def prevalence(predicted_state, population_size, name=None):
"""Computes prevalence of E and I individuals
:param state: the state at a particular timepoint [batch, M, S]
:param population_size: the size of the population
:returns: a dict of mean and 95% credibility intervals for prevalence
in units of infections per person
"""
prev = tf.reduce_sum(predicted_state[:, :, 1:3],
axis=-1) / tf.squeeze(population_size)
return mean_and_ci(prev, name=name)
def rt(input_file, output_file):
"""Reads an array of next generation matrices and
outputs mean (ci) local Rt values.
:param input_file: a pickled xarray of NGMs
:param output_file: a .csv of mean (ci) values
"""
with open(input_file, "rb") as f:
ngm = pkl.load(f)
rt = np.sum(ngm, axis=-2)
rt_summary = mean_and_ci(rt, name="Rt")
exceed = np.mean(rt > 1.0, axis=0)
rt_summary = pd.DataFrame(rt_summary,
index=pd.Index(ngm.coords["dest"],
name="location"))
rt_summary['Rt_exceed'] = exceed
rt_summary.to_csv(output_file)
def predicted_events(events, name=None):
num_events = tf.reduce_sum(events, axis=-1)
return mean_and_ci(num_events, name=name)
for k, v in summary.items():
arr = v
if isinstance(v, tf.Tensor):
arr = v.numpy()
geodata[k] = arr
## GIS here
ltla = gp.read_file(GIS_TEMPLATE, layer="UK2019mod_pop_xgen")
ltla = ltla[ltla["lad19cd"].str.startswith("E")] # England only, for now.
ltla = ltla.sort_values("lad19cd")
rti = tf.reduce_sum(ngms, axis=-2)
geosummary(
ltla,
(
mean_and_ci(rti, name="Rt"),
prev_now,
cases_now,
prev_7,
prev_14,
prev_21,
prev_28,
prev_56,
cases_7,
cases_14,
cases_21,
cases_28,
cases_56,
),
)
def makeGeopackage(pipelineData):
# This function makes the geopackage from the summary data
config = pipelineData['config']
settings = config['GeoSummary']
GIS_TEMPLATE = settings['template']
GIS_OUTPUT = settings['address']
# use the pipelineData if available
# otherwise try to load the processed output
if 'summary' in pipelineData:
summaryData = pipelineData['summary']
else:
print('SummaryData not present in pipelineData')
print('Trying to load processed file instead')
print('Looking for file at', config['SummaryData']['address'])
config['SummaryData']['input'] = 'processed'
summaryData = GetData.SummaryData.process(config)
try:
ltla = gp.read_file(GIS_TEMPLATE, layer="UK2019mod_pop_xgen")
except:
print("Layer UK2019mod_pop_xgen doesn't exist in", GIS_TEMPLATE)
ltla = gp.read_file(GIS_TEMPLATE)
ltla = ltla[ltla["lad19cd"].str.startswith("E")] # England only, for now.
ltla = ltla.sort_values("lad19cd")
# FNC: These likely need to be downselected to just the LADs in the summary data
# do some data checks and throw a warning for now if they're not the same
# we didn't have an example file with more than 43 LADs
geo_lads = ltla.lad19cd.values
config_lads = np.array(summaryData['LADs'])
if geo_lads.size != config_lads.size:
print(
'GEOSUMMARY: Different number of LADs in GIS_TEMPLATE vs summaryData'
)
print('GIS_Template:', geo_lads.size)
print('summaryData:', config_lads.size)
con_in_geo = np.array([x in geo_lads for x in config_lads]).sum()
geo_in_con = np.array([x in config_lads for x in geo_lads]).sum()
if con_in_geo != geo_lads.size:
print('GEOSUMMARY:', con_in_geo, '/', geo_lads.size,
'summary data LADs in GIS Template')
if geo_in_con != config_lads.size:
print('GEOSUMMARY:', geo_in_con, '/', config_lads.size,
'GIS Template LADs in summary data')
rti = tf.reduce_sum(summaryData['metrics']['ngms'], axis=-2)
geosummary(
ltla,
(
mean_and_ci(rti, name="Rt"),
summaryData['prev']['now'],
summaryData['cases']['now'],
summaryData['prev']['7'],
summaryData['prev']['14'],
summaryData['prev']['21'],
summaryData['prev']['28'],
summaryData['prev']['56'],
summaryData['cases']['7'],
summaryData['cases']['14'],
summaryData['cases']['21'],
summaryData['cases']['28'],
summaryData['cases']['56'],
),
)
ltla["Rt_exceed"] = np.mean(rti > 1.0, axis=0)
ltla = ltla.loc[:,
ltla.columns.str.contains(
"(lad19cd|lad19nm$|prev|cases|Rt|popsize|geometry)",
regex=True), ]
ltla.to_file(
GIS_OUTPUT,
driver="GPKG",
)
def calc_prev(state, name=None):
prev = np.sum(state[..., 1:3], axis=-1) / np.squeeze(data["N"])
return mean_and_ci(prev, name=name)
def pred_events(events, name=None):
num_events = np.sum(events, axis=-1)
return mean_and_ci(num_events, name=name)