def independent_2(self):
""" Returns a maximal independent set
(maximal means that no other independent set cantains it,
doesn't mean that it has the most vertices)"""
if self.ver_len() == 0:
return None
else:
#Let 'v' be a vertice closest to (0, 0)
#'v' will be added to the independent set
v = self[0]
odle = dist(v, (0, 0))
for w in self.vertices:
if dist(w, (0, 0)) < odle:
odle = dist(w, (0, 0))
v = w
#Remove neighbors of 'v'
new_graph = self - (self.get_neighbors(v) + [v])
#Repeat the action for the vertices that remained
ind = new_graph.independent_2()
if ind is not None:
return [v] + ind
return [v]
def getSubTrackLength(self, subTrack):
pointList = self.pointLists[subTrack]
totalLength = 0
p0 = point(0,0)
for iP in range(len(pointList)):
p1 = pointList[iP].obj.GetGeometryRef().GetPoint(0)
p1 = point(p1[0], p1[1])
if iP > 0:
totalLength += dist(p0, p1)
else:
pFirst = p1
p0 = p1
return totalLength, dist(pFirst, p1)
def snap_point(self, x: float, y: float) -> Point:
p = Point(x, y)
# snap to active polygon
if self.points:
for p2 in self.points[:-1]:
if dist(p, p2) < SNAP_RADIUS:
return p2
# snap to polygons
for p2 in self.collection.all_points:
if dist(p, p2) < SNAP_RADIUS:
return p2
return p
def __init__(self, name, index=0):
PointElectrode.__init__(self, name, index)
settings = self.settings
try:
self.xyz = settings['point']
except KeyError:
self.xyz = settings['points'][self.index]
self.x, self.y, self.z = self.xyz
# Find the cable this corresponds to
dists = geo.dist((self.x, self.y), (ws.nvt.x, ws.nvt.y))
these = np.where(dists == dists.min())[0]
# print('Points:', these)
# Choose only one cable
cable_i = these[0]
# print('Cable:', ws.nvt.cables[cable_i])
# Locate the section and segment (z value) of the cable
j_sec, z_on_j = anatomy.locate(ws.seclens[cable_i], self.z)
# print('Section and segment:', j_sec, z_on_j)
# Cable and point of the cable to work on
self.point = {'cable': cable_i, 'section': j_sec, 'z': z_on_j}
self.set_stim_info()
def hasCrashed (self):
for i in range(1, len(self.tail) - self.hasEaten):
if geometry.dist(self.pos, self.tail[i].getCenter()) < self.snakeWidth:
return True;
return False;
def multi_dist(self) -> (pd.DataFrame, pd.DataFrame):
'''
This function calculates the distance between source/s and antenna/s and also returns a vector of
source-antenna for every set of source-antenna.
:returns:
- dist_arr: the distances from the source to the antenna
- sa_vec: the vector from source to the antenna in Cartesian coordinate system (x,y,z).
each elements are a list of vector components
:rtype: pandas.Dataframe
'''
dist_arr = pd.DataFrame(np.zeros((self.radar_n, self.source_n)),
columns=self.s_columns)
sa_vec = pd.DataFrame(np.zeros((self.radar_n, self.source_n)),
columns=self.s_columns)
for i in range(self.radar_n):
for j in range(self.source_n):
dist_arr.iloc[i,
j] = geometry.dist(self.source_location[j, :],
self.antenna_location[i, :])
sa_vec.iloc[[i], j] = pd.Series([
-self.source_location[j, :] + self.antenna_location[i, :]
],
index=[i])
return dist_arr, sa_vec
def preprocess_world_state(self, world_state):
"""
Uses historical information about the world to build a more accurate map of where all balls and obstacles are.
Places results back into the world_state dict without adding any new fields.
"""
curr_pos = world_state['pose'][0] # Our current (x,y)
#
# Build obstacle grid
#
# Add all obstacles into a temporary obstacle grid
self.temp_obstacle_grid.occupancy.fill(0)
for static_obstacle in self.field_columns:
self.temp_obstacle_grid.insert_convex_polygon(static_obstacle, 1.0)
for static_obstacle in self.field_trenches:
self.temp_obstacle_grid.insert_rectangular_obstacle(static_obstacle.bounding_box, 1.0)
for dynamic_obstacle in world_state['obstacles']:
self.temp_obstacle_grid.insert_rectangular_obstacle(dynamic_obstacle,
self.obstacle_probability_growth_factor)
# Inflate all obstacles in the temporary obstacle grid
self.temp_obstacle_grid.inflate_obstacles(kernel_size=self.occupancy_grid_dilation_kernel_size)
# Decay the probability of all cells in the master obstacle grid
self.obstacle_grid.decay_probabilities(self.obstacle_probability_decay_factor)
# Update the master obstacle grid with our temporary one
self.obstacle_grid.occupancy += self.temp_obstacle_grid.occupancy
self.obstacle_grid.occupancy = np.clip(self.obstacle_grid.occupancy, a_min=0, a_max=1)
#
# Update ball positions
#
# Recover any balls that are now within the deadzone
if self.prev_obstacles is not None:
for ball in self.prev_obstacles:
if 0.5 < geom.dist(curr_pos, ball) <= self.deadzone_radius and ball not in world_state['balls']:
world_state['balls'].append(ball)
self.prev_obstacles = world_state['balls']
# Decay the probability of all balls
self.ball_grid.decay_probabilities(self.ball_probability_decay_factor)
# Grow the probability of all ball grid cells currently containing balls
for ball_pos in world_state['balls']:
self.ball_grid.grow_probability(ball_pos, self.ball_probability_growth_factor)
# Get final ball positions out of ball grid
world_state['balls'] = []
for i in range(self.ball_grid.occupancy.shape[0]):
for j in range(self.ball_grid.occupancy.shape[1]):
cell_position = self.ball_grid.grid[i][j].position
p_ball = self.ball_grid.occupancy[i][j]
p_occupied = self.obstacle_grid.occupancy[i][j]
if p_ball >= self.ball_probability_threshold and p_occupied < self.obstacle_probability_threshold:
world_state['balls'].append(cell_position)
def project(self, source_point):
"""Finds the closest point on the destination mesh for the given
source_point, and returns the projected point as well as the distance
to that point."""
projected_point_index = self.destination_nearest_neighbors.kneighbors(
source_point
)[1][0][0]
projected_point = self.destination_mesh.vs[projected_point_index]
return projected_point, dist(projected_point, source_point)
def find_element(self, x: float, y: float, radius: float = 20.0):
""" Returns element from collection that is in radius. """
p = Point(x, y)
for p2 in self.collection.points:
if dist(p, p2) < radius:
return p2
for seg in self.collection.segments:
for p2 in seg:
if dist(p, p2) < radius:
return seg
for poly in self.collection.polygons:
for p2 in poly.points:
if dist(p, p2) < radius:
return poly
return None
def testBugEnviroment():
print("#________________TESTING_____________________#")
#Define start and goal positions plus a couple of polygonal obstacles
start = (.3,.3)
#goal = (5.4,4.2)
goal = (3.5,3.5)
Poly1 = pol.Polygon()
Poly1.initiate_list([-0.6,-0.4,0.7,0.6,0.2,-0.296057],[0.3,-0.4,-0.3,0.4,0.3,0.596997])
Poly2 = pol.Polygon()
Poly2.initiate_list([-0.8,-0.1,0.9,0.3,0.102922,-0.3],[-0.4,-0.1,-0.4,0.2,0.598169,0.4])
#Shift polygons into positions along path to create obstacles
Poly1.set_shift(1.5, 1.5)
Poly2.set_shift(2.0, 3.5)
#Need to create a list of these obstacles to define the environment
PolyList = [Poly1,Poly2]
path = []
#Bug algorithm invocation goes here
tic = time.time()
path = bug.computeBug1Path(start,goal,PolyList);
toc = time.time()
print "Time to find path in ms: ", (toc-tic)*1000
totalPathLenght = 0
for i in range(0,len(path)-1):
totalPathLenght += dist(path[i],path[i+1])
print "Total path length: ", totalPathLenght
#Plot path of Bug
drawPolygonAndPoint(PolyList,start,goal,path)
#Plot distance to goal
distanceToGoal = []
total_distance_traveled = 0
for i in range(len(path)):
distanceToGoal.append(dist((path[i][0],path[i][1]),goal))
drawDistanceToGoal(distanceToGoal)
def getSpeedSlope(self, shapeFile, subTrack, slopeSpeedOutFileHdl, paramDict, speedSlopeOutFileHdl):
# one record per point
# get distance
# get duration
# get speed
# get altitudes
# get slope
#write speed and slope
pointList = self.pointLists[subTrack]
if speedSlopeOutFileHdl:
slopeSpeedOutFileHdl.write("ShapeFile" + delim + "SubTrack" + delim + "Distance" + delim + "FromID" + delim +
"ToID" + delim + "Speed" + delim + "Slope" + delim + "\n")
pp0 = point(0,0)
slopeList = []
speedList = []
for iP in range(len(pointList)):
p1 = pointList[iP].obj.GetGeometryRef().GetPoint(0)
pp1 = point(p1[0], p1[1])
distance = 0.0
offset = 1
if iP > 0:
while distance < slopeSpeedResolution and iP+offset < len(pointList):
##print iP+offset, len(pointList)
p2 = pointList[iP+offset].obj.GetGeometryRef().GetPoint(0)
pp2 = point(p2[0], p2[1])
p3 = pointList[iP+offset-1].obj.GetGeometryRef().GetPoint(0)
pp3 = point(p3[0], p3[1])
