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Version 3.1.0.post919+g4ce649c38
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Source code for matplotlib.axes._axes

import collections.abc
import functools
import itertools
import logging
import math
from numbers import Number

import numpy as np
from numpy import ma

from matplotlib import _preprocess_data, rcParams
import matplotlib.cbook as cbook
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.contour as mcontour
import matplotlib.category as _  # <-registers a category unit converter
import matplotlib.dates as _  # <-registers a date unit converter
import matplotlib.docstring as docstring
import matplotlib.image as mimage
import matplotlib.legend as mlegend
import matplotlib.lines as mlines
import matplotlib.markers as mmarkers
import matplotlib.mlab as mlab
import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib.quiver as mquiver
import matplotlib.stackplot as mstack
import matplotlib.streamplot as mstream
import matplotlib.table as mtable
import matplotlib.text as mtext
import matplotlib.ticker as mticker
import matplotlib.transforms as mtransforms
import matplotlib.tri as mtri
from matplotlib.container import BarContainer, ErrorbarContainer, StemContainer
from matplotlib.axes._base import _AxesBase, _process_plot_format
from matplotlib.axes._secondary_axes import SecondaryAxis

try:
    from numpy.lib.histograms import histogram_bin_edges
except ImportError:
    # this function is new in np 1.15
    def histogram_bin_edges(arr, bins, range=None, weights=None):
        # this in True for 1D arrays, and False for None and str
        if np.ndim(bins) == 1:
            return bins

        if isinstance(bins, str):
            # rather than backporting the internals, just do the full
            # computation.  If this is too slow for users, they can
            # update numpy, or pick a manual number of bins
            return np.histogram(arr, bins, range, weights)[1]
        else:
            if bins is None:
                # hard-code numpy's default
                bins = 10
            if range is None:
                range = np.min(arr), np.max(arr)

            return np.linspace(*range, bins + 1)


_log = logging.getLogger(__name__)


def _make_inset_locator(bounds, trans, parent):
    """
    Helper function to locate inset axes, used in
    `.Axes.inset_axes`.

    A locator gets used in `Axes.set_aspect` to override the default
    locations...  It is a function that takes an axes object and
    a renderer and tells `set_aspect` where it is to be placed.

    Here *rect* is a rectangle [l, b, w, h] that specifies the
    location for the axes in the transform given by *trans* on the
    *parent*.
    """
    _bounds = mtransforms.Bbox.from_bounds(*bounds)
    _trans = trans
    _parent = parent

    def inset_locator(ax, renderer):
        bbox = _bounds
        bb = mtransforms.TransformedBbox(bbox, _trans)
        tr = _parent.figure.transFigure.inverted()
        bb = mtransforms.TransformedBbox(bb, tr)
        return bb

    return inset_locator


# The axes module contains all the wrappers to plotting functions.
# All the other methods should go in the _AxesBase class.


[docs]class Axes(_AxesBase): """ The `Axes` contains most of the figure elements: `~.axis.Axis`, `~.axis.Tick`, `~.lines.Line2D`, `~.text.Text`, `~.patches.Polygon`, etc., and sets the coordinate system. The `Axes` instance supports callbacks through a callbacks attribute which is a `~.cbook.CallbackRegistry` instance. The events you can connect to are 'xlim_changed' and 'ylim_changed' and the callback will be called with func(*ax*) where *ax* is the `Axes` instance. Attributes ---------- dataLim : `.BBox` The bounding box enclosing all data displayed in the Axes. viewLim : `.BBox` The view limits in data coordinates. """ ### Labelling, legend and texts @cbook.deprecated("3.1") @property def aname(self): return 'Axes'
[docs] def get_title(self, loc="center"): """ Get an axes title. Get one of the three available axes titles. The available titles are positioned above the axes in the center, flush with the left edge, and flush with the right edge. Parameters ---------- loc : {'center', 'left', 'right'}, str, optional Which title to get, defaults to 'center'. Returns ------- title : str The title text string. """ try: title = {'left': self._left_title, 'center': self.title, 'right': self._right_title}[loc.lower()] except KeyError: raise ValueError("'%s' is not a valid location" % loc) return title.get_text()
[docs] def set_title(self, label, fontdict=None, loc=None, pad=None, **kwargs): """ Set a title for the axes. Set one of the three available axes titles. The available titles are positioned above the axes in the center, flush with the left edge, and flush with the right edge. Parameters ---------- label : str Text to use for the title fontdict : dict A dictionary controlling the appearance of the title text, the default `fontdict` is:: {'fontsize': rcParams['axes.titlesize'], 'fontweight' : rcParams['axes.titleweight'], 'verticalalignment': 'baseline', 'horizontalalignment': loc} loc : {'center', 'left', 'right'}, str, optional Which title to set, defaults to rcParams['axes.titlelocation'] pad : float The offset of the title from the top of the axes, in points. Default is ``None`` to use rcParams['axes.titlepad']. Returns ------- text : :class:`~matplotlib.text.Text` The matplotlib text instance representing the title Other Parameters ---------------- **kwargs : `~matplotlib.text.Text` properties Other keyword arguments are text properties, see :class:`~matplotlib.text.Text` for a list of valid text properties. """ try: if loc is None: loc = rcParams['axes.titlelocation'] title = {'left': self._left_title, 'center': self.title, 'right': self._right_title}[loc.lower()] except KeyError: raise ValueError("'%s' is not a valid location" % loc) default = { 'fontsize': rcParams['axes.titlesize'], 'fontweight': rcParams['axes.titleweight'], 'verticalalignment': 'baseline', 'horizontalalignment': loc.lower()} if pad is None: pad = rcParams['axes.titlepad'] self._set_title_offset_trans(float(pad)) title.set_text(label) title.update(default) if fontdict is not None: title.update(fontdict) title.update(kwargs) return title
[docs] def get_xlabel(self): """ Get the xlabel text string. """ label = self.xaxis.get_label() return label.get_text()
[docs] def set_xlabel(self, xlabel, fontdict=None, labelpad=None, **kwargs): """ Set the label for the x-axis. Parameters ---------- xlabel : str The label text. labelpad : scalar, optional, default: None Spacing in points from the axes bounding box including ticks and tick labels. Other Parameters ---------------- **kwargs : `.Text` properties `.Text` properties control the appearance of the label. See also -------- text : for information on how override and the optional args work """ if labelpad is not None: self.xaxis.labelpad = labelpad return self.xaxis.set_label_text(xlabel, fontdict, **kwargs)
[docs] def get_ylabel(self): """ Get the ylabel text string. """ label = self.yaxis.get_label() return label.get_text()
[docs] def set_ylabel(self, ylabel, fontdict=None, labelpad=None, **kwargs): """ Set the label for the y-axis. Parameters ---------- ylabel : str The label text. labelpad : scalar, optional, default: None Spacing in points from the axes bounding box including ticks and tick labels. Other Parameters ---------------- **kwargs : `.Text` properties `.Text` properties control the appearance of the label. See also -------- text : for information on how override and the optional args work """ if labelpad is not None: self.yaxis.labelpad = labelpad return self.yaxis.set_label_text(ylabel, fontdict, **kwargs)
[docs] def get_legend_handles_labels(self, legend_handler_map=None): """ Return handles and labels for legend ``ax.legend()`` is equivalent to :: h, l = ax.get_legend_handles_labels() ax.legend(h, l) """ # pass through to legend. handles, labels = mlegend._get_legend_handles_labels([self], legend_handler_map) return handles, labels
[docs] @docstring.dedent_interpd def legend(self, *args, **kwargs): """ Place a legend on the axes. Call signatures:: legend() legend(labels) legend(handles, labels) The call signatures correspond to three different ways how to use this method. **1. Automatic detection of elements to be shown in the legend** The elements to be added to the legend are automatically determined, when you do not pass in any extra arguments. In this case, the labels are taken from the artist. You can specify them either at artist creation or by calling the :meth:`~.Artist.set_label` method on the artist:: line, = ax.plot([1, 2, 3], label='Inline label') ax.legend() or:: line.set_label('Label via method') line, = ax.plot([1, 2, 3]) ax.legend() Specific lines can be excluded from the automatic legend element selection by defining a label starting with an underscore. This is default for all artists, so calling `Axes.legend` without any arguments and without setting the labels manually will result in no legend being drawn. **2. Labeling existing plot elements** To make a legend for lines which already exist on the axes (via plot for instance), simply call this function with an iterable of strings, one for each legend item. For example:: ax.plot([1, 2, 3]) ax.legend(['A simple line']) Note: This way of using is discouraged, because the relation between plot elements and labels is only implicit by their order and can easily be mixed up. **3. Explicitly defining the elements in the legend** For full control of which artists have a legend entry, it is possible to pass an iterable of legend artists followed by an iterable of legend labels respectively:: legend((line1, line2, line3), ('label1', 'label2', 'label3')) Parameters ---------- handles : sequence of `.Artist`, optional A list of Artists (lines, patches) to be added to the legend. Use this together with *labels*, if you need full control on what is shown in the legend and the automatic mechanism described above is not sufficient. The length of handles and labels should be the same in this case. If they are not, they are truncated to the smaller length. labels : sequence of strings, optional A list of labels to show next to the artists. Use this together with *handles*, if you need full control on what is shown in the legend and the automatic mechanism described above is not sufficient. Other Parameters ---------------- %(_legend_kw_doc)s Returns ------- legend : `~matplotlib.legend.Legend` Notes ----- Not all kinds of artist are supported by the legend command. See :doc:`/tutorials/intermediate/legend_guide` for details. Examples -------- .. plot:: gallery/text_labels_and_annotations/legend.py """ handles, labels, extra_args, kwargs = mlegend._parse_legend_args( [self], *args, **kwargs) if len(extra_args): raise TypeError('legend only accepts two non-keyword arguments') self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) self.legend_._remove_method = self._remove_legend return self.legend_
def _remove_legend(self, legend): self.legend_ = None
[docs] def inset_axes(self, bounds, *, transform=None, zorder=5, **kwargs): """ Add a child inset axes to this existing axes. Warnings -------- This method is experimental as of 3.0, and the API may change. Parameters ---------- bounds : [x0, y0, width, height] Lower-left corner of inset axes, and its width and height. transform : `.Transform` Defaults to `ax.transAxes`, i.e. the units of *rect* are in axes-relative coordinates. zorder : number Defaults to 5 (same as `.Axes.legend`). Adjust higher or lower to change whether it is above or below data plotted on the parent axes. **kwargs Other *kwargs* are passed on to the `axes.Axes` child axes. Returns ------- Axes The created `.axes.Axes` instance. Examples -------- This example makes two inset axes, the first is in axes-relative coordinates, and the second in data-coordinates:: fig, ax = plt.subplots() ax.plot(range(10)) axin1 = ax.inset_axes([0.8, 0.1, 0.15, 0.15]) axin2 = ax.inset_axes( [5, 7, 2.3, 2.3], transform=ax.transData) """ if transform is None: transform = self.transAxes label = kwargs.pop('label', 'inset_axes') # This puts the rectangle into figure-relative coordinates. inset_locator = _make_inset_locator(bounds, transform, self) bb = inset_locator(None, None) inset_ax = Axes(self.figure, bb.bounds, zorder=zorder, label=label, **kwargs) # this locator lets the axes move if in data coordinates. # it gets called in `ax.apply_aspect() (of all places) inset_ax.set_axes_locator(inset_locator) self.add_child_axes(inset_ax) return inset_ax
[docs] def indicate_inset(self, bounds, inset_ax=None, *, transform=None, facecolor='none', edgecolor='0.5', alpha=0.5, zorder=4.99, **kwargs): """ Add an inset indicator to the axes. This is a rectangle on the plot at the position indicated by *bounds* that optionally has lines that connect the rectangle to an inset axes (`.Axes.inset_axes`). Warnings -------- This method is experimental as of 3.0, and the API may change. Parameters ---------- bounds : [x0, y0, width, height] Lower-left corner of rectangle to be marked, and its width and height. inset_ax : `.Axes` An optional inset axes to draw connecting lines to. Two lines are drawn connecting the indicator box to the inset axes on corners chosen so as to not overlap with the indicator box. transform : `.Transform` Transform for the rectangle co-ordinates. Defaults to `ax.transAxes`, i.e. the units of *rect* are in axes-relative coordinates. facecolor : Matplotlib color Facecolor of the rectangle (default 'none'). edgecolor : Matplotlib color Color of the rectangle and color of the connecting lines. Default is '0.5'. alpha : number Transparency of the rectangle and connector lines. Default is 0.5. zorder : number Drawing order of the rectangle and connector lines. Default is 4.99 (just below the default level of inset axes). **kwargs Other *kwargs* are passed on to the rectangle patch. Returns ------- rectangle_patch : `.Patches.Rectangle` Rectangle artist. connector_lines : 4-tuple of `.Patches.ConnectionPatch` One for each of four connector lines. Two are set with visibility to *False*, but the user can set the visibility to True if the automatic choice is not deemed correct. """ # to make the axes connectors work, we need to apply the aspect to # the parent axes. self.apply_aspect() if transform is None: transform = self.transData label = kwargs.pop('label', 'indicate_inset') xy = (bounds[0], bounds[1]) rectpatch = mpatches.Rectangle(xy, bounds[2], bounds[3], facecolor=facecolor, edgecolor=edgecolor, alpha=alpha, zorder=zorder, label=label, transform=transform, **kwargs) self.add_patch(rectpatch) if inset_ax is not None: # want to connect the indicator to the rect.... connects = [] xr = [bounds[0], bounds[0]+bounds[2]] yr = [bounds[1], bounds[1]+bounds[3]] for xc in range(2): for yc in range(2): xyA = (xc, yc) xyB = (xr[xc], yr[yc]) connects += [mpatches.ConnectionPatch(xyA, xyB, 'axes fraction', 'data', axesA=inset_ax, axesB=self, arrowstyle="-", zorder=zorder, edgecolor=edgecolor, alpha=alpha)] self.add_patch(connects[-1]) # decide which two of the lines to keep visible.... pos = inset_ax.get_position() bboxins = pos.transformed(self.figure.transFigure) rectbbox = mtransforms.Bbox.from_bounds( *bounds).transformed(transform) x0 = rectbbox.x0 < bboxins.x0 x1 = rectbbox.x1 < bboxins.x1 y0 = rectbbox.y0 < bboxins.y0 y1 = rectbbox.y1 < bboxins.y1 connects[0].set_visible(x0 ^ y0) connects[1].set_visible(x0 == y1) connects[2].set_visible(x1 == y0) connects[3].set_visible(x1 ^ y1) return rectpatch, connects
[docs] def indicate_inset_zoom(self, inset_ax, **kwargs): """ Add an inset indicator rectangle to the axes based on the axis limits for an *inset_ax* and draw connectors between *inset_ax* and the rectangle. Warnings -------- This method is experimental as of 3.0, and the API may change. Parameters ---------- inset_ax : `.Axes` Inset axes to draw connecting lines to. Two lines are drawn connecting the indicator box to the inset axes on corners chosen so as to not overlap with the indicator box. **kwargs Other *kwargs* are passed on to `.Axes.inset_rectangle` Returns ------- rectangle_patch : `.Patches.Rectangle` Rectangle artist. connector_lines : 4-tuple of `.Patches.ConnectionPatch` One for each of four connector lines. Two are set with visibility to *False*, but the user can set the visibility to True if the automatic choice is not deemed correct. """ xlim = inset_ax.get_xlim() ylim = inset_ax.get_ylim() rect = [xlim[0], ylim[0], xlim[1] - xlim[0], ylim[1] - ylim[0]] rectpatch, connects = self.indicate_inset( rect, inset_ax, **kwargs) return rectpatch, connects
[docs] @docstring.dedent_interpd def secondary_xaxis(self, location, *, functions=None, **kwargs): """ Add a second x-axis to this axes. For example if we want to have a second scale for the data plotted on the xaxis. %(_secax_docstring)s Examples -------- The main axis shows frequency, and the secondary axis shows period. .. plot:: fig, ax = plt.subplots() ax.loglog(range(1, 360, 5), range(1, 360, 5)) ax.set_xlabel('frequency [Hz]') def invert(x): return 1 / x secax = ax.secondary_xaxis('top', functions=(invert, invert)) secax.set_xlabel('Period [s]') plt.show() """ if (location in ['top', 'bottom'] or isinstance(location, Number)): secondary_ax = SecondaryAxis(self, 'x', location, functions, **kwargs) self.add_child_axes(secondary_ax) return secondary_ax else: raise ValueError('secondary_xaxis location must be either ' 'a float or "top"/"bottom"')
[docs] def secondary_yaxis(self, location, *, functions=None, **kwargs): """ Add a second y-axis to this axes. For example if we want to have a second scale for the data plotted on the yaxis. %(_secax_docstring)s Examples -------- Add a secondary axes that converts from radians to degrees .. plot:: fig, ax = plt.subplots() ax.plot(range(1, 360, 5), range(1, 360, 5)) ax.set_ylabel('degrees') secax = ax.secondary_yaxis('right', functions=(np.deg2rad, np.rad2deg)) secax.set_ylabel('radians') """ if location in ['left', 'right'] or isinstance(location, Number): secondary_ax = SecondaryAxis(self, 'y', location, functions, **kwargs) self.add_child_axes(secondary_ax) return secondary_ax else: raise ValueError('secondary_yaxis location must be either ' 'a float or "left"/"right"')
[docs] @cbook._delete_parameter("3.1", "withdash") def text(self, x, y, s, fontdict=None, withdash=False, **kwargs): """ Add text to the axes. Add the text *s* to the axes at location *x*, *y* in data coordinates. Parameters ---------- x, y : scalars The position to place the text. By default, this is in data coordinates. The coordinate system can be changed using the *transform* parameter. s : str The text. fontdict : dictionary, optional, default: None A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters. withdash : boolean, optional, default: False Creates a `~matplotlib.text.TextWithDash` instance instead of a `~matplotlib.text.Text` instance. Returns ------- text : `.Text` The created `.Text` instance. Other Parameters ---------------- **kwargs : `~matplotlib.text.Text` properties. Other miscellaneous text parameters. Examples -------- Individual keyword arguments can be used to override any given parameter:: >>> text(x, y, s, fontsize=12) The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lower-left and 1,1 is upper-right). The example below places text in the center of the axes:: >>> text(0.5, 0.5, 'matplotlib', horizontalalignment='center', ... verticalalignment='center', transform=ax.transAxes) You can put a rectangular box around the text instance (e.g., to set a background color) by using the keyword `bbox`. `bbox` is a dictionary of `~matplotlib.patches.Rectangle` properties. For example:: >>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5)) """ if fontdict is None: fontdict = {} effective_kwargs = { 'verticalalignment': 'baseline', 'horizontalalignment': 'left', 'transform': self.transData, 'clip_on': False, **fontdict, **kwargs, } # At some point if we feel confident that TextWithDash # is robust as a drop-in replacement for Text and that # the performance impact of the heavier-weight class # isn't too significant, it may make sense to eliminate # the withdash kwarg and simply delegate whether there's # a dash to TextWithDash and dashlength. if (withdash and withdash is not cbook.deprecation._deprecated_parameter): t = mtext.TextWithDash(x, y, text=s) else: t = mtext.Text(x, y, text=s) t.update(effective_kwargs) t.set_clip_path(self.patch) self._add_text(t) return t
[docs] @docstring.dedent_interpd def annotate(self, s, xy, *args, **kwargs): a = mtext.Annotation(s, xy, *args, **kwargs) a.set_transform(mtransforms.IdentityTransform()) if 'clip_on' in kwargs: a.set_clip_path(self.patch) self._add_text(a) return a
annotate.__doc__ = mtext.Annotation.__init__.__doc__ #### Lines and spans
[docs] @docstring.dedent_interpd def axhline(self, y=0, xmin=0, xmax=1, **kwargs): """ Add a horizontal line across the axis. Parameters ---------- y : scalar, optional, default: 0 y position in data coordinates of the horizontal line. xmin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. xmax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. Returns ------- line : :class:`~matplotlib.lines.Line2D` Other Parameters ---------------- **kwargs Valid kwargs are :class:`~matplotlib.lines.Line2D` properties, with the exception of 'transform': %(_Line2D_docstr)s See also -------- hlines : Add horizontal lines in data coordinates. axhspan : Add a horizontal span (rectangle) across the axis. Examples -------- * draw a thick red hline at 'y' = 0 that spans the xrange:: >>> axhline(linewidth=4, color='r') * draw a default hline at 'y' = 1 that spans the xrange:: >>> axhline(y=1) * draw a default hline at 'y' = .5 that spans the middle half of the xrange:: >>> axhline(y=.5, xmin=0.25, xmax=0.75) """ if "transform" in kwargs: raise ValueError( "'transform' is not allowed as a kwarg;" + "axhline generates its own transform.") ymin, ymax = self.get_ybound() # We need to strip away the units for comparison with # non-unitized bounds self._process_unit_info(ydata=y, kwargs=kwargs) yy = self.convert_yunits(y) scaley = (yy < ymin) or (yy > ymax) trans = self.get_yaxis_transform(which='grid') l = mlines.Line2D([xmin, xmax], [y, y], transform=trans, **kwargs) self.add_line(l) self._request_autoscale_view(scalex=False, scaley=scaley) return l
[docs] @docstring.dedent_interpd def axvline(self, x=0, ymin=0, ymax=1, **kwargs): """ Add a vertical line across the axes. Parameters ---------- x : scalar, optional, default: 0 x position in data coordinates of the vertical line. ymin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot. ymax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the bottom of the plot, 1 the top of the plot. Returns ------- line : :class:`~matplotlib.lines.Line2D` Other Parameters ---------------- **kwargs Valid kwargs are :class:`~matplotlib.lines.Line2D` properties, with the exception of 'transform': %(_Line2D_docstr)s Examples -------- * draw a thick red vline at *x* = 0 that spans the yrange:: >>> axvline(linewidth=4, color='r') * draw a default vline at *x* = 1 that spans the yrange:: >>> axvline(x=1) * draw a default vline at *x* = .5 that spans the middle half of the yrange:: >>> axvline(x=.5, ymin=0.25, ymax=0.75) See also -------- vlines : Add vertical lines in data coordinates. axvspan : Add a vertical span (rectangle) across the axis. """ if "transform" in kwargs: raise ValueError( "'transform' is not allowed as a kwarg;" + "axvline generates its own transform.") xmin, xmax = self.get_xbound() # We need to strip away the units for comparison with # non-unitized bounds self._process_unit_info(xdata=x, kwargs=kwargs) xx = self.convert_xunits(x) scalex = (xx < xmin) or (xx > xmax) trans = self.get_xaxis_transform(which='grid') l = mlines.Line2D([x, x], [ymin, ymax], transform=trans, **kwargs) self.add_line(l) self._request_autoscale_view(scalex=scalex, scaley=False) return l
[docs] @docstring.dedent_interpd def axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs): """ Add a horizontal span (rectangle) across the axis. Draw a horizontal span (rectangle) from *ymin* to *ymax*. With the default values of *xmin* = 0 and *xmax* = 1, this always spans the xrange, regardless of the xlim settings, even if you change them, e.g., with the :meth:`set_xlim` command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the *y* location is in data coordinates. Parameters ---------- ymin : float Lower limit of the horizontal span in data units. ymax : float Upper limit of the horizontal span in data units. xmin : float, optional, default: 0 Lower limit of the vertical span in axes (relative 0-1) units. xmax : float, optional, default: 1 Upper limit of the vertical span in axes (relative 0-1) units. Returns ------- Polygon : `~matplotlib.patches.Polygon` Other Parameters ---------------- **kwargs : `~matplotlib.patches.Polygon` properties. %(Polygon)s See Also -------- axvspan : Add a vertical span across the axes. """ trans = self.get_yaxis_transform(which='grid') # process the unit information self._process_unit_info([xmin, xmax], [ymin, ymax], kwargs=kwargs) # first we need to strip away the units xmin, xmax = self.convert_xunits([xmin, xmax]) ymin, ymax = self.convert_yunits([ymin, ymax]) verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin) p = mpatches.Polygon(verts, **kwargs) p.set_transform(trans) self.add_patch(p) self._request_autoscale_view(scalex=False) return p
[docs] def axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs): """ Add a vertical span (rectangle) across the axes. Draw a vertical span (rectangle) from `xmin` to `xmax`. With the default values of `ymin` = 0 and `ymax` = 1. This always spans the yrange, regardless of the ylim settings, even if you change them, e.g., with the :meth:`set_ylim` command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the x location is in data coordinates. Parameters ---------- xmin : scalar Number indicating the first X-axis coordinate of the vertical span rectangle in data units. xmax : scalar Number indicating the second X-axis coordinate of the vertical span rectangle in data units. ymin : scalar, optional Number indicating the first Y-axis coordinate of the vertical span rectangle in relative Y-axis units (0-1). Default to 0. ymax : scalar, optional Number indicating the second Y-axis coordinate of the vertical span rectangle in relative Y-axis units (0-1). Default to 1. Returns ------- rectangle : matplotlib.patches.Polygon Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax). Other Parameters ---------------- **kwargs Optional parameters are properties of the class matplotlib.patches.Polygon. See Also -------- axhspan : Add a horizontal span across the axes. Examples -------- Draw a vertical, green, translucent rectangle from x = 1.25 to x = 1.55 that spans the yrange of the axes. >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5) """ trans = self.get_xaxis_transform(which='grid') # process the unit information self._process_unit_info([xmin, xmax], [ymin, ymax], kwargs=kwargs) # first we need to strip away the units xmin, xmax = self.convert_xunits([xmin, xmax]) ymin, ymax = self.convert_yunits([ymin, ymax]) verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)] p = mpatches.Polygon(verts, **kwargs) p.set_transform(trans) self.add_patch(p) self._request_autoscale_view(scaley=False) return p
