Switched to FNC TierData ingester.

inference.py

@@ -42,14 +42,10 @@ if __name__ == "__main__":
         config = yaml.load(f, Loader=yaml.FullLoader)
 
     inference_period = [
-        np.datetime64(x) for x in config["settings"]["inference_period"]
+        np.datetime64(x) for x in config["Global"]["inference_period"]
     ]
 
-    covar_data = model_spec.read_covariates(
-        config["data"],
-        date_low=inference_period[0],
-        date_high=inference_period[1],
-    )
+    covar_data = model_spec.read_covariates(config)
 
     # We load in cases and impute missing infections first, since this sets the
     # time epoch which we are analysing.

model_spec.py

@@ -19,7 +19,7 @@ XI_FREQ = 14  # baseline transmission changes every 14 days
 NU = tf.constant(0.28, dtype=DTYPE)  # E->I rate assumed known.
 
 
-def read_covariates(paths, date_low, date_high):
+def read_covariates(config):
     """Loads covariate data
 
     :param paths: a dictionary of paths to data with keys {'mobility_matrix',
@@ -27,6 +27,9 @@ def read_covariates(paths, date_low, date_high):
     :returns: a dictionary of covariate information to be consumed by the model
               {'C': commute_matrix, 'W': traffic_flow, 'N': population_size}
     """
+    paths = config["data"]
+    date_low = np.datetime64(config["Global"]["inference_period"][0])
+    date_high = np.datetime64(config["Global"]["inference_period"][1])
     mobility = data.read_mobility(paths["mobility_matrix"])
     popsize = data.read_population(paths["population_size"])
     commute_volume = data.read_traffic_flow(
@@ -35,11 +38,12 @@ def read_covariates(paths, date_low, date_high):
 
     geo = gp.read_file(paths["geopackage"])
     geo = geo.loc[geo["lad19cd"].str.startswith("E")]
-    tier_restriction = data.read_challen_tier_restriction(
-        paths["tier_restriction_csv"],
-        date_low,
-        date_high,
-    )
+    # tier_restriction = data.read_challen_tier_restriction(
+    #     paths["tier_restriction_csv"],
+    #     date_low,
+    #     date_high,
+    # )
+    tier_restriction = data.TierData.process(config)[:, :, 2:]
     weekday = pd.date_range(date_low, date_high).weekday < 5
 
     return dict(

pyproject.toml

@@ -27,6 +27,7 @@ ipython = "^7.18.1"
 jupyter = "^1.0.0"
 flake8 = "^3.8.4"
 black = "^20.8b1"
+pytest = "^6.2.1"
 
 [tool.black]
 line-length = 80

template_config.yaml

-# Covid_ODE model configuration
+# Covid stochastic model configuration
+
+Global:
+  prependID: False
+  prependDate: False
+  inference_period:
+    - 2020-10-11
+    - 2021-01-04
+  time_step: 1.
+
+
+GenerateOutput:
+  storeInputs: False
+  scrapedDataDir: scraped_data
+  storeProcessedInputs: False
+
 
 data:
   mobility_matrix: data/mergedflows.csv
   population_size: data/c2019modagepop.csv
-  commute_volume: data/201211_OFF_SEN_COVID19_road_traffic_national_table.xlsx
-  reported_cases: data/Anonymised Combined Line List 20201109.csv
+  commute_volume: data/201231_OFF_SEN_COVID19_road_traffic_national_table.xlsx
+  reported_cases: data/Anonymised Combined Line List 20201231.csv
   case_date_type: specimen
   pillar: both
-  tier_restriction_csv: data/tidyLAD19Tiers.csv
   geopackage: data/UK2019mod_pop.gpkg
 
-parameter:
-  beta1: 0.291   # R0 2.4
-  beta2: 0.876   # Contact with commuters 1/3rd of the time
-  beta3: 1.0   # lockdown vs normal
-  omega: 1.0   # Non-linearity parameter for commuting volume
-  nu: 0.5      # E -> I transition rate
-  gamma: 0.45  # I -> R transition rate
-  xi:
-    - 2.07
-    - 0.15
-    - -1.08
-    - -0.78
-    - -0.73
-    - -0.51
-
-settings:
-  inference_period:
-    - 2020-07-03
-    - 2020-09-29
-  time_step: 1.
-  prediction_period:
-    - 2020-02-19
-    - 2020-08-01
 
 mcmc:
   dmax: 84
@@ -48,8 +39,23 @@ mcmc:
       concentration: 2.0
       rate: 4.0
 
+
 output:
-  results_dir: $global_scratch/covid19/
+  results_dir: output/testing
   posterior: posterior.hd5
   national_rt: national_rt.xlsx
   geopackage: prediction.gpkg
+
+
+AreaCodeData:
+  input: json
+  address:  "https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LAD_APR_2019_UK_NC/FeatureServer/0/query?where=1%3D1&outFields=LAD19CD,LAD19NM&returnGeometry=false&returnDistinctValues=true&orderByFields=LAD19CD&outSR=4326&f=json"
+  format: ons
+  output: processed_data/processed_lad19cd.csv
+  regions: ["E"]
+
+
+TierData:
+  input: api
+  address: None
+  format: api