def _set_offset(self, range):
# offset of 20,001 is 20,000, for example
locs = self.locs
if locs is None or not len(locs):
self.offset = 0
ave_loc = mean(locs)
if ave_loc: # dont want to take log10(0)
ave_oom = math.floor(math.log10(mean(absolute(locs))))
range_oom = math.floor(math.log10(range))
if absolute(ave_oom-range_oom) >= 3: # four sig-figs
if ave_loc < 0:
self.offset = math.ceil(amax(locs)/10**range_oom)*10**range_oom
else:
self.offset = math.floor(amin(locs)/10**(range_oom))*10**(range_oom)
else: self.offset = 0
from matplotlib.transforms import Bbox, Value, Point, \
get_bbox_transform, unit_bbox
# load the data
t = arange(0.0, 2.0, 0.01)
s1 = sin(2 * pi * t)
s2 = exp(-t)
s3 = sin(2 * pi * t) * exp(-t)
s4 = sin(2 * pi * t) * cos(4 * pi * t)
s5 = s1 * s2
s6 = s1 - s4
s7 = s3 * s4 - s1
signals = s1, s2, s3, s4, s5, s6, s7
for sig in signals:
sig = sig - mean(sig)
lineprops = dict(linewidth=1, color='black', linestyle='-')
fig = figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# The normal matplotlib transformation is the view lim bounding box
# (ax.viewLim) to the axes bounding box (ax.bbox). Where are going to
# define a new transform by defining a new input bounding box. See the
# matplotlib.transforms module helkp for more information on
# transforms
# This bounding reuses the x data of the viewLim for the xscale -10 to
# 10 on the y scale. -10 to 10 means that a signal with a min/max
# amplitude of 10 will span the entire vertical extent of the axes
scale = 10
from matplotlib.transforms import Bbox, Value, Point, \
get_bbox_transform, unit_bbox
# load the data
t = arange(0.0, 2.0, 0.01)
s1 = sin(2*pi*t)
s2 = exp(-t)
s3 = sin(2*pi*t)*exp(-t)
s4 = sin(2*pi*t)*cos(4*pi*t)
s5 = s1*s2
s6 = s1-s4
s7 = s3*s4-s1
signals = s1, s2, s3, s4, s5, s6, s7
for sig in signals:
sig = sig-mean(sig)
lineprops = dict(linewidth=1, color='black', linestyle='-')
fig = figure()
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
# The normal matplotlib transformation is the view lim bounding box
# (ax.viewLim) to the axes bounding box (ax.bbox). Where are going to
# define a new transform by defining a new input bounding box. See the
# matplotlib.transforms module helkp for more information on
# transforms
# This bounding reuses the x data of the viewLim for the xscale -10 to
# 10 on the y scale. -10 to 10 means that a signal with a min/max
# amplitude of 10 will span the entire vertical extent of the axes
scale = 10
