def print_matrix(A, S1, S2, C, filename, parent_dir):
import matplotlib.pyplot as plt
from ensure_dir import ensure_dir
import os
script_dir = os.getcwd()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(A, cmap='Greys')
ax.set_title(
'Matrice interazioni con dati reali e connettanza = {}'.format(C))
ax.set_xlabel('Numero specie piante: S2 = {}'.format(S2))
ax.set_ylabel('Numero specie impollinatori: S1 = {}'.format(S1))
xtics = [x - 0.5 for x in range(0, S2 + 1)]
ytics = [x - 0.5 for x in range(0, S1 + 1)]
ax.grid()
ax.set_yticks(ytics)
ax.set_xticks(xtics)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
path = parent_dir + '/' + filename + '//prova.txt'
ensure_dir(path)
directory = os.path.dirname(path)
os.chdir(directory)
fig.savefig(filename + '_matrix.png')
plt.close()
os.chdir(script_dir)
def print_matrix2(A, S1, S2, C, NODF, re, parent_dir):
import matplotlib.pyplot as plt
from ensure_dir import ensure_dir
import os
script_dir = os.getcwd()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(A, cmap='Greys')
ax.set_title('Matrice per step {} con C = {} e NODF = {}'.format(
re, C, NODF))
ax.set_xlabel('S2 = {}'.format(S2))
ax.set_ylabel('S1 = {}'.format(S1))
xtics = [x - 0.5 for x in range(0, S2 + 1)]
ytics = [x - 0.5 for x in range(0, S1 + 1)]
ax.grid()
ax.set_yticks(ytics)
ax.set_xticks(xtics)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
path = parent_dir + '//prova.txt'
ensure_dir(path)
directory = os.path.dirname(path)
os.chdir(directory)
fig.savefig(repr(C) + '-' + repr(re) + '_matrix.png')
plt.close()
os.chdir(script_dir)
def search_by_reaction(number,folder,SMILES_reaction, PATH):
files_mrv = glob.glob(folder + 'search_' + str(number+1) + '/' + '*.mrv')
ensure_dir.ensure_dir(folder + 'temporary/')
fn=0
for file in files_mrv:
g = open(file)
ggg = g.readlines()
g.close()
fn+=1
if len(ggg)>2:
print(file)
print ('Standardizing...')
try:
sp.call(['standardize', file, '-c', PATH+'/standardizer2.xml', '-f', 'rdf', '-o', file[:-3] + 'rdf'])
except:
print ('Not enough memory for Standardizer!!!')
sys.exit()
print ('Searching by reaction...')
try:
sp.call(['jcsearch', '-q', SMILES_reaction[number*2], '-f', 'MRV', '-o', folder + 'temporary/' + file.split('/')[-1], file[:-3] + 'rdf'])
except:
print ('Not enough memory for jcsearch!!!')
sys.exit()
print (file.split('/')[-1] + ' is done..')
print (str(fn*100//len(files_mrv)) + '%')
sp.call([PATH+'/molconvert_calling.sh', folder.rstrip('/'), str(number+1)])
sp.call(["rm", "-r", folder + 'temporary/'])
print ('Substructure search in jcsearch by reaction is done...')
def write_sta_pv(staname,
ident,
eposta,
Xsta,
Ysta,
Zsta,
Xsta_err,
Ysta_err,
Zsta_err,
pv='P'):
'''write the Position/Velocity of station into a text file.
Parameters
----------
staname : string
name of station
ident : string
'XYZ' or 'UEN'
eposta : array, float
epoch of time series
Xsta : array, float
X- / U- component
Ysta : array, float
Y- / E- component
Zsta : array, float
Z- / N- component
Xsta_err : array, float
formal uncertainty of X- / U- component
Ysta_err : array, float
formal uncertainty of Y- / E- component
Zsta_err : array, float
formal uncertainty of Z- / N- component
pv : string
'P' or 'V'
Returns
----------
None.
