Doc link fix clean

docs/1dplots.py

@@ -24,7 +24,7 @@
 # 1D plotting commands
 # ====================
 #
-# ultraplot adds :ref:`several new features <why_plotting>` to matplotlib's
+# UltraPlot adds :ref:`several new features <why_plotting>` to matplotlib's
 # plotting commands using the intermediate `~ultraplot.axes.PlotAxes` class.
 # For the most part, these additions represent a *superset* of matplotlib -- if
 # you are not interested, you can use the plotting commands just like you would
@@ -54,7 +54,7 @@
 # .. note::
 #
 #    By default, when choosing the *x* or *y* axis limits,
-#    ultraplot ignores out-of-bounds data along the other axis if it was explicitly
+#    UltraPlot ignores out-of-bounds data along the other axis if it was explicitly
 #    fixed by `~matplotlib.axes.Axes.set_xlim` or `~matplotlib.axes.Axes.set_ylim` (or,
 #    equivalently, by passing `xlim` or `ylim` to `ultraplot.axes.CartesianAxes.format`).
 #    This can be useful if you wish to restrict the view along a "dependent" variable
@@ -157,7 +157,7 @@
 #    ``ax.plot(y='y', data=dataset)`` are translated to ``ax.plot(dataset['y'])``.
 #    This is the preferred input style for most `seaborn`_ plotting commands.
 #    Also, if you pass a `pint.Quantity` or `~xarray.DataArray`
-#    containing a `pint.Quantity`, ultraplot will automatically call
+#    containing a `pint.Quantity`, UltraPlot will automatically call
 #    `~pint.UnitRegistry.setup_matplotlib` so that the axes become unit-aware.
 
 # %%
@@ -510,7 +510,7 @@
 #    expressed with the `width` keyword. Note that bar widths can also be passed
 #    as a third positional argument.
 #    Additionally, matplotlib bar widths are always expressed in data units,
-#    while ultraplot bar widths are expressed in step size-relative units by
+#    while UltraPlot bar widths are expressed in step size-relative units by
 #    default. For example, ``width=1`` with a dependent coordinate step
 #    size of ``2`` fills 100% of the space between each bar rather than 50%. This
 #    can be disabled by passing ``absolute_width=True`` to `~ultraplot.axes.Axes.bar`

docs/2dplots.py

@@ -24,7 +24,7 @@
 # 2D plotting commands
 # ====================
 #
-# ultraplot adds :ref:`several new features <why_plotting>` to matplotlib's
+# UltraPlot adds :ref:`several new features <why_plotting>` to matplotlib's
 # plotting commands using the intermediate `~ultraplot.axes.PlotAxes` class.
 # For the most part, these additions represent a *superset* of matplotlib -- if
 # you are not interested, you can use the plotting commands just like you would
@@ -34,7 +34,7 @@
 #
 # .. important::
 #
-#    By default, ultraplot automatically adjusts the width of
+#    By default, UltraPlot automatically adjusts the width of
 #    `~ultraplot.axes.PlotAxes.contourf` and `~ultraplot.axes.PlotAxes.pcolor` edges
 #    to eliminate the appearance of `"white lines" in saved vector graphic files
 #    <https://github.com/jklymak/contourfIssues>`__. However, this can significantly
@@ -67,7 +67,7 @@
 # .. note::
 #
 #    By default, when choosing the colormap :ref:`normalization
-#    range <ug_apply_cmap>`, ultraplot ignores data outside the *x* or *y* axis
+#    range <ug_apply_cmap>`, UltraPlot ignores data outside the *x* or *y* axis
 #    limits if they were previously fixed by `~matplotlib.axes.Axes.set_xlim` or
 #    `~matplotlib.axes.Axes.set_ylim` (or, equivalently, by passing `xlim` or
 #    `ylim` to `ultraplot.axes.CartesianAxes.format`). This can be useful if you
@@ -180,7 +180,7 @@
 #    ``ax.plot(y='y', data=dataset)`` are translated to ``ax.plot(dataset['y'])``.
 #    This is the preferred input style for most `seaborn`_ plotting commands.
 #    Also, if you pass a `pint.Quantity` or `~xarray.DataArray`
-#    containing a `pint.Quantity`, ultraplot will automatically call
+#    containing a `pint.Quantity`, UltraPlot will automatically call
 #    `~pint.UnitRegistry.setup_matplotlib` so that the axes become unit-aware.
 
