Repository: Phinart98/Air-Quality-Analysis
Pull request #2: Feature/time series analysis
Author: login: Phinart98
Labels: name: size:L
description: This PR changes 100-499 lines, ignoring generated files
Created: 2025-05-18T10:07:54Z | Merged: 2025-05-18T11:11:06Z

## **User description**
# Time Series Analysis Feature with Resilient Data Handling

This PR adds a comprehensive time series analysis tab to the dashboard, enabling users to analyze historical pollutant data with robust error handling and fallback mechanisms.

## Key Features
- Station-specific historical data analysis
- Support for analyzing different pollutants (PM10, PM2.5, NO₂, etc.)
- Adjustable time period (7-90 days) via an interactive slider
- Statistical insights (average, maximum, minimum)
- Interactive time series charts with 24-hour moving averages

## Technical Implementation
- Created modular time_series.py component
- Multi-endpoint API integration with fallback strategy
- Comprehensive error handling with user feedback
- Mock data generation for demonstration when API fails
- Responsive Plotly visualizations

## Resilience Features
- Graceful handling of API timeouts and server errors
- Debug information during API failures
- Synthetic data fallback when external sources are unavailable
- Full UI functionality regardless of API status

## Summary by Sourcery

Introduce a modular time series analysis component to fetch, visualize, and summarize historical pollutant data with resilient error handling and fallback mechanisms

New Features:
- Add a new "Time Series Analysis" tab to the dashboard for station-specific pollutant history
- Provide interactive pollutant selection, adjustable 7–90 day time range slider, and Plotly charts with 24-hour moving averages
- Display statistical metrics (average, maximum, minimum) alongside the time series visualization

Enhancements:
- Implement multi-endpoint API integration with fallback strategy and comprehensive error handling
- Generate mock/synthetic data upon API failures to maintain full UI functionality
- Surface debug information and user-friendly feedback during data fetching


___

## **CodeAnt-AI Description**
- Added a new modular `time_series.py` component that enables interactive time series analysis of historical pollutant data for monitoring stations.
- Implemented robust data fetching logic with fallback to alternative API endpoints and comprehensive error handling, including debug feedback for users.
- Provided mock/synthetic data generation to ensure full UI functionality even when external APIs are unavailable.
- Integrated the time series analysis as a new tab in the dashboard, allowing users to select stations, pollutants, and time ranges, and view interactive Plotly charts with 24-hour moving averages and key statistical metrics.

This PR introduces a comprehensive and resilient time series analysis feature to the dashboard, greatly enhancing its analytical capabilities and user experience. The implementation ensures the dashboard remains functional and informative even in the face of data source failures.


___

## **Changes walkthrough**
<table><thead><tr><th></th><th align="left">Relevant files</th></tr></thead><tbody><tr><td><strong>Enhancement
</strong></td><td><table>
<tr>
  <td>
    <details>
      <summary><strong>time_series.py</strong><dd><code>Add modular time series analysis component with resilient data <br>handling</code></dd></summary>
<hr>

src/time_series.py
<li>Introduced a new modular component for time series analysis of <br>pollutant data.<br> <li> Implements robust data fetching with multi-endpoint API fallback and <br>comprehensive error handling.<br> <li> Generates mock/synthetic data for demonstration if API calls fail.<br> <li> Provides interactive Streamlit UI for station, pollutant, and time <br>range selection, with Plotly visualizations and statistical summaries.<br> <br>


</details>


  </td>
  <td><a href="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8">+238/-0</a>&nbsp; </td>
</tr>

<tr>
  <td>
    <details>
      <summary><strong>dashboard.py</strong><dd><code>Integrate time series analysis tab into dashboard UI</code>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; </dd></summary>
<hr>

src/dashboard.py
<li>Integrated the new time series analysis feature as a fourth tab in the <br>dashboard.<br> <li> Imports and invokes the <code>add_time_series_section</code> function to enable <br>interactive historical data analysis.<br> <li> Ensures seamless UI integration with existing dashboard structure.<br>


</details>


  </td>
  <td><a href="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-06135492c180d831917894055e5bb06ed9c4f31998a88b446abd432d50f6a4c7">+7/-2</a>&nbsp; &nbsp; &nbsp; </td>
</tr>
</table></td></tr></tr></tbody></table><details>
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<!-- This is an auto-generated comment: release notes by coderabbit.ai -->

## Summary by CodeRabbit

- **New Features**
  - Introduced a "Time Series Analysis" tab in the dashboard, enabling users to select a monitoring station and pollutant, specify a time range, and visualize historical pollutant data with interactive charts and summary statistics.
  - Added interactive controls for station and pollutant selection, as well as time range adjustment, within the new tab.
  - Users receive feedback and warnings if data is unavailable.

<!-- end of auto-generated comment: release notes by coderabbit.ai --> 
 <div id='description'>
<h3>Summary by Bito</h3>
This PR adds a comprehensive time series analysis feature with a dedicated dashboard tab for visualizations. Users can select stations, pollutants, and adjust time windows for analysis. The implementation includes robust error handling with mock data fallbacks for API failures, enhancing analytical capabilities with interactive visualizations.
<br>
<br>
<b>Unit tests added</b>: False
<br>
<br>
<b>Estimated effort to review (1-5, lower is better)</b>: 2
</div>

<!-- Korbit AI PR Description Start -->
## Description by Korbit AI

### What change is being made?

Add a new "Time Series Analysis" feature to the dashboard, including a dedicated section for historical pollutant data analysis using Streamlit, and integrate the necessary functionalities for data fetching, plotting, and statistical summaries from a new `time_series.py` module.

### Why are these changes being made?

The changes are introduced to enhance the dashboard by providing a detailed temporal view of pollution data, allowing users to analyze trends and fluctuations over specified timeframes. This feature was created to enrich the data insights offered by the dashboard, and it employs mocking for missing data to ensure seamless end-user experience. The new feature leverages Plotly for interactive visualization and requests for data fetching, aligning with the existing framework's technology stack.

> Is this description stale? Ask me to generate a new description by commenting `/korbit-generate-pr-description`
<!-- Korbit AI PR Description End -->


Diff:
diff --git a/src/dashboard.py b/src/dashboard.py
index 9fe2494..0f3c70e 100644
--- a/src/dashboard.py
+++ b/src/dashboard.py
@@ -7,6 +7,7 @@
 import geopandas as gpd
 import requests
 from datetime import datetime, timedelta
+from time_series import add_time_series_section
 
 def get_pollutant_info():
     return {
@@ -111,10 +112,11 @@ def create_dashboard():
         stations_gdf = load_data(selected_countries)
     
     # Main content tabs
-    tab1, tab2, tab3 = st.tabs([
+    tab1, tab2, tab3, tab4 = st.tabs([
         "Network Overview",
         "Station Distribution",
-        "Pollutant Analysis"
+        "Pollutant Analysis",
+        "Time Series Analysis"
     ])
     
     with tab1:
@@ -245,6 +247,9 @@ def create_dashboard():
         )
         fig.update_layout(bargap=0.1)
         st.plotly_chart(fig, use_container_width=True)
+        
+    with tab4:
+        add_time_series_section(stations_gdf, pollutant_info)
 
 if __name__ == "__main__":
     create_dashboard()
diff --git a/src/time_series.py b/src/time_series.py
new file mode 100644
index 0000000..200eb64
--- /dev/null
+++ b/src/time_series.py
@@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
+    available_pollutants = station_row["pollutants"] if isinstance(station_row["pollutants"], list) else []
+    
+    if not available_pollutants:
+        st.warning("No pollutant data available for the selected station.")
+        return
+    
+    selected_pollutant = st.selectbox(
+        "Select pollutant to analyze:",
+        options=[p for p in available_pollutants if p in pollutant_info],
+        format_func=lambda p: pollutant_info[p]["name"]
+    )
+    
+    # Time range selection
+    time_range = st.slider(
+        "Select time range (days):",
+        min_value=7,
+        max_value=90,
+        value=30,
+        step=1
+    )
+    
+    # Fetch and display data
+    with st.spinner("Fetching historical data..."):
+        df = fetch_historical_data(
+            selected_station["id"],
+            selected_pollutant,
+            days=time_range
+        )
+    
+    if not df.empty:
+        fig = plot_time_series(df, pollutant_info)
+        if fig:
+            st.plotly_chart(fig, use_container_width=True)
+            
+            # Basic statistics
+            st.subheader("Statistical Summary")
+            col1, col2, col3 = st.columns(3)
+            
+            with col1:
+                st.metric(
+                    "Average", 
+                    f"{df['value'].mean():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+            
+            with col2:
+                st.metric(
+                    "Maximum", 
+                    f"{df['value'].max():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+                
+            with col3:
+                st.metric(
+                    "Minimum", 
+                    f"{df['value'].min():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+    else:
+        st.warning("No data available for the selected parameters.")


