Skip to content

Commit

Permalink
warn warn warn
Browse files Browse the repository at this point in the history
  • Loading branch information
@afmagee42
afmagee42 committed Dec 18, 2024
1 parent 117a0ad commit b889063
Show file tree
Hide file tree
Showing 3 changed files with 101 additions and 54 deletions.
2 changes: 1 addition & 1 deletion ringvax/__init__.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,7 @@ def run(self) -> None:
bisect.insort_right(infection_queue, (t, id), key=lambda x: x[0])

if len(self.query_people()) >= self.params["max_infections"]:
termination["max_infections"] = True
termination["criterion"] = "max_infections"
min_in_progress = min(
self.infections[parent]["generation"]
for _, parent in infection_queue
Expand Down
132 changes: 88 additions & 44 deletions ringvax/app.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@ def app():
)
max_infections = st.number_input(
"Maximum number of infections",
value=100,
value=1000,
step=10,
min_value=100,
help="",
Expand All @@ -169,7 +169,9 @@ def app():
}

sims = []
with st.spinner("Running simulation..."):
with st.spinner(
"Running simulation... Slow simulations may indicate unreasonable parameter values leading to unrealistically large outbreaks."
):
tic = time.perf_counter()
for i in range(nsim):
sims.append(Simulation(params=params, seed=seed + i))
Expand All @@ -180,60 +182,102 @@ def app():
f"Ran {nsim} simulations in {format_duration(toc - tic)} with an $R_0$ of {infectious_duration * infection_rate:.2f} (the product of the average duration of infection and the infectious rate)."
)

tab1, tab2 = st.tabs(["Simulation summary", "Per-simulation results"])
with tab1:
sim_df = get_all_person_properties(sims)
n_at_max = sum(
1 for sim in sims if sim.termination["criterion"] == "max_infections"
)

pr_control = prob_control_by_gen(sim_df, control_generations)
st.header(
f"Probability of control: {pr_control:.0%}",
help=f"The probability that there are no infections in the {format_control_gens(control_generations)}, or equivalently that the {format_control_gens(control_generations - 1)} do not produce any further infections.",
show = True if n_at_max == 0 else False
if not show:
st.warning(
body=(
f"{n_at_max} simulations hit the specified maximum number of infections ({max_infections})."
),
icon="🚨",
)

st.header("Number of infections")
st.write(
f"Distribution of the total number of infections seen in {n_generations} generations."
)
st.altair_chart(
alt.Chart(get_outbreak_size_df(sim_df))
.mark_bar()
.encode(
x=alt.X("size:Q", bin=True, title="Number of infections"),
y=alt.Y("count()", title="Count"),
)
st.warning(
body=(
"Simulations hitting the maximum likely indicate implausible parameter values. "
'It is recommended that you either adjust simulating parameters or increase "Maximum number of infections".'
),
)

st.header("Summary of dynamics")
infection = summarize_infections(sim_df)
st.write(
f"In these simulations, the average duration of infectiousness was {infection['mean_infectious_duration'][0]:.2f} and $R_e$ was {infection['mean_n_infections'][0]:.2f}"
st.warning(
body=(
"Note that results are summarized only for simulations which do not exceed this maximum. "
"This means that simulations with large final sizes will be missing from the results, biasing results. "
),
)

st.write(
"The following table provides summaries of marginal probabilities regarding detection. Aside from the marginal probability of active detection, these are the observed probabilities that any individual is detected in this manner. The marginal probability of active detection excludes index cases, which are not eligible for active detection."
accept_terms_and_conditions = st.button(
"I accept that the results are biased and may not be meaningful. Please show them anyways."
)
detection = summarize_detections(sim_df)
st.dataframe(
detection.select(
(pl.col(col) * 100).round(0).cast(pl.Int64) for col in detection.columns
if accept_terms_and_conditions:
show = True

if show:
if n_at_max == nsim:
st.error(
"No simulations completed successfully. Please change settings and try again.",
icon="🚨",
)
st.stop()

