""" Classes to support contour plotting and labelling for the Axes class. """ from contextlib import ExitStack import functools import math 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.lines import Line2D from matplotlib.path import Path from matplotlib.text import Text 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 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 cs.axes.contains(event)[0]: 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 : :mpltype:`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. """ # Based on the input arguments, clabel() adds 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 (for automatic label placement) or blocking_input_loop and # _contour_labeler_event_handler (for manual label placement). if fmt is None: fmt = ticker.ScalarFormatter(useOffset=False) fmt.create_dummy_axis() self.labelFmt = fmt self._use_clabeltext = use_clabeltext self.labelManual = manual self.rightside_up = rightside_up self._clabel_zorder = 2 + self.get_zorder() if zorder is None else 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) def print_label(self, linecontour, labelwidth): """Return whether a contour is long enough to hold a label.""" return (len(linecontour) > 10 * labelwidth or (len(linecontour) and (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) 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 _split_path_and_get_label_rotation(self, path, idx, screen_pos, lw, spacing=5): """ Prepare for insertion of a label at index *idx* of *path*. Parameters ---------- path : Path The path where the label will be inserted, in data space. idx : int The vertex index after which the label will be inserted. screen_pos : (float, float) The position where the label will be inserted, in screen space. lw : float The label width, in screen space. spacing : float Extra spacing around the label, in screen space. Returns ------- path : Path The path, broken so that the label can be drawn over it. angle : float The rotation of the label. Notes ----- 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 (label width / 2) away from central point to determine rotation and then to break contour if desired. The extra spacing is taken into account when breaking the path, but not when computing the angle. """ if hasattr(self, "_old_style_split_collections"): vis = False for coll in self._old_style_split_collections: vis |= coll.get_visible() coll.remove() self.set_visible(vis) del self._old_style_split_collections # Invalidate them. xys = path.vertices codes = path.codes # Insert a vertex at idx/pos (converting back to data space), if there isn't yet # a vertex there. With infinite precision one could also always insert the # extra vertex (it will get masked out by the label below anyways), but floating # point inaccuracies (the point can have undergone a data->screen->data # transform loop) can slightly shift the point and e.g. shift the angle computed # below from exactly zero to nonzero. pos = self.get_transform().inverted().transform(screen_pos) if not np.allclose(pos, xys[idx]): xys = np.insert(xys, idx, pos, axis=0) codes = np.insert(codes, idx, Path.LINETO) # Find the connected component where the label will be inserted. Note that a # path always starts with a MOVETO, and we consider there's an implicit # MOVETO (closing the last path) at the end. movetos = (codes == Path.MOVETO).nonzero()[0] start = movetos[movetos <= idx][-1] try: stop = movetos[movetos > idx][0] except IndexError: stop = len(codes) # Restrict ourselves to the connected component. cc_xys = xys[start:stop] idx -= start # If the path is closed, rotate it s.t. it starts at the label. is_closed_path = codes[stop - 1] == Path.CLOSEPOLY if is_closed_path: cc_xys = np.concatenate([cc_xys[idx:-1], cc_xys[:idx+1]]) idx = 0 # Like np.interp, but additionally vectorized over fp. def interp_vec(x, xp, fp): return [np.interp(x, xp, col) for col in fp.T] # Use cumulative path lengths ("cpl") as curvilinear coordinate along contour. screen_xys = self.get_transform().transform(cc_xys) path_cpls = np.insert( np.cumsum(np.hypot(*np.diff(screen_xys, axis=0).T)), 0, 0) path_cpls -= path_cpls[idx] # Use linear interpolation to get end coordinates of label. target_cpls = np.array([-lw/2, lw/2]) if is_closed_path: # For closed paths, target from the other end. target_cpls[0] += (path_cpls[-1] - path_cpls[0]) (sx0, sx1), (sy0, sy1) = interp_vec(target_cpls, path_cpls, screen_xys) angle = np.rad2deg(np.arctan2(sy1 - sy0, sx1 - sx0)) # Screen space. if self.rightside_up: # Fix angle so text is never upside-down angle = (angle + 90) % 180 - 90 target_cpls += [-spacing, +spacing] # Expand range by spacing. # Get indices near points of interest; use -1 as out of bounds marker. i0, i1 = np.interp(target_cpls, path_cpls, range(len(path_cpls)), left=-1, right=-1) i0 = math.floor(i0) i1 = math.ceil(i1) (x0, x1), (y0, y1) = interp_vec(target_cpls, path_cpls, cc_xys) # Actually break contours (dropping zero-len parts). new_xy_blocks = [] new_code_blocks = [] if is_closed_path: if i0 != -1 and i1 != -1: # This is probably wrong in the case that the entire contour would # be