 20).cumsum(axis=0) + i = 0 + for stride in (1, -1): + for key in ('levels', 'values'): + for levels in ( + np.arange(0, 15, 1), # with Normalizer + [0, 1, 2, 5, 10, 15], # with LinearSegmentedNorm + ): + ax = axs[i] + kw = {key: levels[::stride]} + ax.pcolormesh(data, colorbar='b', **kw) + i += 1 diff --git a/proplot/tests/test_docs.py b/proplot/tests/test_docs.py new file mode 100644 index 000000000..cd92b7fec --- /dev/null +++ b/proplot/tests/test_docs.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +""" +Automatically build pytests from jupytext py:percent documentation files. +""" +import glob +import os + +import jupytext +import pytest + +from proplot.config import rc +from proplot.tests import SAVEFIG_KWARGS, TOLERANCE, VERSION_STRING + + +def _init_tests(): + """ + Construct tests from the jupytext docs examples. + """ + # WARNING: This will only work if all jupytext examples consist of single code + # cells or adjacent code cells without intervening markdown or ReST cells. + # Alternative would be to define the entire file as a single test but that + # would make testing and debugging more difficult. + base = os.path.dirname(__file__) + paths = glob.glob(f'{base}/../../docs/*.py') + for path in sorted(paths): + if os.path.basename(path) == 'conf.py': + continue + baseline, _ = os.path.splitext(os.path.basename(path)) + result = jupytext.read(path, fmt='py:percent') + cells = result['cells'] + source = '' + num = 1 + for i, cell in enumerate(cells): + type_ = cell['cell_type'] + if type_ == 'code': + source += '\n' + cell['source'] + if not source: + continue + if i == len(cells) - 1 or type_ != 'code': # end of example definition + _make_cell_test(num, baseline, source) + num += 1 + print(f'\nMade {num} tests from file: {path}', end='') + + +def _make_cell_test(num, baseline, cell_source): + """ + Add a test using the jupytext docs cell. + """ + # WARNING: Ugly kludge to replace e.g. backend='basemap' with backend='cartopy' + # for matplotlib versions incompatible with basemap. Generally these examples + # test basemap and cartopy side-by-side so this will effectively duplicate the + # cartopy tests but keep us from having to dump them. + if baseline == 'test_projections' and VERSION_STRING == 'mpl32': + cell_source = cell_source.replace("'basemap'", "'cartopy'") + if 'pplt.Proj' in cell_source or 'Table' in cell_source: + return # examples that cannot be naively converted + + def run_test(): + rc.reset() + exec(cell_source) + + name = f'{baseline}_cell_{num:02d}' + decorator = pytest.mark.mpl_image_compare( + run_test, + filename=f'{name}_{VERSION_STRING}.png', + baseline_dir='images_docs', + savefig_kwargs=SAVEFIG_KWARGS, + tolerance=TOLERANCE, + style={}, # no mpl style + ) + test = decorator(run_test) + name = f'test_{name}' + test.__name__ = test.__qualname__ = name + globals()[name] = test # for pytest detection + + +# Initialize functions +_init_tests() diff --git a/proplot/tests/test_format.py b/proplot/tests/test_format.py new file mode 100644 index 000000000..4d274e7df --- /dev/null +++ b/proplot/tests/test_format.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python3 +""" +Test format and rc behavior. +""" +import locale + +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +def test_colormap_assign(): + """ + Test below line is possible and naming schemes. + """ + pplt.rc['image.cmap'] = pplt.Colormap('phase', shift=180, left=0.2) + assert pplt.rc['cmap'] == pplt.rc['cmap.sequential'] == '_Phase_copy_s' + pplt.rc['image.cmap'] = pplt.Colormap('magma', reverse=True, right=0.8) + assert pplt.rc['image.cmap'] == pplt.rc['cmap.sequential'] == '_magma_copy_r' + + +def test_ignored_keywords(): + """ + Test ignored keywords and functions. + """ + with pplt.tests.warns(pplt.internals.ProplotWarning) as record: + fig, ax = pplt.subplots( + gridspec_kw={'left': 3}, + subplot_kw={'proj': 'cart'}, + subplotpars={'left': 0.2}, + ) + assert len(record) == 3 + with pplt.tests.warns(pplt.internals.ProplotWarning) as record: + fig.subplots_adjust(left=0.2) + assert len(record) == 1 + + +@pplt.tests.image_compare +def test_init_format(): + """ + Test application of format args on initialization. + """ + fig, axs = pplt.subplots( + ncols=2, xlim=(0, 10), xlabel='xlabel', + abc=True, title='Subplot title', + collabels=['Column 1', 'Column 2'], suptitle='Figure title', + ) + axs[0].format(hatch='xxx', hatchcolor='k', facecolor='blue3') + + +@pplt.tests.image_compare +def test_patch_format(): + """ + Test application of patch args on initialization. + """ + fig = pplt.figure(suptitle='Super title') + fig.subplot( + 121, proj='cyl', labels=True, land=True, latlines=20, + abcloc='l', abc='[A]' + ) + fig.subplot( + 122, facecolor='gray1', color='red', titleloc='l', title='Hello', + abcloc='l', abc='[A]', xticks=0.1, yformatter='scalar' + ) + + +@pplt.tests.image_compare +def test_multi_formatting(): + """ + Support formatting in multiple projections. + """ + fig, axs = pplt.subplots(ncols=2, proj=('cart', 'cyl')) + axs[0].pcolormesh(state.rand(5, 5)) + fig.format( + land=1, labels=1, lonlim=(0, 90), latlim=(0, 90), xlim=(0, 10), ylim=(0, 10), + ) + axs[:1].format( + land=1, labels=1, lonlim=(0, 90), latlim=(0, 90), xlim=(0, 10), ylim=(0, 10), + ) + + +@pplt.tests.image_compare +def test_inner_title_zorder(): + """ + Test prominence of contour labels and whatnot. + """ + fig, ax = pplt.subplots() + ax.format( + title='TITLE', titleloc='upper center', titleweight='bold', titlesize='xx-large' + ) + ax.format(xlim=(0, 1), ylim=(0, 1)) + ax.text( + 0.5, 0.95, 'text', ha='center', va='top', color='red', + weight='bold', size='xx-large' + ) + x = [[0.4, 0.6]] * 2 + y = z = [[0.9, 0.9], [1.0, 1.0]] + ax.contour( + x, y, z, color='k', labels=True, + levels=None, labels_kw={'color': 'blue', 'weight': 'bold', 'size': 'xx-large'} + ) + + +@pplt.tests.image_compare +def test_font_adjustments(): + """ + Test font name application. Somewhat hard to do. + """ + fig, axs = pplt.subplots(ncols=2) + axs.format( + abc='A.', + fontsize=15, + fontname='Fira Math', + xlabel='xlabel', + ylabel='ylabel', + title='Title', + figtitle='Figure title', + collabels=['Column 1', 'Column 2'], + ) + + +@pplt.tests.image_compare +def test_axes_colors(): + """ + Test behavior of passing color to format. + """ + fig, axs = pplt.subplots( + ncols=3, nrows=2, share=False, + proj=('cyl', 'cart', 'polar', 'cyl', 'cart', 'polar'), wratios=(2, 2, 1) + ) + axs[:, 0].format(labels=True) + axs[:3].format(edgecolor='red', gridlabelsize='med-large', gridlabelweight='bold') + axs[:3].format(color='red') # without this just colors the edge + axs[1].format(xticklabelcolor='gray') + # axs[2].format(ticklabelcolor='red') + axs[1].format(tickcolor='blue') + axs[3:].format(color='red') # ensure propagates + # axs[-1].format(gridlabelcolor='green') # should work + + +@pplt.tests.image_compare +def test_locale_formatting(): + """ + Ensure locale formatting works. Also zerotrim should account + for non-period decimal separators. + """ + locale.setlocale(locale.LC_ALL, 'de_DE') + pplt.rc['formatter.use_locale'] = False + pplt.rc['formatter.zerotrim'] = True + with pplt.rc.context({'formatter.use_locale': True}): + fig, ax = pplt.subplots() + ticks = pplt.arange(-1, 1, 0.1) + ax.format(ylim=(min(ticks), max(ticks)), yticks=ticks) + + +@pplt.tests.image_compare +def test_bounds_ticks(): + """ + Test spine bounds and location. Previously applied `fixticks` + automatically but no longer the case. + """ + fig, ax = pplt.subplots() + # ax.format(xlim=(-10, 10)) + ax.format(xloc='top') + ax.format(xlim=(-10, 15), xbounds=(0, 10)) + + +@pplt.tests.image_compare +def test_cutoff_ticks(): + """ + Test spine cutoff ticks. + """ + fig, ax = pplt.subplots() + # ax.format(xlim=(-10, 10)) + ax.format(xlim=(-10, 10), xscale=('cutoff', 0, 2), xloc='top', fixticks=True) + ax.axvspan(0, 100, facecolor='k', alpha=0.1) + + +@pplt.tests.image_compare +def test_spine_side(): + """ + Test automatic spine selection when passing `xspineloc` or `yspineloc`. + """ + fig, ax = pplt.subplots() + ax.plot(pplt.arange(-5, 5), (10 * state.rand(11, 5) - 5).cumsum(axis=0)) + ax.format(xloc='bottom', yloc='zero') + ax.alty(loc='right') + + +@pplt.tests.image_compare +def test_spine_offset(): + """ + Test offset axes. + """ + fig, ax = pplt.subplots() + ax.format(xloc='none') # test none instead of neither + ax.alty(loc=('axes', -0.2), color='red') + # ax.alty(loc=('axes', 1.2), color='blue') + ax.alty(loc=('axes', -0.4), color='blue') + ax.alty(loc=('axes', 1.1), color='green') + + +@pplt.tests.image_compare +def test_tick_direction(): + """ + Test tick direction arguments. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format(tickdir='in') + axs[1].format(xtickdirection='inout', ytickdir='out') # rc setting should be used? + + +@pplt.tests.image_compare +def test_tick_length(): + """ + Test tick length args. Ensure ratios can be applied successively. + """ + fig, ax = pplt.subplots() + ax.format(yticklen=100) + ax.format(xticklen=50, yticklenratio=0.1) + + +@pplt.tests.image_compare +def test_tick_width(): + """ + Test tick width args. Ensure ratios can be applied successively, setting + width to `zero` adjusts length for label padding, and ticks can appear + without spines if requested. