def solve_graph(graph, name):
"""
Runs a planarity test on a predefined graph.
Args:
graph (networkx.Graph): Input graph
name (str): The name of the graph
Returns:
planarity (bool): Planarity of the graph
"""
# graph_nodes = len(graph.nodes())
# graph_edges = len(graph.edges())
# FouldsRobinsonPlanarity.find_planarity(G)
# TarjanHopcroftPlanarity.find_planarity(graph)
planar, bad_graph = KuratowskiPlanarity.find_planarity(graph)
print "Graph %s is planar: %s" % (
name,
planar,
)
GraphUtils.draw_graph(graph, name, bad_graph)
return planar
def get(self):
# Grab initial graph
initialGraph = graph_tool.load_graph(io.StringIO(graphData), fmt='graphml')
graph = initialGraph
# Add in graphincludes
graphIncludes = self.get_argument('graphIncludes', default=None)
if graphIncludes:
includes = graphIncludes.split('|')
#### Turn this into a for-loop, with appropriately named exist queries??
if 'repo' in includes:
addData = exist.buildGraphML('repo' + '.xql')
addGraph = graph_tool.load_graph(io.StringIO(addData), fmt='graphml')
print('\n\n#####################\n JOINING REPO GRAPH\n#######################')
graph = GraphUtils.join_graphs(graph, addGraph)
# Add in search terms
searchString = self.get_argument('searchTerm', default=None)
if searchString:
searchTerms = searchString.split('|')
for st in searchTerms:
addData = exist.buildGraphML('searchTerm.xql?searchString=' + st)
print('\n\n#####################\n JOINING ST', st, 'GRAPH\n#######################')
addGraph = graph_tool.load_graph(io.StringIO(addData), fmt='graphml')
graph = GraphUtils.join_graphs(graph, addGraph)
layout = yield executor.submit(self.get_layout, graph)
###########
self.write(GraphUtils.graph_to_linkurious_json(graph, layout))
self.finish()
def __init__(self, graph, nodeType, alphas=[0.01]):
self.graph = graph
self.type = self.graph.getStringProperty('type')
self.name = self.graph.getStringProperty('name')
self.plainOneModeGraph = self.graph.getSuperGraph().addSubGraph(
'plainOneMode_country')
self.substrateType = nodeType
self.strength = self.plainOneModeGraph.getDoubleProperty(
'Neal strength')
self.nbCatalysts = 0
for node in self.graph.getNodes():
if self.type[node] != self.substrateType:
self.nbCatalysts += 1
self.alphas = alphas
for node in self.graph.getNodes():
if self.type[node] == self.substrateType:
self.plainOneModeGraph.addNode(node)
self.neighborSets = {}
self.probabilities = {}
self.utils = GraphUtils()
def createGraphProcess(functions, domain, gRange):
# Format the expressions so they are ready to be graphed
funcArg = [GraphUtils.formatExpression(f) for f in functions]
funcArg = GraphUtils.replaceCalls(funcArg)
funcArg = [GraphUtils.nestFunctions(e[1], funcArg[:e[0]] + funcArg[e[0] + 1:]) for e in enumerate(funcArg)]
# Open the graphing subprocess
return subprocess.Popen(["python", sys.path[1] + "/Graphing.py", str(len(funcArg))] + funcArg + [domain,gRange])
def get_and_plot_ads_revenue_data(fan_id, fan_token, from_time, to_time):
revenue_data = get_fan_and_admob_revenue_data(fan_id, fan_token, from_time,
to_time)
# we got all the necessary data. We will plot the data into a time series graph now.
GraphUtils.plot_time_series(revenue_data[0], revenue_data[1:4], "Date",
"Revenue", ["FAN", "Admob", "Total"], "$",
"Network", "Pi Music Player Ads Revenue")
def add_van_node(self):
van_pos = (self.ride_data.van_vertex["x"],
self.ride_data.van_vertex["y"])
GraphUtils.add_node(self.graph,
"van",
van_pos,
"van",
grid_graph=None,
on_grid=True)
def testPlanar(self):
# Generate K(4) graph
p_graph = GraphGenerator.make_planar_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(p_graph)
GraphUtils.draw_graph(p_graph, "planar_test", bad_graph)
# print "Test on planar graph:", 'PASS' if planar else 'FAIL'
self.assertTrue(planar) # True means graph is planar
def testK5(self):
# Generate K(5) graph
k5 = GraphGenerator.make_k5_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(k5)
GraphUtils.draw_graph(k5, "K5", bad_graph)
# print "Test on K(5):", 'FAIL' if planar else 'PASS'
self.assertFalse(planar) # False means K(5) subgraph was found
def testK5(self):
# Generate K(5) graph
k5 = GraphGenerator.make_k5_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(k5)
GraphUtils.draw_graph(k5, "K5", bad_graph)
# print "Test on K(5):", 'FAIL' if planar else 'PASS'
self.assertFalse(planar) # False means K(5) subgraph was found
def testK33(self):
# Generate K(3, 3) graph
k33 = GraphGenerator.make_k33_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(k33)
GraphUtils.draw_graph(k33, "K33", bad_graph)
# print "Test on K(3, 3):", 'FAIL' if planar else 'PASS'
self.assertFalse(planar) # False means K(3, 3) subgraph was found
def testPlanar(self):
# Generate K(4) graph
p_graph = GraphGenerator.make_planar_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(p_graph)
GraphUtils.draw_graph(p_graph, "planar_test", bad_graph)
# print "Test on planar graph:", 'PASS' if planar else 'FAIL'
self.assertTrue(planar) # True means graph is planar
def testK33(self):
# Generate K(3, 3) graph
k33 = GraphGenerator.make_k33_graph()
# Test graph
planar, bad_graph = KuratowskiPlanarity.find_planarity(k33)
GraphUtils.draw_graph(k33, "K33", bad_graph)
# print "Test on K(3, 3):", 'FAIL' if planar else 'PASS'
self.assertFalse(planar) # False means K(3, 3) subgraph was found
def __usonic_log(self, tweet_id, span=HOUR): now = datetime.datetime.now() if span == DAY: from_time = now - datetime.timedelta(days=1) axis_span = 120 else: from_time = now - datetime.timedelta(hours=1) axis_span = 10 db = USonicDB() log = db.select_between(from_time, now) Gu.create_usonic_graph(log, USONIC_GRAPH_TEMP, axis_span=axis_span) message = USONIC_LOG_FMT.format(span) self.tweet.update_with_media(USONIC_GRAPH_TEMP, message, tweet_id)
def add_rides_to_graph(self, rides):
for ride in rides:
pu = GraphUtils.add_main_node(self.graph, ride.pickup.name,
ride.pickup.pos, "pickup",
self.ride_data)
do = GraphUtils.add_main_node(self.graph, ride.dropoff.name,
ride.dropoff.pos, "dropoff",
self.ride_data)
self.do_nodes.append(do[0])
self.pu_nodes.append(pu[0])
self.w_nodes.extend(pu[1:])
self.w_nodes.extend(do[1:])
