Web_cohortizer_F_1_www_ready.py

# -*- coding: utf-8 -*-
###############################################################################
#import os, sys
#import os
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import random
import pandas as pd
from collections import Counter    
import time

import warnings


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# get all neighbors of a given orderof ecah node
def all_neighbors_order(g, order):
    g = g.copy()
    
    neighbors_dict = {}
    
    for node in g.nodes():
        neighbors = list(g.neighbors(node))
        
        if(len(neighbors) > 0):
            for o in range(0,order - 1):                               
                neighbors_2 = []
                
                for nei in neighbors:
                    neighbors_2 = neighbors_2 + list(g.neighbors(nei))
                    
                neighbors = neighbors + list(set(neighbors_2))
                neighbors.remove(node)
                neighbors = list(set(neighbors))
            
        neighbors_dict[node] = neighbors
    
    return neighbors_dict
    
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# get all neighbors of a given orderof ecah node
def all_neighbors_multiorder(g, order):
    g = g.copy()
    
    neighbors_dict = {}
    ego_dict = {}
    
    for o in range(1,order+1):
        neighbors_dict[o] = {}
        ego_dict[o] = {}
    
    for node in g.nodes():
        ego = {}
        neigh = {}
        
        #################
        neighbors = list(g.neighbors(node))
        
        #print node
        #print neighbors
        
        ego[0] = set([node])
        ego[1] = set(neighbors)
        
        if(len(neighbors) > 0):
            for distance in range(2,order+1):                               
                neighbors_2 = []
                
                for neighbor in neighbors:
                    neighbors_2 = neighbors_2 + list(g.neighbors(neighbor))
                    
                neighbors = neighbors + list(set(neighbors_2))
                neighbors.remove(node)
                neighbors = list(set(neighbors))
                
                ego[distance] = set(neighbors)
        else:
            for distance in range(2,order+1):                               
                ego[distance] = set([])
      
        #####################

        for distance in range(1,order+1):            
            neigh[distance] = ego[distance].difference(ego[distance-1])
            neighbors_dict[distance][node] = list(neigh[distance]) 
            ego_dict[distance][node]       = list(ego[distance].difference([node])) 
            
    result = {}
    result['neighbors'] = neighbors_dict
    result['ego']       = ego_dict
  
    return result

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# Color Normal  
# uses ordinary random walk to color the nodes
def color_nodes_normal(g, n, p, q, limit, neighbors_dict):
    g = g.copy()
    all_nodes = g.nodes()
    colors = {}
    colored = []
    clusters = {}
    seeds = {}

    for value in all_nodes:
        colors[value]   = 'g'
        clusters[value] = 0
        seeds[value] = 0 

    step  = 0 
    cluster_id = 1 
    
    random_node = random.choice(all_nodes)
    seeds[random_node] = cluster_id

    while(colors.values().count('r') < n):   
        if colors[random_node] == 'g':
            colors[random_node] = 'r'
            colored.append(random_node)
            clusters[random_node] = cluster_id
            last_colored = True
            step = 0  
        else:
            last_colored = False
            step = step + 1
                       
        if random.random() <= p or last_colored == False:
            try:
                random_node = random.choice(neighbors_dict[random_node]) 
            except:
                pass          
        else:
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            seeds[random_node] = cluster_id
                  
        if step > limit:
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            seeds[random_node] = cluster_id
                 
    remaining_colors     = {}
    remaining_colors_clr = {}
    
    for node, color in colors.items():
        if color == 'g':
            remaining_colors[node]     = 'g'
            remaining_colors_clr[node] = 'g'
        elif color == 'r':
            if random.random() <= q: 
                remaining_colors[node]     = 'r'
                remaining_colors_clr[node] = 'r'
            else:
                remaining_colors[node]     = 'y'
                remaining_colors_clr[node] = 'g'

    nx.set_node_attributes(g, 'color_old', colors)
    nx.set_node_attributes(g, 'color_clr', remaining_colors_clr)
    nx.set_node_attributes(g, 'color', remaining_colors)
    nx.set_node_attributes(g, 'cluster', clusters)
    nx.set_node_attributes(g, 'seed', seeds)

