#!/usr/bin/python
# axes3d.py, original mplot3d version by John Porter
# Created: 23 Sep 2005
# Parts fixed by Reinier Heeres <reinier@heeres.eu>
"""
Module containing Axes3D, an object which can plot 3D objects on a
2D matplotlib figure.
"""
from matplotlib.axes import Axes,rcParams
from matplotlib import cbook
from matplotlib.transforms import Bbox
from matplotlib import collections
import numpy as np
from matplotlib.colors import Normalize,colorConverter
import art3d
import proj3d
import axis3d
def sensible_format_data(self, value):
"""Used to generate more comprehensible numbers in status bar"""
if abs(value) > 1e4 or abs(value)<1e-3:
s = '%1.4e' % value
return self._formatSciNotation(s)
else:
return '%4.3f' % value
def unit_bbox():
box = Bbox(np.array([[0, 0], [1, 1]]))
return box
class Axes3D(Axes):
"""
3D axes object.
"""
def __init__(self, fig, rect=None, *args, **kwargs):
if rect is None:
rect = [0.0, 0.0, 1.0, 1.0]
self.fig = fig
self.cids = []
azim = kwargs.pop('azim', -60)
elev = kwargs.pop('elev', 30)
self.xy_viewLim = unit_bbox()
self.zz_viewLim = unit_bbox()
self.xy_dataLim = unit_bbox()
self.zz_dataLim = unit_bbox()
# inihibit autoscale_view until the axises are defined
# they can't be defined until Axes.__init__ has been called
self.view_init(elev, azim)
self._ready = 0
Axes.__init__(self, self.fig, rect,
frameon=True,
xticks=[], yticks=[], *args, **kwargs)
self.M = None
self._ready = 1
self.mouse_init()
self.create_axes()
self.set_top_view()
self.axesPatch.set_linewidth(0)
self.fig.add_axes(self)
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)
Axes.set_xlim(self, -xdwl, xdw)
Axes.set_ylim(self, -ydwl, ydw)
def create_axes(self):
self.w_xaxis = axis3d.XAxis('x', self.xy_viewLim.intervalx,
self.xy_dataLim.intervalx, self)
self.w_yaxis = axis3d.YAxis('y', self.xy_viewLim.intervaly,
self.xy_dataLim.intervaly, self)
self.w_zaxis = axis3d.ZAxis('z', self.zz_viewLim.intervalx,
self.zz_dataLim.intervalx, self)
def unit_cube(self, vals=None):
minx, maxx, miny, maxy, minz, maxz = vals or self.get_w_lims()
xs, ys, zs = ([minx, maxx, maxx, minx, minx, maxx, maxx, minx],
[miny, miny, maxy, maxy, miny, miny, maxy, maxy],
[minz, minz, minz, minz, maxz, maxz, maxz, maxz])
return zip(xs, ys, zs)
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
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
def draw(self, renderer):
# draw the background patch
self.axesPatch.draw(renderer)
self._frameon = False
# 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 zorder them
zlist = [(col.do_3d_projection(renderer), col) \
for col in self.collections]
zlist.sort()
zlist.reverse()
for i, (z, col) in enumerate(zlist):
col.zorder = i
# Calculate projection of patches and zorder them
zlist = [(patch.do_3d_projection(renderer), patch) \
for patch in self.patches]
zlist.sort()
zlist.reverse()
for i, (z, patch) in enumerate(zlist):
patch.zorder = i
self.w_xaxis.draw(renderer)
self.w_yaxis.draw(renderer)
self.w_zaxis.draw(renderer)
Axes.draw(self, renderer)
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 update_datalim(self, xys, **kwargs):
pass
def auto_scale_xyz(self, X, Y, Z=None, had_data=None):
x, y, z = map(np.asarray, (X, Y, Z))
try:
x, y = x.flatten(), y.flatten()
if Z is not None:
z = z.flatten()
except AttributeError:
raise
# This updates the bounding boxes as to keep a record as
# to what the minimum sized rectangular volume holds the
# data.
self.xy_dataLim.update_from_data_xy(np.array([x, y]).T, not had_data)
if z is not None:
self.zz_dataLim.update_from_data_xy(np.array([z, z]).T, not had_data)
# Let autoscale_view figure out how to use this data.
self.autoscale_view()
def autoscale_view(self, scalex=True, scaley=True, scalez=True):
# This method looks at the rectanglular volume (see above)
# of data and decides how to scale the view portal to fit it.
self.set_top_view()
if not self._ready:
return
if not self.get_autoscale_on():
return
if scalex:
self.set_xlim3d(self.xy_dataLim.intervalx)
if scaley:
self.set_ylim3d(self.xy_dataLim.intervaly)
if scalez:
self.set_zlim3d(self.zz_dataLim.intervalx)
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 cbook.iterable(xmin):
xmin, xmax = xmin
if xmin == xmax:
xmin -= 0.5
xmax += 0.5
return (xmin, xmax)
def set_xlim3d(self, *args, **kwargs):
'''Set 3D x limits.'''
lims = self._determine_lims(*args, **kwargs)
self.xy_viewLim.intervalx = lims
return lims
def set_ylim3d(self, *args, **kwargs):
'''Set 3D y limits.'''
lims = self._determine_lims(*args, **kwargs)
self.xy_viewLim.intervaly = lims
return lims
def set_zlim3d(self, *args, **kwargs):
'''Set 3D z limits.'''
lims = self._determine_lims(*args, **kwargs)
self.zz_viewLim.intervalx = lims
return lims
def get_xlim3d(self):
'''Get 3D x limits.'''
return self.xy_viewLim.intervalx
def get_ylim3d(self):
'''Get 3D y limits.'''
return self.xy_viewLim.intervaly
def get_zlim3d(self):
'''Get 3D z limits.'''
return self.zz_viewLim.intervalx
def clabel(self, *args, **kwargs):
return None
def pany(self, numsteps):
print 'numsteps', numsteps
def panpy(self, numsteps):
print 'numsteps', numsteps
def view_init(self, elev, azim):
self.dist = 10
self.elev = elev
self.azim = azim
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 / proj3d.mod(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)
perspM = proj3d.persp_transformation(zfront, zback)
M0 = np.dot(viewM, worldM)
M = np.dot(perspM, M0)
return M
def mouse_init(self):
self.button_pressed = None
canv = self.figure.canvas
if canv != None:
c1 = canv.mpl_connect('motion_notify_event', self._on_move)
c2 = canv.mpl_connect('button_press_event', self._button_press)
c3 = canv.mpl_connect('button_release_event', self._button_release)
self.cids = [c1, c2, c3]
def cla(self):
