def create_pes():
global mvp
pattern = em.EmbPattern()
vertices = [ world_space_to_screen_space(vert) for vert in scene.vertices ]
def to_i4(l):
return l[0] | l[1] << 8 | l[2] << 16 | l[3] << 24
im = list(face_fbo.read(components=1, dtype='i4'))
visible_faces = Counter([ to_i4(im[k:k+4]) for k in range(0,len(im),4) ])
del visible_faces[0xFFFFFFFF]
# TODO count pixels of visible faces and prune any with fewer than some number
print(visible_faces)
# faces = visible_faces
threshold = 10
faces = [ f for f,cnt in visible_faces.items() if cnt >= threshold ]
G = nx.Graph()
# go through all things in scene indices
for idx in faces:
face = scene.mesh_list[0].faces[idx]
verts = [ tuple(vertices[p]) for p in face ]
edges = list(zip(verts, islice(cycle(verts), 1, None)))
G.add_edges_from(edges)
stitches = []
for C in [ G.subgraph(c) for c in nx.connected_components(G) ]:
for (p0, p1) in nx.eulerian_circuit(nx.eulerize(C)):
# needs to be at least 2
for t in np.linspace(0, 1, 2):
stitches.append(((1-t)*p0[0] + t*p1[0], (1-t)*p0[1] + t*p1[1]))
stitches = [ k for k,g in groupby(stitches) ]
for (x, y) in stitches:
pattern.add_stitch_absolute(em.STITCH, x, y)
# create dst format (pes doesn't work?)
print("Saving file...")
em.write_dst(pattern, 'file.dst')
em.write_pes(pattern, 'file.pes')
em.write_txt(pattern, 'file.txt')
print("Done.")
''' TEST ON A SIMPLE TOY '''
print("\nHierholzer eulerian path test on toy example")
toy = nx.Graph()
toy.add_nodes_from(['A', 'B', 'C', 'D', 'E']) # graph with 2 triangles
toy.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D'), ('C', 'E'), ('D', 'E')])
print(nx.is_eulerian(toy), nx.has_eulerian_path(toy))
print(hierholzer(toy))
''' Eulerian Path test i made the test on R and on a toy example because eulerian path on graph of provinces
dosen't exist because the graph of provinces in not strongly connected'''
time_eulerian = []
time_eulerian_nx = []
num_node = [3, 9, 19, 29, 39, 49]
for test in range(len(num_node)):
toy = nx.complete_graph(num_node[test])
toy = nx.eulerize(toy)
# print(nx.is_eulerian(toy))
# print(nx.has_eulerian_path(toy))
# print(nx.has_eulerian_path(R))
# print(nx.has_eulerian_path(P))
'''EULERIAN PATH ON A TOY EXAMPLE USING NETWORKX'''
start = time.time()
path = nx.eulerian_path(toy)
# print(list(path))
end = time.time()
time_eulerian_nx.append(end - start)
'''EULERIAN PATH ON A TOY EXAMPLE USING IMPLEMENTED FUNCTIONS'''
start = time.time()
path = hierholzer(toy)
# print(path)
end = time.time()
print("h) R =", vertex_cover.min_weighted_vertex_cover(G))
print()
# i) Minimum edge cover
print("i) F =", covering.min_edge_cover(G))
print()
# j) Shortest closed path (circuit) that visits every vertex
print("j) W =", end=" ")
for i in range(len(tournament.hamiltonian_path(D))):
print(tournament.hamiltonian_path(D)[i], "-", end=" ")
print(tournament.hamiltonian_path(D)[0])
print()
# k) Shortest closed path (circuit) that visits every edge
H = nx.eulerize(G)
c = list(n for (n, d) in nx.eulerian_circuit(H))
print("k) U =", end=" ")
for i in range(len(c)):
print(c[i], "-", end=" ")
print(c[0])
print()
# m) 2-edge-connected components
print("m) ", list(edge_kcomponents.bridge_components(G)))
print()
# o) MST
MST = nx.Graph(mst.minimum_spanning_tree(G))
print("o) MST = ", MST.nodes)
print("Weight =", sum(c for (a, b, c) in (MST.edges.data('weight'))))
def test_on_complete_graph(self):
G = nx.complete_graph(4)
assert_true(nx.is_eulerian(nx.eulerize(G)))
assert_true(nx.is_eulerian(nx.eulerize(nx.MultiGraph(G))))
def test_on_eulerian_multigraph(self):
G = nx.MultiGraph(nx.cycle_graph(3))
G.add_edge(0, 1)
H = nx.eulerize(G)
assert_true(nx.is_eulerian(H))
def _order_edges_in_block(self, block_data, drop_augmented):
"""Produce an edge sequence for all edges in the component.