    return g, cluster_id
    
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# Color Hybrid 
# uses modified random walk to color the nodes, 
# so it avoids colored nodes among neighbors of the current node
# if there are no uncolored neighbors it colors random neighbor of the cluster 
# clusters have elongated structures
def color_nodes_hybrid(g, n, p, q, limit, neighbors_dict):
    g = g.copy()
    all_nodes = g.nodes()
    colors = {}
    colored = []
    clusters = {} 
    seeds = {}

    for value in all_nodes:
        colors[value]   = 'g'
        seeds[value] = 0 
        clusters[value] = 0

    random_node = random.choice(all_nodes)
    
    step  = 0 
    cluster_id = 1 
    cluster_nodes = []
    cluster_neighbors = []
    
    while(colors.values().count('r') < n):       
        if colors[random_node] == 'g':
            colors[random_node] = 'r'
            colored.append(random_node)
            clusters[random_node] = cluster_id
            cluster_nodes.append(random_node)
            cluster_neighbors += neighbors_dict[random_node]
            cluster_neighbors = list(set(cluster_neighbors))
            last_colored = True
            step = 0
        else:
            last_colored = False
            step = step + 1
            
        if random.random() <= p or last_colored == False:         
            try:   
                #random_node = random.choice(neighbors_dict[random_node]) 
                potential_nodes = list(set(neighbors_dict[random_node]).difference(set(colored)))                
                #potential_nodes = list(set(cluster_neighbors).difference(set(colored)))
                
                if len(potential_nodes) > 0:
                    random_node = random.choice(potential_nodes)                         
                else:
                    potential_nodes = list(set(cluster_neighbors).difference(set(colored)))
                
                    if len(potential_nodes) > 0:
                        random_node = random.choice(potential_nodes)  
                    else:
                        potential_nodes = list(set(all_nodes).difference(set(colored)))
                        random_node = random.choice(potential_nodes)
                        cluster_id += 1
                        cluster_nodes = []
                        cluster_neighbors = []  
                        seeds[random_node] = cluster_id
            except:
                pass            
        else:
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            cluster_nodes = []
            cluster_neighbors = []
                  
        if step > limit:
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            cluster_nodes = []
            cluster_neighbors = []
             
    remaining_colors     = {}
    remaining_colors_clr = {}
    
    for node, color in colors.items():
        if color == 'g':
            remaining_colors[node]     = 'g'
            remaining_colors_clr[node] = 'g'
        elif color == 'r':
            if random.random() <= q: 
                remaining_colors[node]     = 'r'
                remaining_colors_clr[node] = 'r'
            else:
                remaining_colors[node]     = 'y'
                remaining_colors_clr[node] = 'g'
                
    nx.set_node_attributes(g, 'color_old', colors)
    nx.set_node_attributes(g, 'color_clr', remaining_colors_clr)
    nx.set_node_attributes(g, 'color', remaining_colors)
    nx.set_node_attributes(g, 'cluster', clusters)
    nx.set_node_attributes(g, 'seed', seeds)
    
    return g, cluster_id
  
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# Color Compact
# uses compact random walk to color the nodes, 
# it colors random neighbor of the cluster 
def color_nodes_compact(g, n, p, q, limit, neighbors_dict):
    g = g.copy()
    all_nodes = g.nodes()
    colors = {}
    colored = []
    clusters = {}
    seeds = {}

    for value in all_nodes:
        colors[value]   = 'g'
        seeds[value] = 0 
        clusters[value] = 0

    random_node = random.choice(all_nodes)
    jump = True
    
    step  = 0 
    cluster_id = 1 
    cluster_nodes = []
    cluster_neighbors = []
    
    while(colors.values().count('r') < n):
        # coloring of the node if it is uncolored
        if colors[random_node] == 'g':
            if jump == True:
                seeds[random_node] = cluster_id
            
            colors[random_node] = 'r'
            colored.append(random_node)
            clusters[random_node] = cluster_id
            cluster_nodes.append(random_node)
            cluster_neighbors += neighbors_dict[random_node]
            cluster_neighbors = list(set(cluster_neighbors))
            last_colored = True
            step = 0
            jump = False
        else:
            last_colored = False
            step = step + 1
        
