Minor internals cleanup

lukelbd · lukelbd · commit 6da736f7e50c · 2021-10-19T10:49:08.000-06:00
diff --git a/docs/2dplots.py b/docs/2dplots.py
@@ -230,10 +230,11 @@
 # You can also create on-the-fly "qualitative" `~proplot.colors.DiscreteColormap`\ s
 # by passing lists of colors to the keyword `c`, `color`, or `colors`.
 #
-# To apply the default sequential, diverging, cyclic, or qualitative colormap to
-# a plot, pass ``sequential=True``, ``diverging=True``, ``cyclic=True``, or
-# ``qualitative=True`` to any plotting command. The default colormaps of each
-# type are :rc:`cmap.sequential`, :rc:`cmap.diverging`, :rc:`cmap.cyclic`, and
+# To apply the default :ref:`sequential, diverging, cyclic <ug_cmaps_included>`,
+# or :ref:`qualitative <ug_cycles_included>` colormap, pass ``sequential=True``,
+# ``diverging=True``, ``cyclic=True``, or ``qualitative=True`` to any plotting
+# command. The default colormaps of each type are specified with the proplot
+# settings :rc:`cmap.sequential`, :rc:`cmap.diverging`, :rc:`cmap.cyclic`, and
 # :rc:`cmap.qualitative`. Unless otherwise specified, the sequential colormap
 # is used with the default (linear) normalizer when data is strictly positive
 # or negative, and the diverging colormap is used when the data limits or
@@ -251,7 +252,7 @@
 # Custom defaults of each type
 pplt.rc['cmap.sequential'] = 'PuBuGn'
 pplt.rc['cmap.diverging'] = 'PiYG'
-pplt.rc['cmap.cyclic'] = 'corkO'
+pplt.rc['cmap.cyclic'] = 'bamO'
 pplt.rc['cmap.qualitative'] = 'flatui'
 
 # Make plots. Note the default behavior is sequential=True or diverging=True
@@ -303,19 +304,19 @@
 #
 # Changing the normalizer
 # -----------------------
-
-# In matplotlib, data values are translated into
-# colormap colors using so-called `colormap "normalizers"
-# <https://matplotlib.org/stable/tutorials/colors/colormapnorms.html>`__.
-# A normalizer can be selected from its "registered" name using the
+#
+# Matplotlib `colormap "normalizers"
+# <https://matplotlib.org/stable/tutorials/colors/colormapnorms.html>`__
+# translate raw data values into normalized colormap indices. In proplot,
+# you can select the normalizer from its "registered" name using the
 # `~proplot.constructor.Norm` :ref:`constructor function <why_constructor>`. You
 # can also build a normalizer on-the-fly using the `norm` and `norm_kw` keywords,
-# again available with most `~proplot.axes.PlotAxes` 2D plot commands.
+# available with most `~proplot.axes.PlotAxes` 2D plot commands.
 # If you want to work with the normalizer classes directly, they are available in
 # the top-level namespace (e.g., ``norm=pplt.LogNorm(...)`` is allowed). To
 # explicitly set the normalization range, you can pass the usual `vmin` and `vmax`
-# keywords to the plotting command. See the :ref:`next section <ug_discrete>` for
-# more details on colormap normalization.
+# keywords to the plotting command. See :ref:`below <ug_discrete>` for more
+# details on colormap normalization in proplot.
 
