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These are classes to support contour plotting and
labelling for the axes class
from __future__ import (absolute_import, division, print_function,
from matplotlib.externals import six
from matplotlib.externals.six.moves import xrange
import warnings
import matplotlib as mpl
import numpy as np
from numpy import ma
import matplotlib._cntr as _cntr
import matplotlib._contour as _contour
import matplotlib.path as mpath
import matplotlib.ticker as ticker
import as cm
import matplotlib.colors as colors
import matplotlib.collections as mcoll
import matplotlib.font_manager as font_manager
import matplotlib.text as text
import matplotlib.cbook as cbook
import matplotlib.mlab as mlab
import matplotlib.mathtext as mathtext
import matplotlib.patches as mpatches
import matplotlib.texmanager as texmanager
import matplotlib.transforms as mtrans
from matplotlib.cbook import mplDeprecation
# Import needed for adding manual selection capability to clabel
from matplotlib.blocking_input import BlockingContourLabeler
# We can't use a single line collection for contour because a line
# collection can have only a single line style, and we want to be able to have
# dashed negative contours, for example, and solid positive contours.
# We could use a single polygon collection for filled contours, but it
# seems better to keep line and filled contours similar, with one collection
# per level.
class ClabelText(text.Text):
Unlike the ordinary text, the get_rotation returns an updated
angle in the pixel coordinate assuming that the input rotation is
an angle in data coordinate (or whatever transform set).
def get_rotation(self):
angle = text.Text.get_rotation(self)
trans = self.get_transform()
x, y = self.get_position()
new_angles = trans.transform_angles(np.array([angle]),
np.array([[x, y]]))
return new_angles[0]
class ContourLabeler(object):
"""Mixin to provide labelling capability to ContourSet"""
def clabel(self, *args, **kwargs):
Label a contour plot.
Call signature::
clabel(cs, **kwargs)
Adds labels to line contours in *cs*, where *cs* is a
:class:`~matplotlib.contour.ContourSet` object returned by
clabel(cs, v, **kwargs)
only labels contours listed in *v*.
Optional keyword arguments:
size in points or relative size e.g., 'smaller', 'x-large'
- if *None*, the color of each label matches the color of
the corresponding contour
- if one string color, e.g., *colors* = 'r' or *colors* =
'red', all labels will be plotted in this color
- if a tuple of matplotlib color args (string, float, rgb, etc),
different labels will be plotted in different colors in the order
controls whether the underlying contour is removed or
not. Default is *True*.
space in pixels to leave on each side of label when
placing inline. Defaults to 5. This spacing will be
exact for labels at locations where the contour is
straight, less so for labels on curved contours.
a format string for the label. Default is '%1.3f'
Alternatively, this can be a dictionary matching contour
levels with arbitrary strings to use for each contour level
(i.e., fmt[level]=string), or it can be any callable, such
as a :class:`~matplotlib.ticker.Formatter` instance, that
returns a string when called with a numeric contour level.
if *True*, contour labels will be placed manually using
mouse clicks. Click the first button near a contour to
add a label, click the second button (or potentially both
mouse buttons at once) to finish adding labels. The third
button can be used to remove the last label added, but
only if labels are not inline. Alternatively, the keyboard
can be used to select label locations (enter to end label
placement, delete or backspace act like the third mouse button,
and any other key will select a label location).
*manual* can be an iterable object of x,y tuples. Contour labels
will be created as if mouse is clicked at each x,y positions.
if *True* (default), label rotations will always be plus
or minus 90 degrees from level.
if *True* (default is False), ClabelText class (instead of
matplotlib.Text) is used to create labels. ClabelText
recalculates rotation angles of texts during the drawing time,
therefore this can be used if aspect of the axes changes.
.. plot:: mpl_examples/pylab_examples/
NOTES on how this all works:
clabel basically takes the input arguments and uses them to
add a list of "label specific" attributes to the ContourSet
object. These attributes are all of the form label* and names
should be fairly self explanatory.
Once these attributes are set, clabel passes control to the
labels method (case of automatic label placement) or
BlockingContourLabeler (case of manual label placement).
fontsize = kwargs.get('fontsize', None)
inline = kwargs.get('inline', 1)
inline_spacing = kwargs.get('inline_spacing', 5)
self.labelFmt = kwargs.get('fmt', '%1.3f')
_colors = kwargs.get('colors', None)
self._use_clabeltext = kwargs.get('use_clabeltext', False)
# Detect if manual selection is desired and remove from argument list
self.labelManual = kwargs.get('manual', False)
self.rightside_up = kwargs.get('rightside_up', True)
if len(args) == 0:
levels = self.levels
indices = list(xrange(len(self.cvalues)))
elif len(args) == 1:
levlabs = list(args[0])
indices, levels = [], []
for i, lev in enumerate(self.levels):
if lev in levlabs:
if len(levels) < len(levlabs):
msg = "Specified levels " + str(levlabs)
msg += "\n don't match available levels "
msg += str(self.levels)
raise ValueError(msg)
raise TypeError("Illegal arguments to clabel, see help(clabel)")
self.labelLevelList = levels
self.labelIndiceList = indices
self.labelFontProps = font_manager.FontProperties()
font_size_pts = self.labelFontProps.get_size_in_points()
self.labelFontSizeList = [font_size_pts] * len(levels)
if _colors is None:
self.labelMappable = self
self.labelCValueList = np.take(self.cvalues, self.labelIndiceList)
cmap = colors.ListedColormap(_colors, N=len(self.labelLevelList))
self.labelCValueList = list(xrange(len(self.labelLevelList)))
self.labelMappable = cm.ScalarMappable(cmap=cmap,
self.labelXYs = []
if cbook.iterable(self.labelManual):
for x, y in self.labelManual:
self.add_label_near(x, y, inline,
elif self.labelManual:
print('Select label locations manually using first mouse button.')
print('End manual selection with second mouse button.')
if not inline:
print('Remove last label by clicking third mouse button.')
blocking_contour_labeler = BlockingContourLabeler(self)
blocking_contour_labeler(inline, inline_spacing)
self.labels(inline, inline_spacing)
# Hold on to some old attribute names. These are deprecated and will
# be removed in the near future (sometime after 2008-08-01), but
# keeping for now for backwards compatibility = self.labelTexts
self.cl_xy = self.labelXYs
self.cl_cvalues = self.labelCValues
self.labelTextsList = cbook.silent_list('text.Text', self.labelTexts)
return self.labelTextsList
def print_label(self, linecontour, labelwidth):
"Return *False* if contours are too short for a label."
lcsize = len(linecontour)
if lcsize > 10 * labelwidth:
return True
xmax = np.amax(linecontour[:, 0])
xmin = np.amin(linecontour[:, 0])
ymax = np.amax(linecontour[:, 1])
ymin = np.amin(linecontour[:, 1])
lw = labelwidth
if (xmax - xmin) > 1.2 * lw or (ymax - ymin) > 1.2 * lw:
return True
return False
def too_close(self, x, y, lw):
"Return *True* if a label is already near this location."
for loc in self.labelXYs:
d = np.sqrt((x - loc[0]) ** 2 + (y - loc[1]) ** 2)
if d < 1.2 * lw:
return True
return False
def get_label_coords(self, distances, XX, YY, ysize, lw):
Return x, y, and the index of a label location.
