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Commit c2d1b19

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tacaswelltacaswell
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Merge remote-tracking branch 'origin/v2.2.3-doc' into 224_prep
2 parents 6368523 + e2ee53b commit c2d1b19
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‎doc-requirements.txt

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@@ -6,7 +6,7 @@
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# Install the documentation requirements with:
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# pip install -r doc-requirements.txt
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#
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sphinx>=1.3,!=1.5.0,!=1.6.4,!=1.7.3
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sphinx>=1.3,!=1.5.0,!=1.6.4,!=1.7.3,<1.8
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colorspacious
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ipython
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ipywidgets

‎doc/_static/mpl.css

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@@ -623,6 +623,14 @@ table.docutils {
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background-color: #eff3f4;
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}
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/* tables inside class descriptions */
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dl.class table.property-table {
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width: 85%;
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border-spacing: 2px;
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border-collapse: collapse;
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border: 0px;
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}
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/* tables inside parameter descriptions */
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td.field-body table.property-table {
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width: 100%;

‎doc/citing.rst

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@@ -27,6 +27,9 @@ publication, please acknowledge this fact by citing `Hunter et al (2007)
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DOIs
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----
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v2.2.3
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.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1343133.svg
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:target: https://doi.org/10.5281/zenodo.1343133
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v2.2.2
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.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1202077.svg
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:target: https://doi.org/10.5281/zenodo.1202077

‎doc/devel/MEP/MEP24.rst

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@@ -31,7 +31,7 @@ but this seems more generally useful (for example growth rate as a
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function of angle). The assumption in the current code (as I
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understand it) is that the center of the graph is `r==0`, however it
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would be good to be able to set the center to be at any `r` (with any
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value less than the off set clipped).
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value less than the offset clipped).
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Implementation
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==============

‎doc/users/whats_new.rst

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@@ -156,10 +156,10 @@ A new dark blue/yellow colormap named 'cividis' was added. Like
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viridis, cividis is perceptually uniform and colorblind
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friendly. However, cividis also goes a step further: not only is it
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usable by colorblind users, it should actually look effectively
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identical to colorblind and non-colorblind users. For more details,
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see Nunez J, Anderton C, and Renslow R. (submitted). Optimizing
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colormaps with consideration for color vision deficiency to enable
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accurate interpretation of scientific data."
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identical to colorblind and non-colorblind users. For more details
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see `Nuñez J, Anderton C, and Renslow R: "Optimizing colormaps with consideration
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for color vision deficiency to enable accurate interpretation of scientific data"
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<https://doi.org/10.1371/journal.pone.0199239>`_.
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.. plot::
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‎examples/lines_bars_and_markers/fill_betweenx_demo.py

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Fill Betweenx Demo
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==================
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Using ``fill_betweenx`` to color between two horizontal curves.
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Using `~.Axes.fill_betweenx` to color along the horizontal direction between
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two curves.
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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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x1 = np.sin(2 * np.pi * y)
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x2 = 1.2 * np.sin(4 * np.pi * y)
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fig, [ax1, ax2, ax3] = plt.subplots(3, 1, sharex=True)
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fig, [ax1, ax2, ax3] = plt.subplots(1, 3, sharey=True, figsize=(6, 6))
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ax1.fill_betweenx(y, 0, x1)
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ax1.set_ylabel('(x1, 0)')
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ax1.set_title('between (x1, 0)')
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ax2.fill_betweenx(y, x1, 1)
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ax2.set_ylabel('(x1, 1)')
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ax2.set_title('between (x1, 1)')
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ax2.set_xlabel('x')
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ax3.fill_betweenx(y, x1, x2)
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ax3.set_ylabel('(x1, x2)')
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ax3.set_xlabel('x')
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ax3.set_title('between (x1, x2)')
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# now fill between x1 and x2 where a logical condition is met. Note
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# this is different than calling
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# fill_between(y[where], x1[where], x2[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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fig, [ax, ax1] = plt.subplots(1, 2, sharey=True, figsize=(6, 6))
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ax.plot(x1, y, x2, y, color='black')
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ax.fill_betweenx(y, x1, x2, where=x2 >= x1, facecolor='green')
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ax.fill_betweenx(y, x1, x2, where=x2 <= x1, facecolor='red')
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ax.set_title('fill between where')
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ax.set_title('fill_betweenx where')
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# Test support for masked arrays.
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x2 = np.ma.masked_greater(x2, 1.0)
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ax1.plot(x1, y, x2, y, color='black')
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ax1.fill_betweenx(y, x1, x2, where=x2 >= x1, facecolor='green')
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ax1.fill_betweenx(y, x1, x2, where=x2 <= x1, facecolor='red')
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ax1.set_title('Now regions with x2 > 1 are masked')
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ax1.set_title('regions with x2 > 1 are masked')
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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

‎lib/matplotlib/ticker.py

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-----------------
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The default formatter identifies when the x-data being plotted is a
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small range on top of a large off set. To reduce the chances that the
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ticklabels overlap the ticks are labeled as deltas from a fixed offset.
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small range on top of a large offset. To reduce the chances that the
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ticklabels overlap, the ticks are labeled as deltas from a fixed offset.
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For example::
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ax.plot(np.arange(2000, 2010), range(10))

‎tutorials/colors/colormaps.py

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@@ -391,4 +391,4 @@ def plot_color_gradients(cmap_category, cmap_list):
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# .. [bw] http://www.tannerhelland.com/3643/grayscale-image-algorithm-vb6/
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# .. [colorblindness] http://www.color-blindness.com/
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# .. [vischeck] http://www.vischeck.com/vischeck/
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# .. [IBM] http://www.research.ibm.com/people/l/lloydt/color/color.HTM
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# .. [IBM] https://dx.doi.org/10.1109/VISUAL.1995.480803

‎tutorials/introductory/usage.py

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# DPI setting.
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#
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# Here is a summary of the matplotlib renderers (there is an eponymous
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# backed for each; these are *non-interactive backends*, capable of
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# backend for each; these are *non-interactive backends*, capable of
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# writing to a file):
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#
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# ============= ============ ================================================

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