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2, share=False) + ax = axs[0] + ax.format(linewidth=2, ticklen=20, xtickwidthratio=1) + ax.format(ytickwidthratio=0.3) + ax = axs[1] + ax.format(axeslinewidth=0, ticklen=20, tickwidth=2) # should permit ticks + ax = axs[2] + ax.format(tickwidth=0, ticklen=50) # should set length to zero + ax = axs[3] + ax.format(linewidth=0, ticklen=20, tickwidth='5em') # should override linewidth + + +@pplt.tests.image_compare +def test_tick_labels(): + """ + Test default and overwriting properties of auto tick labels. + """ + import pandas as pd + data = state.rand(5, 3) + data = pd.DataFrame(data, index=['foo', 'bar', 'baz', 'bat', 'bot']) + fig, axs = pplt.subplots(abc='A.', abcloc='ul', ncols=2, refwidth=3, span=False) + for i, ax in enumerate(axs): + data.index.name = 'label' + if i == 1: + ax.format(xformatter='null') # overrides result + ax.bar(data, autoformat=True) + if i == 0: + data.index = ['abc', 'def', 'ghi', 'jkl', 'mno'] + data.index.name = 'foobar' # label should be updated + ax.bar(-data, autoformat=True) + + +@pplt.tests.image_compare +def test_label_settings(): + """ + Test label colors and ensure color change does not erase labels. + """ + fig, ax = pplt.subplots() + ax.format(xlabel='xlabel', ylabel='ylabel') + ax.format(labelcolor='red') diff --git a/proplot/tests/test_integration.py b/proplot/tests/test_integration.py new file mode 100644 index 000000000..add57a8e4 --- /dev/null +++ b/proplot/tests/test_integration.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +""" +Test xarray, pandas, pint, seaborn integration. +""" +import numpy as np +import pandas as pd +import pint +import seaborn as sns +import xarray as xr + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_pint_quantities(): + """ + Ensure auto-formatting and column iteration both work. + """ + pplt.rc.unitformat = '~H' + ureg = pint.UnitRegistry() + fig, ax = pplt.subplots() + ax.plot( + np.arange(10), + state.rand(10) * ureg.km, + 'C0', + np.arange(10), + state.rand(10) * ureg.m * 1e2, + 'C1' + ) + + +@pplt.tests.image_compare +def test_data_keyword(): + """ + Make sure `data` keywords work properly. + """ + N = 10 + M = 20 + ds = xr.Dataset( + {'z': (('x', 'y'), state.rand(N, M))}, + coords={ + 'x': ('x', np.arange(N) * 10, {'long_name': 'longitude'}), + 'y': ('y', np.arange(M) * 5, {'long_name': 'latitude'}) + } + ) + fig, ax = pplt.subplots() + # ax.pcolor('z', data=ds, order='F') + ax.pcolor(z='z', data=ds, transpose=True) + ax.format(xformatter='deglat', yformatter='deglon') + + +@pplt.tests.image_compare +def test_keep_guide_labels(): + """ + Preserve metadata when passing mappables and handles to colorbar and + legend subsequently. + """ + fig, ax = pplt.subplots() + df = pd.DataFrame(state.rand(5, 5)) + df.name = 'variable' + m = ax.pcolor(df) + ax.colorbar(m) + + fig, ax = pplt.subplots() + for k in ('foo', 'bar', 'baz'): + s = pd.Series(state.rand(5), index=list('abcde'), name=k) + ax.plot( + s, legend='ul', + legend_kw={ + 'lw': 5, + 'ew': 2, + 'ec': 'r', + 'fc': 'w', + 'handle_kw': {'marker': 'd'} + } + ) + + +@pplt.tests.image_compare +def test_seaborn_swarmplot(): + """ + Test seaborn swarm plots. + """ + tips = sns.load_dataset('tips') + fig = pplt.figure(refwidth=3) + ax = fig.subplot() + sns.swarmplot(ax=ax, x='day', y='total_bill', data=tips, palette='cubehelix') + # fig, ax = pplt.subplots() + # sns.swarmplot(y=state.normal(size=100), ax=ax) + return fig + + +@pplt.tests.image_compare +def test_seaborn_hist(): + """ + Test seaborn histograms. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2) + sns.histplot(state.normal(size=100), ax=axs[0]) + sns.kdeplot(state.rand(100), state.rand(100), ax=axs[1]) + penguins = sns.load_dataset('penguins') + sns.histplot( + data=penguins, x='flipper_length_mm', hue='species', multiple='stack', ax=axs[2] + ) + sns.kdeplot( + data=penguins, x='flipper_length_mm', hue='species', multiple='stack', ax=axs[3] + ) + + +@pplt.tests.image_compare +def test_seaborn_relational(): + """ + Test scatter plots. Disabling seaborn detection creates mismatch between marker + sizes and legend. + """ + fig = pplt.figure() + ax = fig.subplot() + sns.set_theme(style='white') + # Load the example mpg dataset + mpg = sns.load_dataset('mpg') + # Plot miles per gallon against horsepower with other semantics + sns.scatterplot( + x='horsepower', y='mpg', hue='origin', size='weight', + sizes=(40, 400), alpha=.5, palette='muted', + # legend='bottom', + # height=6, + data=mpg, ax=ax + ) + + +@pplt.tests.image_compare +def test_seaborn_heatmap(): + """ + Test seaborn heatmaps. This should work thanks to backwards compatibility support. + """ + fig, ax = pplt.subplots() + sns.heatmap(state.normal(size=(50, 50)), ax=ax[0]) diff --git a/proplot/tests/test_legend.py b/proplot/tests/test_legend.py new file mode 100644 index 000000000..7ff27ec43 --- /dev/null +++ b/proplot/tests/test_legend.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +""" +Test legends. +""" +import numpy as np +import pandas as pd + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_auto_legend(): + """ + Test retrieval of legends from panels, insets, etc. + """ + fig, ax = pplt.subplots() + ax.line(state.rand(5, 3), labels=list('abc')) + px = ax.panel_axes('right', share=False) + px.linex(state.rand(5, 3), labels=list('xyz')) + # px.legend(loc='r') + ix = ax.inset_axes((-0.2, 0.8, 0.5, 0.5), zoom=False) + ix.line(state.rand(5, 2), labels=list('pq')) + ax.legend(loc='b', order='F', edgecolor='red9', edgewidth=3) + + +@pplt.tests.image_compare +def test_singleton_legend(): + """ + Test behavior when singleton lists are passed. + Ensure this does not trigger centered-row legends. + """ + fig, ax = pplt.subplots() + h1 = ax.plot([0, 1, 2], label='a') + h2 = ax.plot([0, 1, 1], label='b') + ax.legend(loc='best') + ax.legend([h1, h2], loc='bottom') + + +@pplt.tests.image_compare +def test_centered_legends(): + """ + Test success of algorithm. + """ + # Basic centered legends + fig, axs = pplt.subplots(ncols=2, nrows=2, axwidth=2) + hs = axs[0].plot(state.rand(10, 6)) + locs = ['l', 't', 'r', 'uc', 'ul', 'll'] + locs = ['l', 't', 'uc', 'll'] + labels = ['a', 'bb', 'ccc', 'ddddd', 'eeeeeeee', 'fffffffff'] + for ax, loc in zip(axs, locs): + ax.legend(hs, loc=loc, ncol=3, labels=labels, center=True) + + # Pass centered legends with keywords or list-of-list input. + fig, ax = pplt.subplots() + hs = ax.plot(state.rand(10, 5), labels=list('abcde')) + ax.legend(hs, center=True, loc='b') + ax.legend(hs + hs[:1], loc='r', ncol=1) + ax.legend([hs[:2], hs[2:], hs[0]], loc='t') + + +@pplt.tests.image_compare +def test_manual_labels(): + """ + Test mixed auto and manual labels. Passing labels but no handles does nothing + This is breaking change but probably best. We should not be "guessing" the + order objects were drawn in then assigning labels to them. Similar to using + OO interface and rejecting pyplot "current axes" and "current figure". + """ + fig, ax = pplt.subplots() + h1, = ax.plot([0, 1, 2], label='label1') + h2, = ax.plot([0, 1, 1], label='label2') + for loc in ('best', 'bottom'): + ax.legend([h1, h2], loc=loc, labels=[None, 'override']) + fig, ax = pplt.subplots() + ax.plot([0, 1, 2]) + ax.plot([0, 1, 1]) + for loc in ('best', 'bottom'): + # ax.legend(loc=loc, labels=['a', 'b']) + ax.legend(['a', 'b'], loc=loc) # same as above + + +@pplt.tests.image_compare +def test_contour_legend(): + """ + Support contour element labels. If has no label should trigger warning. + """ + for label in ('label', None): + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.contour(state.rand(5, 5), color='k', label=label, legend='b') + ax = axs[1] + ax.pcolor(state.rand(5, 5), label=label, legend='b') + + +@pplt.tests.image_compare +def test_histogram_legend(): + """ + Support complex histogram legends. + """ + pplt.rc.inlinefmt = 'svg' + fig, ax = pplt.subplots() + res = ax.hist( + state.rand(500, 2), 4, labels=('label', 'other'), edgefix=True, legend='b' + ) + ax.legend(res, loc='r', ncol=1) # should issue warning after ignoring numpy arrays + df = pd.DataFrame( + { + 'length': [1.5, 0.5, 1.2, 0.9, 3], + 'width': [0.7, 0.2, 0.15, 0.2, 1.1] + }, + index=['pig', 'rabbit', 'duck', 'chicken', 'horse'] + ) + fig, axs = pplt.subplots(ncols=3) + ax = axs[0] + res = ax.hist(df, bins=3, legend=True, lw=3) + ax.legend(loc='b') + for ax, meth in zip(axs[1:], ('bar', 'area')): + hs = getattr(ax, meth)(df, legend='ul', lw=3) + ax.legend(hs, loc='b') + + +@pplt.tests.image_compare +def test_multiple_calls(): + """ + Test successive plotting additions to guides. + """ + fig, ax = pplt.subplots() + ax.pcolor(state.rand(10, 10), colorbar='b') + ax.pcolor(state.rand(10, 5), cmap='grays', colorbar='b') + ax.pcolor(state.rand(10, 5), cmap='grays', colorbar='b') + + fig, ax = pplt.subplots() + data = state.rand(10, 5) + for i in range(data.shape[1]): + ax.plot(data[:, i], colorbar='b', label=f'x{i}', colorbar_kw={'label': 'hello'}) + + +@pplt.tests.image_compare +def test_tuple_handles(): + """ + Test tuple legend handles. + """ + from matplotlib import legend_handler + fig, ax = pplt.subplots(refwidth=3, abc='A.', abcloc='ul', span=False) + patches = ax.fill_between(state.rand(10, 3), stack=True) + lines = ax.line(1 + 0.5 * (state.rand(10, 3) - 0.5).cumsum(axis=0)) + # ax.legend([(handles[0], lines[1])], ['joint label'], loc='bottom', queue=True) + for hs in (lines, patches): + ax.legend( + [tuple(hs[:3]) if len(hs) == 3 else hs], + ['joint label'], + loc='bottom', + queue=True, + ncol=1, + handlelength=4.5, + handleheight=1.5, + handler_map={tuple: legend_handler.HandlerTuple(pad=0, ndivide=3)} + ) diff --git a/proplot/tests/test_projections.py b/proplot/tests/test_projections.py new file mode 100644 index 000000000..4f9ddf789 --- /dev/null +++ b/proplot/tests/test_projections.