Labels are plotted at a location with the smallest
deviation of the contour from a straight line
unless there is another label nearby, in which case
the next best place on the contour is picked up.
If all such candidates are rejected, the beginning
of the contour is chosen.
hysize = int(ysize / 2)
adist = np.argsort(distances)
for ind in adist:
x, y = XX[ind][hysize], YY[ind][hysize]
if self.too_close(x, y, lw):
return x, y, ind
ind = adist[0]
x, y = XX[ind][hysize], YY[ind][hysize]
return x, y, ind
def get_label_width(self, lev, fmt, fsize):
Return the width of the label in points.
if not cbook.is_string_like(lev):
lev = self.get_text(lev, fmt)
lev, ismath = text.Text.is_math_text(lev)
if ismath == 'TeX':
if not hasattr(self, '_TeX_manager'):
self._TeX_manager = texmanager.TexManager()
lw, _, _ = self._TeX_manager.get_text_width_height_descent(lev,
elif ismath:
if not hasattr(self, '_mathtext_parser'):
self._mathtext_parser = mathtext.MathTextParser('bitmap')
img, _ = self._mathtext_parser.parse(lev, dpi=72,
lw = img.get_width() # at dpi=72, the units are PostScript points
# width is much less than "font size"
lw = (len(lev)) * fsize * 0.6
return lw
def get_real_label_width(self, lev, fmt, fsize):
This computes actual onscreen label width.
This uses some black magic to determine onscreen extent of non-drawn
label. This magic may not be very robust.
This method is not being used, and may be modified or removed.
# Find middle of axes
xx = np.mean(np.asarray(, 2), axis=1)
# Temporarily create text object
t = text.Text(xx[0], xx[1])
self.set_label_props(t, self.get_text(lev, fmt), 'k')
# Some black magic to get onscreen extent
# NOTE: This will only work for already drawn figures, as the canvas
# does not have a renderer otherwise. This is the reason this function
# can't be integrated into the rest of the code.
bbox = t.get_window_extent(
# difference in pixel extent of image
lw = np.diff(bbox.corners()[0::2, 0])[0]
return lw
def set_label_props(self, label, text, color):
"set the label properties - color, fontsize, text"
def get_text(self, lev, fmt):
"get the text of the label"
if cbook.is_string_like(lev):
return lev
if isinstance(fmt, dict):
return fmt[lev]
elif six.callable(fmt):
return fmt(lev)
return fmt % lev
def locate_label(self, linecontour, labelwidth):
Find a good place to plot a label (relatively flat
part of the contour).
nsize = len(linecontour)
if labelwidth > 1:
xsize = int(np.ceil(nsize / labelwidth))
xsize = 1
if xsize == 1:
ysize = nsize
ysize = int(labelwidth)
XX = np.resize(linecontour[:, 0], (xsize, ysize))
YY = np.resize(linecontour[:, 1], (xsize, ysize))
# I might have fouled up the following:
yfirst = YY[:, 0].reshape(xsize, 1)
ylast = YY[:, -1].reshape(xsize, 1)
xfirst = XX[:, 0].reshape(xsize, 1)
xlast = XX[:, -1].reshape(xsize, 1)
s = (yfirst - YY) * (xlast - xfirst) - (xfirst - XX) * (ylast - yfirst)
L = np.sqrt((xlast - xfirst) ** 2 + (ylast - yfirst) ** 2).ravel()
dist = np.add.reduce(([(abs(s)[i] / L[i]) for i in range(xsize)]), -1)
x, y, ind = self.get_label_coords(dist, XX, YY, ysize, labelwidth)
# There must be a more efficient way...
lc = [tuple(l) for l in linecontour]
dind = lc.index((x, y))
return x, y, dind
def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
This function calculates the appropriate label rotation given
the linecontour coordinates in screen units, the index of the
label location and the label width.
It will also break contour and calculate inlining if *lc* is
not empty (lc defaults to the empty list if None). *spacing*
is the space around the label in pixels to leave empty.
Do both of these tasks at once to avoid calling mlab.path_length
multiple times, which is relatively costly.
The method used here involves calculating the path length
along the contour in pixel coordinates and then looking
approximately label width / 2 away from central point to
determine rotation and then to break contour if desired.
