def one_simulation(A,
N1=50,
N2=50,
S1=10,
S2=10,
eps=0.05,
p=0.33,
t=1000,
NODF=1,
dir_name='50-50',
rip=1,
g=1,
flag=False,
re=1):
from one_step_bi import one_step
from random import randint
from copy import deepcopy
import time
from grad_nestedness import NODF_calc
start_time = time.time()
name = repr(NODF) + '-' + repr(eps) + '-' + repr(p) + '-' + repr(
rip) + '-' + repr(re)
print('name simulation = ', name)
NODF = NODF_calc(A)
print('NODF = ', NODF)
tau_max1 = int(t / N1)
print('tau = ', tau_max1)
v1 = [randint(0, S1 - 1) for x in range(0, N1)]
n1 = []
Smax1 = max(v1)
for i in range(0, Smax1 + 1):
c1 = v1.count(i)
n1.append(c1)
v2 = [randint(0, S2 - 1) for x in range(0, N2)]
n2 = []
Smax2 = max(v2)
for i in range(0, Smax2 + 1):
c2 = v2.count(i)
n2.append(c2)
import my_output as O
for i in range(1, tau_max1):
#qui il programma è un po' imparziale, perchè tratta la specie 1 come standard
for k in range(0, N1):
(v1, v2, n1, n2) = one_step(N1, N2, v1, v2, n1, n2, A, eps)
#(v1, v2, n1, n2) = one_step(N1, N2, v1, v2, n1, n2, A, eps, rip, True, dir_name)
n_1 = deepcopy(n1)
S1 = len(n_1) - n_1.count(0)
O.print_list_csv(S1, 'S1_' + name, dir_name, d=1)
# O.print_list_csv(n_1, 'n1_'+name, dir_name, d = len(n_1))
n_2 = deepcopy(n2)
S2 = len(n_2) - n_2.count(0)
O.print_list_csv(S2, 'S2_' + name, dir_name, d=1)
#O.print_list_csv(n_2, 'n2_'+name, dir_name, d = len(n_2))
simulation_t = round((time.time() - start_time) / 60, 2)
print("Tempo simulazione: {}".format(simulation_t), 'min. \n')
def N_one_simulation(N1=50,
N2=50,
S1=10,
S2=10,
eps=0.05,
p=0.33,
t=1000,
dir_name='50-50',
rip=1,
g=1,
flag=False,
re=1,
option=0):
from one_step_bi import one_step
from random import randint
from copy import deepcopy
import time
start_time = time.time()
name = repr(eps) + '-' + repr(p) + '-' + repr(rip) + '-' + repr(re)
from bipartite_graph_matrix import adjacency_matrix_nested, adjacency_matrix_nested2
if option == 0: #asymmetric nested / first type
A = adjacency_matrix_nested(S1, S2, p, dir_name, re, g, flag)
else: #balanced nested / second type
A = adjacency_matrix_nested2(S1, S2, p, dir_name, re, g, flag)
tau_max1 = int(t / N1)
v1 = [randint(0, S1 - 1) for x in range(0, N1)]
n1 = []
Smax1 = max(v1)
for i in range(0, Smax1 + 1):
c1 = v1.count(i)
n1.append(c1)
v2 = [randint(0, S2 - 1) for x in range(0, N2)]
n2 = []
Smax2 = max(v2)
for i in range(0, Smax2 + 1):
c2 = v2.count(i)
n2.append(c2)
import my_output as O
for i in range(1, tau_max1):
#qui il programma è un po' imparziale, perchè tratta la specie 1 come standard
for k in range(0, N1):
(v1, v2, n1, n2) = one_step(N1, N2, v1, v2, n1, n2, A, eps)
#(v1, v2, n1, n2) = one_step(N1, N2, v1, v2, n1, n2, A, eps, rip, True, dir_name)
n_1 = deepcopy(n1)
S1 = len(n_1) - n_1.count(0)
O.print_list_csv(S1, 'N_S1_' + name, dir_name, d=1)
O.print_list_csv(n_1, 'N_n1_' + name, dir_name, d=len(n_1))
n_2 = deepcopy(n2)
S2 = len(n_2) - n_2.count(0)
O.print_list_csv(S2, 'N_S2_' + name, dir_name, d=1)
O.print_list_csv(n_2, 'N_n2_' + name, dir_name, d=len(n_2))
simulation_t = round((time.time() - start_time) / 60, 2)
print("Tempo simulazione: {}".format(simulation_t), 'min. \n')
def adjacency_matrix_rnd(S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
curr_dir = os.getcwd()
#print('dir_name = ', dir_name)
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
G = bipartite.random_graph(S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (9,4))
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax = ax1)
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione casuale con C = '+format(p,'.2f'))
ax.set_autoscale_on(True)
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('random_'+format(p,'.2f')+'.png')
plt.close()
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'R_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'R_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