 # %%
@@ -252,7 +252,7 @@
 # You can also create on-the-fly "qualitative" `~ultraplot.colors.DiscreteColormap`\ s
 # by passing lists of colors to the keyword `c`, `color`, or `colors`.
 #
-# ultraplot defines :ref:`global defaults <ug_rc>` for four different colormap
+# UltraPlot defines :ref:`global defaults <ug_rc>` for four different colormap
 # types: :ref:`sequential <ug_cmaps_included>` (setting :rcraw:`cmap.sequential`),
 # :ref:`diverging <ug_cmaps_included>` (setting :rcraw:`cmap.diverging`),
 # :ref:`cyclic <ug_cmaps_included>` (setting :rcraw:`cmap.cyclic`),
@@ -347,7 +347,7 @@
 # Special normalizers
 # -------------------
 #
-# ultraplot includes two new :ref:`"continuous" normalizers <ug_norm>`. The
+# UltraPlot includes two new :ref:`"continuous" normalizers <ug_norm>`. The
 # `~ultraplot.colors.SegmentedNorm` normalizer 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,
@@ -429,7 +429,7 @@
 # Discrete levels
 # ---------------
 #
-# By default, ultraplot uses `~ultraplot.colors.DiscreteNorm` to "discretize"
+# By default, UltraPlot uses `~ultraplot.colors.DiscreteNorm` to "discretize"
 # the possible colormap colors for contour and pseudocolor `~ultraplot.axes.PlotAxes`
 # commands (e.g., `~ultraplot.axes.PlotAxes.contourf`, `~ultraplot.axes.PlotAxes.pcolor`).
 # This is analogous to `matplotlib.colors.BoundaryNorm`, except
@@ -529,7 +529,7 @@
 #
 # By default, colormaps are normalized to span from roughly the minimum
 # data value to the maximum data value. However in the presence of outliers,
-# this is not desirable. ultraplot adds the `robust` keyword to change this
+# this is not desirable. UltraPlot adds the `robust` keyword to change this
 # behavior, inspired by the `xarray keyword
 # <http://xarray.pydata.org/en/stable/user-guide/plotting.html#robust>`__
 # of the same name. Passing ``robust=True`` to a `~ultraplot.axes.PlotAxes`
@@ -543,7 +543,7 @@
 #
 # Additionally, `similar to xarray
 # <http://xarray.pydata.org/en/stable/user-guide/plotting.html#colormaps>`__,
-# ultraplot can automatically detect "diverging" datasets. By default,
+# UltraPlot can automatically detect "diverging" datasets. By default,
 # the 2D `~ultraplot.axes.PlotAxes` commands will apply the diverging colormap
 # :rc:`cmap.diverging` (rather than :rc:`cmap.sequential`) and the diverging
 # normalizer `~ultraplot.colors.DivergingNorm` (rather than `~matplotlib.colors.Normalize`