self.add_edges_for_print(ride)
def __init__(self,
outdir="./",
outfile="yields.txt",
integer_precision=False):
self.columns = []
self.rows = []
self.column_names = []
self.row_names = []
self.gu = GraphUtils()
#
self.out_dir = outdir
self.out_file = outfile
self.integer_precision = integer_precision
def __dht_log(self, tweet_id, span=DAY): now = datetime.datetime.now() if span == DAY: from_time = now - datetime.timedelta(days=1) axis_span = 120 elif span == WEEK: from_time = now - datetime.timedelta(days=7) axis_span = 1000 else: from_time = now - datetime.timedelta(hours=1) axis_span = 10 db = DHTDB() log = db.select_between(from_time, now) Gu.create_dht_graph(log, DHT_GRAPH_TEMP, axis_span=axis_span) message = DHT_LOG_FMT.format(span) self.tweet.update_with_media(DHT_GRAPH_TEMP, message, tweet_id)
def equationChanged(stuff, var, resultString, resultLabel, num):
global fList
def updateResults(rng):
formatted = map(lambda frame: GraphUtils.formatExpression(frame[6].get()), fList[rng[0]: rng[1]])
replaced = GraphUtils.replaceCalls(formatted)
for i in range(len(replaced)):
replaced[i] = GraphUtils.nestFunctions(replaced[i], replaced[:i] + replaced[i + 1:])
for i in range(len(replaced)):
frame = fList[i + rng[0]]
eq = replaced[i]
# Calculate the result of the entered equation
result = MathCalc.readEquation(GraphUtils.removeDependant(eq))
resultType = type(result)
types = [int, long, float]
# Display the result of calculation below the text box
if resultType in types:
frame[7].set("= " + str(result))
frame[3].grid(columnspan=1000)
else:
frame[3].grid_forget()
if GraphUtils.defineFunctions([var.get()] + map(lambda frame: frame[6].get(), fList)):
updateResults([0, len(fList)])
else:
index = int(num.get()[:-1]) - 1
updateResults([index, index + 1])
def add_drop_off_node(self, nn, rides, sh_path_lens, grid_g):
cur_node_dict = self.graph.nodes[nn]
ride_num = int(nn[2])
ride = rides[ride_num - 1]
new_nodes = GraphUtils.add_main_node(self.graph,
ride.dropoff.name,
ride.dropoff.pos,
"dropoff",
self.ride_data,
grid_graph=grid_g.graph,
on_grid=False)
# adds the "detour" attribute to the node attributes dictionary
self.graph.nodes[
ride.dropoff.name]["detour"] = -sh_path_lens[ride.pickup.name]
# Creates weighted edges for the new nodes with the exist nodes
for new_node in new_nodes:
for node in self.graph.nodes:
if all([
node not in self.graph.node[new_node]["wnode"],
node != "van"
]):
self.graph.add_edge(new_node,
node,
weight=nx.shortest_path_length(
grid_g.graph, new_node, node))
def computeBendPoints(pixels, keypixels, vectorpoints):
temp_key_pixels = copy.deepcopy(keypixels)
bend_pixels = []
for i in keypixels:
x = GraphUtils.vectorSearch(np.array(i),
np.array(keypixels),
pixels,
is_start=True,
vector_points=vectorpoints)
if x is not None and len(x) != 0:
# print(x)
i1 = x[0][0]
i2 = x[1][0]
j1 = x[0][1]
j2 = x[1][1]
_cos = (i1 * i2 + j1 * j2) / (np.sqrt(i1**2 + j1**2) *
np.sqrt(i2**2 + j2**2))
tmp = np.arccos(float(_cos))
angle = np.rad2deg(tmp)
# print(angle)
if angle >= 120:
temp_key_pixels.remove(i)
bend_pixels.append(i)
keypixels = temp_key_pixels
return bend_pixels
def __init__(self, multiGraph, M, damp=.8, sieve=3, top=5, eiglim=1.6, wlcf=lambda x,y,z: x): self.rankdata = [] self.remaining = range(len(multiGraph)) self.prevRemaining = range(len(multiGraph)) self.nowins = [] self.M = M self.damp = damp self.sieve = sieve self.top = top self.eiglim = eiglim self.wlcf = wlcf for i in range(len(M)): for j in range(len(M)): self.M[i, j] = self.M[i, j]**.5 for i in multiGraph: if not multiGraph[i]: self.nowins.append(i) self.remaining.remove(i) self.count = len(self.remaining) currM = self.M[np.ix_(self.remaining, self.remaining)] self.prevM = self.M eigval = max(abs(np.linalg.eig(currM)[0])) if not eigval or eigval < eiglim: eigval = eiglim escale = damp*eigval**(-1) K = gu.KRank(currM, escale, 50) rankdata = sorted(zip(range(len(currM)), K), key=lambda v: v[1]) self.currank = [(self.remaining[i], j) for i, j in rankdata]
def random_walk(graph, startVector, r=0.4):
"""
This method can be called from anywhere (such as validation scripts) and does whatever it needs to do to produce a properly formatted output,
using only the given parameters.
@param graph: a networkx graph object containing the entire PPI network
@param startVector: a numpy array that contains the weighted start probabilities for each protein in the network
@returns: a nested list of tuples, in sorted order of probability, where each item contains the name of a gene, and its respective probability as determined by the algorithm
"""
print("INITIALIZING RANDOM WALK")
maxIterations = 500
normThreshold = 10**(-6)
print("creating matrix")
matrix = compute_if_not_cached(create_normalized_matrix,
graph,
fileName=graph.name)
probabilityVector = random_walk_matrix(matrix, startVector, r,
maxIterations, normThreshold)
# format probabilityVector into usable output
print("formatting output")
return GraphUtils.format_output(graph, probabilityVector)
def run_algs(self, policies, n_experiments=None, time_horizon=None):
for n_nodes, budget in zip(self.n_nodes, self.budgets):
network = ut.create_network(n_nodes, self.n_features)
print('\nBudget: ' + str(budget))
print('Number of nodes: ' + str(n_nodes))
env = None
if self.alg_type == 'unknown':
print('Time Horizon: ', str(time_horizon))
print('Number of experiments: ', str(n_experiments))
env = SocialEnvironment(network.copy())
for e in network.edges.values():
e['prob'] = None
for mc_iterations in self.mc_iterations:
print('Montecarlo iterations: ' + str(mc_iterations))
if self.alg_type == 'known':
self.learners = SimplePolicies(network,
mc_iterations,
bandit=False)
self.learners.execute(budget, policies)
self.plot_results()
else:
self.learners = SocialBanditPolicies(
network, budget, mc_iterations, env)
self.learners.execute(n_experiments, time_horizon,
policies)
self.plot_results()
def __init__(self,
inputpath='',
inputfile='',
output=None,
standards=None,
observable=None,
purity=None):
try:
Defaults.__init__(self, standards=standards)
except RuntimeError:
print "Error: cannot initialize defaults class..."