'''
if pv == 'P' or pv == 'p':
lab = 'sta'
elif pv == 'V' or pv == 'v':
lab = 'vel'
else:
print("pv can only be set one of 'PVpv'")
exit()
datname = 'ts_%s/%s_%s.dat' % (lab, staname, ident)
ensure_dir(datname)
fdat = open(datname, 'w')
opfmt = "%%10.5f%s%s" % ("|%15.2f" * 3, "|%10.3f" * 3)
for i, epoi in enumerate(eposta):
print(opfmt % (epoi, Xsta[i], Ysta[i], Zsta[i], Xsta_err[i],
Ysta_err[i], Zsta_err[i]),
file=fdat)
fdat.close()
def info_precision(N, rip, NODFs, eps, p, t, work_dir, parent_dir):
from ensure_dir import ensure_dir
file_path = work_dir + '/info_precision.csv'
ensure_dir(file_path)
with open(file_path, 'a') as f:
print(parent_dir, N, rip, sep=',', end='', file=f)
for i in range(len(NODFs) - 1):
print('', NODFs[i], sep=',', end='', file=f)
print('', NODFs[-1], eps, p, t, sep=',', file=f)
def info_e(N, rip, epsilons, p, t, work_dir, parent_dir):
from ensure_dir import ensure_dir
file_path = work_dir + '/info_e.csv'
ensure_dir(file_path)
with open(file_path, 'a') as f:
print(parent_dir, N, rip, sep=',', end='', file=f)
for i in range(len(epsilons) - 1):
print('', epsilons[i], sep=',', end='', file=f)
print('', epsilons[-1], p, t, sep=',', file=f)
def substructure_search(folder_for_data, folder_for_standardized_data, SMILES):
ensure_dir.ensure_dir(folder_for_data)
files_stand_rdf = glob.glob(folder_for_standardized_data)
for number, smiles in enumerate(SMILES):
ensure_dir.ensure_dir(folder_for_data + 'search_' + str(number+1) + '/')
for file in files_stand_rdf:
print ('Searching of ' + smiles + ' in ' + file.split('/')[-1][0:-9] + 'mrv')
sp.call(['jcsearch', '-q', smiles, '-f', 'MRV', '-o', folder_for_data + 'search_' + str(number+1) + '/' + file.split('/')[-1][0:-9] + 'mrv', file])
print ('Searching ended...')
def print_list_csv(l, name, dir_name, rnd=2, d=2):
from ensure_dir import ensure_dir
file_path = dir_name + '//data_plot//' + name + '.csv'
ensure_dir(file_path)
with open(file_path, 'a') as f:
if d > 1:
for i in range(0, d - 1):
print("{},".format(round(l[i], rnd)), end='', file=f)
print("{}".format(round(l[d - 1], rnd)), file=f)
else:
print(l, file=f)
def write_sou_pv(souname,
eposou,
RAsou,
DCsou,
RAsou_err,
DCsou_err,
corsou,
pv='P'):
'''Write the Position/PM of sources into text files.
Parameters
----------
souname : string
IVS source name
eposou : array, float
epoch of time series
RAsou : array, float
RA component
DCsou : array, float
Dec. component
RAsou_err : array, float
formal uncertainty of RA component
DCsou_err : array, float
formal uncertainty of Dec. component
pv : string
'P' or 'V'
Returns
----------
None.
'''
if pv == 'P' or pv == 'p':
lab = 'sou'
elif pv == 'V' or pv == 'v':
lab = 'pmt'
else:
print("pv can only be set one of 'PVpv'")
exit()
datname = 'ts_%s/%s.dat' % (lab, souname)
ensure_dir(datname)
fdat = open(datname, 'w')
opfmt = "%%10.5f%s%s%s" % ("|%15.2f" * 2, "|%10.3f" * 2, "|%7.4f")
for i, epoi in enumerate(eposou):
print(
opfmt %
(epoi, RAsou[i], DCsou[i], RAsou_err[i], DCsou_err[i], corsou[i]),
file=fdat)
fdat.close()
def sdf_parse(sdf_way, PATH):
f = open(sdf_way)
sdf = f.read().split('$$$$\n')[:-1]
f.close()
ensure_dir.ensure_dir(PATH + '/temp/')
n = 0
for i in range(1, (len(sdf) / 1000) + 2):
out = open(PATH + '/temp/' + str(i) + '.sdf', 'w')
while n < i * 1000 and n <= len(sdf) - 1:
out.write(sdf[n] + '$$$$\n')
n += 1
out.close()
def sdf_parse(sdf_way, PATH):
f = open(sdf_way)
sdf = f.read().split('$$$$\n')[:-1]
f.close()
ensure_dir.ensure_dir(PATH+'/temp/')
n=0
for i in range(1,(len(sdf)/1000)+2):
