from __future__ import division, generators
import math, warnings, new
import numpy as npy
import matplotlib.numerix.npyma as ma
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib import artist as martist
from matplotlib import axis as maxis
from matplotlib import cbook
from matplotlib import collections as mcoll
from matplotlib import colors as mcolors
from matplotlib import contour as mcontour
from matplotlib import dates as mdates
from matplotlib import font_manager
from matplotlib import image as mimage
from matplotlib import legend as mlegend
from matplotlib import lines as mlines
from matplotlib import mlab
from matplotlib import patches as mpatches
from matplotlib import quiver as mquiver
from matplotlib import scale as mscale
from matplotlib import table as mtable
from matplotlib import text as mtext
from matplotlib import ticker as mticker
from matplotlib import transforms as mtransforms
iterable = cbook.iterable
is_string_like = cbook.is_string_like
def delete_masked_points(*args):
"""
Find all masked points in a set of arguments, and return
the arguments with only the unmasked points remaining.
The overall mask is calculated from any masks that are present.
If a mask is found, any argument that does not have the same
dimensions is left unchanged; therefore the argument list may
include arguments that can take string or array values, for
example.
Array arguments must have the same length; masked arguments must
be one-dimensional.
Written as a helper for scatter, but may be more generally
useful.
"""
masks = [ma.getmaskarray(x) for x in args if hasattr(x, 'mask')]
if len(masks) == 0:
return args
mask = reduce(ma.mask_or, masks)
margs = []
for x in args:
if (not is_string_like(x)
and iterable(x)
and len(x) == len(mask)):
if (hasattr(x, 'get_compressed_copy')):
compressed_x = x.get_compressed_copy(mask)
else:
compressed_x = ma.masked_array(x, mask=mask).compressed()
margs.append(compressed_x)
else:
margs.append(x)
return margs
def _process_plot_format(fmt):
"""
Process a matlab(TM) 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
See Line2D.lineStyles and GraphicsContext.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)
return linestyle, marker, color # Yes.
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 mlines.lineStyles.has_key(c):
if linestyle is not None:
raise ValueError(
'Illegal format string "%s"; two linestyle symbols' % fmt)
linestyle = c
elif mlines.lineMarkers.has_key(c):
if marker is not None:
raise ValueError(
'Illegal format string "%s"; two marker symbols' % fmt)
marker = c
elif mcolors.colorConverter.colors.has_key(c):
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
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
"""
defaultColors = ['b','g','r','c','m','y','k']
def __init__(self, axes, command='plot'):
self.axes = axes
self.command = command
self._clear_color_cycle()
def _clear_color_cycle(self):
self.colors = _process_plot_var_args.defaultColors[:]
# if the default line color is a color format string, move it up
# in the que
try: ind = self.colors.index(rcParams['lines.color'])
except ValueError:
self.firstColor = rcParams['lines.color']
else:
self.colors[0], self.colors[ind] = self.colors[ind], self.colors[0]
self.firstColor = self.colors[0]
self.Ncolors = len(self.colors)
self.count = 0
def _get_next_cycle_color(self):
if self.count==0:
color = self.firstColor
else:
color = self.colors[int(self.count % self.Ncolors)]
self.count += 1
return color
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_y(self, y):
if self.axes.yaxis is not None:
b = self.axes.yaxis.update_units(y)
if b: return npy.arange(len(y)), y, False
y = ma.asarray(y)
if len(y.shape) == 1:
y = y[:,npy.newaxis]
nr, nc = y.shape
x = npy.arange(nr)
if len(x.shape) == 1:
x = x[:,npy.newaxis]
return x,y, True
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)
# right now multicol is not supported if either x or y are
# unit enabled but this can be fixed..
if bx or by: return x, y, False
x = ma.asarray(x)
y = ma.asarray(y)
if len(x.shape) == 1:
x = x[:,npy.newaxis]
if len(y.shape) == 1:
y = y[:,npy.newaxis]
nrx, ncx = x.shape
nry, ncy = y.shape
assert nrx == nry, 'Dimensions of x and y are incompatible'
if ncx == ncy:
return x, y, True
if ncx == 1:
x = npy.repeat(x, ncy, axis=1)
if ncy == 1:
y = npy.repeat(y, ncx, axis=1)
assert x.shape == y.shape, 'Dimensions of x and y are incompatible'
return x, y, True
def _plot_1_arg(self, y, **kwargs):
assert self.command == 'plot', 'fill needs at least 2 arguments'
ret = []
x, y, multicol = self._xy_from_y(y)
if multicol:
for j in range(y.shape[1]):
color = self._get_next_cycle_color()
seg = mlines.Line2D(x, y[:,j],
color = color,
axes=self.axes,
)
self.set_lineprops(seg, **kwargs)
ret.append(seg)
else:
color = self._get_next_cycle_color()
seg = mlines.Line2D(x, y,
color = color,
axes=self.axes,
)
self.set_lineprops(seg, **kwargs)
ret.append(seg)
return ret
def _plot_2_args(self, tup2, **kwargs):
ret = []
if is_string_like(tup2[1]):
assert self.command == 'plot', 'fill needs at least 2 non-string arguments'
y, fmt = tup2
x, y, multicol = self._xy_from_y(y)
linestyle, marker, color = _process_plot_format(fmt)
def makeline(x, y):
_color = color
if _color is None:
_color = self._get_next_cycle_color()
seg = mlines.Line2D(x, y,
color=_color,
linestyle=linestyle, marker=marker,
axes=self.axes,
)
self.set_lineprops(seg, **kwargs)
ret.append(seg)
if multicol:
for j in range(y.shape[1]):
makeline(x[:,j], y[:,j])
else:
makeline(x, y)
return ret
else:
x, y = tup2
x, y, multicol = self._xy_from_xy(x, y)
def makeline(x, y):
color = self._get_next_cycle_color()
seg = mlines.Line2D(x, y,
color=color,
axes=self.axes,
)
self.set_lineprops(seg, **kwargs)
ret.append(seg)
def makefill(x, y):
facecolor = self._get_next_cycle_color()
seg = mpatches.Polygon(zip(x, y),
facecolor = facecolor,
fill=True,
)
self.set_patchprops(seg, **kwargs)
ret.append(seg)
if self.command == 'plot': func = makeline
else: func = makefill
if multicol:
for j in range(y.shape[1]):
func(x[:,j], y[:,j])
else:
func(x, y)
return ret
def _plot_3_args(self, tup3, **kwargs):
ret = []
x, y, fmt = tup3
x, y, multicol = self._xy_from_xy(x, y)
linestyle, marker, color = _process_plot_format(fmt)
def makeline(x, y):
_color = color
if _color is None:
_color = self._get_next_cycle_color()
seg = mlines.Line2D(x, y,
color=_color,
linestyle=linestyle, marker=marker,
axes=self.axes,
)
self.set_lineprops(seg, **kwargs)
ret.append(seg)
def makefill(x, y):
facecolor = color
seg = mpatches.Polygon(zip(x, y),
facecolor = facecolor,
fill=True,
)
self.set_patchprops(seg, **kwargs)
ret.append(seg)
if self.command == 'plot': func = makeline
else: func = makefill
if multicol:
for j in range(y.shape[1]):
func(x[:,j], y[:,j])
else:
func(x, y)
return ret
def _grab_next_args(self, *args, **kwargs):
remaining = args
while 1:
if len(remaining)==0: return
if len(remaining)==1:
for seg in self._plot_1_arg(remaining[0], **kwargs):
yield seg
remaining = []
continue
if len(remaining)==2:
for seg in self._plot_2_args(remaining, **kwargs):
yield seg
remaining = []
continue
if len(remaining)==3:
if not is_string_like(remaining[2]):
raise ValueError, 'third arg must be a format string'
for seg in self._plot_3_args(remaining, **kwargs):
yield seg
remaining=[]
continue
if is_string_like(remaining[2]):
for seg in self._plot_3_args(remaining[:3], **kwargs):
yield seg
remaining=remaining[3:]
else:
for seg in self._plot_2_args(remaining[:2], **kwargs):
yield seg
remaining=remaining[2:]
class Axes(martist.Artist):
"""
The Axes contains most of the figure elements: Axis, Tick, Line2D,
Text, Polygon etc, and sets the coordinate system
The Axes instance supports callbacks through a callbacks attribute
which is a 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 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='',
**kwargs
):
"""
Build an Axes instance in Figure with
rect=[left, bottom, width,height in Figure coords
adjustable: ['box' | 'datalim']
alpha: the alpha transparency
anchor: ['C', 'SW', 'S', 'SE', 'E', 'NE', 'N', 'NW', 'W']
aspect: ['auto' | 'equal' | aspect_ratio]
autoscale_on: boolean - whether or not to autoscale the viewlim
axis_bgcolor: any matplotlib color - see help(colors)
axisbelow: draw the grids and ticks below the other artists
cursor_props: a (float, color) tuple
figure: a Figure instance
frame_on: a boolean - draw the axes frame
label: the axes label
navigate: True|False
navigate_mode: the navigation toolbar button status: 'PAN', 'ZOOM', or None
position: [left, bottom, width,height in Figure coords
sharex: an Axes instance to share the x-axis with
sharey: an Axes instance to share the y-axis with
title: the title string
visible: a boolean - 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.set_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 sharey is not None:
self._shared_y_axes.join(self, sharey)
self.set_label(label)
self.set_figure(fig)
# 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._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 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.yaxis = maxis.YAxis(self)
self._update_transScale()
def set_figure(self, fig):
"""
Set the Axes figure
ACCEPTS: a 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._set_lim_and_transforms()
def _set_lim_and_transforms(self):
"""
set the dataLim and viewLim BBox attributes and the
transScale, transData, transLimits and transAxes
transformations.
"""
self.dataLim = mtransforms.Bbox.unit()
self.viewLim = mtransforms.Bbox.unit()
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
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.axes.transData, self.axes.transAxes)
self._yaxis_transform = mtransforms.blended_transform_factory(
self.axes.transAxes, self.axes.transData)
def get_xaxis_transform(self):
"""
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.
This transformation is primarily used by the 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._xaxis_transform
def get_xaxis_text1_transform(self, pad_pixels):
"""
Get the transformation used for drawing x-axis labels, which
will add the given number of pad_pixels 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.
