.. _statistics-errorbar_limits:

statistics example code: errorbar_limits.py
===========================================



.. plot:: /home/tcaswell/source/p/matplotlib/doc/mpl_examples/statistics/errorbar_limits.py

::

    """
    ===========================================================
    Demo of how to include 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.zeros(x.shape) + 0.2
    yerr[[3, 6]] = 0.3
    
    # mock up some limits by modifying previous data
    xlolims = lolims
    xuplims = uplims
    lolims = np.zeros(x.shape)
    uplims = np.zeros(x.shape)
    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()
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)