def SVD(var1,
var2,
num=1,
subspace=-1,
iaxis=Time,
weight1=True,
weight2=True,
matrix='cov'):
"""
Finds coupled EOFs of two fields. Note that the mean/trend/etc. is NOT
removed in this routine.
Parameters
----------
var1, var2 : :class:`Var`
The variables to analyse.
num : integer
The number of EOFs to compute (default is ``1``).
weight1, weight2 : optional
Weights to use for defining orthogonality in the var1, var2 domains,
respectively. Patterns X and Y in the var1 domain are orthogonal if the
sum over X*Y*weights1 is 0. Patterns Z and W in the var2 domain are
orthogonal if the sum over Z*W*weights2 is 0. Default is to use internal
weights defined for var1 accessed by :meth:`Var.getweights()`. If set to
``False`` no weighting is used.
matrix : string, optional ['cov']
Which matrix we are diagonalizing (default is 'cov').
* 'cov': covariance matrix of var1 & var2
* 'cov': correlation matrix of var1 & var2
iaxis : Axis identifier
The principal component / expansion coefficient axis, i.e., the 'time'
axis. Can be an integer (the axis number, leftmost = 0), the axis name
(string), or a Pygeode axis class. If not specified, will try to use
pygeode.timeaxis.Time, and if that fails, the leftmost axis.
Returns
-------
(eof1, pc1, eof2, pc2): tuple
* eof1: The coupled eof patterns for var1.
* pc1: The principal component / expansion coefficients for var1.
* eof2: The coupled eof patterns for var2.
* pc2: The principal component / expansion coefficients for var2.
Notes
-----
Multiple orders of EOFs are concatenated along an 'order' axis.
"""
import numpy as np
from pygeode.timeaxis import Time
from pygeode.var import Var
from pygeode.view import View
from pygeode import MAX_ARRAY_SIZE
from warnings import warn
from pygeode import svdcore as lib
if matrix in ('cov', 'covariance'): matrix = 'cov'
elif matrix in ('cor', 'corr', 'correlation'): matrix = 'cor'
else:
warn("invalid matrix type '%'. Defaulting to covariance." % matrix,
stacklevel=2)
matrix = 'cov'
MAX_ITER = 1000
# Iterate over more EOFs than we need
# (this helps with convergence)
# TODO: a more rigorous formula for the optimum number of EOFs to use
if subspace <= 0: subspace = 2 * num + 8
if subspace < num: subspace = num # Just in case
# Remember the names
prefix1 = var1.name + '_' if var1.name != '' else ''
prefix2 = var2.name + '_' if var2.name != '' else ''
# Apply weights?
# if weight1 is not None: var1 *= weight1.sqrt()
# if weight2 is not None: var2 *= weight2.sqrt()
if weight1 is True: weight1 = var1.getweights()
if weight1 is not False:
assert not weight1.hasaxis(
iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight1.sum() / weight1.size
weight1 /= W
# Apply the weights
var1 *= weight1.sqrt()
if weight2 is True: weight2 = var2.getweights()
if weight2 is not False:
assert not weight2.hasaxis(
iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight2.sum() / weight2.size
weight2 /= W
# Apply the weights
var2 *= weight2.sqrt()
#TODO: allow multiple iteration axes (i.e., time and ensemble)
# if iaxis is None:
# if var1.hasaxis(Time) and var2.hasaxis(Time):
# iaxis1 = var1.whichaxis(Time)
# iaxis2 = var2.whichaxis(Time)
# else:
# iaxis1 = 0
# iaxis2 = 0
# else:
iaxis1 = var1.whichaxis(iaxis)
iaxis2 = var2.whichaxis(iaxis)
assert var1.axes[iaxis1] == var2.axes[
iaxis2], "incompatible iteration axes"
del iaxis # so we don't use this by accident
# Special case: can load entire variable in memory
# This will save some time, especially if the field is stored on disk, or is heavily derived
if var1.size <= MAX_ARRAY_SIZE:
print('preloading ' + repr(var1))
var1 = var1.load()
if var2.size <= MAX_ARRAY_SIZE:
print('preloading ' + repr(var2))
var2 = var2.load()
