def createTemplate():
import SimpSOM as sps
path = pathlib.Path().absolute()
path = str(path) + '\\GE_withstage_20.csv'
#path = "C:\\PR_Proj_Thesis\\PRAD\\SOM_template\\GE_withstage_20.csv"
# Read file
df = pd.read_csv(path)
# Fill na
df.fillna(0, inplace=True)
print("df shape = ", df.shape)
# labels_old = df['TUMOR_STAGE'].values
df.drop(['TUMOR_STAGE', 'PATIENT_ID'], axis=1, inplace=True)
raw_data = df.values
#Create transpose ; such that each column is one patient
raw_data = raw_data.T
# applying scaling to make values between some range 0-1/-1-2 ,as need for Kohens SOM
scaler = MinMaxScaler(copy=True, feature_range=(0, 1))
scaler.fit(raw_data)
raw_data = scaler.transform(raw_data)
ht = 10
wd = 10
no_of_epocs = 1000
net = sps.somNet(ht, wd, raw_data, PBC=False)
net.colorEx = False
Learning_rate = 0.05
net.PCI = True #The weights will be initialised with PCA.
""" Switch to activate periodic boundary conditions. """
net.PBC = True
net.train(Learning_rate, no_of_epocs)
col_num = raw_data.shape[0]
node_list = [i for i in range(col_num)]
new_lbl = [str(j) for j in range(col_num)]
bmu = net.project(raw_data, labels=new_lbl)
pos = bmu
G = nx.chvatal_graph()
nx.draw_networkx_nodes(G,
pos,
nodelist=node_list,
node_color='w',
edgecolors=[0, 0, 0],
node_size=500,
alpha=0.8)
new_lbl_dict = dict(enumerate(new_lbl))
nx.draw_networkx_labels(G, pos, new_lbl_dict, font_size=10)
plt.axis('on')
plt.savefig(str(count) + '_Template.png') #, bbox_inches='tight', dpi=72)
# plt.show()
return pos
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g_i = nx.Graph()
g_i.add_nodes_from([0])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
print("Source of rumor is %s" % source_estimation)
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
g_i = nx.Graph()
g_i.add_nodes_from([0])
sz = AlgorithmSZ()
source_estimation = sz.run(g, g_i)
print("Source of rumor is %s" % source_estimation)
def test_other_graph(self):
g = nx.chvatal_graph()
nx.set_node_attributes(g, 'infected', {n: False for n in g.nodes()})
g.node[0]['infected'] = True
sz = AlgorithmSZ()
source_estimation = sz.run(g, v=0)
print("Source of rumor is %s" % source_estimation)
nx.draw_networkx(g, node_color=['b' if g.node[n]['infected'] else 'r' for n in g])
plt.show()
def small_graph_tests():
if not total_coloring_test("Complete graph on 3 vertices",
networkx.complete_graph(3), 3):
return False
if not total_coloring_test("Cycle of length 5", networkx.cycle_graph(5),
4):
return False
if not total_coloring_test("Star graph on 5 vertices",
networkx.star_graph(4), 5):
return False
if not total_coloring_test("Petersen graph", networkx.petersen_graph(), 4):
return False
if not total_coloring_test("Chvatal graph", networkx.chvatal_graph(), 5):
return False
return True
import networkx as nx
import matplotlib.pylab as plt
from plot_multigraph import plot_multigraph
graphs = [
("bull", nx.bull_graph()),
("chvatal", nx.chvatal_graph()),
("cubical", nx.cubical_graph()),
("desargues", nx.desargues_graph()),
("diamond", nx.diamond_graph()),
("dodecahedral", nx.dodecahedral_graph()),
("frucht", nx.frucht_graph()),
("heawood", nx.heawood_graph()),
("house", nx.house_graph()),
("house_x", nx.house_x_graph()),
("icosahedral", nx.icosahedral_graph()),
("krackhardt_kite", nx.krackhardt_kite_graph()),
("moebius_kantor", nx.moebius_kantor_graph()),
("octahedral", nx.octahedral_graph()),
("pappus", nx.pappus_graph()),
("petersen", nx.petersen_graph()),
("sedgewick_maze", nx.sedgewick_maze_graph()),
("tetrahedral", nx.tetrahedral_graph()),
("truncated_cube", nx.truncated_cube_graph()),
("truncated_tetrahedron", nx.truncated_tetrahedron_graph()),
]
plot_multigraph(graphs, 4, 5, node_size=50)
plt.savefig('graphs/small.png')
def test_chvatal(self):
print '\nchvatal:',
self.g = nx.chvatal_graph()
a = programming_for_tree_decomposition(self.g, True)
self.t = a.tree_decomposition()
self.assertTrue(self.__test_all_conditions())
def test_properties_named_small_graphs(self):
G = nx.bull_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 5
assert sorted(d for n, d in G.degree()) == [1, 1, 2, 3, 3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 2
G = nx.chvatal_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 12 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.cubical_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 8 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.desargues_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
G = nx.diamond_graph()
assert G.number_of_nodes() == 4
assert sorted(d for n, d in G.degree()) == [2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.dodecahedral_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
assert nx.diameter(G) == 5
assert nx.radius(G) == 5
G = nx.frucht_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
assert nx.diameter(G) == 4
assert nx.radius(G) == 3
G = nx.heawood_graph()