out = open(PATH+'/temp/' + str(i) + '.sdf', 'w')
while n<i*1000 and n<=len(sdf)-1:
out.write(sdf[n] + '$$$$\n')
n+=1
out.close()
def overview2(N1, S1, eps, p, rip, t_tot, dir_name):
from ensure_dir import ensure_dir
file_path = 'C:/Users/Utente/Anaconda3/Cooperazione//'+dir_name+'//''overview2.txt'
ensure_dir(file_path)
with open(file_path, 'w') as f:
if f.seek(0,2) == 0:
print('#N1: S1: eps: p: rip: t_tot:', file = f)
print(N1, S1, eps, p, rip, t_tot, sep = ' ', file=f)
file_path2 = 'C:/Users/Utente/Anaconda3/Cooperazione//overview2.txt'
ensure_dir(file_path2)
with open(file_path2, 'a') as f:
if f.seek(0,2) == 0:
print('#N1: S1: eps: p: rip: t_tot:', file = f)
print(N1, S1, eps, p, rip, t_tot, sep = ' ', file=f)
def substructure_search(folder_for_data, folder_for_standardized_data, SMILES):
ensure_dir.ensure_dir(folder_for_data)
files_stand_rdf = glob.glob(folder_for_standardized_data)
for number, smiles in enumerate(SMILES):
ensure_dir.ensure_dir(folder_for_data + 'search_' + str(number + 1) +
'/')
for file in files_stand_rdf:
print('Searching of ' + smiles + ' in ' +
file.split('/')[-1][0:-9] + 'mrv')
sp.call([
'jcsearch', '-q', smiles, '-f', 'MRV', '-o',
folder_for_data + 'search_' + str(number + 1) + '/' +
file.split('/')[-1][0:-9] + 'mrv', file
])
print('Searching ended...')
def print_tuple (K, name, dir_name, d=2, rnd =2):
"""Stampa una lista di tuple fatti di d numeri.
Di default d=2, cioè tratta coppie, e quindi è
una generalizzazione di print_tuple1.
Accetta una lista di tuple. Non ritorna nulla.
"""
from ensure_dir import ensure_dir
file_path = dir_name+'//data_plot//'+ name + '.txt'
ensure_dir(file_path)
with open(file_path, 'a') as fS:
#with open("stat_" + name + ".txt", "w") as fS:
for i in range(0,d-1):
print("{} ".format(round(K[i],rnd)), end ='', file = fS)
print("{}".format(round(K[d-1],rnd)), file = fS)
def print_tuple3 (K, name, dir_name, d=2):
"""Stampa una lista di tuple fatti di d numeri.
Di default d=2, cioè tratta coppie, e quindi è
una generalizzazione di print_tuple1.
Accetta una lista di tuple. Non ritorna nulla.
"""
#import os
from ensure_dir import ensure_dir
file_path = dir_name+"\\data_plot\\"+ name + ".txt"
ensure_dir(file_path)
with open(file_path, 'w') as fS:
#with open("stat_" + name + ".txt", "w") as fS:
for x in K:
for i in range(0,d-1):
print("{} ".format(x[i]), end ='', file = fS)
print("{}".format(x[d-1]), file = fS)
def overview(N1, step, rip, t_tot, dir_name):
from ensure_dir import ensure_dir
file_path = dir_name+'//''overview1.txt'
ensure_dir(file_path)
with open(file_path, 'w') as f:
print('N1 = ', N1, file=f)
print('Numero step compiuti = ', step, file = f)
print('Numero di realizzazioni = ', rip, file = f)
print('Tau max = ', int(step/N1), file = f)
print('Tempo totale impiegato per la simulazione = ', t_tot, 'min', file = f)
file_path2 = 'C:/Users/Utente/Anaconda3/Cooperazione//overview.txt'
ensure_dir(file_path2)
with open(file_path2, 'a') as f:
if f.seek(0,2) == 0:
print('#N1: Step: Rip: Tau: t_tot', file = f)
print(N1, step, int(step/N1), rip, t_tot, sep = ' ', file=f)
def plot_sou_pv(souname, eposou, RAsou, DCsou, RAsou_err, DCsou_err, pv='P'):
'''Plot the Position/PM of sources.
Parameters
----------
souname : string
IVS source name
eposou : array, float
epoch of time series
RAsou : array, float
RA component
DCsou : array, float
Dec. component
RAsou_err : array, float
formal uncertainty of RA component
DCsou_err : array, float
formal uncertainty of Dec. component
pv : string
'P' or 'V'
Returns
----------
None.
