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Commit cca0811

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merge v1.4.0-doc into 1.4.x
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‎INSTALL

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In case Python is not installed for all users (not the default), the
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Microsoft Visual C++ 2008 (
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`64 bit <http://www.microsoft.com/download/en/details.aspx?id=15336>`_
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`64 bit <http://www.microsoft.com/download/en/details.aspx?id=15336>`__
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or
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`32 bit <http://www.microsoft.com/download/en/details.aspx?id=29>`_
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`32 bit <http://www.microsoft.com/download/en/details.aspx?id=29>`__
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for Python 2.6 to 3.2) or Microsoft Visual C++ 2010 (
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`64 bit <http://www.microsoft.com/en-us/download/details.aspx?id=14632>`_
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`64 bit <http://www.microsoft.com/en-us/download/details.aspx?id=14632>`__
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or
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`32 bit <http://www.microsoft.com/en-us/download/details.aspx?id=5555>`_
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`32 bit <http://www.microsoft.com/en-us/download/details.aspx?id=5555>`__
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for Python 3.3 and 3.4) redistributable packages need to be installed.
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Matplotlib depends on `Pillow <https://pypi.python.org/pypi/Pillow>`_
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Optional dependencies
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^^^^^^^^^^^^^^^^^^^^^
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`Pillow <http://python-imaging.github.io/>`_
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`Pillow <http://python-imaging.github.io/>`__
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If Pillow is installed, matplotlib can read and write a larger
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selection of image file formats.
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‎doc/_static/basemap_contour1.png

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‎doc/api/dviread.rst

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****************
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dviread
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****************
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:mod:`matplotlib.dviread`
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=========================

‎doc/api/index.rst

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colorbar_api.rst
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colors_api.rst
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dates_api.rst
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dviread.rst
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figure_api.rst
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finance_api.rst
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font_manager_api.rst
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ticker_api.rst
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tight_layout_api.rst
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tri_api.rst
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type1font.rst
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units_api.rst
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widgets_api.rst

‎doc/api/type1font.rst

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****************
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type1font
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****************
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:mod:`matplotlib.type1font`
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===========================

‎doc/conf.py

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'sphinxext.github',
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'numpydoc']
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exclude_patterns = ['api/api_changes/README.rst']
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# Use IPython's console highlighting by default
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try:

‎doc/devel/gitwash/git_development.rst

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set_up_fork
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configure_git
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development_workflow
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dot2_dot3

‎doc/mpl_toolkits/index.rst

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boundaries, see `basemap <http://matplotlib.org/basemap>`_
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docs.
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.. image:: http://matplotlib.org/basemap/_images/contour1.png
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.. image:: /_static/basemap_contour1.png
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:height: 400px
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tools. An example plot from the
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`Cartopy gallery <http://scitools.org.uk/cartopy/docs/latest/gallery.html>`_:
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.. image:: http://scitools.org.uk/cartopy/docs/latest/_images/hurricane_katrina_01_00.png
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.. image:: /_static/cartopy_hurricane_katrina_01_00.png
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:height: 400px
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toolkit written by Joe Kington to provide interactive "data cursors"
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(clickable annotation boxes) for matplotlib.
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.. _prettyplotlib:
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.. _toolkit_prettyplotlib:
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prettyplotlib
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=============
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Several projects have started to provide a higher-level interface to
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matplotlib. These are independent projects.
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.. _seaborn:
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.. _toolkit_seaborn:
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seaborn
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=======
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aims to make visualization a central part of exploring and
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understanding complex datasets.
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.. _ggplot:
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.. _toolkit_ggplot:
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ggplot
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======

‎doc/users/beginner.rst

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.. _users-guide-index:
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.. _beginners-guide-index:
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################
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Beginner's Guide

‎doc/users/colormaps.rst

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Lightness of matplotlib colormaps
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=================================
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Here we examine the lightness values of the matplotlib colormaps. Note that some documentation on the colormaps is available ([colormaps]_).
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Here we examine the lightness values of the matplotlib colormaps. Note that some documentation on the colormaps is available ([list-colormaps]_).
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Sequential
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----------
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.. [Ware] http://ccom.unh.edu/sites/default/files/publications/Ware_1988_CGA_Color_sequences_univariate_maps.pdf
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.. [Moreland] http://www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf
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.. [colormaps] https://gist.github.com/endolith/2719900#id7
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.. [list-colormaps] https://gist.github.com/endolith/2719900#id7
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.. [mycarta-banding] http://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/
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.. [mycarta-jet] http://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/
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.. [mycarta-lablinear] http://mycarta.wordpress.com/2012/12/06/the-rainbow-is-deadlong-live-the-rainbow-part-5-cie-lab-linear-l-rainbow/