This transformation is primarily used by the 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._xaxis_transform +
mtransforms.Affine2D().translate(0, -1 * pad_pixels),
"top", "center")
def get_xaxis_text2_transform(self, pad_pixels):
"""
Get the transformation used for drawing the secondary x-axis
labels, which will add the given number of pad_pixels 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.
This transformation is primarily used by the 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._xaxis_transform +
mtransforms.Affine2D().translate(0, pad_pixels),
"bottom", "center")
def get_yaxis_transform(self):
"""
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.
This transformation is primarily used by the 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._yaxis_transform
def get_yaxis_text1_transform(self, pad_pixels):
"""
Get the transformation used for drawing y-axis labels, which
will add the given number of pad_pixels 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.
This transformation is primarily used by the 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._yaxis_transform +
mtransforms.Affine2D().translate(-1 * pad_pixels, 0),
"center", "right")
def get_yaxis_text2_transform(self, pad_pixels):
"""
Get the transformation used for drawing the secondary y-axis
labels, which will add the given number of pad_pixels 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.
This transformation is primarily used by the 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._yaxis_transform +
mtransforms.Affine2D().translate(pad_pixels, 0),
"center", "left")
def _update_transScale(self):
self.transScale.set(
mtransforms.blended_transform_factory(
self.xaxis.get_transform(), self.yaxis.get_transform()))
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
There are two position variables: one which is ultimately
used, but which may be modified by apply_aspect, and a second
which is the starting point for apply_aspect.
which = 'active' to change the first;
'original' to change the second;
'both' to change both
ACCEPTS: len(4) sequence of floats, or a Bbox object
"""
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 _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.axes = self
def get_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.
Intended to be overridden by new projection types.
"""
return mpatches.Rectangle((0.0, 0.0), 1.0, 1.0)
def cla(self):
'Clear the current axes'
self.xaxis.cla()
self.yaxis.cla()
self.set_xscale('linear')
self.set_yscale('linear')
self.ignore_existing_data_limits = True
self.callbacks = cbook.CallbackRegistry(('xlim_changed', 'ylim_changed'))
if self._sharex is not None:
self.xaxis.major = self._sharex.xaxis.major
self.xaxis.minor = self._sharex.xaxis.minor
if self._sharey is not None:
self.yaxis.major = self._sharey.yaxis.major
self.yaxis.minor = self._sharey.yaxis.minor
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.legend_ = None
self.collections = [] # collection.Collection instances
self._autoscaleon = True
self.grid(self._gridOn)
props = font_manager.FontProperties(size=rcParams['axes.titlesize'])
self.titleOffsetTrans = mtransforms.Affine2D().translate(0.0, 10.0)
self.title = mtext.Text(
x=0.5, y=1.0, text='',
fontproperties=props,
verticalalignment='bottom',
horizontalalignment='center',
)
self.title.set_transform(self.transAxes + self.titleOffsetTrans)
self.title.set_clip_box(None)
self._set_artist_props(self.title)
self.axesPatch = self.get_axes_patch()
self.axesPatch.set_figure(self.figure)
self.axesPatch.set_facecolor(self._axisbg)
self.axesPatch.set_edgecolor(rcParams['axes.edgecolor'])
self.axesPatch.set_linewidth(rcParams['axes.linewidth'])
self.axesPatch.set_transform(self.transAxes)
self.axesFrame = self.get_axes_patch()
self.axesFrame.set_figure(self.figure)
self.axesFrame.set_facecolor(None)
self.axesFrame.set_edgecolor(rcParams['axes.edgecolor'])
self.axesFrame.set_linewidth(rcParams['axes.linewidth'])
self.axesFrame.set_transform(self.transAxes)
self.axesFrame.set_zorder(2.5)
self.axison = True
self.xaxis.set_clip_path(self.axesPatch)
self.yaxis.set_clip_path(self.axesPatch)
self.titleOffsetTrans.clear().translate(0.0, 10.0)
def clear(self):
'clear the axes'
self.cla()
def ishold(self):
'return the HOLD status of the axes'
return self._hold
def hold(self, b=None):
"""
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.
Eg
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
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:
'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:
'box' - change physical size of axes
'datalim' - change xlim or ylim
anchor:
'C' - centered
'SW' - lower left corner
'S' - middle of bottom edge
'SE' - lower right corner
etc.
ACCEPTS: ['auto' | 'equal' | aspect_ratio]
"""
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']
"""
if adjustable in ('box', 'datalim'):
self._adjustable = adjustable
else:
raise ValueError('argument must be "box", or "datalim"')
def get_anchor(self):
return self._anchor
def set_anchor(self, anchor):
"""
ACCEPTS: ['C', 'SW', 'S', 'SE', 'E', 'NE', 'N', 'NW', 'W']
"""
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()
xsize = max(math.fabs(xmax-xmin), 1e-30)
ymin,ymax = self.get_ybound()
ysize = max(math.fabs(ymax-ymin), 1e-30)
return ysize/xsize
def apply_aspect(self, position=None):
'''
Use self._aspect and self._adjustable to modify the
axes box or the view limits.
'''
if position is None:
position = self.get_position(True)
aspect = self.get_aspect()
if aspect == 'auto':
self.set_position( position , '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:
self._adjustable = 'datalim'
figW,figH = self.get_figure().get_size_inches()
fig_aspect = figH/figW
if self._adjustable == 'box':
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
xmin,xmax = self.get_xbound()
xsize = max(math.fabs(xmax-xmin), 1e-30)
ymin,ymax = self.get_ybound()
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
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
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
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 set_xlim and set_ylim -- see their
docstrings for details
'''
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()
# 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', False)
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
"""
raise DeprecationWarning('Use get_children instead')
def get_frame(self):
'Return the axes Rectangle frame'
return self.axesPatch
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 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 artist to the axes'
a.set_axes(self)
self.artists.append(a)
self._set_artist_props(a)
a.set_clip_path(self.axesPatch)
a._remove_method = lambda h: self.artists.remove(h)
def add_collection(self, collection, autolim=True):
'add a Collection instance to Axes'
label = collection.get_label()
if not label:
collection.set_label('collection%d'%len(self.collections))
self.collections.append(collection)
self._set_artist_props(collection)
collection.set_clip_path(self.axesPatch)
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)
def add_line(self, line):
'Add a line to the list of plot lines'
self._set_artist_props(line)
line.set_clip_path(self.axesPatch)
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)
def _update_line_limits(self, line):
xydata = line.get_xydata()
self.update_datalim( xydata )
def add_patch(self, p):
"""
Add a patch to the list of Axes patches; the clipbox will be
set to the Axes clipping box. If the transform is not set, it
wil be set to self.transData.
"""
self._set_artist_props(p)
p.set_clip_path(self.axesPatch)
self._update_patch_limits(p)
self.patches.append(p)
p._remove_method = lambda h: self.patches.remove(h)
def _update_patch_limits(self, p):
'update the datalimits for patch p'
vertices = p.get_patch_transform().transform(p.get_path().vertices)
if p.get_data_transform() != self.transData:
transform = p.get_data_transform() + self.transData.inverted()
xys = transform.transform(vertices)
self.update_datalim(vertices)
def add_table(self, tab):
'Add a table instance to the list of axes tables'
self._set_artist_props(tab)
self.tables.append(tab)
tab.set_clip_path(self.axesPatch)
tab._remove_method = lambda h: self.tables.remove(h)
def relim(self):
'recompute the datalimits based on current artists'
self.dataLim.ignore(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):
'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 not ma.isMaskedArray(xys):
xys = npy.asarray(xys)
self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits)
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
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 Bbox'
self.dataLim.set(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:
self.xaxis.update_units(xdata)
#print '\tset from xdata', self.xaxis.units
if ydata is not None:
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)
yunits = kwargs.pop('yunits', self.yaxis.units)
if yunits!=self.yaxis.units:
#print '\tkw setting yunits', yunits
self.yaxis.set_units(yunits)
def in_axes(self, mouseevent):
'return True if the given mouseevent (in display coords) is in the Axes'
return self.axesPatch.contains(mouseevent)[0]
def get_autoscale_on(self):
"""
Get whether autoscaling is applied on plot commands
"""
return self._autoscaleon
def set_autoscale_on(self, b):
"""
Set whether autoscaling is applied on plot commands
ACCEPTS: True|False
"""
self._autoscaleon = b
def autoscale_view(self, tight=False, 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.
"""
# if image data only just use the datalim
if not self._autoscaleon: return
if (tight or (len(self.images)>0 and
len(self.lines)==0 and
len(self.patches)==0)):
if scalex: self.set_xbound(self.dataLim.intervalx)
if scaley: self.set_ybound(self.dataLim.intervaly)
return
if scalex:
xl = self.get_xbound()
XL = self.xaxis.get_major_locator().autoscale()
self.set_xbound(XL)
if scaley:
ylocator = self.yaxis.get_major_locator()
yl = self.get_ybound()
YL = ylocator.autoscale()
self.set_ybound(YL)
def update_layout(self, renderer):
pad_pixels = rcParams['xtick.major.pad'] * self.figure.dpi / 72.0
inverse_transFigure = self.figure.transFigure.inverted()
t_text, b_text = self.xaxis.get_text_heights(renderer)
l_text, r_text = self.yaxis.get_text_widths(renderer)
title_height = self.title.get_window_extent(renderer).height
title_height += pad_pixels * 2.0
original_t_text = t_text
((l_text, t_text),
(r_text, b_text),
(dummy, title_height)) = inverse_transFigure.transform(
((l_text, t_text),
(r_text, b_text),
(0.0, title_height)))
x0, y0, x1, y1 = self.get_position(True).extents
# Adjust the title
self.titleOffsetTrans.clear().translate(
0, original_t_text + pad_pixels * 2.0)
new_position = mtransforms.Bbox.from_extents(
x0 + l_text, y0 + b_text,
x1 - r_text, y1 - t_text - title_height)
self.set_position(new_position, 'active')
#### Drawing
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')
self.apply_aspect(self.get_position())
if self.axison and self._frameon:
self.axesPatch.draw(renderer)
artists = []
if len(self.images)<=1 or renderer.option_image_nocomposite():
for im in self.images:
im.draw(renderer)
else:
# make a composite image blending alpha
# list of (mimage.Image, ox, oy)
mag = renderer.get_image_magnification()
ims = [(im.make_image(mag),0,0)
for im in self.images if im.get_visible()]
im = mimage.from_images(self.bbox.height*mag,
self.bbox.width*mag,
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
renderer.draw_image(
l, b, im, self.bbox,
self.axesPatch.get_path(),
self.axesPatch.get_transform())
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_)
if self.axison and self._frameon:
artists.append(self.axesFrame)
dsu = [ (a.zorder, i, a) for i, a in enumerate(artists)
if not a.get_animated() ]
dsu.sort()
for zorder, i, 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()
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 axist 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
def grid(self, b=None, **kwargs):
"""
GRID(self, b=None, **kwargs)
Set the axes grids on or off; b is a boolean
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
kawrgs are used to set the grid line properties, eg
ax.grid(color='r', linestyle='-', linewidth=2)
Valid Line2D kwargs are
%(Line2D)s
"""
if len(kwargs): b = True
self.xaxis.grid(b, **kwargs)
self.yaxis.grid(b, **kwargs)
grid.__doc__ = cbook.dedent(grid.__doc__) % martist.kwdocd
def ticklabel_format(self, **kwargs):
"""
Convenience method for manipulating the ScalarFormatter
used by default for linear axes.
kwargs:
style = 'sci' (or 'scientific') or 'plain';
plain turns off scientific notation
axis = 'x', 'y', or 'both'
Only the major ticks are affected.
