import geometry
import kriging
vmodel = variogram.VariogramModel3D(0.5)
vmodel.add_structure("spherical",1.5,[10,10,10],[0,0,0])
vmodel.add_structure("spherical",1.0,[300,300,300],[0,0,0])
data = np.loadtxt("muestras.csv",delimiter=";")
#create 2d
keys = set()
data2d = []
for d in data:
if (d[0],d[1]) not in keys:
keys.add((d[0],d[1]))
data2d += [d]
data2D = np.array(data2d)
points = data[:,0:3]
cut = data[:,3]
grid = geometry.Grid3D([10,10,1],[40,60,130],[20,30,65])
ret,non_estimated,ret_indices = kriging.kriging3d_block("ordinary",grid,points,cut,vmodel,None,mindata=1,maxdata=5,azimuth=0.0,dip=0.0,plunge=0.0,search_range=100,anisotropy=[1.0,1.0],full=True)
print ret
data_original = alldata_original[:, 3:4]
locations = scaler_locations.fit_transform(locations_original)
data = scaler_data.fit_transform(data_original)
if len(data.shape) < 2:
data = np.expand_dims(data, axis=1)
print locations.shape
print data.shape
nodes = [40, 60, 1]
sizes = [10.0, 10.0, 10.0]
starts = [5.0, 5.0, 125.0]
grid = geometry.Grid3D(nodes, sizes, starts)
dgrid = grid.discretize([4, 4, 1])
nbatch = len(dgrid)
locations_batch = np.empty((nbatch, 3))
model = load_model("muestras-model-locations")
predictions = np.empty(len(grid))
for i, p in enumerate(grid):
locations_batch = scaler_locations.transform(dgrid + p)
p = model.predict(locations_batch)
import kriging
import geometry
'''This example shows how to do a kriging for a grid using the iterator function
'''
#this is the dummy variogram model. It is not a real one
vmodel = variogram.VariogramModel3D(0.5)
vmodel.add_structure("spherical", 1.5, [10, 10, 10], [0, 0, 0])
vmodel.add_structure("spherical", 1.0, [300, 300, 300], [0, 0, 0])
data = np.loadtxt("samples.csv", delimiter=";")
points = data[:, 0:3]
ore = data[:, 3]
grid = geometry.Grid3D([10, 10, 10], [40, 60, 13], [20.0, 30.0, 6.5])
iterator = kriging.kriging3d_block_iterator("ordinary",
grid,
points,
ore,
vmodel,
discretization=None,
mindata=1,
maxdata=5,
azimuth=0.0,
dip=0.0,
plunge=0.0,
search_range=100,
anisotropy=[1.0, 1.0],
full=True)