'''
if pv == 'P' or pv == 'p':
unit = 'mas'
lab = 'sou'
elif pv == 'V' or pv == 'v':
unit = 'mas/yr'
lab = 'pmt'
else:
print("pv can only be set one of 'PVpv'")
exit()
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
ax0.errorbar(eposou, RAsou * 1000, yerr=RAsou_err, fmt='.')
ax0.set_title("R.A. (%s)" % unit)
ax1.errorbar(eposou, DCsou * 1000, yerr=DCsou_err, fmt='.')
ax1.set_title("Dec. (%s)" % unit)
ax1.set_xlabel("Epoch (year)")
ax1.set_xlim([1979.0, 2018.0])
figname = "figures/ts_%s/%s.eps" % (lab, souname)
ensure_dir(figname)
plt.savefig(figname)
plt.close()
def print_tuple2 (K, name, dir_name, d=2, rnd = 2):
"""Stampa una lista di tuple fatti di d numeri.
Di default d=2, cioè tratta coppie, e quindi è
una generalizzazione di print_tuple1.
Accetta una lista di tuple. Non ritorna nulla.
"""
#import os
from ensure_dir import ensure_dir
#attenzione qui ho aggiunto //data_plot
file_path = dir_name+'//data_plot//'+ name + ".txt"
ensure_dir(file_path)
with open(file_path, 'a') as fS:
#with open("stat_" + name + ".txt", "w") as fS:
if fS.seek(0,2) == 0:
for i in range(len(K)):
for j in range(0,d-1):
print("{} ".format(round(K[i][j],rnd)), end ='', file = fS)
print("{}".format(round(K[i][d-1],2)), file = fS)
def guess(N1, S1, p, eps, dir_name):
from ensure_dir import ensure_dir
file_path = 'C:/Users/Utente/Anaconda3/Cooperazione//'+dir_name+'//guess.txt'
ensure_dir(file_path)
V1 = round(1/(S1*p),4)
Ptot = round(1+ eps*S1*p , 2)
T_su_P = round(1 - 1/Ptot,2)
with open(file_path, 'a') as f:
if f.seek(0,2) == 0:
print('#N1: S1: eps: p: V1: P_tot: T/P:', file = f)
print(N1, S1, eps, p, V1, Ptot, T_su_P, sep = ' ', file=f)
#if T_su_P == 0:
# print('Regime competitivo. \n')
#if T_su_P < 0.1 and T_su_P > 0:
# print('Regime perturbativo. \n')
#elif T_su_P > 0.1 and T_su_P < 0.5 :
# print('Regime misto. \n')
#else:
# print('Regime cooperativo. \n')
perc = round(T_su_P*100,2)
print('Regime cooperativo al {} %. \n'.format(perc))
def csv_reader(filename, dir_path):
from ensure_dir import ensure_dir
file_path = dir_path + '//' + filename + ".csv"
ensure_dir(file_path)
with open(file_path, 'r') as f:
l = []
l2 = []
T = 0
while T == 0:
s = f.readline().split(',')
if s[0] != '':
d = len(s)
l2 = []
for i in range(0, d - 1):
l2.append(int(s[i]))
z = s[d - 1].split('\n')
l2.append(int(z[0]))
l.append(l2)
else:
T = 1
break
return l
def tab_reader2 (name, dir_name):
from ensure_dir import ensure_dir
file_path = dir_name+"//data_plot//"+ name + ".txt"
ensure_dir(file_path)
with open (file_path, 'r') as f:
l = []
l2 = []
T = 0
while T == 0:
s = f.readline().split(' ')
if s[0] != '':
d = len(s)
l2 = []
for i in range(0,d-1):
l2.append(int(s[i]))
z = s[d-1].split('\n')
l2.append(int(z[0]))
l.append(l2)
else:
T = 1
break
return l
def search_by_reaction(number, folder, SMILES_reaction, PATH):
files_mrv = glob.glob(folder + 'search_' + str(number + 1) + '/' + '*.mrv')
ensure_dir.ensure_dir(folder + 'temporary/')
fn = 0
for file in files_mrv:
g = open(file)
ggg = g.readlines()
g.close()
fn += 1
if len(ggg) > 2:
print(file)
print('Standardizing...')