If the method is called when the ScalarFormatter is not
the one being used, an AttributeError will be raised with
no additional error message.
Additional capabilities and/or friendlier error checking may be added.
"""
style = kwargs.pop('style', '').lower()
axis = kwargs.pop('axis', 'both').lower()
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"
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)
def set_axis_off(self):
"""
turn off the axis
ACCEPTS: void
"""
self.axison = False
def set_axis_on(self):
"""
turn on the axis
ACCEPTS: void
"""
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 help(colors)
"""
self._axisbg = color
self.axesPatch.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 in the form of 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.
"""
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)
else:
self.set_xlim(lower, upper)
else:
if lower < upper:
self.set_xlim(lower, upper)
else:
self.set_xlim(upper, lower)
def get_xlim(self):
"""
Get the x-axis range [xmin, xmax]
"""
return self.viewLim.intervalx
def set_xlim(self, xmin=None, xmax=None, emit=True, **kwargs):
"""
set_xlim(self, *args, **kwargs):
Set the limits for the xaxis; v = [xmin, xmax]
set_xlim((valmin, valmax))
set_xlim(valmin, valmax)
set_xlim(xmin=1) # xmax unchanged
set_xlim(xmax=1) # xmin unchanged
Valid kwargs:
xmin : the min of the xlim
xmax : the max of the xlim
emit : notify observers of lim change
Returns the current xlimits as a length 2 tuple
ACCEPTS: len(2) sequence of floats
"""
if xmax is None and iterable(xmin):
xmin,xmax = xmin
self._process_unit_info(xdata=(xmin, xmax))
if xmin is not None:
xmin = self.convert_xunits(xmin)
if xmax is not None:
xmax = self.convert_xunits(xmax)
old_xmin,old_xmax = self.get_xlim()
if xmin is None: xmin = old_xmin
if xmax is None: xmax = old_xmax
xmin, xmax = mtransforms.nonsingular(xmin, xmax, increasing=False)
xmin, xmax = self.xaxis.limit_range_for_scale(xmin, xmax)
self.viewLim.intervalx = (xmin, xmax)
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)
if other.figure != self.figure and other.figure.canvas is not None:
other.figure.canvas.draw_idle()
return xmin, xmax
def get_xscale(self):
'return the xaxis scale string: %s' % (
", ".join(mscale.get_scale_names()))
return self.xaxis.get_scale()
def set_xscale(self, value, **kwargs):
"""
SET_XSCALE(value)
Set the xscaling: %(scale)s
If value is 'log', the additional kwargs have the following meaning
* basex: base of the logarithm
* subsx: a sequence of the location of the minor ticks;
None defaults to autosubs, which depend on the number of
decades in the plot. Eg for base 10, subsx=(1,2,5) will
put minor ticks on 1,2,5,11,12,15,21, ....To turn off
minor ticking, set subsx=[]
ACCEPTS: [%(scale)s]
""" % {'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()])}
self.xaxis.set_scale(value, **kwargs)
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))
def set_xticklabels(self, labels, fontdict=None, minor=False, **kwargs):
"""
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 Text properties. Valid properties are
%(Text)s
ACCEPTS: sequence of strings
"""
return self.xaxis.set_ticklabels(labels, fontdict, minor=minor, **kwargs)
set_xticklabels.__doc__ = cbook.dedent(set_xticklabels.__doc__) % martist.kwdocd
def invert_yaxis(self):
"Invert the y-axis."
left, right = self.get_ylim()
self.set_ylim(right, left)
def yaxis_inverted(self):
'Returns True if the y-axis is inverted.'
left, right = self.get_ylim()
return right < left
def get_ybound(self):
"Returns the y-axis numerical bounds in the form of lowerBound < upperBound"
left, right = self.get_ylim()
if left < right:
return left, right
else:
return right, left
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.
"""
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)
else:
self.set_ylim(lower, upper)
else:
if lower < upper:
self.set_ylim(lower, upper)
else:
self.set_ylim(upper, lower)
def get_ylim(self):
"""
Get the y-axis range [xmin, xmax]
"""
return self.viewLim.intervaly
def set_ylim(self, ymin=None, ymax=None, emit=True, **kwargs):
"""
set_ylim(self, *args, **kwargs):
Set the limits for the yaxis; v = [ymin, ymax]
set_ylim((valmin, valmax))
set_ylim(valmin, valmax)
set_ylim(ymin=1) # ymax unchanged
set_ylim(ymax=1) # ymin unchanged
Valid kwargs:
ymin : the min of the ylim
ymax : the max of the ylim
emit : notify observers of lim change
Returns the current ylimits as a length 2 tuple
ACCEPTS: len(2) sequence of floats
"""
if ymax is None and iterable(ymin):
ymin,ymax = ymin
if ymin is not None:
ymin = self.convert_yunits(ymin)
if ymax is not None:
ymax = self.convert_yunits(ymax)
old_ymin,old_ymax = self.get_ylim()
if ymin is None: ymin = old_ymin
if ymax is None: ymax = old_ymax
ymin, ymax = mtransforms.nonsingular(ymin, ymax, increasing=False)
ymin, ymax = self.yaxis.limit_range_for_scale(ymin, ymax)
self.viewLim.intervaly = (ymin, ymax)
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)
if other.figure != self.figure and other.figure.canvas is not None:
other.figure.canvas.draw_idle()
return ymin, ymax
def get_yscale(self):
'return the xaxis scale string: %s' % (
", ".join(mscale.get_scale_names()))
return self.yaxis.get_scale()
def set_yscale(self, value, **kwargs):
"""
SET_YSCALE(value, basey=10, subsy=None)
Set the yscaling: %(scale)s
If value is 'log', the additional kwargs have the following meaning
* basey: base of the logarithm
* subsy: a sequence of the location of the minor ticks;
None defaults to autosubs, which depend on the number of
decades in the plot. Eg for base 10, subsy=(1,2,5) will
put minor ticks on 1,2,5,11,12,15, 21, ....To turn off
minor ticking, set subsy=[]
ACCEPTS: %(scale)s
""" % {'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()])}
self.yaxis.set_scale(value, **kwargs)
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
"""
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))
def set_yticklabels(self, labels, fontdict=None, minor=False, **kwargs):
"""
set_yticklabels(labels, fontdict=None, minor=False, **kwargs)
Set the ytick labels with list of strings labels. Return a list of
Text instances.
kwargs set Text properties for the labels. Valid properties are
%(Text)s
ACCEPTS: sequence of strings
"""
return self.yaxis.set_ticklabels(labels, fontdict, minor=minor, **kwargs)
set_yticklabels.__doc__ = cbook.dedent(set_yticklabels.__doc__) % martist.kwdocd
def xaxis_date(self, tz=None):
"""Sets up x-axis ticks and labels that treat the x data as dates.
tz is the time zone to use in labeling dates. Defaults to rc value.
"""
locator = self.xaxis.get_major_locator()
if not isinstance(locator, mdates.DateLocator):
locator = mdates.AutoDateLocator(tz)
self.xaxis.set_major_locator(locator)
formatter = self.xaxis.get_major_formatter()
if not isinstance(formatter, mdates.DateFormatter):
formatter = mdates.AutoDateFormatter(locator)
self.xaxis.set_major_formatter(formatter)
def yaxis_date(self, tz=None):
"""Sets up y-axis ticks and labels that treat the y data as dates.
tz is the time zone to use in labeling dates. Defaults to rc value.
"""
locator = self.yaxis.get_major_locator()
if not isinstance(locator, mdates.DateLocator):
locator = mdates.AutoDateLocator(tz)
self.yaxis.set_major_locator(locator)
formatter = self.xaxis.get_major_formatter()
if not isinstance(formatter, mdates.DateFormatter):
formatter = mdates.AutoDateFormatter(locator)
self.yaxis.set_major_formatter(formatter)
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 attribute
self.fmt_ydata 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:
x = '???'
if y is None:
y = '???'
xs = self.format_xdata(x)
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;
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
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(),
x = x,
y = y
)
def end_pan(self):
"""
Called when a pan operation completes (when the mouse button
is up.)
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.
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 button == 1:
dx, dy = format_deltas(key, dx, dy)
result = self.bbox.frozen().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 = npy.power(10.0, (dx, dy))
start = p.trans_inverse.transform_point((p.x, p.y))
lim_points = p.lim.get_points()
result = start + alpha * (lim_points - start)
result = mtransforms.Bbox(result)
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 props 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.axesPatch)
children.append(self.axesFrame)
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)
inside = self.axesPatch.contains(mouseevent.x, mouseevent.y)
return inside,{}
def pick(self,*args):
"""
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 matplotlib 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(npy.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, npy.asarray(xt), npy.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 set_title(self, label, fontdict=None, **kwargs):
"""
SET_TITLE(label, fontdict=None, **kwargs):
Set the title for the axes. See the text docstring for information
of how override and the optional args work
kwargs are Text properties:
%(Text)s
ACCEPTS: str
"""
default = {
'fontsize':rcParams['axes.titlesize'],
'verticalalignment' : 'bottom',
'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
set_title.__doc__ = cbook.dedent(set_title.__doc__) % martist.kwdocd
def set_xlabel(self, xlabel, fontdict=None, **kwargs):
"""
SET_XLABEL(xlabel, fontdict=None, **kwargs)
Set the label for the xaxis. See the text docstring for information
of how override and the optional args work.
