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from __future__ import division, generators
import math, sys, warnings, datetime, new
from operator import itemgetter
import itertools
import numpy as np
from numpy import ma
import matplotlib
rcParams = matplotlib.rcParams
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
import matplotlib.axis as maxis
import matplotlib.cbook as cbook
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.contour as mcontour
import matplotlib.dates as mdates
from matplotlib import docstring
import matplotlib.font_manager as font_manager
import matplotlib.image as mimage
import matplotlib.legend as mlegend
import matplotlib.lines as mlines
import matplotlib.mlab as mlab
import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.spines as mspines
import matplotlib.quiver as mquiver
import matplotlib.scale as mscale
import matplotlib.table as mtable
import matplotlib.text as mtext
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
import matplotlib.tri as mtri
iterable = cbook.iterable
is_string_like = cbook.is_string_like
is_sequence_of_strings = cbook.is_sequence_of_strings
def _string_to_bool(s):
if not is_string_like(s):
return s
if s == 'on':
return True
if s == 'off':
return False
raise ValueError("string argument must be either 'on' or 'off'")
def _process_plot_format(fmt):
"""
Process a MATLAB style color/line style format string. Return a
(*linestyle*, *color*) tuple as a result of the processing. Default
values are ('-', 'b'). Example format strings include:
* 'ko': black circles
* '.b': blue dots
* 'r--': red dashed lines
.. seealso::
:func:`~matplotlib.Line2D.lineStyles` and
:func:`~matplotlib.pyplot.colors`
for all possible styles and color format string.
"""
linestyle = None
marker = None
color = None
# Is fmt just a colorspec?
try:
color = mcolors.colorConverter.to_rgb(fmt)
# We need to differentiate grayscale '1.0' from tri_down marker '1'
try:
fmtint = str(int(fmt))
except ValueError:
return linestyle, marker, color # Yes
else:
if fmt != fmtint:
# user definitely doesn't want tri_down marker
return linestyle, marker, color # Yes
else:
# ignore converted color
color = None
except ValueError:
pass # No, not just a color.
# handle the multi char special cases and strip them from the
# string
if fmt.find('--')>=0:
linestyle = '--'
fmt = fmt.replace('--', '')
if fmt.find('-.')>=0:
linestyle = '-.'
fmt = fmt.replace('-.', '')
if fmt.find(' ')>=0:
linestyle = 'None'
fmt = fmt.replace(' ', '')
chars = [c for c in fmt]
for c in chars:
if c in mlines.lineStyles:
if linestyle is not None:
raise ValueError(
'Illegal format string "%s"; two linestyle symbols' % fmt)
linestyle = c
elif c in mlines.lineMarkers:
if marker is not None:
raise ValueError(
'Illegal format string "%s"; two marker symbols' % fmt)
marker = c
elif c in mcolors.colorConverter.colors:
if color is not None:
raise ValueError(
'Illegal format string "%s"; two color symbols' % fmt)
color = c
else:
raise ValueError(
'Unrecognized character %c in format string' % c)
if linestyle is None and marker is None:
linestyle = rcParams['lines.linestyle']
if linestyle is None:
linestyle = 'None'
if marker is None:
marker = 'None'
return linestyle, marker, color
def set_default_color_cycle(clist):
"""
Change the default cycle of colors that will be used by the plot
command. This must be called before creating the
:class:`Axes` to which it will apply; it will
apply to all future axes.
*clist* is a sequence of mpl color specifiers.
See also: :meth:`~matplotlib.axes.Axes.set_color_cycle`.
.. Note:: Deprecated 2010/01/03.
Set rcParams['axes.color_cycle'] directly.
"""
rcParams['axes.color_cycle'] = clist
warnings.warn("Set rcParams['axes.color_cycle'] directly",
DeprecationWarning)
class _process_plot_var_args:
"""
Process variable length arguments to the plot command, so that
plot commands like the following are supported::
plot(t, s)
plot(t1, s1, t2, s2)
plot(t1, s1, 'ko', t2, s2)
plot(t1, s1, 'ko', t2, s2, 'r--', t3, e3)
an arbitrary number of *x*, *y*, *fmt* are allowed
"""
def __init__(self, axes, command='plot'):
self.axes = axes
self.command = command
self.set_color_cycle()
def set_color_cycle(self, clist=None):
if clist is None:
clist = rcParams['axes.color_cycle']
self.color_cycle = itertools.cycle(clist)
def __call__(self, *args, **kwargs):
if self.axes.xaxis is not None and self.axes.yaxis is not None:
xunits = kwargs.pop( 'xunits', self.axes.xaxis.units)
yunits = kwargs.pop( 'yunits', self.axes.yaxis.units)
if xunits!=self.axes.xaxis.units:
self.axes.xaxis.set_units(xunits)
if yunits!=self.axes.yaxis.units:
self.axes.yaxis.set_units(yunits)
ret = self._grab_next_args(*args, **kwargs)
return ret
def set_lineprops(self, line, **kwargs):
assert self.command == 'plot', 'set_lineprops only works with "plot"'
for key, val in kwargs.items():
funcName = "set_%s"%key
if not hasattr(line,funcName):
raise TypeError, 'There is no line property "%s"'%key
func = getattr(line,funcName)
func(val)
def set_patchprops(self, fill_poly, **kwargs):
assert self.command == 'fill', 'set_patchprops only works with "fill"'
for key, val in kwargs.items():
funcName = "set_%s"%key
if not hasattr(fill_poly,funcName):
raise TypeError, 'There is no patch property "%s"'%key
func = getattr(fill_poly,funcName)
func(val)
def _xy_from_xy(self, x, y):
if self.axes.xaxis is not None and self.axes.yaxis is not None:
bx = self.axes.xaxis.update_units(x)
by = self.axes.yaxis.update_units(y)
if self.command!='plot':
# the Line2D class can handle unitized data, with
# support for post hoc unit changes etc. Other mpl
# artists, eg Polygon which _process_plot_var_args
# also serves on calls to fill, cannot. So this is a
# hack to say: if you are not "plot", which is
# creating Line2D, then convert the data now to
# floats. If you are plot, pass the raw data through
# to Line2D which will handle the conversion. So
# polygons will not support post hoc conversions of
# the unit type since they are not storing the orig
# data. Hopefully we can rationalize this at a later
# date - JDH
if bx:
x = self.axes.convert_xunits(x)
if by:
y = self.axes.convert_yunits(y)
x = np.atleast_1d(x) #like asanyarray, but converts scalar to array
y = np.atleast_1d(y)
if x.shape[0] != y.shape[0]:
raise ValueError("x and y must have same first dimension")
if x.ndim > 2 or y.ndim > 2:
raise ValueError("x and y can be no greater than 2-D")
if x.ndim == 1:
x = x[:,np.newaxis]
if y.ndim == 1:
y = y[:,np.newaxis]
return x, y
def _makeline(self, x, y, kw, kwargs):
kw = kw.copy() # Don't modify the original kw.
if not 'color' in kw:
kw['color'] = self.color_cycle.next()
# (can't use setdefault because it always evaluates
# its second argument)
seg = mlines.Line2D(x, y,
axes=self.axes,
**kw
)
self.set_lineprops(seg, **kwargs)
return seg
def _makefill(self, x, y, kw, kwargs):
try:
facecolor = kw['color']
except KeyError:
facecolor = self.color_cycle.next()
seg = mpatches.Polygon(np.hstack(
(x[:,np.newaxis],y[:,np.newaxis])),
facecolor = facecolor,
fill=True,
closed=kw['closed']
)
self.set_patchprops(seg, **kwargs)
return seg
def _plot_args(self, tup, kwargs):
ret = []
if len(tup) > 1 and is_string_like(tup[-1]):
linestyle, marker, color = _process_plot_format(tup[-1])
tup = tup[:-1]
elif len(tup) == 3:
raise ValueError, 'third arg must be a format string'
else:
linestyle, marker, color = None, None, None
kw = {}
for k, v in zip(('linestyle', 'marker', 'color'),
(linestyle, marker, color)):
if v is not None:
kw[k] = v
y = np.atleast_1d(tup[-1])
if len(tup) == 2:
x = np.atleast_1d(tup[0])
else:
x = np.arange(y.shape[0], dtype=float)
x, y = self._xy_from_xy(x, y)
if self.command == 'plot':
func = self._makeline
else:
kw['closed'] = kwargs.get('closed', True)
func = self._makefill
ncx, ncy = x.shape[1], y.shape[1]
for j in xrange(max(ncx, ncy)):
seg = func(x[:,j%ncx], y[:,j%ncy], kw, kwargs)
ret.append(seg)
return ret
def _grab_next_args(self, *args, **kwargs):
remaining = args
while 1:
if len(remaining)==0:
return
if len(remaining) <= 3:
for seg in self._plot_args(remaining, kwargs):
yield seg
return
if is_string_like(remaining[2]):
isplit = 3
else:
isplit = 2
for seg in self._plot_args(remaining[:isplit], kwargs):
yield seg
remaining=remaining[isplit:]
class Axes(martist.Artist):
"""
The :class:`Axes` contains most of the figure elements:
:class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axis.Tick`,
:class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`,
:class:`~matplotlib.patches.Polygon`, etc., and sets the
coordinate system.
The :class:`Axes` instance supports callbacks through a callbacks
attribute which is a :class:`~matplotlib.cbook.CallbackRegistry`
instance. The events you can connect to are 'xlim_changed' and
'ylim_changed' and the callback will be called with func(*ax*)
where *ax* is the :class:`Axes` instance.
"""
name = "rectilinear"
_shared_x_axes = cbook.Grouper()
_shared_y_axes = cbook.Grouper()
def __str__(self):
return "Axes(%g,%g;%gx%g)" % tuple(self._position.bounds)
def __init__(self, fig, rect,
axisbg = None, # defaults to rc axes.facecolor
frameon = True,
sharex=None, # use Axes instance's xaxis info
sharey=None, # use Axes instance's yaxis info
label='',
xscale=None,
yscale=None,
**kwargs
):
"""
Build an :class:`Axes` instance in
:class:`~matplotlib.figure.Figure` *fig* with
*rect=[left, bottom, width, height]* in
:class:`~matplotlib.figure.Figure` coordinates
Optional keyword arguments:
================ =========================================
Keyword Description
================ =========================================
*adjustable* [ 'box' | 'datalim' | 'box-forced']
*alpha* float: the alpha transparency (can be None)
*anchor* [ 'C', 'SW', 'S', 'SE', 'E', 'NE', 'N',
'NW', 'W' ]
*aspect* [ 'auto' | 'equal' | aspect_ratio ]
*autoscale_on* [ *True* | *False* ] whether or not to
autoscale the *viewlim*
*axis_bgcolor* any matplotlib color, see
:func:`~matplotlib.pyplot.colors`
*axisbelow* draw the grids and ticks below the other
artists
*cursor_props* a (*float*, *color*) tuple
*figure* a :class:`~matplotlib.figure.Figure`
instance
*frame_on* a boolean - draw the axes frame
*label* the axes label
*navigate* [ *True* | *False* ]
*navigate_mode* [ 'PAN' | 'ZOOM' | None ] the navigation
toolbar button status
*position* [left, bottom, width, height] in
class:`~matplotlib.figure.Figure` coords
*sharex* an class:`~matplotlib.axes.Axes` instance
to share the x-axis with
*sharey* an class:`~matplotlib.axes.Axes` instance
to share the y-axis with
*title* the title string
*visible* [ *True* | *False* ] whether the axes is
visible
*xlabel* the xlabel
*xlim* (*xmin*, *xmax*) view limits
*xscale* [%(scale)s]
*xticklabels* sequence of strings
*xticks* sequence of floats
*ylabel* the ylabel strings
*ylim* (*ymin*, *ymax*) view limits
*yscale* [%(scale)s]
*yticklabels* sequence of strings
*yticks* sequence of floats
================ =========================================
""" % {'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()])}
martist.Artist.__init__(self)
if isinstance(rect, mtransforms.Bbox):
self._position = rect
else:
self._position = mtransforms.Bbox.from_bounds(*rect)
self._originalPosition = self._position.frozen()
self.set_axes(self)
self.set_aspect('auto')
self._adjustable = 'box'
self.set_anchor('C')
self._sharex = sharex
self._sharey = sharey
if sharex is not None:
self._shared_x_axes.join(self, sharex)
if sharex._adjustable == 'box':
sharex._adjustable = 'datalim'
#warnings.warn(
# 'shared axes: "adjustable" is being changed to "datalim"')
self._adjustable = 'datalim'
if sharey is not None:
self._shared_y_axes.join(self, sharey)
if sharey._adjustable == 'box':
sharey._adjustable = 'datalim'
#warnings.warn(
# 'shared axes: "adjustable" is being changed to "datalim"')
self._adjustable = 'datalim'
self.set_label(label)
self.set_figure(fig)
self.set_axes_locator(kwargs.get("axes_locator", None))
self.spines = self._gen_axes_spines()
# this call may differ for non-sep axes, eg polar
self._init_axis()
if axisbg is None: axisbg = rcParams['axes.facecolor']
self._axisbg = axisbg
self._frameon = frameon
self._axisbelow = rcParams['axes.axisbelow']
self._rasterization_zorder = -30000
self._hold = rcParams['axes.hold']
self._connected = {} # a dict from events to (id, func)
self.cla()
# funcs used to format x and y - fall back on major formatters
self.fmt_xdata = None
self.fmt_ydata = None
self.set_cursor_props((1,'k')) # set the cursor properties for axes
self._cachedRenderer = None
self.set_navigate(True)
self.set_navigate_mode(None)
if xscale:
self.set_xscale(xscale)
if yscale:
self.set_yscale(yscale)
if len(kwargs): martist.setp(self, **kwargs)
if self.xaxis is not None:
self._xcid = self.xaxis.callbacks.connect('units finalize',
self.relim)
if self.yaxis is not None:
self._ycid = self.yaxis.callbacks.connect('units finalize',
self.relim)
def get_window_extent(self, *args, **kwargs):
'''
get the axes bounding box in display space; *args* and
*kwargs* are empty
'''
return self.bbox
def _init_axis(self):
"move this out of __init__ because non-separable axes don't use it"
self.xaxis = maxis.XAxis(self)
self.spines['bottom'].register_axis(self.xaxis)
self.spines['top'].register_axis(self.xaxis)
self.yaxis = maxis.YAxis(self)
self.spines['left'].register_axis(self.yaxis)
self.spines['right'].register_axis(self.yaxis)
self._update_transScale()
def set_figure(self, fig):
"""
Set the class:`~matplotlib.axes.Axes` figure
accepts a class:`~matplotlib.figure.Figure` instance
"""
martist.Artist.set_figure(self, fig)
self.bbox = mtransforms.TransformedBbox(self._position, fig.transFigure)
#these will be updated later as data is added
self.dataLim = mtransforms.Bbox.unit()
self.viewLim = mtransforms.Bbox.unit()
self.transScale = mtransforms.TransformWrapper(
mtransforms.IdentityTransform())
self._set_lim_and_transforms()
def _set_lim_and_transforms(self):
"""
set the *dataLim* and *viewLim*
:class:`~matplotlib.transforms.Bbox` attributes and the
*transScale*, *transData*, *transLimits* and *transAxes*
transformations.
.. note::
This method is primarily used by rectilinear projections
of the :class:`~matplotlib.axes.Axes` class, and is meant
to be overridden by new kinds of projection axes that need
different transformations and limits. (See
:class:`~matplotlib.projections.polar.PolarAxes` for an
example.
"""
self.transAxes = mtransforms.BboxTransformTo(self.bbox)
# Transforms the x and y axis separately by a scale factor.
# It is assumed that this part will have non-linear components
# (e.g. for a log scale).
self.transScale = mtransforms.TransformWrapper(
mtransforms.IdentityTransform())
# An affine transformation on the data, generally to limit the
# range of the axes
self.transLimits = mtransforms.BboxTransformFrom(
mtransforms.TransformedBbox(self.viewLim, self.transScale))
# The parentheses are important for efficiency here -- they
# group the last two (which are usually affines) separately
# from the first (which, with log-scaling can be non-affine).
self.transData = self.transScale + (self.transLimits + self.transAxes)
self._xaxis_transform = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
self._yaxis_transform = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
def get_xaxis_transform(self,which='grid'):
"""
Get the transformation used for drawing x-axis labels, ticks
and gridlines. The x-direction is in data coordinates and the
y-direction is in axis coordinates.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
if which=='grid':
return self._xaxis_transform
elif which=='tick1':
# for cartesian projection, this is bottom spine
return self.spines['bottom'].get_spine_transform()
elif which=='tick2':
# for cartesian projection, this is top spine
return self.spines['top'].get_spine_transform()
else:
raise ValueError('unknown value for which')
def get_xaxis_text1_transform(self, pad_points):
"""
Get the transformation used for drawing x-axis labels, which
will add the given amount of padding (in points) between the
axes and the label. The x-direction is in data coordinates
and the y-direction is in axis coordinates. Returns a
3-tuple of the form::
(transform, valign, halign)
where *valign* and *halign* are requested alignments for the
text.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
return (self.get_xaxis_transform(which='tick1') +
mtransforms.ScaledTranslation(0, -1 * pad_points / 72.0,
self.figure.dpi_scale_trans),
"top", "center")
def get_xaxis_text2_transform(self, pad_points):
"""
Get the transformation used for drawing the secondary x-axis
labels, which will add the given amount of padding (in points)
between the axes and the label. The x-direction is in data
coordinates and the y-direction is in axis coordinates.
Returns a 3-tuple of the form::
(transform, valign, halign)
where *valign* and *halign* are requested alignments for the
text.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
return (self.get_xaxis_transform(which='tick2') +
mtransforms.ScaledTranslation(0, pad_points / 72.0,
self.figure.dpi_scale_trans),
"bottom", "center")
def get_yaxis_transform(self,which='grid'):
"""
Get the transformation used for drawing y-axis labels, ticks
and gridlines. The x-direction is in axis coordinates and the
y-direction is in data coordinates.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
if which=='grid':
return self._yaxis_transform
elif which=='tick1':
# for cartesian projection, this is bottom spine
return self.spines['left'].get_spine_transform()
elif which=='tick2':
# for cartesian projection, this is top spine
return self.spines['right'].get_spine_transform()
else:
raise ValueError('unknown value for which')
def get_yaxis_text1_transform(self, pad_points):
"""
Get the transformation used for drawing y-axis labels, which
will add the given amount of padding (in points) between the
axes and the label. The x-direction is in axis coordinates
and the y-direction is in data coordinates. Returns a 3-tuple
of the form::
(transform, valign, halign)
where *valign* and *halign* are requested alignments for the
text.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
return (self.get_yaxis_transform(which='tick1') +
mtransforms.ScaledTranslation(-1 * pad_points / 72.0, 0,
self.figure.dpi_scale_trans),
"center", "right")
def get_yaxis_text2_transform(self, pad_points):
"""
Get the transformation used for drawing the secondary y-axis
labels, which will add the given amount of padding (in points)
between the axes and the label. The x-direction is in axis
coordinates and the y-direction is in data coordinates.
Returns a 3-tuple of the form::
(transform, valign, halign)
where *valign* and *halign* are requested alignments for the
text.
.. note::
This transformation is primarily used by the
:class:`~matplotlib.axis.Axis` class, and is meant to be
overridden by new kinds of projections that may need to
place axis elements in different locations.
"""
return (self.get_yaxis_transform(which='tick2') +
mtransforms.ScaledTranslation(pad_points / 72.0, 0,
self.figure.dpi_scale_trans),
"center", "left")
def _update_transScale(self):
self.transScale.set(
mtransforms.blended_transform_factory(
self.xaxis.get_transform(), self.yaxis.get_transform()))
if hasattr(self, "lines"):
for line in self.lines:
try:
line._transformed_path.invalidate()
except AttributeError:
pass
def get_position(self, original=False):
'Return the a copy of the axes rectangle as a Bbox'
if original:
return self._originalPosition.frozen()
else:
return self._position.frozen()
def set_position(self, pos, which='both'):
"""
Set the axes position with::
pos = [left, bottom, width, height]
in relative 0,1 coords, or *pos* can be a
:class:`~matplotlib.transforms.Bbox`
There are two position variables: one which is ultimately
used, but which may be modified by :meth:`apply_aspect`, and a
second which is the starting point for :meth:`apply_aspect`.
Optional keyword arguments:
*which*
========== ====================
value description
========== ====================
'active' to change the first
'original' to change the second
'both' to change both
========== ====================
"""
if not isinstance(pos, mtransforms.BboxBase):
pos = mtransforms.Bbox.from_bounds(*pos)
if which in ('both', 'active'):
self._position.set(pos)
if which in ('both', 'original'):
self._originalPosition.set(pos)
def reset_position(self):
'Make the original position the active position'
pos = self.get_position(original=True)
self.set_position(pos, which='active')
def set_axes_locator(self, locator):
"""
set axes_locator
ACCEPT : a callable object which takes an axes instance and renderer and
returns a bbox.
"""
self._axes_locator = locator
def get_axes_locator(self):
"""
return axes_locator
"""
return self._axes_locator
def _set_artist_props(self, a):
'set the boilerplate props for artists added to axes'
a.set_figure(self.figure)
if not a.is_transform_set():
a.set_transform(self.transData)
a.set_axes(self)
def _gen_axes_patch(self):
"""
Returns the patch used to draw the background of the axes. It
is also used as the clipping path for any data elements on the
axes.
In the standard axes, this is a rectangle, but in other
projections it may not be.
.. note::
Intended to be overridden by new projection types.
"""
return mpatches.Rectangle((0.0, 0.0), 1.0, 1.0)
def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'):
"""
Returns a dict whose keys are spine names and values are
Line2D or Patch instances. Each element is used to draw a
spine of the axes.
In the standard axes, this is a single line segment, but in
other projections it may not be.
.. note::
Intended to be overridden by new projection types.
"""
return {
'left':mspines.Spine.linear_spine(self,'left'),
'right':mspines.Spine.linear_spine(self,'right'),
'bottom':mspines.Spine.linear_spine(self,'bottom'),
'top':mspines.Spine.linear_spine(self,'top'),
}
def cla(self):
'Clear the current axes'
# Note: this is called by Axes.__init__()
self.xaxis.cla()
self.yaxis.cla()
for name,spine in self.spines.iteritems():
spine.cla()
self.ignore_existing_data_limits = True
self.callbacks = cbook.CallbackRegistry(('xlim_changed',
'ylim_changed'))
if self._sharex is not None:
# major and minor are class instances with
# locator and formatter attributes
self.xaxis.major = self._sharex.xaxis.major
self.xaxis.minor = self._sharex.xaxis.minor
x0, x1 = self._sharex.get_xlim()
self.set_xlim(x0, x1, emit=False, auto=None)
self.xaxis.set_scale(self._sharex.xaxis.get_scale())
else:
self.xaxis.set_scale('linear')
if self._sharey is not None:
self.yaxis.major = self._sharey.yaxis.major
self.yaxis.minor = self._sharey.yaxis.minor
y0, y1 = self._sharey.get_ylim()
self.set_ylim(y0, y1, emit=False, auto=None)
self.yaxis.set_scale(self._sharey.yaxis.get_scale())
else:
self.yaxis.set_scale('linear')
self._autoscaleXon = True
self._autoscaleYon = True
self._xmargin = 0
self._ymargin = 0
self._tight = False
self._update_transScale() # needed?
self._get_lines = _process_plot_var_args(self)
self._get_patches_for_fill = _process_plot_var_args(self, 'fill')
self._gridOn = rcParams['axes.grid']
self.lines = []
self.patches = []
self.texts = []
self.tables = []
self.artists = []
self.images = []
self._current_image = None # strictly for pyplot via _sci, _gci
self.legend_ = None
self.collections = [] # collection.Collection instances
self.grid(self._gridOn)
props = font_manager.FontProperties(size=rcParams['axes.titlesize'])
self.titleOffsetTrans = mtransforms.ScaledTranslation(
0.0, 5.0 / 72.0, self.figure.dpi_scale_trans)
self.title = mtext.Text(
x=0.5, y=1.0, text='',
fontproperties=props,
verticalalignment='baseline',
horizontalalignment='center',
)
self.title.set_transform(self.transAxes + self.titleOffsetTrans)
self.title.set_clip_box(None)
self._set_artist_props(self.title)
# the patch draws the background of the axes. we want this to
# be below the other artists; the axesPatch name is
# deprecated. We use the frame to draw the edges so we are
# setting the edgecolor to None
self.patch = self.axesPatch = self._gen_axes_patch()
self.patch.set_figure(self.figure)
self.patch.set_facecolor(self._axisbg)
self.patch.set_edgecolor('None')
self.patch.set_linewidth(0)
self.patch.set_transform(self.transAxes)
self.axison = True
self.xaxis.set_clip_path(self.patch)
self.yaxis.set_clip_path(self.patch)
self._shared_x_axes.clean()
self._shared_y_axes.clean()
def get_frame(self):
raise AttributeError('Axes.frame was removed in favor of Axes.spines')
frame = property(get_frame)
def clear(self):
'clear the axes'
self.cla()
def set_color_cycle(self, clist):
"""
Set the color cycle for any future plot commands on this Axes.
*clist* is a list of mpl color specifiers.
"""
self._get_lines.set_color_cycle(clist)
self._get_patches_for_fill.set_color_cycle(clist)
def ishold(self):
'return the HOLD status of the axes'
return self._hold
def hold(self, b=None):
"""
call signature::
hold(b=None)
Set the hold state. If *hold* is *None* (default), toggle the
*hold* state. Else set the *hold* state to boolean value *b*.
Examples:
* toggle hold:
>>> hold()
* turn hold on:
>>> hold(True)
* turn hold off
>>> hold(False)
When hold is True, subsequent plot commands will be added to
the current axes. When hold is False, the current axes and
figure will be cleared on the next plot command
"""
if b is None:
self._hold = not self._hold
else:
self._hold = b
def get_aspect(self):
return self._aspect
def set_aspect(self, aspect, adjustable=None, anchor=None):
"""
*aspect*
======== ================================================
value description
======== ================================================
'auto' automatic; fill position rectangle with data
'normal' same as 'auto'; deprecated
'equal' same scaling from data to plot units for x and y
num a circle will be stretched such that the height
is num times the width. aspect=1 is the same as
aspect='equal'.
======== ================================================
*adjustable*
============ =====================================
value description
============ =====================================
'box' change physical size of axes
'datalim' change xlim or ylim
'box-forced' same as 'box', but axes can be shared
============ =====================================
'box' does not allow axes sharing, as this can cause
unintended side effect. For cases when sharing axes is
fine, use 'box-forced'.
*anchor*
===== =====================
value description
===== =====================
'C' centered
'SW' lower left corner
'S' middle of bottom edge
'SE' lower right corner
etc.
===== =====================
"""
if aspect in ('normal', 'auto'):
self._aspect = 'auto'
elif aspect == 'equal':
self._aspect = 'equal'
else:
self._aspect = float(aspect) # raise ValueError if necessary
if adjustable is not None:
self.set_adjustable(adjustable)
if anchor is not None:
self.set_anchor(anchor)
def get_adjustable(self):
return self._adjustable
def set_adjustable(self, adjustable):
"""
ACCEPTS: [ 'box' | 'datalim' | 'box-forced']
"""
if adjustable in ('box', 'datalim', 'box-forced'):
if self in self._shared_x_axes or self in self._shared_y_axes:
if adjustable == 'box':
raise ValueError(
'adjustable must be "datalim" for shared axes')
self._adjustable = adjustable
else:
raise ValueError('argument must be "box", or "datalim"')
def get_anchor(self):
return self._anchor
def set_anchor(self, anchor):
"""
*anchor*
===== ============
value description
===== ============
'C' Center
'SW' bottom left
'S' bottom
'SE' bottom right
'E' right
'NE' top right
'N' top
'NW' top left
'W' left
===== ============
"""
if anchor in mtransforms.Bbox.coefs.keys() or len(anchor) == 2:
self._anchor = anchor
else:
raise ValueError('argument must be among %s' %
', '.join(mtransforms.BBox.coefs.keys()))
def get_data_ratio(self):
"""
Returns the aspect ratio of the raw data.
This method is intended to be overridden by new projection
types.
"""
xmin,xmax = self.get_xbound()
ymin,ymax = self.get_ybound()
xsize = max(math.fabs(xmax-xmin), 1e-30)
ysize = max(math.fabs(ymax-ymin), 1e-30)
return ysize/xsize
def get_data_ratio_log(self):
"""
Returns the aspect ratio of the raw data in log scale.
Will be used when both axis scales are in log.
"""
xmin,xmax = self.get_xbound()
ymin,ymax = self.get_ybound()
xsize = max(math.fabs(math.log10(xmax)-math.log10(xmin)), 1e-30)
ysize = max(math.fabs(math.log10(ymax)-math.log10(ymin)), 1e-30)
return ysize/xsize
def apply_aspect(self, position=None):
'''
Use :meth:`_aspect` and :meth:`_adjustable` to modify the
axes box or the view limits.
