"""
Classes to support contour plotting and labelling for the Axes class.
"""
import functools
from numbers import Integral
import numpy as np
from numpy import ma
import matplotlib as mpl
from matplotlib import _api, _docstring
from matplotlib.backend_bases import MouseButton
from matplotlib.text import Text
import matplotlib.path as mpath
import matplotlib.ticker as ticker
import matplotlib.cm as cm
import matplotlib.colors as mcolors
import matplotlib.collections as mcoll
import matplotlib.font_manager as font_manager
import matplotlib.cbook as cbook
import matplotlib.patches as mpatches
import matplotlib.transforms as mtransforms
# 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.
@_api.deprecated("3.7", alternative="Text.set_transform_rotates_text")
class ClabelText(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):
new_angle, = self.get_transform().transform_angles(
[super().get_rotation()], [self.get_position()])
return new_angle
def _contour_labeler_event_handler(cs, inline, inline_spacing, event):
canvas = cs.axes.figure.canvas
is_button = event.name == "button_press_event"
is_key = event.name == "key_press_event"
# Quit (even if not in infinite mode; this is consistent with
# MATLAB and sometimes quite useful, but will require the user to
# test how many points were actually returned before using data).
if (is_button and event.button == MouseButton.MIDDLE
or is_key and event.key in ["escape", "enter"]):
canvas.stop_event_loop()
# Pop last click.
elif (is_button and event.button == MouseButton.RIGHT
or is_key and event.key in ["backspace", "delete"]):
# Unfortunately, if one is doing inline labels, then there is currently
# no way to fix the broken contour - once humpty-dumpty is broken, he
# can't be put back together. In inline mode, this does nothing.
if not inline:
cs.pop_label()
canvas.draw()
# Add new click.
elif (is_button and event.button == MouseButton.LEFT
# On macOS/gtk, some keys return None.
or is_key and event.key is not None):
if event.inaxes == cs.axes:
cs.add_label_near(event.x, event.y, transform=False,
inline=inline, inline_spacing=inline_spacing)
canvas.draw()
class ContourLabeler:
"""Mixin to provide labelling capability to `.ContourSet`."""
def clabel(self, levels=None, *,
fontsize=None, inline=True, inline_spacing=5, fmt=None,
colors=None, use_clabeltext=False, manual=False,
rightside_up=True, zorder=None):
"""
Label a contour plot.
Adds labels to line contours in this `.ContourSet` (which inherits from
this mixin class).
Parameters
----------
levels : array-like, optional
A list of level values, that should be labeled. The list must be
a subset of ``cs.levels``. If not given, all levels are labeled.
fontsize : str or float, default: :rc:`font.size`
Size in points or relative size e.g., 'smaller', 'x-large'.
See `.Text.set_size` for accepted string values.
colors : color or colors or None, default: None
The label colors:
- 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 colors (string, float, rgb, etc), different labels
will be plotted in different colors in the order specified.
inline : bool, default: True
If ``True`` the underlying contour is removed where the label is
placed.
inline_spacing : float, default: 5
Space in pixels to leave on each side of label when placing inline.
This spacing will be exact for labels at locations where the
contour is straight, less so for labels on curved contours.
fmt : `.Formatter` or str or callable or dict, optional
How the levels are formatted:
- If a `.Formatter`, it is used to format all levels at once, using
its `.Formatter.format_ticks` method.
- If a str, it is interpreted as a %-style format string.
- If a callable, it is called with one level at a time and should
return the corresponding label.
- If a dict, it should directly map levels to labels.
The default is to use a standard `.ScalarFormatter`.
manual : bool or iterable, default: False
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 also be an iterable object of (x, y) tuples.
Contour labels will be created as if mouse is clicked at each
(x, y) position.
rightside_up : bool, default: True
If ``True``, label rotations will always be plus
or minus 90 degrees from level.
use_clabeltext : bool, default: False
If ``True``, use `.Text.set_transform_rotates_text` to ensure that
label rotation is updated whenever the axes aspect changes.
zorder : float or None, default: ``(2 + contour.get_zorder())``
zorder of the contour labels.
Returns
-------
labels
A list of `.Text` instances for the labels.
"""
# 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).
if fmt is None:
fmt = ticker.ScalarFormatter(useOffset=False)
fmt.create_dummy_axis()
self.labelFmt = fmt
self._use_clabeltext = use_clabeltext
# Detect if manual selection is desired and remove from argument list.
self.labelManual = manual
self.rightside_up = rightside_up
if zorder is None:
self._clabel_zorder = 2+self._contour_zorder
else:
self._clabel_zorder = zorder
if levels is None:
levels = self.levels
indices = list(range(len(self.cvalues)))
else:
levlabs = list(levels)
indices, levels = [], []
for i, lev in enumerate(self.levels):
if lev in levlabs:
indices.append(i)
levels.append(lev)
if len(levels) < len(levlabs):
raise ValueError(f"Specified levels {levlabs} don't match "
f"available levels {self.levels}")
self.labelLevelList = levels
self.labelIndiceList = indices
self._label_font_props = font_manager.FontProperties(size=fontsize)
if colors is None:
self.labelMappable = self
self.labelCValueList = np.take(self.cvalues, self.labelIndiceList)
else:
cmap = mcolors.ListedColormap(colors, N=len(self.labelLevelList))
self.labelCValueList = list(range(len(self.labelLevelList)))
self.labelMappable = cm.ScalarMappable(cmap=cmap,
norm=mcolors.NoNorm())
self.labelXYs = []
if np.iterable(manual):
for x, y in manual:
self.add_label_near(x, y, inline, inline_spacing)
elif manual:
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.')
