Version 3.1.2
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Source code for mpl_toolkits.mplot3d.axes3d

"""
axes3d.py, original mplot3d version by John Porter
Created: 23 Sep 2005

Parts fixed by Reinier Heeres <[email protected]>
Minor additions by Ben Axelrod <[email protected]>
Significant updates and revisions by Ben Root <[email protected]>

Module containing Axes3D, an object which can plot 3D objects on a
2D matplotlib figure.
"""
from collections import defaultdict
from functools import reduce
import math

import numpy as np

from matplotlib import artist
import matplotlib.axes as maxes
import matplotlib.cbook as cbook
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
import matplotlib.docstring as docstring
import matplotlib.projections as proj
import matplotlib.scale as mscale
import matplotlib.transforms as mtransforms
from matplotlib.axes import Axes, rcParams
from matplotlib.colors import Normalize, LightSource
from matplotlib.transforms import Bbox
from matplotlib.tri.triangulation import Triangulation

from . import art3d
from . import proj3d
from . import axis3d


def unit_bbox():
    box = Bbox(np.array([[0, 0], [1, 1]]))
    return box


[docs]class Axes3D(Axes): """ 3D axes object. """ name = '3d' _shared_z_axes = cbook.Grouper() @docstring.dedent_interpd def __init__( self, fig, rect=None, *args, azim=-60, elev=30, zscale=None, sharez=None, proj_type='persp', **kwargs): """ Parameters ---------- fig : Figure The parent figure. rect : (float, float, float, float) The ``(left, bottom, width, height)`` axes position. azim : float, optional Azimuthal viewing angle, defaults to -60. elev : float, optional Elevation viewing angle, defaults to 30. zscale : [%(scale)s], optional The z scale. Note that currently, only a linear scale is supported. sharez : Axes3D, optional Other axes to share z-limits with. proj_type : {'persp', 'ortho'} The projection type, default 'persp'. Notes ----- .. versionadded:: 1.2.1 The *sharez* parameter. """ if rect is None: rect = [0.0, 0.0, 1.0, 1.0] self._cids = [] self.initial_azim = azim self.initial_elev = elev self.set_proj_type(proj_type) self.xy_viewLim = unit_bbox() self.zz_viewLim = unit_bbox() self.xy_dataLim = unit_bbox() self.zz_dataLim = unit_bbox() # inhibit autoscale_view until the axes are defined # they can't be defined until Axes.__init__ has been called self.view_init(self.initial_elev, self.initial_azim) self._ready = 0 self._sharez = sharez if sharez is not None: self._shared_z_axes.join(self, sharez) self._adjustable = 'datalim' super().__init__(fig, rect, frameon=True, *args, **kwargs) # Disable drawing of axes by base class super().set_axis_off() # Enable drawing of axes by Axes3D class self.set_axis_on() self.M = None # func used to format z -- fall back on major formatters self.fmt_zdata = None if zscale is not None: self.set_zscale(zscale) if self.zaxis is not None: self._zcid = self.zaxis.callbacks.connect( 'units finalize', lambda: self._on_units_changed(scalez=True)) else: self._zcid = None self._ready = 1 self.mouse_init() self.set_top_view() self.patch.set_linewidth(0) # Calculate the pseudo-data width and height pseudo_bbox = self.transLimits.inverted().transform([(0, 0), (1, 1)]) self._pseudo_w, self._pseudo_h = pseudo_bbox[1] - pseudo_bbox[0] self.figure.add_axes(self) # mplot3d currently manages its own spines and needs these turned off # for bounding box calculations for k in self.spines.keys(): self.spines[k].set_visible(False)
[docs] def set_axis_off(self): self._axis3don = False self.stale = True
[docs] def set_axis_on(self): self._axis3don = True self.stale = True
[docs] def have_units(self): """ Return *True* if units are set on the *x*, *y*, or *z* axes """ return (self.xaxis.have_units() or self.yaxis.have_units() or self.zaxis.have_units())
[docs] def convert_zunits(self, z): """ For artists in an axes, if the zaxis has units support, convert *z* using zaxis unit type .. versionadded :: 1.2.1 """ return self.zaxis.convert_units(z)
def _process_unit_info(self, xdata=None, ydata=None, zdata=None, kwargs=None): """ Look for unit *kwargs* and update the axis instances as necessary """ super()._process_unit_info(xdata=xdata, ydata=ydata, kwargs=kwargs) if self.xaxis is None or self.yaxis is None or self.zaxis is None: return if zdata is not None: # we only need to update if there is nothing set yet. if not self.zaxis.have_units(): self.zaxis.update_units(xdata) # process kwargs 2nd since these will override default units if kwargs is not None: zunits = kwargs.pop('zunits', self.zaxis.units) if zunits != self.zaxis.units: self.zaxis.set_units(zunits) # If the units being set imply a different converter, # we need to update. if zdata is not None: self.zaxis.update_units(zdata)
[docs] def set_top_view(self): # this happens to be the right view for the viewing coordinates # moved up and to the left slightly to fit labels and axes xdwl = (0.95/self.dist) xdw = (0.9/self.dist) ydwl = (0.95/self.dist) ydw = (0.9/self.dist) # This is purposely using the 2D Axes's set_xlim and set_ylim, # because we are trying to place our viewing pane. super().set_xlim(-xdwl, xdw, auto=None) super().set_ylim(-ydwl, ydw, auto=None)
def _init_axis(self): '''Init 3D axes; overrides creation of regular X/Y axes''' self.xaxis = axis3d.XAxis('x', self.xy_viewLim.intervalx, self.xy_dataLim.intervalx, self) self.yaxis = axis3d.YAxis('y', self.xy_viewLim.intervaly, self.xy_dataLim.intervaly, self) self.zaxis = axis3d.ZAxis('z', self.zz_viewLim.intervalx, self.zz_dataLim.intervalx, self) # Provide old aliases self.w_xaxis = self.xaxis self.w_yaxis = self.yaxis self.w_zaxis = self.zaxis for ax in self.xaxis, self.yaxis, self.zaxis: ax.init3d()
[docs] def get_zaxis(self): '''Return the ``ZAxis`` (`~.axis3d.Axis`) instance.''' return self.zaxis
def _get_axis_list(self): return super()._get_axis_list() + (self.zaxis, )
[docs] def unit_cube(self, vals=None): minx, maxx, miny, maxy, minz, maxz = vals or self.get_w_lims() return [(minx, miny, minz), (maxx, miny, minz), (maxx, maxy, minz), (minx, maxy, minz), (minx, miny, maxz), (maxx, miny, maxz), (maxx, maxy, maxz), (minx, maxy, maxz)]
[docs] def tunit_cube(self, vals=None, M=None): if M is None: M = self.M xyzs = self.unit_cube(vals) tcube = proj3d.proj_points(xyzs, M) return tcube
[docs] def tunit_edges(self, vals=None, M=None): tc = self.tunit_cube(vals, M) edges = [(tc[0], tc[1]), (tc[1], tc[2]), (tc[2], tc[3]), (tc[3], tc[0]), (tc[0], tc[4]), (tc[1], tc[5]), (tc[2], tc[6]), (tc[3], tc[7]), (tc[4], tc[5]), (tc[5], tc[6]), (tc[6], tc[7]), (tc[7], tc[4])] return edges
[docs] @artist.allow_rasterization def draw(self, renderer): # draw the background patch self.patch.draw(renderer) self._frameon = False # first, set the aspect # this is duplicated from `axes._base._AxesBase.draw` # but must be called before any of the artist are drawn as # it adjusts the view limits and the size of the bounding box # of the axes locator = self.get_axes_locator() if locator: pos = locator(self, renderer) self.apply_aspect(pos) else: self.apply_aspect() # add the projection matrix to the renderer self.M = self.get_proj() renderer.M = self.M renderer.vvec = self.vvec renderer.eye = self.eye renderer.get_axis_position = self.get_axis_position # Calculate projection of collections and patches and zorder them. # Make sure they are drawn above the grids. zorder_offset = max(axis.get_zorder() for axis in self._get_axis_list()) + 1 for i, col in enumerate( sorted(self.collections, key=lambda col: col.do_3d_projection(renderer), reverse=True)): col.zorder = zorder_offset + i for i, patch in enumerate( sorted(self.patches, key=lambda patch: patch.do_3d_projection(renderer), reverse=True)): patch.zorder = zorder_offset + i if self._axis3don: # Draw panes first for axis in self._get_axis_list(): axis.draw_pane(renderer) # Then axes for axis in self._get_axis_list(): axis.draw(renderer) # Then rest super().draw(renderer)
[docs] def get_axis_position(self): vals = self.get_w_lims() tc = self.tunit_cube(vals, self.M) xhigh = tc[1][2] > tc[2][2] yhigh = tc[3][2] > tc[2][2] zhigh = tc[0][2] > tc[2][2] return xhigh, yhigh, zhigh
def _on_units_changed(self, scalex=False, scaley=False, scalez=False): """ Callback for processing changes to axis units. Currently forces updates of data limits and view limits. """ self.relim() self.autoscale_view(scalex=scalex, scaley=scaley, scalez=scalez)
[docs] def update_datalim(self, xys, **kwargs): pass
[docs] def get_autoscale_on(self): """ Get whether autoscaling is applied for all axes on plot commands .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ return super().get_autoscale_on() and self.get_autoscalez_on()
[docs] def get_autoscalez_on(self): """ Get whether autoscaling for the z-axis is applied on plot commands .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ return self._autoscaleZon
[docs] def set_autoscale_on(self, b): """ Set whether autoscaling is applied on plot commands .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. Parameters ---------- b : bool """ super().set_autoscale_on(b) self.set_autoscalez_on(b)
[docs] def set_autoscalez_on(self, b): """ Set whether autoscaling for the z-axis is applied on plot commands .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. Parameters ---------- b : bool """ self._autoscaleZon = b
[docs] def set_zmargin(self, m): """ Set padding of Z data limits prior to autoscaling. *m* times the data interval will be added to each end of that interval before it is used in autoscaling. accepts: float in range 0 to 1 .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ if m < 0 or m > 1: raise ValueError("margin must be in range 0 to 1") self._zmargin = m self.stale = True
