Move wrappers.py to axes/plot.py, prepare to add to classes

Author: lukelbd

docs/1dplots.py

@@ -40,7 +40,7 @@
 # It is often desirable to use different `property cycles
 # <https://matplotlib.org/tutorials/intermediate/color_cycle.html#sphx-glr-tutorials-intermediate-color-cycle-py>`__
 # for different axes or different plot elements. To enable this, the
-# `~proplot.wrappers.cycle_changer` adds the `cycle` and `cycle_kw` to the 1D
+# `~proplot.axes.cycle_changer` adds the `cycle` and `cycle_kw` to the 1D
 # plotting methods. These arguments are passed to the
 # `~proplot.constructor.Cycle` constructor function, and the resulting property
 # cycle is used to style the input data. ProPlot iterates through property
@@ -76,9 +76,9 @@
 # Standardized arguments
 # ----------------------
 #
-# The `~proplot.wrappers.standardize_1d` wrapper is used to standardize
+# The `~proplot.axes.standardize_1d` wrapper is used to standardize
 # positional arguments across all 1D plotting methods.
-# `~proplot.wrappers.standardize_1d` allows you to optionally omit *x*
+# `~proplot.axes.standardize_1d` allows you to optionally omit *x*
 # coordinates, in which case they are inferred from the data. It also permits
 # passing 2D *y* coordinate arrays to any plotting method, in which case the
 # plotting method is called for each column of the array.
@@ -122,7 +122,7 @@
 # Pandas and xarray integration
 # -----------------------------
 #
-# The `~proplot.wrappers.standardize_1d` wrapper integrates 1D plotting
+# The `~proplot.axes.standardize_1d` wrapper integrates 1D plotting
 # methods with pandas `~pandas.DataFrame`\ s and xarray `~xarray.DataArray`\ s.
 # When you pass a DataFrame or DataArray to any plotting command, the x-axis
 # label, y-axis label, legend label, colorbar label, and/or title are
@@ -185,20 +185,20 @@
 # Adding error bars
 # -----------------
 #
-# The `~proplot.wrappers.add_errorbars` wrapper lets you draw error bars
+# The `~proplot.axes.add_errorbars` wrapper lets you draw error bars
 # on-the-fly by passing certain keyword arguments to
 # `~matplotlib.axes.Axes.plot`, `~matplotlib.axes.Axes.scatter`,
 # `~matplotlib.axes.Axes.bar`, or `~matplotlib.axes.Axes.barh`.
 #
 # If you pass 2D arrays to these methods with ``means=True`` or
 # ``medians=True``, the means or medians of each column are drawn as points,
 # lines, or bars, and error bars are drawn to represent the spread in each
-# column. `~proplot.wrappers.add_errorbars` lets you draw both thin error
+# column. `~proplot.axes.add_errorbars` lets you draw both thin error
 # "bars" with optional whiskers, and thick error "boxes" overlayed on top of
 # these bars (this can be used to represent different percentil ranges).
 # Instead of using 2D arrays, you can also pass error bar coordinates
 # *manually* with the `bardata` and `boxdata` keyword arguments. See
-# `~proplot.wrappers.add_errorbars` for details.
+# `~proplot.axes.add_errorbars` for details.
 