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
"""
label = self.xaxis.get_label()
label.set_text(xlabel)
if fontdict is not None: label.update(fontdict)
label.update(kwargs)
return label
set_xlabel.__doc__ = cbook.dedent(set_xlabel.__doc__) % martist.kwdocd
def set_ylabel(self, ylabel, fontdict=None, **kwargs):
"""
SET_YLABEL(ylabel, fontdict=None, **kwargs)
Set the label for the yaxis
See the text doctstring for information of how override and
the optional args work
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
"""
label = self.yaxis.get_label()
label.set_text(ylabel)
if fontdict is not None: label.update(fontdict)
label.update(kwargs)
return label
set_ylabel.__doc__ = cbook.dedent(set_ylabel.__doc__) % martist.kwdocd
def text(self, x, y, s, fontdict=None,
withdash=False, **kwargs):
"""
TEXT(x, y, s, fontdict=None, **kwargs)
Add text in string s to axis at location x,y (data coords)
fontdict is a dictionary to override the default text properties.
If fontdict is None, the defaults are determined by your rc
parameters.
withdash=True will create a TextWithDash instance instead
of a 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 lower left and
1,1 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 patches.Rectangle properties (see help
for Rectangle for a list of these). For example
text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))
Valid kwargs are Text properties
%(Text)s
"""
default = {
'verticalalignment' : 'bottom',
'horizontalalignment' : 'left',
#'verticalalignment' : 'top',
'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 kwargs.has_key('clip_on'): t.set_clip_path(self.axesPatch)
return t
text.__doc__ = cbook.dedent(text.__doc__) % martist.kwdocd
def annotate(self, *args, **kwargs):
"""
annotate(s, xy,
xytext=None,
xycoords='data',
textcoords='data',
arrowprops=None,
**props)
%(Annotation)s
"""
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.axesPatch)
self.texts.append(a)
return a
annotate.__doc__ = cbook.dedent(annotate.__doc__) % martist.kwdocd
#### Lines and spans
def axhline(self, y=0, xmin=0, xmax=1, **kwargs):
"""
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 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 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 Line2D properties
%(Line2D)s
"""
trans = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
l, = self.plot([xmin,xmax], [y,y], transform=trans, scalex=False, **kwargs)
return l
axhline.__doc__ = cbook.dedent(axhline.__doc__) % martist.kwdocd
def axvline(self, x=0, ymin=0, ymax=1, **kwargs):
"""
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 xlim settings, even if you change them,
eg with the xlim 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 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
l = axvline(linewidth=4, color='r')
# draw a default vline at x=1 that spans the yrange
l = 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 Line2D properties
%(Line2D)s
"""
trans = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
l, = self.plot([x,x], [ymin,ymax] , transform=trans, scaley=False, **kwargs)
return l
axvline.__doc__ = cbook.dedent(axvline.__doc__) % martist.kwdocd
def axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs):
"""
AXHSPAN(ymin, ymax, xmin=0, xmax=1, **kwargs)
Axis Horizontal Span. ycoords are in data units and x
coords are in axes (relative 0-1) units
Draw a horizontal span (regtangle) from ymin to ymax. With the
default values of xmin=0 and xmax=1, this always span the xrange,
regardless of the xlim settings, even if you change them, eg with the
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.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
Return value is the patches.Polygon instance.
#draws 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 Polygon properties
%(Polygon)s
"""
# convert y axis units
trans = mtransforms.blended_transform_factory(
self.transAxes, self.transData)
verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)
p = mpatches.Polygon(verts, **kwargs)
p.set_transform(trans)
self.add_patch(p)
return p
axhspan.__doc__ = cbook.dedent(axhspan.__doc__) % martist.kwdocd
def axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs):
"""
AXVSPAN(xmin, xmax, ymin=0, ymax=1, **kwargs)
axvspan : Axis Vertical Span. xcoords are in data units and y coords
are in axes (relative 0-1) units
Draw a vertical span (regtangle) from xmin to xmax. With the default
values of ymin=0 and ymax=1, this always span the yrange, regardless
of the ylim settings, even if you change them, eg with the 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.
kwargs are the kwargs to Patch, eg
antialiased, aa
linewidth, lw
edgecolor, ec
facecolor, fc
the terms on the right are aliases
return value is the patches.Polygon instance.
# 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 Polygon properties
%(Polygon)s
"""
# convert x axis units
trans = mtransforms.blended_transform_factory(
self.transData, self.transAxes)
verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]
p = mpatches.Polygon(verts, **kwargs)
p.set_transform(trans)
self.add_patch(p)
return p
axvspan.__doc__ = cbook.dedent(axvspan.__doc__) % martist.kwdocd
def hlines(self, y, xmin, xmax, colors='k', linestyles='solid',
label='', **kwargs):
"""
HLINES(y, xmin, xmax, colors='k', linestyle='solid', **kwargs)
plot horizontal lines at each y from xmin to xmax. xmin or 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
colors is a line collections color args, either a single color
or a len(x) list of colors
linestyle is one of solid|dashed|dashdot|dotted
Returns the LineCollection that was added
"""
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')
if not iterable(y): y = [y]
if not iterable(xmin): xmin = [xmin]
if not iterable(xmax): xmax = [xmax]
y = npy.asarray(y)
if len(xmin)==1:
xmin = xmin*npy.ones(y.shape, y.dtype)
if len(xmax)==1:
xmax = xmax*npy.ones(y.shape, y.dtype)
xmin = npy.asarray(xmin)
xmax = npy.asarray(xmax)
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()
minx, maxx = self.convert_xunits((minx, maxx))
miny, maxy = self.convert_yunits((miny, maxy))
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
return coll
hlines.__doc__ = cbook.dedent(hlines.__doc__)
def vlines(self, x, ymin, ymax, colors='k', linestyles='solid',
label='', **kwargs):
"""
VLINES(x, ymin, ymax, color='k')
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 is a line collections color args, either a single color
or a len(x) list of colors
linestyle is one of solid|dashed|dashdot|dotted
Returns the collections.LineCollection that was added
kwargs are 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, kwargs=kwargs)
if not iterable(x): x = [x]
if not iterable(ymin): ymin = [ymin]
if not iterable(ymax): ymax = [ymax]
x = npy.asarray(x)
ymin = npy.asarray(ymin)
ymax = npy.asarray(ymax)
if len(ymin)==1:
ymin = ymin*npy.ones(x.shape, x.dtype)
if len(ymax)==1:
ymax = ymax*npy.ones(x.shape, x.dtype)
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 = npy.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 = x.min()
maxx = x.max()
miny = min(ymin.min(), ymax.min())
maxy = max(ymin.max(), ymax.max())
minx, maxx = self.convert_xunits((minx, maxx))
miny, maxy = self.convert_yunits((miny, maxy))
corners = (minx, miny), (maxx, maxy)
self.update_datalim(corners)
self.autoscale_view()
return coll
vlines.__doc__ = cbook.dedent(vlines.__doc__) % martist.kwdocd
#### Basic plotting
def plot(self, *args, **kwargs):
"""
PLOT(*args, **kwargs)
Plot lines and/or markers to the 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 the 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 line styles are supported:
- : solid line
-- : dashed line
-. : dash-dot line
: : dotted line
. : points
, : pixels
o : circle symbols
^ : triangle up symbols
v : triangle down symbols
< : triangle left symbols
> : triangle right symbols
s : square symbols
+ : plus symbols
x : cross symbols
D : diamond symbols
d : thin diamond symbols
1 : tripod down symbols
2 : tripod up symbols
3 : tripod left symbols
4 : tripod right symbols
h : hexagon symbols
H : rotated hexagon symbols
p : pentagon symbols
| : vertical line symbols
_ : horizontal line symbols
steps : use gnuplot style 'steps' # kwarg only
The following color abbreviations are supported
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, eg
plot(x1, y1, x2, y2, antialised=False)
Neither line will be antialiased.
The kwargs are Line2D properties:
%(Line2D)s
kwargs scalex and scaley, if defined, are passed on
to autoscale_view to determine whether the x and y axes are
autoscaled; default True. See Axes.autoscale_view for more
information
"""
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
plot.__doc__ = cbook.dedent(plot.__doc__) % martist.kwdocd
def plot_date(self, x, y, fmt='bo', tz=None, xdate=True, ydate=False,
**kwargs):
"""
PLOT_DATE(x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs)
Similar to the plot() command, except the x or y (or both) data
is considered to be dates, and the axis is labeled accordingly.
x or y (or both) can be a sequence of dates represented as
float days since 0001-01-01 UTC.
fmt is a plot format string.
tz is the time zone to use in labelling dates. Defaults to rc value.
If xdate is True, the x-axis will be labeled with dates.
If ydate is 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 plot_date since plot_date will set the default tick locator
to ticker.AutoDateLocator (if the tick locator is not already set to
a ticker.DateLocator instance) and the default tick formatter to
AutoDateFormatter (if the tick formatter is not already set to
a DateFormatter instance).
Valid kwargs are Line2D properties:
%(Line2D)s
See dates for helper functions date2num, num2date
and 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
plot_date.__doc__ = cbook.dedent(plot_date.__doc__) % martist.kwdocd
def loglog(self, *args, **kwargs):
"""
LOGLOG(*args, **kwargs)
Make a loglog plot with log scaling on the a and y axis. The args
to semilog x are the same as the args to plot. See help plot for
more info.
