def find_all_triplets(graph,edge_attr_name):
''' this method runs on the graph and finds triplets of nodes x,i,j such that
the edges x-i and x-j exist but the edge i-j doesn't'''
diff_treshold.print_log("find all triplets start")
triplets_lst = []
for node_x in graph.nodes():
#diff_treshold.print_log("find all triplets new node - " + str(node_x))
nei_lst = graph.neighbors(node_x)
for i in range(0 , len(nei_lst)):
node_i = nei_lst[i]
panel_i = diff_treshold.get_panel_id_substr(node_i)
node_i_neis = graph.neighbors(node_i)
corr_i = graph.get_edge_data(node_x,node_i)[edge_attr_name]
for j in range(i + 1, len(nei_lst)):
if nei_lst[j] not in node_i_neis and panel_i != diff_treshold.get_panel_id_substr(nei_lst[j]):
#node_x and node_i are neighbours, also node_x and node_j
#but node_i and node_j not and they are from different panels
corr_j = graph.get_edge_data(node_x,nei_lst[j])[edge_attr_name]
var = (node_x,node_i,nei_lst[j],corr_i,corr_j)
triplets_lst.append(var)
#print("enter var")
diff_treshold.print_log("find all triplets after loop return list")
return triplets_lst
def analyze_trios(trios_lst, treshold):
same_panel_high_corr = 0
same_panel_low_corr = 0
diff_panel_high_corr = 0
diff_panel_low_corr = 0
for trio in trios_lst:
node1 = trio[1]
node2 = trio[2]
unconnected_corr = trio[5]
diff_panel = False
low_corr = unconnected_corr < treshold
if diff_treshold.get_panel_id_substr(node1) != diff_treshold.get_panel_id_substr(node2):
diff_panel = True
if diff_panel:
if low_corr:
diff_panel_low_corr += 1
else:
diff_panel_high_corr += 1
else:
if low_corr:
same_panel_low_corr += 1
else:
same_panel_high_corr += 1
return (same_panel_high_corr,same_panel_low_corr,diff_panel_high_corr,diff_panel_low_corr)
def graph_corr_dist_between_pannels(graph, panel1, panel2, is_show, attr_name):
''' this method calculates the distribution of the correlation between 2 panels'''
corr_dist = []
for u, v in graph.edges():
panel_a = diff_treshold.get_panel_id_substr(u)
panel_b = diff_treshold.get_panel_id_substr(v)
if (panel_a == panel1
and panel_b == panel2) or (panel_a == panel2
and panel_b == panel1):
corr_dist.append(graph.get_edge_data(u, v)[attr_name])
plt.xlabel("correlation")
plt.ylabel("frequency")
plt.title("frequency for correlation between panels " + panel1 + " and " +
panel2)
plt.hist(corr_dist)
fig_name = panel1 + "_" + panel2 + "_corr_dist_graph.png"
if is_show:
plt.savefig(fig_name)
plt.show()
def graph_pie_chart_neigbours_panel(graph, is_pie, is_save):
''' this method calcs the histogram of neighbours's panel per each panel '''
n = len(diff_treshold.PANELS_LST)
nei_mat = np.zeros(
(n, n)) #matrix with cell for each two panels combination
for u, v in graph.edges():
u_panel = diff_treshold.get_panel_id_substr(u)
v_panel = diff_treshold.get_panel_id_substr(v)
u_panel_idx = diff_treshold.PANELS_LST.index(u_panel)
v_panel_idx = diff_treshold.PANELS_LST.index(v_panel)
nei_mat[u_panel_idx][v_panel_idx] += 1
nei_mat[v_panel_idx][u_panel_idx] += 1 # symetry
#print and save results
labels = diff_treshold.PANELS_LST
colors = ['gold', 'yellow', 'blue', 'red', 'green', 'brown']
for i in range(0, n):
#set the plot
plt.xlabel("panel ID")
plt.ylabel("amount of neigbours")
plt.title("histogram of neigbours of traits from panel P" + str(i + 1))
if is_pie:
plt.pie(nei_mat[i],
colors=colors,
labels=labels,
autopct='%1.1f%%')
else:
#bar plot
plt.bar(np.arange(n), nei_mat[i], align='center')
plt.xticks(np.arange(n), labels)
if is_save:
fig_name = "P" + str(i +
1) + "_neigbours_distribution_full_graph.png"
plt.savefig(fig_name)
plt.show()
def create_graph_from_network(network):
''' This method gets a network and creates a graph from it'''
diff_treshold.print_log("create graph - start")
g = nx.Graph()
for key in network.keys():
g.add_node(key, panel_id = diff_treshold.get_panel_id_substr(key))
#g.add_nodes_from(network.keys())
diff_treshold.print_log("create graph - before adding edges")
#create list of verteces
neigbours_lst = []
for edge in network.keys():
for val in network[edge]:
neigbours_lst.append((edge, val))
g.add_edges_from(neigbours_lst)
diff_treshold.print_log("create graph - after adding edges, return graph")
return g