        # chose node within cluster to color if there are none uncolored try to jump
        if random.random() <= p or last_colored == False:         
            try:                
                potential_nodes = list(set(cluster_neighbors).difference(set(colored)))
                
                if len(potential_nodes) > 0:
                    random_node = random.choice(potential_nodes)  
                else:
                    potential_nodes = list(set(all_nodes).difference(set(colored)))
                    random_node = random.choice(potential_nodes)
                    cluster_id += 1
                    cluster_nodes = []
                    cluster_neighbors = []  
                    jump = True
            except:
                pass
        
        # try to jump by chosing randomly uncolored cluster    
        else: 
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            cluster_nodes = []
            cluster_neighbors = []
            jump = True
        
        # start new cluster if time limit is up          
        if step > limit:
            potential_nodes = list(set(all_nodes).difference(set(colored)))
            random_node = random.choice(potential_nodes)            
            cluster_id += 1
            cluster_nodes = []
            cluster_neighbors = []
            jump = True

    remaining_colors     = {}
    remaining_colors_clr = {}
    
    for node, color in colors.items():
        if color == 'g':
            remaining_colors[node]     = 'g'
            remaining_colors_clr[node] = 'g'
        elif color == 'r':
            if random.random() <= q: 
                remaining_colors[node]     = 'r'
                remaining_colors_clr[node] = 'r'
            else:
                remaining_colors[node]     = 'y'
                remaining_colors_clr[node] = 'g'          
    
    nx.set_node_attributes(g, 'color_old', colors)
    nx.set_node_attributes(g, 'color_clr', remaining_colors_clr)
    nx.set_node_attributes(g, 'color', remaining_colors)
    nx.set_node_attributes(g, 'cluster', clusters)
    nx.set_node_attributes(g, 'seed', seeds)
    
    return g, cluster_id

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def neighbors_order(g, node, order):
    g = g.copy()
    
    neighbors = list(g.neighbors(node))
    
    if(len(neighbors) > 0):
        for o in range(0,order - 1):       
                     
            neighbors_2 = []
            
            for nei in neighbors:
                neighbors_2 = neighbors_2 + list(g.neighbors(nei))
                
            neighbors = neighbors + list(set(neighbors_2))
            neighbors.remove(node)
        
    return neighbors
        
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def color_nodes_order(g, n, p, order):
    g = g.copy()
    colors = {}

    for value in g.nodes():
        colors[value] = 'g'

    random_node = random.choice(g.nodes())
    
    limit = 1000000
    step  = 0 
    
    #while(colors.values().count('r') < n):
    while(list(colors.values()).count('r') < n):   
        colors[random_node] = 'r'
           
        if (random.random() <= p):
            try:
                random_node = random.choice(neighbors_order(g, random_node, order)) 
            except:
                a = 1            
        else:
            random_node = random.choice(g.nodes())
            
        step = step + 1
        if step > limit:
            break

    nx.set_node_attributes(g, 'color', colors)
    #print 'colors: ' +  str(colors.values().count('r'))
    return g
 
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def get_colored_subgraph(g, color = 'r', equality = 'positive'):
    #print equality
    g = g.copy()
    nodes = []
    nodes_neg = []

    for value in g.node:
        if g.node[value]['color'] == color:
            nodes.append(value)
        else:
            nodes_neg.append(value)
            
    if(equality == 'positive'):
        g = g.subgraph(nodes)
    elif(equality == 'negative'):
        g = g.subgraph(nodes_neg)
                    
    return g 

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def get_collective_gene_expression(simulation_data, expressed_genes_under = None, expressed_genes_over = None, phenotype_table = None, mode = 'normal'):        
    start = time.time()
    
    fields        = simulation_data['fields']
    filter_range  = simulation_data['filter_range']

    #expressed_genes_row = {}        
    #expressed_genes_row['threshold'] = simulation_data['threshold']
    
    #print "time in 1  = " + str(time.time() - start)

