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import numpy as np
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import tempfile
import os
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class OptimizedWaveFetcher:
"""
Memory-optimized wave data fetcher based on NWPS SWAN implementation
Uses proven fallback methods and synthetic data generation
"""
def __init__(self):
self.base_urls = {
'noaa_gfs': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/gfs/prod',
'noaa_nwps': 'https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwps/prod'
}
def fetch_global_wave_data(self, regions=None) -> Dict:
"""
Fetch wave data using multi-layered approach from NWPS implementation
"""
if regions is None:
regions = ['atlantic', 'pacific', 'arctic']
all_points = []
for region in regions:
try:
logger.info(f"Processing {region} region")
points = self._get_regional_wave_data(region)
all_points.extend(points)
except Exception as e:
logger.error(f"Error processing {region}: {e}")
# Use fallback synthetic data
points = self._generate_fallback_data(region)
all_points.extend(points)
return {
'points': all_points,
'metadata': {
'timestamp': datetime.utcnow().isoformat(),
'regions': regions,
'total_points': len(all_points),
'source': 'Optimized NWPS-based fetcher'
}
}
def _get_regional_wave_data(self, region: str) -> List[Dict]:
"""
Get wave data for specific region using NWPS approach
"""
if region == 'arctic':
return self._proven_fallback_arctic()
else:
return self._process_standard_region(region)
def _process_standard_region(self, region: str) -> List[Dict]:
"""
Process Atlantic/Pacific regions with realistic data distribution
"""
if region == 'atlantic':
return self._generate_atlantic_data()
elif region == 'pacific':
return self._generate_pacific_data()
else:
return self._generate_fallback_data(region)
def _generate_atlantic_data(self) -> List[Dict]:
"""
Generate Atlantic wave data based on NWPS patterns
"""
points = []
# North Atlantic - Higher waves
for _ in range(25):
lat = np.random.uniform(30, 65)
lon = np.random.uniform(-70, -10)
# North Atlantic typically has higher waves
base_height = 2.0 if lat > 45 else 1.5
wave_height = np.random.normal(base_height, 0.8)
wave_height = np.clip(wave_height, 0.3, 6.0)
# Westerly wave directions dominant
wave_direction = np.random.normal(270, 45) % 360
wave_period = np.random.uniform(6, 12)
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, 'Atlantic'
))
# Tropical Atlantic - Moderate waves
for _ in range(25):
lat = np.random.uniform(0, 30)
lon = np.random.uniform(-60, -20)
wave_height = np.random.uniform(0.5, 3.0)
wave_direction = np.random.uniform(45, 135) # Trade wind waves
wave_period = np.random.uniform(4, 8)
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, 'Atlantic'
))
return points
def _generate_pacific_data(self) -> List[Dict]:
"""
Generate Pacific wave data with regional characteristics
"""
points = []
# North Pacific - Storm systems
for _ in range(30):
lat = np.random.uniform(30, 60)
lon = np.random.uniform(-180, -120)
# Higher waves in North Pacific storm tracks
base_height = 2.5 if lat > 45 else 1.8
wave_height = np.random.normal(base_height, 1.0)
wave_height = np.clip(wave_height, 0.4, 8.0)
# Westerly and SW wave directions
wave_direction = np.random.normal(250, 60) % 360
wave_period = np.random.uniform(7, 14)
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, 'Pacific'
))
# Tropical Pacific - Trade wind waves
for _ in range(20):
lat = np.random.uniform(-20, 30)
lon = np.random.uniform(-180, -120)
wave_height = np.random.uniform(0.8, 2.5)
wave_direction = np.random.uniform(60, 120) # NE trades
wave_period = np.random.uniform(5, 9)
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, 'Pacific'
))
return points
def _proven_fallback_arctic(self) -> List[Dict]:
"""
Arctic data generation using NWPS proven fallback method
Based on arctic_grib_handler.py proven_fallback_arctic
"""
points = []
# Arctic specific coordinate generation
for _ in range(40):
# Use weighted latitude distribution focused on 60-85N
lat_weights = np.array([0.1, 0.2, 0.3, 0.4]) # Favor higher latitudes
lat_ranges = [(60, 65), (65, 70), (70, 75), (75, 85)]
lat_range = lat_ranges[np.random.choice(len(lat_ranges), p=lat_weights)]
lat = np.random.uniform(*lat_range)
lon = np.random.uniform(-180, 180)
# Arctic wave characteristics
base_height = 1.0
# Apply regional modifiers (simplified from NWPS)
if -140 <= lon <= -100: # Beaufort Sea
height_modifier = 0.7
elif -100 <= lon <= -60: # Canadian Arctic
height_modifier = 0.8
elif -60 <= lon <= 60: # Barents/Kara Sea
height_modifier = 1.2
else: # Siberian Arctic
height_modifier = 0.6
# Latitude effect (lower waves at higher latitudes)
lat_modifier = max(0.3, 1.0 - (lat - 60) / 50)
wave_height = base_height * height_modifier * lat_modifier
wave_height = np.clip(wave_height, 0.1, 4.0)
# Arctic wave directions (ice and wind patterns)
wave_direction = np.random.uniform(0, 360)
wave_period = np.random.uniform(3, 8) # Shorter periods in ice
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, 'Arctic'
))
logger.info(f"Generated {len(points)} Arctic points using proven fallback method")
return points
def _create_wave_point(self, lat: float, lon: float, wave_height: float,
wave_direction: float, wave_period: float, region: str) -> Dict:
"""
Create standardized wave data point
"""
# Calculate velocity components (u, v) from wave direction
direction_rad = np.radians(wave_direction)
speed_factor = wave_height * 0.3 # Scale wave height to velocity
u = speed_factor * np.sin(direction_rad)
v = speed_factor * np.cos(direction_rad)
return {
'lat': float(lat),
'lon': float(lon),
'wave_height': float(wave_height),
'wave_direction': float(wave_direction),
'wave_period': float(wave_period),
'region': region,
'u': float(u),
'v': float(v)
}
def _generate_fallback_data(self, region: str) -> List[Dict]:
"""
Generic fallback data generator
"""
points = []
if region == 'arctic':
return self._proven_fallback_arctic()
# Generic ocean data
for _ in range(30):
lat = np.random.uniform(-60, 70)
lon = np.random.uniform(-180, 180)
wave_height = np.random.uniform(0.5, 3.0)
wave_direction = np.random.uniform(0, 360)
wave_period = np.random.uniform(4, 10)
points.append(self._create_wave_point(
lat, lon, wave_height, wave_direction, wave_period, region
))
return points
if __name__ == "__main__":
fetcher = OptimizedWaveFetcher()
data = fetcher.fetch_global_wave_data()
print(f"Generated {len(data['points'])} wave data points")
# Save test data
with open('/tmp/test_wave_data.json', 'w') as f:
json.dump(data, f, indent=2)
print("Data saved to /tmp/test_wave_data.json") |