Optional keyword args supported are any of the kwargs
supported by plot or set_xscale or set_yscale. Notable, for
log scaling:
* basex: base of the x logarithm
* subsx: the location of the minor ticks; None defaults to
autosubs, which depend on the number of decades in the
plot; see set_xscale for details
* basey: base of the y logarithm
* subsy: the location of the minor yticks; None defaults to
autosubs, which depend on the number of decades in the
plot; see set_yscale for details
The remaining valid kwargs are Line2D properties:
%(Line2D)s
"""
if not self._hold: self.cla()
dx = {'basex': kwargs.pop('basex', 10),
'subsx': kwargs.pop('subsx', None),
}
dy = {'basey': kwargs.pop('basey', 10),
'subsy': kwargs.pop('subsy', None),
}
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
loglog.__doc__ = cbook.dedent(loglog.__doc__) % martist.kwdocd
def semilogx(self, *args, **kwargs):
"""
SEMILOGX(*args, **kwargs)
Make a semilog plot with log scaling on the x axis. The args to
semilog x are the same as the args to plot. See help plot for more
info.
Optional keyword args supported are any of the kwargs supported by
plot or set_xscale. Notable, for log scaling:
* basex: base of the logarithm
* subsx: the location of the minor ticks; None defaults to
autosubs, which depend on the number of decades in the
plot; see set_xscale for details
The remaining valid kwargs are Line2D properties:
%(Line2D)s
"""
if not self._hold: self.cla()
d = {'basex': kwargs.pop( 'basex', 10),
'subsx': kwargs.pop( 'subsx', None),
}
self.set_xscale('log', **d)
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
semilogx.__doc__ = cbook.dedent(semilogx.__doc__) % martist.kwdocd
def semilogy(self, *args, **kwargs):
"""
SEMILOGY(*args, **kwargs):
Make a semilog plot with log scaling on the y axis. The args to
semilogy are the same as the args to plot. See help plot for more
info.
Optional keyword args supported are any of the kwargs supported by
plot or set_yscale. Notable, for log scaling:
* basey: base of the logarithm
* subsy: a sequence of the location of the minor ticks;
None defaults to autosubs, which depend on the number of
decades in the plot; see set_yscale for details
The remaining valid kwargs are Line2D properties:
%(Line2D)s
"""
if not self._hold: self.cla()
d = {'basey': kwargs.pop('basey', 10),
'subsy': kwargs.pop('subsy', None),
}
self.set_yscale('log', **d)
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
semilogy.__doc__ = cbook.dedent(semilogy.__doc__) % martist.kwdocd
def acorr(self, x, **kwargs):
"""
ACORR(x, normed=False, detrend=mlab.detrend_none, usevlines=False,
maxlags=None, **kwargs)
Plot the autocorrelation of x. If normed=True, normalize the
data but 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 lags, c, line where lags are a length
2*maxlags+1 lag vector, c is the 2*maxlags+1 auto correlation
vector, and line is a Line2D instance returned by 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 numpy correlate with
mode=2.
If usevlines is True, Axes.vlines rather than Axes.plot is used
to draw vertical lines from the origin to the acorr.
Otherwise the plotstyle is determined by the kwargs, which are
Line2D properties. If usevlines, the return value is lags, c,
linecol, b where linecol is the collections.LineCollection and b is the x-axis
if usevlines=True, kwargs are passed onto Axes.vlines
if usevlines=False, kwargs are passed onto Axes.plot
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.
See the respective function for documentation on valid kwargs
"""
return self.xcorr(x, x, **kwargs)
acorr.__doc__ = cbook.dedent(acorr.__doc__) % martist.kwdocd
def xcorr(self, x, y, normed=False, detrend=mlab.detrend_none, usevlines=False,
maxlags=None, **kwargs):
"""
XCORR(x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, **kwargs):
Plot the cross correlation between x and y. If normed=True,
normalize the data but 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 lags, c, line where lags are a length
2*maxlags+1 lag vector, c is the 2*maxlags+1 auto correlation
vector, and line is a Line2D instance returned by 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 numpy correlate with
mode=2.
If usevlines is True, Axes.vlines rather than Axes.plot is used
to draw vertical lines from the origin to the acorr.
Otherwise the plotstyle is determined by the kwargs, which are
Line2D properties. If usevlines, the return value is lags, c,
linecol, b where linecol is the collections.LineCollection and b is the x-axis
if usevlines=True, kwargs are passed onto Axes.vlines
if usevlines=False, kwargs are passed onto Axes.plot
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.
See the respective function for documentation on valid kwargs
"""
Nx = len(x)
if Nx!=len(y):
raise ValueError('x and y must be equal length')
x = detrend(npy.asarray(x))
y = detrend(npy.asarray(y))
c = npy.correlate(x, y, mode=2)
if normed: c/= npy.sqrt(npy.dot(x,x) * npy.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 = npy.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
xcorr.__doc__ = cbook.dedent(xcorr.__doc__) % martist.kwdocd
def legend(self, *args, **kwargs):
"""
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
USAGE:
Make a legend with existing lines
>>> legend()
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.
# automatically generate the legend from labels
legend( ('label1', 'label2', 'label3') )
# Make a legend for a list of lines and labels
legend( (line1, line2, line3), ('label1', 'label2', 'label3') )
# Make a legend at a given location, using a location argument
# legend( LABELS, LOC ) or
# legend( LINES, LABELS, LOC )
legend( ('label1', 'label2', 'label3'), loc='upper left')
legend( (line1, line2, line3), ('label1', 'label2', 'label3'), loc=2)
The location codes are
'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,
If none of these are suitable, loc can be a 2-tuple giving x,y
in axes coords, ie,
loc = 0, 1 is left top
loc = 0.5, 0.5 is center, center
and so on. The following kwargs are supported:
isaxes=True # whether this is an axes legend
numpoints = 4 # the number of points in the legend line
prop = FontProperties(size='smaller') # the font property
pad = 0.2 # the fractional whitespace inside the legend border
markerscale = 0.6 # the relative size of legend markers vs. original
shadow # if True, draw a shadow behind legend
labelsep = 0.005 # the vertical space between the legend entries
handlelen = 0.05 # the length of the legend lines
handletextsep = 0.02 # the space between the legend line and legend text
axespad = 0.02 # the border between the axes and legend edge
"""
def get_handles():
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)])
return handles
if len(args)==0:
handles = []
labels = []
for handle in get_handles():
label = handle.get_label()
if label is not None and label != '' and not label.startswith('_'):
handles.append(handle)
labels.append(label)
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(get_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(get_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):
'''
step(x, y, *args, **kwargs)
x and y must be 1-D sequences, and it is assumed, but not checked,
that x is uniformly increasing.
Make a step plot. The args and keyword args to step are the same
as the args to plot. See help plot for more info.
Additional keyword args for step:
* where: can be 'pre', 'post' or 'mid'; if 'pre', the
interval from x[i] to x[i+1] has level y[i];
if 'post', that interval has level y[i+1];
and if 'mid', the jumps in y occur half-way
between the x-values. Default is 'pre'.
'''
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)
def bar(self, left, height, width=0.8, bottom=None,
color=None, edgecolor=None, linewidth=None,
yerr=None, xerr=None, ecolor=None, capsize=3,
align='edge', orientation='vertical', log=False,
**kwargs
):
"""
BAR(left, height, width=0.8, bottom=0,
color=None, edgecolor=None, linewidth=None,
yerr=None, xerr=None, ecolor=None, capsize=3,
align='edge', orientation='vertical', log=False)
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 Rectangle patch instances
left - the x coordinates of the left sides of the bars
height - the heights of the bars
Optional arguments:
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 and 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'
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 'center' interprets these values as the x coordinates of
the bar centers. For horizontal bars, 'edge' aligns bars by their
bottom edges in bottom, while '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.
Optional kwargs:
%(Rectangle)s
"""
if not self._hold: self.cla()
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
# do not convert to array here as unit info is lost
#left = npy.asarray(left)
#height = npy.asarray(height)
#width = npy.asarray(width)
#bottom = npy.asarray(bottom)
if len(linewidth) == 1: linewidth = linewidth * nbars
# if color looks like a color string, an RGB tuple or a
# scalar, then repeat it by nbars
if (is_string_like(color) or
(iterable(color) and len(color)==3 and nbars!=3) or
not iterable(color)):
color = [color]*nbars
# if edgecolor looks like a color string, an RGB tuple or a
# scalar, then repeat it by nbars
if (is_string_like(edgecolor) or
(iterable(edgecolor) and len(edgecolor)==3 and nbars!=3) or
not iterable(edgecolor)):
edgecolor = [edgecolor]*nbars
if yerr is not None:
if not iterable(yerr):
yerr = [yerr]*nbars
if xerr is not None:
if not iterable(xerr):
xerr = [xerr]*nbars
assert len(left)==nbars, "argument 'left' must be %d or scalar" % nbars
assert len(height)==nbars, "argument 'height' must be %d or scalar" % nbars
assert len(width)==nbars, "argument 'width' must be %d or scalar" % nbars
assert len(bottom)==nbars, "argument 'bottom' must be %d or scalar" % nbars
assert len(color)==nbars, "argument 'color' must be %d or scalar" % nbars
assert len(edgecolor)==nbars, "argument 'edgecolor' must be %d or scalar" % nbars
assert len(linewidth)==nbars, "argument 'linewidth' must be %d or scalar" % nbars
if yerr is not None and len(yerr)!=nbars:
raise ValueError("bar() argument 'yerr' must be len(%s) or scalar" % nbars)
if xerr is not None and len(xerr)!=nbars:
raise ValueError("bar() argument 'xerr' must be len(%s) or scalar" % nbars)
patches = []
if align == 'edge':
pass
elif align == 'center':
if orientation == 'vertical':
left = [left[i] - width[i]/2. for i in range(len(left))]
elif orientation == 'horizontal':
bottom = [bottom[i] - height[i]/2. for i in range(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,
)
r.update(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, ecolor=ecolor, capsize=capsize)
self.hold(holdstate) # restore previous hold state
if adjust_xlim:
xmin, xmax = self.dataLim.intervalx
xmin = npy.amin(width)
if xerr is not None:
xmin = xmin - npy.amax(xerr)
xmin = max(xmin*0.9, 1e-100)
self.dataLim.intervalx = (xmin, xmax)
if adjust_ylim:
ymin, ymax = self.dataLim.intervaly
ymin = npy.amin(height)
if yerr is not None:
ymin = ymin - npy.amax(yerr)
ymin = max(ymin*0.9, 1e-100)
self.dataLim.intervaly = (ymin, ymax)
self.autoscale_view()
return patches
bar.__doc__ = cbook.dedent(bar.__doc__) % martist.kwdocd
def barh(self, bottom, width, height=0.8, left=None, **kwargs):
"""
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 Rectangle patch instances
bottom - the vertical positions of the bottom edges of the bars
width - the lengths of the bars
Optional arguments:
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 and 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 '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.