'''
if position is None:
position = self.get_position(original=True)
aspect = self.get_aspect()
if self.name != 'polar':
xscale, yscale = self.get_xscale(), self.get_yscale()
if xscale == "linear" and yscale == "linear":
aspect_scale_mode = "linear"
elif xscale == "log" and yscale == "log":
aspect_scale_mode = "log"
elif (xscale == "linear" and yscale == "log") or \
(xscale == "log" and yscale == "linear"):
if aspect is not "auto":
warnings.warn(
'aspect is not supported for Axes with xscale=%s, yscale=%s' \
% (xscale, yscale))
aspect = "auto"
else: # some custom projections have their own scales.
pass
else:
aspect_scale_mode = "linear"
if aspect == 'auto':
self.set_position( position , which='active')
return
if aspect == 'equal':
A = 1
else:
A = aspect
#Ensure at drawing time that any Axes involved in axis-sharing
# does not have its position changed.
if self in self._shared_x_axes or self in self._shared_y_axes:
if self._adjustable == 'box':
self._adjustable = 'datalim'
warnings.warn(
'shared axes: "adjustable" is being changed to "datalim"')
figW,figH = self.get_figure().get_size_inches()
fig_aspect = figH/figW
if self._adjustable in ['box', 'box-forced']:
if aspect_scale_mode == "log":
box_aspect = A * self.get_data_ratio_log()
else:
box_aspect = A * self.get_data_ratio()
pb = position.frozen()
pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect)
self.set_position(pb1.anchored(self.get_anchor(), pb), 'active')
return
# reset active to original in case it had been changed
# by prior use of 'box'
self.set_position(position, which='active')
xmin,xmax = self.get_xbound()
ymin,ymax = self.get_ybound()
if aspect_scale_mode == "log":
xmin, xmax = math.log10(xmin), math.log10(xmax)
ymin, ymax = math.log10(ymin), math.log10(ymax)
xsize = max(math.fabs(xmax-xmin), 1e-30)
ysize = max(math.fabs(ymax-ymin), 1e-30)
l,b,w,h = position.bounds
box_aspect = fig_aspect * (h/w)
data_ratio = box_aspect / A
y_expander = (data_ratio*xsize/ysize - 1.0)
#print 'y_expander', y_expander
# If y_expander > 0, the dy/dx viewLim ratio needs to increase
if abs(y_expander) < 0.005:
#print 'good enough already'
return
if aspect_scale_mode == "log":
dL = self.dataLim
dL_width = math.log10(dL.x1) - math.log10(dL.x0)
dL_height = math.log10(dL.y1) - math.log10(dL.y0)
xr = 1.05 * dL_width
yr = 1.05 * dL_height
else:
dL = self.dataLim
xr = 1.05 * dL.width
yr = 1.05 * dL.height
xmarg = xsize - xr
ymarg = ysize - yr
Ysize = data_ratio * xsize
Xsize = ysize / data_ratio
Xmarg = Xsize - xr
Ymarg = Ysize - yr
xm = 0 # Setting these targets to, e.g., 0.05*xr does not seem to help.
ym = 0
#print 'xmin, xmax, ymin, ymax', xmin, xmax, ymin, ymax
#print 'xsize, Xsize, ysize, Ysize', xsize, Xsize, ysize, Ysize
changex = (self in self._shared_y_axes
and self not in self._shared_x_axes)
changey = (self in self._shared_x_axes
and self not in self._shared_y_axes)
if changex and changey:
warnings.warn("adjustable='datalim' cannot work with shared "
"x and y axes")
return
if changex:
adjust_y = False
else:
#print 'xmarg, ymarg, Xmarg, Ymarg', xmarg, ymarg, Xmarg, Ymarg
if xmarg > xm and ymarg > ym:
adjy = ((Ymarg > 0 and y_expander < 0)
or (Xmarg < 0 and y_expander > 0))
else:
adjy = y_expander > 0
#print 'y_expander, adjy', y_expander, adjy
adjust_y = changey or adjy #(Ymarg > xmarg)
if adjust_y:
yc = 0.5*(ymin+ymax)
y0 = yc - Ysize/2.0
y1 = yc + Ysize/2.0
if aspect_scale_mode == "log":
self.set_ybound((10.**y0, 10.**y1))
else:
self.set_ybound((y0, y1))
#print 'New y0, y1:', y0, y1
#print 'New ysize, ysize/xsize', y1-y0, (y1-y0)/xsize
else:
xc = 0.5*(xmin+xmax)
x0 = xc - Xsize/2.0
x1 = xc + Xsize/2.0
if aspect_scale_mode == "log":
self.set_xbound((10.**x0, 10.**x1))
else:
self.set_xbound((x0, x1))
#print 'New x0, x1:', x0, x1
#print 'New xsize, ysize/xsize', x1-x0, ysize/(x1-x0)
def axis(self, *v, **kwargs):
'''
Convenience method for manipulating the x and y view limits
and the aspect ratio of the plot.
*kwargs* are passed on to :meth:`set_xlim` and
:meth:`set_ylim`
'''
if len(v)==1 and is_string_like(v[0]):
s = v[0].lower()
if s=='on': self.set_axis_on()
elif s=='off': self.set_axis_off()
elif s in ('equal', 'tight', 'scaled', 'normal', 'auto', 'image'):
self.set_autoscale_on(True)
self.set_aspect('auto')
self.autoscale_view(tight=False)
# self.apply_aspect()
if s=='equal':
self.set_aspect('equal', adjustable='datalim')
elif s == 'scaled':
self.set_aspect('equal', adjustable='box', anchor='C')
self.set_autoscale_on(False) # Req. by Mark Bakker
elif s=='tight':
self.autoscale_view(tight=True)
self.set_autoscale_on(False)
elif s == 'image':
self.autoscale_view(tight=True)
self.set_autoscale_on(False)
self.set_aspect('equal', adjustable='box', anchor='C')
else:
raise ValueError('Unrecognized string %s to axis; '
'try on or off' % s)
xmin, xmax = self.get_xlim()
ymin, ymax = self.get_ylim()
return xmin, xmax, ymin, ymax
try: v[0]
except IndexError:
emit = kwargs.get('emit', True)
xmin = kwargs.get('xmin', None)
xmax = kwargs.get('xmax', None)
xmin, xmax = self.set_xlim(xmin, xmax, emit)
ymin = kwargs.get('ymin', None)
ymax = kwargs.get('ymax', None)
ymin, ymax = self.set_ylim(ymin, ymax, emit)
return xmin, xmax, ymin, ymax
v = v[0]
if len(v) != 4:
raise ValueError('v must contain [xmin xmax ymin ymax]')
self.set_xlim([v[0], v[1]])
self.set_ylim([v[2], v[3]])
return v
def get_child_artists(self):
"""
Return a list of artists the axes contains.
.. deprecated:: 0.98
"""
raise DeprecationWarning('Use get_children instead')
def get_frame(self):
'Return the axes Rectangle frame'
warnings.warn('use ax.patch instead', DeprecationWarning)
return self.patch
def get_legend(self):
'Return the legend.Legend instance, or None if no legend is defined'
return self.legend_
def get_images(self):
'return a list of Axes images contained by the Axes'
return cbook.silent_list('AxesImage', self.images)
def get_lines(self):
'Return a list of lines contained by the Axes'
return cbook.silent_list('Line2D', self.lines)
def get_xaxis(self):
'Return the XAxis instance'
return self.xaxis
def get_xgridlines(self):
'Get the x grid lines as a list of Line2D instances'
return cbook.silent_list('Line2D xgridline', self.xaxis.get_gridlines())
def get_xticklines(self):
'Get the xtick lines as a list of Line2D instances'
return cbook.silent_list('Text xtickline', self.xaxis.get_ticklines())
def get_yaxis(self):
'Return the YAxis instance'
return self.yaxis
def get_ygridlines(self):
'Get the y grid lines as a list of Line2D instances'
return cbook.silent_list('Line2D ygridline', self.yaxis.get_gridlines())
def get_yticklines(self):
'Get the ytick lines as a list of Line2D instances'
return cbook.silent_list('Line2D ytickline', self.yaxis.get_ticklines())
#### Adding and tracking artists
def _sci(self, im):
"""
helper for :func:`~matplotlib.pyplot.sci`;
do not use elsewhere.
"""
if isinstance(im, matplotlib.contour.ContourSet):
if im.collections[0] not in self.collections:
raise ValueError(
"ContourSet must be in current Axes")
elif im not in self.images and im not in self.collections:
raise ValueError(
"Argument must be an image, collection, or ContourSet in this Axes")
self._current_image = im
def _gci(self):
"""
helper for :func:`~matplotlib.pyplot.gci`;
do not use elsewhere.
"""
return self._current_image
def has_data(self):
'''Return *True* if any artists have been added to axes.
This should not be used to determine whether the *dataLim*
need to be updated, and may not actually be useful for
anything.
'''
return (
len(self.collections) +
len(self.images) +
len(self.lines) +
len(self.patches))>0
def add_artist(self, a):
'''
Add any :class:`~matplotlib.artist.Artist` to the axes.
Returns the artist.
'''
a.set_axes(self)
self.artists.append(a)
self._set_artist_props(a)
a.set_clip_path(self.patch)
a._remove_method = lambda h: self.artists.remove(h)
return a
def add_collection(self, collection, autolim=True):
'''
Add a :class:`~matplotlib.collections.Collection` instance
to the axes.
Returns the collection.
'''
label = collection.get_label()
if not label:
collection.set_label('_collection%d'%len(self.collections))
self.collections.append(collection)
self._set_artist_props(collection)
if collection.get_clip_path() is None:
collection.set_clip_path(self.patch)
if autolim:
if collection._paths and len(collection._paths):
self.update_datalim(collection.get_datalim(self.transData))
collection._remove_method = lambda h: self.collections.remove(h)
return collection
def add_line(self, line):
'''
Add a :class:`~matplotlib.lines.Line2D` to the list of plot
lines
Returns the line.
'''
self._set_artist_props(line)
if line.get_clip_path() is None:
line.set_clip_path(self.patch)
self._update_line_limits(line)
if not line.get_label():
line.set_label('_line%d'%len(self.lines))
self.lines.append(line)
line._remove_method = lambda h: self.lines.remove(h)
return line
def _update_line_limits(self, line):
p = line.get_path()
if p.vertices.size > 0:
self.dataLim.update_from_path(p, self.ignore_existing_data_limits,
updatex=line.x_isdata,
updatey=line.y_isdata)
self.ignore_existing_data_limits = False
def add_patch(self, p):
"""
Add a :class:`~matplotlib.patches.Patch` *p* to the list of
axes patches; the clipbox will be set to the Axes clipping
box. If the transform is not set, it will be set to
:attr:`transData`.
Returns the patch.
"""
self._set_artist_props(p)
if p.get_clip_path() is None:
p.set_clip_path(self.patch)
self._update_patch_limits(p)
self.patches.append(p)
p._remove_method = lambda h: self.patches.remove(h)
return p
def _update_patch_limits(self, patch):
'update the data limits for patch *p*'
# hist can add zero height Rectangles, which is useful to keep
# the bins, counts and patches lined up, but it throws off log
# scaling. We'll ignore rects with zero height or width in
# the auto-scaling
# cannot check for '==0' since unitized data may not compare to zero
if (isinstance(patch, mpatches.Rectangle) and
((not patch.get_width()) or (not patch.get_height()))):
return
vertices = patch.get_path().vertices
if vertices.size > 0:
xys = patch.get_patch_transform().transform(vertices)
if patch.get_data_transform() != self.transData:
transform = (patch.get_data_transform() +
self.transData.inverted())
xys = transform.transform(xys)
self.update_datalim(xys, updatex=patch.x_isdata,
updatey=patch.y_isdata)
def add_table(self, tab):
'''
Add a :class:`~matplotlib.tables.Table` instance to the
list of axes tables
Returns the table.
'''
self._set_artist_props(tab)
self.tables.append(tab)
tab.set_clip_path(self.patch)
tab._remove_method = lambda h: self.tables.remove(h)
return tab
def relim(self):
"""
Recompute the data limits based on current artists.
At present, :class:`~matplotlib.collections.Collection`
instances are not supported.
"""
# Collections are deliberately not supported (yet); see
# the TODO note in artists.py.
self.dataLim.ignore(True)
self.ignore_existing_data_limits = True
for line in self.lines:
self._update_line_limits(line)
for p in self.patches:
self._update_patch_limits(p)
def update_datalim(self, xys, updatex=True, updatey=True):
'Update the data lim bbox with seq of xy tups or equiv. 2-D array'
# if no data is set currently, the bbox will ignore its
# limits and set the bound to be the bounds of the xydata.
# Otherwise, it will compute the bounds of it's current data
# and the data in xydata
if iterable(xys) and not len(xys): return
if not ma.isMaskedArray(xys):
xys = np.asarray(xys)
self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits,
updatex=updatex, updatey=updatey)
self.ignore_existing_data_limits = False
def update_datalim_numerix(self, x, y):
'Update the data lim bbox with seq of xy tups'
# if no data is set currently, the bbox will ignore it's
# limits and set the bound to be the bounds of the xydata.
# Otherwise, it will compute the bounds of it's current data
# and the data in xydata
if iterable(x) and not len(x): return
self.dataLim.update_from_data(x, y, self.ignore_existing_data_limits)
self.ignore_existing_data_limits = False
def update_datalim_bounds(self, bounds):
'''
Update the datalim to include the given
:class:`~matplotlib.transforms.Bbox` *bounds*
'''
self.dataLim.set(mtransforms.Bbox.union([self.dataLim, bounds]))
def _process_unit_info(self, xdata=None, ydata=None, kwargs=None):
'look for unit *kwargs* and update the axis instances as necessary'
if self.xaxis is None or self.yaxis is None: return
#print 'processing', self.get_geometry()
if xdata is not None:
# we only need to update if there is nothing set yet.
if not self.xaxis.have_units():
self.xaxis.update_units(xdata)
#print '\tset from xdata', self.xaxis.units
if ydata is not None:
# we only need to update if there is nothing set yet.
if not self.yaxis.have_units():
self.yaxis.update_units(ydata)
#print '\tset from ydata', self.yaxis.units
# process kwargs 2nd since these will override default units
if kwargs is not None:
xunits = kwargs.pop( 'xunits', self.xaxis.units)
if xunits!=self.xaxis.units:
#print '\tkw setting xunits', xunits
self.xaxis.set_units(xunits)
# If the units being set imply a different converter,
# we need to update.
if xdata is not None:
self.xaxis.update_units(xdata)
yunits = kwargs.pop('yunits', self.yaxis.units)
if yunits!=self.yaxis.units:
#print '\tkw setting yunits', yunits
self.yaxis.set_units(yunits)
# If the units being set imply a different converter,
# we need to update.
if ydata is not None:
self.yaxis.update_units(ydata)
def in_axes(self, mouseevent):
'''
return *True* if the given *mouseevent* (in display coords)
is in the Axes
'''
return self.patch.contains(mouseevent)[0]
def get_autoscale_on(self):
"""
Get whether autoscaling is applied for both axes on plot commands
"""
return self._autoscaleXon and self._autoscaleYon
def get_autoscalex_on(self):
"""
Get whether autoscaling for the x-axis is applied on plot commands
"""
return self._autoscaleXon
def get_autoscaley_on(self):
"""
Get whether autoscaling for the y-axis is applied on plot commands
"""
return self._autoscaleYon
def set_autoscale_on(self, b):
"""
Set whether autoscaling is applied on plot commands
accepts: [ *True* | *False* ]
"""
self._autoscaleXon = b
self._autoscaleYon = b
def set_autoscalex_on(self, b):
"""
Set whether autoscaling for the x-axis is applied on plot commands
accepts: [ *True* | *False* ]
"""
self._autoscaleXon = b
def set_autoscaley_on(self, b):
"""
Set whether autoscaling for the y-axis is applied on plot commands
accepts: [ *True* | *False* ]
"""
self._autoscaleYon = b
def set_xmargin(self, m):
"""
Set padding of X data limits prior to autoscaling.
*m* times the data interval will be added to each
end of that interval before it is used in autoscaling.
accepts: float in range 0 to 1
"""
if m < 0 or m > 1:
raise ValueError("margin must be in range 0 to 1")
self._xmargin = m
def set_ymargin(self, m):
"""
Set padding of Y data limits prior to autoscaling.
*m* times the data interval will be added to each
end of that interval before it is used in autoscaling.
accepts: float in range 0 to 1
"""
if m < 0 or m > 1:
raise ValueError("margin must be in range 0 to 1")
self._ymargin = m
def margins(self, *args, **kw):
"""
Convenience method to set or retrieve autoscaling margins.
signatures::
margins()
returns xmargin, ymargin
::
margins(margin)
margins(xmargin, ymargin)
margins(x=xmargin, y=ymargin)
margins(..., tight=False)
All three forms above set the xmargin and ymargin parameters.
All keyword parameters are optional. A single argument
specifies both xmargin and ymargin. The *tight* parameter
is passed to :meth:`autoscale_view`, which is executed after
a margin is changed; the default here is *True*, on the
assumption that when margins are specified, no additional
padding to match tick marks is usually desired. Setting
*tight* to *None* will preserve the previous setting.
Specifying any margin changes only the autoscaling; for example,
if *xmargin* is not None, then *xmargin* times the X data
interval will be added to each end of that interval before
it is used in autoscaling.
"""
if not args and not kw:
return self._xmargin, self._ymargin
tight = kw.pop('tight', True)
mx = kw.pop('x', None)
my = kw.pop('y', None)
if len(args) == 1:
mx = my = args[0]
elif len(args) == 2:
mx, my = args
else:
raise ValueError("more than two arguments were supplied")
if mx is not None:
self.set_xmargin(mx)
if my is not None:
self.set_ymargin(my)
scalex = (mx is not None)
scaley = (my is not None)
self.autoscale_view(tight=tight, scalex=scalex, scaley=scaley)
def set_rasterization_zorder(self, z):
"""
Set zorder value below which artists will be rasterized
"""
self._rasterization_zorder = z
def get_rasterization_zorder(self):
"""
Get zorder value below which artists will be rasterized
"""
return self._rasterization_zorder
def autoscale(self, enable=True, axis='both', tight=None):
"""
Convenience method for simple axis view autoscaling.
It turns autoscaling on or off, and then,
if autoscaling for either axis is on, it performs
the autoscaling on the specified axis or axes.
*enable*: [True | False | None]
True (default) turns autoscaling on, False turns it off.
None leaves the autoscaling state unchanged.
*axis*: ['x' | 'y' | 'both']
which axis to operate on; default is 'both'
*tight*: [True | False | None]
If True, set view limits to data limits;
if False, let the locator and margins expand the view limits;
if None, use tight scaling if the only artist is an image,
otherwise treat *tight* as False.
The *tight* setting is retained for future autoscaling
until it is explicitly changed.
Returns None.
"""
if enable is None:
scalex = True
scaley = True
else:
scalex = False
scaley = False
if axis in ['x', 'both']:
self._autoscaleXon = bool(enable)
scalex = self._autoscaleXon
if axis in ['y', 'both']:
self._autoscaleYon = bool(enable)
scaley = self._autoscaleYon
self.autoscale_view(tight=tight, scalex=scalex, scaley=scaley)
def autoscale_view(self, tight=None, scalex=True, scaley=True):
"""
Autoscale the view limits using the data limits. You can
selectively autoscale only a single axis, eg, the xaxis by
setting *scaley* to *False*. The autoscaling preserves any
axis direction reversal that has already been done.
The data limits are not updated automatically when artist
data are changed after the artist has been added to an
Axes instance. In that case, use
:meth:`matplotlib.axes.Axes.relim`
prior to calling autoscale_view.
"""
if tight is None:
# if image data only just use the datalim
_tight = self._tight or (len(self.images)>0 and
len(self.lines)==0 and
len(self.patches)==0)
else:
_tight = self._tight = bool(tight)
if scalex and self._autoscaleXon:
xshared = self._shared_x_axes.get_siblings(self)
dl = [ax.dataLim for ax in xshared]
bb = mtransforms.BboxBase.union(dl)
x0, x1 = bb.intervalx
xlocator = self.xaxis.get_major_locator()
try:
# e.g. DateLocator has its own nonsingular()
x0, x1 = xlocator.nonsingular(x0, x1)
except AttributeError:
# Default nonsingular for, e.g., MaxNLocator
x0, x1 = mtransforms.nonsingular(x0, x1, increasing=False,
expander=0.05)
if self._xmargin > 0:
delta = (x1 - x0) * self._xmargin
x0 -= delta
x1 += delta
if not _tight:
x0, x1 = xlocator.view_limits(x0, x1)
self.set_xbound(x0, x1)
if scaley and self._autoscaleYon:
yshared = self._shared_y_axes.get_siblings(self)
dl = [ax.dataLim for ax in yshared]
bb = mtransforms.BboxBase.union(dl)
y0, y1 = bb.intervaly
ylocator = self.yaxis.get_major_locator()
try:
y0, y1 = ylocator.nonsingular(y0, y1)
except AttributeError:
y0, y1 = mtransforms.nonsingular(y0, y1, increasing=False,
expander=0.05)
if self._ymargin > 0:
delta = (y1 - y0) * self._ymargin
y0 -= delta
y1 += delta
if not _tight:
y0, y1 = ylocator.view_limits(y0, y1)
self.set_ybound(y0, y1)
#### Drawing
@allow_rasterization
def draw(self, renderer=None, inframe=False):
"Draw everything (plot lines, axes, labels)"
if renderer is None:
renderer = self._cachedRenderer
if renderer is None:
raise RuntimeError('No renderer defined')
if not self.get_visible(): return
renderer.open_group('axes')
locator = self.get_axes_locator()
if locator:
pos = locator(self, renderer)
self.apply_aspect(pos)
else:
self.apply_aspect()
artists = []
artists.extend(self.collections)
artists.extend(self.patches)
artists.extend(self.lines)
artists.extend(self.texts)
artists.extend(self.artists)
if self.axison and not inframe:
if self._axisbelow:
self.xaxis.set_zorder(0.5)
self.yaxis.set_zorder(0.5)
else:
self.xaxis.set_zorder(2.5)
self.yaxis.set_zorder(2.5)
artists.extend([self.xaxis, self.yaxis])
if not inframe: artists.append(self.title)
artists.extend(self.tables)
if self.legend_ is not None:
artists.append(self.legend_)
# the frame draws the edges around the axes patch -- we
# decouple these so the patch can be in the background and the
# frame in the foreground.
if self.axison and self._frameon:
artists.extend(self.spines.itervalues())
dsu = [ (a.zorder, a) for a in artists
if not a.get_animated() ]
# add images to dsu if the backend support compositing.
# otherwise, does the manaul compositing without adding images to dsu.
if len(self.images)<=1 or renderer.option_image_nocomposite():
dsu.extend([(im.zorder, im) for im in self.images])
_do_composite = False
else:
_do_composite = True
dsu.sort(key=itemgetter(0))
# rasterize artists with negative zorder
# if the minimum zorder is negative, start rasterization
rasterization_zorder = self._rasterization_zorder
if len(dsu) > 0 and dsu[0][0] < rasterization_zorder:
renderer.start_rasterizing()
dsu_rasterized = [l for l in dsu if l[0] < rasterization_zorder]
dsu = [l for l in dsu if l[0] >= rasterization_zorder]
else:
dsu_rasterized = []
# the patch draws the background rectangle -- the frame below
# will draw the edges
if self.axison and self._frameon:
self.patch.draw(renderer)
if _do_composite:
# make a composite image blending alpha
# list of (mimage.Image, ox, oy)
zorder_images = [(im.zorder, im) for im in self.images \
if im.get_visible()]
zorder_images.sort(key=lambda x: x[0])
mag = renderer.get_image_magnification()
ims = [(im.make_image(mag),0,0) for z,im in zorder_images]
l, b, r, t = self.bbox.extents
width = mag*((round(r) + 0.5) - (round(l) - 0.5))
height = mag*((round(t) + 0.5) - (round(b) - 0.5))
im = mimage.from_images(height,
width,
ims)
im.is_grayscale = False
l, b, w, h = self.bbox.bounds
# composite images need special args so they will not
# respect z-order for now
gc = renderer.new_gc()
gc.set_clip_rectangle(self.bbox)
gc.set_clip_path(mtransforms.TransformedPath(
self.patch.get_path(),
self.patch.get_transform()))
renderer.draw_image(gc, round(l), round(b), im)
gc.restore()
if dsu_rasterized:
for zorder, a in dsu_rasterized:
a.draw(renderer)
renderer.stop_rasterizing()
for zorder, a in dsu:
a.draw(renderer)
renderer.close_group('axes')
self._cachedRenderer = renderer
def draw_artist(self, a):
"""
This method can only be used after an initial draw which
caches the renderer. It is used to efficiently update Axes
data (axis ticks, labels, etc are not updated)
"""
assert self._cachedRenderer is not None
a.draw(self._cachedRenderer)
def redraw_in_frame(self):
"""
This method can only be used after an initial draw which
caches the renderer. It is used to efficiently update Axes
data (axis ticks, labels, etc are not updated)
"""
assert self._cachedRenderer is not None
self.draw(self._cachedRenderer, inframe=True)
def get_renderer_cache(self):
return self._cachedRenderer
def __draw_animate(self):
# ignore for now; broken
if self._lastRenderer is None:
raise RuntimeError('You must first call ax.draw()')
dsu = [(a.zorder, a) for a in self.animated.keys()]
dsu.sort(key=lambda x: x[0])
renderer = self._lastRenderer
renderer.blit()
for tmp, a in dsu:
a.draw(renderer)
#### Axes rectangle characteristics
def get_frame_on(self):
"""
Get whether the axes rectangle patch is drawn
"""
return self._frameon
def set_frame_on(self, b):
"""
Set whether the axes rectangle patch is drawn
ACCEPTS: [ *True* | *False* ]
"""
self._frameon = b
def get_axisbelow(self):
"""
Get whether axis below is true or not
"""
return self._axisbelow
def set_axisbelow(self, b):
"""
Set whether the axis ticks and gridlines are above or below most artists
ACCEPTS: [ *True* | *False* ]
"""
self._axisbelow = b
@docstring.dedent_interpd
def grid(self, b=None, which='major', **kwargs):
"""
call signature::
grid(self, b=None, which='major', **kwargs)
Set the axes grids on or off; *b* is a boolean. (For MATLAB
compatibility, *b* may also be a string, 'on' or 'off'.)
If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If
*kwargs* are supplied, it is assumed that you want a grid and *b*
is thus set to *True*.
*which* can be 'major' (default), 'minor', or 'both' to control
whether major tick grids, minor tick grids, or both are affected.
*kawrgs* are used to set the grid line properties, eg::
ax.grid(color='r', linestyle='-', linewidth=2)
Valid :class:`~matplotlib.lines.Line2D` kwargs are
%(Line2D)s
"""
if len(kwargs):
b = True
b = _string_to_bool(b)
self.xaxis.grid(b, which=which, **kwargs)
self.yaxis.grid(b, which=which, **kwargs)
def ticklabel_format(self, **kwargs):
"""
Convenience method for manipulating the ScalarFormatter
used by default for linear axes.
Optional keyword arguments:
============ =========================================
Keyword Description
============ =========================================
*style* [ 'sci' (or 'scientific') | 'plain' ]
plain turns off scientific notation
*scilimits* (m, n), pair of integers; if *style*
is 'sci', scientific notation will
be used for numbers outside the range
10`-m`:sup: to 10`n`:sup:.
Use (0,0) to include all numbers.
*useOffset* [True | False | offset]; if True,
the offset will be calculated as needed;
if False, no offset will be used; if a
numeric offset is specified, it will be
used.
*axis* [ 'x' | 'y' | 'both' ]
============ =========================================
Only the major ticks are affected.
If the method is called when the
:class:`~matplotlib.ticker.ScalarFormatter` is not the
:class:`~matplotlib.ticker.Formatter` being used, an
:exc:`AttributeError` will be raised.