if lc is None:
lc = []
# Half the label width
hlw = lw / 2.0
# Check if closed and, if so, rotate contour so label is at edge
closed = mlab.is_closed_polygon(slc)
if closed:
slc = np.r_[slc[ind:-1], slc[:ind + 1]]
if len(lc): # Rotate lc also if not empty
lc = np.r_[lc[ind:-1], lc[:ind + 1]]
ind = 0
# Path length in pixel space
pl = mlab.path_length(slc)
pl = pl - pl[ind]
# Use linear interpolation to get points around label
xi = np.array([-hlw, hlw])
if closed: # Look at end also for closed contours
dp = np.array([pl[-1], 0])
dp = np.zeros_like(xi)
ll = mlab.less_simple_linear_interpolation(pl, slc, dp + xi,
# get vector in pixel space coordinates from one point to other
dd = np.diff(ll, axis=0).ravel()
# Get angle of vector - must be calculated in pixel space for
# text rotation to work correctly
if np.all(dd == 0): # Must deal with case of zero length label
rotation = 0.0
rotation = np.arctan2(dd[1], dd[0]) * 180.0 / np.pi
if self.rightside_up:
# Fix angle so text is never upside-down
if rotation > 90:
rotation = rotation - 180.0
if rotation < -90:
rotation = 180.0 + rotation
# Break contour if desired
nlc = []
if len(lc):
# Expand range by spacing
xi = dp + xi + np.array([-spacing, spacing])
# Get indices near points of interest
I = mlab.less_simple_linear_interpolation(
pl, np.arange(len(pl)), xi, extrap=False)
# If those indices aren't beyond contour edge, find x,y
if (not np.isnan(I[0])) and int(I[0]) != I[0]:
xy1 = mlab.less_simple_linear_interpolation(
pl, lc, [xi[0]])
if (not np.isnan(I[1])) and int(I[1]) != I[1]:
xy2 = mlab.less_simple_linear_interpolation(
pl, lc, [xi[1]])
# Round to integer values but keep as float
# To allow check against nan below
I = [np.floor(I[0]), np.ceil(I[1])]
# Actually break contours
if closed:
# This will remove contour if shorter than label
if np.all(~np.isnan(I)):
nlc.append(np.r_[xy2, lc[int(I[1]):int(I[0]) + 1], xy1])
# These will remove pieces of contour if they have length zero
if not np.isnan(I[0]):
nlc.append(np.r_[lc[:int(I[0]) + 1], xy1])
if not np.isnan(I[1]):
nlc.append(np.r_[xy2, lc[int(I[1]):]])
# The current implementation removes contours completely
# covered by labels. Uncomment line below to keep
# original contour if this is the preferred behavior.
# if not len(nlc): nlc = [ lc ]
return rotation, nlc
def _get_label_text(self, x, y, rotation):
dx, dy =, y))
t = text.Text(dx, dy, rotation=rotation,
return t
def _get_label_clabeltext(self, x, y, rotation):
# x, y, rotation is given in pixel coordinate. Convert them to
# the data coordinate and create a label using ClabelText
# class. This way, the roation of the clabel is along the
# contour line always.
transDataInv =
dx, dy = transDataInv.transform_point((x, y))
drotation = transDataInv.transform_angles(np.array([rotation]),
np.array([[x, y]]))
t = ClabelText(dx, dy, rotation=drotation[0],
return t
def _add_label(self, t, x, y, lev, cvalue):
color = self.labelMappable.to_rgba(cvalue, alpha=self.alpha)
_text = self.get_text(lev, self.labelFmt)
self.set_label_props(t, _text, color)
self.labelXYs.append((x, y))
# Add label to plot here - useful for manual mode label selection
def add_label(self, x, y, rotation, lev, cvalue):
Add contour label using :class:`~matplotlib.text.Text` class.
t = self._get_label_text(x, y, rotation)
self._add_label(t, x, y, lev, cvalue)
def add_label_clabeltext(self, x, y, rotation, lev, cvalue):
Add contour label using :class:`ClabelText` class.
# x, y, rotation is given in pixel coordinate. Convert them to
# the data coordinate and create a label using ClabelText
# class. This way, the roation of the clabel is along the
# contour line always.
t = self._get_label_clabeltext(x, y, rotation)
self._add_label(t, x, y, lev, cvalue)
def add_label_near(self, x, y, inline=True, inline_spacing=5,
Add a label near the point (x, y). If transform is None
(default), (x, y) is in data coordinates; if transform is
False, (x, y) is in display coordinates; otherwise, the
specified transform will be used to translate (x, y) into
display coordinates.
controls whether the underlying contour is removed or
not. Default is *True*.
space in pixels to leave on each side of label when
placing inline. Defaults to 5. This spacing will be
exact for labels at locations where the contour is
straight, less so for labels on curved contours.
if transform is None:
transform =
if transform:
x, y = transform.transform_point((x, y))
# find the nearest contour _in screen units_
conmin, segmin, imin, xmin, ymin = self.find_nearest_contour(
x, y, self.labelIndiceList)[:5]
# The calc_label_rot_and_inline routine requires that (xmin,ymin)
# be a vertex in the path. So, if it isn't, add a vertex here
# grab the paths from the collections
paths = self.collections[conmin].get_paths()
# grab the correct segment
active_path = paths[segmin]
# grab it's verticies
lc = active_path.vertices
# sort out where the new vertex should be added data-units
xcmin =[xmin, ymin])
# if there isn't a vertex close enough
if not np.allclose(xcmin, lc[imin]):
# insert new data into the vertex list
lc = np.r_[lc[:imin], np.array(xcmin)[None, :], lc[imin:]]
# replace the path with the new one
paths[segmin] = mpath.Path(lc)
# Get index of nearest level in subset of levels used for labeling
lmin = self.labelIndiceList.index(conmin)
# Coordinates of contour
paths = self.collections[conmin].get_paths()
lc = paths[segmin].vertices
# In pixel/screen space
slc =
# Get label width for rotating labels and breaking contours
lw = self.get_label_width(self.labelLevelList[lmin],
self.labelFmt, self.labelFontSizeList[lmin])
# Figure out label rotation.
if inline:
lcarg = lc
lcarg = None
rotation, nlc = self.calc_label_rot_and_inline(
slc, imin, lw, lcarg,
self.add_label(xmin, ymin, rotation, self.labelLevelList[lmin],
if inline:
# Remove old, not looping over paths so we can do this up front
# Add paths if not empty or single point
for n in nlc:
if len(n) > 1:
def pop_label(self, index=-1):
"""Defaults to removing last label, but any index can be supplied"""
t = self.labelTexts.pop(index)
def labels(self, inline, inline_spacing):
if self._use_clabeltext:
add_label = self.add_label_clabeltext
add_label = self.add_label
for icon, lev, fsize, cvalue in zip(
self.labelIndiceList, self.labelLevelList,
self.labelFontSizeList, self.labelCValueList):
con = self.collections[icon]
trans = con.get_transform()
lw = self.get_label_width(lev, self.labelFmt, fsize)
lw *= / 72.0 # scale to screen coordinates
additions = []
paths = con.get_paths()
for segNum, linepath in enumerate(paths):
