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3010 lines (2821 loc) · 124 KB
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#!/usr/bin/env python3
"""
Wrapper functions applied to various `~proplot.axes.Axes` plotting methods.
In a future version these features will be documented on the individual
plotting methods, but for now they are documented separately.
"""
import sys
import numpy as np
import numpy.ma as ma
import functools
from . import styletools, axistools
from .utils import _warn_proplot, _notNone, edges, edges2d, units
import matplotlib.axes as maxes
import matplotlib.contour as mcontour
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
import matplotlib.patheffects as mpatheffects
import matplotlib.patches as mpatches
import matplotlib.colors as mcolors
import matplotlib.artist as martist
import matplotlib.legend as mlegend
from numbers import Number
from .rctools import rc
try:
from cartopy.crs import PlateCarree
except ModuleNotFoundError:
PlateCarree = object
__all__ = [
'add_errorbars', 'bar_wrapper', 'barh_wrapper', 'boxplot_wrapper',
'default_crs', 'default_latlon', 'default_transform',
'cmap_changer',
'cycle_changer',
'colorbar_wrapper',
'fill_between_wrapper', 'fill_betweenx_wrapper', 'hist_wrapper',
'legend_wrapper', 'plot_wrapper', 'scatter_wrapper',
'standardize_1d', 'standardize_2d', 'text_wrapper',
'violinplot_wrapper',
]
def _load_objects():
"""Delay loading expensive modules. We just want to detect if *input
arrays* belong to these types -- and if this is the case, it means the
module has already been imported! So, we only try loading these classes
within autoformat calls. This saves >~500ms of import time."""
global DataArray, DataFrame, Series, Index, ndarray
ndarray = np.ndarray
DataArray = getattr(sys.modules.get('xarray', None), 'DataArray', ndarray)
DataFrame = getattr(sys.modules.get('pandas', None), 'DataFrame', ndarray)
Series = getattr(sys.modules.get('pandas', None), 'Series', ndarray)
Index = getattr(sys.modules.get('pandas', None), 'Index', ndarray)
_load_objects()
# Keywords for styling cmap overridden plots
# TODO: Deprecate this when #45 merged! Pcolor *already* accepts lw,
# linewidth, *and* linewidths!
STYLE_ARGS_TRANSLATE = {
'contour': {
'colors': 'colors',
'linewidths': 'linewidths',
'linestyles': 'linestyles'},
'hexbin': {
'colors': 'edgecolors',
'linewidths': 'linewidths'},
'tricontour': {
'colors': 'colors',
'linewidths': 'linewidths',
'linestyles': 'linestyles'},
'parametric': {
'colors': 'color',
'linewidths': 'linewidth',
'linestyles': 'linestyle'},
'pcolor': {
'colors': 'edgecolors',
'linewidths': 'linewidth',
'linestyles': 'linestyle'},
'tripcolor': {
'colors': 'edgecolors',
'linewidths': 'linewidth',
'linestyles': 'linestyle'},
'pcolormesh': {
'colors': 'edgecolors',
'linewidths': 'linewidth',
'linestyles': 'linestyle'},
}
def default_latlon(self, func, *args, latlon=True, **kwargs):
"""
Wraps %(methods)s for `~proplot.axes.BasemapAxes`.
With the default `~mpl_toolkits.basemap` API, you need to pass
``latlon=True`` if your data coordinates are longitude and latitude
instead of map projection coordinates. Now, this is the default.
"""
return func(self, *args, latlon=latlon, **kwargs)
def default_transform(self, func, *args, transform=None, **kwargs):
"""
Wraps %(methods)s for `~proplot.axes.GeoAxes`.
With the default `~cartopy.mpl.geoaxes.GeoAxes` API, you need to pass
``transform=cartopy.crs.PlateCarree()`` if your data coordinates are
longitude and latitude instead of map projection coordinates. Now,
this is the default.
"""
# Apply default transform
# TODO: Do some cartopy methods reset backgroundpatch or outlinepatch?
# Deleted comment reported this issue
if transform is None:
transform = PlateCarree()
result = func(self, *args, transform=transform, **kwargs)
return result
def default_crs(self, func, *args, crs=None, **kwargs):
"""
Wraps %(methods)s for `~proplot.axes.GeoAxes` and fixes a
`~cartopy.mpl.geoaxes.GeoAxes.set_extent` bug associated with tight
bounding boxes.
With the default `~cartopy.mpl.geoaxes.GeoAxes` API, you need to pass
``crs=cartopy.crs.PlateCarree()`` if your data coordinates are
longitude and latitude instead of map projection coordinates. Now,
this is the default.