mpl._blocking_input.blocking_input_loop(
self.axes.figure, ["button_press_event", "key_press_event"],
timeout=-1, handler=functools.partial(
_contour_labeler_event_handler,
self, inline, inline_spacing))
else:
self.labels(inline, inline_spacing)
return cbook.silent_list('text.Text', self.labelTexts)
@_api.deprecated("3.7", alternative="cs.labelTexts[0].get_font()")
@property
def labelFontProps(self):
return self._label_font_props
@_api.deprecated("3.7", alternative=(
"[cs.labelTexts[0].get_font().get_size()] * len(cs.labelLevelList)"))
@property
def labelFontSizeList(self):
return [self._label_font_props.get_size()] * len(self.labelLevelList)
@_api.deprecated("3.7", alternative="cs.labelTexts")
@property
def labelTextsList(self):
return cbook.silent_list('text.Text', self.labelTexts)
def print_label(self, linecontour, labelwidth):
"""Return whether a contour is long enough to hold a label."""
return (len(linecontour) > 10 * labelwidth
or (np.ptp(linecontour, axis=0) > 1.2 * labelwidth).any())
def too_close(self, x, y, lw):
"""Return whether a label is already near this location."""
thresh = (1.2 * lw) ** 2
return any((x - loc[0]) ** 2 + (y - loc[1]) ** 2 < thresh
for loc in self.labelXYs)
def _get_nth_label_width(self, nth):
"""Return the width of the *nth* label, in pixels."""
fig = self.axes.figure
renderer = fig._get_renderer()
return (Text(0, 0,
self.get_text(self.labelLevelList[nth], self.labelFmt),
figure=fig, fontproperties=self._label_font_props)
.get_window_extent(renderer).width)
@_api.deprecated("3.7", alternative="Artist.set")
def set_label_props(self, label, text, color):
"""Set the label properties - color, fontsize, text."""
label.set_text(text)
label.set_color(color)
label.set_fontproperties(self._label_font_props)
label.set_clip_box(self.axes.bbox)
def get_text(self, lev, fmt):
"""Get the text of the label."""
if isinstance(lev, str):
return lev
elif isinstance(fmt, dict):
return fmt.get(lev, '%1.3f')
elif callable(getattr(fmt, "format_ticks", None)):
return fmt.format_ticks([*self.labelLevelList, lev])[-1]
elif callable(fmt):
return fmt(lev)
else:
return fmt % lev
def locate_label(self, linecontour, labelwidth):
"""
Find good place to draw a label (relatively flat part of the contour).
"""
ctr_size = len(linecontour)
n_blocks = int(np.ceil(ctr_size / labelwidth)) if labelwidth > 1 else 1
block_size = ctr_size if n_blocks == 1 else int(labelwidth)
# Split contour into blocks of length ``block_size``, filling the last
# block by cycling the contour start (per `np.resize` semantics). (Due
# to cycling, the index returned is taken modulo ctr_size.)
xx = np.resize(linecontour[:, 0], (n_blocks, block_size))
yy = np.resize(linecontour[:, 1], (n_blocks, block_size))
yfirst = yy[:, :1]
ylast = yy[:, -1:]
xfirst = xx[:, :1]
xlast = xx[:, -1:]
s = (yfirst - yy) * (xlast - xfirst) - (xfirst - xx) * (ylast - yfirst)
l = np.hypot(xlast - xfirst, ylast - yfirst)
# Ignore warning that divide by zero throws, as this is a valid option
with np.errstate(divide='ignore', invalid='ignore'):
distances = (abs(s) / l).sum(axis=-1)
# Labels are drawn in the middle of the block (``hbsize``) where the
# contour is the closest (per ``distances``) to a straight line, but
# not `too_close()` to a preexisting label.
hbsize = block_size // 2
adist = np.argsort(distances)
# If all candidates are `too_close()`, go back to the straightest part
# (``adist[0]``).
for idx in np.append(adist, adist[0]):
x, y = xx[idx, hbsize], yy[idx, hbsize]
if not self.too_close(x, y, labelwidth):
break
return x, y, (idx * block_size + hbsize) % ctr_size
def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
"""
Calculate the appropriate label rotation given the linecontour
coordinates in screen units, the index of the label location and the
label width.
If *lc* is not None or empty, also break contours and compute
inlining.
*spacing* is the empty space to leave around the label, in pixels.
Both tasks are done together to avoid calculating path lengths
multiple times, which is relatively costly.
The method used here involves computing 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 = _is_closed_polygon(slc)
if closed:
slc = np.concatenate([slc[ind:-1], slc[:ind + 1]])
if len(lc): # Rotate lc also if not empty
lc = np.concatenate([lc[ind:-1], lc[:ind + 1]])
ind = 0
# Calculate path lengths
pl = np.zeros(slc.shape[0], dtype=float)
dx = np.diff(slc, axis=0)
pl[1:] = np.cumsum(np.hypot(dx[:, 0], dx[:, 1]))
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])
else:
dp = np.zeros_like(xi)
# Get angle of vector between the two ends of the label - must be
# calculated in pixel space for text rotation to work correctly.
(dx,), (dy,) = (np.diff(np.interp(dp + xi, pl, slc_col))
for slc_col in slc.T)
rotation = np.rad2deg(np.arctan2(dy, dx))
if self.rightside_up:
# Fix angle so text is never upside-down
rotation = (rotation + 90) % 180 - 90
# Break contour if desired
nlc = []
if len(lc):
# Expand range by spacing
xi = dp + xi + np.array([-spacing, spacing])
