print i
ecosystem.update_world()
dict_stats = get_eco_state_row(ITERATIONS, ecosystem)
series_counts[i] = ecosystem.populations
if HABITAT_LOSS and i == HABITAT_LOSS_ITER:
ecosystem.apply_habitat_loss()
if i%1000 == 0 or i == ITERATIONS:
net_temp = ecosystem.realised_net.copy()
series = copy(series_counts)
write_adjacency_matrix(i, NETWORK_RECORD, series, net_temp, output_dir)
ecosystem.clear_realised_network() ## WARNING: TO USE OR NOT TO USE!!
write_spatial_state(ecosystem, i, output_dir) ## WARNING: THIS HAPPENS EVERY ITERATION
##############################################
### OUTPUT:
## testing output of adjacency and spatial state. Do they require more than the following?
#net_temp = ecosystem.realised_net.copy()
#series = copy(series_counts)
#write_adjacency_matrix(ITERATIONS, NETWORK_RECORD, series, net_temp, output_dir)
#write_spatial_state(ecosystem, ITERATIONS, output_dir)
header_names = ['species', 'init_tl', 'mutualist', 'mutualistic_producer', 'individuals_init', 'immigrants', 'born', 'dead', 'individuals_final']
file_species = open(output_dir+'/output_species.csv', 'w')
out_species = csv.DictWriter(file_species, header_names)
out_species.writeheader()
out_row_species = dict()
else:
thread = ThreadNetStats(network_stats_lock, out, net_temp, i,
NETWORK_RECORD, series_counts,
eco_thread, INT_STRENGTHS)
threads.append(thread)
thread.start()
ecosystem.clear_realised_network()
##calculate spatial variation metrics
## and save spatial state of system (for further spatial analysis after simulation)
if SPATIAL_VARIATION and (i % RECORD_SPATIAL_VAR == 0
or i == ITERATIONS):
start = datetime.now()
write_spatial_analysis(ecosystem, i)
write_spatial_state(ecosystem, i)
stop = datetime.now()
elapsed = stop - start
print elapsed
for t in threads:
t.join()
file_net.close()
file_eco.close()
# here we write the output file for the species populations dynamics
header_names = ['iteration']
for sp in sorted(ecosystem.species):
header_names.append(sp)
areas_counts)
else:
NetStats(out, net_temp, i, NETWORK_RECORD, series_counts,
INT_STRENGTHS, output_dir)
ecosystem.clear_realised_network()
if HABITAT_LOSS and i == HABITAT_LOSS_ITER:
ecosystem.apply_habitat_loss()
##calculate spatial variation metrics
if SPATIAL_VARIATION and (i % RECORD_SPATIAL_VAR == 0
or i == ITERATIONS):
start = datetime.now()
write_spatial_analysis(ecosystem, i, output_dir)
write_spatial_state(ecosystem, i, output_dir)
stop = datetime.now()
elapsed = stop - start
print(elapsed)
file_net.close()
file_eco.close()
# here we write the output file for the species populations dynamics
header_names = ['iteration']
for sp in sorted(ecosystem.species):
header_names.append(sp)
file_populations = open(output_dir + '/output_populations.csv', 'w')
out_populations = csv.DictWriter(file_populations, header_names)
print i
ecosystem.update_world()
# dict_stats = get_eco_state_row(ITERATIONS, ecosystem)
dict_stats = get_eco_state_row(ITERATIONS, ecosystem)
# series_counts[1] = ecosystem.populations
series_counts[i] = ecosystem.populations
if HABITAT_LOSS and i == HABITAT_LOSS_ITER:
ecosystem.apply_habitat_loss()
if i % 1000 == 0 or i == ITERATIONS:
net_temp = ecosystem.realised_net.copy()
series = copy(series_counts)
write_adjacency_matrix(i, NETWORK_RECORD, series, net_temp, output_dir)
write_spatial_state(ecosystem, i, output_dir)
### OUTPUT:
## testing output of adjacency and spatial state. Do they require more than the following?
# net_temp = ecosystem.realised_net.copy()
# series = copy(series_counts)
# write_adjacency_matrix(ITERATIONS, NETWORK_RECORD, series, net_temp, output_dir)
# write_spatial_state(ecosystem, ITERATIONS, output_dir)
header_names = [
"species",
"init_tl",
"mutualist",
"mutualistic_producer",
"individuals_init",
"immigrants",
##here we obtain the trophic position of each species so at the end we can calculate
##its mean and standard deviation for the species statistics (- as noted in README, this does not work for large networks)
tps, a, b = net_temp.find_trophic_positions()
for sp in cumulative_sps_stats.keys():
if cumulative_sps_stats[sp]['tps'] == 0:
cumulative_sps_stats[sp]['tps'] = []
if tps.has_key(sp):
cumulative_sps_stats[sp]['tps'].append(tps[sp])
if SPATIAL_VARIATION:
NetStats(out, net_temp, i, NETWORK_RECORD, series_counts, INT_STRENGTHS, output_dir, centroids_counts, areas_counts)
else:
NetStats(out, net_temp, i, NETWORK_RECORD, series_counts, INT_STRENGTHS, output_dir)
write_spatial_state(ecosystem,i, output_dir) ## save the spatial state of the system in the case the SPATIAL_VARIATION is switched off.
ecosystem.clear_realised_network()
if HABITAT_LOSS and i == HABITAT_LOSS_ITER:
ecosystem.apply_habitat_loss()
##calculate spatial variation metrics
if SPATIAL_VARIATION and (i%RECORD_SPATIAL_VAR == 0 or i == ITERATIONS):
start = datetime.now()
write_spatial_analysis(ecosystem, i, output_dir)
#write_spatial_state(ecosystem,i, output_dir)
stop = datetime.now()
elapsed = stop-start
print elapsed