file_path2 = "C:/Users/Utente/Anaconda3/UserScripts/Programmi cooperazione/"
directory2 = os.path.dirname(file_path2)
print('directory2 = ', directory2)
print('curr_dir = ', curr_dir)
os.chdir(directory2)
return A1
def adjacency_matrix_nested2(S1=10,
S2=10,
p=0.33,
dir_name='graph',
index=1,
index2=1,
flag=False):
#balanced nested
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
file_path = dir_name + '/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1 + S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1 + S2 - i):
edges.append((i, j))
G.add_edges_from(edges)
if p < 0.55:
#this is where the actual edges are decided in most cases (C < 0.55) - different method
G = adjust_edges1(G, S1, S2, p)
if p > 0.55:
G = adjust_edges2(G, S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x: [0, x] for x in range(S1)}
for j in range(S2):
pos[S1 + j] = [1, j]
colors = ['r' for i in range(0, S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#rimettere if index == 1
if index == 0:
plt.style.use('seaborn')
fig, [ax1, ax] = plt.subplots(1, 2, figsize=(10, 4))
ax1.set_title('Interazioni mutualistiche nidificate')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos=pos, node_color=colors, ax=ax1)
ax.grid()
xtics = [x - 0.5 for x in range(0, S2 + 1)]
ytics = [x - 0.5 for x in range(0, S1 + 1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nidificata con C = ' + format(p, '.2f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap='Greys')
plt.tight_layout()
fig.savefig('nested_' + format(p, '.2f') + '_II.png')
if index == 1:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x - 0.5 for x in range(0, S2 + 1)]
ytics = [x - 0.5 for x in range(0, S1 + 1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Nested II tipo con C = ' + format(p, '.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap='Greys')
if index == 1:
fig.savefig('nested_' + format(p, '.2f') + '_II.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-' + repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-' + repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-' + repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-' + repr(index), dir_name)
plt.close()
os.chdir(script_dir)
return A1
def adjacency_matrix_nested2(S1=10,
S2=10,
p=0.33,
dir_name='graph',
index=1,
index2=1,
flag=False):
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
file_path = dir_name + '/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1 + S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1 + S2 - i):
edges.append((i, j))
G.add_edges_from(edges)
if p < 0.55:
G = adjust_edges1(G, S1, S2, p)
#if p > 0.55:
# G = adjust_edges2(G, S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x: [0, x] for x in range(S1)}
for j in range(S2):
pos[S1 + j] = [1, j]
colors = ['r' for i in range(0, S1)]
for j in range(S2):
colors.append('b')
fig, [ax1, ax] = plt.subplots(1, 2, figsize=(9, 4))
ax1.set_title('Interazioni mutualistiche nested ')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos=pos, node_color=colors, ax=ax1)
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
ax.grid()
xtics = [x - 0.5 for x in range(0, S2 + 1)]
ytics = [x - 0.5 for x in range(0, S1 + 1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nested con C = ' + format(p, '.2f'))
ax.set_autoscale_on(True)
plt.imshow(A1, cmap='Greys')
if index == 1:
fig.savefig('nested_' + format(p, '.2f') + '.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-' + repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-' + repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-' + repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-' + repr(index), dir_name)
plt.close()
file_path2 = "C:/Users/Utente/Anaconda3/UserScripts/Programmi cooperazione/"
directory2 = os.path.dirname(file_path2)
os.chdir(directory2)
return A1