[docs] @_preprocess_data(replace_names=["y", "xmin", "xmax", "colors"], label_namer="y") def hlines(self, y, xmin, xmax, colors='k', linestyles='solid', label='', **kwargs): """ Plot horizontal lines at each *y* from *xmin* to *xmax*. Parameters ---------- y : scalar or sequence of scalar y-indexes where to plot the lines. xmin, xmax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: 'k' linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, optional label : string, optional, default: '' Returns ------- lines : `~matplotlib.collections.LineCollection` Other Parameters ---------------- **kwargs : `~matplotlib.collections.LineCollection` properties. See also -------- vlines : vertical lines axhline: horizontal line across the axes """ # We do the conversion first since not all unitized data is uniform # process the unit information self._process_unit_info([xmin, xmax], y, kwargs=kwargs) y = self.convert_yunits(y) xmin = self.convert_xunits(xmin) xmax = self.convert_xunits(xmax) if not np.iterable(y): y = [y] if not np.iterable(xmin): xmin = [xmin] if not np.iterable(xmax): xmax = [xmax] y, xmin, xmax = cbook.delete_masked_points(y, xmin, xmax) y = np.ravel(y) xmin = np.resize(xmin, y.shape) xmax = np.resize(xmax, y.shape) verts = [((thisxmin, thisy), (thisxmax, thisy)) for thisxmin, thisxmax, thisy in zip(xmin, xmax, y)] lines = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(lines, autolim=False) lines.update(kwargs) if len(y) > 0: minx = min(xmin.min(), xmax.min()) maxx = max(xmin.max(), xmax.max()) miny = y.min() maxy = y.max() corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self._request_autoscale_view() return lines
[docs] @_preprocess_data(replace_names=["x", "ymin", "ymax", "colors"], label_namer="x") def vlines(self, x, ymin, ymax, colors='k', linestyles='solid', label='', **kwargs): """ Plot vertical lines. Plot vertical lines at each *x* from *ymin* to *ymax*. Parameters ---------- x : scalar or 1D array_like x-indexes where to plot the lines. ymin, ymax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: 'k' linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, optional label : string, optional, default: '' Returns ------- lines : `~matplotlib.collections.LineCollection` Other Parameters ---------------- **kwargs : `~matplotlib.collections.LineCollection` properties. See also -------- hlines : horizontal lines axvline: vertical line across the axes """ self._process_unit_info(xdata=x, ydata=[ymin, ymax], kwargs=kwargs) # We do the conversion first since not all unitized data is uniform x = self.convert_xunits(x) ymin = self.convert_yunits(ymin) ymax = self.convert_yunits(ymax) if not np.iterable(x): x = [x] if not np.iterable(ymin): ymin = [ymin] if not np.iterable(ymax): ymax = [ymax] x, ymin, ymax = cbook.delete_masked_points(x, ymin, ymax) x = np.ravel(x) ymin = np.resize(ymin, x.shape) ymax = np.resize(ymax, x.shape) verts = [((thisx, thisymin), (thisx, thisymax)) for thisx, thisymin, thisymax in zip(x, ymin, ymax)] lines = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(lines, autolim=False) lines.update(kwargs) if len(x) > 0: minx = x.min() maxx = x.max() miny = min(ymin.min(), ymax.min()) maxy = max(ymin.max(), ymax.max()) corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self._request_autoscale_view() return lines
[docs] @_preprocess_data(replace_names=["positions", "lineoffsets", "linelengths", "linewidths", "colors", "linestyles"]) @docstring.dedent_interpd def eventplot(self, positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', **kwargs): """ Plot identical parallel lines at the given positions. *positions* should be a 1D or 2D array-like object, with each row corresponding to a row or column of lines. This type of plot is commonly used in neuroscience for representing neural events, where it is usually called a spike raster, dot raster, or raster plot. However, it is useful in any situation where you wish to show the timing or position of multiple sets of discrete events, such as the arrival times of people to a business on each day of the month or the date of hurricanes each year of the last century. Parameters ---------- positions : 1D or 2D array-like object Each value is an event. If *positions* is a 2D array-like, each row corresponds to a row or a column of lines (depending on the *orientation* parameter). orientation : {'horizontal', 'vertical'}, optional Controls the direction of the event collections: - 'horizontal' : the lines are arranged horizontally in rows, and are vertical. - 'vertical' : the lines are arranged vertically in columns, and are horizontal. lineoffsets : scalar or sequence of scalars, optional, default: 1 The offset of the center of the lines from the origin, in the direction orthogonal to *orientation*. linelengths : scalar or sequence of scalars, optional, default: 1 The total height of the lines (i.e. the lines stretches from ``lineoffset - linelength/2`` to ``lineoffset + linelength/2``). linewidths : scalar, scalar sequence or None, optional, default: None The line width(s) of the event lines, in points. If it is None, defaults to its rcParams setting. colors : color, sequence of colors or None, optional, default: None The color(s) of the event lines. If it is None, defaults to its rcParams setting. linestyles : str or tuple or a sequence of such values, optional Default is 'solid'. Valid strings are ['solid', 'dashed', 'dashdot', 'dotted', '-', '--', '-.', ':']. Dash tuples should be of the form:: (offset, onoffseq), where *onoffseq* is an even length tuple of on and off ink in points. **kwargs : optional Other keyword arguments are line collection properties. See :class:`~matplotlib.collections.LineCollection` for a list of the valid properties. Returns ------- list : A list of :class:`~.collections.EventCollection` objects. Contains the :class:`~.collections.EventCollection` that were added. Notes ----- For *linelengths*, *linewidths*, *colors*, and *linestyles*, if only a single value is given, that value is applied to all lines. If an array-like is given, it must have the same length as *positions*, and each value will be applied to the corresponding row of the array. Examples -------- .. plot:: gallery/lines_bars_and_markers/eventplot_demo.py """ self._process_unit_info(xdata=positions, ydata=[lineoffsets, linelengths], kwargs=kwargs) # We do the conversion first since not all unitized data is uniform positions = self.convert_xunits(positions) lineoffsets = self.convert_yunits(lineoffsets) linelengths = self.convert_yunits(linelengths) if not np.iterable(positions): positions = [positions] elif any(np.iterable(position) for position in positions): positions = [np.asanyarray(position) for position in positions] else: positions = [np.asanyarray(positions)] if len(positions) == 0: return [] # prevent 'singular' keys from **kwargs dict from overriding the effect # of 'plural' keyword arguments (e.g. 'color' overriding 'colors') colors = cbook.local_over_kwdict(colors, kwargs, 'color') linewidths = cbook.local_over_kwdict(linewidths, kwargs, 'linewidth') linestyles = cbook.local_over_kwdict(linestyles, kwargs, 'linestyle') if not np.iterable(lineoffsets): lineoffsets = [lineoffsets] if not np.iterable(linelengths): linelengths = [linelengths] if not np.iterable(linewidths): linewidths = [linewidths] if not np.iterable(colors): colors = [colors] if hasattr(linestyles, 'lower') or not np.iterable(linestyles): linestyles = [linestyles] lineoffsets = np.asarray(lineoffsets) linelengths = np.asarray(linelengths) linewidths = np.asarray(linewidths) if len(lineoffsets) == 0: lineoffsets = [None] if len(linelengths) == 0: linelengths = [None] if len(linewidths) == 0: lineoffsets = [None] if len(linewidths) == 0: lineoffsets = [None] if len(colors) == 0: colors = [None] try: # Early conversion of the colors into RGBA values to take care # of cases like colors='0.5' or colors='C1'. (Issue #8193) colors = mcolors.to_rgba_array(colors) except ValueError: # Will fail if any element of *colors* is None. But as long # as len(colors) == 1 or len(positions), the rest of the # code should process *colors* properly. pass if len(lineoffsets) == 1 and len(positions) != 1: lineoffsets = np.tile(lineoffsets, len(positions)) lineoffsets[0] = 0 lineoffsets = np.cumsum(lineoffsets) if len(linelengths) == 1: linelengths = np.tile(linelengths, len(positions)) if len(linewidths) == 1: linewidths = np.tile(linewidths, len(positions)) if len(colors) == 1: colors = list(colors) colors = colors * len(positions) if len(linestyles) == 1: linestyles = [linestyles] * len(positions) if len(lineoffsets) != len(positions): raise ValueError('lineoffsets and positions are unequal sized ' 'sequences') if len(linelengths) != len(positions): raise ValueError('linelengths and positions are unequal sized ' 'sequences') if len(linewidths) != len(positions): raise ValueError('linewidths and positions are unequal sized ' 'sequences') if len(colors) != len(positions): raise ValueError('colors and positions are unequal sized ' 'sequences') if len(linestyles) != len(positions): raise ValueError('linestyles and positions are unequal sized ' 'sequences') colls = [] for position, lineoffset, linelength, linewidth, color, linestyle in \ zip(positions, lineoffsets, linelengths, linewidths, colors, linestyles): coll = mcoll.EventCollection(position, orientation=orientation, lineoffset=lineoffset, linelength=linelength, linewidth=linewidth, color=color, linestyle=linestyle) self.add_collection(coll, autolim=False) coll.update(kwargs) colls.append(coll) if len(positions) > 0: # try to get min/max min_max = [(np.min(_p), np.max(_p)) for _p in positions if len(_p) > 0] # if we have any non-empty positions, try to autoscale if len(min_max) > 0: mins, maxes = zip(*min_max) minpos = np.min(mins) maxpos = np.max(maxes) minline = (lineoffsets - linelengths).min() maxline = (lineoffsets + linelengths).max() if (orientation is not None and orientation.lower() == "vertical"): corners = (minline, minpos), (maxline, maxpos) else: # "horizontal", None or "none" (see EventCollection) corners = (minpos, minline), (maxpos, maxline) self.update_datalim(corners) self._request_autoscale_view() return colls
#### Basic plotting # Uses a custom implementation of data-kwarg handling in # _process_plot_var_args.
[docs] @docstring.dedent_interpd def plot(self, *args, scalex=True, scaley=True, data=None, **kwargs): """ Plot y versus x as lines and/or markers. Call signatures:: plot([x], y, [fmt], *, data=None, **kwargs) plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) The coordinates of the points or line nodes are given by *x*, *y*. The optional parameter *fmt* is a convenient way for defining basic formatting like color, marker and linestyle. It's a shortcut string notation described in the *Notes* section below. >>> plot(x, y) # plot x and y using default line style and color >>> plot(x, y, 'bo') # plot x and y using blue circle markers >>> plot(y) # plot y using x as index array 0..N-1 >>> plot(y, 'r+') # ditto, but with red plusses You can use `.Line2D` properties as keyword arguments for more control on the appearance. Line properties and *fmt* can be mixed. The following two calls yield identical results: >>> plot(x, y, 'go--', linewidth=2, markersize=12) >>> plot(x, y, color='green', marker='o', linestyle='dashed', ... linewidth=2, markersize=12) When conflicting with *fmt*, keyword arguments take precedence. **Plotting labelled data** There's a convenient way for plotting objects with labelled data (i.e. data that can be accessed by index ``obj['y']``). Instead of giving the data in *x* and *y*, you can provide the object in the *data* parameter and just give the labels for *x* and *y*:: >>> plot('xlabel', 'ylabel', data=obj) All indexable objects are supported. This could e.g. be a `dict`, a `pandas.DataFame` or a structured numpy array. **Plotting multiple sets of data** There are various ways to plot multiple sets of data. - The most straight forward way is just to call `plot` multiple times. Example: >>> plot(x1, y1, 'bo') >>> plot(x2, y2, 'go') - Alternatively, if your data is already a 2d array, you can pass it directly to *x*, *y*. A separate data set will be drawn for every column. Example: an array ``a`` where the first column represents the *x* values and the other columns are the *y* columns:: >>> plot(a[0], a[1:]) - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]* groups:: >>> plot(x1, y1, 'g^', x2, y2, 'g-') In this case, any additional keyword argument applies to all datasets. Also this syntax cannot be combined with the *data* parameter. By default, each line is assigned a different style specified by a 'style cycle'. The *fmt* and line property parameters are only necessary if you want explicit deviations from these defaults. Alternatively, you can also change the style cycle using the 'axes.prop_cycle' rcParam. Parameters ---------- x, y : array-like or scalar The horizontal / vertical coordinates of the data points. *x* values are optional and default to `range(len(y))`. Commonly, these parameters are 1D arrays. They can also be scalars, or two-dimensional (in that case, the columns represent separate data sets). These arguments cannot be passed as keywords. fmt : str, optional A format string, e.g. 'ro' for red circles. See the *Notes* section for a full description of the format strings. Format strings are just an abbreviation for quickly setting basic line properties. All of these and more can also be controlled by keyword arguments. This argument cannot be passed as keyword. data : indexable object, optional An object with labelled data. If given, provide the label names to plot in *x* and *y*. .. note:: Technically there's a slight ambiguity in calls where the second label is a valid *fmt*. `plot('n', 'o', data=obj)` could be `plt(x, y)` or `plt(y, fmt)`. In such cases, the former interpretation is chosen, but a warning is issued. You may suppress the warning by adding an empty format string `plot('n', 'o', '', data=obj)`. Other Parameters ---------------- scalex, scaley : bool, optional, default: True These parameters determined if the view limits are adapted to the data limits. The values are passed on to `autoscale_view`. **kwargs : `.Line2D` properties, optional *kwargs* are used to specify properties like a line label (for auto legends), linewidth, antialiasing, marker face color. Example:: >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2) >>> plot([1,2,3], [1,4,9], 'rs', label='line 2') If you make multiple lines with one plot command, the kwargs apply to all those lines. Here is a list of available `.Line2D` properties: %(_Line2D_docstr)s Returns ------- lines A list of `.Line2D` objects representing the plotted data. See Also -------- scatter : XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart). Notes ----- **Format Strings** A format string consists of a part for color, marker and line:: fmt = '[marker][line][color]' Each of them is optional. If not provided, the value from the style cycle is used. Exception: If ``line`` is given, but no ``marker``, the data will be a line without markers. Other combinations such as ``[color][marker][line]`` are also supported, but note that their parsing may be ambiguous. **Markers** ============= =============================== character description ============= =============================== ``'.'`` point marker ``','`` pixel marker ``'o'`` circle marker ``'v'`` triangle_down marker ``'^'`` triangle_up marker ``'<'`` triangle_left marker ``'>'`` triangle_right marker ``'1'`` tri_down marker ``'2'`` tri_up marker ``'3'`` tri_left marker ``'4'`` tri_right marker ``'s'`` square marker ``'p'`` pentagon marker ``'*'`` star marker ``'h'`` hexagon1 marker ``'H'`` hexagon2 marker ``'+'`` plus marker ``'x'`` x marker ``'D'`` diamond marker ``'d'`` thin_diamond marker ``'|'`` vline marker ``'_'`` hline marker ============= =============================== **Line Styles** ============= =============================== character description ============= =============================== ``'-'`` solid line style ``'--'`` dashed line style ``'-.'`` dash-dot line style ``':'`` dotted line style ============= =============================== Example format strings:: 'b' # blue markers with default shape 'or' # red circles '-g' # green solid line '--' # dashed line with default color '^k:' # black triangle_up markers connected by a dotted line **Colors** The supported color abbreviations are the single letter codes ============= =============================== character color ============= =============================== ``'b'`` blue ``'g'`` green ``'r'`` red ``'c'`` cyan ``'m'`` magenta ``'y'`` yellow ``'k'`` black ``'w'`` white ============= =============================== and the ``'CN'`` colors that index into the default property cycle. If the color is the only part of the format string, you can additionally use any `matplotlib.colors` spec, e.g. full names (``'green'``) or hex strings (``'#008000'``). """ kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) lines = [*self._get_lines(*args, data=data, **kwargs)] for line in lines: self.add_line(line) self._request_autoscale_view(scalex=scalex, scaley=scaley) return lines
[docs] @_preprocess_data(replace_names=["x", "y"], label_namer="y") @docstring.dedent_interpd def plot_date(self, x, y, fmt='o', tz=None, xdate=True, ydate=False, **kwargs): """ Plot data that contains dates. Similar to `.plot`, this plots *y* vs. *x* as lines or markers. However, the axis labels are formatted as dates depending on *xdate* and *ydate*. Parameters ---------- x, y : array-like The coordinates of the data points. If *xdate* or *ydate* is *True*, the respective values *x* or *y* are interpreted as :ref:`Matplotlib dates <date-format>`. fmt : str, optional The plot format string. For details, see the corresponding parameter in `.plot`. tz : [ *None* | timezone string | :class:`tzinfo` instance] The time zone to use in labeling dates. If *None*, defaults to rcParam ``timezone``. xdate : bool, optional, default: True If *True*, the *x*-axis will be interpreted as Matplotlib dates. ydate : bool, optional, default: False If *True*, the *y*-axis will be interpreted as Matplotlib dates. Returns ------- lines A list of `~.Line2D` objects representing the plotted data. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- matplotlib.dates : Helper functions on dates. matplotlib.dates.date2num : Convert dates to num. matplotlib.dates.num2date : Convert num to dates. matplotlib.dates.drange : Create an equally spaced sequence of dates. Notes ----- If you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to `.plot_date`. `.plot_date` will set the default tick locator to `.AutoDateLocator` (if the tick locator is not already set to a `.DateLocator` instance) and the default tick formatter to `.AutoDateFormatter` (if the tick formatter is not already set to a `.DateFormatter` instance). """ if xdate: self.xaxis_date(tz) if ydate: self.yaxis_date(tz) ret = self.plot(x, y, fmt, **kwargs) self._request_autoscale_view() return ret
# @_preprocess_data() # let 'plot' do the unpacking..
[docs] @docstring.dedent_interpd def loglog(self, *args, **kwargs): """ Make a plot with log scaling on both the x and y axis. Call signatures:: loglog([x], y, [fmt], data=None, **kwargs) loglog([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) This is just a thin wrapper around `.plot` which additionally changes both the x-axis and the y-axis to log scaling. All of the concepts and parameters of plot can be used here as well. The additional parameters *basex/y*, *subsx/y* and *nonposx/y* control the x/y-axis properties. They are just forwarded to `.Axes.set_xscale` and `.Axes.set_yscale`. Parameters ---------- basex, basey : scalar, optional, default 10 Base of the x/y logarithm. subsx, subsy : sequence, optional The location of the minor x/y ticks. If *None*, reasonable locations are automatically chosen depending on the number of decades in the plot. See `.Axes.set_xscale` / `.Axes.set_yscale` for details. nonposx, nonposy : {'mask', 'clip'}, optional, default 'mask' Non-positive values in x or y can be masked as invalid, or clipped to a very small positive number. Returns ------- lines A list of `~.Line2D` objects representing the plotted data. Other Parameters ---------------- **kwargs All parameters supported by `.plot`. """ dx = {k: kwargs.pop(k) for k in ['basex', 'subsx', 'nonposx'] if k in kwargs} dy = {k: kwargs.pop(k) for k in ['basey', 'subsy', 'nonposy'] if k in kwargs} self.set_xscale('log', **dx) self.set_yscale('log', **dy) l = self.plot(*args, **kwargs) return l
# @_preprocess_data() # let 'plot' do the unpacking..
[docs] @docstring.dedent_interpd def semilogx(self, *args, **kwargs): """ Make a plot with log scaling on the x axis. Call signatures:: semilogx([x], y, [fmt], data=None, **kwargs) semilogx([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) This is just a thin wrapper around `.plot` which additionally changes the x-axis to log scaling. All of the concepts and parameters of plot can be used here as well. The additional parameters *basex*, *subsx* and *nonposx* control the x-axis properties. They are just forwarded to `.Axes.set_xscale`. Parameters ---------- basex : scalar, optional, default 10 Base of the x logarithm. subsx : array_like, optional The location of the minor xticks. If *None*, reasonable locations are automatically chosen depending on the number of decades in the plot. See `.Axes.set_xscale` for details. nonposx : {'mask', 'clip'}, optional, default 'mask' Non-positive values in x can be masked as invalid, or clipped to a very small positive number. Returns ------- lines A list of `~.Line2D` objects representing the plotted data. Other Parameters ---------------- **kwargs All parameters supported by `.plot`. """ d = {k: kwargs.pop(k) for k in ['basex', 'subsx', 'nonposx'] if k in kwargs} self.set_xscale('log', **d) l = self.plot(*args, **kwargs) return l
# @_preprocess_data() # let 'plot' do the unpacking..
[docs] @docstring.dedent_interpd def semilogy(self, *args, **kwargs): """ Make a plot with log scaling on the y axis. Call signatures:: semilogy([x], y, [fmt], data=None, **kwargs) semilogy([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs) This is just a thin wrapper around `.plot` which additionally changes the y-axis to log scaling. All of the concepts and parameters of plot can be used here as well. The additional parameters *basey*, *subsy* and *nonposy* control the y-axis properties. They are just forwarded to `.Axes.set_yscale`. Parameters ---------- basey : scalar, optional, default 10 Base of the y logarithm. subsy : array_like, optional The location of the minor yticks. If *None*, reasonable locations are automatically chosen depending on the number of decades in the plot. See `.Axes.set_yscale` for details. nonposy : {'mask', 'clip'}, optional, default 'mask' Non-positive values in y can be masked as invalid, or clipped to a very small positive number. Returns ------- lines A list of `~.Line2D` objects representing the plotted data. Other Parameters ---------------- **kwargs All parameters supported by `.plot`. """ d = {k: kwargs.pop(k) for k in ['basey', 'subsy', 'nonposy'] if k in kwargs} self.set_yscale('log', **d) l = self.plot(*args, **kwargs) return l
[docs] @_preprocess_data(replace_names=["x"], label_namer="x") def acorr(self, x, **kwargs): """ Plot the autocorrelation of *x*. Parameters ---------- x : array-like detrend : callable, optional, default: `mlab.detrend_none` *x* is detrended by the *detrend* callable. This must be a function ``x = detrend(x)`` accepting and returning an `numpy.array`. Default is no normalization. normed : bool, optional, default: True If ``True``, input vectors are normalised to unit length. usevlines : bool, optional, default: True Determines the plot style. If ``True``, vertical lines are plotted from 0 to the acorr value using `Axes.vlines`. Additionally, a horizontal line is plotted at y=0 using `Axes.axhline`. If ``False``, markers are plotted at the acorr values using `Axes.plot`. maxlags : int, optional, default: 10 Number of lags to show. If ``None``, will return all ``2 * len(x) - 1`` lags. Returns ------- lags : array (length ``2*maxlags+1``) The lag vector. c : array (length ``2*maxlags+1``) The auto correlation vector. line : `.LineCollection` or `.Line2D` `.Artist` added to the axes of the correlation: - `.LineCollection` if *usevlines* is True. - `.Line2D` if *usevlines* is False. b : `.Line2D` or None Horizontal line at 0 if *usevlines* is True None *usevlines* is False. Other Parameters ---------------- linestyle : `.Line2D` property, optional The linestyle for plotting the data points. Only used if *usevlines* is ``False``. marker : str, optional, default: 'o' The marker for plotting the data points. Only used if *usevlines* is ``False``. Notes ----- The cross correlation is performed with :func:`numpy.correlate` with ``mode = "full"``. """ return self.xcorr(x, x, **kwargs)
[docs] @_preprocess_data(replace_names=["x", "y"], label_namer="y") def xcorr(self, x, y, normed=True, detrend=mlab.detrend_none, usevlines=True, maxlags=10, **kwargs): r""" Plot the cross correlation between *x* and *y*. The correlation with lag k is defined as :math:`\sum_n x[n+k] \cdot y^*[n]`, where :math:`y^*` is the complex conjugate of :math:`y`. Parameters ---------- x : array-like of length n y : array-like of length n detrend : callable, optional, default: `mlab.detrend_none` *x* and *y* are detrended by the *detrend* callable. This must be a function ``x = detrend(x)`` accepting and returning an `numpy.array`. Default is no normalization. normed : bool, optional, default: True If ``True``, input vectors are normalised to unit length. usevlines : bool, optional, default: True Determines the plot style. If ``True``, vertical lines are plotted from 0 to the xcorr value using `Axes.vlines`. Additionally, a horizontal line is plotted at y=0 using `Axes.axhline`. If ``False``, markers are plotted at the xcorr values using `Axes.plot`. maxlags : int, optional, default: 10 Number of lags to show. If None, will return all ``2 * len(x) - 1`` lags. Returns ------- lags : array (length ``2*maxlags+1``) The lag vector. c : array (length ``2*maxlags+1``) The auto correlation vector. line : `.LineCollection` or `.Line2D` `.Artist` added to the axes of the correlation: - `.LineCollection` if *usevlines* is True. - `.Line2D` if *usevlines* is False. b : `.Line2D` or None Horizontal line at 0 if *usevlines* is True None *usevlines* is False. Other Parameters ---------------- linestyle : `.Line2D` property, optional The linestyle for plotting the data points. Only used if *usevlines* is ``False``. marker : str, optional, default: 'o' The marker for plotting the data points. Only used if *usevlines* is ``False``. Notes ----- The cross correlation is performed with :func:`numpy.correlate` with ``mode = "full"``. """ Nx = len(x) if Nx != len(y): raise ValueError('x and y must be equal length') x = detrend(np.asarray(x)) y = detrend(np.asarray(y)) correls = np.correlate(x, y, mode="full") if normed: correls /= np.sqrt(np.dot(x, x) * np.dot(y, y)) if maxlags is None: maxlags = Nx - 1 if maxlags >= Nx or maxlags < 1: raise ValueError('maxlags must be None or strictly ' 'positive < %d' % Nx) lags = np.arange(-maxlags, maxlags + 1) correls = correls[Nx - 1 - maxlags:Nx + maxlags] if usevlines: a = self.vlines(lags, [0], correls, **kwargs) # Make label empty so only vertical lines get a legend entry kwargs.pop('label', '') b = self.axhline(**kwargs) else: kwargs.setdefault('marker', 'o') kwargs.setdefault('linestyle', 'None') a, = self.plot(lags, correls, **kwargs) b = None return lags, correls, a, b