[docs] def margins(self, *margins, x=None, y=None, z=None, tight=True): """ Convenience method to set or retrieve autoscaling margins. signatures:: margins() returns xmargin, ymargin, zmargin :: margins(margin) margins(xmargin, ymargin, zmargin) margins(x=xmargin, y=ymargin, z=zmargin) margins(..., tight=False) All forms above set the xmargin, ymargin and zmargin parameters. All keyword parameters are optional. A single positional argument specifies xmargin, ymargin and zmargin. Passing both positional and keyword arguments for xmargin, ymargin, and/or zmargin is invalid. The *tight* parameter is passed to :meth:`autoscale_view`, which is executed after a margin is changed; the default here is *True*, on the assumption that when margins are specified, no additional padding to match tick marks is usually desired. Setting *tight* to *None* will preserve the previous setting. Specifying any margin changes only the autoscaling; for example, if *xmargin* is not None, then *xmargin* times the X data interval will be added to each end of that interval before it is used in autoscaling. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ if margins and x is not None and y is not None and z is not None: raise TypeError('Cannot pass both positional and keyword ' 'arguments for x, y, and/or z.') elif len(margins) == 1: x = y = z = margins[0] elif len(margins) == 3: x, y, z = margins elif margins: raise TypeError('Must pass a single positional argument for all ' 'margins, or one for each margin (x, y, z).') if x is None and y is None and z is None: if tight is not True: cbook._warn_external(f'ignoring tight={tight!r} in get mode') return self._xmargin, self._ymargin, self._zmargin if x is not None: self.set_xmargin(x) if y is not None: self.set_ymargin(y) if z is not None: self.set_zmargin(z) self.autoscale_view( tight=tight, scalex=(x is not None), scaley=(y is not None), scalez=(z is not None) )
[docs] def autoscale(self, enable=True, axis='both', tight=None): """ Convenience method for simple axis view autoscaling. See :meth:`matplotlib.axes.Axes.autoscale` for full explanation. Note that this function behaves the same, but for all three axes. Therefore, 'z' can be passed for *axis*, and 'both' applies to all three axes. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ if enable is None: scalex = True scaley = True scalez = True else: if axis in ['x', 'both']: self._autoscaleXon = scalex = bool(enable) else: scalex = False if axis in ['y', 'both']: self._autoscaleYon = scaley = bool(enable) else: scaley = False if axis in ['z', 'both']: self._autoscaleZon = scalez = bool(enable) else: scalez = False self.autoscale_view(tight=tight, scalex=scalex, scaley=scaley, scalez=scalez)
[docs] def auto_scale_xyz(self, X, Y, Z=None, had_data=None): # This updates the bounding boxes as to keep a record as to what the # minimum sized rectangular volume holds the data. X = np.reshape(X, -1) Y = np.reshape(Y, -1) self.xy_dataLim.update_from_data_xy( np.column_stack([X, Y]), not had_data) if Z is not None: Z = np.reshape(Z, -1) self.zz_dataLim.update_from_data_xy( np.column_stack([Z, Z]), not had_data) # Let autoscale_view figure out how to use this data. self.autoscale_view()
[docs] def autoscale_view(self, tight=None, scalex=True, scaley=True, scalez=True): """ Autoscale the view limits using the data limits. See :meth:`matplotlib.axes.Axes.autoscale_view` for documentation. Note that this function applies to the 3D axes, and as such adds the *scalez* to the function arguments. .. versionchanged :: 1.1.0 Function signature was changed to better match the 2D version. *tight* is now explicitly a kwarg and placed first. .. versionchanged :: 1.2.1 This is now fully functional. """ if not self._ready: return # This method looks at the rectangular volume (see above) # of data and decides how to scale the view portal to fit it. if tight is None: # if image data only just use the datalim _tight = self._tight or ( len(self.images) > 0 and len(self.lines) == len(self.patches) == 0) else: _tight = self._tight = bool(tight) if scalex and self._autoscaleXon: self._shared_x_axes.clean() x0, x1 = self.xy_dataLim.intervalx xlocator = self.xaxis.get_major_locator() x0, x1 = xlocator.nonsingular(x0, x1) if self._xmargin > 0: delta = (x1 - x0) * self._xmargin x0 -= delta x1 += delta if not _tight: x0, x1 = xlocator.view_limits(x0, x1) self.set_xbound(x0, x1) if scaley and self._autoscaleYon: self._shared_y_axes.clean() y0, y1 = self.xy_dataLim.intervaly ylocator = self.yaxis.get_major_locator() y0, y1 = ylocator.nonsingular(y0, y1) if self._ymargin > 0: delta = (y1 - y0) * self._ymargin y0 -= delta y1 += delta if not _tight: y0, y1 = ylocator.view_limits(y0, y1) self.set_ybound(y0, y1) if scalez and self._autoscaleZon: self._shared_z_axes.clean() z0, z1 = self.zz_dataLim.intervalx zlocator = self.zaxis.get_major_locator() z0, z1 = zlocator.nonsingular(z0, z1) if self._zmargin > 0: delta = (z1 - z0) * self._zmargin z0 -= delta z1 += delta if not _tight: z0, z1 = zlocator.view_limits(z0, z1) self.set_zbound(z0, z1)
[docs] def get_w_lims(self): '''Get 3D world limits.''' minx, maxx = self.get_xlim3d() miny, maxy = self.get_ylim3d() minz, maxz = self.get_zlim3d() return minx, maxx, miny, maxy, minz, maxz
def _determine_lims(self, xmin=None, xmax=None, *args, **kwargs): if xmax is None and np.iterable(xmin): xmin, xmax = xmin if xmin == xmax: xmin -= 0.05 xmax += 0.05 return (xmin, xmax)
[docs] def set_xlim3d(self, left=None, right=None, emit=True, auto=False, *, xmin=None, xmax=None): """ Set 3D x limits. See :meth:`matplotlib.axes.Axes.set_xlim` for full documentation. """ if right is None and np.iterable(left): left, right = left if xmin is not None: cbook.warn_deprecated('3.0', name='`xmin`', alternative='`left`', obj_type='argument') if left is not None: raise TypeError('Cannot pass both `xmin` and `left`') left = xmin if xmax is not None: cbook.warn_deprecated('3.0', name='`xmax`', alternative='`right`', obj_type='argument') if right is not None: raise TypeError('Cannot pass both `xmax` and `right`') right = xmax self._process_unit_info(xdata=(left, right)) left = self._validate_converted_limits(left, self.convert_xunits) right = self._validate_converted_limits(right, self.convert_xunits) old_left, old_right = self.get_xlim() if left is None: left = old_left if right is None: right = old_right if left == right: cbook._warn_external( f"Attempting to set identical left == right == {left} results " f"in singular transformations; automatically expanding.") reverse = left > right left, right = self.xaxis.get_major_locator().nonsingular(left, right) left, right = self.xaxis.limit_range_for_scale(left, right) # cast to bool to avoid bad interaction between python 3.8 and np.bool_ left, right = sorted([left, right], reverse=bool(reverse)) self.xy_viewLim.intervalx = (left, right) if auto is not None: self._autoscaleXon = bool(auto) if emit: self.callbacks.process('xlim_changed', self) # Call all of the other x-axes that are shared with this one for other in self._shared_x_axes.get_siblings(self): if other is not self: other.set_xlim(self.xy_viewLim.intervalx, emit=False, auto=auto) if other.figure != self.figure: other.figure.canvas.draw_idle() self.stale = True return left, right
set_xlim = set_xlim3d
[docs] def set_ylim3d(self, bottom=None, top=None, emit=True, auto=False, *, ymin=None, ymax=None): """ Set 3D y limits. See :meth:`matplotlib.axes.Axes.set_ylim` for full documentation. """ if top is None and np.iterable(bottom): bottom, top = bottom if ymin is not None: cbook.warn_deprecated('3.0', name='`ymin`', alternative='`bottom`', obj_type='argument') if bottom is not None: raise TypeError('Cannot pass both `ymin` and `bottom`') bottom = ymin if ymax is not None: cbook.warn_deprecated('3.0', name='`ymax`', alternative='`top`', obj_type='argument') if top is not None: raise TypeError('Cannot pass both `ymax` and `top`') top = ymax self._process_unit_info(ydata=(bottom, top)) bottom = self._validate_converted_limits(bottom, self.convert_yunits) top = self._validate_converted_limits(top, self.convert_yunits) old_bottom, old_top = self.get_ylim() if bottom is None: bottom = old_bottom if top is None: top = old_top if bottom == top: cbook._warn_external( f"Attempting to set identical bottom == top == {bottom} " f"results in singular transformations; automatically " f"expanding.") swapped = bottom > top bottom, top = self.yaxis.get_major_locator().nonsingular(bottom, top) bottom, top = self.yaxis.limit_range_for_scale(bottom, top) if swapped: bottom, top = top, bottom self.xy_viewLim.intervaly = (bottom, top) if auto is not None: self._autoscaleYon = bool(auto) if emit: self.callbacks.process('ylim_changed', self) # Call all of the other y-axes that are shared with this one for other in self._shared_y_axes.get_siblings(self): if other is not self: other.set_ylim(self.xy_viewLim.intervaly, emit=False, auto=auto) if other.figure != self.figure: other.figure.canvas.draw_idle() self.stale = True return bottom, top
set_ylim = set_ylim3d
[docs] def set_zlim3d(self, bottom=None, top=None, emit=True, auto=False, *, zmin=None, zmax=None): """ Set 3D z limits. See :meth:`matplotlib.axes.Axes.set_ylim` for full documentation """ if top is None and np.iterable(bottom): bottom, top = bottom if zmin is not None: cbook.warn_deprecated('3.0', name='`zmin`', alternative='`bottom`', obj_type='argument') if bottom is not None: raise TypeError('Cannot pass both `zmin` and `bottom`') bottom = zmin if zmax is not None: cbook.warn_deprecated('3.0', name='`zmax`', alternative='`top`', obj_type='argument') if top is not None: raise TypeError('Cannot pass both `zmax` and `top`') top = zmax self._process_unit_info(zdata=(bottom, top)) bottom = self._validate_converted_limits(bottom, self.convert_zunits) top = self._validate_converted_limits(top, self.convert_zunits) old_bottom, old_top = self.get_zlim() if bottom is None: bottom = old_bottom if top is None: top = old_top if bottom == top: cbook._warn_external( f"Attempting to set identical bottom == top == {bottom} " f"results in singular transformations; automatically " f"expanding.") swapped = bottom > top bottom, top = self.zaxis.get_major_locator().nonsingular(bottom, top) bottom, top = self.zaxis.limit_range_for_scale(bottom, top) if swapped: bottom, top = top, bottom self.zz_viewLim.intervalx = (bottom, top) if auto is not None: self._autoscaleZon = bool(auto) if emit: self.callbacks.process('zlim_changed', self) # Call all of the other y-axes that are shared with this one for other in self._shared_z_axes.get_siblings(self): if other is not self: other.set_zlim(self.zz_viewLim.intervalx, emit=False, auto=auto) if other.figure != self.figure: other.figure.canvas.draw_idle() self.stale = True return bottom, top
set_zlim = set_zlim3d
[docs] def get_xlim3d(self): return tuple(self.xy_viewLim.intervalx)
get_xlim3d.__doc__ = maxes.Axes.get_xlim.__doc__ get_xlim = get_xlim3d if get_xlim.__doc__ is not None: get_xlim.__doc__ += """ .. versionchanged :: 1.1.0 This function now correctly refers to the 3D x-limits """
[docs] def get_ylim3d(self): return tuple(self.xy_viewLim.intervaly)
get_ylim3d.__doc__ = maxes.Axes.get_ylim.__doc__ get_ylim = get_ylim3d if get_ylim.__doc__ is not None: get_ylim.__doc__ += """ .. versionchanged :: 1.1.0 This function now correctly refers to the 3D y-limits. """
[docs] def get_zlim3d(self): '''Get 3D z limits.''' return tuple(self.zz_viewLim.intervalx)
get_zlim = get_zlim3d
[docs] def get_zscale(self): """ Return the zaxis scale string %s """ % (", ".join(mscale.get_scale_names())) return self.zaxis.get_scale()
