Y = data["NH4N"]
Eastings = data["X_COORD"]
Northings = data["Y_COORD"]
good = np.where(1 - ((np.isnan(X[:,0])) +
(np.isnan(X[:,1])) +
(np.isnan(X[:,2])) +
(np.isnan(Y)) +
(np.isnan(Eastings)) +
(np.isnan(Northings))))
Y = Y[good]
X = X[good]
Eastings = Eastings[good]
Northings = Northings[good]
k = krige.bayes(Y,
Eastings,
Northings,
X
#np.vstack([data["Monitor_Classification"], data["Monitor_Radius_km"]]).transpose()
)
#import cProfile
#cProfile.run("k.Converge()")
k.Converge()
from spatial import krige as krige
import numpy as np
data = np.genfromtxt("./chicagomondataR.csv", dtype = None, delimiter =",", names = True)
X = np.array([1]*data.shape[0])
X.shape = (len(X), 1)
k = krige.bayes(data["PM25_ugm3"],
data["EW_Coord_km"],
data["NS_Coord_km"],
X
#np.vstack([data["Monitor_Classification"], data["Monitor_Radius_km"]]).transpose()
)
k.Converge()