PyCafe - Solara - River Flow Forecast Viewer

app.py
requirements.txt
app.py
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import solara
import zarr
import pandas as pd
import altair as alt
import requests
from io import BytesIO

@solara.component
def Page():
    river_number = 110123714  # Replace with the desired river number
    date = '2024040100'    # Replace with the desired date
    
    forecast_data = get_forecast_data(river_number, date)
    
    if not forecast_data.empty:
        solara.Markdown(f"## Forecast data for RiverNumber {river_number} on {date}:")
        solara.DataFrame(forecast_data)
        
        chart = create_altair_chart(forecast_data)
        solara.Markdown("## Forecast Visualization")
        solara.Viz(chart)
    else:
        solara.Markdown(f"No forecast data found for RiverNumber {river_number} on {date}.")
        
def get_forecast_data(river_number, date):
    s3_bucket_url = f'https://geoglows-v2-forecasts.s3.amazonaws.com/{date}.zarr/'
    
    try:
        rivid_url = s3_bucket_url + 'rivid'
        qout_url = s3_bucket_url + 'Qout'
        time_url = s3_bucket_url + 'time'
        ensemble_url = s3_bucket_url + 'ensemble'
        
        rivid_array = fetch_zarr_array(rivid_url)
        if river_number in rivid_array:
            river_index = list(rivid_array).index(river_number)
            qout_array = fetch_zarr_array(qout_url)[:, :, river_index]
            time_array = fetch_zarr_array(time_url)
            ensemble_array = fetch_zarr_array(ensemble_url)

            forecast_df = pd.DataFrame(qout_array, columns=time_array)
            forecast_df.index = [f"ensemble_{i}" for i in ensemble_array]
            forecast_df.columns = time_array
            forecast_df = forecast_df.transpose()

            return forecast_df
        else:
            return pd.DataFrame()
    except Exception as e:
        print(f"Error accessing data for RiverNumber {river_number} on {date}: {e}")
        return pd.DataFrame()

def fetch_zarr_array(url):
    response = requests.get(url)
    response.raise_for_status()
    store = zarr.storage.ZipStore(BytesIO(response.content))
    zarr_array = zarr.open(store, mode='r')
    return zarr_array[:]

def create_altair_chart(forecast_df):
    forecast_df_reset = forecast_df.reset_index().melt(id_vars=['index'], var_name='Time', value_name='Flow')
    
    chart = alt.Chart(forecast_df_reset).mark_line().encode(
        x='Time:T',
        y='Flow:Q',
        color='index:N'
    ).properties(
        title='Forecast Flow Data',
        width=800,
        height=400
    )
    return chart
Py.Cafe

river-flow-forecast-viewer

River Flow Forecast Viewer
