CoWIN added.

pages/7_CoWIN.py

@@ -0,0 +1,167 @@
+import streamlit as st
+import requests
+import json
+import pandas as pd
+import folium
+from streamlit_folium import st_folium
+import plotly.graph_objects as go
+import numpy as np
+from datetime import datetime
+from branca.colormap import LinearColormap
+
+st.set_page_config(layout="wide", page_title="Real-Time CoWIN Weather Data Dashboard")
+
+@st.cache_data(ttl=300)  # Cache data for 5 minutes (300 seconds)
+def fetch_data():
+    current_time = datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
+    url = f'https://cowin.hku.hk/API/data/CoWIN/map?time={current_time}'
+    response = requests.get(url)
+    return json.loads(response.text)
+
+data = fetch_data()
+
+features = data
+df = pd.json_normalize(features)
+
+df.rename(columns={
+    'station': 'Station',
+    'temp': 'Temperature',
+    'lat': 'Latitude',
+    'lon': 'Longitude',
+    'wd': 'Wind Direction',
+    'ws': 'Wind Speed',
+    'rh': 'Relative Humidity',
+    'uv': 'UV Radiation',
+    'me_name': 'Name'
+}, inplace=True)
+
+attribute = st.selectbox(
+    'Select Weather Attributes to Plot and Map (Data from HKO-HKU CoWIN)',
+    ['Temperature', 'Wind Speed', 'Relative Humidity', 'UV Radiation']
+)
+
+col1, col2, col3 = st.columns([1.65, 2, 1.15])
+
+with col1:
+    attr_series = pd.Series(df[attribute].dropna())
+
+    hist_data = np.histogram(attr_series, bins=10)
+    bin_edges = hist_data[1]
+    counts = hist_data[0]
+
+    def get_color(value, min_value, max_value):
+        ratio = (value - min_value) / (max_value - min_value)
+        r = int(255 * ratio)
+        b = int(255 * (1 - ratio))
+        return f'rgb({r}, 0, {b})'
+
+    fig = go.Figure()
+
+    for i in range(len(bin_edges) - 1):
+        bin_center = (bin_edges[i] + bin_edges[i + 1]) / 2
+        color = get_color(bin_center, bin_edges.min(), bin_edges.max())
+        fig.add_trace(go.Bar(
+            x=[f'{bin_edges[i]:.1f} - {bin_edges[i + 1]:.1f}'],
+            y=[counts[i]],
+            marker_color=color,
+            name=f'{bin_edges[i]:.1f} - {bin_edges[i + 1]:.1f}'
+        ))
+
+    fig.update_layout(
+        xaxis_title=f'{attribute}',
+        yaxis_title='Count',
+        title=f'{attribute} Distribution',
+        bargap=0.2,
+        title_font_size=20,
+        xaxis_title_font_size=14,
+        yaxis_title_font_size=14,
+        height=350,
+        xaxis=dict(title_font_size=14),
+        yaxis=dict(title_font_size=14)
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    with st.container():
+        col_1, col_2 = st.columns([1, 1])
+        with col_1:
+            if attr_series.size > 0:
+                avg_attr = np.mean(attr_series)
+                std_attr = np.std(attr_series)
+                max_attr = np.max(attr_series)
+                min_attr = np.min(attr_series)
+
+                st.metric(label=f"Average {attribute}", value=f"{avg_attr:.2f}")
+                st.metric(label=f"Minimum {attribute}", value=f"{min_attr:.2f}")
+        with col_2:
+            st.metric(label=f"Maximum {attribute}", value=f"{max_attr:.2f}")
+            st.metric(label=f"Std. Dev {attribute}", value=f"{std_attr:.2f}")
+
+def attribute_to_color(value, min_value, max_value):
+    """Convert a value to a color based on the gradient."""
+    ratio = (value - min_value) / (max_value - min_value)
+    return LinearColormap(['blue', 'purple', 'red']).rgb_hex_str(ratio)
+
+with col2:
+    m = folium.Map(location=[22.3547, 114.1483], zoom_start=11, tiles='CartoDB positron')
+
+    min_value = df[attribute].min()
+    max_value = df[attribute].max()
+
+    for _, row in df.iterrows():
+        lat = row['Latitude']
+        lon = row['Longitude']
+        station = row['Station']
+        name = row['Name']
+        value = row[attribute]
+
+        color = attribute_to_color(value, min_value, max_value) if pd.notna(value) else 'gray'
+
+        folium.Marker(
+            location=[lat, lon],
+            popup=(
+                f"<p style='font-size: 12px; background-color: white; padding: 5px; border-radius: 5px;'>"
+                f"Station: {station}<br>"
+                f"Name: {name}<br>"
+                f"{attribute}: {value}<br>"
+                f"</p>"
+            ),
+            icon=folium.DivIcon(
+                html=f'<div style="font-size: 10pt; color: {color}; padding: 2px; border-radius: 5px;">'
+                     f'<strong>{value}</strong></div>'
+            )
+        ).add_to(m)
+
+    # Create a color scale legend
+    colormap = folium.LinearColormap(
+        colors=['blue', 'purple', 'red'],
+        index=[min_value, (min_value + max_value) / 2, max_value],
+        vmin=min_value,
+        vmax=max_value,
+        caption=f'{attribute}'
+    )
+    colormap.add_to(m)
+
+    st_folium(m, width=500, height=600)
+
+with col3:
+    st.markdown(
+        """
+        <style>
+        .dataframe-container {
+            height: 600px;
+            overflow-y: auto;
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+    st.dataframe(df[['Station', 'Name', 'Temperature', 'Wind Speed', 'Relative Humidity', 'UV Radiation', 'Latitude', 'Longitude']], height=600)
+
+if st.button("Refresh Data"):
+    st.experimental_rerun()
+
+if 'last_ran' not in st.session_state or (datetime.now() - st.session_state.last_ran).seconds > 300:
+    st.session_state.last_ran = datetime.now()
+    st.experimental_rerun()