

.. _sphx_glr_gallery_statistics_errorbar_limits.py:


==============================================
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.




.. image:: /gallery/statistics/images/sphx_glr_errorbar_limits_001.png
    :align: center





.. code-block:: python


    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_like(x) + 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()

**Total running time of the script:** ( 0 minutes  0.056 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: errorbar_limits.py <errorbar_limits.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: errorbar_limits.ipynb <errorbar_limits.ipynb>`

.. rst-class:: sphx-glr-signature

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
