def roi_df(args, model_name, roi):
if args.fixed_t:
args.roi = roi # Temporary
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)
global_start = datetime.strptime('01/22/20', '%m/%d/%y')
frame_start = datetime.strptime(t0, '%m/%d/%y')
day_offset = (frame_start - global_start).days
else:
day_offset = 0
model_path = ncs.get_model_path(args.models_path, model_name)
extension = ['csv', 'pkl'][args.fit_format]
rois = ncs.list_rois(args.fits_path, model_name, extension)
if args.rois:
rois = list(set(rois).intersection(args.rois))
fit_path = ncs.get_fit_path(args.fits_path, model_name, roi)
if args.fit_format == 1:
fit = ncs.load_fit(fit_path, model_path)
stats = ncs.get_waic_and_loo(fit)
samples = fit.to_dataframe()
elif args.fit_format == 0:
samples = ncs.extract_samples(args.fits_path, args.models_path,
model_name, roi, args.fit_format)
stats = ncs.get_waic(samples)
df = ncs.make_table(roi,
samples,
args.params,
stats,
quantiles=args.quantiles,
day_offset=day_offset)
return model_name, roi, df
def get_weeks(args, rois):
"""Build dataframe containing roi and number of weeks of data per roi.
Need this to calculate number of parameters per model to then calulate AIC.
Return dataframe, then merge on roi on big table. """
# Create lists: rois, and num weeks.
roi_weeks = {}
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
if not args.totwk:
stan_data, t0, num_weeks = ncs.get_stan_data(csv, args)
if args.totwk:
stan_data, t0, num_weeks = ncs.get_stan_data_weekly_total(
csv, args)
roi_weeks[roi] = num_weeks
df_numweek = pd.DataFrame(roi_weeks.items(), columns=['roi', 'num weeks'])
df_numweek = df_numweek.set_index('roi').sort_index()
return df_numweek
default=0,
help=('Use a fixed time base (where 1/22/20 is t=0)'
'rather than a time base that is relative to the '
'beginning of the data for each region'))
args = parser.parse_args()
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)
default=1,
help=('Number of iterations for eta adaptation (ADVI).'))
args = parser.parse_args()
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
if not args.totwk:
stan_data, t0, num_weeks = ncs.get_stan_data(csv, args)
if args.totwk:
stan_data, t0, num_weeks = ncs.get_stan_data_weekly_total(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
if not args.advi: