/
Helios_model.py
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/
Helios_model.py
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from matplotlib.ticker import NullFormatter # useful for `logit` scale
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib
matplotlib.use('TkAgg')
from pythonis_model import *
import networkx as nx
def createListPanelGraph(flow):
list_panel = []
for i in range(len(flow)):
list_panel.append(i)
return list_panel
def drawGraph(genes_names_list, network_as_list, flow, graph_selected, layout_selected, color_activate_node,
color_inactivate_node, color_active_edge, color_inactivate_edge, activate_widthedge,genes_selected_visu):
G = nx.MultiDiGraph()
value_source = getFlow(flow, graph_selected)
global_gene_state = getRegulationActivation(network_as_list, genes_names_list, value_source)
# ajout des aretes et des noeuds
G = addNodes(global_gene_state, G)
G = addEdges(network_as_list, G)
G = drawFig(G, global_gene_state, layout_selected, color_activate_node, color_inactivate_node,
color_active_edge, color_inactivate_edge, activate_widthedge, genes_selected_visu)
return G
def drawFig(G, global_gene_state, layout_selected, color_activate_node, color_inactivate_node, color_active_edge,
color_inactivate_edge, activate_widthedge, genes_selected_visu):
pos = selectLayout(G, layout_selected)
for node in G.nodes(data=True):
transparency = 1
if node[1]['forme'] == "1":
node_shape = "^"
if node[1]['forme'] == "-1":
node_shape = "v"
if node[1]['forme'] == "0":
node_shape = "o"
if node[1]['active_state'] == 0:
color = color_inactivate_node
if node[1]['active_state'] == 1:
color = color_activate_node
if genes_selected_visu :
transparency = 0.5
for i in range(len(genes_selected_visu)):
if genes_selected_visu[i] == node[0]:
transparency = 1
nx.draw_networkx_nodes(G, pos, nodelist=[node[0]], node_size=1500, node_shape= node_shape, node_color= color, alpha = transparency)
nx.draw_networkx_labels(G, pos)
for edge in G.edges(data=True):
if edge[2]['arrowstyle'] == "-1":
arrowstyle = "-["
if edge[2]['arrowstyle'] == "1":
arrowstyle = "-|>"
if edge[2]['duet'] == "1":
form_arrow = 'arc3, rad = 0.2'
if edge[2]['duet'] == "0":
form_arrow = 'arc3, rad = 0.0'
for i in range(len(global_gene_state)):
if global_gene_state[i][0] == edge[0]:
if global_gene_state[i][1] == 1:
activateedge = color_active_edge
widthedge = activate_widthedge
else:
activateedge = color_inactivate_edge
widthedge = 1
nx.draw_networkx_edges(G, pos, edgelist=[(edge[0], edge[1])], width=widthedge, arrowstyle=arrowstyle,
node_size=1900, connectionstyle=form_arrow, edge_color=activateedge)
plt.gca().yaxis.set_minor_formatter(NullFormatter())
plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0.25,
wspace=0.35)
plt.ioff()
plt.draw()
fig = plt.gcf()
return fig,G
def getFlow(flow, graph_selected):
value_to_reach = graph_selected + 1
value_running = 0
for key, value in flow.items():
# Cle du dictionnaire
value_source = []
source = key
value_running += 1
if value_running == value_to_reach:
for number in source:
value_source.append(number)
return value_source
def getRegulationActivation(network_as_list, genes_names_list, value_source):
positive_gene = []
negative_gene = []
global_gene_state = []
for i in range(len(network_as_list)):
gene_source = network_as_list[i][0]
gene_target = network_as_list[i][2]
interaction = network_as_list[i][1]
if gene_target == gene_source:
if interaction == "1":
positive_gene.append(gene_source)
else:
negative_gene.append(gene_source)
for i in range(len(genes_names_list)):
state_gene = []
gene = genes_names_list[i]
activation = value_source[i]
if gene in positive_gene:
regulation = "1"
if gene in negative_gene:
regulation = "-1"
else:
regulation = "0"
state_gene.append(gene)
state_gene.append(activation)
state_gene.append(regulation)
global_gene_state.append(state_gene)
return global_gene_state
def addEdges(network_as_list, G):
for i in range(len(network_as_list)):
duet = "0"
gene_source = network_as_list[i][0]
gene_target = network_as_list[i][2]
interaction = network_as_list[i][1]
for k in range(len(network_as_list)):
if network_as_list[k][0] == gene_target and network_as_list[k][2] == gene_source:
duet = "1"
G.add_edge(gene_source, gene_target, arrowstyle=interaction, duet=duet)
return G
def addNodes(global_gene_state, G):
for i in range(len(global_gene_state)):
gene_name = global_gene_state[i][0]
activation = global_gene_state[i][1]
regulation = global_gene_state[i][2]
G.add_node(gene_name, forme=regulation, active_state=activation)
return G
def selectLayout(G, layout_selected):
if layout_selected == 'circular_layout':
pos = nx.circular_layout(G)
if layout_selected == 'spring_layout':
pos = nx.spring_layout(G)
if layout_selected == 'kamada_kawai_layout':
pos = nx.kamada_kawai_layout(G)
if layout_selected == 'random_layout':
pos = nx.random_layout(G)
if layout_selected == 'shell_layout':
pos = nx.shell_layout(G)
if layout_selected == 'spectral_layout':
pos = nx.spectral_layout(G)
if layout_selected == 'planar_layout':
pos = nx.planar_layout(G)
if layout_selected == 'fruchterman_reingold_layout':
pos = nx.fruchterman_reingold_layout(G)
if layout_selected == 'spiral_layout':
pos = nx.spiral_layout(G)
return pos
# ------------------------------- Beginning of Matplotlib -----------------------
######Ca c'est bon#########
def draw_figure(canvas, figure, loc=(0, 0)):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)
return figure_canvas_agg
def delete_figure_agg(figure_agg):
figure_agg.get_tk_widget().forget()
plt.close('all')