def border_point(self, xt, yt):
x1 = self.x + self.w / 2.0
y1 = self.y + self.h / 2.0
ac = np.array([x1, y1])
bc = np.array([xt, yt])
lines = []
lines.append([[self.x, self.y], [self.x + self.w, self.y]])
lines.append([[self.x + self.w, self.y],
[self.x + self.w, self.y + self.h]])
lines.append([[self.x, self.y + self.h],
[self.x + self.w, self.y + self.h]])
lines.append([[self.x, self.y], [self.x, self.y + self.h]])
for e in lines:
t1 = np.array(e[0])
t2 = np.array(e[1])
rez = intersection(ac, bc, t1, t2)
if (rez != None):
p, u, v = rez
if u >= 0 and u <= 1 and v >= 0 and v <= 1:
tc = t1 + 0.5 * (t2 - t1)
x1 = p[0]
y1 = p[1]
#self.x1 = tc[0]
#self.y1 = tc[1]
return x1, y1
def diffz_between(g, cornerid1, cornerid2):
'''Returns set of DiffZ nodeids between cornerid1 and cornerid2. The DiffZ
nodes actually tag the ZTag nodes that tag the corners.'''
return intersection(
g.at_last_step(cornerid1, ZTag, DiffZ),
g.at_last_step(cornerid2, ZTag, DiffZ)
)
def cls_add_tag(cls, g, taggees): # HACK
taggees = list(as_iter(taggees))
taggee_containers = intersection(
*[g.member_of(ee) for ee in as_iter(taggees)])
tag = g.make_node(cls, container=taggee_containers)
for taggee in as_iter(taggees):
g.add_edge(tag, tag_port_label, taggee, taggee_port_label)
return tag
def F_reg_pc(par, chd):
"""
Regularization term(Parent-Child)
"""
chd_area = area(chd)
bbox_penalty = (chd_area - intersection(par, chd) + 1e-6) / \
(chd_area + 1e-6)
return bbox_penalty.clamp(min=0)
def __call__(self, *args):
subsets = [self.index[key] for key in enumerate(args)
if key in self.index]
if subsets: # we are able to reduce the pool early
facts = intersection(*sorted(subsets, key=len))
else:
facts = self.facts
return conde(*[[eq(a, b) for a, b in zip(args, fact)]
for fact in facts])
def F_reg_cc(chd_a, chd_b):
"""
Regularization term(among childs)
"""
margin = 0
area_a = area(chd_a)
area_b = area(chd_b)
intersection_area = intersection(chd_a, chd_b)
chds_penalty = F.relu((intersection_area + 1e-6) / (area_a + 1e-6) - margin) + \
F.relu((intersection_area + 1e-6) / (area_b + 1e-6) - margin)
return chds_penalty
def is_match(self, g, tup):
'''tup must have same length as self.port_labels.'''
if len(tup) != len(self.port_labels):
raise (ValueError(
f'tuple {tup} and port_labels {self.port_labels} do not have the same length.'
))
mate_sets = []
for nodeid, port_label in zip(tup, self.port_labels):
mate_sets.append(g.neighbors(nodeid, port_label=port_label))
common_mates = intersection(*mate_sets)
return len(common_mates) == 0
def f(s):
args2 = reify(args, s)
subsets = [self.index[key] for key in enumerate(args) if key in self.index]
if subsets: # we are able to reduce the pool early
facts = intersection(*sorted(subsets, key=len))
else:
facts = self.facts
varinds = [i for i, arg in enumerate(args2) if isvar(arg)]
valinds = [i for i, arg in enumerate(args2) if not isvar(arg)]
vars = index(args2, varinds)
vals = index(args2, valinds)
assert not any(var in s for var in vars)
return (
merge(dict(zip(vars, index(fact, varinds))), s) for fact in self.facts if vals == index(fact, valinds)
)
def make(cls, g, operandids):
'''If any Operators remain that haven't been tried on the operandids
(node ids), returns a ConsumeOperands Action to build a full
arithmetic operation on the operandids, linking them to a new
Operator, whose class is chosen randomly, which links to a result
Block. Otherwise returns None.'''
