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 NODF_launcher(N, S1, S2, C, eps, step, P, rip, t, work_dir, option):
import time
from one_simulation import one_simulation
from bipartite_graph_matrix import adjacency_matrix_nested2, adjacency_matrix_nested
import my_output as O
#from NODF_swap import swap_matrix_element, NODF_swap2
from grad_nestedness import swap_matrix_element, NODF_calc
import os
N1 = N
N2 = N
start = time.time()
if option == 0:
parent_dir = work_dir+'/NODF/op0/'
else:
parent_dir = work_dir+'/NODF/op1/'
dir_name = parent_dir+repr(C)+'-'+repr(eps)+'-'+repr(rip)
matrix_dir = dir_name+'/matrix'
if option == 0:
print('Eseguo matrice nested nuova.')
A = adjacency_matrix_nested2(S1, S2, C, dir_name, 0)
else:
print('Eseguo matrice nested vecchia.')
A = adjacency_matrix_nested(S1, S2, C, dir_name, 0)
NODF = NODF_calc(A)
A0 = A
NODFs = []
NODFs.append(NODF)
#(As, NODFs) = NODF_swap2(S1, S2, C, step, P, dir_name, option)
#from NODF_swap import NODF_swap3
#(As, NODFs) = NODF_swap3(S1, S2, C, step, P, dir_name)
file_path2 = "C:/Users/Utente/Anaconda3/UserScripts/Programmi cooperazione/"
directory2 = os.path.dirname(file_path2)
os.chdir(directory2)
T = True
for i in range(10):
if i == 0:
O.print_matrix2(A0, S1, S2, C, NODF, 0, matrix_dir)
for j in range(rip):
print('Eseguo simulazione numero {} su {} con NODF = {}'.format(j+1,rip, NODF))
one_simulation(A0, N1, N2, S1, S2, eps, C, t, NODF, dir_name, rip, re = j)
O.NODF_print_info_csv(NODFs[i], eps, C, rip, dir_name, parent_dir)
else:
(A, NODF) = swap_matrix_element(A0,P*i)
tentativo = 1
while NODF > NODFs[-1] - 0.04 :
(A, NODF) = swap_matrix_element(A0,P*i)
tentativo = tentativo + 1
if tentativo > 10000:
T = False
break
if T == True:
print('Matrice selezionata al tentativo numero ', tentativo)
NODFs.append(NODF)
O.print_matrix2(A, S1, S2, C, NODF, i, matrix_dir)
for j in range(rip):
print('Eseguo simulazione numero {} su {} con NODF = {}'.format(j+1,rip, NODF))
one_simulation(A, N1, N2, S1, S2, eps, C, t, NODF, dir_name, rip, re = j)
O.NODF_print_info_csv(NODFs[i], eps, C, rip, dir_name, parent_dir)
O.info_NODF(N, rip, NODFs, eps, C, t, work_dir, parent_dir)
end = time.time()
elapsed = round((end-start)/60,2)
print('NODF_launcher eseguito in {} min \n'.format(elapsed))
def NODF_swap2(S1, S2, C, step, P, dir_name, option):
#import matplotlib.pyplot as plt
import numpy as np
from grad_nestedness import swap_matrix_element, NODF_calc
from bipartite_graph_matrix import adjacency_matrix_nested2, adjacency_matrix_nested
import my_output as O
NODFs = []
matrix_dir = dir_name + '/matrix'
if option == 0:
print('Eseguo matrice nested nuova.')
A = adjacency_matrix_nested2(S1, S2, C, dir_name, 0)
else:
print('Eseguo matrice nested vecchia.')
A = adjacency_matrix_nested(S1, S2, C, dir_name, 0)
NODF = NODF_calc(A)
A0 = A
#print('Astart = ', Astart)
#NODFstart = NODF
#print('NODFstart = ', NODFstart)
O.print_matrix2(A, S1, S2, C, NODF, 0, matrix_dir)
NODFs.append(round(NODF, 3))
A1, A2, A3, A4, A5, A6, A7, A8 = [], [], [], [], [], [], [], []
As = [A0, A1, A2, A3, A4, A5, A6, A7, A8]
M = NODF + 0.001
m = 0.25
selection = np.linspace(m, M, 10)
#print('selection = ', selection)
for i in range(len(selection)):
print('selection[-{}] = '.format(i + 1), selection[-i - 1])
counts = 1
tentativo = 1
m2 = min([selection[-counts], NODFs[-1]])
while counts < len(selection) - 1:
(A, NODF) = swap_matrix_element(A0, P)
if NODF < m2 and NODF > selection[-counts - 2]:
NODFs.append(round(NODF, 3))
As[counts] = A
O.print_matrix2(A, S1, S2, C, NODF, counts, matrix_dir)
counts = counts + 1
print(
'Matrice numero {} selezionata al tentativo {} con NODF = {}'.
format(counts, tentativo, NODFs[-1]))
tentativo = 1
m2 = min([selection[-counts], NODFs[-1]])
if counts < len(selection) - 1:
print('Cerco {} < NODF < {}: \n'.format(
round(selection[-counts - 2], 3), round(m2, 3)))
else:
print('NODF trovata = ', NODF)
print('NODF richiesta tra {} e {}'.format(
selection[-counts - 2], min([selection[-counts], NODFs[-1]])))
tentativo = tentativo + 1
#As = [A0, A1, A2, A3, A4, A5, A6, A7, A8, A9]
#questo è da cambiare
O.print_NODF_step2(NODFs, C, P, dir_name)
print('len(As) = ', len(As))
print('len(NODFs) = ', len(NODFs))
for i in range(len(As)):
print(As[i], '\n')
return ([A0, A1, A2, A3, A4, A5, A6, A7, A8], NODFs)