Optional kwargs:
%(Rectangle)s
"""
patches = self.bar(left=left, height=height, width=width, bottom=bottom,
orientation='horizontal', **kwargs)
return patches
barh.__doc__ = cbook.dedent(barh.__doc__) % martist.kwdocd
def broken_barh(self, xranges, yrange, **kwargs):
"""
A collection of horizontal bars spanning yrange with a sequence of
xranges
xranges : sequence of (xmin, xwidth)
yrange : (ymin, ywidth)
kwargs are 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'
"""
col = mcoll.BrokenBarHCollection(xranges, yrange, **kwargs)
self.add_collection(col, autolim=True)
self.autoscale_view()
return col
broken_barh.__doc__ = cbook.dedent(broken_barh.__doc__) % martist.kwdocd
def stem(self, x, y, linefmt='b-', markerfmt='bo', basefmt='r-'):
"""
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 (markerline, stemlines, baseline) .
See
http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html
for details and examples/stem_plot.py for a demo.
"""
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([npy.amin(x), npy.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,
):
"""
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.
- explode, if not None, is a len(x) array which specifies the
fraction of the radius to offset that wedge.
- colors is a sequence of matplotlib color args that the pie chart
will cycle.
- labels, if not None, is a len(x) list of labels.
- autopct, 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 is 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 is the radial distance at which the pie labels are drawn
- shadow, if True, will 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 a list of (patches, texts), where patches
is a sequence of mpatches.Wedge instances and texts is a
list of the label Text instnaces
If autopct is not None, return (patches, texts, autotexts), where
patches and texts are as above, and autotexts is a list of text
instances for the numeric labels
"""
self.set_frame_on(False)
x = npy.asarray(x).astype(npy.float32)
sx = float(x.sum())
if sx>1: x = npy.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())
self.add_patch(shad)
xt = x + labeldistance*radius*math.cos(thetam)
yt = y + labeldistance*radius*math.sin(thetam)
t = self.text(xt, yt, label,
size=rcParams['xtick.labelsize'],
horizontalalignment='center',
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
def errorbar(self, x, y, yerr=None, xerr=None,
fmt='-', ecolor=None, capsize=3,
barsabove=False, lolims=False, uplims=False,
xlolims=False, xuplims=False, **kwargs):
"""
ERRORBAR(x, y, yerr=None, xerr=None,
fmt='b-', ecolor=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
xerr and yerr may be any of:
a rank-0, Nx1 Numpy array - symmetric errorbars +/- value
an N-element list or tuple - symmetric errorbars +/- value
a rank-1, Nx2 Numpy array - asymmetric errorbars -column1/+column2
Alternatively, x, y, xerr, and yerr can all be scalars, which
plots a single error bar at x, y.
fmt is the plot format symbol for y. if fmt is None, just
plot the errorbars with no line symbols. This can be useful
for creating a bar plot with errorbars
ecolor is a matplotlib color arg which gives the color the
errorbar lines; if None, use the marker color.
capsize is the size of the error bar caps in points
barsabove, if True, will plot the errorbars above the plot symbols
- default is below
lolims, uplims, xlolims, xuplims: 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.
kwargs are passed on to the plot command for the markers.
So you can add additional key=value pairs to control the
errorbar 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)
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
Return value is a length 3 tuple. The first element is the
Line2D instance for the y symbol lines. The second element is
a list of error bar cap lines, the third element is a list of
line collections for the horizontal and vertical error ranges
"""
self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs)
if not self._hold: self.cla()
# 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 'linewidth' in kwargs:
lines_kw['linewidth']=kwargs['linewidth']
if 'lw' in kwargs:
lines_kw['lw']=kwargs['lw']
# arrays fine here, they are booleans and hence not units
if not iterable(lolims):
lolims = npy.asarray([lolims]*len(x), bool)
else: lolims = npy.asarray(lolims, bool)
if not iterable(uplims): uplims = npy.array([uplims]*len(x), bool)
else: uplims = npy.asarray(uplims, bool)
if not iterable(xlolims): xlolims = npy.array([xlolims]*len(x), bool)
else: xlolims = npy.asarray(xlolims, bool)
if not iterable(xuplims): xuplims = npy.array([xuplims]*len(x), bool)
else: xuplims = npy.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 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._get_next_cycle_color()
else:
ecolor = l0.get_color()
for l in barcols:
l.set_color(ecolor)
for l in caplines:
l.set_color(ecolor)
self.autoscale_view()
return (l0, caplines, barcols)
errorbar.__doc__ = cbook.dedent(errorbar.__doc__) % martist.kwdocd
def boxplot(self, x, notch=0, sym='b+', vert=1, whis=1.5,
positions=None, widths=None):
"""
boxplot(x, notch=0, sym='+', vert=1, whis=1.5,
positions=None, widths=None)
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.
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.
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.
x is an array or a sequence of vectors.
Returns a list of the lines added.
"""
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 range(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 = npy.ones((col,), float) * widths
# loop through columns, adding each to plot
self.hold(True)
for i,pos in enumerate(positions):
d = npy.ravel(x[i])
row = len(d)
# 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 = npy.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 = npy.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 = npy.compress( d > wisk_hi, d )
flier_lo = npy.compress( d < wisk_lo, d )
flier_hi_x = npy.ones(flier_hi.shape[0]) * pos
flier_lo_x = npy.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 = npy.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:
notch_max = med + 1.57*iq/npy.sqrt(row)
notch_min = med - 1.57*iq/npy.sqrt(row)
if notch_max > q3:
notch_max = q3
if notch_min < q1:
notch_min = q1
# 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]
# vertical or horizontal plot?
if vert:
def doplot(*args):
return self.plot(*args)
else:
def doplot(*args):
shuffled = []
for i in range(0, len(args), 3):
shuffled.extend([args[i+1], args[i], args[i+2]])
return self.plot(*shuffled)
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-'))
boxes.extend(doplot(box_x, box_y, 'b-'))
medians.extend(doplot(med_x, med_y, 'r-'))
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)
def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0, linewidths=None,
faceted=True, verts=None,
**kwargs):
"""
SCATTER(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0, linewidths=None,
faceted=True, **kwargs)
Supported function signatures:
SCATTER(x, y, **kwargs)
SCATTER(x, y, s, **kwargs)
SCATTER(x, y, s, c, **kwargs)
Make a scatter plot of x versus y, where x, y are 1-D sequences
of the same length, N.
Arguments s and c can also be given as kwargs; this is encouraged
for readability.
s is a size in points^2. It is a scalar
or an array of the same length as x and y.
c is a color and 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.
The marker can be one of
's' : square
'o' : circle
'^' : triangle up
'>' : triangle right
'v' : triangle down
'<' : triangle left
'd' : diamond
'p' : pentagram
'h' : hexagon
'8' : octagon
If marker is None and verts is not None, verts is a sequence
of (x,y) vertices for a custom scatter symbol.
s is a size argument in points squared.
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 args; the color mapping and normalization arguments will
be used only if c is an array of floats
* cmap = cm.jet : a colors.Colormap instance from cm.
defaults to rc image.cmap
* norm = colors.Normalize() : colors.Normalize instance
is used to scale luminance data to 0,1.
* vmin=None and vmax=None : 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 =1.0 : the alpha value for the patches
* linewidths, 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 RegularPolyCollection -- see
collections.RegularPolyCollection for details
* faceted: if True, will use the default edgecolor for the
markers. If False, will set the edgecolors to be the same
as the facecolors.
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors=None
edgecolors also can be any mpl color or sequence of colors.
Optional kwargs control the Collection properties:
%(Collection)s
A 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' : (20,0,0), # 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), # pentagram
'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, y, s, c = delete_masked_points(x, y, s, c)
# The inherent ambiguity is resolved in favor of color
# mapping, not interpretation as rgb or rgba.
if not is_string_like(c):
sh = npy.shape(c)
if len(sh) == 1 and sh[0] == len(x):
colors = None # use cmap, norm after collection is created
else:
colors = mcolors.colorConverter.to_rgba_array(c, alpha)
else:
colors = mcolors.colorConverter.to_rgba_array(c, alpha)
if not iterable(s):
scales = (s,)
else:
scales = s
if faceted: edgecolors = None
else: edgecolors = 'None'
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):
symstyle = marker[1]
else:
verts = npy.asarray(marker[0])
if sym is not None:
if symstyle==0:
collection = mcoll.RegularPolyCollection(
self.figure.dpi,
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
elif symstyle==1:
collection = mcoll.StarPolygonCollection(
self.figure.dpi,
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
elif symstyle==2:
collection = mcoll.AsteriskPolygonCollection(
self.figure.dpi,
numsides, rotation, scales,
facecolors = colors,
edgecolors = edgecolors,
linewidths = linewidths,
offsets = zip(x,y),
transOffset = self.transData,
)
else:
# rescale verts
rescale = npy.sqrt(max(verts[:,0]**2+verts[:,1]**2))
verts /= rescale
scales = npy.asarray(scales)
scales = npy.sqrt(scales * self.figure.dpi / 72.)
if len(scales)==1:
verts = [scales[0]*verts]
else:
# todo -- make this nx friendly
verts = [verts*s for s in scales]
collection = mcoll.PolyCollection(
verts,
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))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
collection.set_array(npy.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 = npy.amin(temp_x)
maxx = npy.amax(temp_x)
miny = npy.amin(temp_y)
maxy = npy.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
scatter.__doc__ = cbook.dedent(scatter.__doc__) % martist.kwdocd
def arrow(self, 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
"""
a = mpatches.FancyArrow(x, y, dx, dy, **kwargs)
self.add_artist(a)
return a
arrow.__doc__ = cbook.dedent(arrow.__doc__) % martist.kwdocd
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):
"""
TODO: Document me
"""
q = mquiver.Quiver(self, *args, **kw)
self.add_collection(q, False)
self.update_datalim_numerix(q.X, q.Y)
self.autoscale_view()
return q
quiver.__doc__ = mquiver.Quiver.quiver_doc
def fill(self, *args, **kwargs):
"""
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 plot
for details on the argument parsing. For example, all of the
following are legal, assuming ax is an Axes instance:
ax.fill(x,y) # plot polygon with vertices at x,y
ax.fill(x,y, 'b' ) # plot polygon with vertices at x,y in blue
An arbitrary number of x, y, color groups can be specified, as in
ax.fill(x1, y1, 'g', x2, y2, 'r')
Return value is a list of patches that were added
The same color strings that 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 mlab.poly_between, eg
xs, ys = poly_between(x, 0, y)
axes.fill(xs, ys, facecolor='red', alpha=0.5)
See examples/fill_between.py for more examples.
kwargs control the Polygon properties:
%(Polygon)s
"""
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
fill.__doc__ = cbook.dedent(fill.__doc__) % martist.kwdocd
#### plotting z(x,y): imshow, pcolor and relatives, contour
def imshow(self, X,
cmap = None,
norm = None,
aspect=None,
interpolation=None,
alpha=1.0,
vmin = None,
vmax = None,
origin=None,
extent=None,
shape=None,
filternorm=1,
filterrad=4.0,
imlim=None,
**kwargs):
"""
IMSHOW(X, cmap=None, norm=None, aspect=None, interpolation=None,
alpha=1.0, vmin=None, vmax=None, origin=None, extent=None)
IMSHOW(X) - plot image X to current axes, resampling to scale to axes
size (X may be numarray/Numeric array or PIL image)
IMSHOW(X, **kwargs) - Use keyword args to control image scaling,
colormapping etc. See below for details
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.
A image.AxesImage instance is returned
The following kwargs are allowed:
* cmap is a cm colormap instance, eg cm.jet. If None, default to rc
image.cmap value (Ignored when X has RGB(A) information)
* aspect is one of: auto, equal, or a number. If None, default to rc
image.aspect value
* interpolation is one of:
'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36',
'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',
'lanczos', 'blackman'
if interpolation is None, default to rc
image.interpolation. See also th the filternorm and
filterrad parameters
* norm is a mcolors.Normalize instance; default is
normalization(). This scales luminance -> 0-1 (only used for an
MxN float array).
* vmin and vmax are 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 you pass a norm instance, the settings for vmin and
vmax will be ignored.
* alpha = 1.0 : the alpha blending value
* origin is 'upper' or 'lower', to 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 is (left, right, bottom, top) data values of the
axes. The default assigns zero-based row, column indices
to the x, y centers of the pixels.
* shape is for raw buffer images
* filternorm is a parameter for the antigrain image resize
filter. From the antigrain documentation, if normalize=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, ie when interpolation is one of: 'sinc',
'lanczos' or 'blackman'
Additional kwargs are martist properties
"""
if not self._hold: self.cla()
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
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, **kwargs)
im.set_data(X)
im.set_alpha(alpha)
self._set_artist_props(im)
im.set_clip_path(self.axesPatch)
#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()
xmin, xmax, ymin, ymax = im.get_extent()
corners = (xmin, ymin), (xmax, ymax)
self.update_datalim(corners)
if self._autoscaleon:
self.set_xlim((xmin, xmax))
self.set_ylim((ymin, ymax))
self.images.append(im)
return im
def _pcolorargs(self, funcname, *args):
if len(args)==1:
C = args[0]
numRows, numCols = C.shape
X, Y = npy.meshgrid(npy.arange(numCols+1), npy.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
def pcolor(self, *args, **kwargs):
"""
pcolor(*args, **kwargs): pseudocolor plot of a 2-D array
Function signatures
pcolor(C, **kwargs)
pcolor(X, Y, C, **kwargs)
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 "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.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from cm
* norm = Normalize() : mcolors.Normalize instance
is used to scale luminance data to 0,1.
* vmin=None and vmax=None : 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
* shading = 'flat' : or 'faceted'. If 'faceted', a black grid is
drawn around each rectangle; if 'flat', edges are not drawn.
Default is 'flat', contrary to Matlab(TM).
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors='k'
edgecolors can also be None to specify the rcParams
default, or any mpl color or sequence of colors.
* alpha=1.0 : the alpha blending value
Return value is a mcoll.Collection
object
Grid Orientation
The orientation follows the Matlab(TM) 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 meshgrid:
x = npy.arange(5)
y = npy.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)
Dimensions
Matlab 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 PolyCollection properties:
%(PolyCollection)s
"""
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', 1.0)
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 = npy.newaxis
compress = npy.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 = npy.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))
#verts = zip(zip(X1,Y1),zip(X2,Y2),zip(X3,Y3),zip(X4,Y4))
C = compress(ravelmask, ma.filled(C[0:Ny-1,0:Nx-1]).ravel())
if shading == 'faceted':
edgecolors = (0,0,0,1),
linewidths = (0.25,)
else:
edgecolors = 'None'
linewidths = (0.0,)
kwargs.setdefault('edgecolors', edgecolors)
kwargs.setdefault('antialiaseds', (0,))
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))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
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 = npy.amin(x)
maxx = npy.amax(x)
miny = npy.amin(y)
maxy = npy.amax(y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
self.autoscale_view()
self.add_collection(collection)
return collection
pcolor.__doc__ = cbook.dedent(pcolor.__doc__) % martist.kwdocd
def pcolormesh(self, *args, **kwargs):
"""
PCOLORMESH(*args, **kwargs)
Function signatures
PCOLORMESH(C) - make a pseudocolor plot of matrix C
PCOLORMESH(X, Y, C) - a pseudo color plot of C on the matrices X and Y
PCOLORMESH(C, **kwargs) - Use keyword args to control colormapping and
scaling; see below
C may be a masked array, but X and Y may not. Masked array support
is implemented via cmap and norm; in contrast, pcolor simply does
not draw quadrilaterals with masked colors or vertices.
Optional keyword args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from cm.
* norm = Normalize() : colors.Normalize instance
is used to scale luminance data to 0,1. Instantiate it
with clip=False if C is a masked array.
* vmin=None and vmax=None : 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.
* shading = 'flat' : or 'faceted'. If 'faceted', a black grid is
drawn around each rectangle; if 'flat', edges are not drawn.
Default is 'flat', contrary to Matlab(TM).
This kwarg is deprecated;
please use the edgecolors kwarg instead:
shading='flat' --> edgecolors='None'
shading='faceted --> edgecolors='k'
More flexible specification of edgecolors, as in pcolor,
is not presently supported.
* alpha=1.0 : the alpha blending value
Return value is a collections.Collection
object
See pcolor for an explantion of the grid orientation and the
expansion of 1-D X and/or Y to 2-D arrays.
kwargs can be used to control the collections.QuadMesh polygon
collection properties:
%(QuadMesh)s
"""
if not self._hold: self.cla()
alpha = kwargs.pop('alpha', 1.0)
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')
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
C = ma.ravel(C[0:Ny-1, 0:Nx-1]) # data point in each cell is value at lower left corner
X = X.ravel()
Y = Y.ravel()
coords = npy.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) # kwargs are not used
collection.set_alpha(alpha)
collection.set_array(C)
if norm is not None: assert(isinstance(norm, mcolors.Normalize))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
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 = npy.amin(X)
maxx = npy.amax(X)
miny = npy.amin(Y)
maxy = npy.amax(Y)
corners = (minx, miny), (maxx, maxy)
self.update_datalim( corners)
self.autoscale_view()
self.add_collection(collection)
return collection
pcolormesh.__doc__ = cbook.dedent(pcolormesh.__doc__) % martist.kwdocd
def pcolorfast(self, *args, **kwargs):
"""
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.
pcolor(*args, **kwargs): pseudocolor plot of a 2-D array
Function 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 args are shown with their defaults below (you must
use kwargs for these):
* cmap = cm.jet : a cm Colormap instance from cm
* norm = Normalize() : mcolors.Normalize instance
is used to scale luminance data to 0,1.
* vmin=None and vmax=None : 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=1.0 : 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', 1.0)
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))
if cmap is not None: assert(isinstance(cmap, mcolors.Colormap))
C = args[-1]
nr, nc = C.shape
if len(args) == 1:
style = "image"
x = [0, nc+1]
y = [0, nr+1]
elif len(args) == 3:
x, y = args[:2]
x = npy.asarray(x)
y = npy.asarray(y)
if x.ndim == 1 and y.ndim == 1:
if x.size == 2 and y.size == 2:
style = "image"
else:
dx = npy.diff(x)
dy = npy.diff(y)
if (npy.ptp(dx) < 0.01*npy.abs(dx.mean()) and
npy.ptp(dy) < 0.01*npy.abs(dy.mean())):
style = "image"
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 = npy.empty(((Nx * Ny), 2), npy.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(npy.array([[xl, yb], [xr, yt]]))
self.autoscale_view(tight=True)
return ret
def contour(self, *args, **kwargs):
kwargs['filled'] = False
return mcontour.ContourSet(self, *args, **kwargs)
contour.__doc__ = mcontour.ContourSet.contour_doc
def contourf(self, *args, **kwargs):
kwargs['filled'] = True
return mcontour.ContourSet(self, *args, **kwargs)
contourf.__doc__ = mcontour.ContourSet.contour_doc
def clabel(self, CS, *args, **kwargs):
return CS.clabel(*args, **kwargs)
clabel.__doc__ = mcontour.ContourSet.clabel.__doc__
def table(self, **kwargs):
"""
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 table instance. For
finer grained control over tables, use the Table class and add it
to the axes with add_table.
