8000 Improve example for fill_between · matplotlib/matplotlib@a191e9d · GitHub
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Improve example for fill_between
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‎examples/lines_bars_and_markers/fill_between_demo.py‎

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Filling the area between lines
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==============================
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This example shows how to use ``fill_between`` to color between lines based on
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user-defined logic.
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This example shows how to use `~.axes.Axes.fill_between` to color the area
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between two lines.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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###############################################################################
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#
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# Basic usage
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# -----------
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# The parameters *y1* and *y2* can be a scalar, indicating a horizontal
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# boundary a the given y-values. If only *y1* is given, *y2* defaults to 0.
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x = np.arange(0.0, 2, 0.01)
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y1 = np.sin(2 * np.pi * x)
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y2 = 1.2 * np.sin(4 * np.pi * x)
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###############################################################################
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fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)
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ax1.fill_between(x, 0, y1)
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ax1.fill_between(x, y1)
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ax1.set_ylabel('between y1 and 0')
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ax2.fill_between(x, y1, 1)
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ax3.set_xlabel('x')
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###############################################################################
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# Now fill between y1 and y2 where a logical condition is met. Note
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# this is different than calling
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# ``fill_between(x[where], y1[where], y2[where] ...)``
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# because of edge effects over multiple contiguous regions.
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fig, (ax, ax1) = plt.subplots(2, 1, sharex=True)
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ax.plot(x, y1, x, y2, color='black')
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ax.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green', interpolate=True)
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ax.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True)
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ax.set_title('fill between where')
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# Test support for masked arrays.
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y2 = np.ma.masked_greater(y2, 1.0)
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ax1.plot(x, y1, x, y2, color='black')
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ax1.fill_between(x, y1, y2, where=y2 >= y1,
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facecolor='green', interpolate=True)
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ax1.fill_between(x, y1, y2, where=y2 <= y1,
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facecolor='red', interpolate=True)
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ax1.set_title('Now regions with y2>1 are masked')
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#
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# Example: Confidence bands
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# -------------------------
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# A common application for `~.axes.Axes.fill_between` is the indication of
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# confidence bands.
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#
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# `~.axes.Axes.fill_between` uses the colors of the color cycle as the fill
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# color. These may be a bit strong when applied to fill areas. It is
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# therefore often a good practice to lighten the color by making the area
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# semi-transparent using *alpha*.
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# sphinx_gallery_thumbnail_number = 2
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N = 21
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x = np.linspace(0, 10, 11)
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y = [3.9, 4.4, 10.8, 10.3, 11.2, 13.1, 14.1, 9.9, 13.9, 15.1, 12.5]
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# fit a linear curve an estimate it's y-values and their error.
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a, b = np.polyfit(x, y, deg=1)
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y_est = a * x + b
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y_err = x.std() * np.sqrt(1/len(x) +
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(x - x.mean())**2 / np.sum((x - x.mean())**2))
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fig, ax = plt.subplots()
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ax.plot(x, y_est, '-')
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ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2)
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ax.plot(x, y, 'o', color='tab:brown')
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###############################################################################
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#
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# Selectively filling horizontal regions
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# --------------------------------------
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# The parameter *where* allows to specify the x-ranges to fill. It's a boolean
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# array with the same size as *x*.
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#
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# Only x-ranges of contiguous *True* sequences are filled. As a result the
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# range between neighboring *True* and *False* values is never filled. This
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# often undesired when the data points should represent a contiguous quantity.
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# It is therefore recommended to set ``interpolate=True`` unless the
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# x-distance of the data points is fine enough so that the above effect is not
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# noticeable. Interpolation approximates the actual x position at which the
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# *where* condition will change and extends the filling up to there.
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x = np.array([0, 1, 2, 3])
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y1 = np.array([1, 1, 0, 0])
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y2 = np.array([0, 0, 1, 1])
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fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
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ax1.set_title('interpolation=False')
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ax1.plot(x, y1, 'o--')
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ax1.plot(x, y2, 'o--')
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ax1.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3)
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ax1.fill_between(x, y1, y2, where=(y1 < y2), color='C1', alpha=0.3)
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ax2.set_title('interpolation=True')
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ax2.plot(x, y1, 'o--')
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ax2.plot(x, y2, 'o--')
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ax2.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3,
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interpolate=True)
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ax2.fill_between(x, y1, y2, where=(y1 <= y2), color='C1', alpha=0.3,
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interpolate=True)
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###############################################################################
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# This example illustrates a problem; because of the data
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# gridding, there are undesired unfilled triangles at the crossover
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# points. A brute-force solution would be to interpolate all
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# arrays to a very fine grid before plotting.
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#
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# .. note::
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#
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# Similar gaps will occur if *y1* or *y2* are masked arrays. Since missing
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# values cannot be approximated, *interpolate* has no effect in this case.
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# The gaps around masked values can only be reduced by adding more data
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# points close to the masked values.
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###############################################################################
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# Use transforms to create axes spans where a certain condition is satisfied:
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#
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# Selectively marking horizontal regions across the whole Axes
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# ------------------------------------------------------------
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# The same selection mechanism can be applied to fill the full vertical height
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# of the axes. To be independent of y-limits, we add a transform that
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# interprets the x-values in data coorindates and the y-values in axes
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# coordinates.
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#
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# The following example marks the regions in which the y-data are above a
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# given threshold.
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fig, ax = plt.subplots()
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y = np.sin(4 * np.pi * x)
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x = np.arange(0, 4 * np.pi, 0.01)
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y = np.sin(x)
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ax.plot(x, y, color='black')
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# use data coordinates for the x-axis and the axes coordinates for the y-axis
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import matplotlib.transforms as mtransforms
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trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)
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theta = 0.9
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ax.axhline(theta, color='green', lw=2, alpha=0.5)
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ax.axhline(-theta, color='red', lw=2, alpha=0.5)
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ax.fill_between(x, 0, 1, where=y > theta,
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facecolor='green', alpha=0.5, transform=trans)
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ax.fill_between(x, 0, 1, where=y < -theta,
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facecolor='red', alpha=0.5, transform=trans)
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plt.show()
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threshold = 0.75
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ax.axhline(threshold, color='green', lw=2, alpha=0.7)
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ax.fill_between(x, 0, 1, where=y > threshold,
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color='green', alpha=0.5, transform=trans)
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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.axes.Axes.fill_between
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matplotlib.pyplot.fill_between
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matplotlib.transforms.blended_transform_factory

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