"""
style = kwargs.pop('style', '').lower()
scilimits = kwargs.pop('scilimits', None)
useOffset = kwargs.pop('useOffset', None)
axis = kwargs.pop('axis', 'both').lower()
if scilimits is not None:
try:
m, n = scilimits
m+n+1 # check that both are numbers
except (ValueError, TypeError):
raise ValueError("scilimits must be a sequence of 2 integers")
if style[:3] == 'sci':
sb = True
elif style in ['plain', 'comma']:
sb = False
if style == 'plain':
cb = False
else:
cb = True
raise NotImplementedError, "comma style remains to be added"
elif style == '':
sb = None
else:
raise ValueError, "%s is not a valid style value"
try:
if sb is not None:
if axis == 'both' or axis == 'x':
self.xaxis.major.formatter.set_scientific(sb)
if axis == 'both' or axis == 'y':
self.yaxis.major.formatter.set_scientific(sb)
if scilimits is not None:
if axis == 'both' or axis == 'x':
self.xaxis.major.formatter.set_powerlimits(scilimits)
if axis == 'both' or axis == 'y':
self.yaxis.major.formatter.set_powerlimits(scilimits)
if useOffset is not None:
if axis == 'both' or axis == 'x':
self.xaxis.major.formatter.set_useOffset(useOffset)
if axis == 'both' or axis == 'y':
self.yaxis.major.formatter.set_useOffset(useOffset)
except AttributeError:
raise AttributeError(
"This method only works with the ScalarFormatter.")
def locator_params(self, axis='both', tight=None, **kwargs):
"""
Convenience method for controlling tick locators.
Keyword arguments:
*axis*
['x' | 'y' | 'both'] Axis on which to operate;
default is 'both'.
*tight*
[True | False | None] Parameter passed to :meth:`autoscale_view`.
Default is None, for no change.
Remaining keyword arguments are passed to directly to the
:meth:`~matplotlib.ticker.MaxNLocator.set_params` method.
Typically one might want to reduce the maximum number
of ticks and use tight bounds when plotting small
subplots, for example::
ax.locator_params(tight=True, nbins=4)
Because the locator is involved in autoscaling,
:meth:`autoscale_view` is called automatically after
the parameters are changed.
This presently works only for the
:class:`~matplotlib.ticker.MaxNLocator` used
by default on linear axes, but it may be generalized.
"""
_x = axis in ['x', 'both']
_y = axis in ['y', 'both']
if _x:
self.xaxis.get_major_locator().set_params(**kwargs)
if _y:
self.yaxis.get_major_locator().set_params(**kwargs)
self.autoscale_view(tight=tight, scalex=_x, scaley=_y)
def tick_params(self, axis='both', **kwargs):
"""
Convenience method for changing the appearance of ticks and
tick labels.
Keyword arguments:
*axis*
['x' | 'y' | 'both'] Axis on which to operate;
default is 'both'.
*reset*
[True | False] If *True*, set all parameters to defaults
before processing other keyword arguments. Default is
*False*.
*which*
['major' | 'minor' | 'both'] Default is 'major': apply
arguments to major ticks only.
*direction*
['in' | 'out'] Puts ticks inside or outside the axes.
*length*
Tick length in points.
*width*
Tick width in points.
*color*
Tick color; accepts any mpl color spec.
*pad*
Distance in points between tick and label.
*labelsize*
Tick label font size in points or as a string (e.g. 'large').
*labelcolor*
Tick label color; mpl color spec.
*colors*
Changes the tick color and the label color to the same value:
mpl color spec.
*zorder*
Tick and label zorder.
*bottom*, *top*, *left*, *right*
Boolean or ['on' | 'off'], controls whether to draw the
respective ticks.
*labelbottom*, *labeltop*, *labelleft*, *labelright*
Boolean or ['on' | 'off'], controls whether to draw the
respective tick labels.
Example::
ax.tick_params(direction='out', length=6, width=2, colors='r')
This will make all major ticks be red, pointing out of the box,
and with dimensions 6 points by 2 points. Tick labels will
also be red.
"""
if axis in ['x', 'both']:
xkw = dict(kwargs)
xkw.pop('left', None)
xkw.pop('right', None)
xkw.pop('labelleft', None)
xkw.pop('labelright', None)
self.xaxis.set_tick_params(**xkw)
if axis in ['y', 'both']:
ykw = dict(kwargs)
ykw.pop('top', None)
ykw.pop('bottom', None)
ykw.pop('labeltop', None)
ykw.pop('labelbottom', None)
self.yaxis.set_tick_params(**ykw)
def set_axis_off(self):
"""turn off the axis"""
self.axison = False
def set_axis_on(self):
"""turn on the axis"""
self.axison = True
def get_axis_bgcolor(self):
'Return the axis background color'
return self._axisbg
def set_axis_bgcolor(self, color):
"""
set the axes background color
ACCEPTS: any matplotlib color - see
:func:`~matplotlib.pyplot.colors`
"""
self._axisbg = color
self.patch.set_facecolor(color)
### data limits, ticks, tick labels, and formatting
def invert_xaxis(self):
"Invert the x-axis."
left, right = self.get_xlim()
self.set_xlim(right, left)
def xaxis_inverted(self):
'Returns True if the x-axis is inverted.'
left, right = self.get_xlim()
return right < left
def get_xbound(self):
"""
Returns the x-axis numerical bounds where::
lowerBound < upperBound
"""
left, right = self.get_xlim()
if left < right:
return left, right
else:
return right, left
def set_xbound(self, lower=None, upper=None):
"""
Set the lower and upper numerical bounds of the x-axis.
This method will honor axes inversion regardless of parameter order.
It will not change the _autoscaleXon attribute.
"""
if upper is None and iterable(lower):
lower,upper = lower
old_lower,old_upper = self.get_xbound()
if lower is None: lower = old_lower
if upper is None: upper = old_upper
if self.xaxis_inverted():
if lower < upper:
self.set_xlim(upper, lower, auto=None)
else:
self.set_xlim(lower, upper, auto=None)
else:
if lower < upper:
self.set_xlim(lower, upper, auto=None)
else:
self.set_xlim(upper, lower, auto=None)
def get_xlim(self):
"""
Get the x-axis range [*left*, *right*]
"""
return tuple(self.viewLim.intervalx)
def set_xlim(self, left=None, right=None, emit=True, auto=False, **kw):
"""
call signature::
set_xlim(self, *args, **kwargs):
Set the data limits for the xaxis
Examples::
set_xlim((left, right))
set_xlim(left, right)
set_xlim(left=1) # right unchanged
set_xlim(right=1) # left unchanged
Keyword arguments:
*left*: scalar
the left xlim; *xmin*, the previous name, may still be used
*right*: scalar
the right xlim; *xmax*, the previous name, may still be used
*emit*: [ True | False ]
notify observers of lim change
*auto*: [ True | False | None ]
turn *x* autoscaling on (True), off (False; default),
or leave unchanged (None)
Note: the *left* (formerly *xmin*) value may be greater than
the *right* (formerly *xmax*).
For example, suppose *x* is years before present.
Then one might use::
set_ylim(5000, 0)
so 5000 years ago is on the left of the plot and the
present is on the right.
Returns the current xlimits as a length 2 tuple
ACCEPTS: len(2) sequence of floats
"""
if 'xmin' in kw:
left = kw.pop('xmin')
if 'xmax' in kw:
right = kw.pop('xmax')
if kw:
raise ValueError("unrecognized kwargs: %s" % kw.keys())
if right is None and iterable(left):
left,right = left
self._process_unit_info(xdata=(left, right))
if left is not None:
left = self.convert_xunits(left)
if right is not None:
right = self.convert_xunits(right)
old_left, old_right = self.get_xlim()
if left is None: left = old_left
if right is None: right = old_right
if left==right:
warnings.warn(('Attempting to set identical left==right results\n'
+ 'in singular transformations; automatically expanding.\n'
+ 'left=%s, right=%s') % (left, right))
left, right = mtransforms.nonsingular(left, right, increasing=False)
left, right = self.xaxis.limit_range_for_scale(left, right)
self.viewLim.intervalx = (left, right)
if auto is not None:
self._autoscaleXon = bool(auto)
if emit:
self.callbacks.process('xlim_changed', self)
# Call all of the other x-axes that are shared with this one
for other in self._shared_x_axes.get_siblings(self):
if other is not self:
other.set_xlim(self.viewLim.intervalx,
emit=False, auto=auto)
if (other.figure != self.figure and
other.figure.canvas is not None):
other.figure.canvas.draw_idle()
return left, right
def get_xscale(self):
'return the xaxis scale string: %s' % (
", ".join(mscale.get_scale_names()))
return self.xaxis.get_scale()
@docstring.dedent_interpd
def set_xscale(self, value, **kwargs):
"""
call signature::
set_xscale(value)
Set the scaling of the x-axis: %(scale)s
ACCEPTS: [%(scale)s]
Different kwargs are accepted, depending on the scale:
%(scale_docs)s
"""
self.xaxis.set_scale(value, **kwargs)
self.autoscale_view(scaley=False)
self._update_transScale()
def get_xticks(self, minor=False):
'Return the x ticks as a list of locations'
return self.xaxis.get_ticklocs(minor=minor)
def set_xticks(self, ticks, minor=False):
"""
Set the x ticks with list of *ticks*
ACCEPTS: sequence of floats
"""
return self.xaxis.set_ticks(ticks, minor=minor)
def get_xmajorticklabels(self):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text xticklabel',
self.xaxis.get_majorticklabels())
def get_xminorticklabels(self):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text xticklabel',
self.xaxis.get_minorticklabels())
def get_xticklabels(self, minor=False):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text xticklabel',
self.xaxis.get_ticklabels(minor=minor))
@docstring.dedent_interpd
def set_xticklabels(self, labels, fontdict=None, minor=False, **kwargs):
"""
call signature::
set_xticklabels(labels, fontdict=None, minor=False, **kwargs)
Set the xtick labels with list of strings *labels*. Return a
list of axis text instances.
*kwargs* set the :class:`~matplotlib.text.Text` properties.
Valid properties are
%(Text)s
ACCEPTS: sequence of strings
"""
return self.xaxis.set_ticklabels(labels, fontdict,
minor=minor, **kwargs)
def invert_yaxis(self):
"Invert the y-axis."
bottom, top = self.get_ylim()
self.set_ylim(top, bottom)
def yaxis_inverted(self):
'Returns True if the y-axis is inverted.'
bottom, top = self.get_ylim()
return top < bottom
def get_ybound(self):
"Return y-axis numerical bounds in the form of lowerBound < upperBound"
bottom, top = self.get_ylim()
if bottom < top:
return bottom, top
else:
return top, bottom
def set_ybound(self, lower=None, upper=None):
"""Set the lower and upper numerical bounds of the y-axis.
This method will honor axes inversion regardless of parameter order.
It will not change the _autoscaleYon attribute.
"""
if upper is None and iterable(lower):
lower,upper = lower
old_lower,old_upper = self.get_ybound()
if lower is None: lower = old_lower
if upper is None: upper = old_upper
if self.yaxis_inverted():
if lower < upper:
self.set_ylim(upper, lower, auto=None)
else:
self.set_ylim(lower, upper, auto=None)
else:
if lower < upper:
self.set_ylim(lower, upper, auto=None)
else:
self.set_ylim(upper, lower, auto=None)
def get_ylim(self):
"""
Get the y-axis range [*bottom*, *top*]
"""
return tuple(self.viewLim.intervaly)
def set_ylim(self, bottom=None, top=None, emit=True, auto=False, **kw):
"""
call signature::
set_ylim(self, *args, **kwargs):
Set the data limits for the yaxis
Examples::
set_ylim((bottom, top))
set_ylim(bottom, top)
set_ylim(bottom=1) # top unchanged
set_ylim(top=1) # bottom unchanged
Keyword arguments:
*bottom*: scalar
the bottom ylim; the previous name, *ymin*, may still be used
*top*: scalar
the top ylim; the previous name, *ymax*, may still be used
*emit*: [ True | False ]
notify observers of lim change
*auto*: [ True | False | None ]
turn *y* autoscaling on (True), off (False; default),
or leave unchanged (None)
Note: the *bottom* (formerly *ymin*) value may be greater than
the *top* (formerly *ymax*).
For example, suppose *y* is depth in the ocean.
Then one might use::
set_ylim(5000, 0)
so 5000 m depth is at the bottom of the plot and the
surface, 0 m, is at the top.
Returns the current ylimits as a length 2 tuple
ACCEPTS: len(2) sequence of floats
"""
if 'ymin' in kw:
bottom = kw.pop('ymin')
if 'ymax' in kw:
top = kw.pop('ymax')
if kw:
raise ValueError("unrecognized kwargs: %s" % kw.keys())
if top is None and iterable(bottom):
bottom,top = bottom
if bottom is not None:
bottom = self.convert_yunits(bottom)
if top is not None:
top = self.convert_yunits(top)
old_bottom, old_top = self.get_ylim()
if bottom is None: bottom = old_bottom
if top is None: top = old_top
if bottom==top:
warnings.warn(('Attempting to set identical bottom==top results\n'
+ 'in singular transformations; automatically expanding.\n'
+ 'bottom=%s, top=%s') % (bottom, top))
bottom, top = mtransforms.nonsingular(bottom, top, increasing=False)
bottom, top = self.yaxis.limit_range_for_scale(bottom, top)
self.viewLim.intervaly = (bottom, top)
if auto is not None:
self._autoscaleYon = bool(auto)
if emit:
self.callbacks.process('ylim_changed', self)
# Call all of the other y-axes that are shared with this one
for other in self._shared_y_axes.get_siblings(self):
if other is not self:
other.set_ylim(self.viewLim.intervaly,
emit=False, auto=auto)
if (other.figure != self.figure and
other.figure.canvas is not None):
other.figure.canvas.draw_idle()
return bottom, top
def get_yscale(self):
'return the xaxis scale string: %s' % (
", ".join(mscale.get_scale_names()))
return self.yaxis.get_scale()
@docstring.dedent_interpd
def set_yscale(self, value, **kwargs):
"""
call signature::
set_yscale(value)
Set the scaling of the y-axis: %(scale)s
ACCEPTS: [%(scale)s]
Different kwargs are accepted, depending on the scale:
%(scale_docs)s
"""
self.yaxis.set_scale(value, **kwargs)
self.autoscale_view(scalex=False)
self._update_transScale()
def get_yticks(self, minor=False):
'Return the y ticks as a list of locations'
return self.yaxis.get_ticklocs(minor=minor)
def set_yticks(self, ticks, minor=False):
"""
Set the y ticks with list of *ticks*
ACCEPTS: sequence of floats
Keyword arguments:
*minor*: [ False | True ]
Sets the minor ticks if True
"""
return self.yaxis.set_ticks(ticks, minor=minor)
def get_ymajorticklabels(self):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text yticklabel',
self.yaxis.get_majorticklabels())
def get_yminorticklabels(self):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text yticklabel',
self.yaxis.get_minorticklabels())
def get_yticklabels(self, minor=False):
'Get the xtick labels as a list of Text instances'
return cbook.silent_list('Text yticklabel',
self.yaxis.get_ticklabels(minor=minor))
@docstring.dedent_interpd
def set_yticklabels(self, labels, fontdict=None, minor=False, **kwargs):
"""
call signature::
set_yticklabels(labels, fontdict=None, minor=False, **kwargs)
Set the ytick labels with list of strings *labels*. Return a list of
:class:`~matplotlib.text.Text` instances.
*kwargs* set :class:`~matplotlib.text.Text` properties for the labels.
Valid properties are
%(Text)s
ACCEPTS: sequence of strings
"""
return self.yaxis.set_ticklabels(labels, fontdict,
minor=minor, **kwargs)
def xaxis_date(self, tz=None):
"""Sets up x-axis ticks and labels that treat the x data as dates.
*tz* is a timezone string or :class:`tzinfo` instance.
Defaults to rc value.
"""
# should be enough to inform the unit conversion interface
# dates are coming in
self.xaxis.axis_date(tz)
def yaxis_date(self, tz=None):
"""Sets up y-axis ticks and labels that treat the y data as dates.
*tz* is a timezone string or :class:`tzinfo` instance.
Defaults to rc value.
"""
self.yaxis.axis_date(tz)
def format_xdata(self, x):
"""
Return *x* string formatted. This function will use the attribute
self.fmt_xdata if it is callable, else will fall back on the xaxis
major formatter
"""
try: return self.fmt_xdata(x)
except TypeError:
func = self.xaxis.get_major_formatter().format_data_short
val = func(x)
return val
def format_ydata(self, y):
"""
Return y string formatted. This function will use the
:attr:`fmt_ydata` attribute if it is callable, else will fall
back on the yaxis major formatter
"""
try: return self.fmt_ydata(y)
except TypeError:
func = self.yaxis.get_major_formatter().format_data_short
val = func(y)
return val
def format_coord(self, x, y):
'return a format string formatting the *x*, *y* coord'
if x is None:
xs = '???'
else:
xs = self.format_xdata(x)
if y is None:
ys = '???'
else:
ys = self.format_ydata(y)
return 'x=%s y=%s'%(xs,ys)
#### Interactive manipulation
def can_zoom(self):
"""
Return *True* if this axes support the zoom box
"""
return True
def get_navigate(self):
"""
Get whether the axes responds to navigation commands
"""
return self._navigate
def set_navigate(self, b):
"""
Set whether the axes responds to navigation toolbar commands
ACCEPTS: [ True | False ]
"""
self._navigate = b
def get_navigate_mode(self):
"""
Get the navigation toolbar button status: 'PAN', 'ZOOM', or None
"""
return self._navigate_mode
def set_navigate_mode(self, b):
"""
Set the navigation toolbar button status;
.. warning::
this is not a user-API function.
"""
self._navigate_mode = b
def start_pan(self, x, y, button):
"""
Called when a pan operation has started.
*x*, *y* are the mouse coordinates in display coords.
button is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
.. note::
Intended to be overridden by new projection types.
"""
self._pan_start = cbook.Bunch(
lim = self.viewLim.frozen(),
trans = self.transData.frozen(),
trans_inverse = self.transData.inverted().frozen(),
bbox = self.bbox.frozen(),
x = x,
y = y
)
def end_pan(self):
"""
Called when a pan operation completes (when the mouse button
is up.)
.. note::
Intended to be overridden by new projection types.
"""
del self._pan_start
def drag_pan(self, button, key, x, y):
"""
Called when the mouse moves during a pan operation.
*button* is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
*key* is a "shift" key
*x*, *y* are the mouse coordinates in display coords.
.. note::
Intended to be overridden by new projection types.
"""
def format_deltas(key, dx, dy):
if key=='control':
if(abs(dx)>abs(dy)):
dy = dx
else:
dx = dy
elif key=='x':
dy = 0
elif key=='y':
dx = 0
elif key=='shift':
if 2*abs(dx) < abs(dy):
dx=0
elif 2*abs(dy) < abs(dx):
dy=0
elif(abs(dx)>abs(dy)):
dy=dy/abs(dy)*abs(dx)
else:
dx=dx/abs(dx)*abs(dy)
return (dx,dy)
p = self._pan_start
dx = x - p.x
dy = y - p.y
if dx == 0 and dy == 0:
return
if button == 1:
dx, dy = format_deltas(key, dx, dy)
result = p.bbox.translated(-dx, -dy) \
.transformed(p.trans_inverse)
elif button == 3:
try:
dx = -dx / float(self.bbox.width)
dy = -dy / float(self.bbox.height)
dx, dy = format_deltas(key, dx, dy)
if self.get_aspect() != 'auto':
dx = 0.5 * (dx + dy)
dy = dx
alpha = np.power(10.0, (dx, dy))
start = np.array([p.x, p.y])
oldpoints = p.lim.transformed(p.trans)
newpoints = start + alpha * (oldpoints - start)
result = mtransforms.Bbox(newpoints) \
.transformed(p.trans_inverse)
except OverflowError:
warnings.warn('Overflow while panning')
return
self.set_xlim(*result.intervalx)
self.set_ylim(*result.intervaly)
def get_cursor_props(self):
"""
return the cursor propertiess as a (*linewidth*, *color*)
tuple, where *linewidth* is a float and *color* is an RGBA
tuple
"""
return self._cursorProps
def set_cursor_props(self, *args):
"""
Set the cursor property as::
ax.set_cursor_props(linewidth, color)
or::
ax.set_cursor_props((linewidth, color))
ACCEPTS: a (*float*, *color*) tuple
"""
if len(args)==1:
lw, c = args[0]
elif len(args)==2:
lw, c = args
else:
raise ValueError('args must be a (linewidth, color) tuple')
c =mcolors.colorConverter.to_rgba(c)
self._cursorProps = lw, c
def connect(self, s, func):
"""
Register observers to be notified when certain events occur. Register
with callback functions with the following signatures. The function
has the following signature::
func(ax) # where ax is the instance making the callback.
The following events can be connected to:
'xlim_changed','ylim_changed'
The connection id is is returned - you can use this with
disconnect to disconnect from the axes event
"""
raise DeprecationWarning('use the callbacks CallbackRegistry instance '
'instead')
def disconnect(self, cid):
'disconnect from the Axes event.'
raise DeprecationWarning('use the callbacks CallbackRegistry instance '
'instead')
def get_children(self):
'return a list of child artists'
children = []
children.append(self.xaxis)
children.append(self.yaxis)
children.extend(self.lines)
children.extend(self.patches)
children.extend(self.texts)
children.extend(self.tables)
children.extend(self.artists)
children.extend(self.images)
if self.legend_ is not None:
children.append(self.legend_)
children.extend(self.collections)
children.append(self.title)
children.append(self.patch)
children.extend(self.spines.itervalues())
return children
def contains(self,mouseevent):
"""Test whether the mouse event occured in the axes.
Returns T/F, {}
"""
if callable(self._contains): return self._contains(self,mouseevent)
return self.patch.contains(mouseevent)
def contains_point(self, point):
"""
Returns True if the point (tuple of x,y) is inside the axes
(the area defined by the its patch). A pixel coordinate is
required.
"""
return self.patch.contains_point(point)
def pick(self, *args):
"""
call signature::
pick(mouseevent)
each child artist will fire a pick event if mouseevent is over
the artist and the artist has picker set
"""
if len(args)>1:
raise DeprecationWarning('New pick API implemented -- '
'see API_CHANGES in the src distribution')
martist.Artist.pick(self,args[0])
def __pick(self, x, y, trans=None, among=None):
"""
Return the artist under point that is closest to the *x*, *y*.
If *trans* is *None*, *x*, and *y* are in window coords,
(0,0 = lower left). Otherwise, *trans* is a
:class:`~matplotlib.transforms.Transform` that specifies the
coordinate system of *x*, *y*.
The selection of artists from amongst which the pick function
finds an artist can be narrowed using the optional keyword
argument *among*. If provided, this should be either a sequence
of permitted artists or a function taking an artist as its
argument and returning a true value if and only if that artist
can be selected.
Note this algorithm calculates distance to the vertices of the
polygon, so if you want to pick a patch, click on the edge!
"""
# MGDTODO: Needs updating
if trans is not None:
xywin = trans.transform_point((x,y))
else:
xywin = x,y
def dist_points(p1, p2):
'return the distance between two points'
x1, y1 = p1
x2, y2 = p2
return math.sqrt((x1-x2)**2+(y1-y2)**2)
def dist_x_y(p1, x, y):
'*x* and *y* are arrays; return the distance to the closest point'
x1, y1 = p1
return min(np.sqrt((x-x1)**2+(y-y1)**2))
def dist(a):
if isinstance(a, Text):
bbox = a.get_window_extent()
l,b,w,h = bbox.bounds
verts = (l,b), (l,b+h), (l+w,b+h), (l+w, b)
xt, yt = zip(*verts)
elif isinstance(a, Patch):
path = a.get_path()
tverts = a.get_transform().transform_path(path)
xt, yt = zip(*tverts)
elif isinstance(a, mlines.Line2D):
xdata = a.get_xdata(orig=False)
ydata = a.get_ydata(orig=False)
xt, yt = a.get_transform().numerix_x_y(xdata, ydata)
return dist_x_y(xywin, np.asarray(xt), np.asarray(yt))
artists = self.lines + self.patches + self.texts
if callable(among):
artists = filter(test, artists)
elif iterable(among):
amongd = dict([(k,1) for k in among])
artists = [a for a in artists if a in amongd]
elif among is None:
pass
else:
raise ValueError('among must be callable or iterable')
if not len(artists): return None
ds = [ (dist(a),a) for a in artists]
ds.sort()
return ds[0][1]
#### Labelling
def get_title(self):
"""
Get the title text string.
"""
return self.title.get_text()
@docstring.dedent_interpd
def set_title(self, label, fontdict=None, **kwargs):
"""
call signature::
set_title(label, fontdict=None, **kwargs):
Set the title for the axes.
kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
"""
default = {
'fontsize':rcParams['axes.titlesize'],
'verticalalignment' : 'baseline',
'horizontalalignment' : 'center'
}
self.title.set_text(label)
self.title.update(default)
if fontdict is not None: self.title.update(fontdict)
self.title.update(kwargs)
return self.title
def get_xlabel(self):
"""
Get the xlabel text string.
"""
label = self.xaxis.get_label()
return label.get_text()
@docstring.dedent_interpd
def set_xlabel(self, xlabel, fontdict=None, labelpad=None, **kwargs):
"""
call signature::
set_xlabel(xlabel, fontdict=None, labelpad=None, **kwargs)
Set the label for the xaxis.
*labelpad* is the spacing in points between the label and the x-axis
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
"""
if labelpad is not None: self.xaxis.labelpad = labelpad
return self.xaxis.set_label_text(xlabel, fontdict, **kwargs)
def get_ylabel(self):
"""
Get the ylabel text string.
"""
label = self.yaxis.get_label()
return label.get_text()
@docstring.dedent_interpd
def set_ylabel(self, ylabel, fontdict=None, labelpad=None, **kwargs):
"""
call signature::
set_ylabel(ylabel, fontdict=None, labelpad=None, **kwargs)
Set the label for the yaxis
*labelpad* is the spacing in points between the label and the y-axis
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
"""
if labelpad is not None: self.yaxis.labelpad = labelpad
return self.yaxis.set_label_text(ylabel, fontdict, **kwargs)
@docstring.dedent_interpd
def text(self, x, y, s, fontdict=None,
withdash=False, **kwargs):
"""
call signature::
text(x, y, s, fontdict=None, **kwargs)
Add text in string *s* to axis at location *x*, *y*, data
coordinates.
Keyword arguments:
*fontdict*:
A dictionary to override the default text properties.
If *fontdict* is *None*, the defaults are determined by your rc
parameters.
*withdash*: [ False | True ]
Creates a :class:`~matplotlib.text.TextWithDash` instance
instead of a :class:`~matplotlib.text.Text` instance.
Individual keyword arguments can be used to override any given
parameter::
text(x, y, s, fontsize=12)
The default transform specifies that text is in data coords,
alternatively, you can specify text in axis coords (0,0 is
lower-left and 1,1 is upper-right). The example below places
text in the center of the axes::
text(0.5, 0.5,'matplotlib',
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
You can put a rectangular box around the text instance (eg. to
set a background color) by using the keyword *bbox*. *bbox* is
a dictionary of :class:`matplotlib.patches.Rectangle`
properties. For example::
text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))
Valid kwargs are :class:`matplotlib.text.Text` properties:
%(Text)s
"""
default = {
'verticalalignment' : 'baseline',
'horizontalalignment' : 'left',
'transform' : self.transData,
}
# At some point if we feel confident that TextWithDash
# is robust as a drop-in replacement for Text and that
# the performance impact of the heavier-weight class
# isn't too significant, it may make sense to eliminate
# the withdash kwarg and simply delegate whether there's
# a dash to TextWithDash and dashlength.
if withdash:
t = mtext.TextWithDash(
x=x, y=y, text=s,
)
else:
t = mtext.Text(
x=x, y=y, text=s,
)
self._set_artist_props(t)
t.update(default)
if fontdict is not None: t.update(fontdict)
t.update(kwargs)
self.texts.append(t)
t._remove_method = lambda h: self.texts.remove(h)
#if t.get_clip_on(): t.set_clip_box(self.bbox)
if 'clip_on' in kwargs: t.set_clip_box(self.bbox)
return t
@docstring.dedent_interpd
def annotate(self, *args, **kwargs):
"""
call signature::
annotate(s, xy, xytext=None, xycoords='data',
textcoords='data', arrowprops=None, **kwargs)
Keyword arguments:
%(Annotation)s
.. plot:: mpl_examples/pylab_examples/annotation_demo2.py
"""
a = mtext.Annotation(*args, **kwargs)
a.set_transform(mtransforms.IdentityTransform())
self._set_artist_props(a)
if kwargs.has_key('clip_on'): a.set_clip_path(self.patch)
self.texts.append(a)
a._remove_method = lambda h: self.texts.remove(h)
return a
#### Lines and spans
@docstring.dedent_interpd
def axhline(self, y=0, xmin=0, xmax=1, **kwargs):
"""
call signature::
axhline(y=0, xmin=0, xmax=1, **kwargs)
Axis Horizontal Line
Draw a horizontal line at *y* from *xmin* to *xmax*. With the
default values of *xmin* = 0 and *xmax* = 1, this line will
always span the horizontal extent of the axes, regardless of
the xlim settings, even if you change them, eg. with the
:meth:`set_xlim` command. That is, the horizontal extent is
in axes coords: 0=left, 0.5=middle, 1.0=right but the *y*
location is in data coordinates.
