0.05)) + [0.975, 0.99]
forecast_date = pd.to_datetime(forecast_start)
currentEpiWeek = Week.fromdate(forecast_date)
forecast = {
'quantile': [],
'target_end_date': [],
'value': [],
'type': [],
'location': [],
'target': []
}
for place in places:
prior_samples, mcmc_samples, post_pred_samples, forecast_samples = util.load_samples(
place, path=samples_directory)
forecast_samples = forecast_samples['mean_z_future']
t = pd.date_range(start=forecast_start,
periods=forecast_samples.shape[1],
freq='D')
weekly_df = pd.DataFrame(index=t,
data=np.transpose(forecast_samples)).resample(
"1w", label='left').last()
weekly_df[weekly_df < 0.] = 0.
for time, samples in weekly_df.iterrows():
for q in allQuantiles:
deathPrediction = np.percentile(samples, q * 100)
forecast["quantile"].append("{:.3f}".format(q))
forecast["value"].append(deathPrediction)
forecast["type"].append("quantile")
forecast["location"].append(place)
import pandas as pd
import numpy as np
import sys
import covid.util as util
import covid.models.SEIRD_incident as model_type
### get daily incident
sub_file = sys.argv[1]
samples_directory = sys.argv[2]
model = model_type.SEIRD()
prior_samples, mcmc_samples, post_pred_samples, forecast_samples = util.load_samples(
samples_directory + "US" + ".npz")
forecast_samples = model.get(forecast_samples, 'z', forecast=True)
mean_forecast_samples = np.mean(forecast_samples, axis=0)
data = util.load_state_data()
sub = pd.read_csv(sub_file, dtype=str)
# verify less than 4 weeks
targets = np.unique(sub.target.values)
if (len(targets) > 4):
print("Error: more than 4 week ahead present")
print(targets)
else:
print("Success: only 4 week ahead or fewer present")
# verify fips codes are length 5
fips = np.unique(sub.location.values)
lengths = [len(str(k)) == 2 for k in fips]
if (all(lengths) == False):
import pandas as pd
import numpy as np
import sys
import covid.util as util
import covid.models.SEIRD_incident as model_type
### get daily incident
sub_file = sys.argv[1]
samples_directory = sys.argv[2]
model = model_type.SEIRD()
prior_samples, mcmc_samples, post_pred_samples, forecast_samples = util.load_samples(samples_directory + "CA-Los Angeles" +".npz")
forecast_samples = model.get(forecast_samples, 'dy',forecast=True)
mean_forecast_samples = np.mean(forecast_samples,axis=0)
data = util.load_county_data()
sub = pd.read_csv(sub_file,dtype=str)
# verify less than 4 weeks
targets = np.unique(sub.target.values)
if (len(targets) > 4):
print ("Error: more than 4 week ahead present")
else:
print ("Success: only 4 week ahead or fewer present")
# verify fips codes are length 5