"""
# Apply default crs
name = func.__name__
if crs is None:
crs = PlateCarree()
try:
result = func(self, *args, crs=crs, **kwargs)
except TypeError as err: # duplicate keyword args, i.e. crs is positional
if not args:
raise err
result = func(self, *args[:-1], crs=args[-1], **kwargs)
# Fix extent, so axes tight bounding box gets correct box!
# From this issue:
# https://github.com/SciTools/cartopy/issues/1207#issuecomment-439975083
if name == 'set_extent':
clipped_path = self.outline_patch.orig_path.clip_to_bbox(self.viewLim)
self.outline_patch._path = clipped_path
self.background_patch._path = clipped_path
return result
def _to_iloc(data):
"""Get indexible attribute of array, so we can perform axis
wise operations."""
return getattr(data, 'iloc', data)
def _to_array(data):
"""Convert to ndarray cleanly."""
data = getattr(data, 'values', data)
return np.array(data)
def _atleast_array(data):
"""Converts list of lists to array."""
_load_objects()
if not isinstance(data, (ndarray, DataArray, DataFrame, Series, Index)):
data = np.array(data)
if not np.iterable(data):
data = np.atleast_1d(data)
return data
def _auto_label(data, axis=None, units=True):
"""Gets data and label for pandas or xarray objects or
their coordinates."""
label = ''
_load_objects()
if isinstance(data, ndarray):
if axis is not None and data.ndim > axis:
data = np.arange(data.shape[axis])
# Xarray with common NetCDF attribute names
elif isinstance(data, DataArray):
if axis is not None and data.ndim > axis:
data = data.coords[data.dims[axis]]
label = getattr(data, 'name', '') or ''
for key in ('standard_name', 'long_name'):
label = data.attrs.get(key, label)
if units:
units = data.attrs.get('units', '')
if label and units:
label = f'{label} ({units})'
elif units:
label = units
# Pandas object with name attribute
# if not label and isinstance(data, DataFrame) and data.columns.size == 1:
elif isinstance(data, (DataFrame, Series, Index)):
if axis == 0 and isinstance(data, (DataFrame, Series)):
data = data.index
elif axis == 1 and isinstance(data, DataFrame):
data = data.columns
elif axis is not None:
data = np.arange(len(data)) # e.g. for Index
# DataFrame has no native name attribute but user can add one:
# https://github.com/pandas-dev/pandas/issues/447
label = getattr(data, 'name', '') or ''
return data, str(label).strip()
def standardize_1d(self, func, *args, **kwargs):
"""
Wraps %(methods)s, standardizes acceptable positional args and optionally
modifies the x axis label, y axis label, title, and axis ticks
if a `~xarray.DataArray`, `~pandas.DataFrame`, or `~pandas.Series` is
passed.
Positional args are standardized as follows.
* If a 2D array is passed, the corresponding plot command is called for
each column of data (except for ``boxplot`` and ``violinplot``, in which
case each column is interpreted as a distribution).
* If *x* and *y* or *latitude* and *longitude* coordinates were not
provided, and a `~pandas.DataFrame` or `~xarray.DataArray`, we
try to infer them from the metadata. Otherwise,
``np.arange(0, data.shape[0])`` is used.
"""
# Sanitize input
# TODO: Add exceptions for methods other than 'hist'?
name = func.__name__
_load_objects()
if not args:
return func(self, *args, **kwargs)
elif len(args) == 1:
x = None
y, *args = args
elif len(args) in (2, 3, 4):
x, y, *args = args # same
else:
raise ValueError(f'Too many arguments passed to {name}. Max is 4.')
vert = kwargs.get('vert', None)
if vert is not None:
orientation = ('vertical' if vert else 'horizontal')
else:
orientation = kwargs.get('orientation', 'vertical')
# Iterate through list of ys that we assume are identical
# Standardize based on the first y input
if len(args) >= 1 and 'fill_between' in name:
ys, args = (y, args[0]), args[1:]
else:
ys = (y,)
ys = [_atleast_array(y) for y in ys]
# Auto x coords
y = ys[0] # test the first y input
if x is None:
axis = 1 if (name in ('hist', 'boxplot', 'violinplot') or any(
kwargs.get(s, None) for s in ('means', 'medians'))) else 0
x, _ = _auto_label(y, axis=axis)
x = _atleast_array(x)
if x.ndim != 1:
raise ValueError(
f'x coordinates must be 1-dimensional, but got {x.ndim}.')
# Auto formatting
xi = None # index version of 'x'
if not hasattr(self, 'projection'):
# First handle string-type x-coordinates
kw = {}
xaxis = 'y' if (orientation == 'horizontal') else 'x'
yaxis = 'x' if xaxis == 'y' else 'y'
if _to_array(x).dtype == 'object':
xi = np.arange(len(x))
kw[xaxis + 'locator'] = mticker.FixedLocator(xi)
kw[xaxis + 'formatter'] = mticker.IndexFormatter(x)
kw[xaxis + 'minorlocator'] = mticker.NullLocator()
if name == 'boxplot':
kwargs['labels'] = x
elif name == 'violinplot':
kwargs['positions'] = xi
if name in ('boxplot', 'violinplot'):
kwargs['positions'] = xi
# Next handle labels if 'autoformat' is on
if self.figure._auto_format:
# Ylabel
y, label = _auto_label(y)
if label:
# for histogram, this indicates x coordinate
iaxis = xaxis if name in ('hist',) else yaxis
kw[iaxis + 'label'] = label
# Xlabel
x, label = _auto_label(x)
if label and name not in ('hist',):
kw[xaxis + 'label'] = label
if name != 'scatter' and len(x) > 1 and xi is None and x[1] < x[0]:
kw[xaxis + 'reverse'] = True
# Appply
if kw:
self.format(**kw)
# Standardize args
if xi is not None:
x = xi
if name in ('boxplot', 'violinplot'):
ys = [_to_array(yi) for yi in ys] # store naked array
# Basemap shift x coordiantes without shifting y, we fix this!
if getattr(self, 'name', '') == 'basemap' and kwargs.get('latlon', None):
ix, iys = x, []
xmin, xmax = self.projection.lonmin, self.projection.lonmax
for y in ys:
# Ensure data is monotonic and falls within map bounds
ix, iy = _enforce_bounds(*_standardize_latlon(x, y), xmin, xmax)
iys.append(iy)
x, ys = ix, iys
# WARNING: For some functions, e.g. boxplot and violinplot, we *require*
# cycle_changer is also applied so it can strip 'x' input.
return func(self, x, *ys, *args, **kwargs)
def _interp_poles(y, Z):
"""Adds data points on the poles as the average of highest
latitude data."""
# Get means
with np.errstate(all='ignore'):
p1 = Z[0, :].mean() # pole 1, make sure is not 0D DataArray!
p2 = Z[-1, :].mean() # pole 2
if hasattr(p1, 'item'):
p1 = np.asscalar(p1) # happens with DataArrays
if hasattr(p2, 'item'):
p2 = np.asscalar(p2)
# Concatenate
ps = (-90, 90) if (y[0] < y[-1]) else (90, -90)
Z1 = np.repeat(p1, Z.shape[1])[None, :]
Z2 = np.repeat(p2, Z.shape[1])[None, :]
y = ma.concatenate((ps[:1], y, ps[1:]))
Z = ma.concatenate((Z1, Z, Z2), axis=0)
return y, Z
def _standardize_latlon(x, y):
"""Ensures monotonic longitudes and makes `~numpy.ndarray` copies so the
contents can be modified. Ignores 2D coordinate arrays."""
# Sanitization and bail if 2D
if x.ndim == 1:
x = ma.array(x)
if y.ndim == 1:
y = ma.array(y)
if x.ndim != 1 or all(x < x[0]): # skip monotonic backwards data
return x, y
# Enforce monotonic longitudes
lon1 = x[0]
while True:
filter_ = (x < lon1)
if filter_.sum() == 0:
break
x[filter_] += 360
return x, y
def _enforce_bounds(x, y, xmin, xmax):
"""Ensures data for basemap plots is restricted between the minimum and
maximum longitude of the projection. Input is the ``x`` and ``y``
coordinates. The ``y`` coordinates are rolled along the rightmost axis."""
if x.ndim != 1:
return x, y
# Roll in same direction if some points on right-edge extend
# more than 360 above min longitude; *they* should be on left side
lonroll = np.where(x > xmin + 360)[0] # tuple of ids
if lonroll.size: # non-empty
roll = x.size - lonroll.min()
x = np.roll(x, roll)
y = np.roll(y, roll, axis=-1)
x[:roll] -= 360 # make monotonic
# Set NaN where data not in range xmin, xmax. Must be done
# for regional smaller projections or get weird side-effects due
# to having valid data way outside of the map boundaries
y = y.copy()
if x.size - 1 == y.shape[-1]: # test western/eastern grid cell edges
y[..., (x[1:] < xmin) | (x[:-1] > xmax)] = np.nan
elif x.size == y.shape[-1]: # test the centers and pad by one for safety
where = np.where((x < xmin) | (x > xmax))[0]
y[..., where[1:-1]] = np.nan
return x, y
def standardize_2d(self, func, *args, order='C', globe=False, **kwargs):
"""
Wraps %(methods)s, standardizes acceptable positional args and optionally
modifies the x axis label, y axis label, title, and axis ticks if the
a `~xarray.DataArray`, `~pandas.DataFrame`, or `~pandas.Series` is passed.
Positional args are standardized as follows.
* If *x* and *y* or *latitude* and *longitude* coordinates were not
provided, and a `~pandas.DataFrame` or `~xarray.DataArray` is passed, we
try to infer them from the metadata. Otherwise,
``np.arange(0, data.shape[0])`` and ``np.arange(0, data.shape[1])``
are used.
* For ``pcolor`` and ``pcolormesh``, coordinate *edges* are calculated
if *centers* were provided. For all other methods, coordinate *centers*
are calculated if *edges* were provided.
For `~proplot.axes.GeoAxes` and `~proplot.axes.BasemapAxes`, the
`globe` keyword arg is added, suitable for plotting datasets with global
coverage. Passing ``globe=True`` does the following.
1. "Interpolates" input data to the North and South poles.
2. Makes meridional coverage "circular", i.e. the last longitude coordinate
equals the first longitude coordinate plus 360\N{DEGREE SIGN}.
For `~proplot.axes.BasemapAxes`, 1D longitude vectors are also cycled to
fit within the map edges. For example, if the projection central longitude
is 90\N{DEGREE SIGN}, the data is shifted so that it spans
-90\N{DEGREE SIGN} to 270\N{DEGREE SIGN}.
"""
# Sanitize input
name = func.__name__
_load_objects()
if not args:
return func(self, *args, **kwargs)
elif len(args) > 4:
raise ValueError(f'Too many arguments passed to {name}. Max is 4.')
x, y = None, None
if len(args) > 2:
x, y, *args = args
# Ensure DataArray, DataFrame or ndarray
Zs = []
for Z in args:
Z = _atleast_array(Z)
if Z.ndim != 2:
raise ValueError(f'Z must be 2-dimensional, got shape {Z.shape}.')
Zs.append(Z)
if not all(Zs[0].shape == Z.shape for Z in Zs):
raise ValueError(
f'Zs must be same shape, got shapes {[Z.shape for Z in Zs]}.')
# Retrieve coordinates
if x is None and y is None:
Z = Zs[0]
if order == 'C': # TODO: check order stuff works
idx, idy = 1, 0
else:
idx, idy = 0, 1
if isinstance(Z, ndarray):
x = np.arange(Z.shape[idx])
y = np.arange(Z.shape[idy])
elif isinstance(Z, DataArray): # DataArray
x = Z.coords[Z.dims[idx]]
y = Z.coords[Z.dims[idy]]
else: # DataFrame; never Series or Index because these are 1D
x = Z.index
y = Z.columns
# Check coordinates
x, y = _atleast_array(x), _atleast_array(y)
if x.ndim != y.ndim:
raise ValueError(
f'x coordinates are {x.ndim}-dimensional, '
f'but y coordinates are {y.ndim}-dimensional.')
for s, array in zip(('x', 'y'), (x, y)):
if array.ndim not in (1, 2):
raise ValueError(
f'{s} coordinates are {array.ndim}-dimensional, '
f'but must be 1 or 2-dimensional.')
# Auto formatting
kw = {}
xi, yi = None, None
if not hasattr(self, 'projection'):
# First handle string-type x and y-coordinates
if _to_array(x).dtype == 'object':
xi = np.arange(len(x))
kw['xlocator'] = mticker.FixedLocator(xi)
kw['xformatter'] = mticker.IndexFormatter(x)
kw['xminorlocator'] = mticker.NullLocator()
if _to_array(y).dtype == 'object':
yi = np.arange(len(y))
kw['ylocator'] = mticker.FixedLocator(yi)
kw['yformatter'] = mticker.IndexFormatter(y)
kw['yminorlocator'] = mticker.NullLocator()
# Handle labels if 'autoformat' is on
if self.figure._auto_format:
for key, xy in zip(('xlabel', 'ylabel'), (x, y)):
_, label = _auto_label(xy)
if label:
kw[key] = label
if len(xy) > 1 and all(isinstance(xy, Number)
for xy in xy[:2]) and xy[1] < xy[0]:
kw[key[0] + 'reverse'] = True
if xi is not None:
x = xi
if yi is not None:
y = yi
# Handle figure titles
if self.figure._auto_format:
_, colorbar_label = _auto_label(Zs[0], units=True)
_, title = _auto_label(Zs[0], units=False)
if title:
kw['title'] = title
if kw:
self.format(**kw)
# Enforce edges
if name in ('pcolor', 'pcolormesh'):
# Get centers or raise error. If 2D, don't raise error, but don't fix
# either, because matplotlib pcolor just trims last column and row.
xlen, ylen = x.shape[-1], y.shape[0]
for Z in Zs:
if Z.ndim != 2:
raise ValueError(
f'Input arrays must be 2D, instead got shape {Z.shape}.')
elif Z.shape[1] == xlen and Z.shape[0] == ylen:
if all(z.ndim == 1 and z.size > 1
and z.dtype != 'object' for z in (x, y)):
x = edges(x)
y = edges(y)
else:
if (x.ndim == 2 and x.shape[0] > 1 and x.shape[1] > 1
and x.dtype != 'object'):
x = edges2d(x)
if (y.ndim == 2 and y.shape[0] > 1 and y.shape[1] > 1
and y.dtype != 'object'):
y = edges2d(y)
elif Z.shape[1] != xlen - 1 or Z.shape[0] != ylen - 1:
raise ValueError(
f'Input shapes x {x.shape} and y {y.shape} must match '
f'Z centers {Z.shape} or '
f'Z borders {tuple(i+1 for i in Z.shape)}.')