operandids = list(operandids)
consumers = intersection(
*[g.neighbors(o, port_label='consumer') for o in operandids]
)
consumer_classes = set([g.class_of(c) for c in consumers])
untried_classes = all_operator_classes.difference(consumer_classes)
#print('MAKE', consumers, consumer_classes, untried_classes)
if untried_classes:
return ConsumeOperands(operandids, choice(list(untried_classes)))
else:
return None
def on_blocked(self) -> Actions:
'''Actions to perform when this node is Blocked.'''
agents = self.g.neighbors(self, 'agents')
agents_problems = union(*(self.g.neighbors(agent, 'problem')
for agent in agents))
tags_with_agent, tags_without_agent = [], []
for tag in self.g.tags_of(self, 'Blocked'):
if tag in agents_problems:
tags_with_agent.append(tag)
else:
tags_without_agent.append(tag)
if tags_without_agent:
return BuildAgent(self, choice(tags_without_agent))
else:
return BoostFromTo(
intersection(
agents,
self.g.neighbors(tags_with_agent,
neighbor_label='problem')))
def make_list_2 ():
cwd = os.getcwd()
ext = os.path.splitext(cwd)[1]
if ext == ".pbm":
pbms = glob.glob("problem.*.tex")
if pbms:
langs = []
for p in pbms:
langs.append(p.replace("problem.", "").replace(".tex", ""))
else:
langs = util.intersection(glob.glob("*"), languages)
print(cwd + " " + " ".join(sorted(langs)))
else:
for path in sorted(glob.glob("*")):
if os.path.isdir(path):
os.chdir(path)
make_list_2()
os.chdir(cwd)
def make_srclst_2 ():
global ctr
cwd = os.getcwd()
ext = os.path.splitext(cwd)[1]
if ext == ".pbm":
ctr+=1
pbms = glob.glob("problem.*.tex")
if pbms:
langs = []
for p in pbms:
langs.append(p.replace("problem.", "").replace(".tex", ""))
else:
langs = util.intersection(glob.glob("*"), languages)
for l in langs:
print "P%04d_%s" % (ctr, l), cwd, l
else:
for path in sorted(glob.glob("*")):
if os.path.isdir(path):
os.chdir(path)
make_srclst_2()
os.chdir(cwd)
def line_between(g, cornerid1, cornerid2):
'''Returns id of Line node between cornerid1 and cornerid2.'''
return first(intersection(
g.neighbors(cornerid1, neighbor_class=Line),
g.neighbors(cornerid2, neighbor_class=Line)
))
def find_page(img):
"""This function uses a Hough line transform to detect the four sides of a
piece of paper in the image, applies an inverse warp transformation to
correct for perspective distortion, returning the corrected image.