Parameters
----------
block_data : pandas.DataFrame
A DataFrame representing all the edges within a single block.
drop_augmented : bool
Whether or not to keep any edges that needed to be added to the source edges in order to navigate the
network.
Returns
-------
edges : pandas.DataFrame
The same edges that were input with the edge order and route type as new columns.
"""
logger.debug("order_edges_by_block started")
logger.debug("Received edge data of shape %s", block_data.shape)
# Sort the DataFrame to load right hand arcs into NetworkX first.
# Note that Eulerian paths work in reverse order.
block_data = block_data.sort_values(self.leftrightflag_field,
ascending=False)
block_g = nx.from_pandas_edgelist(block_data,
source=self.source_field,
target=self.target_field,
edge_attr=True,
create_using=self.graph_type)
logger.debug("Block contains %s edges and %s nodes",
block_g.number_of_edges(), block_g.number_of_nodes())
# if the graph is empty it means there is a problem with the source data
# an error is logged, but other blocks are still processed
if nx.is_empty(block_g):
logger.error("Block contains no edges and cannot be sequenced")
return
# Scale nodes that are mid-segment by looking for duplicated ngd_str_uid values
logger.debug(
"Looking for nodes that fall in the middle of a road segment")
block_data['same_ngd_str_uid'] = block_data.duplicated(
subset=[self.struid_field], keep=False)
mid_arc_start_nodes = set(
block_data.loc[block_data['same_ngd_str_uid'] == True,
self.source_field])
mid_arc_end_nodes = set(
block_data.loc[block_data['same_ngd_str_uid'] == True,
self.target_field])
mid_arc_nodes = mid_arc_start_nodes.intersection(mid_arc_end_nodes)
if mid_arc_nodes:
logger.debug("Found mid-segment nodes: %s", mid_arc_nodes)
self._apply_node_scaling_factor(mid_arc_nodes, factor=-0.5)
# initialize the edge sequence counter
edge_sequence = 0
# record what type of path was used to determine the circuit
path_indicator_name = self.path_indicator
path_indicator_edges = {}
# blocks don't necessarily form fully connected graphs, so cycle through the components
logger.debug("Block contains %s connected components",
nx.number_weakly_connected_components(block_g))
for block_comp in sorted(nx.weakly_connected_components(block_g),
key=len,
reverse=True):
logger.debug(
"Creating subgraph from connected component with %s nodes",
len(block_comp))
block_g_comp = block_g.subgraph(block_comp)
# determine the preferred start node for this block component
preferred_sp = self._get_preferred_start_node(block_g_comp.nodes)
logger.debug("Preferred start node for this block: %s",
preferred_sp)
logger.debug("Component contains %s edges and %s nodes",
block_g_comp.number_of_edges(), len(block_g_comp))
# Need to pick an approach to processing this component depending on what type of circuit it forms.
# Ideally things are a Eulerian circuit that can be walked and return to start, but not all blocks form
# these nice circuits. If no good circuit can be found, then sequence numbers are just applied but may
# not form a logical order.
# Track the sequence value in case the enumeration method needs to be reset. This gets used when using
# the preferred start point fails, and also controls if the start node for this component is marked as a
# point we want to cluster on.
seq_val_at_start = edge_sequence
# the preferred option is a Eulerian circuit, so try that first
# logger.debug("Available edges: %s", block_g_comp.edges)
if nx.is_eulerian(block_g_comp):
logger.debug("Block component is eulerian.")
# record all these edges as being eulerian
indicator = dict(
zip(block_g_comp.edges, ['circuit'] * block_g_comp.size()))
path_indicator_edges.update(indicator)
# enumerate the edges and order them directly
logger.debug("Creating Eulerian circuit from node %s",
preferred_sp)
for u, v, k in nx.eulerian_circuit(block_g_comp,
source=preferred_sp,
keys=True):
edge_sequence += 1
block_g.edges[u, v, k][self.eo_name] = edge_sequence
# logger.debug("Sequence applied: (%s, %s, %s) = %s", u, v, k, edge_sequence)
# next best option is a path that stops at a different location from the start point
elif nx.has_eulerian_path(block_g_comp):
logger.debug("Block component forms Eulerian path")
# record all these edges as being a eulerian path
indicator = dict(
zip(block_g_comp.edges, ['path'] * block_g_comp.size()))
path_indicator_edges.update(indicator)
try:
logger.debug(
"Trying to create path from preferred start node %s",
preferred_sp)
for u, v, k in nx.eulerian_path(block_g_comp,
source=preferred_sp,
keys=True):
edge_sequence += 1
# check if the start point is actually in the first edge
if edge_sequence == 1 and not (preferred_sp == u
or preferred_sp == v):
logger.debug(
"Preferred start point not present on starting edge, throwing KeyError."