    #######################################################################
    start = time.time()
    
    tables = {}
    
    for field in fields.keys():
        tables[field + '_under'] = []
        tables[field + '_over'] = []
        tables[field + '_both'] = []
 
    if mode == 'random':
        for key, value in simulation_data['healthy_fields'].items():
            a = "phenotype_table[" + value
            a = a.replace("{", "phenotype_table['")
            a = a.replace("}", "']")
            #a = a.replace("&", "and") 
            a += "]"
            
            healthy_table = eval(a)

    for key, value in fields.items():
        a = "phenotype_table[" + value
        a = a.replace("{", "phenotype_table['")
        a = a.replace("}", "']")
        #a = a.replace("&", "and") 
        a += "]"
        
        subtable = eval(a)  

        if mode == 'normal':
            iter_list = list(subtable.index)
        elif mode == 'random':
            if simulation_data['disease_group_limit'] == False:
                iter_list = random.sample(list(healthy_table.index), len(subtable))
            else:
                iter_list = random.sample(list(healthy_table.index), min(len(list(subtable.index)), simulation_data['disease_group_limit']))               
        elif mode == 'limit': 
            iter_list = random.sample(list(subtable.index), min(len(list(subtable.index)), simulation_data['disease_group_limit']))
            
        for i in iter_list:
            tables[key + '_under'] += expressed_genes_under[i]
            tables[key +  '_over'] += expressed_genes_over[i]
            tables[key +  '_both'] += expressed_genes_under[i] + expressed_genes_over[i] 
     
    #print "time in 2  = " + str(time.time() - start)               
    ###########################################################################
    start = time.time()
    
    counters = {}
    genes = {}
        
    for key, value in tables.items(): 
        try:
            gene = value
        except:
            gene = []
        try:
            counter = Counter(gene)
        except:
            counter = []           
        
        counters[key] = counter
        genes[key] = gene

    #print "time in 3  = " + str(time.time() - start)

    filtered = {}
    reverse_filtered = {}
    flt = {}

    start = time.time()
    
    for key, value in counters.items():  
        for filter_threshold in range(0, filter_range + 1):
            tmp_flt     = dict((subkey, subvalue) for (subkey, subvalue) in dict(value).items() if subvalue > filter_threshold ).keys() 
            tmp_rev_flt = dict((subkey, subvalue) for (subkey, subvalue) in dict(value).items() if ((subvalue <= filter_threshold) & (subvalue > 0)) ).keys()
            
            filtered[str(key) + '_filtered_' + str(filter_threshold)] = tmp_flt
            reverse_filtered[str(key) + '_reverse_filtered_' + str(filter_threshold)] = tmp_rev_flt 
            flt[str(key) + '_flt_' + str(filter_threshold)] = tmp_flt
            
            if filter_threshold != 0:
                flt[str(key) + '_flt_-' + str(filter_threshold)] = tmp_rev_flt
    done = time.time()
    elapsed = done - start
    #print "time in 4 old  = " + str(elapsed)
    
    ###########################################################################
    
    collective_genes = {}
    #collective_genes['filtered'] = filtered
    #collective_genes['reverse_filtered'] = reverse_filtered       
    collective_genes['flt'] = flt
    collective_genes['counters'] = counters
    
    return collective_genes

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def simulate_artificial_disease(disease_params, simulation_data, g, colored_graph):  
    G = disease_params['G']
    D = disease_params['D']
    A = disease_params['A']
    rho_0 = disease_params['rho_0']
    
    rho_D_max = np.min([1, rho_0*G/D])
    rho_D = rho_0 + A*(rho_D_max - rho_0)
    rho_G = rho_0 - (rho_D - rho_0)*D/(G - D)
    
    #print 'rho_0 = ' + str(rho_0) 
    #print 'rho_D = ' + str(rho_D) 
    #print 'rho_G = ' + str(rho_G) 
    
    result = {}
    result['colored_subgraph']   = get_colored_subgraph(colored_graph, color = 'r', equality = 'positive')        
    result['uncolored_subgraph'] = get_colored_subgraph(colored_graph, color = 'r', equality = 'negative')   
     
    all_nodes = colored_graph.nodes()
    dis_nodes = result['colored_subgraph'].nodes()
    hlt_nodes = result['uncolored_subgraph'].nodes()     
    