lc = linepath.vertices # Line contour
slc0 = trans.transform(lc) # Line contour in screen coords
# For closed polygons, add extra point to avoid division by
# zero in print_label and locate_label. Other than these
# functions, this is not necessary and should probably be
# eventually removed.
if mlab.is_closed_polygon(lc):
slc = np.r_[slc0, slc0[1:2, :]]
slc = slc0
# Check if long enough for a label
if self.print_label(slc, lw):
x, y, ind = self.locate_label(slc, lw)
if inline:
lcarg = lc
lcarg = None
rotation, new = self.calc_label_rot_and_inline(
slc0, ind, lw, lcarg,
# Actually add the label
add_label(x, y, rotation, lev, cvalue)
# If inline, add new contours
if inline:
for n in new:
# Add path if not empty or single point
if len(n) > 1:
else: # If not adding label, keep old path
# After looping over all segments on a contour, remove old
# paths and add new ones if inlining
if inline:
del paths[:]
def _find_closest_point_on_leg(p1, p2, p0):
"""find closest point to p0 on line segment connecting p1 and p2"""
# handle degenerate case
if np.all(p2 == p1):
d = np.sum((p0 - p1)**2)
return d, p1
d21 = p2 - p1
d01 = p0 - p1
# project on to line segment to find closest point
proj =, d21) /, d21)
if proj < 0:
proj = 0
if proj > 1:
proj = 1
pc = p1 + proj * d21
# find squared distance
d = np.sum((pc-p0)**2)
return d, pc
def _find_closest_point_on_path(lc, point):
lc: coordinates of vertices
point: coordinates of test point
# find index of closest vertex for this segment
ds = np.sum((lc - point[None, :])**2, 1)
imin = np.argmin(ds)
dmin = np.inf
xcmin = None
legmin = (None, None)
closed = mlab.is_closed_polygon(lc)
# build list of legs before and after this vertex
legs = []
if imin > 0 or closed:
legs.append(((imin-1) % len(lc), imin))
if imin < len(lc) - 1 or closed:
legs.append((imin, (imin+1) % len(lc)))
for leg in legs:
d, xc = _find_closest_point_on_leg(lc[leg[0]], lc[leg[1]], point)
if d < dmin:
dmin = d
xcmin = xc
legmin = leg
return (dmin, xcmin, legmin)
class ContourSet(cm.ScalarMappable, ContourLabeler):
Store a set of contour lines or filled regions.
User-callable method: clabel
Useful attributes:
The axes object in which the contours are drawn
a silent_list of LineCollections or PolyCollections
contour levels
same as levels for line contours; half-way between
levels for filled contours. See :meth:`_process_colors`.
def __init__(self, ax, *args, **kwargs):
Draw contour lines or filled regions, depending on
whether keyword arg 'filled' is *False* (default) or *True*.
The first three arguments must be:
*ax*: axes object.
*levels*: [level0, level1, ..., leveln]
A list of floating point numbers indicating the contour
*allsegs*: [level0segs, level1segs, ...]
List of all the polygon segments for all the *levels*.
For contour lines ``len(allsegs) == len(levels)``, and for
filled contour regions ``len(allsegs) = len(levels)-1``.
level0segs = [polygon0, polygon1, ...]
polygon0 = array_like [[x0,y0], [x1,y1], ...]
*allkinds*: *None* or [level0kinds, level1kinds, ...]
Optional list of all the polygon vertex kinds (code types), as
described and used in Path. This is used to allow multiply-
connected paths such as holes within filled polygons.
If not *None*, len(allkinds) == len(allsegs).
level0kinds = [polygon0kinds, ...]
polygon0kinds = [vertexcode0, vertexcode1, ...]
If *allkinds* is not *None*, usually all polygons for a particular
contour level are grouped together so that
level0segs = [polygon0] and level0kinds = [polygon0kinds].
Keyword arguments are as described in
:class:`~matplotlib.contour.QuadContourSet` object.
.. plot:: mpl_examples/misc/
""" = ax
self.levels = kwargs.get('levels', None)
self.filled = kwargs.get('filled', False)
self.linewidths = kwargs.get('linewidths', None)
self.linestyles = kwargs.get('linestyles', None)
self.hatches = kwargs.get('hatches', [None])
self.alpha = kwargs.get('alpha', None)
self.origin = kwargs.get('origin', None)
self.extent = kwargs.get('extent', None)
cmap = kwargs.get('cmap', None)
self.colors = kwargs.get('colors', None)
norm = kwargs.get('norm', None)
vmin = kwargs.get('vmin', None)
vmax = kwargs.get('vmax', None)
self.extend = kwargs.get('extend', 'neither')
self.antialiased = kwargs.get('antialiased', None)
if self.antialiased is None and self.filled:
self.antialiased = False # eliminate artifacts; we are not
# stroking the boundaries.
# The default for line contours will be taken from
# the LineCollection default, which uses the
# rcParams['lines.antialiased']
self.nchunk = kwargs.get('nchunk', 0)
self.locator = kwargs.get('locator', None)
if (isinstance(norm, colors.LogNorm)
or isinstance(self.locator, ticker.LogLocator)):
self.logscale = True
if norm is None:
norm = colors.LogNorm()
if self.extend is not 'neither':
raise ValueError('extend kwarg does not work yet with log '
' scale')
self.logscale = False
if self.origin not in [None, 'lower', 'upper', 'image']:
raise ValueError("If given, *origin* must be one of [ 'lower' |"
" 'upper' | 'image']")
if self.extent is not None and len(self.extent) != 4:
raise ValueError("If given, *extent* must be '[ *None* |"
" (x0,x1,y0,y1) ]'")
if self.colors is not None and cmap is not None:
raise ValueError('Either colors or cmap must be None')
if self.origin == 'image':
self.origin = mpl.rcParams['image.origin']
self._transform = kwargs.get('transform', None)
self._process_args(*args, **kwargs)
if self.colors is not None:
ncolors = len(self.levels)
if self.filled:
ncolors -= 1
i0 = 0
# Handle the case where colors are given for the extended
# parts of the contour.
extend_min = self.extend in ['min', 'both']
extend_max = self.extend in ['max', 'both']