Return value is the :class:`~matplotlib.lines.Line2D`
instance. kwargs are the same as kwargs to plot, and can be
used to control the line properties. Eg.,
* draw a thick red hline at *y* = 0 that spans the xrange
>>> axhline(linewidth=4, color='r')
* draw a default hline at *y* = 1 that spans the xrange
>>> axhline(y=1)
* draw a default hline at *y* = .5 that spans the the middle half of
the xrange
>>> axhline(y=.5, xmin=0.25, xmax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,
with the exception of 'transform':
%(Line2D)s
.. seealso::
:meth:`axhspan`
for example plot and source code
"""
if "transform" in kwargs:
raise ValueError(
"'transform' is not allowed as a kwarg;"
+ "axhline generates its own transform.")
ymin, ymax = self.get_ybound()
# We need to strip away the units for comparison with
# non-unitized bounds
self._process_unit_info( ydata=y, kwargs=kwargs )
yy = self.convert_yunits( y )
scaley = (yy<ymin) or (yy>ymax)
trans = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
l = mlines.Line2D([xmin,xmax], [y,y], transform=trans, **kwargs)
l.x_isdata = False
self.add_line(l)
self.autoscale_view(scalex=False, scaley=scaley)
return l
@docstring.dedent_interpd
def axvline(self, x=0, ymin=0, ymax=1, **kwargs):
"""
call signature::
axvline(x=0, ymin=0, ymax=1, **kwargs)
Axis Vertical Line
Draw a vertical line at *x* from *ymin* to *ymax*. With the
default values of *ymin* = 0 and *ymax* = 1, this line will
always span the vertical extent of the axes, regardless of the
ylim settings, even if you change them, eg. with the
:meth:`set_ylim` command. That is, the vertical extent is in
axes coords: 0=bottom, 0.5=middle, 1.0=top but the *x* location
is in data coordinates.
Return value is the :class:`~matplotlib.lines.Line2D`
instance. kwargs are the same as kwargs to plot, and can be
used to control the line properties. Eg.,
* draw a thick red vline at *x* = 0 that spans the yrange
>>> axvline(linewidth=4, color='r')
* draw a default vline at *x* = 1 that spans the yrange
>>> axvline(x=1)
* draw a default vline at *x* = .5 that spans the the middle half of
the yrange
>>> axvline(x=.5, ymin=0.25, ymax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,
with the exception of 'transform':
%(Line2D)s
.. seealso::
:meth:`axhspan`
for example plot and source code
"""
if "transform" in kwargs:
raise ValueError(
"'transform' is not allowed as a kwarg;"
+ "axvline generates its own transform.")
xmin, xmax = self.get_xbound()
# We need to strip away the units for comparison with
# non-unitized bounds
self._process_unit_info( xdata=x, kwargs=kwargs )
xx = self.convert_xunits( x )
scalex = (xx<xmin) or (xx>xmax)
trans = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
l = mlines.Line2D([x,x], [ymin,ymax] , transform=trans, **kwargs)
l.y_isdata = False
self.add_line(l)
self.autoscale_view(scalex=scalex, scaley=False)
return l
@docstring.dedent_interpd
def axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs):
"""
call signature::
axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs)
Axis Horizontal Span.
*y* coords are in data units and *x* coords are in axes (relative
0-1) units.
Draw a horizontal span (rectangle) from *ymin* to *ymax*.
With the default values of *xmin* = 0 and *xmax* = 1, this
always spans the xrange, regardless of the xlim settings, even
if you change them, eg. with the :meth:`set_xlim` command.
That is, the horizontal extent is in axes coords: 0=left,
0.5=middle, 1.0=right but the *y* location is in data
coordinates.
Return value is a :class:`matplotlib.patches.Polygon`
instance.
Examples:
* draw a gray rectangle from *y* = 0.25-0.75 that spans the
horizontal extent of the axes
>>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
Valid kwargs are :class:`~matplotlib.patches.Polygon` properties:
%(Polygon)s
**Example:**
.. plot:: mpl_examples/pylab_examples/axhspan_demo.py
"""
trans = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
# process the unit information
self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs )
# first we need to strip away the units
xmin, xmax = self.convert_xunits( [xmin, xmax] )
ymin, ymax = self.convert_yunits( [ymin, ymax] )
verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)
p = mpatches.Polygon(verts, **kwargs)
p.set_transform(trans)
p.x_isdata = False
self.add_patch(p)
self.autoscale_view(scalex=False)
return p
@docstring.dedent_interpd
def axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs):
"""
call signature::
axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs)
Axis Vertical Span.
*x* coords are in data units and *y* coords are in axes (relative
0-1) units.
Draw a vertical span (rectangle) from *xmin* to *xmax*. With
the default values of *ymin* = 0 and *ymax* = 1, this always
spans the yrange, regardless of the ylim settings, even if you
change them, eg. with the :meth:`set_ylim` command. That is,
the vertical extent is in axes coords: 0=bottom, 0.5=middle,
1.0=top but the *y* location is in data coordinates.
Return value is the :class:`matplotlib.patches.Polygon`
instance.
Examples:
* draw a vertical green translucent rectangle from x=1.25 to 1.55 that
spans the yrange of the axes
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
Valid kwargs are :class:`~matplotlib.patches.Polygon`
properties:
%(Polygon)s
.. seealso::
:meth:`axhspan`
for example plot and source code
"""
trans = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
# process the unit information
self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs )
# first we need to strip away the units
xmin, xmax = self.convert_xunits( [xmin, xmax] )
ymin, ymax = self.convert_yunits( [ymin, ymax] )
verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]
p = mpatches.Polygon(verts, **kwargs)
p.set_transform(trans)
p.y_isdata = False
self.add_patch(p)
self.autoscale_view(scaley=False)
return p
@docstring.dedent
def hlines(self, y, xmin, xmax, colors='k', linestyles='solid',
label='', **kwargs):
"""
call signature::
hlines(y, xmin, xmax, colors='k', linestyles='solid', **kwargs)
Plot horizontal lines at each *y* from *xmin* to *xmax*.
Returns the :class:`~matplotlib.collections.LineCollection`
that was added.
Required arguments:
*y*:
a 1-D numpy array or iterable.
*xmin* and *xmax*:
can be scalars or ``len(x)`` numpy arrays. If they are
scalars, then the respective values are constant, else the
widths of the lines are determined by *xmin* and *xmax*.
Optional keyword arguments:
*colors*:
a line collections color argument, either a single color
or a ``len(y)`` list of colors
*linestyles*:
[ 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
**Example:**
.. plot:: mpl_examples/pylab_examples/hline_demo.py
"""
if kwargs.get('fmt') is not None:
raise DeprecationWarning('hlines now uses a '
'collections.LineCollection and not a '
'list of Line2D to draw; see API_CHANGES')
# We do the conversion first since not all unitized data is uniform
# process the unit information
self._process_unit_info( [xmin, xmax], y, kwargs=kwargs )
y = self.convert_yunits( y )
xmin = self.convert_xunits(xmin)
xmax = self.convert_xunits(xmax)
if not iterable(y): y = [y]
if not iterable(xmin): xmin = [xmin]
if not iterable(xmax): xmax = [xmax]
y = np.asarray(y)
xmin = np.asarray(xmin)
xmax = np.asarray(xmax)
if len(xmin)==1:
xmin = np.resize( xmin, y.shape )
if len(xmax)==1:
xmax = np.resize( xmax, y.shape )
if len(xmin)!=len(y):
raise ValueError, 'xmin and y are unequal sized sequences'
if len(xmax)!=len(y):
raise ValueError, 'xmax and y are unequal sized sequences'
verts = [ ((thisxmin, thisy), (thisxmax, thisy))
for thisxmin, thisxmax, thisy in zip(xmin, xmax, y)]
coll = mcoll.LineCollection(verts, colors=colors,
linestyles=linestyles, label=label)
self.add_collection(coll)
coll.update(kwargs)
minx = min(xmin.min(), xmax.min())
maxx = max(xmin.max(), xmax.max())
miny = y.min()
maxy = y.max()
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
return coll
@docstring.dedent_interpd
def vlines(self, x, ymin, ymax, colors='k', linestyles='solid',
label='', **kwargs):
"""
call signature::
vlines(x, ymin, ymax, color='k', linestyles='solid')
Plot vertical lines at each *x* from *ymin* to *ymax*. *ymin*
or *ymax* can be scalars or len(*x*) numpy arrays. If they are
scalars, then the respective values are constant, else the
heights of the lines are determined by *ymin* and *ymax*.
*colors*
a line collections color args, either a single color
or a len(*x*) list of colors
*linestyles*
one of [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
Returns the :class:`matplotlib.collections.LineCollection`
that was added.
kwargs are :class:`~matplotlib.collections.LineCollection` properties:
%(LineCollection)s
"""
if kwargs.get('fmt') is not None:
raise DeprecationWarning('vlines now uses a '
'collections.LineCollection and not a '
'list of Line2D to draw; see API_CHANGES')
self._process_unit_info(xdata=x, ydata=[ymin, ymax], kwargs=kwargs)
# We do the conversion first since not all unitized data is uniform
x = self.convert_xunits( x )
ymin = self.convert_yunits( ymin )
ymax = self.convert_yunits( ymax )
if not iterable(x): x = [x]
if not iterable(ymin): ymin = [ymin]
if not iterable(ymax): ymax = [ymax]
x = np.asarray(x)
ymin = np.asarray(ymin)
ymax = np.asarray(ymax)
if len(ymin)==1:
ymin = np.resize( ymin, x.shape )
if len(ymax)==1:
ymax = np.resize( ymax, x.shape )
if len(ymin)!=len(x):
raise ValueError, 'ymin and x are unequal sized sequences'
if len(ymax)!=len(x):
raise ValueError, 'ymax and x are unequal sized sequences'
Y = np.array([ymin, ymax]).T
verts = [ ((thisx, thisymin), (thisx, thisymax))
for thisx, (thisymin, thisymax) in zip(x,Y)]
#print 'creating line collection'
coll = mcoll.LineCollection(verts, colors=colors,
linestyles=linestyles, label=label)
self.add_collection(coll)
coll.update(kwargs)
minx = min( x )
maxx = max( x )
miny = min( min(ymin), min(ymax) )
maxy = max( max(ymin), max(ymax) )
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
return coll
#### Basic plotting
@docstring.dedent_interpd
def plot(self, *args, **kwargs):
"""
Plot lines and/or markers to the
:class:`~matplotlib.axes.Axes`. *args* is a variable length
argument, allowing for multiple *x*, *y* pairs with an
optional format string. For example, each of the following is
legal::
plot(x, y) # plot x and y using default line style and color
plot(x, y, 'bo') # plot x and y using blue circle markers
plot(y) # plot y using x as index array 0..N-1
plot(y, 'r+') # ditto, but with red plusses
If *x* and/or *y* is 2-dimensional, then the corresponding columns
will be plotted.
An arbitrary number of *x*, *y*, *fmt* groups can be
specified, as in::
a.plot(x1, y1, 'g^', x2, y2, 'g-')
Return value is a list of lines that were added.
The following format string characters are accepted to control
the line style or marker:
================ ===============================
character description
================ ===============================
``'-'`` solid line style
``'--'`` dashed line style
``'-.'`` dash-dot line style
``':'`` dotted line style
``'.'`` point marker
``','`` pixel marker
``'o'`` circle marker
``'v'`` triangle_down marker
``'^'`` triangle_up marker
``'<'`` triangle_left marker
``'>'`` triangle_right marker
``'1'`` tri_down marker
``'2'`` tri_up marker
``'3'`` tri_left marker
``'4'`` tri_right marker
``'s'`` square marker
``'p'`` pentagon marker
``'*'`` star marker
``'h'`` hexagon1 marker
``'H'`` hexagon2 marker
``'+'`` plus marker
``'x'`` x marker
``'D'`` diamond marker
``'d'`` thin_diamond marker
``'|'`` vline marker
``'_'`` hline marker
================ ===============================
The following color abbreviations are supported:
========== ========
character color
========== ========
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white
========== ========
In addition, you can specify colors in many weird and
wonderful ways, including full names (``'green'``), hex
strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or
grayscale intensities as a string (``'0.8'``). Of these, the
string specifications can be used in place of a ``fmt`` group,
but the tuple forms can be used only as ``kwargs``.
Line styles and colors are combined in a single format string, as in
``'bo'`` for blue circles.
The *kwargs* can be used to set line properties (any property that has
a ``set_*`` method). You can use this to set a line label (for auto
legends), linewidth, anitialising, marker face color, etc. Here is an
example::
plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
plot([1,2,3], [1,4,9], 'rs', label='line 2')
axis([0, 4, 0, 10])
legend()
If you make multiple lines with one plot command, the kwargs
apply to all those lines, e.g.::
plot(x1, y1, x2, y2, antialised=False)
Neither line will be antialiased.
You do not need to use format strings, which are just
abbreviations. All of the line properties can be controlled
by keyword arguments. For example, you can set the color,
marker, linestyle, and markercolor with::
plot(x, y, color='green', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=12). See
:class:`~matplotlib.lines.Line2D` for details.
The kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
kwargs *scalex* and *scaley*, if defined, are passed on to
:meth:`~matplotlib.axes.Axes.autoscale_view` to determine
whether the *x* and *y* axes are autoscaled; the default is
*True*.
"""
scalex = kwargs.pop( 'scalex', True)
scaley = kwargs.pop( 'scaley', True)
if not self._hold: self.cla()
lines = []
for line in self._get_lines(*args, **kwargs):
self.add_line(line)
lines.append(line)
self.autoscale_view(scalex=scalex, scaley=scaley)
return lines
@docstring.dedent_interpd
def plot_date(self, x, y, fmt='bo', tz=None, xdate=True, ydate=False,
**kwargs):
"""
call signature::
plot_date(x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs)
Similar to the :func:`~matplotlib.pyplot.plot` command, except
the *x* or *y* (or both) data is considered to be dates, and the
axis is labeled accordingly.
*x* and/or *y* can be a sequence of dates represented as float
days since 0001-01-01 UTC.
Keyword arguments:
*fmt*: string
The plot format string.
*tz*: [ None | timezone string | :class:`tzinfo` instance]
The time zone to use in labeling dates. If *None*, defaults to rc
value.
*xdate*: [ True | False ]
If *True*, the *x*-axis will be labeled with dates.
*ydate*: [ False | True ]
If *True*, the *y*-axis will be labeled with dates.
Note if you are using custom date tickers and formatters, it
may be necessary to set the formatters/locators after the call
to :meth:`plot_date` since :meth:`plot_date` will set the
default tick locator to
:class:`matplotlib.dates.AutoDateLocator` (if the tick
locator is not already set to a
:class:`matplotlib.dates.DateLocator` instance) and the
default tick formatter to
:class:`matplotlib.dates.AutoDateFormatter` (if the tick
formatter is not already set to a
:class:`matplotlib.dates.DateFormatter` instance).
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:mod:`~matplotlib.dates`
for helper functions
:func:`~matplotlib.dates.date2num`,
:func:`~matplotlib.dates.num2date` and
:func:`~matplotlib.dates.drange`
for help on creating the required floating point
dates.
"""
if not self._hold: self.cla()
ret = self.plot(x, y, fmt, **kwargs)
if xdate:
self.xaxis_date(tz)
if ydate:
self.yaxis_date(tz)
self.autoscale_view()
return ret
@docstring.dedent_interpd
def loglog(self, *args, **kwargs):
"""
call signature::
loglog(*args, **kwargs)
Make a plot with log scaling on the *x* and *y* axis.
:func:`~matplotlib.pyplot.loglog` supports all the keyword
arguments of :func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale`.
Notable keyword arguments:
*basex*/*basey*: scalar > 1
base of the *x*/*y* logarithm
*subsx*/*subsy*: [ None | sequence ]
the location of the minor *x*/*y* ticks; *None* defaults
to autosubs, which depend on the number of decades in the
plot; see :meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale` for details
*nonposx*/*nonposy*: ['mask' | 'clip' ]
non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/log_demo.py
"""
if not self._hold: self.cla()
dx = {'basex': kwargs.pop('basex', 10),
'subsx': kwargs.pop('subsx', None),
'nonposx': kwargs.pop('nonposx', 'mask'),
}
dy = {'basey': kwargs.pop('basey', 10),
'subsy': kwargs.pop('subsy', None),
'nonposy': kwargs.pop('nonposy', 'mask'),
}
self.set_xscale('log', **dx)
self.set_yscale('log', **dy)
b = self._hold
self._hold = True # we've already processed the hold
l = self.plot(*args, **kwargs)
self._hold = b # restore the hold
return l
@docstring.dedent_interpd
def semilogx(self, *args, **kwargs):
"""
call signature::
semilogx(*args, **kwargs)
Make a plot with log scaling on the *x* axis.
:func:`semilogx` supports all the keyword arguments of
:func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale`.
Notable keyword arguments:
*basex*: scalar > 1
base of the *x* logarithm
*subsx*: [ None | sequence ]
The location of the minor xticks; *None* defaults to
autosubs, which depend on the number of decades in the
plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for
details.
*nonposx*: ['mask' | 'clip' ]
non-positive values in *x* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`loglog`
For example code and figure
"""
if not self._hold: self.cla()
d = {'basex': kwargs.pop( 'basex', 10),
'subsx': kwargs.pop( 'subsx', None),
'nonposx': kwargs.pop('nonposx', 'mask'),
}
self.set_xscale('log', **d)
self.set_yscale('linear')
b = self._hold
self._hold = True # we've already processed the hold
l = self.plot(*args, **kwargs)
self._hold = b # restore the hold
return l
@docstring.dedent_interpd
def semilogy(self, *args, **kwargs):
"""
call signature::
semilogy(*args, **kwargs)
Make a plot with log scaling on the *y* axis.
:func:`semilogy` supports all the keyword arguments of
:func:`~matplotlib.pylab.plot` and
:meth:`matplotlib.axes.Axes.set_yscale`.
Notable keyword arguments:
*basey*: scalar > 1
Base of the *y* logarithm
*subsy*: [ None | sequence ]
The location of the minor yticks; *None* defaults to
autosubs, which depend on the number of decades in the
plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for
details.
*nonposy*: ['mask' | 'clip' ]
non-positive values in *y* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`loglog`
For example code and figure
"""
if not self._hold: self.cla()
d = {'basey': kwargs.pop('basey', 10),
'subsy': kwargs.pop('subsy', None),
'nonposy': kwargs.pop('nonposy', 'mask'),
}
self.set_yscale('log', **d)
self.set_xscale('linear')
b = self._hold
self._hold = True # we've already processed the hold
l = self.plot(*args, **kwargs)
self._hold = b # restore the hold
return l
@docstring.dedent_interpd
def acorr(self, x, **kwargs):
"""
call signature::
acorr(x, normed=True, detrend=mlab.detrend_none, usevlines=True,
maxlags=10, **kwargs)
Plot the autocorrelation of *x*. If *normed* = *True*,
normalize the data by the autocorrelation at 0-th lag. *x* is
detrended by the *detrend* callable (default no normalization).
Data are plotted as ``plot(lags, c, **kwargs)``
Return value is a tuple (*lags*, *c*, *line*) where:
- *lags* are a length 2*maxlags+1 lag vector
- *c* is the 2*maxlags+1 auto correlation vector
- *line* is a :class:`~matplotlib.lines.Line2D` instance
returned by :meth:`plot`
The default *linestyle* is None and the default *marker* is
``'o'``, though these can be overridden with keyword args.
The cross correlation is performed with
:func:`numpy.correlate` with *mode* = 2.
If *usevlines* is *True*, :meth:`~matplotlib.axes.Axes.vlines`
rather than :meth:`~matplotlib.axes.Axes.plot` is used to draw
vertical lines from the origin to the acorr. Otherwise, the
plot style is determined by the kwargs, which are
:class:`~matplotlib.lines.Line2D` properties.
*maxlags* is a positive integer detailing the number of lags
to show. The default value of *None* will return all
:math:`2 \times \mathrm{len}(x) - 1` lags.
The return value is a tuple (*lags*, *c*, *linecol*, *b*)
where
- *linecol* is the
:class:`~matplotlib.collections.LineCollection`
- *b* is the *x*-axis.
.. seealso::
:meth:`~matplotlib.axes.Axes.plot` or
:meth:`~matplotlib.axes.Axes.vlines`
For documentation on valid kwargs.
**Example:**
:func:`~matplotlib.pyplot.xcorr` above, and
:func:`~matplotlib.pyplot.acorr` below.
**Example:**
.. plot:: mpl_examples/pylab_examples/xcorr_demo.py
"""
return self.xcorr(x, x, **kwargs)
@docstring.dedent_interpd
def xcorr(self, x, y, normed=True, detrend=mlab.detrend_none,
usevlines=True, maxlags=10, **kwargs):
"""
call signature::
def xcorr(self, x, y, normed=True, detrend=mlab.detrend_none,
usevlines=True, maxlags=10, **kwargs):
Plot the cross correlation between *x* and *y*. If *normed* =
*True*, normalize the data by the cross correlation at 0-th
lag. *x* and y are detrended by the *detrend* callable
(default no normalization). *x* and *y* must be equal length.
Data are plotted as ``plot(lags, c, **kwargs)``
Return value is a tuple (*lags*, *c*, *line*) where:
- *lags* are a length ``2*maxlags+1`` lag vector
- *c* is the ``2*maxlags+1`` auto correlation vector
- *line* is a :class:`~matplotlib.lines.Line2D` instance
returned by :func:`~matplotlib.pyplot.plot`.
The default *linestyle* is *None* and the default *marker* is
'o', though these can be overridden with keyword args. The
cross correlation is performed with :func:`numpy.correlate`
with *mode* = 2.
If *usevlines* is *True*:
:func:`~matplotlib.pyplot.vlines`
rather than :func:`~matplotlib.pyplot.plot` is used to draw
vertical lines from the origin to the xcorr. Otherwise the
plotstyle is determined by the kwargs, which are
:class:`~matplotlib.lines.Line2D` properties.
The return value is a tuple (*lags*, *c*, *linecol*, *b*)
where *linecol* is the
:class:`matplotlib.collections.LineCollection` instance and
*b* is the *x*-axis.
*maxlags* is a positive integer detailing the number of lags to show.
The default value of *None* will return all ``(2*len(x)-1)`` lags.
**Example:**
:func:`~matplotlib.pyplot.xcorr` above, and
:func:`~matplotlib.pyplot.acorr` below.
**Example:**
.. plot:: mpl_examples/pylab_examples/xcorr_demo.py
"""
Nx = len(x)
if Nx!=len(y):
raise ValueError('x and y must be equal length')
x = detrend(np.asarray(x))
y = detrend(np.asarray(y))
c = np.correlate(x, y, mode=2)
if normed: c/= np.sqrt(np.dot(x,x) * np.dot(y,y))
if maxlags is None: maxlags = Nx - 1
if maxlags >= Nx or maxlags < 1:
raise ValueError('maglags must be None or strictly '
'positive < %d'%Nx)
lags = np.arange(-maxlags,maxlags+1)
c = c[Nx-1-maxlags:Nx+maxlags]
if usevlines:
a = self.vlines(lags, [0], c, **kwargs)
b = self.axhline(**kwargs)
else:
kwargs.setdefault('marker', 'o')
kwargs.setdefault('linestyle', 'None')
a, = self.plot(lags, c, **kwargs)
b = None
return lags, c, a, b
def _get_legend_handles(self):
"return artists that will be used as handles for legend"
handles = self.lines[:]
handles.extend(self.patches)
handles.extend([c for c in self.collections
if isinstance(c, mcoll.LineCollection)])
handles.extend([c for c in self.collections
if isinstance(c, mcoll.RegularPolyCollection)])
handles.extend([c for c in self.collections
if isinstance(c, mcoll.CircleCollection)])
return handles
def get_legend_handles_labels(self):
"""
return handles and labels for legend
ax.legend() is equivalent to ::
h, l = ax.get_legend_handles_labels()
ax.legend(h, l)
"""
handles = []
labels = []
for handle in self._get_legend_handles():
label = handle.get_label()
if (label is not None and
label != '' and not label.startswith('_')):
handles.append(handle)
labels.append(label)
return handles, labels
def legend(self, *args, **kwargs):
"""
call signature::
legend(*args, **kwargs)
Place a legend on the current axes at location *loc*. Labels are a
sequence of strings and *loc* can be a string or an integer specifying
the legend location.
To make a legend with existing lines::
legend()
:meth:`legend` by itself will try and build a legend using the label
property of the lines/patches/collections. You can set the label of
a line by doing::
plot(x, y, label='my data')
or::
line.set_label('my data').
If label is set to '_nolegend_', the item will not be shown in
legend.
To automatically generate the legend from labels::
legend( ('label1', 'label2', 'label3') )
To make a legend for a list of lines and labels::
legend( (line1, line2, line3), ('label1', 'label2', 'label3') )
To make a legend at a given location, using a location argument::
legend( ('label1', 'label2', 'label3'), loc='upper left')
or::
legend( (line1, line2, line3), ('label1', 'label2', 'label3'), loc=2)
The location codes are
=============== =============
Location String Location Code
=============== =============
'best' 0
'upper right' 1
'upper left' 2
'lower left' 3
'lower right' 4
'right' 5
'center left' 6
'center right' 7
'lower center' 8
'upper center' 9
'center' 10
=============== =============
Users can specify any arbitrary location for the legend using the
*bbox_to_anchor* keyword argument. bbox_to_anchor can be an instance
of BboxBase(or its derivatives) or a tuple of 2 or 4 floats.
For example,
loc = 'upper right', bbox_to_anchor = (0.5, 0.5)
will place the legend so that the upper right corner of the legend at
the center of the axes.
The legend location can be specified in other coordinate, by using the
*bbox_transform* keyword.
The loc itslef can be a 2-tuple giving x,y of the lower-left corner of
the legend in axes coords (*bbox_to_anchor* is ignored).
Keyword arguments:
*prop*: [ None | FontProperties | dict ]
A :class:`matplotlib.font_manager.FontProperties`
instance. If *prop* is a dictionary, a new instance will be
created with *prop*. If *None*, use rc settings.
*numpoints*: integer
The number of points in the legend for line
*scatterpoints*: integer
The number of points in the legend for scatter plot
*scatteroffsets*: list of floats
a list of yoffsets for scatter symbols in legend
*markerscale*: [ None | scalar ]
The relative size of legend markers vs. original. If *None*, use rc
settings.
*frameon*: [ True | False ]
if True, draw a frame. Default is True
*fancybox*: [ None | False | True ]
if True, draw a frame with a round fancybox. If None, use rc
*shadow*: [ None | False | True ]
If *True*, draw a shadow behind legend. If *None*, use rc settings.
*ncol* : integer
number of columns. default is 1
*mode* : [ "expand" | None ]
if mode is "expand", the legend will be horizontally expanded
to fill the axes area (or *bbox_to_anchor*)
*bbox_to_anchor* : an instance of BboxBase or a tuple of 2 or 4 floats
the bbox that the legend will be anchored.
*bbox_transform* : [ an instance of Transform | None ]
the transform for the bbox. transAxes if None.
*title* : string
the legend title
Padding and spacing between various elements use following
keywords parameters. These values are measure in font-size
units. E.g., a fontsize of 10 points and a handlelength=5
implies a handlelength of 50 points. Values from rcParams
will be used if None.
================ ==================================================================
Keyword Description
================ ==================================================================
borderpad the fractional whitespace inside the legend border
labelspacing the vertical space between the legend entries
handlelength the length of the legend handles
handletextpad the pad between the legend handle and text
borderaxespad the pad between the axes and legend border
columnspacing the spacing between columns
================ ==================================================================
**Example:**
.. plot:: mpl_examples/api/legend_demo.py
Also see :ref:`plotting-guide-legend`.
"""
if len(args)==0:
handles, labels = self.get_legend_handles_labels()
if len(handles) == 0:
warnings.warn("No labeled objects found. "
"Use label='...' kwarg on individual plots.")
return None
elif len(args)==1:
# LABELS
labels = args[0]
handles = [h for h, label in zip(self._get_legend_handles(),
labels)]
elif len(args)==2:
if is_string_like(args[1]) or isinstance(args[1], int):
# LABELS, LOC
labels, loc = args
handles = [h for h, label in zip(self._get_legend_handles(),
labels)]
kwargs['loc'] = loc
else:
# LINES, LABELS
handles, labels = args
elif len(args)==3:
# LINES, LABELS, LOC
handles, labels, loc = args
kwargs['loc'] = loc
else:
raise TypeError('Invalid arguments to legend')
handles = cbook.flatten(handles)
self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
return self.legend_
#### Specialized plotting
def step(self, x, y, *args, **kwargs):
'''
call signature::
step(x, y, *args, **kwargs)
Make a step plot. Additional keyword args to :func:`step` are the same
as those for :func:`~matplotlib.pyplot.plot`.
*x* and *y* must be 1-D sequences, and it is assumed, but not checked,
that *x* is uniformly increasing.