"""
rows, cols = img.shape[:2]
img2 = cv2.GaussianBlur(img, (params.BLUR_RADIUS, params.BLUR_RADIUS), 0)
"""
util.show('Blurred', img2)
"""
# use Canny edge deduction to obtain an edge map
edge_map_bw = cv2.Canny(img2, params.CANNY_THRESHOLD_LOW, params.CANNY_THRESHOLD_HIGH)
"""
util.show('Edges', edge_map_bw)
"""
# use the probablistic Hough transform
lines = cv2.HoughLinesP(
edge_map_bw,
params.HOUGH_DISTANCE_RESOLUTION,
params.HOUGH_ANGLE_RESOLUTION,
params.HOUGH_THRESHOLD,
minLineLength=params.HOUGH_MIN_LINE_LENGTH,
maxLineGap=params.HOUGH_MAX_LINE_GAP,
)
if lines is None:
util.debug("detected no image outlines")
return
else:
lines = lines[0].tolist()
util.debug("detected %d image outlines: %s" % (len(lines), str(lines)))
edge_map_color = cv2.cvtColor(edge_map_bw, cv2.COLOR_GRAY2BGR)
while True:
for a in lines:
"""
util.debug('current line: %s' % str(a))
"""
too_similar = False
for b in lines:
if a == b:
continue
t = util.angle(a, b)
d = util.distance(a, b)
"""
util.debug('other line: %s (angle: %f)' % (str(b), t))
"""
if d < params.DISTANCE_THRESHOLD and t < params.ANGLE_THRESHOLD:
too_similar = True
break
if too_similar:
"""
util.debug('removing %s' % str(a))
"""
lines.remove(a)
break
if not too_similar:
break
util.debug("kept %d image outlines: %s" % (len(lines), str(lines)))
for x1, y1, x2, y2 in lines:
cv2.line(edge_map_color, (x1, y1), (x2, y2), util.RED, params.LINE_THICKNESS)
"""
util.show('Lines', edge_map_color)
"""
if len(lines) != 4:
return None
corners = set()
for a in lines:
for b in lines:
if a == b:
continue
i = util.intersection(a, b)
if i[0] >= 0 and i[0] < cols and i[1] >= 0 and i[1] < cols:
corners.add(i)
# take the most extreme four corners to be the corners of the page
asc_by_x = lambda seq: sorted(seq, cmp=lambda a, b: int(a[0] - b[0]))
asc_by_y = lambda seq: sorted(seq, cmp=lambda a, b: int(a[1] - b[1]))
dsc_by_x = lambda seq: sorted(seq, cmp=lambda a, b: int(b[0] - a[0]))
dsc_by_y = lambda seq: sorted(seq, cmp=lambda a, b: int(b[1] - a[1]))
ul = asc_by_y(asc_by_x(corners))[0]
corners.remove(ul)
ur = asc_by_y(dsc_by_x(corners))[0]
corners.remove(ur)
ll = dsc_by_y(asc_by_x(corners))[0]
corners.remove(ll)
lr = dsc_by_y(dsc_by_x(corners))[0]
corners.remove(lr)
for x, y in [ul, ur, ll, lr]:
cv2.circle(edge_map_color, (x, y), 3, util.GREEN, -1)
"""
util.show('Lines & corners', edge_map_color)
"""
# use these four corners to construct a transformation matrix
# for the height/width of the new image, we use a bounding box
rows2 = max(ll[1], lr[1]) - min(ul[1], ur[1])
cols2 = max(ur[0], lr[0]) - min(ul[0], ll[0])
"""
print("source: ", numpy.array([ul, ur, ll, lr]).astype('float32'))
print("dest: ", numpy.array(
[
[0, 0],
[cols2 - 1, 0],
[cols2 - 1, rows2 - 1],
[0, rows2 - 1]
]
).astype('float32'))
"""
m = cv2.getPerspectiveTransform(
numpy.array([ul, ur, ll, lr]).astype("float32"),
numpy.array([[0, 0], [cols2 - 1, 0], [cols2 - 1, rows2 - 1], [0, rows2 - 1]]).astype("float32"),
)
corrected = cv2.warpPerspective(img, m, (cols2, rows2))
"""
util.show('Corrected', corrected)
"""
return corrected
LMaj = u.line(pH, pT)
pMaj = (pH, pT)
pMin = (pW1, pW2)
# if u.longer((pW1, pW2), (pH, pT)):
# LMaj = u.line(pW1, pW2)
# pMaj = (pW1, pW2)
# pMin = (pH, pT)
# Find the equations of the long and short edges of the bounding box
le = (u.linePointSlope(LMaj, pMin[0]),
u.linePointSlope(LMaj, pMin[1]))
se = (u.linePointSlopeInverted(LMaj, pMaj[0]),
u.linePointSlopeInverted(LMaj, pMaj[1]))
# Find the vertices of the bounding box from the lines
v = (u.intersection(le[0], se[0]),
u.intersection(le[0], se[1]),
u.intersection(le[1], se[0]),
u.intersection(le[1], se[1]))
# Show the raw vertices without epsilon
if SHOW:
plt.scatter(np.array(v)[:,0], np.array(v)[:,1])
# Calculate the diagonal lines of the bounding box
diags = (u.line(v[0], v[3]), u.line(v[1], v[2]))
# Calculate the center, width, height, and angle of the bounding box
c = u.intersection(diags[0], diags[1]) # Find the center by looking at the intersection
w = u.length(v[0], v[1]) + EPSILON # Find the width
h = u.length(v[0], v[2]) + EPSILON # Find the height
def find_page(img):
"""This function uses a Hough line transform to detect the four sides of a
piece of paper in the image, applies an inverse warp transformation to
correct for perspective distortion, returning the corrected image.
