Default style changes and miscellaneous plotting improvements

CHANGELOG.rst

@@ -71,6 +71,9 @@ ProPlot v0.6.0 (2020-##-##)
 
 There are quite a lot of deprecations for this release.
 
+- Rename `add_errorbars` to `~proplot.axes.plot.indicate_error` and rename
+  various keyword args (:pr:`166`, :commit:`d8c50a8d`).
+- Remove ``'rgbcycle'`` setting (:commit:`6653b7f0`).
 - Deprecate support for "parametric" plots inside `~matplotlib.axes.Axes.plot`,
   instead use `~proplot.axes.Axes.parametric` (:commit:`64210bce`).
 - Change `~proplot.utils.units` ``units`` keyword argument to more natural
@@ -115,10 +118,25 @@ There are quite a lot of deprecations for this release.
 
 .. rubric:: Features
 
-- Support building a colormap and `DiscreteNorm` inside `~matplotlib.axes.Axes.scatter`,
-  just like `contourf` and `pcolormesh` (:pr:`162`).
+- Add options to `~proplot.axes.plot.indicate_error` for adding *shading*
+  to arbitrary plots (:pr:`166`, :commit:`d8c50a8d`). Also support automatic legend
+  entries for shading and ensure `indicate_error` preserves metadata.
+- Wrap ``pcolorfast`` just like ``pcolor`` and ``pcolormesh`` are
+  wrapped (:commit:`50a262dd`).
+- Add ``negpos`` feature to `~proplot.axes.plot.bar_wrapper` and new
+  :rcraw:`negcolor` and :rcraw:`poscolor` rc keyword arguments (:commit:`ab4d6746`).
+- Increase default line width from ``0.6`` to ``0.8`` to match matplotlib defaults
+  (:commit:`f801852b`; try to avoid frivolously changing defaults).
+- Change default resolution for geographic features from ``'lo'`` to ``'med'``
+  (:commit:`f801852b`).
+- Change default line style for geographic gridlines from ``':'`` to ``'-'``
+  and match style from primary gridlines (:commit:`f801852b`).
+- Support cartopy inline meridian and parallel gridlines and support
+  changing the gridline padding (:commit:`###`).
 - Support `cartopy 0.18 <https://scitools.org.uk/cartopy/docs/latest/whats_new.html>`__
   locators, formatters, deprecations, and new labelling features (:pr:`158`).
+- Support building a colormap and `DiscreteNorm` inside `~matplotlib.axes.Axes.scatter`,
+  just like `contourf` and `pcolormesh` (:pr:`162`).
 - Add :rcraw:`geogrid.labelpad` and :rcraw:`geogrid.rotatelabels` settings
   for cartopy gridline labels (:pr:`158`).
 - Support more `~proplot.ticker.AutoFormatter` features on
@@ -161,6 +179,8 @@ There are quite a lot of deprecations for this release.
 
 .. rubric:: Bug fixes
 
+- Fix various issues with axis label sharing and axis sharing for
+  twinned axes and panel axes (:pr:`164`).
 - Fix issue drawing bar plots with datetime *x* axes (:pr:`156`).
 - Fix issue where `~proplot.ticker.AutoFormatter` tools were not locale-aware, i.e. use
   comma as decimal point sometimes (:commit:`c7636296`).
@@ -206,9 +226,8 @@ There are quite a lot of deprecations for this release.
   sensible behavior.
 - Turn some private `~proplot.config` functions into static
   methods (:commit:`6121de03`).
+- Remove "smart bounds" feature from `FuncScale` (:commit:`9ac149ea`).
 - Clean up axes iterators (:commit:`c8a0768a`).
-- Clean up locator and formatter sharing stuff (:commit:`###`).
-- Configure label sharing at draw-time rather than label-time (:commit:`###`).
 
 .. rubric:: Documentation
 
@@ -245,7 +264,6 @@ ProPlot v0.5.0 (2020-02-10)
 
 .. rubric:: Bug fixes
 
-- Fix issue where colormaps cannot have "dot" in name (:commit:`###`).
 - Fix issue where `~proplot.styletools.show_cmaps` and
   `~proplot.styletools.show_cycles` colormap names were messed up
   (:commit:`13045599`)
@@ -256,9 +274,9 @@ ProPlot v0.5.0 (2020-02-10)
   `~proplot.wrappers.colorbar_wrapper` were sometimes ignored
   (:commit:`fd4f8d5f`).
 - Permit passing *lists of colors* to manually shade line contours and filled
-  contours in `~proplot.wrappers.cmap_changer` (:commit:`###`).
+  contours in `~proplot.wrappers.cmap_changer`.
 - Prevent formatting rightmost meridian label as ``1e-10`` on cartopy map
-  projections (:commit:`37fdd1eb]`).
+  projections (:commit:`37fdd1eb`).
 - Support CF-time axes by fixing bug in `~proplot.wrappers.standardize_1d`
   and `~proplot.wrappers.standardize_2d` (:issue:`103`, :pr:`121`).
 - Redirect to the "default" location when using ``legend=True`` and

docs/1dplots.py

@@ -94,7 +94,7 @@
     fig, axs = plot.subplots(ncols=2, share=False)
     x = np.linspace(-5, 5, N)
     y = state.rand(N, 5)
-    axs.format(xlabel='xlabel', ylabel='ylabel', ymargin=0.05)
+    axs.format(xlabel='xlabel', ylabel='ylabel')
     axs.format(suptitle='Standardized arguments demonstration')
 