discarded, but ensures that a valid path is returned and is # consistent with behavior of mpl <3.8 points = cc_xys[i1:i0+1] new_xy_blocks.extend([[(x1, y1)], points, [(x0, y0)]]) nlines = len(points) + 1 new_code_blocks.extend([[Path.MOVETO], [Path.LINETO] * nlines]) else: if i0 != -1: new_xy_blocks.extend([cc_xys[:i0 + 1], [(x0, y0)]]) new_code_blocks.extend([[Path.MOVETO], [Path.LINETO] * (i0 + 1)]) if i1 != -1: new_xy_blocks.extend([[(x1, y1)], cc_xys[i1:]]) new_code_blocks.extend([ [Path.MOVETO], [Path.LINETO] * (len(cc_xys) - i1)]) # Back to the full path. xys = np.concatenate([xys[:start], *new_xy_blocks, xys[stop:]]) codes = np.concatenate([codes[:start], *new_code_blocks, codes[stop:]]) return angle, Path(xys, codes) @_api.deprecated("3.8") 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.vstack([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.vstack([lc[:I[0]+1], xy1])) if I[1] != -1: nlc.append(np.vstack([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 a contour label, respecting whether *use_clabeltext* was set.""" 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.get_alpha()), fontproperties=self._label_font_props, clip_box=self.axes.bbox) if self._use_clabeltext: data_rotation, = self.axes.transData.inverted().transform_angles( [rotation], [[x, y]]) t.set(rotation=data_rotation, transform_rotates_text=True) 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) @_api.deprecated("3.8", alternative="add_label") def add_label_clabeltext(self, x, y, rotation, lev, cvalue): """Add contour label with `.Text.set_transform_rotates_text`.""" with cbook._setattr_cm(self, _use_clabeltext=True): self.add_label(x, y, rotation, lev, cvalue) 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)) idx_level_min, idx_vtx_min, proj = self._find_nearest_contour( (x, y), self.labelIndiceList) path = self._paths[idx_level_min] level = self.labelIndiceList.index(idx_level_min) label_width = self._get_nth_label_width(level) rotation, path = self._split_path_and_get_label_rotation( path, idx_vtx_min, proj, label_width, inline_spacing) self.add_label(*proj, rotation, self.labelLevelList[idx_level_min], self.labelCValueList[idx_level_min]) if inline: self._paths[idx_level_min] = path 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): for idx, (icon, lev, cvalue) in enumerate(zip( self.labelIndiceList, self.labelLevelList, self.labelCValueList, )): trans = self.get_transform() label_width = self._get_nth_label_width(idx) additions = [] for subpath in self._paths[icon]._iter_connected_components(): screen_xys = trans.transform(subpath.vertices) # Check if long enough for a label if self.print_label(screen_xys, label_width): x, y, idx = self.locate_label(screen_xys, label_width) rotation, path = self._split_path_and_get_label_rotation( subpath, idx, (x, y), label_width, inline_spacing) self.add_label(x, y, rotation, lev, cvalue) # Really add label. if inline: # If inline, add new contours additions.append(path) else: # If not adding label, keep old path additions.append(subpath) # After looping over all segments on a contour, replace old path by new one # if inlining. if inline: self._paths[icon] = Path.make_compound_path(*additions) def remove(self): super().remove() 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(ContourLabeler, mcoll.Collection): """ 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, clip_path=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`. """ if antialiased is None and filled: # Eliminate artifacts; we are not stroking the boundaries. antialiased = False # The default for line contours will be taken from the # LineCollection default, which uses :rc:`lines.antialiased`. super().__init__( antialiaseds=antialiased, alpha=alpha, clip_path=clip_path, transform=transform, ) self.axes = ax self.levels = levels self.filled = filled self.hatches = hatches self.origin = origin self.extent = extent self.colors = colors self.extend = extend 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._orig_linestyles = linestyles # Only kept for user access. 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]) # label lists must be initialized here self.labelTexts = [] self.labelCValues = [] self.set_cmap(cmap) if norm is not None: self.set_norm(norm) with self.norm.callbacks.blocked(signal="changed"): if vmin is not None: self.norm.vmin = vmin if vmax is not None: self.norm.vmax = vmax self.norm._changed() self._process_colors() if self._paths is None: self._paths = self._make_paths_from_contour_generator() if self.filled: if linewidths is not None: _api.warn_external('linewidths is ignored by contourf') # Lower and upper contour levels. lowers, uppers = self._get_lowers_and_uppers() self.set( edgecolor="none", # Default zorder taken from Collection zorder=kwargs.pop("zorder", 1), ) else: self.set( facecolor="none", linewidths=self._process_linewidths(linewidths), linestyle=self._process_linestyles(linestyles), # Default zorder taken from LineCollection, which is higher # than for filled contours so that lines are displayed on top. zorder=kwargs.pop("zorder", 2), label="_nolegend_", ) self.axes.add_collection(self, autolim=False) self.sticky_edges.x[:] = [self._mins[0], self._maxs[0]] self.