#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script demonstrates:
    * Creating a Matérn FEM approximation model in 2 dimensions.
    * Generate samples from this model.
    * Compute correlation (and compare with theoretical correlation).
    * Conditional distribution give observations of two points.
    * Estimating the range parameter.


This file is part of Fieldosophy, a toolkit for random fields.

Copyright (C) 2021 <NAME> <NAME> <<EMAIL>>

This Source Code is subject to the terms of the BSD 3-Clause License.
If a copy of the license was not distributed with this file, you can obtain one at https://opensource.org/licenses/BSD-3-Clause.

"""


# Import package
from fieldosophy.GRF import FEM
from fieldosophy.GRF import GRF
from fieldosophy import mesh as mesher


from matplotlib import pyplot as plt
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize


        
print("Running two-dimensional FEM test case")

plt.figure(1)
plt.clf()
plt.figure(2)
plt.clf()


# %% Create 2D mesh


# Limits of coordinates
coordinateLims = np.array( [ [0,1], [0, 1] ] )
# Define original minimum corelation length
corrMin = 0.4
extension = corrMin*1.5
# Create fake data points to force mesh
lats = np.linspace(coordinateLims[1,0], coordinateLims[1,-1], num = int( np.ceil( np.diff(coordinateLims[1,:])[0] / (corrMin/7) ) ) )
lons = np.linspace(coordinateLims[0,0], coordinateLims[0,-1], num = int( np.ceil( np.diff(coordinateLims[0,:])[0] / (corrMin/7) ) ) )
dataGrid = np.meshgrid( lons, lats )
dataPoints = np.hstack( (dataGrid[0].reshape(-1,1), dataGrid[1].reshape(-1,1)) )

# Mesh
print("Compute Mesh")
meshPlane = mesher.regularMesh.meshInPlaneRegular( coordinateLims + extension * np.array([-1,1]).reshape((1,2)), corrMin/5/np.sqrt(2) )
# Remove all nodes too far from active points    
meshPlane = meshPlane.cutOutsideMesh( dataPoints.transpose(), extension )



print("Plot mesh")

plt.figure(1)
ax = plt.subplot(221)
plt.cla()
ax.set_title( "Mesh" )    
meshPlotter = mesher.MeshPlotter(meshPlane)
edges = meshPlotter.getLines()
plt.plot(edges[0], edges[1], color="blue")
edges = meshPlotter.getBoundaryLines()
plt.plot(edges[0], edges[1], color="red")

plt.scatter(dataPoints[:,0], dataPoints[:,1], c="red")




# %% Create FEM system

print("Set up FEM system")

# Define the random field
r =  0.48
nu = 1.3
sigma = 1
sigmaEps = 2e-2
BCDirichlet = np.NaN * np.ones((meshPlane.N))
BCDirichlet[meshPlane.getBoundary()["nodes"]] = 0
BCDirichlet = None
BCRobin = np.ones( (meshPlane.getBoundary()["edges"].shape[0], 2) )
BCRobin[:, 0] = 0  # Association with constant
BCRobin[:, 1] = - 1 # Association with function
# BCRobin = None

# Create FEM object
fem = FEM.MaternFEM( mesh = meshPlane, childParams = {'r':r}, nu = nu, sigma = sigma, BCDirichlet = BCDirichlet, BCRobin = BCRobin )









# %% Sample

# Acquire realizations
print("Generate realizations")

M = int(2e3)

Z = fem.generateRandom( M )

# Set observation points
lats = np.linspace(coordinateLims[1,0], coordinateLims[1,-1], num = int( 60 ) )
lons = np.linspace(coordinateLims[0,0], coordinateLims[0,-1], num = int( 60 ) )
obsPoints = np.meshgrid( lons, lats )

# Get observation matrix
print("Acquire observation matrix")
obsMat = fem.mesh.getObsMat( np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ))
ZObs = obsMat.tocsr() * Z + stats.norm.rvs( loc = 0, scale = sigmaEps, size = M*obsMat.shape[0] ).reshape((obsMat.shape[0], M))





print("Plot covariances")


plt.figure(1)

ax = plt.subplot(222)
plt.cla()
ax.set_title( "Stationary covariance plot" )