Thanks to John Gill for providing the class and table.
kwargs control the Table properties:
%(Table)s
"""
return mtable.table(self, **kwargs)
table.__doc__ = cbook.dedent(table.__doc__) % martist.kwdocd
def twinx(self):
"""
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(), sharex=self, frameon=False)
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position('right')
self.yaxis.tick_left()
return ax2
def twiny(self):
"""
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(), sharey=self, frameon=False)
ax2.xaxis.tick_top()
ax2.xaxis.set_label_position('top')
self.xaxis.tick_bottom()
return ax2
#### Data analysis
def hist(self, x, bins=10, normed=0, bottom=None,
align='edge', orientation='vertical', width=None,
log=False, **kwargs):
"""
HIST(x, bins=10, normed=0, bottom=None,
align='edge', orientation='vertical', width=None,
log=False, **kwargs)
Compute the histogram of x. bins is either an integer number of
bins or a sequence giving the bins. x are the data to be binned.
The return values is (n, bins, patches)
If normed is true, the first element of the return tuple will
be the counts normalized to form a probability density, ie,
n/(len(x)*dbin). In a probability density, the integral of
the histogram should be one (we assume equally spaced bins);
you can verify that with
# trapezoidal integration of the probability density function
pdf, bins, patches = ax.hist(...)
print npy.trapz(pdf, bins)
align = 'edge' | 'center'. Interprets bins either as edge
or center values
orientation = 'horizontal' | 'vertical'. If horizontal, barh
will be used and the "bottom" kwarg will be the left edges.
width: the width of the bars. If None, automatically compute
the width.
log: if True, the histogram axis will be set to a log scale
kwargs are used to update the properties of the
hist Rectangles:
%(Rectangle)s
"""
if not self._hold: self.cla()
n, bins = npy.histogram(x, bins, range=None, normed=normed)
if width is None: width = 0.9*(bins[1]-bins[0])
if orientation == 'horizontal':
patches = self.barh(bins, n, height=width, left=bottom,
align=align, log=log)
elif orientation == 'vertical':
patches = self.bar(bins, n, width=width, bottom=bottom,
align=align, log=log)
else:
raise ValueError, 'invalid orientation: %s' % orientation
for p in patches:
p.update(kwargs)
return n, bins, cbook.silent_list('Patch', patches)
hist.__doc__ = cbook.dedent(hist.__doc__) % martist.kwdocd
def psd(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, **kwargs):
"""
PSD(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, **kwargs)
The power spectral density by Welches average periodogram method. The
vector x is divided into NFFT length segments. Each segment is
detrended by function detrend and windowed by function window.
noperlap gives the length of the overlap between segments. The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing. Fs is the
sampling frequency.
* NFFT is the length of the fft segment; must be a power of 2
* Fs is the sampling frequency.
* Fc is the center frequency of x (defaults to 0), which offsets
the yextents of the image to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
* detrend - the function applied to each segment before fft-ing,
designed to remove the mean or linear trend. Unlike in matlab,
where the detrend parameter is a vector, in matplotlib is it a
function. The mlab module defines detrend_none, detrend_mean,
detrend_linear, but you can use a custom function as well.
* window - the function used to window the segments. window is a
function, unlike in matlab(TM) where it is a vector. mlab defines
window_none, window_hanning, but you can use a custom function
as well.
* noverlap gives the length of the overlap between segments.
Returns the tuple Pxx, freqs
For plotting, the power is plotted as 10*npy.log10(pxx) for decibels,
though pxx itself is returned
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
%(Line2D)s
"""
if not self._hold: self.cla()
pxx, freqs = mlab.psd(x, NFFT, Fs, detrend, window, noverlap)
pxx.shape = len(freqs),
freqs += Fc
self.plot(freqs, 10*npy.log10(pxx), **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Power Spectrum (dB)')
self.grid(True)
vmin, vmax = self.viewLim.intervaly
intv = vmax-vmin
logi = int(npy.log10(intv))
if logi==0: logi=.1
step = 10*logi
#print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1
ticks = npy.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxx, freqs
psd.__doc__ = cbook.dedent(psd.__doc__) % martist.kwdocd
def csd(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, **kwargs):
"""
CSD(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=window_hanning, noverlap=0, **kwargs)
The cross spectral density Pxy by Welches 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 Pxy, with a scaling to correct for power loss
due to windowing.
See the PSD help for a description of the optional parameters.
Returns the tuple Pxy, freqs. Pxy is the cross spectrum (complex
valued), and 10*npy.log10(|Pxy|) is plotted
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
%(Line2D)s
"""
if not self._hold: self.cla()
pxy, freqs = mlab.csd(x, y, NFFT, Fs, detrend, window, noverlap)
pxy.shape = len(freqs),
# pxy is complex
freqs += Fc
self.plot(freqs, 10*npy.log10(npy.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(npy.log10(intv))
ticks = npy.arange(math.floor(vmin), math.ceil(vmax)+1, step)
self.set_yticks(ticks)
return pxy, freqs
csd.__doc__ = cbook.dedent(csd.__doc__) % martist.kwdocd
def cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, **kwargs):
"""
COHERE(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
window = mlab.window_hanning, noverlap=0, **kwargs)
cohere the coherence between x and y. Coherence is the normalized
cross spectral density
Cxy = |Pxy|^2/(Pxx*Pyy)
The return value is (Cxy, f), where f are the frequencies of the
coherence vector.
See the PSD help for a description of the optional parameters.
kwargs are applied to the lines
Returns the tuple Cxy, freqs
Refs: Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties of the coherence plot:
%(Line2D)s
"""
if not self._hold: self.cla()
cxy, freqs = mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap)
freqs += Fc
self.plot(freqs, cxy, **kwargs)
self.set_xlabel('Frequency')
self.set_ylabel('Coherence')
self.grid(True)
return cxy, freqs
cohere.__doc__ = cbook.dedent(cohere.__doc__) % martist.kwdocd
def specgram(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap = None, xextent=None):
"""
SPECGRAM(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window = mlab.window_hanning, noverlap=128,
cmap=None, xextent=None)
Compute a spectrogram of data in x. Data are split into NFFT length
segements 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.
* cmap is a colormap; if None use default determined by rc
* xextent is the image extent in the xaxes xextent=xmin, xmax -
default 0, max(bins), 0, max(freqs) where bins is the return
value from mlab.specgram
* See help(psd) for information on the other keyword arguments.
Return value is (Pxx, freqs, bins, im), where
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 image.AxesImage.
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.
"""
if not self._hold: self.cla()
Pxx, freqs, bins = mlab.specgram(x, NFFT, Fs, detrend,
window, noverlap)
Z = 10*npy.log10(Pxx)
Z = npy.flipud(Z)
if xextent is None: xextent = 0, npy.amax(bins)
xmin, xmax = xextent
freqs += Fc
extent = xmin, xmax, freqs[0], freqs[-1]
im = self.imshow(Z, cmap, extent=extent)
self.axis('auto')
return Pxx, freqs, bins, im
def spy(self, Z, precision=None, marker=None, markersize=None,
aspect='equal', **kwargs):
"""
spy(Z) plots the sparsity pattern of the 2-D array Z
If precision is None, any non-zero value will be plotted;
else, values of absolute(Z)>precision will be plotted.
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 scipy.sparse.spmatrix instances.
If marker and markersize are None, an image will be
returned and any remaining kwargs are passed to imshow;
else, a Line2D object will be returned with the value
of marker determining the marker type, and any remaining
kwargs passed to the axes plot method.
If marker and markersize are None, useful kwargs include:
cmap
alpha
See documentation for imshow() for details.
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
See documentation for plot() for details.
Useful values for marker include:
's' square (default)
'o' circle
'.' point
',' pixel
"""
if marker is None and markersize is None:
if hasattr(Z, 'tocoo'):
raise TypeError, "Image mode does not support scipy.sparse arrays"
Z = npy.asarray(Z)
if precision is None: mask = Z!=0.
else: mask = npy.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()
y = c.row
x = c.col
z = c.data
else:
Z = npy.asarray(Z)
if precision is None: mask = Z!=0.
else: mask = npy.absolute(Z)>precision
y,x,z = mlab.get_xyz_where(mask, mask)
if marker is None: marker = 's'
if markersize is None: markersize = 10
lines = self.plot(x, y, linestyle='None',
marker=marker, markersize=markersize, **kwargs)
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 = lines
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 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 imshow. 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 image.AxesImage instance
'''
Z = npy.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
class SubplotBase:
"""
Base class for subplots, which are Axes instances with additional
methods to facilitate generating and manipulating a set of Axes
within a figure.
"""
def __init__(self, fig, *args, **kwargs):
"""
fig is a figure instance
args is 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
right 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:
s = str(args[0])
if len(s) != 3:
raise ValueError('Argument to subplot must be a 3 digits long')
rows, cols, num = map(int, s)
elif len(args)==3:
rows, cols, num = args
else:
raise ValueError( 'Illegal argument to subplot')
total = rows*cols
num -= 1 # convert from matlab to python indexing ie num in range(0,total)
if num >= total:
raise ValueError( 'Subplot number exceeds total subplots')
self._rows = rows
self._cols = cols
self._num = num
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'
return self._rows, self._cols, self._num+1
# 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._rows = numrows
self._cols = numcols
self._num = num-1
self.update_params()
self.set_position(self.figbox)
def update_params(self):
'update the subplot position from fig.subplotpars'
rows = self._rows
cols = self._cols
num = self._num
pars = self.figure.subplotpars
left = pars.left
right = pars.right
bottom = pars.bottom
top = pars.top
wspace = pars.wspace
hspace = pars.hspace
totWidth = right-left
totHeight = top-bottom
figH = totHeight/(rows + hspace*(rows-1))
sepH = hspace*figH
figW = totWidth/(cols + wspace*(cols-1))
sepW = wspace*figW
rowNum, colNum = divmod(num, cols)
figBottom = top - (rowNum+1)*figH - rowNum*sepH
figLeft = left + colNum*(figW + sepW)
self.figbox = mtransforms.Bbox.from_bounds(figLeft, figBottom, figW, figH)
self.rowNum = rowNum
self.colNum = colNum
self.numRows = rows
self.numCols = cols
if 0:
print 'rcn', rows, cols, num
print 'lbrt', left, bottom, right, top
print 'self.figBottom', self.figBottom
print 'self.figLeft', self.figLeft
print 'self.figW', self.figW
print 'self.figH', self.figH
print 'self.rowNum', self.rowNum
print 'self.colNum', self.colNum
print 'self.numRows', self.numRows
print 'self.numCols', self.numCols
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()
martist.kwdocd['Axes'] = martist.kwdocd['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))
"""