Reviews:
user: login: codereviewbot-ai[bot]
state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:08:39Z
user: login: korbit-ai[bot]
body: ### Review by Korbit AI

###### Korbit automatically attempts to detect when you fix issues in new commits.

| Category | Issue | Status |
|----------|-------|----------|
| <sub>![Security](https://img.shields.io/badge/Security-e11d48)</sub> | Unfiltered Exception Message Exposure ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466363 ) ||
| <sub>![Performance](https://img.shields.io/badge/Performance-4f46e5)</sub> | Inefficient Row Lookup ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466365 ) ||
| <sub>![Logging](https://img.shields.io/badge/Logging-4f46e5)</sub> | UI-based debugging instead of proper logging ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466374 ) ||
| <sub>![Performance](https://img.shields.io/badge/Performance-4f46e5)</sub> | Inefficient DataFrame Iteration ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466375 ) ||
| <sub>![Performance](https://img.shields.io/badge/Performance-4f46e5)</sub> | Unnecessary DataFrame Copy ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466367 ) ||
| <sub>![Documentation](https://img.shields.io/badge/Documentation-7c3aed)</sub> | Incomplete return value documentation ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466368 ) ||
| <sub>![Documentation](https://img.shields.io/badge/Documentation-7c3aed)</sub> | Missing pollutant_info structure documentation ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466371 ) ||
| <sub>![Error Handling](https://img.shields.io/badge/Error%20Handling-ea580c)</sub> | Insufficient Error Context in API Exception Handler ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466372 ) ||
| <sub>![Error Handling](https://img.shields.io/badge/Error%20Handling-ea580c)</sub> | Undifferentiated HTTP Error Handling ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466373 ) ||
| <sub>![Readability](https://img.shields.io/badge/Readability-0284c7)</sub> | Unexplained Magic Numbers in Mock Data Generation ▹ [view]( https://github.com/Phinart98/Air-Quality-Analysis/pull/2#discussion_r2094466376 ) ||






<details>
  <summary>Files scanned</summary>
&#8203;



| File Path | Reviewed |
| ------------------ | -------------- |
| src/dashboard.py | ✅ |
| src/time_series.py | ✅ |


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state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:09:09Z
user: login: greptile-apps[bot]
body: 
## PR Summary

Added a comprehensive time series analysis feature to the air quality dashboard, enabling historical pollutant data visualization and analysis with robust error handling.

- Implemented resilient API data fetching in `src/time_series.py` with fallback to mock data generation
- Added interactive time series visualization with 24-hour moving averages and statistical metrics
- Created station/pollutant selection interface with dynamic filtering based on available data
- Integrated multi-endpoint API strategy with automatic failover between endpoints
- Added debug logging and user feedback for API connection issues

<sub>💡 (2/5) Greptile learns from your feedback when you react with 👍/👎!</sub>

<sub>2 file(s) reviewed, 5 comment(s)</sub>
<sub>[Edit PR Review Bot Settings](https://app.greptile.com/review/github) | [Greptile](https://greptile.com?utm_source=greptile_expert&utm_medium=github&utm_campaign=code_reviews&utm_content=air-quality-analysis_2)</sub>
state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:09:20Z
user: login: coderabbitai[bot]
body: **Actionable comments posted: 5**

<details>
<summary>🧹 Nitpick comments (3)</summary><blockquote>

<details>
<summary>src/dashboard.py (1)</summary><blockquote>

`251-253`: **Good separation of concerns**

Delegating the heavy lifting to `add_time_series_section()` keeps `dashboard.py` concise and maintains single-responsibility.  


If you anticipate multiple analytical sub-modules in the future, consider registering them through a small registry/dict to avoid an ever-growing list of `tabX` variables.

</blockquote></details>
<details>
<summary>src/time_series.py (2)</summary><blockquote>

`40-61`: **Gate verbose `st.info` logs behind a debug flag**

Frequent calls to `st.info` for every fetch can flood the sidebar and degrade UX. Wrap them in a verbosity check:

```diff
-        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        if st.session_state.get("debug", False):
+            st.info(f"Fetching data from {base_url} with parameters: {params}")
```

Do the same for subsequent `st.info` calls.

---

`138-140`: **Set `min_periods` to avoid an all-NaN moving average for <24-hour spans**

When users choose a 7-day window, the first 23 points render `NaN`. Improve visual continuity:

```diff
-    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24, min_periods=1).mean()
```

</blockquote></details>

</blockquote></details>

<details>
<summary>📜 Review details</summary>

**Configuration used: CodeRabbit UI**
**Review profile: CHILL**
**Plan: Pro**
**Cache: Disabled due to data retention organization setting**
**Knowledge Base: Disabled due to data retention organization setting**


<details>
<summary>📥 Commits</summary>

Reviewing files that changed from the base of the PR and between 76c9389c9f5f96bd5a902787f96fafe4cade1d0a and 93d0fdc5d4d753ae7cbc1511812ae97045cd751d.

</details>

<details>
<summary>📒 Files selected for processing (2)</summary>

* `src/dashboard.py` (3 hunks)
* `src/time_series.py` (1 hunks)

</details>

<details>
<summary>🧰 Additional context used</summary>

<details>
<summary>🧬 Code Graph Analysis (1)</summary>

<details>
<summary>src/dashboard.py (1)</summary><blockquote>

<details>
<summary>src/time_series.py (1)</summary>

* `add_time_series_section` (151-238)

</details>

</blockquote></details>

</details>

</details>

<details>
<summary>⏰ Context from checks skipped due to timeout of 90000ms (2)</summary>

* GitHub Check: Codacy Static Code Analysis
* GitHub Check: Trag Review

</details>

<details>
<summary>🔇 Additional comments (1)</summary><blockquote>

<details>
<summary>src/dashboard.py (1)</summary>

`115-120`: **Nice seamless integration of the new tab**

The additional `tab4` for “Time Series Analysis” cleanly extends the UI without disturbing existing logic.

</details>

</blockquote></details>

</details>

<!-- This is an auto-generated comment by CodeRabbit for review status -->
state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:10:59Z
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state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:12:40Z
user: login: sourcery-ai[bot]
body: Hey @Phinart98 - I've reviewed your changes and they look great!

<details>
<summary>Here's what I looked at during the review</summary>

- 🟡 **General issues**: 5 issues found
- 🟢 **Security**: all looks good
- 🟢 **Testing**: all looks good
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state: COMMENTED
author_association: NONE
submitted_at: 2025-05-18T10:12:51Z
user: login: bito-code-review[bot]
body: <div>
<h3>Code Review Agent Run #a2cfef</h3>

<div>
<details>
<summary>
<b>Actionable Suggestions -  1</b>
</summary>

<ul>

<li>
<div id="suggestion">
src/time_series.py - <b>1</b>

<ul>
<li>Catching blind exception · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#r2094467321">Line 87-87</a></li>
</ul>

</div>
</li>

</ul>

</details>
</div>


<div>
<details>
<summary>
<b>Additional Suggestions - 16</b>
</summary>

<ul>

<li>
<div id="secondary_suggestion">
src/time_series.py - <b>15</b>

<ul>
<li>Missing type annotations in function definition · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R9">Line 9-9</a></li><li>Remove debug statements from production UI · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R39">Line 39-60</a></li><li>Missing type annotations in function definition · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R105">Line 105-105</a></li><li>Missing type annotations in function definition · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R151">Line 151-151</a></li><li>Missing timezone in datetime.now() call · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R21">Line 21-21</a></li><li>Missing module docstring · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R1">Line 1-1</a></li><li>Unsorted import statements · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R1">Line 1-8</a></li><li>Magic number in comparison · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R46">Line 46-46</a></li><li>Generic DataFrame variable name · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R66">Line 66-69</a></li><li>Unnecessary assignment before return · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R69">Line 69-69</a></li><li>Docstring format needs improvement · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R10">Line 10-20</a></li><li>Missing trailing comma in dictionary · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R36">Line 36-36</a></li><li>Unnecessary elif after return · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R70">Line 70-70</a></li><li>Trailing whitespace in code · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R130">Line 130-130</a></li><li>Unnecessary dict() call · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R146">Line 146-146</a></li>
</ul>

</div>
</li>

<li>
<div id="secondary_suggestion">
src/dashboard.py - <b>1</b>

<ul>
<li>Missing trailing comma in multiline list · <a href ="https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-06135492c180d831917894055e5bb06ed9c4f31998a88b446abd432d50f6a4c7R119">Line 119-119</a></li>
</ul>

</div>
</li>

</ul>

</details>
</div>






<div>
<details>
<summary>
<b>Review Details</b>
</summary>

<ul>
<li>
<div id="file_reviewed">
Files reviewed - <b>2</b> · Commit Range: <code>d846704..93d0fdc</code>
<ul>
<li>src/dashboard.py</li><li>src/time_series.py</li>
</ul>
</div>
</li>

<li>
<div id="file_skipped">
Files skipped - <b>0</b>
<ul>
      
</ul>
</div>
</li>


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<ul>
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</div>
state: CHANGES_REQUESTED
author_association: NONE
submitted_at: 2025-05-18T10:13:11Z

Review comments:
diff_hunk: @@ -245,6 +247,9 @@ def create_dashboard():
         )
         fig.update_layout(bargap=0.1)
         st.plotly_chart(fig, use_container_width=True)
+        
+    with tab4:
+        add_time_series_section(stations_gdf, pollutant_info)
path: src/dashboard.py
user: login: greptile-apps[bot]
body: style: Consider adding error handling around add_time_series_section() call in case the function raises exceptions
created_at: 2025-05-18T10:08:31Z
author_association: NONE
diff_hunk: @@ -111,10 +112,11 @@ def create_dashboard():
         stations_gdf = load_data(selected_countries)
     
     # Main content tabs
-    tab1, tab2, tab3 = st.tabs([
+    tab1, tab2, tab3, tab4 = st.tabs([
         "Network Overview",
         "Station Distribution",
-        "Pollutant Analysis"
+        "Pollutant Analysis",
+        "Time Series Analysis"
     ])
     
     with tab1:
path: src/dashboard.py
user: login: codereviewbot-ai[bot]
body: ### Potential Performance Issue with Data Loading
The function `load_data` is called within a `with st.spinner` block, which is good for user experience as it shows a loading spinner. However, the actual data loading function `load_data` might be a performance bottleneck if not properly optimized, especially if it handles large datasets or complex transformations.