# Get node closest to middle
midPoint = np.mean( coordinateLims, axis = 1 )
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - midPoint
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) )[orderInd[0], :]
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = runx[orderInd]

# Compute estimated covariance from realization
runySample = ( ZObs[orderInd[0], :] - np.mean(ZObs[orderInd[0], :]) ).reshape((1,-1)) * (ZObs - np.mean(ZObs, axis=1).reshape((-1,1)))
runySample = np.mean(runySample, axis=1)
runySample = runySample[orderInd]

# Plot empirical covariance
plt.plot(runx, runySample, label = "SPDE empirical", color="green", linestyle="dashed")

# Compute SPDE correlation
runyFEM = obsMat.tocsr()[orderInd, :] * fem.multiplyWithCovariance(obsMat.tocsr()[orderInd[0], :].transpose())
# Plot true covariance from model
plt.plot(runx, runyFEM, label = "SPDE", color="red", linewidth=2)
      
# Compute theoretical Matérn correlation
runyMatern = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/r )
plt.plot(runx, runyMatern, label = "Matern", color="blue")
plt.legend()
plt.xlabel("Time [s]")




ax = plt.subplot(2,2,3)
plt.cla()
ax.set_title( "A realization" )
# temp = obsMat * np.sqrt(np.sum(fem.mesh.nodes**2, axis=1))
# plt.imshow( temp.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
#             extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.imshow( ZObs[:,0].reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
            extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()




ax = plt.subplot(224)
plt.cla()
ax.set_title( "Covariance" )
# Compute SPDE covariance
runy = fem.mesh.getObsMat( midPoint.reshape((1,-1)) )
runy = runy.transpose()
runy = fem.multiplyWithCovariance(runy)
runy = obsMat.tocsr() * runy
plt.imshow( runy.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
           extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()











        




# %% Conditional distribution

# Set condition points
condPoints = np.array( [ [0.4,0.4], [0.55,0.45], [0.83, 0.9] ] )
# Get observation matrix
condObsMat = fem.mesh.getObsMat( condPoints ).tocsc()
# set conditional values
condVal = np.array( [1.2, 1.1, -0.5] )
# Compute conditional distribution
condDistr = fem.cond(condVal, condObsMat, sigmaEps)
# Get conditional mean at observation points
condMean = obsMat.tocsr() * condDistr.mu

# Plot
plt.figure(2)
ax = plt.subplot(2,2,1)
plt.cla()
ax.set_title( "Conditional mean" )
plt.imshow( condMean.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
            extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()







    
# %% Parameter estimation

print("Estimate range parameter")

# Get a copy of the original distribution
paramEst = fem.copy()
rangeLim = np.array( [corrMin, extension] )
def optimTrans( x ):
    return stats.logistic.cdf(x)*np.diff(rangeLim)[0] + rangeLim[0]
def optimTransInv( x ):
    return stats.logistic.ppf((x-rangeLim[0])/np.diff(rangeLim)[0])
# Define function to optimize
def optimFunc( x ):
    # Transform from unconstrained to constrained value
    r = optimTrans( x[0] )
    # Update current system
    paramEst.updateSystem( {'r':r}, nu=nu, sigma=sigma, BCRobin=BCRobin )
    # Return minus log-likelihood
    return - paramEst.loglik( ZObs, obsMat.tocsc(), sigmaEps=sigmaEps) 
# Set initial value
x0 = [ optimTransInv( 9/10*rangeLim[0] + 1/10*rangeLim[1] ) ]
# Optimize ("BFGS")
# resultOptim = optimize.minimize( optimFunc, x0, method='BFGS', options={'disp': True, "maxiter":20} )
resultOptim = optimize.minimize( optimFunc, x0, method='Nelder-Mead', options={'disp': True, "maxiter":10} )
# Get result
rEst = optimTrans( resultOptim.x[0] )
print( "Found range: " + str(rEst) )

    

print( "" )
print( "Plotting fitted compared to actual covariances" )



plt.figure(2)
ax = plt.subplot(2,2,2)
plt.cla()
ax.set_title( "Found covariance" )

