Commit 4991f523 authored by Chris Jewell's avatar Chris Jewell
Browse files

Added selection for report vs specimen data in linelisting ingester.

parent 9face1b9
......@@ -90,7 +90,7 @@ def _read_csv(filename, date_format="%m/%d/%Y"):
return data
def phe_case_data(linelisting_file, date_range=None, pillar=None):
def phe_case_data(linelisting_file, date_range=None, date_type='specimen', pillar=None):
read_file = dict(csv=_read_csv, xlsx=pd.read_excel)
match_extension = re.match(r"(.*)\.(.*)$", linelisting_file)
......@@ -106,7 +106,11 @@ def phe_case_data(linelisting_file, date_range=None, pillar=None):
if pillar is not None:
ll = ll.loc[ll["pillar"] == pillar]
date = ll["specimen_date"]
date_type_map = {'specimen': 'specimen_type',
'report': 'lab_report_date'}
date = ll[date_type_map[date_type]]
ltla_region = ll["LTLA_code"]
# Merged regions
......
......@@ -65,7 +65,7 @@ covar_data = load_data(config["data"], settings, DTYPE)
# We load in cases and impute missing infections first, since this sets the
# time epoch which we are analysing.
cases = phe_case_data(config["data"]["reported_cases"], settings["inference_period"])
cases = phe_case_data(config["data"]["reported_cases"], date_range=settings["inference_period"], date_type='report')
ei_events, lag_ei = impute_previous_cases(cases, 0.44)
se_events, lag_se = impute_previous_cases(ei_events, 2.0)
ir_events = np.pad(cases, ((0, 0), (lag_ei + lag_se - 2, 0)))
......@@ -145,7 +145,7 @@ def logp(theta, xi, events):
concentration=tf.constant(1.0, dtype=DTYPE), rate=tf.constant(1.0, dtype=DTYPE)
)
sigma = tf.constant(0.1, dtype=DTYPE)
sigma = tf.constant(0.01, dtype=DTYPE)
phi = tf.constant(12.0, dtype=DTYPE)
kernel = tfp.math.psd_kernels.MaternThreeHalves(sigma, phi)
idx_pts = tf.cast(tf.range(events.shape[1] // xi_freq) * xi_freq, dtype=DTYPE)
......@@ -376,7 +376,7 @@ theta_scale = tf.constant(
theta_scale = theta_scale * 0.2 / theta_scale.shape[0]
xi_scale = tf.eye(current_state[1].shape[0], dtype=DTYPE)
xi_scale = xi_scale * 0.001 / xi_scale.shape[0]
xi_scale = xi_scale * 0.0001 / xi_scale.shape[0]
# We loop over successive calls to sample because we have to dump results
# to disc, or else end OOM (even on a 32GB system).
......
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