**Recommendation:**
- Ensure that `load_data` is optimized for performance. Consider implementing caching or more efficient data processing techniques if the data volume is large. Use profiling tools to identify slow parts of the data loading process and optimize them.
created_at: 2025-05-18T10:08:39Z
author_association: NONE
diff_hunk: @@ -245,6 +247,9 @@ def create_dashboard():
         )
         fig.update_layout(bargap=0.1)
         st.plotly_chart(fig, use_container_width=True)
+        
+    with tab4:
+        add_time_series_section(stations_gdf, pollutant_info)
 
 if __name__ == "__main__":
     create_dashboard()
path: src/dashboard.py
user: login: codereviewbot-ai[bot]
body: ### Inefficient Data Handling in Plotting
The code directly uses the `plotly_chart` function to render a histogram without any prior data aggregation or checks. This could lead to performance issues if the dataset is large, as every single data point is processed for plotting.

**Recommendation:**
- Before plotting, consider aggregating or summarizing the data if the dataset is large. This can reduce the load on the plotting library and improve the responsiveness of the dashboard. Use pandas functions like `groupby` or `aggregate` to summarize the data before plotting.
created_at: 2025-05-18T10:08:39Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
path: src/time_series.py
user: login: codereviewbot-ai[bot]
body: The exception handling in the `fetch_historical_data` function is too generic, catching all exceptions broadly. This can obscure the source of errors and make debugging more difficult. It's recommended to catch specific exceptions related to HTTP requests, such as `requests.exceptions.RequestException`, to handle potential connectivity issues more gracefully. Additionally, consider logging the error details to help with troubleshooting.

**Suggested Change:**
```python
except requests.exceptions.RequestException as e:
    st.error(f"HTTP request failed: {e}")
except Exception as e:
    st.error(f"An unexpected error occurred: {e}")
```
created_at: 2025-05-18T10:08:39Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
path: src/time_series.py
user: login: codereviewbot-ai[bot]
body: The function attempts to fetch data from a primary API endpoint and, if it fails, tries an alternative endpoint. This approach, while robust, could be optimized. If the primary endpoint consistently fails, it might be more efficient to dynamically select the endpoint based on historical success rates or to implement a more intelligent retry mechanism.

**Suggested Change:**
Consider implementing a strategy to dynamically select the API endpoint based on historical response success. This could involve maintaining a simple success/failure record for each endpoint and selecting the one with the highest success rate for the initial request. This strategy can reduce unnecessary requests and improve the overall responsiveness of your application.
created_at: 2025-05-18T10:08:39Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
path: src/time_series.py
user: login: greptile-apps[bot]
body: logic: raise_for_status() called after attempting alternative endpoint but before checking its status code. Could raise exception even if alternative endpoint succeeded.

```suggestion
        if response.status_code != 200:
            response.raise_for_status()
        data = response.json()
```
created_at: 2025-05-18T10:09:05Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
path: src/time_series.py
user: login: greptile-apps[bot]
body: logic: No error handling for case where station_id lookup fails. iloc[0] will raise IndexError if station not found.

```suggestion
    matching_stations = stations_gdf[stations_gdf["id"] == selected_station["id"]]
    if matching_stations.empty:
        st.error(f"Station {selected_station['id']} not found")
        return
    station_row = matching_stations.iloc[0]
```
created_at: 2025-05-18T10:09:06Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
path: src/time_series.py
user: login: greptile-apps[bot]
body: logic: No validation that required columns 'pollutant' and 'value' exist in DataFrame before accessing. Could raise KeyError.

```suggestion
    if "pollutant" not in df.columns or "value" not in df.columns:
        st.error("Required columns 'pollutant' and 'value' not found in data")
        return None
    pollutant = df["pollutant"].iloc[0]
    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
```
created_at: 2025-05-18T10:09:06Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
path: src/time_series.py
user: login: greptile-apps[bot]
body: style: Debug info exposed to end users in production. Consider using logging instead of st.info for API debugging details.
created_at: 2025-05-18T10:09:07Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Unfiltered Exception Message Exposure <sub>![category Security](https://img.shields.io/badge/Security-e11d48)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
Raw exception messages are being displayed to end users.


###### Why this matters
Exception messages may contain sensitive implementation details or system information that could be exploited by attackers.

###### Suggested change ∙ *Feature Preview*
Use a generic error message instead:
```python
st.error("Unable to fetch historical data. Please try again later.")
logging.error(f"Data fetch error: {e}")
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/348cde98-0c3f-4942-8193-a884d7e0b24b/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/348cde98-0c3f-4942-8193-a884d7e0b24b?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/348cde98-0c3f-4942-8193-a884d7e0b24b?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/348cde98-0c3f-4942-8193-a884d7e0b24b?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/348cde98-0c3f-4942-8193-a884d7e0b24b)
</details>

<sub>

💬 Looking for more details? Reply to this comment to chat with Korbit.
</sub>

<!--- korbi internal id:8057061a-b9a8-47be-8bb5-82b68216035a -->


[](8057061a-b9a8-47be-8bb5-82b68216035a)
created_at: 2025-05-18T10:09:08Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Inefficient Row Lookup <sub>![category Performance](https://img.shields.io/badge/Performance-4f46e5)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
Inefficient row lookup using boolean indexing followed by iloc.


###### Why this matters
This operation scans the entire DataFrame to create a boolean mask, which is inefficient when you only need a single row.

###### Suggested change ∙ *Feature Preview*
Use loc with index if id is the index, or use DataFrame.query():
```python
station_row = stations_gdf.loc[stations_gdf['id'] == selected_station['id']].squeeze()
# or if many lookups are needed, set index first:
# stations_gdf = stations_gdf.set_index('id')
# station_row = stations_gdf.loc[selected_station['id']]
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/e8f96da1-e3d9-4c43-b7b0-af0da1e6c241/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/e8f96da1-e3d9-4c43-b7b0-af0da1e6c241?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/e8f96da1-e3d9-4c43-b7b0-af0da1e6c241?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/e8f96da1-e3d9-4c43-b7b0-af0da1e6c241?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/e8f96da1-e3d9-4c43-b7b0-af0da1e6c241)
</details>

<sub>

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</sub>

<!--- korbi internal id:a1028598-af2c-4d41-ab8b-070a2fe82bf6 -->


[](a1028598-af2c-4d41-ab8b-070a2fe82bf6)
created_at: 2025-05-18T10:09:08Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Unnecessary DataFrame Copy <sub>![category Performance](https://img.shields.io/badge/Performance-4f46e5)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
Unnecessary DataFrame copy created for calculating rolling average.


###### Why this matters
Creating a full copy of the DataFrame consumes additional memory, especially problematic with large datasets.

