def deletion_links_effect(k, periodoborr, minputchar_a, minputchar_b,
minputchar_a_mask, minputchar_b_mask, ldev_inf):
minputchar_a, fil, col = delete_link(minputchar_a)
minputchar_b[col][fil] = 0
minpeq_a = minputchar_a * minputchar_a_mask
minpeq_b = minputchar_b * minputchar_b_mask
sgcom.inform_user(ldev_inf, \
"Day:%d (year %d). Deleted links plant %d <-> pollinator %d"\
% (k, k // sgGL.DAYS_YEAR , int_to_ext_rep(fil), int_to_ext_rep(col)))
return minpeq_a, minpeq_b, minputchar_a, minputchar_b,
def init_random_links_removal(eliminarenlaces, periods, ldev_inf, minputchar_a):
periodoborr = periods
if eliminarenlaces > 0:
cuenta = cuentaenlaces(minputchar_a)
hayqueborrar = math.trunc(eliminarenlaces * cuenta)
sgcom.inform_user(ldev_inf, "Links %d. Will be deleted %d" %\
(cuentaenlaces(minputchar_a), hayqueborrar))
if hayqueborrar > 0:
periodoborr = periods // (hayqueborrar)
return periodoborr
def deletion_links_effect(k, periodoborr, minputchar_a, minputchar_b,
minputchar_a_mask, minputchar_b_mask, ldev_inf):
minputchar_a, fil, col = delete_link(minputchar_a)
minputchar_b[col][fil] = 0
minpeq_a = minputchar_a * minputchar_a_mask
minpeq_b = minputchar_b * minputchar_b_mask
sgcom.inform_user(ldev_inf, \
"Day:%d (year %d). Deleted links plant %d <-> pollinator %d"\
% (k, k // sgGL.DAYS_YEAR , int_to_ext_rep(fil), int_to_ext_rep(col)))
return minpeq_a, minpeq_b, minputchar_a, minputchar_b,
def init_random_links_removal(eliminarenlaces, periods, ldev_inf,
minputchar_a):
periodoborr = periods
if eliminarenlaces > 0:
cuenta = cuentaenlaces(minputchar_a)
hayqueborrar = math.trunc(eliminarenlaces * cuenta)
sgcom.inform_user(ldev_inf, "Links %d. Will be deleted %d" %\
(cuentaenlaces(minputchar_a), hayqueborrar))
if hayqueborrar > 0:
periodoborr = periods // (hayqueborrar)
return periodoborr
def init_blossom_pertubation_params(BssDat, numspecies_a, year_periods,
ldev_inf):
# Blossom variability analysis
global pendiente, periodo
pBssvar_species = []
pendiente = periodo = 0
hay_bssvar = (BssDat.Bssvar_sd > 0.0) & (len(BssDat.Bssvar_species) > 0)
if (hay_bssvar):
if (BssDat.Bssvar_modulationtype_list[0] == 'linear'):
strvalor = (", Slope %0.2f " %
(BssDat.Bssvar_modulationtype_list[1]))
else:
if (BssDat.Bssvar_modulationtype_list[0] == 'sin'):
strvalor = (", Period %0.2f " %
(BssDat.Bssvar_modulationtype_list[1]))
else:
strvalor = ''
straff = (". Affected species:%s" % (BssDat.Bssvar_species))
sgcom.inform_user(ldev_inf, "Blossom variability active. Period: %0.2f (%% of year), Initial moment standard dev.: %0.04f (%% of year), Type: %s%s%s "\
% (BssDat.Bssvar_period, BssDat.Bssvar_sd, BssDat.Bssvar_modulationtype_list[0],
strvalor, straff))
if (str(BssDat.Bssvar_species[0]).upper() == 'ALL'):
listaspecies = list(range(numspecies_a))
else:
listaspecies = [i - 1 for i in BssDat.Bssvar_species]
bssvar_allones = np.ones((year_periods, ), dtype=float)
#for i in range(numspecies_a):
for i in range_species_a:
if i in listaspecies:
if (BssDat.Bssvar_modulationtype_list[0] == 'None'):
varspecies = calcbssvarespecie(BssDat.Bssvar_period,
BssDat.Bssvar_sd,
bss_pert_no_modulation,
year_periods)
elif (BssDat.Bssvar_modulationtype_list[0] == 'linear'):
pendiente = float(BssDat.Bssvar_modulationtype_list[1])
varspecies = calcbssvarespecie(BssDat.Bssvar_period,
BssDat.Bssvar_sd,
bss_pert_lineal,
year_periods)
elif (BssDat.Bssvar_modulationtype_list[0] == 'sin'):
periodo = int(BssDat.Bssvar_modulationtype_list[1])
varspecies = calcbssvarespecie(BssDat.Bssvar_period,
BssDat.Bssvar_sd,
bss_pert_sinusoidal,
year_periods)
pBssvar_species.append(np.array(varspecies))
else:
pBssvar_species.append(bssvar_allones)
return pBssvar_species, hay_bssvar, pendiente, periodo
def init_blossom_pertubation_params(BssDat, numspecies_a,
year_periods, ldev_inf):
# Blossom variability analysis
global pendiente, periodo
pBssvar_species = []
pendiente = periodo = 0
hay_bssvar = (BssDat.Bssvar_sd > 0.0) & (len(BssDat.Bssvar_species) > 0)
if (hay_bssvar):
if (BssDat.Bssvar_modulationtype_list[0] == 'linear'):
strvalor = (", Slope %0.2f " % (BssDat.Bssvar_modulationtype_list[1]))
else:
if (BssDat.Bssvar_modulationtype_list[0] == 'sin'):
strvalor = (", Period %0.2f " % (BssDat.Bssvar_modulationtype_list[1]))
else:
strvalor = ''
straff = (". Affected species:%s" % (BssDat.Bssvar_species))
sgcom.inform_user(ldev_inf, "Blossom variability active. Period: %0.2f (%% of year), Initial moment standard dev.: %0.04f (%% of year), Type: %s%s%s "\
% (BssDat.Bssvar_period, BssDat.Bssvar_sd, BssDat.Bssvar_modulationtype_list[0],
strvalor, straff))
if (str(BssDat.Bssvar_species[0]).upper() == 'ALL'):
listaspecies = list(range(numspecies_a))
else:
listaspecies = [i-1 for i in BssDat.Bssvar_species]
bssvar_allones = np.ones((year_periods,), dtype=float)
#for i in range(numspecies_a):
for i in range_species_a:
if i in listaspecies:
if (BssDat.Bssvar_modulationtype_list[0] == 'None'):
varspecies = calcbssvarespecie(BssDat.Bssvar_period, BssDat.Bssvar_sd,
bss_pert_no_modulation, year_periods)
elif (BssDat.Bssvar_modulationtype_list[0] == 'linear'):
pendiente = float(BssDat.Bssvar_modulationtype_list[1])
varspecies = calcbssvarespecie(BssDat.Bssvar_period, BssDat.Bssvar_sd,
bss_pert_lineal, year_periods)
elif (BssDat.Bssvar_modulationtype_list[0] == 'sin'):