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +""" +Test projection features. +""" +import cartopy.crs as ccrs +import matplotlib.pyplot as plt +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_aspect_ratios(): + """ + Test aspect ratio adjustments. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format(aspect=1.5) + + fig, axs = pplt.subplots(ncols=2, proj=('cart', 'cyl'), aspect=2) + axs[0].set_aspect(1) + + +if pplt.internals._version_mpl <= '3.2': + @pplt.tests.image_compare + def test_basemap_labels(): + """ + Add basemap labels. + """ + fig, axs = pplt.subplots(ncols=2, proj='robin', refwidth=3, basemap=True) + axs.format(coast=True, labels='rt') + + +@pplt.tests.image_compare +def test_cartopy_labels(): + """ + Add cartopy labels. + """ + fig, axs = pplt.subplots(ncols=2, proj='robin', refwidth=3) + axs.format(coast=True, labels=True) + axs[0].format(inlinelabels=True) + axs[1].format(rotatelabels=True) + + +@pplt.tests.image_compare +def test_cartopy_contours(): + """ + Test bug with cartopy contours. Sometimes results in global coverage + with single color sometimes not. + """ + N = 10 + fig = plt.figure(figsize=(5, 2.5)) + ax = fig.add_subplot(projection=ccrs.Mollweide()) + ax.coastlines() + x = np.linspace(-180, 180, N) + y = np.linspace(-90, 90, N) + z = state.rand(N, N) * 10 - 5 + m = ax.contourf( + x, y, z, + transform=ccrs.PlateCarree(), + cmap='RdBu_r', + vmin=-5, + vmax=5, + ) + fig.colorbar(m, ax=ax) + + fig = pplt.figure() + ax = fig.add_subplot( + projection=pplt.Mollweide(), + extent='auto' + ) + ax.coastlines() + N = 10 + m = ax.contourf( + np.linspace(0, 180, N), + np.linspace(0, 90, N)[1::2], + state.rand(N // 2, N) * 10 + 5, + cmap='BuRd', + transform=pplt.PlateCarree(), + edgefix=False + ) + fig.colorbar(m, ax=ax) + + +@pplt.tests.image_compare +def test_cartopy_manual(): + """ + Test alternative workflow without classes. + """ + fig = pplt.figure() + proj = pplt.Proj('npstere') + # fig.add_axes([0.1, 0.1, 0.9, 0.9], proj='geo', map_projection=proj) + fig.add_subplot(111, proj='geo', land=True, map_projection=proj) + + +@pplt.tests.image_compare +def test_three_axes(): + """ + Test basic 3D axes here. + """ + pplt.rc['tick.minor'] = False + fig, ax = pplt.subplots(proj='3d', outerpad=3) + + +@pplt.tests.image_compare +def test_projection_dicts(): + """ + Test projection dictionaries. + """ + fig = pplt.figure(refnum=1) + a = [ + [1, 0], + [1, 4], + [2, 4], + [2, 4], + [3, 4], + [3, 0] + ] + fig.subplots(a, proj={1: 'cyl', 2: 'cart', 3: 'cart', 4: 'cart'}) + + +@pplt.tests.image_compare +def test_polar_projections(): + """ + Rigorously test polar features here. + """ + fig, ax = pplt.subplots(proj='polar') + ax.format( + rlabelpos=45, + thetadir=-1, + thetalines=90, + thetalim=(0, 270), + theta0='N', + r0=0, + rlim=(0.5, 1), + rlines=0.25, + ) diff --git a/proplot/tests/test_subplots.py b/proplot/tests/test_subplots.py new file mode 100644 index 000000000..15b160265 --- /dev/null +++ b/proplot/tests/test_subplots.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Test subplot layout. +""" +import numpy as np + +import proplot as pplt + + +@pplt.tests.image_compare +def test_align_labels(): + """ + Test spanning and aligned labels. + """ + fig, axs = pplt.subplots( + [[2, 1, 4], [2, 3, 5]], refnum=2, refwidth=1.5, align=1, span=0 + ) + fig.format(xlabel='xlabel', ylabel='ylabel', abc='A.', abcloc='ul') + axs[0].format(ylim=(10000, 20000)) + axs[-1].panel_axes('bottom', share=False) + + +@pplt.tests.image_compare +def test_share_all_basic(): + """ + Test sharing level all. + """ + # Simple example + N = 10 + fig, axs = pplt.subplots(nrows=1, ncols=2, refwidth=1.5, share='all') + axs[0].plot(np.arange(N) * 1e2, np.arange(N) * 1e4) + # Complex example + fig, axs = pplt.subplots(nrows=2, ncols=2, refwidth=1.5, share='all') + axs[0].panel('b') + pax = axs[0].panel('r') + pax.format(ylabel='label') + axs[0].plot(np.arange(N) * 1e2, np.arange(N) * 1e4) + + +@pplt.tests.image_compare +def test_span_labels(): + """ + Rigorous tests of spanning and aligned labels feature. + """ + fig, axs = pplt.subplots([[1, 2, 4], [1, 3, 5]], refwidth=1.5, share=0, span=1) + fig.format(xlabel='xlabel', ylabel='ylabel', abc='A.', abcloc='ul') + axs[1].format() # xlabel='xlabel') + axs[2].format() + + +@pplt.tests.image_compare +def test_title_deflection(): + """ + Test the deflection of titles above and below panels. + """ + fig, ax = pplt.subplots() + # ax.format(abc='A.', title='Title', titleloc='left', titlepad=30) + tax = ax.panel_axes('top') + ax.format(titleabove=False) # redirects to bottom + ax.format(abc='A.', title='Title', titleloc='left', titlepad=50) + ax.format(xlabel='xlabel', ylabel='ylabel', ylabelpad=50) + tax.format(title='Fear Me', title_kw={'size': 'x-large'}) + tax.format(ultitle='Inner', titlebbox=True, title_kw={'size': 'med-large'}) + + +@pplt.tests.image_compare +def test_complex_ticks(): + """ + Normally title offset with these different tick arrangements is tricky + but `_update_title_position` accounts for edge cases. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format( + xtickloc='both', + xticklabelloc='top', + xlabelloc='top', + title='title', + xlabel='xlabel', + suptitle='Test', + ) + axs[1].format( + xtickloc='both', + xticklabelloc='top', + # xlabelloc='top', + xlabel='xlabel', + title='title', + suptitle='Test', + ) + + +@pplt.tests.image_compare +def test_both_ticklabels(): + """ + Test both tick labels. + """ + fig, ax = pplt.subplots() # when both, have weird bug + ax.format(xticklabelloc='both', title='title', suptitle='Test') + fig, ax = pplt.subplots() # when *just top*, bug disappears + ax.format(xtickloc='top', xticklabelloc='top', title='title', suptitle='Test') + fig, ax = pplt.subplots() # not sure here + ax.format(xtickloc='both', xticklabelloc='neither', suptitle='Test') + fig, ax = pplt.subplots() # doesn't seem to change the title offset though + ax.format(xtickloc='top', xticklabelloc='neither', suptitle='Test') + + +@pplt.tests.image_compare +def test_gridspec_copies(): + """ + Test whether gridspec copies work. + """ + fig1, ax = pplt.subplots(ncols=2) + gs = fig1.gridspec.copy(left=5, wspace=0, right=5) + fig2 = pplt.figure() + fig2.add_subplots(gs) + fig = pplt.figure() + with pplt.tests.raises(ValueError): + fig.add_subplots(gs) # should raise error + return (fig1, fig2) + + +@pplt.tests.image_compare +def test_aligned_outer_guides(): + """ + Test alignment adjustment. + """ + fig, ax = pplt.subplot() + h1 = ax.plot(np.arange(5), label='foo') + h2 = ax.plot(np.arange(5) + 1, label='bar') + h3 = ax.plot(np.arange(5) + 2, label='baz') + ax.legend(h1, loc='bottom', align='left') + ax.legend(h2, loc='bottom', align='right') + ax.legend(h3, loc='b', align='c') + ax.colorbar('magma', loc='right', align='top', shrink=0.4) # same as length + ax.colorbar('magma', loc='right', align='bottom', shrink=0.4) + ax.colorbar('magma', loc='left', align='top', length=0.6) # should offset + ax.colorbar('magma', loc='left', align='bottom', length=0.6) + ax.legend(h1, loc='top', align='right', pad='4pt', frame=False) + ax.format(title='Very long title', titlepad=6, titleloc='left') + + +def test_reference_aspect(): + """ + Rigorous test of reference aspect ratio accuracy. + """ + # A simple test + refwidth = 1.5 + fig, axs = pplt.subplots(ncols=2, refwidth=refwidth) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0]) + + # A test with funky layout + refwidth = 1.5 + fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], ref=3, refwidth=refwidth) + axs[1].panel_axes('left') + axs.format(xlocator=0.2, ylocator=0.2) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0]) + + # A test with panels + refwidth = 2.0 + fig, axs = pplt.subplots( + [[1, 1, 2], [3, 4, 5], [3, 4, 6]], hratios=(2, 1, 1), refwidth=refwidth + ) + axs[2].panel_axes('right', width=0.5) + axs[0].panel_axes('bottom', width=0.5) + axs[3].panel_axes('left', width=0.5) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0]) From 1f06f10ef41be37388ef342efabf8b6d65771ebc Mon Sep 17 00:00:00 2001 From: Luke Davis Date: Sun, 26 Mar 2023 17:59:35 -0600 Subject: [PATCH 8/8] Add tests and ci instructions [Luke's tests] --- proplot/tests/test_1dplots.py | 344 ++++++++++++++++++++++++++++++ proplot/tests/test_2dplots.py | 322 ++++++++++++++++++++++++++++ proplot/tests/test_axes.py | 128 +++++++++++ proplot/tests/test_colorbar.py | 228 ++++++++++++++++++++ proplot/tests/test_docs.py | 78 +++++++ proplot/tests/test_format.py | 271 +++++++++++++++++++++++ proplot/tests/test_integration.py | 139 ++++++++++++ proplot/tests/test_journals.py | 11 - proplot/tests/test_legend.py | 160 ++++++++++++++ proplot/tests/test_projections.py | 139 ++++++++++++ proplot/tests/test_subplots.py | 168 +++++++++++++++ 11 files changed, 1977 insertions(+), 11 deletions(-) create mode 100644 proplot/tests/test_1dplots.py create mode 100644 proplot/tests/test_2dplots.py create mode 100644 proplot/tests/test_axes.py create mode 100644 proplot/tests/test_colorbar.py create mode 100644 proplot/tests/test_docs.py create mode 100644 proplot/tests/test_format.py create mode 100644 proplot/tests/test_integration.py delete mode 100644 proplot/tests/test_journals.py create mode 100644 proplot/tests/test_legend.py create mode 100644 proplot/tests/test_projections.py create mode 100644 proplot/tests/test_subplots.py diff --git a/proplot/tests/test_1dplots.py b/proplot/tests/test_1dplots.py new file mode 100644 index 000000000..aa7ab327d --- /dev/null +++ b/proplot/tests/test_1dplots.py @@ -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') diff --git a/proplot/tests/test_2dplots.py b/proplot/tests/test_2dplots.py new file mode 100644 index 000000000..8e8fee55b --- /dev/null +++ b/proplot/tests/test_2dplots.py @@ -0,0 +1,322 @@ +#!