>>> """
==============================================
Including upper and lower limits in error bars
==============================================

In matplotlib, errors bars can have "limits". Applying limits to the
error bars essentially makes the error unidirectional. Because of that,
upper and lower limits can be applied in both the y- and x-directions
via the ``uplims``, ``lolims``, ``xuplims``, and ``xlolims`` parameters,
respectively. These parameters can be scalar or boolean arrays.

For example, if ``xlolims`` is ``True``, the x-error bars will only
extend from the data towards increasing values. If ``uplims`` is an
array filled with ``False`` except for the 4th and 7th values, all of the
y-error bars will be bidirectional, except the 4th and 7th bars, which
will extend from the data towards decreasing y-values.
"""
... 
... import numpy as np
... import matplotlib.pyplot as plt
... 
... # example data
... x = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0])
... y = np.exp(-x)
... xerr = 0.1
... yerr = 0.2
... 
... # lower & upper limits of the error
... lolims = np.array([0, 0, 1, 0, 1, 0, 0, 0, 1, 0], dtype=bool)
... uplims = np.array([0, 1, 0, 0, 0, 1, 0, 0, 0, 1], dtype=bool)
... ls = 'dotted'
... 
... fig, ax = plt.subplots(figsize=(7, 4))
... 
... # standard error bars
... ax.errorbar(x, y, xerr=xerr, yerr=yerr, linestyle=ls)
... 
... # including upper limits
... ax.errorbar(x, y + 0.5, xerr=xerr, yerr=yerr, uplims=uplims,
...             linestyle=ls)
... 
... # including lower limits
... ax.errorbar(x, y + 1.0, xerr=xerr, yerr=yerr, lolims=lolims,
...             linestyle=ls)
... 
... # including upper and lower limits
... ax.errorbar(x, y + 1.5, xerr=xerr, yerr=yerr,
...             lolims=lolims, uplims=uplims,
...             marker='o', markersize=8,
...             linestyle=ls)
... 
... # Plot a series with lower and upper limits in both x & y
... # constant x-error with varying y-error
... xerr = 0.2
... yerr = np.full_like(x, 0.2)
... yerr[[3, 6]] = 0.3
... 
... # mock up some limits by modifying previous data
... xlolims = lolims
... xuplims = uplims
... lolims = np.zeros_like(x)
... uplims = np.zeros_like(x)
... lolims[[6]] = True  # only limited at this index
... uplims[[3]] = True  # only limited at this index
... 
... # do the plotting
... ax.errorbar(x, y + 2.1, xerr=xerr, yerr=yerr,
...             xlolims=xlolims, xuplims=xuplims,
...             uplims=uplims, lolims=lolims,
...             marker='o', markersize=8,
...             linestyle='none')
... 
... # tidy up the figure
... ax.set_xlim((0, 5.5))
... ax.set_title('Errorbar upper and lower limits')
... plt.show()
... 
... #############################################################################
... #
... # .. admonition:: References
... #
... #    The use of the following functions, methods, classes and modules is shown
... #    in this example:
... #
... #    - `matplotlib.axes.Axes.errorbar` / `matplotlib.pyplot.errorbar`
...