#### Specialized plotting # @_preprocess_data() # let 'plot' do the unpacking..
[docs] def step(self, x, y, *args, where='pre', data=None, **kwargs): """ Make a step plot. Call signatures:: step(x, y, [fmt], *, data=None, where='pre', **kwargs) step(x, y, [fmt], x2, y2, [fmt2], ..., *, where='pre', **kwargs) This is just a thin wrapper around `.plot` which changes some formatting options. Most of the concepts and parameters of plot can be used here as well. Parameters ---------- x : array_like 1-D sequence of x positions. It is assumed, but not checked, that it is uniformly increasing. y : array_like 1-D sequence of y levels. fmt : str, optional A format string, e.g. 'g' for a green line. See `.plot` for a more detailed description. Note: While full format strings are accepted, it is recommended to only specify the color. Line styles are currently ignored (use the keyword argument *linestyle* instead). Markers are accepted and plotted on the given positions, however, this is a rarely needed feature for step plots. data : indexable object, optional An object with labelled data. If given, provide the label names to plot in *x* and *y*. where : {'pre', 'post', 'mid'}, optional, default 'pre' Define where the steps should be placed: - 'pre': The y value is continued constantly to the left from every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the value ``y[i]``. - 'post': The y value is continued constantly to the right from every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the value ``y[i]``. - 'mid': Steps occur half-way between the *x* positions. Returns ------- lines A list of `.Line2D` objects representing the plotted data. Other Parameters ---------------- **kwargs Additional parameters are the same as those for `.plot`. Notes ----- .. [notes section required to get data note injection right] """ if where not in ('pre', 'post', 'mid'): raise ValueError("'where' argument to step must be " "'pre', 'post' or 'mid'") kwargs['drawstyle'] = 'steps-' + where return self.plot(x, y, *args, data=data, **kwargs)
@staticmethod def _convert_dx(dx, x0, xconv, convert): """ Small helper to do logic of width conversion flexibly. *dx* and *x0* have units, but *xconv* has already been converted to unitless (and is an ndarray). This allows the *dx* to have units that are different from *x0*, but are still accepted by the ``__add__`` operator of *x0*. """ # x should be an array... assert type(xconv) is np.ndarray if xconv.size == 0: # xconv has already been converted, but maybe empty... return convert(dx) try: # attempt to add the width to x0; this works for # datetime+timedelta, for instance # only use the first element of x and x0. This saves # having to be sure addition works across the whole # vector. This is particularly an issue if # x0 and dx are lists so x0 + dx just concatenates the lists. # We can't just cast x0 and dx to numpy arrays because that # removes the units from unit packages like `pint` that # wrap numpy arrays. try: x0 = x0[0] except (TypeError, IndexError, KeyError): x0 = x0 try: x = xconv[0] except (TypeError, IndexError, KeyError): x = xconv delist = False if not np.iterable(dx): dx = [dx] delist = True dx = [convert(x0 + ddx) - x for ddx in dx] if delist: dx = dx[0] except (ValueError, TypeError, AttributeError): # if the above fails (for any reason) just fallback to what # we do by default and convert dx by itself. dx = convert(dx) return dx
[docs] @_preprocess_data() @docstring.dedent_interpd def bar(self, x, height, width=0.8, bottom=None, *, align="center", **kwargs): r""" Make a bar plot. The bars are positioned at *x* with the given *align*\ment. Their dimensions are given by *width* and *height*. The vertical baseline is *bottom* (default 0). Each of *x*, *height*, *width*, and *bottom* may either be a scalar applying to all bars, or it may be a sequence of length N providing a separate value for each bar. Parameters ---------- x : sequence of scalars The x coordinates of the bars. See also *align* for the alignment of the bars to the coordinates. height : scalar or sequence of scalars The height(s) of the bars. width : scalar or array-like, optional The width(s) of the bars (default: 0.8). bottom : scalar or array-like, optional The y coordinate(s) of the bars bases (default: 0). align : {'center', 'edge'}, optional, default: 'center' Alignment of the bars to the *x* coordinates: - 'center': Center the base on the *x* positions. - 'edge': Align the left edges of the bars with the *x* positions. To align the bars on the right edge pass a negative *width* and ``align='edge'``. Returns ------- container : `.BarContainer` Container with all the bars and optionally errorbars. Other Parameters ---------------- color : scalar or array-like, optional The colors of the bar faces. edgecolor : scalar or array-like, optional The colors of the bar edges. linewidth : scalar or array-like, optional Width of the bar edge(s). If 0, don't draw edges. tick_label : string or array-like, optional The tick labels of the bars. Default: None (Use default numeric labels.) xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional If not *None*, add horizontal / vertical errorbars to the bar tips. The values are +/- sizes relative to the data: - scalar: symmetric +/- values for all bars - shape(N,): symmetric +/- values for each bar - shape(2,N): Separate - and + values for each bar. First row contains the lower errors, the second row contains the upper errors. - *None*: No errorbar. (Default) See :doc:`/gallery/statistics/errorbar_features` for an example on the usage of ``xerr`` and ``yerr``. ecolor : scalar or array-like, optional, default: 'black' The line color of the errorbars. capsize : scalar, optional The length of the error bar caps in points. Default: None, which will take the value from :rc:`errorbar.capsize`. error_kw : dict, optional Dictionary of kwargs to be passed to the `~.Axes.errorbar` method. Values of *ecolor* or *capsize* defined here take precedence over the independent kwargs. log : bool, optional, default: False If *True*, set the y-axis to be log scale. orientation : {'vertical', 'horizontal'}, optional *This is for internal use only.* Please use `barh` for horizontal bar plots. Default: 'vertical'. See also -------- barh: Plot a horizontal bar plot. Notes ----- The optional arguments *color*, *edgecolor*, *linewidth*, *xerr*, and *yerr* can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: *xerr* and *yerr* are passed directly to :meth:`errorbar`, so they can also have shape 2xN for independent specification of lower and upper errors. Other optional kwargs: %(Rectangle)s """ kwargs = cbook.normalize_kwargs(kwargs, mpatches.Patch) color = kwargs.pop('color', None) if color is None: color = self._get_patches_for_fill.get_next_color() edgecolor = kwargs.pop('edgecolor', None) linewidth = kwargs.pop('linewidth', None) # Because xerr and yerr will be passed to errorbar, most dimension # checking and processing will be left to the errorbar method. xerr = kwargs.pop('xerr', None) yerr = kwargs.pop('yerr', None) error_kw = kwargs.pop('error_kw', {}) ezorder = error_kw.pop('zorder', None) if ezorder is None: ezorder = kwargs.get('zorder', None) if ezorder is not None: # If using the bar zorder, increment slightly to make sure # errorbars are drawn on top of bars ezorder += 0.01 error_kw.setdefault('zorder', ezorder) ecolor = kwargs.pop('ecolor', 'k') capsize = kwargs.pop('capsize', rcParams["errorbar.capsize"]) error_kw.setdefault('ecolor', ecolor) error_kw.setdefault('capsize', capsize) orientation = kwargs.pop('orientation', 'vertical') log = kwargs.pop('log', False) label = kwargs.pop('label', '') tick_labels = kwargs.pop('tick_label', None) adjust_ylim = False adjust_xlim = False y = bottom # Matches barh call signature. if orientation == 'vertical': if bottom is None: if self.get_yscale() == 'log': adjust_ylim = True y = 0 elif orientation == 'horizontal': if x is None: if self.get_xscale() == 'log': adjust_xlim = True x = 0 if orientation == 'vertical': self._process_unit_info(xdata=x, ydata=height, kwargs=kwargs) if log: self.set_yscale('log', nonposy='clip') elif orientation == 'horizontal': self._process_unit_info(xdata=width, ydata=y, kwargs=kwargs) if log: self.set_xscale('log', nonposx='clip') else: raise ValueError('invalid orientation: %s' % orientation) # lets do some conversions now since some types cannot be # subtracted uniformly if self.xaxis is not None: x0 = x x = np.asarray(self.convert_xunits(x)) width = self._convert_dx(width, x0, x, self.convert_xunits) if xerr is not None: xerr = self._convert_dx(xerr, x0, x, self.convert_xunits) if self.yaxis is not None: y0 = y y = np.asarray(self.convert_yunits(y)) height = self._convert_dx(height, y0, y, self.convert_yunits) if yerr is not None: yerr = self._convert_dx(yerr, y0, y, self.convert_yunits) x, height, width, y, linewidth = np.broadcast_arrays( # Make args iterable too. np.atleast_1d(x), height, width, y, linewidth) # Now that units have been converted, set the tick locations. if orientation == 'vertical': tick_label_axis = self.xaxis tick_label_position = x elif orientation == 'horizontal': tick_label_axis = self.yaxis tick_label_position = y linewidth = itertools.cycle(np.atleast_1d(linewidth)) color = itertools.chain(itertools.cycle(mcolors.to_rgba_array(color)), # Fallback if color == "none". itertools.repeat('none')) if edgecolor is None: edgecolor = itertools.repeat(None) else: edgecolor = itertools.chain( itertools.cycle(mcolors.to_rgba_array(edgecolor)), # Fallback if edgecolor == "none". itertools.repeat('none')) # We will now resolve the alignment and really have # left, bottom, width, height vectors if align == 'center': if orientation == 'vertical': try: left = x - width / 2 except TypeError as e: raise TypeError(f'the dtypes of parameters x ({x.dtype}) ' f'and width ({width.dtype}) ' f'are incompatible') from e bottom = y elif orientation == 'horizontal': try: bottom = y - height / 2 except TypeError as e: raise TypeError(f'the dtypes of parameters y ({y.dtype}) ' f'and height ({height.dtype}) ' f'are incompatible') from e left = x elif align == 'edge': left = x bottom = y else: raise ValueError('invalid alignment: %s' % align) patches = [] args = zip(left, bottom, width, height, color, edgecolor, linewidth) for l, b, w, h, c, e, lw in args: r = mpatches.Rectangle( xy=(l, b), width=w, height=h, facecolor=c, edgecolor=e, linewidth=lw, label='_nolegend_', ) r.update(kwargs) r.get_path()._interpolation_steps = 100 if orientation == 'vertical': r.sticky_edges.y.append(b) elif orientation == 'horizontal': r.sticky_edges.x.append(l) self.add_patch(r) patches.append(r) if xerr is not None or yerr is not None: if orientation == 'vertical': # using list comps rather than arrays to preserve unit info ex = [l + 0.5 * w for l, w in zip(left, width)] ey = [b + h for b, h in zip(bottom, height)] elif orientation == 'horizontal': # using list comps rather than arrays to preserve unit info ex = [l + w for l, w in zip(left, width)] ey = [b + 0.5 * h for b, h in zip(bottom, height)] error_kw.setdefault("label", '_nolegend_') errorbar = self.errorbar(ex, ey, yerr=yerr, xerr=xerr, fmt='none', **error_kw) else: errorbar = None if adjust_xlim: xmin, xmax = self.dataLim.intervalx xmin = min(w for w in width if w > 0) if xerr is not None: xmin = xmin - np.max(xerr) xmin = max(xmin * 0.9, 1e-100) self.dataLim.intervalx = (xmin, xmax) if adjust_ylim: ymin, ymax = self.dataLim.intervaly ymin = min(h for h in height if h > 0) if yerr is not None: ymin = ymin - np.max(yerr) ymin = max(ymin * 0.9, 1e-100) self.dataLim.intervaly = (ymin, ymax) self._request_autoscale_view() bar_container = BarContainer(patches, errorbar, label=label) self.add_container(bar_container) if tick_labels is not None: tick_labels = np.broadcast_to(tick_labels, len(patches)) tick_label_axis.set_ticks(tick_label_position) tick_label_axis.set_ticklabels(tick_labels) return bar_container
[docs] @docstring.dedent_interpd def barh(self, y, width, height=0.8, left=None, *, align="center", **kwargs): r""" Make a horizontal bar plot. The bars are positioned at *y* with the given *align*\ment. Their dimensions are given by *width* and *height*. The horizontal baseline is *left* (default 0). Each of *y*, *width*, *height*, and *left* may either be a scalar applying to all bars, or it may be a sequence of length N providing a separate value for each bar. Parameters ---------- y : scalar or array-like The y coordinates of the bars. See also *align* for the alignment of the bars to the coordinates. width : scalar or array-like The width(s) of the bars. height : sequence of scalars, optional, default: 0.8 The heights of the bars. left : sequence of scalars The x coordinates of the left sides of the bars (default: 0). align : {'center', 'edge'}, optional, default: 'center' Alignment of the base to the *y* coordinates*: - 'center': Center the bars on the *y* positions. - 'edge': Align the bottom edges of the bars with the *y* positions. To align the bars on the top edge pass a negative *height* and ``align='edge'``. Returns ------- container : `.BarContainer` Container with all the bars and optionally errorbars. Other Parameters ---------------- color : scalar or array-like, optional The colors of the bar faces. edgecolor : scalar or array-like, optional The colors of the bar edges. linewidth : scalar or array-like, optional Width of the bar edge(s). If 0, don't draw edges. tick_label : string or array-like, optional The tick labels of the bars. Default: None (Use default numeric labels.) xerr, yerr : scalar or array-like of shape(N,) or shape(2,N), optional If not ``None``, add horizontal / vertical errorbars to the bar tips. The values are +/- sizes relative to the data: - scalar: symmetric +/- values for all bars - shape(N,): symmetric +/- values for each bar - shape(2,N): Separate - and + values for each bar. First row contains the lower errors, the second row contains the upper errors. - *None*: No errorbar. (default) See :doc:`/gallery/statistics/errorbar_features` for an example on the usage of ``xerr`` and ``yerr``. ecolor : scalar or array-like, optional, default: 'black' The line color of the errorbars. capsize : scalar, optional The length of the error bar caps in points. Default: None, which will take the value from :rc:`errorbar.capsize`. error_kw : dict, optional Dictionary of kwargs to be passed to the `~.Axes.errorbar` method. Values of *ecolor* or *capsize* defined here take precedence over the independent kwargs. log : bool, optional, default: False If ``True``, set the x-axis to be log scale. See also -------- bar: Plot a vertical bar plot. Notes ----- The optional arguments *color*, *edgecolor*, *linewidth*, *xerr*, and *yerr* can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: *xerr* and *yerr* are passed directly to :meth:`errorbar`, so they can also have shape 2xN for independent specification of lower and upper errors. Other optional kwargs: %(Rectangle)s """ kwargs.setdefault('orientation', 'horizontal') patches = self.bar(x=left, height=height, width=width, bottom=y, align=align, **kwargs) return patches
[docs] @_preprocess_data() @docstring.dedent_interpd def broken_barh(self, xranges, yrange, **kwargs): """ Plot a horizontal sequence of rectangles. A rectangle is drawn for each element of *xranges*. All rectangles have the same vertical position and size defined by *yrange*. This is a convenience function for instantiating a `.BrokenBarHCollection`, adding it to the axes and autoscaling the view. Parameters ---------- xranges : sequence of tuples (*xmin*, *xwidth*) The x-positions and extends of the rectangles. For each tuple (*xmin*, *xwidth*) a rectangle is drawn from *xmin* to *xmin* + *xwidth*. yrange : (*ymin*, *yheight*) The y-position and extend for all the rectangles. Other Parameters ---------------- **kwargs : :class:`.BrokenBarHCollection` properties Each *kwarg* can be either a single argument applying to all rectangles, e.g.:: facecolors='black' or a sequence of arguments over which is cycled, e.g.:: facecolors=('black', 'blue') would create interleaving black and blue rectangles. Supported keywords: %(BrokenBarHCollection)s Returns ------- collection : A :class:`~.collections.BrokenBarHCollection` """ # process the unit information if len(xranges): xdata = cbook.safe_first_element(xranges) else: xdata = None if len(yrange): ydata = cbook.safe_first_element(yrange) else: ydata = None self._process_unit_info(xdata=xdata, ydata=ydata, kwargs=kwargs) xranges_conv = [] for xr in xranges: if len(xr) != 2: raise ValueError('each range in xrange must be a sequence ' 'with two elements (i.e. an Nx2 array)') # convert the absolute values, not the x and dx... x_conv = np.asarray(self.convert_xunits(xr[0])) x1 = self._convert_dx(xr[1], xr[0], x_conv, self.convert_xunits) xranges_conv.append((x_conv, x1)) yrange_conv = self.convert_yunits(yrange) col = mcoll.BrokenBarHCollection(xranges_conv, yrange_conv, **kwargs) self.add_collection(col, autolim=True) self._request_autoscale_view() return col
[docs] @_preprocess_data() def stem(self, *args, linefmt=None, markerfmt=None, basefmt=None, bottom=0, label=None, use_line_collection=False): """ Create a stem plot. A stem plot plots vertical lines at each *x* location from the baseline to *y*, and places a marker there. Call signature:: stem([x,] y, linefmt=None, markerfmt=None, basefmt=None) The x-positions are optional. The formats may be provided either as positional or as keyword-arguments. Parameters ---------- x : array-like, optional The x-positions of the stems. Default: (0, 1, ..., len(y) - 1). y : array-like The y-values of the stem heads. linefmt : str, optional A string defining the properties of the vertical lines. Usually, this will be a color or a color and a linestyle: ========= ============= Character Line Style ========= ============= ``'-'`` solid line ``'--'`` dashed line ``'-.'`` dash-dot line ``':'`` dotted line ========= ============= Default: 'C0-', i.e. solid line with the first color of the color cycle. Note: While it is technically possible to specify valid formats other than color or color and linestyle (e.g. 'rx' or '-.'), this is beyond the intention of the method and will most likely not result in a reasonable reasonable plot. markerfmt : str, optional A string defining the properties of the markers at the stem heads. Default: 'C0o', i.e. filled circles with the first color of the color cycle. basefmt : str, optional A format string defining the properties of the baseline. Default: 'C3-' ('C2-' in classic mode). bottom : float, optional, default: 0 The y-position of the baseline. label : str, optional, default: None The label to use for the stems in legends. use_line_collection : bool, optional, default: False If ``True``, store and plot the stem lines as a `~.collections.LineCollection` instead of individual lines. This significantly increases performance, and will become the default option in Matplotlib 3.3. If ``False``, defaults to the old behavior of using a list of `.Line2D` objects. Returns ------- container : :class:`~matplotlib.container.StemContainer` The container may be treated like a tuple (*markerline*, *stemlines*, *baseline*) Notes ----- .. seealso:: The MATLAB function `stem <http://www.mathworks.com/help/techdoc/ref/stem.html>`_ which inspired this method. """ if not 1 <= len(args) <= 5: raise TypeError('stem expected between 1 and 5 positional ' 'arguments, got {}'.format(args)) y = np.asarray(args[0]) args = args[1:] # Try a second one if not args: x = np.arange(len(y)) else: x = y y = np.asarray(args[0], dtype=float) args = args[1:] self._process_unit_info(xdata=x, ydata=y) x = self.convert_xunits(x) y = self.convert_yunits(y) # defaults for formats if linefmt is None: try: # fallback to positional argument linefmt = args[0] except IndexError: linecolor = 'C0' linemarker = 'None' linestyle = '-' else: linestyle, linemarker, linecolor = \ _process_plot_format(linefmt) else: linestyle, linemarker, linecolor = _process_plot_format(linefmt) if markerfmt is None: try: # fallback to positional argument markerfmt = args[1] except IndexError: markercolor = 'C0' markermarker = 'o' markerstyle = 'None' else: markerstyle, markermarker, markercolor = \ _process_plot_format(markerfmt) else: markerstyle, markermarker, markercolor = \ _process_plot_format(markerfmt) if basefmt is None: try: # fallback to positional argument basefmt = args[2] except IndexError: if rcParams['_internal.classic_mode']: basecolor = 'C2' else: basecolor = 'C3' basemarker = 'None' basestyle = '-' else: basestyle, basemarker, basecolor = \ _process_plot_format(basefmt) else: basestyle, basemarker, basecolor = _process_plot_format(basefmt) # New behaviour in 3.1 is to use a LineCollection for the stemlines if use_line_collection: stemlines = [((xi, bottom), (xi, yi)) for xi, yi in zip(x, y)] if linestyle is None: linestyle = rcParams['lines.linestyle'] stemlines = mcoll.LineCollection(stemlines, linestyles=linestyle, colors=linecolor, label='_nolegend_') self.add_collection(stemlines) # Old behaviour is to plot each of the lines individually else: cbook._warn_external( 'In Matplotlib 3.3 individual lines on a stem plot will be ' 'added as a LineCollection instead of individual lines. ' 'This significantly improves the performance of a stem plot. ' 'To remove this warning and switch to the new behaviour, ' 'set the "use_line_collection" keyword argument to True.') stemlines = [] for xi, yi in zip(x, y): l, = self.plot([xi, xi], [bottom, yi], color=linecolor, linestyle=linestyle, marker=linemarker, label="_nolegend_") stemlines.append(l) markerline, = self.plot(x, y, color=markercolor, linestyle=markerstyle, marker=markermarker, label="_nolegend_") baseline, = self.plot([np.min(x), np.max(x)], [bottom, bottom], color=basecolor, linestyle=basestyle, marker=basemarker, label="_nolegend_") stem_container = StemContainer((markerline, stemlines, baseline), label=label) self.add_container(stem_container) return stem_container
[docs] @_preprocess_data(replace_names=["x", "explode", "labels", "colors"]) def pie(self, x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False): """ Plot a pie chart. Make a pie chart of array *x*. The fractional area of each wedge is given by ``x/sum(x)``. If ``sum(x) < 1``, then the values of *x* give the fractional area directly and the array will not be normalized. The resulting pie will have an empty wedge of size ``1 - sum(x)``. The wedges are plotted counterclockwise, by default starting from the x-axis. Parameters ---------- x : array-like The wedge sizes. explode : array-like, optional, default: None If not *None*, is a ``len(x)`` array which specifies the fraction of the radius with which to offset each wedge. labels : list, optional, default: None A sequence of strings providing the labels for each wedge colors : array-like, optional, default: None A sequence of matplotlib color args through which the pie chart will cycle. If *None*, will use the colors in the currently active cycle. autopct : None (default), string, or function, optional If not *None*, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be ``fmt%pct``. If it is a function, it will be called. pctdistance : float, optional, default: 0.6 The ratio between the center of each pie slice and the start of the text generated by *autopct*. Ignored if *autopct* is *None*. shadow : bool, optional, default: False Draw a shadow beneath the pie. labeldistance : float or None, optional, default: 1.1 The radial distance at which the pie labels are drawn. If set to ``None``, label are not drawn, but are stored for use in ``legend()`` startangle : float, optional, default: None If not *None*, rotates the start of the pie chart by *angle* degrees counterclockwise from the x-axis. radius : float, optional, default: None The radius of the pie, if *radius* is *None* it will be set to 1. counterclock : bool, optional, default: True Specify fractions direction, clockwise or counterclockwise. wedgeprops : dict, optional, default: None Dict of arguments passed to the wedge objects making the pie. For example, you can pass in ``wedgeprops = {'linewidth': 3}`` to set the width of the wedge border lines equal to 3. For more details, look at the doc/arguments of the wedge object. By default ``clip_on=False``. textprops : dict, optional, default: None Dict of arguments to pass to the text objects. center : list of float, optional, default: (0, 0) Center position of the chart. Takes value (0, 0) or is a sequence of 2 scalars. frame : bool, optional, default: False Plot axes frame with the chart if true. rotatelabels : bool, optional, default: False Rotate each label to the angle of the corresponding slice if true. Returns ------- patches : list A sequence of :class:`matplotlib.patches.Wedge` instances texts : list A list of the label :class:`matplotlib.text.Text` instances. autotexts : list A list of :class:`~matplotlib.text.Text` instances for the numeric labels. This will only be returned if the parameter *autopct* is not *None*. Notes ----- The pie chart will probably look best if the figure and axes are square, or the Axes aspect is equal. This method sets the aspect ratio of the axis to "equal". The axes aspect ratio can be controlled with `Axes.set_aspect`. """ self.set_aspect('equal') # The use of float32 is "historical", but can't be changed without # regenerating the test baselines. x = np.asarray(x, np.float32) if x.ndim != 1 and x.squeeze().ndim <= 1: cbook.warn_deprecated( "3.1", message="Non-1D inputs to pie() are currently " "squeeze()d, but this behavior is deprecated since %(since)s " "and will be removed %(removal)s; pass a 1D array instead.") x = np.atleast_1d(x.squeeze()) sx = x.sum() if sx > 1: x = x / sx if labels is None: labels = [''] * len(x) if explode is None: explode = [0] * len(x) if len(x) != len(labels): raise ValueError("'label' must be of length 'x'") if len(x) != len(explode): raise ValueError("'explode' must be of length 'x'") if colors is None: get_next_color = self._get_patches_for_fill.get_next_color else: color_cycle = itertools.cycle(colors) def get_next_color(): return next(color_cycle) if radius is None: radius = 1 # Starting theta1 is the start fraction of the circle if startangle is None: theta1 = 0 else: theta1 = startangle / 360.0 # set default values in wedge_prop if wedgeprops is None: wedgeprops = {} wedgeprops.setdefault('clip_on', False) if textprops is None: textprops = {} textprops.setdefault('clip_on', False) texts = [] slices = [] autotexts = [] for frac, label, expl in zip(x, labels, explode): x, y = center theta2 = (theta1 + frac) if counterclock else (theta1 - frac) thetam = 2 * np.pi * 0.5 * (theta1 + theta2) x += expl * math.cos(thetam) y += expl * math.sin(thetam) w = mpatches.Wedge((x, y), radius, 360. * min(theta1, theta2), 360. * max(theta1, theta2), facecolor=get_next_color(), **wedgeprops) slices.append(w) self.add_patch(w) w.set_label(label) if shadow: # make sure to add a shadow after the call to # add_patch so the figure and transform props will be # set shad = mpatches.Shadow(w, -0.02, -0.02) shad.set_zorder(0.9 * w.get_zorder()) shad.set_label('_nolegend_') self.add_patch(shad) if labeldistance is not None: xt = x + labeldistance * radius * math.cos(thetam) yt = y + labeldistance * radius * math.sin(thetam) label_alignment_h = 'left' if xt > 0 else 'right' label_alignment_v = 'center' label_rotation = 'horizontal' if rotatelabels: label_alignment_v = 'bottom' if yt > 0 else 'top' label_rotation = (np.rad2deg(thetam) + (0 if xt > 0 else 180)) props = dict(horizontalalignment=label_alignment_h, verticalalignment=label_alignment_v, rotation=label_rotation, size=rcParams['xtick.labelsize']) props.update(textprops) t = self.text(xt, yt, label, **props) texts.append(t) if autopct is not None: xt = x + pctdistance * radius * math.cos(thetam) yt = y + pctdistance * radius * math.sin(thetam) if isinstance(autopct, str): s = autopct % (100. * frac) elif callable(autopct): s = autopct(100. * frac) else: raise TypeError( 'autopct must be callable or a format string') props = dict(horizontalalignment='center', verticalalignment='center') props.update(textprops) t = self.text(xt, yt, s, **props) autotexts.append(t) theta1 = theta2 if not frame: self.set_frame_on(False) self.set_xlim((-1.25 + center[0], 1.25 + center[0])) self.set_ylim((-1.25 + center[1], 1.25 + center[1])) self.set_xticks([]) self.set_yticks([]) if autopct is None: return slices, texts else: return slices, texts, autotexts
