def normal_left_down(self, event):
super(MapCanvas, self).normal_left_down(event)
if self.current_hole is not None:
# and not event.handled
ca = self.calibration_item
if ca is not None:
if hasattr(event, 'item'):
if hasattr(ca, 'right'):
if event.item.right is ca.right:
return
rot = ca.rotation
cpos = ca.center
aff = AffineTransform()
aff.translate(*cpos)
aff.rotate(rot)
aff.translate(-cpos[0], -cpos[1])
aff.translate(*cpos)
mpos = self.mp.get_hole_pos(self.current_hole)
# dpos = aff.transformPt(mpos)
dpos = aff.transform(*mpos)
spos = self.map_data((event.x, event.y))
# not much point in adding an indicator because the hole
# renders its own
# self.markupdict['tweak'] = Indicator(*spos, canvas = self)
tweak = spos[0] - dpos[0], spos[1] - dpos[1]
ca.tweak_dict[self.current_hole] = tweak
self.request_redraw()
def normal_mouse_move(self, event):
# over a hole
ca = self.calibration_item
if ca:
for obj in self.mp.sample_holes:
hole = obj.id
pos = obj.x, obj.y
rot = ca.rotation
cpos = ca.center
aff = AffineTransform()
aff.translate(*cpos)
aff.rotate(rot)
aff.translate(-cpos[0], -cpos[1])
aff.translate(*cpos)
dpos = aff.transformPt(pos)
pos = self.map_screen([dpos])[0]
if abs(pos[0] - event.x) <= 10 and abs(pos[1] - event.y) <= 10:
event.window.set_pointer(self.select_pointer)
event.handled = True
self.current_hole = hole
break
if not event.handled:
self.current_hole = None
super(MapCanvas, self).normal_mouse_move(event)
def _calculate_affine_transform(self, pts):
rps, ps = zip(*pts)
s, r, t = calculate_rigid_transform(rps, ps)
self.A = AffineTransform()
self.A.scale(s, s)
self.A.rotate(r)
self.A.translate(-t[0], -t[1])
print self.A
def map_to_uncalibration(self, pos, cpos=None, rot=None, scale=None):
cpos, rot, scale = self._get_calibration_params(cpos, rot, scale)
a = AffineTransform()
a.scale(1 / scale, 1 / scale)
a.rotate(-rot)
a.translate(cpos[0], cpos[1])
# a.translate(-cpos[0], -cpos[1])
# a.translate(*cpos)
# a.rotate(-rot)
# a.translate(-cpos[0], -cpos[1])
pos = a.transform(*pos)
return pos
def rubberband_pattern(cx, cy, offset, l, rotation):
p1 = cx - offset, cy + offset
p2 = cx + l + offset, cy + offset
p3 = cx + l + offset, cy - offset
p4 = cx - offset, cy - offset
a = AffineTransform()
a.translate(cx, cy)
a.rotate(rotation)
a.translate(-cx, -cy)
ps = (p1, p2, p3, p4, p1)
for p in ps:
yield a.transform(*p)
def line_pattern(cx, cy, length, rotation, n):
p1 = (cx, cy)
p2 = (cx + length, cy)
for i in xrange(n):
a = AffineTransform()
a.translate(cx, cy)
a.rotate(rotation)
a.translate(-cx, -cy)
if i % 2 == 0:
ps = (p1, p2)
else:
ps = (p2, p1)
for x, y in ps:
yield a.transform(x, y)
def raster_rubberband_pattern(cx, cy, offset, l, dx, rotation, single_pass):
a = AffineTransform()
a.translate(cx, cy)
a.rotate(rotation)
a.translate(-cx, -cy)
# print offset, l
n = int((l + 2 * offset) / dx)
if n*dx<=l+2*offset:
n = n+1 if n%2 else n
dx = (l+2*offset)/float(n+1)
n = int((l + 2 * offset) / dx)
for i in xrange(0, n+1):
y = cy - offset if i % 2 else cy + offset
yield a.transform(cx - offset + dx * i, y)
if not single_pass:
for i in xrange(0, n+1):
y = cy - offset if i % 2 else cy + offset