periodo = int(BssDat.Bssvar_modulationtype_list[1])
varspecies = calcbssvarespecie(BssDat.Bssvar_period, BssDat.Bssvar_sd,
bss_pert_sinusoidal, year_periods)
pBssvar_species.append(np.array(varspecies))
else:
pBssvar_species.append(bssvar_allones)
return pBssvar_species, hay_bssvar, pendiente, periodo
def predators_population_evolution(hay_foodweb, ldev_inf, numspecies_a,
Nindividuals_a, numspecies_b,
Nindividuals_b, K_c, Nindividuals_c, r_c,
numspecies_c, minputchar_d, j, k):
if hay_foodweb:
rowNi = []
for n in range(numspecies_c):
coef_bij_matrix = c_devorados = r_eqsum = 0
signo = signfunc(r_c[n])
if (Nindividuals_c[k][n] > 0):
for j in range_species_a:
coef_bij_matrix = Nindividuals_a[k][j] * minputchar_d[j][n]
r_eqsum += coef_bij_matrix
for j in range_species_b:
coef_bij_matrix = Nindividuals_b[k][j] *\
minputchar_d[j + numspecies_a][n]
r_eqsum += coef_bij_matrix
rperiodequivalente = calc_r_periodo_vh(r_eqsum, invperiod)
term_c = round(Nindividuals_c[k][n] *\
(1 - Nindividuals_c[k][n] / K_c[n]))
if term_c > 1:
incPredatoria = np.random.binomial(
term_c, -math.expm1(-rperiodequivalente))
else:
incPredatoria = 0
incNmalth = np.random.binomial(
Nindividuals_c[k][n],
-math.expm1(-calc_r_periodo_vh(abs(r_c[n]), invperiod)))
signo = signfunc(r_c[n])
pop_c = Nindividuals_c[k][n] + incPredatoria + signo * incNmalth
if (pop_c == 0):
sgcom.inform_user(ldev_inf, \
"Predator species %d extinction in day %d" % (n, k))
else:
pop_c = 0
rowNi.append(pop_c)
Nindividuals_c.append(rowNi)
def predators_population_evolution(hay_foodweb, ldev_inf, numspecies_a,
Nindividuals_a, numspecies_b, Nindividuals_b,
K_c, Nindividuals_c, r_c, numspecies_c,
minputchar_d, j, k):
if hay_foodweb:
rowNi = []
for n in range(numspecies_c):
coef_bij_matrix = c_devorados = r_eqsum = 0
signo = signfunc(r_c[n])
if (Nindividuals_c[k][n] > 0):
for j in range_species_a:
coef_bij_matrix = Nindividuals_a[k][j] * minputchar_d[j][n]
r_eqsum += coef_bij_matrix
for j in range_species_b:
coef_bij_matrix = Nindividuals_b[k][j] *\
minputchar_d[j + numspecies_a][n]
r_eqsum += coef_bij_matrix
rperiodequivalente = calc_r_periodo_vh(r_eqsum, invperiod)
term_c = round(Nindividuals_c[k][n] *\
(1 - Nindividuals_c[k][n] / K_c[n]))
if term_c > 1:
incPredatoria = np.random.binomial(term_c,
-math.expm1(-rperiodequivalente))
else:
incPredatoria = 0
incNmalth = np.random.binomial(Nindividuals_c[k][n],
-math.expm1(-calc_r_periodo_vh(abs(r_c[n]), invperiod)))
signo = signfunc(r_c[n])
pop_c = Nindividuals_c[k][n] + incPredatoria + signo * incNmalth
if (pop_c == 0):
sgcom.inform_user(ldev_inf, \
"Predator species %d extinction in day %d" % (n, k))
else:
pop_c = 0
rowNi.append(pop_c)
Nindividuals_c.append(rowNi)
def init_forced_external_pertubations(pl_ext, pol_ext, yearperiods,
hayextplantas, hayextpolin, ldev_inf,
lfich_inf):
global forced_extinctions_in_course
forced_extinctions_in_course = False
if hayextplantas:
inicioextplantas = pl_ext['start'] * sgGL.DAYS_YEAR
nperpl = pl_ext['numperiod']
periodoextpl = pl_ext['period']
spikepl = round(periodoextpl * pl_ext['spike'])
species_list_text = sgcom.create_list_species_affected(','.join(
[str(i) for i in pl_ext['species']]))
sgcom.inform_user(ldev_inf,
"Perturbations. Plants species %s, period (years): %0.2f, numperiods: %d, spike (fraction of period): %0.2f, rate: %.03f, start (year): %d"\
% (species_list_text, periodoextpl / sgGL.DAYS_YEAR,
nperpl, pl_ext['spike'], float(pl_ext['rate']),
pl_ext['start']))
else:
inicioextplantas = nperpl = periodoextpl = spikepl = 0
if hayextpolin:
inicioextpol = pol_ext['start'] * sgGL.DAYS_YEAR
nperpol = pol_ext['numperiod']
periodoextpol = pol_ext['period']
spikepol = round(periodoextpol * pol_ext['spike'])
species_list_text = sgcom.create_list_species_affected(','.join(
[str(i) for i in pol_ext['species']]))
sgcom.inform_user(ldev_inf,
"Perturbations. Pollinators species %s, period (years): %d, numperiods: %d, spike (fraction of period): %0.2f, rate: %.03f, start (year): %d"\
% (species_list_text, periodoextpol / sgGL.DAYS_YEAR,
nperpol, pol_ext['spike'], float(pol_ext['rate']),
pol_ext['start']))
else:
inicioextpol = nperpol = periodoextpol = spikepol = 0
perturbation_conditions = sgcom.ExternalPerturbationConditions(
nperpl, inicioextplantas, periodoextpl, spikepl, nperpol, inicioextpol,
periodoextpol, spikepol)
return (perturbation_conditions)
def init_forced_external_pertubations(pl_ext, pol_ext, yearperiods,
hayextplantas, hayextpolin,
ldev_inf, lfich_inf):
global forced_extinctions_in_course
forced_extinctions_in_course = False
if hayextplantas:
inicioextplantas = pl_ext['start'] * sgGL.DAYS_YEAR
nperpl = pl_ext['numperiod']
periodoextpl = pl_ext['period']
spikepl = round(periodoextpl * pl_ext['spike'])
species_list_text = sgcom.create_list_species_affected(','.join([str(i) for i in pl_ext['species']] ))
sgcom.inform_user(ldev_inf,
"Perturbations. Plants species %s, period (years): %0.2f, numperiods: %d, spike (fraction of period): %0.2f, rate: %.03f, start (year): %d"\
% (species_list_text, periodoextpl / sgGL.DAYS_YEAR,
nperpl, pl_ext['spike'], float(pl_ext['rate']),
pl_ext['start']))
else:
inicioextplantas = nperpl = periodoextpl = spikepl = 0
if hayextpolin:
inicioextpol = pol_ext['start'] * sgGL.DAYS_YEAR