Keyword arguments:
*where*: [ 'pre' | 'post' | 'mid' ]
If 'pre', the interval from x[i] to x[i+1] has level y[i+1]
If 'post', that interval has level y[i]
If 'mid', the jumps in *y* occur half-way between the
*x*-values.
'''
where = kwargs.pop('where', 'pre')
if where not in ('pre', 'post', 'mid'):
raise ValueError("'where' argument to step must be "
"'pre', 'post' or 'mid'")
kwargs['linestyle'] = 'steps-' + where
return self.plot(x, y, *args, **kwargs)
@docstring.dedent_interpd
def bar(self, left, height, width=0.8, bottom=None, **kwargs):
"""
call signature::
bar(left, height, width=0.8, bottom=0, **kwargs)
Make a bar plot with rectangles bounded by:
*left*, *left* + *width*, *bottom*, *bottom* + *height*
(left, right, bottom and top edges)
*left*, *height*, *width*, and *bottom* can be either scalars
or sequences
Return value is a list of
:class:`matplotlib.patches.Rectangle` instances.
Required arguments:
======== ===============================================
Argument Description
======== ===============================================
*left* the x coordinates of the left sides of the bars
*height* the heights of the bars
======== ===============================================
Optional keyword arguments:
=============== ==========================================
Keyword Description
=============== ==========================================
*width* the widths of the bars
*bottom* the y coordinates of the bottom edges of
the bars
*color* the colors of the bars
*edgecolor* the colors of the bar edges
*linewidth* width of bar edges; None means use default
linewidth; 0 means don't draw edges.
*xerr* if not None, will be used to generate
errorbars on the bar chart
*yerr* if not None, will be used to generate
errorbars on the bar chart
*ecolor* specifies the color of any errorbar
*capsize* (default 3) determines the length in
points of the error bar caps
*error_kw* dictionary of kwargs to be passed to
errorbar method. *ecolor* and *capsize*
may be specified here rather than as
independent kwargs.
*align* 'edge' (default) | 'center'
*orientation* 'vertical' | 'horizontal'
*log* [False|True] False (default) leaves the
orientation axis as-is; True sets it to
log scale
=============== ==========================================
For vertical bars, *align* = 'edge' aligns bars by their left
edges in left, while *align* = 'center' interprets these
values as the *x* coordinates of the bar centers. For
horizontal bars, *align* = 'edge' aligns bars by their bottom
edges in bottom, while *align* = 'center' interprets these
values as the *y* coordinates of the bar centers.
The optional arguments *color*, *edgecolor*, *linewidth*,
*xerr*, and *yerr* can be either scalars or sequences of
length equal to the number of bars. This enables you to use
bar as the basis for stacked bar charts, or candlestick plots.
Detail: *xerr* and *yerr* are passed directly to
:meth:`errorbar`, so they can also have shape 2xN for
independent specification of lower and upper errors.
Other optional kwargs:
%(Rectangle)s
**Example:** A stacked bar chart.
.. plot:: mpl_examples/pylab_examples/bar_stacked.py
"""
if not self._hold: self.cla()
color = kwargs.pop('color', None)
edgecolor = kwargs.pop('edgecolor', None)
linewidth = kwargs.pop('linewidth', None)
# Because xerr and yerr will be passed to errorbar,
# most dimension checking and processing will be left
# to the errorbar method.
xerr = kwargs.pop('xerr', None)
yerr = kwargs.pop('yerr', None)
error_kw = kwargs.pop('error_kw', dict())
ecolor = kwargs.pop('ecolor', None)
capsize = kwargs.pop('capsize', 3)
error_kw.setdefault('ecolor', ecolor)
error_kw.setdefault('capsize', capsize)
align = kwargs.pop('align', 'edge')
orientation = kwargs.pop('orientation', 'vertical')
log = kwargs.pop('log', False)
label = kwargs.pop('label', '')
def make_iterable(x):
if not iterable(x):
return [x]
else:
return x
# make them safe to take len() of
_left = left
left = make_iterable(left)
height = make_iterable(height)
width = make_iterable(width)
_bottom = bottom
bottom = make_iterable(bottom)
linewidth = make_iterable(linewidth)
adjust_ylim = False
adjust_xlim = False
if orientation == 'vertical':
self._process_unit_info(xdata=left, ydata=height, kwargs=kwargs)
if log:
self.set_yscale('log')
# size width and bottom according to length of left
if _bottom is None:
if self.get_yscale() == 'log':
bottom = [1e-100]
adjust_ylim = True
else:
bottom = [0]
nbars = len(left)
if len(width) == 1:
width *= nbars
if len(bottom) == 1:
bottom *= nbars
elif orientation == 'horizontal':
self._process_unit_info(xdata=width, ydata=bottom, kwargs=kwargs)
if log:
self.set_xscale('log')
# size left and height according to length of bottom
if _left is None:
if self.get_xscale() == 'log':
left = [1e-100]
adjust_xlim = True
else:
left = [0]
nbars = len(bottom)
if len(left) == 1:
left *= nbars
if len(height) == 1:
height *= nbars
else:
raise ValueError, 'invalid orientation: %s' % orientation
if len(linewidth) < nbars:
linewidth *= nbars
if color is None:
color = [None] * nbars
else:
color = list(mcolors.colorConverter.to_rgba_array(color))
if len(color) == 0: # until to_rgba_array is changed
color = [[0,0,0,0]]
if len(color) < nbars:
color *= nbars
if edgecolor is None:
edgecolor = [None] * nbars
else:
edgecolor = list(mcolors.colorConverter.to_rgba_array(edgecolor))
if len(edgecolor) == 0: # until to_rgba_array is changed
edgecolor = [[0,0,0,0]]
if len(edgecolor) < nbars:
edgecolor *= nbars
# FIXME: convert the following to proper input validation
# raising ValueError; don't use assert for this.
assert len(left)==nbars, "incompatible sizes: argument 'left' must be length %d or scalar" % nbars
assert len(height)==nbars, ("incompatible sizes: argument 'height' must be length %d or scalar" %
nbars)
assert len(width)==nbars, ("incompatible sizes: argument 'width' must be length %d or scalar" %
nbars)
assert len(bottom)==nbars, ("incompatible sizes: argument 'bottom' must be length %d or scalar" %
nbars)
patches = []
# lets do some conversions now since some types cannot be
# subtracted uniformly
if self.xaxis is not None:
left = self.convert_xunits( left )
width = self.convert_xunits( width )
if xerr is not None:
xerr = self.convert_xunits( xerr )
if self.yaxis is not None:
bottom = self.convert_yunits( bottom )
height = self.convert_yunits( height )
if yerr is not None:
yerr = self.convert_yunits( yerr )
if align == 'edge':
pass
elif align == 'center':
if orientation == 'vertical':
left = [left[i] - width[i]/2. for i in xrange(len(left))]
elif orientation == 'horizontal':
bottom = [bottom[i] - height[i]/2. for i in xrange(len(bottom))]
else:
raise ValueError, 'invalid alignment: %s' % align
args = zip(left, bottom, width, height, color, edgecolor, linewidth)
for l, b, w, h, c, e, lw in args:
if h<0:
b += h
h = abs(h)
if w<0:
l += w
w = abs(w)
r = mpatches.Rectangle(
xy=(l, b), width=w, height=h,
facecolor=c,
edgecolor=e,
linewidth=lw,
label=label
)
label = '_nolegend_'
r.update(kwargs)
r.get_path()._interpolation_steps = 100
#print r.get_label(), label, 'label' in kwargs
self.add_patch(r)
patches.append(r)
holdstate = self._hold
self.hold(True) # ensure hold is on before plotting errorbars
if xerr is not None or yerr is not None:
if orientation == 'vertical':
# using list comps rather than arrays to preserve unit info
x = [l+0.5*w for l, w in zip(left, width)]
y = [b+h for b,h in zip(bottom, height)]
elif orientation == 'horizontal':
# using list comps rather than arrays to preserve unit info
x = [l+w for l,w in zip(left, width)]
y = [b+0.5*h for b,h in zip(bottom, height)]
self.errorbar(
x, y,
yerr=yerr, xerr=xerr,
fmt=None, **error_kw)
self.hold(holdstate) # restore previous hold state
if adjust_xlim:
xmin, xmax = self.dataLim.intervalx
xmin = np.amin([w for w in width if w > 0])
if xerr is not None:
xmin = xmin - np.amax(xerr)
xmin = max(xmin*0.9, 1e-100)
self.dataLim.intervalx = (xmin, xmax)
if adjust_ylim:
ymin, ymax = self.dataLim.intervaly
ymin = np.amin([h for h in height if h > 0])
if yerr is not None:
ymin = ymin - np.amax(yerr)
ymin = max(ymin*0.9, 1e-100)
self.dataLim.intervaly = (ymin, ymax)
self.autoscale_view()
return patches
@docstring.dedent_interpd
def barh(self, bottom, width, height=0.8, left=None, **kwargs):
"""
call signature::
barh(bottom, width, height=0.8, left=0, **kwargs)
Make a horizontal bar plot with rectangles bounded by:
*left*, *left* + *width*, *bottom*, *bottom* + *height*
(left, right, bottom and top edges)
*bottom*, *width*, *height*, and *left* can be either scalars
or sequences
Return value is a list of
:class:`matplotlib.patches.Rectangle` instances.
Required arguments:
======== ======================================================
Argument Description
======== ======================================================
*bottom* the vertical positions of the bottom edges of the bars
*width* the lengths of the bars
======== ======================================================
Optional keyword arguments:
=============== ==========================================
Keyword Description
=============== ==========================================
*height* the heights (thicknesses) of the bars
*left* the x coordinates of the left edges of the
bars
*color* the colors of the bars
*edgecolor* the colors of the bar edges
*linewidth* width of bar edges; None means use default
linewidth; 0 means don't draw edges.
*xerr* if not None, will be used to generate
errorbars on the bar chart
*yerr* if not None, will be used to generate
errorbars on the bar chart
*ecolor* specifies the color of any errorbar
*capsize* (default 3) determines the length in
points of the error bar caps
*align* 'edge' (default) | 'center'
*log* [False|True] False (default) leaves the
horizontal axis as-is; True sets it to log
scale
=============== ==========================================
Setting *align* = 'edge' aligns bars by their bottom edges in
bottom, while *align* = 'center' interprets these values as
the *y* coordinates of the bar centers.
The optional arguments *color*, *edgecolor*, *linewidth*,
*xerr*, and *yerr* can be either scalars or sequences of
length equal to the number of bars. This enables you to use
barh as the basis for stacked bar charts, or candlestick
plots.
other optional kwargs:
%(Rectangle)s
"""
patches = self.bar(left=left, height=height, width=width, bottom=bottom,
orientation='horizontal', **kwargs)
return patches
@docstring.dedent_interpd
def broken_barh(self, xranges, yrange, **kwargs):
"""
call signature::
broken_barh(self, xranges, yrange, **kwargs)
A collection of horizontal bars spanning *yrange* with a sequence of
*xranges*.
Required arguments:
========= ==============================
Argument Description
========= ==============================
*xranges* sequence of (*xmin*, *xwidth*)
*yrange* sequence of (*ymin*, *ywidth*)
========= ==============================
kwargs are
:class:`matplotlib.collections.BrokenBarHCollection`
properties:
%(BrokenBarHCollection)s
these can either be a single argument, ie::
facecolors = 'black'
or a sequence of arguments for the various bars, ie::
facecolors = ('black', 'red', 'green')
**Example:**
.. plot:: mpl_examples/pylab_examples/broken_barh.py
"""
col = mcoll.BrokenBarHCollection(xranges, yrange, **kwargs)
self.add_collection(col, autolim=True)
self.autoscale_view()
return col
def stem(self, x, y, linefmt='b-', markerfmt='bo', basefmt='r-'):
"""
call signature::
stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')
A stem plot plots vertical lines (using *linefmt*) at each *x*
location from the baseline to *y*, and places a marker there
using *markerfmt*. A horizontal line at 0 is is plotted using
*basefmt*.
Return value is a tuple (*markerline*, *stemlines*,
*baseline*).
.. seealso::
`this document`__
for details
:file:`examples/pylab_examples/stem_plot.py`
for a demo
__ http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html
"""
remember_hold=self._hold
if not self._hold: self.cla()
self.hold(True)
markerline, = self.plot(x, y, markerfmt)
stemlines = []
for thisx, thisy in zip(x, y):
l, = self.plot([thisx,thisx], [0, thisy], linefmt)
stemlines.append(l)
baseline, = self.plot([np.amin(x), np.amax(x)], [0,0], basefmt)
self.hold(remember_hold)
return markerline, stemlines, baseline
def pie(self, x, explode=None, labels=None, colors=None,
autopct=None, pctdistance=0.6, shadow=False,
labeldistance=1.1):
r"""
call signature::
pie(x, explode=None, labels=None,
colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'),
autopct=None, pctdistance=0.6, labeldistance=1.1, shadow=False)
Make a pie chart of array *x*. The fractional area of each
wedge is given by x/sum(x). If sum(x) <= 1, then the values
of x give the fractional area directly and the array will not
be normalized.
Keyword arguments:
*explode*: [ None | len(x) sequence ]
If not *None*, is a len(*x*) array which specifies the
fraction of the radius with which to offset each wedge.
*colors*: [ None | color sequence ]
A sequence of matplotlib color args through which the pie chart
will cycle.
*labels*: [ None | len(x) sequence of strings ]
A sequence of strings providing the labels for each wedge
*autopct*: [ None | format string | format function ]
If not *None*, is a string or function used to label the
wedges with their numeric value. The label will be placed inside
the wedge. If it is a format string, the label will be ``fmt%pct``.
If it is a function, it will be called.
*pctdistance*: scalar
The ratio between the center of each pie slice and the
start of the text generated by *autopct*. Ignored if
*autopct* is *None*; default is 0.6.
*labeldistance*: scalar
The radial distance at which the pie labels are drawn
*shadow*: [ False | True ]
Draw a shadow beneath the pie.
The pie chart will probably look best if the figure and axes are
square. Eg.::
figure(figsize=(8,8))
ax = axes([0.1, 0.1, 0.8, 0.8])
Return value:
If *autopct* is None, return the tuple (*patches*, *texts*):
- *patches* is a sequence of
:class:`matplotlib.patches.Wedge` instances
- *texts* is a list of the label
:class:`matplotlib.text.Text` instances.
If *autopct* is not *None*, return the tuple (*patches*,
*texts*, *autotexts*), where *patches* and *texts* are as
above, and *autotexts* is a list of
:class:`~matplotlib.text.Text` instances for the numeric
labels.
"""
self.set_frame_on(False)
x = np.asarray(x).astype(np.float32)
sx = float(x.sum())
if sx>1: x = np.divide(x,sx)
if labels is None: labels = ['']*len(x)
if explode is None: explode = [0]*len(x)
assert(len(x)==len(labels))
assert(len(x)==len(explode))
if colors is None: colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w')
center = 0,0
radius = 1
theta1 = 0
i = 0
texts = []
slices = []
autotexts = []
for frac, label, expl in cbook.safezip(x,labels, explode):
x, y = center
theta2 = theta1 + frac
thetam = 2*math.pi*0.5*(theta1+theta2)
x += expl*math.cos(thetam)
y += expl*math.sin(thetam)
w = mpatches.Wedge((x,y), radius, 360.*theta1, 360.*theta2,
facecolor=colors[i%len(colors)])
slices.append(w)
self.add_patch(w)
w.set_label(label)
if shadow:
# make sure to add a shadow after the call to
# add_patch so the figure and transform props will be
# set
shad = mpatches.Shadow(w, -0.02, -0.02,
#props={'facecolor':w.get_facecolor()}
)
shad.set_zorder(0.9*w.get_zorder())
shad.set_label('_nolegend_')
self.add_patch(shad)
xt = x + labeldistance*radius*math.cos(thetam)
yt = y + labeldistance*radius*math.sin(thetam)
label_alignment = xt > 0 and 'left' or 'right'
t = self.text(xt, yt, label,
size=rcParams['xtick.labelsize'],
horizontalalignment=label_alignment,
verticalalignment='center')
texts.append(t)
if autopct is not None:
xt = x + pctdistance*radius*math.cos(thetam)
yt = y + pctdistance*radius*math.sin(thetam)
if is_string_like(autopct):
s = autopct%(100.*frac)
elif callable(autopct):
s = autopct(100.*frac)
else:
raise TypeError(
'autopct must be callable or a format string')
t = self.text(xt, yt, s,
horizontalalignment='center',
verticalalignment='center')
autotexts.append(t)
theta1 = theta2
i += 1
self.set_xlim((-1.25, 1.25))
self.set_ylim((-1.25, 1.25))
self.set_xticks([])
self.set_yticks([])
if autopct is None: return slices, texts
else: return slices, texts, autotexts
@docstring.dedent_interpd
def errorbar(self, x, y, yerr=None, xerr=None,
fmt='-', ecolor=None, elinewidth=None, capsize=3,
barsabove=False, lolims=False, uplims=False,
xlolims=False, xuplims=False, **kwargs):
"""
call signature::
errorbar(x, y, yerr=None, xerr=None,
fmt='-', ecolor=None, elinewidth=None, capsize=3,
barsabove=False, lolims=False, uplims=False,
xlolims=False, xuplims=False)
Plot *x* versus *y* with error deltas in *yerr* and *xerr*.
Vertical errorbars are plotted if *yerr* is not *None*.
Horizontal errorbars are plotted if *xerr* is not *None*.
*x*, *y*, *xerr*, and *yerr* can all be scalars, which plots a
single error bar at *x*, *y*.
Optional keyword arguments:
*xerr*/*yerr*: [ scalar | N, Nx1, or 2xN array-like ]
If a scalar number, len(N) array-like object, or an Nx1 array-like
object, errorbars are drawn +/- value.
If a sequence of shape 2xN, errorbars are drawn at -row1 and
+row2
*fmt*: '-'
The plot format symbol. If *fmt* is *None*, only the
errorbars are plotted. This is used for adding
errorbars to a bar plot, for example.
*ecolor*: [ None | mpl color ]
a matplotlib color arg which gives the color the errorbar lines;
if *None*, use the marker color.
*elinewidth*: scalar
the linewidth of the errorbar lines. If *None*, use the linewidth.
*capsize*: scalar
the size of the error bar caps in points
*barsabove*: [ True | False ]
if *True*, will plot the errorbars above the plot
symbols. Default is below.
*lolims*/*uplims*/*xlolims*/*xuplims*: [ False | True ]
These arguments can be used to indicate that a value gives
only upper/lower limits. In that case a caret symbol is
used to indicate this. lims-arguments may be of the same
type as *xerr* and *yerr*.
All other keyword arguments are passed on to the plot command for the
markers. For example, this code makes big red squares with
thick green edges::
x,y,yerr = rand(3,10)
errorbar(x, y, yerr, marker='s',
mfc='red', mec='green', ms=20, mew=4)
where *mfc*, *mec*, *ms* and *mew* are aliases for the longer
property names, *markerfacecolor*, *markeredgecolor*, *markersize*
and *markeredgewith*.
valid kwargs for the marker properties are
%(Line2D)s
Returns (*plotline*, *caplines*, *barlinecols*):
*plotline*: :class:`~matplotlib.lines.Line2D` instance
*x*, *y* plot markers and/or line
*caplines*: list of error bar cap
:class:`~matplotlib.lines.Line2D` instances
*barlinecols*: list of
:class:`~matplotlib.collections.LineCollection` instances for
the horizontal and vertical error ranges.
**Example:**
.. plot:: mpl_examples/pylab_examples/errorbar_demo.py
"""
self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
if not self._hold: self.cla()
holdstate = self._hold
self._hold = True
# make sure all the args are iterable; use lists not arrays to
# preserve units
if not iterable(x):
x = [x]
if not iterable(y):
y = [y]
if xerr is not None:
if not iterable(xerr):
xerr = [xerr]*len(x)
if yerr is not None:
if not iterable(yerr):
yerr = [yerr]*len(y)
l0 = None
if barsabove and fmt is not None:
l0, = self.plot(x,y,fmt,**kwargs)
barcols = []
caplines = []
lines_kw = {'label':'_nolegend_'}
if elinewidth:
lines_kw['linewidth'] = elinewidth
else:
if 'linewidth' in kwargs:
lines_kw['linewidth']=kwargs['linewidth']
if 'lw' in kwargs:
lines_kw['lw']=kwargs['lw']
if 'transform' in kwargs:
lines_kw['transform'] = kwargs['transform']
# arrays fine here, they are booleans and hence not units
if not iterable(lolims):
lolims = np.asarray([lolims]*len(x), bool)
else: lolims = np.asarray(lolims, bool)
if not iterable(uplims): uplims = np.array([uplims]*len(x), bool)
else: uplims = np.asarray(uplims, bool)
if not iterable(xlolims): xlolims = np.array([xlolims]*len(x), bool)
else: xlolims = np.asarray(xlolims, bool)
if not iterable(xuplims): xuplims = np.array([xuplims]*len(x), bool)
else: xuplims = np.asarray(xuplims, bool)
def xywhere(xs, ys, mask):
"""
return xs[mask], ys[mask] where mask is True but xs and
ys are not arrays
"""
assert len(xs)==len(ys)
assert len(xs)==len(mask)
xs = [thisx for thisx, b in zip(xs, mask) if b]
ys = [thisy for thisy, b in zip(ys, mask) if b]
return xs, ys
if capsize > 0:
plot_kw = {
'ms':2*capsize,
'label':'_nolegend_'}
if 'markeredgewidth' in kwargs:
plot_kw['markeredgewidth']=kwargs['markeredgewidth']
if 'mew' in kwargs:
plot_kw['mew']=kwargs['mew']
if 'transform' in kwargs:
plot_kw['transform'] = kwargs['transform']
if xerr is not None:
if (iterable(xerr) and len(xerr)==2 and
iterable(xerr[0]) and iterable(xerr[1])):
# using list comps rather than arrays to preserve units
left = [thisx-thiserr for (thisx, thiserr)
in cbook.safezip(x,xerr[0])]
right = [thisx+thiserr for (thisx, thiserr)
in cbook.safezip(x,xerr[1])]
else:
# using list comps rather than arrays to preserve units
left = [thisx-thiserr for (thisx, thiserr)
in cbook.safezip(x,xerr)]
right = [thisx+thiserr for (thisx, thiserr)
in cbook.safezip(x,xerr)]
barcols.append( self.hlines(y, left, right, **lines_kw ) )
if capsize > 0:
if xlolims.any():
# can't use numpy logical indexing since left and
# y are lists
leftlo, ylo = xywhere(left, y, xlolims)
caplines.extend(
self.plot(leftlo, ylo, ls='None',
marker=mlines.CARETLEFT, **plot_kw) )
xlolims = ~xlolims
leftlo, ylo = xywhere(left, y, xlolims)
caplines.extend( self.plot(leftlo, ylo, 'k|', **plot_kw) )
else:
caplines.extend( self.plot(left, y, 'k|', **plot_kw) )
if xuplims.any():
rightup, yup = xywhere(right, y, xuplims)
caplines.extend(
self.plot(rightup, yup, ls='None',
marker=mlines.CARETRIGHT, **plot_kw) )
xuplims = ~xuplims
rightup, yup = xywhere(right, y, xuplims)
caplines.extend( self.plot(rightup, yup, 'k|', **plot_kw) )
else:
caplines.extend( self.plot(right, y, 'k|', **plot_kw) )
if yerr is not None:
if (iterable(yerr) and len(yerr)==2 and
iterable(yerr[0]) and iterable(yerr[1])):
# using list comps rather than arrays to preserve units
lower = [thisy-thiserr for (thisy, thiserr)
in cbook.safezip(y,yerr[0])]
upper = [thisy+thiserr for (thisy, thiserr)
in cbook.safezip(y,yerr[1])]
else:
# using list comps rather than arrays to preserve units
lower = [thisy-thiserr for (thisy, thiserr)
in cbook.safezip(y,yerr)]
upper = [thisy+thiserr for (thisy, thiserr)
in cbook.safezip(y,yerr)]
barcols.append( self.vlines(x, lower, upper, **lines_kw) )
if capsize > 0:
if lolims.any():
xlo, lowerlo = xywhere(x, lower, lolims)
caplines.extend(
self.plot(xlo, lowerlo, ls='None',
marker=mlines.CARETDOWN, **plot_kw) )
lolims = ~lolims
xlo, lowerlo = xywhere(x, lower, lolims)
caplines.extend( self.plot(xlo, lowerlo, 'k_', **plot_kw) )
else:
caplines.extend( self.plot(x, lower, 'k_', **plot_kw) )
if uplims.any():
xup, upperup = xywhere(x, upper, uplims)
caplines.extend(
self.plot(xup, upperup, ls='None',
marker=mlines.CARETUP, **plot_kw) )
uplims = ~uplims
xup, upperup = xywhere(x, upper, uplims)
caplines.extend( self.plot(xup, upperup, 'k_', **plot_kw) )
else:
caplines.extend( self.plot(x, upper, 'k_', **plot_kw) )
if not barsabove and fmt is not None:
l0, = self.plot(x,y,fmt,**kwargs)
if ecolor is None:
if l0 is None:
ecolor = self._get_lines.color_cycle.next()
else:
ecolor = l0.get_color()
for l in barcols:
l.set_color(ecolor)
for l in caplines:
l.set_color(ecolor)
self.autoscale_view()
self._hold = holdstate
return (l0, caplines, barcols)
def boxplot(self, x, notch=0, sym='b+', vert=1, whis=1.5,
positions=None, widths=None, patch_artist=False,
bootstrap=None):
"""
call signature::
boxplot(x, notch=0, sym='+', vert=1, whis=1.5,
positions=None, widths=None, patch_artist=False)
Make a box and whisker plot for each column of *x* or each
vector in sequence *x*. The box extends from the lower to
upper quartile values of the data, with a line at the median.
The whiskers extend from the box to show the range of the
data. Flier points are those past the end of the whiskers.
*x* is an array or a sequence of vectors.
- *notch* = 0 (default) produces a rectangular box plot.
- *notch* = 1 will produce a notched box plot
*sym* (default 'b+') is the default symbol for flier points.
Enter an empty string ('') if you don't want to show fliers.
- *vert* = 1 (default) makes the boxes vertical.
- *vert* = 0 makes horizontal boxes. This seems goofy, but
that's how MATLAB did it.
*whis* (default 1.5) defines the length of the whiskers as
a function of the inner quartile range. They extend to the
most extreme data point within ( ``whis*(75%-25%)`` ) data range.
*bootstrap* (default None) specifies whether to bootstrap the
confidence intervals around the median for notched
boxplots. If bootstrap==None, no bootstrapping is performed,
and notches are calculated using a Gaussian-based asymptotic
approximation (see McGill, R., Tukey, J.W., and Larsen, W.A.,
1978, and Kendall and Stuart, 1967). Otherwise, bootstrap
specifies the number of times to bootstrap the median to
determine it's 95% confidence intervals. Values between 1000
and 10000 are recommended.
*positions* (default 1,2,...,n) sets the horizontal positions of
the boxes. The ticks and limits are automatically set to match
the positions.
*widths* is either a scalar or a vector and sets the width of
each box. The default is 0.5, or ``0.15*(distance between extreme
positions)`` if that is smaller.
- *patch_artist* = False (default) produces boxes with the Line2D artist
- *patch_artist* = True produces boxes with the Patch artist
Returns a dictionary mapping each component of the boxplot
to a list of the :class:`matplotlib.lines.Line2D`
instances created.