)
raise KeyError("Invalid starting edge")
# Sometimes the preferred start point means walking over the same edge twice, which will leave
# a data gap (the previous edge order value will be overwritten). If this happens, throw a
# KeyError
if block_g.edges[u, v, k].get(self.eo_name):
logger.debug("Edge already sequenced.")
raise KeyError(
"Preferred start point results in backtracking."
)
block_g.edges[u, v, k][self.eo_name] = edge_sequence
# logger.debug("Sequence applied: (%s, %s, %s) = %s", u, v, k, edge_sequence)
if edge_sequence < block_g_comp.number_of_edges():
logger.debug("It looks like some edges got missed")
raise KeyError("Missing edges on path")
logger.debug(
"Path was created from desired start point %s",
preferred_sp)
except KeyError:
# preferred start point failed; let networkx pick and start over
logger.debug(
"Preferred start node did not create a path. Trying a different one."
)
# reset the path listing since a new point will be picked
logger.debug("Reset edge_sequence value to %s",
seq_val_at_start)
edge_sequence = seq_val_at_start
for u, v, k in nx.eulerian_path(block_g_comp, keys=True):
edge_sequence += 1
block_g.edges[u, v, k][self.eo_name] = edge_sequence
# logger.debug("Sequence applied: (%s, %s, %s) = %s", u, v, k, edge_sequence)
# No good path exists, which means someone will have to backtrack
else:
logger.debug(
"Non-eulerian block is not easily traversable. Eulerizing it."
)
# Record all these edges as being augmented.
indicator = dict(
zip(block_g_comp.edges,
['augmented'] * block_g_comp.size()))
path_indicator_edges.update(indicator)
# Send this data to the anomaly folder so that it can be investigated later. It could have addressable
# issues that operations can correct for the next run.
logger.debug("Writing anomaly set for this block")
bf_uid_set = list(
nx.get_edge_attributes(
block_g_comp, self.edge_uid_field).values()).pop()
anomaly_file_name = f"anomaly_block_component.{bf_uid_set}.yaml"
nx.write_yaml(block_g_comp, (self.anomaly_folder /
anomaly_file_name).as_posix())
# You cannot eulerize a directed graph, so create an undirected one
logger.debug("Creating MultiGraph from directed graph.")
temp_graph = nx.MultiGraph()
for u, v, data in block_g_comp.edges(data=True):
key = temp_graph.add_edge(u, v, **data)
# logger.debug("Adding edge (%s, %s, %s) to temporary graph.", u, v, key)
logger.debug("Created temporary MultiGraph with %s edges",
temp_graph.number_of_edges())
# Convert the temporary graph to a proper Euler circuit so that it can be traversed.
logger.debug("Eulerizing MultiGraph")
euler_block = nx.eulerize(temp_graph)
logger.debug("Added %s edges to the block",
(euler_block.size() - temp_graph.size()))
logger.debug("Number of vertices in eulerized graph: %s",
euler_block.number_of_nodes())
# As we try to traverse the undirected graph, we need to keep track of places already visited to make
# sure arcs are not skipped.
visited_edges = Counter()
# augmented edges will throw the node weights off, so don't bother trying the preferred start node
logger.debug("Generating path through augmented block")
for u, v, k in nx.eulerian_circuit(euler_block,
preferred_sp,
keys=True):
# augmented edges have no attributes, so look for one and skip the edge if nothing is returned
if drop_augmented and not euler_block.edges[u, v, k].get(
self.edge_uid_field):
logger.debug("Ignoring augmented edge (%s, %s, %s)", u,
v, k)
continue
# Increment the sequence value for each edge we see.
edge_sequence += 1
# Since we formed an undirected MultiGraph we need to check the orientation of the nodes on the
# edge to assign the sequence back to the directed graph.
start_node = u
end_node = v
available_edge_count = block_g.number_of_edges(
start_node, end_node)
