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from scipy.interpolate import RegularGridInterpolator
import numpy as np
# import os
# os.chdir("C:\\Users\\KN\\Python Projects\\PT\\Solar GSA Model")
#%% Interpolator definition
class SolarEnergyInterpolator:
def __init__(self, data_dir=None):
"""
1. Loads 3D grid file
2. Initializes interpolator
"""
import os
if data_dir is None:
data_file = "pv_potential_3d.npz"
else:
data_file = os.path.join(data_dir, "pv_potential_3d.npz")
print(f"Loading efficiency data from: {data_file}")
print(f"File exists: {os.path.exists(data_file)}")
data = np.load(data_file)
print("File loaded")
# Initialize lats, lons
pv_data = data["pv_data"]
lons = data["lons"]
lats = data["lats"]
months_axis = np.arange(1, 13)
# Create the interpolator (ignoring NaNs)
self.interpolator = RegularGridInterpolator(
(lons, lats, months_axis),
pv_data,
method='linear', # Can be 'nearest' or 'cubic' as well
bounds_error=False, # Does NOT raise error when input is out of bounds
fill_value=0 # Will return 0 if out of bounds
)
print("Grid interpolator initialized")
def get_solar_efficiency(self, latitude, longitude):
"""
Interpolates the solar energy for the given lat, long, and month of year.
Calculates efficiency based on yearly average energy.
Parameters:
latitude (float): Latitude of the query point. (-60 to 65)
longitude (float): Longitude of the query point. (-180 to 180)
Returns:
float: Interpolated solar efficiency value or 0 if out of range.
"""
days_vec = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
month = list(range(1,13)) # Create month vector
specEnergy = self.interpolator((longitude, latitude, month)) # Compute (daily) specific energy vector
efficiency = round(np.average(specEnergy)*(365/100),2) # Average over 1 year
return efficiency
#%% Create interpolator instance (commented out - instantiated on demand)
# GSA_3Dinterpolate = SolarEnergyInterpolator()
#%% SAMPLE QUERY (commented out)
# lat, lon = -22.0, -67.0
# efficiency = GSA_3Dinterpolate.get_solar_efficiency(lat, lon)
# print("Efficiency:", efficiency,"%")
# %%
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