# We need to slightly redefine these to pass scalez=False # to their calls of autoscale_view.
[docs] def set_xscale(self, value, **kwargs): self.xaxis._set_scale(value, **kwargs) self.autoscale_view(scaley=False, scalez=False) self._update_transScale()
if maxes.Axes.set_xscale.__doc__ is not None: set_xscale.__doc__ = maxes.Axes.set_xscale.__doc__ + """ .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """
[docs] def set_yscale(self, value, **kwargs): self.yaxis._set_scale(value, **kwargs) self.autoscale_view(scalex=False, scalez=False) self._update_transScale() self.stale = True
if maxes.Axes.set_yscale.__doc__ is not None: set_yscale.__doc__ = maxes.Axes.set_yscale.__doc__ + """ .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """
[docs] def set_zscale(self, value, **kwargs): """ Set the z-axis scale. Parameters ---------- value : {"linear", "log", "symlog", "logit", ...} The axis scale type to apply. **kwargs Different keyword arguments are accepted, depending on the scale. See the respective class keyword arguments: - `matplotlib.scale.LinearScale` - `matplotlib.scale.LogScale` - `matplotlib.scale.SymmetricalLogScale` - `matplotlib.scale.LogitScale` Notes ----- Currently, Axes3D objects only supports linear scales. Other scales may or may not work, and support for these is improving with each release. By default, Matplotlib supports the above mentioned scales. Additionally, custom scales may be registered using `matplotlib.scale.register_scale`. These scales may then also be used here as support is added. """ self.zaxis._set_scale(value, **kwargs) self.autoscale_view(scalex=False, scaley=False) self._update_transScale() self.stale = True
[docs] def set_zticks(self, *args, **kwargs): """ Set z-axis tick locations. See :meth:`matplotlib.axes.Axes.set_yticks` for more details. .. note:: Minor ticks are not supported. .. versionadded:: 1.1.0 """ return self.zaxis.set_ticks(*args, **kwargs)
[docs] def get_zticks(self, minor=False): """ Return the z ticks as a list of locations See :meth:`matplotlib.axes.Axes.get_yticks` for more details. .. note:: Minor ticks are not supported. .. versionadded:: 1.1.0 """ return self.zaxis.get_ticklocs(minor=minor)
[docs] def get_zmajorticklabels(self): """ Get the ztick labels as a list of Text instances .. versionadded :: 1.1.0 """ return cbook.silent_list('Text zticklabel', self.zaxis.get_majorticklabels())
[docs] def get_zminorticklabels(self): """ Get the ztick labels as a list of Text instances .. note:: Minor ticks are not supported. This function was added only for completeness. .. versionadded :: 1.1.0 """ return cbook.silent_list('Text zticklabel', self.zaxis.get_minorticklabels())
[docs] def set_zticklabels(self, *args, **kwargs): """ Set z-axis tick labels. See :meth:`matplotlib.axes.Axes.set_yticklabels` for more details. .. note:: Minor ticks are not supported by Axes3D objects. .. versionadded:: 1.1.0 """ return self.zaxis.set_ticklabels(*args, **kwargs)
[docs] def get_zticklabels(self, minor=False): """ Get ztick labels as a list of Text instances. See :meth:`matplotlib.axes.Axes.get_yticklabels` for more details. .. note:: Minor ticks are not supported. .. versionadded:: 1.1.0 """ return cbook.silent_list('Text zticklabel', self.zaxis.get_ticklabels(minor=minor))
[docs] def zaxis_date(self, tz=None): """ Sets up z-axis ticks and labels that treat the z data as dates. *tz* is a timezone string or :class:`tzinfo` instance. Defaults to rc value. .. note:: This function is merely provided for completeness. Axes3D objects do not officially support dates for ticks, and so this may or may not work as expected. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ self.zaxis.axis_date(tz)
[docs] def get_zticklines(self): """ Get ztick lines as a list of Line2D instances. Note that this function is provided merely for completeness. These lines are re-calculated as the display changes. .. versionadded:: 1.1.0 """ return self.zaxis.get_ticklines()
[docs] def clabel(self, *args, **kwargs): """ This function is currently not implemented for 3D axes. Returns *None*. """ return None
[docs] def view_init(self, elev=None, azim=None): """ Set the elevation and azimuth of the axes in degrees (not radians). This can be used to rotate the axes programmatically. 'elev' stores the elevation angle in the z plane (in degrees). 'azim' stores the azimuth angle in the x,y plane (in degrees). if elev or azim are None (default), then the initial value is used which was specified in the :class:`Axes3D` constructor. """ self.dist = 10 if elev is None: self.elev = self.initial_elev else: self.elev = elev if azim is None: self.azim = self.initial_azim else: self.azim = azim
[docs] def set_proj_type(self, proj_type): """ Set the projection type. Parameters ---------- proj_type : str Type of projection, accepts 'persp' and 'ortho'. """ if proj_type == 'persp': self._projection = proj3d.persp_transformation elif proj_type == 'ortho': self._projection = proj3d.ortho_transformation else: raise ValueError("unrecognized projection: %s" % proj_type)
[docs] def get_proj(self): """ Create the projection matrix from the current viewing position. elev stores the elevation angle in the z plane azim stores the azimuth angle in the x,y plane dist is the distance of the eye viewing point from the object point. """ relev, razim = np.pi * self.elev/180, np.pi * self.azim/180 xmin, xmax = self.get_xlim3d() ymin, ymax = self.get_ylim3d() zmin, zmax = self.get_zlim3d() # transform to uniform world coordinates 0-1.0,0-1.0,0-1.0 worldM = proj3d.world_transformation(xmin, xmax, ymin, ymax, zmin, zmax) # look into the middle of the new coordinates R = np.array([0.5, 0.5, 0.5]) xp = R[0] + np.cos(razim) * np.cos(relev) * self.dist yp = R[1] + np.sin(razim) * np.cos(relev) * self.dist zp = R[2] + np.sin(relev) * self.dist E = np.array((xp, yp, zp)) self.eye = E self.vvec = R - E self.vvec = self.vvec / np.linalg.norm(self.vvec) if abs(relev) > np.pi/2: # upside down V = np.array((0, 0, -1)) else: V = np.array((0, 0, 1)) zfront, zback = -self.dist, self.dist viewM = proj3d.view_transformation(E, R, V) projM = self._projection(zfront, zback) M0 = np.dot(viewM, worldM) M = np.dot(projM, M0) return M
[docs] def mouse_init(self, rotate_btn=1, zoom_btn=3): """ Initializes mouse button callbacks to enable 3D rotation of the axes. Also optionally sets the mouse buttons for 3D rotation and zooming. Parameters ---------- rotate_btn : int or list of int The mouse button or buttons to use for 3D rotation of the axes; defaults to 1. zoom_btn : int or list of int The mouse button or buttons to use to zoom the 3D axes; defaults to 3. """ self.button_pressed = None self._cids = [ self.figure.canvas.mpl_connect( 'motion_notify_event', self._on_move), self.figure.canvas.mpl_connect( 'button_press_event', self._button_press), self.figure.canvas.mpl_connect( 'button_release_event', self._button_release), ] # coerce scalars into array-like, then convert into # a regular list to avoid comparisons against None # which breaks in recent versions of numpy. self._rotate_btn = np.atleast_1d(rotate_btn).tolist() self._zoom_btn = np.atleast_1d(zoom_btn).tolist()
[docs] def can_zoom(self): """ Return *True* if this axes supports the zoom box button functionality. 3D axes objects do not use the zoom box button. """ return False
[docs] def can_pan(self): """ Return *True* if this axes supports the pan/zoom button functionality. 3D axes objects do not use the pan/zoom button. """ return False
[docs] def cla(self): """ Clear axes """ # Disabling mouse interaction might have been needed a long # time ago, but I can't find a reason for it now - BVR (2012-03) #self.disable_mouse_rotation() super().cla() self.zaxis.cla() if self._sharez is not None: self.zaxis.major = self._sharez.zaxis.major self.zaxis.minor = self._sharez.zaxis.minor z0, z1 = self._sharez.get_zlim() self.set_zlim(z0, z1, emit=False, auto=None) self.zaxis._set_scale(self._sharez.zaxis.get_scale()) else: self.zaxis._set_scale('linear') try: self.set_zlim(0, 1) except TypeError: pass self._autoscaleZon = True self._zmargin = 0 self.grid(rcParams['axes3d.grid'])
[docs] def disable_mouse_rotation(self): """Disable mouse button callbacks. """ # Disconnect the various events we set. for cid in self._cids: self.figure.canvas.mpl_disconnect(cid) self._cids = []
def _button_press(self, event): if event.inaxes == self: self.button_pressed = event.button self.sx, self.sy = event.xdata, event.ydata def _button_release(self, event): self.button_pressed = None
[docs] def format_zdata(self, z): """ Return *z* string formatted. This function will use the :attr:`fmt_zdata` attribute if it is callable, else will fall back on the zaxis major formatter """ try: return self.fmt_zdata(z) except (AttributeError, TypeError): func = self.zaxis.get_major_formatter().format_data_short val = func(z) return val
[docs] def format_coord(self, xd, yd): """ Given the 2D view coordinates attempt to guess a 3D coordinate. Looks for the nearest edge to the point and then assumes that the point is at the same z location as the nearest point on the edge. """ if self.M is None: return '' if self.button_pressed in self._rotate_btn: return 'azimuth={:.0f} deg, elevation={:.0f} deg '.format( self.azim, self.elev) # ignore xd and yd and display angles instead # nearest edge p0, p1 = min(self.tunit_edges(), key=lambda edge: proj3d._line2d_seg_dist( edge[0], edge[1], (xd, yd))) # scale the z value to match x0, y0, z0 = p0 x1, y1, z1 = p1 d0 = np.hypot(x0-xd, y0-yd) d1 = np.hypot(x1-xd, y1-yd) dt = d0+d1 z = d1/dt * z0 + d0/dt * z1 x, y, z = proj3d.inv_transform(xd, yd, z, self.M) xs = self.format_xdata(x) ys = self.format_ydata(y) zs = self.format_zdata(z) return 'x=%s, y=%s, z=%s' % (xs, ys, zs)