# Get (integer) indices near points of interest; use -1 as marker
# for out of bounds.
I = np.interp(xi, pl, np.arange(len(pl)), left=-1, right=-1)
I = [np.floor(I[0]).astype(int), np.ceil(I[1]).astype(int)]
if I[0] != -1:
xy1 = [np.interp(xi[0], pl, lc_col) for lc_col in lc.T]
if I[1] != -1:
xy2 = [np.interp(xi[1], pl, lc_col) for lc_col in lc.T]
# Actually break contours
if closed:
# This will remove contour if shorter than label
if all(i != -1 for i in I):
nlc.append(np.row_stack([xy2, lc[I[1]:I[0]+1], xy1]))
else:
# These will remove pieces of contour if they have length zero
if I[0] != -1:
nlc.append(np.row_stack([lc[:I[0]+1], xy1]))
if I[1] != -1:
nlc.append(np.row_stack([xy2, lc[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 add_label(self, x, y, rotation, lev, cvalue):
"""Add contour label without `.Text.set_transform_rotates_text`."""
data_x, data_y = self.axes.transData.inverted().transform((x, y))
t = Text(
data_x, data_y,
text=self.get_text(lev, self.labelFmt),
rotation=rotation,
horizontalalignment='center', verticalalignment='center',
zorder=self._clabel_zorder,
color=self.labelMappable.to_rgba(cvalue, alpha=self.alpha),
fontproperties=self._label_font_props,
clip_box=self.axes.bbox)
self.labelTexts.append(t)
self.labelCValues.append(cvalue)
self.labelXYs.append((x, y))
# Add label to plot here - useful for manual mode label selection
self.axes.add_artist(t)
def add_label_clabeltext(self, x, y, rotation, lev, cvalue):
"""Add contour label with `.Text.set_transform_rotates_text`."""
self.add_label(x, y, rotation, lev, cvalue)
# Grab the last added text, and reconfigure its rotation.
t = self.labelTexts[-1]
data_rotation, = self.axes.transData.inverted().transform_angles(
[rotation], [[x, y]])
t.set(rotation=data_rotation, transform_rotates_text=True)
def add_label_near(self, x, y, inline=True, inline_spacing=5,
transform=None):
"""
Add a label near the point ``(x, y)``.
Parameters
----------
x, y : float
The approximate location of the label.
inline : bool, default: True
If *True* remove the segment of the contour beneath the label.
inline_spacing : int, default: 5
Space in pixels to leave on each side of label when placing
inline. This spacing will be exact for labels at locations where
the contour is straight, less so for labels on curved contours.
transform : `.Transform` or `False`, default: ``self.axes.transData``
A transform applied to ``(x, y)`` before labeling. The default
causes ``(x, y)`` to be interpreted as data coordinates. `False`
is a synonym for `.IdentityTransform`; i.e. ``(x, y)`` should be
interpreted as display coordinates.
"""
if transform is None:
transform = self.axes.transData
if transform:
x, y = transform.transform((x, y))
# find the nearest contour _in screen units_
conmin, segmin, imin, xmin, ymin = self.find_nearest_contour(
x, y, self.labelIndiceList)[:5]
# calc_label_rot_and_inline() requires that (xmin, ymin)
# be a vertex in the path. So, if it isn't, add a vertex here
paths = self.collections[conmin].get_paths() # paths of correct coll.
lc = paths[segmin].vertices # vertices of correct segment
# Where should the new vertex be added in data-units?
xcmin = self.axes.transData.inverted().transform([xmin, ymin])
if not np.allclose(xcmin, lc[imin]):
# No vertex is close enough, so add a new point in the vertices and
# replace the path by the new one.
lc = np.insert(lc, imin, xcmin, axis=0)
paths[segmin] = mpath.Path(lc)
# Get index of nearest level in subset of levels used for labeling
lmin = self.labelIndiceList.index(conmin)
# Get label width for rotating labels and breaking contours
lw = self._get_nth_label_width(lmin)
# Figure out label rotation.
rotation, nlc = self.calc_label_rot_and_inline(
self.axes.transData.transform(lc), # to pixel space.
imin, lw, lc if inline else None, inline_spacing)
self.add_label(xmin, ymin, rotation, self.labelLevelList[lmin],
self.labelCValueList[lmin])
if inline:
# Remove old, not looping over paths so we can do this up front
paths.pop(segmin)
# Add paths if not empty or single point
paths.extend([mpath.Path(n) 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"""
self.labelCValues.pop(index)
t = self.labelTexts.pop(index)
t.remove()
def labels(self, inline, inline_spacing):
if self._use_clabeltext:
add_label = self.add_label_clabeltext
else:
add_label = self.add_label
for idx, (icon, lev, cvalue) in enumerate(zip(
self.labelIndiceList,
self.labelLevelList,
self.labelCValueList,
)):
con = self.collections[icon]
trans = con.get_transform()
lw = self._get_nth_label_width(idx)
additions = []
paths = con.get_paths()
for segNum, linepath in enumerate(paths):
lc = linepath.vertices # Line contour
slc = trans.transform(lc) # Line contour in screen coords
# Check if long enough for a label
if self.print_label(slc, lw):
x, y, ind = self.locate_label(slc, lw)
rotation, new = self.calc_label_rot_and_inline(
slc, ind, lw, lc if inline else None, inline_spacing)
# 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:
additions.append(mpath.Path(n))
else: # If not adding label, keep old path
additions.append(linepath)
# After looping over all segments on a contour, replace old paths
# by new ones if inlining.
if inline:
paths[:] = additions
def remove(self):
for text in self.labelTexts:
text.remove()
def _is_closed_polygon(X):
"""
Return whether first and last object in a sequence are the same. These are
presumably coordinates on a polygonal curve, in which case this function
tests if that curve is closed.