nperpol = pol_ext['numperiod']
periodoextpol = pol_ext['period']
spikepol = round(periodoextpol * pol_ext['spike'])
species_list_text = sgcom.create_list_species_affected(','.join([str(i) for i in pol_ext['species']] ))
sgcom.inform_user(ldev_inf,
"Perturbations. Pollinators species %s, period (years): %d, numperiods: %d, spike (fraction of period): %0.2f, rate: %.03f, start (year): %d"\
% (species_list_text, periodoextpol / sgGL.DAYS_YEAR,
nperpol, pol_ext['spike'], float(pol_ext['rate']),
pol_ext['start']))
else:
inicioextpol = nperpol = periodoextpol = spikepol = 0
perturbation_conditions = sgcom.ExternalPerturbationConditions(nperpl,
inicioextplantas, periodoextpl, spikepl,
nperpol, inicioextpol, periodoextpol,
spikepol)
return(perturbation_conditions)
def predators_param_init(filename, hay_foodweb, direntrada, ldev_inf,
lfich_inf, dt):
K_c = []
Nindividuals_c = []
rowNindividuals_c = []
r_c = []
r_cperiod = []
minputchar_c = []
minputchar_d = []
numspecies_c = 0
if hay_foodweb > 0:
filename_c = filename + '_c.txt'
filename_d = filename + '_d.txt'
sgcom.inform_user(lfich_inf, "Predators matrix c: <a href='file:///" +\
dt + "/input/" + filename_c + "' target=_BLANK>" +\
filename_c + "<a>")
sgcom.inform_user(lfich_inf, "Predators matrix d: <a href='file:///" +\
dt + "/input/" + filename_d + "' target=_BLANK>" +\
filename_d + "<a>")
try:
l_minputchar_c = sgcom.dlmreadlike(filename_c, direntrada)
minputchar_c = np.array(l_minputchar_c, dtype=float)
nrows_c = len(minputchar_c)
ncols_c = len(minputchar_c[0])
numspecies_c = ncols_c
except:
print("INPUT FILE BAD FORMAT")
else:
sgcom.inform_user(ldev_inf, "Predator species : %d" % numspecies_c)
for n in range(numspecies_c):
rowNindividuals_c.append(int(minputchar_c[nrows_c - 3][n]))
K_c.append(int(minputchar_c[nrows_c - 2][n]))
r_c.append(minputchar_c[nrows_c - 1][n])
r_cperiod.append(
calc_r_periodo_vh(minputchar_c[nrows_c - 1][n], invperiod))
Nindividuals_c.append(rowNindividuals_c)
l_minputchar_d = sgcom.dlmreadlike(filename_d, direntrada)
minputchar_d = np.array(l_minputchar_d, dtype=float)
nrows_d = len(minputchar_d)
ncols_d = len(minputchar_d[0])
numspecies_d = ncols_d
return Nindividuals_c, minputchar_c, numspecies_c, K_c, r_c, minputchar_d
def predators_param_init(filename, hay_foodweb, direntrada, ldev_inf, lfich_inf,
dt):
K_c = []
Nindividuals_c = []
rowNindividuals_c = []
r_c = []
r_cperiod = []
minputchar_c = []; minputchar_d = []
numspecies_c = 0
if hay_foodweb > 0:
filename_c = filename + '_c.txt'
filename_d = filename + '_d.txt'
sgcom.inform_user(lfich_inf, "Predators matrix c: <a href='file:///" +\
dt + "/input/" + filename_c + "' target=_BLANK>" +\
filename_c + "<a>")
sgcom.inform_user(lfich_inf, "Predators matrix d: <a href='file:///" +\
dt + "/input/" + filename_d + "' target=_BLANK>" +\
filename_d + "<a>")
try:
l_minputchar_c = sgcom.dlmreadlike(filename_c, direntrada)
minputchar_c = np.array(l_minputchar_c, dtype=float)
nrows_c = len(minputchar_c)
ncols_c = len(minputchar_c[0])
numspecies_c = ncols_c
except:
print("INPUT FILE BAD FORMAT")
else:
sgcom.inform_user(ldev_inf, "Predator species : %d" % numspecies_c)
for n in range(numspecies_c):
rowNindividuals_c.append(int(minputchar_c[nrows_c - 3][n]))
K_c.append(int(minputchar_c[nrows_c - 2][n]))
r_c.append(minputchar_c[nrows_c - 1][n])
r_cperiod.append(calc_r_periodo_vh(minputchar_c[nrows_c - 1][n],
invperiod))
Nindividuals_c.append(rowNindividuals_c)
l_minputchar_d = sgcom.dlmreadlike(filename_d, direntrada)
minputchar_d = np.array(l_minputchar_d, dtype=float)
nrows_d = len(minputchar_d)
ncols_d = len(minputchar_d[0])
numspecies_d = ncols_d
return Nindividuals_c, minputchar_c, numspecies_c, K_c, r_c, minputchar_d
def food_render(simulation_params,
sig_ret_val,
displayinic,
periods,
verbose=True):
global ax
sgGL.ldev_inf, sgGL.lfich_inf = sgcom.open_info_channels(
verbose, simulation_params.fichreport, 'a')
matplotlib.rc('xtick', labelsize=8)
matplotlib.rc('ytick', labelsize=8)
factorescala = 1.2
numspecies_a = len(sig_ret_val.Nindividuals_a[0])
numspecies_b = len(sig_ret_val.Nindividuals_b[0])
numspecies_c = len(sig_ret_val.Nindividuals_c[0])
plt.figure('Mutualist network simulation. Input file: ' +\
simulation_params.filename,
dpi=resolucion, figsize=(ancho, alto))
ax = plt.subplot(3, 1, 1)
plt.title('Plants')
plt.ylabel('Individuals')
# plt.xlabel('Days')
plt.grid(True)
for i in range(numspecies_a):
graf = []
x = []
for k in range(displayinic, periods):
graf.append(sig_ret_val.Nindividuals_a[k][i])
x.append(k)
plt.plot(x,
graf,
color=cm.Set1(i / (numspecies_a)),
lw=calc_lw_width(numspecies_a))
a = plt.gca()
a.set_ylim([0, factorescala * sig_ret_val.maxminval.maxa_individuos])
if numspecies_b < 11:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
ax = plt.subplot(3, 1, 2)
plt.title('Pollinators')
plt.ylabel('Individuals')
# plt.xlabel('Days')
plt.grid(True)
for i in range(numspecies_b):
graf = []
x = []
for k in range(displayinic, periods):
graf.append(sig_ret_val.Nindividuals_b[k][i])
x.append(k)
plt.plot(x,
graf,
color=cm.Paired(i / (numspecies_b)),
lw=calc_lw_width(numspecies_b))
a = plt.gca()
a.set_ylim([0, factorescala * sig_ret_val.maxminval.maxb_individuos])
if numspecies_b < 11:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
ax = plt.subplot(3, 1, 3)