self.__outputpath = inputpath
self.__filename = os.path.join(inputpath, inputfile)
self.__output = output
self.__standards = standards
self.__observable = observable
self.__purity = purity
#
self.__analysis = standards.analysis
self.__luminosity = standards.luminosity
self.__gu = GraphUtils()
self.__fm = Format(standards)
self.__histo_list = []
self.__analysis_ready = self.analyze_file()
self.__visual_ready = self.visualize()
self.__final_ready = self.finalize()
def xmlizeFiles(self, directory, fileType):
# stat table contains triples of (time, file, stat)
statTable = []
fields = self.request().fields()
for f in findSysFiles(directory):
s = os.stat(os.path.join(basicPathName, directory, f))
md5sum = self.readMd5Sum(directory, f)
statTable.append((s[stat.ST_MTIME], f, s, md5sum))
# sort first by reverse time, then by name
statTable.sort(lambda a,b: cmp(b[0], a[0]) or FormUtils.alphanumericCompare(a[1], b[1]))
result = self.doc.createElement(directory)
for sysMtime, eachFile, fileStat, md5Sum in statTable:
sysBytes = fileStat[stat.ST_SIZE]
sysTimeStr = time.strftime('%Y/%m/%d %H:%M',
time.localtime(sysMtime))
fileEl = self.doc.createElement('file')
fileEl.setAttribute('name', eachFile)
fileEl.setAttribute(
'href', '/mgmt/download?f=%s&type=%s' % (eachFile, fileType))
# bytes are for column sorting
fileEl.setAttribute('bytes', '%d' % sysBytes)
fileEl.setAttribute('sizeStr', GraphUtils.scale(
sysBytes, GraphUtils.SCALER_HUNDREDS_OF_BYTES))
# timestamp is for column sorting
fileEl.setAttribute('timestamp', '%d' % sysMtime)
fileEl.setAttribute('timestring', sysTimeStr)
fileEl.setAttribute('md5sum', '%s' % md5Sum)
result.appendChild(fileEl)
self.doc.documentElement.appendChild(result)
self.writeXmlDoc()
def inputExpression():
while 1:
while 1:
print "\nPlease enter expression to evaluate [OR] q to return to the main menu"
expression = raw_input(">> ").strip()
if not expression:
continue
break
if expression.lower() == "q":
return None
formatted = GraphUtils.formatExpression(expression)
if not GraphUtils.validateFunction(formatted):
print "The expression entered has unmatched brackets.\nPlease try again.\n"
continue
return expression
def create_normalized_matrix(ppiGraph):
"""
Generates normalized adjacency matrix.
@param ppiGraph: a networkx graph containing the entire PPI network
@returns: a numpy array that contains the normalized adjacency matrix
"""
return np.asarray(
GraphUtils.normalize_adjacency_matrix(nx.to_numpy_matrix(ppiGraph)))
def calc_data(self): self.M = gu.MG_to_M(self.multiGraph) data = gu.MG_to_G(self.multiGraph) self.G = data[0] self.D = data[1] self.A = gu.G_to_A(self.G) self.DA = np.matrix([[0 for col in self.M] for col in self.M]) for i in self.D: for j in self.D[i]: self.DA[i, j] = 1 self.W = self.A + (.5)*self.DA # TODO: implement a function to scale results from 0 to 1 self.partition = gu.SCC(self.G) self.inCycle = gu.record_cycles(self.G, self.partition) self.isBelow = gu.record_isBelow(self.G, self.inCycle) self.stronglyBelow = gu.record_stronglyBelow(self.G, self.isBelow, self.inCycle)
def updateResults(rng):
formatted = map(lambda frame: GraphUtils.formatExpression(frame[6].get()), fList[rng[0]: rng[1]])
replaced = GraphUtils.replaceCalls(formatted)
for i in range(len(replaced)):
replaced[i] = GraphUtils.nestFunctions(replaced[i], replaced[:i] + replaced[i + 1:])
for i in range(len(replaced)):
frame = fList[i + rng[0]]
eq = replaced[i]
# Calculate the result of the entered equation
result = MathCalc.readEquation(GraphUtils.removeDependant(eq))
resultType = type(result)
types = [int, long, float]
# Display the result of calculation below the text box
if resultType in types:
frame[7].set("= " + str(result))
frame[3].grid(columnspan=1000)
else:
frame[3].grid_forget()
def reduce(self): while len(self.currank) > self.top: self.rankdata.append(self.currank) #find point of change after cutoff point n = max(1, len(self.currank)/self.sieve) while self.currank[n] == self.currank[n+1]: n += 1 for i in [i for i,j in self.currank[:n]]: self.remaining.remove(i) currM = self.M[np.ix_(self.remaining, self.remaining)] eigval = max(abs(np.linalg.eig(currM)[0])) if not eigval or eigval < self.eiglim: eigval = self.eiglim escale = self.damp*eigval**(-1) # used for scaling losses in wlcf lscale = float(self.count - len(self.remaining))/self.count K = gu.KRank(currM, escale, 50) KL = gu.KRank(currM, escale, 50, direction=0) D = [self.wlcf(i, j, lscale) for i, j in zip(K, KL)] drankdata = sorted(zip(range(len(currM)), D), key=lambda v: v[1]) self.currank = [(self.remaining[i], j) for i, j in drankdata] self.prevM = currM self.prevRemain = self.remaining # I don't understand why I need to make this check # If I don't, rankdata somehow gets the last currank appended twice if self.rankdata[-1] != self.currank: self.rankdata.append(self.currank)
def main():
"""
Entry point into the program.
"""
num_nodes = 20
# Use binomial coefficient to find algorithm runtime (algorithm is O(n choose 6 + n choose 5))
# K33 check complexity
complexity_k33 = math.factorial(num_nodes) / (
math.factorial(6) * math.factorial(num_nodes - 6))
# K5 check complexity
complexity_k5 = math.factorial(num_nodes) / (math.factorial(5) *
math.factorial(num_nodes - 5))
# Overall complexity formatted with commas
complexity = GraphUtils.format_commas(complexity_k5 + complexity_k33)
print "[kuratowski] Iterations (worst case) for %s nodes: %s" % (
num_nodes,
complexity,
)
# Random graph, usually nonplanar
solve_random_graph(num_nodes, 0.7,
"graph1") # Graph is likely to be nonplanar
# Random graph, usually planar
solve_random_graph(8, 0.4, "graph2") # Graph is usually planar
# K(3, 3) graph
k33 = GraphGenerator.make_k33_graph()
solve_graph(k33, "k33")
# K(5) graph
k5 = GraphGenerator.make_k5_graph()
solve_graph(k5, "k5")
# Guaranteed planar graph
planar_graph = GraphGenerator.make_planar_graph()
solve_graph(planar_graph, "planar_graph")
# Display each graph (blocking command, run last)
plt.show()
def approximation(edges, vectors, i, P_list, S_list, O_list):
O = edges[i][0]
S = edges[i][-1]
OM = vectors[i]
OS = (S[0] - O[0], S[1] - O[1])
c = np.sqrt((O[0] - S[0])**2 + (O[1] - S[1])**2)
length = GraphUtils.fullLen(edges[i])
cos_alpha = (OM[0] * OS[0] + OM[1] * OS[1]) / (
np.sqrt(OM[0]**2 + OM[1]**2) * np.sqrt(OS[0]**2 + OS[1]**2))
if cos_alpha != 1:
a = (c**2 - length**2) / (2 * (c * cos_alpha - length))
cos_beta = OM[0] / np.sqrt(OM[0]**2 + OM[1]**2)
sin_beta = np.sqrt(1 - cos_beta**2)
delta_x = a * cos_beta
delta_y = a * sin_beta
P = O[0] + delta_x, O[1] + delta_y
OP = P[0] - O[0], P[1] - O[1]
# С ОТРАЖЕНИЕМ
if (OS[0] * OM[1] - OS[1] * OM[0]) * (OS[0] * OP[1] -
OS[1] * OP[0]) >= 0:
