Merge pull request #97 from nickcanz/implement_streamlit_caching

Implement streamlit caching

Author: quinn-dougherty

penn_chime/models.py

@@ -1,9 +1,11 @@
 from typing import Tuple
 
 import numpy as np
+import streamlit as st
 
 
 # The SIR model, one time step
+@st.cache
 def sir(y, beta, gamma, N):
     S, I, R = y
     Sn = (-beta * S * I) + S
@@ -21,6 +23,7 @@ def sir(y, beta, gamma, N):
 
 
 # Run the SIR model forward in time
+@st.cache
 def sim_sir(
     S, I, R, beta, gamma, n_days, beta_decay=0
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
@@ -37,7 +40,7 @@ def sim_sir(
     s, i, r = np.array(s), np.array(i), np.array(r)
     return s, i, r
 
-
+@st.cache
 def get_hospitalizations(
     infected: np.ndarray, rates: Tuple[float, float, float], market_share: float
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:

penn_chime/utils.py

@@ -1,7 +1,8 @@
 import numpy as np
 import pandas as pd
+import streamlit as st
 
-
+@st.cache
 def build_admissions_df(n_days, hosp, icu, vent) -> pd.DataFrame:
     days = np.array(range(0, n_days + 1))
     data_dict = dict(zip(["day", "hosp", "icu", "vent"], [days, hosp, icu, vent]))
@@ -12,7 +13,7 @@ def build_admissions_df(n_days, hosp, icu, vent) -> pd.DataFrame:
     projection_admits["day"] = range(projection_admits.shape[0])
     return projection_admits
 
-
+@st.cache
 def build_census_df(projection_admits, hosp_los, icu_los, vent_los) -> pd.DataFrame:
     """ALOS for each category of COVID-19 case (total guesses)"""
     n_days = np.shape(projection_admits)[0]