# Disconnect the various events we set.
for cid in self.cids:
self.figure.canvas.mpl_disconnect(cid)
self.cids = []
Axes.cla(self)
self.grid(rcParams['axes3d.grid'])
def _button_press(self, event):
self.button_pressed = event.button
self.sx, self.sy = event.xdata, event.ydata
def _button_release(self, event):
self.button_pressed = None
def format_xdata(self, x):
"""
Return x string formatted. This function will use the attribute
self.fmt_xdata if it is callable, else will fall back on the xaxis
major formatter
"""
try:
return self.fmt_xdata(x)
except TypeError:
fmt = self.w_xaxis.get_major_formatter()
return sensible_format_data(fmt, x)
def format_ydata(self, y):
"""
Return y string formatted. This function will use the attribute
self.fmt_ydata if it is callable, else will fall back on the yaxis
major formatter
"""
try:
return self.fmt_ydata(y)
except TypeError:
fmt = self.w_yaxis.get_major_formatter()
return sensible_format_data(fmt, y)
def format_zdata(self, z):
"""
Return z string formatted. This function will use the attribute
self.fmt_zdata if it is callable, else will fall back on the yaxis
major formatter
"""
try:
return self.fmt_zdata(z)
except (AttributeError, TypeError):
fmt = self.w_zaxis.get_major_formatter()
return sensible_format_data(fmt, z)
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 == 1:
return 'azimuth=%d deg, elevation=%d deg ' % (self.azim, self.elev)
# ignore xd and yd and display angles instead
p = (xd, yd)
edges = self.tunit_edges()
#lines = [proj3d.line2d(p0,p1) for (p0,p1) in edges]
ldists = [(proj3d.line2d_seg_dist(p0, p1, p), i) for \
i, (p0, p1) in enumerate(edges)]
ldists.sort()
# nearest edge
edgei = ldists[0][1]
p0, p1 = edges[edgei]
# 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_ydata(z)
return 'x=%s, y=%s, z=%s' % (xs, ys, zs)
def _on_move(self, event):
"""Mouse moving
button-1 rotates
button-3 zooms
"""
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 == None:
return
dx, dy = x - self.sx, y - self.sy
x0, x1 = self.get_xlim()
y0, y1 = self.get_ylim()
w = (x1-x0)
h = (y1-y0)
self.sx, self.sy = x, y
if self.button_pressed == 1:
# 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.figure.canvas.draw()
elif self.button_pressed == 2:
# pan view
# project xv,yv,zv -> xw,yw,zw
# pan
pass
elif self.button_pressed == 3:
# 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()
def set_xlabel(self, xlabel, fontdict=None, **kwargs):
'''Set xlabel. '''
label = self.w_xaxis.get_label()
label.set_text(xlabel)
if fontdict is not None:
label.update(fontdict)
label.update(kwargs)
return label
def set_ylabel(self, ylabel, fontdict=None, **kwargs):
'''Set ylabel.'''
label = self.w_yaxis.get_label()
label.set_text(ylabel)
if fontdict is not None:
label.update(fontdict)
label.update(kwargs)
return label
def set_zlabel(self, zlabel, fontdict=None, **kwargs):
'''Set zlabel.'''
label = self.w_zaxis.get_label()
label.set_text(zlabel)
if fontdict is not None:
label.update(fontdict)
label.update(kwargs)
return label
def grid(self, on=True, **kwargs):
'''
Set / unset 3D grid.
'''
self._draw_grid = on
def text(self, x, y, z, s, zdir=None):
'''Add text to the plot.'''
text = Axes.text(self, x, y, s)
art3d.text_2d_to_3d(text, z, zdir)
return text
text3D = text
def plot(self, xs, ys, *args, **kwargs):
'''
Plot 2D or 3D data.
========== ================================================
Argument Description
========== ================================================
*xs*, *ys* X, y coordinates of vertices
*zs* z value(s), either one for all points or one for
each point.
*zdir* Which direction to use as z ('x', 'y' or 'z')
when plotting a 2d set.
========== ================================================
Other arguments are passed on to
:func:`~matplotlib.axes.Axes.plot`
'''
had_data = self.has_data()
zs = kwargs.pop('zs', 0)
zdir = kwargs.pop('zdir', 'z')
argsi = 0
# First argument is array of zs
if len(args) > 0 and cbook.iterable(args[0]) and \
len(xs) == len(args[0]) and cbook.is_scalar(args[0][0]):
zs = args[argsi]
argsi += 1
# First argument is z value
elif len(args) > 0 and cbook.is_scalar(args[0]):
zs = args[argsi]
argsi += 1
# Match length
if not cbook.iterable(zs):
zs = np.ones(len(xs)) * zs
lines = Axes.plot(self, xs, ys, *args[argsi:], **kwargs)
for line in lines:
art3d.line_2d_to_3d(line, zs=zs, zdir=zdir)
self.auto_scale_xyz(xs, ys, zs, had_data)
return lines
plot3D = plot
def plot_surface(self, X, Y, Z, *args, **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.