plt.title('Predators')
plt.ylabel('Individuals')
plt.xlabel('Years')
plt.grid(True)
lmaxc = max(sig_ret_val.Nindividuals_c)
maxc_individuos = max(lmaxc)
for i in range(numspecies_c):
graf = []
x = []
for k in range(displayinic, periods):
graf.append(sig_ret_val.Nindividuals_c[k][i])
x.append(k)
plt.plot(x,
graf,
color=cm.Paired(i / (numspecies_c)),
lw=calc_lw_width(numspecies_c))
a = plt.gca()
a.set_ylim([0, factorescala * maxc_individuos])
if numspecies_c < 11:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
nsal = 'output_pict_foodweb_' + simulation_params.filename + '_' +\
simulation_params.algorithm + '_' + simulation_params.output_suff +\
'_' + str(periods) + '.png'
dt = simulation_params.dirtrabajo.replace('\\', '/')
plt.savefig(str(dt + '/' + simulation_params.dirsal.replace('\\', '/') +
nsal),
bbox_inches=0)
plt.close()
sgcom.inform_user(sgGL.lfich_inf,
"<P align=left><br>Foodweb effect picture<br>")
sgcom.inform_user(sgGL.lfich_inf,\
"<IMG SRC=file:///%s ALIGN=LEFT width=1200 BORDER=0>" %\
str(dt + '/' + simulation_params.dirsal.replace('\\', '/') + nsal))
sgcom.inform_user(sgGL.lfich_inf, '<p>')
sgcom.close_info_channels(sgGL.lfich_inf)
def mutual_render(simulation_params,
sig_ret_val,
displayinic,
periods,
verbose=True):
global ax
# if (len(sig_ret_val.pBssvar_species)):
# nrows = 3
# else:
# nrows = 2
nrows = 3
matplotlib.rc('xtick', labelsize=8)
matplotlib.rc('ytick', labelsize=8)
years = periods // sgGL.DAYS_YEAR
sgGL.ldev_inf, sgGL.lfich_inf = sgcom.open_info_channels(
verbose, simulation_params.fichreport, 'a')
factorescala = 1.1
numspecies_a = len(sig_ret_val.Nindividuals_a[0])
numspecies_b = len(sig_ret_val.Nindividuals_b[0])
plt.figure('Mutualist network simulation. Input file: ' +\
simulation_params.filename, dpi=resolucion,
figsize=(ancho, alto))
ax = plt.subplot(nrows, 2, 1)
pintasubplot(sig_ret_val.Nindividuals_a, 0,
sig_ret_val.maxminval.maxa_individuos, displayinic, periods,
factorescala, numspecies_a, 'Plants', 'Individuals')
ax = plt.subplot(nrows, 2, 3)
pintasubplot(sig_ret_val.ra_eff, sig_ret_val.maxminval.min_reff,
sig_ret_val.maxminval.max_reff, displayinic, periods,
factorescala, numspecies_a, '', 'Efficient growth rate')
plt.xlabel('Years')
# if (len(sig_ret_val.pBssvar_species)):
# for i in range(numspecies_a):
# graf = [sig_ret_val.pBssvar_species[i][0]]
# x = [0]
# for k in range (0, years):
# graf.append(sig_ret_val.pBssvar_species[i][k])
# x.append(1 + k)
# ax = plt.subplot(nrows, 2, 5)
# listacoefs = np.array(sig_ret_val.pBssvar_species)
# listacoefs = np.c_[listacoefs[:,0],listacoefs]
# listacoefs = list(listacoefs.transpose())
# pintasubplot(listacoefs, 0, 1, displayinic, years, factorescala,
# numspecies_a, '', 'Blossom variability coeffs.',
# periodsinyears = True)
# plt.xlabel('Years')
ax = plt.subplot(nrows, 2, 2)
pintasubplot(sig_ret_val.Nindividuals_b, 0,
sig_ret_val.maxminval.maxb_individuos, displayinic, periods,
factorescala, numspecies_b, 'Polllinators', '')
ax = plt.subplot(nrows, 2, 4)
pintasubplot(sig_ret_val.rb_eff, sig_ret_val.maxminval.min_reff,
sig_ret_val.maxminval.max_reff, displayinic, periods,
factorescala, numspecies_b, '', '')
plt.xlabel('Years')
# Network Growth Power
ax = plt.subplot(nrows, 2, 5)
pintasubplot(sig_ret_val.network_growth_power,
min(sig_ret_val.network_growth_power),
max(sig_ret_val.network_growth_power),
displayinic,
periods,
factorescala,
1,
'',
'Network Growth Power',
displaylegend=False)
dt = simulation_params.dirtrabajo.replace('\\', '/')
nsal = 'output_pict_plantsandpols_' + simulation_params.filename +\
'_' + simulation_params.algorithm + '_' + simulation_params.output_suff +\
'_' + str(years) + '.png'
plt.savefig(str(dt + '/' + simulation_params.dirsal.replace('\\', '/') +
nsal),
bbox_inches=0)
sgcom.inform_user(sgGL.lfich_inf,
"<p align=left>Populations evolution picture")
sgcom.inform_user(sgGL.lfich_inf, \
"<IMG SRC=file:///%s ALIGN=LEFT width=1200 BORDER=0>" %\
str(dt + '/' + simulation_params.dirsal.replace('\\', '/') + nsal))
sgcom.inform_user(sgGL.lfich_inf, '</p><br>')
sgcom.close_info_channels(sgGL.lfich_inf)
plt.close()
def bino_mutual(sim_cond=''):
global cuentaperpl, cuentaperpol
global Logistic_abs
global model_r_alpha
global pendiente, blossomperiod, sd, periodo
global count_collapse_years
global forced_extinctions_in_course
global init_collapse_years
global range_species_a, range_species_b
sgGL.ldev_inf, sgGL.lfich_inf, periods, systemextinction,\
May, haymut, model_r_alpha, count_collapse_years, init_collapse_years = \
init_simulation_environment(sim_cond.year_periods, sim_cond.fichreport,
sim_cond.algorithm, sim_cond.verbose)
tinic = time()
sgcom.start_report(sgGL.ldev_inf, sim_cond.filename, sim_cond.com,
sim_cond.year_periods, sim_cond.algorithm,
sim_cond.release, sim_cond.hay_foodweb)
numspecies_a, minputchar_a, nrows_a, ncols_a = \
sgcom.read_simulation_matrix(sim_cond.filename,
sim_cond.dirtrabajo,
sim_cond.direntrada,
'_a.txt',
'Plants',
sim_cond.N0plants,
lfich_inf = sgGL.lfich_inf)
rowNindividuals_a, Alpha_a, cAlpha_a, r_a, rd_a, Nindividuals_a,\
ra_eff, ra_equs, r_tot_eqsum_a = init_lists_pop(periods, numspecies_a, minputchar_a)
lcompatibplantas = init_blossom_perturbation_lists(
sim_cond.plants_blossom_prob, sim_cond.blossom_pert_list, numspecies_a)
