Weather forecast on dashboard

src/Weather_forecast_on_dashboard.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from herbie import FastHerbie
+from datetime import datetime, timedelta
+import numpy as np
+import os
+
+# Define the points with the provided coordinates and create a human-readable location column
+points = pd.DataFrame(
+    {
+        "latitude": [
+            27.035, 27.188, 27.085, 27.055, 27.186, 27.138, 26.97, 26.898,
+            26.806, 26.706, 26.698, 26.708, 26.679, 26.8, 26.704, 26.5,
+            26.566, 26.679, 26.201, 26.501, 26.463, 26.718, 26.63, 24.142,
+            23.956, 23.657
+        ],
+        "longitude": [
+            70.515, 70.661, 70.638, 70.72, 70.81, 71.024, 70.917, 70.99599,
+            70.732, 70.81, 70.875, 70.982, 71.027, 71.128, 71.12699, 71.285,
+            71.369, 71.452, 71.295, 72.512, 72.836, 73.049, 73.581, 74.73099,
+            74.625, 74.772
+        ],
+        "stid": [
+            "aa", "bb", "cc", "dd", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll",
+            "mm", "nn", "oo", "pp", "qq", "rr", "ss", "tt", "uu", "vv", "ww", "xx", "yy", "zz"
+        ]
+    }
+)
+
+# Add a location column for human-readable representation
+points["location"] = points["latitude"].astype(str) + ", " + points["longitude"].astype(str)
+
+# Function to get weather data based on the selected parameter
+def get_weather_data(date_time, points, parameter):
+    try:
+        FH = FastHerbie([date_time], model="ifs", fxx=range(0, 12, 3), fast=True)
+        FH.download()  # Ensure the file is downloaded
+
+        variable_subset = ":10[u|v]" if parameter == "u10:v10" else ":100[u|v]" if parameter == "u100:v100" else "2t"
+        ds = FH.xarray(variable_subset, remove_grib=False)
+        if isinstance(ds, list):
+            ds = ds[0]
+
+        interpolated_data = []
+        for i, row in points.iterrows():
+            lat = row['latitude']
+            lon = row['longitude']
+            for step in ds.step:
+                if parameter == "u10:v10":
+                    u = ds['u10'].interp(latitude=lat, longitude=lon, method="nearest", step=step).values
+                    v = ds['v10'].interp(latitude=lat, longitude=lon, method="nearest", step=step).values
+                    value = np.sqrt(u**2 + v**2)  # Calculate wind speed at 10m
+                elif parameter == "u100:v100":
+                    u = ds['u100'].interp(latitude=lat, longitude=lon, method="nearest", step=step).values
+                    v = ds['v100'].interp(latitude=lat, longitude=lon, method="nearest", step=step).values
+                    value = np.sqrt(u**2 + v**2)  # Calculate wind speed at 100m
+                else:  # temp
+                    value = ds['t2m'].interp(latitude=lat, longitude=lon, method="nearest", step=step).values - 273.15  # Convert temperature from Kelvin to Celsius
+
+                interpolated_data.append({
+                    'date_time': date_time + step.values,
+                    'location': row['location'],
+                    'latitude': lat,
+                    'longitude': lon,
+                    'value': value
+                })
+
+        return pd.DataFrame(interpolated_data)
+    except FileNotFoundError as fnf_error:
+        st.error(f"File not found: {fnf_error}")
+        st.warning(f"Check if the model data is available for {date_time}. Ensure the directory '{os.path.dirname(fnf_error.filename)}' exists.")
+        return pd.DataFrame()
+    except Exception as e:
+        st.error(f"Error processing data for {date_time}: {e}")
+        return pd.DataFrame()
+
+# Streamlit app
+st.title("Weather Data Visualization")
+
+# Dropdown menu mapping user-friendly names to internal parameters
+parameter_mapping = {
+    "Wind Speed (10m)": "u10:v10",
+    "Wind Speed (100m)": "u100:v100",
+    "Temperature": "2t"
+}
+
+# Sidebar for parameter selection
+parameter_option = st.sidebar.selectbox(
+    "Select Parameter",
+    options=list(parameter_mapping.keys())  # Display user-friendly names
+)
+
+# Get the actual parameter value to use in the Herbie function
+parameter_value = parameter_mapping[parameter_option]
+
+# Sidebar for site selection
+site_option = st.sidebar.selectbox(
+    "Select Site",
+    options=["All Sites", "Mean of All Sites"] + points["location"].tolist()
+)
+
+# Input date range
+start_date = st.date_input("Select start date", datetime(2024, 7, 20))
+end_date = st.date_input("Select end date", start_date + timedelta(days=1))
+
+# Fetch data button
+if st.button("Fetch Data"):
+    date_range = pd.date_range(start=start_date, end=end_date, freq="6h")
+
+    all_data = []
+    for current_date in date_range:
+        data = get_weather_data(current_date, points, parameter_value)
+        if not data.empty:
+            all_data.append(data)
+
+    if all_data:
+        final_data = pd.concat(all_data, ignore_index=True)
+
+        if site_option == "Mean of All Sites":
+            # Calculate the mean value across all sites
+            mean_data = final_data.groupby('date_time')['value'].mean().reset_index()
+            mean_data['location'] = 'Mean of All Sites'
+
+            # Plot: Mean value over time
+            fig = px.line(
+                mean_data,
+                x='date_time',
+                y='value',
+                labels={
+                    'date_time': 'Date and Time',
+                    'value': f'Mean {parameter_option} ({"m/s" if "Wind Speed" in parameter_option else "°C"})',
+                    'location': 'Location'
+                },
+                title=f"Mean {parameter_option} Over Time"
+            )
+            st.plotly_chart(fig)
+
+        else:
+            # Filter data by selected location
+            if site_option != "All Sites":
+                final_data = final_data[final_data['location'] == site_option]
+
+            # Plot: Value over time
+            fig = px.line(
+                final_data,
+                x='date_time',
+                y='value',
+                color='location' if site_option == "All Sites" else None,
+                labels={
+                    'date_time': 'Date and Time',
+                    'value': f'{parameter_option} ({"m/s" if "Wind Speed" in parameter_option else "°C"})',
+                    'location': 'Location'
+                },
+                title=f"{parameter_option} Over Time for {'All Sites' if site_option == 'All Sites' else site_option}"
+            )
+            st.plotly_chart(fig)
+
+        # Download as CSV button
+        csv = final_data.to_csv(index=False)
+        start_datetime_str = start_date.strftime('%Y-%m-%d_%H-%M')
+        file_name = f"{start_datetime_str}_{parameter_option}_data.csv"
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name=file_name,
+            mime='text/csv',
+        )
+    else:
+        st.warning("No data was collected.")