"""
return np.allclose(X[0], X[-1], rtol=1e-10, atol=1e-13)
def _find_closest_point_on_path(xys, p):
"""
Parameters
----------
xys : (N, 2) array-like
Coordinates of vertices.
p : (float, float)
Coordinates of point.
Returns
-------
d2min : float
Minimum square distance of *p* to *xys*.
proj : (float, float)
Projection of *p* onto *xys*.
imin : (int, int)
Consecutive indices of vertices of segment in *xys* where *proj* is.
Segments are considered as including their end-points; i.e. if the
closest point on the path is a node in *xys* with index *i*, this
returns ``(i-1, i)``. For the special case where *xys* is a single
point, this returns ``(0, 0)``.
"""
if len(xys) == 1:
return (((p - xys[0]) ** 2).sum(), xys[0], (0, 0))
dxys = xys[1:] - xys[:-1] # Individual segment vectors.
norms = (dxys ** 2).sum(axis=1)
norms[norms == 0] = 1 # For zero-length segment, replace 0/0 by 0/1.
rel_projs = np.clip( # Project onto each segment in relative 0-1 coords.
((p - xys[:-1]) * dxys).sum(axis=1) / norms,
0, 1)[:, None]
projs = xys[:-1] + rel_projs * dxys # Projs. onto each segment, in (x, y).
d2s = ((projs - p) ** 2).sum(axis=1) # Squared distances.
imin = np.argmin(d2s)
return (d2s[imin], projs[imin], (imin, imin+1))
_docstring.interpd.update(contour_set_attributes=r"""
Attributes
----------
ax : `~matplotlib.axes.Axes`
The Axes object in which the contours are drawn.
collections : `.silent_list` of `.PathCollection`\s
The `.Artist`\s representing the contour. This is a list of
`.PathCollection`\s for both line and filled contours.
levels : array
The values of the contour levels.
layers : array
Same as levels for line contours; half-way between
levels for filled contours. See ``ContourSet._process_colors``.
""")
@_docstring.dedent_interpd
class ContourSet(cm.ScalarMappable, ContourLabeler):
"""
Store a set of contour lines or filled regions.
User-callable method: `~.Axes.clabel`
Parameters
----------
ax : `~matplotlib.axes.Axes`
levels : [level0, level1, ..., leveln]
A list of floating point numbers indicating the contour levels.
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``. The lists
should look like ::
level0segs = [polygon0, polygon1, ...]
polygon0 = [[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)``. The lists
should look like ::
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]``.
**kwargs
Keyword arguments are as described in the docstring of
`~.Axes.contour`.
%(contour_set_attributes)s
"""
def __init__(self, ax, *args,
levels=None, filled=False, linewidths=None, linestyles=None,
hatches=(None,), alpha=None, origin=None, extent=None,
cmap=None, colors=None, norm=None, vmin=None, vmax=None,
extend='neither', antialiased=None, nchunk=0, locator=None,
transform=None, negative_linestyles=None,
**kwargs):
"""
Draw contour lines or filled regions, depending on
whether keyword arg *filled* is ``False`` (default) or ``True``.
Call signature::
ContourSet(ax, levels, allsegs, [allkinds], **kwargs)
Parameters
----------
ax : `~matplotlib.axes.Axes`
The `~.axes.Axes` object to draw on.
levels : [level0, level1, ..., leveln]
A list of floating point numbers indicating the contour
levels.
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``. The lists
should look like ::
level0segs = [polygon0, polygon1, ...]
polygon0 = [[x0, y0], [x1, y1], ...]
allkinds : [level0kinds, level1kinds, ...], optional
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)``. The lists
should look like ::
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]``.
**kwargs
Keyword arguments are as described in the docstring of
`~.Axes.contour`.
"""
self.axes = ax
self.levels = levels
self.filled = filled
self.linewidths = linewidths
self.linestyles = linestyles
self.hatches = hatches
self.alpha = alpha
self.origin = origin
self.extent = extent
self.colors = colors
self.extend = extend
self.antialiased = antialiased
if self.antialiased is None and self.filled:
# Eliminate artifacts; we are not stroking the boundaries.
self.antialiased = False
# The default for line contours will be taken from the
# LineCollection default, which uses :rc:`lines.antialiased`.
self.nchunk = nchunk
self.locator = locator
if (isinstance(norm, mcolors.LogNorm)
or isinstance(self.locator, ticker.LogLocator)):
self.logscale = True
if norm is None:
norm = mcolors.LogNorm()
else:
self.logscale = False
_api.check_in_list([None, 'lower', 'upper', 'image'], origin=origin)
if self.extent is not None and len(self.extent) != 4:
raise ValueError(
"If given, 'extent' must be None or (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 = transform
self.negative_linestyles = negative_linestyles
# If negative_linestyles was not defined as a keyword argument, define
# negative_linestyles with rcParams
if self.negative_linestyles is None:
self.negative_linestyles = \
mpl.rcParams['contour.negative_linestyle']
kwargs = self._process_args(*args, **kwargs)
self._process_levels()
self._extend_min = self.extend in ['min', 'both']
self._extend_max = self.extend in ['max', 'both']
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.