P_list.append(P)
S_list.append(S)
O_list.append(O)
else:
S_list.append(S)
O_list.append(O)
# TODO: отражение точки относительно отрезка
temp_line = Line(0, xy1=O, xy2=S)
A, B, C = temp_line.A, temp_line.B, temp_line.C
first_matrix = np.array([[A, B], [B, -A]], dtype=np.float32)
second_vector = np.array([-C, B * P[0] - A * P[1]],
dtype=np.float32)
x1, y1 = np.linalg.solve(first_matrix, second_vector)
P = (2 * x1 - P[0], 2 * y1 - P[1])
P_list.append(P)
else:
P_list.append(edges[i][len(edges[i]) // 2])
O_list.append(O)
S_list.append(S)
def calc_data(self):
self.M = gu.MG_to_M(self.multiGraph)
data = gu.MG_to_G(self.multiGraph)
self.G = data[0]
self.D = data[1]
self.A = gu.G_to_A(self.G)
self.DA = np.matrix([[0 for col in self.M] for col in self.M])
for i in self.D:
for j in self.D[i]:
self.DA[i, j] = 1
self.W = self.A + (.5) * self.DA
# TODO: implement a function to scale results from 0 to 1
self.partition = gu.SCC(self.G)
self.inCycle = gu.record_cycles(self.G, self.partition)
self.isBelow = gu.record_isBelow(self.G, self.inCycle)
self.stronglyBelow = gu.record_stronglyBelow(self.G, self.isBelow,
self.inCycle)
def __init__(self, graph, nodeType, alphas = [0.01]):
self.graph = graph
self.type = self.graph.getStringProperty('type')
self.name = self.graph.getStringProperty('name')
self.plainOneModeGraph = self.graph.getSuperGraph().addSubGraph('plainOneMode_country')
self.substrateType = nodeType
self.strength = self.plainOneModeGraph.getDoubleProperty('Neal strength')
self.nbCatalysts = 0
for node in self.graph.getNodes():
if self.type[node] != self.substrateType:
self.nbCatalysts += 1
self.alphas = alphas
for node in self.graph.getNodes():
if self.type[node] == self.substrateType:
self.plainOneModeGraph.addNode(node)
self.neighborSets = {}
self.probabilities = {}
self.utils = GraphUtils()
def final_graph_plotting(self, path, rides):
self.add_rides_to_graph(rides)
self.add_van_node()
path_edge_list = GraphUtils.get_edge_list_from_path(path)
plt.figure(figsize=(15, 15))
self.set_nodes_and_edge_colors()
nx.draw(self.graph,
self.positions,
node_size=15,
node_color=self.node_colors,
edge_color=self.edge_colors)
nx.draw_networkx_nodes(self.graph,
self.positions,
self.w_nodes,
node_size=40,
node_color='y')
nx.draw_networkx_nodes(self.graph,
self.positions,
self.pu_nodes,
node_size=40,
node_color='r')
nx.draw_networkx_nodes(self.graph,
self.positions,
self.do_nodes,
node_size=40,
node_color='b')
nx.draw_networkx_nodes(self.graph,
self.positions, ["van"],
node_size=40,
node_color='g')
nx.draw_networkx_edges(self.graph,
self.positions,
path_edge_list,
width=2,
edge_color='red')
plt.show()
def load_nodes(self, gridG, rides, van_vertex, for_final_plot=False):
van_pos = (van_vertex["x"], van_vertex["y"])
GraphUtils.add_node(self.graph,
"van",
van_pos,
"van",
grid_graph=gridG.graph,
on_grid=False)
for ride in rides:
GraphUtils.add_main_node(self.graph,
ride.pickup.name,
ride.pickup.pos,
"pickup",
self.ride_data,
grid_graph=gridG.graph,
on_grid=False)
if for_final_plot:
GraphUtils.add_main_node(self.graph,
ride.dropoff.name,
ride.dropoff.pos,
"dropoff",
self.ride_data,
grid_graph=gridG.graph,
on_grid=False)
self.graph.add_edge(ride.pickup.name,
ride.dropoff.name,
color="green")
else:
for i, j in itertools.combinations(self.graph.nodes, 2):
if i == "van" or j == "van" or j not in self.graph.node[i][
"wnode"]:
self.graph.add_edge(i,
j,
weight=nx.shortest_path_length(
gridG.graph,
self.graph.node[i]["position"],
self.graph.node[j]["position"]))
class OneModeProjection(object):
'''
this class implements Z. Neal's technique for computing
a one mode projection of a bipartite network
we agree to distinguish nodes by assinging them a type
stored in a string property
for convenience we call nodes either substrates or catalysts
to distinguish these two types
substrates are those nodes of the indicated type
'''
def __init__(self, graph, nodeType, alphas = [0.01]):
self.graph = graph
self.type = self.graph.getStringProperty('type')
self.name = self.graph.getStringProperty('name')
self.plainOneModeGraph = self.graph.getSuperGraph().addSubGraph('plainOneMode_country')
self.substrateType = nodeType
self.strength = self.plainOneModeGraph.getDoubleProperty('Neal strength')
self.nbCatalysts = 0
for node in self.graph.getNodes():
if self.type[node] != self.substrateType:
self.nbCatalysts += 1
self.alphas = alphas
for node in self.graph.getNodes():
if self.type[node] == self.substrateType:
self.plainOneModeGraph.addNode(node)
self.neighborSets = {}
self.probabilities = {}
self.utils = GraphUtils()
def computeNeighborSets(self):
for node in self.graph.getNodes():
if self.type[node] == self.substrateType:
neighbors = []
for neigh in self.graph.getInOutNodes(node):
neighbors.append(neigh.id)
self.neighborSets[node.id] = frozenset(neighbors)
def probability(self, P1, P2, C):
E = self.nbCatalysts
p = 1.0
p *= binomial(E, C)
p *= binomial(E - C, P1 - C)
p *= binomial(E - P1, P2 - C)
p /= binomial(E, P1)
p /= binomial(E, P2)
return p
def cumulProbability(self, P1, P2, C):
s = 0.0
for c in range(C+1):
s += self.probability(P1, P2, c)
return s
def inverseCPF(self, P1, P2, alpha):
'''
(CPF stands for cumulatice probability function)
determine C such that the probability that
P1 and P2 share C common neighbors or less is at least alpha
'''
C = min(P1, P2) + 1
cumulProb = 0.0
while cumulProb < alpha:
C -= 1
cumulProb += self.probability(P1, P2, C)
return C + 1
def induceEdge(self, node1, node2):
try:
set1 = self.neighborSets[node1.id]
except KeyError:
print node1, self.type[node1], self.name[node1]
P1 = len(set1)
set2 = self.neighborSets[node2.id]
P2 = len(set2)
intersection = set1.intersection(set2)
C = len(intersection)
for i in range(len(self.alphas)):
alpha = self.alphas[i]
oneModeGraph = self.oneModeGraphs[i]
if C >= self.inverseCPF(P1, P2, alpha):
edge = self.utils.findEdge(node1, node2, oneModeGraph)
#print edge
if edge == None:
edge = oneModeGraph.addEdge(node1, node2)
self.strength[edge] = self.cumulProbability(P1, P2, C)
label = '(' + str(node1.id) + ', ' + str(node2.id) + ', ' + str(self.inverseCPF(P1, P2, alpha)) + ')'
self.invCPFProperty[edge] = label
def plainProjection(self):
'''
take all pairs of distance-2 neighbors of type A
(connected through a type B common neighbor)
induce an edge between nodes in a unimode (type A) graph
'''
visited = set()
queue = Queue()
'''
find a node of proper type
'''
for node in self.sandBoxGraph.getNodes():
if self.type[node] == self.substrateType:
queue.put(node)
break
nbProcessedEdges = 0
while not queue.empty():
node1 = queue.get()
visited.add(node1.id)
if len(visited) % 10 == 0:
print 'processed ' + str(len(visited)) + ' substrate nodes'