========== ================================================
Argument Description
========== ================================================
*X*, *Y*, Data values as numpy.arrays
*Z*
*rstride* Array row stride (step size)
*cstride* Array column stride (step size)
*color* Color of the surface patches
*cmap* A colormap for the surface patches.
========== ================================================
'''
had_data = self.has_data()
rows, cols = Z.shape
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
rstride = kwargs.pop('rstride', 10)
cstride = kwargs.pop('cstride', 10)
color = kwargs.pop('color', 'b')
color = np.array(colorConverter.to_rgba(color))
cmap = kwargs.get('cmap', None)
polys = []
normals = []
avgz = []
for rs in np.arange(0, rows-1, rstride):
for cs in np.arange(0, cols-1, cstride):
ps = []
corners = []
for a, ta in [(X, tX), (Y, tY), (Z, tZ)]:
ztop = a[rs][cs:min(cols, cs+cstride+1)]
zleft = ta[min(cols-1, cs+cstride)][rs:min(rows, rs+rstride+1)]
zbase = a[min(rows-1, rs+rstride)][cs:min(cols, cs+cstride+1):]
zbase = zbase[::-1]
zright = ta[cs][rs:min(rows, rs+rstride+1):]
zright = zright[::-1]
corners.append([ztop[0], ztop[-1], zbase[0], zbase[-1]])
z = np.concatenate((ztop, zleft, zbase, zright))
ps.append(z)
# The construction leaves the array with duplicate points, which
# are removed here.
ps = zip(*ps)
lastp = np.array([])
ps2 = []
avgzsum = 0.0
for p in ps:
if p != lastp:
ps2.append(p)
lastp = p
avgzsum += p[2]
polys.append(ps2)
avgz.append(avgzsum / len(ps2))
v1 = np.array(ps2[0]) - np.array(ps2[1])
v2 = np.array(ps2[2]) - np.array(ps2[0])
normals.append(np.cross(v1, v2))
polyc = art3d.Poly3DCollection(polys, *args, **kwargs)
if cmap is not None:
polyc.set_array(np.array(avgz))
polyc.set_linewidth(0)
else:
colors = self._shade_colors(color, normals)
polyc.set_facecolors(colors)
self.add_collection(polyc)
self.auto_scale_xyz(X, Y, Z, had_data)
return polyc
def _generate_normals(self, polygons):
'''
Generate normals for polygons by using the first three points.
This normal of course might not make sense for polygons with
more than three points not lying in a plane.
'''
normals = []
for verts in polygons:
v1 = np.array(verts[0]) - np.array(verts[1])
v2 = np.array(verts[2]) - np.array(verts[0])
normals.append(np.cross(v1, v2))
return normals
def _shade_colors(self, color, normals):
shade = []
for n in normals:
n = n / proj3d.mod(n) * 5
shade.append(np.dot(n, [-1, -1, 0.5]))
shade = np.array(shade)
mask = ~np.isnan(shade)
if len(shade[mask]) > 0:
norm = Normalize(min(shade[mask]), max(shade[mask]))
color = color.copy()
color[3] = 1
colors = [color * (0.5 + norm(v) * 0.5) for v in shade]
else:
colors = color.copy()
return colors
def plot_wireframe(self, X, Y, Z, *args, **kwargs):
'''
Plot a 3D wireframe.
========== ================================================
Argument Description
========== ================================================
*X*, *Y*, Data values as numpy.arrays
*Z*
*rstride* Array row stride (step size)
*cstride* Array column stride (step size)
========== ================================================
Keyword arguments are passed on to
:func:`matplotlib.collections.LineCollection.__init__`.
