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Source code for matplotlib.contour

These are classes to support contour plotting and labelling for the Axes class.
from __future__ import (absolute_import, division, print_function,

import six
from six.moves import xrange

from numbers import Integral
import warnings

import matplotlib as mpl
import numpy as np
from numpy import ma
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.mathtext as mathtext
import matplotlib.patches as mpatches
import matplotlib.texmanager as texmanager
import matplotlib.transforms as mtransforms

# 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.

[docs]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). """
[docs] 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]
[docs]class ContourLabeler(object): """Mixin to provide labelling capability to `.ContourSet`."""
[docs] 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 contour. :: clabel(cs, v, **kwargs) only labels contours listed in *v*. Parameters ---------- fontsize : string or float, optional Size in points or relative size e.g., 'smaller', 'x-large'. See `Text.set_size` for accepted string values. colors : Color of each label - 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 specified inline : bool, optional If ``True`` the underlying contour is removed where the label is placed. Default is ``True``. inline_spacing : float, optional 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. fmt : string or dict, optional 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. manual : bool or iterable, optional 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 positions. rightside_up : bool, optional If ``True``, label rotations will always be plus or minus 90 degrees from level. Default is ``True``. use_clabeltext : bool, optional If ``True``, `ClabelText` class (instead of `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. Default is ``False``. """ """ 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: indices.append(i) levels.append(lev) if len(levels) < len(levlabs): raise ValueError("Specified levels {} don't match available " "levels {}".format(levlabs, self.levels)) else: raise TypeError("Illegal arguments to clabel, see help(clabel)") self.labelLevelList = levels self.labelIndiceList = indices self.labelFontProps = font_manager.FontProperties() self.labelFontProps.set_size(fontsize) 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) else: cmap = colors.ListedColormap(_colors, N=len(self.labelLevelList)) self.labelCValueList = list(xrange(len(self.labelLevelList))) self.labelMappable = cm.ScalarMappable(cmap=cmap, norm=colors.NoNorm()) self.labelXYs = [] if cbook.iterable(self.labelManual): for x, y in self.labelManual: self.add_label_near(x, y, inline, inline_spacing) 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) else: 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
[docs] def print_label(self, linecontour, labelwidth): "Return *False* if contours are too short for a label." return (len(linecontour) > 10 * labelwidth or (np.ptp(linecontour, axis=0) > 1.2 * labelwidth).any())
[docs] 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
[docs] 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): continue return x, y, ind ind = adist[0] x, y = XX[ind][hysize], YY[ind][hysize] return x, y, ind
[docs] def get_label_width(self, lev, fmt, fsize): """ Return the width of the label in points. """ if not isinstance(lev, six.string_types): 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, fsize) elif ismath: if not hasattr(self, '_mathtext_parser'): self._mathtext_parser = mathtext.MathTextParser('bitmap') img, _ = self._mathtext_parser.parse(lev, dpi=72, prop=self.labelFontProps) lw = img.get_width() # at dpi=72, the units are PostScript points else: # width is much less than "font size" lw = (len(lev)) * fsize * 0.6 return lw
[docs] @cbook.deprecated("2.2") 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
[docs] 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.labelFontProps) label.set_clip_box(
[docs] def get_text(self, lev, fmt): """Get the text of the label.""" if isinstance(lev, six.string_types): return lev else: if isinstance(fmt, dict): return fmt.get(lev, '%1.3f') elif callable(fmt): return fmt(lev) else: return fmt % lev
[docs] def locate_label(self, linecontour, labelwidth): """ Find good place to draw a label (relatively flat part of the contour). """ # Number of contour points nsize = len(linecontour) if labelwidth > 1: xsize = int(np.ceil(nsize / labelwidth)) else: xsize = 1 if xsize == 1: ysize = nsize else: 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[:, :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'): dist = np.sum(np.abs(s) / L, axis=-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
[docs] 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 calculating path lengths 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 = _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 # 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 _get_label_text(self, x, y, rotation): dx, dy =, y)) t = text.Text(dx, dy, rotation=rotation, horizontalalignment='center', verticalalignment='center') 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], horizontalalignment='center', verticalalignment='center') 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.labelTexts.append(t) self.labelCValues.append(cvalue) self.labelXYs.append((x, y)) # Add label to plot here - useful for manual mode label selection
[docs] 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)
[docs] 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)
[docs] def add_label_near(self, x, y, inline=True, inline_spacing=5, transform=None): """ 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. Parameters ---------- x, y : float The approximate location of the label. inline : bool, optional, default: True If *True* remove the segment of the contour beneath the label. inline_spacing : int, optional, 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. """ 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 its vertices 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]) # lw is in points. lw *= / 72.0 # scale to screen coordinates # now lw in pixels # Figure out label rotation. if inline: lcarg = lc else: lcarg = None rotation, nlc = self.calc_label_rot_and_inline( slc, imin, lw, lcarg, 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 for n in nlc: if len(n) > 1: paths.append(mpath.Path(n))