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)) ) allsegs = property(lambda self: [ [subp.vertices for subp in p._iter_connected_components()] for p in self.get_paths()]) allkinds = property(lambda self: [ [subp.codes for subp in p._iter_connected_components()] for p in self.get_paths()]) tcolors = _api.deprecated("3.8")(property(lambda self: [ (tuple(rgba),) for rgba in self.to_rgba(self.cvalues, self.alpha)])) tlinewidths = _api.deprecated("3.8")(property(lambda self: [ (w,) for w in self.get_linewidths()])) alpha = property(lambda self: self.get_alpha()) linestyles = property(lambda self: self._orig_linestyles) @_api.deprecated("3.8", alternative="set_antialiased or get_antialiased", addendum="Note that get_antialiased returns an array.") @property def antialiased(self): return all(self.get_antialiased()) @antialiased.setter def antialiased(self, aa): self.set_antialiased(aa) @_api.deprecated("3.8") @property def collections(self): # On access, make oneself invisible and instead add the old-style collections # (one PathCollection per level). We do not try to further split contours into # connected components as we already lost track of what pairs of contours need # to be considered as single units to draw filled regions with holes. if not hasattr(self, "_old_style_split_collections"): self.set_visible(False) fcs = self.get_facecolor() ecs = self.get_edgecolor() lws = self.get_linewidth() lss = self.get_linestyle() self._old_style_split_collections = [] for idx, path in enumerate(self._paths): pc = mcoll.PathCollection( [path] if len(path.vertices) else [], alpha=self.get_alpha(), antialiaseds=self._antialiaseds[idx % len(self._antialiaseds)], transform=self.get_transform(), zorder=self.get_zorder(), label="_nolegend_", facecolor=fcs[idx] if len(fcs) else "none", edgecolor=ecs[idx] if len(ecs) else "none", linewidths=[lws[idx % len(lws)]], linestyles=[lss[idx % len(lss)]], ) if self.filled: pc.set(hatch=self.hatches[idx % len(self.hatches)]) self._old_style_split_collections.append(pc) for col in self._old_style_split_collections: self.axes.add_collection(col) return self._old_style_split_collections 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._paths) for idx in range(n_levels): artists.append(mpatches.Rectangle( (0, 0), 1, 1, facecolor=self.get_facecolor()[idx], hatch=self.hatches[idx % len(self.hatches)], )) lower = str_format(lowers[idx]) upper = str_format(uppers[idx]) if idx == 0 and self.extend in ('min', 'both'): labels.append(fr'${variable_name} \leq {lower}s$') elif idx == 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 idx, level in enumerate(self.levels): artists.append(Line2D( [], [], color=self.get_edgecolor()[idx], linewidth=self.get_linewidths()[idx], linestyle=self.get_linestyles()[idx], )) labels.append(fr'${variable_name} = {str_format(level)}$') 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] allsegs = args[1] allkinds = args[2] if len(args) > 2 else None self.zmax = np.max(self.levels) self.zmin = np.min(self.levels) if allkinds is None: allkinds = [[None] * len(segs) for segs in allsegs] # Check lengths of levels and allsegs. if self.filled: if len(allsegs) != len(self.levels) - 1: raise ValueError('must be one less number of segments as ' 'levels') else: if len(allsegs) != len(self.levels): raise ValueError('must be same number of segments as levels') # Check length of allkinds. if len(allkinds) != len(allsegs): raise ValueError('allkinds has different length to allsegs') # Determine x, y bounds and update axes data limits. flatseglist = [s for seg in allsegs for s in seg] points = np.concatenate(flatseglist, axis=0) self._mins = points.min(axis=0) self._maxs = points.max(axis=0) # Each entry in (allsegs, allkinds) is a list of (segs, kinds): segs is a list # of (N, 2) arrays of xy coordinates, kinds is a list of arrays of corresponding # pathcodes. However, kinds can also be None; in which case all paths in that # list are codeless (this case is normalized above). These lists are used to # construct paths, which then get concatenated. self._paths = [Path.make_compound_path(*map(Path, segs, kinds)) for segs, kinds in zip(allsegs, allkinds)] return kwargs def _make_paths_from_contour_generator(self): """Compute ``paths`` using C extension.""" if self._paths is not None: return self._paths cg = self._contour_generator empty_path = Path(np.empty((0, 2))) vertices_and_codes = ( map(cg.create_filled_contour, *self._get_lowers_and_uppers()) if self.filled else map(cg.create_contour, self.levels)) return [Path(np.concatenate(vs), np.concatenate(cs)) if len(vs) else empty_path for vs, cs in vertices_and_codes] 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 changed(self): if not hasattr(self, "cvalues"): self._process_colors() # Sets cvalues. # 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) self.set_array(self.cvalues) self.update_scalarmappable() alphas = np.broadcast_to(self.get_alpha(), len(self.cvalues)) for label, cv, alpha in