def precision_simul(N, S1, S2, C, eps, rip, t, work_dir):
import time
from one_simulation import one_simulation
import my_output as O
from grad_nestedness import NODF_calc
N1 = N
N2 = N
start = time.time()
parent_dir = work_dir + '/precision/'
dir_name1 = parent_dir + '/nuova/'
dir_name2 = parent_dir + '/vecchia/'
from bipartite_graph_matrix import adjacency_matrix_nested2, adjacency_matrix_rnd2
print('Eseguo matrice nested nuova.')
A1 = adjacency_matrix_nested2(S1, S2, C, dir_name1, 1)
NODF1 = NODF_calc(A1)
for i in range(rip):
one_simulation(A1,
N1,
N2,
S1,
S2,
eps,
C,
t,
NODF1,
dir_name1,
rip,
re=i)
O.NODF_print_info_csv(NODF1, eps, C, rip, dir_name1, parent_dir)
O.info_precision(N, rip, [NODF1], eps, C, t, work_dir, parent_dir)
print('Eseguo matrice random.')
A2 = adjacency_matrix_rnd2(S1, S2, C, dir_name2, 1)
NODF2 = NODF_calc(A2)
for i in range(rip):
one_simulation(A2,
N1,
N2,
S1,
S2,
eps,
C,
t,
NODF2,
dir_name2,
rip,
re=i)
O.NODF_print_info_csv(NODF2, eps, C, rip, dir_name2, parent_dir)
O.info_precision(N, rip, [NODF2], 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 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 NODF_swap3(S1, S2, C, step, P, dir_name):
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_rnd2
import my_output as O
NODFs = []
matrix_dir = dir_name + '/matrix'
print('Eseguo matrice nested nuova.')
A = adjacency_matrix_nested2(S1, S2, C, dir_name, 0)
NODF = NODF_calc(A)
#print('NODFstart = ', NODFstart)
O.print_matrix2(A, S1, S2, C, NODF, 0, matrix_dir)
NODFs.append(round(NODF, 3))
As = []
As.append(A)
for i in range(100):
A = adjacency_matrix_rnd2(S1, S2, C)
NODF = NODF_calc(A)
NODFs.append(round(NODF, 3))
As.append(A)
O.print_matrix2(As[-1], S1, S2, C, NODF, i, matrix_dir)
return (As, NODFs)
def NODF_swap(S1, S2, C, step, P, dir_name):
from grad_nestedness import swap_matrix_element, NODF_calc
from bipartite_graph_matrix import adjacency_matrix_nested2
import my_output as O
NODFs = []
matrix_dir = dir_name + '/matrix_step'
A = adjacency_matrix_nested2(S1, S2, C, dir_name, 0)
NODF = NODF_calc(A)
O.print_matrix2(A, S1, S2, C, NODF, 0, matrix_dir)
NODFs.append(NODF)
for i in range(step):
(A, NODF) = swap_matrix_element(A, P)
O.print_matrix2(A, S1, S2, C, NODF, i + 1, matrix_dir)
NODFs.append(NODF)
O.print_NODF_step(NODFs, C, P, dir_name)
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)
def adjacency_matrix_rnd(S1=10,
S2=10,
p=0.35,
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
from grad_nestedness import NODF_calc
plt.style.use('seaborn')
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)
k = int(p * S1 * S2)
G = bipartite.gnmk_random_graph(S1, S2, k)
G1 = ordina_grafo_1(G, S1, S2)
G1 = ordina_grafo_2(G1, S1, S2)
deg = list(G1.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:])
NODF = NODF_calc(A1)
print('NODF prima del riordino = ', NODF, '\n')
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('Configurazione casuale con C = ' + format(p, '.2f'))
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap='Greys')
plt.tight_layout()
fig.savefig('random_' + format(p, '.2f') + '.png')
plt.close()
A = nx.to_numpy_matrix(G1)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#NODF = NODF_calc(A1)
#print('NODF dopo il riordino = ', NODF, '\n')
#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('Configurazione casuale con C = '+format(p,'.2f'))
#plt.setp(ax.get_xticklabels(), visible=False)
#plt.setp(ax.get_yticklabels(), visible=False)
#plt.imshow(A1, cmap = 'Greys')
#plt.tight_layout()
#fig.savefig('random_'+format(p,'.2f')+'_ordinata.png')
#plt.close()
fig, [ax1, ax] = plt.subplots(1, 2, figsize=(10, 4))
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G1, 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 casuale 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('random_' + format(p, '.2f') + '_ordinata.png')
plt.close()
print('curr_dir = ', curr_dir)
os.chdir(curr_dir)
return A1