Weather forecast on dashboard

requirements.txt

@@ -20,3 +20,4 @@ ocf_blosc2==0.0.4
 zarr==2.14.2
 elexonpy
 fiona==1.9.6
+herbie

src/Weather_forecast_on_dashboard.py

@@ -0,0 +1,133 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+from herbie import FastHerbie
+from datetime import datetime, timedelta, time
+import numpy as np
+from concurrent.futures import ThreadPoolExecutor
+
+@st.cache_data(show_spinner=False)
+def fetch_data_for_init_time(init_time, forecast_date, lat, lon, parameter, model="ifs"):
+    # Adjust the initialization datetime based on the init_time offset in hours
+    init_datetime = forecast_date - timedelta(hours=init_time)  # This will be a datetime object
+
+    FH = FastHerbie([init_datetime], model=model, fxx=range(0, 24, 1), fast=True)
+
+    try:
+        FH.download()  # Ensure the file is downloaded
+    except Exception as e:
+        st.error(f"Failed to download data for initialization time {init_datetime}. Error: {e}")
+        return None, None
+
+    variable_subset = ":10[u|v]" if parameter == "u10:v10" else ":100[u|v]" if parameter == "u100:v100" else "2t"
+
+    try:
+        ds = FH.xarray(variable_subset, remove_grib=False)
+        if isinstance(ds, list):
+            ds = ds[0]
+    except EOFError as e:
+        st.error(f"Failed to read data file for {init_datetime}. Error: {e}")
+        return None, None
+    except Exception as e:
+        st.error(f"An unexpected error occurred while reading data: {e}")
+        return None, None
+
+    return ds, init_datetime
+
+def process_initialization(init_time, forecast_date, lat, lon, parameter, model="ifs"):
+    ds, init_datetime = fetch_data_for_init_time(init_time, forecast_date, lat, lon, parameter, model)
+    if ds is None:
+        return []
+
+    interpolated_data = []
+
+    time_dim = ds['step'] if 'step' in ds.dims else ds['time'] if 'time' in ds.dims else None
+    if time_dim is None:
+        st.error("Neither 'step' nor 'time' dimension is present in the dataset. Unable to proceed with forecast.")
+        return []
+
+    for time_val in time_dim:
+        actual_forecast_time = init_datetime + pd.to_timedelta(time_val.values)
+        if actual_forecast_time.date() == forecast_date.date():
+            if parameter == "u10:v10":
+                u = ds['u10'].interp(latitude=lat, longitude=lon, method="nearest", step=time_val)
+                v = ds['v10'].interp(latitude=lat, longitude=lon, method="nearest", step=time_val)
+                value = np.sqrt(u**2 + v**2)
+            elif parameter == "u100:v100":
+                u = ds['u100'].interp(latitude=lat, longitude=lon, method="nearest", step=time_val)
+                v = ds['v100'].interp(latitude=lat, longitude=lon, method="nearest", step=time_val)
+                value = np.sqrt(u**2 + v**2)
+            else:  # temp
+                value = ds['t2m'].interp(latitude=lat, longitude=lon, method="nearest", step=time_val) - 273.15
+
+            interpolated_data.append({
+                'date_time': actual_forecast_time,
+                'latitude': lat,
+                'longitude': lon,
+                'value': value.compute().item(),
+                'init_time': f"{init_time} hours before"
+            })
+
+    return interpolated_data
+
+def get_forecast(forecast_date, lat, lon, parameter, init_times, model="ifs"):
+    all_data = []
+    with ThreadPoolExecutor() as executor:
+        results = list(executor.map(lambda init_time: process_initialization(init_time, forecast_date, lat, lon, parameter, model), init_times))
+    for result in results:
+        all_data.extend(result)
+    return pd.DataFrame(all_data)
+
+def weather_forecast_page():
+    # Streamlit app
+    st.title("Weather Forecasts from Different Initialization Times")
+
+    parameter_mapping = {
+        "Wind Speed (10m)": "u10:v10",
+        "Wind Speed (100m)": "u100:v100",
+        "Temperature": "2t"
+    }
+
+    parameter_option = st.sidebar.selectbox("Select Parameter", options=list(parameter_mapping.keys()))
+    parameter_value = parameter_mapping[parameter_option]
+    lat = st.sidebar.number_input("Enter Latitude", value=27.035, format="%.6f")
+    lon = st.sidebar.number_input("Enter Longitude", value=70.515, format="%.6f")
+
+    # Use datetime.combine to set the forecast_date to midnight
+    forecast_date = st.sidebar.date_input("Select Forecast Date", datetime.today().date())
+    forecast_date = datetime.combine(forecast_date, time(hour=0))
+
+    # Multiselect for initialization times in hours
+    init_time_options = st.sidebar.multiselect(
+        "Select Initialization Times (in hours before forecast date)",
+        options=[24, 18, 12, 6, 0],
+        default=[24, 12, 0]  # Default selections: 24 hours before, 12 hours before, and current day
+    )
+
+    if st.button("Fetch Data"):
+        if not init_time_options:
+            st.warning("Please select at least one initialization time.")
+        else:
+            st.write("Note: Data loading may take up to 30 seconds. Please be patient...")
+            data = get_forecast(forecast_date, lat, lon, parameter_value, init_times=init_time_options )
+
+            if not data.empty:
+                fig = px.line(
+                    data,
+                    x='date_time',
+                    y='value',
+                    color='init_time',
+                    labels={
+                        'date_time': 'Date and Time',
+                        'value': f'{parameter_option} ({"m/s" if "Wind Speed" in parameter_option else "°C"})',
+                        'init_time': 'Initialization Time'
+                    },
+                    title=f"Forecast of {parameter_option} at ({lat}, {lon}) for {forecast_date.strftime('%Y-%m-%d')}"
+                )
+                st.plotly_chart(fig)
+
+                csv = data.to_csv(index=False)
+                file_name = f"{forecast_date.strftime('%Y-%m-%d')}_{parameter_option}_comparison_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.")

src/main_india.py

@@ -13,6 +13,7 @@
 from satellite_page import satellite_page
 from status import status_page
 from users import user_page
+from weather_forecast import weather_forecast_page
 
 st.get_option("theme.primaryColor")
 st.set_page_config(layout="wide", page_title="OCF Dashboard")
@@ -26,6 +27,7 @@
         "API Users": user_page,
         "NWP": nwp_page,
         "Satellite": satellite_page,
+        "Weather Forecast": weather_forecast_page, 
     }
 
     demo_name = st.sidebar.selectbox("Choose a page", page_names_to_funcs.keys(), 1)