 # %%
 import proplot as pplt
@@ -343,15 +344,15 @@
 # Special normalizers
 # -------------------
 #
-# Proplot includes two new "continuous" normalizers. `~proplot.colors.SegmentedNorm`
-# provides even color gradations with respect to *index* for an arbitrary
-# monotonically increasing or decreasing list of levels. This is automatically
-# applied if you pass unevenly spaced `levels` to a plotting command, or it can be
-# manually applied using e.g. ``norm='segmented'``. This can be useful for datasets
-# with unusual statistical distributions or spanning a wide range of magnitudes.
+# Proplot includes two new :ref:`"continuous" normalizers <ug_norm>`.
+# `~proplot.colors.SegmentedNorm` provides even color gradations with respect to
+# index for an arbitrary monotonically increasing or decreasing list of levels. This is
+# automatically applied if you pass unevenly spaced `levels` to a plotting command, or
+# it can be manually applied using e.g. ``norm='segmented'``. This can be useful for
+# datasets with unusual statistical distributions or spanning many orders of magnitudes.
 #
-# The `~proplot.colors.DivergingNorm` normalizer ensures the colormap midpoint lies
-# on some *central* data value (usually 0), even if `vmin`, `vmax`, or `levels`
+# The `~proplot.colors.DivergingNorm` normalizer ensures that colormap midpoints lie
+# on some central data value (usually ``0``), even if `vmin`, `vmax`, or `levels`
 # are asymmetric with respect to the central value. This is automatically applied
 # if your data contains negative and positive values (see :ref:`below <ug_autonorm>`),
 # or it can be manually applied using e.g. ``diverging=True`` or ``norm='diverging'``.
@@ -365,7 +366,7 @@
 #   should be used with care, as it may lead you to misinterpret your data.
 #
 # The below examples demonstrate how these normalizers
-# affect the interpretation of colormap plots.
+# affect the interpretation of different datasets.
 
 # %%
 import proplot as pplt
@@ -421,18 +422,17 @@
 # ---------------
 #
 # By default, proplot uses `~proplot.colors.DiscreteNorm` to "discretize"
-# the possible colormap colors for contour and pseudocolor plotting commands,
-# including `~proplot.axes.PlotAxes.contourf` and `~proplot.axes.PlotAxes.pcolor`.
+# the possible colormap colors for contour and pseudocolor plotting commands
+# (e.g., `~proplot.axes.PlotAxes.contourf`, `~proplot.axes.PlotAxes.pcolor`).
 # This is analogous to `matplotlib.colors.BoundaryNorm`, except
 # `~proplot.colors.DiscreteNorm` can be paired with arbitrary
-# continuous normalizers like `~matplotlib.colors.LogNorm`,
-# `~proplot.colors.DivergingNorm`, or `~proplot.colors.SegmentedNorm`.
+# continuous normalizers specified by `norm` (see :ref:`above <ug_norm>`).
 # Discrete color levels can help readers discern exact numeric values and
 # tend to reveal qualitative structure in the data. `~proplot.colors.DiscreteNorm`
 # also repairs the colormap end-colors by ensuring the following conditions are met:
 #
-# #. All colormaps always span the *entire color range*,
-#    independent of the `extend` setting.
+# #. All colormaps always span the *entire color range*
+#    regardless of the `extend` parameter.
 # #. Cyclic colormaps always have *distinct color levels*
 #    on either end of the colorbar.
 #
@@ -445,13 +445,13 @@
 # keywords (or to the `N` keyword) to automatically generate approximately that many
 # level edges or centers at "nice" intervals. The algorithm used to generate levels
 # is similar to matplotlib's algorithm for selecting contour levels. The default
-# number of levels is controlled by :rcraw:`cmap.levels`, and the algorithm is
-# constrained by the keywords `vmin`, `vmax`, `locator`, and `locator_kw` -- for
+# number of levels is controlled by :rcraw:`cmap.levels`, and the level selection
+# is constrainted by the keywords `vmin`, `vmax`, `locator`, and `locator_kw` -- for
 # example, ``vmin=100`` ensures the minimum level is greater than or equal to ``100``,
-# and ``locator=5`` ensures a level step size of 5 (see the :ref:`axis locators
-# <ug_locators>` section for details). You can also use the keywords `positive`,
-# `negative`, and `symmetric` to ensure that your levels are strictly positive,
-# strictly negative, or symmetric about zero, or use the `nozero` keyword to remove
+# and ``locator=5`` ensures a level step size of 5 (see :ref:`this section
+# <ug_locators>` for more on locators). You can also use the keywords `negative`,
+# `positive`, or `symmetric` to ensure that your levels are strictly negative,
+# positive, or symmetric about zero, or use the `nozero` keyword to remove
 # the zero level (useful for single-color `~proplot.axes.PlotAxes.contour` plots).
 