## Collect slopes on the way (as a list). Enable average slopes by the end.
distance += dist(pp3, pp2)
startId = pointList[iP].obj.GetField(pointIdAttributeName)
endId = pointList[iP+offset].obj.GetField(pointIdAttributeName)
startTime = pointList[iP].dateTime
endTime = pointList[iP+offset].dateTime
offset += 1
deltaTimeHours = (endTime - startTime).seconds / 3600.00
rasterVal1 = paramDict["Altitude"].getCellValueAtGeolocation(pp2.x, pp2.y)
rasterVal0 = paramDict["Altitude"].getCellValueAtGeolocation(pp0.x, pp0.y)
deltaAltitude = rasterVal1 - rasterVal0
if distance != 0:
## Calc slopePCT as the average oof all segments slope
slopePct = (deltaAltitude * 100.00) / distance
else:
slopePct = 0
speed = (distance / 1000) / deltaTimeHours
if slopePct > minSlope and slopePct < maxSlope and speed > minSpeed and speed < maxSpeed:
slopeSpeedOutFileHdl.write(shapeFile + delim + str(subTrack) + delim + str(distance) + delim + str(startId) + delim +
str(endId) + delim + str(speed) + delim + str(slopePct) + "\n")
slopeList.append(slopePct)
speedList.append(speed)
p0 = p1
pp0 = point(p0[0], p0[1])
##print len(slopeList), len(speedList), len([slopeList, speedList])
return [slopeList, speedList]
def compare(a: Union[Point, Segment], b: Union[Point, Segment]):
# get current ray
ray = ray_getter()
if isinstance(a, Point) and isinstance(b, Point):
return dist(ray.p1, a) - dist(ray.p2, b)
if isinstance(a, Point) and isinstance(b, Segment):
pb = intersection(*ray,
*b,
restriction_1='ray',
restriction_2='segment')
dist_a = dist(ray.p1, a)
dist_b = dist(ray.p1, pb)
if dist_a == dist_b:
return -1
return dist_a - dist_b
if isinstance(a, Segment) and isinstance(b, Point):
pa = intersection(*ray,
*a,
restriction_1='ray',
restriction_2='segment')
dist_a = dist(ray.p1, pa)
dist_b = dist(ray.p1, b)
if dist_a == dist_b:
return 1
return dist_a - dist_b
if isinstance(a, Segment) and isinstance(b, Segment):
pa = intersection(*ray,
*a,
restriction_1='ray',
restriction_2='segment')
pb = intersection(*ray,
*b,
restriction_1='ray',
restriction_2='segment')
dist_a = dist(ray.p1, pa)
dist_b = dist(ray.p1, pb)
if dist_a == dist_b:
pa2 = a.p1 if a.p2 == pa else a.p2
pb2 = b.p1 if b.p2 == pb else b.p2
return angle_between_points(ray.p1, ray.p2,
pa2) - angle_between_points(
ray.p1, ray.p2, pb2)
return dist_a - dist_b
raise ValueError(
f'Comparator got unexpected types: {type(a)}, {type(b)}')
def pm6_error(params):
# Run Gaussian jobs with new parameters
for i, example in enumerate(examples):
running_jobs = [job('%s-%d-%d' % (name, counter[0], i),
'PM6=(Input,Print) Opt=Loose',
example.atoms,
extra_section=param_string % tuple(params),
queue=queue,
force=True)]
# Wait for all jobs to finish
for j in running_jobs:
j.wait()
# Get forces and energies resulting from new parameters
geom_error = 0.0
for i, example in enumerate(examples):
try:
new_energy, new_atoms = parse_atoms(
'%s-%d-%d' % (name, counter[0], i),
check_convergence=False)
except:
print '%s-%d-%d' % (name, counter[0], i), 'has no data'
exit()
if parse_atoms('%s-%d-%d' % (name, counter[0], i)) is None:
print '%s-%d-%d' % (name, counter[0], i),\
'did not converge fully'
# Compare results
for b in example.bonds:
d1 = geometry.dist(example.atoms[b[0]], example.atoms[b[1]])
d2 = geometry.dist(new_atoms[b[0]], new_atoms[b[1]])
geom_error += (d1 - d2)**2
error = geom_error / n_bonds
print error**0.5, params
counter[0] += 1
return error
def getSamples(self, subIndex, angleWidth, length, numPoints, targetFileHdl, rasterDataDict={}, header=False):
##print length
pointList = self.pointLists[subIndex]
lastPoint = pointList[-1].obj
##import pdb;pdb.set_trace()
p3 = lastPoint.GetGeometryRef().GetPoint(0)
paramListStr = ""
orgLength = length
for key in rasterDataDict.keys():
paramListStr = paramListStr + delim + key
if header:
targetFileHdl.write("FileName" + delim + "SubTrack" + delim + "Id" + delim + "Choice" + delim + "Distance" + delim + "X" + delim + "Y" + delim + "AngPrev" + delim + "AngLast" + paramListStr + "\n")
for i in range(len(pointList)):
length = orgLength
if i == 0:
presentPoint = pointList[0].obj
if i == 1:
previousPoint = presentPoint
presentPoint = pointList[1].obj
if i > 1:
for ii in range(len(pointList)):
if ii >= i:
pp1 = presentPoint.GetGeometryRef().GetPoint(0)
pp1 = point(pp1[0], pp1[1])
pp2 = pointList[ii].obj.GetGeometryRef().GetPoint(0)
pp2 = point(pp2[0], pp2[1])
akkuLength = dist(pp1, pp2)
##import pdb;pdb.set_trace()
##print "Length", length, "akkuLength", akkuLength
if akkuLength >= length:
samplePoint = pointList[ii].obj
##pp2 = nextPoint.GetGeometryRef().GetPoint(0)
break
else:
##nextPoint = pointList[ii - 1].obj
pass
length = akkuLength
nextPoint = pointList[i].obj
id = presentPoint.GetField(pointIdAttributeName)
p0 = previousPoint.GetGeometryRef().GetPoint(0)
p1 = presentPoint.GetGeometryRef().GetPoint(0)
p2 = samplePoint.GetGeometryRef().GetPoint(0)
samples = samplePoints(point(p1[0], p1[1]), point(p2[0], p2[1]), angleWidth, length, numPoints, point(p0[0], p0[1]), point(p3[0], p3[1]))
for p in samples:
## Here raster values are collected.
paramValueStr = ""
for key in rasterDataDict.keys():
paramValueStr = paramValueStr + delim + str(rasterDataDict[key].getCellValueAtGeolocation(p.x, p.y))
targetFileHdl.write(self.fileName + delim + str(subIndex) + delim + str(id) + delim + str(p.selected) + delim + str("%.2f" % p.length) + delim + str(p.x) + delim + str(p.y) + delim + str("%.4f" % p.angleToLast) + delim + str("%.4f" % p.angleFromPrevious) + paramValueStr + "\n")
previousPoint = presentPoint
presentPoint = nextPoint
def get_tanget_vector(self, q):
p1, p2, distance = self.get_closest_segment(q)
closest_point = None
if geo.dist(q, p1) == distance:
closest_point = p1
elif geo.dist(q, p2) == distance:
closest_point = p2
print closest_point
if closest_point != None:
# Need to rotate vector -90 degrees corresponding to origin point
# Rotating by -90 degrees (-PI /2 rads)
computed_point = (closest_point[0] - q[0], closest_point[1] - q[1]) # Computed line
#lot = (cp[0]*4.0,cp[1]*4.0)
normal_point = (q[0] + computed_point[0], q[1] + computed_point[1])
theta = -math.pi/2
rp = geo.rotate_point_around_fixed_point(normal_point, q, theta)
else:
# Segment closest
x = p2[0] - p1[0]
y = p2[1] - p1[1]
rp = (x, y)
return rp
def get_tangent_vector(self, point):
min_vertex_dist = float("inf")
min_vertex = None
min_edge_dist = float("inf")
min_edge = None
# first find the closest vertex
for vertex in self.points:
dist_to_vertex = dist(point, vertex)
if dist_to_vertex < min_vertex_dist:
min_vertex_dist = dist_to_vertex
min_vertex = vertex
# next, find the closest edge
for i, vertex1 in enumerate(self.points):
if i < len(self.points) - 1:
vertex2 = self.points[i + 1]
else:
vertex2 = self.points[0]
if vertex1[0] == vertex2[0] and vertex1[1] == vertex2[1]:
break
dist_to_edge = get_distance_to_segment(point, vertex1, vertex2)
if dist_to_edge < min_edge_dist:
min_edge_dist = dist_to_edge
min_edge = [vertex1, vertex2]
# compare closest edge and closest vertex
if min_vertex_dist <= min_edge_dist + 10e-5:
# then closest point on polygon is min_vertex
# compute unit vector from point to min_vertex
x_component = (min_vertex[0] - point[0]) / dist(min_vertex, point)
y_component = (min_vertex[1] - point[1]) / dist(min_vertex, point)
# now rotate this vector by -pi/2 to get counterclockwise tangent
return (y_component, -x_component)
else: # the closest point on the polygon is min_edge
x_component = (min_edge[1][0] - min_edge[0][0]) / dist(min_edge[0], min_edge[1])
y_component = (min_edge[1][1] - min_edge[0][1]) / dist(min_edge[0], min_edge[1])
return (x_component, y_component)
def cond(self, edge):
"""This is a condition that must be satisfied by an edge to be included
in creating a graph
It says that distance between vertices connected by it must be between
1 and 2"""
if Graph.cond(self, edge):
v = list(edge)[0]
w = list(edge)[1]
return between(1, dist(v, w), 2)
else:
return False
def nearbyPoints(pointXyz, pdbD):
'''finds the nearest atom to each surface point, makes list'''
outputList = []
for pointCoord in pointXyz:
pointNum = pointCoord[0]
xyz = pointCoord[1:4]
closestIndex, closestDist = 0, 1000000000000.