    # for each individual mark if it is sick
    #print "phenotype table"

    result['phenotype_table'] = pd.DataFrame(columns = ['disease']) 
    result['expressed_genes_under'] = []
    result['expressed_genes_over']  = []
            
    #print "disease"

    for i in range(0, disease_params['S']):    
        patient = {}
        patient['disease'] = 1
        result['phenotype_table'] = result['phenotype_table'].append(patient, ignore_index=True)  

        exp_genes = []
        
        for node in dis_nodes:                
            if(float(np.random.uniform(0,1,1)) <= rho_D):
                exp_genes.append(node)
                
        for node in hlt_nodes:                
            if(float(np.random.uniform(0,1,1)) <= rho_G):
                exp_genes.append(node)
        
        result['expressed_genes_under'].append(dis_nodes) #note that underexpressed genes are used as a disease picture
        result['expressed_genes_over'].append(exp_genes)        
    
    #print "healthy"
    for i in range(disease_params['S'], disease_params['patients_number']):    
        patient = {}
        patient['disease'] = 0
        result['phenotype_table'] = result['phenotype_table'].append(patient, ignore_index=True)  
        
        exp_genes = []
        
        for node in all_nodes:                
            if(float(np.random.uniform(0,1,1)) <= rho_0):
                exp_genes.append(node)
        
        result['expressed_genes_under'].append([]) #note that underexpressed genes are used as a disease picture
        #result['expressed_genes_under'].append(dis_nodes) #note that underexpressed genes are used as a disease picture 
        result['expressed_genes_over'].append(exp_genes)
           
    # if not give to it random expression pattern
    # if sick take some expressed genes from pattern and some at random
    #disease_params['prefix'] += '_p_' + str(disease_params['p']) + '_re_' + str(disease_params['realisation'])
    
    return result

############################################################################### 
    
#def simulate_series(simulation_data, disease_params):
def simulate_series(simulation_data):
    disease_params = simulation_data    
    #disease_params['order'] = 1
    simulation_data['G']     = simulation_data['network_n'] # genes
    
    simulation_data['S'] = simulation_data['P']
    simulation_data['patients_number'] = simulation_data['S']    

    # make network
    if(simulation_data['network_type'] == 'BA'):
        g = nx.barabasi_albert_graph(simulation_data['network_n'], simulation_data['network_m'])
    elif(simulation_data['network_type'] == 'ER'):
        g = nx.erdos_renyi_graph(simulation_data['network_n'], simulation_data['network_p'])  
    elif(simulation_data['model'] == '2D'):
        g = nx.grid_2d_graph(simulation_data['network_x'], simulation_data['network_y'], periodic = True)
    elif(simulation_data['model']  == 'CYC'):
        g = nx.cycle_graph(simulation_data['network_n'])
    elif(simulation_data['model']  == 'REG'):
        g = nx.random_regular_graph(simulation_data['network_d'], simulation_data['network_n'])

    #neighbors_dict = all_neighbors_order(g, simulation_params['order'])
    colored_graph = color_nodes_order(g, disease_params['D'], disease_params['p'], disease_params['order'])
    #colored_graph = color_nodes_order(g, disease_params['D'], disease_params['p'], disease_params['order'])
    
    g_strip = g.copy()
        
    solitary = [ n for n,d in g_strip.degree_iter() if d == 0 ]
    g_strip.remove_nodes_from(solitary)                       
    layout = nx.spring_layout(g_strip)
    
    result = {}
    #result['layout'] = layout
    #result['g'] = g
    #result['g_strip'] = g_strip
 
    for disease_params['rho_0'] in disease_params['rho_0_list']:
        result[str(disease_params['rho_0'])] = {}

        result_disease = simulate_artificial_disease(disease_params, simulation_data, colored_graph, colored_graph)
        collective_genes = get_collective_gene_expression(simulation_data, result_disease['expressed_genes_under'], result_disease['expressed_genes_over'], result_disease['phenotype_table'], mode = 'normal')        
        filtered = collective_genes['flt']
        
        for flt in simulation_data['f_list'] :
            #result[str(disease_params['rho_0'])][str(flt)] = {}
            
