PyCafe - Solara - River Flow Forecast Visualization

app.py
requirements.txt
app.py
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import zarr
import s3fs
import pandas as pd
import altair as alt
import solara
from tqdm import tqdm

def get_forecast_data(river_number, date):
    s3_bucket_url = f's3://geoglows-v2-forecasts/{date}.zarr/'
    s3 = s3fs.S3FileSystem(anon=True)

    try:
        mapper = s3fs.S3Map(root=s3_bucket_url, s3=s3, check=False)
        zarr_group = zarr.open_group(mapper, mode='r')

        rivid_array = zarr_group['rivid'][:]
        if river_number in rivid_array:
            river_index = list(rivid_array).index(river_number)
            qout_array = zarr_group['Qout'][:, :, river_index]
            time_array = zarr_group['time'][:]
            ensemble_array = zarr_group['ensemble'][:]
            
            forecast_df = pd.DataFrame(qout_array, columns=time_array)
            forecast_df.index = [f"ensemble_{i}" for i in ensemble_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 list_s3_contents(bucket_url):
    s3 = s3fs.S3FileSystem(anon=True)

    try:
        contents = s3.ls(bucket_url)
        return contents
    except Exception as e:
        print(f"Error accessing S3 bucket: {e}")
        return []

def visualize_forecast_data(forecast_df, river_number, date):
    forecast_df = forecast_df.reset_index().melt(id_vars=['index'], var_name='Ensemble', value_name='Flow')
    forecast_df.columns = ['Time', 'Ensemble', 'Flow']

    chart = alt.Chart(forecast_df).mark_line().encode(
        x='Time:T',
        y='Flow:Q',
        color='Ensemble:N'
    ).properties(
        title=f'Forecast for RiverNumber {river_number} on {date}',
        width=800,
        height=400
    )

    return chart

# Example usage:
river_number = 110123714  # Replace with actual river number
selected_date = '2024040100'  # Replace with actual date

# Fetch forecast data
forecast_df = get_forecast_data(river_number, selected_date)

# Visualize forecast data if available
if not forecast_df.empty:
    chart = visualize_forecast_data(forecast_df, river_number, selected_date)
    solara.FigureAltair(chart)
else:
    print(f"No data available for RiverNumber {river_number} on {selected_date}")
Py.Cafe

solara-river-forecast

River Flow Forecast Visualization