# Optionally re-order
# TODO: Double check this
if order == 'F':
x, y = x.T, y.T # in case they are 2-dimensional
Zs = (Z.T for Z in Zs)
elif order != 'C':
raise ValueError(
f'Invalid order {order!r}. Choose from '
'"C" (row-major, default) and "F" (column-major).')
# Enforce centers
else:
# Get centers given edges. If 2D, don't raise error, let matplotlib
# raise error down the line.
xlen, ylen = x.shape[-1], y.shape[0]
for Z in Zs:
if Z.ndim != 2:
raise ValueError(
f'Input arrays must be 2D, instead got shape {Z.shape}.')
elif Z.shape[1] == xlen - 1 and Z.shape[0] == ylen - 1:
if all(z.ndim == 1 and z.size > 1
and z.dtype != 'object' for z in (x, y)):
x = (x[1:] + x[:-1]) / 2
y = (y[1:] + y[:-1]) / 2
else:
if (x.ndim == 2 and x.shape[0] > 1 and x.shape[1] > 1
and x.dtype != 'object'):
x = 0.25 * (x[:-1, :-1] + x[:-1, 1:]
+ x[1:, :-1] + x[1:, 1:])
if (y.ndim == 2 and y.shape[0] > 1 and y.shape[1] > 1
and y.dtype != 'object'):
y = 0.25 * (y[:-1, :-1] + y[:-1, 1:]
+ y[1:, :-1] + y[1:, 1:])
elif Z.shape[1] != xlen or Z.shape[0] != ylen:
raise ValueError(
f'Input shapes x {x.shape} and y {y.shape} '
f'must match Z centers {Z.shape} '
f'or Z borders {tuple(i+1 for i in Z.shape)}.')
# Optionally re-order
# TODO: Double check this
if order == 'F':
x, y = x.T, y.T # in case they are 2-dimensional
Zs = (Z.T for Z in Zs)
elif order != 'C':
raise ValueError(
f'Invalid order {order!r}. Choose from '
'"C" (row-major, default) and "F" (column-major).')
# Cartopy projection axes
if (getattr(self, 'name', '') == 'geo'
and isinstance(kwargs.get('transform', None), PlateCarree)):
x, y = _standardize_latlon(x, y)
ix, iZs = x, []
for Z in Zs:
if globe and x.ndim == 1 and y.ndim == 1:
# Fix holes over poles by *interpolating* there
y, Z = _interp_poles(y, Z)
# Fix seams by ensuring circular coverage. Unlike basemap,
# cartopy can plot across map edges.
if (x[0] % 360) != ((x[-1] + 360) % 360):
ix = ma.concatenate((x, [x[0] + 360]))
Z = ma.concatenate((Z, Z[:, :1]), axis=1)
iZs.append(Z)
x, Zs = ix, iZs
# Basemap projection axes
elif getattr(self, 'name', '') == 'basemap' and kwargs.get('latlon', None):
# Fix grid
xmin, xmax = self.projection.lonmin, self.projection.lonmax
x, y = _standardize_latlon(x, y)
ix, iZs = x, []
for Z in Zs:
# Ensure data is within map bounds
ix, Z = _enforce_bounds(x, Z, xmin, xmax)
# Globe coverage fixes
if globe and ix.ndim == 1 and y.ndim == 1:
# Fix holes over poles by interpolating there (equivalent to
# simple mean of highest/lowest latitude points)
y, Z = _interp_poles(y, Z)
# Fix seams at map boundary; 3 scenarios here:
# Have edges (e.g. for pcolor), and they fit perfectly against
# basemap seams. Does not augment size.
if ix[0] == xmin and ix.size - 1 == Z.shape[1]:
pass # do nothing
# Have edges (e.g. for pcolor), and the projection edge is
# in-between grid cell boundaries. Augments size by 1.
elif ix.size - 1 == Z.shape[1]: # just add grid cell
ix = ma.append(xmin, ix)
ix[-1] = xmin + 360
Z = ma.concatenate((Z[:, -1:], Z), axis=1)
# Have centers (e.g. for contourf), and we need to interpolate
# to left/right edges of the map boundary. Augments size by 2.
elif ix.size == Z.shape[1]:
xi = np.array([ix[-1], ix[0] + 360]) # x
if xi[0] != xi[1]:
Zq = ma.concatenate((Z[:, -1:], Z[:, :1]), axis=1)
xq = xmin + 360
Zq = (Zq[:, :1] * (xi[1] - xq) + Zq[:, 1:]
* (xq - xi[0])) / (xi[1] - xi[0])
ix = ma.concatenate(([xmin], ix, [xmin + 360]))
Z = ma.concatenate((Zq, Z, Zq), axis=1)
else:
raise ValueError(
'Unexpected shape of longitude/latitude/data arrays.')
