
import numpy as np     
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import IPython
import gstlearn as gl
import gstlearn.document as gdoc
import gstlearn.plot3D as gop
import os

gdoc.setNoScroll()


gl.defineDefaultSpace(gl.ESpaceType.SN)

mesh = gl.MeshSphericalExt()
mesh.resetFromDb(None,None,triswitch = "-r5",verbose=False)

model = gl.Model.createFromParam(gl.ECov.BESSEL_K,range=0.2,param=1)

simu = gl.SimuSphericalParam(0,1000)
simu.display()
result = gl.simsph_mesh(mesh,model,simu,seed=23234,verbose=True)

Option for constructing the covariance spectrum
-----------------------------------------------
For all standard covariances
Number of basic functions = 1000
Number of discretization  = 360
Spectrum Tolerance        = 1e-05
 Random generation seed    = 23234
 Number of frequencies     = 116
 Cumulated Spectrum        = 1.000166
 Sum of negative weights   = 0.000000
 Maximum degree            = 115
 Minimum order             = -96
 Maximum order             = 83


simu = gop.SurfaceOnMesh(mesh, intensity=result, opacity=1, showlegend=True)


name = gdoc.loadData("boundaries", "world.poly")
poly = gl.Polygons.createFromNF(name)
boundaries = gop.PolygonOnSphere(poly)


fig = go.Figure(data = [simu, boundaries])
f = fig.show()

Spectral Simulation on Sphere¶