Merge remote-tracking branch 'upstream/develop' into pip_installation_scripts

Author: Nick Canzoneri

src/penn_chime/models.py

@@ -93,6 +93,8 @@ def __init__(self, p: Parameters) -> SimSirModel:
         self.dispositions_df = dispositions_df
         self.admits_df = admits_df
         self.census_df = census_df
+        self.daily_growth = daily_growth_helper(p.doubling_time)
+        self.daily_growth_t = daily_growth_helper(doubling_time_t)
 
 
 def sir(
@@ -171,3 +173,11 @@ def build_census_df(
             for key, los in lengths_of_stay.items()
         }
     })
+
+  
+def daily_growth_helper(doubling_time):
+    """Calculates average daily growth rate from doubling time"""
+    result = 0
+    if doubling_time != 0:
+        result = (np.power(2, 1.0 / doubling_time) - 1) * 100
+    return result

src/penn_chime/presentation.py

@@ -81,10 +81,11 @@ def display_header(st, m, p):
 {infected_population_warning_str}
 
 An initial doubling time of **{doubling_time}** days and a recovery time of **{recovery_days}** days imply an $R_0$ of
-**{r_naught:.2f}**.
-
+ **{r_naught:.2f}** and daily growth rate of **{daily_growth:.2f}%**. 
+ 
 **Mitigation**: A **{relative_contact_rate:.0%}** reduction in social contact after the onset of the
-outbreak **{impact_statement:s} {doubling_time_t:.1f}** days, implying an effective $R_t$ of **${r_t:.2f}$**.
+outbreak **{impact_statement:s} {doubling_time_t:.1f}** days, implying an effective $R_t$ of **${r_t:.2f}$** 
+and daily growth rate of **{daily_growth_t:.2f}%**.
 """.format(
             total_infections=m.infected,
             initial_infections=p.known_infected,
@@ -100,6 +101,8 @@ def display_header(st, m, p):
             r_t=m.r_t,
             doubling_time_t=abs(m.doubling_time_t),
             impact_statement=("halves the infections every" if m.r_t < 1 else "reduces the doubling time to"),
+            daily_growth=m.daily_growth,
+            daily_growth_t=m.daily_growth_t,
             docs_url=DOCS_URL,
             infection_warning_str=infection_warning_str,
             infected_population_warning_str=infected_population_warning_str

tests/test_app.py

@@ -8,7 +8,7 @@
 import altair as alt  # type: ignore
 
 from src.penn_chime.charts import new_admissions_chart, admitted_patients_chart, chart_descriptions
-from src.penn_chime.models import SimSirModel, sir, sim_sir_df, build_admits_df
+from src.penn_chime.models import SimSirModel, sir, sim_sir_df, build_admits_df, daily_growth_helper
 from src.penn_chime.parameters import Parameters
 from src.penn_chime.presentation import display_header
 from src.penn_chime.settings import DEFAULTS
@@ -27,7 +27,21 @@
     n_days=60,
 )
 
+HALVING_PARAM = Parameters(
+    current_hospitalized=100,
+    doubling_time=6.0,
+    known_infected=5000,
+    market_share=0.05,
+    relative_contact_rate=0.7,
+    susceptible=500000,
+    hospitalized=RateLos(0.05, 7),
+    icu=RateLos(0.02, 9),
+    ventilated=RateLos(0.01, 10),
+    n_days=60,
+)
+
 MODEL = SimSirModel(PARAM)
+HALVING_MODEL = SimSirModel(HALVING_PARAM)
 
 
 # set up
@@ -58,46 +72,38 @@ def cleanup(self):
 # test presentation
 
 
+def header_test_helper(expected_str, model, param):
+    st.cleanup()
+    display_header(st, model, param)
+    assert [s for s in st.render_store if expected_str in s],\
+        "Expected the string '{expected}' in the display header".format(expected=expected_str)
+    st.cleanup()
+
+
 def test_penn_logo_in_header():
     penn_css = '<link rel="stylesheet" href="https://www1.pennmedicine.org/styles/shared/penn-medicine-header.css">'
-    display_header(st, MODEL, PARAM)
-    assert len(
-        list(filter(lambda s: penn_css in s, st.render_store))
-    ), "The Penn Medicine header should be printed"
+    header_test_helper(penn_css, MODEL, PARAM)
 
 
 def test_the_rest_of_header_shows_up():
     random_part_of_header = "implying an effective $R_t$ of"
-    assert len(
-        list(filter(lambda s: random_part_of_header in s, st.render_store))
-    ), "The whole header should render"
+    header_test_helper(random_part_of_header, MODEL, PARAM)
 
 
 def test_mitigation_statement():
-    st.cleanup()
     expected_doubling = "outbreak **reduces the doubling time to 7.8** days"
-    display_header(st, MODEL, PARAM)
-    assert [s for s in st.render_store if expected_doubling in s]
-    # assert len((list(filter(lambda s: expected_doubling in s, st.render_store))))
-    st.cleanup()
     expected_halving = "outbreak **halves the infections every 51.9** days"
-    halving_params = Parameters(
-        current_hospitalized=100,
-        doubling_time=6.0,
-        known_infected=5000,
-        market_share=0.05,
-        relative_contact_rate=0.7,
-        susceptible=500000,
-        hospitalized=RateLos(0.05, 7),
-        icu=RateLos(0.02, 9),
-        ventilated=RateLos(0.01, 10),
-        n_days=60,
-    )
-    halving_model = SimSirModel(halving_params)
-    display_header(st, halving_model, halving_params)
-    assert [s for s in st.render_store if expected_halving in s]
-    #assert len((list(filter(lambda s: expected_halving in s, st.render_store))))
-    st.cleanup()
+    header_test_helper(expected_doubling, MODEL, PARAM)
+    header_test_helper(expected_halving, HALVING_MODEL, HALVING_PARAM)
+
+
+def test_daily_growth_presentation():
+    initial_growth = "and daily growth rate of **12.25%**."
+    mitigated_growth = "and daily growth rate of **9.34%**."
+    mitigated_halving = "and daily growth rate of **-1.33%**."
+    header_test_helper(initial_growth, MODEL, PARAM)
+    header_test_helper(mitigated_growth, MODEL, PARAM)
+    header_test_helper(mitigated_halving, HALVING_MODEL, HALVING_PARAM)
 
 
 st.cleanup()
@@ -262,6 +268,12 @@ def test_model(model=MODEL, param=PARAM):
     assert (diff.abs() < 0.1).all()
 
 
+def test_daily_growth_helper():
+    assert np.round(daily_growth_helper(5), decimals=4) == 14.8698
+    assert np.round(daily_growth_helper(0), decimals=4) == 0.0
+    assert np.round(daily_growth_helper(-4), decimals=4) == -15.9104
+
+
 def test_chart_descriptions(p=PARAM):
     # new admissions chart
     projection_admits = pd.read_csv('tests/projection_admits.csv')