            tmp_result = {}
            
            tmp_result['extracted_genes']      = list(set(filtered['dis_over_flt_' + str(flt)]))
            tmp_result['disease_genes']        = list(set(filtered['dis_under_flt_' + str(flt)]))
            tmp_result['true_poositive_genes'] = list(set(filtered['dis_under_flt_' + str(flt)]) & set(filtered['dis_over_flt_' + str(flt)]))
            tmp_result['disease_params'] = disease_params
            
            tmp_result['layout'] = layout
            tmp_result['g'] = g
            tmp_result['g_strip'] = g_strip
            
            tmp_result['rho_0'] = disease_params['rho_0']
            tmp_result['flt'] = flt
            
            
            result[str(disease_params['rho_0'])][str(flt)] = tmp_result
            
    return result
            
###############################################################################

def plot_network(tmp_result):  
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore",category=DeprecationWarning)
        g_strip = tmp_result['g_strip']
        layout = tmp_result['layout']
        
        simulation_data['G']     = simulation_data['network_n'] # genes
        simulation_data['S'] = simulation_data['P']
        simulation_data['patients_number'] = simulation_data['S']  
        
        disease_params = tmp_result['disease_params']
       
        plt.figure(figsize=(6,6))
        
        g_over = g_strip.subgraph(tmp_result['extracted_genes'])
    
        solitary_over = len([ n for n,d in g_over.degree_iter() if d == 0 ])
    
        n = len(g_over.nodes()) 
        if n > 0:     
            c = float(n - solitary_over)/float(n) 
        else:
            c = 0
            
        c_G = 1.0 - np.power(1.0 - nx.density(g_strip), n - 1)                    
        delta_c = c - c_G
     
        F_s  = len(tmp_result['extracted_genes'])
        V_G  = len(tmp_result['disease_genes'])
        Vs_G = len(tmp_result['true_poositive_genes'])
        
        try:
            PPV = float(Vs_G)/float(F_s)
        except:
            PPV = 0
    
        try:
            TPR = float(Vs_G)/float(V_G)  
        except:
            TPR = 0
            
        Q = PPV*TPR
        
        #######################################################################
        
        nx.draw_networkx(g_strip,pos=layout,
                         with_labels=False,
                         node_size = 10,
                         node_color= 'k',
                         alpha=0.2
                         )
        
        #######################################################################
        
        # false negative
        nx.draw_networkx(g_strip.subgraph(tmp_result['disease_genes']), pos=layout,
                         with_labels=False,
                         node_size = 100,
                         node_color= 'orange',
                         edge_color = 'orange',
                         width = 4.0,
                         alpha=1.0
                         )   
                         
        #######################################################################
        
        # false positive
        nx.draw_networkx(g_strip.subgraph(tmp_result['extracted_genes']), pos=layout,
                         with_labels=False,
                         node_size = 100,
                         node_color= 'lime',
                         edge_color = 'lime',
                         width = 4.0,
                         alpha=1.0
                         )  
                         
        #######################################################################
        
        # true positive
        nx.draw_networkx(g_strip.subgraph(tmp_result['true_poositive_genes']), pos=layout,
                         with_labels=False,
                         node_size = 200,
                         node_color= 'r',
                         edge_color = 'r',
                         width = 8.0,
                         alpha=1.0
                         )             
                         
        #######################################################################
    
        suffix = '_dc_' + str(round(delta_c,2)) + '_Q_' + str(round(Q,2))
        
        try:
            PPV = float(Vs_G)/float(F_s)
        except:
            PPV = 0
         
        try: 
            TPR = float(Vs_G)/float(V_G) 
        except:
            TPR = 0        
        
        Q = PPV*TPR
          
        plt.xticks([])
        plt.yticks([])
        xylim = [-0.1,1.1]
        
        plt.xlim(xylim) 
        plt.ylim(xylim) 
        #plt.title(title, size = 22)    
        