Returns a :class:`~mpl_toolkits.mplot3d.art3d.Line3DCollection`
'''
rstride = kwargs.pop("rstride", 1)
cstride = kwargs.pop("cstride", 1)
had_data = self.has_data()
rows, cols = Z.shape
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
rii = [i for i in range(0, rows, rstride)]+[rows-1]
cii = [i for i in range(0, cols, cstride)]+[cols-1]
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 = [zip(xl, yl, zl) for xl, yl, zl in \
zip(xlines, ylines, zlines)]
lines += [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
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):
topverts = art3d.paths_to_3d_segments(linec.get_paths(), z - dz)
botverts = art3d.paths_to_3d_segments(linec.get_paths(), z + dz)
color = linec.get_color()[0]
polyverts = []
normals = []
nsteps = 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(round(nsteps)) - 1):
i1 = int(round(i * stepsize))
i2 = int(round((i + 1) * stepsize))
polyverts.append([topverts[0][i1],
topverts[0][i2],
botverts[0][i2],
botverts[0][i1]])
v1 = np.array(topverts[0][i1]) - np.array(topverts[0][i2])
v2 = np.array(topverts[0][i1]) - np.array(botverts[0][i1])
normals.append(np.cross(v1, v2))
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)
def contour(self, X, Y, Z, levels=10, **kwargs):
'''
Create a 3D contour plot.
========== ================================================
Argument Description
========== ================================================
*X*, *Y*, Data values as numpy.arrays
*Z*
*levels* Number of levels to use, defaults to 10. Can
also be a tuple of specific levels.
*extend3d* Whether to extend contour in 3D (default: False)
*stride* Stride (step size) for extending contour
========== ================================================
Other keyword arguments are passed on to
:func:`~matplotlib.axes.Axes.contour`
'''
extend3d = kwargs.pop('extend3d', False)
stride = kwargs.pop('stride', 5)
nlevels = kwargs.pop('nlevels', 15)
had_data = self.has_data()
cset = Axes.contour(self, X, Y, Z, levels, **kwargs)
if extend3d:
self._3d_extend_contour(cset, stride)
else:
for z, linec in zip(cset.levels, cset.collections):
art3d.line_collection_2d_to_3d(linec, z)
self.auto_scale_xyz(X, Y, Z, had_data)
return cset
contour3D = contour
def contourf(self, X, Y, Z, *args, **kwargs):
'''
Plot filled 3D contours.
*X*, *Y*, *Z*: data points.
Keyword arguments are passed on to
:func:`~matplotlib.axes.Axes.contour`
'''
had_data = self.has_data()
cset = Axes.contourf(self, X, Y, Z, *args, **kwargs)
levels = cset.levels
colls = cset.collections
for z1, z2, linec in zip(levels, levels[1:], colls):
art3d.poly_collection_2d_to_3d(linec, z1)
linec.set_sort_zpos(z1)
self.auto_scale_xyz(X, Y, Z, had_data)
return cset
contourf3D = contourf
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
- LineColleciton
- PatchCollection
'''
if type(col) is collections.PolyCollection:
art3d.poly_collection_2d_to_3d(col, zs=zs, zdir=zdir)
col.set_sort_zpos(min(zs))
elif type(col) is collections.LineCollection:
art3d.line_collection_2d_to_3d(col, zs=zs, zdir=zdir)
col.set_sort_zpos(min(zs))
elif type(col) is collections.PatchCollection:
art3d.patch_collection_2d_to_3d(col, zs=zs, zdir=zdir)
col.set_sort_zpos(min(zs))
Axes.add_collection(self, col)
def scatter(self, xs, ys, zs=0, zdir='z', *args, **kwargs):
'''
Create a scatter plot.
========== ================================================
Argument Description
========== ================================================
*xs*, *ys* Positions of data points.
*zs* Either an array of the same length as *xs* and
*ys* or a single value to place all points in
the same plane. Default is 0.
*zdir* Which direction to use as z ('x', 'y' or 'z')
when plotting a 2d set.
========== ================================================
Keyword arguments are passed on to
:func:`~matplotlib.axes.Axes.scatter`.