 # %%
 import proplot as plot
@@ -254,8 +254,8 @@
 # ------------------------
 #
 # The `~matplotlib.axes.Axes.bar` and `~matplotlib.axes.Axes.barh` methods
-# are wrapped by `~proplot.wrappers.bar_wrapper`,
-# `~proplot.wrappers.cycle_changer`, and `~proplot.wrappers.standardize_1d`.
+# are wrapped by `~proplot.axes.bar_wrapper`,
+# `~proplot.axes.cycle_changer`, and `~proplot.axes.standardize_1d`.
 # You can now *group* or *stack* columns of data by passing 2D arrays to
 # `~matplotlib.axes.Axes.bar` or `~matplotlib.axes.Axes.barh`, just like in
 # `pandas`. Also, `~matplotlib.axes.Axes.bar` and `~matplotlib.axes.Axes.barh`
@@ -266,8 +266,8 @@
 # `~proplot.axes.Axes.areax` methods. These are alises for
 # `~matplotlib.axes.Axes.fill_between` and
 # `~matplotlib.axes.Axes.fill_betweenx`, which are now wrapped by
-# `~proplot.wrappers.fill_between_wrapper` and
-# `~proplot.wrappers.fill_betweenx_wrapper`. You can now *stack* or *overlay*
+# `~proplot.axes.fill_between_wrapper` and
+# `~proplot.axes.fill_betweenx_wrapper`. You can now *stack* or *overlay*
 # columns of data by passing 2D arrays to `~proplot.axes.Axes.area` and
 # `~proplot.axes.Axes.areax`, just like in `pandas`. You can also now draw
 # area plots that *change color* when the fill boundaries cross each other by
@@ -350,9 +350,9 @@
 # --------------------------
 #
 # The `~matplotlib.axes.Axes.boxplot` and `~matplotlib.axes.Axes.violinplot`
-# methods are now wrapped with `~proplot.wrappers.boxplot_wrapper`,
-# `~proplot.wrappers.violinplot_wrapper`, `~proplot.wrappers.cycle_changer`,
-# and `~proplot.wrappers.standardize_1d`. These wrappers add some useful
+# methods are now wrapped with `~proplot.axes.boxplot_wrapper`,
+# `~proplot.axes.violinplot_wrapper`, `~proplot.axes.cycle_changer`,
+# and `~proplot.axes.standardize_1d`. These wrappers add some useful
 # options and apply aesthetically pleasing default settings. They also
 # automatically apply axis labels based on the `~pandas.DataFrame` column
 # labels or the input *x* coordinate labels.
@@ -456,8 +456,8 @@
 # -------------
 #
 # The `~matplotlib.axes.Axes.scatter` method is now wrapped by
-# `~proplot.wrappers.scatter_wrapper`, `~proplot.wrappers.cycle_changer`, and
-# `~proplot.wrappers.standardize_1d`. This means that
+# `~proplot.axes.scatter_wrapper`, `~proplot.axes.cycle_changer`, and
+# `~proplot.axes.standardize_1d`. This means that
 # `~matplotlib.axes.Axes.scatter` now accepts 2D arrays, just like
 # `~matplotlib.axes.Axes.plot`. Also, successive calls to
 # `~matplotlib.axes.Axes.scatter` now use the property cycler properties

docs/2dplots.py

@@ -39,14 +39,14 @@
 #
 # It is often desirable to create ProPlot colormaps on-the-fly, without
 # explicitly using the `~proplot.constructor.Colormap` constructor function.
-# To enable this, the `~proplot.wrappers.cmap_changer` wrapper adds the
+# To enable this, the `~proplot.axes.cmap_changer` wrapper adds the
 # `cmap` and `cmap_kw` arguments to every 2D plotting method. These
 # arguments are passed to the `~proplot.constructor.Colormap` constructor
 # function, and the resulting colormap is used for the input data. For
 # example, to create and apply a monochromatic colormap, you can simply use
 # ``cmap='color name'``.
 
