def _graph_factory(self, with_image=False):
g = Graph(
container_dict=dict(
padding=0
))
g.new_plot(
bounds=[250, 250],
resizable='',
padding=[30, 0, 0, 30])
cx = self.cx
cy = self.cy
cbx = self.xbounds
cby = self.ybounds
tr = self.target_radius
# if with_image:
# px = self.pxpermm #px is in mm
# cbx, cby = self._get_crop_bounds()
# #g.set_axis_traits(tick_label_formatter=lambda x: '{:0.2f}'.format((x - w / 2) / px))
# #g.set_axis_traits(tick_label_formatter=lambda x: '{:0.2f}'.format((x - h / 2) / px), axis='y')
#
# bx, by = g.plots[0].bounds
# g.plots[0].x_axis.mapper = LinearMapper(high_pos=bx,
# range=DataRange1D(low_setting=self.xbounds[0],
# high_setting=self.xbounds[1]))
# g.plots[0].y_axis.mapper = LinearMapper(high_pos=by,
# range=DataRange1D(low_setting=self.ybounds[0],
# high_setting=self.ybounds[1]))
# cx += self.image_width / 2
# cy += self.image_height / 2
# tr *= px
g.set_x_limits(*cbx)
g.set_y_limits(*cby)
lp, _plot = g.new_series()
t = TargetOverlay(component=lp,
cx=cx,
cy=cy,
target_radius=tr)
lp.overlays.append(t)
overlap_overlay = OverlapOverlay(component=lp,
visible=self.show_overlap
)
lp.overlays.append(overlap_overlay)
g.new_series(type='scatter', marker='circle')
g.new_series(type='line', color='red')
return g
xs, ys = poly.T
cx, cy = xs.mean(), ys.mean()
P = poly.T
xs = np.hstack((xs, xs[0]))
ys = np.hstack((ys, ys[0]))
else:
xs, ys = poly.T
xs = np.hstack((xs, xs[0]))
ys = np.hstack((ys, ys[0]))
cx, cy = xs.mean(), ys.mean()
# plot original
g.new_series(xs, ys)
g.set_x_limits(min(xs), max(xs), pad='0.1')
g.set_y_limits(min(ys), max(ys), pad='0.1')
for ps in npoints:
for i in range(0, len(ps), 2):
p1, p2 = ps[i], ps[i + 1]
g.new_series((p1[0], p2[0]),
(p1[1], p2[1]), color='black')
# plot offset polygon
# poly = sort_clockwise(poly, poly)
opoly = polygon_offset(poly, -500)
if use_convex_hull:
opoly = convex_hull(opoly)
xs, ys, _ = opoly.T
xs = np.hstack((xs, xs[0]))
ys = np.hstack((ys, ys[0]))
mswds1.append(ms1)
rratios1.append(ratios1)
if tau % 5 == 0:
g.new_series(x, ratios1, plotid=1, type='line')
i = int((tau / 5) - 1)
# g.set_series_label('p', plotid=1, series=i)
# g.set_series_label('{} (ns)'.format(tau), plotid=1, series=i)
# g.plots[1].legend.plots = dict([(k, v[0]) for k, v in g.plots[1].plots.iteritems()])
# print g.plots[1].legend.plots
g.new_series(taus, mswds1, plotid=0, type='line_scatter')
g.set_y_limits(min(mswds1) - 5, max(mswds1) + 5, plotid=0)
# fit parabola and find minimum.
coeffs1 = polyfit(taus, mswds1, 2)
# min at dy=0 (ax2+bx+c)dx=dy==2ax+b
# 2ax+b=0 , x=-b/(2a)
dt = -coeffs1[1] / (2 * coeffs1[0])
print 'dead time1= ', dt
g.set_x_title('# Airshots', plotid=1)
g.set_x_title('Dead Time (ns)', plotid=0)
g.set_y_title('40Ar/36Ar', plotid=1)
g.set_y_title('MSWD', plotid=0)
g.add_vertical_rule(dt)