range_periods = range(periods - 1)
range_species_a = range(numspecies_a)
numspecies_b, minputchar_b, nrows_b, ncols_b = \
sgcom.read_simulation_matrix(sim_cond.filename,
sim_cond.dirtrabajo,
sim_cond.direntrada,
'_b.txt', 'Pollinators ',
sim_cond.N0pols,
lfich_inf = sgGL.lfich_inf)
rowNindividuals_b, Alpha_b, cAlpha_b, r_b,\
rd_b, Nindividuals_b, rb_eff, rb_equs, r_tot_eqsum_b = init_lists_pop(periods, numspecies_b,
minputchar_b)
range_species_b = range(numspecies_b)
add_report_simulation_conditions(sim_cond.plants_blossom_prob,
sim_cond.plants_blossom_sd,
sim_cond.plants_blossom_type,
sim_cond.blossom_pert_list, sgGL.ldev_inf,
numspecies_a, rowNindividuals_a,
numspecies_b, rowNindividuals_b)
network_growth_power = np.zeros(periods, dtype=float)
mut_brake_a = Alpha_a
mut_brake_b = Alpha_b
# Random links removal
periodoborr = init_random_links_removal(sim_cond.eliminarenlaces, periods,
sgGL.ldev_inf, minputchar_a)
# Extinction analysis. Forced death rate increases
# inicioextplantas, inicioextpol, hayextplantas, hayextpolin, j =\
hayextplantas, hayextpolin, inner_pl_ext, inner_pol_ext, j = init_external_perturbation_lists(
sim_cond.pl_ext, sim_cond.pol_ext, numspecies_a, numspecies_b)
pert_cond = init_forced_external_pertubations(
sim_cond.pl_ext, sim_cond.pol_ext, sim_cond.year_periods,
hayextplantas, hayextpolin, sgGL.ldev_inf, sgGL.lfich_inf)
# Extinction analysis. Blossom pertubations
bloss_species, hay_bssvar, pBssvar_species = \
init_blossom_perturbations_conditions(sim_cond.year_periods,
sim_cond.blossom_pert_list,
sim_cond.Bssvar_data,
sgGL.ldev_inf,
numspecies_a)
Nindividuals_c, minputchar_c, numspecies_c, K_c, r_c, minputchar_d = \
predators_param_init(sim_cond.filename,
sim_cond.hay_foodweb,
sim_cond.direntrada,
sgGL.ldev_inf,
sgGL.lfich_inf,
sim_cond.dirtrabajo.replace('\\', '/'))
for k in range_periods:
''' The compatibilty matrixes masks are created when the year starts '''
if not (k %
sgGL.DAYS_YEAR): # Much faster than if ((k%sgGL.DAYS_YEAR)==0)
if (not (systemextinction)):
systemextinction = check_system_extinction(
sim_cond.algorithm, k, lcompatibplantas,
sim_cond.plants_blossom_prob, ra_equs, rb_equs, ra_eff,
rb_eff)
forced_extinctions_in_course = False
if systemextinction:
sgcom.inform_user(sgGL.ldev_inf,\
"ALARM !!!. System will collapse. Day %d (year %d). NGP %0.3f" %\
(k, k // sgGL.DAYS_YEAR,network_growth_power[k]))
if sim_cond.exit_on_extinction:
sim_ret_val = sgcom.SimulationReturnValues(
Nindividuals_a, Nindividuals_b, Nindividuals_c,
ra_eff, rb_eff, ra_equs, rb_equs, [],
systemextinction, pBssvar_species,
network_growth_power)
return (sim_ret_val)
minputchar_a_mask, minputchar_b_mask, lcompatibplantas =\
calc_random_blossom_effect(numspecies_a, nrows_a, ncols_a,
nrows_b, ncols_b, numspecies_b,
sim_cond,
blossom_pert_list=bloss_species[:])
minpeq_a, minpeq_b = calc_perturbed_coeffs(
k, hay_bssvar, pBssvar_species, minputchar_a,
minputchar_a_mask, numspecies_a, minputchar_b,
minputchar_b_mask, numspecies_b)
# Eliminacion aleatoria de enlaces
if (sim_cond.eliminarenlaces > 0) & (k > 0) & (k % periodoborr == 0):
minpeq_a, minpeq_b , minputchar_a, minputchar_b = \
deletion_links_effect(k, periodoborr,
minputchar_a, minputchar_b,
minputchar_a_mask, minputchar_b_mask,
sgGL.ldev_inf)
if haymut:
r_tot_eqsum_a = np.dot(np.transpose(minpeq_a[0:numspecies_b, :]),\
Nindividuals_b[k, :])
r_tot_eqsum_b = np.dot(np.transpose(minpeq_b[0:numspecies_a, :]),\
Nindividuals_a[k, :])
mut_brake_a = Alpha_a + cAlpha_a * r_tot_eqsum_a
mut_brake_b = Alpha_b + cAlpha_b * r_tot_eqsum_b
brake_a = mut_brake_a * np.transpose(Nindividuals_a[k, ])
brake_b = mut_brake_b * np.transpose(Nindividuals_b[k, ])
# pepars = [PopEvoPars(n, "Plant",
# numspecies_a, sim_cond.algorithm,
# sim_cond.hay_foodweb, inner_pl_ext,
# May, haymut, ra_eff, ra_equs,
# minpeq_a, j, cAlpha_a, Alpha_a, r_a, rd_a,
# Nindividuals_a, numspecies_b, Nindividuals_b,
# Nindividuals_c, minputchar_c, numspecies_c,
# pert_cond.inicioextplantas, hayextplantas,
# pert_cond.nperpl, pert_cond.periodoextpl,
# pert_cond.spikepl, k, model_r_alpha, r_tot_eqsum_a[n],
# sgGL.ldev_inf) for n in range_species_a if Nindividuals_a[k,n]>0]
# with Pool(3) as p:
# (p.map(populations_evolution_parallel, pepars))
# list(map(populations_evolution_parallel, pepars))
#[populations_evolution_parallel(x) for x in pepars]
[
populations_evolution(
n,
numspecies_a, #sim_cond.algorithm,
sim_cond.hay_foodweb,
inner_pl_ext,
#May,
haymut,
ra_eff,
ra_equs,
minpeq_a,
j, #cAlpha_a, Alpha_a,
r_a,
rd_a,
Nindividuals_a,
numspecies_b,
Nindividuals_b,
Nindividuals_c,
minputchar_c,
numspecies_c,
pert_cond.inicioextplantas,
hayextplantas,
pert_cond.nperpl,
pert_cond.periodoextpl,
pert_cond.spikepl,
k,
model_r_alpha,
r_tot_eqsum_a[n],
brake_a[n],
sgGL.ldev_inf,
isplant=True) for n in range_species_a
if Nindividuals_a[k, n] > 0
]
[
populations_evolution(
n,
numspecies_b, #sim_cond.algorithm,
sim_cond.hay_foodweb,
inner_pol_ext,
#May,
haymut,
rb_eff,
rb_equs,
minpeq_b,
j, #cAlpha_b, Alpha_b,
r_b,
rd_b,
Nindividuals_b,
numspecies_a,
Nindividuals_a,
Nindividuals_c,
minputchar_c,
numspecies_c,
pert_cond.inicioextpol,
hayextpolin,
pert_cond.nperpol,
pert_cond.periodoextpol,
pert_cond.spikepol,
k,
model_r_alpha,
r_tot_eqsum_b[n],
brake_b[n],