-# The `~proplot.wrappers.cmap_changer` wrapper also
+# The `~proplot.axes.cmap_changer` wrapper also
 # adds the `norm` and `norm_kw` arguments. They are passed to the
 # `~proplot.constructor.Norm` constructor function, and the resulting
 # normalizer is used for the input data. For more information on colormaps
@@ -80,7 +80,7 @@
 # Discrete colormap levels
 # ------------------------
 #
-# The `~proplot.wrappers.cmap_changer` wrapper also applies the
+# The `~proplot.axes.cmap_changer` wrapper also applies the
 # `~proplot.colors.DiscreteNorm` normalizer to every colormap plot.
 # `~proplot.colors.DiscreteNorm` converts data values to colormap colors by (1)
 # transforming data using an arbitrary *continuous* normalizer (e.g.
@@ -212,7 +212,7 @@
 # Standardized arguments
 # ----------------------
 #
-# The `~proplot.wrappers.standardize_2d` wrapper is used to standardize
+# The `~proplot.axes.standardize_2d` wrapper is used to standardize
 # positional arguments across all 2D plotting methods.  Among other things,
 # it guesses coordinate *edges* for `~matplotlib.axes.Axes.pcolor` and
 # `~matplotlib.axes.Axes.pcolormesh` plots when you supply coordinate
@@ -255,7 +255,7 @@
 # Pandas and xarray integration
 # -----------------------------
 #
-# The `~proplot.wrappers.standardize_2d` wrapper also integrates 2D
+# The `~proplot.axes.standardize_2d` wrapper also integrates 2D
 # plotting methods with pandas `~pandas.DataFrame`\ s and xarray
 # `~xarray.DataArray`\ s. When you pass a DataFrame or DataArray to any
 # plotting command, the x-axis label, y-axis label, legend label, colorbar
@@ -328,19 +328,19 @@
 # Contour and gridbox labels
 # --------------------------
 #
-# The `~proplot.wrappers.cmap_changer` wrapper also allows you to quickly add
+# The `~proplot.axes.cmap_changer` wrapper also allows you to quickly add
 # *labels* to `~proplot.axes.Axes.heatmap`, `~matplotlib.axes.Axes.pcolor`,
 # `~matplotlib.axes.Axes.pcolormesh`, `~matplotlib.axes.Axes.contour`, and
 # `~matplotlib.axes.Axes.contourf` plots by simply using ``labels=True``.
 # Critically, the label text is colored black or white depending on the
 # luminance of the underlying grid box or filled contour.
 #
-# `~proplot.wrappers.cmap_changer` draws contour labels with
+# `~proplot.axes.cmap_changer` draws contour labels with
 # `~matplotlib.axes.Axes.clabel` and grid box labels with
 # `~matplotlib.axes.Axes.text`. You can pass keyword arguments to these
 # functions using the `labels_kw` dictionary keyword argument, and change the
 # label precision with the `precision` keyword argument. See
-# `~proplot.wrappers.cmap_changer` for details.
+# `~proplot.axes.cmap_changer` for details.
 
 # %%
 import proplot as plot

docs/api.rst

@@ -28,7 +28,7 @@ Axes classes
 
 .. automodsumm:: proplot.axes
    :toctree: api
-   :skip: ProjAxes, XYAxes
+   :classes-only:
 
 
 Constructor functions
@@ -52,13 +52,15 @@ Configuration tools
 Plotting wrappers
 =================
 
-.. automodule:: proplot.wrappers
+.. automodule:: proplot.axes.plot
 
-.. automodsumm:: proplot.wrappers
+.. automodsumm:: proplot.axes
    :toctree: api
+   :functions-only:
+   :skip: ProjAxes, XYAxes
 
 
-Show functions
+Demo functions
 ==============
 
 .. automodule:: proplot.demos

docs/axis.py

@@ -269,8 +269,8 @@
 # %% [raw] raw_mimetype="text/restructuredtext"
 # .. _ug_scales:
 #
-# Axis scales
-# -----------
+# Changing the axis scale
+# -----------------------
 #
 # "Axis scales" like ``'linear'`` and ``'log'`` control the *x* and *y* axis
 # coordinate system. To change the axis scale, simply pass e.g.
@@ -448,8 +448,7 @@
 # In the latter case, the scale's transforms are used for the forward and
 # inverse functions, and the scale's default locators and formatters are used
 # for the default `~proplot.scale.FuncScale` locators and formatters.
-#
-# Notably, the "parent" axis scale is now *arbitrary* -- in the first example
+# Note that the "parent" axis scale is now arbitrary -- in the first example
 # shown below, we create a `~proplot.axes.CartesianAxes.dualx` axis for an
 # axis scaled by the ``'symlog'`` scale.
 