for index, atom in enumerate(pdbD.coords):
thisDist = geometry.dist(xyz, atom, metric="L2SQUARED") # l2^2 monotonic
if thisDist < closestDist:
closestDist = thisDist
closestIndex = index
outputList.append([pointNum, closestIndex+1])
return outputList
def compareColumns(
tmNode1, tmNode2, columnList, columnsToMean, columnsToStddev):
'''compares 2 nodes, report dist between vectors of z-scores
'''
zScores1, zScores2 = [], []
for col in columnList:
colMean = columnsToMean[col]
colStddev = columnsToStddev[col]
zScore1 = (tmNode1.attributes[col] - colMean) / colStddev
zScore2 = (tmNode2.attributes[col] - colMean) / colStddev
zScores1.append(zScore1)
zScores2.append(zScore2)
#distZ = geometry.distL2(zScores1, zScores2)
distZ = geometry.dist(zScores1, zScores2, metric='L1') # L1 seems to be best
return distZ
def check_colors(self):
"""Returns True if a graph is colored properly, that is no two neighbors
have the same color, False otherwise"""
for v in self.vertices:
i = self.vertices.index(v)
for w in self.vertices:
j = self.vertices.index(w)
if (self.are_neighbors(v, w)
and self.colors[i] == self.colors[j]):
print('Bad colors :(')
print('vertices:', v, w)
print('dist:', dist(v, w))
print('colors:', self.colors[i], self.colors[j])
return False
print('Good colors :)')
return True
def findFragments(self, dist_cutoff=5.0):
"""
Take lines of pdb in self.atom_lines and find breaks in chain.
"""
# For every residue in the pdb file, grab the CA and N lines. This
# assumes that there are no duplicate residues and that all residues
# have CA and N. If there is a duplicate residue or missing atom, an
# error is raised.
residue_list = []
for line in self.atom_lines:
if line[21:26] not in residue_list:
residue_list.append(line[21:26])
ca_list = []
n_list = []
for resid in residue_list:
resid_atoms = [l for l in self.atom_lines if l[21:26] == resid]
ca = [l for l in resid_atoms if l[13:16] == "CA "]
n = [l for l in resid_atoms if l[13:16] == "N "]
if len(ca) > 1 or len(n) > 1:
err = "Residue \"%s\" is duplicated!" % resid
raise PdbContainerError(err)
elif len(ca) == 0 or len(n) == 0:
err = "Residue \"%s\" has missing CA or N atoms!" % resid
raise PdbContainerError(err)
ca_list.append(ca[0])
n_list.append(n[0])
# Grab coordinates of CA atoms and index of residues indexes of
# amide nitrogens.
ca_coord = [[float(l[30 + 8 * i:38 + 8 * i]) for i in range(3)]
for l in ca_list]
res_index = [self.atom_lines.index(n) for n in n_list]
# Check the distance between every ca carbon and the next. If these
# are further apart than dist_cutoff, place in different fragments.
self.frag_index = []
for i in range(1, len(ca_list)):
if dist(ca_coord[i - 1], ca_coord[i]) > dist_cutoff:
self.frag_index.append(res_index[i])
def findFragments(self,dist_cutoff=5.0):
"""
Take lines of pdb in self.atom_lines and find breaks in chain.
"""
# For every residue in the pdb file, grab the CA and N lines. This
# assumes that there are no duplicate residues and that all residues
# have CA and N. If there is a duplicate residue or missing atom, an
# error is raised.
residue_list = []
for line in self.atom_lines:
if line[21:26] not in residue_list:
residue_list.append(line[21:26])
ca_list = []
n_list = []
for resid in residue_list:
resid_atoms = [l for l in self.atom_lines if l[21:26] == resid]
ca = [l for l in resid_atoms if l[13:16] == "CA "]
n = [l for l in resid_atoms if l[13:16] == "N "]
if len(ca) > 1 or len(n) > 1:
err = "Residue \"%s\" is duplicated!" % resid
raise PdbContainerError(err)
elif len(ca) == 0 or len(n) == 0:
err = "Residue \"%s\" has missing CA or N atoms!" % resid
raise PdbContainerError(err)
ca_list.append(ca[0])
n_list.append(n[0])
# Grab coordinates of CA atoms and index of residues indexes of
# amide nitrogens.
ca_coord = [[float(l[30+8*i:38+8*i]) for i in range(3)] for l in ca_list]
res_index = [self.atom_lines.index(n) for n in n_list]
# Check the distance between every ca carbon and the next. If these
# are further apart than dist_cutoff, place in different fragments.
self.frag_index = []
for i in range(1,len(ca_list)):
if dist(ca_coord[i-1],ca_coord[i]) > dist_cutoff:
self.frag_index.append(res_index[i])
def crop_head_using_skeleton(image, skeleton):
""" Given an image taken from the kinect RGB in PIL format, and a set
of skeleton coordinates, crop the head and return the cropped image.
"""
# We will cut off a rectangle with the center in the head
# and the dimension 2 * head-neck distance.
neck_length = dist(skeleton['head']['X'], skeleton['head']['Y'],
skeleton['neck']['X'], skeleton['neck']['Y'])
head_rect = (skeleton['head']['X'] - neck_length,
skeleton['head']['Y'] - neck_length,
skeleton['head']['X'] + neck_length,
skeleton['head']['Y'] + neck_length)
# Intersect our rectangle with the actual image, and crop it
image_rect = (0, 0, image.size[0], image.size[1])
cropping_rect = rectangle_intersection(head_rect, image_rect)
cropping_rect = (int(cropping_rect[0]), int(cropping_rect[1]),
int(cropping_rect[2]), int(cropping_rect[3]))
return image.crop(cropping_rect)
def visible_vertices(point: Point, points: Iterable[Point],
segments: Dict[Point, List[Segment]]) -> Iterator[Point]:
""" Yields points from given points that can be seen from given start point. """
# remove point from points
points = filter(lambda x: x != point, points)
# sort points first by angle and then by distance from point
points = sorted(points,
key=lambda x: (angle_to_xaxis(point, x), dist(point, x)))
# create sorted list from segments that cross starting ray
# list is sorted using ray that has to be updated
ray = Segment(point, Point(point.x + 1, point.y))
status = SortedList(
iterable=(seg for seg in set(chain(*segments.values()))
if intersection(
*ray, *seg, restriction_1='ray', restriction_2='segment')
and point not in seg),
key=status_key(lambda: ray))
# for each point (they are sorted by angle)
for p in points:
# update ray
ray = Segment(point, p)
# if p is visible yield it
if status.bisect_left(p) == 0:
yield p
# remove segments from this point
for seg in segments[p]:
if orient(point, p, seg.p1 if seg.p2 == p else seg.p2) < 0:
status.remove(seg)
# add segments to this point
for seg in segments[p]:
if orient(point, p, seg.p1 if seg.p2 == p else seg.p2) > 0:
status.add(seg)
def set_stimulation(self):
""" Connect grounds """
# Go straigth to connect the wanted points to ground
for (x, y, z) in ws.electrodes_settings[self.name]["points"]:
# Find the cable this corresponds to
dists = geo.dist((x, y), (ws.nvt.x, ws.nvt.y))
these = np.where(dists == dists.min())[0]
# print('Points:', these)
# Choose only one cable
cable_i = these[0]
# print('Cable:', ws.nvt.cables[cable_i])
# Locate the section and segment (z value) of the cable
j_sec, z_on_j = anatomy.locate(ws.seclens[cable_i], z)
# Cable and point of the cable to work on
point = {'cable': cable_i, 'section': j_sec, 'z': z_on_j}
# Ground it
ground_segment(point)
def update (self, fruit):
prev = self.tail[0].getCenter()
self.pos = geometry.add(self.pos, self.vel)
self.tail[0].move(self.vel.getX(), self.vel.getY())
for i in range(1, len(self.tail) - self.hasEaten):
temp = self.tail[i].getCenter()
vector = geometry.sub(prev, temp)
self.tail[i].move(vector.getX(), vector.getY())
prev = temp
self.hasEaten = 0
if geometry.dist(self.pos, fruit.getCenter()) < self.snakeWidth:
self.score += 100
self.hasEaten = 1
newSegment = self.tail[len(self.tail) - 1].clone()
# newSegment.setFill(color_rgb(175, 175, 175))
newSegment.setFill("white")
self.tail.append(newSegment)
self.tail[len(self.tail) - 1].draw(self.window)
return True;
return False;
def get_distance_from_unproof_focuses(self, p, foc):
return abs(
abs(geom.dist(p, foc[0]) - geom.dist(p, foc[1])) - self.delta)
def turbo_independent(self):
"""Returns a maximal independent set
(maximal means that no other independent set cantains it,
doesn't mean that it has the most vertices)"""
#Return 'None' if there are no vertices
if self.ver_len() == 0:
return None
#Let 'v' be a vertice closest to (0, 0)
v = self[0]
dist_v = dist(v, (0, 0))
for w in self.vertices:
d = dist(w, (0, 0))
if d < dist_v:
v = w
dist_v = d
"""'close_circle' is now a circle with a center in 'v' and radius of
length 1, this means that no vertice contained in 'close_circle' is
connected with 'v'"""
close_circle = Circle(v, 1)
"""Let 'close_vertices' be a list containing vertices that lay in
'close_circle'"""
close_vertices = []
for w in self.vertices:
if w in close_circle:
close_vertices.append(w)
"""'the_circle' will be a closed circle that will contain vertices
chosen as elements of returned set, it has to have diameter of length 1
so that no two vertices that lay in it are connected and it has to
contain 'v'
'max_card' will be the number of those vertices"""
max_card = 0
the_circle = ClosedCircle(v, 0.5)
#Count vertices in 'the_circle'