sgGL.ldev_inf,
isplant=False) for n in range_species_b
if Nindividuals_b[k, n] > 0
]
if (np.sum(Nindividuals_a[k, ]) + np.sum(Nindividuals_b[k, ])) > 0:
network_growth_power[k+1] = np.average(list(np.extract(Nindividuals_a[k,]>0,ra_equs[k+1,]))+\
list(np.extract(Nindividuals_b[k,]>0,rb_equs[k+1,])))
else:
network_growth_power[k + 1:] = network_growth_power[k]
if (sim_cond.hay_foodweb):
predators_population_evolution(sim_cond.hay_foodweb, sgGL.ldev_inf,
numspecies_a, Nindividuals_a,
numspecies_b, Nindividuals_b, K_c,
Nindividuals_c, r_c, numspecies_c,
minputchar_d, j, k)
ra_eff[0, ] = ra_eff[1, ]
rb_eff[0, ] = rb_eff[1, ]
network_growth_power[0] = network_growth_power[1]
network_growth_power = np.transpose(network_growth_power)
maxminval = sgcom.find_max_values(Nindividuals_a, Nindividuals_b, ra_eff,
rb_eff, ra_equs, rb_equs)
tfin = time()
sgcom.end_report(sgGL.ldev_inf, sgGL.lfich_inf, sim_cond, tfin, tinic,
periods, Nindividuals_a, ra_eff, ra_equs, Nindividuals_b,
rb_eff, rb_equs, network_growth_power, Nindividuals_c)
sim_ret_val = sgcom.SimulationReturnValues(Nindividuals_a, Nindividuals_b,
Nindividuals_c, ra_eff, rb_eff,
ra_equs, rb_equs, maxminval,
systemextinction,
pBssvar_species,
network_growth_power)
return (sim_ret_val)
def add_report_simulation_conditions(plants_blossom_prob, plants_blossom_sd,
plants_blossom_type, blossom_pert_list,
ldev_inf, numspecies_a, rowNindividuals_a,
numspecies_b, rowNindividuals_b):
sgcom.inform_user(ldev_inf, "Plant species: %d" % numspecies_a)
sgcom.inform_user(ldev_inf, "Plant initial populations %s" %\
rowNindividuals_a)
sgcom.inform_user(ldev_inf, "Pollinator species: %d" % numspecies_b)
sgcom.inform_user(ldev_inf,
"Pollinator initial populations %s" % rowNindividuals_b)
if (plants_blossom_type == 'Binary'):
sgcom.inform_user(ldev_inf,
"Blossom probability %s, type %s. Plant affected species:%s" %\
(plants_blossom_prob, plants_blossom_type, str(blossom_pert_list)))
else:
sgcom.inform_user(ldev_inf,
"Blossom probability, type %s, mean %s, standard dev. %s. Plant affected species:%s" %\
(plants_blossom_type, plants_blossom_prob, plants_blossom_sd,
str(blossom_pert_list)))
def populations_evolution(n, numspecies_p, #algorithm,
hay_foodweb,
p_ext, #May,
haymut, rp_eff, rp_eq,
minputchar_p, j, #cAlpha_p, Alpha_p,
r_p, rd_p,
Nindividuals_p, numspecies_q, Nindividuals_q,
Nindividuals_c, minputchar_c, numspecies_c, inicioext,
hayext, nper, periodoext, spike, k, model_r_a, r_tot_eqsum_n,
brake_p_n, ldev_inf, isplant = True):
global cuentaperpl, cuentaperpol
p_devorados = 0
r_muerte = rd_p[n]
# Function is only executed if Nindividuals_p[k, n]>0
if hayext:
if (isplant) & (cuentaperpl < nper):
r_muerte, cuentaperpl = perturbation(p_ext, n, rd_p[n],
cuentaperpl, inicioext,
periodoext, spike, k)
elif not(isplant) & (cuentaperpol < nper):
r_muerte, cuentaperpol = perturbation(p_ext, n, rd_p[n],
cuentaperpol, inicioext,
periodoext, spike, k)
# CODIGO OBSOLETO, May eliminado
#
# elif (May):
# for j in range(numspecies_q):
# r_eqsum, term_May, rMay = calc_mutualism_params(minputchar_p,
# Alpha_p, Nindividuals_p, Nindividuals_q,
# r_eqsum, term_May, rMay, k, j, n, r_p,
# r_muerte)
rtot_p = r_p[n] + r_tot_eqsum_n - r_muerte
# Predators effect
if hay_foodweb:
p_devorados, j, rceff = predators_effect(p_devorados, j,
Nindividuals_p,
Nindividuals_c,
minputchar_c,
numspecies_c, n, k)
# New algorithm
# ciclo_verhulst Inline code following Vwn Rossum's advice
#rcal = rtot_p - ( Alpha_p[n] + cAlpha_p[n] * r_tot_eqsum_n) * Nindividuals_p[k, n]
#rcal = rtot_p - mut_brake_p_n* Nindividuals_p[k, n]
rcal = rtot_p - brake_p_n
#rcal = 0
inc_pop = Nindividuals_p[k, n]
absrcal = abs(rcal)
rcalpos = (absrcal == rcal)
# IF rcal is tiny, what happens when populations are stable return
# zero. Approximation to speed up algorithm
#if absrcal*Nindividuals_p[k, n]>sgGL.IGNORE_REFF:
rspneq = math.pow(1 + absrcal, invperiod ) - 1
incNmalth = np.random.binomial(Nindividuals_p[k, n], -math.expm1(-rspneq))
if rcalpos:
inc_pop += incNmalth
else:
inc_pop -= incNmalth
pop_p = inc_pop - p_devorados
# Codigo obsoleto que contemplaba los modelos viejos
#
# elif not (May):
# retl = ciclo_new_model(rtot_p, Nindividuals_p[k, n],
# 1 / Alpha_p[n], Logistic_abs)
# else:
# retl = ciclo_May(r_p[n] - r_muerte, rMay, term_May,
# Nindividuals_p[k, n], Alpha_p[n])
# Species extinction
if not(pop_p):
if isplant:
strtype = "Plant"
else:
strtype = "Pollinator"
sgcom.inform_user(ldev_inf,
"Day %d (year %d). %s species %d extincted" %\
(k, k // sgGL.DAYS_YEAR, strtype,
int_to_ext_rep(n)))
rp_eff[k + 1:, n], rp_eq[k + 1:, n] = rp_eff[k, n], rp_eq[k, n]
return
Nindividuals_p[k + 1, n] = pop_p
rp_eff[k + 1, n], rp_eq[k + 1, n] = rcal, rtot_p
def add_report_simulation_conditions(plants_blossom_prob, plants_blossom_sd,
plants_blossom_type, blossom_pert_list,
ldev_inf, numspecies_a, rowNindividuals_a,
numspecies_b, rowNindividuals_b):
sgcom.inform_user(ldev_inf, "Plant species: %d" % numspecies_a)
sgcom.inform_user(ldev_inf, "Plant initial populations %s" %\
rowNindividuals_a)
sgcom.inform_user(ldev_inf, "Pollinator species: %d" % numspecies_b)
sgcom.inform_user(ldev_inf,
"Pollinator initial populations %s" % rowNindividuals_b)
if (plants_blossom_type == 'Binary'):
sgcom.inform_user(ldev_inf,
