incremental progress on debugging

Author: quinn-dougherty

src/penn_chime/models.py

@@ -13,7 +13,6 @@
 import pandas as pd  # type: ignore
 
 from .parameters import Parameters
-from .utils import SimSirModelAttributes
 
 class SimSirModel:
 
@@ -98,6 +97,9 @@ def __init__(self, p: Parameters):
         self.admits_df = admits_df
         self.census_df = census_df
 
+        #if p.n_days_since_first_hospitalized is None:
+        #    continue
+
         if p.n_days_since_first_hospitalized is not None and p.doubling_time is None:
             # optimize doubling_time
             argmin_dt = None
@@ -130,12 +132,13 @@ def __init__(self, p: Parameters):
                 recovered,
                 beta,
                 gamma,
-                p.n_days #+ p.n_days_since_first_hospitalized
+                p.n_days # + p.n_days_since_first_hospitalized
             )
             dispositions_df = build_dispositions_df(raw_df, rates, p.market_share)
             admits_df = build_admits_df(dispositions_df, p.n_days_since_first_hospitalized)
             census_df = build_census_df(admits_df, lengths_of_stay, p.n_days_since_first_hospitalized)
 
+            self.population = p.population
             self.infected = current_infected
             self.intrinsic_growth_rate = intrinsic_growth_rate
             self.gamma = gamma
@@ -160,7 +163,7 @@ def run_projection(self, p: Parameters, doubling_time: float) -> Tuple[pd.DataFr
         market_share = p.market_share
         initial_i = 1 / p.hospitalized.rate / market_share
 
-        S, I, R = self.susceptible, initial_i, self.recovered
+        S, I, R = p.population - initial_i, initial_i, self.recovered
 
         # mean recovery rate (inv_recovery_days)
         gamma = 1 / recovery_days
@@ -203,6 +206,9 @@ def loss_dt(self, p: Parameters) -> float:
         # we shall optimize squared distance
         return (p.current_hospitalized - predicted_current_hospitalized) ** 2
 
+    def loss_ds(self, p: Parameters) -> float:
+        pass
+    
 
     @staticmethod
     def _intrinsic_growth_rate(doubling_time: Optional[float]) -> float:

src/penn_chime/utils.py

@@ -16,23 +16,6 @@
 RateLos = namedtuple("RateLos", ("rate", "length_of_stay"))
 
 
-SimSirModelAttributes = namedtuple(
-    "SimSirModelAttributes",
-    (
-        "doubling_time",
-        "intrinsic_growth_rate",
-        "gamma",
-        "beta",
-        "r_t",
-        "r_0",
-        "doubling_time_t",
-        "raw_df",
-        "dispositions_df",
-        "admits_df",
-        "census_df"
-    )
-)
-
 
 
 def add_date_column(
@@ -72,7 +55,7 @@ def add_date_column(
         start = today - delta
     else:
         start = today
-    end = start + timedelta(days=n_days + 1)
+    end = start + timedelta(days=n_days + 1) + delta # the +delta part is a hypothesis by phil
     # And pick dates present in frame
     dates = pd.date_range(start=start, end=end, freq="D")[df.day.tolist()]