8000 Doc: Scatter Hist example update · matplotlib/matplotlib@4e63c18 · GitHub
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Doc: Scatter Hist example update
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‎.flake8

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examples/animation/frame_grabbing_sgskip.py: E402
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examples/axes_grid1/inset_locator_demo.py: E402
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examples/axes_grid1/scatter_hist_locatable_axes.py: E401, E402
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examples/axisartist/demo_curvelinear_grid.py: E402
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examples/color/color_by_yvalue.py: E402
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examples/color/color_cycle_default.py: E402
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examples/lines_bars_and_markers/filled_step.py: E402
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examples/lines_bars_and_markers/horizontal_barchart_distribution.py: E402
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examples/lines_bars_and_markers/joinstyle.py: E402
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examples/lines_bars_and_markers/scatter_hist.py: E402
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examples/lines_bars_and_markers/scatter_piecharts.py: E402
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examples/lines_bars_and_markers/scatter_with_legend.py: E402
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examples/lines_bars_and_markers/span_regions.py: E402

‎examples/axes_grid1/scatter_hist_locatable_axes.py

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"""
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============
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Scatter Hist
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============
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==================================
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Scatter Histogram (Locatable Axes)
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==================================
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Show the marginal distributions of a scatter as histograms at the sides of
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the plot.
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For a nice alignment of the main axes with the marginals, the axes positions
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are defined by a ``Divider``, produced via `make_axes_locatable`.
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An alternative method to produce a similar figure is shown in the
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:doc:`/gallery/lines_bars_and_markers/scatter_hist` example. The advantage of
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the locatable axes method shown below is that the marginal axes follow the
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fixed aspect ratio of the main axes.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# the random data
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x = np.random.randn(1000)
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y = np.random.randn(1000)
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# the scatter plot:
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axScatter.scatter(x, y)
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# Set aspect of the main axes.
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axScatter.set_aspect(1.)
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# create new axes on the right and on the top of the current axes
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# The first argument of the new_vertical(new_horizontal) method is
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# the height (width) of the axes to be created in inches.
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divider = make_axes_locatable(axScatter)
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axHistx = divider.append_axes("top", 1.2, pad=0.1, sharex=axScatter)
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axHisty = divider.append_axes("right", 1.2, pad=0.1, sharey=axScatter)
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axHistx = divider.append_axes("top", 1.2, pad=0.1, # height and pad in inches
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sharex=axScatter)
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axHisty = divider.append_axes("right", 1.2, pad=0.1, # width and pad in inches
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sharey=axScatter)
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# make some labels invisible
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axHistx.xaxis.set_tick_params(labelbottom=False)
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# axis.
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axHistx.set_yticks([0, 50, 100])
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axHisty.set_xticks([0, 50, 100])
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods and classes is shown
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# in this example:
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import matplotlib, mpl_toolkits
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mpl_toolkits.axes_grid1.make_axes_locatable
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matplotlib.axes.Axes.set_aspect
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matplotlib.axes.Axes.scatter
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matplotlib.axes.Axes.hist