for vertex in close_vertices:
if vertex in the_circle:
max_card += 1
#We want 'the_circle' to contain the most vertices possible
for w in close_vertices:
if w == v:
continue
"""'circ_1' and 'circ_1' are closed circles with diameter of length
1, such that 'v' and 'w' lay on edges of 'circ_1' and 'circ_1'"""
circles = find_circles(v, w, 0.5, type='closed')
circ_1 = circles[0]
circ_2 = circles[1]
#'card_1' will be the number of vertices that lay in 'circ_1'
card_1 = 0
#Count vertices in 'circ_1'
for vertex in close_vertices:
if vertex in circ_1:
card_1 += 1
"""If 'circ_1' contains more vertices than 'the_ circle' swap them
with one another"""
if card_1 > max_card:
the_circle = circ_1
max_card = card_1
#'card_2' will be the number of vertices that lay in 'circ_2'
card_2 = 0
#Count vertices in 'circ_2'
for vertex in close_vertices:
if vertex in circ_2:
card_2 += 1
"""If 'circ_2' contains more vertices than 'the_ circle' swap them
with one another"""
if card_2 > max_card:
the_circle = circ_2
max_card = card_2
#'chosen_vertices' will be a list of all vertices from 'the_circle'
chosen_vertices = []
for w in close_vertices:
if w in the_circle:
chosen_vertices.append(w)
"""'big_circle' is a circle that contains vertices that can be connected
with vertices from 'the_circle'"""
big_circle = Circle(the_circle.center, 2.5)
#'rejested' will be a list of vertices from 'big_cirle'
rejected = []
for w in self.vertices:
if w in big_circle:
rejected.append(w)
"""remove vertices from 'rejected' because it contains alredy chosen
vertices as well as their potential neighbors"""
new_graph = self - rejected
#Repeat the action for what remained
ind = new_graph.turbo_independent()
if ind is None:
return chosen_vertices
else:
return ind + chosen_vertices
def behavior_planning(self, world_state):
"""
Identifies a goal state and places it into world_state['goal']. Also identifies whether to run the intake or
outtake and places it into world_state['tube_mode'] as one of 'INTAKE', 'OUTTAKE', 'NONE'. Also identifies which
direction to drive in, one of '1', '-1', or '0'
"""
curr_pos = world_state['pose'][0] # Our current (x,y)
tube_mode = 'NONE'
direction = 0
field_outtake = False
flail = False
goal = None
if geom.dist(curr_pos, self.scoring_zone) <= 0.15 and world_state['numIngestedBalls'] > 0:
# If we're in the scoring zone with some balls then run the outtake
tube_mode = 'OUTTAKE'
direction = 0
field_outtake = False
flail = False
goal = self.scoring_zone
elif geom.dist(curr_pos, self.chute_pos) <= 0.15 and world_state['numIngestedBalls'] <= 5:
# If we're in the human player station and don't yet have 5 balls then request another ball from the field
tube_mode = 'INTAKE'
direction = -1
field_outtake = True
flail = False
goal = self.scoring_zone
elif self.obstacle_grid.get_occupancy(curr_pos):
# If we're currently inside an obstacle, flail!
tube_mode = 'INTAKE'
direction = 1
field_outtake = False
flail = True
goal = None
elif world_state['numIngestedBalls'] >= 5:
# If we have >=5 balls then drive backwards towards the goal
tube_mode = 'INTAKE'
direction = -1
field_outtake = False
flail = False
goal = self.scoring_zone
elif len(world_state['balls']) == 0:
# If we can't see any more balls to go towards, then go towards the human player station
tube_mode = 'INTAKE'
direction = 1
field_outtake = False
flail = False
goal = self.chute_pos
else:
# The rest of the time, just run the intake and go towards the closest unobstructed ball
tube_mode = 'INTAKE'
direction = 1
field_outtake = False
flail = False
# Find the closest ball
min_ball_dist = np.inf
for ball_pos in world_state['balls']:
ball_dist = geom.dist(curr_pos, ball_pos)
if ball_dist < min_ball_dist and not self.obstacle_grid.get_occupancy(ball_pos):
min_ball_dist = ball_dist
goal = ball_pos
world_state['tube_mode'] = tube_mode
world_state['direction'] = direction
world_state['field_outtake'] = field_outtake
world_state['flail'] = flail
world_state['goal'] = goal
def calcTrochoidalMilling(self):
new_paths=[]
lastPoint = None
radius = self.trochoidalDiameter.getValue()/2.0
distPerRev = self.trochoidalStepover.getValue()
rampdown=self.rampdown.getValue()
steps_per_rev = 50
stock_poly = None
if self.source is not None:
stock_poly = self.source.getStockPolygon()
#for path_index, path in enumerate(self.path.path):
newpath=[]
angle = 0
for p_index, p in enumerate(self.path.path):
# when plunging, check if we already cut this part before
cutting = True
plunging = False
for cp in self.path.path[0:p_index]:
if cp.position is None or p.position is None:
continue;
if lastPoint is not None and lastPoint.position[2]>p.position[2] \
and geometry.dist(p.position, cp.position) < min(i for i in [radius, cp.dist_from_model] if i is not None ):
cutting = False
if p.rapid or p.order>self.trochoidalOrder.getValue() or p.dist_from_model< self.trochoidalOuterDist.getValue() or not cutting :
newpath.append(GPoint(position = (p.position), rapid = p.rapid, inside_model=p.inside_model, in_contact=p.in_contact))
else:
if p.order%self.trochoidalSkip.getValue()==0: #skip paths
if lastPoint is not None:
if lastPoint.position[2] > p.position[2]:
plunging = True
else:
plunging = False
dist=sqrt((p.position[0]-lastPoint.position[0])**2 + (p.position[1]-lastPoint.position[1])**2 + (p.position[2]-lastPoint.position[2])**2)
distPerRev = self.trochoidalStepover.getValue()
if plunging:
dradius = radius
if p.dist_from_model is not None:
dradius = min(min(radius, p.dist_from_model), self.tool.diameter.getValue()/2.0)
if rampdown>0.0:
distPerRev = rampdown*(dradius*2.0*pi)
steps = int(float(steps_per_rev)*dist/distPerRev)+1
dradius = 0.0
for i in range(0, steps):
angle -= (dist/float(distPerRev) / float(steps)) * 2.0*PI
dradius = radius
bore_expansion = False
if p.dist_from_model is not None and lastPoint.dist_from_model is not None:
dradius = min(radius, lastPoint.dist_from_model*(1.0-(float(i)/steps)) + p.dist_from_model*(float(i)/steps))
if p.dist_from_model is not None and lastPoint.dist_from_model is None:
dradius = min(radius, p.dist_from_model)
# if plunging and radius is larger than tool diameter, bore at smaller radius and expand out
if plunging:
if dradius>self.tool.diameter.getValue():
dradius = self.tool.diameter.getValue()/2.0
bore_expansion = True
x = lastPoint.position[0]*(1.0-(float(i)/steps)) + p.position[0]*(float(i)/steps) + dradius * sin(angle)
y = lastPoint.position[1]*(1.0-(float(i)/steps)) + p.position[1]*(float(i)/steps) + dradius * cos(angle)
z = lastPoint.position[2]*(1.0-(float(i)/steps)) + p.position[2]*(float(i)/steps)
cutting = True
if stock_poly is not None and not stock_poly.pointInside((x, y, z)):
cutting = False
for cp in self.path.path[0:p_index]:
if cp.dist_from_model is not None and geometry.dist((x, y, z), cp.position) < min(radius, cp.dist_from_model) - 0.5*self.trochoidalStepover.getValue():
cutting = False
if cutting:
feedrate=None
if plunging:
feedrate=self.plunge_feedrate.getValue()
newpath.append(GPoint(position=(x, y, z), rapid=p.rapid, inside_model=p.inside_model,in_contact=p.in_contact, feedrate = feedrate))
if bore_expansion:
distPerRev = self.trochoidalStepover.getValue()
dist = min(radius, p.dist_from_model) - dradius + distPerRev
steps = int(float(steps_per_rev) * (dist / distPerRev) )
for i in range(0, steps):
angle -= (dist / float(distPerRev) / float(steps)) * 2.0 * PI
dradius += dist/steps
if dradius>p.dist_from_model:
dradius=p.dist_from_model
x = p.position[0] + dradius * sin(angle)
y = p.position[1] + dradius * cos(angle)
z = p.position[2]
cutting = True
if stock_poly is not None and not stock_poly.pointInside((x, y, z)):
cutting = False
if cutting:
newpath.append(GPoint(position = (x, y, z), rapid = p.rapid, inside_model=p.inside_model, in_contact=p.in_contact))
lastPoint = p
#remove non-cutting points
# cleanpath=[]
# for p in newpath:
# cutting = True
# for cp in cleanpath:
# if geometry.dist(p.position, cp.position) < min(radius, cp.dist_from_model):
# cutting = False
# if cutting:
# cleanpath.append(p)
new_paths.append(GCode(newpath))
self.outpaths=new_paths
self.updateView()
def are_neighbors(self, v, w):
"""Returns True if v and w are neighbors, False otherwise"""
return between(1, dist(v, w), 2)
def pickle_training_set(run_name,
training_sets_folder="training_set",
pickle_file_name="training_set",
high_energy_cutoff=500.0,
system_x_offset=1000.0,
verbose=False,
extra_parameters={}):
"""
A function to pickle together the training set in a manner that is
readable for MCSMRFF. This is a single LAMMPs data file with each
training set offset alongst the x-axis by system_x_offset. The pickle
file, when read in later, holds a list of two objects. The first is
the entire system as described above. The second is a dictionary of all
molecules in the system, organized by composition.
