8000 parent 29a3707a48bfe2abdf7bd58cfa449015742b5aa4 · matplotlib/matplotlib@a516cbd · GitHub
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Lucas Ricci
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parent 29a3707
author Lucas Ricci <llricci@aluno.crb.g12.br> 1636671767 -0300 committer Lucas Ricci <llricci@aluno.crb.g12.br> 1636755318 -0300 parent 29a3707 author Lucas Ricci <llricci@aluno.crb.g12.br> 1636671767 -0300 committer Lucas Ricci <llricci@aluno.crb.g12.br> 1636755293 -0300 deleted and updated baseline image for test_hexbin_linear() Resolve issue #21165, Add Hexbin Linear test a little correction in the code update Update update update a few corrections Resolve issue #21165, Add Hexbin Linear test a little correction in the code Resolve issue #21165, Add Hexbin Linear test a little correction in the code update Update update update a few corrections removing a typo where hexbon_linear() were duplicated
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‎lib/matplotlib/tests/baseline_images/test_axes/hexbin_linear.png

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‎lib/matplotlib/tests/test_axes.py

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@@ -786,6 +786,55 @@ def test_hexbin_log():
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plt.colorbar(h)
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<<<<<<< HEAD
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@image_comparison(baseline_images=['hexbin_linear'],
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extensions=['png'], style='mpl20')
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def test_hexbin_linear():
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np.random.seed(19680801)
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n = 100000
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x = np.random.standard_normal(n)
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y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
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fig, ax = plt.subplots()
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h = ax.hexbin(x, y, marginals=True, reduce_C_function=np.sum)
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plt.colorbar(h)
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@image_comparison(['hexbin_linear.png'], style='mpl20')
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def test_hexbin_linear():
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# Issue #21165 (add a hexbin_linear test)
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plt.rcParams['pcolormesh.snap'] = False
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np.random.seed(19680801)
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n = 100000
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x = np.random.standard_normal(n)
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y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
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y = np.power(2, y * 0.5)
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fig, ax = plt.subplots()
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h = ax.hexbin(x, y, yscale='log', bins='log',
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marginals=True, reduce_C_function=np.sum)
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plt.colorbar(h)
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=======
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>>>>>>> f2fa2e58e5... removing a typo where hexbon_linear() were duplicated
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@image_comparison(baseline_images=['hexbin_linear'],
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extensions=['png'], style='mpl20')
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def test_hexbin_linear():
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np.random.seed(19680801)
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n = 100 7969 000
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x = np.random.standard_normal(n)
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y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
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fig, ax = plt.subplots()
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h = ax.hexbin(x, y, marginals=True, reduce_C_function=np.sum)
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plt.colorbar(h)
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def test_hexbin_log_clim():
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x, y = np.arange(200).reshape((2, 100))
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fig, ax = plt.subplots()

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