if args.n_threads == 0:
args.n_threads = args.n_chains
if args.n_iter < args.n_warmups:
args.n_warmups = int(args.n_iter / 2)
csv = Path(args.data_path) / ("covidtimeseries_%s.csv" % args.roi)
csv = csv.resolve()
assert csv.exists(), "No such csv file: %s" % csv
stan_data, t0 = ncs.get_stan_data(csv, args)
if stan_data is None:
print("No data for %s; skipping fit." % args.roi)
sys.exit(0)
if args.n_data_only:
print(ncs.get_n_data(stan_data))
sys.exit(0)
init_fun = ncs.get_init_fun(args, stan_data)
model_path = Path(args.models_path) / ('%s.stan' % args.model_name)
model_path = model_path.resolve()
assert model_path.is_file(), "No such .stan file: %s" % model_path
control = {'adapt_delta': args.adapt_delta}
stanrunmodel = ncs.load_or_compile_stan_model(
args.model_name, args.models_path, force_recompile=args.force_recompile)
# Fit Stan
# fit = stanrunmodel.sampling(data=stan_data, init=init_fun, control=control,
# chains=args.n_chains,
# chain_id=np.arange(args.n_chains),
import pandas as pd
from pathlib import Path
import niddk_covid_sicr as ncs
# Parse all the command-line arguments
parser = argparse.ArgumentParser(description='')
parser.add_argument('-dp',
'--data-path',
default='./data',
help='Path to directory containing the data files')
parser.add_argument('-ld',
'--last-date',
help=('Last date to use in the data; dates past this '
'will be ignored'))
args = parser.parse_args()
# Get all model_names, roi combinations
rois = ncs.list_rois(args.data_path, 'covidtimeseries', '.csv')
df = pd.DataFrame(index=rois, columns=['n_data_pts'], dtype=int)
for roi in rois:
csv = Path(args.data_path) / ("covidtimeseries_%s.csv" % roi)
csv = csv.resolve()
assert csv.exists(), "No such csv file: %s" % csv
stan_data, t0 = ncs.get_stan_data(csv, args)
n_data = ncs.get_n_data(stan_data)
df.loc[roi, 'n_data_pts'] = int(n_data)
df.loc[roi, 't0'] = t0
df.index.name = 'roi'
df['n_data_pts'] = df['n_data_pts'].astype(int)
df['t0'] = df['t0'].astype('datetime64')
df.to_csv('n_data.csv')