for node in self.sandBoxGraph.getInOutNodes(node1):
for node2 in self.sandBoxGraph.getInOutNodes(node):
if node2.id not in visited:
queue.put(node2)
edge = self.utils.findEdge(node1, node2, self.plainOneModeGraph)
if edge == None:
edge = self.plainOneModeGraph.addEdge(node1, node2)
nbProcessedEdges += 1
def computeEdgeStrength(self, node1, node2):
set1 = self.neighborSets[node1.id]
P1 = len(set1)
set2 = self.neighborSets[node2.id]
P2 = len(set2)
intersection = set1.intersection(set2)
C = len(intersection)
strength = 0
for k in range(C+1):
strength += self.probability(P1, P2, k)
return strength
def projectNeal(self, alphas):
'''
this is Zachary Neal's trick
take all pairs of distance-2 neighbors of type A
(connected through a type B common neighbor)
if number of common neighbors (of type B)
is at lest equal to inverseCPF(alpha)
induce an edge between nodes in a unimode (type A) graph
'''
visited = set()
queue = Queue()
'''
find a node of proper type
'''
for node in self.sandBoxGraph.getNodes():
if self.type[node] == self.substrateType:
queue.put(node)
break
nbProcessedEdges = 0
while not queue.empty():
substrate1 = queue.get()
visited.add(substrate1.id)
if len(visited) % 10 == 0:
print 'processed ' + str(len(visited)) + ' substrate nodes'
for catalyst in self.sandBoxGraph.getInOutNodes(substrate1):
for substrate2 in self.sandBoxGraph.getInOutNodes(catalyst):
if substrate2.id not in visited:
queue.put(substrate2)
self.induceEdge(substrate1, substrate2, alpha)
nbProcessedEdges += 1
def efficientProject(self):
'''
parse all nodes of type 'not substrate'
for each,
grab their neighbors (they are of type 'substrate')
build a clique (add all missing edges, as some might already be there)
'''
denom = self.graph.numberOfNodes() - self.plainOneModeGraph.numberOfNodes()
numer = 0
percentProcessedNodes = 0.0
for node in self.graph.getNodes():
if self.type[node] != self.substrateType:
numer += 1
for neigh1 in self.graph.getInOutNodes(node):
for neigh2 in self.graph.getInOutNodes(node):
if neigh1.id != neigh2.id:
edge = self.utils.findEdge(neigh1, neigh2, self.plainOneModeGraph)
if edge == None:
edge = self.plainOneModeGraph.addEdge(neigh1, neigh2)
self.strength[edge] = self.computeEdgeStrength(neigh1, neigh2)
newPercentProcessedNodes = round(float(numer)/denom * 100)
if newPercentProcessedNodes > percentProcessedNodes:
percentProcessedNodes = newPercentProcessedNodes
print 'Processed ', percentProcessedNodes, '% nodes, (', numer, ' non substrate) nodes'
def efficientNealProject(self):
for i in range(len(self.alphas)):
alpha = self.alphas[i]
print ' Computing Neal projection' + str(alpha)
nodeSet = set()
self.plainOneModeGraph.getBooleanProperty('select')
for edge in self.plainOneModeGraph.getEdges():
node1 = self.plainOneModeGraph.source(edge)
set1 = self.neighborSets[node1.id]
P1 = len(set1)
node2 = self.plainOneModeGraph.target(edge)
set2 = self.neighborSets[node2.id]
P2 = len(set2)
intersection = set1.intersection(set2)
C = len(intersection)
if C >= self.inverseCPF(P1, P2, alpha):
nodeSet.add(node1)
nodeSet.add(node2)
subGraph = self.plainOneModeGraph.inducedSubGraph(nodeSet)
subGraph.setName('plainOneMode_country_' + str(alpha))
print ' Finished - Computing Neal projection' + str(alpha)
class Yields(object):
def __init__(self,
outdir="./",
outfile="yields.txt",
integer_precision=False):
self.columns = []
self.rows = []
self.column_names = []
self.row_names = []
self.gu = GraphUtils()
#
self.out_dir = outdir
self.out_file = outfile
self.integer_precision = integer_precision
def __ncol(self):
return len(self.column_names) + 1
def __get_latex_table_top(self):
column_names = self.column_names
column_names.append("Ratio")
ncolumns = "l" * (self.__ncol())
titles = " & ".join(column_names)
multicolumn = ""
for i in xrange(1, self.__ncol() + 1):
multicolumn += "{#%i}" % i
return """\\begin{table}
\\renewcommand{\\arraystretch}{1.3}
\\begin{center}
\\begin{tabular}{%s}\hline
Sample & %s \\\ \hline \hline \n""" % (ncolumns, titles)
def __get_latex_table_bottom(self):
caption = ", ".join(self.column_names)
label = "_".join(self.column_names)
return """\hline
\end{tabular}
\end{center}
\caption{%s yields.}
\label{tb:yields_%s}
\end{table}\n\n""" % (caption, label)
def add_column(self, icolumn=None):
if icolumn:
self.columns.append(icolumn)
self.column_names.append(icolumn.name)
def add_row(self, irow=None):
if irow:
self.rows.append(irow)
self.row_names.append(irow.name)
def store(self):
#upper table
outbuffer = self.__get_latex_table_top()
#organize columns
ref_column = self.columns[0]
total_prefit = 0
total_postfit = 0
error_prefit2 = 0
error_postfit2 = 0
for sample, histo in ref_column.column.items():
#prefit
error = ROOT.Double()
area = histo.IntegralAndError(-1, -1, error, "")
if self.integer_precision:
outbuffer += "%-*s & %-*i $\pm$ %-*i" % (
15, sample, 15, int(area), 15, int(error))
else:
outbuffer += "%-*s & %-*.4f $\pm$ %-*.4f" % (15, sample, 15,
area, 15, error)
total_prefit += area
error_prefit2 += error**2
#postfit
for icolumn in self.columns:
if icolumn.name == ref_column.name:
continue
for isample, ihisto in icolumn.column.items():
if isample == sample:
ierror = ROOT.Double()
iarea = ihisto.IntegralAndError(-1, -1, ierror, "")
if self.integer_precision:
outbuffer += " & %-*i $\pm$ %-*i" % (
15, int(iarea), 15, int(ierror))
else:
outbuffer += " & %-*.4f $\pm$ %-*.4f" % (
15, iarea, 15, ierror)
#ratio
ratio_val = iarea / area
ratio_err = ratio_val * math.sqrt(
math.pow(ierror / iarea, 2) +
math.pow(error / area, 2))
outbuffer += " & %-*.2f $\pm$ %-*.2f" % (15, ratio_val,
15, ratio_err)
total_postfit += iarea
error_postfit2 += ierror**2
outbuffer += "\\\\ \n"
#
error_prefit = math.sqrt(error_prefit2)
error_postfit = math.sqrt(error_postfit2)
total_ratio_val = total_postfit / total_prefit
total_ratio_error = total_ratio_val * math.sqrt(
math.pow(error_prefit / total_prefit, 2) +
math.pow(error_postfit / total_postfit, 2))
outbuffer += "\hline \n"
outbuffer += "%-*s & %-*.4f $\pm$ %-*.4f" % (
15, "Total", 15, total_prefit, 15, error_prefit)
outbuffer += " & %-*.4f $\pm$ %-*.4f" % (15, total_postfit, 15,
error_postfit)
outbuffer += " & %-*.2f $\pm$ %-*.2f" % (15, total_ratio_val, 15,
total_ratio_error)
outbuffer += "\\\\ \n"
outbuffer += "\hline \n"
#organize rows
for irow in self.rows:
content, error_low, error_high = self.gu.area_and_poissonian_errors(
irow.graph)
outbuffer += "%-*s & \multicolumn{%i}{c}{$%i^{+%i}_{-%i}$} & \\\\ \n" % (
15, irow.name, self.__ncol() - 2, content, error_high,
error_low)
total_ratio_prefit = content / total_prefit
total_error_prefit_high = total_ratio_prefit * math.sqrt(
math.pow(error_prefit / total_prefit, 2) +