/usr/bin/env python3 +""" +Test 2D plotting overrides. +""" +import numpy as np +import pytest +import xarray as xr + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_colormap_vcenter(): + """ + Test colormap vcenter. + """ + fig, axs = pplt.subplots(ncols=3) + data = 10 * state.rand(10, 10) - 3 + axs[0].pcolor(data, vcenter=0) + axs[1].pcolor(data, vcenter=1) + axs[2].pcolor(data, vcenter=2) + + +@pplt.tests.image_compare +def test_auto_diverging(): + """ + Test that auto diverging works. + """ + # Test with basic data + fig = pplt.figure() + # fig.format(collabels=('Auto sequential', 'Auto diverging'), suptitle='Default') + ax = fig.subplot(121) + ax.pcolor(state.rand(10, 10) * 5, colorbar='b') + ax = fig.subplot(122) + ax.pcolor(state.rand(10, 10) * 5 - 3.5, colorbar='b') + fig.format(toplabels=('Sequential', 'Diverging')) + + # Test with explicit vcenter + fig, axs = pplt.subplots(ncols=3) + data = 5 * state.rand(10, 10) + axs[0].pcolor(data, vcenter=0, colorbar='b') # otherwise should be disabled + axs[1].pcolor(data, vcenter=1.5, colorbar='b') + axs[2].pcolor(data, vcenter=4, colorbar='b', symmetric=True) + + # Test when cmap input disables auto diverging. + fig, axs = pplt.subplots(ncols=2, nrows=2, refwidth=2) + cmap = pplt.Colormap(('red7', 'red3', 'red1', 'blue1', 'blue3', 'blue7'), listmode='discrete') # noqa: E501 + data1 = 10 * state.rand(10, 10) + data2 = data1 - 2 + for i, cmap in enumerate(('RdBu_r', cmap)): + for j, data in enumerate((data1, data2)): + cmap = pplt.Colormap(pplt.Colormap(cmap)) + axs[i, j].pcolormesh(data, cmap=cmap, colorbar='b') + + fig, axs = pplt.subplots(ncols=3) + data = state.rand(5, 5) * 10 - 5 + for i, ax in enumerate(axs[:2]): + ax.pcolor(data, sequential=bool(i), colorbar='b') + axs[2].pcolor(data, diverging=False, colorbar='b') # should have same effect + + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) * 10 + 2 + for ax, norm in zip(axs, (None, 'div')): + ax.pcolor(data, norm=norm, colorbar='b') + + +@pplt.tests.image_compare +def test_colormap_mode(): + """ + Test auto extending, auto discrete. Should issue warnings. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2, share=False) + axs[0].pcolor(state.rand(5, 5) % 0.3, extend='both', cyclic=True, colorbar='b') + axs[1].pcolor(state.rand(5, 5), sequential=True, diverging=True, colorbar='b') + axs[2].pcolor(state.rand(5, 5), discrete=False, qualitative=True, colorbar='b') + pplt.rc['cmap.discrete'] = False # should be ignored below + axs[3].contourf(state.rand(5, 5), colorbar='b') + + +@pplt.tests.image_compare +def test_contour_labels(): + """ + Test contour labels. We use a separate `contour` object when adding labels to + filled contours or else weird stuff happens (see below). We could just modify + filled contour edges when not adding labels but that would be inconsistent with + behavior when labels are active. + """ + data = state.rand(5, 5) * 10 - 5 + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.contourf( + data, edgecolor='k', linewidth=1.5, + labels=True, labels_kw={'color': 'k', 'size': 'large'} + ) + ax = axs[1] + m = ax.contourf(data) + ax.clabel(m, colors='black', fontsize='large') # looks fine without this + for o in m.collections: + o.set_linewidth(1.5) + o.set_edgecolor('k') + + +@pplt.tests.image_compare +def test_contour_negative(): + """ + Ensure `cmap.monochrome` properly assigned. + """ + fig = pplt.figure(share=False) + ax = fig.subplot(131) + data = state.rand(10, 10) * 10 - 5 + ax.contour(data, color='k') + ax = fig.subplot(132) + ax.tricontour(*(state.rand(3, 20) * 10 - 5), color='k') + ax = fig.subplot(133) + ax.contour(data, cmap=['black']) # fails but that's ok + + +@pplt.tests.image_compare +def test_contour_single(): + """ + Test whether single contour works. + """ + da = xr.DataArray( + np.array( + [ + [0, 1, 2], + [3, 4, 5], + [6, 7, 8], + [9, 10, 11] + ] + ), + dims=['y', 'x'] + ) + fig, ax = pplt.subplots() + ax.contour(da, levels=[5.0], color='r') + + +@pplt.tests.image_compare +def test_edge_fix(): + """ + Test edge fix applied to 1D plotting utilities. + """ + # Test basic application + # TODO: This should make no difference for PNG plots? + pplt.rc.edgefix = 1 + fig, axs = pplt.subplots(ncols=2, share=False) + axs.format(grid=False) + axs[0].bar(state.rand(10,) * 10 - 5, width=1, negpos=True) + axs[1].area(state.rand(5, 3), stack=True) + + # Test whether ignored for transparent colorbars + data = state.rand(10, 10) + cmap = 'magma' + fig, axs = pplt.subplots(nrows=3, ncols=2, refwidth=2.5, share=False) + for i, iaxs in enumerate((axs[:2], axs[2:4])): + if i == 0: + cmap = pplt.Colormap('magma', alpha=0.5) + alpha = None + iaxs.format(title='Colormap alpha') + else: + cmap = 'magma' + alpha = 0.5 + iaxs.format(title='Single alpha') + iaxs[0].contourf(data, cmap=cmap, colorbar='b', alpha=alpha) + iaxs[1].pcolormesh(data, cmap=cmap, colorbar='b', alpha=alpha) + axs[4].bar(data[:3, :3], alpha=0.5) + axs[5].area(data[:3, :3], alpha=0.5, stack=True) + + +@pplt.tests.image_compare +def test_flow_functions(): + """ + These are seldom used and missing from documentation. Be careful + not to break anything basic. + """ + fig, ax = pplt.subplots() + for _ in range(2): + ax.streamplot(state.rand(10, 10), 5 * state.rand(10, 10), label='label') + + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.quiver( + state.rand(10, 10), 5 * state.rand(10, 10), c=state.rand(10, 10), + label='label' + ) + ax = axs[1] + ax.quiver(state.rand(10), state.rand(10), label='single') + + +@pplt.tests.image_compare +def test_gray_adjustment(): + """ + Test gray adjustments when creating segmented colormaps. + """ + fig, ax = pplt.subplots() + data = state.rand(5, 5) * 10 - 5 + cmap = pplt.Colormap(['blue', 'grey3', 'red']) + ax.pcolor(data, cmap=cmap, colorbar='b') + + +@pplt.tests.image_compare +def test_ignore_message(): + """ + Test various ignored argument warnings. + """ + warning = pplt.internals.ProplotWarning + fig, axs = pplt.subplots(ncols=2, nrows=2) + with pytest.warns(warning): + axs[0].contour( + state.rand(5, 5) * 10, levels=pplt.arange(10), symmetric=True + ) + with pytest.warns(warning): + axs[1].contourf( + state.rand(10, 10), levels=np.linspace(0, 1, 10), + locator=5, locator_kw={} + ) + with pytest.warns(warning): + axs[2].contourf( + state.rand(10, 10), + levels=pplt.arange(0, 1, 0.2), + vmin=0, + vmax=2, + locator=3, + colorbar='b' + ) + with pytest.warns(warning): + axs[3].hexbin( + state.rand(1000), + state.rand(1000), + levels=pplt.arange(0, 20), + gridsize=10, + locator=2, + colorbar='b', + cmap='blues', + ) + + +@pplt.tests.image_compare +def test_levels_with_vmin_vmax(): + """ + Make sure `vmin` and `vmax` go into level generation algorithm. + """ + # Sample data + state = np.random.RandomState(51423) + x = y = np.array([-10, -5, 0, 5, 10]) + data = state.rand(y.size, x.size) + + # Figure + fig = pplt.figure(refwidth=2.3, share=False) + axs = fig.subplots() + m = axs.pcolormesh(x, y, data, vmax=1.35123) + axs.colorbar([m], loc='r') + + +@pplt.tests.image_compare +def test_level_restriction(): + """ + Test `negative`, `positive`, and `symmetric` with and without discrete. + """ + fig, axs = pplt.subplots(ncols=3, nrows=2) + data = 20 * state.rand(10, 10) - 5 + keys = ('negative', 'positive', 'symmetric') + for i, grp in enumerate((axs[:3], axs[3:])): + for j, ax in enumerate(grp): + kw = {keys[j]: True, 'discrete': bool(1 - i)} + ax.pcolor(data, **kw, colorbar='b') + + +@pplt.tests.image_compare +def test_qualitative_colormaps(): + """ + Test both `colors` and `cmap` input and ensure extend setting is used for + extreme only if unset. + """ + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) + colors = pplt.get_colors('set3') + for ax, extend in zip(axs, ('both', 'neither')): + ax.pcolor( + data, extend=extend, + colors=colors, colorbar='b' + ) + + fig, axs = pplt.subplots(ncols=2) + data = state.rand(5, 5) + cmap = pplt.Colormap('set3') + cmap.set_under('black') # does not overwrite + for ax, extend in zip(axs, ('both', 'neither')): + ax.pcolor( + data, extend=extend, cmap=cmap, colorbar='b' + ) + + +@pplt.tests.image_compare +def test_segmented_norm(): + """ + Test segmented norm with non-discrete levels. + """ + fig, ax = pplt.subplots() + ax.pcolor( + state.rand(5, 5) * 10, + discrete=False, + norm='segmented', + norm_kw={'levels': [0, 2, 10]}, + colorbar='b' + ) + + +@pplt.tests.image_compare +def test_triangular_functions(): + """ + Test triangular functions. Here there is no remotely sensible way to infer + coordinates so we skip standardize function. + """ + fig, ax = pplt.subplots() + N = 30 + y = state.rand(N) * 20 + x = state.rand(N) * 50 + da = xr.DataArray(state.rand(N), dims=('x',), coords={'x': x, 'y': ('x', y)}) + ax.tricontour(da.x, da.y, da, labels=True) diff --git a/proplot/tests/test_axes.py b/proplot/tests/test_axes.py new file mode 100644 index 000000000..6252d9c1c --- /dev/null +++ b/proplot/tests/test_axes.py @@ -0,0 +1,128 @@ +#!