File Downloads

import os
import requests
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.environ.get('SPIRE_API_KEY')
BASE_URL = 'https://api.wx.spire.com'
HEADERS = {'spire-api-key': API_KEY}

# Get list of available forecast files
params = {
    'bundles': 'basic',
    'regions': 'global'
}

response = requests.get(
    f'{BASE_URL}/forecast/file',
    headers=HEADERS,
    params=params
)
response.raise_for_status()
file_list = response.json()

print(f"Total files available: {file_list['meta']['count']}")
print()
print("First 10 files:")
for f in file_list['files'][:10]:
    print(f"  {f}")

import re
from dataclasses import dataclass
from datetime import datetime

@dataclass
class FileInfo:
    system: str
    date: datetime
    hour: int
    resolution: object
    bundle: str
    region: str
    lead_hours: int
    format: str
    filename: str

def parse_filename(filename):
    """Parse a forecast filename into components."""
    pattern = r'^([\w-]+)\.(\d{8})\.t(\d{2})z\.(\w+)\.([\w-]+)\.([\w-]+)\.(?:f(\d{3})(?:m\d{2})?|[DW](\d{3}))\.(\w+)$'
    match = re.match(pattern, filename)
    
    if not match:
        return None
    
    system, date_str, hour, res_str, bundle, region, lead, period, fmt = match.groups()
    
    return FileInfo(
        system=system,
        date=datetime.strptime(date_str, '%Y%m%d'),
        hour=int(hour),
        resolution=float(res_str.replace('p', '.')) if 'p' in res_str else res_str,
        bundle=bundle,
        region=region,
        lead_hours=int(lead) if lead is not None else int(period) * 24,
        format=fmt,
        filename=filename
    )

# Parse all files
parsed_files = [parse_filename(f) for f in file_list['files']]
parsed_files = [f for f in parsed_files if f is not None]

# Show example
if parsed_files:
    example = parsed_files[0]
    print(f"Example: {example.filename}")
    print(f"  System: {example.system}")
    print(f"  Date: {example.date.strftime('%Y-%m-%d')}")
    print(f"  Issuance: {example.hour:02d}Z")
    print(f"  Resolution: {example.resolution}°")
    print(f"  Lead time: {example.lead_hours} hours")

# Get unique lead times
lead_times = sorted(set(f.lead_hours for f in parsed_files))
print(f"Available lead times: {lead_times}")

# Filter to first 24 hours only
short_range = [f for f in parsed_files if f.lead_hours <= 24]
print(f"\nFiles with lead time <= 24 hours: {len(short_range)}")

# Create output directory
output_dir = 'downloaded_data'
os.makedirs(output_dir, exist_ok=True)

# Download the first file
if file_list['files']:
    filename = file_list['files'][0]
    print(f"Downloading: {filename}")
    
    file_response = requests.get(
        f'{BASE_URL}/forecast/file/{filename}',
        headers=HEADERS,
        allow_redirects=True  # Important!
    )
    file_response.raise_for_status()
    
    filepath = os.path.join(output_dir, filename)
    with open(filepath, 'wb') as f:
        f.write(file_response.content)
    
    print(f"Saved to: {filepath}")
    print(f"File size: {os.path.getsize(filepath) / 1024 / 1024:.2f} MB")

def download_file(filename, output_dir='downloaded_data'):
    """Download a single file from the API."""
    filepath = os.path.join(output_dir, filename)
    
    # Skip if already exists
    if os.path.exists(filepath):
        return filepath, 'skipped'
    
    response = requests.get(
        f'{BASE_URL}/forecast/file/{filename}',
        headers=HEADERS,
        allow_redirects=True
    )
    response.raise_for_status()
    
    with open(filepath, 'wb') as f:
        f.write(response.content)
    
    return filepath, 'downloaded'

# Download first 3 files (for demonstration)
files_to_download = file_list['files'][:3]

for filename in files_to_download:
    filepath, status = download_file(filename)
    print(f"{status}: {filename}")

try:
    import xarray as xr
    
    # Find a downloaded file
    grib_files = [f for f in os.listdir(output_dir) if f.endswith('.grib2')]
    
    if grib_files:
        filepath = os.path.join(output_dir, grib_files[0])
        print(f"Opening: {filepath}")
        
        # Open with cfgrib engine
        ds = xr.open_dataset(filepath, engine='cfgrib')
        print("\nDataset:")
        print(ds)
    else:
        print("No GRIB2 files found. Download some files first.")
        
except ImportError:
    print("cfgrib not installed. Install with: pip install cfgrib")
except Exception as e:
    print(f"Error opening file: {e}")

try:
    if 'ds' in dir():
        print("Variables in dataset:")
        for var in ds.data_vars:
            print(f"  {var}: {ds[var].dims}")
        
        print("\nCoordinates:")
        for coord in ds.coords:
            print(f"  {coord}: {ds.coords[coord].shape}")
except NameError:
    print("Dataset not loaded.")

# Use the latest endpoint to get most recent files regardless of issuance
response = requests.get(
    f'{BASE_URL}/forecast/latest/file',
    headers=HEADERS,
    params={'bundles': 'basic'}
)
response.raise_for_status()
latest_files = response.json()

print(f"Latest files: {latest_files['meta']['count']}")
print()
for f in latest_files['files'][:5]:
    print(f"  {f}")

# Optionally remove downloaded files
# import shutil
# shutil.rmtree(output_dir)
# print(f"Removed {output_dir}")