     # Plot by passing both x and y coordinates
@@ -182,66 +182,70 @@
 # %% [raw] raw_mimetype="text/restructuredtext"
 # .. _ug_errorbars:
 #
-# Adding error bars
-# -----------------
+# Error bars and shading
+# ----------------------
 #
-# The `~proplot.axes.add_errorbars` wrapper lets you draw error bars
-# on-the-fly by passing certain keyword arguments to
+# The `~proplot.axes.indicate_error` wrapper lets you draw error bars
+# and error shading 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.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.axes.add_errorbars` for details.
+# lines, or bars, and error bars or shading is drawn to represent the spread in
+# each column. `~proplot.axes.indicate_error` lets you draw thin error
+# bars with optional whiskers, thick "boxes" overlayed on top of these bars
+# (think of this as a miniature boxplot), or up to 2 intervals of shading.
+# Instead of using 2D arrays, you can also pass the error bounds
+# *manually* with the `bardata`, `boxdata`, `shadedata`, and `fadedata` keyword
+# arguments. See `~proplot.axes.indicate_error` for details.
 
-# %%
 import proplot as plot
 import numpy as np
 import pandas as pd
 plot.rc['title.loc'] = 'uc'
-plot.rc['axes.ymargin'] = plot.rc['axes.xmargin'] = 0.05
+
+# Generate sample data
 state = np.random.RandomState(51423)
-data = (
-    state.rand(20, 8).cumsum(axis=0).cumsum(axis=1)[:, ::-1]
-    + 20 * state.normal(size=(20, 8)) + 30
-)
+data = state.rand(20, 8).cumsum(axis=0).cumsum(axis=1)[:, ::-1]
+data = data + 20 * state.normal(size=(20, 8)) + 30
+data = pd.DataFrame(data, columns=np.arange(0, 16, 2))
+data.name = 'variable'
+
+# Generate figure
 fig, axs = plot.subplots(
     nrows=3, aspect=1.5, axwidth=4,
     share=0, hratios=(2, 1, 1)
 )
-axs.format(suptitle='Error bars with various plotting commands')
+axs.format(suptitle='Indicating error bounds with various plotting commands')
 axs[1:].format(xlabel='column number', xticks=1, xgrid=False)
 
-# Asking add_errorbars to calculate bars
+# Automatically calculate medians and display default percentile range
 ax = axs[0]
-obj = ax.barh(data, color='red orange', means=True)
+obj = ax.barh(
+    data, color='light red', legend=True,
+    medians=True, barpctiles=True, boxpctiles=True
+)
 ax.format(title='Column statistics')
 ax.format(ylabel='column number', title='Bar plot', ygrid=False)
 
-# Showing a standard deviation range instead of percentile range
+# Automatically calculate means and display requested standard deviation range
 ax = axs[1]
 ax.scatter(
-    data, color='k', marker='_', markersize=50,
-    medians=True, barstd=True, boxes=False, capsize=2,
-    barcolor='gray6', barrange=(-1, 1), barzorder=0, barlw=1,
+    data, color='denim', marker='x', markersize=8**2, linewidth=0.8,
+    means=True, shadestds=(-1, 1), legend='ll',
 )
 ax.format(title='Scatter plot')
 
