Run black: Fix all the formatting!!!

Author: Michael Becker

src/app.py

@@ -4,7 +4,7 @@
 import streamlit as st  # type: ignore
 
 from penn_chime.presentation import (
-    #additional_projections_chart,
+    # additional_projections_chart,
     display_header,
     display_sidebar,
     display_n_days_slider,
@@ -17,9 +17,14 @@
     write_definitions,
     write_footer,
 )
-from penn_chime.settings import DEFAULTS 
+from penn_chime.settings import DEFAULTS
 from penn_chime.models import sim_sir_df, build_admissions_df, build_census_df
-from penn_chime.charts import additional_projections_chart, admitted_patients_chart, new_admissions_chart
+from penn_chime.charts import (
+    additional_projections_chart,
+    admitted_patients_chart,
+    new_admissions_chart,
+)
+
 # This is somewhat dangerous:
 # Hide the main menu with "Rerun", "run on Save", "clear cache", and "record a screencast"
 # This should not be hidden in prod, but removed
@@ -32,27 +37,23 @@
 
 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)"
-    show_more_info_about_this_tool(
-        st=st,
-        parameters=p,
-        inputs=DEFAULTS,
-        notes=notes
-    )
+    show_more_info_about_this_tool(st=st, parameters=p, inputs=DEFAULTS, notes=notes)
 
 # PRESENTATION
 # Two more combination variable initialization / input element creation
 as_date = st.checkbox(label="Present result as dates instead of days", value=False)
 display_n_days_slider(st, p, DEFAULTS)
 
 # begin format data
-admissions_df = build_admissions_df(p=p) # p.n_days, *p.dispositions)
+admissions_df = build_admissions_df(p=p)  # p.n_days, *p.dispositions)
 census_df = build_census_df(admissions_df, parameters=p)
 # end format data
 
 st.subheader("New Admissions")
 st.markdown("Projected number of **daily** COVID-19 admissions at Penn hospitals")
 st.altair_chart(
-    new_admissions_chart(alt, admissions_df, parameters=p, as_date=as_date), use_container_width=True
+    new_admissions_chart(alt, admissions_df, parameters=p, as_date=as_date),
+    use_container_width=True,
 )
 if st.checkbox("Show Projected Admissions in tabular form"):
     draw_projected_admissions_table(st, admissions_df, as_date=as_date)
@@ -61,7 +62,8 @@
     "Projected **census** of COVID-19 patients, accounting for arrivals and discharges at Penn hospitals"
 )
 st.altair_chart(
-    admitted_patients_chart(alt=alt, census=census_df, parameters=p, as_date=as_date), use_container_width=True
+    admitted_patients_chart(alt=alt, census=census_df, parameters=p, as_date=as_date),
+    use_container_width=True,
 )
 if st.checkbox("Show Projected Census in tabular form"):
     draw_census_table(st, census_df, as_date=as_date)
@@ -70,10 +72,8 @@
 )
 if st.checkbox("Show Additional Projections"):
     show_additional_projections(
-        st, alt,
-        additional_projections_chart,
-        parameters=p,
-        as_date=as_date)
+        st, alt, additional_projections_chart, parameters=p, as_date=as_date
+    )
     if st.checkbox("Show Raw SIR Simulation Data"):
         draw_raw_sir_simulation_table(st, parameters=p, as_date=as_date)
 write_definitions(st)

src/cli.py

@@ -37,30 +37,63 @@ def validate(string):
 
 def parse_args():
     """Parse args."""
-    parser = ArgumentParser(description='CHIME')
+    parser = ArgumentParser(description="CHIME")
 
-    parser.add_argument('--file', type=open, action=FromFile)
+    parser.add_argument("--file", type=open, action=FromFile)
     parser.add_argument(
-        '--prefix',
-        type=str,
-        default=datetime.now().strftime("%Y.%m.%d.%H.%M."),
+        "--prefix", type=str, default=datetime.now().strftime("%Y.%m.%d.%H.%M."),
     )
 