###### Suggested change ∙ *Feature Preview*
Calculate rolling average without creating a copy:
```python
rolling_avg = df["value"].rolling(window=24).mean()
fig.add_scatter(x=df.index, y=rolling_avg, mode="lines", name="24-hour Moving Average", line=dict(width=2, dash="dash"))
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/0a9ac03d-7102-49d3-aec2-51f042c5db7c/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/0a9ac03d-7102-49d3-aec2-51f042c5db7c?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/0a9ac03d-7102-49d3-aec2-51f042c5db7c?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/0a9ac03d-7102-49d3-aec2-51f042c5db7c?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/0a9ac03d-7102-49d3-aec2-51f042c5db7c)
</details>

<sub>

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</sub>

<!--- korbi internal id:227e8cf4-ffe7-483d-b19d-c031ca4443ed -->


[](227e8cf4-ffe7-483d-b19d-c031ca4443ed)
created_at: 2025-05-18T10:09:08Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Incomplete return value documentation <sub>![category Documentation](https://img.shields.io/badge/Documentation-7c3aed)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
The docstring doesn't describe the structure of the returned DataFrame or handling of error cases.


###### Why this matters
Missing information about DataFrame columns and error handling makes it harder for other developers to properly handle the function's output and error conditions.

###### Suggested change ∙ *Feature Preview*
"""Fetch historical pollutant data for a specific station.

Args:
    station_id (str): The ID of the monitoring station
    pollutant (str): The pollutant code (pm10, pm25, etc.)
    days (int): Number of days of historical data to fetch

Returns:
    pandas.DataFrame: Time series data with columns:
        - value: pollutant concentration
        - pollutant: pollutant code
        - station_name: name of the station
    Returns empty DataFrame if API request fails or no data available.
"""


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/d2debfe5-dd7d-42d7-890b-d5ea8b20d875/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/d2debfe5-dd7d-42d7-890b-d5ea8b20d875?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/d2debfe5-dd7d-42d7-890b-d5ea8b20d875?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/d2debfe5-dd7d-42d7-890b-d5ea8b20d875?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/d2debfe5-dd7d-42d7-890b-d5ea8b20d875)
</details>

<sub>

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</sub>

<!--- korbi internal id:e04d58e7-75b6-4622-a2f7-0f04bf11a4da -->


[](e04d58e7-75b6-4622-a2f7-0f04bf11a4da)
created_at: 2025-05-18T10:09:08Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Missing pollutant_info structure documentation <sub>![category Documentation](https://img.shields.io/badge/Documentation-7c3aed)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
The pollutant_info dictionary structure is not documented.


###### Why this matters
Without knowing the expected structure of pollutant_info, developers might pass incorrectly formatted metadata.

###### Suggested change ∙ *Feature Preview*
"""Create a time series plot for pollutant data.

Args:
    df (pandas.DataFrame): DataFrame with datetime index and pollutant values
    pollutant_info (dict): Dictionary with pollutant metadata in format:
        {pollutant_code: {"name": str, "unit": str, "color": str}}
"


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/84b031d8-d860-4084-9ce1-93eea02eff0f/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/84b031d8-d860-4084-9ce1-93eea02eff0f?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/84b031d8-d860-4084-9ce1-93eea02eff0f?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/84b031d8-d860-4084-9ce1-93eea02eff0f?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/84b031d8-d860-4084-9ce1-93eea02eff0f)
</details>

<sub>

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</sub>

<!--- korbi internal id:99cecbda-81ad-412a-8cee-e03a8aa85a7b -->


[](99cecbda-81ad-412a-8cee-e03a8aa85a7b)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Insufficient Error Context in API Exception Handler <sub>![category Error Handling](https://img.shields.io/badge/Error%20Handling-ea580c)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
The catch block uses a broad Exception class and only displays the error message without capturing additional context about the failed request.


###### Why this matters
When debugging API issues, lack of request details (URL, parameters, response headers) makes it difficult to diagnose the root cause of failures.

###### Suggested change ∙ *Feature Preview*
```python
except Exception as e:
    error_context = {
        'url': response.url if 'response' in locals() else base_url,
        'params': params,
        'error': str(e),
        'response_status': response.status_code if 'response' in locals() else None,
        'response_headers': dict(response.headers) if 'response' in locals() else None
    }
    st.error(f"Error fetching historical data: {error_context}")
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/62c3bd9e-d216-4716-a234-38d779ffd4bd/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/62c3bd9e-d216-4716-a234-38d779ffd4bd?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/62c3bd9e-d216-4716-a234-38d779ffd4bd?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/62c3bd9e-d216-4716-a234-38d779ffd4bd?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/62c3bd9e-d216-4716-a234-38d779ffd4bd)
</details>

<sub>

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</sub>

<!--- korbi internal id:1407efc9-4a07-4317-8c7c-6906a5af7937 -->


[](1407efc9-4a07-4317-8c7c-6906a5af7937)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Undifferentiated HTTP Error Handling <sub>![category Error Handling](https://img.shields.io/badge/Error%20Handling-ea580c)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
The error handling combines both error detection and recovery in the same block without distinguishing between different types of HTTP errors.


###### Why this matters
Not all non-200 status codes should trigger the fallback endpoint - some errors (like 401, 403) would fail on both endpoints and should be handled differently.

###### Suggested change ∙ *Feature Preview*
```python
if response.status_code in [404, 400]:  # Only retry for specific errors
    # Try alternative endpoint format
    alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
    # ... rest of alternative endpoint code ...
elif response.status_code in [401, 403]:
    st.error("Authentication or authorization error. Please check your API credentials.")
    return pd.DataFrame()
else:
    response.raise_for_status()
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/1d092e77-2c16-4f93-89ad-318383f37d27/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/1d092e77-2c16-4f93-89ad-318383f37d27?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/1d092e77-2c16-4f93-89ad-318383f37d27?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/1d092e77-2c16-4f93-89ad-318383f37d27?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/1d092e77-2c16-4f93-89ad-318383f37d27)
</details>

<sub>

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</sub>

<!--- korbi internal id:132140f7-aa7e-4f60-ac9f-1ef1ebbc1318 -->


[](132140f7-aa7e-4f60-ac9f-1ef1ebbc1318)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### UI-based debugging instead of proper logging <sub>![category Logging](https://img.shields.io/badge/Logging-4f46e5)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
Using Streamlit's info() for debugging API requests instead of proper logging framework.


###### Why this matters
Streamlit's info() messages are only visible in the UI and not persisted, making it impossible to debug production issues or maintain an audit trail of API interactions.

###### Suggested change ∙ *Feature Preview*
```python
import logging

try:
    logging.info(f"Fetching data from {base_url} with parameters: {params}")
    response = requests.get(base_url, params=params, timeout=15)
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/8ba2bf04-b6e4-41f8-a5d6-7570e10c7651/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/8ba2bf04-b6e4-41f8-a5d6-7570e10c7651?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/8ba2bf04-b6e4-41f8-a5d6-7570e10c7651?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/8ba2bf04-b6e4-41f8-a5d6-7570e10c7651?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/8ba2bf04-b6e4-41f8-a5d6-7570e10c7651)
</details>

<sub>

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</sub>

<!--- korbi internal id:0ba33e36-0dd5-476b-830d-0644c2884611 -->


[](0ba33e36-0dd5-476b-830d-0644c2884611)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Inefficient DataFrame Iteration <sub>![category Performance](https://img.shields.io/badge/Performance-4f46e5)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
Using iterrows() for DataFrame iteration is inefficient as it creates a new Series object for each row.


###### Why this matters
This operation has poor performance for large DataFrames and can significantly slow down the application when there are many stations.

###### Suggested change ∙ *Feature Preview*
Replace iterrows() with vectorized operations or list comprehension:
```python
station_options = [
    {"id": row.id, "name": f"{row.name} ({row.city_name or 'Unknown City'}, {row.country_id})"}
    for _, row in stations_gdf[stations_gdf['name'].notna() & stations_gdf['id'].notna()].itertuples()
]
```


###### Provide feedback to improve future suggestions
[![Nice Catch](https://img.shields.io/badge/👍%20Nice%20Catch-71BC78)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/7ce7605b-b053-4570-950e-f4652ab4fff5/upvote) [![Incorrect](https://img.shields.io/badge/👎%20Incorrect-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/7ce7605b-b053-4570-950e-f4652ab4fff5?what_not_true=true)  [![Not in Scope](https://img.shields.io/badge/👎%20Out%20of%20PR%20scope-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/7ce7605b-b053-4570-950e-f4652ab4fff5?what_out_of_scope=true) [![Not in coding standard](https://img.shields.io/badge/👎%20Not%20in%20our%20standards-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/7ce7605b-b053-4570-950e-f4652ab4fff5?what_not_in_standard=true) [![Other](https://img.shields.io/badge/👎%20Other-white)](https://app.korbit.ai/feedback/da42cc13-e7cd-4a0b-bab0-6a030a96cc5e/7ce7605b-b053-4570-950e-f4652ab4fff5)
</details>

<sub>

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</sub>

<!--- korbi internal id:b0c5430e-00f5-444c-9e06-e41c0734c981 -->


[](b0c5430e-00f5-444c-9e06-e41c0734c981)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
path: src/time_series.py
user: login: korbit-ai[bot]
body: ### Unexplained Magic Numbers in Mock Data Generation <sub>![category Readability](https://img.shields.io/badge/Readability-0284c7)</sub>

<details>
  <summary>Tell me more</summary>

###### What is the issue?
The magic numbers 10, 5, 24, -2, 2 in the mock data generation formula lack explanation of their significance.


###### Why this matters
Future maintainers won't understand what these numbers represent or how to adjust them appropriately for testing different scenarios.