assert G.number_of_nodes() == 14
assert G.number_of_edges() == 21
assert list(d for n, d in G.degree()) == 14 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.hoffman_singleton_graph()
assert G.number_of_nodes() == 50
assert G.number_of_edges() == 175
assert list(d for n, d in G.degree()) == 50 * [7]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 6
assert sorted(d for n, d in G.degree()) == [2, 2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_x_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 8
assert sorted(d for n, d in G.degree()) == [2, 3, 3, 4, 4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.icosahedral_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 30
assert (list(
d for n, d in G.degree()) == [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.krackhardt_kite_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 18
assert (sorted(
d for n, d in G.degree()) == [1, 2, 3, 3, 3, 4, 4, 5, 5, 6])
G = nx.moebius_kantor_graph()
assert G.number_of_nodes() == 16
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 16 * [3]
assert nx.diameter(G) == 4
G = nx.octahedral_graph()
assert G.number_of_nodes() == 6
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 6 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.pappus_graph()
assert G.number_of_nodes() == 18
assert G.number_of_edges() == 27
assert list(d for n, d in G.degree()) == 18 * [3]
assert nx.diameter(G) == 4
G = nx.petersen_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 15
assert list(d for n, d in G.degree()) == 10 * [3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.sedgewick_maze_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 10
assert sorted(d for n, d in G.degree()) == [1, 2, 2, 2, 3, 3, 3, 4]
G = nx.tetrahedral_graph()
assert G.number_of_nodes() == 4
assert G.number_of_edges() == 6
assert list(d for n, d in G.degree()) == [3, 3, 3, 3]
assert nx.diameter(G) == 1
assert nx.radius(G) == 1
G = nx.truncated_cube_graph()
assert G.number_of_nodes() == 24
assert G.number_of_edges() == 36
assert list(d for n, d in G.degree()) == 24 * [3]
G = nx.truncated_tetrahedron_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
G = nx.tutte_graph()
assert G.number_of_nodes() == 46
assert G.number_of_edges() == 69
assert list(d for n, d in G.degree()) == 46 * [3]
# Test create_using with directed or multigraphs on small graphs
pytest.raises(nx.NetworkXError,
nx.tutte_graph,
create_using=nx.DiGraph)
MG = nx.tutte_graph(create_using=nx.MultiGraph)
assert sorted(MG.edges()) == sorted(G.edges())
import pytest
import os
from allegedb import ORM
import networkx as nx
scalefreestart = nx.MultiDiGraph(name='scale_free_graph_5')
scalefreestart.add_edges_from([(0, 1), (1, 2), (2, 0)])
testgraphs = [
nx.chvatal_graph(),
nx.scale_free_graph(5, create_using=scalefreestart),
# nx.chordal_cycle_graph(5, create_using=nx.MultiGraph(name='chordal_cycle_graph_5')),
# The standard networkx edges iterator decides to flip some edges about in arbitrary-seeming
# ways that I haven't been able to replicate and it doesn't seem worth it.
]
# have to name it after creation because it clears the create_using
path_graph_9 = nx.path_graph(9)
path_graph_9.name = 'path_graph_9'
testgraphs.append(path_graph_9)
@pytest.fixture
def db():
name = 'allegedb_load_test.db'
if os.path.exists(name):
os.remove(name)
with ORM('sqlite:///' + name) as orm:
for graph in testgraphs:
{
import networkx as nx
import matplotlib.pylab as plt
from plot_multigraph import plot_multigraph
graphs = [
("bull", nx.bull_graph()),
("chvatal", nx.chvatal_graph()),
("cubical", nx.cubical_graph()),
("desargues", nx.desargues_graph()),
("diamond", nx.diamond_graph()),
("dodecahedral", nx.dodecahedral_graph()),
("frucht", nx.frucht_graph()),
("heawood", nx.heawood_graph()),
("house", nx.house_graph()),
("house_x", nx.house_x_graph()),
("icosahedral", nx.icosahedral_graph()),
("krackhardt_kite", nx.krackhardt_kite_graph()),
("moebius_kantor", nx.moebius_kantor_graph()),
("octahedral", nx.octahedral_graph()),
("pappus", nx.pappus_graph()),
("petersen", nx.petersen_graph()),
("sedgewick_maze", nx.sedgewick_maze_graph()),
("tetrahedral", nx.tetrahedral_graph()),
("truncated_cube", nx.truncated_cube_graph()),
("truncated_tetrahedron", nx.truncated_tetrahedron_graph()),
]
plot_multigraph(graphs, 4, 5, node_size=50)
plt.savefig('graphs/small.png')
vertices = int(words[2])
graph.add_nodes_from(range(vertices))
if words[0] == 'e':
graph.add_edge(int(words[1]) - 1, int(words[2]) - 1)
return graph
def test(nx_graph, k, draw=False):
graph = nx.to_numpy_matrix(nx_graph).astype(int).tolist()
coloring = tabucol(graph, k, debug=True)
if draw:
values = [coloring[node] for node in nx_graph]
nx.draw(nx_graph, node_color=values, pos=nx.shell_layout(nx_graph))
plt.show()
except ImportError:
print("Need networkx and matplotlib installed for testing.")