**Parameters**
run_name: *str*
Name of final training set.
training_sets_folder: *str, optional*
Path to the folder where all the training set data is.
pickle_file_name: *str, optional*
A name for the pickle file and training set system.
high_energy_cutoff: *float, optional*
A cutoff for systems that are too large in energy, as MD is likely
never to sample them.
system_x_offset: *float, optional*
The x offset for the systems to be added by.
verbose: *bool, optional*
Whether to have additional stdout or not.
extra_parameters: *dict, optional*
A dictionaries for additional parameters that do not exist
in the default OPLSAA parameter file.
**Returns**
system: *System*
The entire training set system.
systems_by_composition: *dict, list, Molecule*
Each molecule organized in this hash table.
"""
# Take care of pickle file I/O
if training_sets_folder.endswith("/"):
training_sets_folder = training_sets_folder[:-1]
if pickle_file_name is not None and pickle_file_name.endswith(".pickle"):
pickle_file_name = pickle_file_name.split(".pickle")[0]
pfile = training_sets_folder + "/" + pickle_file_name + ".pickle"
sys_name = pickle_file_name
if os.path.isfile(pfile):
raise Exception("Pickled training set already exists!")
# Generate empty system for your training set
system = None
system = structures.System(box_size=[1e3, 100.0, 100.0], name=sys_name)
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file we
# want and write the energies and forces for that file
for name in os.listdir(training_sets_folder):
# We'll read in any training subset that succeeded and print a warning
# on those that failed
try:
result = orca.read("%s/%s/%s.out"
% (training_sets_folder, name, name))
except IOError:
print("Warning - Training Subset %s not included as \
out file not found..." % name)
continue
# Check for convergence
if not result.converged:
print("Warning - Results for %s have not converged." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("%s/%s/%s.orca.engrad"
% (training_sets_folder, name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
return units.convert_dist("Ang", "Bohr",
units.convert_energy("Ha",
"kcal",
x)
)
for a, b in zip(result.atoms, forces):
a.fx, a.fy, a.fz = convert(b.fx), convert(b.fy), convert(b.fz)
except (IndexError, IOError):
print("Warning - Training Subset %s not included as \
results not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("%s/%s/%s.cml"
% (training_sets_folder, name, name),
extra_parameters=extra_parameters,
allow_errors=True,
test_charges=False)
# Copy over the forces read in into the system that has the bonding
# information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
# sanity check on atom positions
if geometry.dist(a, b) > 1e-4:
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z)
)
# Rename and save energy
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that our
# training set takes into account. This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate:
# (1) xyz file of various systems as different time steps
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first
# in the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, and convert energy units
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > high_energy_cutoff:
to_delete.append([composition, j])
continue
# For testing purposes, output
if verbose:
print "Using:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * system_x_offset)
# Delete the system_names that we aren't actually using due to energy
# being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
if verbose:
print "Warning - Training Subset %s not included as energy \
is too high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all our
# systems
system.xhi = len(system.molecules) * system_x_offset + 100.0
# Write all of the states we are using to training_sets.xyz
files.write_xyz(xyz_atoms, training_sets_folder + '/' + pickle_file_name)
# Generate our pickle file
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# Now we have the data, save it to files for this simulation of
# "run_name" and return parameters
if not os.path.isdir(run_name):
os.mkdir(run_name)
os.chdir(run_name)
mcsmrff_files.write_system_and_training_data(run_name,
system,
systems_by_composition
)
os.chdir("../")
shutil.copyfile(pfile, "%s/%s.pickle" % (run_name, run_name))
return system, systems_by_composition
def build_from_json(self):
"""
Build the nerve using the parameters stored in json files
"""
# Build contours
self.c_reduction = ws.anatomy_settings["cross-section"][
"contours point reduction"]
self.build_contours()
contour = self.contour
contour_hd = self.contour_hd
contour_pslg = self.contour_pslg
contour_pslg_nerve = self.contour_pslg_nerve
# Build internal elements
x = []
y = []
r = []
cables = OrderedDict()
free_areas = []
start_positions = []
cables_tissues = OrderedDict()
segments = {}
len_seg = {}
len_con = {}
numberof = {'Axon': 0, 'NAELC': 0}
models = {}
itpath = ws.anatomy_settings["cross-section"]["internal topology file"]
topology = read_from_json(itpath, object_pairs_hook=OrderedDict)
# Read the dictionary and crete the necessary variables from it
for i, c in topology['cables'].items():
i = int(i)
cables[i] = c['type']
x.append(c['x'])
y.append(c['y'])
r.append(c['r'])
free_areas.append(c['free extracellular area'])
start_positions.append(c['start position'])
cables_tissues[i] = OrderedDict()
cables_tissues[i]['endoneurium'] = c['endoneurium']
cables_tissues[i]['epineurium'] = c['epineurium']
numberof[c['type']] += 1
# Axon model
try:
models[i] = c['model']
except KeyError:
# It's not an axon
models[i] = cables[i]
# Turn the cables dictionary into a sorted list
# And also sort everything else
sortorder = np.argsort(np.array(list(cables.keys())))
cables = np.array(list(cables.values()))[sortorder]
x = np.array(x)[sortorder]
y = np.array(y)[sortorder]
r = np.array(r)[sortorder]
free_areas = np.array(free_areas)[sortorder]
start_positions = np.array(start_positions)[sortorder]
# Now pairs...
for p in topology['pairs'].values():
i, j = p['pair']
pair = (i, j)
s = p['separator segment']
a = s['a']
b = s['b']
seg = geo.Segment(((a['x'], a['y']), (b['x'], b['y'])))
segments[pair] = seg
len_seg[pair] = seg.length
len_con[pair] = geo.dist((x[i], y[i]), (x[j], y[j]))
# Save things as attributes
# self.x = np.array(x)
# self.y = np.array(y)
# self.r = np.array(r)
# self.free_areas = np.array(free_areas)
# self.start_positions = np.array(start_positions)
self.x = x
self.y = y
self.r = r
self.free_areas = free_areas
self.start_positions = start_positions
self.cables_tissues = cables_tissues
self.segments = segments
self.len_seg = len_seg
self.len_con = len_con
self.pairs = len_con.keys()
self.cables = cables
self.models = models
# Unique list of models
self.models_set = set(self.models.values())
# print('cables:', cables)
# print('r:', r)
# for c_, r_ in zip(cables, r):
# print(c_, r_)
self.nc = len(cables)
self.naxons_total = numberof['Axon']
self.nNAELC = numberof['NAELC']
# Build power diagram
pd = tess.PowerDiagram(self.x, self.y, self.r, contour_pslg_nerve)
# for p, s in zip(self.pairs, self.segments.values()):
# print(p, str(s))
pd.build_preexisting(self.pairs, self.segments)
self.trios = pd.trios
self.pd = pd
self.circ_areas = pd.circ_areas
# Total endoneurial cross-sectional free area
self.endo_free_cs_area = self.fas_total_area - self.circ_areas.sum()
def get_training_set(run_name, use_pickle=True, pickle_file_name=None):
# Take care of pickle file I/O
# Get file name
if pickle_file_name is None:
pfile = "training_sets/training_set.pickle"
else:
pfile = pickle_file_name
system = None
# If the pickle file does not exist, then make it
# If use_pickle is False, then make the read in the data from the
# training_sets folder
if not os.path.isfile(pfile) or not use_pickle:
if pickle_file_name is not None:
raise Exception("Requested file %s, but unable to read it in."