zip(self.labelTexts, self.labelCValues, alphas): label.set_alpha(alpha) label.set_color(self.labelMappable.to_rgba(cv)) super().changed() 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.norm.autoscale_None(self.levels) self.set_array(self.cvalues) self.update_scalarmappable() if self.extend in ('both', 'max', 'min'): self.norm.clip = False def _process_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'] return [default_linewidth] * Nlev elif not np.iterable(linewidths): return [linewidths] * Nlev else: linewidths = list(linewidths) return (linewidths * math.ceil(Nlev / len(linewidths)))[:Nlev] def _process_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 _find_nearest_contour(self, xy, indices=None): """ Find the point in the unfilled contour plot that is closest (in screen space) to point *xy*. Parameters ---------- xy : tuple[float, float] The reference point (in screen space). indices : list of int or None, default: None Indices of contour levels to consider. If None (the default), all levels are considered. Returns ------- idx_level_min : int The index of the contour level closest to *xy*. idx_vtx_min : int The index of the `.Path` segment closest to *xy* (at that level). proj : (float, float) The point in the contour plot closest to *xy*. """ # Convert each contour segment to pixel coordinates and then compare the given # point to those coordinates for each contour. This is fast enough in normal # cases, but speedups may be possible. if self.filled: raise ValueError("Method does not support filled contours") if indices is None: indices = range(len(self._paths)) d2min = np.inf idx_level_min = idx_vtx_min = proj_min = None for idx_level in indices: path = self._paths[idx_level] idx_vtx_start = 0 for subpath in path._iter_connected_components(): if not len(subpath.vertices): continue lc = self.get_transform().transform(subpath.vertices) d2, proj, leg = _find_closest_point_on_path(lc, xy) if d2 < d2min: d2min = d2 idx_level_min = idx_level idx_vtx_min = leg[1] + idx_vtx_start proj_min = proj idx_vtx_start += len(subpath) return idx_level_min, idx_vtx_min, proj_min 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 ------- path : int The index of the path that is closest to ``(x, y)``. Each path corresponds to one contour level. subpath : int The index within that closest path of the subpath that is closest to ``(x, y)``. Each subpath corresponds to one unbroken contour line. index : int The index of the vertices within that subpath that are 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)``. """ segment = index = d2 = None with ExitStack() as stack: if not pixel: # _find_nearest_contour works in pixel space. We want axes space, so # effectively disable the transformation here by setting to identity. stack.enter_context(self._cm_set( transform=mtransforms.IdentityTransform())) i_level, i_vtx, (xmin, ymin) = self._find_nearest_contour((x, y), indices) if i_level is not None: cc_cumlens = np.cumsum( [*map(len, self._paths[i_level]._iter_connected_components())]) segment = cc_cumlens.searchsorted(i_vtx, "right") index = i_vtx if segment == 0 else i_vtx - cc_cumlens[segment - 1] d2 = (xmin-x)**2 + (ymin-y)**2 return (i_level, segment, index, xmin, ymin, d2) def draw(self, renderer): paths = self._paths n_paths = len(paths) if not self.filled or all(hatch is None for hatch in self.hatches): super().draw(renderer) return # In presence of hatching, draw contours one at a time. edgecolors = self.get_edgecolors() if edgecolors.size == 0: edgecolors = ("none",) for idx in range(n_paths): with cbook._setattr_cm(self, _paths=[paths[idx]]), self._cm_set( hatch=self.hatches[idx % len(self.hatches)], array=[self.get_array()[idx]], linewidths=[self.get_linewidths()[idx % len(self.get_linewidths())]], linestyles=[self.get_linestyles()[idx % len(self.get_linestyles())]], edgecolors=edgecolors[idx % len(edgecolors)], ): super().draw(renderer) @_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 args and 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 0 < nargs <= 2: z, *args = args z = ma.asarray(z) x, y = self._initialize_x_y(z) elif 2 < 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 = z.max().astype(float) self.zmin = z.min().astype(float) 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 = z.min().astype(float) 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 : :mpltype:`color` or list of :mpltype:`color`, 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 *False*. 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. algorithm : {'mpl2005', 'mpl2014', 'serial', 'threaded'}, optional Which contouring algorithm to use to calculate the contour lines and polygons. The algorithms are implemented in `ContourPy `_, consult the `ContourPy documentation `_ for further information. The default is taken from :rc:`contour.algorithm`. clip_path : `~matplotlib.patches.Patch` or `.Path` or `.TransformedPath` Set the clip path. See `~matplotlib.artist.Artist.set_clip_path`. .. versionadded:: 3.8 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 `_ algorithm to compute contour locations. More information can be found in `ContourPy documentation `_. """ % _docstring.interpd.params)