math.pow(error_high / content, 2))
total_error_prefit_low = total_ratio_prefit * math.sqrt(
math.pow(error_prefit / total_prefit, 2) +
math.pow(error_low / content, 2))
total_ratio_postfit = content / total_postfit
total_error_postfit_high = total_ratio_postfit * math.sqrt(
math.pow(error_postfit / total_postfit, 2) +
math.pow(error_high / content, 2))
total_error_postfit_low = total_ratio_postfit * math.sqrt(
math.pow(error_postfit / total_postfit, 2) +
math.pow(error_low / content, 2))
outbuffer += "\hline \n"
outbuffer += "%-*s" % (15, irow.name + "/Total")
outbuffer += " & $%.4f^{+%.4f}_{-%.4f}$" % (
total_ratio_prefit, total_error_prefit_high,
total_error_prefit_low)
outbuffer += " & $%.4f^{+%.4f}_{+%.4f}$" % (
total_ratio_postfit, total_error_postfit_high,
total_error_postfit_low)
outbuffer += " & "
outbuffer += "\\\\ \n"
outbuffer += "\hline \n"
#bottom table
outbuffer += self.__get_latex_table_bottom() + "\n"
fname = os.path.join(self.out_dir, self.out_file)
f = open(fname, "w")
f.write(outbuffer)
f.close()
msg.info("Yields::store", "Result is stored in '%s'" % (fname))
def page_rank(graph, startVector, priorBias, beta=BETA):
return GraphUtils.format_output(
graph, rank_genes(graph, startVector, priorBias, beta))
def compute_matrix(graph):
return np.asarray(
GraphUtils.normalize_adjacency_matrix(nx.to_numpy_matrix(graph)))
def simplefitter_3D_NoFormDataValidation():
formTextData = request.form['textdata']
formEquation = request.form['equation']
formFittingTarget = request.form['target']
if formEquation == 'Linear':
equation = pyeq2.Models_3D.Polynomial.Linear(formFittingTarget)
elif formEquation == 'FullQuadratic':
equation = pyeq2.Models_3D.Polynomial.FullQuadratic(formFittingTarget)
elif formEquation == 'FullCubic':
equation = pyeq2.Models_3D.Polynomial.FullCubic(formFittingTarget)
elif formEquation == 'MonkeySaddleA':
equation = pyeq2.Models_3D.Miscellaneous.MonkeySaddleA(
formFittingTarget)
elif formEquation == 'GaussianCurvatureOfWhitneysUmbrellaA':
equation = pyeq2.Models_3D.Miscellaneous.GaussianCurvatureOfWhitneysUmbrellaA(
formFittingTarget)
elif formEquation == 'NIST_NelsonAutolog':
equation = pyeq2.Models_3D.NIST.NIST_NelsonAutolog(formFittingTarget)
elif formEquation == 'CustomPolynomialOne': # X order 3, Y order 1 in this example - passed as integers
equation = pyeq2.Models_3D.Polynomial.UserSelectablePolynomial(
formFittingTarget, "Default", 3, 1)
# the name of the data here is from the form
# check for functions requiring non-zero nor non-negative data such as 1/x, etc.
try:
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
formTextData, equation, False)
except:
return equation.reasonWhyDataRejected
# check for number of coefficients > number of data points to be fitted
coeffCount = len(equation.GetCoefficientDesignators())
dataCount = len(equation.dataCache.allDataCacheDictionary['DependentData'])
if coeffCount > dataCount:
return "This equation requires a minimum of " + repr(
coeffCount) + " data points, you supplied " + repr(dataCount) + "."
equation.Solve()
equation.CalculateModelErrors(equation.solvedCoefficients,
equation.dataCache.allDataCacheDictionary)
equation.CalculateCoefficientAndFitStatistics()
# save fit statistics to a text file
fitStatisticsFilePath = "static/fitstatistics_3D.txt" # simplefitter_3D
TextUtils.SaveCoefficientAndFitStatistics(fitStatisticsFilePath, equation)
# save source code to a single text file, all available languages
sourceCodeFilePath = "static/sourcecode_3D.html" # simplefitter_3D
TextUtils.SaveSourceCode(sourceCodeFilePath, equation)
# create graphs
graphFilePath_Surface = "static/surface.png" # surface plot
graphFilePath_Contour = "static/contour.png" # contour plot
surfaceTitle = "Example Surface Plot"
contourTitle = "Example Contour Plot"
xAxisLabel = "X data"
yAxisLabel = "Y data"
zAxisLabel = "Z data"
GraphUtils.SurfaceAndContourPlots(graphFilePath_Surface,
graphFilePath_Contour, equation,
surfaceTitle, contourTitle, xAxisLabel,
yAxisLabel, zAxisLabel)
absErrorPlotFilePath = "static/abs_error_3D.png" # simplefitter_3D
title = "Absolute Error For An HTML FORM Model"
GraphUtils.SaveAbsErrorScatterPlot(absErrorPlotFilePath, equation, title,
zAxisLabel)
absErrorHistFilePath = "static/abs_error_hist_3D.png" # simplefitter_3D
title = "Absolute Error"
GraphUtils.SaveDataHistogram(absErrorHistFilePath,
equation.modelAbsoluteError, title)
if equation.dataCache.DependentDataContainsZeroFlag != 1:
perErrorPlotFilePath = "static/per_error_3D.png" # simplefitter_3D
title = "Percent Error For An HTML FORM Model"
GraphUtils.SavePercentErrorScatterPlot(perErrorPlotFilePath, equation,
title, zAxisLabel)
perErrorHistFilePath = "static/per_error_hist_3D.png" # simplefitter_3D
title = "Percent Error"
GraphUtils.SaveDataHistogram(perErrorHistFilePath,
equation.modelPercentError, title)
# generate HTML
htmlToReturn = ''
htmlToReturn += equation.GetDisplayName() + '<br><br>\n'
htmlToReturn += equation.GetDisplayHTML() + '<br><br>\n'
htmlToReturn += '<a href="' + fitStatisticsFilePath + '">Link to parameter and fit statistics</a><br><br>\n'
htmlToReturn += '<a href="' + sourceCodeFilePath + '">Link to source code, all available languages</a><br><br>\n'
htmlToReturn += '<img src="' + graphFilePath_Surface + '"><br><br>\n'
htmlToReturn += '<img src="' + graphFilePath_Contour + '"><br><br>\n'
htmlToReturn += '<img src="' + absErrorPlotFilePath + '"><br><br>\n'
htmlToReturn += '<img src="' + absErrorHistFilePath + '"><br><br>\n'
if equation.dataCache.DependentDataContainsZeroFlag != 1:
htmlToReturn += '<img src="' + perErrorPlotFilePath + '"><br><br>\n'
htmlToReturn += '<img src="' + perErrorHistFilePath + '"><br><br>\n'
return '<html><body>' + htmlToReturn + '</body></html>'
def simplefitter_2D_NoFormDataValidation():
formTextData = request.form['textdata']
formEquation = request.form['equation']
formFittingTarget = request.form['target']
if formEquation == 'Linear':
equation = pyeq2.Models_2D.Polynomial.Linear(formFittingTarget)
elif formEquation == 'Quadratic':
equation = pyeq2.Models_2D.Polynomial.Quadratic(formFittingTarget)
elif formEquation == 'Cubic':
equation = pyeq2.Models_2D.Polynomial.Cubic(formFittingTarget)
elif formEquation == 'WitchA':
equation = pyeq2.Models_2D.Miscellaneous.WitchOfAgnesiA(
formFittingTarget)
elif formEquation == 'VanDeemter':
equation = pyeq2.Models_2D.Engineering.VanDeemterChromatography(
formFittingTarget)
elif formEquation == 'GammaRayDegreesB':
equation = pyeq2.Models_2D.LegendrePolynomial.GammaRayAngularDistributionDegreesB(
formFittingTarget)
elif formEquation == 'ExponentialWithOffset':
equation = pyeq2.Models_2D.Exponential.Exponential(
formFittingTarget, 'Offset')