try:
sp.call([
'standardize', file, '-c', PATH + '/standardizer2.xml',
'-f', 'rdf', '-o', file[:-3] + 'rdf'
])
except:
print('Not enough memory for Standardizer!!!')
sys.exit()
print('Searching by reaction...')
try:
sp.call([
'jcsearch', '-q', SMILES_reaction[number * 2], '-f', 'MRV',
'-o', folder + 'temporary/' + file.split('/')[-1],
file[:-3] + 'rdf'
])
except:
print('Not enough memory for jcsearch!!!')
sys.exit()
print(file.split('/')[-1] + ' is done..')
print(str(fn * 100 // len(files_mrv)) + '%')
sp.call(
[PATH + '/molconvert_calling.sh',
folder.rstrip('/'),
str(number + 1)])
sp.call(["rm", "-r", folder + 'temporary/'])
print('Substructure search in jcsearch by reaction is done...')
def tab_reader (name, dir_name, d = 2):
#import os
from ensure_dir import ensure_dir
#file_path = "C:\Users\Utente\Anaconda3\Cooperazione\\"+dir_name+"\\data_plot\\"+ name + ".txt"
file_path = dir_name+"//data_plot//"+ name + ".txt"
ensure_dir(file_path)
with open (file_path, 'r') as f:
l = []
l2 = []
T = 0
while T == 0:
s = f.readline().split(' ')
if s[0] != '':
d = len(s)
l2 = []
for i in range(1,d-1):
l2.append(int(s[i]))
z = s[d-1].split('\n')
l2.append(int(z[0]))
l.append((int(s[0]),l2))
else:
T = 1
break
return l
n7=int(sys.argv[4])
n6=int(sys.argv[5])
else:
n1 = int(input('Parse?: '))
n2 = int(input('Standardize?: '))
n5 = int(input('Cross comparing?: '))
n7 = int(input('Substructure searches?: '))
n6 = int(input('Maximum number of dynamic bonds?: '))
PATH = path.dirname(__file__)
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#=======================PARSING OF INITIAL DATABASE=================================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
ensure_dir.ensure_dir(PATH + '/RDFs_from_INITIAL/')
if n1 == 1:
rdf_parser.rdf_parser(PATH + '/INITIAL/*.rdf', PATH + '/RDFs_from_INITIAL/', PATH+'/descriptions.csv')
print('Database is parsed...')
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#====================STANDARDIZING OF PARSED DATABASE===============================
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
files_mols_rdf = glob.glob(PATH+'/RDFs_from_INITIAL/*.mols.rdf')
ensure_dir.ensure_dir(PATH+'/STANDARDIZED_RDFs/')
if n2 == 1:
for file in files_mols_rdf:
print ('Standardizing of ' + file[18:])
sp.call(['standardize', file, '-c', PATH + '/standardizer.xml', '-f', 'rdf', '-o', PATH+'/STANDARDIZED_RDFs/' + file.split('/')[-1][0:-9] + '.stand.rdf'])
print ('Files are standardized!')
def print_string_csv(string, name, dir_name):
from ensure_dir import ensure_dir
file_path = dir_name + '//' + name + 'files.csv'
ensure_dir(file_path)
with open(file_path, 'a') as f:
print(string, file=f)
def plot_sta_pv(staname,
ident,
eposta,
Xsta,
Ysta,
Zsta,
Xsta_err,
Ysta_err,
Zsta_err,
pv='P'):
'''Plot the Position/Velocity of stations.
Parameters
----------
staname : string
name of station
ident : string
'XYZ' or 'UEN'
eposta : array, float
epoch of time series
Xsta : array, float
X- / U- component
Ysta : array, float
Y- / E- component
Zsta : array, float
Z- / N- component
Xsta_err : array, float
formal uncertainty of X- / U- component
Ysta_err : array, float
formal uncertainty of Y- / E- component
Zsta_err : array, float
formal uncertainty of Z- / N- component
pv : string
'P' or 'V'
Returns
----------
None.