‎examples/lines_bars_and_markers/scatter_hist.py

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Scatter plot with histograms
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============================
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Create a scatter plot with histograms to its sides.
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Show the marginal distributions of a scatter as histograms at the sides of
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the plot.
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For a nice alignment of the main axes with the marginals, two options are shown
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below.
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* the axes positions are defined in terms of rectangles in figure coordinates
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* the axes positions are defined via a gridspec
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An alternative method to produce a similar figure using the ``axes_grid1``
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toolkit is shown in the
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:doc:`/gallery/axes_grid1/scatter_hist_locatable_axes` example.
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Let us first define a function that takes x and y data as input, as well
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as three axes, the main axes for the scatter, and two marginal axes. It will
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then create the scatter and histograms inside the provided axes.
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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# Fixing random state for reproducibility
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np.random.seed(19680801)
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# the random data
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# some random data
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x = np.random.randn(1000)
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y = np.random.randn(1000)
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def scatter_hist(x, y, axScatter, axHistx, axHisty):
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# no labels
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axHistx.tick_params(axis="x", labelbottom=False)
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axHisty.tick_params(axis="y", labelleft=False)
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# the scatter plot:
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axScatter.scatter(x, y)
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# now determine nice limits by hand:
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binwidth = 0.25
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xymax = max(np.max(np.abs(x)), np.max(np.abs(y)))
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lim = (int(xymax/binwidth) + 1) * binwidth
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bins = np.arange(-lim, lim + binwidth, binwidth)
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axHistx.hist(x, bins=bins)
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axHisty.hist(y, bins=bins, orientation='horizontal')
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#############################################################################
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#
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# Axes in figure coordinates
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# --------------------------
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#
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# To define the axes positions `Figure.add_axes` is provided with a rectangle
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# ``[left, bottom, width, height]`` in figure coordinates. The marginal axes
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# share one dimension with the main axes.
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# definitions for the axes
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left, width = 0.1, 0.65
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bottom, height = 0.1, 0.65
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rect_histx = [left, bottom + height + spacing, width, 0.2]
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rect_histy = [left + width + spacing, bottom, 0.2, height]
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# start with a rectangular Figure
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plt.figure(figsize=(8, 8))
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# start with a square Figure
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fig = plt.figure(figsize=(8, 8))
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axScatter = fig.add_axes(rect_scatter)
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axHistx = fig.add_axes(rect_histx, sharex=axScatter)
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axHisty = fig.add_axes(rect_histy, sharey=axScatter)
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# use the previously defined function
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scatter_hist(x, y, axScatter, axHistx, axHisty)
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plt.show()
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ax_scatter = plt.axes(rect_scatter)
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ax_scatter.tick_params(direction='in', top=True, right=True)
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ax_histx = plt.axes(rect_histx)
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ax_histx.tick_params(direction='in', labelbottom=False)
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ax_histy = plt.axes(rect_histy)
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ax_histy.tick_params(direction='in', labelleft=False)
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#############################################################################
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#
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# Using a gridspec
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# ----------------
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#
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# We may equally define a gridspec with unequal width- and height-ratios to
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# achieve desired layout. Also see the :doc:`/tutorials/intermediate/gridspec`
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# tutorial.
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# the scatter plot:
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ax_scatter.scatter(x, y)
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# start with a square Figure
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fig = plt.figure(figsize=(8, 8))
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# now determine nice limits by hand:
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binwidth = 0.25
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lim = np.ceil(np.abs([x, y]).max() / binwidth) * binwidth
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ax_scatter.set_xlim((-lim, lim))
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ax_scatter.set_ylim((-lim, lim))
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# Add a gridspec with two rows and two columns and a ratio of 2 to 7 between
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# the size of the marginal axes and the main axes in both directions.
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# Also adjust the subplot parameters for a square plot.
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gs = fig.add_gridspec(2, 2, width_ratios=(7, 2), height_ratios=(2, 7),
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left=0.1, right=0.9, bottom=0.1, top=0.9,
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wspace=0.05, hspace=0.05)
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bins = np.arange(-lim, lim + binwidth, binwidth)
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ax_histx.hist(x, bins=bins)
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ax_histy.hist(y, bins=bins, orientation='horizontal')
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axScatter = fig.add_subplot(gs[1, 0])
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axHistx = fig.add_subplot(gs[0, 0], sharex=axScatter)
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axHisty = fig.add_subplot(gs[1, 1], sharey=axScatter)
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ax_histx.set_xlim(ax_scatter.get_xlim())
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ax_histy.set_ylim(ax_scatter.get_ylim())
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# use the previously defined function
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scatter_hist(x, y, axScatter, axHistx, axHisty)
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plt.show()
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#############################################################################
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#
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# ------------
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#
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# References
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# """"""""""
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#
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# The use of the following functions, methods and classes is shown
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# in this example:
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import matplotlib
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matplotlib.figure.Figure.add_axes
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matplotlib.figure.Figure.add_subplot
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matplotlib.figure.Figure.add_gridspec
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matplotlib.axes.Axes.scatter
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matplotlib.axes.Axes.hist

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