# Use correlation instead of covariance?
# (normalize by standard deviation)
if matrix == 'cor':
print('computing standard deviations')
std1 = var1.stdev(iaxis1).load()
std2 = var2.stdev(iaxis2).load()
# account for grid points with zero standard deviation?
std1.values = std1.values + (std1.values == 0)
std2.values = std2.values + (std2.values == 0)
var1 /= std1
var2 /= std2
eofshape1 = (subspace, ) + var1.shape[:iaxis1] + var1.shape[iaxis1 + 1:]
eofshape2 = (subspace, ) + var2.shape[:iaxis2] + var2.shape[iaxis2 + 1:]
pcshape1 = (var1.shape[iaxis1], subspace)
pcshape2 = (var2.shape[iaxis2], subspace)
# number of spatial grid points
NX1 = var1.size // var1.shape[iaxis1]
assert NX1 <= MAX_ARRAY_SIZE, 'field is too large!'
NX2 = var2.size // var2.shape[iaxis2]
assert NX2 <= MAX_ARRAY_SIZE, 'field is too large!'
# Total number of timesteps
NT = var1.shape[iaxis1]
# Number of timesteps we can do in one fetch
dt = MAX_ARRAY_SIZE // max(NX1, NX2)
pcs1 = np.empty(pcshape1, dtype='d')
pcs2 = np.empty(pcshape2, dtype='d')
X = np.empty(eofshape2, dtype='d')
U = np.empty(eofshape1, dtype='d')
# Seed with sinusoids superimposed on random values
Y = np.random.rand(*eofshape1)
V = np.random.rand(*eofshape2)
from math import pi
for i in range(subspace):
Y[i, ...].reshape(NX1)[:] += np.cos(
np.arange(NX1, dtype='d') / NX1 * 2 * pi * (i + 1))
V[i, ...].reshape(NX2)[:] += np.cos(
np.arange(NX2, dtype='d') / NX2 * 2 * pi * (i + 1))
# raise Exception
# Workspace for C code
UtAX = np.empty([subspace, subspace], dtype='d')
XtAtU = np.empty([subspace, subspace], dtype='d')
VtV = np.empty([subspace, subspace], dtype='d')
YtY = np.empty([subspace, subspace], dtype='d')
# Views over whole variables
# (rearranged to be compatible with our output eof arrays)
view1 = View((var1.axes[iaxis1], ) + var1.axes[:iaxis1] +
var1.axes[iaxis1 + 1:])
view2 = View((var2.axes[iaxis2], ) + var2.axes[:iaxis2] +
var2.axes[iaxis2 + 1:])
for iter_num in range(1, MAX_ITER + 1):
print('iter_num: %d' % iter_num)
assert Y.shape == U.shape
assert X.shape == V.shape
U, Y = Y, U
X, V = V, X
# Reset the accumulation arrays for the next approximations
Y[()] = 0
V[()] = 0
# Apply the covariance/correlation matrix
for t in range(0, NT, dt):
# number of timesteps we actually have
nt = min(dt, NT - t)
# Read the data
chunk1 = view1.modify_slice(0, slice(t, t + nt)).get(var1)
chunk1 = np.ascontiguousarray(chunk1, dtype='d')
chunk2 = view2.modify_slice(0, slice(t, t + nt)).get(var2)
chunk2 = np.ascontiguousarray(chunk2, dtype='d')
ier = lib.build_svds(subspace, nt, NX1, NX2, chunk1, chunk2, X, Y,
pcs2[t, ...])
assert ier == 0
ier = lib.build_svds(subspace, nt, NX2, NX1, chunk2, chunk1, U, V,
pcs1[t, ...])