iZs.append(Z)
x, Zs = ix, iZs
# Convert to projection coordinates
if x.ndim == 1 and y.ndim == 1:
x, y = np.meshgrid(x, y)
x, y = self.projection(x, y)
kwargs['latlon'] = False
# Finally return result
# WARNING: Must apply default colorbar label *here* in case metadata
# was stripped by globe=True.
colorbar_kw = kwargs.pop('colorbar_kw', None) or {}
colorbar_kw.setdefault('label', colorbar_label)
return func(self, x, y, *Zs, colorbar_kw=colorbar_kw, **kwargs)
def _errorbar_values(data, idata, bardata=None, barrange=None, barstd=False):
"""Returns values that can be passed to the `~matplotlib.axes.Axes.errorbar`
`xerr` and `yerr` keyword args."""
if bardata is not None:
err = np.array(bardata)
if err.ndim == 1:
err = err[:, None]
if err.ndim != 2 or err.shape[0] != 2 \
or err.shape[1] != idata.shape[-1]:
raise ValueError(
f'bardata must have shape (2, {idata.shape[-1]}), '
f'but got {err.shape}.')
elif barstd:
err = np.array(idata) + np.std(
data, axis=0)[None, :] * np.array(barrange)[:, None]
else:
err = np.percentile(data, barrange, axis=0)
err = err - np.array(idata)
err[0, :] *= -1 # array now represents error bar sizes
return err
def add_errorbars(
self, func, *args,
medians=False, means=False,
boxes=None, bars=None,
boxdata=None, bardata=None,
boxstd=False, barstd=False,
boxmarker=True, boxmarkercolor='white',
boxrange=(25, 75), barrange=(5, 95), boxcolor=None, barcolor=None,
boxlw=None, barlw=None, capsize=None,
boxzorder=3, barzorder=3,
**kwargs):
"""
Wraps %(methods)s, adds support for drawing error bars. Includes
options for interpreting columns of data as ranges, representing the mean
or median of each column with lines, points, or bars, and drawing error
bars representing percentile ranges or standard deviation multiples for
the data in each column.
Parameters
----------
*args
The input data.
bars : bool, optional
Toggles *thin* error bars with optional "whiskers" (i.e. caps). Default
is ``True`` when `means` is ``True``, `medians` is ``True``, or
`bardata` is not ``None``.
boxes : bool, optional
Toggles *thick* boxplot-like error bars with a marker inside
representing the mean or median. Default is ``True`` when `means` is
``True``, `medians` is ``True``, or `boxdata` is not ``None``.
means : bool, optional
Whether to plot the means of each column in the input data.
medians : bool, optional
Whether to plot the medians of each column in the input data.
bardata, boxdata : 2xN ndarray, optional
Arrays that manually specify the thin and thick error bar coordinates.
The first row contains lower bounds, and the second row contains
upper bounds. Columns correspond to points in the dataset.
barstd, boxstd : bool, optional
Whether `barrange` and `boxrange` refer to multiples of the standard
deviation, or percentile ranges. Default is ``False``.
barrange : (float, float), optional
Percentile ranges or standard deviation multiples for drawing thin
error bars. The defaults are ``(-3,3)`` (i.e. +/-3 standard deviations)
when `barstd` is ``True``, and ``(0,100)`` (i.e. the full data range)
when `barstd` is ``False``.
boxrange : (float, float), optional
Percentile ranges or standard deviation multiples for drawing thick
error bars. The defaults are ``(-1,1)`` (i.e. +/-1 standard deviation)
when `boxstd` is ``True``, and ``(25,75)`` (i.e. the middle 50th
percentile) when `boxstd` is ``False``.
barcolor, boxcolor : color-spec, optional
Colors for the thick and thin error bars. Default is ``'k'``.
barlw, boxlw : float, optional
Line widths for the thin and thick error bars, in points. Default
`barlw` is ``0.7`` and default `boxlw` is ``4*barlw``.
boxmarker : bool, optional
Whether to draw a small marker in the middle of the box denoting
the mean or median position. Ignored if `boxes` is ``False``.