**Example:**
.. plot:: pyplots/boxplot_demo.py
"""
if not self._hold: self.cla()
holdStatus = self._hold
whiskers, caps, boxes, medians, fliers = [], [], [], [], []
# convert x to a list of vectors
if hasattr(x, 'shape'):
if len(x.shape) == 1:
if hasattr(x[0], 'shape'):
x = list(x)
else:
x = [x,]
elif len(x.shape) == 2:
nr, nc = x.shape
if nr == 1:
x = [x]
elif nc == 1:
x = [x.ravel()]
else:
x = [x[:,i] for i in xrange(nc)]
else:
raise ValueError, "input x can have no more than 2 dimensions"
if not hasattr(x[0], '__len__'):
x = [x]
col = len(x)
# get some plot info
if positions is None:
positions = range(1, col + 1)
if widths is None:
distance = max(positions) - min(positions)
widths = min(0.15*max(distance,1.0), 0.5)
if isinstance(widths, float) or isinstance(widths, int):
widths = np.ones((col,), float) * widths
# loop through columns, adding each to plot
self.hold(True)
for i,pos in enumerate(positions):
d = np.ravel(x[i])
row = len(d)
if row==0:
# no data, skip this position
continue
# get median and quartiles
q1, med, q3 = mlab.prctile(d,[25,50,75])
# get high extreme
iq = q3 - q1
hi_val = q3 + whis*iq
wisk_hi = np.compress( d <= hi_val , d )
if len(wisk_hi) == 0:
wisk_hi = q3
else:
wisk_hi = max(wisk_hi)
# get low extreme
lo_val = q1 - whis*iq
wisk_lo = np.compress( d >= lo_val, d )
if len(wisk_lo) == 0:
wisk_lo = q1
else:
wisk_lo = min(wisk_lo)
# get fliers - if we are showing them
flier_hi = []
flier_lo = []
flier_hi_x = []
flier_lo_x = []
if len(sym) != 0:
flier_hi = np.compress( d > wisk_hi, d )
flier_lo = np.compress( d < wisk_lo, d )
flier_hi_x = np.ones(flier_hi.shape[0]) * pos
flier_lo_x = np.ones(flier_lo.shape[0]) * pos
# get x locations for fliers, whisker, whisker cap and box sides
box_x_min = pos - widths[i] * 0.5
box_x_max = pos + widths[i] * 0.5
wisk_x = np.ones(2) * pos
cap_x_min = pos - widths[i] * 0.25
cap_x_max = pos + widths[i] * 0.25
cap_x = [cap_x_min, cap_x_max]
# get y location for median
med_y = [med, med]
# calculate 'regular' plot
if notch == 0:
# make our box vectors
box_x = [box_x_min, box_x_max, box_x_max, box_x_min, box_x_min ]
box_y = [q1, q1, q3, q3, q1 ]
# make our median line vectors
med_x = [box_x_min, box_x_max]
# calculate 'notch' plot
else:
if bootstrap is not None:
# Do a bootstrap estimate of notch locations.
def bootstrapMedian(data, N=5000):
# determine 95% confidence intervals of the median
M = len(data)
percentile = [2.5,97.5]
estimate = np.zeros(N)
for n in range(N):
bsIndex = np.random.random_integers(0,M-1,M)
bsData = data[bsIndex]
estimate[n] = mlab.prctile(bsData, 50)
CI = mlab.prctile(estimate, percentile)
return CI
# get conf. intervals around median
CI = bootstrapMedian(d, N=bootstrap)
notch_max = CI[1]
notch_min = CI[0]
else:
# Estimate notch locations using Gaussian-based
# asymptotic approximation.
#
# For discussion: McGill, R., Tukey, J.W.,
# and Larsen, W.A. (1978) "Variations of
# Boxplots", The American Statistician, 32:12-16.
notch_max = med + 1.57*iq/np.sqrt(row)
notch_min = med - 1.57*iq/np.sqrt(row)
# make our notched box vectors
box_x = [box_x_min, box_x_max, box_x_max, cap_x_max, box_x_max,
box_x_max, box_x_min, box_x_min, cap_x_min, box_x_min,
box_x_min ]
box_y = [q1, q1, notch_min, med, notch_max, q3, q3, notch_max,
med, notch_min, q1]
# make our median line vectors
med_x = [cap_x_min, cap_x_max]
med_y = [med, med]
def to_vc(xs,ys):
# convert arguments to verts and codes
verts = []
#codes = []
for xi,yi in zip(xs,ys):
verts.append( (xi,yi) )
verts.append( (0,0) ) # ignored
codes = [mpath.Path.MOVETO] + \
[mpath.Path.LINETO]*(len(verts)-2) + \
[mpath.Path.CLOSEPOLY]
return verts,codes
def patch_list(xs,ys):
verts,codes = to_vc(xs,ys)
path = mpath.Path( verts, codes )
patch = mpatches.PathPatch(path)
self.add_artist(patch)
return [patch]
# vertical or horizontal plot?
if vert:
def doplot(*args):
return self.plot(*args)
def dopatch(xs,ys):
return patch_list(xs,ys)
else:
def doplot(*args):
shuffled = []
for i in xrange(0, len(args), 3):
shuffled.extend([args[i+1], args[i], args[i+2]])
return self.plot(*shuffled)
def dopatch(xs,ys):
xs,ys = ys,xs # flip X, Y
return patch_list(xs,ys)
if patch_artist:
median_color = 'k'
else:
median_color = 'r'
whiskers.extend(doplot(wisk_x, [q1, wisk_lo], 'b--',
wisk_x, [q3, wisk_hi], 'b--'))
caps.extend(doplot(cap_x, [wisk_hi, wisk_hi], 'k-',
cap_x, [wisk_lo, wisk_lo], 'k-'))
if patch_artist:
boxes.extend(dopatch(box_x, box_y))
else:
boxes.extend(doplot(box_x, box_y, 'b-'))
medians.extend(doplot(med_x, med_y, median_color+'-'))
fliers.extend(doplot(flier_hi_x, flier_hi, sym,
flier_lo_x, flier_lo, sym))
# fix our axes/ticks up a little
if 1 == vert:
setticks, setlim = self.set_xticks, self.set_xlim
else:
setticks, setlim = self.set_yticks, self.set_ylim
newlimits = min(positions)-0.5, max(positions)+0.5
setlim(newlimits)
setticks(positions)
# reset hold status
self.hold(holdStatus)
return dict(whiskers=whiskers, caps=caps, boxes=boxes,
medians=medians, fliers=fliers)
@docstring.dedent_interpd
def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=None, linewidths=None,
faceted=True, verts=None,
**kwargs):
"""
call signatures::
scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=None, linewidths=None,
verts=None, **kwargs)
Make a scatter plot of *x* versus *y*, where *x*, *y* are
converted to 1-D sequences which must be of the same length, *N*.
Keyword arguments:
*s*:
size in points^2. It is a scalar or an array of the same
length as *x* and *y*.
*c*:
a color. *c* can be a single color format string, or a
sequence of color specifications of length *N*, or a
sequence of *N* numbers to be mapped to colors using the
*cmap* and *norm* specified via kwargs (see below). Note
that *c* should not be a single numeric RGB or RGBA
sequence because that is indistinguishable from an array
of values to be colormapped. *c* can be a 2-D array in
which the rows are RGB or RGBA, however.
*marker*:
can be one of:
======= ==============
Value Description
======= ==============
``'s'`` square
``'o'`` circle
``'^'`` triangle up
``'>'`` triangle right
``'v'`` triangle down
``'<'`` triangle left
``'d'`` diamond
``'p'`` pentagon
``'h'`` hexagon
``'8'`` octagon
``'+'`` plus
``'x'`` cross
======= ==============
The marker can also be a tuple (*numsides*, *style*,
*angle*), which will create a custom, regular symbol.
*numsides*:
the number of sides
*style*:
the style of the regular symbol:
===== =============================================
Value Description
===== =============================================
0 a regular polygon
1 a star-like symbol
2 an asterisk
3 a circle (*numsides* and *angle* is ignored)
===== =============================================
*angle*:
the angle of rotation of the symbol
Finally, *marker* can be (*verts*, 0): *verts* is a
sequence of (*x*, *y*) vertices for a custom scatter
symbol. Alternatively, use the kwarg combination
*marker* = *None*, *verts* = *verts*.
Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in
which case all masks will be combined and only unmasked points
will be plotted.
Other keyword arguments: the color mapping and normalization
arguments will be used only if *c* is an array of floats.
*cmap*: [ None | Colormap ]
A :class:`matplotlib.colors.Colormap` instance or registered
name. If *None*, defaults to rc ``image.cmap``. *cmap* is
only used if *c* is an array of floats.
*norm*: [ None | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0, 1. If *None*, use the default
:func:`normalize`. *norm* is only used if *c* is an array
of floats.
*vmin*/*vmax*:
*vmin* and *vmax* are used in conjunction with norm to
normalize luminance data. If either are None, the min and
max of the color array *C* is used. Note if you pass a
*norm* instance, your settings for *vmin* and *vmax* will
be ignored.
*alpha*: 0 <= scalar <= 1 or None
The alpha value for the patches
*linewidths*: [ None | scalar | sequence ]
If *None*, defaults to (lines.linewidth,). Note that this
is a tuple, and if you set the linewidths argument you
must set it as a sequence of floats, as required by
:class:`~matplotlib.collections.RegularPolyCollection`.
Optional kwargs control the
:class:`~matplotlib.collections.Collection` properties; in
particular:
*edgecolors*:
The string 'none' to plot faces with no outlines
*facecolors*:
The string 'none' to plot unfilled outlines
Here are the standard descriptions of all the
:class:`~matplotlib.collections.Collection` kwargs:
%(Collection)s
A :class:`~matplotlib.collections.Collection` instance is
returned.
"""
if not self._hold: self.cla()
syms = { # a dict from symbol to (numsides, angle)
's' : (4,math.pi/4.0,0), # square
'o' : (0,0,3), # circle
'^' : (3,0,0), # triangle up
'>' : (3,math.pi/2.0,0), # triangle right
'v' : (3,math.pi,0), # triangle down
'<' : (3,3*math.pi/2.0,0), # triangle left
'd' : (4,0,0), # diamond
'p' : (5,0,0), # pentagon
'h' : (6,0,0), # hexagon
'8' : (8,0,0), # octagon
'+' : (4,0,2), # plus
'x' : (4,math.pi/4.0,2) # cross
}
self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
x = self.convert_xunits(x)
y = self.convert_yunits(y)
# np.ma.ravel yields an ndarray, not a masked array,
# unless its argument is a masked array.
x = np.ma.ravel(x)
y = np.ma.ravel(y)
if x.size != y.size:
raise ValueError("x and y must be the same size")
s = np.ma.ravel(s) # This doesn't have to match x, y in size.
c_is_stringy = is_string_like(c) or is_sequence_of_strings(c)
if not c_is_stringy:
c = np.asanyarray(c)
if c.size == x.size:
c = np.ma.ravel(c)
x, y, s, c = cbook.delete_masked_points(x, y, s, c)
scales = s # Renamed for readability below.
if c_is_stringy:
colors = mcolors.colorConverter.to_rgba_array(c, alpha)
else:
# The inherent ambiguity is resolved in favor of color
# mapping, not interpretation as rgb or rgba:
if c.size == x.size:
colors = None # use cmap, norm after collection is created
else:
colors = mcolors.colorConverter.to_rgba_array(c, alpha)
if faceted:
edgecolors = None
else:
edgecolors = 'none'
warnings.warn(
'''replace "faceted=False" with "edgecolors='none'"''',
DeprecationWarning) #2008/04/18
sym = None
symstyle = 0
# to be API compatible
if marker is None and not (verts is None):
marker = (verts, 0)
verts = None
if is_string_like(marker):
# the standard way to define symbols using a string character
sym = syms.get(marker)
if sym is None and verts is None:
raise ValueError('Unknown marker symbol to scatter')
numsides, rotation, symstyle = syms[marker]
elif iterable(marker):
# accept marker to be:
# (numsides, style, [angle])
# or
# (verts[], style, [angle])
if len(marker)<2 or len(marker)>3:
raise ValueError('Cannot create markersymbol from marker')
if cbook.is_numlike(marker[0]):
# (numsides, style, [angle])
if len(marker)==2:
numsides, rotation = marker[0], 0.
elif len(marker)==3:
numsides, rotation = marker[0], marker[2]
sym = True
if marker[1] in (1,2,3):
symstyle = marker[1]
else:
verts = np.asarray(marker[0])
if sym is not None:
if symstyle==0:
collection = mcoll.RegularPolyCollection(
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
elif symstyle==1:
collection = mcoll.StarPolygonCollection(
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
elif symstyle==2:
collection = mcoll.AsteriskPolygonCollection(
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
elif symstyle==3:
collection = mcoll.CircleCollection(
scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
else:
rescale = np.sqrt(max(verts[:,0]**2+verts[:,1]**2))
verts /= rescale
collection = mcoll.PolyCollection(
(verts,), scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
collection.set_transform(mtransforms.IdentityTransform())
collection.set_alpha(alpha)
collection.update(kwargs)
if colors is None:
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
collection.set_array(np.asarray(c))
collection.set_cmap(cmap)
collection.set_norm(norm)
if vmin is not None or vmax is not None:
collection.set_clim(vmin, vmax)
else:
collection.autoscale_None()
temp_x = x
temp_y = y
minx = np.amin(temp_x)
maxx = np.amax(temp_x)
miny = np.amin(temp_y)
maxy = np.amax(temp_y)
w = maxx-minx
h = maxy-miny
# the pad is a little hack to deal with the fact that we don't
# want to transform all the symbols whose scales are in points
# to data coords to get the exact bounding box for efficiency
# reasons. It can be done right if this is deemed important
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
self.update_datalim( corners)
self.autoscale_view()
# add the collection last
self.add_collection(collection)
return collection
@docstring.dedent_interpd
def hexbin(self, x, y, C = None, gridsize = 100, bins = None,
xscale = 'linear', yscale = 'linear', extent = None,
cmap=None, norm=None, vmin=None, vmax=None,
alpha=None, linewidths=None, edgecolors='none',
reduce_C_function = np.mean, mincnt=None, marginals=False,
**kwargs):
"""
call signature::
hexbin(x, y, C = None, gridsize = 100, bins = None,
xscale = 'linear', yscale = 'linear',
cmap=None, norm=None, vmin=None, vmax=None,
alpha=None, linewidths=None, edgecolors='none'
reduce_C_function = np.mean, mincnt=None, marginals=True
**kwargs)
Make a hexagonal binning plot of *x* versus *y*, where *x*,
*y* are 1-D sequences of the same length, *N*. If *C* is None
(the default), this is a histogram of the number of occurences
of the observations at (x[i],y[i]).
If *C* is specified, it specifies values at the coordinate
(x[i],y[i]). These values are accumulated for each hexagonal
bin and then reduced according to *reduce_C_function*, which
defaults to numpy's mean function (np.mean). (If *C* is
specified, it must also be a 1-D sequence of the same length
as *x* and *y*.)
*x*, *y* and/or *C* may be masked arrays, in which case only
unmasked points will be plotted.
Optional keyword arguments:
*gridsize*: [ 100 | integer ]
The number of hexagons in the *x*-direction, default is
100. The corresponding number of hexagons in the
*y*-direction is chosen such that the hexagons are
approximately regular. Alternatively, gridsize can be a
tuple with two elements specifying the number of hexagons
in the *x*-direction and the *y*-direction.
*bins*: [ None | 'log' | integer | sequence ]
If *None*, no binning is applied; the color of each hexagon
directly corresponds to its count value.
If 'log', use a logarithmic scale for the color
map. Internally, :math:`log_{10}(i+1)` is used to
determine the hexagon color.
If an integer, divide the counts in the specified number
of bins, and color the hexagons accordingly.
If a sequence of values, the values of the lower bound of
the bins to be used.
*xscale*: [ 'linear' | 'log' ]
Use a linear or log10 scale on the horizontal axis.
*scale*: [ 'linear' | 'log' ]
Use a linear or log10 scale on the vertical axis.
*mincnt*: None | a positive integer
If not None, only display cells with more than *mincnt*
number of points in the cell
*marginals*: True|False
if marginals is True, plot the marginal density as
colormapped rectagles along the bottom of the x-axis and
left of the y-axis
*extent*: [ None | scalars (left, right, bottom, top) ]
The limits of the bins. The default assigns the limits
based on gridsize, x, y, xscale and yscale.
Other keyword arguments controlling color mapping and normalization
arguments:
*cmap*: [ None | Colormap ]
a :class:`matplotlib.cm.Colormap` instance. If *None*,
defaults to rc ``image.cmap``.
*norm*: [ None | Normalize ]
:class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1.
*vmin*/*vmax*: scalar
*vmin* and *vmax* are used in conjunction with *norm* to normalize
luminance data. If either are *None*, the min and max of the color
array *C* is used. Note if you pass a norm instance, your settings
for *vmin* and *vmax* will be ignored.
*alpha*: scalar between 0 and 1, or None
the alpha value for the patches
*linewidths*: [ None | scalar ]
If *None*, defaults to rc lines.linewidth. Note that this
is a tuple, and if you set the linewidths argument you
must set it as a sequence of floats, as required by
:class:`~matplotlib.collections.RegularPolyCollection`.
Other keyword arguments controlling the Collection properties:
*edgecolors*: [ None | mpl color | color sequence ]
If 'none', draws the edges in the same color as the fill color.
This is the default, as it avoids unsightly unpainted pixels
between the hexagons.
If *None*, draws the outlines in the default color.
If a matplotlib color arg or sequence of rgba tuples, draws the
outlines in the specified color.
Here are the standard descriptions of all the
:class:`~matplotlib.collections.Collection` kwargs:
%(Collection)s
The return value is a
:class:`~matplotlib.collections.PolyCollection` instance; use
:meth:`~matplotlib.collection.PolyCollection.get_array` on
this :class:`~matplotlib.collections.PolyCollection` to get
the counts in each hexagon.. If marginals is True, horizontal
bar and vertical bar (both PolyCollections) will be attached
to the return collection as attributes *hbar* and *vbar*
**Example:**
.. plot:: mpl_examples/pylab_examples/hexbin_demo.py
"""
if not self._hold: self.cla()
self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
x, y, C = cbook.delete_masked_points(x, y, C)
# Set the size of the hexagon grid
if iterable(gridsize):
nx, ny = gridsize
else:
nx = gridsize
ny = int(nx/math.sqrt(3))
# Count the number of data in each hexagon
x = np.array(x, float)
y = np.array(y, float)
if xscale=='log':
if np.any(x <= 0.0):
raise ValueError("x contains non-positive values, so can not be log-scaled")
x = np.log10(x)
if yscale=='log':
if np.any(y <= 0.0):
raise ValueError("y contains non-positive values, so can not be log-scaled")
y = np.log10(y)
if extent is not None:
xmin, xmax, ymin, ymax = extent
else:
xmin = np.amin(x)
xmax = np.amax(x)
ymin = np.amin(y)
ymax = np.amax(y)
# In the x-direction, the hexagons exactly cover the region from
# xmin to xmax. Need some padding to avoid roundoff errors.
padding = 1.e-9 * (xmax - xmin)
xmin -= padding
xmax += padding
sx = (xmax-xmin) / nx
sy = (ymax-ymin) / ny
if marginals:
xorig = x.copy()
yorig = y.copy()
x = (x-xmin)/sx
y = (y-ymin)/sy
ix1 = np.round(x).astype(int)
iy1 = np.round(y).astype(int)
ix2 = np.floor(x).astype(int)
iy2 = np.floor(y).astype(int)
nx1 = nx + 1
ny1 = ny + 1
nx2 = nx
ny2 = ny
n = nx1*ny1+nx2*ny2
d1 = (x-ix1)**2 + 3.0 * (y-iy1)**2
d2 = (x-ix2-0.5)**2 + 3.0 * (y-iy2-0.5)**2
bdist = (d1<d2)
if C is None:
accum = np.zeros(n)
# Create appropriate views into "accum" array.
lattice1 = accum[:nx1*ny1]
lattice2 = accum[nx1*ny1:]
lattice1.shape = (nx1,ny1)
lattice2.shape = (nx2,ny2)
for i in xrange(len(x)):
if bdist[i]:
if ((ix1[i] >= 0) and (ix1[i] < nx1) and
(iy1[i] >= 0) and (iy1[i] < ny1)):
lattice1[ix1[i], iy1[i]]+=1
else:
if ((ix2[i] >= 0) and (ix2[i] < nx2) and
(iy2[i] >= 0) and (iy2[i] < ny2)):
lattice2[ix2[i], iy2[i]]+=1
# threshold
if mincnt is not None:
for i in xrange(nx1):
for j in xrange(ny1):
if lattice1[i,j]<mincnt:
lattice1[i,j] = np.nan
for i in xrange(nx2):
for j in xrange(ny2):
if lattice2[i,j]<mincnt:
lattice2[i,j] = np.nan
accum = np.hstack((
lattice1.astype(float).ravel(), lattice2.astype(float).ravel()))
good_idxs = ~np.isnan(accum)
else:
if mincnt is None:
mincnt = 0
# create accumulation arrays
lattice1 = np.empty((nx1,ny1),dtype=object)
for i in xrange(nx1):
for j in xrange(ny1):
lattice1[i,j] = []
lattice2 = np.empty((nx2,ny2),dtype=object)
for i in xrange(nx2):
for j in xrange(ny2):
lattice2[i,j] = []
for i in xrange(len(x)):
if bdist[i]:
if ((ix1[i] >= 0) and (ix1[i] < nx1) and
(iy1[i] >= 0) and (iy1[i] < ny1)):
lattice1[ix1[i], iy1[i]].append( C[i] )
else:
if ((ix2[i] >= 0) and (ix2[i] < nx2) and
(iy2[i] >= 0) and (iy2[i] < ny2)):
lattice2[ix2[i], iy2[i]].append( C[i] )
for i in xrange(nx1):
for j in xrange(ny1):
vals = lattice1[i,j]
if len(vals)>mincnt:
lattice1[i,j] = reduce_C_function( vals )
else:
lattice1[i,j] = np.nan
for i in xrange(nx2):
for j in xrange(ny2):
vals = lattice2[i,j]
if len(vals)>mincnt:
lattice2[i,j] = reduce_C_function( vals )
else:
lattice2[i,j] = np.nan
accum = np.hstack((
lattice1.astype(float).ravel(), lattice2.astype(float).ravel()))
good_idxs = ~np.isnan(accum)
px = xmin + sx * np.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0])
py = ymin + sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0
polygons = np.zeros((6, n, 2), float)
polygons[:,:nx1*ny1,0] = np.repeat(np.arange(nx1), ny1)
polygons[:,:nx1*ny1,1] = np.tile(np.arange(ny1), nx1)
polygons[:,nx1*ny1:,0] = np.repeat(np.arange(nx2) + 0.5, ny2)
polygons[:,nx1*ny1:,1] = np.tile(np.arange(ny2), nx2) + 0.5
# remove accumulation bins with no data
polygons = polygons[:,good_idxs,:]
accum = accum[good_idxs]
polygons = np.transpose(polygons, axes=[1,0,2])
polygons[:,:,0] *= sx
polygons[:,:,1] *= sy
polygons[:,:,0] += px
polygons[:,:,1] += py
if xscale=='log':
polygons[:,:,0] = 10**(polygons[:,:,0])
xmin = 10**xmin
xmax = 10**xmax
self.set_xscale('log')
if yscale=='log':
polygons[:,:,1] = 10**(polygons[:,:,1])
ymin = 10**ymin
ymax = 10**ymax
self.set_yscale('log')
if edgecolors=='none':
edgecolors = 'face'
collection = mcoll.PolyCollection(
polygons,
edgecolors = edgecolors,
linewidths = linewidths,
transOffset = self.transData,
)
if isinstance(norm, mcolors.LogNorm):
if (accum==0).any():
# make sure we have not zeros
accum += 1
# autoscale the norm with curren accum values if it hasn't
# been set
if norm is not None:
if norm.vmin is None and norm.vmax is None:
norm.autoscale(accum)
# Transform accum if needed
if bins=='log':
accum = np.log10(accum+1)
elif bins!=None:
if not iterable(bins):
minimum, maximum = min(accum), max(accum)
bins-=1 # one less edge than bins
bins = minimum + (maximum-minimum)*np.arange(bins)/bins
bins = np.sort(bins)
accum = bins.searchsorted(accum)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
collection.set_array(accum)
collection.set_cmap(cmap)
collection.set_norm(norm)
collection.set_alpha(alpha)
collection.update(kwargs)
if vmin is not None or vmax is not None:
collection.set_clim(vmin, vmax)
else:
collection.autoscale_None()
corners = ((xmin, ymin), (xmax, ymax))
self.update_datalim( corners)
self.autoscale_view(tight=True)
# add the collection last
self.add_collection(collection)
if not marginals:
return collection
if C is None:
C = np.ones(len(x))
def coarse_bin(x, y, coarse):
ind = coarse.searchsorted(x).clip(0, len(coarse)-1)
mus = np.zeros(len(coarse))
for i in range(len(coarse)):
mu = reduce_C_function(y[ind==i])
mus[i] = mu
return mus
coarse = np.linspace(xmin, xmax, gridsize)
xcoarse = coarse_bin(xorig, C, coarse)
valid = ~np.isnan(xcoarse)
verts, values = [], []
for i,val in enumerate(xcoarse):
thismin = coarse[i]
if i<len(coarse)-1:
thismax = coarse[i+1]
else:
thismax = thismin + np.diff(coarse)[-1]
if not valid[i]: continue
verts.append([(thismin, 0), (thismin, 0.05), (thismax, 0.05), (thismax, 0)])
values.append(val)
values = np.array(values)
trans = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
hbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face')
hbar.set_array(values)
hbar.set_cmap(cmap)
hbar.set_norm(norm)
hbar.set_alpha(alpha)
hbar.update(kwargs)
self.add_collection(hbar)
coarse = np.linspace(ymin, ymax, gridsize)
ycoarse = coarse_bin(yorig, C, coarse)
valid = ~np.isnan(ycoarse)
verts, values = [], []
for i,val in enumerate(ycoarse):
thismin = coarse[i]
if i<len(coarse)-1:
thismax = coarse[i+1]
else:
thismax = thismin + np.diff(coarse)[-1]
if not valid[i]: continue
verts.append([(0, thismin), (0.0, thismax), (0.05, thismax), (0.05, thismin)])
values.append(val)
values = np.array(values)
trans = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
vbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face')
vbar.set_array(values)
vbar.set_cmap(cmap)
vbar.set_norm(norm)
vbar.set_alpha(alpha)
vbar.update(kwargs)
self.add_collection(vbar)
collection.hbar = hbar
collection.vbar = vbar
def on_changed(collection):
hbar.set_cmap(collection.get_cmap())
hbar.set_clim(collection.get_clim())
vbar.set_cmap(collection.get_cmap())
vbar.set_clim(collection.get_clim())
collection.callbacksSM.connect('changed', on_changed)
return collection
@docstring.dedent_interpd
def arrow(self, x, y, dx, dy, **kwargs):
"""
call signature::
arrow(x, y, dx, dy, **kwargs)
Draws arrow on specified axis from (*x*, *y*) to (*x* + *dx*,
*y* + *dy*).
Optional kwargs control the arrow properties:
%(FancyArrow)s
**Example:**
.. plot:: mpl_examples/pylab_examples/arrow_demo.py
"""
a = mpatches.FancyArrow(x, y, dx, dy, **kwargs)
self.add_artist(a)
return a
def quiverkey(self, *args, **kw):
qk = mquiver.QuiverKey(*args, **kw)
self.add_artist(qk)
return qk
quiverkey.__doc__ = mquiver.QuiverKey.quiverkey_doc
def quiver(self, *args, **kw):
if not self._hold: self.cla()
q = mquiver.Quiver(self, *args, **kw)
self.add_collection(q, False)
self.update_datalim(q.XY)
self.autoscale_view()
return q
quiver.__doc__ = mquiver.Quiver.quiver_doc
@docstring.dedent_interpd
def barbs(self, *args, **kw):
"""
%(barbs_doc)s
**Example:**
.. plot:: mpl_examples/pylab_examples/barb_demo.py
"""
if not self._hold: self.cla()
b = mquiver.Barbs(self, *args, **kw)
self.add_collection(b)
self.update_datalim(b.get_offsets())
self.autoscale_view()
return b
@docstring.dedent_interpd
def fill(self, *args, **kwargs):
"""
call signature::
fill(*args, **kwargs)
Plot filled polygons. *args* is a variable length argument,
allowing for multiple *x*, *y* pairs with an optional color
format string; see :func:`~matplotlib.pyplot.plot` for details
on the argument parsing. For example, to plot a polygon with
vertices at *x*, *y* in blue.::
ax.fill(x,y, 'b' )
An arbitrary number of *x*, *y*, *color* groups can be specified::
ax.fill(x1, y1, 'g', x2, y2, 'r')
Return value is a list of :class:`~matplotlib.patches.Patch`
instances that were added.
The same color strings that :func:`~matplotlib.pyplot.plot`
supports are supported by the fill format string.
If you would like to fill below a curve, eg. shade a region
between 0 and *y* along *x*, use :meth:`fill_between`
The *closed* kwarg will close the polygon when *True* (default).
kwargs control the Polygon properties:
%(Polygon)s
**Example:**
.. plot:: mpl_examples/pylab_examples/fill_demo.py
"""
if not self._hold: self.cla()
patches = []
for poly in self._get_patches_for_fill(*args, **kwargs):
self.add_patch( poly )
patches.append( poly )
self.autoscale_view()
return patches
@docstring.dedent_interpd
def fill_between(self, x, y1, y2=0, where=None, interpolate=False,
**kwargs):
"""
call signature::
fill_between(x, y1, y2=0, where=None, **kwargs)
Create a :class:`~matplotlib.collections.PolyCollection`
filling the regions between *y1* and *y2* where
``where==True``
*x*
an N length np array of the x data
*y1*
an N length scalar or np array of the y data
*y2*
an N length scalar or np array of the y data
*where*
if None, default to fill between everywhere. If not None,
it is a a N length numpy boolean array and the fill will
only happen over the regions where ``where==True``
*interpolate*
If True, interpolate between the two lines to find the
precise point of intersection. Otherwise, the start and
end points of the filled region will only occur on explicit
values in the *x* array.