"""
rows, cols = img.shape[:2]
img2 = cv2.GaussianBlur(img, (params.BLUR_RADIUS, params.BLUR_RADIUS), 0)
"""
util.show('Blurred', img2)
"""
# use Canny edge deduction to obtain an edge map
edge_map_bw = cv2.Canny(img2, params.CANNY_THRESHOLD_LOW,
params.CANNY_THRESHOLD_HIGH)
"""
util.show('Edges', edge_map_bw)
"""
# use the probablistic Hough transform
lines = cv2.HoughLinesP(edge_map_bw,
params.HOUGH_DISTANCE_RESOLUTION,
params.HOUGH_ANGLE_RESOLUTION,
params.HOUGH_THRESHOLD,
minLineLength=params.HOUGH_MIN_LINE_LENGTH,
maxLineGap=params.HOUGH_MAX_LINE_GAP)
if lines is None:
util.debug('detected no image outlines')
return
else:
lines = lines[0].tolist()
util.debug('detected %d image outlines: %s' % (len(lines), str(lines)))
edge_map_color = cv2.cvtColor(edge_map_bw, cv2.COLOR_GRAY2BGR)
while True:
for a in lines:
"""
util.debug('current line: %s' % str(a))
"""
too_similar = False
for b in lines:
if a == b:
continue
t = util.angle(a, b)
d = util.distance(a, b)
"""
util.debug('other line: %s (angle: %f)' % (str(b), t))
"""
if d < params.DISTANCE_THRESHOLD and t < params.ANGLE_THRESHOLD:
too_similar = True
break
if too_similar:
"""
util.debug('removing %s' % str(a))
"""
lines.remove(a)
break
if not too_similar:
break
util.debug('kept %d image outlines: %s' % (len(lines), str(lines)))
for x1, y1, x2, y2 in lines:
cv2.line(edge_map_color, (x1, y1), (x2, y2), util.RED,
params.LINE_THICKNESS)
"""
util.show('Lines', edge_map_color)
"""
if len(lines) != 4:
return None
corners = set()
for a in lines:
for b in lines:
if a == b:
continue
i = util.intersection(a, b)
if i[0] >= 0 and i[0] < cols and i[1] >= 0 and i[1] < cols:
corners.add(i)
# take the most extreme four corners to be the corners of the page
asc_by_x = lambda seq: sorted(seq, cmp=lambda a, b: int(a[0] - b[0]))
asc_by_y = lambda seq: sorted(seq, cmp=lambda a, b: int(a[1] - b[1]))
dsc_by_x = lambda seq: sorted(seq, cmp=lambda a, b: int(b[0] - a[0]))
dsc_by_y = lambda seq: sorted(seq, cmp=lambda a, b: int(b[1] - a[1]))
ul = asc_by_y(asc_by_x(corners))[0]
corners.remove(ul)
ur = asc_by_y(dsc_by_x(corners))[0]
corners.remove(ur)
ll = dsc_by_y(asc_by_x(corners))[0]
corners.remove(ll)
lr = dsc_by_y(dsc_by_x(corners))[0]
corners.remove(lr)
for x, y in [ul, ur, ll, lr]:
cv2.circle(edge_map_color, (x, y), 3, util.GREEN, -1)
"""
util.show('Lines & corners', edge_map_color)
"""
# use these four corners to construct a transformation matrix
# for the height/width of the new image, we use a bounding box
rows2 = max(ll[1], lr[1]) - min(ul[1], ur[1])
cols2 = max(ur[0], lr[0]) - min(ul[0], ll[0])
"""
print("source: ", numpy.array([ul, ur, ll, lr]).astype('float32'))
print("dest: ", numpy.array(
[
[0, 0],
[cols2 - 1, 0],
[cols2 - 1, rows2 - 1],
[0, rows2 - 1]
]
).astype('float32'))
"""
m = cv2.getPerspectiveTransform(
numpy.array([ul, ur, ll, lr]).astype('float32'),
numpy.array([[0, 0], [cols2 - 1, 0], [cols2 - 1, rows2 - 1],
[0, rows2 - 1]]).astype('float32'))
corrected = cv2.warpPerspective(img, m, (cols2, rows2))
"""
util.show('Corrected', corrected)
"""
return corrected
def renderAsJson(self, filename):
bb = self.schedule.GetStopBoundingBox()
longitude = (bb[1] + bb[3]) / 2.