[docs] 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()
[docs] def labels(self, inline, inline_spacing): if self._use_clabeltext: add_label = self.add_label_clabeltext else: 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 _is_closed_polygon(lc): slc = np.r_[slc0, slc0[1:2, :]] else: 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 else: lcarg = None rotation, new = self.calc_label_rot_and_inline( slc0, ind, lw, lcarg, 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, remove old # paths and add new ones if inlining if inline: del paths[:] paths.extend(additions)
def _find_closest_point_on_leg(p1, p2, p0): """Find the 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 _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.all(X[0] == X[-1]) 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 = _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)
[docs]class ContourSet(cm.ScalarMappable, ContourLabeler): """ Store a set of contour lines or filled regions. User-callable method: `~.axes.Axes.clabel` Parameters ---------- ax : `~.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 = 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)``. 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.Axes.contour`. Attributes ---------- ax: The axes object in which the contours are drawn. collections: A silent_list of LineCollections or PolyCollections. levels: Contour levels. layers: 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``. Call signature:: ContourSet(ax, levels, allsegs, [allkinds], **kwargs) Parameters ---------- ax : 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 = array_like [[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.Axes.contour`. """ = ax self.levels = kwargs.pop('levels', None) self.filled = kwargs.pop('filled', False) self.linewidths = kwargs.pop('linewidths', None) self.linestyles = kwargs.pop('linestyles', None) self.hatches = kwargs.pop('hatches', [None]) self.alpha = kwargs.pop('alpha', None) self.origin = kwargs.pop('origin', None) self.extent = kwargs.pop('extent', None) cmap = kwargs.pop('cmap', None) self.colors = kwargs.pop('colors', None) norm = kwargs.pop('norm', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) self.extend = kwargs.pop('extend', 'neither') self.antialiased = kwargs.pop('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.pop('nchunk', 0) self.locator = kwargs.pop('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 != 'neither': raise ValueError('extend kwarg does not work yet with log ' ' scale') else: 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.pop('transform', None) kwargs = self._process_args(*args, **kwargs) self._process_levels() 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: cmap.set_under(self.colors[0]) if extend_max: cmap.set_over(self.colors[-1]) if self.filled: self.collections = cbook.silent_list('mcoll.PathCollection') else: 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._process_colors() 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) # Default zorder taken from Collection zorder = kwargs.pop('zorder', 1) for level, level_upper, segs, kinds in \ zip(lowers, uppers, self.allsegs, self.allkinds): paths = self._make_paths(segs, kinds) col = mcoll.PathCollection( paths, antialiaseds=(self.antialiased,), edgecolors='none', alpha=self.alpha, transform=self.get_transform(), zorder=zorder), autolim=False) self.collections.append(col) else: tlinewidths = self._process_linewidths() self.tlinewidths = tlinewidths tlinestyles = self._process_linestyles() aa = self.antialiased if aa is not None: aa = (self.antialiased,) # Default zorder taken from LineCollection zorder = kwargs.pop('zorder', 2) for level, width, lstyle, segs in \ zip(self.levels, tlinewidths, tlinestyles, self.allsegs): col = mcoll.LineCollection( segs, antialiaseds=aa, linewidths=width, linestyles=[lstyle], alpha=self.alpha, transform=self.get_transform(), zorder=zorder) col.set_label('_nolegend_'), autolim=False) self.collections.append(col) for col in self.collections: col.sticky_edges.x[:] = [self._mins[0], self._maxs[0]] col.sticky_edges.y[:] = [self._mins[1], self._maxs[1]][self._mins, self._maxs]) self.changed() # set the colors if kwargs: s = ", ".join(map(repr, kwargs)) warnings.warn('The following kwargs were not used by contour: ' + s)
[docs] 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, mtransforms.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
[docs] def legend_elements(self, variable_name='x', str_format=str): """ Return a list of artists and labels suitable for passing through to :func:`plt.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(r'$%s \leq %s$' % (variable_name, lower)) elif i == n_levels - 1 and self.extend in ('max', 'both'): labels.append(r'$%s > %s$' % (variable_name, upper)) else: labels.append(r'$%s < %s \leq %s$' % (lower, variable_name, 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(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 limits. """ self.levels = args[0] self.allsegs = args[1] self.allkinds = len(args) > 2 and args[2] or None self.zmax = np.max(self.levels) self.zmin = np.min(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 ' '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): """ 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 else: 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)] else: return [mpath.Path(seg) for seg in segs]
[docs] 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, self.collections): if self.filled: collection.set_facecolor(color) # update the collection's hatch (may be None) collection.set_hatch(hatch) else: collection.set_color(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. 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) self._auto = True return lev def _contour_level_args(self, z, args): """ Determine the contour levels and store in self.levels. """ if self.filled: fn = 'contourf' else: fn = 'contour' self._auto = False if self.levels is None: if len(args) == 0: levels_arg = 7 # Default, hard-wired. else: levels_arg = args[0] else: levels_arg = self.levels if isinstance(levels_arg, Integral): self.levels = self._autolev(levels_arg) else: self.levels = np.asarray(levels_arg).astype(np.float64) if not self.filled: inside = (self.levels > self.zmin) & (self.levels < self.zmax) self.levels = self.levels[inside] if len(self.levels) == 0: self.levels = [self.zmin] warnings.warn("No contour levels were found" " within the data range.") 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.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 return # 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 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(colors.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: tlinewidths = [(mpl.rcParams['lines.linewidth'],)] * Nlev else: if not cbook.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: 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 else: if isinstance(linestyles, six.string_types): 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] else: raise ValueError("Unrecognized type for linestyles kwarg") return tlinestyles
