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 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):
from one_step_bi import one_step
from random import randint
from copy import deepcopy
import time
start_time = time.time()
name = 'N-' + format(p, '.2f') + '-' + repr(eps) + '-' + repr(rip)
from matrici import adjacency_matrix_nested
A = adjacency_matrix_nested(S1, S2, p, dir_name, rip, g, flag)
#print('A = ', A)
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)
#print('n1[0] = ', n1[0])
#print('n1[-1] = ', n1[-1])
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)
#print('n2[0] = ', n2[0])
#print('n2[-1] = ', n2[-1])
from my_print import print_tuple
#t_conv = 0
S_range_1 = 0
S_range_2 = 0
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)
print_tuple([i, S1], 'S1_' + name, dir_name)
print_tuple(n_1, 'n1_' + name, dir_name, d=len(n_1))
if S_range_1 < S1:
S_range_1 = S1
n_2 = deepcopy(n2)
S2 = len(n_2) - n_2.count(0)
print_tuple([i, S2], 'S2_' + name, dir_name)
print_tuple(n_2, 'n2_' + name, dir_name, d=len(n_2))
if S_range_2 < S2:
S_range_2 = S2
#from my_print import print_tuple3
#from my_print import print_gnuplot, print_gnuplot3, print_gnuplot_sys
from data_analysis import asymptotic_S_1, Pn, t_start, Pn_cumulative2
from my_print import tab_reader
from my_print import tab_reader2
t_S1 = tab_reader('S1_' + name, dir_name)
v_n1 = tab_reader2('n1_' + name, dir_name)
#print('v_n1 = ', v_n1 )
t_S2 = tab_reader('S2_' + name, dir_name)
v_n2 = tab_reader2('n2_' + name, dir_name)
t_start = t_start(t_S1, eps, N1)
#qui è da cambiare la parte in eps-nu
S_mean1 = asymptotic_S_1(t_S1, eps, N1)
S_mean2 = asymptotic_S_1(t_S2, eps, N2)
#print('j = {}'.format(j))
from my_print import my_pyplot
if rip == 1:
print('Eseguo my_pyplot.')
info_1 = {
'xlab': 'Tempo ' + u"\u03C4" + ' [step/N]',
'ylab1': 'Numero specie S1',
'ylab2': 'Numero specie S2',
'tit1':
'S1(' + u"\u03C4" + ') per ' + u"\u03B5" + ' = ' + repr(eps),
'tit2':
'S2(' + u"\u03C4" + ') per ' + u"\u03B5" + ' = ' + repr(eps)
}
my_pyplot('S1_' + name, 'S2_' + name, dir_name, info_1)
#Pn1 = Pn(v_n1, N1, t_start)
Pn1 = Pn_cumulative2(v_n1, N1, t_start)
#Pn2 = Pn(v_n2, N2, t_start)
Pn2 = Pn_cumulative2(v_n2, N2, t_start)
simulation_t = round((time.time() - start_time) / 60, 2)
print("Tempo simulazione: {}".format(simulation_t), 'min.')
return (S_mean1, S_mean2, Pn1, Pn2, simulation_t)
