def closeto_seq(xs, ys):
return alltrue([closeto(x, y) for x, y in zip(xs, ys)])
def closeto_bbox(b1, b2):
xmin1, xmax1 = b1.intervalx().get_bounds()
ymin1, ymax1 = b1.intervaly().get_bounds()
xmin2, xmax2 = b2.intervalx().get_bounds()
ymin2, ymax2 = b2.intervaly().get_bounds()
pairs = ((xmin1, xmin2), (xmax1, xmax2), (ymin1, ymin2), (ymax1, ymax2))
return alltrue([closeto(x, y) for x, y in pairs])
ll = Point(Value(10), Value(10))
ur = Point(Value(200), Value(40))
bbox = Bbox(ll, ur)
assert (bbox.xmin() == 10)
assert (bbox.width() == 190)
assert (bbox.height() == 30)
ll.x().set(12.0)
assert (bbox.xmin() == 12)
assert (bbox.width() == 188)
assert (bbox.height() == 30)
a = Value(10)
b = Value(0)
def rand_point():
xy = rand(2)
return Point(Value(xy[0]), Value(xy[1]))
hist(im, 100)
xticks([-1, -.5, 0, .5, 1])
yticks([])
xlabel('intensity')
ylabel('MRI density')
if 1: # plot the EEG
# load the data
numSamples, numRows = 800, 4
data = fromstring(file('data/eeg.dat', 'rb').read(), float)
data.shape = numSamples, numRows
t = arange(numSamples) / float(numSamples) * 10.0
ticklocs = []
ax = subplot(212)
boxin = Bbox(Point(ax.viewLim.ll().x(), Value(-20)),
Point(ax.viewLim.ur().x(), Value(20)))
height = ax.bbox.ur().y() - ax.bbox.ll().y()
boxout = Bbox(Point(ax.bbox.ll().x(),
Value(-1) * height),
Point(ax.bbox.ur().x(),
Value(1) * height))
transOffset = get_bbox_transform(
unit_bbox(),
Bbox(Point(Value(0),
ax.bbox.ll().y()), Point(Value(1),
ax.bbox.ur().y())))
for i in range(numRows):
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
boxin = Bbox(Point(ax.viewLim.ll().x(), Value(-scale)),
Point(ax.viewLim.ur().x(), Value(scale)))
# height is a lazy value
height = ax.bbox.ur().y() - ax.bbox.ll().y()
boxout = Bbox(Point(ax.bbox.ll().x(),
Value(-0.5) * height),
Point(ax.bbox.ur().x(),
Value(0.5) * height))
# matplotlib transforms can accepts an offset, which is defined as a
# point and another transform to map that point to display. This
# transform maps x as identity and maps the 0-1 y interval to the
# vertical extent of the yaxis. This will be used to offset the lines
# and ticks vertically
def rand_point():
return Point( rand_val(), rand_val() )
def closeto_seq(xs, ys):
return alltrue([closeto(x, y) for x, y in zip(xs, ys)])
def closeto_bbox(b1, b2):
xmin1, xmax1 = b1.intervalx().get_bounds()
ymin1, ymax1 = b1.intervaly().get_bounds()
xmin2, xmax2 = b2.intervalx().get_bounds()
ymin2, ymax2 = b2.intervaly().get_bounds()
pairs = ((xmin1, xmin2), (xmax1, xmax2), (ymin1, ymin2), (ymax1, ymax2))
return alltrue([closeto(x, y) for x, y in pairs])
ll = Point(Value(10), Value(10))
ur = Point(Value(200), Value(40))
bbox = Bbox(ll, ur)
assert (bbox.xmin() == 10)
assert (bbox.width() == 190)
assert (bbox.height() == 30)
ll.x().set(12.0)
assert (bbox.xmin() == 12)
assert (bbox.width() == 188)
assert (bbox.height() == 30)
a = Value(10)
b = Value(0)
# Timing tests -- print time per plot
TIME_PY = 1
TIME_EXT = 1
#################
# Test Parameters
#################
# Bounding box to use in testing
ll_x = 320
ll_y = 240
ur_x = 640
ur_y = 480
BBOX = Bbox(Point(Value(ll_x), Value(ll_y)),
Point(Value(ur_x), Value(ur_y)))
# Number of iterations for timing
NITERS = 25
###############################################################################
#
# Testing framework
#
def time_loop(function, args):
i = 0