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(self._extend_min) +
int(self._extend_max))
if (len(self.colors) == total_levels and
(self._extend_min or self._extend_max)):
use_set_under_over = True
if self._extend_min:
i0 = 1
cmap = mcolors.ListedColormap(self.colors[i0:None], N=ncolors)
if use_set_under_over:
if self._extend_min:
cmap.set_under(self.colors[0])
if self._extend_max:
cmap.set_over(self.colors[-1])
self.collections = cbook.silent_list(None)
# 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._process_colors()
if getattr(self, 'allsegs', None) is None:
self.allsegs, self.allkinds = self._get_allsegs_and_allkinds()
elif self.allkinds is None:
# allsegs specified in constructor may or may not have allkinds as
# well. Must ensure allkinds can be zipped below.
self.allkinds = [None] * len(self.allsegs)
if self.filled:
if self.linewidths is not None:
_api.warn_external('linewidths is ignored by contourf')
# Lower and upper contour levels.
lowers, uppers = self._get_lowers_and_uppers()
# Default zorder taken from Collection
self._contour_zorder = kwargs.pop('zorder', 1)
self.collections[:] = [
mcoll.PathCollection(
self._make_paths(segs, kinds),
antialiaseds=(self.antialiased,),
edgecolors='none',
alpha=self.alpha,
transform=self.get_transform(),
zorder=self._contour_zorder)
for level, level_upper, segs, kinds
in zip(lowers, uppers, self.allsegs, self.allkinds)]
else:
self.tlinewidths = tlinewidths = self._process_linewidths()
tlinestyles = self._process_linestyles()
aa = self.antialiased
if aa is not None:
aa = (self.antialiased,)
# Default zorder taken from LineCollection, which is higher than
# for filled contours so that lines are displayed on top.
self._contour_zorder = kwargs.pop('zorder', 2)
self.collections[:] = [
mcoll.PathCollection(
self._make_paths(segs, kinds),
facecolors="none",
antialiaseds=aa,
linewidths=width,
linestyles=[lstyle],
alpha=self.alpha,
transform=self.get_transform(),
zorder=self._contour_zorder,
label='_nolegend_')
for level, width, lstyle, segs, kinds
in zip(self.levels, tlinewidths, tlinestyles, self.allsegs,
self.allkinds)]
for col in self.collections:
self.axes.add_collection(col, autolim=False)
col.sticky_edges.x[:] = [self._mins[0], self._maxs[0]]
col.sticky_edges.y[:] = [self._mins[1], self._maxs[1]]
self.axes.update_datalim([self._mins, self._maxs])
self.axes.autoscale_view(tight=True)
self.changed() # set the colors
if kwargs:
_api.warn_external(
'The following kwargs were not used by contour: ' +
", ".join(map(repr, kwargs))
)
def get_transform(self):
"""Return the `.Transform` instance used by this ContourSet."""
if self._transform is None:
self._transform = self.axes.transData
elif (not isinstance(self._transform, mtransforms.Transform)
and hasattr(self._transform, '_as_mpl_transform')):
self._transform = self._transform._as_mpl_transform(self.axes)
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 artists and labels suitable for passing through
to `~.Axes.legend` which represent this ContourSet.
The labels have the form "0 < x <= 1" stating the data ranges which
the artists represent.
Parameters
----------
variable_name : str
The string used inside the inequality used on the labels.
str_format : function: float -> str
Function used to format the numbers in the labels.
Returns
-------
artists : list[`.Artist`]
A list of the artists.
labels : list[str]
A list of 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,
facecolor=collection.get_facecolor()[0],
hatch=collection.get_hatch(),
alpha=collection.get_alpha())
artists.append(patch)
lower = str_format(lower)
upper = str_format(upper)
if i == 0 and self.extend in ('min', 'both'):
labels.append(fr'${variable_name} \leq {lower}s$')
elif i == n_levels - 1 and self.extend in ('max', 'both'):
labels.append(fr'${variable_name} > {upper}s$')
else:
labels.append(fr'${lower} < {variable_name} \leq {upper}$')
else:
for collection, level in zip(self.collections, self.levels):
patch = mcoll.LineCollection(None)
patch.update_from(collection)
artists.append(patch)
# format the level for insertion into the labels
level = str_format(level)
labels.append(fr'${variable_name} = {level}$')
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 limits.
"""
self.levels = args[0]
self.allsegs = args[1]
self.allkinds = args[2] if len(args) > 2 else None
self.zmax = np.max(self.levels)
self.zmin = np.min(self.levels)
# 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 '
'levels')
else:
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.
flatseglist = [s for seg in self.allsegs for s in seg]
points = np.concatenate(flatseglist, axis=0)
self._mins = points.min(axis=0)
self._maxs = points.max(axis=0)
return kwargs
def _get_allsegs_and_allkinds(self):
"""Compute ``allsegs`` and ``allkinds`` using C extension."""
allsegs = []
allkinds = []
if self.filled:
lowers, uppers = self._get_lowers_and_uppers()
for level, level_upper in zip(lowers, uppers):
vertices, kinds = \
self._contour_generator.create_filled_contour(
level, level_upper)
allsegs.append(vertices)
allkinds.append(kinds)
else:
for level in self.levels:
vertices, kinds = self._contour_generator.create_contour(level)
allsegs.append(vertices)
allkinds.append(kinds)
return allsegs, 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
else:
lowers[0] -= 1
uppers = self._levels[1:]
return (lowers, uppers)
def _make_paths(self, segs, kinds):
"""
Create and return Path objects for the specified segments and optional
kind codes. *segs* is a list of numpy arrays, each array is either a
closed line loop or open line strip of 2D points with a shape of
(npoints, 2). *kinds* is either None or a list (with the same length
as *segs*) of numpy arrays, each array is of shape (npoints,) and
contains the kind codes for the corresponding line in *segs*. If
*kinds* is None then the Path constructor creates the kind codes
assuming that the line is an open strip.