-# Supplying error bar data manually
+# Manually supply error bar data and legend labels
 ax = axs[2]
-boxdata = np.percentile(data, (25, 75), axis=0)
-bardata = np.percentile(data, (5, 95), axis=0)
+means = data.mean(axis=0)
+means.name = data.name
+shadedata = np.percentile(data, (25, 75), axis=0)  # dark shading
+fadedata = np.percentile(data, (5, 95), axis=0)  # light shading
 ax.plot(
-    data.mean(axis=0), boxes=True,
-    edgecolor='k', color='gray9',
-    boxdata=boxdata, bardata=bardata, barzorder=0,
-    boxcolor='gray7', barcolor='gray7', boxmarker=False,
+    means, shadedata=shadedata, fadedata=fadedata,
+    color='ocean blue', barzorder=0, boxmarker=False, legend='ll',
 )
 ax.format(title='Line plot')
 plot.rc.reset()
@@ -258,9 +262,10 @@
 # `~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`
-# now employ "default" *x* coordinates if you failed to provide them
-# explicitly, just like `~matplotlib.axes.Axes.plot`.
+# `pandas`, or use different colors for negative and positive bars by
+# passing ``negpos=True``. Also, `~matplotlib.axes.Axes.bar` and
+# `~matplotlib.axes.Axes.barh` now employ "default" *x* coordinates if you
+# failed to provide them explicitly.
 #
 # To make filled "area" plots, use the new `~proplot.axes.Axes.area` and
 # `~proplot.axes.Axes.areax` methods. These are alises for
@@ -270,7 +275,7 @@
 # `~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
+# area plots that change color when the fill boundaries cross each other by
 # passing ``negpos=True`` to `~matplotlib.axes.Axes.fill_between`. The most
 # common use case for this is highlighting negative and positive areas with
 # different colors, as shown below.
@@ -280,7 +285,6 @@
 import numpy as np
 import pandas as pd
 plot.rc.titleloc = 'uc'
-plot.rc.margin = 0.05
 fig, axs = plot.subplots(nrows=2, aspect=2, axwidth=5, share=0, hratios=(3, 2))
 state = np.random.RandomState(51423)
 data = state.rand(5, 5).cumsum(axis=0).cumsum(axis=1)[:, ::-1]
@@ -313,33 +317,39 @@
 # %%
 import proplot as plot
 import numpy as np
-plot.rc.margin = 0
-fig, axs = plot.subplots(array=[[1, 2], [3, 3]], hratios=(1, 0.8), share=0)
-axs.format(xlabel='xlabel', ylabel='ylabel', suptitle='Area plot demo')
+fig, axs = plot.subplots(array=[[1, 2], [3, 3]], hratios=(1, 1.5), share=0)
+axs.format(grid=False, xlabel='xlabel', ylabel='ylabel', suptitle='Area plot demo')
 state = np.random.RandomState(51423)
 data = state.rand(5, 3).cumsum(axis=0)
 cycle = ('gray3', 'gray5', 'gray7')
 
-# Overlaid and stacked area patches
+# Overlaid area patches
 ax = axs[0]
 ax.area(
-    np.arange(5), data, data + state.rand(5)[:, None], cycle=cycle, alpha=0.5,
+    np.arange(5), data, data + state.rand(5)[:, None], cycle=cycle, alpha=0.7,
     legend='uc', legend_kw={'center': True, 'ncols': 2, 'labels': ['z', 'y', 'qqqq']},
 )
 ax.format(title='Fill between columns')
+
+# Stacked area patches
 ax = axs[1]
 ax.area(
     np.arange(5), data, stacked=True, cycle=cycle, alpha=0.8,
     legend='ul', legend_kw={'center': True, 'ncols': 2, 'labels': ['z', 'y', 'qqqq']},
 )
 ax.format(title='Stack between columns')
 
-# Positive and negative color area patches
+# Positive and negative color bars and area patches
 ax = axs[2]
-data = 5 * (state.rand(20) - 0.5)
-ax.area(data, negpos=True, negcolor='blue7', poscolor='red7')
-ax.format(title='Positive and negative colors', xlabel='xlabel', ylabel='ylabel')
-axs.format(grid=False)
+data = 4 * (state.rand(20) - 0.5)
+ax.bar(data, bottom=-2, width=1, edgecolor='none', negpos=True)
+ax.area(data + 2, y2=2, negpos=True)
+for offset in (-2, 2):
+    ax.axhline(offset, color='k', linewidth=1, linestyle='--')
+ax.format(
+    xmargin=0, xlabel='xlabel', ylabel='ylabel',
+    title='Positive and negative colors demo', titleweight='bold',
+)
 plot.rc.reset()
 
 
@@ -361,18 +371,19 @@
 import proplot as plot
 import numpy as np
 import pandas as pd
+
+# Generate sample data
 N = 500
 state = np.random.RandomState(51423)
-fig, axs = plot.subplots(ncols=2, axwidth=2.5)
 data = state.normal(size=(N, 5)) + 2 * (state.rand(N, 5) - 0.5) * np.arange(5)
 data = pd.DataFrame(
     data,
     columns=pd.Index(['a', 'b', 'c', 'd', 'e'], name='xlabel')
 )
-axs.format(
-    ymargin=0.1, xmargin=0.1, grid=False,
-    suptitle='Boxes and violins demo'
-)
+
+# Generate figure
+fig, axs = plot.subplots(ncols=2, axwidth=2.5)
+axs.format(grid=False, suptitle='Boxes and violins demo')
 