###### Suggested change ∙ *Feature Preview*
```python
# Define constants with meaningful names
BASE_POLLUTANT_VALUE = 10  # Base pollution level
DAILY_VARIATION = 5        # Maximum daily variation
RANDOM_NOISE_RANGE = 2     # Random noise range (+/-)
HOURS_PER_DAY = 24

mock_values = [
    BASE_POLLUTANT_VALUE + 
    DAILY_VARIATION * math.sin(i/HOURS_PER_DAY * math.pi) + 
    random.uniform(-RANDOM_NOISE_RANGE, RANDOM_NOISE_RANGE) 
    for i in range(len(dates))
]
```


###### Provide feedback to improve future suggestions
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</details>

<sub>

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</sub>

<!--- korbi internal id:4a152993-396d-49be-8bf2-5459655eb36f -->


[](4a152993-396d-49be-8bf2-5459655eb36f)
created_at: 2025-05-18T10:09:09Z
author_association: NONE
diff_hunk: @@ -7,6 +7,7 @@
 import geopandas as gpd
 import requests
 from datetime import datetime, timedelta
+from time_series import add_time_series_section
path: src/dashboard.py
user: login: coderabbitai[bot]
body: _⚠️ Potential issue_

**Use a package-relative import to avoid runtime import errors**

`dashboard.py` resides in the same `src` package as `time_series.py`, but `from time_series import …` relies on the repository root being on `PYTHONPATH`. When the app is executed from outside `src/`, this import will fail because Python will look for a top-level module named `time_series`.

```diff
-from time_series import add_time_series_section
+from .time_series import add_time_series_section
```

Alternatively, mark `src` as a proper package (add an `__init__.py`) and use `import src.time_series as ts` if absolute imports are preferred.  



<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

```suggestion
from .time_series import add_time_series_section
```

</details>

<!-- suggestion_end -->

<details>
<summary>🤖 Prompt for AI Agents</summary>

```
In src/dashboard.py at line 10, replace the current import statement with a
package-relative import to avoid runtime errors when the app is run from outside
the src directory. Change the import to use a relative import like from
.time_series import add_time_series_section, or alternatively, ensure src is a
package by adding an __init__.py file and use an absolute import such as import
src.time_series as ts.
```

</details>

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-05-18T10:10:58Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
+    available_pollutants = station_row["pollutants"] if isinstance(station_row["pollutants"], list) else []
+    
+    if not available_pollutants:
+        st.warning("No pollutant data available for the selected station.")
+        return
+    
+    selected_pollutant = st.selectbox(
+        "Select pollutant to analyze:",
+        options=[p for p in available_pollutants if p in pollutant_info],
+        format_func=lambda p: pollutant_info[p]["name"]
+    )
+    
+    # Time range selection
+    time_range = st.slider(
+        "Select time range (days):",
+        min_value=7,
+        max_value=90,
+        value=30,
+        step=1
+    )
+    
+    # Fetch and display data
+    with st.spinner("Fetching historical data..."):
+        df = fetch_historical_data(
+            selected_station["id"],
+            selected_pollutant,
+            days=time_range
+        )
+    
+    if not df.empty:
+        fig = plot_time_series(df, pollutant_info)
+        if fig:
+            st.plotly_chart(fig, use_container_width=True)
+            
+            # Basic statistics
+            st.subheader("Statistical Summary")
+            col1, col2, col3 = st.columns(3)
+            
+            with col1:
+                st.metric(
+                    "Average", 
+                    f"{df['value'].mean():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+            
+            with col2:
+                st.metric(
+                    "Maximum", 
+                    f"{df['value'].max():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+                
+            with col3:
+                st.metric(
+                    "Minimum", 
+                    f"{df['value'].min():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
path: src/time_series.py
user: login: coderabbitai[bot]
body: _🛠️ Refactor suggestion_

**Guard against missing or non-numeric values before computing stats**

If any `value` entries are `NaN`, the metrics will display `nan`. Drop or coerce invalid rows first:

```python
clean_values = pd.to_numeric(df["value"], errors="coerce").dropna()
if clean_values.empty:
    st.warning("No valid numeric readings to compute statistics.")
else:
    st.metric("Average", f"{clean_values.mean():.2f} …")
```

This keeps the UI consistent and prevents misleading outputs.

<details>
<summary>🤖 Prompt for AI Agents</summary>

```
In src/time_series.py around lines 211 to 236, the current code calculates
statistics directly on the 'value' column without handling NaN or non-numeric
entries, which can cause metrics to display 'nan'. To fix this, first convert
the 'value' column to numeric using pd.to_numeric with errors='coerce', then
drop NaN values. Check if the resulting series is empty; if so, display a
warning message instead of metrics. Otherwise, compute and display the
statistics using this cleaned data to ensure accurate and meaningful outputs.
```

</details>

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-05-18T10:10:58Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
path: src/time_series.py
user: login: coderabbitai[bot]
body: _⚠️ Potential issue_

**Handle pagination or enlarge the `limit` parameter**

The primary API caps results at `limit=1000`, but a 90-day, hourly query (~2160 rows) will silently truncate data. Either:

1. Page through results while the API reports `next` links/offsets, or  
2. Increase `limit` to comfortably exceed the maximum expected rows (e.g., 2500).

```diff
-        "limit": 1000
+        "limit": 3000  # supports up to 4 months of hourly data
```

Even better—loop until no more pages are returned.  



<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

```suggestion
    # Try the main API endpoint first
    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
    params = {
        "station_id": station_id,
        "pollutant": pollutant,
        "start_date": start_str,
        "end_date": end_str,
        "format": "json",  # Added format parameter
        "limit": 3000  # supports up to 4 months of hourly data
    }
```

</details>

<!-- suggestion_end -->

<details>
<summary>🤖 Prompt for AI Agents</summary>

```
In src/time_series.py around lines 28 to 37, the API request uses a fixed limit
of 1000, which truncates data for queries returning more than 1000 rows. To fix
this, implement pagination by checking the API response for a next page
indicator (like a 'next' link or offset) and loop to fetch all pages until no
more data is returned. Alternatively, increase the limit parameter to a value
exceeding the maximum expected rows, such as 2500, to avoid truncation for
typical queries.
```

</details>

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-05-18T10:10:58Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
path: src/time_series.py
user: login: coderabbitai[bot]
body: _🛠️ Refactor suggestion_

**Replace a blanket `except Exception` with targeted exception handling**

Catching all exceptions obscures bugs and makes debugging harder. Catch `requests.exceptions.RequestException` and `ValueError` separately; re-raise unexpected errors after logging.

```diff
-    except Exception as e:
-        st.error(f"Error fetching historical data: {e}")
+    except (requests.exceptions.RequestException, ValueError) as e:
+        st.error(f"Error fetching historical data: {e}")
```

Consider toggling mock-data generation behind `st.session_state.get("debug")` to avoid silently showing fake data in production scenarios.  



<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

```suggestion
    except (requests.exceptions.RequestException, ValueError) as e:
        st.error(f"Error fetching historical data: {e}")
        
        # For development/testing: create mock data
        st.info("Generating mock data for demonstration purposes")
        dates = pd.date_range(start=start_date, end=end_date, freq='H')
        mock_values = [
            10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2)
            for i in range(len(dates))
        ]
        
        mock_df = pd.DataFrame({
            'datetime': dates,
            'value': mock_values,
            'pollutant': pollutant,
            'station_name': f"Station {station_id}"
        })
        mock_df = mock_df.set_index('datetime')
        return mock_df
```

</details>

<!-- suggestion_end -->

<details>
<summary>🤖 Prompt for AI Agents</summary>

```
In src/time_series.py around lines 86 to 103, replace the broad 'except
Exception' with specific exception handlers for
'requests.exceptions.RequestException' and 'ValueError'. Log the errors
accordingly and re-raise any other unexpected exceptions after logging.
Additionally, wrap the mock data generation code inside a condition that checks
if 'st.session_state.get("debug")' is True, so mock data is only generated
during debugging and not in production.
```

</details>

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-05-18T10:10:58Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
path: src/time_series.py
user: login: coderabbitai[bot]
body: _⚠️ Potential issue_

**Ensure numeric dtype for the `value` column**

API responses sometimes serialise numbers as strings. Down-stream stats (`mean`, `max`, etc.) will crash or yield `NaN` if the dtype is object.

```diff
-            df = pd.DataFrame(data["results"])
+            df = pd.DataFrame(data["results"])
+            if "value" in df.columns:
+                df["value"] = pd.to_numeric(df["value"], errors="coerce")
```

Repeat for the alternative-structure branch (lines 70-83).  