def _on_move(self, event): """Mouse moving button-1 rotates by default. Can be set explicitly in mouse_init(). button-3 zooms by default. Can be set explicitly in mouse_init(). """ if not self.button_pressed: return if self.M is None: return x, y = event.xdata, event.ydata # In case the mouse is out of bounds. if x is None: return dx, dy = x - self.sx, y - self.sy w = self._pseudo_w h = self._pseudo_h self.sx, self.sy = x, y # Rotation if self.button_pressed in self._rotate_btn: # rotate viewing point # get the x and y pixel coords if dx == 0 and dy == 0: return self.elev = art3d._norm_angle(self.elev - (dy/h)*180) self.azim = art3d._norm_angle(self.azim - (dx/w)*180) self.get_proj() self.stale = True self.figure.canvas.draw_idle() # elif self.button_pressed == 2: # pan view # project xv,yv,zv -> xw,yw,zw # pan # pass # Zoom elif self.button_pressed in self._zoom_btn: # zoom view # hmmm..this needs some help from clipping.... minx, maxx, miny, maxy, minz, maxz = self.get_w_lims() df = 1-((h - dy)/h) dx = (maxx-minx)*df dy = (maxy-miny)*df dz = (maxz-minz)*df self.set_xlim3d(minx - dx, maxx + dx) self.set_ylim3d(miny - dy, maxy + dy) self.set_zlim3d(minz - dz, maxz + dz) self.get_proj() self.figure.canvas.draw_idle()
[docs] def set_zlabel(self, zlabel, fontdict=None, labelpad=None, **kwargs): ''' Set zlabel. See doc for :meth:`set_ylabel` for description. ''' if labelpad is not None: self.zaxis.labelpad = labelpad return self.zaxis.set_label_text(zlabel, fontdict, **kwargs)
[docs] def get_zlabel(self): """ Get the z-label text string. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ label = self.zaxis.get_label() return label.get_text()
#### Axes rectangle characteristics
[docs] def get_frame_on(self): """ Get whether the 3D axes panels are drawn. .. versionadded :: 1.1.0 """ return self._frameon
[docs] def set_frame_on(self, b): """ Set whether the 3D axes panels are drawn. .. versionadded :: 1.1.0 Parameters ---------- b : bool """ self._frameon = bool(b) self.stale = True
[docs] def grid(self, b=True, **kwargs): ''' Set / unset 3D grid. .. note:: Currently, this function does not behave the same as :meth:`matplotlib.axes.Axes.grid`, but it is intended to eventually support that behavior. .. versionchanged :: 1.1.0 This function was changed, but not tested. Please report any bugs. ''' # TODO: Operate on each axes separately if len(kwargs): b = True self._draw_grid = b self.stale = True
[docs] def ticklabel_format( self, *, style='', scilimits=None, useOffset=None, axis='both'): """ Convenience method for manipulating the ScalarFormatter used by default for linear axes in Axed3D objects. See :meth:`matplotlib.axes.Axes.ticklabel_format` for full documentation. Note that this version applies to all three axes of the Axes3D object. Therefore, the *axis* argument will also accept a value of 'z' and the value of 'both' will apply to all three axes. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ style = style.lower() axis = axis.lower() if scilimits is not None: try: m, n = scilimits m+n+1 # check that both are numbers except (ValueError, TypeError): raise ValueError("scilimits must be a sequence of 2 integers") if style[:3] == 'sci': sb = True elif style == 'plain': sb = False elif style == '': sb = None else: raise ValueError("%s is not a valid style value") try: if sb is not None: if axis in ['both', 'z']: self.xaxis.major.formatter.set_scientific(sb) if axis in ['both', 'y']: self.yaxis.major.formatter.set_scientific(sb) if axis in ['both', 'z']: self.zaxis.major.formatter.set_scientific(sb) if scilimits is not None: if axis in ['both', 'x']: self.xaxis.major.formatter.set_powerlimits(scilimits) if axis in ['both', 'y']: self.yaxis.major.formatter.set_powerlimits(scilimits) if axis in ['both', 'z']: self.zaxis.major.formatter.set_powerlimits(scilimits) if useOffset is not None: if axis in ['both', 'x']: self.xaxis.major.formatter.set_useOffset(useOffset) if axis in ['both', 'y']: self.yaxis.major.formatter.set_useOffset(useOffset) if axis in ['both', 'z']: self.zaxis.major.formatter.set_useOffset(useOffset) except AttributeError: raise AttributeError( "This method only works with the ScalarFormatter.")
[docs] def locator_params(self, axis='both', tight=None, **kwargs): """ Convenience method for controlling tick locators. See :meth:`matplotlib.axes.Axes.locator_params` for full documentation. Note that this is for Axes3D objects, therefore, setting *axis* to 'both' will result in the parameters being set for all three axes. Also, *axis* can also take a value of 'z' to apply parameters to the z axis. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ _x = axis in ['x', 'both'] _y = axis in ['y', 'both'] _z = axis in ['z', 'both'] if _x: self.xaxis.get_major_locator().set_params(**kwargs) if _y: self.yaxis.get_major_locator().set_params(**kwargs) if _z: self.zaxis.get_major_locator().set_params(**kwargs) self.autoscale_view(tight=tight, scalex=_x, scaley=_y, scalez=_z)
[docs] def tick_params(self, axis='both', **kwargs): """ Convenience method for changing the appearance of ticks and tick labels. See :meth:`matplotlib.axes.Axes.tick_params` for more complete documentation. The only difference is that setting *axis* to 'both' will mean that the settings are applied to all three axes. Also, the *axis* parameter also accepts a value of 'z', which would mean to apply to only the z-axis. Also, because of how Axes3D objects are drawn very differently from regular 2D axes, some of these settings may have ambiguous meaning. For simplicity, the 'z' axis will accept settings as if it was like the 'y' axis. .. note:: While this function is currently implemented, the core part of the Axes3D object may ignore some of these settings. Future releases will fix this. Priority will be given to those who file bugs. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ cbook._check_in_list(['x', 'y', 'z', 'both'], axis=axis) if axis in ['x', 'y', 'both']: super().tick_params(axis, **kwargs) if axis in ['z', 'both']: zkw = dict(kwargs) zkw.pop('top', None) zkw.pop('bottom', None) zkw.pop('labeltop', None) zkw.pop('labelbottom', None) self.zaxis.set_tick_params(**zkw)
### data limits, ticks, tick labels, and formatting
[docs] def invert_zaxis(self): """ Invert the z-axis. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ bottom, top = self.get_zlim() self.set_zlim(top, bottom, auto=None)
[docs] def zaxis_inverted(self): ''' Returns True if the z-axis is inverted. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. ''' bottom, top = self.get_zlim() return top < bottom
[docs] def get_zbound(self): """ Returns the z-axis numerical bounds where:: lowerBound < upperBound .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ bottom, top = self.get_zlim() if bottom < top: return bottom, top else: return top, bottom
[docs] def set_zbound(self, lower=None, upper=None): """ Set the lower and upper numerical bounds of the z-axis. This method will honor axes inversion regardless of parameter order. It will not change the :attr:`_autoscaleZon` attribute. .. versionadded :: 1.1.0 This function was added, but not tested. Please report any bugs. """ if upper is None and np.iterable(lower): lower, upper = lower old_lower, old_upper = self.get_zbound() if lower is None: lower = old_lower if upper is None: upper = old_upper if self.zaxis_inverted(): if lower < upper: self.set_zlim(upper, lower, auto=None) else: self.set_zlim(lower, upper, auto=None) else: if lower < upper: self.set_zlim(lower, upper, auto=None) else: self.set_zlim(upper, lower, auto=None)
[docs] def text(self, x, y, z, s, zdir=None, **kwargs): ''' Add text to the plot. kwargs will be passed on to Axes.text, except for the `zdir` keyword, which sets the direction to be used as the z direction. ''' text = super().text(x, y, s, **kwargs) art3d.text_2d_to_3d(text, z, zdir) return text
text3D = text text2D = Axes.text
[docs] def plot(self, xs, ys, *args, zdir='z', **kwargs): """ Plot 2D or 3D data. Parameters ---------- xs : 1D array-like x coordinates of vertices. ys : 1D array-like y coordinates of vertices. zs : scalar or 1D array-like z coordinates of vertices; either one for all points or one for each point. zdir : {'x', 'y', 'z'} When plotting 2D data, the direction to use as z ('x', 'y' or 'z'); defaults to 'z'. **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.plot`. """ had_data = self.has_data() # `zs` can be passed positionally or as keyword; checking whether # args[0] is a string matches the behavior of 2D `plot` (via # `_process_plot_var_args`). if args and not isinstance(args[0], str): zs = args[0] args = args[1:] if 'zs' in kwargs: raise TypeError("plot() for multiple values for argument 'z'") else: zs = kwargs.pop('zs', 0) # Match length zs = np.broadcast_to(zs, len(xs)) lines = super().plot(xs, ys, *args, **kwargs) for line in lines: art3d.line_2d_to_3d(line, zs=zs, zdir=zdir) xs, ys, zs = art3d.juggle_axes(xs, ys, zs, zdir) self.auto_scale_xyz(xs, ys, zs, had_data) return lines
