def set_bounds(self, vmin, vmax):
'''
Set dataInterval and viewInterval from numeric vmin, vmax.
This is for stand-alone use of Formatters and/or
Locators that require these intervals; that is, for
cases where the Intervals do not need to be updated
automatically.
'''
self.dataInterval = mtrans.Interval(mtrans.Value(vmin),
mtrans.Value(vmax))
self.viewInterval = mtrans.Interval(mtrans.Value(vmin),
mtrans.Value(vmax))
def _set_transform(self):
ax = self.ax
if self.units in ('x', 'y'):
if self.units == 'x':
dx0 = ax.viewLim.ur().x() - ax.viewLim.ll().x()
dx1 = ax.bbox.ur().x() - ax.bbox.ll().x()
else:
dx0 = ax.viewLim.ur().y() - ax.viewLim.ll().y()
dx1 = ax.bbox.ur().y() - ax.bbox.ll().y()
dx = dx1/dx0
else:
if self.units == 'width':
dx = ax.bbox.ur().x() - ax.bbox.ll().x()
elif self.units == 'height':
dx = ax.bbox.ur().y() - ax.bbox.ll().y()
elif self.units == 'dots':
dx = T.Value(1)
elif self.units == 'inches':
dx = ax.figure.dpi
else:
raise ValueError('unrecognized units')
bb = T.Bbox(T.origin(), T.Point(dx, dx))
trans = T.get_bbox_transform(T.unit_bbox(), bb)
self.set_transform(trans)
return trans
def _ticker(self):
'''
Return two sequences: ticks (colorbar data locations)
and ticklabels (strings).
'''
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator()
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if isinstance(self.norm, colors.NoNorm):
intv = transforms.Interval(transforms.Value(self._values[0]),
transforms.Value(self._values[-1]))
else:
intv = transforms.Interval(transforms.Value(self.vmin),
transforms.Value(self.vmax))
locator.set_view_interval(intv)
locator.set_data_interval(intv)
formatter.set_view_interval(intv)
formatter.set_data_interval(intv)
b = npy.array(locator())
b, ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t) for t in b]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
def __init__(self, Q, X, Y, U, label, **kw):
Artist.__init__(self)
self.Q = Q
self.X = X
self.Y = Y
self.U = U
self.coord = kw.pop('coordinates', 'axes')
self.color = kw.pop('color', None)
self.label = label
self.labelsep = T.Value(kw.pop('labelsep', 0.1)) * Q.ax.figure.dpi
self.labelpos = kw.pop('labelpos', 'N')
self.labelcolor = kw.pop('labelcolor', None)
self.fontproperties = kw.pop('fontproperties', dict())
self.kw = kw
self.text = Text(text=label,
horizontalalignment=self.halign[self.labelpos],
verticalalignment=self.valign[self.labelpos],
fontproperties=FontProperties(**self.fontproperties))
if self.labelcolor is not None:
self.text.set_color(self.labelcolor)
self._initialized = False
self.zorder = Q.zorder + 0.1
transOffset=a.transData)
# Note: the first argument to the collection initializer
# must be a list of sequences of x,y tuples; we have only
# one sequence, but we still have to put it in a list.
a.add_collection(col, autolim=True)
# autolim=True enables autoscaling. For collections with
# offsets like this, it is neither efficient nor accurate,
# but it is good enough to generate a plot that you can use
# as a starting point. If you know beforehand the range of
# x and y that you want to show, it is better to set them
# explicitly, leave out the autolim kwarg (or set it to False),
# and omit the 'a.autoscale_view()' call below.
# Make a transform for the line segments such that their size is
# given in points:
trans = transforms.scale_transform(fig.dpi / transforms.Value(72.),
fig.dpi / transforms.Value(72.))
col.set_transform(trans) # the points to pixels transform
col.set_color(colors)
a.autoscale_view() # See comment above, after a.add_collection.
a.set_title('LineCollection using offsets')
# The same data as above, but fill the curves.
a = fig.add_subplot(2, 2, 2)
col = collections.PolyCollection([spiral],
offsets=xyo,
transOffset=a.transData)
a.add_collection(col, autolim=True)