 # Box plots
 ax = axs[0]
@@ -428,7 +439,6 @@
     x, y, c, cmap=cmap, lw=7, interp=5, capstyle='round', joinstyle='round'
 )
 ax.format(xlabel='xlabel', ylabel='ylabel', title='Line with smooth gradations')
-ax.format(xmargin=0.05, ymargin=0.05)
 ax.colorbar(m, loc='b', label='parametric coordinate', locator=5)
 
 # Parametric line with stepped gradations
@@ -463,12 +473,6 @@
 # `~matplotlib.axes.Axes.scatter` now optionally accepts keywords that look
 # like `~matplotlib.axes.Axes.plot` keywords (e.g. `color` instead of `c` and
 # `markersize` instead of `s`).
-#
-# We are also considering supporting 2D array input and property cycle
-# iteration for more obscure matplotlib plotting commands like
-# `~matplotlib.axes.Axes.stem`, `~matplotlib.axes.Axes.step`,
-# `~matplotlib.axes.Axes.vlines`, and `~matplotlib.axes.Axes.hlines`. Stay
-# tuned.
 
 # %%
 import proplot as plot

docs/basics.py

@@ -200,7 +200,7 @@
     xlabel='x-axis', ylabel='y-axis',
     xscale='log',
     xlim=(1, 10), xticks=1,
-    ylim=(-2, 2), yticks=plot.arange(-2, 2),
+    ymargin=0.05, yticks=plot.arange(-2, 2),
     yticklabels=('a', 'bb', 'c', 'dd', 'e'),
     ytickloc='both', yticklabelloc='both',
     xtickdir='inout', xtickminor=False, ygridminor=True,

docs/configuration.rst

@@ -89,9 +89,10 @@ Key               Description
 ``linewidth``     Thickness of axes spines and major tick lines.
 ``lut``           The number of colors to put in the colormap lookup table.
 ``margin``        The margin of space between axes edges and objects plotted inside the axes, if ``xlim`` and ``ylim`` are unset.
+``negcolor``      The color for negative bars and shaded areas when using ``negpos=True``.
 ``ocean``         Boolean, toggles ocean patches on and off.
+``poscolor``      The color for positive bars and shaded areas when using ``negpos=True``.
 ``reso``          Resolution of geographic features, one of ``'lo'``, ``'med'``, or ``'hi'``
-``rgbcycle``      If ``True``, and ``colorblind`` is the current cycle, this registers the ``colorblind`` colors as ``'r'``, ``'b'``, ``'g'``, etc., like in `seaborn <https://seaborn.pydata.org/tutorial/color_palettes.html>`__.
 ``rivers``        Boolean, toggles river lines on and off.
 ``share``         The axis sharing level, one of ``0``, ``1``, ``2``, or ``3``. See `~proplot.ui.subplots` for details.
 ``small``         Font size for legend text, tick labels, axis labels, and text generated with `~matplotlib.axes.Axes.text`.

docs/projections.py

@@ -220,12 +220,6 @@
             fig.colorbar(m, loc='b', span=i + 1, label='values', extendsize='1.7em')
         else:
             ax.pcolor(lon, lat, data, cmap=cmap, globe=globe, extend='both')
-        if globe:
-            continue
-        xi = offset + np.linspace(0, 360, 20)
-        for cmd in (np.sin, np.cos):
-            yi = cmd(xi * np.pi / 180) * 60
-            ax.plot(xi, yi, color='k', lw=0, marker='o')
     axs.format(
         suptitle=titles[globe],
         collabels=['Cartopy example', 'Basemap example'],

docs/sphinxext/custom_roles.py

@@ -1,6 +1,6 @@
 from docutils import nodes
 from os.path import sep
-from proplot.internals import defaults
+from proplot.internals import rcsetup
 
 
 def get_nodes(rawtext, text, inliner):
@@ -20,7 +20,7 @@ def get_nodes(rawtext, text, inliner):
 def rc_role(name, rawtext, text, lineno, inliner, options={}, content=[]):  # noqa: U100
     node_list = get_nodes(rawtext, text, inliner)
     try:
-        default = defaults._get_default_param(text)
+        default = rcsetup._get_default_param(text)
     except KeyError:
         pass
     else:

docs/why.rst

@@ -400,8 +400,8 @@ designed to make your life easier.
   `~matplotlib.axes.Axes.fill_betweenx`, you will be able to use different
   colors for positive/negative bars.
 * All :ref:`1D plotting methods <ug_1dplots>` can be used to :ref:`add error
-  bars <ug_errorbars>` using various specialized keyword arguments. You no
-  longer have to work with the `~matplotlib.axes.Axes.errorbar` method
+  bars or error shading <ug_errorbars>` using various specialized keyword arguments.
+  You no longer have to work with the `~matplotlib.axes.Axes.errorbar` method
   directly.  They also accept a
   :ref:`"cycle" keyword argument <ug_cycle_changer>`
   interpreted by `~proplot.constructor.Cycle` and optional