plot3D = plot
[docs] def plot_surface(self, X, Y, Z, *args, norm=None, vmin=None, vmax=None, lightsource=None, **kwargs): """ Create a surface plot. By default it will be colored in shades of a solid color, but it also supports color mapping by supplying the *cmap* argument. .. note:: The *rcount* and *ccount* kwargs, which both default to 50, determine the maximum number of samples used in each direction. If the input data is larger, it will be downsampled (by slicing) to these numbers of points. Parameters ---------- X, Y, Z : 2d arrays Data values. rcount, ccount : int Maximum number of samples used in each direction. If the input data is larger, it will be downsampled (by slicing) to these numbers of points. Defaults to 50. .. versionadded:: 2.0 rstride, cstride : int Downsampling stride in each direction. These arguments are mutually exclusive with *rcount* and *ccount*. If only one of *rstride* or *cstride* is set, the other defaults to 10. 'classic' mode uses a default of ``rstride = cstride = 10`` instead of the new default of ``rcount = ccount = 50``. color : color-like Color of the surface patches. cmap : Colormap Colormap of the surface patches. facecolors : array-like of colors. Colors of each individual patch. norm : Normalize Normalization for the colormap. vmin, vmax : float Bounds for the normalization. shade : bool Whether to shade the facecolors. Defaults to True. Shading is always disabled when `cmap` is specified. lightsource : `~matplotlib.colors.LightSource` The lightsource to use when `shade` is True. **kwargs Other arguments are forwarded to `.Poly3DCollection`. """ had_data = self.has_data() if Z.ndim != 2: raise ValueError("Argument Z must be 2-dimensional.") if np.any(np.isnan(Z)): cbook._warn_external( "Z contains NaN values. This may result in rendering " "artifacts.") # TODO: Support masked arrays X, Y, Z = np.broadcast_arrays(X, Y, Z) rows, cols = Z.shape has_stride = 'rstride' in kwargs or 'cstride' in kwargs has_count = 'rcount' in kwargs or 'ccount' in kwargs if has_stride and has_count: raise ValueError("Cannot specify both stride and count arguments") rstride = kwargs.pop('rstride', 10) cstride = kwargs.pop('cstride', 10) rcount = kwargs.pop('rcount', 50) ccount = kwargs.pop('ccount', 50) if rcParams['_internal.classic_mode']: # Strides have priority over counts in classic mode. # So, only compute strides from counts # if counts were explicitly given compute_strides = has_count else: # If the strides are provided then it has priority. # Otherwise, compute the strides from the counts. compute_strides = not has_stride if compute_strides: rstride = int(max(np.ceil(rows / rcount), 1)) cstride = int(max(np.ceil(cols / ccount), 1)) if 'facecolors' in kwargs: fcolors = kwargs.pop('facecolors') else: color = kwargs.pop('color', None) if color is None: color = self._get_lines.get_next_color() color = np.array(mcolors.to_rgba(color)) fcolors = None cmap = kwargs.get('cmap', None) shade = kwargs.pop('shade', cmap is None) if shade is None: cbook.warn_deprecated( "3.1", message="Passing shade=None to Axes3D.plot_surface() is " "deprecated since matplotlib 3.1 and will change its " "semantic or raise an error in matplotlib 3.3. " "Please use shade=False instead.") # evenly spaced, and including both endpoints row_inds = list(range(0, rows-1, rstride)) + [rows-1] col_inds = list(range(0, cols-1, cstride)) + [cols-1] colset = [] # the sampled facecolor polys = [] for rs, rs_next in zip(row_inds[:-1], row_inds[1:]): for cs, cs_next in zip(col_inds[:-1], col_inds[1:]): ps = [ # +1 ensures we share edges between polygons cbook._array_perimeter(a[rs:rs_next+1, cs:cs_next+1]) for a in (X, Y, Z) ] # ps = np.stack(ps, axis=-1) ps = np.array(ps).T polys.append(ps) if fcolors is not None: colset.append(fcolors[rs][cs]) # note that the striding causes some polygons to have more coordinates # than others polyc = art3d.Poly3DCollection(polys, *args, **kwargs) if fcolors is not None: if shade: colset = self._shade_colors( colset, self._generate_normals(polys), lightsource) polyc.set_facecolors(colset) polyc.set_edgecolors(colset) elif cmap: # doesn't vectorize because polys is jagged avg_z = np.array([ps[:, 2].mean() for ps in polys]) polyc.set_array(avg_z) if vmin is not None or vmax is not None: polyc.set_clim(vmin, vmax) if norm is not None: polyc.set_norm(norm) else: if shade: colset = self._shade_colors( color, self._generate_normals(polys), lightsource) else: colset = color polyc.set_facecolors(colset) self.add_collection(polyc) self.auto_scale_xyz(X, Y, Z, had_data) return polyc
def _generate_normals(self, polygons): """ Takes a list of polygons and return an array of their normals. Normals point towards the viewer for a face with its vertices in counterclockwise order, following the right hand rule. Uses three points equally spaced around the polygon. This normal of course might not make sense for polygons with more than three points not lying in a plane, but it's a plausible and fast approximation. Parameters ---------- polygons: list of (M_i, 3) array_like, or (..., M, 3) array_like A sequence of polygons to compute normals for, which can have varying numbers of vertices. If the polygons all have the same number of vertices and array is passed, then the operation will be vectorized. Returns ------- normals: (..., 3) array_like A normal vector estimated for the polygon. """ if isinstance(polygons, np.ndarray): # optimization: polygons all have the same number of points, so can # vectorize n = polygons.shape[-2] i1, i2, i3 = 0, n//3, 2*n//3 v1 = polygons[..., i1, :] - polygons[..., i2, :] v2 = polygons[..., i2, :] - polygons[..., i3, :] else: # The subtraction doesn't vectorize because polygons is jagged. v1 = np.empty((len(polygons), 3)) v2 = np.empty((len(polygons), 3)) for poly_i, ps in enumerate(polygons): n = len(ps) i1, i2, i3 = 0, n//3, 2*n//3 v1[poly_i, :] = ps[i1, :] - ps[i2, :] v2[poly_i, :] = ps[i2, :] - ps[i3, :] return np.cross(v1, v2) def _shade_colors(self, color, normals, lightsource=None): """ Shade *color* using normal vectors given by *normals*. *color* can also be an array of the same length as *normals*. """ if lightsource is None: # chosen for backwards-compatibility lightsource = LightSource(azdeg=225, altdeg=19.4712) with np.errstate(invalid="ignore"): shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True)) @ lightsource.direction) mask = ~np.isnan(shade) if mask.any(): # convert dot product to allowed shading fractions in_norm = Normalize(-1, 1) out_norm = Normalize(0.3, 1).inverse def norm(x): return out_norm(in_norm(x)) shade[~mask] = 0 color = mcolors.to_rgba_array(color) # shape of color should be (M, 4) (where M is number of faces) # shape of shade should be (M,) # colors should have final shape of (M, 4) alpha = color[:, 3] colors = norm(shade)[:, np.newaxis] * color colors[:, 3] = alpha else: colors = np.asanyarray(color).copy() return colors
[docs] def plot_wireframe(self, X, Y, Z, *args, **kwargs): """ Plot a 3D wireframe. .. note:: The *rcount* and *ccount* kwargs, which both default to 50, determine the maximum number of samples used in each direction. If the input data is larger, it will be downsampled (by slicing) to these numbers of points. Parameters ---------- X, Y, Z : 2d arrays Data values. rcount, ccount : int Maximum number of samples used in each direction. If the input data is larger, it will be downsampled (by slicing) to these numbers of points. Setting a count to zero causes the data to be not sampled in the corresponding direction, producing a 3D line plot rather than a wireframe plot. Defaults to 50. .. versionadded:: 2.0 rstride, cstride : int Downsampling stride in each direction. These arguments are mutually exclusive with *rcount* and *ccount*. If only one of *rstride* or *cstride* is set, the other defaults to 1. Setting a stride to zero causes the data to be not sampled in the corresponding direction, producing a 3D line plot rather than a wireframe plot. 'classic' mode uses a default of ``rstride = cstride = 1`` instead of the new default of ``rcount = ccount = 50``. **kwargs Other arguments are forwarded to `.Line3DCollection`. """ had_data = self.has_data() if Z.ndim != 2: raise ValueError("Argument Z must be 2-dimensional.") # FIXME: Support masked arrays X, Y, Z = np.broadcast_arrays(X, Y, Z) rows, cols = Z.shape has_stride = 'rstride' in kwargs or 'cstride' in kwargs has_count = 'rcount' in kwargs or 'ccount' in kwargs if has_stride and has_count: raise ValueError("Cannot specify both stride and count arguments") rstride = kwargs.pop('rstride', 1) cstride = kwargs.pop('cstride', 1) rcount = kwargs.pop('rcount', 50) ccount = kwargs.pop('ccount', 50) if rcParams['_internal.classic_mode']: # Strides have priority over counts in classic mode. # So, only compute strides from counts # if counts were explicitly given if has_count: rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 else: # If the strides are provided then it has priority. # Otherwise, compute the strides from the counts. if not has_stride: rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 # We want two sets of lines, one running along the "rows" of # Z and another set of lines running along the "columns" of Z. # This transpose will make it easy to obtain the columns. tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z) if rstride: rii = list(range(0, rows, rstride)) # Add the last index only if needed if rows > 0 and rii[-1] != (rows - 1): rii += [rows-1] else: rii = [] if cstride: cii = list(range(0, cols, cstride)) # Add the last index only if needed if cols > 0 and cii[-1] != (cols - 1): cii += [cols-1] else: cii = [] if rstride == 0 and cstride == 0: raise ValueError("Either rstride or cstride must be non zero") # If the inputs were empty, then just # reset everything. if Z.size == 0: rii = [] cii = [] xlines = [X[i] for i in rii] ylines = [Y[i] for i in rii] zlines = [Z[i] for i in rii] txlines = [tX[i] for i in cii] tylines = [tY[i] for i in cii] tzlines = [tZ[i] for i in cii] lines = ([list(zip(xl, yl, zl)) for xl, yl, zl in zip(xlines, ylines, zlines)] + [list(zip(xl, yl, zl)) for xl, yl, zl in zip(txlines, tylines, tzlines)]) linec = art3d.Line3DCollection(lines, *args, **kwargs) self.add_collection(linec) self.auto_scale_xyz(X, Y, Z, had_data) return linec
