Merge branch 'develop' of github.com:CodeForPhilly/chime into phil-census-forecast

Author: jlubken

.github/workflows/dockerpublish.yml

@@ -92,7 +92,7 @@ jobs:
           echo "::set-env name=VERSION::$VERSION"
 
           # Update image version in application manifest
-          sed -Ei "s,(- image: docker.pkg.github.com)(.*),\1/${{ github.repository }}/$IMAGE_NAME:$VERSION," k8s/app.yaml
+          sed -Ei "s,(- image: docker.pkg.github.com)(.*),\1/${GITHUB_REPOSITORY,,}/$IMAGE_NAME:$VERSION," k8s/app.yaml
 
           # Show updated manifest file
           cat k8s/app.yaml

.github/workflows/pythonapp.yml

@@ -44,4 +44,6 @@ jobs:
       uses: cypress-io/[email protected]
       with:
         working-directory: e2e
+        wait-on: 'http://localhost:8000'
+        wait-on-timeout: 300
 

e2e/cypress/integration/tests/actions.spec.js

@@ -10,7 +10,8 @@ context('Actions', () => {
 
     // This gets the "first" input from the sidebar. From clicking step up,
     // the Regional Population should increase from default 4119405 to 4219405.
-    cy.get('input.st-al').eq(1)
-      .should('has.value', '4119406')
+    // Updated to 3600000
+    cy.get('input.st-al').eq(0)
+      .should('has.value', '3600000')
   })
 });

setup.py

@@ -1,6 +1,6 @@
 """Setup file for chime
 """
-__version__ = "0.2.0"
+__version__ = "1.1.0"
 __author__ = "Predictive Healthcare @ Penn Medicine"
 
 from os import path

src/app.py

@@ -34,14 +34,14 @@
 display_header(st, m, p)
 
 if st.checkbox("Show more info about this tool"):
-    notes = "The total size of the susceptible population will be the entire catchment area for Penn Medicine entities (HUP, PAH, PMC, CCH)"
+    notes = "The total size of the susceptible population will be the entire catchment area for our hospitals."
     display_more_info(st=st, model=m, parameters=p, defaults=DEFAULTS, notes=notes)
 
 st.subheader("New Admissions")
-st.markdown("Projected number of **daily** COVID-19 admissions at Penn hospitals")
+st.markdown("Projected number of **daily** COVID-19 admissions. \n\n _NOTE: Now including estimates of prior admissions for comparison._")
 admits_chart = build_admits_chart(alt=alt, admits_df=m.admits_df, max_y_axis=p.max_y_axis)
 st.altair_chart(admits_chart, use_container_width=True)
-st.markdown(build_descriptions(chart=admits_chart, labels=p.labels))
+st.markdown(build_descriptions(chart=admits_chart, labels=p.labels, suffix=" Admissions"))
 display_download_link(
     st,
     filename=f"{p.current_date}_projected_admits.csv",
@@ -60,7 +60,7 @@
 
 
 st.subheader("Admitted Patients (Census)")
-st.markdown("Projected **census** of COVID-19 patients, accounting for arrivals and discharges at Penn hospitals")
+st.markdown("Projected **census** of COVID-19 patients, accounting for arrivals and discharges \n\n _NOTE: Now including estimates of prior census for comparison._")
 census_chart = build_census_chart(alt=alt, census_df=m.census_df, max_y_axis=p.max_y_axis)
 st.altair_chart(census_chart, use_container_width=True)
 st.markdown(build_descriptions(chart=census_chart, labels=p.labels, suffix=" Census"))

src/penn_chime/constants.py

@@ -14,5 +14,5 @@
 
 EPSILON = 1.0e-7
 
-FLOAT_INPUT_MIN = 0.001
-FLOAT_INPUT_STEP = FLOAT_INPUT_MIN
+FLOAT_INPUT_MIN = 0.0001
+FLOAT_INPUT_STEP = 0.1

src/penn_chime/models.py

@@ -91,9 +91,7 @@ def __init__(self, p: Parameters):
 
             self.beta_t = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, p.relative_contact_rate)
             self.run_projection(p)
-            self.infected = self.raw_df['infected'].values[i_day]
-            self.susceptible = self.raw_df['susceptible'].values[i_day]
-            self.recovered = self.raw_df['recovered'].values[i_day]
+
             self.r_t = self.beta_t / gamma * susceptible
             self.r_naught = self.beta / gamma * susceptible
             logger.info('Set i_day = %s', i_day)
@@ -144,6 +142,10 @@ def __init__(self, p: Parameters):
         logger.info('len(np.arange(-i_day, n_days+1)): %s', len(np.arange(-self.i_day, p.n_days+1)))
         logger.info('len(raw_df): %s', len(self.raw_df))
 
+        self.infected = self.raw_df['infected'].values[self.i_day]
+        self.susceptible = self.raw_df['susceptible'].values[self.i_day]
+        self.recovered = self.raw_df['recovered'].values[self.i_day]
+
         self.r_t = self.beta_t / gamma * susceptible
         self.r_naught = self.beta / gamma * susceptible
 
@@ -180,7 +182,10 @@ def get_loss(self) -> float:
 
 
 def get_argmin_ds(census_df: pd.DataFrame, current_hospitalized: float) -> float:
-    losses_df = (census_df.hospitalized - current_hospitalized) ** 2.0
+    # By design, this forbids choosing a day after the peak
+    # If that's a problem, see #381
+    peak_day = census_df.hospitalized.argmax()
+    losses_df = (census_df.hospitalized[:peak_day] - current_hospitalized) ** 2.0
     return losses_df.argmin()
 
 

src/penn_chime/parameters.py

@@ -59,7 +59,7 @@ def __init__(
         infectious_days: int = 14,
         market_share: float = 1.0,
         max_y_axis: Optional[int] = None,
-        n_days: int = 60,
+        n_days: int = 100,
         population: Optional[int] = None,
         recovered: int = 0,
         region: Optional[Regions] = None,