‎doc/users/configuration.rst

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.. _users-guide-index:
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.. _configuration-guide-index:
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###################
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Configuration Guide

‎doc/users/developer.rst

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.. _users-guide-index:
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.. _advanced-guide-index:
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###############
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Advanced Guide
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event_handling.rst
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transforms_tutorial.rst
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path_tutorial.rst
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patheffects_guide.rst
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recipes.rst
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‎doc/users/screenshots.rst

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* :ref:`user_interfaces-embedding_in_gtk2`
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* :ref:`user_interfaces-embedding_in_wx2`
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* :ref:`user_interfaces-mpl_with_glade`
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* :ref:`user_interfaces-embedding_in_qt`
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* :ref:`user_interfaces-embedding_in_qt4`
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* :ref:`user_interfaces-embedding_in_tk`
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XKCD-style sketch plots

‎doc/users/whats_new.rst

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will now display correctly. Second, the 'best' placement of a legend has
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See :ref:`legend-complex-plots` for more detailed explanation and
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See the :ref:`plotting-guide-legend` for more detailed explanation and
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examples.
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.. plot:: mpl_examples/pylab_examples/legend_demo4.py

‎examples/color/named_colors.py

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sorted_colors = [colors_[i] for i in ind]
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n = len(sorted_colors)
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ncols = 3
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ncols = 4
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nrows = int(np.ceil(1. * n / ncols))
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fig = plt.figure(figsize=(ncols*2.5, nrows*2))
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fig = plt.figure()
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ax = fig.add_subplot(111)
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X, Y = fig.get_dpi() * fig.get_size_inches()
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# row height
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h = Y / (nrows + 1)
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# col width
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w = X / ncols
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for i, (name, color) in enumerate(sorted_colors):
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ax = fig.add_subplot(nrows, ncols, i + 1)
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ax.text(0.55, 0.5, name, fontsize=12,
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col = i % ncols
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row = int(i / ncols)
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y = Y - (row * h) - h
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xi_line = w * (col + 0.05)
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xf_line = w * (col + 0.25)
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xi_text = w * (col + 0.3)
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# clear colors more visible.
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ax.hlines(0.5, 0, 0.5, color='black', linewidth=10)
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ax.hlines(0.5, 0, 0.5, color=color, linewidth=8)
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.set_axis_off()
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fig.subplots_adjust(left=0.01, right=0.99,
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# clear colors more visible.
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ax.hlines(y, xi_line, xf_line, color='black', linewidth=(h * 0.7))
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ax.hlines(y + h * 0.1, xi_line, xf_line, color=color, linewidth=(h * 0.6))
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ax.set_xlim(0, X)
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ax.set_ylim(0, Y)
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ax.set_axis_off()
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fig.subplots_adjust(left=0, right=1,
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hspace=0, wspace=0)
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plt.show()

‎lib/matplotlib/cbook.py

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Non-bootstrapping approach to confidence interval uses Gaussian-based
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.. math:: \mathrm{med} \pm 1.57 \times \frac{\mathrm{iqr}}{\sqrt{N}}
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.. math::
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\mathrm{med} \pm 1.57 \\times \\frac{\mathrm{iqr}}{\sqrt{N}}
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General approach from:
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McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of

‎lib/matplotlib/mlab.py

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Return the phase (unwrapped angle) of the frequency spectrum of a signal
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:func:`detrend_mean`
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Remove the mean from a line.
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:func:`demean`
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Remove the mean from a line. These functions differ in their defaults.
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Remove the mean from a line. This function is the same as as
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:func:`detrend_mean` except for the default *axis*.
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:func:`detrend_linear`
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Remove the best fit line from a line.

‎lib/matplotlib/patheffects.py

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----------
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shadow_rgbFace : color
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The shadow color.
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alpha : float
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The offset to apply to the path, in points.
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shadow_color : :ref:`color <mpl-color-spec>`
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shadow_color : color
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The shadow color. Default is black.
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A value of ``None`` takes the original artist's color
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with a scale factor of `rho`.

‎lib/matplotlib/tri/triangulation.py

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Attributes
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edges
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neighbors
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`edges`
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`neighbors`
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is_delaunay : bool
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Whether the Triangulation is a calculated Delaunay
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triangulation (where `triangles` was not specified) or not.

‎lib/matplotlib/tri/tritools.py

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`scale_factors`
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"""
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def __init__(self, triangulation):

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