[docs] def plot_trisurf(self, *args, color=None, norm=None, vmin=None, vmax=None, lightsource=None, **kwargs): """ Plot a triangulated surface. The (optional) triangulation can be specified in one of two ways; either:: plot_trisurf(triangulation, ...) where triangulation is a :class:`~matplotlib.tri.Triangulation` object, or:: plot_trisurf(X, Y, ...) plot_trisurf(X, Y, triangles, ...) plot_trisurf(X, Y, triangles=triangles, ...) in which case a Triangulation object will be created. See :class:`~matplotlib.tri.Triangulation` for a explanation of these possibilities. The remaining arguments are:: plot_trisurf(..., Z) where *Z* is the array of values to contour, one per point in the triangulation. Parameters ---------- X, Y, Z : array-like Data values as 1D arrays. color Color of the surface patches. cmap A colormap for the surface patches. norm : Normalize An instance of Normalize to map values to colors. vmin, vmax : scalar, optional, default: None Minimum and maximum value to map. shade : bool Whether to shade the facecolors. Defaults to True. Shading is always disabled when *cmap* is specified. lightsource : `~matplotlib.colors.LightSource` The lightsource to use when *shade* is True. **kwargs All other arguments are passed on to :class:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection` Examples -------- .. plot:: gallery/mplot3d/trisurf3d.py .. plot:: gallery/mplot3d/trisurf3d_2.py .. versionadded:: 1.2.0 This plotting function was added for the v1.2.0 release. """ had_data = self.has_data() # TODO: Support custom face colours if color is None: color = self._get_lines.get_next_color() color = np.array(mcolors.to_rgba(color)) cmap = kwargs.get('cmap', None) shade = kwargs.pop('shade', cmap is None) tri, args, kwargs = \ Triangulation.get_from_args_and_kwargs(*args, **kwargs) if 'Z' in kwargs: z = np.asarray(kwargs.pop('Z')) else: z = np.asarray(args[0]) # We do this so Z doesn't get passed as an arg to PolyCollection args = args[1:] triangles = tri.get_masked_triangles() xt = tri.x[triangles] yt = tri.y[triangles] zt = z[triangles] verts = np.stack((xt, yt, zt), axis=-1) polyc = art3d.Poly3DCollection(verts, *args, **kwargs) if cmap: # average over the three points of each triangle avg_z = verts[:, :, 2].mean(axis=1) polyc.set_array(avg_z) if vmin is not None or vmax is not None: polyc.set_clim(vmin, vmax) if norm is not None: polyc.set_norm(norm) else: if shade: normals = self._generate_normals(verts) colset = self._shade_colors(color, normals, lightsource) else: colset = color polyc.set_facecolors(colset) self.add_collection(polyc) self.auto_scale_xyz(tri.x, tri.y, z, had_data) return polyc
def _3d_extend_contour(self, cset, stride=5): ''' Extend a contour in 3D by creating ''' levels = cset.levels colls = cset.collections dz = (levels[1] - levels[0]) / 2 for z, linec in zip(levels, colls): paths = linec.get_paths() if not paths: continue topverts = art3d._paths_to_3d_segments(paths, z - dz) botverts = art3d._paths_to_3d_segments(paths, z + dz) color = linec.get_color()[0] polyverts = [] normals = [] nsteps = np.round(len(topverts[0]) / stride) if nsteps <= 1: if len(topverts[0]) > 1: nsteps = 2 else: continue stepsize = (len(topverts[0]) - 1) / (nsteps - 1) for i in range(int(np.round(nsteps)) - 1): i1 = int(np.round(i * stepsize)) i2 = int(np.round((i + 1) * stepsize)) polyverts.append([topverts[0][i1], topverts[0][i2], botverts[0][i2], botverts[0][i1]]) # all polygons have 4 vertices, so vectorize polyverts = np.array(polyverts) normals = self._generate_normals(polyverts) colors = self._shade_colors(color, normals) colors2 = self._shade_colors(color, normals) polycol = art3d.Poly3DCollection(polyverts, facecolors=colors, edgecolors=colors2) polycol.set_sort_zpos(z) self.add_collection3d(polycol) for col in colls: self.collections.remove(col)
[docs] def add_contour_set( self, cset, extend3d=False, stride=5, zdir='z', offset=None): zdir = '-' + zdir if extend3d: self._3d_extend_contour(cset, stride) else: for z, linec in zip(cset.levels, cset.collections): if offset is not None: z = offset art3d.line_collection_2d_to_3d(linec, z, zdir=zdir)
[docs] def add_contourf_set(self, cset, zdir='z', offset=None): zdir = '-' + zdir for z, linec in zip(cset.levels, cset.collections): if offset is not None: z = offset art3d.poly_collection_2d_to_3d(linec, z, zdir=zdir) linec.set_sort_zpos(z)
[docs] def contour(self, X, Y, Z, *args, extend3d=False, stride=5, zdir='z', offset=None, **kwargs): """ Create a 3D contour plot. Parameters ---------- X, Y, Z : array-likes Input data. extend3d : bool Whether to extend contour in 3D; defaults to False. stride : int Step size for extending contour. zdir : {'x', 'y', 'z'} The direction to use; defaults to 'z'. offset : scalar If specified, plot a projection of the contour lines at this position in a plane normal to zdir *args, **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.contour`. Returns ------- matplotlib.contour.QuadContourSet """ had_data = self.has_data() jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) cset = super().contour(jX, jY, jZ, *args, **kwargs) self.add_contour_set(cset, extend3d, stride, zdir, offset) self.auto_scale_xyz(X, Y, Z, had_data) return cset
contour3D = contour
[docs] def tricontour(self, *args, extend3d=False, stride=5, zdir='z', offset=None, **kwargs): """ Create a 3D contour plot. .. versionchanged:: 1.3.0 Added support for custom triangulations .. note:: This method currently produces incorrect output due to a longstanding bug in 3D PolyCollection rendering. Parameters ---------- X, Y, Z : array-likes Input data. extend3d : bool Whether to extend contour in 3D; defaults to False. stride : int Step size for extending contour. zdir : {'x', 'y', 'z'} The direction to use; defaults to 'z'. offset : scalar If specified, plot a projection of the contour lines at this position in a plane normal to zdir *args, **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.tricontour`. Returns ------- matplotlib.tri.tricontour.TriContourSet """ had_data = self.has_data() tri, args, kwargs = Triangulation.get_from_args_and_kwargs( *args, **kwargs) X = tri.x Y = tri.y if 'Z' in kwargs: Z = kwargs.pop('Z') else: Z = args[0] # We do this so Z doesn't get passed as an arg to Axes.tricontour args = args[1:] jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) tri = Triangulation(jX, jY, tri.triangles, tri.mask) cset = super().tricontour(tri, jZ, *args, **kwargs) self.add_contour_set(cset, extend3d, stride, zdir, offset) self.auto_scale_xyz(X, Y, Z, had_data) return cset
[docs] def contourf(self, X, Y, Z, *args, zdir='z', offset=None, **kwargs): """ Create a 3D filled contour plot. Parameters ---------- X, Y, Z : array-likes Input data. zdir : {'x', 'y', 'z'} The direction to use; defaults to 'z'. offset : scalar If specified, plot a projection of the contour lines at this position in a plane normal to zdir *args, **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.contourf`. Returns ------- matplotlib.contour.QuadContourSet Notes ----- .. versionadded:: 1.1.0 The *zdir* and *offset* parameters. """ had_data = self.has_data() jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) cset = super().contourf(jX, jY, jZ, *args, **kwargs) self.add_contourf_set(cset, zdir, offset) self.auto_scale_xyz(X, Y, Z, had_data) return cset
contourf3D = contourf
[docs] def tricontourf(self, *args, zdir='z', offset=None, **kwargs): """ Create a 3D filled contour plot. .. note:: This method currently produces incorrect output due to a longstanding bug in 3D PolyCollection rendering. Parameters ---------- X, Y, Z : array-likes Input data. zdir : {'x', 'y', 'z'} The direction to use; defaults to 'z'. offset : scalar If specified, plot a projection of the contour lines at this position in a plane normal to zdir *args, **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.tricontourf`. Returns ------- matplotlib.tri.tricontour.TriContourSet Notes ----- .. versionadded:: 1.1.0 The *zdir* and *offset* parameters. .. versionchanged:: 1.3.0 Added support for custom triangulations """ had_data = self.has_data() tri, args, kwargs = Triangulation.get_from_args_and_kwargs( *args, **kwargs) X = tri.x Y = tri.y if 'Z' in kwargs: Z = kwargs.pop('Z') else: Z = args[0] # We do this so Z doesn't get passed as an arg to Axes.tricontourf args = args[1:] jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) tri = Triangulation(jX, jY, tri.triangles, tri.mask) cset = super().tricontourf(tri, jZ, *args, **kwargs) self.add_contourf_set(cset, zdir, offset) self.auto_scale_xyz(X, Y, Z, had_data) return cset