Returns a :class:`~mpl_toolkits.mplot3d.art3d.Patch3DCollection`
'''
had_data = self.has_data()
patches = Axes.scatter(self, xs, ys, *args, **kwargs)
if not cbook.iterable(zs):
is_2d = True
zs = np.ones(len(xs)) * zs
else:
is_2d = False
art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir)
#FIXME: why is this necessary?
if not is_2d:
self.auto_scale_xyz(xs, ys, zs, had_data)
return patches
scatter3D = scatter
def bar(self, left, height, zs=0, zdir='z', *args, **kwargs):
'''
Add 2D bar(s).
========== ================================================
Argument Description
========== ================================================
*left* The x coordinates of the left sides of the bars.
*height* The height of the bars.
*zs* Z coordinate of bars, if one value is specified
they will all be placed at the same z.
*zdir* Which direction to use as z ('x', 'y' or 'z')
when plotting a 2d set.
========== ================================================
Keyword arguments are passed onto :func:`~matplotlib.axes.Axes.bar`.
Returns a :class:`~mpl_toolkits.mplot3d.art3d.Patch3DCollection`
'''
had_data = self.has_data()
patches = Axes.bar(self, left, height, *args, **kwargs)
if not cbook.iterable(zs):
zs = np.ones(len(left)) * zs
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, zs, zdir)
if 'alpha' in kwargs:
p.set_alpha(kwargs['alpha'])
xs, ys = zip(*verts)
xs, ys, verts_zs = art3d.juggle_axes(xs, ys, verts_zs, zdir)
self.auto_scale_xyz(xs, ys, verts_zs, had_data)
return patches
def bar3d(self, x, y, z, dx, dy, dz, color='b'):
'''
Generate a 3D bar, or multiple bars.
When generating multiple bars, x, y, z have to be arrays.
dx, dy, dz can still be scalars.
'''
had_data = self.has_data()
if not cbook.iterable(x):
x, y, z = [x], [y], [z]
if not cbook.iterable(dx):
dx, dy, dz = [dx], [dy], [dz]
if len(dx) == 1:
dx = dx * len(x)
dy = dy * len(x)
dz = dz * len(x)
minx, miny, minz = 1e20, 1e20, 1e20
maxx, maxy, maxz = -1e20, -1e20, -1e20
polys = []
for xi, yi, zi, dxi, dyi, dzi in zip(x, y, z, dx, dy, dz):
minx = min(xi, minx)
maxx = max(xi + dxi, maxx)
miny = min(yi, miny)
maxy = max(yi + dyi, maxy)
minz = min(zi, minz)
maxz = max(zi + dzi, maxz)
polys.extend([
((xi, yi, zi), (xi + dxi, yi, zi),
(xi + dxi, yi + dyi, zi), (xi, yi + dyi, zi)),
((xi, yi, zi + dzi), (xi + dxi, yi, zi + dzi),
(xi + dxi, yi + dyi, zi + dzi), (xi, yi + dyi, zi + dzi)),
((xi, yi, zi), (xi + dxi, yi, zi),
(xi + dxi, yi, zi + dzi), (xi, yi, zi + dzi)),
((xi, yi + dyi, zi), (xi + dxi, yi + dyi, zi),
(xi + dxi, yi + dyi, zi + dzi), (xi, yi + dyi, zi + dzi)),
((xi, yi, zi), (xi, yi + dyi, zi),
(xi, yi + dyi, zi + dzi), (xi, yi, zi + dzi)),
((xi + dxi, yi, zi), (xi + dxi, yi + dyi, zi),
(xi + dxi, yi + dyi, zi + dzi), (xi + dxi, yi, zi + dzi)),
])
color = np.array(colorConverter.to_rgba(color))
normals = self._generate_normals(polys)
colors = self._shade_colors(color, normals)
col = art3d.Poly3DCollection(polys, facecolor=colors)
self.add_collection(col)
self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data)
def get_test_data(delta=0.05):
'''
Return a tuple X, Y, Z with a test data set.
'''
from matplotlib.mlab import bivariate_normal
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1
X = X * 10
Y = Y * 10
Z = Z * 500
return X, Y, Z
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