# Get node closest to middle
midPoint = np.mean( coordinateLims, axis = 1 )
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - midPoint
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) )[orderInd[0], :]
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = runx[orderInd]
      
# Compute SPDE correlation
runy = obsMat.tocsr()[orderInd, :] * fem.multiplyWithCovariance(obsMat.tocsr()[orderInd[0], :].transpose())
# Plot true covariance from model
plt.plot(runx, runy, label = "SPDE", color="red", linewidth=2)
      
# Compute theoretical Matérn correlation
runy = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/r )
plt.plot(runx, runy, label = "Matern", color="blue")


# Compute theoretical Matérn correlation
runy = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/rEst )
plt.plot(runx, runy, label = "Matern estimated", color="green")
plt.legend()
plt.xlabel("Time [s]")

    



plt.show()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script demonstrates:
    * Creating a Matérn FEM approximation model in 2 dimensions.
    * Generate samples from this model.
    * Compute correlation (and compare with theoretical correlation).
    * Conditional distribution give observations of two points.
    * Estimating the range parameter.


This file is part of Fieldosophy, a toolkit for random fields.

Copyright (C) 2021 <NAME> <NAME> <<EMAIL>>

This Source Code is subject to the terms of the BSD 3-Clause License.
If a copy of the license was not distributed with this file, you can obtain one at https://opensource.org/licenses/BSD-3-Clause.

"""


# Import package
from fieldosophy.GRF import FEM
from fieldosophy.GRF import GRF
from fieldosophy import mesh as mesher


from matplotlib import pyplot as plt
import numpy as np
from scipy import sparse
from scipy import stats
from scipy import optimize


        
print("Running two-dimensional FEM test case")

plt.figure(1)
plt.clf()
plt.figure(2)
plt.clf()


# %% Create 2D mesh


# Limits of coordinates
coordinateLims = np.array( [ [0,1], [0, 1] ] )
# Define original minimum corelation length
corrMin = 0.4
extension = corrMin*1.5
# Create fake data points to force mesh
lats = np.linspace(coordinateLims[1,0], coordinateLims[1,-1], num = int( np.ceil( np.diff(coordinateLims[1,:])[0] / (corrMin/7) ) ) )
lons = np.linspace(coordinateLims[0,0], coordinateLims[0,-1], num = int( np.ceil( np.diff(coordinateLims[0,:])[0] / (corrMin/7) ) ) )
dataGrid = np.meshgrid( lons, lats )
dataPoints = np.hstack( (dataGrid[0].reshape(-1,1), dataGrid[1].reshape(-1,1)) )

# Mesh
print("Compute Mesh")
meshPlane = mesher.regularMesh.meshInPlaneRegular( coordinateLims + extension * np.array([-1,1]).reshape((1,2)), corrMin/5/np.sqrt(2) )
# Remove all nodes too far from active points    
meshPlane = meshPlane.cutOutsideMesh( dataPoints.transpose(), extension )



print("Plot mesh")

plt.figure(1)
ax = plt.subplot(221)
plt.cla()
ax.set_title( "Mesh" )    
meshPlotter = mesher.MeshPlotter(meshPlane)
edges = meshPlotter.getLines()
plt.plot(edges[0], edges[1], color="blue")
edges = meshPlotter.getBoundaryLines()
plt.plot(edges[0], edges[1], color="red")

plt.scatter(dataPoints[:,0], dataPoints[:,1], c="red")




# %% Create FEM system

print("Set up FEM system")

# Define the random field
r =  0.48
nu = 1.3
sigma = 1
sigmaEps = 2e-2
BCDirichlet = np.NaN * np.ones((meshPlane.N))
BCDirichlet[meshPlane.getBoundary()["nodes"]] = 0
BCDirichlet = None
BCRobin = np.ones( (meshPlane.getBoundary()["edges"].shape[0], 2) )
BCRobin[:, 0] = 0  # Association with constant
BCRobin[:, 1] = - 1 # Association with function
# BCRobin = None

# Create FEM object
fem = FEM.MaternFEM( mesh = meshPlane, childParams = {'r':r}, nu = nu, sigma = sigma, BCDirichlet = BCDirichlet, BCRobin = BCRobin )