[docs] def add_collection3d(self, col, zs=0, zdir='z'): ''' Add a 3D collection object to the plot. 2D collection types are converted to a 3D version by modifying the object and adding z coordinate information. Supported are: - PolyCollection - LineCollection - PatchCollection ''' zvals = np.atleast_1d(zs) zsortval = (np.min(zvals) if zvals.size else 0) # FIXME: arbitrary default # FIXME: use issubclass() (although, then a 3D collection # object would also pass.) Maybe have a collection3d # abstract class to test for and exclude? if type(col) is mcoll.PolyCollection: art3d.poly_collection_2d_to_3d(col, zs=zs, zdir=zdir) col.set_sort_zpos(zsortval) elif type(col) is mcoll.LineCollection: art3d.line_collection_2d_to_3d(col, zs=zs, zdir=zdir) col.set_sort_zpos(zsortval) elif type(col) is mcoll.PatchCollection: art3d.patch_collection_2d_to_3d(col, zs=zs, zdir=zdir) col.set_sort_zpos(zsortval) super().add_collection(col)
[docs] def scatter(self, xs, ys, zs=0, zdir='z', s=20, c=None, depthshade=True, *args, **kwargs): """ Create a scatter plot. Parameters ---------- xs, ys : array-like The data positions. zs : float or array-like, optional, default: 0 The z-positions. Either an array of the same length as *xs* and *ys* or a single value to place all points in the same plane. zdir : {'x', 'y', 'z', '-x', '-y', '-z'}, optional, default: 'z' The axis direction for the *zs*. This is useful when plotting 2D data on a 3D Axes. The data must be passed as *xs*, *ys*. Setting *zdir* to 'y' then plots the data to the x-z-plane. See also :doc:`/gallery/mplot3d/2dcollections3d`. s : scalar or array-like, optional, default: 20 The marker size in points**2. Either an array of the same length as *xs* and *ys* or a single value to make all markers the same size. 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 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. For more details see the *c* argument of `~.axes.Axes.scatter`. depthshade : bool, optional, default: True Whether to shade the scatter markers to give the appearance of depth. **kwargs All other arguments are passed on to `~.axes.Axes.scatter`. Returns ------- paths : `~matplotlib.collections.PathCollection` """ had_data = self.has_data() xs, ys, zs = np.broadcast_arrays( *[np.ravel(np.ma.filled(t, np.nan)) for t in [xs, ys, zs]]) s = np.ma.ravel(s) # This doesn't have to match x, y in size. xs, ys, zs, s, c = cbook.delete_masked_points(xs, ys, zs, s, c) patches = super().scatter(xs, ys, s=s, c=c, *args, **kwargs) art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir, depthshade=depthshade) if self._zmargin < 0.05 and xs.size > 0: self.set_zmargin(0.05) self.auto_scale_xyz(xs, ys, zs, had_data) return patches
scatter3D = scatter
[docs] def bar(self, left, height, zs=0, zdir='z', *args, **kwargs): """ Add 2D bar(s). Parameters ---------- left : 1D array-like The x coordinates of the left sides of the bars. height : 1D array-like The height of the bars. zs : scalar or 1D array-like Z coordinate of bars; if a single value is specified, it will be used for all bars. zdir : {'x', 'y', 'z'} When plotting 2D data, the direction to use as z ('x', 'y' or 'z'); defaults to 'z'. **kwargs Other arguments are forwarded to `matplotlib.axes.Axes.bar`. Returns ------- mpl_toolkits.mplot3d.art3d.Patch3DCollection """ had_data = self.has_data() patches = super().bar(left, height, *args, **kwargs) zs = np.broadcast_to(zs, len(left)) verts = [] verts_zs = [] for p, z in zip(patches, zs): vs = art3d._get_patch_verts(p) verts += vs.tolist() verts_zs += [z] * len(vs) art3d.patch_2d_to_3d(p, z, zdir) if 'alpha' in kwargs: p.set_alpha(kwargs['alpha']) if len(verts) > 0: # the following has to be skipped if verts is empty # NOTE: Bugs could still occur if len(verts) > 0, # but the "2nd dimension" is empty. xs, ys = zip(*verts) else: xs, ys = [], [] xs, ys, verts_zs = art3d.juggle_axes(xs, ys, verts_zs, zdir) self.auto_scale_xyz(xs, ys, verts_zs, had_data) return patches
[docs] def bar3d(self, x, y, z, dx, dy, dz, color=None, zsort='average', shade=True, *args, **kwargs): """Generate a 3D barplot. This method creates three dimensional barplot where the width, depth, height, and color of the bars can all be uniquely set. Parameters ---------- x, y, z : array-like The coordinates of the anchor point of the bars. dx, dy, dz : scalar or array-like The width, depth, and height of the bars, respectively. color : sequence of valid color specifications, optional The color of the bars can be specified globally or individually. This parameter can be: - A single color value, to color all bars the same color. - An array of colors of length N bars, to color each bar independently. - An array of colors of length 6, to color the faces of the bars similarly. - An array of colors of length 6 * N bars, to color each face independently. When coloring the faces of the boxes specifically, this is the order of the coloring: 1. -Z (bottom of box) 2. +Z (top of box) 3. -Y 4. +Y 5. -X 6. +X zsort : str, optional The z-axis sorting scheme passed onto `~.art3d.Poly3DCollection` shade : bool, optional (default = True) When true, this shades the dark sides of the bars (relative to the plot's source of light). **kwargs Any additional keyword arguments are passed onto `~.art3d.Poly3DCollection`. Returns ------- collection : `~.art3d.Poly3DCollection` A collection of three dimensional polygons representing the bars. """ had_data = self.has_data() x, y, z, dx, dy, dz = np.broadcast_arrays( np.atleast_1d(x), y, z, dx, dy, dz) minx = np.min(x) maxx = np.max(x + dx) miny = np.min(y) maxy = np.max(y + dy) minz = np.min(z) maxz = np.max(z + dz) # shape (6, 4, 3) # All faces are oriented facing outwards - when viewed from the # outside, their vertices are in a counterclockwise ordering. cuboid = np.array([ # -z ( (0, 0, 0), (0, 1, 0), (1, 1, 0), (1, 0, 0), ), # +z ( (0, 0, 1), (1, 0, 1), (1, 1, 1), (0, 1, 1), ), # -y ( (0, 0, 0), (1, 0, 0), (1, 0, 1), (0, 0, 1), ), # +y ( (0, 1, 0), (0, 1, 1), (1, 1, 1), (1, 1, 0), ), # -x ( (0, 0, 0), (0, 0, 1), (0, 1, 1), (0, 1, 0), ), # +x ( (1, 0, 0), (1, 1, 0), (1, 1, 1), (1, 0, 1), ), ]) # indexed by [bar, face, vertex, coord] polys = np.empty(x.shape + cuboid.shape) # handle each coordinate separately for i, p, dp in [(0, x, dx), (1, y, dy), (2, z, dz)]: p = p[..., np.newaxis, np.newaxis] dp = dp[..., np.newaxis, np.newaxis] polys[..., i] = p + dp * cuboid[..., i] # collapse the first two axes polys = polys.reshape((-1,) + polys.shape[2:]) facecolors = [] if color is None: color = [self._get_patches_for_fill.get_next_color()] if len(color) == len(x): # bar colors specified, need to expand to number of faces for c in color: facecolors.extend([c] * 6) else: # a single color specified, or face colors specified explicitly facecolors = list(mcolors.to_rgba_array(color)) if len(facecolors) < len(x): facecolors *= (6 * len(x)) if shade: normals = self._generate_normals(polys) sfacecolors = self._shade_colors(facecolors, normals) else: sfacecolors = facecolors col = art3d.Poly3DCollection(polys, zsort=zsort, facecolor=sfacecolors, *args, **kwargs) self.add_collection(col) self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data) return col
[docs] def set_title(self, label, fontdict=None, loc='center', **kwargs): # docstring inherited ret = super().set_title(label, fontdict=fontdict, loc=loc, **kwargs) (x, y) = self.title.get_position() self.title.set_y(0.92 * y) return ret
[docs] def quiver(self, *args, length=1, arrow_length_ratio=.3, pivot='tail', normalize=False, **kwargs): """ ax.quiver(X, Y, Z, U, V, W, /, length=1, arrow_length_ratio=.3, \ pivot='tail', normalize=False, **kwargs) Plot a 3D field of arrows. The arguments could be array-like or scalars, so long as they they can be broadcast together. The arguments can also be masked arrays. If an element in any of argument is masked, then that corresponding quiver element will not be plotted. Parameters ---------- X, Y, Z : array-like The x, y and z coordinates of the arrow locations (default is tail of arrow; see *pivot* kwarg) U, V, W : array-like The x, y and z components of the arrow vectors length : float The length of each quiver, default to 1.0, the unit is the same with the axes arrow_length_ratio : float The ratio of the arrow head with respect to the quiver, default to 0.3 pivot : {'tail', 'middle', 'tip'} The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name *pivot*. Default is 'tail' normalize : bool When True, all of the arrows will be the same length. This defaults to False, where the arrows will be different lengths depending on the values of u,v,w. **kwargs Any additional keyword arguments are delegated to :class:`~matplotlib.collections.LineCollection` """ def calc_arrow(uvw, angle=15): """ To calculate the arrow head. uvw should be a unit vector. We normalize it here: """ # get unit direction vector perpendicular to (u,v,w) norm = np.linalg.norm(uvw[:2]) if norm > 0: x = uvw[1] / norm y = -uvw[0] / norm else: x, y = 0, 1 # compute the two arrowhead direction unit vectors ra = math.radians(angle) c = math.cos(ra) s = math.sin(ra) # construct the rotation matrices Rpos = np.array([[c+(x**2)*(1-c), x*y*(1-c), y*s], [y*x*(1-c), c+(y**2)*(1-c), -x*s], [-y*s, x*s, c]]) # opposite rotation negates all the sin terms Rneg = Rpos.copy() Rneg[[0, 1, 2, 2], [2, 2, 0, 1]] = \ -Rneg[[0, 1, 2, 2], [2, 2, 0, 1]] # multiply them to get the rotated vector return Rpos.dot(uvw), Rneg.dot(uvw) had_data = self.has_data() # handle args argi = 6 if len(args) < argi: raise ValueError('Wrong number of arguments. Expected %d got %d' % (argi, len(args))) # first 6 arguments are X, Y, Z, U, V, W input_args = args[:argi] # extract the masks, if any masks = [k.mask for k in input_args if isinstance(k, np.ma.MaskedArray)] # broadcast to match the shape bcast = np.broadcast_arrays(*input_args, *masks) input_args = bcast[:argi] masks = bcast[argi:] if masks: # combine the masks into one mask = reduce(np.logical_or, masks) # put mask on and compress input_args = [np.ma.array(k, mask=mask).compressed() for k in input_args] else: input_args = [np.ravel(k) for k in input_args] if any(len(v) == 0 for v in input_args): # No quivers, so just make an empty collection and return early linec = art3d.Line3DCollection([], *args[argi:], **kwargs) self.add_collection(linec) return linec shaft_dt = np.array([0, length]) arrow_dt = shaft_dt * arrow_length_ratio if pivot == 'tail': shaft_dt -= length elif pivot == 'middle': shaft_dt -= length/2. elif pivot != 'tip': raise ValueError('Invalid pivot argument: ' + str(pivot)) XYZ = np.column_stack(input_args[:3]) UVW = np.column_stack(input_args[3:argi]).astype(float) # Normalize rows of UVW norm = np.linalg.norm(UVW, axis=1) # If any row of UVW is all zeros, don't make a quiver for it mask = norm > 0 XYZ = XYZ[mask] if normalize: UVW = UVW[mask] / norm[mask].reshape((-1, 1)) else: UVW = UVW[mask] if len(XYZ) > 0: # compute the shaft lines all at once with an outer product shafts = (XYZ - np.multiply.outer(shaft_dt, UVW)).swapaxes(0, 1) # compute head direction vectors, n heads x 2 sides x 3 dimensions head_dirs = np.array([calc_arrow(d) for d in UVW]) # compute all head lines at once, starting from the shaft ends heads = shafts[:, :1] - np.multiply.outer(arrow_dt, head_dirs) # stack left and right head lines together heads.shape = (len(arrow_dt), -1, 3) # transpose to get a list of lines heads = heads.swapaxes(0, 1) lines = [*shafts, *heads] else: lines = [] linec = art3d.Line3DCollection(lines, *args[argi:], **kwargs) self.add_collection(linec) self.auto_scale_xyz(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], had_data) return linec
quiver3D = quiver
[docs] def voxels(self, *args, facecolors=None, edgecolors=None, shade=True, lightsource=None, **kwargs): """ ax.voxels([x, y, z,] /, filled, facecolors=None, edgecolors=None, \ **kwargs) Plot a set of filled voxels All voxels are plotted as 1x1x1 cubes on the axis, with filled[0,0,0] placed with its lower corner at the origin. Occluded faces are not plotted. .. versionadded:: 2.1 Parameters ---------- filled : 3D np.array of bool A 3d array of values, with truthy values indicating which voxels to fill x, y, z : 3D np.array, optional The coordinates of the corners of the voxels. This should broadcast to a shape one larger in every dimension than the shape of `filled`. These can be used to plot non-cubic voxels. If not specified, defaults to increasing integers along each axis, like those returned by :func:`~numpy.indices`. As indicated by the ``/`` in the function signature, these arguments can only be passed positionally. facecolors, edgecolors : array_like, optional The color to draw the faces and edges of the voxels. Can only be passed as keyword arguments. This parameter can be: - A single color value, to color all voxels the same color. This can be either a string, or a 1D rgb/rgba array - ``None``, the default, to use a single color for the faces, and the style default for the edges. - A 3D ndarray of color names, with each item the color for the corresponding voxel. The size must match the voxels. - A 4D ndarray of rgb/rgba data, with the components along the last axis. shade : bool Whether to shade the facecolors. Defaults to True. Shading is always disabled when *cmap* is specified. .. versionadded:: 3.1 lightsource : `~matplotlib.colors.LightSource` The lightsource to use when *shade* is True. .. versionadded:: 3.1 **kwargs Additional keyword arguments to pass onto :func:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection` Returns ------- faces : dict A dictionary indexed by coordinate, where ``faces[i,j,k]`` is a `Poly3DCollection` of the faces drawn for the voxel ``filled[i,j,k]``. If no faces were drawn for a given voxel, either because it was not asked to be drawn, or it is fully occluded, then ``(i,j,k) not in faces``. Examples -------- .. plot:: gallery/mplot3d/voxels.py .. plot:: gallery/mplot3d/voxels_rgb.py .. plot:: gallery/mplot3d/voxels_torus.py .. plot:: gallery/mplot3d/voxels_numpy_logo.py """ # work out which signature we should be using, and use it to parse # the arguments. Name must be voxels for the correct error message if len(args) >= 3: # underscores indicate position only def voxels(__x, __y, __z, filled, **kwargs): return (__x, __y, __z), filled, kwargs else: def voxels(filled, **kwargs): return None, filled, kwargs xyz, filled, kwargs = voxels(*args, **kwargs) # check dimensions if filled.ndim != 3: raise ValueError("Argument filled must be 3-dimensional") size = np.array(filled.shape, dtype=np.intp) # check xyz coordinates, which are one larger than the filled shape coord_shape = tuple(size + 1) if xyz is None: x, y, z = np.indices(coord_shape) else: x, y, z = (np.broadcast_to(c, coord_shape) for c in xyz) def _broadcast_color_arg(color, name): if np.ndim(color) in (0, 1): # single color, like "red" or [1, 0, 0] return np.broadcast_to(color, filled.shape + np.shape(color)) elif np.ndim(color) in (3, 4): # 3D array of strings, or 4D array with last axis rgb if np.shape(color)[:3] != filled.shape: raise ValueError( "When multidimensional, {} must match the shape of " "filled".format(name)) return color else: raise ValueError("Invalid {} argument".format(name)) # broadcast and default on facecolors if facecolors is None: facecolors = self._get_patches_for_fill.get_next_color() facecolors = _broadcast_color_arg(facecolors, 'facecolors') # broadcast but no default on edgecolors edgecolors = _broadcast_color_arg(edgecolors, 'edgecolors') # scale to the full array, even if the data is only in the center self.auto_scale_xyz(x, y, z) # points lying on corners of a square square = np.array([ [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0], ], dtype=np.intp) voxel_faces = defaultdict(list) def permutation_matrices(n): """Generator of cyclic permutation matrices.""" mat = np.eye(n, dtype=np.intp) for i in range(n): yield mat mat = np.roll(mat, 1, axis=0) # iterate over each of the YZ, ZX, and XY orientations, finding faces # to render for permute in permutation_matrices(3): # find the set of ranges to iterate over pc, qc, rc = permute.T.dot(size) pinds = np.arange(pc) qinds = np.arange(qc) rinds = np.arange(rc) square_rot_pos = square.dot(permute.T) square_rot_neg = square_rot_pos[::-1] # iterate within the current plane for p in pinds: for q in qinds: # iterate perpendicularly to the current plane, handling # boundaries. We only draw faces between a voxel and an # empty space, to avoid drawing internal faces. # draw lower faces p0 = permute.dot([p, q, 0]) i0 = tuple(p0) if filled[i0]: voxel_faces[i0].append(p0 + square_rot_neg) # draw middle faces for r1, r2 in zip(rinds[:-1], rinds[1:]): p1 = permute.dot([p, q, r1]) p2 = permute.dot([p, q, r2]) i1 = tuple(p1) i2 = tuple(p2) if filled[i1] and not filled[i2]: voxel_faces[i1].append(p2 + square_rot_pos) elif not filled[i1] and filled[i2]: voxel_faces[i2].append(p2 + square_rot_neg) # draw upper faces pk = permute.dot([p, q, rc-1]) pk2 = permute.dot([p, q, rc]) ik = tuple(pk) if filled[ik]: voxel_faces[ik].append(pk2 + square_rot_pos) # iterate over the faces, and generate a Poly3DCollection for each # voxel polygons = {} for coord, faces_inds in voxel_faces.items(): # convert indices into 3D positions if xyz is None: faces = faces_inds else: faces = [] for face_inds in faces_inds: ind = face_inds[:, 0], face_inds[:, 1], face_inds[:, 2] face = np.empty(face_inds.shape) face[:, 0] = x[ind] face[:, 1] = y[ind] face[:, 2] = z[ind] faces.append(face) # shade the faces facecolor = facecolors[coord] edgecolor = edgecolors[coord] if shade: normals = self._generate_normals(faces) facecolor = self._shade_colors(facecolor, normals, lightsource) if edgecolor is not None: edgecolor = self._shade_colors( edgecolor, normals, lightsource ) poly = art3d.Poly3DCollection(faces, facecolors=facecolor, edgecolors=edgecolor, **kwargs ) self.add_collection3d(poly) polygons[coord] = poly return polygons
def get_test_data(delta=0.05): ''' Return a tuple X, Y, Z with a test data set. ''' x = y = np.arange(-3.0, 3.0, delta) X, Y = np.meshgrid(x, y) Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi) Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) / (2 * np.pi * 0.5 * 1.5)) Z = Z2 - Z1 X = X * 10 Y = Y * 10 Z = Z * 500 return X, Y, Z ######################################################## # Register Axes3D as a 'projection' object available # for use just like any other axes ######################################################## proj.projection_registry.register(Axes3D)