% pickle_file_name)
# Generate the pickle itself if it doesn't exist
# Create the size of the box to be 1000 x 100 x 100 to hold your
# training sets
system = structures.System(box_size=[1e3, 100.0, 100.0],
name="training_set")
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file
# we want and write the energies and forces for that file
for name in os.listdir("training_sets"):
# We'll read in any training subset that succeeded and print
# a warning on those that failed
try:
result = orca.read("training_sets/%s/%s.out" % (name, name))
except IOError:
print("Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("training_sets/%s/%s.orca.engrad"
% (name, name), pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
units.convert_dist("Ang",
"Bohr",
units.convert_energy("Ha",
"kcal",
x))
for a, b in zip(result.atoms, forces):
a.fx = convert(b.fx)
a.fy = convert(b.fy)
a.fz = convert(b.fz)
except (IndexError, IOError):
print("Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule(
"training_sets/%s/system.cml" % name,
extra_parameters=extra_Pb,
test_charges=False)
# Copy over the forces read in into the system that has the
# bonding information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
if geometry.dist(a, b) > 1e-4:
# sanity check on atom positions
raise Exception('Atoms are different:',
(a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename some things
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that
# our training set takes into account
# This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate (1) xyz file of various systems as different time steps and
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first in
# the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, convert units of
# the energy
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > 500.0:
to_delete.append([composition, j])
continue
# For testing purposes, output
print "DEBUG:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * 1000.0)
# Delete the system_names that we aren't actually using due to
# energy being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
print "Warning - Training Subset %s not included as energy is too \
high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all
# our systems
system.xhi = len(system.molecules) * 1000.0 + 100.0
# Write all of the states we are using to training_sets.xyz
if not os.path.isdir("training_sets"):
os.mkdir("training_sets")
os.chdir("training_sets")
files.write_xyz(xyz_atoms, 'training_sets')
os.chdir("../")
# Generate our pickle file if desired
if use_pickle:
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# If use_pickle is true AND the pickle file exists, then we can just
# read it in
if system is None and use_pickle:
print("Reading pickle file %s..." % pfile)
fptr = open(pfile, "rb")
system, systems_by_composition = pickle.load(fptr)
system.name = run_name
fptr.close()
elif system is None:
raise Exception("Requested file %s, but unable to read it in." % pfile)
# Now we have the data, save it to files for this simulation of "run_name"
# and return parameters
if not os.path.isdir("lammps"):
os.mkdir("lammps")
if not os.path.isdir("lammps/%s" % run_name):
os.mkdir("lammps/%s" % run_name)
os.chdir("lammps/%s" % run_name)
mcsmrff_files.write_system_and_training_data(run_name,
system,
systems_by_composition)
os.chdir("../../")
return system, systems_by_composition
def run(self, plan_state):
"""
Calculates controls given the current plan state
:param plan_state: Dict containing robot's current pose and some goal state
:return: Dict containing motor speeds for each motor on the robot
"""
curr_time = time.time()
vehicle_commands = {
'leftDriveMotorSpeed': 0, # Left drive motor speed (-512 - 512)
'rightDriveMotorSpeed': 0, # Right drive motor speed (-512 - 512)
'intakeCenterMotorSpeed':
0, # Intake center motor speed (-512 - 512)
'intakeLeftMotorSpeed': 0, # Intake left motor speed (-512 - 512)
'intakeRightMotorSpeed':
0, # Intake right motor speed (-512 - 512)
'tubeMotorSpeed': 0, # Tube motor speed (-512 - 512)
'timerStartStop': 0, # Timer start/stop (0 or 1)
'reset': 0, # Reset (0 or 1)
'outtake': 0, # Outtake (0 or 1)
'draw': [] # List of shapes to draw
}
# Determine left and right drive motor speeds
direction = plan_state['direction']
tube_mode = plan_state['tube_mode']
if plan_state['flail']:
curr_time = time.time()
left_drive_speed = int(self.max_forward_speed *
math.sin(2 * curr_time))
right_drive_speed = int(self.max_forward_speed *
math.sin(2.1 * curr_time))
elif plan_state['trajectory'] is None or direction == 0:
# Nothing to do
left_drive_speed = 0
right_drive_speed = 0
else:
pose = plan_state['pose']
start = np.array(plan_state['trajectory'][0])
goal = np.array(plan_state['trajectory'][1])
curr_heading = pose[1] % (2 * np.pi)
vec_start_to_goal = goal - start
desired_heading = np.arctan2(vec_start_to_goal[1],
vec_start_to_goal[0]) % (2 * np.pi)
if direction == -1:
desired_heading += np.pi
desired_heading = desired_heading % (2 * np.pi)
# If we're not facing the right way, turn in place. Else move straight.
heading_norm_vector = np.array(
[np.cos(curr_heading),
np.sin(curr_heading)])
goal_norm_vector = np.array(
[np.cos(desired_heading),
np.sin(desired_heading)])
x1 = heading_norm_vector[0]
y1 = heading_norm_vector[1]
x2 = goal_norm_vector[0]
y2 = goal_norm_vector[1]
heading_error = math.atan2(x1 * y2 - y1 * x2, x1 * x2 + y1 * y2)
if abs(heading_error) >= self.heading_error_threshold:
left_drive_speed = -int(
self.left_drive_pid.run(0, heading_error, curr_time))
right_drive_speed = int(
self.right_drive_pid.run(0, heading_error, curr_time))
else:
distance_to_goal = geom.dist(start, goal)
speed = int(
abs(self.straight_pid.run(distance_to_goal, 0, curr_time)))
speed = min(speed, self.max_forward_speed)
left_drive_speed = speed * direction
right_drive_speed = speed * direction
# Run the intake/outtake motors
intake_speed = self.max_intake_speed if tube_mode == 'INTAKE' else 0
outtake_speed = self.max_outtake_speed if tube_mode == 'OUTTAKE' else 0
# Request the field to outtake a ball
if plan_state['field_outtake']:
self.field_outtake = not self.field_outtake
vehicle_commands['leftDriveMotorSpeed'] = left_drive_speed
vehicle_commands['rightDriveMotorSpeed'] = right_drive_speed
vehicle_commands['intakeCenterMotorSpeed'] = intake_speed
vehicle_commands['intakeLeftMotorSpeed'] = intake_speed
vehicle_commands['intakeRightMotorSpeed'] = intake_speed
vehicle_commands['tubeMotorSpeed'] = outtake_speed
vehicle_commands['outtake'] = int(self.field_outtake)
return vehicle_commands
def callback_strip_solvents(fpl_obj):
xyz = fpl_obj.data[-1]
system = fpl_obj.system
## Store end of last LAMMPs simulation to system.atoms variable
for a, b in zip(system.atoms, xyz[-1]):
a.x, a.y, a.z = b.x, b.y, b.z
if any([np.isnan(x) for x in (a.x, a.y, a.z)]):
return None
## Grab only molecules we're interested in. Here we find relative distances to the solute in question
if fpl_obj.solute:
molecules_in_cluster = []
# Generate a list of all ion atoms
ion_list = [
a for m in system.molecules for a in m.atoms
if a.element == fpl_obj.ion
]
all_solutes = [
m for m in system.molecules
if fpl_obj.ion in [a.element for a in m.atoms]
]
if len(ion_list) == 0:
raise Exception("Could not find %s!" % fpl_obj.ion)
# Get distance between every ion and oxygen per molecule, saving molecule and min r
for m in system.molecules:
# Calculate the distance from the oxygen atom to all pb atoms.
oxypos = [a for a in m.atoms if a.type.element in [8, "O"]]
if oxypos == []: continue
# Get a list of the closest oxygen of molecule m to the ions
r_min = min(
[geometry.dist(i, o) for i in ion_list for o in oxypos])
# If close enough, save
if r_min < fpl_obj.R_cutoff:
molecules_in_cluster.append((m, r_min))
else:
all_solutes = []
origin = structures.Atom('X', 0.0, 0.0, 0.0)
molecules_in_cluster = [(m, geometry.dist(origin, m.atoms[0]))
for m in system.molecules]
# Now, if we want only N solvents, grab closest N
molecules_in_cluster = sorted(molecules_in_cluster, key=lambda x: x[1])
if fpl_obj.num_solvents is not None:
molecules_in_cluster = all_solutes + [
m[0] for m in molecules_in_cluster[:fpl_obj.num_solvents]
]
else:
molecules_in_cluster = all_solutes + [
m[0] for m in molecules_in_cluster
]
# Set indices
for j, m in enumerate(molecules_in_cluster):
for i, a in enumerate(m.atoms):
a.index = i + 1
## Generate the new system
system = None
system = structures.System(box_size=(100, 100, 100), name=fpl_obj.run_name)
for m in molecules_in_cluster:
system.add(m)
fpl_obj.system = system
def pickle_training_set(run_name,
training_sets_folder="training_set",
pickle_file_name="training_set",
high_energy_cutoff=500.0,
system_x_offset=1000.0,
verbose=False,
extra_parameters={}):
"""
A function to pickle together the training set in a manner that is
readable for MCSMRFF. This is a single LAMMPs data file with each
training set offset alongst the x-axis by system_x_offset. The pickle
file, when read in later, holds a list of two objects. The first is
the entire system as described above. The second is a dictionary of all
molecules in the system, organized by composition.
