Updated the sidebar to include our parameters. Does not yet have work…

…ing import-export functionality.

src/penn_chime/hc_param_import_export.py

@@ -0,0 +1,81 @@
+import base64
+from datetime import (
+    datetime, 
+    date,
+)
+import json
+import io
+from typing import Tuple
+
+from .parameters import (
+    Parameters, 
+    Regions, 
+    Disposition,
+)
+
+
+def constants_from_uploaded_file(file: io.StringIO) -> Tuple[Parameters, dict]:
+    imported_params = json.loads(file.read())
+    constants = Parameters(
+        region=Regions(area=imported_params["RegionalPopulation"]),
+        doubling_time=float(imported_params["DoublingTimeBeforeSocialDistancing"]),
+        # known_infected=imported_params.get("CurrentlyKnownRegionalInfections", 510),
+        n_days=imported_params["NumberOfDaysToProject"],
+        market_share=float(imported_params["HospitalMarketShare"]),
+        relative_contact_rate=float(imported_params["SocialDistancingPercentReduction"]),
+        hospitalized=Disposition(float(imported_params["HospitalizationPercentage"]), imported_params["HospitalLengthOfStay"]),
+        icu=Disposition(float(imported_params["ICUPercentage"]), imported_params["ICULengthOfStay"]),
+        ventilators=Disposition(float(imported_params["VentilatorsPercentage"]),imported_params["VentLengthOfStay"]),
+
+        # total_beds=imported_params.get("TotalNumberOfBeds", 10),
+        total_non_covid_beds=imported_params["TotalNumberOfBedsForNCPatients"],
+        # total_icu_beds=imported_params.get("TotalNumberOfICUBeds", 10),
+        total_non_covid_icu_beds=imported_params["TotalNumberOfICUBedsForNCPatients"],
+        # total_vents=imported_params.get("TotalNumberOfVents", 10),
+        total_non_covid_vents=imported_params["TotalNumberOfVentsForNCPatients"],
+
+        current_hospitalized=imported_params["CurrentlyHospitalizedCovidPatients"],
+        census_date = date.fromisoformat(imported_params.get("CurrentlyHospitalizedCovidPatientsDate", date.today().isoformat())),
+        selected_offset = imported_params.get("SelectedOffsetDays", -1)
+    )
+    return constants, imported_params
+
+def param_download_widget(st, parameters, as_date, max_y_axis_set, max_y_axis):
+    if parameters.author == "Jane Doe" or parameters.scenario == "COVID Model":
+        st.sidebar.markdown("""
+        **Enter a unique author name and scenario name to enable parameter download.**""")
+    else:
+        filename = "ModelParameters" + "_" + parameters.author + "_" + parameters.scenario + "_" + datetime.now().isoformat() + ".json"
+        out_obj = {
+            "Author": parameters.author,
+            "Scenario": parameters.scenario,
+            "NumberOfDaysToProject": parameters.n_days,
+            "DoublingTimeBeforeSocialDistancing": parameters.doubling_time,
+            "SocialDistancingPercentReduction": parameters.relative_contact_rate,
+            "HospitalizationPercentage": parameters.hospitalized.rate,
+            "ICUPercentage": parameters.icu.rate,
+            "VentilatorsPercentage": parameters.ventilators.rate,
+            "HospitalLengthOfStay": parameters.hospitalized.length_of_stay,
+            "ICULengthOfStay": parameters.icu.length_of_stay,
+            "VentLengthOfStay": parameters.ventilators.length_of_stay,
+            "HospitalMarketShare": parameters.market_share,
+            "RegionalPopulation": parameters.susceptible,
+            "PresentResultAsDates": as_date,
+            "MaxYAxisSet":max_y_axis_set,
+            "MaxYAxis":max_y_axis,
+
+            "TotalNumberOfBedsForNCPatients": parameters.total_non_covid_beds,
+            "TotalNumberOfICUBedsForNCPatients": parameters.total_non_covid_icu_beds,
+            "TotalNumberOfVentsForNCPatients": parameters.total_non_covid_vents,
+
+            "CurrentlyHospitalizedCovidPatients": parameters.current_hospitalized,
+            "CurrentlyHospitalizedCovidPatientsDate": parameters.census_date.isoformat(),
+            "SelectedOffsetDays": parameters.selected_offset,
+        }
+        out_json = json.dumps(out_obj)
+        b64_json = base64.b64encode(out_json.encode()).decode()
+        st.sidebar.markdown(
+            """<a download="{filename}" href="data:text/plain;base64,{b64_json}" style="padding:.75em;border-radius:10px;background-color:#00aeff;color:white;font-family:sans-serif;text-decoration:none;">Save Scenario</a>"""
+            .format(b64_json=b64_json,filename=filename), 
+            unsafe_allow_html=True,
+        )

src/penn_chime/models.py

@@ -89,7 +89,7 @@ def __init__(self, p: Parameters):
             self.i_day = 0 # seed to the full length
             self.beta_t = self.beta
             self.run_projection(p)
-            self.i_day = i_day = int(get_argmin_ds(self.census_df, p.current_hospitalized))
+            self.i_day = i_day = int(get_argmin_ds(self.census_df, p.covid_census_value))
 