use_set_under_over = False
# if we are extending the lower end, and we've been given enough
# colors then skip the first color in the resulting cmap. For the
# extend_max case we don't need to worry about passing more colors
# than ncolors as ListedColormap will clip.
total_levels = ncolors + int(extend_min) + int(extend_max)
if (len(self.colors) == total_levels and
any([extend_min, extend_max])):
use_set_under_over = True
if extend_min:
i0 = 1
cmap = colors.ListedColormap(self.colors[i0:None], N=ncolors)
if use_set_under_over:
if extend_min:
if extend_max:
if self.filled:
self.collections = cbook.silent_list('mcoll.PathCollection')
self.collections = cbook.silent_list('mcoll.LineCollection')
# label lists must be initialized here
self.labelTexts = []
self.labelCValues = []
kw = {'cmap': cmap}
if norm is not None:
kw['norm'] = norm
# sets self.cmap, norm if needed;
cm.ScalarMappable.__init__(self, **kw)
if vmin is not None:
self.norm.vmin = vmin
if vmax is not None:
self.norm.vmax = vmax
self.allsegs, self.allkinds = self._get_allsegs_and_allkinds()
if self.filled:
if self.linewidths is not None:
warnings.warn('linewidths is ignored by contourf')
# Lower and upper contour levels.
lowers, uppers = self._get_lowers_and_uppers()
# Ensure allkinds can be zipped below.
if self.allkinds is None:
self.allkinds = [None] * len(self.allsegs)
for level, level_upper, segs, kinds in \
zip(lowers, uppers, self.allsegs, self.allkinds):
paths = self._make_paths(segs, kinds)
# Default zorder taken from Collection
zorder = kwargs.get('zorder', 1)
col = mcoll.PathCollection(
margins=False), autolim=False)
tlinewidths = self._process_linewidths()
self.tlinewidths = tlinewidths
tlinestyles = self._process_linestyles()
aa = self.antialiased
if aa is not None:
aa = (self.antialiased,)
for level, width, lstyle, segs in \
zip(self.levels, tlinewidths, tlinestyles, self.allsegs):
# Default zorder taken from LineCollection
zorder = kwargs.get('zorder', 2)
col = mcoll.LineCollection(
col.set_label('_nolegend_'), autolim=False)
self.changed() # set the colors
def get_transform(self):
Return the :class:`~matplotlib.transforms.Transform`
instance used by this ContourSet.
if self._transform is None:
self._transform =
elif (not isinstance(self._transform, mtrans.Transform)
and hasattr(self._transform, '_as_mpl_transform')):
self._transform = self._transform._as_mpl_transform(
return self._transform
def __getstate__(self):
state = self.__dict__.copy()
# the C object _contour_generator cannot currently be pickled. This
# isn't a big issue as it is not actually used once the contour has
# been calculated.
state['_contour_generator'] = None
return state
def legend_elements(self, variable_name='x', str_format=str):
Return a list of artist and labels suitable for passing through
to :func:`plt.legend` which represent this ContourSet.
*variable_name*: the string used inside the inequality used
on the labels
*str_format*: function used to format the numbers in the labels
artists = []
labels = []
if self.filled:
lowers, uppers = self._get_lowers_and_uppers()
n_levels = len(self.collections)
for i, (collection, lower, upper) in enumerate(
zip(self.collections, lowers, uppers)):
patch = mpatches.Rectangle(
(0, 0), 1, 1,
lower = str_format(lower)
upper = str_format(upper)
if i == 0 and self.extend in ('min', 'both'):
labels.append(r'$%s \leq %s$' % (variable_name,
elif i == n_levels - 1 and self.extend in ('max', 'both'):
labels.append(r'$%s > %s$' % (variable_name,
labels.append(r'$%s < %s \leq %s$' % (lower,
for collection, level in zip(self.collections, self.levels):
patch = mcoll.LineCollection(None)
# format the level for insertion into the labels
level = str_format(level)
labels.append(r'$%s = %s$' % (variable_name, level))
return artists, labels
def _process_args(self, *args, **kwargs):
Process *args* and *kwargs*; override in derived classes.
Must set self.levels, self.zmin and self.zmax, and update axes
self.levels = args[0]
self.allsegs = args[1]
self.allkinds = len(args) > 2 and args[2] or None
self.zmax = np.amax(self.levels)
self.zmin = np.amin(self.levels)
self._auto = False
# Check lengths of levels and allsegs.
if self.filled:
if len(self.allsegs) != len(self.levels) - 1:
raise ValueError('must be one less number of segments as '
if len(self.allsegs) != len(self.levels):
raise ValueError('must be same number of segments as levels')
# Check length of allkinds.
if (self.allkinds is not None and
len(self.allkinds) != len(self.allsegs)):
raise ValueError('allkinds has different length to allsegs')
# Determine x,y bounds and update axes data limits.
havelimits = False
for segs in self.allsegs:
for seg in segs:
seg = np.asarray(seg)
if havelimits:
min = np.minimum(min, seg.min(axis=0))
max = np.maximum(max, seg.max(axis=0))
min = seg.min(axis=0)
max = seg.max(axis=0)
havelimits = True
if havelimits:[min, max])
def _get_allsegs_and_allkinds(self):
Override in derived classes to create and return allsegs and allkinds.
allkinds can be None.
return self.allsegs, self.allkinds
def _get_lowers_and_uppers(self):
Return (lowers,uppers) for filled contours.
lowers = self._levels[:-1]
if self.zmin == lowers[0]:
# Include minimum values in lowest interval
lowers = lowers.copy() # so we don't change self._levels
if self.logscale:
lowers[0] = 0.99 * self.zmin
lowers[0] -= 1
uppers = self._levels[1:]
return (lowers, uppers)
def _make_paths(self, segs, kinds):
if kinds is not None:
return [mpath.Path(seg, codes=kind)
for seg, kind in zip(segs, kinds)]
return [mpath.Path(seg) for seg in segs]
def changed(self):
tcolors = [(tuple(rgba),)
for rgba in self.to_rgba(self.cvalues, alpha=self.alpha)]
self.tcolors = tcolors
hatches = self.hatches * len(tcolors)
for color, hatch, collection in zip(tcolors, hatches,
if self.filled:
# update the collection's hatch (may be None)
for label, cv in zip(self.labelTexts, self.labelCValues):
# add label colors
def _autolev(self, z, N):
Select contour levels to span the data.