 # %%
@@ -529,12 +529,11 @@
 #
 # Additionally, `similar to xarray
 # <http://xarray.pydata.org/en/stable/user-guide/plotting.html#colormaps>`__,
-# proplot can automatically detect "diverging" datasets. That is, the
-# `~proplot.axes.PlotAxes` 2D plot commands will by default apply the diverging colormap
-# :rc:`cmap.diverging` (rather than the default colormap :rc:`cmap.sequential`) and
-# the diverging normalizer `~proplot.colors.DivergingNorm` (rather than the
-# linear normalizer `~matplotlib.colors.Normalize` -- see :ref:`above <ug_norm>`)
-# if the following conditions are met:
+# proplot can automatically detect "diverging" datasets. By default, the
+# `~proplot.axes.PlotAxes` 2D plot commands will apply the diverging colormap
+# :rc:`cmap.diverging` (rather than :rc:`cmap.sequential`) and the diverging
+# normalizer `~proplot.colors.DivergingNorm` (rather than `~matplotlib.colors.Normalize`
+# -- see :ref:`above <ug_norm>`) if the following conditions are met:
 #
 # #. If discrete levels are enabled (see :ref:`above <ug_discrete>`) and the
 #    level list includes at least 2 negative and 2 positive values.
diff --git a/docs/basics.py b/docs/basics.py
@@ -223,8 +223,8 @@
 # `two different interfaces <https://matplotlib.org/stable/api/index.html>`__:
 # an object-oriented interface and a MATLAB-style `~matplotlib.pyplot` interface
 # (which uses the object-oriented interface internally). Plotting with proplot is just
-# like plotting with matplotlib's *object-oriented* interface. Proplot builds upon
-# the `proplot.axes.Axes` class with an intermediate `proplot.axes.PlotAxes` subclass.
+# like plotting with matplotlib's *object-oriented* interface. The added plotting
+# features are implemented with an intermediate `proplot.axes.PlotAxes` subclass.
 # This subclass adds several new plotting commands and adds new features to existing
 # commands. These additions do not change the usage or syntax of existing commands,
 # which means a shallow learning curve for the average matplotlib user.
@@ -275,12 +275,12 @@
 # arguments accepted by ``format`` fall into the following groups:
 #
 # * Figure settings. These are related to row labels, column labels, and
-#   figure "super" titles -- for example, ``fig.format(suptitle='Super title')``
-#   changes the "super" title. See e.g. `proplot.figure.Figure.format` for details.
+#   figure "super" titles -- for example, ``fig.format(suptitle='Super title')``.
+#   See `proplot.figure.Figure.format` for details.
 #
 # * General axes settings. These are related to background patches,
 #   a-b-c labels, and axes titles -- for example, ``ax.format(title='Title')``
-#   changes the axes title. See e.g. `proplot.axes.Axes.format` for details.
+#   See `proplot.axes.Axes.format` for details.
 #
 # * Cartesian axes settings (valid only for `~proplot.axes.CartesianAxes`).
 #   These are related to *x* and *y* axis ticks, spines, bounds, and labels --
@@ -363,8 +363,7 @@
 # * `~proplot.gridspec.SubplotGrid` permits array-like 2D indexing, e.g.
 #   ``axs[1, 0]``. Indexing the `~proplot.gridspec.SubplotGrid` is similar
 #   to indexing a `~proplot.gridspec.GridSpec`. The result is a
-#   `~proplot.gridspec.SubplotGrid` of subplots that occupy the indexed
-#   `~proplot.gridspec.GridSpec` slot(s).
+#   `~proplot.gridspec.SubplotGrid` of subplots that occupy the indexed slot(s).
 # * `~proplot.gridspec.SubplotGrid` permits list-like 1D indexing, e.g. ``axs[0]``.
 #   The default order can be switched from row-major to column-major by passing
 #   ``order='F'`` to `~proplot.ui.subplots`.
@@ -376,10 +375,13 @@
 # `~proplot.figure.Figure.add_subplot`, a `~proplot.gridspec.SubplotGrid` containing
 # the numbered subplots is available via the `proplot.figure.Figure.subplotgrid`
 # property. `~proplot.gridspec.SubplotGrid` is especially useful because it lets you
-# call ``format``, ``colorbar``, ``legend``, ``panel``, ``inset``, and the various
-# twin axis commands simultaneously for all subplots in the grid. In the below
-# example, we use `proplot.gridspec.SubplotGrid.format` on the grid returned
-# by `~proplot.ui.subplots` to format several subplots all at once.
+# call e.g. `~proplot.gridspec.SubplotGrid.format`,
+# `~proplot.gridspec.SubplotGrid.panel_axes`,
+# `~proplot.gridspec.SubplotGrid.inset_axes`,
+# `~proplot.gridspec.SubplotGrid.altx`,
+# and `~proplot.gridspec.SubplotGrid.alty` for all subplots in the grid at once.
+# In the below example, we use `proplot.gridspec.SubplotGrid.format` on the grid
+# returned by `~proplot.ui.subplots` to format different groups of subplots.
 