[docs] @_preprocess_data(replace_names=["x", "y", "xerr", "yerr"], label_namer="y") @docstring.dedent_interpd def errorbar(self, x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, **kwargs): """ Plot y versus x as lines and/or markers with attached errorbars. *x*, *y* define the data locations, *xerr*, *yerr* define the errorbar sizes. By default, this draws the data markers/lines as well the errorbars. Use fmt='none' to draw errorbars without any data markers. Parameters ---------- x, y : scalar or array-like The data positions. xerr, yerr : scalar or array-like, shape(N,) or shape(2,N), optional The errorbar sizes: - scalar: Symmetric +/- values for all data points. - shape(N,): Symmetric +/-values for each data point. - shape(2,N): Separate - and + values for each bar. First row contains the lower errors, the second row contains the upper errors. - *None*: No errorbar. Note that all error arrays should have *positive* values. See :doc:`/gallery/statistics/errorbar_features` for an example on the usage of ``xerr`` and ``yerr``. fmt : plot format string, optional, default: '' The format for the data points / data lines. See `.plot` for details. Use 'none' (case insensitive) to plot errorbars without any data markers. ecolor : mpl color, optional, default: None A matplotlib color arg which gives the color the errorbar lines. If None, use the color of the line connecting the markers. elinewidth : scalar, optional, default: None The linewidth of the errorbar lines. If None, the linewidth of the current style is used. capsize : scalar, optional, default: None The length of the error bar caps in points. If None, it will take the value from :rc:`errorbar.capsize`. capthick : scalar, optional, default: None An alias to the keyword argument *markeredgewidth* (a.k.a. *mew*). This setting is a more sensible name for the property that controls the thickness of the error bar cap in points. For backwards compatibility, if *mew* or *markeredgewidth* are given, then they will over-ride *capthick*. This may change in future releases. barsabove : bool, optional, default: False If True, will plot the errorbars above the plot symbols. Default is below. lolims, uplims, xlolims, xuplims : bool, optional, default: None These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. *lims*-arguments may be of the same type as *xerr* and *yerr*. To use limits with inverted axes, :meth:`set_xlim` or :meth:`set_ylim` must be called before :meth:`errorbar`. errorevery : int or (int, int), optional, default: 1 draws error bars on a subset of the data. *errorevery* =N draws error bars on the points (x[::N], y[::N]). *errorevery* =(start, N) draws error bars on the points (x[start::N], y[start::N]). e.g. errorevery=(6, 3) adds error bars to the data at (x[6], x[9], x[12], x[15], ...). Used to avoid overlapping error bars when two series share x-axis values. Returns ------- container : :class:`~.container.ErrorbarContainer` The container contains: - plotline: :class:`~matplotlib.lines.Line2D` instance of x, y plot markers and/or line. - caplines: A tuple of :class:`~matplotlib.lines.Line2D` instances of the error bar caps. - barlinecols: A tuple of :class:`~matplotlib.collections.LineCollection` with the horizontal and vertical error ranges. Other Parameters ---------------- **kwargs All other keyword arguments are passed on to the plot command for the markers. For example, this code makes big red squares with thick green edges:: x,y,yerr = rand(3,10) errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4) where *mfc*, *mec*, *ms* and *mew* are aliases for the longer property names, *markerfacecolor*, *markeredgecolor*, *markersize* and *markeredgewidth*. Valid kwargs for the marker properties are `.Lines2D` properties: %(_Line2D_docstr)s """ kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) # anything that comes in as 'None', drop so the default thing # happens down stream kwargs = {k: v for k, v in kwargs.items() if v is not None} kwargs.setdefault('zorder', 2) try: offset, errorevery = errorevery except TypeError: offset = 0 if errorevery < 1 or int(errorevery) != errorevery: raise ValueError( 'errorevery must be positive integer or tuple of integers') if int(offset) != offset: raise ValueError("errorevery's starting index must be an integer") self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) plot_line = (fmt.lower() != 'none') label = kwargs.pop("label", None) if fmt == '': fmt_style_kwargs = {} else: fmt_style_kwargs = {k: v for k, v in zip(('linestyle', 'marker', 'color'), _process_plot_format(fmt)) if v is not None} if fmt == 'none': # Remove alpha=0 color that _process_plot_format returns fmt_style_kwargs.pop('color') if ('color' in kwargs or 'color' in fmt_style_kwargs or ecolor is not None): base_style = {} if 'color' in kwargs: base_style['color'] = kwargs.pop('color') else: base_style = next(self._get_lines.prop_cycler) base_style['label'] = '_nolegend_' base_style.update(fmt_style_kwargs) if 'color' not in base_style: base_style['color'] = 'C0' if ecolor is None: ecolor = base_style['color'] # make sure all the args are iterable; use lists not arrays to # preserve units if not np.iterable(x): x = [x] if not np.iterable(y): y = [y] if xerr is not None: if not np.iterable(xerr): xerr = [xerr] * len(x) if yerr is not None: if not np.iterable(yerr): yerr = [yerr] * len(y) # make the style dict for the 'normal' plot line plot_line_style = { **base_style, **kwargs, 'zorder': (kwargs['zorder'] - .1 if barsabove else kwargs['zorder'] + .1), } # make the style dict for the line collections (the bars) eb_lines_style = dict(base_style) eb_lines_style.pop('marker', None) eb_lines_style.pop('linestyle', None) eb_lines_style['color'] = ecolor if elinewidth: eb_lines_style['linewidth'] = elinewidth elif 'linewidth' in kwargs: eb_lines_style['linewidth'] = kwargs['linewidth'] for key in ('transform', 'alpha', 'zorder', 'rasterized'): if key in kwargs: eb_lines_style[key] = kwargs[key] # set up cap style dictionary eb_cap_style = dict(base_style) # eject any marker information from format string eb_cap_style.pop('marker', None) eb_lines_style.pop('markerfacecolor', None) eb_lines_style.pop('markeredgewidth', None) eb_lines_style.pop('markeredgecolor', None) eb_cap_style.pop('ls', None) eb_cap_style['linestyle'] = 'none' if capsize is None: capsize = rcParams["errorbar.capsize"] if capsize > 0: eb_cap_style['markersize'] = 2. * capsize if capthick is not None: eb_cap_style['markeredgewidth'] = capthick # For backwards-compat, allow explicit setting of # 'markeredgewidth' to over-ride capthick. for key in ('markeredgewidth', 'transform', 'alpha', 'zorder', 'rasterized'): if key in kwargs: eb_cap_style[key] = kwargs[key] eb_cap_style['color'] = ecolor data_line = None if plot_line: data_line = mlines.Line2D(x, y, **plot_line_style) self.add_line(data_line) barcols = [] caplines = [] # arrays fine here, they are booleans and hence not units lolims = np.broadcast_to(lolims, len(x)).astype(bool) uplims = np.broadcast_to(uplims, len(x)).astype(bool) xlolims = np.broadcast_to(xlolims, len(x)).astype(bool) xuplims = np.broadcast_to(xuplims, len(x)).astype(bool) everymask = np.zeros(len(x), bool) everymask[offset::errorevery] = True def xywhere(xs, ys, mask): """ return xs[mask], ys[mask] where mask is True but xs and ys are not arrays """ assert len(xs) == len(ys) assert len(xs) == len(mask) xs = [thisx for thisx, b in zip(xs, mask) if b] ys = [thisy for thisy, b in zip(ys, mask) if b] return xs, ys def extract_err(err, data): """ Private function to parse *err* and subtract/add it to *data*. Both *err* and *data* are already iterables at this point. """ try: # Asymmetric error: pair of 1D iterables. a, b = err iter(a) iter(b) except (TypeError, ValueError): a = b = err # Symmetric error: 1D iterable. # This could just be `np.ndim(a) > 1 and np.ndim(b) > 1`, except # for the (undocumented, but tested) support for (n, 1) arrays. a_sh = np.shape(a) b_sh = np.shape(b) if (len(a_sh) > 2 or (len(a_sh) == 2 and a_sh[1] != 1) or len(b_sh) > 2 or (len(b_sh) == 2 and b_sh[1] != 1)): raise ValueError( "err must be a scalar or a 1D or (2, n) array-like") if len(a_sh) == 2 or len(b_sh) == 2: cbook.warn_deprecated( "3.1", message="Support for passing a (n, 1)-shaped error " "array to errorbar() is deprecated since Matplotlib " "%(since)s and will be removed %(removal)s; pass a 1D " "array instead.") # Using list comprehensions rather than arrays to preserve units. for e in [a, b]: if len(data) != len(e): raise ValueError( f"The lengths of the data ({len(data)}) and the " f"error {len(e)} do not match") low = [v - e for v, e in zip(data, a)] high = [v + e for v, e in zip(data, b)] return low, high if xerr is not None: left, right = extract_err(xerr, x) # select points without upper/lower limits in x and # draw normal errorbars for these points noxlims = ~(xlolims | xuplims) if noxlims.any() or len(noxlims) == 0: yo, _ = xywhere(y, right, noxlims & everymask) lo, ro = xywhere(left, right, noxlims & everymask) barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) if capsize > 0: caplines.append(mlines.Line2D(lo, yo, marker='|', **eb_cap_style)) caplines.append(mlines.Line2D(ro, yo, marker='|', **eb_cap_style)) if xlolims.any(): yo, _ = xywhere(y, right, xlolims & everymask) lo, ro = xywhere(x, right, xlolims & everymask) barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) rightup, yup = xywhere(right, y, xlolims & everymask) if self.xaxis_inverted(): marker = mlines.CARETLEFTBASE else: marker = mlines.CARETRIGHTBASE caplines.append( mlines.Line2D(rightup, yup, ls='None', marker=marker, **eb_cap_style)) if capsize > 0: xlo, ylo = xywhere(x, y, xlolims & everymask) caplines.append(mlines.Line2D(xlo, ylo, marker='|', **eb_cap_style)) if xuplims.any(): yo, _ = xywhere(y, right, xuplims & everymask) lo, ro = xywhere(left, x, xuplims & everymask) barcols.append(self.hlines(yo, lo, ro, **eb_lines_style)) leftlo, ylo = xywhere(left, y, xuplims & everymask) if self.xaxis_inverted(): marker = mlines.CARETRIGHTBASE else: marker = mlines.CARETLEFTBASE caplines.append( mlines.Line2D(leftlo, ylo, ls='None', marker=marker, **eb_cap_style)) if capsize > 0: xup, yup = xywhere(x, y, xuplims & everymask) caplines.append(mlines.Line2D(xup, yup, marker='|', **eb_cap_style)) if yerr is not None: lower, upper = extract_err(yerr, y) # select points without upper/lower limits in y and # draw normal errorbars for these points noylims = ~(lolims | uplims) if noylims.any() or len(noylims) == 0: xo, _ = xywhere(x, lower, noylims & everymask) lo, uo = xywhere(lower, upper, noylims & everymask) barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) if capsize > 0: caplines.append(mlines.Line2D(xo, lo, marker='_', **eb_cap_style)) caplines.append(mlines.Line2D(xo, uo, marker='_', **eb_cap_style)) if lolims.any(): xo, _ = xywhere(x, lower, lolims & everymask) lo, uo = xywhere(y, upper, lolims & everymask) barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) xup, upperup = xywhere(x, upper, lolims & everymask) if self.yaxis_inverted(): marker = mlines.CARETDOWNBASE else: marker = mlines.CARETUPBASE caplines.append( mlines.Line2D(xup, upperup, ls='None', marker=marker, **eb_cap_style)) if capsize > 0: xlo, ylo = xywhere(x, y, lolims & everymask) caplines.append(mlines.Line2D(xlo, ylo, marker='_', **eb_cap_style)) if uplims.any(): xo, _ = xywhere(x, lower, uplims & everymask) lo, uo = xywhere(lower, y, uplims & everymask) barcols.append(self.vlines(xo, lo, uo, **eb_lines_style)) xlo, lowerlo = xywhere(x, lower, uplims & everymask) if self.yaxis_inverted(): marker = mlines.CARETUPBASE else: marker = mlines.CARETDOWNBASE caplines.append( mlines.Line2D(xlo, lowerlo, ls='None', marker=marker, **eb_cap_style)) if capsize > 0: xup, yup = xywhere(x, y, uplims & everymask) caplines.append(mlines.Line2D(xup, yup, marker='_', **eb_cap_style)) for l in caplines: self.add_line(l) self._request_autoscale_view() errorbar_container = ErrorbarContainer((data_line, tuple(caplines), tuple(barcols)), has_xerr=(xerr is not None), has_yerr=(yerr is not None), label=label) self.containers.append(errorbar_container) return errorbar_container # (l0, caplines, barcols)
[docs] @cbook._rename_parameter("3.1", "manage_xticks", "manage_ticks") @_preprocess_data() def boxplot(self, x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None): """ Make a box and whisker plot. Make a box and whisker plot for each column of ``x`` or each vector in sequence ``x``. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. Parameters ---------- x : Array or a sequence of vectors. The input data. notch : bool, optional (False) If `True`, will produce a notched box plot. Otherwise, a rectangular boxplot is produced. The notches represent the confidence interval (CI) around the median. See the entry for the ``bootstrap`` parameter for information regarding how the locations of the notches are computed. .. note:: In cases where the values of the CI are less than the lower quartile or greater than the upper quartile, the notches will extend beyond the box, giving it a distinctive "flipped" appearance. This is expected behavior and consistent with other statistical visualization packages. sym : str, optional The default symbol for flier points. Enter an empty string ('') if you don't want to show fliers. If `None`, then the fliers default to 'b+' If you want more control use the flierprops kwarg. vert : bool, optional (True) If `True` (default), makes the boxes vertical. If `False`, everything is drawn horizontally. whis : float, sequence, or string (default = 1.5) As a float, determines the reach of the whiskers to the beyond the first and third quartiles. In other words, where IQR is the interquartile range (`Q3-Q1`), the upper whisker will extend to last datum less than `Q3 + whis*IQR`). Similarly, the lower whisker will extend to the first datum greater than `Q1 - whis*IQR`. Beyond the whiskers, data are considered outliers and are plotted as individual points. Set this to an unreasonably high value to force the whiskers to show the min and max values. Alternatively, set this to an ascending sequence of percentile (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, ``whis`` can be the string ``'range'`` to force the whiskers to the min and max of the data. bootstrap : int, optional Specifies whether to bootstrap the confidence intervals around the median for notched boxplots. If ``bootstrap`` is None, no bootstrapping is performed, and notches are calculated using a Gaussian-based asymptotic approximation (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and Kendall and Stuart, 1967). Otherwise, bootstrap specifies the number of times to bootstrap the median to determine its 95% confidence intervals. Values between 1000 and 10000 are recommended. usermedians : array-like, optional An array or sequence whose first dimension (or length) is compatible with ``x``. This overrides the medians computed by matplotlib for each element of ``usermedians`` that is not `None`. When an element of ``usermedians`` is None, the median will be computed by matplotlib as normal. conf_intervals : array-like, optional Array or sequence whose first dimension (or length) is compatible with ``x`` and whose second dimension is 2. When the an element of ``conf_intervals`` is not None, the notch locations computed by matplotlib are overridden (provided ``notch`` is `True`). When an element of ``conf_intervals`` is `None`, the notches are computed by the method specified by the other kwargs (e.g., ``bootstrap``). positions : array-like, optional Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to `range(1, N+1)` where N is the number of boxes to be drawn. widths : scalar or array-like Sets the width of each box either with a scalar or a sequence. The default is 0.5, or ``0.15*(distance between extreme positions)``, if that is smaller. patch_artist : bool, optional (False) If `False` produces boxes with the Line2D artist. Otherwise, boxes and drawn with Patch artists. labels : sequence, optional Labels for each dataset. Length must be compatible with dimensions of ``x``. manage_ticks : bool, optional (True) If True, the tick locations and labels will be adjusted to match the boxplot positions. autorange : bool, optional (False) When `True` and the data are distributed such that the 25th and 75th percentiles are equal, ``whis`` is set to ``'range'`` such that the whisker ends are at the minimum and maximum of the data. meanline : bool, optional (False) If `True` (and ``showmeans`` is `True`), will try to render the mean as a line spanning the full width of the box according to ``meanprops`` (see below). Not recommended if ``shownotches`` is also True. Otherwise, means will be shown as points. zorder : scalar, optional (None) Sets the zorder of the boxplot. Other Parameters ---------------- showcaps : bool, optional (True) Show the caps on the ends of whiskers. showbox : bool, optional (True) Show the central box. showfliers : bool, optional (True) Show the outliers beyond the caps. showmeans : bool, optional (False) Show the arithmetic means. capprops : dict, optional (None) Specifies the style of the caps. boxprops : dict, optional (None) Specifies the style of the box. whiskerprops : dict, optional (None) Specifies the style of the whiskers. flierprops : dict, optional (None) Specifies the style of the fliers. medianprops : dict, optional (None) Specifies the style of the median. meanprops : dict, optional (None) Specifies the style of the mean. Returns ------- result : dict A dictionary mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. That dictionary has the following keys (assuming vertical boxplots): - ``boxes``: the main body of the boxplot showing the quartiles and the median's confidence intervals if enabled. - ``medians``: horizontal lines at the median of each box. - ``whiskers``: the vertical lines extending to the most extreme, non-outlier data points. - ``caps``: the horizontal lines at the ends of the whiskers. - ``fliers``: points representing data that extend beyond the whiskers (fliers). - ``means``: points or lines representing the means. """ # Missing arguments default to rcParams. if whis is None: whis = rcParams['boxplot.whiskers'] if bootstrap is None: bootstrap = rcParams['boxplot.bootstrap'] bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap, labels=labels, autorange=autorange) if notch is None: notch = rcParams['boxplot.notch'] if vert is None: vert = rcParams['boxplot.vertical'] if patch_artist is None: patch_artist = rcParams['boxplot.patchartist'] if meanline is None: meanline = rcParams['boxplot.meanline'] if showmeans is None: showmeans = rcParams['boxplot.showmeans'] if showcaps is None: showcaps = rcParams['boxplot.showcaps'] if showbox is None: showbox = rcParams['boxplot.showbox'] if showfliers is None: showfliers = rcParams['boxplot.showfliers'] if boxprops is None: boxprops = {} if whiskerprops is None: whiskerprops = {} if capprops is None: capprops = {} if medianprops is None: medianprops = {} if meanprops is None: meanprops = {} if flierprops is None: flierprops = {} if patch_artist: boxprops['linestyle'] = 'solid' # Not consistent with bxp. if 'color' in boxprops: boxprops['edgecolor'] = boxprops.pop('color') # if non-default sym value, put it into the flier dictionary # the logic for providing the default symbol ('b+') now lives # in bxp in the initial value of final_flierprops # handle all of the `sym` related logic here so we only have to pass # on the flierprops dict. if sym is not None: # no-flier case, which should really be done with # 'showfliers=False' but none-the-less deal with it to keep back # compatibility if sym == '': # blow away existing dict and make one for invisible markers flierprops = dict(linestyle='none', marker='', color='none') # turn the fliers off just to be safe showfliers = False # now process the symbol string else: # process the symbol string # discarded linestyle _, marker, color = _process_plot_format(sym) # if we have a marker, use it if marker is not None: flierprops['marker'] = marker # if we have a color, use it if color is not None: # assume that if color is passed in the user want # filled symbol, if the users want more control use # flierprops flierprops['color'] = color flierprops['markerfacecolor'] = color flierprops['markeredgecolor'] = color # replace medians if necessary: if usermedians is not None: if (len(np.ravel(usermedians)) != len(bxpstats) or np.shape(usermedians)[0] != len(bxpstats)): raise ValueError('usermedians length not compatible with x') else: # reassign medians as necessary for stats, med in zip(bxpstats, usermedians): if med is not None: stats['med'] = med if conf_intervals is not None: if np.shape(conf_intervals)[0] != len(bxpstats): err_mess = 'conf_intervals length not compatible with x' raise ValueError(err_mess) else: for stats, ci in zip(bxpstats, conf_intervals): if ci is not None: if len(ci) != 2: raise ValueError('each confidence interval must ' 'have two values') else: if ci[0] is not None: stats['cilo'] = ci[0] if ci[1] is not None: stats['cihi'] = ci[1] artists = self.bxp(bxpstats, positions=positions, widths=widths, vert=vert, patch_artist=patch_artist, shownotches=notch, showmeans=showmeans, showcaps=showcaps, showbox=showbox, boxprops=boxprops, flierprops=flierprops, medianprops=medianprops, meanprops=meanprops, meanline=meanline, showfliers=showfliers, capprops=capprops, whiskerprops=whiskerprops, manage_ticks=manage_ticks, zorder=zorder) return artists
[docs] @cbook._rename_parameter("3.1", "manage_xticks", "manage_ticks") def bxp(self, bxpstats, positions=None, widths=None, vert=True, patch_artist=False, shownotches=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, whiskerprops=None, flierprops=None, medianprops=None, capprops=None, meanprops=None, meanline=False, manage_ticks=True, zorder=None): """ Drawing function for box and whisker plots. Make a box and whisker plot for each column of *x* or each vector in sequence *x*. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. Parameters ---------- bxpstats : list of dicts A list of dictionaries containing stats for each boxplot. Required keys are: - ``med``: The median (scalar float). - ``q1``: The first quartile (25th percentile) (scalar float). - ``q3``: The third quartile (75th percentile) (scalar float). - ``whislo``: Lower bound of the lower whisker (scalar float). - ``whishi``: Upper bound of the upper whisker (scalar float). Optional keys are: - ``mean``: The mean (scalar float). Needed if ``showmeans=True``. - ``fliers``: Data beyond the whiskers (sequence of floats). Needed if ``showfliers=True``. - ``cilo`` & ``cihi``: Lower and upper confidence intervals about the median. Needed if ``shownotches=True``. - ``label``: Name of the dataset (string). If available, this will be used a tick label for the boxplot positions : array-like, default = [1, 2, ..., n] Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. widths : array-like, default = None Either a scalar or a vector and sets the width of each box. The default is ``0.15*(distance between extreme positions)``, clipped to no less than 0.15 and no more than 0.5. vert : bool, default = True If `True` (default), makes the boxes vertical. If `False`, makes horizontal boxes. patch_artist : bool, default = False If `False` produces boxes with the `~matplotlib.lines.Line2D` artist. If `True` produces boxes with the `~matplotlib.patches.Patch` artist. shownotches : bool, default = False If `False` (default), produces a rectangular box plot. If `True`, will produce a notched box plot showmeans : bool, default = False If `True`, will toggle on the rendering of the means showcaps : bool, default = True If `True`, will toggle on the rendering of the caps showbox : bool, default = True If `True`, will toggle on the rendering of the box showfliers : bool, default = True If `True`, will toggle on the rendering of the fliers boxprops : dict or None (default) If provided, will set the plotting style of the boxes whiskerprops : dict or None (default) If provided, will set the plotting style of the whiskers capprops : dict or None (default) If provided, will set the plotting style of the caps flierprops : dict or None (default) If provided will set the plotting style of the fliers medianprops : dict or None (default) If provided, will set the plotting style of the medians meanprops : dict or None (default) If provided, will set the plotting style of the means meanline : bool, default = False If `True` (and *showmeans* is `True`), will try to render the mean as a line spanning the full width of the box according to *meanprops*. Not recommended if *shownotches* is also True. Otherwise, means will be shown as points. manage_ticks : bool, default = True If True, the tick locations and labels will be adjusted to match the boxplot positions. zorder : scalar, default = None The zorder of the resulting boxplot. Returns ------- result : dict A dictionary mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. That dictionary has the following keys (assuming vertical boxplots): - ``boxes``: the main body of the boxplot showing the quartiles and the median's confidence intervals if enabled. - ``medians``: horizontal lines at the median of each box. - ``whiskers``: the vertical lines extending to the most extreme, non-outlier data points. - ``caps``: the horizontal lines at the ends of the whiskers. - ``fliers``: points representing data that extend beyond the whiskers (fliers). - ``means``: points or lines representing the means. Examples -------- .. plot:: gallery/statistics/bxp.py """ # lists of artists to be output whiskers = [] caps = [] boxes = [] medians = [] means = [] fliers = [] # empty list of xticklabels datalabels = [] # Use default zorder if none specified if zorder is None: zorder = mlines.Line2D.zorder zdelta = 0.1 def line_props_with_rcdefaults(subkey, explicit, zdelta=0): d = {k.split('.')[-1]: v for k, v in rcParams.items() if k.startswith(f'boxplot.{subkey}')} d['zorder'] = zorder + zdelta if explicit is not None: d.update( cbook.normalize_kwargs(explicit, mlines.Line2D._alias_map)) return d # box properties if patch_artist: final_boxprops = dict( linestyle=rcParams['boxplot.boxprops.linestyle'], linewidth=rcParams['boxplot.boxprops.linewidth'], edgecolor=rcParams['boxplot.boxprops.color'], facecolor=('white' if rcParams['_internal.classic_mode'] else rcParams['patch.facecolor']), zorder=zorder, ) if boxprops is not None: final_boxprops.update( cbook.normalize_kwargs( boxprops, mpatches.PathPatch._alias_map)) else: final_boxprops = line_props_with_rcdefaults('boxprops', boxprops) final_whiskerprops = line_props_with_rcdefaults( 'whiskerprops', whiskerprops) final_capprops = line_props_with_rcdefaults( 'capprops', capprops) final_flierprops = line_props_with_rcdefaults( 'flierprops', flierprops) final_medianprops = line_props_with_rcdefaults( 'medianprops', medianprops, zdelta) final_meanprops = line_props_with_rcdefaults( 'meanprops', meanprops, zdelta) removed_prop = 'marker' if meanline else 'linestyle' # Only remove the property if it's not set explicitly as a parameter. if meanprops is None or removed_prop not in meanprops: final_meanprops[removed_prop] = '' def to_vc(xs, ys): # convert arguments to verts and codes, append (0, 0) (ignored). verts = np.append(np.column_stack([xs, ys]), [(0, 0)], 0) codes = ([mpath.Path.MOVETO] + [mpath.Path.LINETO] * (len(verts) - 2) + [mpath.Path.CLOSEPOLY]) return verts, codes def patch_list(xs, ys, **kwargs): verts, codes = to_vc(xs, ys) path = mpath.Path(verts, codes) patch = mpatches.PathPatch(path, **kwargs) self.add_artist(patch) return [patch] # vertical or horizontal plot? if vert: def doplot(*args, **kwargs): return self.plot(*args, **kwargs) def dopatch(xs, ys, **kwargs): return patch_list(xs, ys, **kwargs) else: def doplot(*args, **kwargs): shuffled = [] for i in range(0, len(args), 2): shuffled.extend([args[i + 1], args[i]]) return self.plot(*shuffled, **kwargs) def dopatch(xs, ys, **kwargs): xs, ys = ys, xs # flip X, Y return patch_list(xs, ys, **kwargs) # input validation N = len(bxpstats) datashape_message = ("List of boxplot statistics and `{0}` " "values must have same the length") # check position if positions is None: positions = list(range(1, N + 1)) elif len(positions) != N: raise ValueError(datashape_message.format("positions")) positions = np.array(positions) if len(positions) > 0 and not isinstance(positions[0], Number): raise TypeError("positions should be an iterable of numbers") # width if widths is None: widths = [np.clip(0.15 * np.ptp(positions), 0.15, 0.5)] * N elif np.isscalar(widths): widths = [widths] * N elif len(widths) != N: raise ValueError(datashape_message.format("widths")) for pos, width, stats in zip(positions, widths, bxpstats): # try to find a new label datalabels.append(stats.get('label', pos)) # whisker coords whisker_x = np.ones(2) * pos whiskerlo_y = np.array([stats['q1'], stats['whislo']]) whiskerhi_y = np.array([stats['q3'], stats['whishi']]) # cap coords cap_left = pos - width * 0.25 cap_right = pos + width * 0.25 cap_x = np.array([cap_left, cap_right]) cap_lo = np.ones(2) * stats['whislo'] cap_hi = np.ones(2) * stats['whishi'] # box and median coords box_left = pos - width * 0.5 box_right = pos + width * 0.5 med_y = [stats['med'], stats['med']] # notched boxes if shownotches: box_x = [box_left, box_right, box_right, cap_right, box_right, box_right, box_left, box_left, cap_left, box_left, box_left] box_y = [stats['q1'], stats['q1'], stats['cilo'], stats['med'], stats['cihi'], stats['q3'], stats['q3'], stats['cihi'], stats['med'], stats['cilo'], stats['q1']] med_x = cap_x # plain boxes else: box_x = [box_left, box_right, box_right, box_left, box_left] box_y = [stats['q1'], stats['q1'], stats['q3'], stats['q3'], stats['q1']] med_x = [box_left, box_right] # maybe draw the box: if showbox: if patch_artist: boxes.extend(dopatch(box_x, box_y, **final_boxprops)) else: boxes.extend(doplot(box_x, box_y, **final_boxprops)) # draw the whiskers whiskers.extend(doplot( whisker_x, whiskerlo_y, **final_whiskerprops )) whiskers.extend(doplot( whisker_x, whiskerhi_y, **final_whiskerprops )) # maybe draw the caps: if showcaps: caps.extend(doplot(cap_x, cap_lo, **final_capprops)) caps.extend(doplot(cap_x, cap_hi, **final_capprops)) # draw the medians medians.extend(doplot(med_x, med_y, **final_medianprops)) # maybe draw the means if showmeans: if meanline: means.extend(doplot( [box_left, box_right], [stats['mean'], stats['mean']], **final_meanprops )) else: means.extend(doplot( [pos], [stats['mean']], **final_meanprops )) # maybe draw the fliers if showfliers: # fliers coords flier_x = np.full(len(stats['fliers']), pos, dtype=np.float64) flier_y = stats['fliers'] fliers.extend(doplot( flier_x, flier_y, **final_flierprops )) if manage_ticks: axis_name = "x" if vert else "y" interval = getattr(self.dataLim, f"interval{axis_name}") axis = getattr(self, f"{axis_name}axis") positions = axis.convert_units(positions) # The 0.5 additional padding ensures reasonable-looking boxes # even when drawing a single box. We set the sticky edge to # prevent margins expansion, in order to match old behavior (back # when separate calls to boxplot() would completely reset the axis # limits regardless of what was drawn before). The sticky edges # are attached to the median lines, as they are always present. interval[:] = (min(interval[0], min(positions) - .5), max(interval[1], max(positions) + .5)) for median, position in zip(medians, positions): getattr(median.sticky_edges, axis_name).extend( [position - .5, position + .5]) # Modified from Axis.set_ticks and Axis.set_ticklabels. locator = axis.get_major_locator() if not isinstance(axis.get_major_locator(), mticker.FixedLocator): locator = mticker.FixedLocator([]) axis.set_major_locator(locator) locator.locs = np.array([*locator.locs, *positions]) formatter = axis.get_major_formatter() if not isinstance(axis.get_major_formatter(), mticker.FixedFormatter): formatter = mticker.FixedFormatter([]) axis.set_major_formatter(formatter) formatter.seq = [*formatter.seq, *datalabels] self._request_autoscale_view( scalex=self._autoscaleXon, scaley=self._autoscaleYon) return dict(whiskers=whiskers, caps=caps, boxes=boxes, medians=medians, fliers=fliers, means=means)