docs/basics.py

@@ -24,10 +24,10 @@
 # Creating figures
 # ----------------
 #
-# ultraplot works by `subclassing
+# UltraPlot works by `subclassing
 # <https://docs.python.org/3/tutorial/classes.html#inheritance>`__
-# three fundamental matplotlib classes: `ultraplot.figure.Figure` replaces
-# `matplotlib.figure.Figure`, `ultraplot.axes.Axes` replaces `matplotlib.axes.Axes`,
+# three fundamental matplotlib classes: :class:`ultraplot.figure.Figure` replaces
+# :class:`matplotlib.figure.Figure`, :class:`ultraplot.axes.Axes` replaces :class:`matplotlib.axes.Axes`,
 # and `ultraplot.gridspec.GridSpec` replaces `matplotlib.gridspec.GridSpec`
 # (see this `tutorial
 # <https://matplotlib.org/stable/tutorials/intermediate/gridspec.html>`__
@@ -44,11 +44,11 @@
 # %% [raw] raw_mimetype="text/restructuredtext"
 # .. note::
 #
-#    ultraplot changes the default :rcraw:`figure.facecolor`
+#    UltraPlot changes the default :rcraw:`figure.facecolor`
 #    so that the figure backgrounds shown by the `matplotlib backend
 #    <https://matplotlib.org/faq/usage_faq#what-is-a-backend>`__ are light gray
 #    (the :rcraw:`savefig.facecolor` applied to saved figures is still white).
-#    ultraplot also controls the appearance of figures in Jupyter notebooks
+#    UltraPlot also controls the appearance of figures in Jupyter notebooks
 #    using the new :rcraw:`inlineformat` setting, which is passed to
 #    `~ultraplot.config.config_inline_backend` on import. This
 #    imposes a higher-quality default `"inline" format
@@ -57,7 +57,7 @@
 #    ``InlineBackend.print_figure_kwargs``, ensuring that the figures you save
 #    look like the figures displayed by the backend.
 #
-#    ultraplot also changes the default :rcraw:`savefig.format`
+#    UltraPlot also changes the default :rcraw:`savefig.format`
 #    from PNG to PDF for the following reasons:
 #
 #        #. Vector graphic formats are infinitely scalable.
@@ -67,7 +67,7 @@
 #        #. The EPS format is outdated and does not support transparent graphic
 #           elements.
 #
-#    In case you *do* need a raster format like PNG, ultraplot increases the
+#    In case you *do* need a raster format like PNG, UltraPlot increases the
 #    default :rcraw:`savefig.dpi` to 1000 dots per inch, which is
 #    `recommended <https://www.pnas.org/page/authors/format>`__ by most journals
 #    as the minimum resolution for figures containing lines and text. See the
@@ -98,7 +98,7 @@
 # `ultraplot.axes.Axes`. To add subplots all at once, use
 # `ultraplot.figure.Figure.add_subplots` (or its shorthand,
 # `ultraplot.figure.Figure.subplots`). Note that under the hood, the top-level
-# ultraplot command `~ultraplot.ui.subplots` simply calls `~ultraplot.ui.figure`
+# UltraPlot command `~ultraplot.ui.subplots` simply calls `~ultraplot.ui.figure`
 # followed by `ultraplot.figure.Figure.add_subplots`.
 #
 # * With no arguments, `~ultraplot.figure.Figure.add_subplots` returns a subplot
@@ -135,7 +135,7 @@
 #
 # .. warning::
 #
-#    ultraplot employs :ref:`automatic axis sharing <ug_share>` by default. This lets
+#    UltraPlot employs :ref:`automatic axis sharing <ug_share>` by default. This lets
 #    subplots in the same row or column share the same axis limits, scales, ticks,
 #    and labels. This is often convenient, but may be annoying for some users. To
 #    keep this feature turned off, simply :ref:`change the default settings <ug_rc>`
@@ -232,7 +232,7 @@
 # -----------------
 #
 # If you create subplots all-at-once with e.g. `~ultraplot.ui.subplots`,
-# ultraplot returns a `~ultraplot.gridspec.SubplotGrid` of subplots. This list-like,
+# UltraPlot returns a `~ultraplot.gridspec.SubplotGrid` of subplots. This list-like,
 # array-like object provides some useful features and unifies the behavior of the
 # three possible return types used by `matplotlib.pyplot.subplots`:
 #
@@ -307,8 +307,8 @@
 # a python-style object-oriented interface with axes-level commands
 # like `matplotlib.axes.Axes.plot`, and a MATLAB-style `~matplotlib.pyplot` interface
 # with global commands like `matplotlib.pyplot.plot` that track the "current" axes.
-# ultraplot builds upon the python-style interface using the `ultraplot.axes.PlotAxes`
-# class. Since every axes used by ultraplot is a child of `~ultraplot.axes.PlotAxes`, we
+# UltraPlot builds upon the python-style interface using the `ultraplot.axes.PlotAxes`
+# class. Since every axes used by UltraPlot is a child of `~ultraplot.axes.PlotAxes`, we
 # are able to add features directly to the axes-level commands rather than relying
 # on a separate library of commands  (note that while some of these features may be
 # accessible via `~matplotlib.pyplot` commands, this is not officially supported).
@@ -368,7 +368,7 @@
 # that tracks current axes and provides global commands like
 # `matplotlib.pyplot.title`.
 #
-# ultraplot provides the ``format`` command as an
+# UltraPlot provides the ``format`` command as an
 # alternative "python-style" command for formatting a variety of plot elements.
 # While matplotlib's one-liner commands still work, ``format`` only needs to be
 # called once and tends to cut down on boilerplate code. You can call
@@ -477,7 +477,7 @@
 # :ref:`ultraplot settings <ug_rcultraplot>`. The matplotlib-specific settings are
 # stored in `~ultraplot.config.rc_matplotlib` (our name for `matplotlib.rcParams`) and
 # the ultraplot-specific settings are stored in `~ultraplot.config.rc_ultraplot`.
-# ultraplot also includes a :rcraw:`style` setting that can be used to
+# UltraPlot also includes a :rcraw:`style` setting that can be used to
 # switch between `matplotlib stylesheets
 # <https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html>`__.
 # See the :ref:`configuration section <ug_config>` for details.