if __name__ == "__main__":
graph = [[0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0],
[1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0],
[1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1],
[1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0],
[1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0]]
test(nx.chvatal_graph(), 4, True)
return mcl
def clean_attributes(g):
for v in g.nodes():
if 'visited' in g.node[v]:
del g.node[v]['visited']
for u, v in g.edges():
if 'visited' in g[u][v]:
del g[u][v]['visited']
if __name__ == '__main__':
targets = {
'bull': nx.bull_graph(), # 1-connected planar
'chvatal': nx.chvatal_graph(), # 4-connected non-planar
'cubical': nx.cubical_graph(), # 3-connected planar
'desargues': nx.desargues_graph(), # 3-connected non-planar
'diamond': nx.diamond_graph(), # 2-connected planar
'dodecahedral': nx.dodecahedral_graph(), # 3-connected planar
'frucht': nx.frucht_graph(), # 3-connected planar
'heawood': nx.heawood_graph(), # 3-connected non-planar
'house': nx.house_graph(), # 2-connected planar
'house_x': nx.house_x_graph(), # 2-connected planar
'icosahedral': nx.icosahedral_graph(), # 5-connected planar
'krackhardt': nx.krackhardt_kite_graph(), # 1-connected planar
'moebius': nx.moebius_kantor_graph(), # non-planar
'octahedral': nx.octahedral_graph(), # 4-connected planar
'pappus': nx.pappus_graph(), # 3-connected non-planar
'petersen': nx.petersen_graph(), # 3-connected non-planar
'sedgewick': nx.sedgewick_maze_graph(), # 1-connected planar
def create_RGB_Images(pos, classlbl):
gene_list_top14 = [
'SPOP', 'FOXA1', 'CTNNB1', 'CLPTM1L', 'DPYSL2', 'NEIL1', 'PITPNM2',
'ATM', 'EMG1', 'ETV3', 'BRAF', 'NKX3-1', 'ZMYM3', 'SALL1'
]
gene_list = gene_list_top14
path = pathlib.Path().absolute()
pathGE = str(path) + '\\Mergefile_top20.csv'
# Read file
df = pd.read_csv(pathGE)
df = df.loc[df['TUMOR_STAGE'] == classlbl]
df.drop(['TUMOR_STAGE', 'PATIENT_ID'], axis=1, inplace=True)
col_num = len(gene_list) #14
#Create node list of numbers then create lables and dict
node_list = [i for i in range(col_num)]
new_lbl = [str(j) for j in range(col_num)]
new_lbl_dict = dict(enumerate(new_lbl))
# loop through all rows and get RGB values for each patient
row_count = df.shape[0]
df.reset_index(inplace=True)
for i in range(row_count): #loop through all rows
nodecolor = []
for j in range(
len(gene_list)): # loop through all nodes (i.e 14 nodes)
r_prefix = 'GE_' + gene_list[j]
g_prefix = 'DM_' + gene_list[j]
b_prefix = 'CNA_' + gene_list[j]
#Check if tht column exixts
R = round(df[r_prefix][i], 5) if (r_prefix in df.columns) else 0.0
G = round(df[g_prefix][i], 5) if (g_prefix in df.columns) else 0.0
B = round(df[b_prefix][i], 5) if (b_prefix in df.columns) else 0.0
nodecolor.append([R, G, B])
# print("node_color size = " ,len(nodecolor))
G = nx.chvatal_graph()
try:
nx.draw_networkx_nodes(G,
pos,
nodelist=node_list,
edgecolors=[0, 0, 0],
node_color=nodecolor,
node_size=500,
alpha=0.8)
nx.draw_networkx_labels(G, pos, new_lbl_dict, font_size=10)
plt.axis('on')
filename = str(i) + '_Template.png'
path = "./training/" + str(classlbl)
plt.savefig(os.path.join(path, filename))
except:
print("row = ", i, " node_color = ", nodecolor)
# print(("\r Preparing Images... "+str(int(i*100.0/row_count))+"%" ), end=' ')
print("\r Done Preparing Training Images ....for :", classlbl)
return
import pytest
import os
from allegedb import ORM
import networkx as nx
scalefreestart = nx.MultiDiGraph(name='scale_free_graph_5')
scalefreestart.add_edges_from([(0, 1), (1, 2), (2, 0)])
