structured repo for huggingface spaces

streamlit_app.py → app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 from datetime import time, date
-from utils import generate_random_data, evaluate_alarm_state, aggregate_data
+from utils import generate_random_data, evaluate_alarm_state, aggregate_data, re_aggregate_data
 from textwrap import dedent
 from matplotlib import pyplot as plt
 
@@ -20,7 +20,7 @@ def main():
 
     if not st.session_state.df.empty:
         display_dataframe("Raw Event Data", st.session_state.df)
-        st.scatter_chart(st.session_state.df.set_index("Timestamp"))
+        st.line_chart(st.session_state.df.set_index("Timestamp"))
 
     # Section 2 - Calculate Aggregations
     st.header("Section 2 - Calculate Aggregations")
@@ -34,7 +34,7 @@ def main():
             key='aggregation_function_input__storage',
             help="Select the aggregation function for visualizing the data."
         )
-        st.line_chart(st.session_state.aggregated_df.set_index("Timestamp")[st.session_state.aggregation_function_input__storage])
+        st.line_chart(st.session_state.aggregated_df.set_index("Timestamp")[aggregation_function_input__storage])
 
     # Section 3 - Summary Data Aggregated by Period
     st.header("Section 3 - Summary Data Aggregated by Period")
@@ -48,7 +48,7 @@ def main():
             key='aggregation_function_input__alarm',
             help="Select the aggregation function for visualizing the data."
         )
-        st.line_chart(st.session_state.summary_by_period_df.set_index("Timestamp")[st.session_state.aggregation_function_input__alarm])
+        st.line_chart(st.session_state.summary_by_period_df.set_index("Timestamp")[aggregation_function_input__alarm])
 
     # Section 4 - Evaluate Alarm State
     st.header("Section 4 - Evaluate Alarm State")
@@ -96,8 +96,8 @@ def aggregation_form() -> None:
 
 def summary_by_period_form() -> None:
     period_length_input = st.selectbox("Period Length", ['1min', '5min', '15min'], key='period_length_input', help="Select the period length for aggregating the summary data.")
-    if not st.session_state.df.empty:
-        st.session_state.summary_by_period_df = aggregate_data(st.session_state.df, period_length_input)
+    if not st.session_state.aggregated_df.empty:
+        st.session_state.summary_by_period_df = re_aggregate_data(st.session_state.aggregated_df, period_length_input)
 
 def alarm_state_form() -> None:
     threshold_input = st.slider("Threshold (ms)", min_value=50, max_value=300, value=150, key='threshold_input', help="Specify the threshold value for evaluating the alarm state.")
@@ -238,3 +238,4 @@ def display_key_tables() -> None:
 
 if __name__ == "__main__":
     main()
+

requirements.dev.txt

@@ -0,0 +1,3 @@
+ipykernel
+jupyterlab
+watchdog

requirements.txt

@@ -1,6 +1,4 @@
 pandas
 numpy
-ipykernel
-jupyterlab
 streamlit
 matplotlib

utils.py

@@ -42,13 +42,14 @@ def calculate_percentile(
     freq: str,
     percentile: float
 ) -> pd.DataFrame:
-    percentile_df: pd.DataFrame = df.groupby(pd.Grouper(key='Timestamp', freq=freq))["ResponseTime(ms)"]                                    .quantile(percentile).reset_index(name=f"p{int(percentile * 100)}_ResponseTime(ms)")
+    percentile_df: pd.DataFrame = df.groupby(pd.Grouper(key='Timestamp', freq=freq))["ResponseTime(ms)"]\
+                                    .quantile(percentile).reset_index(name=f"p{int(percentile * 100)}_ResponseTime(ms)")
     percentile_df.replace(to_replace=np.nan, value=None, inplace=True)
     return percentile_df
 
 def aggregate_data(
     df: pd.DataFrame,
-    period_length: str
+    period_length: str,
 ) -> pd.DataFrame:
     if df.empty:
         return pd.DataFrame()  # Return an empty DataFrame if input is empty
@@ -72,6 +73,33 @@ def aggregate_data(
     ).reset_index()
     return summary_df
 
+def re_aggregate_data(
+    df: pd.DataFrame,
+    period_length: str,
+) -> pd.DataFrame:
+    if df.empty:
+        return pd.DataFrame()  # Return an empty DataFrame if input is empty
+
+    aggregation_funcs = {
+        'p50': lambda x: np.percentile(x.dropna(), 50) if not x.dropna().empty else np.nan,
+        'p95': lambda x: np.percentile(x.dropna(), 95) if not x.dropna().empty else np.nan,
+        'p99': lambda x: np.percentile(x.dropna(), 99) if not x.dropna().empty else np.nan,
+        'max': lambda x: np.max(x.dropna()) if not x.dropna().empty else np.nan,
+        'min': lambda x: np.min(x.dropna()) if not x.dropna().empty else np.nan,
+        'average': lambda x: np.mean(x.dropna()) if not x.dropna().empty else np.nan
+    }
+
+    summary_df = df.groupby(pd.Grouper(key='Timestamp', freq=period_length)).agg(
+        p50=('p50', aggregation_funcs['p50']),
+        p95=('p95', aggregation_funcs['p95']),
+        p99=('p99', aggregation_funcs['p99']),
+        max=('max', aggregation_funcs['max']),
+        min=('min', aggregation_funcs['min']),
+        average=('average', aggregation_funcs['average']),
+    ).reset_index()
+    return summary_df
+
+
 def chunk_list(input_list: List[Any], size: int = 3) -> Iterator[List[Any]]:
     while input_list:
         chunk: List[Any] = input_list[:size]