"""
if kinds is None:
return [mpath.Path(seg) for seg in segs]
else:
return [mpath.Path(seg, codes=kind) for seg, kind
in zip(segs, kinds)]
def changed(self):
if not hasattr(self, "cvalues"):
# Just return after calling the super() changed function
cm.ScalarMappable.changed(self)
return
# Force an autoscale immediately because self.to_rgba() calls
# autoscale_None() internally with the data passed to it,
# so if vmin/vmax are not set yet, this would override them with
# content from *cvalues* rather than levels like we want
self.norm.autoscale_None(self.levels)
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,
self.collections):
if self.filled:
collection.set_facecolor(color)
# update the collection's hatch (may be None)
collection.set_hatch(hatch)
else:
collection.set_edgecolor(color)
for label, cv in zip(self.labelTexts, self.labelCValues):
label.set_alpha(self.alpha)
label.set_color(self.labelMappable.to_rgba(cv))
# add label colors
cm.ScalarMappable.changed(self)
def _autolev(self, N):
"""
Select contour levels to span the data.
The target number of levels, *N*, is used only when the
scale is not log and default locator is used.
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()
else:
self.locator = ticker.MaxNLocator(N + 1, min_n_ticks=1)
lev = self.locator.tick_values(self.zmin, self.zmax)
try:
if self.locator._symmetric:
return lev
except AttributeError:
pass
# Trim excess levels the locator may have supplied.
under = np.nonzero(lev < self.zmin)[0]
i0 = under[-1] if len(under) else 0
over = np.nonzero(lev > self.zmax)[0]
i1 = over[0] + 1 if len(over) else len(lev)
if self.extend in ('min', 'both'):
i0 += 1
if self.extend in ('max', 'both'):
i1 -= 1
if i1 - i0 < 3:
i0, i1 = 0, len(lev)
return lev[i0:i1]
def _process_contour_level_args(self, args, z_dtype):
"""
Determine the contour levels and store in self.levels.
"""
if self.levels is None:
if args:
levels_arg = args[0]
elif np.issubdtype(z_dtype, bool):
if self.filled:
levels_arg = [0, .5, 1]
else:
levels_arg = [.5]
else:
levels_arg = 7 # Default, hard-wired.
else:
levels_arg = self.levels
if isinstance(levels_arg, Integral):
self.levels = self._autolev(levels_arg)
else:
self.levels = np.asarray(levels_arg, np.float64)
if self.filled and len(self.levels) < 2:
raise ValueError("Filled contours require at least 2 levels.")
if len(self.levels) > 1 and np.min(np.diff(self.levels)) <= 0.0:
raise ValueError("Contour levels must be increasing")
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.
"""
# 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.logscale:
lower, upper = 1e-250, 1e250
else:
lower, upper = -1e250, 1e250
if self.extend in ('both', 'min'):
self._levels.insert(0, lower)
if self.extend in ('both', 'max'):
self._levels.append(upper)
self._levels = np.asarray(self._levels)
if not self.filled:
self.layers = self.levels
return
# Layer values are mid-way between levels in screen space.
if self.logscale:
# Avoid overflow by taking sqrt before multiplying.
self.layers = (np.sqrt(self._levels[:-1])
* np.sqrt(self._levels[1:]))
else:
self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
def _process_colors(self):
"""
Color argument processing for contouring.
Note that we base the colormapping 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
levels.
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.set_norm(mcolors.NoNorm())
else:
self.cvalues = self.layers
self.set_array(self.levels)
self.autoscale_None()
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:
default_linewidth = mpl.rcParams['contour.linewidth']
if default_linewidth is None:
default_linewidth = mpl.rcParams['lines.linewidth']
tlinewidths = [(default_linewidth,)] * Nlev
else:
if not np.iterable(linewidths):
linewidths = [linewidths] * Nlev
else:
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:
eps = - (self.zmax - self.zmin) * 1e-15
for i, lev in enumerate(self.levels):
if lev < eps:
tlinestyles[i] = self.negative_linestyles
else:
if isinstance(linestyles, str):
tlinestyles = [linestyles] * Nlev
elif np.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]
else:
raise ValueError("Unrecognized type for linestyles kwarg")
return tlinestyles
def get_alpha(self):
"""Return alpha to be applied to all ContourSet artists."""
return self.alpha
def set_alpha(self, alpha):
"""
Set the alpha blending value for all ContourSet artists.
*alpha* must be between 0 (transparent) and 1 (opaque).
"""
self.alpha = alpha
self.changed()
def find_nearest_contour(self, x, y, indices=None, pixel=True):
"""
Find the point in the contour plot that is closest to ``(x, y)``.
This method does not support filled contours.
Parameters
----------
x, y : float
The reference point.
indices : list of int or None, default: None
Indices of contour levels to consider. If None (the default), all
levels are considered.
pixel : bool, default: True
If *True*, measure distance in pixel (screen) space, which is
useful for manual contour labeling; else, measure distance in axes
space.
Returns
-------
contour : `.Collection`
The contour that is closest to ``(x, y)``.
segment : int
The index of the `.Path` in *contour* that is closest to
``(x, y)``.
index : int
The index of the path segment in *segment* that is closest to
``(x, y)``.
xmin, ymin : float
The point in the contour plot that is closest to ``(x, y)``.
d2 : float
The squared distance from ``(xmin, ymin)`` to ``(x, y)``.
"""
# 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 self.filled:
raise ValueError("Method does not support filled contours.")
if indices is None:
indices = range(len(self.collections))
d2min = np.inf
conmin = None
segmin = None
imin = 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)
d2, xc, leg = _find_closest_point_on_path(lc, point)
if d2 < d2min:
d2min = d2
conmin = icon
segmin = segNum
imin = leg[1]
xmin = xc[0]
ymin = xc[1]
return (conmin, segmin, imin, xmin, ymin, d2min)
def remove(self):
super().remove()
for coll in self.collections:
coll.remove()
@_docstring.dedent_interpd
class QuadContourSet(ContourSet):
"""
Create and store a set of contour lines or filled regions.