assert ier == 0
# Useful dot products
lib.dot(subspace, NX1, U, Y, UtAX)
lib.dot(subspace, NX2, V, V, VtV)
lib.dot(subspace, NX1, Y, U, XtAtU)
lib.dot(subspace, NX1, Y, Y, YtY)
# Compute surrogate matrices (using all available information from this iteration)
A1, residues, rank, s = np.linalg.lstsq(UtAX, VtV, rcond=1e-30)
A2, residues, rank, s = np.linalg.lstsq(XtAtU, YtY, rcond=1e-30)
# Eigendecomposition on surrogate matrices
Dy, Qy = np.linalg.eig(np.dot(A1, A2))
Dv, Qv = np.linalg.eig(np.dot(A2, A1))
# Sort by eigenvalue (largest first)
S = np.argsort(np.real(Dy))[::-1]
Dy = Dy[S]
Qy = np.ascontiguousarray(Qy[:, S], dtype='d')
S = np.argsort(np.real(Dv))[::-1]
Dv = Dv[S]
Qv = np.ascontiguousarray(Qv[:, S], dtype='d')
# get estimate of true eigenvalues
D = np.sqrt(Dy) # should also = np.sqrt(Dv) in theory
print(D)
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX1, Qy, Y)
lib.transform(subspace, NX2, Qv, V)
# Normalize
lib.normalize(subspace, NX1, Y)
lib.normalize(subspace, NX2, V)
if not np.allclose(U[:num, ...], Y[:num, ...], atol=0): continue
if not np.allclose(X[:num, ...], V[:num, ...], atol=0): continue
print('converged after %d iterations' % iter_num)
break
assert iter_num != MAX_ITER, "no convergence"
# Flip the sign of the var2 EOFs and PCs so that the covariance is positive
lib.fixcov(subspace, NT, NX2, pcs1, pcs2, V)
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
orderaxis = order(num)
eof1 = np.array(Y[:num])
pc1 = np.array(pcs1[..., :num]).transpose()
eof1 = Var((orderaxis, ) + var1.axes[:iaxis1] + var1.axes[iaxis1 + 1:],
values=eof1)
pc1 = Var((orderaxis, var1.axes[iaxis1]), values=pc1)
eof2 = np.array(V[:num])
pc2 = np.array(pcs2[..., :num]).transpose()
eof2 = Var((orderaxis, ) + var2.axes[:iaxis2] + var2.axes[iaxis2 + 1:],
values=eof2)
pc2 = Var((orderaxis, var2.axes[iaxis2]), values=pc2)
# Apply weights?
if weight1 is not False: eof1 /= weight1.sqrt()
if weight2 is not False: eof2 /= weight2.sqrt()
# Use correlation instead of covariance?
# Re-scale the fields by standard deviation
if matrix == 'cor':
eof1 *= std1
eof2 *= std2
# Give it a name
eof1.name = prefix1 + "EOF"
pc1.name = prefix1 + "PC"
eof2.name = prefix2 + "EOF"
pc2.name = prefix2 + "PC"
return eof1, pc1, eof2, pc2
def EOF_iter (x, num=1, iaxis=None, subspace = -1, max_iter=1000, weight=True, out=None):
"""
(See svd.SVD for documentation on a similar function, but replace each xxx1 and xxx2 parameter with a single xxx parameter.)
"""
import numpy as np
from pygeode import libpath
from pygeode.view import View
from math import sqrt
from pygeode.varoperations import fill
from pygeode import svdcore as lib
# Iterate over more EOFs than we need
# (this helps with convergence)
# TODO: a more rigorous formula for the optimum number of EOFs to use
if subspace <= 0: subspace = 2*num + 8
if subspace < num: subspace = num
# Run the single-pass guess to seed the first iteration
guess_eof, guess_eig, guess_pc = EOF_guess (x, subspace, iaxis, weight=weight, out=None)
# Convert NaNs to zeros so they don't screw up the matrix operations
guess_eof = fill (guess_eof, 0)
x, time, space = prep(var=x, iaxis=iaxis, weight=weight, out=out)
del iaxis
eofshape = (subspace,) + space.shape
pcshape = time.shape + (subspace,)
pcs = np.empty(pcshape,dtype='d')
oldeofs = np.empty(eofshape,dtype='d')
# Seed with initial guess (in the weighted space)
neweofs = apply_weights (guess_eof, weight=weight).get()
neweofs = np.array(neweofs, dtype='d') # so we can write
# neweofs = np.random.rand(*eofshape)
# Workspace for smaller representative matrix
work1 = np.empty([subspace,subspace], dtype='d')
work2 = np.empty([subspace,subspace], dtype='d')
NX = space.size
# Variance accumulation (on first iteration only)
variance = 0.0
for iter_num in range(1,max_iter+1):
print 'iter_num:', iter_num
neweofs, oldeofs = oldeofs, neweofs
# Reset the accumulation arrays for the next approximations
neweofs[()] = 0
# Apply the covariance matrix
for inview in View(x.axes).loop_mem():
X = np.ascontiguousarray(inview.get(x), dtype='d')
assert X.size >= space.size, "spatial pattern is too large"
nt = inview.shape[0]
time_offset = inview.slices[0].start
ier = lib.build_eofs (subspace, nt, NX, X, oldeofs,
neweofs, pcs[time_offset,...])