# If no edges exist, invert the nodes and check again.
# This also checks to see if we've already encountered all the edges between these nodes, indicating
# we need to process the inverse of the start and end values
if available_edge_count == 0 or (
((start_node, end_node) in visited_edges) and
(available_edge_count
== visited_edges[(start_node, end_node)])):
logger.debug(
"Nothing to process between (%s, %s), inverting nodes.",
start_node, end_node)
start_node = v
end_node = u
available_edge_count = block_g.number_of_edges(
start_node, end_node)
logger.debug(
"Number of edges available between (%s, %s): %s",
start_node, end_node, available_edge_count)
# Apply the edge_sequence to the first edge that hasn't received one yet
for ki in range(available_edge_count):
if not block_g.edges[start_node, end_node, ki].get(
self.eo_name):
logger.debug(
"Edge sequence applied: (%s, %s, %s) = %s",
start_node, end_node, ki, edge_sequence)
block_g.edges[start_node, end_node,
ki][self.eo_name] = edge_sequence
visited_edges[(start_node, end_node)] += 1
break
# At this point every edge should be accounted for, but in case something somehow slips through the cracks
# it needs to be given a sequence label. The label almost certainly won't make much sense in terms of a
# logical ordering, but this is just trying ot make sure it is counted.
logger.debug("Looking for any missed edges in block component")
for u, v, k in block_g_comp.edges:
if not block_g.edges[u, v, k].get(self.eo_name):
edge_sequence += 1
block_g.edges[u, v, k][self.eo_name] = edge_sequence
logger.warning(
"Applied out of order sequence to component edge (%s, %s, %s): %s",
u, v, k, edge_sequence)
# just log the last sequence value to make tracing easier
logger.debug("Final edge sequence value for component: %s",
edge_sequence)
# apply a sequence value to all the edges that were discovered
logger.debug("Edge order results: %s",
nx.get_edge_attributes(block_g_comp, self.eo_name))
# To help cluster the start nodes, mark which node was used as the start point in this block
if seq_val_at_start == 1:
self._mark_chosen_start_node(block_g_comp, preferred_sp)
logger.debug("Finished processing component")
# record that block processing is finished
logger.debug("Block processing complete")
# nx.set_edge_attributes(block_g, block_sequence_labels, self.eo_name)
nx.set_edge_attributes(block_g, path_indicator_edges,
path_indicator_name)
# check to see if the counts line up
if not block_g.number_of_edges() == edge_sequence:
logger.debug(
"Edge sequence (%s) and edge count (%s) do not match in block",
edge_sequence, block_g.number_of_edges())
# help start point clustering by apply a scaling factor to all nodes that were touched
logger.debug(
"Applying scaling factor to nodes in this block, except start point"
)
nodes_in_block = set(block_g.nodes())
nodes_in_block.remove(
preferred_sp) # don't scale the preferred start point
self._apply_node_scaling_factor(nodes_in_block)
logger.debug("Final node data for block: %s",
self.graph.subgraph(block_g.nodes).nodes(data=True))
logger.debug("Returning pandas DataFrame from block graph.")
return nx.to_pandas_edgelist(block_g,
source=self.source_field,
target=self.target_field)
def test_on_eulerian_multigraph(self):
G = nx.MultiGraph(nx.cycle_graph(3))
G.add_edge(0, 1)
H = nx.eulerize(G)
assert nx.is_eulerian(H)
def test_on_empty_graph(self):
with pytest.raises(nx.NetworkXError):
nx.eulerize(nx.empty_graph(3))
def test_null_graph(self):
nx.eulerize(nx.Graph())
def test_disconnected(self):
G = nx.from_edgelist([(0, 1), (2, 3)])
nx.eulerize(G)
n = ps[0]
m = ps[1]
graph = nx.Graph()
for i in range(0, m):
s = f.readline()
ps = [float(i) for i in s.split()]
v = int(ps[0])
u = int(ps[1])
graph.add_edge(v, u, weight = 1)
f.close()
f = open("outputPy.txt", "w")
f.write("maximum stable set = " + str(approximation.independent_set.maximum_independent_set(graph)) + '\n')