Default is ``True``.
boxmarkercolor : color-spec, optional
Color for the `boxmarker` marker. Default is ``'w'``.
capsize : float, optional
The cap size for thin error bars, in points.
barzorder, boxzorder : float, optional
The "zorder" for the thin and thick error bars.
lw, linewidth : float, optional
If passed, this is used for the default `barlw`.
edgecolor : float, optional
If passed, this is used for the default `barcolor` and `boxcolor`.
"""
name = func.__name__
x, y, *args = args
# Sensible defaults
if boxdata is not None:
bars = _notNone(bars, True)
if bardata is not None:
boxes = _notNone(boxes, True)
if boxdata is not None or bardata is not None:
# e.g. if boxdata passed but bardata not passed, use bars=False
bars = _notNone(bars, False)
boxes = _notNone(boxes, False)
# Get means or medians for plotting
iy = y
if (means or medians):
bars = _notNone(bars, True)
boxes = _notNone(boxes, True)
if y.ndim != 2:
raise ValueError(
f'Need 2D data array for means=True or medians=True, '
f'got {y.ndim}D array.')
if means:
iy = np.mean(y, axis=0)
elif medians:
iy = np.percentile(y, 50, axis=0)
# Call function, accounting for different signatures of plot and violinplot
get = kwargs.pop if name == 'violinplot' else kwargs.get
lw = _notNone(get('lw', None), get('linewidth', None), 0.7)
get = kwargs.pop if name != 'bar' else kwargs.get
edgecolor = _notNone(get('edgecolor', None), 'k')
if name == 'violinplot':
xy = (x, y) # full data
else:
xy = (x, iy) # just the stats
obj = func(self, *xy, *args, **kwargs)
if not boxes and not bars:
return obj
# Account for horizontal bar plots
if 'vert' in kwargs:
orientation = 'vertical' if kwargs['vert'] else 'horizontal'
else:
orientation = kwargs.get('orientation', 'vertical')
if orientation == 'horizontal':
axis = 'x' # xerr
xy = (iy, x)
else:
axis = 'y' # yerr
xy = (x, iy)
# Defaults settings
barlw = _notNone(barlw, lw)
boxlw = _notNone(boxlw, 4 * barlw)
capsize = _notNone(capsize, 3)
barcolor = _notNone(barcolor, edgecolor)
boxcolor = _notNone(boxcolor, edgecolor)
# Draw boxes and bars
if boxes:
default = (-1, 1) if barstd else (25, 75)
boxrange = _notNone(boxrange, default)
err = _errorbar_values(y, iy, boxdata, boxrange, boxstd)
if boxmarker:
self.scatter(*xy, marker='o', color=boxmarkercolor,
s=boxlw, zorder=5)
self.errorbar(*xy, **{
axis + 'err': err, 'capsize': 0, 'zorder': boxzorder,
'color': boxcolor, 'linestyle': 'none', 'linewidth': boxlw})
if bars: # now impossible to make thin bar width different from cap width!
default = (-3, 3) if barstd else (0, 100)
barrange = _notNone(barrange, default)
err = _errorbar_values(y, iy, bardata, barrange, barstd)
self.errorbar(*xy, **{
axis + 'err': err, 'capsize': capsize, 'zorder': barzorder,
'color': barcolor, 'linewidth': barlw, 'linestyle': 'none',
'markeredgecolor': barcolor, 'markeredgewidth': barlw})
return obj
def plot_wrapper(self, func, *args, cmap=None, values=None, **kwargs):
"""
Wraps %(methods)s, draws a "colormap line" if the `cmap` argument was
passed. "Colormap lines" change color as a function of the parametric
coordinate `values` using the input colormap `cmap`.
Parameters
----------
*args : (y,), (x,y), or (x,y,fmt)
Passed to `~matplotlib.axes.Axes.plot`.
cmap, values : optional
Passed to `~proplot.axes.Axes.parametric`.
**kwargs
`~matplotlib.lines.Line2D` properties.
"""
if len(args) > 3: # e.g. with fmt string
raise ValueError(f'Expected 1-3 positional args, got {len(args)}.')
if cmap is None:
lines = func(self, *args, values=values, **kwargs)
else:
lines = self.parametric(*args, cmap=cmap, values=values, **kwargs)
return lines
def scatter_wrapper(
self, func, *args,
s=None, size=None, markersize=None,
c=None, color=None, markercolor=None,
smin=None, smax=None,
cmap=None, cmap_kw=None, vmin=None, vmax=None, norm=None, norm_kw=None,
lw=None, linewidth=None, linewidths=None,
markeredgewidth=None, markeredgewidths=None,
edgecolor=None, edgecolors=None,
markeredgecolor=None, markeredgecolors=None,
**kwargs):
"""
Wraps `~matplotlib.axes.Axes.scatter`, adds optional keyword args
more consistent with the `~matplotlib.axes.Axes.plot` keywords.