docs/cartesian.py

@@ -126,7 +126,7 @@
 # directly, they are available in the top-level namespace
 # (e.g., ``xformatter=uplt.SciFormatter(...)`` is allowed).
 #
-# ultraplot also changes the default tick formatter to
+# UltraPlot also changes the default tick formatter to
 # `~ultraplot.ticker.AutoFormatter`. This class trims trailing zeros by
 # default, can optionally omit or wrap tick values within particular
 # number ranges, and can add prefixes and suffixes to each label. See
@@ -384,7 +384,7 @@
 # coordinate system. To change the axis scale, pass e.g. ``xscale='log'`` or
 # ``yscale='log'`` to `~ultraplot.axes.Axes.format`. This is powered by the
 # `~ultraplot.constructor.Scale` :ref:`constructor function <why_constructor>`.
-# ultraplot makes several changes to the axis scale API:
+# UltraPlot makes several changes to the axis scale API:
 #
 # * The `~ultraplot.ticker.AutoFormatter` formatter is now used for all axis scales
 #   by default, including ``'log'`` and ``'symlog'``. Matplotlib's behavior can
@@ -398,14 +398,14 @@
 #   addition to "registered" names like ``'log'``.
 # * While matplotlib axis scales must be instantiated with an
 #   `~matplotlib.axis.Axis` instance (for backwards compatibility reasons),
-#   ultraplot axis scales can be instantiated without the axis instance
+#   UltraPlot axis scales can be instantiated without the axis instance
 #   (e.g., ``uplt.LogScale()`` instead of ``uplt.LogScale(ax.xaxis)``).
 # * The default `subs` for the ``'symlog'`` axis scale is now ``np.arange(1, 10)``,
 #   and the default `linthresh` is now ``1``. Also the ``'log'`` and ``'symlog'``
 #   axis scales now accept the keywords `base`, `linthresh`, `linscale`, and
 #   `subs` rather than keywords with trailing ``x`` or ``y``.
 #
-# ultraplot also includes a few new axis scales. The ``'cutoff'`` scale
+# UltraPlot also includes a few new axis scales. The ``'cutoff'`` scale
 # `~ultraplot.scale.CutoffScale` is useful when the statistical distribution
 # of your data is very unusual. The ``'sine'`` scale `~ultraplot.scale.SineLatitudeScale`
 # scales the axis with a sine function (resulting in an area-weighted spherical latitude
@@ -547,13 +547,13 @@
 #
 # The `matplotlib.axes.Axes` class includes `~matplotlib.axes.Axes.twinx`
 # and `~matplotlib.axes.Axes.twiny` commands for drawing "twin" *x* and
-# *y* axes in the same subplot. ultraplot expands on these commands and adds
+# *y* axes in the same subplot. UltraPlot expands on these commands and adds
 # the arguably more intuitive `~ultraplot.axes.CartesianAxes.altx` and
 # `~ultraplot.axes.CartesianAxes.alty` options. Here `~ultraplot.axes.CartesianAxes.altx`
 # is equivalent to `~ultraplot.axes.CartesianAxes.twiny` (makes an alternate *x*
 # axes and an identical twin *y* axes) and `~ultraplot.axes.CartesianAxes.alty`
 # is equivalent to `~ultraplot.axes.CartesianAxes.twinx` (makes an alternate *y*
-# axes and an identical twin *x* axes). The ultraplot versions can be quickly
+# axes and an identical twin *x* axes). The UltraPlot versions can be quickly
 # formatted by passing `ultraplot.axes.CartesianAxes.format` keyword arguments
 # to the commands (e.g., ``ax.alty(ycolor='red')`` or, since the ``y`` prefix in
 # this context is redundant, just ``ax.alty(color='red')``). They also enforce
@@ -562,7 +562,7 @@
 #
 # .. note::
 #
-#    Unlike matplotlib, ultraplot adds alternate axes as `children
+#    Unlike matplotlib, UltraPlot adds alternate axes as `children
 #    <https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.add_child_axes.html>`__
 #    of the original axes. This helps simplify the :ref:`tight layout algorithm
 #    <ug_tight>` but means that the drawing order is controlled by the difference