f.write("maximum matching = " + str(nx.max_weight_matching(graph)) + '\n')
f.write("minimum vertex cover = " + str(approximation.vertex_cover.min_weighted_vertex_cover(graph)) + '\n')
f.write("minimum edge cover = " + str(nx.min_edge_cover(graph)))
f.write("minimum path that visits every edge: " + str(list(nx.eulerian_circuit(nx.eulerize(graph)))))
dists = dict(nx.all_pairs_shortest_path_length(graph))
paths = dict(nx.all_pairs_shortest_path(graph))
distsArr = []
listsCnt = 0
for i in range(1, n + 1):
if i == 9 or i == 27 or i == 19 or i == 46 or i == 18:
continue
distsArr.append([])
listsCnt += 1
for j in range(1, n + 1):
if j == 9 or j == 27 or j == 19 or j == 46 or j == 18:
continue
distsArr[listsCnt - 1].append(dists[i][j])
vertexes = []
for i in range(1, n + 1):
pos,
edgelist=esmall,
width=2,
alpha=0.5,
edge_color='b',
style='dashed')
# labels
nx.draw_networkx_labels(G,
pos,
font_size=11,
font_family='sans-serif',
font_color='blue')
nx.draw_networkx_edge_labels(G, pos=nx.spectral_layout(G), font_size=5)
nx.eulerize(G)
plt.axis('off')
plt.savefig("weighted_graph.png") # save as png
plt.tight_layout(True)
plt.suptitle('Eulerized Graph, Distance Model (miles)')
plt.show() # display
print(
startGreen +
'------------- Algorithmic Path Experiments post-Eulerian Graph'
+ ' -------------' + endColor, '\n')
print(
startBlue +
'\nShortest path from Pensacola to Phoenix, Eulerized Graph:\n' + endColor,
fin_country = open("europe.txt")
fin_edges = open("distances.txt")
country = [] # список стран
edges = [] # список рёбер
G = nx.Graph() # граф
matrix = [[0] * V for i in range(V)]
for i in range(V):
pars_one = fin_country.readline().split('.')
if i != V - 1:
num, name = pars_one[0], pars_one[1][:-1]
else:
num, name = pars_one[0], pars_one[1]
country.append(name)
for i in range(E):
pars_two = fin_edges.readline().split()
a, b = int(pars_two[0]), int(pars_two[1])
matrix[a][b] = matrix[b][a] = 1
edges.append([a, b])
# создадим граф
for i in range(E):
a, b = edges[i]
G.add_edge(country[a], country[b])
# Построим кратчайший замкнутый путь, с помощью функции либы networkx
a = list(nx.eulerian_circuit(nx.eulerize(G)))
for i in a:
v1, v2 = i
print('(', v1, '-->', v2, end=') ')
def test_disconnected(self):
with pytest.raises(nx.NetworkXError):
G = nx.from_edgelist([(0, 1), (2, 3)])
nx.eulerize(G)
def test_null_multigraph(self):
nx.eulerize(nx.MultiGraph())
def test_null_multigraph(self):
with pytest.raises(nx.NetworkXPointlessConcept):
nx.eulerize(nx.MultiGraph())
def test_on_empty_graph(self):
nx.eulerize(nx.empty_graph(3))
def test_on_eulerian(self):
G = nx.cycle_graph(3)
H = nx.eulerize(G)
assert nx.is_isomorphic(G, H)
def test_on_eulerian(self):
G = nx.cycle_graph(3)
H = nx.eulerize(G)
assert_true(nx.is_isomorphic(G, H))
def test_on_complete_graph(self):
G = nx.complete_graph(4)
assert nx.is_eulerian(nx.eulerize(G))
assert nx.is_eulerian(nx.eulerize(nx.MultiGraph(G)))
G.add_edge('B', 'G', weight=35)
G.add_edge('C', 'D', weight=70)
G.add_edge('C', 'F', weight=28)
G.add_edge('D', 'E', weight=80)
G.add_edge('E', 'F', weight=62)
G.add_edge('F', 'G', weight=40)
posicoes = {
'A': (0, 3),
'B': (3, 2),
'C': (6, 1),
'D': (10, 0),
'E': (9, 5),
'F': (7, 3),
'G': (2, 5)
}
# rotulos = nx.get_edge_attributes(G, 'weight')
nx.draw(G, posicoes, with_labels=True)
# nx.draw_networkx_edge_labels(G, posicoes, edge_labels=rotulos)
print("É Euleriano?", nx.is_eulerian(G))
print("Arestas:", G.edges(data=True))
print("# de arestas:", G.number_of_edges())
print("Soma total dos pesos:", G.size(weight='weight'))
G_eulerizado = nx.eulerize(G)
print("\nÉ Euleriano?", nx.is_eulerian(G_eulerizado))
print("Arestas:", G_eulerizado.edges(data=True))
print("# de arestas:", G_eulerizado.number_of_edges())
print("Soma total dos pesos:", G_eulerizado.size(weight='weight'))