@staticmethod def _parse_scatter_color_args(c, edgecolors, kwargs, xsize, get_next_color_func): """ Helper function to process color related arguments of `.Axes.scatter`. Argument precedence for facecolors: - c (if not None) - kwargs['facecolors'] - kwargs['facecolor'] - kwargs['color'] (==kwcolor) - 'b' if in classic mode else the result of ``get_next_color_func()`` Argument precedence for edgecolors: - edgecolors (is an explicit kw argument in scatter()) - kwargs['edgecolor'] - kwargs['color'] (==kwcolor) - 'face' if not in classic mode else None Parameters ---------- c : color or sequence or sequence of color or None See argument description of `.Axes.scatter`. edgecolors : color or sequence of color or {'face', 'none'} or None See argument description of `.Axes.scatter`. kwargs : dict Additional kwargs. If these keys exist, we pop and process them: 'facecolors', 'facecolor', 'edgecolor', 'color' Note: The dict is modified by this function. xsize : int The size of the x and y arrays passed to `.Axes.scatter`. get_next_color_func : callable A callable that returns a color. This color is used as facecolor if no other color is provided. Note, that this is a function rather than a fixed color value to support conditional evaluation of the next color. As of the current implementation obtaining the next color from the property cycle advances the cycle. This must only happen if we actually use the color, which will only be decided within this method. Returns ------- c The input *c* if it was not *None*, else some color specification derived from the other inputs or defaults. colors : array(N, 4) or None The facecolors as RGBA values or *None* if a colormap is used. edgecolors The edgecolor specification. """ facecolors = kwargs.pop('facecolors', None) facecolors = kwargs.pop('facecolor', facecolors) edgecolors = kwargs.pop('edgecolor', edgecolors) kwcolor = kwargs.pop('color', None) if kwcolor is not None and c is not None: raise ValueError("Supply a 'c' argument or a 'color'" " kwarg but not both; they differ but" " their functionalities overlap.") if kwcolor is not None: try: mcolors.to_rgba_array(kwcolor) except ValueError: raise ValueError("'color' kwarg must be an mpl color" " spec or sequence of color specs.\n" "For a sequence of values to be color-mapped," " use the 'c' argument instead.") if edgecolors is None: edgecolors = kwcolor if facecolors is None: facecolors = kwcolor if edgecolors is None and not rcParams['_internal.classic_mode']: edgecolors = rcParams['scatter.edgecolors'] c_was_none = c is None if c is None: c = (facecolors if facecolors is not None else "b" if rcParams['_internal.classic_mode'] else get_next_color_func()) c_is_string_or_strings = ( isinstance(c, str) or (isinstance(c, collections.abc.Iterable) and len(c) > 0 and isinstance(cbook.safe_first_element(c), str))) def invalid_shape_exception(csize, xsize): return ValueError( f"'c' argument has {csize} elements, which is inconsistent " f"with 'x' and 'y' with size {xsize}.") c_is_mapped = False # Unless proven otherwise below. valid_shape = True # Unless proven otherwise below. if not c_was_none and kwcolor is None and not c_is_string_or_strings: try: # First, does 'c' look suitable for value-mapping? c = np.asanyarray(c, dtype=float) except ValueError: pass # Failed to convert to float array; must be color specs. else: # If c can be either mapped values or a RGB(A) color, prefer # the former if shapes match, the latter otherwise. if c.size == xsize: c = c.ravel() c_is_mapped = True else: # Wrong size; it must not be intended for mapping. if c.shape in ((3,), (4,)): _log.warning( "'c' argument looks like a single numeric RGB or " "RGBA sequence, which should be avoided as value-" "mapping will have precedence in case its length " "matches with 'x' & 'y'. Please use a 2-D array " "with a single row if you really want to specify " "the same RGB or RGBA value for all points.") valid_shape = False if not c_is_mapped: try: # Is 'c' acceptable as PathCollection facecolors? colors = mcolors.to_rgba_array(c) except ValueError: if not valid_shape: raise invalid_shape_exception(c.size, xsize) # Both the mapping *and* the RGBA conversion failed: pretty # severe failure => one may appreciate a verbose feedback. raise ValueError( f"'c' argument must be a mpl color, a sequence of mpl " f"colors, or a sequence of numbers, not {c}.") else: if len(colors) not in (0, 1, xsize): # NB: remember that a single color is also acceptable. # Besides *colors* will be an empty array if c == 'none'. raise invalid_shape_exception(len(colors), xsize) else: colors = None # use cmap, norm after collection is created return c, colors, edgecolors
[docs] @_preprocess_data(replace_names=["x", "y", "s", "linewidths", "edgecolors", "c", "facecolor", "facecolors", "color"], label_namer="y") @cbook._delete_parameter("3.2", "verts") def scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, plotnonfinite=False, **kwargs): """ A scatter plot of *y* vs *x* with varying marker size and/or color. Parameters ---------- x, y : scalar or array_like, shape (n, ) The data positions. s : scalar or array_like, shape (n, ), optional The marker size in points**2. Default is ``rcParams['lines.markersize'] ** 2``. c : color, sequence, or sequence of color, optional The marker color. Possible values: - A single color format string. - A sequence of color specifications of length n. - A scalar or sequence of n numbers to be mapped to colors using *cmap* and *norm*. - A 2-D array in which the rows are RGB or RGBA. Note that *c* should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2-D array with a single row. Otherwise, value- matching will have precedence in case of a size matching with *x* and *y*. Defaults to ``None``. In that case the marker color is determined by the value of ``color``, ``facecolor`` or ``facecolors``. In case those are not specified or ``None``, the marker color is determined by the next color of the ``Axes``' current "shape and fill" color cycle. This cycle defaults to :rc:`axes.prop_cycle`. marker : `~matplotlib.markers.MarkerStyle`, optional The marker style. *marker* can be either an instance of the class or the text shorthand for a particular marker. Defaults to ``None``, in which case it takes the value of :rc:`scatter.marker` = 'o'. See `~matplotlib.markers` for more information about marker styles. cmap : `~matplotlib.colors.Colormap`, optional, default: None A `.Colormap` instance or registered colormap name. *cmap* is only used if *c* is an array of floats. If ``None``, defaults to rc ``image.cmap``. norm : `~matplotlib.colors.Normalize`, optional, default: None A `.Normalize` instance is used to scale luminance data to 0, 1. *norm* is only used if *c* is an array of floats. If *None*, use the default `.colors.Normalize`. vmin, vmax : scalar, optional, default: None *vmin* and *vmax* are used in conjunction with *norm* to normalize luminance data. If None, the respective min and max of the color array is used. *vmin* and *vmax* are ignored if you pass a *norm* instance. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque). linewidths : scalar or array_like, optional, default: None The linewidth of the marker edges. Note: The default *edgecolors* is 'face'. You may want to change this as well. If *None*, defaults to rcParams ``lines.linewidth``. edgecolors : {'face', 'none', *None*} or color or sequence of color, \ optional. The edge color of the marker. Possible values: - 'face': The edge color will always be the same as the face color. - 'none': No patch boundary will be drawn. - A Matplotlib color or sequence of color. Defaults to ``None``, in which case it takes the value of :rc:`scatter.edgecolors` = 'face'. For non-filled markers, the *edgecolors* kwarg is ignored and forced to 'face' internally. plotnonfinite : boolean, optional, default: False Set to plot points with nonfinite *c*, in conjunction with `~matplotlib.colors.Colormap.set_bad`. Returns ------- paths : `~matplotlib.collections.PathCollection` Other Parameters ---------------- **kwargs : `~matplotlib.collections.Collection` properties See Also -------- plot : To plot scatter plots when markers are identical in size and color. Notes ----- * The `.plot` function will be faster for scatterplots where markers don't vary in size or color. * Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. * Fundamentally, scatter works with 1-D arrays; *x*, *y*, *s*, and *c* may be input as N-D arrays, but within scatter they will be flattened. The exception is *c*, which will be flattened only if its size matches the size of *x* and *y*. """ # Process **kwargs to handle aliases, conflicts with explicit kwargs: self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x = self.convert_xunits(x) y = self.convert_yunits(y) # np.ma.ravel yields an ndarray, not a masked array, # unless its argument is a masked array. x = np.ma.ravel(x) y = np.ma.ravel(y) if x.size != y.size: raise ValueError("x and y must be the same size") if s is None: s = (20 if rcParams['_internal.classic_mode'] else rcParams['lines.markersize'] ** 2.0) s = np.ma.ravel(s) if len(s) not in (1, x.size): raise ValueError("s must be a scalar, or the same size as x and y") c, colors, edgecolors = \ self._parse_scatter_color_args( c, edgecolors, kwargs, x.size, get_next_color_func=self._get_patches_for_fill.get_next_color) if plotnonfinite and colors is None: c = np.ma.masked_invalid(c) x, y, s, edgecolors, linewidths = \ cbook._combine_masks(x, y, s, edgecolors, linewidths) else: x, y, s, c, colors, edgecolors, linewidths = \ cbook._combine_masks( x, y, s, c, colors, edgecolors, linewidths) scales = s # Renamed for readability below. # load default marker from rcParams if marker is None: marker = rcParams['scatter.marker'] if isinstance(marker, mmarkers.MarkerStyle): marker_obj = marker else: marker_obj = mmarkers.MarkerStyle(marker) path = marker_obj.get_path().transformed( marker_obj.get_transform()) if not marker_obj.is_filled(): edgecolors = 'face' linewidths = rcParams['lines.linewidth'] offsets = np.ma.column_stack([x, y]) collection = mcoll.PathCollection( (path,), scales, facecolors=colors, edgecolors=edgecolors, linewidths=linewidths, offsets=offsets, transOffset=kwargs.pop('transform', self.transData), alpha=alpha ) collection.set_transform(mtransforms.IdentityTransform()) collection.update(kwargs) if colors is None: if norm is not None and not isinstance(norm, mcolors.Normalize): raise ValueError( "'norm' must be an instance of 'mcolors.Normalize'") collection.set_array(c) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() # Classic mode only: # ensure there are margins to allow for the # finite size of the symbols. In v2.x, margins # are present by default, so we disable this # scatter-specific override. if rcParams['_internal.classic_mode']: if self._xmargin < 0.05 and x.size > 0: self.set_xmargin(0.05) if self._ymargin < 0.05 and x.size > 0: self.set_ymargin(0.05) self.add_collection(collection) self._request_autoscale_view() return collection
[docs] @_preprocess_data(replace_names=["x", "y"], label_namer="y") @docstring.dedent_interpd def hexbin(self, x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='face', reduce_C_function=np.mean, mincnt=None, marginals=False, **kwargs): """ Make a hexagonal binning plot. Make a hexagonal binning plot of *x* versus *y*, where *x*, *y* are 1-D sequences of the same length, *N*. If *C* is *None* (the default), this is a histogram of the number of occurrences of the observations at (x[i],y[i]). If *C* is specified, it specifies values at the coordinate (x[i], y[i]). These values are accumulated for each hexagonal bin and then reduced according to *reduce_C_function*, which defaults to `numpy.mean`. (If *C* is specified, it must also be a 1-D sequence of the same length as *x* and *y*.) Parameters ---------- x, y : array or masked array C : array or masked array, optional, default is *None* gridsize : int or (int, int), optional, default is 100 The number of hexagons in the *x*-direction, default is 100. The corresponding number of hexagons in the *y*-direction is chosen such that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the *x*-direction and the *y*-direction. bins : 'log' or int or sequence, optional, default is *None* If *None*, no binning is applied; the color of each hexagon directly corresponds to its count value. If 'log', use a logarithmic scale for the color map. Internally, :math:`log_{10}(i+1)` is used to determine the hexagon color. If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly. If a sequence of values, the values of the lower bound of the bins to be used. xscale : {'linear', 'log'}, optional, default is 'linear' Use a linear or log10 scale on the horizontal axis. yscale : {'linear', 'log'}, optional, default is 'linear' Use a linear or log10 scale on the vertical axis. mincnt : int > 0, optional, default is *None* If not *None*, only display cells with more than *mincnt* number of points in the cell marginals : bool, optional, default is *False* if marginals is *True*, plot the marginal density as colormapped rectangles along the bottom of the x-axis and left of the y-axis extent : scalar, optional, default is *None* The limits of the bins. The default assigns the limits based on *gridsize*, *x*, *y*, *xscale* and *yscale*. If *xscale* or *yscale* is set to 'log', the limits are expected to be the exponent for a power of 10. E.g. for x-limits of 1 and 50 in 'linear' scale and y-limits of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3). Order of scalars is (left, right, bottom, top). Other Parameters ---------------- cmap : object, optional, default is *None* a :class:`matplotlib.colors.Colormap` instance. If *None*, defaults to rc ``image.cmap``. norm : object, optional, default is *None* :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. vmin, vmax : scalar, optional, default is *None* *vmin* and *vmax* are used in conjunction with *norm* to normalize luminance data. If *None*, the min and max of the color array *C* are used. Note if you pass a norm instance your settings for *vmin* and *vmax* will be ignored. alpha : scalar between 0 and 1, optional, default is *None* the alpha value for the patches linewidths : scalar, optional, default is *None* If *None*, defaults to 1.0. edgecolors : {'face', 'none', *None*} or color, optional If 'face' (the default), draws the edges in the same color as the fill color. If 'none', no edge is drawn; this can sometimes lead to unsightly unpainted pixels between the hexagons. If *None*, draws outlines in the default color. If a matplotlib color arg, draws outlines in the specified color. Returns ------- polycollection A `.PolyCollection` instance; use `.PolyCollection.get_array` on this to get the counts in each hexagon. If *marginals* is *True*, horizontal bar and vertical bar (both PolyCollections) will be attached to the return collection as attributes *hbar* and *vbar*. Notes ----- The standard descriptions of all the :class:`~matplotlib.collections.Collection` parameters: %(Collection)s """ self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x, y, C = cbook.delete_masked_points(x, y, C) # Set the size of the hexagon grid if np.iterable(gridsize): nx, ny = gridsize else: nx = gridsize ny = int(nx / math.sqrt(3)) # Count the number of data in each hexagon x = np.array(x, float) y = np.array(y, float) if xscale == 'log': if np.any(x <= 0.0): raise ValueError("x contains non-positive values, so can not" " be log-scaled") x = np.log10(x) if yscale == 'log': if np.any(y <= 0.0): raise ValueError("y contains non-positive values, so can not" " be log-scaled") y = np.log10(y) if extent is not None: xmin, xmax, ymin, ymax = extent else: xmin, xmax = (np.min(x), np.max(x)) if len(x) else (0, 1) ymin, ymax = (np.min(y), np.max(y)) if len(y) else (0, 1) # to avoid issues with singular data, expand the min/max pairs xmin, xmax = mtransforms.nonsingular(xmin, xmax, expander=0.1) ymin, ymax = mtransforms.nonsingular(ymin, ymax, expander=0.1) # In the x-direction, the hexagons exactly cover the region from # xmin to xmax. Need some padding to avoid roundoff errors. padding = 1.e-9 * (xmax - xmin) xmin -= padding xmax += padding sx = (xmax - xmin) / nx sy = (ymax - ymin) / ny if marginals: xorig = x.copy() yorig = y.copy() x = (x - xmin) / sx y = (y - ymin) / sy ix1 = np.round(x).astype(int) iy1 = np.round(y).astype(int) ix2 = np.floor(x).astype(int) iy2 = np.floor(y).astype(int) nx1 = nx + 1 ny1 = ny + 1 nx2 = nx ny2 = ny n = nx1 * ny1 + nx2 * ny2 d1 = (x - ix1) ** 2 + 3.0 * (y - iy1) ** 2 d2 = (x - ix2 - 0.5) ** 2 + 3.0 * (y - iy2 - 0.5) ** 2 bdist = (d1 < d2) if C is None: lattice1 = np.zeros((nx1, ny1)) lattice2 = np.zeros((nx2, ny2)) c1 = (0 <= ix1) & (ix1 < nx1) & (0 <= iy1) & (iy1 < ny1) & bdist c2 = (0 <= ix2) & (ix2 < nx2) & (0 <= iy2) & (iy2 < ny2) & ~bdist np.add.at(lattice1, (ix1[c1], iy1[c1]), 1) np.add.at(lattice2, (ix2[c2], iy2[c2]), 1) if mincnt is not None: lattice1[lattice1 < mincnt] = np.nan lattice2[lattice2 < mincnt] = np.nan accum = np.concatenate([lattice1.ravel(), lattice2.ravel()]) good_idxs = ~np.isnan(accum) else: if mincnt is None: mincnt = 0 # create accumulation arrays lattice1 = np.empty((nx1, ny1), dtype=object) for i in range(nx1): for j in range(ny1): lattice1[i, j] = [] lattice2 = np.empty((nx2, ny2), dtype=object) for i in range(nx2): for j in range(ny2): lattice2[i, j] = [] for i in range(len(x)): if bdist[i]: if 0 <= ix1[i] < nx1 and 0 <= iy1[i] < ny1: lattice1[ix1[i], iy1[i]].append(C[i]) else: if 0 <= ix2[i] < nx2 and 0 <= iy2[i] < ny2: lattice2[ix2[i], iy2[i]].append(C[i]) for i in range(nx1): for j in range(ny1): vals = lattice1[i, j] if len(vals) > mincnt: lattice1[i, j] = reduce_C_function(vals) else: lattice1[i, j] = np.nan for i in range(nx2): for j in range(ny2): vals = lattice2[i, j] if len(vals) > mincnt: lattice2[i, j] = reduce_C_function(vals) else: lattice2[i, j] = np.nan accum = np.hstack((lattice1.astype(float).ravel(), lattice2.astype(float).ravel())) good_idxs = ~np.isnan(accum) offsets = np.zeros((n, 2), float) offsets[:nx1 * ny1, 0] = np.repeat(np.arange(nx1), ny1) offsets[:nx1 * ny1, 1] = np.tile(np.arange(ny1), nx1) offsets[nx1 * ny1:, 0] = np.repeat(np.arange(nx2) + 0.5, ny2) offsets[nx1 * ny1:, 1] = np.tile(np.arange(ny2), nx2) + 0.5 offsets[:, 0] *= sx offsets[:, 1] *= sy offsets[:, 0] += xmin offsets[:, 1] += ymin # remove accumulation bins with no data offsets = offsets[good_idxs, :] accum = accum[good_idxs] polygon = [sx, sy / 3] * np.array( [[.5, -.5], [.5, .5], [0., 1.], [-.5, .5], [-.5, -.5], [0., -1.]]) if linewidths is None: linewidths = [1.0] if xscale == 'log' or yscale == 'log': polygons = np.expand_dims(polygon, 0) + np.expand_dims(offsets, 1) if xscale == 'log': polygons[:, :, 0] = 10.0 ** polygons[:, :, 0] xmin = 10.0 ** xmin xmax = 10.0 ** xmax self.set_xscale(xscale) if yscale == 'log': polygons[:, :, 1] = 10.0 ** polygons[:, :, 1] ymin = 10.0 ** ymin ymax = 10.0 ** ymax self.set_yscale(yscale) collection = mcoll.PolyCollection( polygons, edgecolors=edgecolors, linewidths=linewidths, ) else: collection = mcoll.PolyCollection( [polygon], edgecolors=edgecolors, linewidths=linewidths, offsets=offsets, transOffset=mtransforms.IdentityTransform(), offset_position="data" ) # Check for valid norm if norm is not None and not isinstance(norm, mcolors.Normalize): msg = "'norm' must be an instance of 'mcolors.Normalize'" raise ValueError(msg) # Set normalizer if bins is 'log' if bins == 'log': if norm is not None: cbook._warn_external("Only one of 'bins' and 'norm' " "arguments can be supplied, ignoring " "bins={}".format(bins)) else: norm = mcolors.LogNorm() bins = None if isinstance(norm, mcolors.LogNorm): if (accum == 0).any(): # make sure we have no zeros accum += 1 # autoscale the norm with curren accum values if it hasn't # been set if norm is not None: if norm.vmin is None and norm.vmax is None: norm.autoscale(accum) if bins is not None: if not np.iterable(bins): minimum, maximum = min(accum), max(accum) bins -= 1 # one less edge than bins bins = minimum + (maximum - minimum) * np.arange(bins) / bins bins = np.sort(bins) accum = bins.searchsorted(accum) collection.set_array(accum) collection.set_cmap(cmap) collection.set_norm(norm) collection.set_alpha(alpha) collection.update(kwargs) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() corners = ((xmin, ymin), (xmax, ymax)) self.update_datalim(corners) self._request_autoscale_view(tight=True) # add the collection last self.add_collection(collection, autolim=False) if not marginals: return collection if C is None: C = np.ones(len(x)) def coarse_bin(x, y, coarse): ind = coarse.searchsorted(x).clip(0, len(coarse) - 1) mus = np.zeros(len(coarse)) for i in range(len(coarse)): yi = y[ind == i] if len(yi) > 0: mu = reduce_C_function(yi) else: mu = np.nan mus[i] = mu return mus coarse = np.linspace(xmin, xmax, gridsize) xcoarse = coarse_bin(xorig, C, coarse) valid = ~np.isnan(xcoarse) verts, values = [], [] for i, val in enumerate(xcoarse): thismin = coarse[i] if i < len(coarse) - 1: thismax = coarse[i + 1] else: thismax = thismin + np.diff(coarse)[-1] if not valid[i]: continue verts.append([(thismin, 0), (thismin, 0.05), (thismax, 0.05), (thismax, 0)]) values.append(val) values = np.array(values) trans = self.get_xaxis_transform(which='grid') hbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face') hbar.set_array(values) hbar.set_cmap(cmap) hbar.set_norm(norm) hbar.set_alpha(alpha) hbar.update(kwargs) self.add_collection(hbar, autolim=False) coarse = np.linspace(ymin, ymax, gridsize) ycoarse = coarse_bin(yorig, C, coarse) valid = ~np.isnan(ycoarse) verts, values = [], [] for i, val in enumerate(ycoarse): thismin = coarse[i] if i < len(coarse) - 1: thismax = coarse[i + 1] else: thismax = thismin + np.diff(coarse)[-1] if not valid[i]: continue verts.append([(0, thismin), (0.0, thismax), (0.05, thismax), (0.05, thismin)]) values.append(val) values = np.array(values) trans = self.get_yaxis_transform(which='grid') vbar = mcoll.PolyCollection(verts, transform=trans, edgecolors='face') vbar.set_array(values) vbar.set_cmap(cmap) vbar.set_norm(norm) vbar.set_alpha(alpha) vbar.update(kwargs) self.add_collection(vbar, autolim=False) collection.hbar = hbar collection.vbar = vbar def on_changed(collection): hbar.set_cmap(collection.get_cmap()) hbar.set_clim(collection.get_clim()) vbar.set_cmap(collection.get_cmap()) vbar.set_clim(collection.get_clim()) collection.callbacksSM.connect('changed', on_changed) return collection
[docs] @docstring.dedent_interpd def arrow(self, x, y, dx, dy, **kwargs): """ Add an arrow to the axes. This draws an arrow from ``(x, y)`` to ``(x+dx, y+dy)``. Parameters ---------- x, y : float The x and y coordinates of the arrow base. dx, dy : float The length of the arrow along x and y direction. Returns ------- arrow : `.FancyArrow` The created `.FancyArrow` object. Other Parameters ---------------- **kwargs Optional kwargs (inherited from `.FancyArrow` patch) control the arrow construction and properties: %(FancyArrow)s Notes ----- The resulting arrow is affected by the axes aspect ratio and limits. This may produce an arrow whose head is not square with its stem. To create an arrow whose head is square with its stem, use :meth:`annotate` for example: >>> ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0), ... arrowprops=dict(arrowstyle="->")) """ # Strip away units for the underlying patch since units # do not make sense to most patch-like code x = self.convert_xunits(x) y = self.convert_yunits(y) dx = self.convert_xunits(dx) dy = self.convert_yunits(dy) a = mpatches.FancyArrow(x, y, dx, dy, **kwargs) self.add_artist(a) return a
[docs] @docstring.copy(mquiver.QuiverKey.__init__) def quiverkey(self, Q, X, Y, U, label, **kw): qk = mquiver.QuiverKey(Q, X, Y, U, label, **kw) self.add_artist(qk) return qk
# Handle units for x and y, if they've been passed def _quiver_units(self, args, kw): if len(args) > 3: x, y = args[0:2] self._process_unit_info(xdata=x, ydata=y, kwargs=kw) x = self.convert_xunits(x) y = self.convert_yunits(y) return (x, y) + args[2:] return args # args can by a combination if X, Y, U, V, C and all should be replaced
[docs] @_preprocess_data() def quiver(self, *args, **kw): # Make sure units are handled for x and y values args = self._quiver_units(args, kw) q = mquiver.Quiver(self, *args, **kw) self.add_collection(q, autolim=True) self._request_autoscale_view() return q
quiver.__doc__ = mquiver.Quiver.quiver_doc # args can be some combination of X, Y, U, V, C and all should be replaced
[docs] @_preprocess_data() @docstring.dedent_interpd def barbs(self, *args, **kw): """ %(barbs_doc)s """ # Make sure units are handled for x and y values args = self._quiver_units(args, kw) b = mquiver.Barbs(self, *args, **kw) self.add_collection(b, autolim=True) self._request_autoscale_view() return b