docs/colorbars_legends.py

@@ -18,7 +18,7 @@
 # Colorbars and legends
 # =====================
 #
-# ultraplot includes some useful changes to the matplotlib API that make
+# UltraPlot includes some useful changes to the matplotlib API that make
 # working with colorbars and legends :ref:`easier <why_colorbars_legends>`.
 # Notable features include "inset" colorbars, "outer" legends,
 # on-the-fly colorbars and legends, colorbars built from artists,
@@ -36,7 +36,7 @@
 # posssible using the somewhat opaque `bbox_to_anchor` keyword (see `here
 # <https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#legend-location>`__)
 # and "inset" colorbars are not possible without manually creating and positioning
-# the associated axes. ultraplot tries to improve this behavior:
+# the associated axes. UltraPlot tries to improve this behavior:
 #
 # * `ultraplot.axes.Axes.legend` can draw both "inset" legends when you request an inset
 #   location (e.g., ``loc='upper right'`` or the shorthand ``loc='ur'``) and "outer"
@@ -45,7 +45,7 @@
 #   or colorbars on one side, they are "stacked" on top of each other. Unlike using
 #   `bbox_to_anchor`, the "outer" legend position is adjusted automatically when the
 #   :ref:`tight layout algorithm <ug_tight>` is active.
-# * ultraplot adds the axes command `ultraplot.axes.Axes.colorbar`,
+# * UltraPlot adds the axes command `ultraplot.axes.Axes.colorbar`,
 #   analogous to `ultraplot.axes.Axes.legend` and equivalent to
 #   calling `ultraplot.figure.Figure.colorbar` with an `ax` keyword.
 #   `~ultraplot.axes.Axes.colorbar` can draw both "outer" colorbars when you request
@@ -65,14 +65,14 @@
 #
 # .. important::
 #
-#    Unlike matplotlib, ultraplot adds "outer" colorbars and legends by allocating
+#    Unlike matplotlib, UltraPlot adds "outer" colorbars and legends by allocating
 #    new rows and columns in the `~ultraplot.gridspec.GridSpec` rather than
 #    "stealing" space from the parent subplot (note that subsequently indexing
 #    the `~ultraplot.gridspec.GridSpec` will ignore the slots allocated for
 #    colorbars and legends). This approach means that "outer" colorbars and
 #    legends :ref:`do not affect subplot aspect ratios <ug_autosize>`
 #    and :ref:`do not affect subplot spacing <ug_tight>`, which lets
-#    ultraplot avoid relying on complicated `"constrained layout" algorithms
+#    UltraPlot avoid relying on complicated `"constrained layout" algorithms
 #    <https://matplotlib.org/stable/tutorials/intermediate/constrainedlayout_guide.html>`__
 #    and tends to improve the appearance of figures with even the most
 #    complex arrangements of subplots, colorbars, and legends.
@@ -302,12 +302,12 @@
 #   keyword with :ref:`1D commands <ug_1dplots>` like `~ultraplot.axes.PlotAxes.plot`.
 # * The associated :ref:`colormap normalizer <ug_norm>` can be specified with the
 #   `vmin`, `vmax`, `norm`, and `norm_kw` keywords. The `~ultraplot.colors.DiscreteNorm`
-#   levels can be specified with `values`, or ultraplot will infer them from the
+#   levels can be specified with `values`, or UltraPlot will infer them from the
 #   `~matplotlib.artist.Artist` labels (non-numeric labels will be applied to
 #   the colorbar as tick labels). This can be useful for labeling discrete plot
 #   elements that bear some numeric relationship to each other.
 #
-# ultraplot also includes improvements for adding ticks and tick labels to colorbars.
+# UltraPlot also includes improvements for adding ticks and tick labels to colorbars.
 # Similar to `ultraplot.axes.CartesianAxes.format`, you can flexibly specify
 # major tick locations, minor tick locations, and major tick labels using the
 # `locator`, `minorlocator`, `formatter`, `ticks`, `minorticks`, and `ticklabels`
@@ -317,7 +317,7 @@
 # are restricted based on the axis length using `~ultraplot.ticker.DiscreteLocator`.
 # You can easily toggle minor ticks using ``tickminor=True``.
 #
-# Similar to :ref:`axes panels <ug_panels>`, the geometry of ultraplot colorbars is
+# Similar to :ref:`axes panels <ug_panels>`, the geometry of UltraPlot colorbars is
 # specified with :ref:`physical units <ug_units>` (this helps avoid the common issue
 # where colorbars appear "too skinny" or "too fat" and preserves their appearance
 # when the figure size changes). You can specify the colorbar width locally using the