# the name of the data here is from the form
# check for functions requiring non-zero nor non-negative data such as 1/x, etc.
try:
pyeq2.dataConvertorService().ConvertAndSortColumnarASCII(
formTextData, equation, False)
except:
return equation.reasonWhyDataRejected
# check for number of coefficients > number of data points to be fitted
coeffCount = len(equation.GetCoefficientDesignators())
dataCount = len(equation.dataCache.allDataCacheDictionary['DependentData'])
if coeffCount > dataCount:
return "This equation requires a minimum of " + repr(
coeffCount) + " data points, you supplied " + repr(dataCount) + "."
equation.Solve()
equation.CalculateModelErrors(equation.solvedCoefficients,
equation.dataCache.allDataCacheDictionary)
equation.CalculateCoefficientAndFitStatistics()
# save fit statistics to a text file
fitStatisticsFilePath = "static/fitstatistics_2D.txt" # simplefitter_2D
TextUtils.SaveCoefficientAndFitStatistics(fitStatisticsFilePath, equation)
# save source code to a single text file, all available languages
sourceCodeFilePath = "static/sourcecode_2D.html" # simplefitter_2D
TextUtils.SaveSourceCode(sourceCodeFilePath, equation)
# create graph
graphFilePath = "static/model_and_scatterplot_2D.png" # simplefitter_2D
title = "Example Of An HTML FORM Model"
xAxisLabel = "X data"
yAxisLabel = "Y data"
GraphUtils.SaveModelScatterConfidence(graphFilePath, equation, title,
xAxisLabel, yAxisLabel)
absErrorPlotFilePath = "static/abs_error_2D.png" # simplefitter_2D
title = "Absolute Error For An HTML FORM Model"
GraphUtils.SaveAbsErrorScatterPlot(absErrorPlotFilePath, equation, title,
yAxisLabel)
absErrorHistFilePath = "static/abs_error_hist_2D.png" # simplefitter_2D
title = "Absolute Error"
GraphUtils.SaveDataHistogram(absErrorHistFilePath,
equation.modelAbsoluteError, title)
if equation.dataCache.DependentDataContainsZeroFlag != 1:
percentErrorPlotFilePath = "static/per_error_2D.png" # simplefitter_2D
title = "Percent Error For An HTML FORM Model"
GraphUtils.SavePercentErrorScatterPlot(percentErrorPlotFilePath,
equation, title, yAxisLabel)
perErrorHistFilePath = "static/per_error_hist_2D.png" # simplefitter_2D
title = "Percent Error"
GraphUtils.SaveDataHistogram(perErrorHistFilePath,
equation.modelPercentError, title)
# generate HTML
htmlToReturn = ''
htmlToReturn += equation.GetDisplayName() + '<br><br>\n'
htmlToReturn += equation.GetDisplayHTML() + '<br><br>\n'
htmlToReturn += '<a href="' + fitStatisticsFilePath + '">Link to parameter and fit statistics</a><br><br>\n'
htmlToReturn += '<a href="' + sourceCodeFilePath + '">Link to source code, all available languages</a><br><br>\n'
htmlToReturn += '<img src="' + graphFilePath + '"> <br>\n'
htmlToReturn += '<img src="' + absErrorPlotFilePath + '"><br>\n'
htmlToReturn += '<img src="' + absErrorHistFilePath + '"><br>\n'
if equation.dataCache.DependentDataContainsZeroFlag != 1:
htmlToReturn += '<img src="' + percentErrorPlotFilePath + '"><br><br>\n'
htmlToReturn += '<img src="' + perErrorHistFilePath + '"><br><br>\n'
return '<html><body>' + htmlToReturn + '</body></html>'
def textInterface():
# Get user input for expression
def inputExpression():
while 1:
while 1:
print "\nPlease enter expression to evaluate [OR] q to return to the main menu"
expression = raw_input(">> ").strip()
if not expression:
continue
break
if expression.lower() == "q":
return None
formatted = GraphUtils.formatExpression(expression)
if not GraphUtils.validateFunction(formatted):
print "The expression entered has unmatched brackets.\nPlease try again.\n"
continue
return expression
# Menu options
options = ["Evaluate an expression", "Set domain and range", "View a function in text",
"View a function in the graph window", "Perform calculations from a text file",
"Exit the program"]
domain, rnge = [-10, 10], [-7.5, 7.5]
# Pattern to match floats
floatPattern = r"(\-|\+)?[0-9]+(\.[0-9]*)?"
currentDom, currentRange = "%s,%s" % (str(domain[0]), str(domain[1])), "%s,%s" % (str(rnge[0]), str(rnge[1]))
graphProcess = None
running = True
while running:
# Display options to the user
print "What would you like to do?"
for i, option in enumerate(options):
print " %d. %s" % (i + 1, option)
choice = raw_input(">> ").strip()
if choice == "1":
expression = inputExpression()
if expression == None:
continue
formatted = GraphUtils.formatExpression(expression)
result = MathCalc.readEquation(formatted)
if result == None:
print "\nSorry the expression was not evaluated successfully.\n"
else:
print "\nResult: %s\n" % str(result)
elif choice == "2":
# Display the current domain and range to user
print "\nCurrent domain and range:"
print "%s <= x <= %s %s <= y <= %s\n" % (str(domain[0]), str(domain[1]), str(rnge[0]), str(rnge[1]))
# Domain entry
while 1:
print "Please input the domain [OR] nothing to not alter it"
print "Format: 'lowerX,upperX'"
inp = raw_input(">> ").strip()
if not inp: break
# Match the user entered domain
match = re.search(r"(%s) *\, *(%s)" % (floatPattern, floatPattern), inp)
if match == None:
print "Invalid input please try again.\n"
continue
domain = map(lambda s: float(s.strip()), match.group().split(","))
currentDom = "%s,%s" % (str(domain[0]), str(domain[1]))
break
print
# Range entry
while 1:
print "Please input the range [OR] nothing to not alter it"
print "Format: 'lowerY,upperY'"
inp = raw_input(">> ").strip()
if not inp: break
# Match the user entered range
match = re.search(r"(%s) *\, *(%s)" % (floatPattern, floatPattern), inp)
if match == None:
print "Invalid input please try again.\n"
continue
rnge = map(lambda s: float(s.strip()), match.group().split(","))
currentRange = "%s,%s" % (str(rnge[0]), str(rnge[1]))
break
print
elif choice == "3":
expression = inputExpression()
if not expression:
continue
# Format the entered expression and draw the text graph
expression = GraphUtils.formatExpression(expression)
Graphing.commandLineDraw(expression, domain, rnge)
elif choice == "4":
expression = inputExpression()
if expression == None:
continue
if graphProcess != None:
if graphProcess.poll() == None:
# Terminate previous graphing process
graphProcess.terminate()
graphProcess = createGraphProcess([expression], currentDom, currentRange)
else:
graphProcess = createGraphProcess([expression], currentDom, currentRange)
else:
graphProcess = createGraphProcess([expression], currentDom, currentRange)
print
elif choice == "5":
print
while 1:
print "Please input the path to the file [OR] q to return to the main menu"
inp = raw_input(">> ")
if inp.lower() == "q":
break
try:
fileContents = CLineReadFile.readFile(inp)
except:
print "Sorry", inp, "cannot be found or opened.\n"
continue
expressions = CLineReadFile.parseContents(fileContents)
results = CLineReadFile.evalExpressions(expressions)
# Display results
print "============Results============"
CLineReadFile.printResults(results)
break
print
elif choice == "6":
print "Goodbye!"
