def eval_simulation_mp(name,
site,
sim_file=None,
plots=False,
no_mp=False,
fix_closure=True,
qc=True):
"""Evaluate using multiple processes if necessary"""
# will only work if simulations are already run.
try:
datasets = get_sites(site)
except KeyError: # Unknown site, single site
eval_simulation(name,
site,
sim_file,
plots=plots,
fix_closure=fix_closure,
qc=qc)
else:
if no_mp:
for s in datasets:
eval_simulation(name,
s,
plots=plots,
fix_closure=fix_closure,
qc=qc)
else:
f_args = [[name, s, None, plots, fix_closure, qc]
for s in datasets]
ncores = min(os.cpu_count(), 1 + int(os.cpu_count() * 0.5))
with Pool(ncores) as p:
p.starmap(eval_simulation, f_args)
def run_simulation_mp(name,
site,
no_mp=False,
multivariate=False,
overwrite=False,
fix_closure=True):
"""Multi-processor run handling."""
# TODO: refactor to work with above caller.
model = get_model(name)
if site in ['all', 'PLUMBER_ext', 'PLUMBER']:
logger.info('Running {n} at {s} sites'.format(n=name, s=site))
datasets = get_sites(site)
if no_mp:
for s in datasets:
run_simulation(model, name, s, multivariate, overwrite,
fix_closure)
else:
f_args = [(model, name, s, multivariate, overwrite, fix_closure)
for s in datasets]
ncores = get_suitable_ncores()
if site is not 'debug' and hasattr(model, 'memory_requirement'):
ncores = max(
1,
int((psutil.virtual_memory().total / 2) //
model.memory_requirement))
logger.info("Running on %d core(s)" % ncores)
with Pool(ncores) as p:
p.starmap(run_simulation, f_args)
else:
run_simulation(model, name, site, multivariate, overwrite, fix_closure)
return
def get_PLUMBER_metrics(name, site='all', variables=['Qle', 'Qh', 'NEE']):
"""get metrics dataframe from a site, with benchmarks for comparison
:returns: dataframe with metrics for model at site
"""
csv_file = './source/models/{n}/metrics/{n}_{s}_metrics.csv'
# benchmark_names = ['1lin', '2lin', '3km27']
benchmark_names = ['S_lin', 'ST_lin', 'STH_km27']
if site == 'all':
sites = get_sites('PLUMBER_ext')
else:
sites = [site]
metric_df = []
failures = []
for s in sites:
try:
site_metrics = pd.read_csv(csv_file.format(n=name, s=s))
site_metrics = pd.melt(site_metrics, id_vars='metric')
site_metrics['name'] = name
site_metrics['site'] = s
metric_df.append(
site_metrics[site_metrics.variable.isin(variables)])
for b in benchmark_names:
benchmark_metrics = pd.read_csv(csv_file.format(n=b, s=s))
benchmark_metrics = pd.melt(benchmark_metrics,
id_vars='metric')
benchmark_metrics['name'] = b
benchmark_metrics['site'] = s
metric_df.append(benchmark_metrics[
benchmark_metrics.variable.isin(variables)])
except Exception:
failures.append(s)
continue
if len(failures) > 0:
logging.warning('Skipped {l} sites: {f}'.format(l=len(failures),
f=', '.join(failures)))
if len(metric_df) == 0:
logger.error(
'Failed to load any csv files for {n} at {s} - skipping plot.'.
format(n=name, s=site))
return
metric_df = pd.concat(metric_df).reset_index(drop=True)
metric_df = rank_metric_df(metric_df)
return metric_df
def get_train_test_data(site,
met_vars,
flux_vars,
use_names,
qc=True,
fix_closure=True):
"""Gets training and testing data, PLUMBER style (leave one out)
Set the training set using pals.data.set_config(['datasets', 'train'])"""
if site == 'debug':
train_sites = ['Amplero']
test_site = 'Tumba'
# Use non-quality controlled data, to ensure there's enough to train
qc = False
else:
train_sites = get_sites(get_config(['datasets', 'train']))
test_site = site
if test_site not in train_sites:
# Running on a non-training site, train on all training sites.
train_sites = train_sites
else:
# Running on a training site, so leave it out.
train_sites = [s for s in train_sites if s != test_site]
print("Training with {n} datasets".format(n=len(train_sites)))
train_dict = get_train_data(train_sites,
met_vars,
flux_vars,
use_names=use_names,
qc=qc,
fix_closure=fix_closure)
test_dict = get_test_data(test_site, met_vars, use_names=use_names, qc=qc)
train_test_data = train_dict
train_test_data.update(test_dict)
train_test_data['site'] = site
with open('new_data.txt', 'w') as f:
for k in sorted(train_test_data.keys()):
print(k, ':\n', train_test_data[k], file=f)
return train_test_data
def main_rst_gen_mp(name, site, sim_file=None, no_mp=False):
"""Generate rst files using multiple processes if necessary
:name: TODO
:site: TODO
:sim_file: TODO
:returns: TODO
"""
if site in ['all', 'PLUMBER_ext']:
# will only work if simulations are already evaluated.
datasets = get_sites('PLUMBER_ext')
if no_mp:
for s in datasets:
main_rst_gen(name, s)
else:
f_args = [[name, s] for s in datasets]
ncores = min(os.cpu_count(), 2 + int(os.cpu_count() * 0.25))
with Pool(ncores) as p:
p.starmap(main_rst_gen, f_args)
else:
main_rst_gen(name, site)