     for arg, cast, min_value, max_value, help in (
-        ('--current-hospitalized', int, 0, None, "Currently Hospitalized COVID-19 Patients (>= 0)"),
-        ('--doubling-time', float, 0.0, None, "Doubling time before social distancing (days)"),
-        ('--hospitalized-los', int, 0, None, "Hospitalized Length of Stay (days)"),
-        ('--hospitalized-rate', float, 0.00001, 1.0, "Hospitalized Rate: 0.00001 - 1.0"),
-        ('--icu-los', int, 0, None, "ICU Length of Stay (days)"),
-        ('--icu-rate', float, 0.0, 1.0, "ICU Rate: 0.0 - 1.0"),
-        ('--known-infected', int, 0, None,
-            "Currently Known Regional Infections (>=0) (only used to compute detection rate - does not change projections)"),
-        ('--market_share', float, 0.00001, 1.0, "Hospital Market Share (0.00001 - 1.0)"),
-        ('--n-days', int, 0, None, "Nuber of days to project >= 0"),
-        ('--relative-contact-rate', float, 0.0, 1.0, "Social Distancing Reduction Rate: 0.0 - 1.0"),
-        ('--susceptible', int, 1, None, "Regional Population >= 1"),
-        ('--ventilated-los', int, 0, None, "Hospitalized Length of Stay (days)"),
-        ('--ventilated-rate', float, 0.0, 1.0, "Ventilated Rate: 0.0 - 1.0"),
+        (
+            "--current-hospitalized",
+            int,
+            0,
+            None,
+            "Currently Hospitalized COVID-19 Patients (>= 0)",
+        ),
+        (
+            "--doubling-time",
+            float,
+            0.0,
+            None,
+            "Doubling time before social distancing (days)",
+        ),
+        ("--hospitalized-los", int, 0, None, "Hospitalized Length of Stay (days)"),
+        (
+            "--hospitalized-rate",
+            float,
+            0.00001,
+            1.0,
+            "Hospitalized Rate: 0.00001 - 1.0",
+        ),
+        ("--icu-los", int, 0, None, "ICU Length of Stay (days)"),
+        ("--icu-rate", float, 0.0, 1.0, "ICU Rate: 0.0 - 1.0"),
+        (
+            "--known-infected",
+            int,
+            0,
+            None,
+            "Currently Known Regional Infections (>=0) (only used to compute detection rate - does not change projections)",
+        ),
+        (
+            "--market_share",
+            float,
+            0.00001,
+            1.0,
+            "Hospital Market Share (0.00001 - 1.0)",
+        ),
+        ("--n-days", int, 0, None, "Nuber of days to project >= 0"),
+        (
+            "--relative-contact-rate",
+            float,
+            0.0,
+            1.0,
+            "Social Distancing Reduction Rate: 0.0 - 1.0",
+        ),
+        ("--susceptible", int, 1, None, "Regional Population >= 1"),
+        ("--ventilated-los", int, 0, None, "Hospitalized Length of Stay (days)"),
+        ("--ventilated-rate", float, 0.0, 1.0, "Ventilated Rate: 0.0 - 1.0"),
     ):
         parser.add_argument(arg, type=validator(cast, min_value, max_value))
     return parser.parse_args()
@@ -83,22 +116,24 @@ def main():
         ventilated=RateLos(a.ventilated_rate, a.ventilated_los),
     )
 
-    raw_df = DataFrame({
-        "Susceptible": p.susceptible_v,
-        "Infected": p.infected_v,
-        "Recovered": p.recovered_v,
-    })
+    raw_df = DataFrame(
+        {
+            "Susceptible": p.susceptible_v,
+            "Infected": p.infected_v,
+            "Recovered": p.recovered_v,
+        }
+    )
     admits_df = build_admissions_df(p.n_days, *p.dispositions)
     census_df = build_census_df(admits_df, *p.lengths_of_stay)
 
     prefix = a.prefix
     for df, name in (
-        (raw_df, 'raw'),
-        (admits_df, 'admits'),
-        (census_df, 'census'),
+        (raw_df, "raw"),
+        (admits_df, "admits"),
+        (census_df, "census"),
     ):
-        df.to_csv(prefix + name + '.csv')
+        df.to_csv(prefix + name + ".csv")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

src/penn_chime/charts.py

@@ -1,4 +1,3 @@
-
 from altair import Chart  # type: ignore
 import pandas as pd  # type: ignore
 import numpy as np  # type: ignore
@@ -8,10 +7,7 @@
 
 
 def new_admissions_chart(
-    alt,
-    projection_admits: pd.DataFrame,
-    parameters: Parameters,
-    as_date: bool = False,
+    alt, projection_admits: pd.DataFrame, parameters: Parameters, as_date: bool = False,
 ) -> Chart:
     """docstring"""
     plot_projection_days = parameters.n_days - 10
@@ -49,10 +45,7 @@ def new_admissions_chart(
 
 
 def admitted_patients_chart(
-    alt,
-    census: pd.DataFrame,
-    parameters: Parameters,
-    as_date: bool = False
+    alt, census: pd.DataFrame, parameters: Parameters, as_date: bool = False
 ) -> Chart:
     """docstring"""
 
