Merge pull request pennsignals#27 from pennsignals/fix_22

app: Support floating point for Market Share

app.py

@@ -22,7 +22,9 @@
 initial_infections = st.sidebar.number_input(
     "Currently Known Regional Infections", value=known_infections, step=10, format="%i"
 )
-current_hosp = st.sidebar.number_input("Currently Hospitalized COVID-19 Patients", value=2, step=1, format="%i")
+current_hosp = st.sidebar.number_input(
+    "Currently Hospitalized COVID-19 Patients", value=2, step=1, format="%i"
+)
 doubling_time = st.sidebar.number_input(
     "Doubling Time (days)", value=6, step=1, format="%i"
 )
@@ -42,7 +44,7 @@
 vent_los = st.sidebar.number_input("Vent LOS", value=10, step=1, format="%i")
 Penn_market_share = (
     st.sidebar.number_input(
-        "Hospital Market Share (%)", 0, 100, value=15, step=1, format="%i"
+        "Hospital Market Share (%)", 0.0, 100.0, value=15.0, step=1.0, format="%f"
     )
     / 100.0
 )
@@ -84,10 +86,12 @@
 
 - The $\\gamma$ parameter represents 1 over the mean recovery time in days. Since the CDC is recommending 14 days of self-quarantine, we'll use $\\gamma = 1/14$. 
 - Next, the AHA says to expect a doubling time $T_d$ of 7-10 days. That means an early-phase rate of growth can be computed by using the doubling time formula:
-""")
+"""
+    )
     st.latex("g = 2^{1/T_d} - 1")
 
-    st.markdown("""
+    st.markdown(
+        """
 - Since the rate of new infections in the SIR model is $g = \\beta S - \\gamma$, and we've already computed $\\gamma$, $\\beta$ becomes a function of the initial population size of susceptible individuals.
 $$\\beta = (g + \\gamma)/s$$
 
@@ -252,7 +256,9 @@ def sim_sir(S, I, R, beta, gamma, n_days, beta_decay=None):
     """**Click the checkbox below to view additional data generated by this simulation**"""
 )
 if st.checkbox("Show Additional Projections"):
-    st.subheader("The number of infected and recovered individuals in the hospital catchment region at any given moment")
+    st.subheader(
+        "The number of infected and recovered individuals in the hospital catchment region at any given moment"
+    )
     fig, ax = plt.subplots(1, 1, figsize=(10, 4))
     ax.plot(i, label="Infected")
     ax.plot(r, label="Recovered")