# %% Sample

# Acquire realizations
print("Generate realizations")

M = int(2e3)

Z = fem.generateRandom( M )

# Set observation points
lats = np.linspace(coordinateLims[1,0], coordinateLims[1,-1], num = int( 60 ) )
lons = np.linspace(coordinateLims[0,0], coordinateLims[0,-1], num = int( 60 ) )
obsPoints = np.meshgrid( lons, lats )

# Get observation matrix
print("Acquire observation matrix")
obsMat = fem.mesh.getObsMat( np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ))
ZObs = obsMat.tocsr() * Z + stats.norm.rvs( loc = 0, scale = sigmaEps, size = M*obsMat.shape[0] ).reshape((obsMat.shape[0], M))





print("Plot covariances")


plt.figure(1)

ax = plt.subplot(222)
plt.cla()
ax.set_title( "Stationary covariance plot" )

# Get node closest to middle
midPoint = np.mean( coordinateLims, axis = 1 )
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - midPoint
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) )[orderInd[0], :]
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = runx[orderInd]

# Compute estimated covariance from realization
runySample = ( ZObs[orderInd[0], :] - np.mean(ZObs[orderInd[0], :]) ).reshape((1,-1)) * (ZObs - np.mean(ZObs, axis=1).reshape((-1,1)))
runySample = np.mean(runySample, axis=1)
runySample = runySample[orderInd]

# Plot empirical covariance
plt.plot(runx, runySample, label = "SPDE empirical", color="green", linestyle="dashed")

# Compute SPDE correlation
runyFEM = obsMat.tocsr()[orderInd, :] * fem.multiplyWithCovariance(obsMat.tocsr()[orderInd[0], :].transpose())
# Plot true covariance from model
plt.plot(runx, runyFEM, label = "SPDE", color="red", linewidth=2)
      
# Compute theoretical Matérn correlation
runyMatern = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/r )
plt.plot(runx, runyMatern, label = "Matern", color="blue")
plt.legend()
plt.xlabel("Time [s]")




ax = plt.subplot(2,2,3)
plt.cla()
ax.set_title( "A realization" )
# temp = obsMat * np.sqrt(np.sum(fem.mesh.nodes**2, axis=1))
# plt.imshow( temp.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
#             extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.imshow( ZObs[:,0].reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
            extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()




ax = plt.subplot(224)
plt.cla()
ax.set_title( "Covariance" )
# Compute SPDE covariance
runy = fem.mesh.getObsMat( midPoint.reshape((1,-1)) )
runy = runy.transpose()
runy = fem.multiplyWithCovariance(runy)
runy = obsMat.tocsr() * runy
plt.imshow( runy.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
           extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()











        




# %% Conditional distribution

# Set condition points
condPoints = np.array( [ [0.4,0.4], [0.55,0.45], [0.83, 0.9] ] )
# Get observation matrix
condObsMat = fem.mesh.getObsMat( condPoints ).tocsc()
# set conditional values
condVal = np.array( [1.2, 1.1, -0.5] )
# Compute conditional distribution
condDistr = fem.cond(condVal, condObsMat, sigmaEps)
# Get conditional mean at observation points
condMean = obsMat.tocsr() * condDistr.mu

# Plot
plt.figure(2)
ax = plt.subplot(2,2,1)
plt.cla()
ax.set_title( "Conditional mean" )
plt.imshow( condMean.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \
            extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) )
plt.colorbar()







    
# %% Parameter estimation

print("Estimate range parameter")