             self.beta_t = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, p.relative_contact_rate)
             self.run_projection(p)
@@ -114,7 +114,7 @@ def __init__(self, p: Parameters):
             min_loss = 2.0**99
             dts = np.linspace(1, 15, 29)
             losses = np.zeros(dts.shape[0])
-            self.current_hospitalized = p.current_hospitalized
+            self.covid_census_value = p.covid_census_value
             for i, i_dt in enumerate(dts):
                 intrinsic_growth_rate = get_growth_rate(i_dt)
                 self.beta = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, 0.0)
@@ -184,14 +184,14 @@ def run_projection(self, p):
     def get_loss(self) -> float:
         """Squared error: predicted vs. actual current hospitalized."""
         predicted = self.census_df.hospitalized.loc[self.i_day]
-        return (self.current_hospitalized - predicted) ** 2.0
+        return (self.covid_census_value - predicted) ** 2.0
 
 
-def get_argmin_ds(census_df: pd.DataFrame, current_hospitalized: float) -> float:
+def get_argmin_ds(census_df: pd.DataFrame, covid_census_value: float) -> float:
     # 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
+    losses_df = (census_df.hospitalized[:peak_day] - covid_census_value) ** 2.0
     return losses_df.argmin()
 
 

src/penn_chime/parameters.py

@@ -5,7 +5,7 @@
 """
 
 from collections import namedtuple
-from datetime import date
+import datetime
 from typing import Optional
 
 from .validators import (
@@ -51,13 +51,18 @@ class Parameters:
     def __init__(
         self,
         *,
-        current_hospitalized: int,
+        covid_census_value: int, # used to be current_hospitalized
+        covid_census_date: datetime.date, # added by Health Catalyst team
+        total_covid_beds: int,
+        icu_covid_beds: int,
+        covid_ventilators: int,
         hospitalized: Disposition,
         icu: Disposition,
         relative_contact_rate: float,
-        ventilated: Disposition,
-        current_date: date = date.today(),
-        date_first_hospitalized: Optional[date] = None,
+        ventilators: Disposition, # used to be ventilated
+        current_date: datetime.date = datetime.date.today() - datetime.timedelta(hours=6),
+        social_distancing_date: datetime.date = datetime.date.today()  - datetime.timedelta(hours=6),
+        date_first_hospitalized: Optional[datetime.date] = None,
         doubling_time: Optional[float] = None,
         infectious_days: int = 14,
         market_share: float = 1.0,
@@ -66,17 +71,25 @@ def __init__(
         population: Optional[int] = None,
         recovered: int = 0,
         region: Optional[Regions] = None,
+        # Added by the Health Catalyst Team
     ):
-        self.current_hospitalized = StrictlyPositive(value=current_hospitalized)
+        self.covid_census_value = StrictlyPositive(value=covid_census_value)
+        self.covid_census_date = Date(value=covid_census_date)
+        self.current_date = Date(value=current_date)
         self.relative_contact_rate = Rate(value=relative_contact_rate)
 
-        Rate(value=hospitalized.rate), Rate(value=icu.rate), Rate(value=ventilated.rate)
-        StrictlyPositive(value=hospitalized.days), StrictlyPositive(value=icu.days),
-        StrictlyPositive(value=ventilated.days)
+        self.total_covid_beds = StrictlyPositive(value=total_covid_beds)
+        self.icu_covid_beds = StrictlyPositive(value=icu_covid_beds)
+        self.covid_ventilators = StrictlyPositive(value=covid_ventilators)
+        Rate(value=hospitalized.rate)
+        Rate(value=icu.rate)
+        StrictlyPositive(value=hospitalized.days)
+        StrictlyPositive(value=icu.days),
 
         self.hospitalized = hospitalized
         self.icu = icu
-        self.ventilated = ventilated
+
+        self.ventilators = ventilators
 
         if region is not None and population is None:
             self.region = region
@@ -87,7 +100,7 @@ def __init__(
         else:
             raise AssertionError('population or regions must be provided.')
 
-        self.current_date = Date(value=current_date)
+        self.social_distancing_date = Date(value=social_distancing_date)
 
         self.date_first_hospitalized = OptionalDate(value=date_first_hospitalized)
         self.doubling_time = OptionalStrictlyPositive(value=doubling_time)
@@ -101,7 +114,7 @@ def __init__(
         self.labels = {
             "hospitalized": "Hospitalized",
             "icu": "ICU",
-            "ventilated": "Ventilated",
+            "ventilators": "Ventilators",
             "day": "Day",
             "date": "Date",
             "susceptible": "Susceptible",
@@ -112,5 +125,5 @@ def __init__(
         self.dispositions = {
             "hospitalized": hospitalized,
             "icu": icu,
-            "ventilated": ventilated,
+            "ventilators": ventilators,
         }