Parameters
----------
s, size, markersize : float or list of float, optional
Aliases for the marker size.
smin, smax : float, optional
Used to scale the `s` array. These are the minimum and maximum marker
sizes. Defaults are the minimum and maximum of the `s` array.
c, color, markercolor : color-spec or list thereof, or array, optional
Aliases for the marker fill color. If just an array of values, the
colors will be generated by passing the values through the `norm`
normalizer and drawing from the `cmap` colormap.
cmap : colormap-spec, optional
The colormap specifer, passed to the `~proplot.styletools.Colormap`
constructor.
cmap_kw : dict-like, optional
Passed to `~proplot.styletools.Colormap`.
vmin, vmax : float, optional
Used to generate a `norm` for scaling the `c` array. These are the
values corresponding to the leftmost and rightmost colors in the
colormap. Defaults are the minimum and maximum values of the `c` array.
norm : normalizer spec, optional
The colormap normalizer, passed to the `~proplot.styletools.Norm`
constructor.
norm_kw : dict, optional
Passed to `~proplot.styletools.Norm`.
lw, linewidth, linewidths, markeredgewidth, markeredgewidths : \
float or list thereof, optional
Aliases for the marker edge width.
edgecolors, markeredgecolor, markeredgecolors : \
color-spec or list thereof, optional
Aliases for the marker edge color.
**kwargs
Passed to `~matplotlib.axes.Axes.scatter`.
"""
# Manage input arguments
# NOTE: Parse 1D must come before this
nargs = len(args)
if len(args) > 4:
raise ValueError(f'Expected 1-4 positional args, got {nargs}.')
args = list(args)
if len(args) == 4:
c = args.pop(1)
if len(args) == 3:
s = args.pop(0)
# Format cmap and norm
cmap_kw = cmap_kw or {}
norm_kw = norm_kw or {}
if cmap is not None:
cmap = styletools.Colormap(cmap, **cmap_kw)
if norm is not None:
norm = styletools.Norm(norm, **norm_kw)
# Apply some aliases for keyword arguments
c = _notNone(c, color, markercolor, None,
names=('c', 'color', 'markercolor'))
s = _notNone(s, size, markersize, None,
names=('s', 'size', 'markersize'))
lw = _notNone(
lw, linewidth, linewidths, markeredgewidth, markeredgewidths, None,
names=(
'lw', 'linewidth', 'linewidths',
'markeredgewidth', 'markeredgewidths'
))
ec = _notNone(
edgecolor, edgecolors, markeredgecolor, markeredgecolors, None,
names=(
'edgecolor', 'edgecolors', 'markeredgecolor', 'markeredgecolors'
))
# Scale s array
if np.iterable(s):
smin_true, smax_true = min(s), max(s)
if smin is None:
smin = smin_true
if smax is None:
smax = smax_true
s = smin + (smax - smin) * (np.array(s) - smin_true) / \
(smax_true - smin_true)
return func(self, *args, c=c, s=s,
cmap=cmap, vmin=vmin, vmax=vmax,
norm=norm, linewidths=lw, edgecolors=ec,
**kwargs)
def _fill_between_apply(self, func, *args,
negcolor='blue', poscolor='red', negpos=False,
**kwargs):
"""Parse args and call function."""
# Allow common keyword usage
x = 'y' if 'x' in func.__name__ else 'y'
y = 'x' if x == 'y' else 'y'
if x in kwargs:
args = (kwargs.pop(x), *args)
for y in (y + '1', y + '2'):
if y in kwargs:
args = (*args, kwargs.pop(y))
if len(args) == 1:
args = (np.arange(len(args[0])), *args)
if len(args) == 2:
if kwargs.get('stacked', False):
args = (*args, 0)
else:
args = (args[0], 0, args[1]) # default behavior
if len(args) != 3:
raise ValueError(f'Expected 2-3 positional args, got {len(args)}.')
if not negpos:
obj = func(self, *args, **kwargs)
return obj
# Get zero points
objs = []
kwargs.setdefault('interpolate', True)
y1, y2 = np.atleast_1d(
args[-2]).squeeze(), np.atleast_1d(args[-1]).squeeze()
if y1.ndim > 1 or y2.ndim > 1:
raise ValueError(f'When "negpos" is True, y must be 1-dimensional.')
if kwargs.get('where', None) is not None:
raise ValueError(
'When "negpos" is True, you cannot set the "where" keyword.')
for i in range(2):
kw = {**kwargs}
kw.setdefault('color', negcolor if i == 0 else poscolor)
where = (y2 < y1) if i == 0 else (y2 >= y1)
obj = func(self, *args, where=where, **kw)
objs.append(obj)
return (*objs,)
def fill_between_wrapper(self, func, *args, **kwargs):
"""
Wraps `~matplotlib.axes.Axes.fill_between`, also accessible via the
`~proplot.axes.Axes.area` alias.