tab1, tab2 = st.tabs(["Simulation summary", "Per-simulation results"])
with tab1:
sim_df = get_all_person_properties(sims)

pr_control = prob_control_by_gen(sim_df, control_generations)
st.header(
f"Probability of control: {pr_control:.0%}",
help=f"The probability that there are no infections in the {format_control_gens(control_generations)}, or equivalently that the {format_control_gens(control_generations - 1)} do not produce any further infections.",
)
.with_columns(
pl.concat_str([pl.col(col), pl.lit("%")], separator="")
for col in detection.columns

st.header("Number of infections")
st.write(
f"Distribution of the total number of infections seen in {n_generations} generations."
)
.rename(
{
"prob_detect": "Any detection",
"prob_active": "Active detection",
"prob_passive": "Passive detection",
"prob_detect_before_infectious": "Detection before onset of infectiousness",
}
st.altair_chart(
alt.Chart(get_outbreak_size_df(sim_df))
.mark_bar()
.encode(
x=alt.X("size:Q", bin=True, title="Number of infections"),
y=alt.Y("count()", title="Count"),
)
)

st.header("Summary of dynamics")
infection = summarize_infections(sim_df)
st.write(
f"In these simulations, the average duration of infectiousness was {infection['mean_infectious_duration'][0]:.2f} and $R_e$ was {infection['mean_n_infections'][0]:.2f}"
)

st.write(
"The following table provides summaries of marginal probabilities regarding detection. Aside from the marginal probability of active detection, these are the observed probabilities that any individual is detected in this manner. The marginal probability of active detection excludes index cases, which are not eligible for active detection."
)
detection = summarize_detections(sim_df)
st.dataframe(
detection.select(
(pl.col(col) * 100).round(0).cast(pl.Int64)
for col in detection.columns
)
.with_columns(
pl.concat_str([pl.col(col), pl.lit("%")], separator="")
for col in detection.columns
)
.rename(
{
"prob_detect": "Any detection",
"prob_active": "Active detection",
"prob_passive": "Passive detection",
"prob_detect_before_infectious": "Detection before onset of infectiousness",
}
)
)
)

with tab2:
st.header("Graph of infections")
show_graph(sims=sims)
with tab2:
st.header("Graph of infections")
show_graph(sims=sims)


if __name__ == "__main__":
Expand Down
21 changes: 12 additions & 9 deletions ringvax/summary.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,9 @@ def prepare_for_df(infection: dict) -> dict:
return dfable


def get_all_person_properties(sims: Sequence[Simulation]) -> pl.DataFrame:
def get_all_person_properties(
sims: Sequence[Simulation], exclude_termination_if: list[str] = ["max_infections"]
) -> pl.DataFrame:
"""
Get a dataframe of all properties of all infections
"""
Expand All @@ -54,14 +56,15 @@ def get_all_person_properties(sims: Sequence[Simulation]) -> pl.DataFrame:

per_sim = []
for idx, sim in enumerate(sims):
sims_dict = {k: [] for k in infection_schema.keys()} | {
"simulation": [idx] * len(sim.infections)
}
for infection in sim.infections.values():
prep = prepare_for_df(infection)
for k in infection_schema.keys():
sims_dict[k].append(prep[k])
per_sim.append(pl.DataFrame(sims_dict).cast(infection_schema)) # type: ignore
if sim.termination["criterion"] not in exclude_termination_if:
sims_dict = {k: [] for k in infection_schema.keys()} | {
"simulation": [idx] * len(sim.infections)
}
for infection in sim.infections.values():
prep = prepare_for_df(infection)
for k in infection_schema.keys():
sims_dict[k].append(prep[k])
per_sim.append(pl.DataFrame(sims_dict).cast(infection_schema)) # type: ignore
return pl.concat(per_sim)


Expand Down

0 comments on commit b889063

Please sign in to comment.