We need two more levels for filled contours than for
line contours, because for the latter we need to specify
the lower and upper boundary of each range. For example,
a single contour boundary, say at z = 0, requires only
one contour line, but two filled regions, and therefore
three levels to provide boundaries for both regions.
if self.locator is None:
if self.logscale:
self.locator = ticker.LogLocator()
self.locator = ticker.MaxNLocator(N + 1)
zmax = self.zmax
zmin = self.zmin
lev = self.locator.tick_values(zmin, zmax)
self._auto = True
if self.filled:
return lev
# For line contours, drop levels outside the data range.
return lev[(lev > zmin) & (lev < zmax)]
def _contour_level_args(self, z, args):
Determine the contour levels and store in self.levels.
if self.filled:
fn = 'contourf'
fn = 'contour'
self._auto = False
if self.levels is None:
if len(args) == 0:
lev = self._autolev(z, 7)
level_arg = args[0]
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
lev = np.asarray(level_arg).astype(np.float64)
raise TypeError(
"Last %s arg must give levels; see help(%s)" %
(fn, fn))
self.levels = lev
if self.filled and len(self.levels) < 2:
raise ValueError("Filled contours require at least 2 levels.")
if len(self.levels) > 1 and np.amin(np.diff(self.levels)) <= 0.0:
if hasattr(self, '_corner_mask') and self._corner_mask == 'legacy':
warnings.warn("Contour levels are not increasing")
raise ValueError("Contour levels must be increasing")
def vmin(self):
warnings.warn("vmin is deprecated and will be removed in 2.2 "
"and not replaced.",
return getattr(self, '_vmin', None)
def vmax(self):
warnings.warn("vmax is deprecated and will be removed in 2.2 "
"and not replaced.",
return getattr(self, '_vmax', None)
def _process_levels(self):
Assign values to :attr:`layers` based on :attr:`levels`,
adding extended layers as needed if contours are filled.
For line contours, layers simply coincide with levels;
a line is a thin layer. No extended levels are needed
with line contours.
# following are deprecated and will be removed in 2.2
self._vmin = np.amin(self.levels)
self._vmax = np.amax(self.levels)
# Make a private _levels to include extended regions; we
# want to leave the original levels attribute unchanged.
# (Colorbar needs this even for line contours.)
self._levels = list(self.levels)
if self.extend in ('both', 'min'):
self._levels.insert(0, min(self.levels[0], self.zmin) - 1)
if self.extend in ('both', 'max'):
self._levels.append(max(self.levels[-1], self.zmax) + 1)
self._levels = np.asarray(self._levels)
if not self.filled:
self.layers = self.levels
# layer values are mid-way between levels
self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
# ...except that extended layers must be outside the
# normed range:
if self.extend in ('both', 'min'):
self.layers[0] = -1e150
if self.extend in ('both', 'max'):
self.layers[-1] = 1e150
def _process_colors(self):
Color argument processing for contouring.
Note that we base the color mapping on the contour levels
and layers, not on the actual range of the Z values. This
means we don't have to worry about bad values in Z, and we
always have the full dynamic range available for the selected
The color is based on the midpoint of the layer, except for
extended end layers. By default, the norm vmin and vmax
are the extreme values of the non-extended levels. Hence,
the layer color extremes are not the extreme values of
the colormap itself, but approach those values as the number
of levels increases. An advantage of this scheme is that
line contours, when added to filled contours, take on
colors that are consistent with those of the filled regions;
for example, a contour line on the boundary between two
regions will have a color intermediate between those
of the regions.
self.monochrome = self.cmap.monochrome
if self.colors is not None:
# Generate integers for direct indexing.
i0, i1 = 0, len(self.levels)
if self.filled:
i1 -= 1
# Out of range indices for over and under:
if self.extend in ('both', 'min'):
i0 = -1
if self.extend in ('both', 'max'):
i1 += 1
self.cvalues = list(range(i0, i1))
self.cvalues = self.layers
if self.extend in ('both', 'max', 'min'):
self.norm.clip = False
# self.tcolors are set by the "changed" method
def _process_linewidths(self):
linewidths = self.linewidths
Nlev = len(self.levels)
if linewidths is None:
tlinewidths = [(mpl.rcParams['lines.linewidth'],)] * Nlev
if not cbook.iterable(linewidths):
linewidths = [linewidths] * Nlev
linewidths = list(linewidths)
if len(linewidths) < Nlev:
nreps = int(np.ceil(Nlev / len(linewidths)))
linewidths = linewidths * nreps
if len(linewidths) > Nlev:
linewidths = linewidths[:Nlev]
tlinewidths = [(w,) for w in linewidths]
return tlinewidths
def _process_linestyles(self):
linestyles = self.linestyles
Nlev = len(self.levels)
if linestyles is None:
tlinestyles = ['solid'] * Nlev
if self.monochrome:
neg_ls = mpl.rcParams['contour.negative_linestyle']
eps = - (self.zmax - self.zmin) * 1e-15
for i, lev in enumerate(self.levels):
if lev < eps:
tlinestyles[i] = neg_ls
if cbook.is_string_like(linestyles):
tlinestyles = [linestyles] * Nlev
elif cbook.iterable(linestyles):
tlinestyles = list(linestyles)
if len(tlinestyles) < Nlev:
nreps = int(np.ceil(Nlev / len(linestyles)))
tlinestyles = tlinestyles * nreps
if len(tlinestyles) > Nlev:
tlinestyles = tlinestyles[:Nlev]
raise ValueError("Unrecognized type for linestyles kwarg")
return tlinestyles
def get_alpha(self):
"""returns alpha to be applied to all ContourSet artists"""
return self.alpha
def set_alpha(self, alpha):
"""sets alpha for all ContourSet artists"""
self.alpha = alpha
def find_nearest_contour(self, x, y, indices=None, pixel=True):
Finds contour that is closest to a point. Defaults to
measuring distance in pixels (screen space - useful for manual
contour labeling), but this can be controlled via a keyword
Returns a tuple containing the contour, segment, index of
segment, x & y of segment point and distance to minimum point.
Call signature::
conmin,segmin,imin,xmin,ymin,dmin = find_nearest_contour(
self, x, y, indices=None, pixel=True )
Optional keyword arguments:
Indexes of contour levels to consider when looking for
nearest point. Defaults to using all levels.
If *True*, measure distance in pixel space, if not, measure
distance in axes space. Defaults to *True*.
# This function uses a method that is probably quite
# inefficient based on converting each contour segment to
# pixel coordinates and then comparing the given point to
# those coordinates for each contour. This will probably be
# quite slow for complex contours, but for normal use it works
# sufficiently well that the time is not noticeable.
# Nonetheless, improvements could probably be made.
if indices is None:
indices = list(xrange(len(self.levels)))
dmin = np.inf
conmin = None
segmin = None
xmin = None
ymin = None
point = np.array([x, y])
for icon in indices:
con = self.collections[icon]
trans = con.get_transform()
paths = con.get_paths()
for segNum, linepath in enumerate(paths):
lc = linepath.vertices
# transfer all data points to screen coordinates if desired
if pixel:
lc = trans.transform(lc)
d, xc, leg = _find_closest_point_on_path(lc, point)
if d < dmin:
dmin = d
conmin = icon
segmin = segNum
imin = leg[1]
xmin = xc[0]
ymin = xc[1]
return (conmin, segmin, imin, xmin, ymin, dmin)
class QuadContourSet(ContourSet):
Create and store a set of contour lines or filled regions.
