def _offset(ax, x, y):
"""Provide offset in pixels
Parameters
----------
x : int
Offset in pixels for x
y : int
Offset in pixels for y
Idea borrowed from
http://www.scipy.org/Cookbook/Matplotlib/Transformations
but then heavily extended to be compatible with many
reincarnations of matplotlib
"""
d = dir(mlt)
if 'offset_copy' in d:
# tested with python-matplotlib 0.98.3-5
# ??? if pukes, might need to replace 2nd parameter from
# ax to ax.get_figure()
return mlt.offset_copy(ax.transData, ax, x=x, y=y, units='dots')
elif 'BlendedAffine2D' in d:
# some newer versions of matplotlib
return ax.transData + \
mlt.Affine2D().translate(x,y)
elif 'blend_xy_sep_transform' in d:
trans = mlt.blend_xy_sep_transform(ax.transData, ax.transData)
# Now we set the offset in pixels
trans.set_offset((x, y), mlt.identity_transform())
return trans
else:
raise RuntimeError, \
"Lacking needed functions in matplotlib.transform " \
"for _offset. Please upgrade"
def _offset(ax, x, y):
"""Provide offset in pixels
Parameters
----------
x : int
Offset in pixels for x
y : int
Offset in pixels for y
Idea borrowed from
http://www.scipy.org/Cookbook/Matplotlib/Transformations
but then heavily extended to be compatible with many
reincarnations of matplotlib
"""
d = dir(mlt)
if "offset_copy" in d:
# tested with python-matplotlib 0.98.3-5
# ??? if pukes, might need to replace 2nd parameter from
# ax to ax.get_figure()
return mlt.offset_copy(ax.transData, ax, x=x, y=y, units="dots")
elif "BlendedAffine2D" in d:
# some newer versions of matplotlib
return ax.transData + mlt.Affine2D().translate(x, y)
elif "blend_xy_sep_transform" in d:
trans = mlt.blend_xy_sep_transform(ax.transData, ax.transData)
# Now we set the offset in pixels
trans.set_offset((x, y), mlt.identity_transform())
return trans
else:
raise RuntimeError, "Lacking needed functions in matplotlib.transform " "for _offset. Please upgrade"
def drawLine(self, xData, yData, renderer): line = Line2D( xdata=xData, ydata=yData, color=rcParams['grid.color'], #linestyle=rcParams['grid.linestyle'], linewidth=rcParams['grid.linewidth'], antialiased=True, ) line.set_transform( blend_xy_sep_transform( self.axes.transData, self.axes.transData) ) line.set_clip_box(self.axes.bbox) #self._set_artist_props(line) line.draw(renderer)
def __init__(self, ax, onselect, minspan=None, useblit=False, rectprops=None):
"""
Create a span selector in ax. When a selection is made, clear
the span and call onselect with
onselect(xmin, xmax)
and clear the span.
If minspan is not None, ignore events smaller than minspan
The span rect is drawn with rectprops; default
rectprops = dict(facecolor='red', alpha=0.5)
set the visible attribute to False if you want to turn off
the functionality of the span selector
"""
if rectprops is None:
rectprops = dict(facecolor='red', alpha=0.5)
self.ax = ax
self.visible = True
self.canvas = ax.figure.canvas
self.canvas.mpl_connect('motion_notify_event', self.onmove)
self.canvas.mpl_connect('button_press_event', self.press)
self.canvas.mpl_connect('button_release_event', self.release)
self.canvas.mpl_connect('draw_event', self.update_background)
self.rect = None
self.background = None
self.rectprops = rectprops
self.onselect = onselect
self.useblit = useblit
self.minspan = minspan
trans = blend_xy_sep_transform(self.ax.transData, self.ax.transAxes)
self.rect = Rectangle( (0,0), 0, 1,
transform=trans,
visible=False,
**self.rectprops
)
if not self.useblit: self.ax.add_patch(self.rect)
self.pressx = None
def offset(ax, x, y):
# This trick makes a shallow copy of ax.transData (but fails for polar plots):
trans = blend_xy_sep_transform(ax.transData, ax.transData)
# Now we set the offset in pixels
trans.set_offset((x, y), identity_transform())
return trans
def offset(ax, x, y):
# This trick makes a shallow copy of ax.transData (but fails for polar plots):
trans = blend_xy_sep_transform(ax.transData, ax.transData)
# Now we set the offset in pixels
trans.set_offset((x,y), identity_transform())
return trans
class Line:
def __init__(self):
self._transform = identity_transform()
def set_transform(self, t):
self._transform = t
x, y = rand(2, 10000)
indStart, indEnd = 30, 350
for i in range(indEnd):
for j in range(20):
l = Line()
t1 = rand_transform()
t2 = rand_transform()
trans = blend_xy_sep_transform(t1, t2)
l.set_transform(trans)
xt, yt = trans.numerix_x_y(x, y)
xytup = tuple(rand(2))
txytup = trans.xy_tup(xytup)
ixytup = trans.inverse_xy_tup(xytup)
seqt = trans.seq_xy_tups(zip(x, y))
gc.collect()
val = report_memory(i)
if i == indStart:
start = val # wait a few cycles for memory usage to stabilize
end = val
print 'Average memory consumed per loop: %1.4fk bytes\n' % (
(end - start) / float(indEnd - indStart))
class Line:
def __init__(self):
self._transform = identity_transform()
def set_transform(self, t):
self._transform = t
x, y = rand(2,10000)
indStart, indEnd = 30, 350
for i in range(indEnd):
for j in range(20):
l = Line()
t1 = rand_transform()
t2 = rand_transform()
trans = blend_xy_sep_transform( t1, t2)
l.set_transform(trans)
xt, yt = trans.numerix_x_y(x, y)
xytup = tuple(rand(2))
txytup = trans.xy_tup(xytup)
ixytup = trans.inverse_xy_tup(xytup)
seqt = trans.seq_xy_tups(zip(x,y))
gc.collect()
val = report_memory(i)
if i==indStart: start = val # wait a few cycles for memory usage to stabilize
end = val
print 'Average memory consumed per loop: %1.4fk bytes\n' % ((end-start)/float(indEnd-indStart))
def rand_transform():
b1 = rand_bbox()
b2 = rand_bbox()
return get_bbox_transform(b1, b2)
class Line:
def __init__(self):
self._transform = identity_transform()
def set_transform(self, t):
self._transform = t
indStart, indEnd = 30, 250
for i in range(indEnd):
l = Line()
t1 = rand_transform()
t2 = rand_transform()
l.set_transform(blend_xy_sep_transform(t1, t2))
val = report_memory(i)
if i == indStart:
start = val # wait a few cycles for memory usage to stabilize
end = val
print 'Average memory consumed per loop: %1.4fk bytes\n' % (
(end - start) / float(indEnd - indStart))
return Bbox(ll, ur)
def rand_transform():
b1 = rand_bbox()
b2 = rand_bbox()
return get_bbox_transform(b1, b2)
class Line:
def __init__(self):
self._transform = identity_transform()
def set_transform(self, t):
self._transform = t
indStart, indEnd = 30, 250
for i in range(indEnd):
l = Line()
t1 = rand_transform()
t2 = rand_transform()
l.set_transform(blend_xy_sep_transform( t1, t2))
val = report_memory(i)
if i==indStart: start = val # wait a few cycles for memory usage to stabilize
end = val
print 'Average memory consumed per loop: %1.4fk bytes\n' % ((end-start)/float(indEnd-indStart))