zone = (int((longitude + 180)/6) % 60) + 1
utm17 = pyproj.Proj(proj='utm', zone=zone, ellps='WGS84')
features = []
for edge in self.graph.edges:
if not self.graph.edges[edge]['junction']:
A, B = edge
A, B = fullEdge(A)
currentEdge = self.graph.edges[edge]
#self.graph.edges[edge]['offsets'] = {}
self.graph.node[A]['points'] = {}
self.graph.node[B]['points'] = {}
centreGeom = currentEdge['geom'].simplify(self.totalThickness / 2.)
__, self.graph.node[A]['dir'] = getDirection(centreGeom, True)
__, self.graph.node[B]['dir'] = getDirection(centreGeom, False)
#offset = -self.graph.edges[edge]['width'] / 2.
for route in currentEdge['sequence']:
route_color = self.schedule.GetRoute(route).route_color
#offset += self._getTotalThickness(edge, route) / 2.
offset = currentEdge['offsets'][route]
geom = centreGeom.parallel_offset(offset, 'left')
#self.graph.edges[edge]['offsets'][route] = offset
if offset >= 0:
self.graph.node[A]['points'][route] = geom.coords[0]
self.graph.node[B]['points'][route] = geom.coords[-1]
else:
self.graph.node[A]['points'][route] = geom.coords[-1]
self.graph.node[B]['points'][route] = geom.coords[0]
#offset += self._getTotalThickness(edge, route) / 2.
coord_list = [list(utm17(inverse=True, *c))
for c in geom.coords]
features.append({"type": "Feature",
"properties": {
'stroke-width': self._getLineThickness(edge, route),
'stroke': '#' + route_color,
'stroke-opacity': 1,
'route': route,
'sides': str(self.graph.edges[edge]['sides'].items())
},
"geometry": {
"type": "LineString",
"coordinates": coord_list
}})
for edge in self.graph.edges:
if self.graph.edges[edge]['junction']:
A, B = edge
currentEdge = self.graph.edges[edge]
edgeA = self.graph.edges[fullEdge(A)]
edgeB = self.graph.edges[fullEdge(B)]
seqA = list(edgeA['sequence'])
seqB = list(edgeB['sequence'])
if firstNode(A) == firstNode(B):
seqB.reverse()
# __, dA = getDirection(edgeA['geom'], firstNode(A))
# __, dB = getDirection(edgeB['geom'], firstNode(B))
dA = self.graph.node[A]['dir']
dB = self.graph.node[B]['dir']
# if self.maxAngle > currentEdge['angle']:
#
#
# continue
sinAB = np.cross(dA, dB)
cosAB = np.dot(dA, dB)
angle = np.arctan2(abs(sinAB), cosAB)
right = sinAB > 0
if right and firstNode(A) or not right and not firstNode(A):
seqA.reverse()
seqB.reverse()
routesA = [r for r in seqA if r in currentEdge['routes']]
routesB = [r for r in seqB if r in currentEdge['routes']]
routes = [r for r in currentEdge['routes']
if r in routesA and r in routesB]
if not routes:
continue
offsetA0 = edgeA['offsets'][routesA[0]]
offsetB0 = edgeB['offsets'][routesB[0]]