        """
        title =  'G = '  + str(disease_params['G'])
        title += ',   D = '  + str(disease_params['D']) 
        title += ',   S = ' + str(disease_params['S'])
        title += ',   A = '   + str(disease_params['A'])
        title += ',   p = '   + str(disease_params['p'])  + ' \n'
        """
        
        title = 'f = ' + str(tmp_result['flt']) 
        title += ',    $\\rho_0$ = ' + str(disease_params['rho_0']) # + ' \n'
        
        #c_text = 'c = ' +  str(round(c,2)) + ' \n'
        #c_text += 'c$_G$ = ' + str(round(c_G,2)) + ' \n'
        #c_text += '$\Delta$c = ' + str(round(delta_c,2)) # + ' \n'
        c_text = '$\Delta$c = ' + str(round(delta_c,2)) # + ' \n'
        
        f_text = 'PPV = ' +  str(round(PPV,2)) + ' \n'
        f_text += 'TPR = ' + str(round(TPR,2)) + ' \n'
        f_text += 'Q = ' + str(round(Q,2)) 
        
        plt.text(0.375, 0.0, c_text, horizontalalignment='left',  verticalalignment='top', size = 18)  
    
        #plt.text(0.0, 1.0, c_text, horizontalalignment='left',  verticalalignment='top', size = 20)                          
        #plt.text(0.8, 1.0, f_text, horizontalalignment='left',  verticalalignment='top', size = 20)                          
        #plt.text(0.325, 0.0, title, horizontalalignment='left',  verticalalignment='top', size = 20) 
        #plt.title(title, horizontalalignment='left',  verticalalignment='top', size = 20) 
        
        #if row_number == 1:
        plt.title('$\\rho_0$ = ' + str(tmp_result['rho_0']) + ',   f = ' + str(tmp_result['flt']), size = 18)
            
        #if col_number == 1:
        #plt.ylabel('f = ' + str(tmp_result['flt']) , size = 32) 
            
        #filename = simulation_data['img'] + prefix + '_flt_' + str(flt) + '_rho_0_' + str(disease_params['rho_0']) + suffix 
        #filename = simulation_data['img'] + prefix      
        #filename = filename.replace('.','_')
        
        plt.tight_layout()
        #plt.savefig(filename + '_network_spring.png')
        #plt.savefig(filename + '_network_spring.pdf')                  
        plt.show() 
        plt.close() 
        
###############################################################################
###############################################################################
###############################################################################

#neighbors_dict = all_neighbors_order(g, simulation_params['order'])

###############################################################################
################################ SETUP ########################################
###############################################################################

simulation_data = {}

#################################### COMMON ###################################

simulation_data['stats_simple'] = True
simulation_data['make_z_scores'] = True

########################## COLLECTIVE EXPRESSION ##############################

simulation_data['shuffle'] = True
simulation_data['shuffle'] = False
simulation_data['shuffled_samples'] = 2

simulation_data['excluding'] = True
simulation_data['excluding'] = False
simulation_data['filter_range'] = 11

simulation_data['fields'] = {'dis':'{disease} == 1'}

simulation_data['multiproc'] = False
#simulation_data['multiproc'] = True
simulation_data['processors'] = 32

############################ DISEASE PARAMS ###################################

simulation_data['order'] = 1 #order of clustering neighborhood 
simulation_data['patients_number'] = 100 # pn

############################ NETWORK MODEL ####################################

simulation_data['rho_0_list'] = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2] #expression density              #expression density
simulation_data['f_list']     = range(0,11)

#simulation_data = {}
simulation_data['order'] = 1 #order of clustering neighborhood 

simulation_data['network_type']  = 'ER'

"""
###############################################################################
################################ SETUP ########################################
###############################################################################

# Erdos Renyi network 
simulation_data['network_n'] = 1000
simulation_data['network_p'] = 0.006   

# disease 
simulation_data['D'] = 50   #disease related genes  
simulation_data['p'] = 0.5  #clustering probability

# cohort
simulation_data['P'] = 15 #patiens number
simulation_data['A'] = 0.5  #disease expression density scaling

###############################################################################
############################### RUN ###########################################
###############################################################################

#start = time.time()

series = simulate_series(simulation_data) 
plot_network(series[str(0.02)][str(2)]) 
#print "whole time = " + str(time.time() - start)    
"""