*kwargs*
keyword args passed on to the :class:`PolyCollection`
kwargs control the Polygon properties:
%(PolyCollection)s
.. plot:: mpl_examples/pylab_examples/fill_between_demo.py
.. seealso::
:meth:`fill_betweenx`
for filling between two sets of x-values
"""
# Handle united data, such as dates
self._process_unit_info(xdata=x, ydata=y1, kwargs=kwargs)
self._process_unit_info(ydata=y2)
# Convert the arrays so we can work with them
x = np.asanyarray(self.convert_xunits(x))
y1 = np.asanyarray(self.convert_yunits(y1))
y2 = np.asanyarray(self.convert_yunits(y2))
if y1.ndim == 0:
y1 = np.ones_like(x)*y1
if y2.ndim == 0:
y2 = np.ones_like(x)*y2
if where is None:
where = np.ones(len(x), np.bool)
else:
where = np.asarray(where, np.bool)
if not (x.shape == y1.shape == y2.shape == where.shape):
raise ValueError("Argument dimensions are incompatible")
mask = reduce(ma.mask_or,
[ma.getmask(x), ma.getmask(y1), ma.getmask(y2)])
if mask is not ma.nomask:
where &= ~mask
polys = []
for ind0, ind1 in mlab.contiguous_regions(where):
theseverts = []
xslice = x[ind0:ind1]
y1slice = y1[ind0:ind1]
y2slice = y2[ind0:ind1]
if not len(xslice):
continue
N = len(xslice)
X = np.zeros((2*N+2, 2), np.float)
if interpolate:
def get_interp_point(ind):
im1 = max(ind-1, 0)
x_values = x[im1:ind+1]
diff_values = y1[im1:ind+1] - y2[im1:ind+1]
y1_values = y1[im1:ind+1]
if len(diff_values) == 2:
if np.ma.is_masked(diff_values[1]):
return x[im1], y1[im1]
elif np.ma.is_masked(diff_values[0]):
return x[ind], y1[ind]
diff_order = diff_values.argsort()
diff_root_x = np.interp(
0, diff_values[diff_order], x_values[diff_order])
diff_root_y = np.interp(diff_root_x, x_values, y1_values)
return diff_root_x, diff_root_y
start = get_interp_point(ind0)
end = get_interp_point(ind1)
else:
# the purpose of the next two lines is for when y2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the y1 sample points do
start = xslice[0], y2slice[0]
end = xslice[-1], y2slice[-1]
X[0] = start
X[N+1] = end
X[1:N+1,0] = xslice
X[1:N+1,1] = y1slice
X[N+2:,0] = xslice[::-1]
X[N+2:,1] = y2slice[::-1]
polys.append(X)
collection = mcoll.PolyCollection(polys, **kwargs)
# now update the datalim and autoscale
XY1 = np.array([x[where], y1[where]]).T
XY2 = np.array([x[where], y2[where]]).T
self.dataLim.update_from_data_xy(XY1, self.ignore_existing_data_limits,
updatex=True, updatey=True)
self.dataLim.update_from_data_xy(XY2, self.ignore_existing_data_limits,
updatex=False, updatey=True)
self.add_collection(collection)
self.autoscale_view()
return collection
@docstring.dedent_interpd
def fill_betweenx(self, y, x1, x2=0, where=None, **kwargs):
"""
call signature::
fill_between(y, x1, x2=0, where=None, **kwargs)
Create a :class:`~matplotlib.collections.PolyCollection`
filling the regions between *x1* and *x2* where
``where==True``
*y*
an N length np array of the y data
*x1*
an N length scalar or np array of the x data
*x2*
an N length scalar or np array of the x data
*where*
if None, default to fill between everywhere. If not None,
it is a a N length numpy boolean array and the fill will
only happen over the regions where ``where==True``
*kwargs*
keyword args passed on to the :class:`PolyCollection`
kwargs control the Polygon properties:
%(PolyCollection)s
.. plot:: mpl_examples/pylab_examples/fill_betweenx_demo.py
.. seealso::
:meth:`fill_between`
for filling between two sets of y-values
"""
# Handle united data, such as dates
self._process_unit_info(ydata=y, xdata=x1, kwargs=kwargs)
self._process_unit_info(xdata=x2)
# Convert the arrays so we can work with them
y = np.asanyarray(self.convert_yunits(y))
x1 = np.asanyarray(self.convert_xunits(x1))
x2 = np.asanyarray(self.convert_xunits(x2))
if x1.ndim == 0:
x1 = np.ones_like(y)*x1
if x2.ndim == 0:
x2 = np.ones_like(y)*x2
if where is None:
where = np.ones(len(y), np.bool)
else:
where = np.asarray(where, np.bool)
if not (y.shape == x1.shape == x2.shape == where.shape):
raise ValueError("Argument dimensions are incompatible")
mask = reduce(ma.mask_or,
[ma.getmask(y), ma.getmask(x1), ma.getmask(x2)])
if mask is not ma.nomask:
where &= ~mask
polys = []
for ind0, ind1 in mlab.contiguous_regions(where):
theseverts = []
yslice = y[ind0:ind1]
x1slice = x1[ind0:ind1]
x2slice = x2[ind0:ind1]
if not len(yslice):
continue
N = len(yslice)
Y = np.zeros((2*N+2, 2), np.float)
# the purpose of the next two lines is for when x2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the x1 sample points do
Y[0] = x2slice[0], yslice[0]
Y[N+1] = x2slice[-1], yslice[-1]
Y[1:N+1,0] = x1slice
Y[1:N+1,1] = yslice
Y[N+2:,0] = x2slice[::-1]
Y[N+2:,1] = yslice[::-1]
polys.append(Y)
collection = mcoll.PolyCollection(polys, **kwargs)
# now update the datalim and autoscale
X1Y = np.array([x1[where], y[where]]).T
X2Y = np.array([x2[where], y[where]]).T
self.dataLim.update_from_data_xy(X1Y, self.ignore_existing_data_limits,
updatex=True, updatey=True)
self.dataLim.update_from_data_xy(X2Y, self.ignore_existing_data_limits,
updatex=False, updatey=True)
self.add_collection(collection)
self.autoscale_view()
return collection
#### plotting z(x,y): imshow, pcolor and relatives, contour
@docstring.dedent_interpd
def imshow(self, X, cmap=None, norm=None, aspect=None,
interpolation=None, alpha=None, vmin=None, vmax=None,
origin=None, extent=None, shape=None, filternorm=1,
filterrad=4.0, imlim=None, resample=None, url=None, **kwargs):
"""
call signature::
imshow(X, cmap=None, norm=None, aspect=None, interpolation=None,
alpha=None, vmin=None, vmax=None, origin=None, extent=None,
**kwargs)
Display the image in *X* to current axes. *X* may be a float
array, a uint8 array or a PIL image. If *X* is an array, *X*
can have the following shapes:
* MxN -- luminance (grayscale, float array only)
* MxNx3 -- RGB (float or uint8 array)
* MxNx4 -- RGBA (float or uint8 array)
The value for each component of MxNx3 and MxNx4 float arrays should be
in the range 0.0 to 1.0; MxN float arrays may be normalised.
An :class:`matplotlib.image.AxesImage` instance is returned.
Keyword arguments:
*cmap*: [ None | Colormap ]
A :class:`matplotlib.cm.Colormap` instance, eg. cm.jet.
If *None*, default to rc ``image.cmap`` value.
*cmap* is ignored when *X* has RGB(A) information
*aspect*: [ None | 'auto' | 'equal' | scalar ]
If 'auto', changes the image aspect ratio to match that of the axes
If 'equal', and *extent* is *None*, changes the axes
aspect ratio to match that of the image. If *extent* is
not *None*, the axes aspect ratio is changed to match that
of the extent.
If *None*, default to rc ``image.aspect`` value.
*interpolation*:
Acceptable values are *None*, 'nearest', 'bilinear',
'bicubic', 'spline16', 'spline36', 'hanning', 'hamming',
'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian',
'bessel', 'mitchell', 'sinc', 'lanczos'
If *interpolation* is *None*, default to rc
``image.interpolation``. See also the *filternorm* and
*filterrad* parameters
*norm*: [ None | Normalize ]
An :class:`matplotlib.colors.Normalize` instance; if
*None*, default is ``normalization()``. This scales
luminance -> 0-1
*norm* is only used for an MxN float array.
*vmin*/*vmax*: [ None | scalar ]
Used to scale a luminance image to 0-1. If either is
*None*, the min and max of the luminance values will be
used. Note if *norm* is not *None*, the settings for
*vmin* and *vmax* will be ignored.
*alpha*: scalar
The alpha blending value, between 0 (transparent) and 1 (opaque)
or *None*
*origin*: [ None | 'upper' | 'lower' ]
Place the [0,0] index of the array in the upper left or lower left
corner of the axes. If *None*, default to rc ``image.origin``.
*extent*: [ None | scalars (left, right, bottom, top) ]
Data limits for the axes. The default assigns zero-based row,
column indices to the *x*, *y* centers of the pixels.
*shape*: [ None | scalars (columns, rows) ]
For raw buffer images
*filternorm*:
A parameter for the antigrain image resize filter. From the
antigrain documentation, if *filternorm* = 1, the filter normalizes
integer values and corrects the rounding errors. It doesn't do
anything with the source floating point values, it corrects only
integers according to the rule of 1.0 which means that any sum of
pixel weights must be equal to 1.0. So, the filter function must
produce a graph of the proper shape.
*filterrad*:
The filter radius for filters that have a radius
parameter, i.e. when interpolation is one of: 'sinc',
'lanczos' or 'blackman'
Additional kwargs are :class:`~matplotlib.artist.Artist` properties:
%(Artist)s
**Example:**
.. plot:: mpl_examples/pylab_examples/image_demo.py
"""
if not self._hold: self.cla()
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
if aspect is None: aspect = rcParams['image.aspect']
self.set_aspect(aspect)
im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent,
filternorm=filternorm,
filterrad=filterrad, resample=resample, **kwargs)
im.set_data(X)
im.set_alpha(alpha)
self._set_artist_props(im)
if im.get_clip_path() is None:
# image does not already have clipping set, clip to axes patch
im.set_clip_path(self.patch)
#if norm is None and shape is None:
# im.set_clim(vmin, vmax)
if vmin is not None or vmax is not None:
im.set_clim(vmin, vmax)
else:
im.autoscale_None()
im.set_url(url)
# update ax.dataLim, and, if autoscaling, set viewLim
# to tightly fit the image, regardless of dataLim.
im.set_extent(im.get_extent())
self.images.append(im)
im._remove_method = lambda h: self.images.remove(h)
return im
def _pcolorargs(self, funcname, *args):
if len(args)==1:
C = args[0]
numRows, numCols = C.shape
X, Y = np.meshgrid(np.arange(numCols+1), np.arange(numRows+1) )
elif len(args)==3:
X, Y, C = args
else:
raise TypeError(
'Illegal arguments to %s; see help(%s)' % (funcname, funcname))
Nx = X.shape[-1]
Ny = Y.shape[0]
if len(X.shape) <> 2 or X.shape[0] == 1:
x = X.reshape(1,Nx)
X = x.repeat(Ny, axis=0)
if len(Y.shape) <> 2 or Y.shape[1] == 1:
y = Y.reshape(Ny, 1)
Y = y.repeat(Nx, axis=1)
if X.shape != Y.shape:
raise TypeError(
'Incompatible X, Y inputs to %s; see help(%s)' % (
funcname, funcname))
return X, Y, C
@docstring.dedent_interpd
def pcolor(self, *args, **kwargs):
"""
call signatures::
pcolor(C, **kwargs)
pcolor(X, Y, C, **kwargs)
Create a pseudocolor plot of a 2-D array.
*C* is the array of color values.
*X* and *Y*, if given, specify the (*x*, *y*) coordinates of
the colored quadrilaterals; the quadrilateral for C[i,j] has
corners at::
(X[i, j], Y[i, j]),
(X[i, j+1], Y[i, j+1]),
(X[i+1, j], Y[i+1, j]),
(X[i+1, j+1], Y[i+1, j+1]).
Ideally the dimensions of *X* and *Y* should be one greater
than those of *C*; if the dimensions are the same, then the
last row and column of *C* will be ignored.
Note that the the column index corresponds to the
*x*-coordinate, and the row index corresponds to *y*; for
details, see the :ref:`Grid Orientation
<axes-pcolor-grid-orientation>` section below.
If either or both of *X* and *Y* are 1-D arrays or column vectors,
they will be expanded as needed into the appropriate 2-D arrays,
making a rectangular grid.
*X*, *Y* and *C* may be masked arrays. If either C[i, j], or one
of the vertices surrounding C[i,j] (*X* or *Y* at [i, j], [i+1, j],
[i, j+1],[i+1, j+1]) is masked, nothing is plotted.
Keyword arguments:
*cmap*: [ None | Colormap ]
A :class:`matplotlib.cm.Colormap` instance. If *None*, use
rc settings.
norm: [ None | Normalize ]
An :class:`matplotlib.colors.Normalize` instance is used
to scale luminance data to 0,1. If *None*, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either are *None*, the min
and max of the color array *C* is used. If you pass a
*norm* instance, *vmin* and *vmax* will be ignored.
*shading*: [ 'flat' | 'faceted' ]
If 'faceted', a black grid is drawn around each rectangle; if
'flat', edges are not drawn. Default is 'flat', contrary to
MATLAB.
This kwarg is deprecated; please use 'edgecolors' instead:
* shading='flat' -- edgecolors='none'
* shading='faceted -- edgecolors='k'
*edgecolors*: [ None | 'none' | color | color sequence]
If *None*, the rc setting is used by default.
If 'none', edges will not be visible.
An mpl color or sequence of colors will set the edge color
*alpha*: 0 <= scalar <= 1 or *None*
the alpha blending value
Return value is a :class:`matplotlib.collection.Collection`
instance.
.. _axes-pcolor-grid-orientation:
The grid orientation follows the MATLAB convention: an
array *C* with shape (*nrows*, *ncolumns*) is plotted with
the column number as *X* and the row number as *Y*, increasing
up; hence it is plotted the way the array would be printed,
except that the *Y* axis is reversed. That is, *C* is taken
as *C*(*y*, *x*).
Similarly for :func:`~matplotlib.pyplot.meshgrid`::
x = np.arange(5)
y = np.arange(3)
X, Y = meshgrid(x,y)
is equivalent to:
X = array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]])
Y = array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2]])
so if you have::
C = rand( len(x), len(y))
then you need::
pcolor(X, Y, C.T)
or::
pcolor(C.T)
MATLAB :func:`pcolor` always discards the last row and column
of *C*, but matplotlib displays the last row and column if *X* and
*Y* are not specified, or if *X* and *Y* have one more row and
column than *C*.
kwargs can be used to control the
:class:`~matplotlib.collection.PolyCollection` properties:
%(PolyCollection)s
Note: the default *antialiaseds* is taken from
rcParams['patch.antialiased'], which defaults to *True*.
In some cases, particularly if *alpha* is 1,
you may be able to reduce rendering artifacts (light or
dark patch boundaries) by setting it to *False*. An
alternative it to set *edgecolors* to 'face'. Unfortunately,
there seems to be no single combination of parameters that
eliminates artifacts under all conditions.
"""
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
shading = kwargs.pop('shading', 'flat')
X, Y, C = self._pcolorargs('pcolor', *args)
Ny, Nx = X.shape
# convert to MA, if necessary.
C = ma.asarray(C)
X = ma.asarray(X)
Y = ma.asarray(Y)
mask = ma.getmaskarray(X)+ma.getmaskarray(Y)
xymask = mask[0:-1,0:-1]+mask[1:,1:]+mask[0:-1,1:]+mask[1:,0:-1]
# don't plot if C or any of the surrounding vertices are masked.
mask = ma.getmaskarray(C)[0:Ny-1,0:Nx-1]+xymask
newaxis = np.newaxis
compress = np.compress
ravelmask = (mask==0).ravel()
X1 = compress(ravelmask, ma.filled(X[0:-1,0:-1]).ravel())
Y1 = compress(ravelmask, ma.filled(Y[0:-1,0:-1]).ravel())
X2 = compress(ravelmask, ma.filled(X[1:,0:-1]).ravel())
Y2 = compress(ravelmask, ma.filled(Y[1:,0:-1]).ravel())
X3 = compress(ravelmask, ma.filled(X[1:,1:]).ravel())
Y3 = compress(ravelmask, ma.filled(Y[1:,1:]).ravel())
X4 = compress(ravelmask, ma.filled(X[0:-1,1:]).ravel())
Y4 = compress(ravelmask, ma.filled(Y[0:-1,1:]).ravel())
npoly = len(X1)
xy = np.concatenate((X1[:,newaxis], Y1[:,newaxis],
X2[:,newaxis], Y2[:,newaxis],
X3[:,newaxis], Y3[:,newaxis],
X4[:,newaxis], Y4[:,newaxis],
X1[:,newaxis], Y1[:,newaxis]),
axis=1)
verts = xy.reshape((npoly, 5, 2))
C = compress(ravelmask, ma.filled(C[0:Ny-1,0:Nx-1]).ravel())
if shading == 'faceted':
edgecolors = 'k',
else:
edgecolors = 'none'
linewidths = (0.25,)
# Not sure if we want to have the following, or just trap
# invalid kwargs and raise an exception.
if 'edgecolor' in kwargs:
kwargs['edgecolors'] = kwargs.pop('edgecolor')
if 'linewidth' in kwargs:
kwargs['linewidths'] = kwargs.pop('linewidth')
if 'antialiased' in kwargs:
kwargs['antialiaseds'] = kwargs.pop('antialiased')
kwargs.setdefault('edgecolors', edgecolors)
kwargs.setdefault('linewidths', linewidths)
collection = mcoll.PolyCollection(verts, **kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
collection.set_cmap(cmap)
collection.set_norm(norm)
if vmin is not None or vmax is not None:
collection.set_clim(vmin, vmax)
else:
collection.autoscale_None()
self.grid(False)
x = X.compressed()
y = Y.compressed()
minx = np.amin(x)
maxx = np.amax(x)
miny = np.amin(y)
maxy = np.amax(y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
self.autoscale_view()
self.add_collection(collection)
return collection
@docstring.dedent_interpd
def pcolormesh(self, *args, **kwargs):
"""
call signatures::
pcolormesh(C)
pcolormesh(X, Y, C)
pcolormesh(C, **kwargs)
*C* may be a masked array, but *X* and *Y* may not. Masked
array support is implemented via *cmap* and *norm*; in
contrast, :func:`~matplotlib.pyplot.pcolor` simply does not
draw quadrilaterals with masked colors or vertices.
Keyword arguments:
*cmap*: [ None | Colormap ]
A :class:`matplotlib.cm.Colormap` instance. If None, use
rc settings.
*norm*: [ None | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1. If None, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either are *None*, the min
and max of the color array *C* is used. If you pass a
*norm* instance, *vmin* and *vmax* will be ignored.
*shading*: [ 'flat' | 'faceted' | 'gouraud' ]
If 'faceted', a black grid is drawn around each rectangle; if
'flat', edges are not drawn. Default is 'flat', contrary to
MATLAB.
This kwarg is deprecated; please use 'edgecolors' instead:
* shading='flat' -- edgecolors='None'
* shading='faceted -- edgecolors='k'
*edgecolors*: [ None | 'None' | color | color sequence]
If None, the rc setting is used by default.
If 'None', edges will not be visible.
An mpl color or sequence of colors will set the edge color
*alpha*: 0 <= scalar <= 1 or *None*
the alpha blending value
Return value is a :class:`matplotlib.collection.QuadMesh`
object.
kwargs can be used to control the
:class:`matplotlib.collections.QuadMesh`
properties:
%(QuadMesh)s
.. seealso::
:func:`~matplotlib.pyplot.pcolor`
For an explanation of the grid orientation and the
expansion of 1-D *X* and/or *Y* to 2-D arrays.
"""
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
shading = kwargs.pop('shading', 'flat').lower()
edgecolors = kwargs.pop('edgecolors', 'None')
antialiased = kwargs.pop('antialiased', False)
X, Y, C = self._pcolorargs('pcolormesh', *args)
Ny, Nx = X.shape
# convert to one dimensional arrays
if shading != 'gouraud':
C = ma.ravel(C[0:Ny-1, 0:Nx-1]) # data point in each cell is value at
# lower left corner
else:
C = C.ravel()
X = X.ravel()
Y = Y.ravel()
coords = np.zeros(((Nx * Ny), 2), dtype=float)
coords[:, 0] = X
coords[:, 1] = Y
if shading == 'faceted' or edgecolors != 'None':
showedges = 1
else:
showedges = 0
collection = mcoll.QuadMesh(
Nx - 1, Ny - 1, coords, showedges,
antialiased=antialiased, shading=shading, **kwargs)
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
collection.set_cmap(cmap)
collection.set_norm(norm)
if vmin is not None or vmax is not None:
collection.set_clim(vmin, vmax)
else:
collection.autoscale_None()
self.grid(False)
minx = np.amin(X)
maxx = np.amax(X)
miny = np.amin(Y)
maxy = np.amax(Y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
self.autoscale_view()
self.add_collection(collection)
return collection
@docstring.dedent_interpd
def pcolorfast(self, *args, **kwargs):
"""
pseudocolor plot of a 2-D array
Experimental; this is a version of pcolor that
does not draw lines, that provides the fastest
possible rendering with the Agg backend, and that
can handle any quadrilateral grid.
Call signatures::
pcolor(C, **kwargs)
pcolor(xr, yr, C, **kwargs)
pcolor(x, y, C, **kwargs)
pcolor(X, Y, C, **kwargs)
C is the 2D array of color values corresponding to quadrilateral
cells. Let (nr, nc) be its shape. C may be a masked array.
``pcolor(C, **kwargs)`` is equivalent to
``pcolor([0,nc], [0,nr], C, **kwargs)``
*xr*, *yr* specify the ranges of *x* and *y* corresponding to the
rectangular region bounding *C*. If::
xr = [x0, x1]
and::
yr = [y0,y1]
then *x* goes from *x0* to *x1* as the second index of *C* goes
from 0 to *nc*, etc. (*x0*, *y0*) is the outermost corner of
cell (0,0), and (*x1*, *y1*) is the outermost corner of cell
(*nr*-1, *nc*-1). All cells are rectangles of the same size.
This is the fastest version.
*x*, *y* are 1D arrays of length *nc* +1 and *nr* +1, respectively,
giving the x and y boundaries of the cells. Hence the cells are
rectangular but the grid may be nonuniform. The speed is
intermediate. (The grid is checked, and if found to be
uniform the fast version is used.)
*X* and *Y* are 2D arrays with shape (*nr* +1, *nc* +1) that specify
the (x,y) coordinates of the corners of the colored
quadrilaterals; the quadrilateral for C[i,j] has corners at
(X[i,j],Y[i,j]), (X[i,j+1],Y[i,j+1]), (X[i+1,j],Y[i+1,j]),
(X[i+1,j+1],Y[i+1,j+1]). The cells need not be rectangular.
This is the most general, but the slowest to render. It may
produce faster and more compact output using ps, pdf, and
svg backends, however.
Note that the the column index corresponds to the x-coordinate,
and the row index corresponds to y; for details, see
the "Grid Orientation" section below.
Optional keyword arguments:
*cmap*: [ None | Colormap ]
A cm Colormap instance from cm. If None, use rc settings.
*norm*: [ None | Normalize ]
An mcolors.Normalize instance is used to scale luminance data to
0,1. If None, defaults to normalize()
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* are used in conjunction with norm to normalize
luminance data. If either are *None*, the min and max of the color
array *C* is used. If you pass a norm instance, *vmin* and *vmax*
will be *None*.
*alpha*: 0 <= scalar <= 1 or *None*
the alpha blending value
Return value is an image if a regular or rectangular grid
is specified, and a QuadMesh collection in the general
quadrilateral case.
"""
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', None)
norm = kwargs.pop('norm', None)
cmap = kwargs.pop('cmap', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
C = args[-1]
nr, nc = C.shape
if len(args) == 1:
style = "image"
x = [0, nc]
y = [0, nr]
elif len(args) == 3:
x, y = args[:2]
x = np.asarray(x)
y = np.asarray(y)
if x.ndim == 1 and y.ndim == 1:
if x.size == 2 and y.size == 2:
style = "image"
else:
dx = np.diff(x)
dy = np.diff(y)
if (np.ptp(dx) < 0.01*np.abs(dx.mean()) and
np.ptp(dy) < 0.01*np.abs(dy.mean())):
style = "image"
else:
style = "pcolorimage"
elif x.ndim == 2 and y.ndim == 2:
style = "quadmesh"
else:
raise TypeError("arguments do not match valid signatures")
else:
raise TypeError("need 1 argument or 3 arguments")
if style == "quadmesh":
# convert to one dimensional arrays
# This should also be moved to the QuadMesh class
C = ma.ravel(C) # data point in each cell is value
# at lower left corner
X = x.ravel()
Y = y.ravel()
Nx = nc+1
Ny = nr+1
# The following needs to be cleaned up; the renderer
# requires separate contiguous arrays for X and Y,
# but the QuadMesh class requires the 2D array.
coords = np.empty(((Nx * Ny), 2), np.float64)
coords[:, 0] = X
coords[:, 1] = Y
# The QuadMesh class can also be changed to
# handle relevant superclass kwargs; the initializer
# should do much more than it does now.
collection = mcoll.QuadMesh(nc, nr, coords, 0)
collection.set_alpha(alpha)
collection.set_array(C)
collection.set_cmap(cmap)
collection.set_norm(norm)
self.add_collection(collection)
xl, xr, yb, yt = X.min(), X.max(), Y.min(), Y.max()
ret = collection
else:
# One of the image styles:
xl, xr, yb, yt = x[0], x[-1], y[0], y[-1]
if style == "image":
im = mimage.AxesImage(self, cmap, norm,
interpolation='nearest',
origin='lower',
extent=(xl, xr, yb, yt),
**kwargs)
im.set_data(C)
im.set_alpha(alpha)
self.images.append(im)
ret = im
if style == "pcolorimage":
im = mimage.PcolorImage(self, x, y, C,
cmap=cmap,
norm=norm,
alpha=alpha,
**kwargs)
self.images.append(im)
ret = im
self._set_artist_props(ret)
if vmin is not None or vmax is not None:
ret.set_clim(vmin, vmax)
else:
ret.autoscale_None()
self.update_datalim(np.array([[xl, yb], [xr, yt]]))
self.autoscale_view(tight=True)
return ret
def contour(self, *args, **kwargs):
if not self._hold: self.cla()
kwargs['filled'] = False
return mcontour.QuadContourSet(self, *args, **kwargs)
contour.__doc__ = mcontour.QuadContourSet.contour_doc
def contourf(self, *args, **kwargs):
if not self._hold: self.cla()
kwargs['filled'] = True
return mcontour.QuadContourSet(self, *args, **kwargs)
contourf.__doc__ = mcontour.QuadContourSet.contour_doc
def clabel(self, CS, *args, **kwargs):
return CS.clabel(*args, **kwargs)
clabel.__doc__ = mcontour.ContourSet.clabel.__doc__
@docstring.dedent_interpd
def table(self, **kwargs):
"""
call signature::
table(cellText=None, cellColours=None,
cellLoc='right', colWidths=None,
rowLabels=None, rowColours=None, rowLoc='left',
colLabels=None, colColours=None, colLoc='center',
loc='bottom', bbox=None):
Add a table to the current axes. Returns a
:class:`matplotlib.table.Table` instance. For finer grained
control over tables, use the :class:`~matplotlib.table.Table`
class and add it to the axes with
:meth:`~matplotlib.axes.Axes.add_table`.
Thanks to John Gill for providing the class and table.
kwargs control the :class:`~matplotlib.table.Table`
properties:
%(Table)s
"""
return mtable.table(self, **kwargs)
def twinx(self):
"""
call signature::
ax = twinx()
create a twin of Axes for generating a plot with a sharex
x-axis but independent y axis. The y-axis of self will have
ticks on left and the returned axes will have ticks on the
right
"""
ax2 = self.figure.add_axes(self.get_position(True), sharex=self,
frameon=False)
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
ax2.yaxis.set_offset_position('right')
self.yaxis.tick_left()
ax2.xaxis.set_visible(False)
return ax2
def twiny(self):
"""
call signature::
ax = twiny()
create a twin of Axes for generating a plot with a shared
y-axis but independent x axis. The x-axis of self will have
ticks on bottom and the returned axes will have ticks on the
top
"""
ax2 = self.figure.add_axes(self.get_position(True), sharey=self,
frameon=False)
ax2.xaxis.tick_top()
ax2.xaxis.set_label_position('top')
self.xaxis.tick_bottom()
ax2.yaxis.set_visible(False)
return ax2
def get_shared_x_axes(self):
'Return a copy of the shared axes Grouper object for x axes'
return self._shared_x_axes
def get_shared_y_axes(self):
'Return a copy of the shared axes Grouper object for y axes'
return self._shared_y_axes
#### Data analysis
@docstring.dedent_interpd
def hist(self, x, bins=10, range=None, normed=False, weights=None,
cumulative=False, bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False,
color=None, label=None,
**kwargs):
"""
call signature::
hist(x, bins=10, range=None, normed=False, cumulative=False,
bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False, **kwargs)
Compute and draw the histogram of *x*. The return value is a
tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,
[*patches0*, *patches1*,...]) if the input contains multiple
data.