**Parameters**
run_name: *str*
Name of final training set.
training_sets_folder: *str, optional*
Path to the folder where all the training set data is.
pickle_file_name: *str, optional*
A name for the pickle file and training set system.
high_energy_cutoff: *float, optional*
A cutoff for systems that are too large in energy, as MD is likely
never to sample them.
system_x_offset: *float, optional*
The x offset for the systems to be added by.
verbose: *bool, optional*
Whether to have additional stdout or not.
extra_parameters: *dict, optional*
A dictionaries for additional parameters that do not exist
in the default OPLSAA parameter file.
**Returns**
system: *System*
The entire training set system.
systems_by_composition: *dict, list, Molecule*
Each molecule organized in this hash table.
"""
# Take care of pickle file I/O
if training_sets_folder.endswith("/"):
training_sets_folder = training_sets_folder[:-1]
if pickle_file_name is not None and pickle_file_name.endswith(".pickle"):
pickle_file_name = pickle_file_name.split(".pickle")[0]
pfile = training_sets_folder + "/" + pickle_file_name + ".pickle"
sys_name = pickle_file_name
if os.path.isfile(pfile):
raise Exception("Pickled training set already exists!")
# Generate empty system for your training set
system = None
system = structures.System(box_size=[1e3, 100.0, 100.0], name=sys_name)
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file we
# want and write the energies and forces for that file
for name in os.listdir(training_sets_folder):
# We'll read in any training subset that succeeded and print a warning
# on those that failed
try:
result = orca.read("%s/%s/%s.out" %
(training_sets_folder, name, name))
except IOError:
print(
"Warning - Training Subset %s not included as \
out file not found..." % name)
continue
# Check for convergence
if not result.converged:
print("Warning - Results for %s have not converged." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("%s/%s/%s.orca.engrad" %
(training_sets_folder, name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
return units.convert_dist(
"Ang", "Bohr", units.convert_energy("Ha", "kcal", x))
for a, b in zip(result.atoms, forces):
a.fx, a.fy, a.fz = convert(b.fx), convert(b.fy), convert(b.fz)
except (IndexError, IOError):
print(
"Warning - Training Subset %s not included as \
results not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("%s/%s/%s.cml" %
(training_sets_folder, name, name),
extra_parameters=extra_parameters,
allow_errors=True,
test_charges=False)
# Copy over the forces read in into the system that has the bonding
# information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
# sanity check on atom positions
if geometry.dist(a, b) > 1e-4:
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename and save energy
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that our
# training set takes into account. This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate:
# (1) xyz file of various systems as different time steps
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first
# in the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, and convert energy units
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > high_energy_cutoff:
to_delete.append([composition, j])
continue
# For testing purposes, output
if verbose:
print "Using:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * system_x_offset)
# Delete the system_names that we aren't actually using due to energy
# being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
if verbose:
print "Warning - Training Subset %s not included as energy \
is too high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all our
# systems
system.xhi = len(system.molecules) * system_x_offset + 100.0
# Write all of the states we are using to training_sets.xyz
files.write_xyz(xyz_atoms, training_sets_folder + '/' + pickle_file_name)
# Generate our pickle file
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# Now we have the data, save it to files for this simulation of
# "run_name" and return parameters
if not os.path.isdir(run_name):
os.mkdir(run_name)
os.chdir(run_name)
mcsmrff_files.write_system_and_training_data(run_name, system,
systems_by_composition)
os.chdir("../")
shutil.copyfile(pfile, "%s/%s.pickle" % (run_name, run_name))
return system, systems_by_composition
def snap_point(self, x: float, y: float) -> Point:
p = Point(x, y)
for p2 in self.collection.all_points:
if dist(p, p2) < SNAP_RADIUS:
return p2
return p
def get_distance_from_focuses(self, p):
return abs(
abs(geom.dist(p, self.focuses[0]) -
geom.dist(p, self.focuses[1])) - self.delta)
def get_training_set(run_name, use_pickle=True, pickle_file_name=None):
# Take care of pickle file I/O
# Get file name
if pickle_file_name is None:
pfile = "training_sets/training_set.pickle"
else:
pfile = pickle_file_name
system = None
# If the pickle file does not exist, then make it
# If use_pickle is False, then make the read in the data from the
# training_sets folder
if not os.path.isfile(pfile) or not use_pickle:
if pickle_file_name is not None:
raise Exception("Requested file %s, but unable to read it in." %
pickle_file_name)
# Generate the pickle itself if it doesn't exist
# Create the size of the box to be 1000 x 100 x 100 to hold your
# training sets
system = structures.System(box_size=[1e3, 100.0, 100.0],
name="training_set")
systems_by_composition = {}
# For each folder in the training_sets folder lets get the cml file
# we want and write the energies and forces for that file
for name in os.listdir("training_sets"):
# We'll read in any training subset that succeeded and print
# a warning on those that failed
try:
result = orca.read("training_sets/%s/%s.out" % (name, name))
except IOError:
print(
"Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Parse the force output and change units. In the case of no force
# found, do not use this set of data
try:
forces = orca.engrad_read("training_sets/%s/%s.orca.engrad" %
(name, name),
pos="Ang")[0]
# Convert force from Ha/Bohr to kcal/mol-Ang
def convert(x):
units.convert_dist("Ang", "Bohr",
units.convert_energy("Ha", "kcal", x))
for a, b in zip(result.atoms, forces):
a.fx = convert(b.fx)
a.fy = convert(b.fy)
a.fz = convert(b.fz)
except (IndexError, IOError):
print(
"Warning - Training Subset %s not included as results \
not found..." % name)
continue
# Get the bonding information
with_bonds = structures.Molecule("training_sets/%s/system.cml" %
name,
extra_parameters=extra_Pb,
test_charges=False)
# Copy over the forces read in into the system that has the
# bonding information
for a, b in zip(with_bonds.atoms, result.atoms):
a.fx, a.fy, a.fz = b.fx, b.fy, b.fz
if geometry.dist(a, b) > 1e-4:
# sanity check on atom positions
raise Exception('Atoms are different:', (a.x, a.y, a.z),
(b.x, b.y, b.z))
# Rename some things
with_bonds.energy = result.energy
with_bonds.name = name
# Now, we read in all the potential three-body interactions that
# our training set takes into account
# This will be in a 1D array
composition = ' '.join(sorted([a.element for a in result.atoms]))
if composition not in systems_by_composition:
systems_by_composition[composition] = []
systems_by_composition[composition].append(with_bonds)
# Generate (1) xyz file of various systems as different time steps and
# (2) system to simulate
xyz_atoms = []
to_delete = []
for i, composition in enumerate(systems_by_composition):
# Sort so that the lowest energy training subset is first in
# the system
systems_by_composition[composition].sort(key=lambda s: s.energy)
baseline_energy = systems_by_composition[composition][0].energy
# Offset the energies by the lowest energy, convert units of
# the energy
for j, s in enumerate(systems_by_composition[composition]):
s.energy -= baseline_energy
s.energy = units.convert_energy("Ha", "kcal/mol", s.energy)
# Don't use high-energy systems, because these will not likely
# be sampled in MD
if s.energy > 500.0:
to_delete.append([composition, j])
continue
# For testing purposes, output
print "DEBUG:", s.name, s.energy
xyz_atoms.append(s.atoms)
system.add(s, len(system.molecules) * 1000.0)
# Delete the system_names that we aren't actually using due to
# energy being too high
to_delete = sorted(to_delete, key=lambda x: x[1])[::-1]
for d1, d2 in to_delete:
print "Warning - Training Subset %s not included as energy is too \