testgraphs = [
nx.chvatal_graph(),
nx.scale_free_graph(5, create_using=scalefreestart),
# nx.chordal_cycle_graph(5, create_using=nx.MultiGraph(name='chordal_cycle_graph_5')),
# The standard networkx edges iterator decides to flip some edges about in arbitrary-seeming
# ways that I haven't been able to replicate and it doesn't seem worth it.
]
# have to name it after creation because it clears the create_using
path_graph_9 = nx.path_graph(9)
path_graph_9.name = 'path_graph_9'
testgraphs.append(path_graph_9)
@pytest.fixture
def db():
name = 'allegedb_load_test.db'
if os.path.exists(name):
os.remove(name)
with ORM('sqlite:///' + name) as orm:
for graph in testgraphs:
{
nx.Graph: orm.new_graph,
nx.DiGraph: orm.new_digraph,
def test_chvatal(self):
expected = False
actual = is_planar(nx.chvatal_graph())
self.assertEqual(expected, actual)
r_prefix = 'GE_' + gene_list[j]
g_prefix = 'DM_' + gene_list[j]
b_prefix = 'CNA_' + gene_list[j]
#Check if tht column exixts
R = round(df1[r_prefix][i], 5) if (r_prefix
in df1.columns) else 0.0
G = round(df1[g_prefix][i], 5) if (g_prefix
in df1.columns) else 0.0
B = round(df1[b_prefix][i], 5) if (b_prefix
in df1.columns) else 0.0
nodecolor.append([R, G, B])
G = nx.chvatal_graph()
try:
nx.draw_networkx_nodes(G,
pos,
nodelist=node_list,
edgecolors=[0, 0, 0],
node_color=nodecolor,
node_size=500,
alpha=0.8)
nx.draw_networkx_labels(G, pos, new_lbl_dict, font_size=10)
plt.axis('on')
filename = str(i) + '_Template.png'
path = "./training/3A"
plt.savefig(os.path.join(
def small_graphs():
print("Make small graph")
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [1, 3], [2, 4], [1, 3]]])
draw_graph(G)
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [3], [4], []]])
draw_graph(G)
G = nx.make_small_graph(
["edgelist", "C_4", 4, [[1, 2], [3, 4], [2, 3], [4, 1]]])
draw_graph(G)
print("LCF graph")
G = nx.LCF_graph(6, [3, -3], 3)
draw_graph(G)
G = nx.LCF_graph(14, [5, -5], 7)
draw_graph(G)
print("Bull graph")
G = nx.bull_graph()
draw_graph(G)
print("Chvátal graph")
G = nx.chvatal_graph()
draw_graph(G)
print("Cubical graph")
G = nx.cubical_graph()
draw_graph(G)
print("Desargues graph")
G = nx.desargues_graph()
draw_graph(G)
print("Diamond graph")
G = nx.diamond_graph()
draw_graph(G)
print("Dodechaedral graph")
G = nx.dodecahedral_graph()
draw_graph(G)
print("Frucht graph")
G = nx.frucht_graph()
draw_graph(G)
print("Heawood graph")
G = nx.heawood_graph()
draw_graph(G)
print("House graph")
G = nx.house_graph()
draw_graph(G)
print("House X graph")
G = nx.house_x_graph()
draw_graph(G)
print("Icosahedral graph")
G = nx.icosahedral_graph()
draw_graph(G)
print("Krackhardt kite graph")
G = nx.krackhardt_kite_graph()
draw_graph(G)
print("Moebius kantor graph")
G = nx.moebius_kantor_graph()
draw_graph(G)
print("Octahedral graph")
G = nx.octahedral_graph()
draw_graph(G)
print("Pappus graph")
G = nx.pappus_graph()
draw_graph(G)
print("Petersen graph")
G = nx.petersen_graph()
draw_graph(G)
print("Sedgewick maze graph")
G = nx.sedgewick_maze_graph()
draw_graph(G)
print("Tetrahedral graph")
G = nx.tetrahedral_graph()
draw_graph(G)
print("Truncated cube graph")
G = nx.truncated_cube_graph()
draw_graph(G)
print("Truncated tetrahedron graph")
G = nx.truncated_tetrahedron_graph()
draw_graph(G)
print("Tutte graph")
G = nx.tutte_graph()
draw_graph(G)