Multiple data can be provided via *x* as a list of datasets
of potentially different length ([*x0*, *x1*, ...]), or as
a 2-D ndarray in which each column is a dataset. Note that
the ndarray form is transposed relative to the list form.
Masked arrays are not supported at present.
Keyword arguments:
*bins*:
Either an integer number of bins or a sequence giving the
bins. If *bins* is an integer, *bins* + 1 bin edges
will be returned, consistent with :func:`numpy.histogram`
for numpy version >= 1.3, and with the *new* = True argument
in earlier versions.
Unequally spaced bins are supported if *bins* is a sequence.
*range*:
The lower and upper range of the bins. Lower and upper outliers
are ignored. If not provided, *range* is (x.min(), x.max()).
Range has no effect if *bins* is a sequence.
If *bins* is a sequence or *range* is specified, autoscaling
is based on the specified bin range instead of the
range of x.
*normed*:
If *True*, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
``n/(len(x)*dbin)``. In a probability density, the integral of
the histogram should be 1; you can verify that with a
trapezoidal integration of the probability density function::
pdf, bins, patches = ax.hist(...)
print np.sum(pdf * np.diff(bins))
.. Note:: Until numpy release 1.5, the underlying numpy
histogram function was incorrect with *normed*=*True*
if bin sizes were unequal. MPL inherited that
error. It is now corrected within MPL when using
earlier numpy versions
*weights*
An array of weights, of the same shape as *x*. Each value in
*x* only contributes its associated weight towards the bin
count (instead of 1). If *normed* is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
*cumulative*:
If *True*, then a histogram is computed where each bin
gives the counts in that bin plus all bins for smaller values.
The last bin gives the total number of datapoints. If *normed*
is also *True* then the histogram is normalized such that the
last bin equals 1. If *cumulative* evaluates to less than 0
(e.g. -1), the direction of accumulation is reversed. In this
case, if *normed* is also *True*, then the histogram is normalized
such that the first bin equals 1.
*histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ]
The type of histogram to draw.
- 'bar' is a traditional bar-type histogram. If multiple data
are given the bars are aranged side by side.
- 'barstacked' is a bar-type histogram where multiple
data are stacked on top of each other.
- 'step' generates a lineplot that is by default
unfilled.
- 'stepfilled' generates a lineplot that is by default
filled.
*align*: ['left' | 'mid' | 'right' ]
Controls how the histogram is plotted.
- 'left': bars are centered on the left bin edges.
- 'mid': bars are centered between the bin edges.
- 'right': bars are centered on the right bin edges.
*orientation*: [ 'horizontal' | 'vertical' ]
If 'horizontal', :func:`~matplotlib.pyplot.barh` will be
used for bar-type histograms and the *bottom* kwarg will be
the left edges.
*rwidth*:
The relative width of the bars as a fraction of the bin
width. If *None*, automatically compute the width. Ignored
if *histtype* = 'step' or 'stepfilled'.
*log*:
If *True*, the histogram axis will be set to a log scale.
If *log* is *True* and *x* is a 1D array, empty bins will
be filtered out and only the non-empty (*n*, *bins*,
*patches*) will be returned.
*color*:
Color spec or sequence of color specs, one per
dataset. Default (*None*) uses the standard line
color sequence.
*label*:
String, or sequence of strings to match multiple
datasets. Bar charts yield multiple patches per
dataset, but only the first gets the label, so
that the legend command will work as expected::
ax.hist(10+2*np.random.randn(1000), label='men')
ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5)
ax.legend()
kwargs are used to update the properties of the
:class:`~matplotlib.patches.Patch` instances returned by *hist*:
%(Patch)s
**Example:**
.. plot:: mpl_examples/pylab_examples/histogram_demo.py
"""
if not self._hold: self.cla()
# NOTE: the range keyword overwrites the built-in func range !!!
# needs to be fixed in numpy !!!
# Validate string inputs here so we don't have to clutter
# subsequent code.
if histtype not in ['bar', 'barstacked', 'step', 'stepfilled']:
raise ValueError("histtype %s is not recognized" % histtype)
if align not in ['left', 'mid', 'right']:
raise ValueError("align kwarg %s is not recognized" % align)
if orientation not in [ 'horizontal', 'vertical']:
raise ValueError(
"orientation kwarg %s is not recognized" % orientation)
if kwargs.get('width') is not None:
raise DeprecationWarning(
'hist now uses the rwidth to give relative width '
'and not absolute width')
# Massage 'x' for processing.
# NOTE: Be sure any changes here is also done below to 'weights'
if isinstance(x, np.ndarray) or not iterable(x[0]):
# TODO: support masked arrays;
x = np.asarray(x)
if x.ndim == 2:
x = x.T # 2-D input with columns as datasets; switch to rows
elif x.ndim == 1:
x = x.reshape(1, x.shape[0]) # new view, single row
else:
raise ValueError("x must be 1D or 2D")
if x.shape[1] < x.shape[0]:
warnings.warn('2D hist input should be nsamples x nvariables;\n '
'this looks transposed (shape is %d x %d)' % x.shape[::-1])
else:
# multiple hist with data of different length
x = [np.array(xi) for xi in x]
nx = len(x) # number of datasets
if color is None:
color = [self._get_lines.color_cycle.next()
for i in xrange(nx)]
else:
color = mcolors.colorConverter.to_rgba_array(color)
if len(color) != nx:
raise ValueError("color kwarg must have one color per dataset")
# We need to do to 'weights' what was done to 'x'
if weights is not None:
if isinstance(weights, np.ndarray) or not iterable(weights[0]) :
w = np.array(weights)
if w.ndim == 2:
w = w.T
elif w.ndim == 1:
w.shape = (1, w.shape[0])
else:
raise ValueError("weights must be 1D or 2D")
else:
w = [np.array(wi) for wi in weights]
if len(w) != nx:
raise ValueError('weights should have the same shape as x')
for i in xrange(nx):
if len(w[i]) != len(x[i]):
raise ValueError(
'weights should have the same shape as x')
else:
w = [None]*nx
# Save autoscale state for later restoration; turn autoscaling
# off so we can do it all a single time at the end, instead
# of having it done by bar or fill and then having to be redone.
_saved_autoscalex = self.get_autoscalex_on()
_saved_autoscaley = self.get_autoscaley_on()
self.set_autoscalex_on(False)
self.set_autoscaley_on(False)
# Save the datalimits for the same reason:
_saved_bounds = self.dataLim.bounds
# Check whether bins or range are given explicitly. In that
# case use those values for autoscaling.
binsgiven = (cbook.iterable(bins) or range != None)
# If bins are not specified either explicitly or via range,
# we need to figure out the range required for all datasets,
# and supply that to np.histogram.
if not binsgiven:
xmin = np.inf
xmax = -np.inf
for xi in x:
xmin = min(xmin, xi.min())
xmax = max(xmax, xi.max())
range = (xmin, xmax)
#hist_kwargs = dict(range=range, normed=bool(normed))
# We will handle the normed kwarg within mpl until we
# get to the point of requiring numpy >= 1.5.
hist_kwargs = dict(range=range)
if np.__version__ < "1.3": # version 1.1 and 1.2
hist_kwargs['new'] = True
n = []
for i in xrange(nx):
# this will automatically overwrite bins,
# so that each histogram uses the same bins
m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
if normed:
db = np.diff(bins)
m = (m.astype(float) / db) / m.sum()
n.append(m)
if normed and db.std() > 0.01 * db.mean():
warnings.warn("""
This release fixes a normalization bug in the NumPy histogram
function prior to version 1.5, occuring with non-uniform
bin widths. The returned and plotted value is now a density:
n / (N * bin width),
where n is the bin count and N the total number of points.
""")
if cumulative:
slc = slice(None)
if cbook.is_numlike(cumulative) and cumulative < 0:
slc = slice(None,None,-1)
if normed:
n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n]
else:
n = [m[slc].cumsum()[slc] for m in n]
patches = []
if histtype.startswith('bar'):
totwidth = np.diff(bins)
if rwidth is not None:
dr = min(1.0, max(0.0, rwidth))
elif len(n)>1:
dr = 0.8
else:
dr = 1.0
if histtype=='bar':
width = dr*totwidth/nx
dw = width
if nx > 1:
boffset = -0.5*dr*totwidth*(1.0-1.0/nx)
else:
boffset = 0.0
stacked = False
elif histtype=='barstacked':
width = dr*totwidth
boffset, dw = 0.0, 0.0
stacked = True
if align == 'mid' or align == 'edge':
boffset += 0.5*totwidth
elif align == 'right':
boffset += totwidth
if orientation == 'horizontal':
_barfunc = self.barh
else: # orientation == 'vertical'
_barfunc = self.bar
for m, c in zip(n, color):
patch = _barfunc(bins[:-1]+boffset, m, width, bottom,
align='center', log=log,
color=c)
patches.append(patch)
if stacked:
if bottom is None:
bottom = 0.0
bottom += m
boffset += dw
elif histtype.startswith('step'):
x = np.zeros( 2*len(bins), np.float )
y = np.zeros( 2*len(bins), np.float )
x[0::2], x[1::2] = bins, bins
minimum = min(bins)
if align == 'left' or align == 'center':
x -= 0.5*(bins[1]-bins[0])
elif align == 'right':
x += 0.5*(bins[1]-bins[0])
if log:
y[0],y[-1] = minimum, minimum
if orientation == 'horizontal':
self.set_xscale('log')
else: # orientation == 'vertical'
self.set_yscale('log')
fill = (histtype == 'stepfilled')
for m, c in zip(n, color):
y[1:-1:2], y[2::2] = m, m
if log:
y[y<minimum]=minimum
if orientation == 'horizontal':
x,y = y,x
if fill:
patches.append( self.fill(x, y,
closed=False, facecolor=c) )
else:
patches.append( self.fill(x, y,
closed=False, edgecolor=c, fill=False) )
# adopted from adjust_x/ylim part of the bar method
if orientation == 'horizontal':
xmin0 = max(_saved_bounds[0]*0.9, minimum)
xmax = self.dataLim.intervalx[1]
for m in n:
xmin = np.amin(m[m!=0]) # filter out the 0 height bins
xmin = max(xmin*0.9, minimum)
xmin = min(xmin0, xmin)
self.dataLim.intervalx = (xmin, xmax)
elif orientation == 'vertical':
ymin0 = max(_saved_bounds[1]*0.9, minimum)
ymax = self.dataLim.intervaly[1]
for m in n:
ymin = np.amin(m[m!=0]) # filter out the 0 height bins
ymin = max(ymin*0.9, minimum)
ymin = min(ymin0, ymin)
self.dataLim.intervaly = (ymin, ymax)
if label is None:
labels = ['_nolegend_']
elif is_string_like(label):
labels = [label]
elif is_sequence_of_strings(label):
labels = list(label)
else:
raise ValueError(
'invalid label: must be string or sequence of strings')
if len(labels) < nx:
labels += ['_nolegend_'] * (nx - len(labels))
for (patch, lbl) in zip(patches, labels):
for p in patch:
p.update(kwargs)
p.set_label(lbl)
lbl = '_nolegend_'
if binsgiven:
if orientation == 'vertical':
self.update_datalim([(bins[0],0), (bins[-1],0)], updatey=False)
else:
self.update_datalim([(0,bins[0]), (0,bins[-1])], updatex=False)
self.set_autoscalex_on(_saved_autoscalex)
self.set_autoscaley_on(_saved_autoscaley)
self.autoscale_view()
if nx == 1:
return n[0], bins, cbook.silent_list('Patch', patches[0])
else:
return n, bins, cbook.silent_list('Lists of Patches', patches)
@docstring.dedent_interpd
def psd(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs):
"""
call signature::
psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
The power spectral density by Welch's average periodogram
method. The vector *x* is divided into *NFFT* length
segments. Each segment is detrended by function *detrend* and
windowed by function *window*. *noverlap* gives the length of
the overlap between segments. The :math:`|\mathrm{fft}(i)|^2`
of each segment :math:`i` are averaged to compute *Pxx*, with a
scaling to correct for power loss due to windowing. *Fs* is the
sampling frequency.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
Returns the tuple (*Pxx*, *freqs*).
For plotting, the power is plotted as
:math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself
is returned.
References:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/psd_demo.py
"""
if not self._hold: self.cla()
pxx, freqs = mlab.psd(x, NFFT, Fs, detrend, window, noverlap, pad_to,
sides, scale_by_freq)
pxx.shape = len(freqs),
freqs += Fc
if scale_by_freq in (None, True):
psd_units = 'dB/Hz'
else:
psd_units = 'dB'
self.plot(freqs, 10*np.log10(pxx), **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Power Spectral Density (%s)' % psd_units)
self.grid(True)
vmin, vmax = self.viewLim.intervaly
intv = vmax-vmin
logi = int(np.log10(intv))
if logi==0: logi=.1
step = 10*logi
#print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1
ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxx, freqs
@docstring.dedent_interpd
def csd(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs):
"""
call signature::
csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
The cross spectral density :math:`P_{xy}` by Welch's average
periodogram method. The vectors *x* and *y* are divided into
*NFFT* length segments. Each segment is detrended by function
*detrend* and windowed by function *window*. The product of
the direct FFTs of *x* and *y* are averaged over each segment
to compute :math:`P_{xy}`, with a scaling to correct for power
loss due to windowing.
Returns the tuple (*Pxy*, *freqs*). *P* is the cross spectrum
(complex valued), and :math:`10\log_{10}|P_{xy}|` is
plotted.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
References:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/csd_demo.py
.. seealso:
:meth:`psd`
For a description of the optional parameters.
"""
if not self._hold: self.cla()
pxy, freqs = mlab.csd(x, y, NFFT, Fs, detrend, window, noverlap,
pad_to, sides, scale_by_freq)
pxy.shape = len(freqs),
# pxy is complex
freqs += Fc
self.plot(freqs, 10*np.log10(np.absolute(pxy)), **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Cross Spectrum Magnitude (dB)')
self.grid(True)
vmin, vmax = self.viewLim.intervaly
intv = vmax-vmin
step = 10*int(np.log10(intv))
ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxy, freqs
@docstring.dedent_interpd
def cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs):
"""
call signature::
cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
window = mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
:meth:`cohere` the coherence between *x* and *y*. Coherence
is the normalized cross spectral density:
.. math::
C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}}
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
The return value is a tuple (*Cxy*, *f*), where *f* are the
frequencies of the coherence vector.
kwargs are applied to the lines.
References:
* Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the :class:`~matplotlib.lines.Line2D`
properties of the coherence plot:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/cohere_demo.py
"""
if not self._hold: self.cla()
cxy, freqs = mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap,
scale_by_freq)
freqs += Fc
self.plot(freqs, cxy, **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Coherence')
self.grid(True)
return cxy, freqs
@docstring.dedent_interpd
def specgram(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, **kwargs):
"""
call signature::
specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, **kwargs)
Compute a spectrogram of data in *x*. Data are split into
*NFFT* length segments and the PSD of each section is
computed. The windowing function *window* is applied to each
segment, and the amount of overlap of each segment is
specified with *noverlap*.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the y extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
*cmap*:
A :class:`matplotlib.cm.Colormap` instance; if *None* use
default determined by rc
*xextent*:
The image extent along the x-axis. xextent = (xmin,xmax)
The default is (0,max(bins)), where bins is the return
value from :func:`mlab.specgram`
*kwargs*:
Additional kwargs are passed on to imshow which makes the
specgram image
Return value is (*Pxx*, *freqs*, *bins*, *im*):
- *bins* are the time points the spectrogram is calculated over
- *freqs* is an array of frequencies
- *Pxx* is a len(times) x len(freqs) array of power
- *im* is a :class:`matplotlib.image.AxesImage` instance
Note: If *x* is real (i.e. non-complex), only the positive
spectrum is shown. If *x* is complex, both positive and
negative parts of the spectrum are shown. This can be
overridden using the *sides* keyword argument.
**Example:**
.. plot:: mpl_examples/pylab_examples/specgram_demo.py
"""
if not self._hold: self.cla()
Pxx, freqs, bins = mlab.specgram(x, NFFT, Fs, detrend,
window, noverlap, pad_to, sides, scale_by_freq)
Z = 10. * np.log10(Pxx)
Z = np.flipud(Z)
if xextent is None: xextent = 0, np.amax(bins)
xmin, xmax = xextent
freqs += Fc
extent = xmin, xmax, freqs[0], freqs[-1]
im = self.imshow(Z, cmap, extent=extent, **kwargs)
self.axis('auto')
return Pxx, freqs, bins, im
def spy(self, Z, precision=0, marker=None, markersize=None,
aspect='equal', **kwargs):
"""
call signature::
spy(Z, precision=0, marker=None, markersize=None,
aspect='equal', **kwargs)
``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*.
If *precision* is 0, any non-zero value will be plotted;
else, values of :math:`|Z| > precision` will be plotted.
For :class:`scipy.sparse.spmatrix` instances, there is a
special case: if *precision* is 'present', any value present in
the array will be plotted, even if it is identically zero.
The array will be plotted as it would be printed, with
the first index (row) increasing down and the second
index (column) increasing to the right.
By default aspect is 'equal', so that each array element
occupies a square space; set the aspect kwarg to 'auto'
to allow the plot to fill the plot box, or to any scalar
number to specify the aspect ratio of an array element
directly.
Two plotting styles are available: image or marker. Both
are available for full arrays, but only the marker style
works for :class:`scipy.sparse.spmatrix` instances.
If *marker* and *markersize* are *None*, an image will be
returned and any remaining kwargs are passed to
:func:`~matplotlib.pyplot.imshow`; else, a
:class:`~matplotlib.lines.Line2D` object will be returned with
the value of marker determining the marker type, and any
remaining kwargs passed to the
:meth:`~matplotlib.axes.Axes.plot` method.
If *marker* and *markersize* are *None*, useful kwargs include:
* *cmap*
* *alpha*
.. seealso::
:func:`~matplotlib.pyplot.imshow`
For image options.
For controlling colors, e.g. cyan background and red marks,
use::
cmap = mcolors.ListedColormap(['c','r'])
If *marker* or *markersize* is not *None*, useful kwargs include:
* *marker*
* *markersize*
* *color*
Useful values for *marker* include:
* 's' square (default)
* 'o' circle
* '.' point
* ',' pixel
.. seealso::
:func:`~matplotlib.pyplot.plot`
For plotting options
"""
if precision is None:
precision = 0
warnings.DeprecationWarning("Use precision=0 instead of None")
# 2008/10/03
if marker is None and markersize is None and hasattr(Z, 'tocoo'):
marker = 's'
if marker is None and markersize is None:
Z = np.asarray(Z)
mask = np.absolute(Z)>precision
if 'cmap' not in kwargs:
kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'],
name='binary')
nr, nc = Z.shape
extent = [-0.5, nc-0.5, nr-0.5, -0.5]
ret = self.imshow(mask, interpolation='nearest', aspect=aspect,
extent=extent, origin='upper', **kwargs)
else:
if hasattr(Z, 'tocoo'):
c = Z.tocoo()
if precision == 'present':
y = c.row
x = c.col
else:
nonzero = np.absolute(c.data) > precision
y = c.row[nonzero]
x = c.col[nonzero]
else:
Z = np.asarray(Z)
nonzero = np.absolute(Z)>precision
y, x = np.nonzero(nonzero)
if marker is None: marker = 's'
if markersize is None: markersize = 10
marks = mlines.Line2D(x, y, linestyle='None',
marker=marker, markersize=markersize, **kwargs)
self.add_line(marks)
nr, nc = Z.shape
self.set_xlim(xmin=-0.5, xmax=nc-0.5)
self.set_ylim(ymin=nr-0.5, ymax=-0.5)
self.set_aspect(aspect)
ret = marks
self.title.set_y(1.05)
self.xaxis.tick_top()
self.xaxis.set_ticks_position('both')
self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9,
steps=[1, 2, 5, 10],
integer=True))
self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9,
steps=[1, 2, 5, 10],
integer=True))
return ret
def matshow(self, Z, **kwargs):
'''
Plot a matrix or array as an image.
The matrix will be shown the way it would be printed,
with the first row at the top. Row and column numbering
is zero-based.
Argument:
*Z* anything that can be interpreted as a 2-D array
kwargs all are passed to :meth:`~matplotlib.axes.Axes.imshow`.
:meth:`matshow` sets defaults for *extent*, *origin*,
*interpolation*, and *aspect*; use care in overriding the
*extent* and *origin* kwargs, because they interact. (Also,
if you want to change them, you probably should be using
imshow directly in your own version of matshow.)
Returns: an :class:`matplotlib.image.AxesImage` instance.
'''
Z = np.asarray(Z)
nr, nc = Z.shape
extent = [-0.5, nc-0.5, nr-0.5, -0.5]
kw = {'extent': extent,
'origin': 'upper',
'interpolation': 'nearest',
'aspect': 'equal'} # (already the imshow default)
kw.update(kwargs)
im = self.imshow(Z, **kw)
self.title.set_y(1.05)
self.xaxis.tick_top()
self.xaxis.set_ticks_position('both')
self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9,
steps=[1, 2, 5, 10],
integer=True))
self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9,
steps=[1, 2, 5, 10],
integer=True))
return im
def get_tightbbox(self, renderer):
"""
return the tight bounding box of the axes.
The dimension of the Bbox in canvas coordinate.
"""
artists = []
bb = []
artists.append(self)
if self.title.get_visible():
artists.append(self.title)
if self.xaxis.get_visible():
artists.append(self.xaxis.label)
bbx1, bbx2 = self.xaxis.get_ticklabel_extents(renderer)
bb.extend([bbx1, bbx2])
if self.yaxis.get_visible():
artists.append(self.yaxis.label)
bby1, bby2 = self.yaxis.get_ticklabel_extents(renderer)
bb.extend([bby1, bby2])
bb.extend([c.get_window_extent(renderer) for c in artists if c.get_visible()])
_bbox = mtransforms.Bbox.union([b for b in bb if b.width!=0 or b.height!=0])
return _bbox
def minorticks_on(self):
'Add autoscaling minor ticks to the axes.'
for ax in (self.xaxis, self.yaxis):
if ax.get_scale() == 'log':
s = ax._scale
ax.set_minor_locator(mticker.LogLocator(s.base, s.subs))
else:
ax.set_minor_locator(mticker.AutoMinorLocator())
def minorticks_off(self):
'Remove minor ticks from the axes.'
self.xaxis.set_minor_locator(mticker.NullLocator())
self.yaxis.set_minor_locator(mticker.NullLocator())
def tricontour(self, *args, **kwargs):
return mtri.tricontour(self, *args, **kwargs)
tricontour.__doc__ = mtri.TriContourSet.tricontour_doc
def tricontourf(self, *args, **kwargs):
return mtri.tricontourf(self, *args, **kwargs)
tricontourf.__doc__ = mtri.TriContourSet.tricontour_doc
def tripcolor(self, *args, **kwargs):
return mtri.tripcolor(self, *args, **kwargs)
tripcolor.__doc__ = mtri.tripcolor.__doc__
def triplot(self, *args, **kwargs):
mtri.triplot(self, *args, **kwargs)
triplot.__doc__ = mtri.triplot.__doc__
from matplotlib.gridspec import GridSpec, SubplotSpec
class SubplotBase:
"""
Base class for subplots, which are :class:`Axes` instances with
additional methods to facilitate generating and manipulating a set
of :class:`Axes` within a figure.
"""
def __init__(self, fig, *args, **kwargs):
"""
*fig* is a :class:`matplotlib.figure.Figure` instance.
*args* is the tuple (*numRows*, *numCols*, *plotNum*), where
the array of subplots in the figure has dimensions *numRows*,
*numCols*, and where *plotNum* is the number of the subplot
being created. *plotNum* starts at 1 in the upper left
corner and increases to the right.
If *numRows* <= *numCols* <= *plotNum* < 10, *args* can be the
decimal integer *numRows* * 100 + *numCols* * 10 + *plotNum*.
"""
self.figure = fig
if len(args) == 1:
if isinstance(args[0], SubplotSpec):
self._subplotspec = args[0]
else:
try:
s = str(int(args[0]))
rows, cols, num = map(int, s)
except ValueError:
raise ValueError(
'Single argument to subplot must be a 3-digit integer')
self._subplotspec = GridSpec(rows, cols)[num-1]
# num - 1 for converting from MATLAB to python indexing
elif len(args)==3:
rows, cols, num = args
rows = int(rows)
cols = int(cols)
if isinstance(num, tuple) and len(num) == 2:
num = [int(n) for n in num]
self._subplotspec = GridSpec(rows, cols)[num[0]-1:num[1]]
else:
self._subplotspec = GridSpec(rows, cols)[int(num)-1]
# num - 1 for converting from MATLAB to python indexing
else:
raise ValueError('Illegal argument(s) to subplot: %s' % (args,))
self.update_params()
# _axes_class is set in the subplot_class_factory
self._axes_class.__init__(self, fig, self.figbox, **kwargs)
def get_geometry(self):
'get the subplot geometry, eg 2,2,3'
rows, cols, num1, num2 = self.get_subplotspec().get_geometry()
return rows, cols, num1+1 # for compatibility
# COVERAGE NOTE: Never used internally or from examples
def change_geometry(self, numrows, numcols, num):
'change subplot geometry, eg. from 1,1,1 to 2,2,3'
self._subplotspec = GridSpec(numrows, numcols)[num-1]
self.update_params()
self.set_position(self.figbox)
def get_subplotspec(self):
'get the SubplotSpec instance associated with the subplot'
return self._subplotspec
def set_subplotspec(self, subplotspec):
'set the SubplotSpec instance associated with the subplot'
self._subplotspec = subplotspec
def update_params(self):
'update the subplot position from fig.subplotpars'
self.figbox, self.rowNum, self.colNum, self.numRows, self.numCols = \
self.get_subplotspec().get_position(self.figure,
return_all=True)
def is_first_col(self):
return self.colNum==0
def is_first_row(self):
return self.rowNum==0
def is_last_row(self):
return self.rowNum==self.numRows-1
def is_last_col(self):
return self.colNum==self.numCols-1
# COVERAGE NOTE: Never used internally or from examples
def label_outer(self):
"""
set the visible property on ticklabels so xticklabels are
visible only if the subplot is in the last row and yticklabels
are visible only if the subplot is in the first column
"""
lastrow = self.is_last_row()
firstcol = self.is_first_col()
for label in self.get_xticklabels():
label.set_visible(lastrow)
for label in self.get_yticklabels():
label.set_visible(firstcol)
_subplot_classes = {}
def subplot_class_factory(axes_class=None):
# This makes a new class that inherits from SubclassBase and the
# given axes_class (which is assumed to be a subclass of Axes).
# This is perhaps a little bit roundabout to make a new class on
# the fly like this, but it means that a new Subplot class does
# not have to be created for every type of Axes.
if axes_class is None:
axes_class = Axes
new_class = _subplot_classes.get(axes_class)
if new_class is None:
new_class = new.classobj("%sSubplot" % (axes_class.__name__),
(SubplotBase, axes_class),
{'_axes_class': axes_class})
_subplot_classes[axes_class] = new_class
return new_class
# This is provided for backward compatibility
Subplot = subplot_class_factory()
docstring.interpd.update(Axes=martist.kwdoc(Axes))
docstring.interpd.update(Subplot=martist.kwdoc(Axes))
"""
# this is some discarded code I was using to find the minimum positive
# data point for some log scaling fixes. I realized there was a
# cleaner way to do it, but am keeping this around as an example for
# how to get the data out of the axes. Might want to make something
# like this a method one day, or better yet make get_verts an Artist
# method
minx, maxx = self.get_xlim()
if minx<=0 or maxx<=0:
# find the min pos value in the data
xs = []
for line in self.lines:
xs.extend(line.get_xdata(orig=False))
for patch in self.patches:
xs.extend([x for x,y in patch.get_verts()])
for collection in self.collections:
xs.extend([x for x,y in collection.get_verts()])
posx = [x for x in xs if x>0]
if len(posx):
minx = min(posx)
maxx = max(posx)
# warning, probably breaks inverted axis
self.set_xlim((0.1*minx, maxx))
"""