 # %%
 import proplot as pplt
diff --git a/docs/cartesian.py b/docs/cartesian.py
@@ -297,10 +297,10 @@
 # `~matplotlib.spines.Spine.set_position` value, e.g. ``'zero'`` or
 # ``('axes', 1.5)``. The top or right spine is used when the coordinate is
 # more than halfway across the axes. This is often convenient when passing
-# e.g. `loc` or `spineloc` to :ref:`"alternate" axes commands <ug_alt>`.
-# These keywords provide the functionality of matplotlib's
-# `~matplotlib.axis.YAxis.tick_left`, `~matplotlib.axis.YAxis.tick_right`,
-# `~matplotlib.axis.XAxis.tick_top`, and `~matplotlib.axis.XAxis.tick_bottom`,
+# e.g. `loc` to :ref:`"alternate" axes commands <ug_alt>`. These keywords
+# provide the functionality of matplotlib's `~matplotlib.axis.YAxis.tick_left`,
+# `~matplotlib.axis.YAxis.tick_right`, `~matplotlib.axis.XAxis.tick_top`, and
+# `~matplotlib.axis.XAxis.tick_bottom`, and `~matplotlib.spines.Spine.set_position`,
 # but with additional flexibility.
 
 # %%
diff --git a/docs/subplots.py b/docs/subplots.py
@@ -304,8 +304,8 @@
 #
 # Figures with lots of subplots often have :ref:`redundant labels <why_redundant>`.
 # To help address this, the matplotlib command `matplotlib.pyplot.subplots` includes
-# `sharex` and `sharey` keywords that permit sharing axis limits, ticks, and tick labels
-# between like rows and columns of subplots. Proplot builds on this feature by...
+# `sharex` and `sharey` keywords that permit sharing axis limits and ticks between
+# like rows and columns of subplots. Proplot builds on this feature by...
 #
 # #. Automatically sharing axes between subplots and :ref:`panels <ug_panels>`
 #    occupying the same rows or columns of the `~proplot.gridspec.GridSpec`. This
diff --git a/proplot/internals/rcsetup.py b/proplot/internals/rcsetup.py
@@ -34,8 +34,8 @@
 BLACK = 'black'
 CYCLE = 'colorblind'
 CMAPCYC = 'twilight'
-CMAPDIV = 'burd'
-CMAPSEQ = 'fire'
+CMAPDIV = 'BuRd'
+CMAPSEQ = 'Fire'
 CMAPCAT = 'colorblind10'
 DIVERGING = 'div'
 FRAMEALPHA = 0.8  # legend and colorbar

Original file line number	Diff line number	Diff line change
`@@ -304,8 +304,8 @@`
`304`	`304`	`#`
`305`	`305`	# Figures with lots of subplots often have :ref:`redundant labels <why_redundant>`.
`306`	`306`	# To help address this, the matplotlib command `matplotlib.pyplot.subplots` includes
`307`		-# `sharex` and `sharey` keywords that permit sharing axis limits, ticks, and tick labels
`308`		`-# between like rows and columns of subplots. Proplot builds on this feature by...`
	`307`	+# `sharex` and `sharey` keywords that permit sharing axis limits and ticks between
	`308`	`+# like rows and columns of subplots. Proplot builds on this feature by...`
`309`	`309`	`#`
`310`	`310`	# #. Automatically sharing axes between subplots and :ref:`panels <ug_panels>`
`311`	`311`	# occupying the same rows or columns of the `~proplot.gridspec.GridSpec`. This