@@ -63,7 +56,7 @@ def admitted_patients_chart(
         x_kwargs = {"shorthand": "date:T", "title": "Date"}
         idx = "date:T"
     else:
-        x_kwargs ={"shorthand": "day", "title": "Days from today"}
+        x_kwargs = {"shorthand": "day", "title": "Days from today"}
         idx = "day"
 
     y_scale = alt.Scale()
@@ -91,11 +84,7 @@ def admitted_patients_chart(
 
 
 def additional_projections_chart(
-    alt,
-    i: np.ndarray,
-    r: np.ndarray,
-    as_date: bool = False,
-    max_y_axis: int = None
+    alt, i: np.ndarray, r: np.ndarray, as_date: bool = False, max_y_axis: int = None
 ) -> Chart:
     dat = pd.DataFrame({"Infected": i, "Recovered": r})
     dat["day"] = dat.index

src/penn_chime/defaults.py

@@ -5,6 +5,7 @@
 
 class Regions:
     """Arbitrary number of counties."""
+
     def __init__(self, **kwargs):
         susceptible = 0
         for key, value in kwargs.items():
@@ -19,18 +20,18 @@ def susceptible(self):
 
 class Constants:
     def __init__(
-        self, *,
+        self,
+        *,
         current_hospitalized: int,
         doubling_time: int,
         known_infected: int,
         n_days: int,
         relative_contact_rate: int,
         region: Regions,
-
         hospitalized: RateLos,
         icu: RateLos,
         ventilated: RateLos,
-        market_share: float = 1.0
+        market_share: float = 1.0,
     ):
         self.region = region
         self.known_infected = known_infected

src/penn_chime/models.py

@@ -7,8 +7,7 @@
 
 
 def sir(
-    s: float, i: float, r: float,
-    beta: float, gamma: float, n: float
+    s: float, i: float, r: float, beta: float, gamma: float, n: float
 ) -> Tuple[float, float, float]:
     """The SIR model, one time step."""
     s_n = (-beta * s * i) + s
@@ -26,8 +25,7 @@ def sir(
 
 
 def gen_sir(
-    s: float, i: float, r: float,
-    beta: float, gamma: float, n_days: int
+    s: float, i: float, r: float, beta: float, gamma: float, n_days: int
 ) -> Generator[Tuple[float, float, float], None, None]:
     """Simulate SIR model forward in time yielding tuples."""
     s, i, r = (float(v) for v in (s, i, r))
@@ -38,8 +36,7 @@ def gen_sir(
 
 
 def sim_sir(
-    s: float, i: float, r: float,
-    beta: float, gamma: float, n_days: int
+    s: float, i: float, r: float, beta: float, gamma: float, n_days: int
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     """Simulate the SIR model forward in time."""
     s, i, r = (float(v) for v in (s, i, r))
@@ -79,20 +76,20 @@ def get_dispositions(
 def build_admissions_df(p) -> pd.DataFrame:
     """Build admissions dataframe from Parameters."""
     days = np.array(range(0, p.n_days + 1))
-    data_dict = dict(zip(["day", "Hospitalized", "ICU", "Ventilated"],
-                         [days] + [disposition for disposition in p.dispositions]
-    ))
+    data_dict = dict(
+        zip(
+            ["day", "Hospitalized", "ICU", "Ventilated"],
+            [days] + [disposition for disposition in p.dispositions],
+        )
+    )
     projection = pd.DataFrame.from_dict(data_dict)
     # New cases
     projection_admits = projection.iloc[:-1, :] - projection.shift(1)
     projection_admits["day"] = range(projection_admits.shape[0])
     return projection_admits
 
 
-def build_census_df(
-        projection_admits: pd.DataFrame,
-        parameters
-) -> pd.DataFrame:
+def build_census_df(projection_admits: pd.DataFrame, parameters) -> pd.DataFrame:
     """ALOS for each category of COVID-19 case (total guesses)"""
     n_days = np.shape(projection_admits)[0]
     hosp_los, icu_los, vent_los = parameters.lengths_of_stay
@@ -115,8 +112,9 @@ def build_census_df(
     census_df = census_df[["day", "Hospitalized", "ICU", "Ventilated"]]
     census_df = census_df.head(n_days)
     census_df = census_df.rename(
-        columns={disposition: f"{disposition}"
-                 for disposition
-                 in ("Hospitalized", "ICU", "Ventilated")}
+        columns={
+            disposition: f"{disposition}"
+            for disposition in ("Hospitalized", "ICU", "Ventilated")
+        }
     )
     return census_df