high..." % systems_by_composition[d1][d2].name
del systems_by_composition[d1][d2]
# Make the box just a little bigger (100) so that we can fit all
# our systems
system.xhi = len(system.molecules) * 1000.0 + 100.0
# Write all of the states we are using to training_sets.xyz
if not os.path.isdir("training_sets"):
os.mkdir("training_sets")
os.chdir("training_sets")
files.write_xyz(xyz_atoms, 'training_sets')
os.chdir("../")
# Generate our pickle file if desired
if use_pickle:
print("Saving pickle file %s..." % pfile)
fptr = open(pfile, "wb")
pickle.dump([system, systems_by_composition], fptr)
fptr.close()
# If use_pickle is true AND the pickle file exists, then we can just
# read it in
if system is None and use_pickle:
print("Reading pickle file %s..." % pfile)
fptr = open(pfile, "rb")
system, systems_by_composition = pickle.load(fptr)
system.name = run_name
fptr.close()
elif system is None:
raise Exception("Requested file %s, but unable to read it in." % pfile)
# Now we have the data, save it to files for this simulation of "run_name"
# and return parameters
if not os.path.isdir("lammps"):
os.mkdir("lammps")
if not os.path.isdir("lammps/%s" % run_name):
os.mkdir("lammps/%s" % run_name)
os.chdir("lammps/%s" % run_name)
mcsmrff_files.write_system_and_training_data(run_name, system,
systems_by_composition)
os.chdir("../../")
return system, systems_by_composition
def calcEdgeGridDist(pt1, pt2, mins, maxs, gridSize, metric="L1"):
return geometry.dist(getIndices(mins, gridSize, pt1), getIndices(mins, gridSize, pt2), metric=metric)
def build(self, params):
"""
Build the tessellation for the nerve
"""
# Get parameters
# self.get_params(params)
self.__dict__.update(params)
mnd = self.mnd
min_sep = self.min_sep
rmin = self.rmin
rmax = self.rmax
circp_tol = self.circp_tol
max_axs_pf = self.max_axs_pf
numberofaxons = self.numberofaxons
locations = self.locations
radii = self.radii
# models = self.models
# Build contours
self.build_contours()
contour = self.contour
contour_hd = self.contour_hd
contour_pslg = self.contour_pslg
contour_pslg_nerve = self.contour_pslg_nerve
##################################################################
# Triangulate
# Max. area for the triangles,
# inverse to the minimum NAELC density
maxarea = 1. / mnd
# Triangulate
# Instead, triangulate without taking the fascicles' contours
# into account
tri = triangle.triangulate(contour_pslg_nerve, 'a%f' % maxarea)
tri = triangle.triangulate(contour_pslg, 'a%f' % maxarea)
# Vertices
tv = tri['vertices']
self.original_points = tv.T
##################################################################
# Fill fascicles
# If the axons have fixed locations, get their locations and
# radii first, and then they will be added to the different
# fascicles when needed
if self.packing_type == "fixed locations":
try:
xx_, yy_ = np.array(locations).T
except ValueError:
# Something went wrong or simply there are no axons
xx_ = yy_ = rr_ = np.array([])
else:
rr_ = np.array(radii)
# Axon models
self.models = self.models['fixed']
# else:
# # Axon models. Create them according to the proportions
# Remove points inside the fascicles
remove_these = []
axons = {}
naxons = {}
naxons_total = 0
print('about to fill the contours')
for k, v in contour.items():
varr = np.array(v)
# Remove points outside the nerve
if 'Nerve' in k:
plpol = pl.Polygon(v)
for i, p in enumerate(tv):
# Remove any points in tv falling outside the nerve
# and outside its boundaries
# Note: that means that I don't remove the points ON the
# boundaries
if not (plpol.contains_point(p) or np.isin(p, varr).all()):
remove_these.append(i)
# Remove points from the fascicles
if 'Fascicle' in k:
print(k)
plpol = pl.Polygon(v)
for i, p in enumerate(tv):
# Remove the points of the fascicle's contours from tv
inclc = plpol.contains_point(p) and (not np.isin(
p, varr).all())
# Actually, don't remove the fascicle's contours
# Remove any points in tv contained in a fascicle
notic = plpol.contains_point(p) or np.isin(p, varr).all()
if notic:
remove_these.append(i)
# Fill the fascicle
# Dictionary of axons
axons[k] = {}
# Create the circles
# Different packing strategies yield different results
if self.packing_type == "uniform":
xx, yy, rr = cp.fill(contour_hd[k],
rmin,
rmax,
min_sep,
nmaxpf=max_axs_pf,
tolerance=circp_tol)
elif self.packing_type == "gamma":
distr_params = {
"mean": self.avg_r,
"shape": self.gamma_shape
}
xx, yy, rr = cp.fill(contour_hd[k],
rmin,
rmax,
min_sep,
nmaxpf=max_axs_pf,
tolerance=circp_tol,
distribution="gamma",
distr_params=distr_params)
print('Filled %s' % k)
elif self.packing_type == "fixed locations":
# Iterate over axons and get those inside
# the fascicle
xx, yy, rr = [], [], []
for x_, y_, r_ in zip(xx_, yy_, rr_):
if plpol.contains_point((x_, y_)):
xx.append(x_)
yy.append(y_)
rr.append(r_)
xx = np.array(xx)
yy = np.array(yy)
rr = np.array(rr)
# Store information in a clean way
axons[k]['x'] = xx
axons[k]['y'] = yy
axons[k]['r'] = rr
# axons[k]['models'] = models[:]
naxons[k] = len(xx)
naxons_total += naxons[k]
rps = tv[remove_these]
self.removed_points = rps
keep_these = np.array(
list(set(range(tv.shape[0])) - set(remove_these)))
# print("keep_these:", keep_these)
# print("remove_these:", remove_these)
tv = tv[keep_these]
# List x, y and r once some points have been removed
tv = tv.T
x, y = tv
nc = x.size
r = np.zeros_like(x)
# Dictionaries for:
# cables (their type)
cables = OrderedDict()
models = {}
# Points corresponding to epineurium
for ic in range(nc):
cables[ic] = 'NAELC'
# NAELC model indexing
models[ic] = 'NAELC'
print(ic, models, self.models)
# Add axons to the existing points for the nerve
for k in axons:
x = np.array(x.tolist() + axons[k]['x'].tolist())
y = np.array(y.tolist() + axons[k]['y'].tolist())
r = np.array(r.tolist() + axons[k]['r'].tolist())
nc = x.size
nNAELC = nc - naxons_total
# Axon models
if self.packing_type != 'fixed locations':
# Now that the axons have been placed, determine their models according to the proportions
# ninst: 'number of instances' (of each model)
ninst = {}
proportions = self.models['proportions']
keys = list(proportions.keys())
for m, p in proportions.items():
ninst[m] = int(p * naxons_total)
# Remainder
rem = naxons_total - sum(list(ninst.values()))
# Just add the remaining in an arbitrary (not random) way
for i in range(rem):
ninst[keys[i]] += 1
# Now select the indices of the axons for each model
axon_indices = (np.arange(naxons_total) + nNAELC).tolist()
remaining_axon_indices = axon_indices[:]
# Dictionary for the axon indices for each model
inds = {}
# Dictionary for the model names for each index
model_by_index = {}
for m, p in proportions.items():
sample = random.sample(remaining_axon_indices, ninst[m])
remaining_axon_indices = list(
set(remaining_axon_indices) - set(sample))
inds[m] = sample[:]
for i in sample:
model_by_index[i] = m
# Points corresponding to axons
for ik, i in enumerate(np.arange(nNAELC, nc, 1)):
cables[i] = 'Axon'
# Axon model indexing
if self.packing_type == 'fixed locations':
models[i] = self.models[ik]
else:
models[i] = model_by_index[i]
print(ik, i, models, self.models)
##################################################################
# Power diagram
# Zero-valued radii: Voronoi diagram
# Build power diagram
pd = tess.PowerDiagram(x, y, r, contour_pslg_nerve)
pd.build()
# Lengths of the segments and the connections
pdpairs = pd.pairs
pdsegments = pd.segments
pairs = []
segments = {}
len_seg = {}
len_con = {}
for pair in pdpairs:
# if len(pdsegments[pair]) > 1:
if pdsegments[pair] is not None:
seg = pdsegments[pair]
pairs.append(pair)
segments[pair] = seg
a, b = seg.a, seg.b
len_seg[pair] = geo.dist(a, b).mean()
i, j = pair
len_con[pair] = geo.dist((x[i], y[i]), (x[j], y[j]))
# Store relevant stuff in the attributes
self.pd = pd
self.x = x
self.y = y
self.r = r
self.nc = nc
self.axons_dict = axons
self.pairs = pairs
self.cables = cables
self.models = models
# Unique list of models
self.models_set = set(self.models.values())
self.segments = segments
self.trios = pd.trios
self.len_con = len_con
self.len_seg = len_seg
self.free_areas = pd.free_areas
self.circ_areas = pd.circ_areas
# Total endoneurial cross-sectional free area
self.endo_free_cs_area = self.fas_total_area - self.circ_areas.sum()
self.naxons = naxons
self.naxons_total = naxons_total
self.nNAELC = nNAELC