<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

```suggestion
        if "results" in data and len(data["results"]) > 0:
            df = pd.DataFrame(data["results"])
            if "value" in df.columns:
                df["value"] = pd.to_numeric(df["value"], errors="coerce")
            df["datetime"] = pd.to_datetime(df["datetime"])
            df = df.set_index("datetime")
            return df
```

</details>

<!-- suggestion_end -->

<details>
<summary>🤖 Prompt for AI Agents</summary>

```
In src/time_series.py around lines 65 to 69, the 'value' column in the DataFrame
may be of object dtype due to numbers serialized as strings, causing downstream
statistical functions to fail. Convert the 'value' column to a numeric dtype
using pandas' to_numeric method with errors='coerce' after creating the
DataFrame and before returning it. Apply the same conversion in the
alternative-structure branch between lines 70 and 83 to ensure consistency.
```

</details>

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-05-18T10:10:58Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
+    available_pollutants = station_row["pollutants"] if isinstance(station_row["pollutants"], list) else []
+    
+    if not available_pollutants:
+        st.warning("No pollutant data available for the selected station.")
+        return
+    
+    selected_pollutant = st.selectbox(
+        "Select pollutant to analyze:",
+        options=[p for p in available_pollutants if p in pollutant_info],
+        format_func=lambda p: pollutant_info[p]["name"]
+    )
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Validate and filter the pollutant list before using it in the selectbox to ensure valid options. [Input Validation]
```suggestion
    filtered_pollutants = [p for p in available_pollutants if p in pollutant_info]
    if not filtered_pollutants:
        st.warning("No valid pollutant data available for selection.")
        return
    selected_pollutant = st.selectbox(
        "Select pollutant to analyze:",
        options=filtered_pollutants,
        format_func=lambda p: pollutant_info[p]["name"]
    )
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Use a module-level requests.Session for connection pooling and replace direct requests.get calls. [Connection Pooling]
```suggestion
        # Add at the top of the module, after imports:
        session = requests.Session()

        response = session.get(base_url, params=params, timeout=15)
        ...
        response = session.get(alt_base_url, params=alt_params, timeout=15)
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
+            })
+    
+    if not station_options:
+        st.warning("No stations available for time series analysis.")
+        return
+    
+    selected_station = st.selectbox(
+        "Select a monitoring station:",
+        options=station_options,
+        format_func=lambda x: x["name"]
+    )
+    
+    # Pollutant selection
+    station_row = stations_gdf[stations_gdf["id"] == selected_station["id"]].iloc[0]
+    available_pollutants = station_row["pollutants"] if isinstance(station_row["pollutants"], list) else []
+    
+    if not available_pollutants:
+        st.warning("No pollutant data available for the selected station.")
+        return
+    
+    selected_pollutant = st.selectbox(
+        "Select pollutant to analyze:",
+        options=[p for p in available_pollutants if p in pollutant_info],
+        format_func=lambda p: pollutant_info[p]["name"]
+    )
+    
+    # Time range selection
+    time_range = st.slider(
+        "Select time range (days):",
+        min_value=7,
+        max_value=90,
+        value=30,
+        step=1
+    )
+    
+    # Fetch and display data
+    with st.spinner("Fetching historical data..."):
+        df = fetch_historical_data(
+            selected_station["id"],
+            selected_pollutant,
+            days=time_range
+        )
+    
+    if not df.empty:
+        fig = plot_time_series(df, pollutant_info)
+        if fig:
+            st.plotly_chart(fig, use_container_width=True)
+            
+            # Basic statistics
+            st.subheader("Statistical Summary")
+            col1, col2, col3 = st.columns(3)
+            
+            with col1:
+                st.metric(
+                    "Average", 
+                    f"{df['value'].mean():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+            
+            with col2:
+                st.metric(
+                    "Maximum", 
+                    f"{df['value'].max():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
+                
+            with col3:
+                st.metric(
+                    "Minimum", 
+                    f"{df['value'].min():.2f} {pollutant_info[selected_pollutant]['unit']}"
+                )
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Aggregate statistical computations in a single call to minimize iterations over the data. [Aggregation Optimization]
```suggestion
            stats = df["value"].agg(["mean", "max", "min"])
            with col1:
                st.metric(
                    "Average", 
                    f"{stats['mean']:.2f} {pollutant_info[selected_pollutant]['unit']}"
                )
            with col2:
                st.metric(
                    "Maximum", 
                    f"{stats['max']:.2f} {pollutant_info[selected_pollutant]['unit']}"
                )
            with col3:
                st.metric(
                    "Minimum", 
                    f"{stats['min']:.2f} {pollutant_info[selected_pollutant]['unit']}"
                )
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Prevent unintended production behavior by removing mock data fallback in the exception block. [Error Handling Improvement]
```suggestion
    except Exception as e:
        st.error(f"Error fetching historical data: {e}")
        return pd.DataFrame()
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Wrap debug logging with a condition checking a debug flag to avoid exposing sensitive API details in production. [Sensitive Logging]
```suggestion
        import os
        if os.getenv("DEBUG", "False") == "True":
            st.info(f"Fetching data from {base_url} with parameters: {params}")
        response = session.get(base_url, params=params, timeout=15)
        if os.getenv("DEBUG", "False") == "True":
            st.info(f"Response status code: {response.status_code}")
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
path: src/time_series.py
user: login: codeant-ai[bot]
body: **Suggestion:** Validate the station_id using a regex pattern to secure dynamic URL construction. [Input Validation]
```suggestion
            import re
            if not re.match(r'^[\w-]+$', station_id):
                st.error("Invalid station ID")
                return pd.DataFrame()
            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
```
created_at: 2025-05-18T10:12:40Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion (performance):** Cache historical data to avoid redundant API calls

Decorate this function with @st.cache_data (or @st.experimental_memo) to reuse cached results for identical station, pollutant, and time-range requests instead of re-querying the API.

```suggestion
@st.cache_data
def fetch_historical_data(station_id, pollutant, days=30):
```
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion:** Avoid using st.info for internal debug logs

Use Python’s logging module or Streamlit’s logging utilities instead of st.info to keep debug output out of the UI.
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion (bug_risk):** Narrow exception handling to specific error types

Catch requests.RequestException and json.JSONDecodeError instead of Exception to handle API and parsing errors precisely.
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion (bug_risk):** Handle potential pagination when fetching historical data

Implement pagination or raise the limit to avoid truncating results at 1000 records.
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion:** Specify min_periods for rolling average to avoid NaNs

Set min_periods=1 in rolling(window=24) to compute the average from the first data point and avoid NaNs for the initial 23 entries.

```suggestion
    df_rolling["24h_avg"] = df["value"].rolling(window=24, min_periods=1).mean()
```
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
+    Add time series analysis section to the dashboard.
+    
+    Args:
+        stations_gdf (geopandas.GeoDataFrame): GeoDataFrame with station data
+        pollutant_info (dict): Dictionary with pollutant metadata
+    """
+    st.header("📈 Time Series Analysis")
+    
+    # Station selection
+    station_options = []
+    for _, row in stations_gdf.iterrows():
+        if row["name"] and row["id"]:
+            station_options.append({
+                "id": row["id"],
+                "name": f"{row['name']} ({row['city_name'] if row['city_name'] else 'Unknown City'}, {row['country_id']})"
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **suggestion (code-quality):** Replace if-expression with `or` ([`or-if-exp-identity`](https://docs.sourcery.ai/Reference/Rules-and-In-Line-Suggestions/Python/Default-Rules/or-if-exp-identity))

