Remove inferrable parameter

covid19_drdfm/streamlit/Dashboard.py

@@ -1,15 +1,10 @@
-from datetime import datetime
 import json
 import time
 from pathlib import Path
 
 import pandas as pd
 import plotly.io as pio
-import plotly_express as px
-import statsmodels.api as sm
 import streamlit as st
-from rich import print as pprint
-from sklearn.preprocessing import MinMaxScaler
 
 from covid19_drdfm.constants import FACTORS
 from covid19_drdfm.dfm import run_parameterized_model

covid19_drdfm/streamlit/pages/2_Comparative_Run_Analysis.py

@@ -3,6 +3,7 @@
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import plotly.io as pio
 import plotly_express as px
 import pymc as pm
@@ -70,19 +71,20 @@ def delta_failures(run_dir: Path, run_name: str):
     return min_failures - num_failures(run_dir, run_name)
 
 
-def get_summary(df, run_name):
+def get_summary(df: pd.DataFrame):
     # Median metrics
+    run_name = df.Run.iloc[0]
     col1, col2, col3, col4 = st.columns(4)
     col1.metric("Number of Failed States", num_failures(run_dir, run_name), delta_failures(run_dir, run_name))
     col2.metric("Median Log Likelihood", df["Log Likelihood"].median())
     col3.metric("Median AIC", df["AIC"].median())
     col4.metric("Median EM Iterations", df["EM Iterations"].median())
 
 
-def show_summary(df):
+def show_summary(df: pd.DataFrame):
     run = st.selectbox("Select a run", df["Run"].unique())
     filtered_df = df[(df["Run"] == run)]
-    return get_summary(filtered_df, run)
+    return get_summary(filtered_df)
 
 
 def run_normal(df, metric):