moved build_*_df funcs into models.py since they have mathematical content

Author: quinn-dougherty

src/app.py

@@ -17,9 +17,8 @@
     write_definitions,
     write_footer,
 )
-from penn_chime.utils import build_admissions_df, build_census_df
-from penn_chime.settings import DEFAULTS
-from penn_chime.models import sim_sir_df
+from penn_chime.settings import DEFAULTS 
+from penn_chime.models import sim_sir_df, build_admissions_df, build_census_df
 from penn_chime.charts import additional_projections_chart, admitted_patients_chart, new_admissions_chart
 # This is somewhat dangerous:
 # Hide the main menu with "Rerun", "run on Save", "clear cache", and "record a screencast"
@@ -61,7 +60,6 @@
 st.markdown(
     "Projected **census** of COVID-19 patients, accounting for arrivals and discharges at Penn hospitals"
 )
-st.dataframe(census_df)
 st.altair_chart(
     admitted_patients_chart(alt=alt, census=census_df, parameters=p, as_date=as_date), use_container_width=True
 )

src/penn_chime/models.py

@@ -74,3 +74,51 @@ def get_dispositions(
 ) -> Tuple[np.ndarray, ...]:
     """Get dispositions of infected adjusted by rate and market_share."""
     return (*(infected * rate * market_share for rate in rates),)
+
+
+
+def build_admissions_df(p) -> pd.DataFrame:
+    """Build admissions dataframe from Parameters."""
+    days = np.array(range(0, p.n_days + 1))
+    data_dict = dict(zip(["day", "Hospitalized", "ICU", "Ventilated"],
+                         [days] + [disposition for disposition in p.dispositions]
+    ))
+    projection = pd.DataFrame.from_dict(data_dict)
+    # New cases
+    projection_admits = projection.iloc[:-1, :] - projection.shift(1)
+    projection_admits[projection_admits < 0] = 0
+    projection_admits["day"] = range(projection_admits.shape[0])
+    return projection_admits
+
+
+def build_census_df(
+        projection_admits: pd.DataFrame,
+        parameters
+) -> pd.DataFrame:
+    """ALOS for each category of COVID-19 case (total guesses)"""
+    n_days = np.shape(projection_admits)[0]
+    hosp_los, icu_los, vent_los = parameters.lengths_of_stay
+    los_dict = {
+        "Hospitalized": hosp_los,
+        "ICU": icu_los,
+        "Ventilated": vent_los,
+    }
+
+    census_dict = dict()
+    for k, los in los_dict.items():
+        census = (
+            projection_admits.cumsum().iloc[:-los, :]
+            - projection_admits.cumsum().shift(los).fillna(0)
+        ).apply(np.ceil)
+        census_dict[k] = census[k]
+
+    census_df = pd.DataFrame(census_dict)
+    census_df["day"] = census_df.index
+    census_df = census_df[["day", "Hospitalized", "ICU", "Ventilated"]]
+    census_df = census_df.head(n_days)
+    census_df = census_df.rename(
+        columns={disposition: f"{disposition} Census"
+                 for disposition
+                 in ("Hospitalized", "ICU", "Ventilated")}
+    )
+    return census_df