```suggestion
                "name": f"{row['name']} ({row['city_name'] or 'Unknown City'}, {row['country_id']})"
```

<br/><details><summary>Explanation</summary>Here we find ourselves setting a value if it evaluates to `True`, and otherwise
using a default.

The 'After' case is a bit easier to read and avoids the duplication of
`input_currency`.

It works because the left-hand side is evaluated first. If it evaluates to
true then `currency` will be set to this and the right-hand side will not be
evaluated. If it evaluates to false the right-hand side will be evaluated and
`currency` will be set to `DEFAULT_CURRENCY`.
</details>
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
+        st.error(f"Error fetching historical data: {e}")
+        
+        # For development/testing: create mock data
+        st.info("Generating mock data for demonstration purposes")
+        dates = pd.date_range(start=start_date, end=end_date, freq='H')
+        mock_values = [10 + 5 * math.sin(i/24 * math.pi) + random.uniform(-2, 2) for i in range(len(dates))]
+        
+        mock_df = pd.DataFrame({
+            'datetime': dates,
+            'value': mock_values,
+            'pollutant': pollutant,
+            'station_name': f"Station {station_id}"
+        })
+        mock_df = mock_df.set_index('datetime')
+        return mock_df
+
+
+def plot_time_series(df, pollutant_info):
+    """
+    Create a time series plot for pollutant data.
+    
+    Args:
+        df (pandas.DataFrame): DataFrame with datetime index and pollutant values
+        pollutant_info (dict): Dictionary with pollutant metadata
+        
+    Returns:
+        plotly.graph_objects.Figure: Interactive time series plot
+    """
+    if df.empty:
+        st.warning("No historical data available for the selected station and pollutant.")
+        return None
+    
+    pollutant = df["pollutant"].iloc[0]
+    station_name = df["station_name"].iloc[0] if "station_name" in df.columns else "Unknown Station"
+    
+    # Get pollutant info
+    p_name = pollutant_info.get(pollutant, {}).get("name", pollutant.upper())
+    p_unit = pollutant_info.get(pollutant, {}).get("unit", "")
+    p_color = pollutant_info.get(pollutant, {}).get("color", "blue")
+    
+    # Create plot
+    fig = px.line(
+        df, 
+        y="value",
+        title=f"{p_name} Levels at {station_name}",
+        labels={"value": f"Concentration ({p_unit})", "datetime": "Date"},
+        color_discrete_sequence=[p_color]
+    )
+    
+    # Add rolling average
+    df_rolling = df.copy()
+    df_rolling["24h_avg"] = df["value"].rolling(window=24).mean()
+    
+    fig.add_scatter(
+        x=df_rolling.index, 
+        y=df_rolling["24h_avg"],
+        mode="lines",
+        name="24-hour Moving Average",
+        line=dict(width=2, dash="dash")
+    )
+    
+    return fig
+
+def add_time_series_section(stations_gdf, pollutant_info):
+    """
path: src/time_series.py
user: login: sourcery-ai[bot]
body: **issue (code-quality):** We've found these issues:

- Convert for loop into list comprehension ([`list-comprehension`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/list-comprehension/))
- Use named expression to simplify assignment and conditional ([`use-named-expression`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/use-named-expression/))
- Swap if/else branches ([`swap-if-else-branches`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/swap-if-else-branches/))
- Merge else clause's nested if statement into elif ([`merge-else-if-into-elif`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/merge-else-if-into-elif/))
- Extract code out into function ([`extract-method`](https://docs.sourcery.ai/Reference/Default-Rules/refactorings/extract-method/))
created_at: 2025-05-18T10:12:51Z
author_association: NONE
diff_hunk: @@ -0,0 +1,238 @@
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import requests
+from datetime import datetime, timedelta
+import math
+import random
+
+def fetch_historical_data(station_id, pollutant, days=30):
+    """
+    Fetch historical pollutant data for a specific station.
+    
+    Args:
+        station_id (str): The ID of the monitoring station
+        pollutant (str): The pollutant code (pm10, pm25, etc.)
+        days (int): Number of days of historical data to fetch
+        
+    Returns:
+        pandas.DataFrame: Time series data with datetime index and pollutant values
+    """
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    
+    # Format dates for API
+    start_str = start_date.strftime("%Y-%m-%d")
+    end_str = end_date.strftime("%Y-%m-%d")
+    
+    # Try the main API endpoint first
+    base_url = "https://api.energyandcleanair.org/measurements"  # Removed /v1/
+    params = {
+        "station_id": station_id,
+        "pollutant": pollutant,
+        "start_date": start_str,
+        "end_date": end_str,
+        "format": "json",  # Added format parameter
+        "limit": 1000
+    }
+    
+    try:
+        st.info(f"Fetching data from {base_url} with parameters: {params}")
+        response = requests.get(base_url, params=params, timeout=15)
+        
+        # Debug information
+        st.info(f"Response status code: {response.status_code}")
+        
+        if response.status_code != 200:
+            # Try alternative endpoint format
+            alt_base_url = f"https://api.energyandcleanair.org/stations/{station_id}/measurements"
+            alt_params = {
+                "pollutant": pollutant,
+                "start_date": start_str,
+                "end_date": end_str,
+                "format": "json",
+                "limit": 1000
+            }
+            
+            st.info(f"Trying alternative endpoint: {alt_base_url}")
+            response = requests.get(alt_base_url, params=alt_params, timeout=15)
+            st.info(f"Alternative response status code: {response.status_code}")
+        
+        response.raise_for_status()
+        data = response.json()
+        
+        # Convert to DataFrame
+        if "results" in data and len(data["results"]) > 0:
+            df = pd.DataFrame(data["results"])
+            df["datetime"] = pd.to_datetime(df["datetime"])
+            df = df.set_index("datetime")
+            return df
+        elif isinstance(data, list) and len(data) > 0:
+            # Handle case where API returns a list directly
+            df = pd.DataFrame(data)
+            if "date" in df.columns:
+                df["datetime"] = pd.to_datetime(df["date"])
+            elif "timestamp" in df.columns:
+                df["datetime"] = pd.to_datetime(df["timestamp"])
+            else:
+                st.warning("Could not find date column in response")
+                return pd.DataFrame()
+                
+            df = df.set_index("datetime")
+            return df
+        else:
+            st.warning(f"API returned empty or unexpected data structure: {data}")
+            return pd.DataFrame()
+            
+    except Exception as e:
path: src/time_series.py
user: login: bito-code-review[bot]
body: 
<div>


<div id="suggestion">
<div id="issue"><b>Catching blind exception</b></div>
<div id="fix">

Do not catch blind exception: `Exception`. Catch specific exceptions instead to avoid masking unexpected errors and to make error handling more precise.
</div>
<details>
<summary>
<b>Code suggestion</b>
</summary>
<blockquote>Check the AI-generated fix before applying</blockquote>
<div id="code">


```
 -        st.info(f"Fetching data from {base_url} with parameters: {params}")
 +        st.info(f"Fetching data from {base_url} with parameters: {params}")
          response = requests.get(base_url, params=params, timeout=15)
         
          # Debug information
 @@ -84,7 +84,7 @@ def fetch_historical_data(station_id, pollutant, days=30):
              st.warning(f"API returned empty or unexpected data structure: {data}")
              return pd.DataFrame()
             
 -    except Exception as e:
 +    except (requests.RequestException, ValueError, KeyError) as e:
          st.error(f"Error fetching historical data: {e}")
         
          # For development/testing: create mock data
```

</div>
</details>
</div>


<small><i>Code Review Run <a href=https://github.com/Phinart98/Air-Quality-Analysis/pull/2#issuecomment-2888899787>#a2cfef</a></i></small>
</div>

---
Should Bito avoid suggestions like this for future reviews? (<a href=https://alpha.bito.ai/home/ai-agents/review-rules>Manage Rules</a>)
- [ ] Yes, avoid them
created_at: 2025-05-18T10:13:11Z
author_association: NONE

PR comments:
user: login: codeant-ai[bot]
created_at: 2025-05-18T10:07:58Z
body: CodeAnt AI is reviewing your PR.
author_association: NONE
user: login: sourcery-ai[bot]
created_at: 2025-05-18T10:07:58Z
body: <!-- Generated by sourcery-ai[bot]: start review_guide -->

## Reviewer's Guide

Introduces a dedicated Time Series Analysis feature by adding a new dashboard tab and a modular time_series component that fetches historical pollutant data with resilient multi-endpoint and mock fallbacks, renders interactive Plotly charts with rolling averages, and surfaces key statistics.

#### Sequence Diagram: Successful Data Fetching for Time Series Analysis

```mermaid
sequenceDiagram
    actor User
    participant Dashboard
    participant TimeSeriesComponent as "time_series.py"
    participant PrimaryAPI as "Primary API"

    User->>Dashboard: Selects station, pollutant, time range in UI
    Dashboard->>TimeSeriesComponent: add_time_series_section(...)
    TimeSeriesComponent->>TimeSeriesComponent: fetch_historical_data(...)
    TimeSeriesComponent->>PrimaryAPI: GET /measurements (station_id, pollutant, dates)
    PrimaryAPI-->>TimeSeriesComponent: Historical Data (JSON)
    TimeSeriesComponent->>TimeSeriesComponent: Process data (to DataFrame)
    TimeSeriesComponent->>TimeSeriesComponent: plot_time_series(df, pollutant_info)
    TimeSeriesComponent-->>Dashboard: Display chart and stats
    Dashboard-->>User: Shows Time Series Analysis
```

#### Sequence Diagram: Data Fetching Fallback to Alternative API

```mermaid
sequenceDiagram
    actor User
    participant Dashboard
    participant TimeSeriesComponent as "time_series.py"
    participant PrimaryAPI as "Primary API"
    participant AlternativeAPI as "Alternative API"

    User->>Dashboard: Selects station, pollutant, time range in UI
    Dashboard->>TimeSeriesComponent: add_time_series_section(...)
    TimeSeriesComponent->>TimeSeriesComponent: fetch_historical_data(...)
    TimeSeriesComponent->>PrimaryAPI: GET /measurements (station_id, pollutant, dates)
    PrimaryAPI-->>TimeSeriesComponent: Error (e.g., non-200 status)
    TimeSeriesComponent->>AlternativeAPI: GET /stations/{station_id}/measurements (pollutant, dates)
    AlternativeAPI-->>TimeSeriesComponent: Historical Data (JSON)
    TimeSeriesComponent->>TimeSeriesComponent: Process data (to DataFrame)
    TimeSeriesComponent->>TimeSeriesComponent: plot_time_series(df, pollutant_info)
    TimeSeriesComponent-->>Dashboard: Display chart and stats
    Dashboard-->>User: Shows Time Series Analysis
```

#### Sequence Diagram: Data Fetching Fallback to Mock Data

```mermaid
sequenceDiagram
    actor User
    participant Dashboard
    participant TimeSeriesComponent as "time_series.py"
    participant PrimaryAPI as "Primary API"
    participant AlternativeAPI as "Alternative API"