User-callable method: :meth:`clabel`
Useful attributes:
The axes object in which the contours are drawn
A silent_list of LineCollections or PolyCollections
Contour levels
Same as levels for line contours; half-way between
levels for filled contours. See :meth:`_process_colors` method.
def __init__(self, ax, *args, **kwargs):
Calculate and draw contour lines or filled regions, depending
on whether keyword arg 'filled' is False (default) or True.
The first argument of the initializer must be an axes
object. The remaining arguments and keyword arguments
are described in QuadContourSet.contour_doc.
ContourSet.__init__(self, ax, *args, **kwargs)
def _process_args(self, *args, **kwargs):
Process args and kwargs.
if isinstance(args[0], QuadContourSet):
if self.levels is None:
self.levels = args[0].levels
self.zmin = args[0].zmin
self.zmax = args[0].zmax
self._corner_mask = args[0]._corner_mask
if self._corner_mask == 'legacy':
contour_generator = args[0].Cntr
contour_generator = args[0]._contour_generator
self._corner_mask = kwargs.get('corner_mask', None)
if self._corner_mask is None:
self._corner_mask = mpl.rcParams['contour.corner_mask']
x, y, z = self._contour_args(args, kwargs)
_mask = ma.getmask(z)
if _mask is ma.nomask or not _mask.any():
_mask = None
if self._corner_mask == 'legacy':
alternative='corner_mask=False or True')
contour_generator = _cntr.Cntr(x, y, z.filled(), _mask)
contour_generator = _contour.QuadContourGenerator(
x, y, z.filled(), _mask, self._corner_mask, self.nchunk)
t = self.get_transform()
# if the transform is not trans data, and some part of it
# contains transData, transform the xs and ys to data coordinates
if (t != and
trans_to_data = t -
pts = (np.vstack([x.flat, y.flat]).T)
transformed_pts = trans_to_data.transform(pts)
x = transformed_pts[..., 0]
y = transformed_pts[..., 1]
x0 = ma.minimum(x)
x1 = ma.maximum(x)
y0 = ma.minimum(y)
y1 = ma.maximum(y)[(x0, y0), (x1, y1)])
if self._corner_mask == 'legacy':
self.Cntr = contour_generator
self._contour_generator = contour_generator
def _get_allsegs_and_allkinds(self):
Create and return allsegs and allkinds by calling underlying C code.
allsegs = []
if self.filled:
lowers, uppers = self._get_lowers_and_uppers()
allkinds = []
for level, level_upper in zip(lowers, uppers):
if self._corner_mask == 'legacy':
nlist = self.Cntr.trace(level, level_upper,
nseg = len(nlist) // 2
vertices = nlist[:nseg]
kinds = nlist[nseg:]
vertices, kinds = \
level, level_upper)
allkinds = None
for level in self.levels:
if self._corner_mask == 'legacy':
nlist = self.Cntr.trace(level)
nseg = len(nlist) // 2
vertices = nlist[:nseg]
vertices = self._contour_generator.create_contour(level)
return allsegs, allkinds
def _contour_args(self, args, kwargs):
if self.filled:
fn = 'contourf'
fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=np.float64)
x, y = self._initialize_x_y(z)
args = args[1:]
elif Nargs <= 4:
x, y, z = self._check_xyz(args[:3], kwargs)
args = args[3:]
raise TypeError("Too many arguments to %s; see help(%s)" %
(fn, fn))
z = ma.masked_invalid(z, copy=False)
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <= 0 have been masked')
self.zmin = z.min()
self._contour_level_args(z, args)
return (x, y, z)
def _check_xyz(self, args, kwargs):
For functions like contour, check that the dimensions
of the input arrays match; if x and y are 1D, convert
them to 2D using meshgrid.
Possible change: I think we should make and use an ArgumentError
Exception class (here and elsewhere).
x, y = args[:2], ydata=y, kwargs=kwargs)
x =
y =
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
z = ma.asarray(args[2], dtype=np.float64)
if z.ndim != 2:
raise TypeError("Input z must be a 2D array.")
Ny, Nx = z.shape
if x.ndim != y.ndim:
raise TypeError("Number of dimensions of x and y should match.")
if x.ndim == 1:
nx, = x.shape
ny, = y.shape
if nx != Nx:
raise TypeError("Length of x must be number of columns in z.")
if ny != Ny:
raise TypeError("Length of y must be number of rows in z.")
x, y = np.meshgrid(x, y)
elif x.ndim == 2:
if x.shape != z.shape:
raise TypeError("Shape of x does not match that of z: found "
"{0} instead of {1}.".format(x.shape, z.shape))
if y.shape != z.shape:
raise TypeError("Shape of y does not match that of z: found "
"{0} instead of {1}.".format(y.shape, z.shape))
raise TypeError("Inputs x and y must be 1D or 2D.")
return x, y, z
def _initialize_x_y(self, z):
Return X, Y arrays such that contour(Z) will match imshow(Z)
if origin is not None.
The center of pixel Z[i,j] depends on origin:
if origin is None, x = j, y = i;
if origin is 'lower', x = j + 0.5, y = i + 0.5;
if origin is 'upper', x = j + 0.5, y = Nrows - i - 0.5
If extent is not None, x and y will be scaled to match,
as in imshow.
If origin is None and extent is not None, then extent
will give the minimum and maximum values of x and y.
if z.ndim != 2:
raise TypeError("Input must be a 2D array.")
Ny, Nx = z.shape
if self.origin is None: # Not for image-matching.
if self.extent is None:
return np.meshgrid(np.arange(Nx), np.arange(Ny))
x0, x1, y0, y1 = self.extent
x = np.linspace(x0, x1, Nx)
y = np.linspace(y0, y1, Ny)
return np.meshgrid(x, y)
# Match image behavior:
if self.extent is None:
x0, x1, y0, y1 = (0, Nx, 0, Ny)
x0, x1, y0, y1 = self.extent
dx = float(x1 - x0) / Nx
dy = float(y1 - y0) / Ny
x = x0 + (np.arange(Nx) + 0.5) * dx
y = y0 + (np.arange(Ny) + 0.5) * dy
if self.origin == 'upper':
y = y[::-1]
return np.meshgrid(x, y)
contour_doc = """
Plot contours.
