app.py

import os
# Set memory optimization environment variables
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
os.environ['ANEMOI_INFERENCE_NUM_CHUNKS'] = '16'

import gradio as gr
import datetime
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.tri as tri
from anemoi.inference.runners.simple import SimpleRunner
from ecmwf.opendata import Client as OpendataClient
import earthkit.data as ekd
import earthkit.regrid as ekr

# Define parameters (updating to match notebook.py)
PARAM_SFC = ["10u", "10v", "2d", "2t", "msl", "skt", "sp", "tcw", "lsm", "z", "slor", "sdor"]
PARAM_SOIL = ["vsw", "sot"]
PARAM_PL = ["gh", "t", "u", "v", "w", "q"]
LEVELS = [1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50]
SOIL_LEVELS = [1, 2]
DEFAULT_DATE = OpendataClient().latest()

# First organize variables into categories
VARIABLE_GROUPS = {
    "Surface Variables": {
        "10u": "10m U Wind Component",
        "10v": "10m V Wind Component",
        "2d": "2m Dewpoint Temperature",
        "2t": "2m Temperature",
        "msl": "Mean Sea Level Pressure",
        "skt": "Skin Temperature",
        "sp": "Surface Pressure",
        "tcw": "Total Column Water",
        "lsm": "Land-Sea Mask",
        "z": "Surface Geopotential",
        "slor": "Slope of Sub-gridscale Orography",
        "sdor": "Standard Deviation of Orography",
    },
    "Soil Variables": {
        "stl1": "Soil Temperature Level 1",
        "stl2": "Soil Temperature Level 2",
        "swvl1": "Soil Water Volume Level 1",
        "swvl2": "Soil Water Volume Level 2",
    },
    "Pressure Level Variables": {}  # Will fill this dynamically
}

# Add pressure level variables dynamically
for var in ["t", "u", "v", "w", "q", "z"]:
    var_name = {
        "t": "Temperature",
        "u": "U Wind Component",
        "v": "V Wind Component",
        "w": "Vertical Velocity",
        "q": "Specific Humidity",
        "z": "Geopotential"
    }[var]
    
    for level in LEVELS:
        var_id = f"{var}_{level}"
        VARIABLE_GROUPS["Pressure Level Variables"][var_id] = f"{var_name} at {level}hPa"

def get_open_data(param, levelist=[]):
    fields = {}
    # Get the data for the current date and the previous date
    for date in [DEFAULT_DATE - datetime.timedelta(hours=6), DEFAULT_DATE]:
        data = ekd.from_source("ecmwf-open-data", date=date, param=param, levelist=levelist)
        for f in data:
            assert f.to_numpy().shape == (721, 1440)
            values = np.roll(f.to_numpy(), -f.shape[1] // 2, axis=1)
            values = ekr.interpolate(values, {"grid": (0.25, 0.25)}, {"grid": "N320"})
            name = f"{f.metadata('param')}_{f.metadata('levelist')}" if levelist else f.metadata("param")
            if name not in fields:
                fields[name] = []
            fields[name].append(values)
    
    # Create a single matrix for each parameter
    for param, values in fields.items():
        fields[param] = np.stack(values)
    
    return fields

def run_forecast(date, lead_time, device):
    # Get all required fields
    fields = {}
    
    # Get surface fields
    fields.update(get_open_data(param=PARAM_SFC))
    
    # Get soil fields and rename them
    soil = get_open_data(param=PARAM_SOIL, levelist=SOIL_LEVELS)
    mapping = {
        'sot_1': 'stl1', 'sot_2': 'stl2',
        'vsw_1': 'swvl1', 'vsw_2': 'swvl2'
    }
    for k, v in soil.items():
        fields[mapping[k]] = v
    
    # Get pressure level fields
    fields.update(get_open_data(param=PARAM_PL, levelist=LEVELS))
    
    # Convert geopotential height to geopotential
    for level in LEVELS:
        gh = fields.pop(f"gh_{level}")
        fields[f"z_{level}"] = gh * 9.80665
    
    input_state = dict(date=date, fields=fields)
    runner = SimpleRunner("aifs-single-mse-1.0.ckpt", device=device)
    results = []
    for state in runner.run(input_state=input_state, lead_time=lead_time):
        results.append(state)
    return results[-1]

def plot_forecast(state, selected_variable):
    latitudes, longitudes = state["latitudes"], state["longitudes"]
    values = state["fields"][selected_variable]
    fig, ax = plt.subplots(figsize=(11, 6), subplot_kw={"projection": ccrs.PlateCarree()})
    ax.coastlines()
    ax.add_feature(cfeature.BORDERS, linestyle=":")
    triangulation = tri.Triangulation(longitudes, latitudes)
    
    # Use 'RdBu_r' instead of 'RdBu' to reverse the color scheme
    contour = ax.tricontourf(triangulation, values, levels=20, 
                            transform=ccrs.PlateCarree(), 
                            cmap='RdBu_r')
    plt.title(f"{selected_variable} at {state['date']}")
    plt.colorbar(contour)
    return fig

# Create dropdown choices with groups
DROPDOWN_CHOICES = []
for group_name, variables in VARIABLE_GROUPS.items():
    # Add group separator
    DROPDOWN_CHOICES.append((f"── {group_name} ──", None))
    # Add variables in this group
    for var_id, desc in sorted(variables.items()):
        DROPDOWN_CHOICES.append((f"{desc} ({var_id})", var_id))

def gradio_interface(date_str, lead_time, device, selected_variable):
    try:
        date = datetime.datetime.strptime(date_str, "%Y-%m-%d")
    except ValueError:
        raise gr.Error("Please enter a valid date in YYYY-MM-DD format")
    state = run_forecast(date, lead_time, device)
    return plot_forecast(state, selected_variable)

demo = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(value=DEFAULT_DATE.strftime("%Y-%m-%d"), label="Forecast Date (YYYY-MM-DD)"),
        gr.Slider(minimum=6, maximum=48, step=6, value=12, label="Lead Time (Hours)"),
        gr.Radio(choices=["cuda", "cpu"], value="cuda", label="Compute Device"),
        gr.Dropdown(
            choices=DROPDOWN_CHOICES,
            value="2t",  # Default to 2m temperature
            label="Select Variable to Plot",
            info="Choose a meteorological variable to visualize"
        )
    ],
    outputs=gr.Plot(),
    title="AIFS Weather Forecast",
    description="Interactive visualization of ECMWF AIFS weather forecasts. Select a date, forecast lead time, and meteorological variable to plot."
)

demo.launch()