# lastRadius = self.minRadius - self._getTotalThickness(edge, routeIndexes[0][1]) / 2.
# delta = 0
radii = {}
for r in routes:
offsetA = abs(offsetA0 - edgeA['offsets'][r])
offsetB = abs(offsetB0 - edgeB['offsets'][r])
radii[r] = self.minRadius + min(offsetA, offsetB)
# for i, r in sorted(routeIndexes):
# thickness = self._getTotalThickness(edge, r)
# if i != lastIndex:
# lastRadius += delta
# delta = 0
# radii[r] = lastRadius + thickness / 2.
# delta += thickness
# lastIndex = i
for route in self.graph.edges[edge]['routes']:
route_color = self.schedule.GetRoute(route).route_color
if route not in edgeA['sequence'] or route not in edgeB['sequence']:
continue
ptA = np.array(self.graph.node[A]['points'][route])
ptB = np.array(self.graph.node[B]['points'][route])
# offsetA = edgeA['offsets'][route]
# offsetB = edgeB['offsets'][route]
# if firstNode(A):
# ptA = edgeA['geom'].interpolate(0, normalized=True)
# dirA = getDirection(edgeA['geom'], 0)
# else:
# ptA = edgeA['geom'].interpolate(1, normalized=True)
# dirA = getDirection(edgeA['geom'], 1)
# if firstNode(B):
# ptB = edgeB['geom'].interpolate(0, normalized=True)
# dirB = getDirection(edgeB['geom'], 0)
# else:
# ptB = edgeB['geom'].interpolate(1, normalized=True)
# dirB = getDirection(edgeB['geom'], 1)
# ptA = Point(ptA.x - dirA[1] * offsetA, ptA.y + dirA[0] * offsetA)
# ptB = Point(ptB.x - dirB[1] * offsetB, ptB.y + dirB[0] * offsetB)
radius = radii[route]
corner = intersection(ptA, dA, ptB, dB)
setback = radius / np.tan(angle/2)
delta = abs(setback-radius)/radius
filler = ''
if np.sin(self.maxAngle) > np.sin(angle) or delta > 1:
# hack for now with straight line
dAB = ptB - ptA
dAB /= np.linalg.norm(dAB)
dAR = np.array([dA[1], -dA[0]])
dBR = np.array([dB[1], -dB[0]])
crossAAB = np.cross(dA, dAB)
crossBAB = np.cross(dB, dAB)
dotAAB = np.dot(dA, dAB)
dotBAB = np.dot(dB, dAB)
offsetsA = [o
for (r, o) in edgeA['offsets'].items()
if r in routes]
offsetsB = [o
for (r, o) in edgeB['offsets'].items()
if r in routes]
if crossAAB < 0: # left
if firstNode(A):
offsetA0 = min(offsetsA)
else:
offsetA0 = max(offsetsA)
else: # right
if firstNode(A):
offsetA0 = max(offsetsA)
else:
offsetA0 = min(offsetsA)
dAR = -dAR
if crossBAB > 0: # left
if firstNode(B):
offsetB0 = min(offsetsB)
else:
offsetB0 = max(offsetsB)
else: # right
if firstNode(B):
offsetB0 = max(offsetsB)
else:
offsetB0 = min(offsetsB)
dBR = - dBR
radiusA = self.minRadius + abs(offsetA0 - edgeA['offsets'][route])
radiusB = self.minRadius + abs(offsetB0 - edgeB['offsets'][route])
centreA = ptA + dAR * radiusA
angleA = np.arctan2(-dAR[1], -dAR[0])
centreB = ptB + dBR * radiusB
angleB = np.arctan2(-dBR[1], -dBR[0])
l = np.linalg.norm(centreA - centreB)
#if radius + radius <= l:
__, dC = getDirection(LineString([centreA, centreB]))
angleC = np.arctan2(dC[1], dC[0])
if (crossAAB > 0 and crossBAB > 0) or (crossAAB < 0 and crossBAB < 0):
thetaB = np.arccos((radiusA-radiusB)/l)