assert ier == 0
# Compute variance?
if iter_num == 1:
variance += (X**2).sum()
# Useful dot products
lib.dot(subspace, NX, oldeofs, neweofs, work1)
lib.dot(subspace, NX, neweofs, neweofs, work2)
# Compute surrogate matrix (using all available information from this iteration)
A, residues, rank, s = np.linalg.lstsq(work1,work2,rcond=1e-30)
# Eigendecomposition on surrogate matrix
w, P = np.linalg.eig(A)
# Sort by eigenvalue
S = np.argsort(w)[::-1]
w = w[S]
print w
# assert P.dtype.name == 'float64', P.dtype.name
P = np.ascontiguousarray(P[:,S], dtype='d')
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX, P, neweofs)
# Normalize
lib.normalize (subspace, NX, neweofs)
# # verify orthogonality
# for i in range(num):
# print [np.dot(neweofs[i,...].flatten(), neweofs[j,...].flatten()) for j in range(num)]
if np.allclose(oldeofs[:num,...],neweofs[:num,...], atol=0):
print 'converged after %d iterations'%iter_num
break
assert iter_num != max_iter, "no convergence"
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
eof = neweofs[:num]
pc = pcs[...,:num].transpose()
# Extract the eigenvalues
# (compute magnitude of pc arrays)
#TODO: keep eigenvalues as a separate variable in the iteration loop
eig = np.array([sqrt( (pc[i,...]**2).sum() ) for i in range(pc.shape[0]) ])
pc = np.dot(np.diag(1/eig), pc)
return finalize (x, time, space, eof, eig, pc, variance, weight=weight, out=out)
def SVD (var1, var2, num=1, subspace=-1, iaxis=Time, weight1=True, weight2=True, matrix='cov'):
"""
Finds coupled EOFs of two fields. Note that the mean/trend/etc. is NOT
removed in this routine.
Parameters
----------
var1, var2 : :class:`Var`
The variables to analyse.
num : integer
The number of EOFs to compute (default is ``1``).
weight1, weight2 : optional
Weights to use for defining orthogonality in the var1, var2 domains,
respectively. Patterns X and Y in the var1 domain are orthogonal if the
sum over X*Y*weights1 is 0. Patterns Z and W in the var2 domain are
orthogonal if the sum over Z*W*weights2 is 0. Default is to use internal
weights defined for var1 accessed by :meth:`Var.getweights()`. If set to
``False`` no weighting is used.
matrix : string, optional ['cov']
Which matrix we are diagonalizing (default is 'cov').
* 'cov': covariance matrix of var1 & var2
* 'cov': correlation matrix of var1 & var2
iaxis : Axis identifier
The principal component / expansion coefficient axis, i.e., the 'time'
axis. Can be an integer (the axis number, leftmost = 0), the axis name
(string), or a Pygeode axis class. If not specified, will try to use
pygeode.timeaxis.Time, and if that fails, the leftmost axis.
Returns
-------
(eof1, pc1, eof2, pc2): tuple
* eof1: The coupled eof patterns for var1.
* pc1: The principal component / expansion coefficients for var1.
* eof2: The coupled eof patterns for var2.
* pc2: The principal component / expansion coefficients for var2.
Notes
-----
Multiple orders of EOFs are concatenated along an 'order' axis.
"""
import numpy as np
from pygeode.timeaxis import Time
from pygeode.var import Var
from pygeode.view import View
from pygeode import MAX_ARRAY_SIZE
from warnings import warn
from pygeode import svdcore as lib
if matrix in ('cov', 'covariance'): matrix = 'cov'
elif matrix in ('cor', 'corr', 'correlation'): matrix = 'cor'
else:
warn ("invalid matrix type '%'. Defaulting to covariance."%matrix, stacklevel=2)
matrix = 'cov'
MAX_ITER = 1000
# Iterate over more EOFs than we need
# (this helps with convergence)
# TODO: a more rigorous formula for the optimum number of EOFs to use
if subspace <= 0: subspace = 2*num + 8
if subspace < num: subspace = num # Just in case
# Remember the names
prefix1 = var1.name+'_' if var1.name != '' else ''
prefix2 = var2.name+'_' if var2.name != '' else ''