'''
if pv == 'P' or pv == 'p':
unit = 'mm'
elif pv == 'V' or pv == 'v':
unit = 'mm/yr'
else:
print("pv can only be set one of 'PVpv'")
exit()
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharex=True)
ax0.errorbar(eposta, Xsta, yerr=Xsta_err, fmt='.')
ax0.set_title("%s (%s)" % (ident[0], unit))
# ax0.set_ylim([-500, 500])
ax1.errorbar(eposta, Ysta, yerr=Ysta_err, fmt='.')
ax1.set_title("%s (%s)" % (ident[1], unit))
# ax1.set_ylim([-200, 800])
ax2.errorbar(eposta, Zsta, yerr=Zsta_err, fmt='.')
ax2.set_title("%s (%s)" % (ident[2], unit))
ax2.set_xlabel("Epoch (year)")
ax2.set_xlim([1979.0, 2018.0])
figname = "figures/ts_sta/%s_%s.eps" % (staname, ident)
ensure_dir(figname)
plt.savefig(figname)
plt.close()
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
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 my_pyplot(name1, name2, dir_name, info, d=2):
from ensure_dir import ensure_dir
#file_path = "C:\Users\Utente\Anaconda3\Cooperazione\\"+dir_name+"\\data_plot\\"+ name + ".txt"
file_path1 = dir_name+"//data_plot//"+ name1 + ".txt"
ensure_dir(file_path1)
with open (file_path1, 'r') as f:
l1 = []
for i in range(d):
l1.append([])
T = 0
while T == 0:
s = f.readline().split(' ')
if s[0] != '':
b = len(s)
l1[0].append(int(s[0]))
for j in range(1,b-1):
l1[j].append(int(s[j]))
z = s[b-1].split('\n')
l1[b-1].append(int(z[0]))
else:
T = 1
break
file_path2 = dir_name+"//data_plot//"+ name2 + ".txt"
ensure_dir(file_path2)
with open (file_path2, 'r') as f:
l2 = []
for i in range(d):
l2.append([])
T = 0
while T == 0:
s = f.readline().split(' ')
if s[0] != '':
b = len(s)
l2[0].append(int(s[0]))
for j in range(1,b-1):
l2[j].append(int(s[j]))
z = s[b-1].split('\n')
l2[b-1].append(int(z[0]))
else:
T = 1
break
import matplotlib.pyplot as plt
plt.style.use('seaborn')
#fig, [ax1,ax2] = plt.subplots(1,2, figsize = (9,4))
fig, [ax1,ax2] = plt.subplots(2,1,figsize = (4,8))
#fig, [ax1,ax2] = plt.subplots(2,1)
ax1.plot(l1[0], l1[1], 'r.')
ax1.set(ylabel= info['ylab1'], title = info['tit1'])
ax1.grid()
ax1.set_ylim(0)
ax1.set_autoscale_on(True)
ax2.plot(l2[0], l2[1], 'r.')
ax2.set(xlabel = info['xlab'] , ylabel= info['ylab2'], title = info['tit2'])
ax2.grid()
ax2.set_ylim(0)
ax2.set_autoscale_on(True)
plt.tight_layout()
import os
path = dir_name
os.chdir(path)
fig.savefig(name1+"_plot.png")
plt.close()
def adjacency_matrix_rnd2(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
script_dir = os.getcwd()
#print('dir_name = ', dir_name)
#k is the number of non-null elements of the mutualistic matrix
#in this case p = C in each realization
k = int(round(S1 * S2 * p, 0))
G = bipartite.gnmk_random_graph(S1, S2, k)
#G = bipartite.random_graph(S1, S2, p)
num_conn = G.number_of_edges()
if num_conn != k: #this checks out if the number of connections of the random graphs is really k
print('Problema numero connessioni.') #prints an alert otherwise
print('# = ', num_conn)
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:])
if index == 1:
plt.style.use('seaborn')
file_path = dir_name + '/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
fig, [ax1, ax] = plt.subplots(1, 2, figsize=(10, 4))
#fig, [ax1, ax] = plt.subplots(1,2)
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)
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, '.3f'))
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') + '.png')
plt.close()
else:
file_path = dir_name + '/random_matrix/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
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)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_title('Matrice random realizzazione {}'.format(index))
ax.set_xlabel('S2 = {}'.format(S2))
ax.set_ylabel('S1 = {}'.format(S1))
ax.imshow(A1, cmap='Greys')
fig.savefig('random_' + format(p, '.2f') + '-' + repr(index) + '.png')
plt.close()
#else:
# O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
# O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
os.chdir(script_dir)
return A1
def adjacency_matrix_nested (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
curr_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:
G = adjust_edges(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:
my.print_tuple2(deg1, 'deg1_N-'+repr(index), dir_name)
my.print_tuple2(deg2, 'deg2_N-'+repr(index), dir_name)
plt.close()
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_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