This class is typically not instantiated directly by the user but by
`~.Axes.contour` and `~.Axes.contourf`.
%(contour_set_attributes)s
"""
def _process_args(self, *args, corner_mask=None, algorithm=None, **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
contour_generator = args[0]._contour_generator
self._mins = args[0]._mins
self._maxs = args[0]._maxs
self._algorithm = args[0]._algorithm
else:
import contourpy
if algorithm is None:
algorithm = mpl.rcParams['contour.algorithm']
mpl.rcParams.validate["contour.algorithm"](algorithm)
self._algorithm = algorithm
if corner_mask is None:
if self._algorithm == "mpl2005":
# mpl2005 does not support corner_mask=True so if not
# specifically requested then disable it.
corner_mask = False
else:
corner_mask = mpl.rcParams['contour.corner_mask']
self._corner_mask = corner_mask
x, y, z = self._contour_args(args, kwargs)
contour_generator = contourpy.contour_generator(
x, y, z, name=self._algorithm, corner_mask=self._corner_mask,
line_type=contourpy.LineType.SeparateCode,
fill_type=contourpy.FillType.OuterCode,
chunk_size=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 != self.axes.transData and
any(t.contains_branch_seperately(self.axes.transData))):
trans_to_data = t - self.axes.transData
pts = np.vstack([x.flat, y.flat]).T
transformed_pts = trans_to_data.transform(pts)
x = transformed_pts[..., 0]
y = transformed_pts[..., 1]
self._mins = [ma.min(x), ma.min(y)]
self._maxs = [ma.max(x), ma.max(y)]
self._contour_generator = contour_generator
return kwargs
def _contour_args(self, args, kwargs):
if self.filled:
fn = 'contourf'
else:
fn = 'contour'
nargs = len(args)
if nargs <= 2:
z, *args = args
z = ma.asarray(z)
x, y = self._initialize_x_y(z)
elif nargs <= 4:
x, y, z_orig, *args = args
x, y, z = self._check_xyz(x, y, z_orig, kwargs)
else:
raise _api.nargs_error(fn, takes="from 1 to 4", given=nargs)
z = ma.masked_invalid(z, copy=False)
self.zmax = float(z.max())
self.zmin = float(z.min())
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
_api.warn_external('Log scale: values of z <= 0 have been masked')
self.zmin = float(z.min())
self._process_contour_level_args(args, z.dtype)
return (x, y, z)
def _check_xyz(self, x, y, z, kwargs):
"""
Check that the shapes of the input arrays match; if x and y are 1D,
convert them to 2D using meshgrid.
"""
x, y = self.axes._process_unit_info([("x", x), ("y", y)], kwargs)
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
z = ma.asarray(z)
if z.ndim != 2:
raise TypeError(f"Input z must be 2D, not {z.ndim}D")
if z.shape[0] < 2 or z.shape[1] < 2:
raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
f"but has shape {z.shape}")
Ny, Nx = z.shape
if x.ndim != y.ndim:
raise TypeError(f"Number of dimensions of x ({x.ndim}) and y "
f"({y.ndim}) do not match")
if x.ndim == 1:
nx, = x.shape
ny, = y.shape
if nx != Nx:
raise TypeError(f"Length of x ({nx}) must match number of "
f"columns in z ({Nx})")
if ny != Ny:
raise TypeError(f"Length of y ({ny}) must match number of "
f"rows in z ({Ny})")
x, y = np.meshgrid(x, y)
elif x.ndim == 2:
if x.shape != z.shape:
raise TypeError(
f"Shapes of x {x.shape} and z {z.shape} do not match")
if y.shape != z.shape:
raise TypeError(
f"Shapes of y {y.shape} and z {z.shape} do not match")
else:
raise TypeError(f"Inputs x and y must be 1D or 2D, not {x.ndim}D")
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(f"Input z must be 2D, not {z.ndim}D")
elif z.shape[0] < 2 or z.shape[1] < 2:
raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
f"but has shape {z.shape}")
else:
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))
else:
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)
else:
x0, x1, y0, y1 = self.extent
dx = (x1 - x0) / Nx
dy = (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)
_docstring.interpd.update(contour_doc="""
`.contour` and `.contourf` draw contour lines and filled contours,
respectively. Except as noted, function signatures and return values
are the same for both versions.
Parameters
----------
X, Y : array-like, optional
The coordinates of the values in *Z*.
*X* and *Y* must both be 2D with the same shape as *Z* (e.g.
created via `numpy.meshgrid`), or they must both be 1-D such
that ``len(X) == N`` is the number of columns in *Z* and
``len(Y) == M`` is the number of rows in *Z*.
*X* and *Y* must both be ordered monotonically.
If not given, they are assumed to be integer indices, i.e.
``X = range(N)``, ``Y = range(M)``.
Z : (M, N) array-like
The height values over which the contour is drawn. Color-mapping is
controlled by *cmap*, *norm*, *vmin*, and *vmax*.
levels : int or array-like, optional
Determines the number and positions of the contour lines / regions.
If an int *n*, use `~matplotlib.ticker.MaxNLocator`, which tries
to automatically choose no more than *n+1* "nice" contour levels
between minimum and maximum numeric values of *Z*.
If array-like, draw contour lines at the specified levels.
The values must be in increasing order.
Returns
-------
`~.contour.QuadContourSet`
Other Parameters
----------------
corner_mask : bool, default: :rc:`contour.corner_mask`
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.
colors : color string or sequence of colors, optional
The colors of the levels, i.e. the lines for `.contour` and the
areas for `.contourf`.