[docs] def get_alpha(self): """returns alpha to be applied to all ContourSet artists""" return self.alpha
[docs] 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()
[docs] 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 argument. Returns a tuple containing the contour, segment, index of segment, x & y of segment point and distance to minimum point. Optional keyword arguments: *indices*: Indexes of contour levels to consider when looking for nearest point. Defaults to using all levels. *pixel*: 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)
[docs]class QuadContourSet(ContourSet): """ Create and store a set of contour lines or filled regions. User-callable method: `~axes.Axes.clabel` Attributes ---------- ax: The axes object in which the contours are drawn. collections: A silent_list of LineCollections or PolyCollections. levels: Contour levels. layers: Same as levels for line contours; half-way between levels for filled contours. See :meth:`_process_colors` method. """ 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 contour_generator = args[0]._contour_generator self._mins = args[0]._mins self._maxs = args[0]._maxs else: self._corner_mask = kwargs.pop('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 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 any(t.contains_branch_seperately( 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] self._mins = [ma.min(x), ma.min(y)] self._maxs = [ma.max(x), ma.max(y)] self._contour_generator = contour_generator return kwargs def _get_allsegs_and_allkinds(self): """Compute ``allsegs`` and ``allkinds`` using C extension.""" allsegs = [] if self.filled: lowers, uppers = self._get_lowers_and_uppers() allkinds = [] 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: allkinds = None for level in self.levels: vertices = self._contour_generator.create_contour(level) allsegs.append(vertices) return allsegs, allkinds def _contour_args(self, args, kwargs): if self.filled: fn = 'contourf' else: 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:] else: raise TypeError("Too many arguments to %s; see help(%s)" % (fn, fn)) 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) warnings.warn('Log scale: values of z <= 0 have been masked') self.zmin = float(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] kwargs =, 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.") elif z.shape[0] < 2 or z.shape[1] < 2: raise TypeError("Input z must be at least a 2x2 array.") else: 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)) else: 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.") elif z.shape[0] < 2 or z.shape[1] < 2: raise TypeError("Input z must be at least a 2x2 array.") 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) _contour_doc = """ Plot contours. Call signature:: contour([X, Y,] Z, [levels], **kwargs) :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. Parameters ---------- X, Y : array-like, optional The coordinates of the values in *Z*. *X* and *Y* must both be 2-D with the same shape as *Z* (e.g. created via :func:`numpy.meshgrid`), or they must both be 1-D such that ``len(X) == M`` is the number of columns in *Z* and ``len(Y) == N`` is the number of rows in *Z*. If not given, they are assumed to be integer indices, i.e. ``X = range(M)``, ``Y = range(N)``. Z : array-like(N, M) The height values over which the contour is drawn. levels : int or array-like, optional Determines the number and positions of the contour lines / regions. If an int *n*, use *n* data intervals; i.e. draw *n+1* contour lines. The level heights are automatically chosen. If array-like, draw contour lines at the specified levels. The values must be in increasing order. Returns ------- :class:`~matplotlib.contour.QuadContourSet` Other Parameters ---------------- corner_mask : bool, optional 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. Defaults to ``rcParams['contour.corner_mask']``, which defaults to ``True``. 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, optional The alpha blending value, between 0 (transparent) and 1 (opaque). cmap : str or `.Colormap`, optional A `.Colormap` instance or registered colormap name. The colormap maps the level values to colors. Defaults to :rc:`image.cmap`. If given, *colors* take precedence over *cmap*. norm : `~matplotlib.colors.Normalize`, optional If a colormap is used, the `.Normalize` instance scales the level values to the canonical colormap range [0, 1] for mapping to colors. If not given, the default linear scaling is used. vmin, vmax : float, optional If not *None*, either or both of these values will be supplied to the `.Normalize` instance, overriding the default color scaling based on *levels*. origin : {*None*, 'upper', 'lower', 'image'}, optional 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`. Note: The value *None* in the rcParam is currently handled as 'lower'. extent : (x0, x1, y0, y1), optional 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 : 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'}, optional 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 : registered units, optional Override axis units by specifying an instance of a :class:`matplotlib.units.ConversionInterface`. antialiased : bool, optinal 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 sequence of float, optional *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. Defaults 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 take their linestyle from :rc:`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. 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. Notes ----- 1. :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`. 2. 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. """