/usr/bin/env python3 +""" +Test twin, inset, and panel axes. +""" +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_inset_colors(): + """ + Test color application for zoom boxes. + """ + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes( + (0.5, 0.5, 0.3, 0.3), zoom=True, zoom_kw={'color': 'r', 'fc': 'r', 'ec': 'b'} + ) # zoom_kw={'alpha': 1}) + # ix = ax.inset_axes((40, 40, 20, 20), zoom=True, transform='data') + ix.format(xlim=(10, 20), ylim=(10, 20), grid=False) + + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes( + (0.3, 0.5, 0.5, 0.3), zoom=True, + zoom_kw={'lw': 3, 'ec': 'red9', 'a': 1, 'c': pplt.set_alpha('red4', 0.5)} + ) + ix.format(xlim=(10, 20), ylim=(10, 20)) + + +@pplt.tests.image_compare +def test_inset_zoom_update(): + """ + Test automatic limit adjusment with successive changes. Without the extra + lines in `draw()` and `get_tight_bbox()` this fails. + """ + fig, ax = pplt.subplots() + ax.format(xlim=(0, 100), ylim=(0, 100)) + ix = ax.inset_axes((40, 40, 20, 20), zoom=True, transform='data') + ix.format(xlim=(10, 20), ylim=(10, 20), grid=False) + + ix.format(xlim=(10, 20), ylim=(10, 30)) + fig.show() + + ax.format(ylim=(0, 300)) + fig.show() + + +@pplt.tests.image_compare +def test_panels_with_sharing(): + """ + Previously the below text would hide the second y label. + """ + fig, axs = pplt.subplots(ncols=2, share=False, refwidth=1.5) + axs.panel('left') + fig.format(ylabel='ylabel', xlabel='xlabel') + + +@pplt.tests.image_compare +def test_panels_without_sharing(): + """ + What should happen if `share=False` but figure-wide sharing enabled? + Strange use case but behavior appears "correct." + """ + fig, axs = pplt.subplots(ncols=2, share=True, refwidth=1.5, includepanels=False) + axs.panel('left', share=False) + fig.format(ylabel='ylabel', xlabel='xlabel') + + fig, axs = pplt.subplots(ncols=2, refwidth=1.5, includepanels=True) + for _ in range(3): + p = axs[0].panel('l', space=0) + p.format(xlabel='label') + fig.format(xlabel='xlabel') + + +@pplt.tests.image_compare +@pplt.tests.image_compare +def test_panels_suplabels(): + """ + Test label sharing for `includepanels=True`. + """ + for b in range(3): + for ncols in range(1, 3): + fig, axs = pplt.subplots(ncols=ncols, refwidth=1.5, includepanels=(b == 2)) + if b: + for _ in range(3): + axs[0].panel('l') + axs.format(xlabel='xlabel\nxlabel\nxlabel', ylabel='ylabel', suptitle='sup') + + +def test_twin_axes(): + """ + Adjust twin axis positions. Should allow easily switching the location. + """ + # Test basic twin creation and tick, spine, label location changes + fig = pplt.figure() + ax = fig.subplot() + ax.format( + ycolor='blue', ylabel='orig', ylabelcolor='blue9', + yspineloc='l', labelweight='bold', xlabel='xlabel', + xtickloc='t', xlabelloc='b', + ) + ax.alty( + loc='r', color='r', labelcolor='red9', label='other', labelweight='bold' + ) + + # Simple example but doesn't quite work. Figure out how to specify left vs. right + # spines for 'offset' locations... maybe needs another keyword. + fig, ax = pplt.subplots() + ax.format(ymax=10, ylabel='Reference') + ax.alty(color='green', label='Green', max=8) + ax.alty(color='red', label='Red', max=15, loc=('axes', -0.2)) + ax.alty(color='blue', label='Blue', max=5, loc=('axes', 1.2), ticklabeldir='out') + + # A worked example from Riley Brady + # Uses auto-adjusting limits + fig, ax = pplt.subplots() + axs = [ax, ax.twinx(), ax.twinx()] + axs[-1].spines['right'].set_position(('axes', 1.2)) + colors = ('Green', 'Red', 'Blue') + for ax, color in zip(axs, colors): + data = state.random(1) * state.random(10) + ax.plot(data, marker='o', linestyle='none', color=color) + ax.format(ylabel='%s Thing' % color, ycolor=color) + axs[0].format(xlabel='xlabel') diff --git a/proplot/tests/test_colorbar.py b/proplot/tests/test_colorbar.py new file mode 100644 index 000000000..0476d2888 --- /dev/null +++ b/proplot/tests/test_colorbar.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python3 +""" +Test colorbars. +""" +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_outer_align(): + """ + Test various align options. + """ + fig, ax = pplt.subplots() + ax.plot(np.empty((0, 4)), labels=list('abcd')) + ax.legend(loc='bottom', align='right', ncol=2) + ax.legend(loc='left', align='bottom', ncol=1) + ax.colorbar('magma', loc='r', align='top', shrink=0.5, label='label', extend='both') + ax.colorbar( + 'magma', loc='top', ticklen=0, tickloc='bottom', align='left', + shrink=0.5, label='Title', extend='both', labelloc='top', labelweight='bold' + ) + # ax.colorbar( + # 'magma', loc='top', align='left', shrink=0.5, label='label', extend='both' + # ) + ax.colorbar('magma', loc='right', extend='both', label='test extensions') + fig.suptitle('Align demo') + + +@pplt.tests.image_compare +def test_colorbar_ticks(): + """ + Test ticks modification. + """ + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.colorbar( + 'magma', loc='bottom', ticklen=10, linewidth=3, tickminor=True + ) + ax = axs[1] + ax.colorbar( + 'magma', loc='bottom', ticklen=10, linewidth=3, tickwidth=1.5, tickminor=True + ) + + +@pplt.tests.image_compare +def test_discrete_ticks(): + """ + Test `DiscreteLocator`. + """ + levels = pplt.arange(0, 2, 0.1) + data = state.rand(5, 5) * 2 + for j, width in enumerate((0.8, 1.2, 2)): + fig, axs = pplt.subplots(share=False, ncols=2, nrows=2, refwidth=width) + for i, ax in enumerate(axs): + cmd = ax.contourf if i // 2 == 0 else ax.pcolormesh + m = cmd(data, levels=levels, extend='both') + ax.colorbar(m, loc='t' if i // 2 == 0 else 'b') + ax.colorbar(m, loc='l' if i % 2 == 0 else 'r') + fig.save(f'~/Downloads/tmp{j}.png', dpi=300) + + +@pplt.tests.image_compare +def test_discrete_vs_fixed(): + """ + Test `DiscreteLocator` for numeric on-the-fly + mappable ticks and `FixedLocator` otherwise. + """ + fig, axs = pplt.subplots(ncols=2, nrows=3, refwidth=1.3, share=False) + axs[0].plot(state.rand(10, 5), labels=list('xyzpq'), colorbar='b') # fixed + axs[1].plot(state.rand(10, 5), labels=np.arange(5), colorbar='b') # discrete + axs[2].contourf( + state.rand(10, 10), + colorbar='b', colorbar_kw={'ticklabels': list('xyzpq')} # fixed + ) + axs[3].contourf(state.rand(10, 10), colorbar='b') # discrete + axs[4].pcolormesh( + state.rand(10, 10) * 20, colorbar='b', levels=[0, 2, 4, 6, 8, 10, 15, 20] + ) # fixed + axs[5].pcolormesh( + state.rand(10, 10) * 20, colorbar='b', levels=pplt.arange(0, 20, 2) + ) # discrete + + +@pplt.tests.image_compare +def test_uneven_levels(): + """ + Test even and uneven levels with discrete cmap. Ensure minor ticks are disabled. + """ + N = 20 + state = np.random.RandomState(51423) + data = np.cumsum(state.rand(N, N), axis=1) * 12 + colors = [ + 'white', + 'indigo1', 'indigo3', 'indigo5', 'indigo7', 'indigo9', + 'yellow1', 'yellow3', 'yellow5', 'yellow7', 'yellow9', + 'violet1', 'violet3' + ] + levels_even = pplt.arange(1, 12, 1) + levels_uneven = [1., 1.25, 1.5, 2., 2.5, 3., 3.75, 4.5, 6., 7.5, 9., 12.] + fig, axs = pplt.subplots(ncols=2, refwidth=3.) + axs[0].pcolor( + data, levels=levels_uneven, colors=colors, colorbar='r', extend='both' + ) + axs[1].pcolor( + data, levels=levels_even, colors=colors, colorbar='r', extend='both' + ) + + +@pplt.tests.image_compare +def test_on_the_fly_mappable(): + """ + Test on-the-fly mappable generation. + """ + fig, axs = pplt.subplots(ncols=2, nrows=3, space=3) + axs.format(aspect=0.5) + axs[0].colorbar('magma', vmin=None, vmax=100, values=[0, 1, 2, 3, 4], loc='bottom') + axs[1].colorbar('magma', vmin=None, vmax=100, loc='bottom') + axs[2].colorbar('colorblind', vmin=None, vmax=None, values=[0, 1, 2], loc='bottom') + axs[3].colorbar('colorblind', vmin=None, vmax=None, loc='bottom') + axs[4].colorbar(['r', 'b', 'g', 'k', 'w'], values=[0, 1, 2], loc='b') + axs[5].colorbar(['r', 'b', 'g', 'k', 'w'], loc='bottom') + + # Passing labels to plot function. + fig, ax = pplt.subplots() + ax.scatter(state.rand(10, 4), labels=['foo', 'bar', 'baz', 'xyz'], colorbar='b') + + # Passing string value lists. This helps complete the analogy with legend 'labels'. + fig, ax = pplt.subplots() + hs = ax.line(state.rand(20, 5)) + ax.colorbar(hs, loc='b', values=['abc', 'def', 'ghi', 'pqr', 'xyz']) + + +@pplt.tests.image_compare +def test_inset_colorbars(): + """ + Test basic functionality. + """ + # Simple example + fig, ax = pplt.subplots() + ax.colorbar('magma', loc='ul') + + # Colorbars from lines + fig = pplt.figure(share=False, refwidth=2) + ax = fig.subplot(121) + state = np.random.RandomState(51423) + data = 1 + (state.rand(12, 10) - 0.45).cumsum(axis=0) + cycle = pplt.Cycle('algae') + hs = ax.line( + data, lw=4, cycle=cycle, colorbar='lr', + colorbar_kw={'length': '8em', 'label': 'line colorbar'} + ) + ax.colorbar( + hs, loc='t', values=np.arange(0, 10), + label='line colorbar', ticks=2 + ) + + # Colorbars from a mappable + ax = fig.subplot(122) + m = ax.contourf( + data.T, extend='both', cmap='algae', + levels=pplt.arange(0, 3, 0.5) + ) + fig.colorbar( + m, loc='r', length=1, # length is relative + label='interior ticks', tickloc='left' + ) + ax.colorbar( + m, loc='ul', length=6, # length is em widths + label='inset colorbar', tickminor=True, alpha=0.5, + ) + fig.format( + suptitle='Colorbar formatting demo', + xlabel='xlabel', ylabel='ylabel', titleabove=False + ) + + +@pplt.tests.image_compare +def test_segmented_norm_center(): + """ + Test various align options. + """ + fig, ax = pplt.subplots() + cmap = pplt.Colormap('NegPos', cut=0.1) + data = state.rand(10, 10) * 10 - 2 + levels = [-4, -3, -2, -1, 0, 1, 2, 4, 8, 16, 32, 64, 128] + norm = pplt.SegmentedNorm(levels, vcenter=0, fair=1) + ax.pcolormesh(data, levels=levels, norm=norm, cmap=cmap, colorbar='b') + + +@pplt.tests.image_compare +def test_segmented_norm_ticks(): + """ + Ensure segmented norm ticks show up in center when `values` are passed. + """ + fig, ax = pplt.subplots() + data = state.rand(10, 10) * 10 + values = (1, 5, 5.5, 6, 10) + ax.contourf( + data, + values=values, + colorbar='ll', + colorbar_kw={'tickminor': True, 'minorlocator': np.arange(-20, 20, 0.5)}, + ) + + +@pplt.tests.image_compare +def test_reversed_levels(): + """ + Test negative levels with a discrete norm and segmented norm. + """ + # %% + fig, axs = pplt.subplots(ncols=4, nrows=2, refwidth=1.8) + data = state.rand(20, 20).cumsum(axis=0) + i = 0 + for stride in (1, -1): + for key in ('levels', 'values'): + for levels in ( + np.arange(0, 15, 1), # with Normalizer + [0, 1, 2, 5, 10, 15], # with LinearSegmentedNorm + ): + ax = axs[i] + kw = {key: levels[::stride]} + ax.pcolormesh(data, colorbar='b', **kw) + i += 1 diff --git a/proplot/tests/test_docs.py b/proplot/tests/test_docs.py new file mode 100644 index 000000000..cd92b7fec --- /dev/null +++ b/proplot/tests/test_docs.