# Uses a custom implementation of data-kwarg handling in # _process_plot_var_args.
[docs] def fill(self, *args, data=None, **kwargs): """ Plot filled polygons. Parameters ---------- *args : sequence of x, y, [color] Each polygon is defined by the lists of *x* and *y* positions of its nodes, optionally followed by a *color* specifier. See :mod:`matplotlib.colors` for supported color specifiers. The standard color cycle is used for polygons without a color specifier. You can plot multiple polygons by providing multiple *x*, *y*, *[color]* groups. For example, each of the following is legal:: ax.fill(x, y) # a polygon with default color ax.fill(x, y, "b") # a blue polygon ax.fill(x, y, x2, y2) # two polygons ax.fill(x, y, "b", x2, y2, "r") # a blue and a red polygon data : indexable object, optional An object with labelled data. If given, provide the label names to plot in *x* and *y*, e.g.:: ax.fill("time", "signal", data={"time": [0, 1, 2], "signal": [0, 1, 0]}) Returns ------- a list of :class:`~matplotlib.patches.Polygon` Other Parameters ---------------- **kwargs : :class:`~matplotlib.patches.Polygon` properties Notes ----- Use :meth:`fill_between` if you would like to fill the region between two curves. """ # For compatibility(!), get aliases from Line2D rather than Patch. kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) patches = [] for poly in self._get_patches_for_fill(*args, data=data, **kwargs): self.add_patch(poly) patches.append(poly) self._request_autoscale_view() return patches
[docs] @_preprocess_data(replace_names=["x", "y1", "y2", "where"]) @docstring.dedent_interpd def fill_between(self, x, y1, y2=0, where=None, interpolate=False, step=None, **kwargs): """ Fill the area between two horizontal curves. The curves are defined by the points (*x*, *y1*) and (*x*, *y2*). This creates one or multiple polygons describing the filled area. You may exclude some horizontal sections from filling using *where*. By default, the edges connect the given points directly. Use *step* if the filling should be a step function, i.e. constant in between *x*. Parameters ---------- x : array (length N) The x coordinates of the nodes defining the curves. y1 : array (length N) or scalar The y coordinates of the nodes defining the first curve. y2 : array (length N) or scalar, optional, default: 0 The y coordinates of the nodes defining the second curve. where : array of bool (length N), optional, default: None Define *where* to exclude some horizontal regions from being filled. The filled regions are defined by the coordinates ``x[where]``. More precisely, fill between ``x[i]`` and ``x[i+1]`` if ``where[i] and where[i+1]``. Note that this definition implies that an isolated *True* value between two *False* values in *where* will not result in filling. Both sides of the *True* position remain unfilled due to the adjacent *False* values. interpolate : bool, optional This option is only relevant if *where* is used and the two curves are crossing each other. Semantically, *where* is often used for *y1* > *y2* or similar. By default, the nodes of the polygon defining the filled region will only be placed at the positions in the *x* array. Such a polygon cannot describe the above semantics close to the intersection. The x-sections containing the intersection are simply clipped. Setting *interpolate* to *True* will calculate the actual intersection point and extend the filled region up to this point. step : {'pre', 'post', 'mid'}, optional Define *step* if the filling should be a step function, i.e. constant in between *x*. The value determines where the step will occur: - 'pre': The y value is continued constantly to the left from every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the value ``y[i]``. - 'post': The y value is continued constantly to the right from every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the value ``y[i]``. - 'mid': Steps occur half-way between the *x* positions. Other Parameters ---------------- **kwargs All other keyword arguments are passed on to `.PolyCollection`. They control the `.Polygon` properties: %(PolyCollection)s Returns ------- `.PolyCollection` A `.PolyCollection` containing the plotted polygons. See Also -------- fill_betweenx : Fill between two sets of x-values. Notes ----- .. [notes section required to get data note injection right] """ if not rcParams['_internal.classic_mode']: kwargs = cbook.normalize_kwargs(kwargs, mcoll.Collection) if not any(c in kwargs for c in ('color', 'facecolor')): kwargs['facecolor'] = \ self._get_patches_for_fill.get_next_color() # Handle united data, such as dates self._process_unit_info(xdata=x, ydata=y1, kwargs=kwargs) self._process_unit_info(ydata=y2) # Convert the arrays so we can work with them x = ma.masked_invalid(self.convert_xunits(x)) y1 = ma.masked_invalid(self.convert_yunits(y1)) y2 = ma.masked_invalid(self.convert_yunits(y2)) for name, array in [('x', x), ('y1', y1), ('y2', y2)]: if array.ndim > 1: raise ValueError('Input passed into argument "%r"' % name + 'is not 1-dimensional.') if where is None: where = True where = where & ~functools.reduce(np.logical_or, map(np.ma.getmask, [x, y1, y2])) x, y1, y2 = np.broadcast_arrays(np.atleast_1d(x), y1, y2) polys = [] for ind0, ind1 in cbook.contiguous_regions(where): xslice = x[ind0:ind1] y1slice = y1[ind0:ind1] y2slice = y2[ind0:ind1] if step is not None: step_func = cbook.STEP_LOOKUP_MAP["steps-" + step] xslice, y1slice, y2slice = step_func(xslice, y1slice, y2slice) if not len(xslice): continue N = len(xslice) X = np.zeros((2 * N + 2, 2), float) if interpolate: def get_interp_point(ind): im1 = max(ind - 1, 0) x_values = x[im1:ind + 1] diff_values = y1[im1:ind + 1] - y2[im1:ind + 1] y1_values = y1[im1:ind + 1] if len(diff_values) == 2: if np.ma.is_masked(diff_values[1]): return x[im1], y1[im1] elif np.ma.is_masked(diff_values[0]): return x[ind], y1[ind] diff_order = diff_values.argsort() diff_root_x = np.interp( 0, diff_values[diff_order], x_values[diff_order]) x_order = x_values.argsort() diff_root_y = np.interp(diff_root_x, x_values[x_order], y1_values[x_order]) return diff_root_x, diff_root_y start = get_interp_point(ind0) end = get_interp_point(ind1) else: # the purpose of the next two lines is for when y2 is a # scalar like 0 and we want the fill to go all the way # down to 0 even if none of the y1 sample points do start = xslice[0], y2slice[0] end = xslice[-1], y2slice[-1] X[0] = start X[N + 1] = end X[1:N + 1, 0] = xslice X[1:N + 1, 1] = y1slice X[N + 2:, 0] = xslice[::-1] X[N + 2:, 1] = y2slice[::-1] polys.append(X) collection = mcoll.PolyCollection(polys, **kwargs) # now update the datalim and autoscale XY1 = np.array([x[where], y1[where]]).T XY2 = np.array([x[where], y2[where]]).T self.dataLim.update_from_data_xy(XY1, self.ignore_existing_data_limits, updatex=True, updatey=True) self.ignore_existing_data_limits = False self.dataLim.update_from_data_xy(XY2, self.ignore_existing_data_limits, updatex=False, updatey=True) self.add_collection(collection, autolim=False) self._request_autoscale_view() return collection
[docs] @_preprocess_data(replace_names=["y", "x1", "x2", "where"]) @docstring.dedent_interpd def fill_betweenx(self, y, x1, x2=0, where=None, step=None, interpolate=False, **kwargs): """ Fill the area between two vertical curves. The curves are defined by the points (*x1*, *y*) and (*x2*, *y*). This creates one or multiple polygons describing the filled area. You may exclude some vertical sections from filling using *where*. By default, the edges connect the given points directly. Use *step* if the filling should be a step function, i.e. constant in between *y*. Parameters ---------- y : array (length N) The y coordinates of the nodes defining the curves. x1 : array (length N) or scalar The x coordinates of the nodes defining the first curve. x2 : array (length N) or scalar, optional, default: 0 The x coordinates of the nodes defining the second curve. where : array of bool (length N), optional, default: None Define *where* to exclude some vertical regions from being filled. The filled regions are defined by the coordinates ``y[where]``. More precisely, fill between ``y[i]`` and ``y[i+1]`` if ``where[i] and where[i+1]``. Note that this definition implies that an isolated *True* value between two *False* values in *where* will not result in filling. Both sides of the *True* position remain unfilled due to the adjacent *False* values. interpolate : bool, optional This option is only relevant if *where* is used and the two curves are crossing each other. Semantically, *where* is often used for *x1* > *x2* or similar. By default, the nodes of the polygon defining the filled region will only be placed at the positions in the *y* array. Such a polygon cannot describe the above semantics close to the intersection. The y-sections containing the intersection are simply clipped. Setting *interpolate* to *True* will calculate the actual intersection point and extend the filled region up to this point. step : {'pre', 'post', 'mid'}, optional Define *step* if the filling should be a step function, i.e. constant in between *y*. The value determines where the step will occur: - 'pre': The y value is continued constantly to the left from every *x* position, i.e. the interval ``(x[i-1], x[i]]`` has the value ``y[i]``. - 'post': The y value is continued constantly to the right from every *x* position, i.e. the interval ``[x[i], x[i+1])`` has the value ``y[i]``. - 'mid': Steps occur half-way between the *x* positions. Other Parameters ---------------- **kwargs All other keyword arguments are passed on to `.PolyCollection`. They control the `.Polygon` properties: %(PolyCollection)s Returns ------- `.PolyCollection` A `.PolyCollection` containing the plotted polygons. See Also -------- fill_between : Fill between two sets of y-values. Notes ----- .. [notes section required to get data note injection right] """ if not rcParams['_internal.classic_mode']: kwargs = cbook.normalize_kwargs(kwargs, mcoll.Collection) if not any(c in kwargs for c in ('color', 'facecolor')): kwargs['facecolor'] = \ self._get_patches_for_fill.get_next_color() # Handle united data, such as dates self._process_unit_info(ydata=y, xdata=x1, kwargs=kwargs) self._process_unit_info(xdata=x2) # Convert the arrays so we can work with them y = ma.masked_invalid(self.convert_yunits(y)) x1 = ma.masked_invalid(self.convert_xunits(x1)) x2 = ma.masked_invalid(self.convert_xunits(x2)) for name, array in [('y', y), ('x1', x1), ('x2', x2)]: if array.ndim > 1: raise ValueError('Input passed into argument "%r"' % name + 'is not 1-dimensional.') if where is None: where = True where = where & ~functools.reduce(np.logical_or, map(np.ma.getmask, [y, x1, x2])) y, x1, x2 = np.broadcast_arrays(np.atleast_1d(y), x1, x2) polys = [] for ind0, ind1 in cbook.contiguous_regions(where): yslice = y[ind0:ind1] x1slice = x1[ind0:ind1] x2slice = x2[ind0:ind1] if step is not None: step_func = cbook.STEP_LOOKUP_MAP["steps-" + step] yslice, x1slice, x2slice = step_func(yslice, x1slice, x2slice) if not len(yslice): continue N = len(yslice) Y = np.zeros((2 * N + 2, 2), float) if interpolate: def get_interp_point(ind): im1 = max(ind - 1, 0) y_values = y[im1:ind + 1] diff_values = x1[im1:ind + 1] - x2[im1:ind + 1] x1_values = x1[im1:ind + 1] if len(diff_values) == 2: if np.ma.is_masked(diff_values[1]): return x1[im1], y[im1] elif np.ma.is_masked(diff_values[0]): return x1[ind], y[ind] diff_order = diff_values.argsort() diff_root_y = np.interp( 0, diff_values[diff_order], y_values[diff_order]) y_order = y_values.argsort() diff_root_x = np.interp(diff_root_y, y_values[y_order], x1_values[y_order]) return diff_root_x, diff_root_y start = get_interp_point(ind0) end = get_interp_point(ind1) else: # the purpose of the next two lines is for when x2 is a # scalar like 0 and we want the fill to go all the way # down to 0 even if none of the x1 sample points do start = x2slice[0], yslice[0] end = x2slice[-1], yslice[-1] Y[0] = start Y[N + 1] = end Y[1:N + 1, 0] = x1slice Y[1:N + 1, 1] = yslice Y[N + 2:, 0] = x2slice[::-1] Y[N + 2:, 1] = yslice[::-1] polys.append(Y) collection = mcoll.PolyCollection(polys, **kwargs) # now update the datalim and autoscale X1Y = np.array([x1[where], y[where]]).T X2Y = np.array([x2[where], y[where]]).T self.dataLim.update_from_data_xy(X1Y, self.ignore_existing_data_limits, updatex=True, updatey=True) self.ignore_existing_data_limits = False self.dataLim.update_from_data_xy(X2Y, self.ignore_existing_data_limits, updatex=True, updatey=False) self.add_collection(collection, autolim=False) self._request_autoscale_view() return collection
#### plotting z(x,y): imshow, pcolor and relatives, contour
[docs] @_preprocess_data() @cbook._delete_parameter("3.1", "shape") @cbook._delete_parameter("3.1", "imlim") def imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, **kwargs): """ Display an image, i.e. data on a 2D regular raster. Parameters ---------- X : array-like or PIL image The image data. Supported array shapes are: - (M, N): an image with scalar data. The data is visualized using a colormap. - (M, N, 3): an image with RGB values (0-1 float or 0-255 int). - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int), i.e. including transparency. The first two dimensions (M, N) define the rows and columns of the image. Out-of-range RGB(A) values are clipped. cmap : str or `~matplotlib.colors.Colormap`, optional The Colormap instance or registered colormap name used to map scalar data to colors. This parameter is ignored for RGB(A) data. Defaults to :rc:`image.cmap`. norm : `~matplotlib.colors.Normalize`, optional The `Normalize` instance used to scale scalar data to the [0, 1] range before mapping to colors using *cmap*. By default, a linear scaling mapping the lowest value to 0 and the highest to 1 is used. This parameter is ignored for RGB(A) data. aspect : {'equal', 'auto'} or float, optional Controls the aspect ratio of the axes. The aspect is of particular relevance for images since it may distort the image, i.e. pixel will not be square. This parameter is a shortcut for explicitly calling `.Axes.set_aspect`. See there for further details. - 'equal': Ensures an aspect ratio of 1. Pixels will be square (unless pixel sizes are explicitly made non-square in data coordinates using *extent*). - 'auto': The axes is kept fixed and the aspect is adjusted so that the data fit in the axes. In general, this will result in non-square pixels. If not given, use :rc:`image.aspect` (default: 'equal'). interpolation : str, optional The interpolation method used. If *None* :rc:`image.interpolation` is used, which defaults to 'nearest'. Supported values are 'none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'. If *interpolation* is 'none', then no interpolation is performed on the Agg, ps, pdf and svg backends. Other backends will fall back to 'nearest'. Note that most SVG renders perform interpolation at rendering and that the default interpolation method they implement may differ. See :doc:`/gallery/images_contours_and_fields/interpolation_methods` for an overview of the supported interpolation methods. Some interpolation methods require an additional radius parameter, which can be set by *filterrad*. Additionally, the antigrain image resize filter is controlled by the parameter *filternorm*. alpha : scalar, optional The alpha blending value, between 0 (transparent) and 1 (opaque). This parameter is ignored for RGBA input data. vmin, vmax : scalar, optional When using scalar data and no explicit *norm*, *vmin* and *vmax* define the data range that the colormap covers. By default, the colormap covers the complete value range of the supplied data. *vmin*, *vmax* are ignored if the *norm* parameter is used. origin : {'upper', 'lower'}, optional Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, :rc:`image.origin` is used, defaulting to 'upper'. Note that the vertical axes points upward for 'lower' but downward for 'upper'. extent : scalars (left, right, bottom, top), optional The bounding box in data coordinates that the image will fill. The image is stretched individually along x and y to fill the box. The default extent is determined by the following conditions. Pixels have unit size in data coordinates. Their centers are on integer coordinates, and their center coordinates range from 0 to columns-1 horizontally and from 0 to rows-1 vertically. Note that the direction of the vertical axis and thus the default values for top and bottom depend on *origin*: - For ``origin == 'upper'`` the default is ``(-0.5, numcols-0.5, numrows-0.5, -0.5)``. - For ``origin == 'lower'`` the default is ``(-0.5, numcols-0.5, -0.5, numrows-0.5)``. See the example :doc:`/tutorials/intermediate/imshow_extent` for a more detailed description. filternorm : bool, optional, default: True A parameter for the antigrain image resize filter (see the antigrain documentation). If *filternorm* is set, the filter normalizes integer values and corrects the rounding errors. It doesn't do anything with the source floating point values, it corrects only integers according to the rule of 1.0 which means that any sum of pixel weights must be equal to 1.0. So, the filter function must produce a graph of the proper shape. filterrad : float > 0, optional, default: 4.0 The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: 'sinc', 'lanczos' or 'blackman'. resample : bool, optional When *True*, use a full resampling method. When *False*, only resample when the output image is larger than the input image. url : str, optional Set the url of the created `.AxesImage`. See `.Artist.set_url`. Returns ------- image : `~matplotlib.image.AxesImage` Other Parameters ---------------- **kwargs : `~matplotlib.artist.Artist` properties These parameters are passed on to the constructor of the `.AxesImage` artist. See also -------- matshow : Plot a matrix or an array as an image. Notes ----- Unless *extent* is used, pixel centers will be located at integer coordinates. In other words: the origin will coincide with the center of pixel (0, 0). There are two common representations for RGB images with an alpha channel: - Straight (unassociated) alpha: R, G, and B channels represent the color of the pixel, disregarding its opacity. - Premultiplied (associated) alpha: R, G, and B channels represent the color of the pixel, adjusted for its opacity by multiplication. `~matplotlib.pyplot.imshow` expects RGB images adopting the straight (unassociated) alpha representation. """ if norm is not None and not isinstance(norm, mcolors.Normalize): raise ValueError( "'norm' must be an instance of 'mcolors.Normalize'") if aspect is None: aspect = rcParams['image.aspect'] self.set_aspect(aspect) im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent, filternorm=filternorm, filterrad=filterrad, resample=resample, **kwargs) im.set_data(X) im.set_alpha(alpha) if im.get_clip_path() is None: # image does not already have clipping set, clip to axes patch im.set_clip_path(self.patch) if vmin is not None or vmax is not None: im.set_clim(vmin, vmax) else: im.autoscale_None() im.set_url(url) # update ax.dataLim, and, if autoscaling, set viewLim # to tightly fit the image, regardless of dataLim. im.set_extent(im.get_extent()) self.add_image(im) return im
@staticmethod def _pcolorargs(funcname, *args, allmatch=False): # If allmatch is True, then the incoming X, Y, C must have matching # dimensions, taking into account that X and Y can be 1-D rather than # 2-D. This perfect match is required for Gouraud shading. For flat # shading, X and Y specify boundaries, so we need one more boundary # than color in each direction. For convenience, and consistent with # Matlab, we discard the last row and/or column of C if necessary to # meet this condition. This is done if allmatch is False. if len(args) == 1: C = np.asanyarray(args[0]) numRows, numCols = C.shape if allmatch: X, Y = np.meshgrid(np.arange(numCols), np.arange(numRows)) else: X, Y = np.meshgrid(np.arange(numCols + 1), np.arange(numRows + 1)) C = cbook.safe_masked_invalid(C) return X, Y, C if len(args) == 3: # Check x and y for bad data... C = np.asanyarray(args[2]) X, Y = [cbook.safe_masked_invalid(a) for a in args[:2]] if funcname == 'pcolormesh': if np.ma.is_masked(X) or np.ma.is_masked(Y): raise ValueError( 'x and y arguments to pcolormesh cannot have ' 'non-finite values or be of type ' 'numpy.ma.core.MaskedArray with masked values') # safe_masked_invalid() returns an ndarray for dtypes other # than floating point. if isinstance(X, np.ma.core.MaskedArray): X = X.data # strip mask as downstream doesn't like it... if isinstance(Y, np.ma.core.MaskedArray): Y = Y.data numRows, numCols = C.shape else: raise TypeError( 'Illegal arguments to %s; see help(%s)' % (funcname, funcname)) Nx = X.shape[-1] Ny = Y.shape[0] if X.ndim != 2 or X.shape[0] == 1: x = X.reshape(1, Nx) X = x.repeat(Ny, axis=0) if Y.ndim != 2 or Y.shape[1] == 1: y = Y.reshape(Ny, 1) Y = y.repeat(Nx, axis=1) if X.shape != Y.shape: raise TypeError( 'Incompatible X, Y inputs to %s; see help(%s)' % ( funcname, funcname)) if allmatch: if (Nx, Ny) != (numCols, numRows): raise TypeError('Dimensions of C %s are incompatible with' ' X (%d) and/or Y (%d); see help(%s)' % ( C.shape, Nx, Ny, funcname)) else: if not (numCols in (Nx, Nx - 1) and numRows in (Ny, Ny - 1)): raise TypeError('Dimensions of C %s are incompatible with' ' X (%d) and/or Y (%d); see help(%s)' % ( C.shape, Nx, Ny, funcname)) C = C[:Ny - 1, :Nx - 1] C = cbook.safe_masked_invalid(C) return X, Y, C
[docs] @_preprocess_data() @docstring.dedent_interpd def pcolor(self, *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, **kwargs): r""" Create a pseudocolor plot with a non-regular rectangular grid. Call signature:: pcolor([X, Y,] C, **kwargs) *X* and *Y* can be used to specify the corners of the quadrilaterals. .. hint:: ``pcolor()`` can be very slow for large arrays. In most cases you should use the similar but much faster `~.Axes.pcolormesh` instead. See there for a discussion of the differences. Parameters ---------- C : array_like A scalar 2-D array. The values will be color-mapped. X, Y : array_like, optional The coordinates of the quadrilateral corners. The quadrilateral for ``C[i,j]`` has corners at:: (X[i+1, j], Y[i+1, j]) (X[i+1, j+1], Y[i+1, j+1]) +--------+ | C[i,j] | +--------+ (X[i, j], Y[i, j]) (X[i, j+1], Y[i, j+1]), Note that the column index corresponds to the x-coordinate, and the row index corresponds to y. For details, see the :ref:`Notes <axes-pcolor-grid-orientation>` section below. The dimensions of *X* and *Y* should be one greater than those of *C*. Alternatively, *X*, *Y* and *C* may have equal dimensions, in which case the last row and column of *C* will be ignored. If *X* and/or *Y* are 1-D arrays or column vectors they will be expanded as needed into the appropriate 2-D arrays, making a rectangular grid. cmap : str or `~matplotlib.colors.Colormap`, optional A Colormap instance or registered colormap name. The colormap maps the *C* values to colors. Defaults to :rc:`image.cmap`. norm : `~matplotlib.colors.Normalize`, optional The Normalize instance scales the data values to the canonical colormap range [0, 1] for mapping to colors. By default, the data range is mapped to the colorbar range using linear scaling. vmin, vmax : scalar, optional, default: None The colorbar range. If *None*, suitable min/max values are automatically chosen by the `~.Normalize` instance (defaults to the respective min/max values of *C* in case of the default linear scaling). edgecolors : {'none', None, 'face', color, color sequence}, optional The color of the edges. Defaults to 'none'. Possible values: - 'none' or '': No edge. - *None*: :rc:`patch.edgecolor` will be used. Note that currently :rc:`patch.force_edgecolor` has to be True for this to work. - 'face': Use the adjacent face color. - An mpl color or sequence of colors will set the edge color. The singular form *edgecolor* works as an alias. alpha : scalar, optional, default: None The alpha blending value of the face color, between 0 (transparent) and 1 (opaque). Note: The edgecolor is currently not affected by this. snap : bool, optional, default: False Whether to snap the mesh to pixel boundaries. Returns ------- collection : `matplotlib.collections.Collection` Other Parameters ---------------- antialiaseds : bool, optional, default: False The default *antialiaseds* is False if the default *edgecolors*\ ="none" is used. This eliminates artificial lines at patch boundaries, and works regardless of the value of alpha. If *edgecolors* is not "none", then the default *antialiaseds* is taken from :rc:`patch.antialiased`, which defaults to True. Stroking the edges may be preferred if *alpha* is 1, but will cause artifacts otherwise. **kwargs Additionally, the following arguments are allowed. They are passed along to the `~matplotlib.collections.PolyCollection` constructor: %(PolyCollection)s See Also -------- pcolormesh : for an explanation of the differences between pcolor and pcolormesh. imshow : If *X* and *Y* are each equidistant, `~.Axes.imshow` can be a faster alternative. Notes ----- **Masked arrays** *X*, *Y* and *C* may be masked arrays. If either ``C[i, j]``, or one of the vertices surrounding ``C[i,j]`` (*X* or *Y* at ``[i, j], [i+1, j], [i, j+1], [i+1, j+1]``) is masked, nothing is plotted. .. _axes-pcolor-grid-orientation: **Grid orientation** The grid orientation follows the standard matrix convention: An array *C* with shape (nrows, ncolumns) is plotted with the column number as *X* and the row number as *Y*. **Handling of pcolor() end-cases** ``pcolor()`` displays all columns of *C* if *X* and *Y* are not specified, or if *X* and *Y* have one more column than *C*. If *X* and *Y* have the same number of columns as *C* then the last column of *C* is dropped. Similarly for the rows. Note: This behavior is different from MATLAB's ``pcolor()``, which always discards the last row and column of *C*. """ X, Y, C = self._pcolorargs('pcolor', *args, allmatch=False) Ny, Nx = X.shape # unit conversion allows e.g. datetime objects as axis values self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs) X = self.convert_xunits(X) Y = self.convert_yunits(Y) # convert to MA, if necessary. C = ma.asarray(C) X = ma.asarray(X) Y = ma.asarray(Y) mask = ma.getmaskarray(X) + ma.getmaskarray(Y) xymask = (mask[0:-1, 0:-1] + mask[1:, 1:] + mask[0:-1, 1:] + mask[1:, 0:-1]) # don't plot if C or any of the surrounding vertices are masked. mask = ma.getmaskarray(C) + xymask unmask = ~mask X1 = ma.filled(X[:-1, :-1])[unmask] Y1 = ma.filled(Y[:-1, :-1])[unmask] X2 = ma.filled(X[1:, :-1])[unmask] Y2 = ma.filled(Y[1:, :-1])[unmask] X3 = ma.filled(X[1:, 1:])[unmask] Y3 = ma.filled(Y[1:, 1:])[unmask] X4 = ma.filled(X[:-1, 1:])[unmask] Y4 = ma.filled(Y[:-1, 1:])[unmask] npoly = len(X1) xy = np.stack([X1, Y1, X2, Y2, X3, Y3, X4, Y4, X1, Y1], axis=-1) verts = xy.reshape((npoly, 5, 2)) C = ma.filled(C[:Ny - 1, :Nx - 1])[unmask] linewidths = (0.25,) if 'linewidth' in kwargs: kwargs['linewidths'] = kwargs.pop('linewidth') kwargs.setdefault('linewidths', linewidths) if 'edgecolor' in kwargs: kwargs['edgecolors'] = kwargs.pop('edgecolor') ec = kwargs.setdefault('edgecolors', 'none') # aa setting will default via collections to patch.antialiased # unless the boundary is not stroked, in which case the # default will be False; with unstroked boundaries, aa # makes artifacts that are often disturbing. if 'antialiased' in kwargs: kwargs['antialiaseds'] = kwargs.pop('antialiased') if 'antialiaseds' not in kwargs and cbook._str_lower_equal(ec, "none"): kwargs['antialiaseds'] = False kwargs.setdefault('snap', False) collection = mcoll.PolyCollection(verts, **kwargs) collection.set_alpha(alpha) collection.set_array(C) if norm is not None and not isinstance(norm, mcolors.Normalize): raise ValueError( "'norm' must be an instance of 'mcolors.Normalize'") collection.set_cmap(cmap) collection.set_norm(norm) collection.set_clim(vmin, vmax) collection.autoscale_None() self.grid(False) x = X.compressed() y = Y.compressed() # Transform from native to data coordinates? t = collection._transform if (not isinstance(t, mtransforms.Transform) and hasattr(t, '_as_mpl_transform')): t = t._as_mpl_transform(self.axes) if t and any(t.contains_branch_seperately(self.transData)): trans_to_data = t - self.transData pts = np.vstack([x, y]).T.astype(float) transformed_pts = trans_to_data.transform(pts) x = transformed_pts[..., 0] y = transformed_pts[..., 1] self.add_collection(collection, autolim=False) minx = np.min(x) maxx = np.max(x) miny = np.min(y) maxy = np.max(y) collection.sticky_edges.x[:] = [minx, maxx] collection.sticky_edges.y[:] = [miny, maxy] corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self._request_autoscale_view() return collection