The sequence is cycled for the levels in ascending order. If the
sequence is shorter than the number of levels, it's repeated.
As a shortcut, single color strings may be used in place of
one-element lists, i.e. ``'red'`` instead of ``['red']`` to color
all levels with the same color. This shortcut does only work for
color strings, not for other ways of specifying colors.
By default (value *None*), the colormap specified by *cmap*
will be used.
alpha : float, default: 1
The alpha blending value, between 0 (transparent) and 1 (opaque).
%(cmap_doc)s
This parameter is ignored if *colors* is set.
%(norm_doc)s
This parameter is ignored if *colors* is set.
%(vmin_vmax_doc)s
If *vmin* or *vmax* are not given, the default color scaling is based on
*levels*.
This parameter is ignored if *colors* is set.
origin : {*None*, 'upper', 'lower', 'image'}, default: None
Determines the orientation and exact position of *Z* by specifying
the position of ``Z[0, 0]``. This is only relevant, if *X*, *Y*
are not given.
- *None*: ``Z[0, 0]`` is at X=0, Y=0 in the lower left corner.
- 'lower': ``Z[0, 0]`` is at X=0.5, Y=0.5 in the lower left corner.
- 'upper': ``Z[0, 0]`` is at X=N+0.5, Y=0.5 in the upper left
corner.
- 'image': Use the value from :rc:`image.origin`.
extent : (x0, x1, y0, y1), optional
If *origin* is not *None*, then *extent* is interpreted as in
`.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 argument is ignored if *X* and *Y* are specified in the call
to contour.
locator : ticker.Locator subclass, optional
The locator is used to determine the contour levels if they
are not given explicitly via *levels*.
Defaults to `~.ticker.MaxNLocator`.
extend : {'neither', 'both', 'min', 'max'}, default: 'neither'
Determines the ``contourf``-coloring of values that are outside the
*levels* range.
If 'neither', values outside the *levels* range are not colored.
If 'min', 'max' or 'both', color the values below, above or below
and above the *levels* range.
Values below ``min(levels)`` and above ``max(levels)`` are mapped
to the under/over values of the `.Colormap`. Note that most
colormaps do not have dedicated colors for these by default, so
that the over and under values are the edge values of the colormap.
You may want to set these values explicitly using
`.Colormap.set_under` and `.Colormap.set_over`.
.. note::
An existing `.QuadContourSet` does not get notified if
properties of its colormap are changed. Therefore, an explicit
call `.QuadContourSet.changed()` is needed after modifying the
colormap. The explicit call can be left out, if a colorbar is
assigned to the `.QuadContourSet` because it internally calls
`.QuadContourSet.changed()`.
Example::
x = np.arange(1, 10)
y = x.reshape(-1, 1)
h = x * y
cs = plt.contourf(h, levels=[10, 30, 50],
colors=['#808080', '#A0A0A0', '#C0C0C0'], extend='both')
cs.cmap.set_over('red')
cs.cmap.set_under('blue')
cs.changed()
xunits, yunits : registered units, optional
Override axis units by specifying an instance of a
:class:`matplotlib.units.ConversionInterface`.
antialiased : bool, optional
Enable antialiasing, overriding the defaults. For
filled contours, the default is *True*. For line contours,
it is taken from :rc:`lines.antialiased`.
nchunk : int >= 0, optional
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*.
linewidths : float or array-like, default: :rc:`contour.linewidth`
*Only applies to* `.contour`.
The line width of the contour lines.
If a number, all levels will be plotted with this linewidth.
If a sequence, the levels in ascending order will be plotted with
the linewidths in the order specified.
If None, this falls back to :rc:`lines.linewidth`.
linestyles : {*None*, 'solid', 'dashed', 'dashdot', 'dotted'}, optional
*Only applies to* `.contour`.
If *linestyles* is *None*, the default is 'solid' unless the lines are
monochrome. In that case, negative contours will instead take their
linestyle from the *negative_linestyles* argument.
*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.
negative_linestyles : {*None*, 'solid', 'dashed', 'dashdot', 'dotted'}, \
optional
*Only applies to* `.contour`.
If *linestyles* is *None* and the lines are monochrome, this argument
specifies the line style for negative contours.
If *negative_linestyles* is *None*, the default is taken from
:rc:`contour.negative_linestyles`.
*negative_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.
hatches : list[str], optional
*Only applies to* `.contourf`.
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.
algorithm : {'mpl2005', 'mpl2014', 'serial', 'threaded'}, optional
Which contouring algorithm to use to calculate the contour lines and
polygons. The algorithms are implemented in
`ContourPy <https://github.com/contourpy/contourpy>`_, consult the
`ContourPy documentation <https://contourpy.readthedocs.io>`_ for
further information.
The default is taken from :rc:`contour.algorithm`.
data : indexable object, optional
DATA_PARAMETER_PLACEHOLDER
Notes
-----
1. `.contourf` differs from the MATLAB version in that it does not draw
the polygon edges. To draw edges, add line contours with calls to
`.contour`.
2. `.contourf` fills intervals that are closed at the top; that is, for
boundaries *z1* and *z2*, the filled region is::
z1 < Z <= z2
except for the lowest interval, which is closed on both sides (i.e.
it includes the lowest value).
3. `.contour` and `.contourf` use a `marching squares
<https://en.wikipedia.org/wiki/Marching_squares>`_ algorithm to
compute contour locations. More information can be found in
`ContourPy documentation <https://contourpy.readthedocs.io>`_.
""" % _docstring.interpd.params)