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +""" +Automatically build pytests from jupytext py:percent documentation files. +""" +import glob +import os + +import jupytext +import pytest + +from proplot.config import rc +from proplot.tests import SAVEFIG_KWARGS, TOLERANCE, VERSION_STRING + + +def _init_tests(): + """ + Construct tests from the jupytext docs examples. + """ + # WARNING: This will only work if all jupytext examples consist of single code + # cells or adjacent code cells without intervening markdown or ReST cells. + # Alternative would be to define the entire file as a single test but that + # would make testing and debugging more difficult. + base = os.path.dirname(__file__) + paths = glob.glob(f'{base}/../../docs/*.py') + for path in sorted(paths): + if os.path.basename(path) == 'conf.py': + continue + baseline, _ = os.path.splitext(os.path.basename(path)) + result = jupytext.read(path, fmt='py:percent') + cells = result['cells'] + source = '' + num = 1 + for i, cell in enumerate(cells): + type_ = cell['cell_type'] + if type_ == 'code': + source += '\n' + cell['source'] + if not source: + continue + if i == len(cells) - 1 or type_ != 'code': # end of example definition + _make_cell_test(num, baseline, source) + num += 1 + print(f'\nMade {num} tests from file: {path}', end='') + + +def _make_cell_test(num, baseline, cell_source): + """ + Add a test using the jupytext docs cell. + """ + # WARNING: Ugly kludge to replace e.g. backend='basemap' with backend='cartopy' + # for matplotlib versions incompatible with basemap. Generally these examples + # test basemap and cartopy side-by-side so this will effectively duplicate the + # cartopy tests but keep us from having to dump them. + if baseline == 'test_projections' and VERSION_STRING == 'mpl32': + cell_source = cell_source.replace("'basemap'", "'cartopy'") + if 'pplt.Proj' in cell_source or 'Table' in cell_source: + return # examples that cannot be naively converted + + def run_test(): + rc.reset() + exec(cell_source) + + name = f'{baseline}_cell_{num:02d}' + decorator = pytest.mark.mpl_image_compare( + run_test, + filename=f'{name}_{VERSION_STRING}.png', + baseline_dir='images_docs', + savefig_kwargs=SAVEFIG_KWARGS, + tolerance=TOLERANCE, + style={}, # no mpl style + ) + test = decorator(run_test) + name = f'test_{name}' + test.__name__ = test.__qualname__ = name + globals()[name] = test # for pytest detection + + +# Initialize functions +_init_tests() diff --git a/proplot/tests/test_format.py b/proplot/tests/test_format.py new file mode 100644 index 000000000..4d274e7df --- /dev/null +++ b/proplot/tests/test_format.py @@ -0,0 +1,271 @@ +#!/usr/bin/env python3 +""" +Test format and rc behavior. +""" +import locale + +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +def test_colormap_assign(): + """ + Test below line is possible and naming schemes. + """ + pplt.rc['image.cmap'] = pplt.Colormap('phase', shift=180, left=0.2) + assert pplt.rc['cmap'] == pplt.rc['cmap.sequential'] == '_Phase_copy_s' + pplt.rc['image.cmap'] = pplt.Colormap('magma', reverse=True, right=0.8) + assert pplt.rc['image.cmap'] == pplt.rc['cmap.sequential'] == '_magma_copy_r' + + +def test_ignored_keywords(): + """ + Test ignored keywords and functions. + """ + with pplt.tests.warns(pplt.internals.ProplotWarning) as record: + fig, ax = pplt.subplots( + gridspec_kw={'left': 3}, + subplot_kw={'proj': 'cart'}, + subplotpars={'left': 0.2}, + ) + assert len(record) == 3 + with pplt.tests.warns(pplt.internals.ProplotWarning) as record: + fig.subplots_adjust(left=0.2) + assert len(record) == 1 + + +@pplt.tests.image_compare +def test_init_format(): + """ + Test application of format args on initialization. + """ + fig, axs = pplt.subplots( + ncols=2, xlim=(0, 10), xlabel='xlabel', + abc=True, title='Subplot title', + collabels=['Column 1', 'Column 2'], suptitle='Figure title', + ) + axs[0].format(hatch='xxx', hatchcolor='k', facecolor='blue3') + + +@pplt.tests.image_compare +def test_patch_format(): + """ + Test application of patch args on initialization. + """ + fig = pplt.figure(suptitle='Super title') + fig.subplot( + 121, proj='cyl', labels=True, land=True, latlines=20, + abcloc='l', abc='[A]' + ) + fig.subplot( + 122, facecolor='gray1', color='red', titleloc='l', title='Hello', + abcloc='l', abc='[A]', xticks=0.1, yformatter='scalar' + ) + + +@pplt.tests.image_compare +def test_multi_formatting(): + """ + Support formatting in multiple projections. + """ + fig, axs = pplt.subplots(ncols=2, proj=('cart', 'cyl')) + axs[0].pcolormesh(state.rand(5, 5)) + fig.format( + land=1, labels=1, lonlim=(0, 90), latlim=(0, 90), xlim=(0, 10), ylim=(0, 10), + ) + axs[:1].format( + land=1, labels=1, lonlim=(0, 90), latlim=(0, 90), xlim=(0, 10), ylim=(0, 10), + ) + + +@pplt.tests.image_compare +def test_inner_title_zorder(): + """ + Test prominence of contour labels and whatnot. + """ + fig, ax = pplt.subplots() + ax.format( + title='TITLE', titleloc='upper center', titleweight='bold', titlesize='xx-large' + ) + ax.format(xlim=(0, 1), ylim=(0, 1)) + ax.text( + 0.5, 0.95, 'text', ha='center', va='top', color='red', + weight='bold', size='xx-large' + ) + x = [[0.4, 0.6]] * 2 + y = z = [[0.9, 0.9], [1.0, 1.0]] + ax.contour( + x, y, z, color='k', labels=True, + levels=None, labels_kw={'color': 'blue', 'weight': 'bold', 'size': 'xx-large'} + ) + + +@pplt.tests.image_compare +def test_font_adjustments(): + """ + Test font name application. Somewhat hard to do. + """ + fig, axs = pplt.subplots(ncols=2) + axs.format( + abc='A.', + fontsize=15, + fontname='Fira Math', + xlabel='xlabel', + ylabel='ylabel', + title='Title', + figtitle='Figure title', + collabels=['Column 1', 'Column 2'], + ) + + +@pplt.tests.image_compare +def test_axes_colors(): + """ + Test behavior of passing color to format. + """ + fig, axs = pplt.subplots( + ncols=3, nrows=2, share=False, + proj=('cyl', 'cart', 'polar', 'cyl', 'cart', 'polar'), wratios=(2, 2, 1) + ) + axs[:, 0].format(labels=True) + axs[:3].format(edgecolor='red', gridlabelsize='med-large', gridlabelweight='bold') + axs[:3].format(color='red') # without this just colors the edge + axs[1].format(xticklabelcolor='gray') + # axs[2].format(ticklabelcolor='red') + axs[1].format(tickcolor='blue') + axs[3:].format(color='red') # ensure propagates + # axs[-1].format(gridlabelcolor='green') # should work + + +@pplt.tests.image_compare +def test_locale_formatting(): + """ + Ensure locale formatting works. Also zerotrim should account + for non-period decimal separators. + """ + locale.setlocale(locale.LC_ALL, 'de_DE') + pplt.rc['formatter.use_locale'] = False + pplt.rc['formatter.zerotrim'] = True + with pplt.rc.context({'formatter.use_locale': True}): + fig, ax = pplt.subplots() + ticks = pplt.arange(-1, 1, 0.1) + ax.format(ylim=(min(ticks), max(ticks)), yticks=ticks) + + +@pplt.tests.image_compare +def test_bounds_ticks(): + """ + Test spine bounds and location. Previously applied `fixticks` + automatically but no longer the case. + """ + fig, ax = pplt.subplots() + # ax.format(xlim=(-10, 10)) + ax.format(xloc='top') + ax.format(xlim=(-10, 15), xbounds=(0, 10)) + + +@pplt.tests.image_compare +def test_cutoff_ticks(): + """ + Test spine cutoff ticks. + """ + fig, ax = pplt.subplots() + # ax.format(xlim=(-10, 10)) + ax.format(xlim=(-10, 10), xscale=('cutoff', 0, 2), xloc='top', fixticks=True) + ax.axvspan(0, 100, facecolor='k', alpha=0.1) + + +@pplt.tests.image_compare +def test_spine_side(): + """ + Test automatic spine selection when passing `xspineloc` or `yspineloc`. + """ + fig, ax = pplt.subplots() + ax.plot(pplt.arange(-5, 5), (10 * state.rand(11, 5) - 5).cumsum(axis=0)) + ax.format(xloc='bottom', yloc='zero') + ax.alty(loc='right') + + +@pplt.tests.image_compare +def test_spine_offset(): + """ + Test offset axes. + """ + fig, ax = pplt.subplots() + ax.format(xloc='none') # test none instead of neither + ax.alty(loc=('axes', -0.2), color='red') + # ax.alty(loc=('axes', 1.2), color='blue') + ax.alty(loc=('axes', -0.4), color='blue') + ax.alty(loc=('axes', 1.1), color='green') + + +@pplt.tests.image_compare +def test_tick_direction(): + """ + Test tick direction arguments. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format(tickdir='in') + axs[1].format(xtickdirection='inout', ytickdir='out') # rc setting should be used? + + +@pplt.tests.image_compare +def test_tick_length(): + """ + Test tick length args. Ensure ratios can be applied successively. + """ + fig, ax = pplt.subplots() + ax.format(yticklen=100) + ax.format(xticklen=50, yticklenratio=0.1) + + +@pplt.tests.image_compare +def test_tick_width(): + """ + Test tick width args. Ensure ratios can be applied successively, setting + width to `zero` adjusts length for label padding, and ticks can appear + without spines if requested. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2, share=False) + ax = axs[0] + ax.format(linewidth=2, ticklen=20, xtickwidthratio=1) + ax.format(ytickwidthratio=0.3) + ax = axs[1] + ax.format(axeslinewidth=0, ticklen=20, tickwidth=2) # should permit ticks + ax = axs[2] + ax.format(tickwidth=0, ticklen=50) # should set length to zero + ax = axs[3] + ax.format(linewidth=0, ticklen=20, tickwidth='5em') # should override linewidth + + +@pplt.tests.image_compare +def test_tick_labels(): + """ + Test default and overwriting properties of auto tick labels. + """ + import pandas as pd + data = state.rand(5, 3) + data = pd.DataFrame(data, index=['foo', 'bar', 'baz', 'bat', 'bot']) + fig, axs = pplt.subplots(abc='A.', abcloc='ul', ncols=2, refwidth=3, span=False) + for i, ax in enumerate(axs): + data.index.name = 'label' + if i == 1: + ax.format(xformatter='null') # overrides result + ax.bar(data, autoformat=True) + if i == 0: + data.index = ['abc', 'def', 'ghi', 'jkl', 'mno'] + data.index.name = 'foobar' # label should be updated + ax.bar(-data, autoformat=True) + + +@pplt.tests.image_compare +def test_label_settings(): + """ + Test label colors and ensure color change does not erase labels. + """ + fig, ax = pplt.subplots() + ax.format(xlabel='xlabel', ylabel='ylabel') + ax.format(labelcolor='red') diff --git a/proplot/tests/test_integration.py b/proplot/tests/test_integration.py new file mode 100644 index 000000000..add57a8e4 --- /dev/null +++ b/proplot/tests/test_integration.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +""" +Test xarray, pandas, pint, seaborn integration. +""" +import numpy as np +import pandas as pd +import pint +import seaborn as sns +import xarray as xr + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_pint_quantities(): + """ + Ensure auto-formatting and column iteration both work. + """ + pplt.rc.unitformat = '~H' + ureg = pint.UnitRegistry() + fig, ax = pplt.subplots() + ax.plot( + np.arange(10), + state.rand(10) * ureg.km, + 'C0', + np.arange(10), + state.rand(10) * ureg.m * 1e2, + 'C1' + ) + + +@pplt.tests.image_compare +def test_data_keyword(): + """ + Make sure `data` keywords work properly. + """ + N = 10 + M = 20 + ds = xr.Dataset( + {'z': (('x', 'y'), state.rand(N, M))}, + coords={ + 'x': ('x', np.arange(N) * 10, {'long_name': 'longitude'}), + 'y': ('y', np.arange(M) * 5, {'long_name': 'latitude'}) + } + ) + fig, ax = pplt.subplots() + # ax.pcolor('z', data=ds, order='F') + ax.pcolor(z='z', data=ds, transpose=True) + ax.format(xformatter='deglat', yformatter='deglon') + + +@pplt.tests.image_compare +def test_keep_guide_labels(): + """ + Preserve metadata when passing mappables and handles to colorbar and + legend subsequently. + """ + fig, ax = pplt.subplots() + df = pd.DataFrame(state.rand(5, 5)) + df.name = 'variable' + m = ax.pcolor(df) + ax.colorbar(m) + + fig, ax = pplt.subplots() + for k in ('foo', 'bar', 'baz'): + s = pd.Series(state.rand(5), index=list('abcde'), name=k) + ax.plot( + s, legend='ul', + legend_kw={ + 'lw': 5, + 'ew': 2, + 'ec': 'r', + 'fc': 'w', + 'handle_kw': {'marker': 'd'} + } + ) + + +@pplt.tests.image_compare +def test_seaborn_swarmplot(): + """ + Test seaborn swarm plots. + """ + tips = sns.load_dataset('tips') + fig = pplt.figure(refwidth=3) + ax = fig.subplot() + sns.swarmplot(ax=ax, x='day', y='total_bill', data=tips, palette='cubehelix') + # fig, ax = pplt.subplots() + # sns.swarmplot(y=state.normal(size=100), ax=ax) + return fig + + +@pplt.tests.image_compare +def test_seaborn_hist(): + """ + Test seaborn histograms. + """ + fig, axs = pplt.subplots(ncols=2, nrows=2) + sns.histplot(state.normal(size=100), ax=axs[0]) + sns.kdeplot(state.rand(100), state.rand(100), ax=axs[1]) + penguins = sns.load_dataset('penguins') + sns.histplot( + data=penguins, x='flipper_length_mm', hue='species', multiple='stack', ax=axs[2] + ) + sns.kdeplot( + data=penguins, x='flipper_length_mm', hue='species', multiple='stack', ax=axs[3] + ) + + +@pplt.tests.image_compare +def test_seaborn_relational(): + """ + Test scatter plots. Disabling seaborn detection creates mismatch between marker + sizes and legend. + """ + fig = pplt.figure() + ax = fig.subplot() + sns.set_theme(style='white') + # Load the example mpg dataset + mpg = sns.load_dataset('mpg') + # Plot miles per gallon against horsepower with other semantics + sns.scatterplot( + x='horsepower', y='mpg', hue='origin', size='weight', + sizes=(40, 400), alpha=.5, palette='muted', + # legend='bottom', + # height=6, + data=mpg, ax=ax + ) + + +@pplt.tests.image_compare +def test_seaborn_heatmap(): + """ + Test seaborn heatmaps. This should work thanks to backwards compatibility support. + """ + fig, ax = pplt.subplots() + sns.heatmap(state.normal(size=(50, 50)), ax=ax[0]) diff --git a/proplot/tests/test_journals.py b/proplot/tests/test_journals.py deleted file mode 100644 index 5fe002027..000000000 --- a/proplot/tests/test_journals.py +++ /dev/null @@ -1,11 +0,0 @@ -import pytest - -import proplot as pplt -from proplot.subplots import JOURNAL_SPECS - - -# Loop through all available journals. -@pytest.mark.parametrize('journal', JOURNAL_SPECS.keys()) -def test_journal_subplots(journal): - """Tests that subplots can be generated with journal specifications.""" - f, axs = pplt.subplots(journal=journal) diff --git a/proplot/tests/test_legend.py b/proplot/tests/test_legend.py new file mode 100644 index 000000000..7ff27ec43 --- /dev/null +++ b/proplot/tests/test_legend.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python3 +""" +Test legends. +""" +import numpy as np +import pandas as pd + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_auto_legend(): + """ + Test retrieval of legends from panels, insets, etc. + """ + fig, ax = pplt.subplots() + ax.line(state.rand(5, 3), labels=list('abc')) + px = ax.panel_axes('right', share=False) + px.linex(state.rand(5, 3), labels=list('xyz')) + # px.legend(loc='r') + ix = ax.inset_axes((-0.2, 0.8, 0.5, 0.5), zoom=False) + ix.line(state.rand(5, 2), labels=list('pq')) + ax.legend(loc='b', order='F', edgecolor='red9', edgewidth=3) + + +@pplt.tests.image_compare +def test_singleton_legend(): + """ + Test behavior when singleton lists are passed. + Ensure this does not trigger centered-row legends. + """ + fig, ax = pplt.subplots() + h1 = ax.plot([0, 1, 2], label='a') + h2 = ax.plot([0, 1, 1], label='b') + ax.legend(loc='best') + ax.legend([h1, h2], loc='bottom') + + +@pplt.tests.image_compare +def test_centered_legends(): + """ + Test success of algorithm. + """ + # Basic centered legends + fig, axs = pplt.subplots(ncols=2, nrows=2, axwidth=2) + hs = axs[0].plot(state.rand(10, 6)) + locs = ['l', 't', 'r', 'uc', 'ul', 'll'] + locs = ['l', 't', 'uc', 'll'] + labels = ['a', 'bb', 'ccc', 'ddddd', 'eeeeeeee', 'fffffffff'] + for ax, loc in zip(axs, locs): + ax.legend(hs, loc=loc, ncol=3, labels=labels, center=True) + + # Pass centered legends with keywords or list-of-list input. + fig, ax = pplt.subplots() + hs = ax.plot(state.rand(10, 5), labels=list('abcde')) + ax.legend(hs, center=True, loc='b') + ax.legend(hs + hs[:1], loc='r', ncol=1) + ax.legend([hs[:2], hs[2:], hs[0]], loc='t') + + +@pplt.tests.image_compare +def test_manual_labels(): + """ + Test mixed auto and manual labels. Passing labels but no handles does nothing + This is breaking change but probably best. We should not be "guessing" the + order objects were drawn in then assigning labels to them. Similar to using + OO interface and rejecting pyplot "current axes" and "current figure". + """ + fig, ax = pplt.subplots() + h1, = ax.plot([0, 1, 2], label='label1') + h2, = ax.plot([0, 1, 1], label='label2') + for loc in ('best', 'bottom'): + ax.legend([h1, h2], loc=loc, labels=[None, 'override']) + fig, ax = pplt.subplots() + ax.plot([0, 1, 2]) + ax.plot([0, 1, 1]) + for loc in ('best', 'bottom'): + # ax.legend(loc=loc, labels=['a', 'b']) + ax.legend(['a', 'b'], loc=loc) # same as above + + +@pplt.tests.image_compare +def test_contour_legend(): + """ + Support contour element labels. If has no label should trigger warning. + """ + for label in ('label', None): + fig, axs = pplt.subplots(ncols=2) + ax = axs[0] + ax.contour(state.rand(5, 5), color='k', label=label, legend='b') + ax = axs[1] + ax.pcolor(state.rand(5, 5), label=label, legend='b') + + +@pplt.tests.image_compare +def test_histogram_legend(): + """ + Support complex histogram legends. + """ + pplt.rc.inlinefmt = 'svg' + fig, ax = pplt.subplots() + res = ax.hist( + state.rand(500, 2), 4, labels=('label', 'other'), edgefix=True, legend='b' + ) + ax.legend(res, loc='r', ncol=1) # should issue warning after ignoring numpy arrays + df = pd.DataFrame( + { + 'length': [1.5, 0.5, 1.2, 0.9, 3], + 'width': [0.7, 0.2, 0.15, 0.2, 1.1] + }, + index=['pig', 'rabbit', 'duck', 'chicken', 'horse'] + ) + fig, axs = pplt.subplots(ncols=3) + ax = axs[0] + res = ax.hist(df, bins=3, legend=True, lw=3) + ax.legend(loc='b') + for ax, meth in zip(axs[1:], ('bar', 'area')): + hs = getattr(ax, meth)(df, legend='ul', lw=3) + ax.legend(hs, loc='b') + + +@pplt.tests.image_compare +def test_multiple_calls(): + """ + Test successive plotting additions to guides. + """ + fig, ax = pplt.subplots() + ax.pcolor(state.rand(10, 10), colorbar='b') + ax.pcolor(state.rand(10, 5), cmap='grays', colorbar='b') + ax.pcolor(state.rand(10, 5), cmap='grays', colorbar='b') + + fig, ax = pplt.subplots() + data = state.rand(10, 5) + for i in range(data.shape[1]): + ax.plot(data[:, i], colorbar='b', label=f'x{i}', colorbar_kw={'label': 'hello'}) + + +@pplt.tests.image_compare +def test_tuple_handles(): + """ + Test tuple legend handles. + """ + from matplotlib import legend_handler + fig, ax = pplt.subplots(refwidth=3, abc='A.', abcloc='ul', span=False) + patches = ax.fill_between(state.rand(10, 3), stack=True) + lines = ax.line(1 + 0.5 * (state.rand(10, 3) - 0.5).cumsum(axis=0)) + # ax.legend([(handles[0], lines[1])], ['joint label'], loc='bottom', queue=True) + for hs in (lines, patches): + ax.legend( + [tuple(hs[:3]) if len(hs) == 3 else hs], + ['joint label'], + loc='bottom', + queue=True, + ncol=1, + handlelength=4.5, + handleheight=1.5, + handler_map={tuple: legend_handler.HandlerTuple(pad=0, ndivide=3)} + ) diff --git a/proplot/tests/test_projections.py b/proplot/tests/test_projections.py new file mode 100644 index 000000000..4f9ddf789 --- /dev/null +++ b/proplot/tests/test_projections.