[docs] @_preprocess_data() @docstring.dedent_interpd def pcolormesh(self, *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, shading='flat', antialiased=False, **kwargs): """ Create a pseudocolor plot with a non-regular rectangular grid. Call signature:: pcolor([X, Y,] C, **kwargs) *X* and *Y* can be used to specify the corners of the quadrilaterals. .. note:: `~Axes.pcolormesh` is similar to `~Axes.pcolor`. It's much faster and preferred in most cases. For a detailed discussion on the differences see :ref:`Differences between pcolor() and pcolormesh() <differences-pcolor-pcolormesh>`. Parameters ---------- C : array_like A scalar 2-D array. The values will be color-mapped. X, Y : array_like, optional The coordinates of the quadrilateral corners. The quadrilateral for ``C[i,j]`` has corners at:: (X[i+1, j], Y[i+1, j]) (X[i+1, j+1], Y[i+1, j+1]) +--------+ | C[i,j] | +--------+ (X[i, j], Y[i, j]) (X[i, j+1], Y[i, j+1]), Note that the column index corresponds to the x-coordinate, and the row index corresponds to y. For details, see the :ref:`Notes <axes-pcolormesh-grid-orientation>` section below. The dimensions of *X* and *Y* should be one greater than those of *C*. Alternatively, *X*, *Y* and *C* may have equal dimensions, in which case the last row and column of *C* will be ignored. If *X* and/or *Y* are 1-D arrays or column vectors they will be expanded as needed into the appropriate 2-D arrays, making a rectangular grid. cmap : str or `~matplotlib.colors.Colormap`, optional A Colormap instance or registered colormap name. The colormap maps the *C* values to colors. Defaults to :rc:`image.cmap`. norm : `~matplotlib.colors.Normalize`, optional The Normalize instance scales the data values to the canonical colormap range [0, 1] for mapping to colors. By default, the data range is mapped to the colorbar range using linear scaling. vmin, vmax : scalar, optional, default: None The colorbar range. If *None*, suitable min/max values are automatically chosen by the `~.Normalize` instance (defaults to the respective min/max values of *C* in case of the default linear scaling). edgecolors : {'none', None, 'face', color, color sequence}, optional The color of the edges. Defaults to 'none'. Possible values: - 'none' or '': No edge. - *None*: :rc:`patch.edgecolor` will be used. Note that currently :rc:`patch.force_edgecolor` has to be True for this to work. - 'face': Use the adjacent face color. - An mpl color or sequence of colors will set the edge color. The singular form *edgecolor* works as an alias. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque). shading : {'flat', 'gouraud'}, optional The fill style, Possible values: - 'flat': A solid color is used for each quad. The color of the quad (i, j), (i+1, j), (i, j+1), (i+1, j+1) is given by ``C[i,j]``. - 'gouraud': Each quad will be Gouraud shaded: The color of the corners (i', j') are given by ``C[i',j']``. The color values of the area in between is interpolated from the corner values. When Gouraud shading is used, *edgecolors* is ignored. snap : bool, optional, default: False Whether to snap the mesh to pixel boundaries. Returns ------- mesh : `matplotlib.collections.QuadMesh` Other Parameters ---------------- **kwargs Additionally, the following arguments are allowed. They are passed along to the `~matplotlib.collections.QuadMesh` constructor: %(QuadMesh)s See Also -------- pcolor : An alternative implementation with slightly different features. For a detailed discussion on the differences see :ref:`Differences between pcolor() and pcolormesh() <differences-pcolor-pcolormesh>`. imshow : If *X* and *Y* are each equidistant, `~.Axes.imshow` can be a faster alternative. Notes ----- **Masked arrays** *C* may be a masked array. If ``C[i, j]`` is masked, the corresponding quadrilateral will be transparent. Masking of *X* and *Y* is not supported. Use `~.Axes.pcolor` if you need this functionality. .. _axes-pcolormesh-grid-orientation: **Grid orientation** The grid orientation follows the standard matrix convention: An array *C* with shape (nrows, ncolumns) is plotted with the column number as *X* and the row number as *Y*. .. _differences-pcolor-pcolormesh: **Differences between pcolor() and pcolormesh()** Both methods are used to create a pseudocolor plot of a 2-D array using quadrilaterals. The main difference lies in the created object and internal data handling: While `~.Axes.pcolor` returns a `.PolyCollection`, `~.Axes.pcolormesh` returns a `.QuadMesh`. The latter is more specialized for the given purpose and thus is faster. It should almost always be preferred. There is also a slight difference in the handling of masked arrays. Both `~.Axes.pcolor` and `~.Axes.pcolormesh` support masked arrays for *C*. However, only `~.Axes.pcolor` supports masked arrays for *X* and *Y*. The reason lies in the internal handling of the masked values. `~.Axes.pcolor` leaves out the respective polygons from the PolyCollection. `~.Axes.pcolormesh` sets the facecolor of the masked elements to transparent. You can see the difference when using edgecolors. While all edges are drawn irrespective of masking in a QuadMesh, the edge between two adjacent masked quadrilaterals in `~.Axes.pcolor` is not drawn as the corresponding polygons do not exist in the PolyCollection. Another difference is the support of Gouraud shading in `~.Axes.pcolormesh`, which is not available with `~.Axes.pcolor`. """ shading = shading.lower() kwargs.setdefault('edgecolors', 'None') allmatch = (shading == 'gouraud') X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch) Ny, Nx = X.shape X = X.ravel() Y = Y.ravel() # unit conversion allows e.g. datetime objects as axis values self._process_unit_info(xdata=X, ydata=Y, kwargs=kwargs) X = self.convert_xunits(X) Y = self.convert_yunits(Y) # convert to one dimensional arrays C = C.ravel() coords = np.column_stack((X, Y)).astype(float, copy=False) collection = mcoll.QuadMesh(Nx - 1, Ny - 1, coords, antialiased=antialiased, shading=shading, **kwargs) collection.set_alpha(alpha) collection.set_array(C) if norm is not None and not isinstance(norm, mcolors.Normalize): raise ValueError( "'norm' must be an instance of 'mcolors.Normalize'") collection.set_cmap(cmap) collection.set_norm(norm) collection.set_clim(vmin, vmax) collection.autoscale_None() self.grid(False) # Transform from native to data coordinates? t = collection._transform if (not isinstance(t, mtransforms.Transform) and hasattr(t, '_as_mpl_transform')): t = t._as_mpl_transform(self.axes) if t and any(t.contains_branch_seperately(self.transData)): trans_to_data = t - self.transData coords = trans_to_data.transform(coords) self.add_collection(collection, autolim=False) minx, miny = np.min(coords, axis=0) maxx, maxy = np.max(coords, axis=0) collection.sticky_edges.x[:] = [minx, maxx] collection.sticky_edges.y[:] = [miny, maxy] corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self._request_autoscale_view() return collection
[docs] @_preprocess_data() @docstring.dedent_interpd def pcolorfast(self, *args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, **kwargs): """ Create a pseudocolor plot with a non-regular rectangular grid. Call signature:: ax.pcolorfast([X, Y], C, /, **kwargs) This method is similar to ~.Axes.pcolor` and `~.Axes.pcolormesh`. It's designed to provide the fastest pcolor-type plotting with the Agg backend. To achieve this, it uses different algorithms internally depending on the complexity of the input grid (regular rectangular, non-regular rectangular or arbitrary quadrilateral). .. warning:: This method is experimental. Compared to `~.Axes.pcolor` or `~.Axes.pcolormesh` it has some limitations: - It supports only flat shading (no outlines) - It lacks support for log scaling of the axes. - It does not have a have a pyplot wrapper. Parameters ---------- C : array-like(M, N) The image data. Supported array shapes are: - (M, N): an image with scalar data. The data is visualized using a colormap. - (M, N, 3): an image with RGB values (0-1 float or 0-255 int). - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int), i.e. including transparency. The first two dimensions (M, N) define the rows and columns of the image. This parameter can only be passed positionally. X, Y : tuple or array-like, default: ``(0, N)``, ``(0, M)`` *X* and *Y* are used to specify the coordinates of the quadrilaterals. There are different ways to do this: - Use tuples ``X=(xmin, xmax)`` and ``Y=(ymin, ymax)`` to define a *uniform rectangular grid*. The tuples define the outer edges of the grid. All individual quadrilaterals will be of the same size. This is the fastest version. - Use 1D arrays *X*, *Y* to specify a *non-uniform rectangular grid*. In this case *X* and *Y* have to be monotonic 1D arrays of length *N+1* and *M+1*, specifying the x and y boundaries of the cells. The speed is intermediate. Note: The grid is checked, and if found to be uniform the fast version is used. - Use 2D arrays *X*, *Y* if you need an *arbitrary quadrilateral grid* (i.e. if the quadrilaterals are not rectangular). In this case *X* and *Y* are 2D arrays with shape (M + 1, N + 1), specifying the x and y coordinates of the corners of the colored quadrilaterals. This is the most general, but the slowest to render. It may produce faster and more compact output using ps, pdf, and svg backends, however. These arguments can only be passed positionally. cmap : str or `~matplotlib.colors.Colormap`, optional A Colormap instance or registered colormap name. The colormap maps the *C* values to colors. Defaults to :rc:`image.cmap`. norm : `~matplotlib.colors.Normalize`, optional The Normalize instance scales the data values to the canonical colormap range [0, 1] for mapping to colors. By default, the data range is mapped to the colorbar range using linear scaling. vmin, vmax : scalar, optional, default: None The colorbar range. If *None*, suitable min/max values are automatically chosen by the `~.Normalize` instance (defaults to the respective min/max values of *C* in case of the default linear scaling). alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque). snap : bool, optional, default: False Whether to snap the mesh to pixel boundaries. Returns ------- image : `.AxesImage` or `.PcolorImage` or `.QuadMesh` The return type depends on the type of grid: - `.AxesImage` for a regular rectangular grid. - `.PcolorImage` for a non-regular rectangular grid. - `.QuadMesh` for a non-rectangular grid. Notes ----- .. [notes section required to get data note injection right] """ if norm is not None and not isinstance(norm, mcolors.Normalize): raise ValueError( "'norm' must be an instance of 'mcolors.Normalize'") C = args[-1] nr, nc = np.shape(C)[:2] if len(args) == 1: style = "image" x = [0, nc] y = [0, nr] elif len(args) == 3: x, y = args[:2] x = np.asarray(x) y = np.asarray(y) if x.ndim == 1 and y.ndim == 1: if x.size == 2 and y.size == 2: style = "image" else: dx = np.diff(x) dy = np.diff(y) if (np.ptp(dx) < 0.01 * np.abs(dx.mean()) and np.ptp(dy) < 0.01 * np.abs(dy.mean())): style = "image" else: style = "pcolorimage" elif x.ndim == 2 and y.ndim == 2: style = "quadmesh" else: raise TypeError("arguments do not match valid signatures") else: raise TypeError("need 1 argument or 3 arguments") if style == "quadmesh": # data point in each cell is value at lower left corner coords = np.stack([x, y], axis=-1) if np.ndim(C) == 2: qm_kwargs = {"array": np.ma.ravel(C)} elif np.ndim(C) == 3: qm_kwargs = {"color": np.ma.reshape(C, (-1, C.shape[-1]))} else: raise ValueError("C must be 2D or 3D") collection = mcoll.QuadMesh( nc, nr, coords, **qm_kwargs, alpha=alpha, cmap=cmap, norm=norm, antialiased=False, edgecolors="none") self.add_collection(collection, autolim=False) xl, xr, yb, yt = x.min(), x.max(), y.min(), y.max() ret = collection else: # It's one of the two image styles. extent = xl, xr, yb, yt = x[0], x[-1], y[0], y[-1] if style == "image": im = mimage.AxesImage( self, cmap, norm, data=C, alpha=alpha, extent=extent, interpolation='nearest', origin='lower', **kwargs) elif style == "pcolorimage": im = mimage.PcolorImage( self, x, y, C, cmap=cmap, norm=norm, alpha=alpha, extent=extent, **kwargs) self.add_image(im) ret = im if vmin is not None or vmax is not None: ret.set_clim(vmin, vmax) elif np.ndim(C) == 2: # C.ndim == 3 is RGB(A) so doesn't need scaling. ret.autoscale_None() ret.sticky_edges.x[:] = [xl, xr] ret.sticky_edges.y[:] = [yb, yt] self.update_datalim(np.array([[xl, yb], [xr, yt]])) self._request_autoscale_view(tight=True) return ret
[docs] @_preprocess_data() def contour(self, *args, **kwargs): kwargs['filled'] = False contours = mcontour.QuadContourSet(self, *args, **kwargs) self._request_autoscale_view() return contours
contour.__doc__ = mcontour.QuadContourSet._contour_doc
[docs] @_preprocess_data() def contourf(self, *args, **kwargs): kwargs['filled'] = True contours = mcontour.QuadContourSet(self, *args, **kwargs) self._request_autoscale_view() return contours
contourf.__doc__ = mcontour.QuadContourSet._contour_doc
[docs] def clabel(self, CS, *args, **kwargs): return CS.clabel(*args, **kwargs)
clabel.__doc__ = mcontour.ContourSet.clabel.__doc__ #### Data analysis
[docs] @_preprocess_data(replace_names=["x", 'weights'], label_namer="x") def hist(self, x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, **kwargs): """ Plot a histogram. Compute and draw the histogram of *x*. The return value is a tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*, [*patches0*, *patches1*,...]) if the input contains multiple data. See the documentation of the *weights* parameter to draw a histogram of already-binned data. Multiple data can be provided via *x* as a list of datasets of potentially different length ([*x0*, *x1*, ...]), or as a 2-D ndarray in which each column is a dataset. Note that the ndarray form is transposed relative to the list form. Masked arrays are not supported at present. Parameters ---------- x : (n,) array or sequence of (n,) arrays Input values, this takes either a single array or a sequence of arrays which are not required to be of the same length. bins : int or sequence or str, optional If an integer is given, ``bins + 1`` bin edges are calculated and returned, consistent with `numpy.histogram`. If `bins` is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, `bins` is returned unmodified. All but the last (righthand-most) bin is half-open. In other words, if `bins` is:: [1, 2, 3, 4] then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which *includes* 4. Unequally spaced bins are supported if *bins* is a sequence. With Numpy 1.11 or newer, you can alternatively provide a string describing a binning strategy, such as 'auto', 'sturges', 'fd', 'doane', 'scott', 'rice' or 'sqrt', see `numpy.histogram`. The default is taken from :rc:`hist.bins`. range : tuple or None, optional The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, *range* is ``(x.min(), x.max())``. Range has no effect if *bins* is a sequence. If *bins* is a sequence or *range* is specified, autoscaling is based on the specified bin range instead of the range of x. Default is ``None`` density : bool, optional If ``True``, the first element of the return tuple will be the counts normalized to form a probability density, i.e., the area (or integral) under the histogram will sum to 1. This is achieved by dividing the count by the number of observations times the bin width and not dividing by the total number of observations. If *stacked* is also ``True``, the sum of the histograms is normalized to 1. Default is ``None`` for both *normed* and *density*. If either is set, then that value will be used. If neither are set, then the args will be treated as ``False``. If both *density* and *normed* are set an error is raised. weights : (n, ) array_like or None, optional An array of weights, of the same shape as *x*. Each value in *x* only contributes its associated weight towards the bin count (instead of 1). If *normed* or *density* is ``True``, the weights are normalized, so that the integral of the density over the range remains 1. Default is ``None``. This parameter can be used to draw a histogram of data that has already been binned, e.g. using `np.histogram` (by treating each bin as a single point with a weight equal to its count) :: counts, bins = np.histogram(data) plt.hist(bins[:-1], bins, weights=counts) (or you may alternatively use `~.bar()`). cumulative : bool, optional If ``True``, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If *normed* or *density* is also ``True`` then the histogram is normalized such that the last bin equals 1. If *cumulative* evaluates to less than 0 (e.g., -1), the direction of accumulation is reversed. In this case, if *normed* and/or *density* is also ``True``, then the histogram is normalized such that the first bin equals 1. Default is ``False`` bottom : array_like, scalar, or None Location of the bottom baseline of each bin. If a scalar, the base line for each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0. Default is ``None`` histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are arranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. Default is 'bar' align : {'left', 'mid', 'right'}, optional Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. Default is 'mid' orientation : {'horizontal', 'vertical'}, optional If 'horizontal', `~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. rwidth : scalar or None, optional The relative width of the bars as a fraction of the bin width. If ``None``, automatically compute the width. Ignored if *histtype* is 'step' or 'stepfilled'. Default is ``None`` log : bool, optional If ``True``, the histogram axis will be set to a log scale. If *log* is ``True`` and *x* is a 1D array, empty bins will be filtered out and only the non-empty ``(n, bins, patches)`` will be returned. Default is ``False`` color : color or array_like of colors or None, optional Color spec or sequence of color specs, one per dataset. Default (``None``) uses the standard line color sequence. Default is ``None`` label : str or None, optional String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected. default is ``None`` stacked : bool, optional If ``True``, multiple data are stacked on top of each other If ``False`` multiple data are arranged side by side if histtype is 'bar' or on top of each other if histtype is 'step' Default is ``False`` Returns ------- n : array or list of arrays The values of the histogram bins. See *density* and *weights* for a description of the possible semantics. If input *x* is an array, then this is an array of length *nbins*. If input is a sequence of arrays ``[data1, data2,..]``, then this is a list of arrays with the values of the histograms for each of the arrays in the same order. The dtype of the array *n* (or of its element arrays) will always be float even if no weighting or normalization is used. bins : array The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in. patches : list or list of lists Silent list of individual patches used to create the histogram or list of such list if multiple input datasets. Other Parameters ---------------- **kwargs : `~matplotlib.patches.Patch` properties See also -------- hist2d : 2D histograms """ # Avoid shadowing the builtin. bin_range = range from builtins import range if np.isscalar(x): x = [x] if bins is None: bins = rcParams['hist.bins'] # Validate string inputs here to avoid cluttering subsequent code. cbook._check_in_list(['bar', 'barstacked', 'step', 'stepfilled'], histtype=histtype) cbook._check_in_list(['left', 'mid', 'right'], align=align) cbook._check_in_list(['horizontal', 'vertical'], orientation=orientation) if histtype == 'barstacked' and not stacked: stacked = True # basic input validation input_empty = np.size(x) == 0 # Massage 'x' for processing. x = cbook._reshape_2D(x, 'x') nx = len(x) # number of datasets # Process unit information # Unit conversion is done individually on each dataset self._process_unit_info(xdata=x[0], kwargs=kwargs) x = [self.convert_xunits(xi) for xi in x] if bin_range is not None: bin_range = self.convert_xunits(bin_range) # We need to do to 'weights' what was done to 'x' if weights is not None: w = cbook._reshape_2D(weights, 'weights') else: w = [None] * nx if len(w) != nx: raise ValueError('weights should have the same shape as x') for xi, wi in zip(x, w): if wi is not None and len(wi) != len(xi): raise ValueError( 'weights should have the same shape as x') if color is None: color = [self._get_lines.get_next_color() for i in range(nx)] else: color = mcolors.to_rgba_array(color) if len(color) != nx: error_message = ( "color kwarg must have one color per data set. %d data " "sets and %d colors were provided" % (nx, len(color))) raise ValueError(error_message) hist_kwargs = dict() # if the bin_range is not given, compute without nan numpy # does not do this for us when guessing the range (but will # happily ignore nans when computing the histogram). if bin_range is None: xmin = np.inf xmax = -np.inf for xi in x: if len(xi): # python's min/max ignore nan, # np.minnan returns nan for all nan input xmin = min(xmin, np.nanmin(xi)) xmax = max(xmax, np.nanmax(xi)) # make sure we have seen at least one non-nan and finite # value before we reset the bin range if not np.isnan([xmin, xmax]).any() and not (xmin > xmax): bin_range = (xmin, xmax) # If bins are not specified either explicitly or via range, # we need to figure out the range required for all datasets, # and supply that to np.histogram. if not input_empty and len(x) > 1: if weights is not None: _w = np.concatenate(w) else: _w = None bins = histogram_bin_edges(np.concatenate(x), bins, bin_range, _w) else: hist_kwargs['range'] = bin_range density = bool(density) if density and not stacked: hist_kwargs['density'] = density # List to store all the top coordinates of the histograms tops = [] mlast = None # Loop through datasets for i in range(nx): # this will automatically overwrite bins, # so that each histogram uses the same bins m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs) m = m.astype(float) # causes problems later if it's an int if mlast is None: mlast = np.zeros(len(bins)-1, m.dtype) if stacked: m += mlast mlast[:] = m tops.append(m) # If a stacked density plot, normalize so the area of all the stacked # histograms together is 1 if stacked and density: db = np.diff(bins) for m in tops: m[:] = (m / db) / tops[-1].sum() if cumulative: slc = slice(None) if isinstance(cumulative, Number) and cumulative < 0: slc = slice(None, None, -1) if density: tops = [(m * np.diff(bins))[slc].cumsum()[slc] for m in tops] else: tops = [m[slc].cumsum()[slc] for m in tops] patches = [] # Save autoscale state for later restoration; turn autoscaling # off so we can do it all a single time at the end, instead # of having it done by bar or fill and then having to be redone. _saved_autoscalex = self.get_autoscalex_on() _saved_autoscaley = self.get_autoscaley_on() self.set_autoscalex_on(False) self.set_autoscaley_on(False) if histtype.startswith('bar'): totwidth = np.diff(bins) if rwidth is not None: dr = np.clip(rwidth, 0, 1) elif (len(tops) > 1 and ((not stacked) or rcParams['_internal.classic_mode'])): dr = 0.8 else: dr = 1.0 if histtype == 'bar' and not stacked: width = dr * totwidth / nx dw = width boffset = -0.5 * dr * totwidth * (1 - 1 / nx) elif histtype == 'barstacked' or stacked: width = dr * totwidth boffset, dw = 0.0, 0.0 if align == 'mid': boffset += 0.5 * totwidth elif align == 'right': boffset += totwidth if orientation == 'horizontal': _barfunc = self.barh bottom_kwarg = 'left' else: # orientation == 'vertical' _barfunc = self.bar bottom_kwarg = 'bottom' for m, c in zip(tops, color): if bottom is None: bottom = np.zeros(len(m)) if stacked: height = m - bottom else: height = m patch = _barfunc(bins[:-1]+boffset, height, width, align='center', log=log, color=c, **{bottom_kwarg: bottom}) patches.append(patch) if stacked: bottom[:] = m boffset += dw elif histtype.startswith('step'): # these define the perimeter of the polygon x = np.zeros(4 * len(bins) - 3) y = np.zeros(4 * len(bins) - 3) x[0:2*len(bins)-1:2], x[1:2*len(bins)-1:2] = bins, bins[:-1] x[2*len(bins)-1:] = x[1:2*len(bins)-1][::-1] if bottom is None: bottom = np.zeros(len(bins) - 1) y[1:2*len(bins)-1:2], y[2:2*len(bins):2] = bottom, bottom y[2*len(bins)-1:] = y[1:2*len(bins)-1][::-1] if log: if orientation == 'horizontal': self.set_xscale('log', nonposx='clip') logbase = self.xaxis._scale.base else: # orientation == 'vertical' self.set_yscale('log', nonposy='clip') logbase = self.yaxis._scale.base # Setting a minimum of 0 results in problems for log plots if np.min(bottom) > 0: minimum = np.min(bottom) elif density or weights is not None: # For data that is normed to form a probability density, # set to minimum data value / logbase # (gives 1 full tick-label unit for the lowest filled bin) ndata = np.array(tops) minimum = (np.min(ndata[ndata > 0])) / logbase else: # For non-normed (density = False) data, # set the min to 1 / log base, # again so that there is 1 full tick-label unit # for the lowest bin minimum = 1.0 / logbase y[0], y[-1] = minimum, minimum else: minimum = 0 if align == 'left': x -= 0.5*(bins[1]-bins[0]) elif align == 'right': x += 0.5*(bins[1]-bins[0]) # If fill kwarg is set, it will be passed to the patch collection, # overriding this fill = (histtype == 'stepfilled') xvals, yvals = [], [] for m in tops: if stacked: # starting point for drawing polygon y[0] = y[1] # top of the previous polygon becomes the bottom y[2*len(bins)-1:] = y[1:2*len(bins)-1][::-1] # set the top of this polygon y[1:2*len(bins)-1:2], y[2:2*len(bins):2] = (m + bottom, m + bottom) if log: y[y < minimum] = minimum if orientation == 'horizontal': xvals.append(y.copy()) yvals.append(x.copy()) else: xvals.append(x.copy()) yvals.append(y.copy()) # stepfill is closed, step is not split = -1 if fill else 2 * len(bins) # add patches in reverse order so that when stacking, # items lower in the stack are plotted on top of # items higher in the stack for x, y, c in reversed(list(zip(xvals, yvals, color))): patches.append(self.fill( x[:split], y[:split], closed=True if fill else None, facecolor=c, edgecolor=None if fill else c, fill=fill if fill else None)) for patch_list in patches: for patch in patch_list: if orientation == 'vertical': patch.sticky_edges.y.append(minimum) elif orientation == 'horizontal': patch.sticky_edges.x.append(minimum) # we return patches, so put it back in the expected order patches.reverse() self.set_autoscalex_on(_saved_autoscalex) self.set_autoscaley_on(_saved_autoscaley) self._request_autoscale_view() if label is None: labels = [None] elif isinstance(label, str): labels = [label] elif not np.iterable(label): labels = [str(label)] else: labels = [str(lab) for lab in label] for patch, lbl in itertools.zip_longest(patches, labels): if patch: p = patch[0] p.update(kwargs) if lbl is not None: p.set_label(lbl) for p in patch[1:]: p.update(kwargs) p.set_label('_nolegend_') if nx == 1: return tops[0], bins, cbook.silent_list('Patch', patches[0]) else: return tops, bins, cbook.silent_list('Lists of Patches', patches)
[docs] @_preprocess_data(replace_names=["x", "y", "weights"]) @cbook._rename_parameter("3.1", "normed", "density") def hist2d(self, x, y, bins=10, range=None, density=False, weights=None, cmin=None, cmax=None, **kwargs): """ Make a 2D histogram plot. Parameters ---------- x, y : array_like, shape (n, ) Input values bins : None or int or [int, int] or array_like or [array, array] The bin specification: - If int, the number of bins for the two dimensions (nx=ny=bins). - If ``[int, int]``, the number of bins in each dimension (nx, ny = bins). - If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). - If ``[array, array]``, the bin edges in each dimension (x_edges, y_edges = bins). The default value is 10. range : array_like shape(2, 2), optional, default: None The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters): ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range will be considered outliers and not tallied in the histogram. density : bool, optional, default: False Normalize histogram. *normed* is a deprecated synonym for this parameter. weights : array_like, shape (n, ), optional, default: None An array of values w_i weighing each sample (x_i, y_i). cmin : scalar, optional, default: None All bins that has count less than cmin will not be displayed and these count values in the return value count histogram will also be set to nan upon return cmax : scalar, optional, default: None All bins that has count more than cmax will not be displayed (set to none before passing to imshow) and these count values in the return value count histogram will also be set to nan upon return Returns ------- h : 2D array The bi-dimensional histogram of samples x and y. Values in x are histogrammed along the first dimension and values in y are histogrammed along the second dimension. xedges : 1D array The bin edges along the x axis. yedges : 1D array The bin edges along the y axis. image : `~.matplotlib.collections.QuadMesh` Other Parameters ---------------- cmap : Colormap or str, optional A `.colors.Colormap` instance. If not set, use rc settings. norm : Normalize, optional A `.colors.Normalize` instance is used to scale luminance data to ``[0, 1]``. If not set, defaults to `.colors.Normalize()`. vmin/vmax : None or scalar, optional Arguments passed to the `~.colors.Normalize` instance. alpha : ``0 <= scalar <= 1`` or ``None``, optional The alpha blending value. See also -------- hist : 1D histogram plotting Notes ----- - Currently ``hist2d`` calculates it's own axis limits, and any limits previously set are ignored. - Rendering the histogram with a logarithmic color scale is accomplished by passing a `.colors.LogNorm` instance to the *norm* keyword argument. Likewise, power-law normalization (similar in effect to gamma correction) can be accomplished with `.colors.PowerNorm`. """ h, xedges, yedges = np.histogram2d(x, y, bins=bins, range=range, normed=density, weights=weights) if cmin is not None: h[h < cmin] = None if cmax is not None: h[h > cmax] = None pc = self.pcolormesh(xedges, yedges, h.T, **kwargs) self.set_xlim(xedges[0], xedges[-1]) self.set_ylim(yedges[0], yedges[-1]) return h, xedges, yedges, pc