# Get a copy of the original distribution
paramEst = fem.copy()
rangeLim = np.array( [corrMin, extension] )
def optimTrans( x ):
    return stats.logistic.cdf(x)*np.diff(rangeLim)[0] + rangeLim[0]
def optimTransInv( x ):
    return stats.logistic.ppf((x-rangeLim[0])/np.diff(rangeLim)[0])
# Define function to optimize
def optimFunc( x ):
    # Transform from unconstrained to constrained value
    r = optimTrans( x[0] )
    # Update current system
    paramEst.updateSystem( {'r':r}, nu=nu, sigma=sigma, BCRobin=BCRobin )
    # Return minus log-likelihood
    return - paramEst.loglik( ZObs, obsMat.tocsc(), sigmaEps=sigmaEps) 
# Set initial value
x0 = [ optimTransInv( 9/10*rangeLim[0] + 1/10*rangeLim[1] ) ]
# Optimize ("BFGS")
# resultOptim = optimize.minimize( optimFunc, x0, method='BFGS', options={'disp': True, "maxiter":20} )
resultOptim = optimize.minimize( optimFunc, x0, method='Nelder-Mead', options={'disp': True, "maxiter":10} )
# Get result
rEst = optimTrans( resultOptim.x[0] )
print( "Found range: " + str(rEst) )

    

print( "" )
print( "Plotting fitted compared to actual covariances" )



plt.figure(2)
ax = plt.subplot(2,2,2)
plt.cla()
ax.set_title( "Found covariance" )

# Get node closest to middle
midPoint = np.mean( coordinateLims, axis = 1 )
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - midPoint
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) ) - np.hstack( (obsPoints[0].reshape(-1,1), obsPoints[1].reshape(-1,1)) )[orderInd[0], :]
runx = np.sqrt(np.sum(runx**2, axis=1))
orderInd =  np.argsort(runx)
runx = runx[orderInd]
      
# Compute SPDE correlation
runy = obsMat.tocsr()[orderInd, :] * fem.multiplyWithCovariance(obsMat.tocsr()[orderInd[0], :].transpose())
# Plot true covariance from model
plt.plot(runx, runy, label = "SPDE", color="red", linewidth=2)
      
# Compute theoretical Matérn correlation
runy = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/r )
plt.plot(runx, runy, label = "Matern", color="blue")


# Compute theoretical Matérn correlation
runy = GRF.MaternCorr( runx, nu = nu, kappa = np.sqrt(8*nu)/rEst )
plt.plot(runx, runy, label = "Matern estimated", color="green")
plt.legend()
plt.xlabel("Time [s]")

    



plt.show()
#!/usr/bin/env python3 # -*- coding: utf-8 -*- This script demonstrates:     * Creating a Matérn FEM approximation model in 2 dimensions.     * Generate samples from this model.     * Compute correlation (and compare with theoretical correlation).     * Conditional distribution give observations of two points.     * Estimating the range parameter.   This file is part of Fieldosophy, a toolkit for random fields.  Copyright (C) 2021 <NAME> <NAME> <<EMAIL>>  This Source Code is subject to the terms of the BSD 3-Clause License. If a copy of the license was not distributed with this file, you can obtain one at https://opensource.org/licenses/BSD-3-Clause. # Import package # %% Create 2D mesh # Limits of coordinates # Define original minimum corelation length # Create fake data points to force mesh # Mesh # Remove all nodes too far from active points # %% Create FEM system # Define the random field # Association with constant # Association with function # BCRobin = None # Create FEM object # %% Sample # Acquire realizations # Set observation points # Get observation matrix # Get node closest to middle # Compute estimated covariance from realization # Plot empirical covariance # Compute SPDE correlation # Plot true covariance from model # Compute theoretical Matérn correlation # temp = obsMat * np.sqrt(np.sum(fem.mesh.nodes**2, axis=1)) # plt.imshow( temp.reshape(obsPoints[0].shape), origin="lower", aspect="equal", \ #             extent = ( coordinateLims[0,0], coordinateLims[0,1], coordinateLims[1,0], coordinateLims[1,1] ) ) # Compute SPDE covariance # %% Conditional distribution # Set condition points # Get observation matrix # set conditional values # Compute conditional distribution # Get conditional mean at observation points # Plot # %% Parameter estimation # Get a copy of the original distribution # Define function to optimize # Transform from unconstrained to constrained value # Update current system # Return minus log-likelihood # Set initial value # Optimize ("BFGS") # resultOptim = optimize.minimize( optimFunc, x0, method='BFGS', options={'disp': True, "maxiter":20} ) # Get result # Get node closest to middle # Compute SPDE correlation # Plot true covariance from model # Compute theoretical Matérn correlation # Compute theoretical Matérn correlation