# Apply weights?
# if weight1 is not None: var1 *= weight1.sqrt()
# if weight2 is not None: var2 *= weight2.sqrt()
if weight1 is True: weight1 = var1.getweights()
if weight1 is not False:
assert not weight1.hasaxis(iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight1.sum() / weight1.size
weight1 /= W
# Apply the weights
var1 *= weight1.sqrt()
if weight2 is True: weight2 = var2.getweights()
if weight2 is not False:
assert not weight2.hasaxis(iaxis), "Can't handle weights along the record axis"
# Normalize the weights
W = weight2.sum() / weight2.size
weight2 /= W
# Apply the weights
var2 *= weight2.sqrt()
#TODO: allow multiple iteration axes (i.e., time and ensemble)
# if iaxis is None:
# if var1.hasaxis(Time) and var2.hasaxis(Time):
# iaxis1 = var1.whichaxis(Time)
# iaxis2 = var2.whichaxis(Time)
# else:
# iaxis1 = 0
# iaxis2 = 0
# else:
iaxis1 = var1.whichaxis(iaxis)
iaxis2 = var2.whichaxis(iaxis)
assert var1.axes[iaxis1] == var2.axes[iaxis2], "incompatible iteration axes"
del iaxis # so we don't use this by accident
# Special case: can load entire variable in memory
# This will save some time, especially if the field is stored on disk, or is heavily derived
if var1.size <= MAX_ARRAY_SIZE:
print('preloading '+repr(var1))
var1 = var1.load()
if var2.size <= MAX_ARRAY_SIZE:
print('preloading '+repr(var2))
var2 = var2.load()
# Use correlation instead of covariance?
# (normalize by standard deviation)
if matrix == 'cor':
print('computing standard deviations')
std1 = var1.stdev(iaxis1).load()
std2 = var2.stdev(iaxis2).load()
# account for grid points with zero standard deviation?
std1.values = std1.values + (std1.values == 0)
std2.values = std2.values + (std2.values == 0)
var1 /= std1
var2 /= std2
eofshape1 = (subspace,) + var1.shape[:iaxis1] + var1.shape[iaxis1+1:]
eofshape2 = (subspace,) + var2.shape[:iaxis2] + var2.shape[iaxis2+1:]
pcshape1 = (var1.shape[iaxis1], subspace)
pcshape2 = (var2.shape[iaxis2], subspace)
# number of spatial grid points
NX1 = var1.size // var1.shape[iaxis1]
assert NX1 <= MAX_ARRAY_SIZE, 'field is too large!'
NX2 = var2.size // var2.shape[iaxis2]
assert NX2 <= MAX_ARRAY_SIZE, 'field is too large!'
# Total number of timesteps
NT = var1.shape[iaxis1]
# Number of timesteps we can do in one fetch
dt = MAX_ARRAY_SIZE // max(NX1,NX2)
pcs1 = np.empty(pcshape1,dtype='d')
pcs2 = np.empty(pcshape2,dtype='d')
X = np.empty(eofshape2,dtype='d')
U = np.empty(eofshape1,dtype='d')
# Seed with sinusoids superimposed on random values
Y = np.random.rand(*eofshape1)
V = np.random.rand(*eofshape2)
from math import pi
for i in range(subspace):
Y[i,...].reshape(NX1)[:] += np.cos( np.arange(NX1,dtype='d') / NX1 * 2 * pi * (i+1))
V[i,...].reshape(NX2)[:] += np.cos( np.arange(NX2,dtype='d') / NX2 * 2 * pi * (i+1))
# raise Exception
# Workspace for C code
UtAX = np.empty([subspace,subspace], dtype='d')
XtAtU = np.empty([subspace,subspace], dtype='d')
VtV = np.empty([subspace,subspace], dtype='d')
YtY = np.empty([subspace,subspace], dtype='d')
# Views over whole variables
# (rearranged to be compatible with our output eof arrays)
view1 = View( (var1.axes[iaxis1],) + var1.axes[:iaxis1] + var1.axes[iaxis1+1:] )
view2 = View( (var2.axes[iaxis2],) + var2.axes[:iaxis2] + var2.axes[iaxis2+1:] )
for iter_num in range(1,MAX_ITER+1):
print('iter_num: %d'%iter_num)
assert Y.shape == U.shape
assert X.shape == V.shape
U, Y = Y, U
X, V = V, X
# Reset the accumulation arrays for the next approximations
Y[()] = 0
V[()] = 0
# Apply the covariance/correlation matrix
for t in range(0,NT,dt):
# number of timesteps we actually have
nt = min(dt,NT-t)
# Read the data
chunk1 = view1.modify_slice(0, slice(t,t+nt)).get(var1)
chunk1 = np.ascontiguousarray(chunk1, dtype='d')
chunk2 = view2.modify_slice(0, slice(t,t+nt)).get(var2)
chunk2 = np.ascontiguousarray(chunk2, dtype='d')
ier = lib.build_svds (subspace, nt, NX1, NX2, chunk1, chunk2,
X, Y, pcs2[t,...])