[docs] @_preprocess_data(replace_names=["x"]) @docstring.dedent_interpd def psd(self, x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, **kwargs): r""" Plot the power spectral density. The power spectral density :math:`P_{xx}` by Welch's average periodogram method. The vector *x* is divided into *NFFT* length segments. Each segment is detrended by function *detrend* and windowed by function *window*. *noverlap* gives the length of the overlap between segments. The :math:`|\mathrm{fft}(i)|^2` of each segment :math:`i` are averaged to compute :math:`P_{xx}`, with a scaling to correct for power loss due to windowing. If len(*x*) < *NFFT*, it will be zero padded to *NFFT*. Parameters ---------- x : 1-D array or sequence Array or sequence containing the data %(Spectral)s %(PSD)s noverlap : int The number of points of overlap between segments. The default value is 0 (no overlap). Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. return_line : bool Whether to include the line object plotted in the returned values. Default is False. Returns ------- Pxx : 1-D array The values for the power spectrum `P_{xx}` before scaling (real valued). freqs : 1-D array The frequencies corresponding to the elements in *Pxx*. line : a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Only returned if *return_line* is True. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- :func:`specgram` :func:`specgram` differs in the default overlap; in not returning the mean of the segment periodograms; in returning the times of the segments; and in plotting a colormap instead of a line. :func:`magnitude_spectrum` :func:`magnitude_spectrum` plots the magnitude spectrum. :func:`csd` :func:`csd` plots the spectral density between two signals. Notes ----- For plotting, the power is plotted as :math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself is returned. References ---------- Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ if Fc is None: Fc = 0 pxx, freqs = mlab.psd(x=x, NFFT=NFFT, Fs=Fs, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq) freqs += Fc if scale_by_freq in (None, True): psd_units = 'dB/Hz' else: psd_units = 'dB' line = self.plot(freqs, 10 * np.log10(pxx), **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Power Spectral Density (%s)' % psd_units) self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax - vmin logi = int(np.log10(intv)) if logi == 0: logi = .1 step = 10 * logi ticks = np.arange(math.floor(vmin), math.ceil(vmax) + 1, step) self.set_yticks(ticks) if return_line is None or not return_line: return pxx, freqs else: return pxx, freqs, line
[docs] @_preprocess_data(replace_names=["x", "y"], label_namer="y") @docstring.dedent_interpd def csd(self, x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, **kwargs): """ Plot the cross-spectral density. The cross spectral density :math:`P_{xy}` by Welch's average periodogram method. The vectors *x* and *y* are divided into *NFFT* length segments. Each segment is detrended by function *detrend* and windowed by function *window*. *noverlap* gives the length of the overlap between segments. The product of the direct FFTs of *x* and *y* are averaged over each segment to compute :math:`P_{xy}`, with a scaling to correct for power loss due to windowing. If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero padded to *NFFT*. Parameters ---------- x, y : 1-D arrays or sequences Arrays or sequences containing the data. %(Spectral)s %(PSD)s noverlap : int The number of points of overlap between segments. The default value is 0 (no overlap). Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. return_line : bool Whether to include the line object plotted in the returned values. Default is False. Returns ------- Pxy : 1-D array The values for the cross spectrum `P_{xy}` before scaling (complex valued). freqs : 1-D array The frequencies corresponding to the elements in *Pxy*. line : a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Only returned if *return_line* is True. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- :func:`psd` :func:`psd` is the equivalent to setting y=x. Notes ----- For plotting, the power is plotted as :math:`10\\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself is returned. References ---------- Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ if Fc is None: Fc = 0 pxy, freqs = mlab.csd(x=x, y=y, NFFT=NFFT, Fs=Fs, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq) # pxy is complex freqs += Fc line = self.plot(freqs, 10 * np.log10(np.abs(pxy)), **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Cross Spectrum Magnitude (dB)') self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax - vmin step = 10 * int(np.log10(intv)) ticks = np.arange(math.floor(vmin), math.ceil(vmax) + 1, step) self.set_yticks(ticks) if return_line is None or not return_line: return pxy, freqs else: return pxy, freqs, line
[docs] @_preprocess_data(replace_names=["x"]) @docstring.dedent_interpd def magnitude_spectrum(self, x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, **kwargs): """ Plot the magnitude spectrum. Compute the magnitude spectrum of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. Parameters ---------- x : 1-D array or sequence Array or sequence containing the data. %(Spectral)s %(Single_Spectrum)s scale : {'default', 'linear', 'dB'} The scaling of the values in the *spec*. 'linear' is no scaling. 'dB' returns the values in dB scale, i.e., the dB amplitude (20 * log10). 'default' is 'linear'. Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns ------- spectrum : 1-D array The values for the magnitude spectrum before scaling (real valued). freqs : 1-D array The frequencies corresponding to the elements in *spectrum*. line : a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- :func:`psd` :func:`psd` plots the power spectral density.`. :func:`angle_spectrum` :func:`angle_spectrum` plots the angles of the corresponding frequencies. :func:`phase_spectrum` :func:`phase_spectrum` plots the phase (unwrapped angle) of the corresponding frequencies. :func:`specgram` :func:`specgram` can plot the magnitude spectrum of segments within the signal in a colormap. """ if Fc is None: Fc = 0 if scale is None or scale == 'default': scale = 'linear' spec, freqs = mlab.magnitude_spectrum(x=x, Fs=Fs, window=window, pad_to=pad_to, sides=sides) freqs += Fc if scale == 'linear': Z = spec yunits = 'energy' elif scale == 'dB': Z = 20. * np.log10(spec) yunits = 'dB' else: raise ValueError('Unknown scale %s', scale) lines = self.plot(freqs, Z, **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Magnitude (%s)' % yunits) return spec, freqs, lines[0]
[docs] @_preprocess_data(replace_names=["x"]) @docstring.dedent_interpd def angle_spectrum(self, x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, **kwargs): """ Plot the angle spectrum. Compute the angle spectrum (wrapped phase spectrum) of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. Parameters ---------- x : 1-D array or sequence Array or sequence containing the data. %(Spectral)s %(Single_Spectrum)s Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns ------- spectrum : 1-D array The values for the angle spectrum in radians (real valued). freqs : 1-D array The frequencies corresponding to the elements in *spectrum*. line : a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- :func:`magnitude_spectrum` :func:`angle_spectrum` plots the magnitudes of the corresponding frequencies. :func:`phase_spectrum` :func:`phase_spectrum` plots the unwrapped version of this function. :func:`specgram` :func:`specgram` can plot the angle spectrum of segments within the signal in a colormap. """ if Fc is None: Fc = 0 spec, freqs = mlab.angle_spectrum(x=x, Fs=Fs, window=window, pad_to=pad_to, sides=sides) freqs += Fc lines = self.plot(freqs, spec, **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Angle (radians)') return spec, freqs, lines[0]
[docs] @_preprocess_data(replace_names=["x"]) @docstring.dedent_interpd def phase_spectrum(self, x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, **kwargs): """ Plot the phase spectrum. Compute the phase spectrum (unwrapped angle spectrum) of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. Parameters ---------- x : 1-D array or sequence Array or sequence containing the data %(Spectral)s %(Single_Spectrum)s Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns ------- spectrum : 1-D array The values for the phase spectrum in radians (real valued). freqs : 1-D array The frequencies corresponding to the elements in *spectrum*. line : a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s See Also -------- :func:`magnitude_spectrum` :func:`magnitude_spectrum` plots the magnitudes of the corresponding frequencies. :func:`angle_spectrum` :func:`angle_spectrum` plots the wrapped version of this function. :func:`specgram` :func:`specgram` can plot the phase spectrum of segments within the signal in a colormap. """ if Fc is None: Fc = 0 spec, freqs = mlab.phase_spectrum(x=x, Fs=Fs, window=window, pad_to=pad_to, sides=sides) freqs += Fc lines = self.plot(freqs, spec, **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Phase (radians)') return spec, freqs, lines[0]
[docs] @_preprocess_data(replace_names=["x", "y"]) @docstring.dedent_interpd def cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs): """ Plot the coherence between *x* and *y*. Plot the coherence between *x* and *y*. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}} Parameters ---------- %(Spectral)s %(PSD)s noverlap : int The number of points of overlap between blocks. The default value is 0 (no overlap). Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns ------- Cxy : 1-D array The coherence vector. freqs : 1-D array The frequencies for the elements in *Cxy*. Other Parameters ---------------- **kwargs Keyword arguments control the :class:`~matplotlib.lines.Line2D` properties: %(_Line2D_docstr)s References ---------- Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ cxy, freqs = mlab.cohere(x=x, y=y, NFFT=NFFT, Fs=Fs, detrend=detrend, window=window, noverlap=noverlap, scale_by_freq=scale_by_freq) freqs += Fc self.plot(freqs, cxy, **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Coherence') self.grid(True) return cxy, freqs
[docs] @_preprocess_data(replace_names=["x"]) @docstring.dedent_interpd def specgram(self, x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, **kwargs): """ Plot a spectrogram. Compute and plot a spectrogram of data in *x*. Data are split into *NFFT* length segments and the spectrum of each section is computed. The windowing function *window* is applied to each segment, and the amount of overlap of each segment is specified with *noverlap*. The spectrogram is plotted as a colormap (using imshow). Parameters ---------- x : 1-D array or sequence Array or sequence containing the data. %(Spectral)s %(PSD)s mode : {'default', 'psd', 'magnitude', 'angle', 'phase'} What sort of spectrum to use. Default is 'psd', which takes the power spectral density. 'magnitude' returns the magnitude spectrum. 'angle' returns the phase spectrum without unwrapping. 'phase' returns the phase spectrum with unwrapping. noverlap : int The number of points of overlap between blocks. The default value is 128. scale : {'default', 'linear', 'dB'} The scaling of the values in the *spec*. 'linear' is no scaling. 'dB' returns the values in dB scale. When *mode* is 'psd', this is dB power (10 * log10). Otherwise this is dB amplitude (20 * log10). 'default' is 'dB' if *mode* is 'psd' or 'magnitude' and 'linear' otherwise. This must be 'linear' if *mode* is 'angle' or 'phase'. Fc : int The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. cmap A :class:`matplotlib.colors.Colormap` instance; if *None*, use default determined by rc xextent : *None* or (xmin, xmax) The image extent along the x-axis. The default sets *xmin* to the left border of the first bin (*spectrum* column) and *xmax* to the right border of the last bin. Note that for *noverlap>0* the width of the bins is smaller than those of the segments. **kwargs Additional kwargs are passed on to imshow which makes the specgram image. Returns ------- spectrum : 2-D array Columns are the periodograms of successive segments. freqs : 1-D array The frequencies corresponding to the rows in *spectrum*. t : 1-D array The times corresponding to midpoints of segments (i.e., the columns in *spectrum*). im : instance of class :class:`~matplotlib.image.AxesImage` The image created by imshow containing the spectrogram See Also -------- :func:`psd` :func:`psd` differs in the default overlap; in returning the mean of the segment periodograms; in not returning times; and in generating a line plot instead of colormap. :func:`magnitude_spectrum` A single spectrum, similar to having a single segment when *mode* is 'magnitude'. Plots a line instead of a colormap. :func:`angle_spectrum` A single spectrum, similar to having a single segment when *mode* is 'angle'. Plots a line instead of a colormap. :func:`phase_spectrum` A single spectrum, similar to having a single segment when *mode* is 'phase'. Plots a line instead of a colormap. Notes ----- The parameters *detrend* and *scale_by_freq* do only apply when *mode* is set to 'psd'. """ if NFFT is None: NFFT = 256 # same default as in mlab.specgram() if Fc is None: Fc = 0 # same default as in mlab._spectral_helper() if noverlap is None: noverlap = 128 # same default as in mlab.specgram() if mode == 'complex': raise ValueError('Cannot plot a complex specgram') if scale is None or scale == 'default': if mode in ['angle', 'phase']: scale = 'linear' else: scale = 'dB' elif mode in ['angle', 'phase'] and scale == 'dB': raise ValueError('Cannot use dB scale with angle or phase mode') spec, freqs, t = mlab.specgram(x=x, NFFT=NFFT, Fs=Fs, detrend=detrend, window=window, noverlap=noverlap, pad_to=pad_to, sides=sides, scale_by_freq=scale_by_freq, mode=mode) if scale == 'linear': Z = spec elif scale == 'dB': if mode is None or mode == 'default' or mode == 'psd': Z = 10. * np.log10(spec) else: Z = 20. * np.log10(spec) else: raise ValueError('Unknown scale %s', scale) Z = np.flipud(Z) if xextent is None: # padding is needed for first and last segment: pad_xextent = (NFFT-noverlap) / Fs / 2 xextent = np.min(t) - pad_xextent, np.max(t) + pad_xextent xmin, xmax = xextent freqs += Fc extent = xmin, xmax, freqs[0], freqs[-1] im = self.imshow(Z, cmap, extent=extent, vmin=vmin, vmax=vmax, **kwargs) self.axis('auto') return spec, freqs, t, im
[docs] @docstring.dedent_interpd def spy(self, Z, precision=0, marker=None, markersize=None, aspect='equal', origin="upper", **kwargs): """ Plot the sparsity pattern of a 2D array. This visualizes the non-zero values of the array. Two plotting styles are available: image and marker. Both are available for full arrays, but only the marker style works for `scipy.sparse.spmatrix` instances. **Image style** If *marker* and *markersize* are *None*, `~.Axes.imshow` is used. Any extra remaining kwargs are passed to this method. **Marker style** If *Z* is a `scipy.sparse.spmatrix` or *marker* or *markersize* are *None*, a `~matplotlib.lines.Line2D` object will be returned with the value of marker determining the marker type, and any remaining kwargs passed to `~.Axes.plot`. Parameters ---------- Z : array-like (M, N) The array to be plotted. precision : float or 'present', optional, default: 0 If *precision* is 0, any non-zero value will be plotted. Otherwise, values of :math:`|Z| > precision` will be plotted. For :class:`scipy.sparse.spmatrix` instances, you can also pass 'present'. In this case any value present in the array will be plotted, even if it is identically zero. origin : {'upper', 'lower'}, optional Place the [0,0] index of the array in the upper left or lower left corner of the axes. The convention 'upper' is typically used for matrices and images. If not given, :rc:`image.origin` is used, defaulting to 'upper'. aspect : {'equal', 'auto', None} or float, optional Controls the aspect ratio of the axes. The aspect is of particular relevance for images since it may distort the image, i.e. pixel will not be square. This parameter is a shortcut for explicitly calling `.Axes.set_aspect`. See there for further details. - 'equal': Ensures an aspect ratio of 1. Pixels will be square. - 'auto': The axes is kept fixed and the aspect is adjusted so that the data fit in the axes. In general, this will result in non-square pixels. - *None*: Use :rc:`image.aspect` (default: 'equal'). Default: 'equal' Returns ------- ret : `~matplotlib.image.AxesImage` or `.Line2D` The return type depends on the plotting style (see above). Other Parameters ---------------- **kwargs The supported additional parameters depend on the plotting style. For the image style, you can pass the following additional parameters of `~.Axes.imshow`: - *cmap* - *alpha* - *url* - any `.Artist` properties (passed on to the `.AxesImage`) For the marker style, you can pass any `.Line2D` property except for *linestyle*: %(_Line2D_docstr)s """ if marker is None and markersize is None and hasattr(Z, 'tocoo'): marker = 's' if marker is None and markersize is None: Z = np.asarray(Z) mask = np.abs(Z) > precision if 'cmap' not in kwargs: kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'], name='binary') if 'interpolation' in kwargs: raise TypeError( "spy() got an unexpected keyword argument 'interpolation'") ret = self.imshow(mask, interpolation='nearest', aspect=aspect, origin=origin, **kwargs) else: if hasattr(Z, 'tocoo'): c = Z.tocoo() if precision == 'present': y = c.row x = c.col else: nonzero = np.abs(c.data) > precision y = c.row[nonzero] x = c.col[nonzero] else: Z = np.asarray(Z) nonzero = np.abs(Z) > precision y, x = np.nonzero(nonzero) if marker is None: marker = 's' if markersize is None: markersize = 10 if 'linestyle' in kwargs: raise TypeError( "spy() got an unexpected keyword argument 'linestyle'") marks = mlines.Line2D(x, y, linestyle='None', marker=marker, markersize=markersize, **kwargs) self.add_line(marks) nr, nc = Z.shape self.set_xlim(-0.5, nc - 0.5) self.set_ylim(nr - 0.5, -0.5) self.set_aspect(aspect) ret = marks self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return ret
[docs] def matshow(self, Z, **kwargs): """ Plot the values of a 2D matrix or array as color-coded image. The matrix will be shown the way it would be printed, with the first row at the top. Row and column numbering is zero-based. Parameters ---------- Z : array-like(M, N) The matrix to be displayed. Returns ------- image : `~matplotlib.image.AxesImage` Other Parameters ---------------- **kwargs : `~matplotlib.axes.Axes.imshow` arguments See Also -------- imshow : More general function to plot data on a 2D regular raster. Notes ----- This is just a convenience function wrapping `.imshow` to set useful defaults for a displaying a matrix. In particular: - Set ``origin='upper'``. - Set ``interpolation='nearest'``. - Set ``aspect='equal'``. - Ticks are placed to the left and above. - Ticks are formatted to show integer indices. """ Z = np.asanyarray(Z) kw = {'origin': 'upper', 'interpolation': 'nearest', 'aspect': 'equal', # (already the imshow default) **kwargs} im = self.imshow(Z, **kw) self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return im
[docs] @_preprocess_data(replace_names=["dataset"]) def violinplot(self, dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, quantiles=None, points=100, bw_method=None): """ Make a violin plot. Make a violin plot for each column of *dataset* or each vector in sequence *dataset*. Each filled area extends to represent the entire data range, with optional lines at the mean, the median, the minimum, the maximum, and user-specified quantiles. Parameters ---------- dataset : Array or a sequence of vectors. The input data. positions : array-like, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, creates a vertical violin plot. Otherwise, creates a horizontal violin plot. widths : array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans : bool, default = False If `True`, will toggle rendering of the means. showextrema : bool, default = True If `True`, will toggle rendering of the extrema. showmedians : bool, default = False If `True`, will toggle rendering of the medians. quantiles : array-like, default = None If not None, set a list of floats in interval [0, 1] for each violin, which stands for the quantiles that will be rendered for that violin. points : scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimations at. bw_method : str, scalar or callable, optional The method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a callable. If a scalar, this will be used directly as `kde.factor`. If a callable, it should take a `GaussianKDE` instance as its only parameter and return a scalar. If None (default), 'scott' is used. Returns ------- result : dict A dictionary mapping each component of the violinplot to a list of the corresponding collection instances created. The dictionary has the following keys: - ``bodies``: A list of the `~.collections.PolyCollection` instances containing the filled area of each violin. - ``cmeans``: A `~.collections.LineCollection` instance that marks the mean values of each of the violin's distribution. - ``cmins``: A `~.collections.LineCollection` instance that marks the bottom of each violin's distribution. - ``cmaxes``: A `~.collections.LineCollection` instance that marks the top of each violin's distribution. - ``cbars``: A `~.collections.LineCollection` instance that marks the centers of each violin's distribution. - ``cmedians``: A `~.collections.LineCollection` instance that marks the median values of each of the violin's distribution. - ``cquantiles``: A `~.collections.LineCollection` instance created to identify the quantile values of each of the violin's distribution. """ def _kde_method(X, coords): # fallback gracefully if the vector contains only one value if np.all(X[0] == X): return (X[0] == coords).astype(float) kde = mlab.GaussianKDE(X, bw_method) return kde.evaluate(coords) vpstats = cbook.violin_stats(dataset, _kde_method, points=points, quantiles=quantiles) return self.violin(vpstats, positions=positions, vert=vert, widths=widths, showmeans=showmeans, showextrema=showextrema, showmedians=showmedians)
[docs] def violin(self, vpstats, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False): """Drawing function for violin plots. Draw a violin plot for each column of `vpstats`. Each filled area extends to represent the entire data range, with optional lines at the mean, the median, the minimum, the maximum, and the quantiles values. Parameters ---------- vpstats : list of dicts A list of dictionaries containing stats for each violin plot. Required keys are: - ``coords``: A list of scalars containing the coordinates that the violin's kernel density estimate were evaluated at. - ``vals``: A list of scalars containing the values of the kernel density estimate at each of the coordinates given in *coords*. - ``mean``: The mean value for this violin's dataset. - ``median``: The median value for this violin's dataset. - ``min``: The minimum value for this violin's dataset. - ``max``: The maximum value for this violin's dataset. Optional keys are: - ``quantiles``: A list of scalars containing the quantile values for this violin's dataset. positions : array-like, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, plots the violins vertically. Otherwise, plots the violins horizontally. widths : array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans : bool, default = False If true, will toggle rendering of the means. showextrema : bool, default = True If true, will toggle rendering of the extrema. showmedians : bool, default = False If true, will toggle rendering of the medians. Returns ------- result : dict A dictionary mapping each component of the violinplot to a list of the corresponding collection instances created. The dictionary has the following keys: - ``bodies``: A list of the `~.collections.PolyCollection` instances containing the filled area of each violin. - ``cmeans``: A `~.collections.LineCollection` instance that marks the mean values of each of the violin's distribution. - ``cmins``: A `~.collections.LineCollection` instance that marks the bottom of each violin's distribution. - ``cmaxes``: A `~.collections.LineCollection` instance that marks the top of each violin's distribution. - ``cbars``: A `~.collections.LineCollection` instance that marks the centers of each violin's distribution. - ``cmedians``: A `~.collections.LineCollection` instance that marks the median values of each of the violin's distribution. - ``cquantiles``: A `~.collections.LineCollection` instance created to identify the quantiles values of each of the violin's distribution. """ # Statistical quantities to be plotted on the violins means = [] mins = [] maxes = [] medians = [] quantiles = np.asarray([]) # Collections to be returned artists = {} N = len(vpstats) datashape_message = ("List of violinplot statistics and `{0}` " "values must have the same length") # Validate positions if positions is None: positions = range(1, N + 1) elif len(positions) != N: raise ValueError(datashape_message.format("positions")) # Validate widths if np.isscalar(widths): widths = [widths] * N elif len(widths) != N: raise ValueError(datashape_message.format("widths")) # Calculate ranges for statistics lines pmins = -0.25 * np.array(widths) + positions pmaxes = 0.25 * np.array(widths) + positions # Check whether we are rendering vertically or horizontally if vert: fill = self.fill_betweenx perp_lines = self.hlines par_lines = self.vlines else: fill = self.fill_between perp_lines = self.vlines par_lines = self.hlines if rcParams['_internal.classic_mode']: fillcolor = 'y' edgecolor = 'r' else: fillcolor = edgecolor = self._get_lines.get_next_color() # Render violins bodies = [] for stats, pos, width in zip(vpstats, positions, widths): # The 0.5 factor reflects the fact that we plot from v-p to # v+p vals = np.array(stats['vals']) vals = 0.5 * width * vals / vals.max() bodies += [fill(stats['coords'], -vals + pos, vals + pos, facecolor=fillcolor, alpha=0.3)] means.append(stats['mean']) mins.append(stats['min']) maxes.append(stats['max']) medians.append(stats['median']) q = stats.get('quantiles') if q is not None: # If exist key quantiles, assume it's a list of floats quantiles = np.concatenate((quantiles, q)) artists['bodies'] = bodies # Render means if showmeans: artists['cmeans'] = perp_lines(means, pmins, pmaxes, colors=edgecolor) # Render extrema if showextrema: artists['cmaxes'] = perp_lines(maxes, pmins, pmaxes, colors=edgecolor) artists['cmins'] = perp_lines(mins, pmins, pmaxes, colors=edgecolor) artists['cbars'] = par_lines(positions, mins, maxes, colors=edgecolor) # Render medians if showmedians: artists['cmedians'] = perp_lines(medians, pmins, pmaxes, colors=edgecolor) # Render quantile values if quantiles.size > 0: # Recalculate ranges for statistics lines for quantiles. # ppmins are the left end of quantiles lines ppmins = np.asarray([]) # pmaxes are the right end of quantiles lines ppmaxs = np.asarray([]) for stats, cmin, cmax in zip(vpstats, pmins, pmaxes): q = stats.get('quantiles') if q is not None: ppmins = np.concatenate((ppmins, [cmin] * np.size(q))) ppmaxs = np.concatenate((ppmaxs, [cmax] * np.size(q))) # Start rendering artists['cquantiles'] = perp_lines(quantiles, ppmins, ppmaxs, colors=edgecolor) return artists
# Methods that are entirely implemented in other modules. table = mtable.table # args can by either Y or y1,y2,... and all should be replaced stackplot = _preprocess_data()(mstack.stackplot) streamplot = _preprocess_data( replace_names=["x", "y", "u", "v", "start_points"])(mstream.streamplot) tricontour = mtri.tricontour tricontourf = mtri.tricontourf tripcolor = mtri.tripcolor triplot = mtri.triplot