fixing lint

Author: quinn-dougherty

src/penn_chime/presentation.py

@@ -281,7 +281,7 @@ def show_more_info_about_this_tool(
 $\\gamma$ is the inverse of the mean recovery time, in days.  I.e.: if $\\gamma = 1/{recovery_days}$, then the average infection will clear in {recovery_days} days.
 
 An important descriptive parameter is the _basic reproduction number_, or $R_0$.  This represents the average number of people who will be infected by any given infected person.  When $R_0$ is greater than 1, it means that a disease will grow.  Higher $R_0$'s imply more rapid growth.  It is defined as """.format(
-            recovery_days=int(recovery_days)
+            recovery_days=int(parameters.recovery_days)
         )
     )
     st.latex("R_0 = \\beta /\\gamma")

src/penn_chime/utils.py

@@ -15,52 +15,6 @@
 RateLos = namedtuple('RateLos', ('rate', 'length_of_stay'))
 
 
-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
-
 
 def add_date_column(
     df: pd.DataFrame,