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python3 +""" +Test projection features. +""" +import cartopy.crs as ccrs +import matplotlib.pyplot as plt +import numpy as np + +import proplot as pplt + +state = np.random.RandomState(51423) + + +@pplt.tests.image_compare +def test_aspect_ratios(): + """ + Test aspect ratio adjustments. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format(aspect=1.5) + + fig, axs = pplt.subplots(ncols=2, proj=('cart', 'cyl'), aspect=2) + axs[0].set_aspect(1) + + +if pplt.internals._version_mpl <= '3.2': + @pplt.tests.image_compare + def test_basemap_labels(): + """ + Add basemap labels. + """ + fig, axs = pplt.subplots(ncols=2, proj='robin', refwidth=3, basemap=True) + axs.format(coast=True, labels='rt') + + +@pplt.tests.image_compare +def test_cartopy_labels(): + """ + Add cartopy labels. + """ + fig, axs = pplt.subplots(ncols=2, proj='robin', refwidth=3) + axs.format(coast=True, labels=True) + axs[0].format(inlinelabels=True) + axs[1].format(rotatelabels=True) + + +@pplt.tests.image_compare +def test_cartopy_contours(): + """ + Test bug with cartopy contours. Sometimes results in global coverage + with single color sometimes not. + """ + N = 10 + fig = plt.figure(figsize=(5, 2.5)) + ax = fig.add_subplot(projection=ccrs.Mollweide()) + ax.coastlines() + x = np.linspace(-180, 180, N) + y = np.linspace(-90, 90, N) + z = state.rand(N, N) * 10 - 5 + m = ax.contourf( + x, y, z, + transform=ccrs.PlateCarree(), + cmap='RdBu_r', + vmin=-5, + vmax=5, + ) + fig.colorbar(m, ax=ax) + + fig = pplt.figure() + ax = fig.add_subplot( + projection=pplt.Mollweide(), + extent='auto' + ) + ax.coastlines() + N = 10 + m = ax.contourf( + np.linspace(0, 180, N), + np.linspace(0, 90, N)[1::2], + state.rand(N // 2, N) * 10 + 5, + cmap='BuRd', + transform=pplt.PlateCarree(), + edgefix=False + ) + fig.colorbar(m, ax=ax) + + +@pplt.tests.image_compare +def test_cartopy_manual(): + """ + Test alternative workflow without classes. + """ + fig = pplt.figure() + proj = pplt.Proj('npstere') + # fig.add_axes([0.1, 0.1, 0.9, 0.9], proj='geo', map_projection=proj) + fig.add_subplot(111, proj='geo', land=True, map_projection=proj) + + +@pplt.tests.image_compare +def test_three_axes(): + """ + Test basic 3D axes here. + """ + pplt.rc['tick.minor'] = False + fig, ax = pplt.subplots(proj='3d', outerpad=3) + + +@pplt.tests.image_compare +def test_projection_dicts(): + """ + Test projection dictionaries. + """ + fig = pplt.figure(refnum=1) + a = [ + [1, 0], + [1, 4], + [2, 4], + [2, 4], + [3, 4], + [3, 0] + ] + fig.subplots(a, proj={1: 'cyl', 2: 'cart', 3: 'cart', 4: 'cart'}) + + +@pplt.tests.image_compare +def test_polar_projections(): + """ + Rigorously test polar features here. + """ + fig, ax = pplt.subplots(proj='polar') + ax.format( + rlabelpos=45, + thetadir=-1, + thetalines=90, + thetalim=(0, 270), + theta0='N', + r0=0, + rlim=(0.5, 1), + rlines=0.25, + ) diff --git a/proplot/tests/test_subplots.py b/proplot/tests/test_subplots.py new file mode 100644 index 000000000..15b160265 --- /dev/null +++ b/proplot/tests/test_subplots.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +""" +Test subplot layout. +""" +import numpy as np + +import proplot as pplt + + +@pplt.tests.image_compare +def test_align_labels(): + """ + Test spanning and aligned labels. + """ + fig, axs = pplt.subplots( + [[2, 1, 4], [2, 3, 5]], refnum=2, refwidth=1.5, align=1, span=0 + ) + fig.format(xlabel='xlabel', ylabel='ylabel', abc='A.', abcloc='ul') + axs[0].format(ylim=(10000, 20000)) + axs[-1].panel_axes('bottom', share=False) + + +@pplt.tests.image_compare +def test_share_all_basic(): + """ + Test sharing level all. + """ + # Simple example + N = 10 + fig, axs = pplt.subplots(nrows=1, ncols=2, refwidth=1.5, share='all') + axs[0].plot(np.arange(N) * 1e2, np.arange(N) * 1e4) + # Complex example + fig, axs = pplt.subplots(nrows=2, ncols=2, refwidth=1.5, share='all') + axs[0].panel('b') + pax = axs[0].panel('r') + pax.format(ylabel='label') + axs[0].plot(np.arange(N) * 1e2, np.arange(N) * 1e4) + + +@pplt.tests.image_compare +def test_span_labels(): + """ + Rigorous tests of spanning and aligned labels feature. + """ + fig, axs = pplt.subplots([[1, 2, 4], [1, 3, 5]], refwidth=1.5, share=0, span=1) + fig.format(xlabel='xlabel', ylabel='ylabel', abc='A.', abcloc='ul') + axs[1].format() # xlabel='xlabel') + axs[2].format() + + +@pplt.tests.image_compare +def test_title_deflection(): + """ + Test the deflection of titles above and below panels. + """ + fig, ax = pplt.subplots() + # ax.format(abc='A.', title='Title', titleloc='left', titlepad=30) + tax = ax.panel_axes('top') + ax.format(titleabove=False) # redirects to bottom + ax.format(abc='A.', title='Title', titleloc='left', titlepad=50) + ax.format(xlabel='xlabel', ylabel='ylabel', ylabelpad=50) + tax.format(title='Fear Me', title_kw={'size': 'x-large'}) + tax.format(ultitle='Inner', titlebbox=True, title_kw={'size': 'med-large'}) + + +@pplt.tests.image_compare +def test_complex_ticks(): + """ + Normally title offset with these different tick arrangements is tricky + but `_update_title_position` accounts for edge cases. + """ + fig, axs = pplt.subplots(ncols=2) + axs[0].format( + xtickloc='both', + xticklabelloc='top', + xlabelloc='top', + title='title', + xlabel='xlabel', + suptitle='Test', + ) + axs[1].format( + xtickloc='both', + xticklabelloc='top', + # xlabelloc='top', + xlabel='xlabel', + title='title', + suptitle='Test', + ) + + +@pplt.tests.image_compare +def test_both_ticklabels(): + """ + Test both tick labels. + """ + fig, ax = pplt.subplots() # when both, have weird bug + ax.format(xticklabelloc='both', title='title', suptitle='Test') + fig, ax = pplt.subplots() # when *just top*, bug disappears + ax.format(xtickloc='top', xticklabelloc='top', title='title', suptitle='Test') + fig, ax = pplt.subplots() # not sure here + ax.format(xtickloc='both', xticklabelloc='neither', suptitle='Test') + fig, ax = pplt.subplots() # doesn't seem to change the title offset though + ax.format(xtickloc='top', xticklabelloc='neither', suptitle='Test') + + +@pplt.tests.image_compare +def test_gridspec_copies(): + """ + Test whether gridspec copies work. + """ + fig1, ax = pplt.subplots(ncols=2) + gs = fig1.gridspec.copy(left=5, wspace=0, right=5) + fig2 = pplt.figure() + fig2.add_subplots(gs) + fig = pplt.figure() + with pplt.tests.raises(ValueError): + fig.add_subplots(gs) # should raise error + return (fig1, fig2) + + +@pplt.tests.image_compare +def test_aligned_outer_guides(): + """ + Test alignment adjustment. + """ + fig, ax = pplt.subplot() + h1 = ax.plot(np.arange(5), label='foo') + h2 = ax.plot(np.arange(5) + 1, label='bar') + h3 = ax.plot(np.arange(5) + 2, label='baz') + ax.legend(h1, loc='bottom', align='left') + ax.legend(h2, loc='bottom', align='right') + ax.legend(h3, loc='b', align='c') + ax.colorbar('magma', loc='right', align='top', shrink=0.4) # same as length + ax.colorbar('magma', loc='right', align='bottom', shrink=0.4) + ax.colorbar('magma', loc='left', align='top', length=0.6) # should offset + ax.colorbar('magma', loc='left', align='bottom', length=0.6) + ax.legend(h1, loc='top', align='right', pad='4pt', frame=False) + ax.format(title='Very long title', titlepad=6, titleloc='left') + + +def test_reference_aspect(): + """ + Rigorous test of reference aspect ratio accuracy. + """ + # A simple test + refwidth = 1.5 + fig, axs = pplt.subplots(ncols=2, refwidth=refwidth) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0]) + + # A test with funky layout + refwidth = 1.5 + fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], ref=3, refwidth=refwidth) + axs[1].panel_axes('left') + axs.format(xlocator=0.2, ylocator=0.2) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0]) + + # A test with panels + refwidth = 2.0 + fig, axs = pplt.subplots( + [[1, 1, 2], [3, 4, 5], [3, 4, 6]], hratios=(2, 1, 1), refwidth=refwidth + ) + axs[2].panel_axes('right', width=0.5) + axs[0].panel_axes('bottom', width=0.5) + axs[3].panel_axes('left', width=0.5) + fig.auto_layout() + assert np.isclose(refwidth, axs[fig._refnum - 1]._get_size_inches()[0])
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