docs/colorbars_legends.py

@@ -43,8 +43,8 @@
 # `~matplotlib.axes.Axes.contourf`). To draw a legend or colorbar-legend in
 # one go, pass a location (e.g. ``legend='r'`` or ``colorbar='r'``) to
 # methods that accept a `cycle` argument (e.g. `~matplotlib.axes.Axes.plot`).
-# This feature is powered by the `~proplot.wrappers.cmap_changer` and
-# `~proplot.wrappers.cycle_changer` wrappers.
+# This feature is powered by the `~proplot.axes.cmap_changer` and
+# `~proplot.axes.cycle_changer` wrappers.
 #
 # Finally, just like matplotlib "inset" legends, ProPlot also supports
 # "inset" *colorbars*. To draw an inset colorbar, pass an inset location to
@@ -185,7 +185,7 @@
 #
 # The `~proplot.figure.Figure` `~proplot.figure.Figure.colorbar` and
 # `~proplot.axes.Axes` `~proplot.axes.Axes.colorbar` methods are wrapped by
-# `~proplot.wrappers.colorbar_wrapper`, which adds several new features.
+# `~proplot.axes.colorbar_wrapper`, which adds several new features.
 #
 # You can now draw colorbars from *lists of colors* or *lists of artists* by
 # passing a list instead of a mappable object. Colorbar minor ticks are now
@@ -243,7 +243,7 @@
 #
 # The `~proplot.figure.Figure` `~proplot.figure.Figure.legend` and
 # `~proplot.axes.Axes` `~proplot.axes.Axes.legend` methods are wrapped by
-# `~proplot.wrappers.legend_wrapper`, which adds several new features.
+# `~proplot.axes.legend_wrapper`, which adds several new features.
 #
 # You can draw legends with *centered legend rows*, either by passing
 # ``center=True`` or by passing *list of lists* of plot handles. This is

docs/colormaps.py

@@ -139,7 +139,7 @@
 # your convenience, most of these features can be accessed via the
 # `~proplot.constructor.Colormap` constructor function. Note that every
 # plotting command that accepts a `cmap` keyword passes it through this
-# function (see `~proplot.wrappers.cmap_changer`).
+# function (see `~proplot.axes.cmap_changer`).
 #
 # To make `~proplot.colors.PerceptuallyUniformColormap`\ s from scratch, you
 # have the following three options:

docs/cycles.py

@@ -58,7 +58,7 @@
 # commands like `~matplotlib.axes.Axes.plot` and
 # `~matplotlib.axes.Axes.scatter` now accept a `cycle` keyword arg, which is
 # passed to `~proplot.constructor.Cycle` (see
-# `~proplot.wrappers.cycle_changer`). To save your color cycle data and use
+# `~proplot.axes.cycle_changer`). To save your color cycle data and use
 # it every time ProPlot is imported, simply pass ``save=True`` to
 # `~proplot.constructor.Cycle`. If you want to change the global property
 # cycler, pass a *name* to the :rcraw:`cycle` setting or pass the result of

docs/projections.py

@@ -181,8 +181,8 @@
 #
 # The below example demonstrates how to plot geographic data with ProPlot.
 # It is mostly the same as cartopy, but with some new features powered by the
-# `~proplot.wrappers.standardize_2d`, `~proplot.wrappers.default_transform`,
-# and `~proplot.wrappers.default_latlon` wrappers.
+# `~proplot.axes.standardize_2d`, `~proplot.axes.default_transform`,
+# and `~proplot.axes.default_latlon` wrappers.
 #
 # * For both basemap and cartopy projections, you can pass ``globe=True`` to
 #   2D plotting commands to ensure global data coverage.

docs/why.rst

@@ -441,10 +441,10 @@ designed to make your life easier.
 
 ..
   All positional arguments for 1D plotting methods are standardized by
-  `~proplot.wrappers.standardize_1d`. All positional arguments for 2D
-  plotting methods are standardized by `~proplot.wrappers.standardize_2d`.
-  See :ref:`1D plotting wrappers` and :ref:`2D plotting wrappers` for
-  details.
+  `~proplot.axes.standardize_1d`. All positional arguments for 2D
+  plotting methods are standardized by `~proplot.axes.standardize_2d`.
+  See :ref:`1D plotting methods <1d_plots>` and :ref:`2D plotting methods <2d_plots>`
+  for details.
 
 
 .. _why_xarray_pandas:

proplot/__init__.py

@@ -15,7 +15,6 @@
     from .scale import *  # noqa: F401 F403
     from .gridspec import *  # noqa: F401 F403
     from .constructor import *  # noqa: F401 F403
-    from .wrappers import *  # noqa: F401 F403
     from .axes import *  # noqa: F401 F403
     from .figure import *  # noqa: F401 F403
     from .ui import *  # noqa: F401 F403