"Blossom probability %s, type %s. Plant affected species:%s" %\
(plants_blossom_prob, plants_blossom_type, str(blossom_pert_list)))
else:
sgcom.inform_user(ldev_inf,
"Blossom probability, type %s, mean %s, standard dev. %s. Plant affected species:%s" %\
(plants_blossom_type, plants_blossom_prob, plants_blossom_sd,
str(blossom_pert_list)))
def populations_evolution(
n,
numspecies_p, #algorithm,
hay_foodweb,
p_ext, #May,
haymut,
rp_eff,
rp_eq,
minputchar_p,
j, #cAlpha_p, Alpha_p,
r_p,
rd_p,
Nindividuals_p,
numspecies_q,
Nindividuals_q,
Nindividuals_c,
minputchar_c,
numspecies_c,
inicioext,
hayext,
nper,
periodoext,
spike,
k,
model_r_a,
r_tot_eqsum_n,
brake_p_n,
ldev_inf,
isplant=True):
global cuentaperpl, cuentaperpol
p_devorados = 0
r_muerte = rd_p[n]
# Function is only executed if Nindividuals_p[k, n]>0
if hayext:
if (isplant) & (cuentaperpl < nper):
r_muerte, cuentaperpl = perturbation(p_ext, n, rd_p[n],
cuentaperpl, inicioext,
periodoext, spike, k)
elif not (isplant) & (cuentaperpol < nper):
r_muerte, cuentaperpol = perturbation(p_ext, n, rd_p[n],
cuentaperpol, inicioext,
periodoext, spike, k)
# CODIGO OBSOLETO, May eliminado
#
# elif (May):
# for j in range(numspecies_q):
# r_eqsum, term_May, rMay = calc_mutualism_params(minputchar_p,
# Alpha_p, Nindividuals_p, Nindividuals_q,
# r_eqsum, term_May, rMay, k, j, n, r_p,
# r_muerte)
rtot_p = r_p[n] + r_tot_eqsum_n - r_muerte
# Predators effect
if hay_foodweb:
p_devorados, j, rceff = predators_effect(p_devorados, j,
Nindividuals_p,
Nindividuals_c, minputchar_c,
numspecies_c, n, k)
# New algorithm
# ciclo_verhulst Inline code following Vwn Rossum's advice
#rcal = rtot_p - ( Alpha_p[n] + cAlpha_p[n] * r_tot_eqsum_n) * Nindividuals_p[k, n]
#rcal = rtot_p - mut_brake_p_n* Nindividuals_p[k, n]
rcal = rtot_p - brake_p_n
#rcal = 0
inc_pop = Nindividuals_p[k, n]
absrcal = abs(rcal)
rcalpos = (absrcal == rcal)
# IF rcal is tiny, what happens when populations are stable return
# zero. Approximation to speed up algorithm
#if absrcal*Nindividuals_p[k, n]>sgGL.IGNORE_REFF:
rspneq = math.pow(1 + absrcal, invperiod) - 1
incNmalth = np.random.binomial(Nindividuals_p[k, n], -math.expm1(-rspneq))
if rcalpos:
inc_pop += incNmalth
else:
inc_pop -= incNmalth
pop_p = inc_pop - p_devorados
# Codigo obsoleto que contemplaba los modelos viejos
#
# elif not (May):
# retl = ciclo_new_model(rtot_p, Nindividuals_p[k, n],
# 1 / Alpha_p[n], Logistic_abs)
# else:
# retl = ciclo_May(r_p[n] - r_muerte, rMay, term_May,
# Nindividuals_p[k, n], Alpha_p[n])
# Species extinction
if not (pop_p):
if isplant:
strtype = "Plant"
else:
strtype = "Pollinator"
sgcom.inform_user(ldev_inf,
"Day %d (year %d). %s species %d extincted" %\
(k, k // sgGL.DAYS_YEAR, strtype,
int_to_ext_rep(n)))
rp_eff[k + 1:, n], rp_eq[k + 1:, n] = rp_eff[k, n], rp_eq[k, n]
return
Nindividuals_p[k + 1, n] = pop_p
rp_eff[k + 1, n], rp_eq[k + 1, n] = rcal, rtot_p
def bino_mutual(sim_cond = ''):
global cuentaperpl, cuentaperpol
global Logistic_abs
global model_r_alpha
global pendiente, blossomperiod, sd, periodo
global count_collapse_years
global forced_extinctions_in_course
global init_collapse_years
global range_species_a, range_species_b
sgGL.ldev_inf, sgGL.lfich_inf, periods, systemextinction,\
May, haymut, model_r_alpha, count_collapse_years, init_collapse_years = \
init_simulation_environment(sim_cond.year_periods, sim_cond.fichreport,
sim_cond.algorithm, sim_cond.verbose)
tinic = time()
sgcom.start_report(sgGL.ldev_inf, sim_cond.filename, sim_cond.com,
sim_cond.year_periods, sim_cond.algorithm,
sim_cond.release, sim_cond.hay_foodweb)
numspecies_a, minputchar_a, nrows_a, ncols_a = \
sgcom.read_simulation_matrix(sim_cond.filename,
sim_cond.dirtrabajo,
sim_cond.direntrada,
'_a.txt',
'Plants',
sim_cond.N0plants,
lfich_inf = sgGL.lfich_inf)
rowNindividuals_a, Alpha_a, cAlpha_a, r_a, rd_a, Nindividuals_a,\
ra_eff, ra_equs, r_tot_eqsum_a = init_lists_pop(periods, numspecies_a, minputchar_a)
lcompatibplantas = init_blossom_perturbation_lists(sim_cond.plants_blossom_prob,
sim_cond.blossom_pert_list,
numspecies_a)
range_periods = range(periods-1)
range_species_a = range(numspecies_a)
numspecies_b, minputchar_b, nrows_b, ncols_b = \
sgcom.read_simulation_matrix(sim_cond.filename,
sim_cond.dirtrabajo,
sim_cond.direntrada,
'_b.txt', 'Pollinators ',
sim_cond.N0pols,
lfich_inf = sgGL.lfich_inf)
rowNindividuals_b, Alpha_b, cAlpha_b, r_b,\
rd_b, Nindividuals_b, rb_eff, rb_equs, r_tot_eqsum_b = init_lists_pop(periods, numspecies_b,
minputchar_b)
range_species_b = range(numspecies_b)
add_report_simulation_conditions(sim_cond.plants_blossom_prob,
sim_cond.plants_blossom_sd,
sim_cond.plants_blossom_type,
sim_cond.blossom_pert_list,
sgGL.ldev_inf, numspecies_a,
rowNindividuals_a, numspecies_b,
rowNindividuals_b)
network_growth_power = np.zeros(periods, dtype=float)
mut_brake_a = Alpha_a
mut_brake_b = Alpha_b
# Random links removal
periodoborr = init_random_links_removal(sim_cond.eliminarenlaces, periods,
sgGL.ldev_inf, minputchar_a)
# Extinction analysis. Forced death rate increases
# inicioextplantas, inicioextpol, hayextplantas, hayextpolin, j =\
hayextplantas, hayextpolin, inner_pl_ext, inner_pol_ext, j =init_external_perturbation_lists(
sim_cond.pl_ext,
sim_cond.pol_ext,
numspecies_a, numspecies_b)