:func:`~matplotlib.pyplot.contour` and
:func:`~matplotlib.pyplot.contourf` draw contour lines and
filled contours, respectively. Except as noted, function
signatures and return values are the same for both versions.
:func:`~matplotlib.pyplot.contourf` differs from the MATLAB
version in that it does not draw the polygon edges.
To draw edges, add line contours with
calls to :func:`~matplotlib.pyplot.contour`.
Call signatures::
make a contour plot of an array *Z*. The level values are chosen
*X*, *Y* specify the (x, y) coordinates of the surface
contour up to *N* automatically-chosen levels.
draw contour lines at the values specified in sequence *V*,
which must be in increasing order.
contourf(..., V)
fill the ``len(V)-1`` regions between the values in *V*,
which must be in increasing order.
contour(Z, **kwargs)
Use keyword args to control colors, linewidth, origin, cmap ... see
below for more details.
*X* and *Y* must both be 2-D with the same shape as *Z*, or they
must both be 1-D such that ``len(X)`` is the number of columns in
*Z* and ``len(Y)`` is the number of rows in *Z*.
``C = contour(...)`` returns a
:class:`~matplotlib.contour.QuadContourSet` object.
Optional keyword arguments:
*corner_mask*: [ *True* | *False* | 'legacy' ]
Enable/disable corner masking, which only has an effect if *Z* is
a masked array. If *False*, any quad touching a masked point is
masked out. If *True*, only the triangular corners of quads
nearest those points are always masked out, other triangular
corners comprising three unmasked points are contoured as usual.
If 'legacy', the old contouring algorithm is used, which is
equivalent to *False* and is deprecated, only remaining whilst the
new algorithm is tested fully.
If not specified, the default is taken from
rcParams['contour.corner_mask'], which is True unless it has
been modified.
*colors*: [ *None* | string | (mpl_colors) ]
If *None*, the colormap specified by cmap will be used.
If a string, like 'r' or 'red', all levels will be plotted in this
If a tuple of matplotlib color args (string, float, rgb, etc),
different levels will be plotted in different colors in the order
*alpha*: float
The alpha blending value
*cmap*: [ *None* | Colormap ]
A cm :class:`~matplotlib.colors.Colormap` instance or
*None*. If *cmap* is *None* and *colors* is *None*, a
default Colormap is used.
*norm*: [ *None* | Normalize ]
A :class:`matplotlib.colors.Normalize` instance for
scaling data values to colors. If *norm* is *None* and
*colors* is *None*, the default linear scaling is used.
*vmin*, *vmax*: [ *None* | scalar ]
If not *None*, either or both of these values will be
supplied to the :class:`matplotlib.colors.Normalize`
instance, overriding the default color scaling based on
*levels*: [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw, in increasing order; e.g., to draw just
the zero contour pass ``levels=[0]``
*origin*: [ *None* | 'upper' | 'lower' | 'image' ]
If *None*, the first value of *Z* will correspond to the
lower left corner, location (0,0). If 'image', the rc
value for ``image.origin`` will be used.
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*extent*: [ *None* | (x0,x1,y0,y1) ]
If *origin* is not *None*, then *extent* is interpreted as
in :func:`matplotlib.pyplot.imshow`: it gives the outer
pixel boundaries. In this case, the position of Z[0,0]
is the center of the pixel, not a corner. If *origin* is
*None*, then (*x0*, *y0*) is the position of Z[0,0], and
(*x1*, *y1*) is the position of Z[-1,-1].
This keyword is not active if *X* and *Y* are specified in
the call to contour.
*locator*: [ *None* | ticker.Locator subclass ]
If *locator* is *None*, the default
:class:`~matplotlib.ticker.MaxNLocator` is used. The
locator is used to determine the contour levels if they
are not given explicitly via the *V* argument.
*extend*: [ 'neither' | 'both' | 'min' | 'max' ]
Unless this is 'neither', contour levels are automatically
added to one or both ends of the range so that all data
are included. These added ranges are then mapped to the
special colormap values which default to the ends of the
colormap range, but can be set via
:meth:`matplotlib.colors.Colormap.set_under` and
:meth:`matplotlib.colors.Colormap.set_over` methods.
*xunits*, *yunits*: [ *None* | registered units ]
Override axis units by specifying an instance of a
*antialiased*: [ *True* | *False* ]
enable antialiasing, overriding the defaults. For
filled contours, the default is *True*. For line contours,
it is taken from rcParams['lines.antialiased'].
*nchunk*: [ 0 | integer ]
If 0, no subdivision of the domain. Specify a positive integer to
divide the domain into subdomains of *nchunk* by *nchunk* quads.
Chunking reduces the maximum length of polygons generated by the
contouring algorithm which reduces the rendering workload passed
on to the backend and also requires slightly less RAM. It can
however introduce rendering artifacts at chunk boundaries depending
on the backend, the *antialiased* flag and value of *alpha*.
contour-only keyword arguments:
*linewidths*: [ *None* | number | tuple of numbers ]
If *linewidths* is *None*, the default width in
``lines.linewidth`` in ``matplotlibrc`` is used.
If a number, all levels will be plotted with this linewidth.
If a tuple, different levels will be plotted with different
linewidths in the order specified.
*linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
If *linestyles* is *None*, the default is 'solid' unless
the lines are monochrome. In that case, negative
contours will take their linestyle from the ``matplotlibrc``
``contour.negative_linestyle`` setting.
*linestyles* can also be an iterable of the above strings
specifying a set of linestyles to be used. If this
iterable is shorter than the number of contour levels
it will be repeated as necessary.
contourf-only keyword arguments:
A list of cross hatch patterns to use on the filled areas.
If None, no hatching will be added to the contour.
Hatching is supported in the PostScript, PDF, SVG and Agg
backends only.
Note: contourf fills intervals that are closed at the top; that
is, for boundaries *z1* and *z2*, the filled region is::
z1 < z <= z2
There is one exception: if the lowest boundary coincides with
the minimum value of the *z* array, then that minimum value
will be included in the lowest interval.
.. plot:: mpl_examples/pylab_examples/
.. plot:: mpl_examples/pylab_examples/
.. plot:: mpl_examples/pylab_examples/
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