    User->>Dashboard: Selects station, pollutant, time range in UI
    Dashboard->>TimeSeriesComponent: add_time_series_section(...)
    TimeSeriesComponent->>TimeSeriesComponent: fetch_historical_data(...)
    TimeSeriesComponent->>PrimaryAPI: GET /measurements (station_id, pollutant, dates)
    PrimaryAPI-->>TimeSeriesComponent: Error (e.g., non-200 status)
    TimeSeriesComponent->>AlternativeAPI: GET /stations/{station_id}/measurements (pollutant, dates)
    AlternativeAPI-->>TimeSeriesComponent: Error (e.g., non-200 status or exception)
    TimeSeriesComponent->>TimeSeriesComponent: Generate Mock Data
    TimeSeriesComponent->>TimeSeriesComponent: plot_time_series(mock_df, pollutant_info)
    TimeSeriesComponent-->>Dashboard: Display chart and stats (with mock data)
    Dashboard-->>User: Shows Time Series Analysis (with mock data warning)
```

#### Class Diagram: `time_series.py` and `dashboard.py` Updates

```mermaid
classDiagram
  class time_series_py {
    <<New Module: time_series.py>>
    +fetch_historical_data(station_id: str, pollutant: str, days: int) : pd.DataFrame
    +plot_time_series(df: pd.DataFrame, pollutant_info: dict) : go.Figure
    +add_time_series_section(stations_gdf: gpd.GeoDataFrame, pollutant_info: dict) : void
  }
  note for time_series_py "New module for time series analysis. Handles data fetching (with API fallbacks and mock data generation), plotting, and Streamlit UI section creation."

  class dashboard_py {
    <<Modified Module: dashboard.py>>
    +create_dashboard() void
    # Other existing functions like get_pollutant_info() not detailed as unchanged by this PR.
  }
  note for dashboard_py "Modified create_dashboard() to:
- Add a new 'Time Series Analysis' tab.
- Import and call add_time_series_section from time_series.py to render the new section."

  dashboard_py ..> time_series_py : uses
```

### File-Level Changes

| Change | Details | Files |
| ------ | ------- | ----- |
| Integrated Time Series Analysis tab into main dashboard | <ul><li>Imported add_time_series_section from time_series module</li><li>Expanded tabs in create_dashboard to include ’Time Series Analysis’</li><li>Inserted with tab block calling add_time_series_section(stations_gdf, pollutant_info)</li></ul> | `src/dashboard.py` |
| Created time_series.py module for data fetching, plotting, and UI | <ul><li>Implemented fetch_historical_data with primary/alternative API endpoints, debug info, error handling, and mock data fallback</li><li>Built plot_time_series using Plotly to plot pollutant values and 24-hour rolling average</li><li>Developed add_time_series_section with station and pollutant selectors, time-range slider, spinner, plot rendering, and statistical metrics</li></ul> | `src/time_series.py` |

---

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<!-- Generated by sourcery-ai[bot]: end review_guide -->
author_association: NONE
user: login: coderabbitai[bot]
created_at: 2025-05-18T10:08:00Z
body: <!-- This is an auto-generated comment: summarize by coderabbit.ai -->
<!-- walkthrough_start -->

## Walkthrough

A new "Time Series Analysis" tab was added to the dashboard, integrating a new module for analyzing pollutant data trends over time. The module provides functions to fetch historical pollutant data, visualize it with interactive charts, and display basic statistics, all accessible through the dashboard’s user interface.

## Changes

| File(s)                           | Change Summary                                                                                                                      |
|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|
| src/dashboard.py                   | Added a "Time Series Analysis" tab to the dashboard, invoking a new function to render its contents.                               |
| src/time_series.py                 | Introduced a new module with functions to fetch, process, and visualize historical pollutant data, and to integrate these in the dashboard UI. |

## Sequence Diagram(s)

```mermaid
sequenceDiagram
    participant User
    participant Dashboard (Streamlit)
    participant TimeSeriesModule

    User->>Dashboard (Streamlit): Selects "Time Series Analysis" tab
    Dashboard (Streamlit)->>TimeSeriesModule: add_time_series_section(stations_gdf, pollutant_info)
    TimeSeriesModule->>User: Presents station, pollutant, and time range selectors
    User->>TimeSeriesModule: Chooses station, pollutant, and time range
    TimeSeriesModule->>TimeSeriesModule: fetch_historical_data(...)
    TimeSeriesModule->>TimeSeriesModule: plot_time_series(...)
    TimeSeriesModule->>User: Displays interactive chart and statistics
```

## Poem

> A dashboard grows with a brand new view,  
> Time series charts for pollutants too!  
> Data fetched and lines that gleam,  
> Stats and sliders—an analyst’s dream.  
> Now bunnies can hop through time with glee,  
> Exploring trends as clear as can be!  
> 🐇📈

<!-- walkthrough_end -->

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<details open="true">
<summary>✨ Finishing Touches</summary>

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author_association: NONE
user: login: trag-bot[bot]
created_at: 2025-05-18T10:09:09Z
body: @trag-bot didn't find any issues in the code! ✅✨
author_association: NONE
user: login: codeant-ai[bot]
created_at: 2025-05-18T10:09:28Z
body: ## Pull Request Feedback 🔍

<table>
<tr><td>🔒&nbsp;<strong>No security issues identified</strong></td></tr>
<tr><td>⚡&nbsp;<strong>Recommended areas for review</strong><br><br>

<a href='https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-06135492c180d831917894055e5bb06ed9c4f31998a88b446abd432d50f6a4c7R10-R253'><strong>Dashboard Integration</strong></a><br>The new "Time Series Analysis" tab is added by importing and calling `add_time_series_section`. Please verify that the tab layout (including the added "Pollutant Analysis" and "Time Series Analysis" tabs) integrates seamlessly with existing dashboard functionality. Also, ensure that any errors from the time series section are handled gracefully to maintain a smooth user experience.

<a href='https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R40-R60'><strong>Debug Messaging</strong></a><br>Several debug messages (using `st.info` and `st.warning`) are displayed during API calls in `fetch_historical_data`. In a production setting, such messages might expose internal state information. Consider using a dedicated logging framework or wrapping these calls in a debug-mode flag to control their visibility.

<a href='https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8R87-R102'><strong>Mock Data Fallback</strong></a><br>The fallback mechanism in `fetch_historical_data` automatically generates mock data when an exception occurs. While this ensures continuity during API failures, it may lead to unintended use of synthetic data in a production environment. It is advisable to gate this behavior behind a configuration flag or an environment check.

</td></tr>
</table>

author_association: NONE
user: login: trag-bot[bot]
created_at: 2025-05-18T10:09:32Z
body: ### Pull request summary

- Added a new module `time_series.py` to handle historical pollutant data fetching and time series plotting.
- Implemented the `fetch_historical_data` function to retrieve pollutant data from a specified API, with error handling and mock data generation for testing.
- Created the `plot_time_series` function to generate interactive time series plots using Plotly, including a 24-hour moving average.
- Introduced the `add_time_series_section` function to integrate time series analysis into the existing dashboard, allowing users to select monitoring stations and pollutants.
- Updated the `create_dashboard` function to include a new tab for "Time Series Analysis" in the dashboard interface.
- Enhanced the user experience by providing statistical summaries (average, maximum, minimum) of the selected pollutant data.
- Improved API request handling by adding a format parameter and an alternative endpoint for data retrieval.
- Ensured that the dashboard dynamically updates based on user selections for stations and pollutants.
author_association: NONE
user: login: fumedev[bot]
created_at: 2025-05-18T10:10:44Z
body: ## Summary
- Introduces a new time series analysis tab to the dashboard for analyzing historical pollutant data.
- Supports station-specific analysis for various pollutants with selectable time periods from 7 to 90 days.
- Includes interactive charts displaying statistical insights, such as average, maximum, and minimum pollutant levels.
- Implements comprehensive error handling and fallback strategies for API failures, including mock data generation for demonstration.
- Features responsive Plotly visualizations and maintains full UI functionality even during API outages.
author_association: NONE
user: login: bito-code-review[bot]
created_at: 2025-05-18T10:13:10Z
body: <div>
<h2>Changelist by <a href="https://bito.ai">Bito</a></h2>
<p>This pull request implements the following key changes.</p>

<div>
<table width="100%">
<tbody>
  <tr>
    <th>Key Change</th>
    <th>Files Impacted</th>
  </tr>

<tr>
<td width="30%">
    <b>New Feature</b> - Time Series Analysis Implementation
</td>
<td width="70%">


<div>

-&nbsp;[`dashboard.py`](https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-06135492c180d831917894055e5bb06ed9c4f31998a88b446abd432d50f6a4c7) - Enhanced the dashboard by adding a new 'Time Series Analysis' tab and updating the tab layout to integrate time series functionalities.
<div>

-&nbsp;[`time_series.py`](https://github.com/Phinart98/Air-Quality-Analysis/pull/2/files#diff-41c0879657f3dd8be9b7c276d83f37825dbdb1f614db5765c18781766c0abfe8) - Introduced a new module that fetches, visualizes, and analyzes historical pollutant data with robust error handling and fallback mechanisms.
</div>
</td>
</tr>

</tbody>
</table>
</div>
</div>
author_association: NONE