running = False
def logFiles(self):
fields = self.request().fields()
baseName = fields.get('logPrefix', '') + 'messages'
msgFileSeq = logDownload.findMessageFiles(baseName)
result = self.doc.createElement('logfiles')
if baseName in msgFileSeq:
plainSize = os.stat('/var/log/' + baseName)[stat.ST_SIZE]
plainSize = '(%s)' % \
GraphUtils.scale(plainSize,
GraphUtils.SCALER_TENTHS_OF_BYTES,
precision=1)
fileEl = self.doc.createElement('file')
fileEl.setAttribute('logName', 'Current Log')
fileEl.setAttribute('uncompressedTitle',
'Download the current log as plain text')
fileEl.setAttribute(
'uncompressedHref',
'/mgmt/download?f=%s&type=plainlog' % baseName)
fileEl.setAttribute('compressedTitle', '')
fileEl.setAttribute('compressedHref', '')
fileEl.setAttribute('plainSize', plainSize)
result.appendChild(fileEl)
msgFileSeq.remove(baseName)
downloads = []
for num in [mf.split('.')[1] for mf in msgFileSeq]:
if num not in downloads:
downloads.append(num)
downloads.sort(FormUtils.alphanumericCompare)
downloads = [(num, '%s.%s' % (baseName, num),
'%s.%s.gz' % (baseName, num))
for num in downloads]
downloads = [(num,
plain in msgFileSeq and plain or compressed,
compressed in msgFileSeq and compressed or '')
for num, plain, compressed in downloads]
for num, plain, compressed in downloads:
fileEl = self.doc.createElement('file')
fileEl.setAttribute('logName', 'Archived log # ' + num)
fileEl.setAttribute('uncompressedTitle',
'Download archived log # %s as plain text' % num)
plainSize = ''
if plain.endswith('.gz'):
fileEl.setAttribute('uncompressedHref',
'/mgmt/download?f=%s&type=gunzippedlog' % plain)
else:
fileEl.setAttribute('uncompressedHref',
'/mgmt/download?f=%s&type=plainlog' % plain)
plainSize = os.stat('/var/log/' + plain)[stat.ST_SIZE]
plainSize = '(%s)' % \
GraphUtils.scale(plainSize,
GraphUtils.SCALER_TENTHS_OF_BYTES,
precision=1)
compressedSize = ''
if compressed:
fileEl.setAttribute('compressedTitle',
'Download archived log # %s in gzip format' % num)
fileEl.setAttribute('compressedHref',
'/mgmt/download?f=%s&type=gzippedlog' % compressed)
compressedSize = os.stat('/var/log/' + compressed)[stat.ST_SIZE]
compressedSize = '(%s)' % \
GraphUtils.scale(compressedSize,
GraphUtils.SCALER_TENTHS_OF_BYTES,
precision=1)
else:
fileEl.setAttribute('compressedTitle', '')
fileEl.setAttribute('compressedHref', '')
fileEl.setAttribute('plainSize', plainSize)
fileEl.setAttribute('compressedSize', compressedSize)
result.appendChild(fileEl)
self.doc.documentElement.appendChild(result)
self.writeXmlDoc()
import networkx as nx
import GraphUtils
import bellMan, Djikstra
gConf = GraphUtils.GraphUtils()
# graphWhereDjikstraFails = {0: [(1, -1), (2, -2)], 1: [(2, 3), (3, 2), (4, 2)], 2: [], 3: [(2, -5), (1, 1)], 4: [(3, 3)]}
graph = {
0: [(1, -1), (2, -2)],
1: [(2, 3), (3, 2), (4, 2)],
2: [],
3: [(2, 5), (1, 1)],
4: [(3, 3)]
}
labels = {0: "Hospital", 1: "Work", 2: "Shop", 3: "Grocery Store", 4: "Home"}
source = 0
def djikstra(gConf, graph, G, labels, pos, source):
try:
# Calculating shortest distances and shortest path
# from source node to every node
path = Djikstra.driver(graph, source)
# Creating graph from the above obtained path
djiGraph, labels, eLabels = gConf.createGraphFromPath(G, path, labels)
# Displaying the above created graph
gConf.displayGraph(G=djiGraph,
name="djikstraPath.jpeg",
labels=labels,
eLabels=eLabels,
pos=pos)
except ValueError:
def find_planarity(G):
"""
Searches *graph* for any subgraphs isomorphic to K(3, 3) or K(5).
Complexity:
O((n choose 6) + (n choose 5))
Args:
graph (networkx.Graph): The graph to be searched
Returns:
bool, networkx.Graph: Planarity of the graph and either None or the detected Kuratowski graph
"""
planar = True
offending_subgraph = None
# Optimization step:
# Remove nodes from graph with edge count > 1 and assign to new graph (don't alter original)
outdeg = G.degree()
to_keep = [n for n in outdeg if outdeg[n] > 1]
graph = G.subgraph(to_keep)
num_nodes = len(graph.nodes())
it = 0
k33 = GraphGenerator.make_k33_graph()
k5 = GraphGenerator.make_k5_graph()
if num_nodes > 5:
# Test if graph contains a K(3, 3) subgraph.
for subgraph_nodes in itertools.combinations(graph.nodes(), 6):
it += 1
subgraph = graph.subgraph(subgraph_nodes)
# If the subgraph is bipartite, get each set
if bipartite.is_bipartite(graph): # subgraph?
set1, set2 = bipartite.sets(graph) # subgraph?
# If one set in a 6-node bipartite graph has 3 nodes, then the other
# set has 3 nodes, making it a K(3, 3) graph.
if len(set1) == 3:
planar = False
offending_subgraph = subgraph
# Test for isomorphism
if nx.is_isomorphic(subgraph, k33):
planar = False
offending_subgraph = subgraph
if planar and num_nodes > 4:
# Test if graph contains a K(5) subgraph.
for subgraph_nodes in itertools.combinations(graph.nodes(), 5):
it += 1
subgraph = graph.subgraph(subgraph_nodes)
# If the graph is complete, it's a K(5) graph
if GraphUtils.check_completeness(subgraph):
planar = False
offending_subgraph = subgraph
# Test isomorphism
if nx.is_isomorphic(subgraph, k5):
planar = False
offending_subgraph = subgraph
print "Iterations (actual):", GraphUtils.format_commas(it)
return planar, offending_subgraph
def set_nodes_and_edge_colors(self):
[self.node_colors, self.positions,
self.edge_colors] = GraphUtils.set_nodes_and_edge_colors(self.graph)
