def asymptotic_deg_n (N, name1, name2, dir_name, tau_start):
import my_print as my
import statistics as st
S_deg = my.tab_reader2(name2, dir_name)
n = my.tab_reader2(name1, dir_name)
m = [ [] for x in range( len(n[0]) )]
for i in range(tau_start, len(n) ):
for j in range(len(n[0]) ):
m[j].append(n[i][j]/N )
x = []
for i in range(len(m)):
x.append(st.mean(m[i]) )
deg_n = []
deg = []
for i in range(len(S_deg)):
deg.append(S_deg[i][1])
M = max(deg)
#print('len(deg) = ', len(deg))
#print('x = ', x)
#print('S_deg = ', S_deg)
#print('deg = ', deg)
if len(S_deg) != len(x):
print('Attenzione: len(S_deg) = {} e len(x) = {} '.format(len(S_deg),len(x)))
print('S_deg = ', S_deg)
print('x = ', x)
for j in range(0, M+1):
#print('j = ', j)
for i in range(len(S_deg) ):
if deg[i] == j:
deg_n.append( (S_deg[i][1], x[i]) )
return deg_n
def init_simulation2(N, S, dir_name, name):
#import math
from random import randint
from copy import deepcopy
import my_print as my
#S = int(math.sqrt(N))
#print("S = {}".format(S))
nu = 0
v = [randint(0, S - 1) for x in range(0, N)]
n = []
Smax = max(v)
for i in range(0, Smax + 1):
c = v.count(i)
n.append(c)
from one_step_bi import one_step_mono
t_conv = 0
i = 0
while t_conv == 0:
i = i + 1
for j in range(N):
(v, n) = one_step_mono(N, nu, v, n, i)
n1 = deepcopy(n)
my.print_tuple(n, 'n_' + name, dir_name, d=len(n))
S = len(n1) - n1.count(0)
if S == 1:
print("Convergenza raggiunta a tau {}.".format(i))
t_conv = i
v_n = my.tab_reader2('n_' + name, dir_name)
info = {}
my.mono_plot(v_n, name, dir_name, info)
def asymptotic_n (name, dir_name):
import my_print as my
import statistics as st
n = my.tab_reader2(name, dir_name)
m = [ [] for x in range( len(n[0]) )]
for i in range(int(len(n)/2), len(n) ):
for j in range(len(n[0]) ):
m[j].append(n[i][j] )
x = []
for i in range(len(m)):
x.append(st.mean(m[i]) )
return x
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)