pert_cond = init_forced_external_pertubations(sim_cond.pl_ext,
sim_cond.pol_ext, sim_cond.year_periods,
hayextplantas, hayextpolin,
sgGL.ldev_inf, sgGL.lfich_inf)
# Extinction analysis. Blossom pertubations
bloss_species, hay_bssvar, pBssvar_species = \
init_blossom_perturbations_conditions(sim_cond.year_periods,
sim_cond.blossom_pert_list,
sim_cond.Bssvar_data,
sgGL.ldev_inf,
numspecies_a)
Nindividuals_c, minputchar_c, numspecies_c, K_c, r_c, minputchar_d = \
predators_param_init(sim_cond.filename,
sim_cond.hay_foodweb,
sim_cond.direntrada,
sgGL.ldev_inf,
sgGL.lfich_inf,
sim_cond.dirtrabajo.replace('\\', '/'))
for k in range_periods:
''' The compatibilty matrixes masks are created when the year starts '''
if not(k % sgGL.DAYS_YEAR): # Much faster than if ((k%sgGL.DAYS_YEAR)==0)
if (not(systemextinction)):
systemextinction = check_system_extinction(sim_cond.algorithm, k,
lcompatibplantas,
sim_cond.plants_blossom_prob,
ra_equs, rb_equs, ra_eff, rb_eff)
forced_extinctions_in_course = False
if systemextinction:
sgcom.inform_user(sgGL.ldev_inf,\
"ALARM !!!. System will collapse. Day %d (year %d). NGP %0.3f" %\
(k, k // sgGL.DAYS_YEAR,network_growth_power[k]))
if sim_cond.exit_on_extinction:
sim_ret_val = sgcom.SimulationReturnValues(
Nindividuals_a, Nindividuals_b,
Nindividuals_c, ra_eff, rb_eff,
ra_equs, rb_equs,
[],
systemextinction, pBssvar_species,
network_growth_power)
return(sim_ret_val)
minputchar_a_mask, minputchar_b_mask, lcompatibplantas =\
calc_random_blossom_effect(numspecies_a, nrows_a, ncols_a,
nrows_b, ncols_b, numspecies_b,
sim_cond,
blossom_pert_list=bloss_species[:])
minpeq_a, minpeq_b = calc_perturbed_coeffs(k, hay_bssvar,
pBssvar_species, minputchar_a,
minputchar_a_mask,
numspecies_a, minputchar_b,
minputchar_b_mask,
numspecies_b)
# Eliminacion aleatoria de enlaces
if (sim_cond.eliminarenlaces > 0) & (k > 0) & (k % periodoborr == 0):
minpeq_a, minpeq_b , minputchar_a, minputchar_b = \
deletion_links_effect(k, periodoborr,
minputchar_a, minputchar_b,
minputchar_a_mask, minputchar_b_mask,
sgGL.ldev_inf)
if haymut:
r_tot_eqsum_a = np.dot(np.transpose(minpeq_a[0:numspecies_b, :]),\
Nindividuals_b[k, :])
r_tot_eqsum_b = np.dot(np.transpose(minpeq_b[0:numspecies_a, :]),\
Nindividuals_a[k, :])
mut_brake_a = Alpha_a + cAlpha_a * r_tot_eqsum_a
mut_brake_b = Alpha_b + cAlpha_b * r_tot_eqsum_b
brake_a = mut_brake_a * np.transpose(Nindividuals_a[k,])
brake_b = mut_brake_b * np.transpose(Nindividuals_b[k,])
# pepars = [PopEvoPars(n, "Plant",
# numspecies_a, sim_cond.algorithm,
# sim_cond.hay_foodweb, inner_pl_ext,
# May, haymut, ra_eff, ra_equs,
# minpeq_a, j, cAlpha_a, Alpha_a, r_a, rd_a,
# Nindividuals_a, numspecies_b, Nindividuals_b,
# Nindividuals_c, minputchar_c, numspecies_c,
# pert_cond.inicioextplantas, hayextplantas,
# pert_cond.nperpl, pert_cond.periodoextpl,
# pert_cond.spikepl, k, model_r_alpha, r_tot_eqsum_a[n],
# sgGL.ldev_inf) for n in range_species_a if Nindividuals_a[k,n]>0]
# with Pool(3) as p:
# (p.map(populations_evolution_parallel, pepars))
# list(map(populations_evolution_parallel, pepars))
#[populations_evolution_parallel(x) for x in pepars]
[populations_evolution(n, numspecies_a, #sim_cond.algorithm,
sim_cond.hay_foodweb, inner_pl_ext,
#May,
haymut, ra_eff, ra_equs,
minpeq_a, j, #cAlpha_a, Alpha_a,
r_a, rd_a,
Nindividuals_a, numspecies_b, Nindividuals_b,
Nindividuals_c, minputchar_c, numspecies_c,
pert_cond.inicioextplantas, hayextplantas,
pert_cond.nperpl, pert_cond.periodoextpl,
pert_cond.spikepl, k, model_r_alpha,
r_tot_eqsum_a[n], brake_a[n],
sgGL.ldev_inf, isplant = True)
for n in range_species_a if Nindividuals_a[k,n]>0]
[populations_evolution(n, numspecies_b, #sim_cond.algorithm,
sim_cond.hay_foodweb, inner_pol_ext,
#May,
haymut, rb_eff, rb_equs,
minpeq_b, j, #cAlpha_b, Alpha_b,
r_b, rd_b,
Nindividuals_b, numspecies_a, Nindividuals_a,
Nindividuals_c, minputchar_c, numspecies_c,
pert_cond.inicioextpol, hayextpolin, pert_cond.nperpol,
pert_cond.periodoextpol, pert_cond.spikepol, k,
model_r_alpha, r_tot_eqsum_b[n], brake_b[n],
sgGL.ldev_inf, isplant = False)
for n in range_species_b if Nindividuals_b[k,n]>0 ]
if ( np.sum(Nindividuals_a[k,]) + np.sum(Nindividuals_b[k,]) ) > 0:
network_growth_power[k+1] = np.average(list(np.extract(Nindividuals_a[k,]>0,ra_equs[k+1,]))+\
list(np.extract(Nindividuals_b[k,]>0,rb_equs[k+1,])))
else:
network_growth_power[k+1:] = network_growth_power[k]
if (sim_cond.hay_foodweb):
predators_population_evolution(sim_cond.hay_foodweb,
sgGL.ldev_inf, numspecies_a,
Nindividuals_a, numspecies_b,
Nindividuals_b, K_c, Nindividuals_c, r_c,
numspecies_c, minputchar_d, j, k)
ra_eff[0, ] = ra_eff[1, ]
rb_eff[0, ] = rb_eff[1, ]
network_growth_power[0] = network_growth_power[1]
network_growth_power = np.transpose(network_growth_power)
maxminval = sgcom.find_max_values(Nindividuals_a, Nindividuals_b,
ra_eff, rb_eff, ra_equs, rb_equs)
tfin = time()
sgcom.end_report(sgGL.ldev_inf, sgGL.lfich_inf, sim_cond, tfin, tinic,
periods, Nindividuals_a, ra_eff, ra_equs, Nindividuals_b,
rb_eff, rb_equs, network_growth_power, Nindividuals_c)
sim_ret_val = sgcom.SimulationReturnValues(Nindividuals_a, Nindividuals_b,
Nindividuals_c, ra_eff, rb_eff,
ra_equs, rb_equs, maxminval,
systemextinction,
pBssvar_species, network_growth_power)
return(sim_ret_val)