thetaA = thetaB
elif radiusA + radiusB <= l:
thetaA = np.arccos((radiusA+radiusB)/l)
thetaB = np.pi - thetaA
else:
thetaA = 0
thetaB = np.pi
if crossAAB > 0: # left
angleAC = angleC + thetaA
else:
angleAC = angleC - thetaA
if crossBAB < 0: # left
angleBC = angleC - thetaB
else:
angleBC = angleC + thetaB
while angleAC - angleA > np.pi:
angleAC -= 2 * np.pi
while angleA - angleAC > np.pi:
angleAC += 2 * np.pi
while angleBC - angleB > np.pi:
angleBC -= 2 * np.pi
while angleB - angleBC > np.pi:
angleBC += 2 * np.pi
pts = [centreA + radiusA * np.array([np.cos(a), np.sin(a)]) for a in np.linspace(angleA, angleAC, 5)]
pts += [centreB + radiusB * np.array([np.cos(a), np.sin(a)]) for a in np.linspace(angleBC, angleB, 5)]
pts.insert(0, ptA)
pts.append(ptB)
filler = str((angleA, angleAC, angleBC, angleB, crossAAB, crossBAB, dotAAB, dotBAB, angleC, thetaA, thetaB, radiusA, radiusB))
# else:
# pts = [ptA, ptB]
# coord_list = [list(utm17(*ptA, inverse=True)),
# list(utm17(*ptB, inverse=True))]
else:
# radius = radii[route]
# corner = intersection(ptA, dA, ptB, dB)
# setback = radius / np.tan(angle/2)
endA = corner + dA * setback
if right:
# if firstNode(A):
# setback = radius / np.tan(currentEdge['angle']/2)
# else:
# setback = radius * np.tan(currentEdge['angle']/2)
# endA = corner + dA * setback
dAR = np.array([-dA[1], dA[0]])
dBR = np.array([dB[1], -dB[0]])
angleA = np.arctan2(-dAR[1], -dAR[0])
angleB = np.arctan2(-dBR[1], -dBR[0])
if angleB > angleA:
angleB -= 2. * np.pi
else:
# if firstNode(A):
# setback = radius * np.tan(currentEdge['angle']/2)
# else:
# setback = radius / np.tan(currentEdge['angle']/2)
# endA = corner + dA * setback
dAR = np.array([dA[1], -dA[0]])
dBR = np.array([-dB[1], dB[0]])
angleA = np.arctan2(-dAR[1], -dAR[0])
angleB = np.arctan2(-dBR[1], -dBR[0])
if angleB < angleA:
angleB += 2. * np.pi
centre = endA + radius * dAR
steps = int(abs(angleA - angleB)*4) # for now, point at least every .25 radians
angles = np.linspace(angleA, angleB, steps + 2)
pts = [centre + [radius*np.cos(a), radius*np.sin(a)]
for a in angles]
pts.append(ptB)
pts.insert(0, ptA)
coord_list = [list(utm17(*pt, inverse=True))
for pt in pts if pt[0] < 1e29]
thickness = min(self._getLineThickness(edgeA, route),
self._getLineThickness(edgeB, route))
features.append({"type": "Feature",
"properties": {
'stroke-width': thickness,
'stroke': '#' + route_color,
'stroke-opacity': 1,
'angles': filler
# 'setback': min(setback, 1e30),
# 'radius': radii[route]
},
"geometry": {
"type": "LineString",
"coordinates": coord_list
}})
data = {"type": "FeatureCollection",
"features": features}
with open(filename, 'w') as fp:
json.dump(data, fp)
def is_already_consumed(self, g):
consumers = intersection(
*[g.neighbors(o, port_label='consumer') for o in self.operandids]
)
consumer_classes = set([g.class_of(c) for c in consumers])
return self.operator_class in consumer_classes