yield a.transform(cx +l+offset - dx * i, y)
yield a.transform(cx-offset, cy+offset)
def map_to_calibration(self, pos, cpos=None, rot=None,
use_modified=False,
scale=None,
translate=None):
cpos, rot, scale = self._get_calibration_params(cpos, rot, scale)
a = AffineTransform()
# if translate:
# a.translate(*translate)
# if scale:
a.scale(scale, scale)
if use_modified:
a.translate(*cpos)
# print cpos, rot, scale
a.rotate(rot)
a.translate(-cpos[0], -cpos[1])
if use_modified:
a.translate(*cpos)
pos = a.transform(*pos)
return pos
def trough_pattern(cx, cy, length, width, rotation, use_x):
"""
1 -------------- 2
| |
4 -------------- 3
"""
p1 = (cx, cy)
p2 = (cx + length, cy)
p3 = (cx + length, cy - width)
p4 = (cx, cy - width)
a = AffineTransform()
a.translate(cx, cy)
a.rotate(rotation)
a.translate(-cx, -cy)
if use_x:
ps = (p1, p2, p4, p3, p1)
else:
ps = (p1, p2, p3, p4, p1)
for p in ps:
yield a.transform(*p)
def predict_values(self, refresh=False):
self.debug('predict values {}'.format(refresh))
try:
x, y, z, ze, j, je, sj, sje = self._extract_position_arrays()
t = AffineTransform()
t.rotate(self.rotation)
x, y = t.transforms(x, y)
# print(x)
except ValueError as e:
self.debug('no monitor positions to fit, {}'.format(e))
return
# print(x)
# print(y)
# print(z)
# print(ze)
n = x.shape[0]
if n >= 3 or self.plotter_options.model_kind in (WEIGHTED_MEAN,
MATCHING, BRACKETING):
# n = z.shape[0] * 10
r = max((max(abs(x)), max(abs(y))))
# r *= 1.25
reg = self._regressor_factory(x, y, z, ze)
self._regressor = reg
else:
msg = 'Not enough monitor positions. At least 3 required. Currently only {} active'.format(
n)
self.debug(msg)
self.information_dialog(msg)
return
options = self.plotter_options
ipositions = self.unknown_positions + self.monitor_positions
if options.model_kind == LEAST_SQUARES_1D:
k = options.one_d_axis.lower()
pts = array([getattr(p, k) for p in ipositions])
else:
pts = array([[p.x, p.y] for p in ipositions])
if options.use_monte_carlo and options.model_kind not in (MATCHING,
BRACKETING,
NN):
fe = FluxEstimator(options.monte_carlo_ntrials, reg)
split = len(self.unknown_positions)
nominals, errors = fe.estimate(pts)
if options.position_error:
_, pos_errors = fe.estimate_position_err(
pts, options.position_error)
else:
pos_errors = zeros(pts.shape[0])
for positions, s, e in ((self.unknown_positions, 0, split),
(self.monitor_positions, split, None)):
noms, es, ps = nominals[s:e], errors[s:e], pos_errors[s:e]
for p, j, je, pe in zip(positions, noms, es, ps):
oj = p.saved_j
p.j = j
p.jerr = je
p.position_jerr = pe
p.dev = (oj - j) / j * 100
else:
js = reg.predict(pts)
jes = reg.predict_error(pts)
for j, je, p in zip(js, jes, ipositions):
p.j = float(j)
p.jerr = float(je)
p.dev = (p.saved_j - j) / j * 100
p.mean_dev = (p.mean_j - j) / j * 100
if options.plot_kind == '2D':
self._graph_contour(x, y, z, r, reg, refresh)
elif options.plot_kind == 'Grid':
self._graph_grid(x, y, z, ze, r, reg, refresh)
else:
if options.model_kind in (LEAST_SQUARES_1D, WEIGHTED_MEAN_1D):
self._graph_linear_j(x, y, r, reg, refresh)
else:
self._graph_hole_vs_j(x, y, r, reg, refresh)