assert ier == 0
ier = lib.build_svds (subspace, nt, NX2, NX1, chunk2, chunk1,
U, V, pcs1[t,...])
assert ier == 0
# Useful dot products
lib.dot(subspace, NX1, U, Y, UtAX)
lib.dot(subspace, NX2, V, V, VtV)
lib.dot(subspace, NX1, Y, U, XtAtU)
lib.dot(subspace, NX1, Y, Y, YtY)
# Compute surrogate matrices (using all available information from this iteration)
A1, residues, rank, s = np.linalg.lstsq(UtAX,VtV,rcond=1e-30)
A2, residues, rank, s = np.linalg.lstsq(XtAtU,YtY,rcond=1e-30)
# Eigendecomposition on surrogate matrices
Dy, Qy = np.linalg.eig(np.dot(A1,A2))
Dv, Qv = np.linalg.eig(np.dot(A2,A1))
# Sort by eigenvalue (largest first)
S = np.argsort(np.real(Dy))[::-1]
Dy = Dy[S]
Qy = np.ascontiguousarray(Qy[:,S], dtype='d')
S = np.argsort(np.real(Dv))[::-1]
Dv = Dv[S]
Qv = np.ascontiguousarray(Qv[:,S], dtype='d')
# get estimate of true eigenvalues
D = np.sqrt(Dy) # should also = np.sqrt(Dv) in theory
print(D)
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX1, Qy, Y)
lib.transform(subspace, NX2, Qv, V)
# Normalize
lib.normalize (subspace, NX1, Y)
lib.normalize (subspace, NX2, V)
if not np.allclose(U[:num,...],Y[:num,...], atol=0): continue
if not np.allclose(X[:num,...],V[:num,...], atol=0): continue
print('converged after %d iterations'%iter_num)
break
assert iter_num != MAX_ITER, "no convergence"
# Flip the sign of the var2 EOFs and PCs so that the covariance is positive
lib.fixcov (subspace, NT, NX2, pcs1, pcs2, V)
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
orderaxis = order(num)
eof1 = np.array(Y[:num])
pc1 = np.array(pcs1[...,:num]).transpose()
eof1 = Var((orderaxis,)+var1.axes[:iaxis1]+var1.axes[iaxis1+1:], values=eof1)
pc1 = Var((orderaxis,var1.axes[iaxis1]), values = pc1)
eof2 = np.array(V[:num])
pc2 = np.array(pcs2[...,:num]).transpose()
eof2 = Var((orderaxis,)+var2.axes[:iaxis2]+var2.axes[iaxis2+1:], values=eof2)
pc2 = Var((orderaxis,var2.axes[iaxis2]), values = pc2)
# Apply weights?
if weight1 is not False: eof1 /= weight1.sqrt()
if weight2 is not False: eof2 /= weight2.sqrt()
# Use correlation instead of covariance?
# Re-scale the fields by standard deviation
if matrix == 'cor':
eof1 *= std1
eof2 *= std2
# Give it a name
eof1.name = prefix1 + "EOF"
pc1.name = prefix1 + "PC"
eof2.name = prefix2 + "EOF"
pc2.name = prefix2 + "PC"
return eof1, pc1, eof2, pc2
def EOF_iter (x, num=1, iaxis=None, subspace = -1, max_iter=1000, weight=True, out=None):
"""
(See svd.SVD for documentation on a similar function, but replace each xxx1 and xxx2 parameter with a single xxx parameter.)
"""
import numpy as np
from pygeode import libpath
from pygeode.view import View
from math import sqrt
from pygeode.varoperations import fill
from pygeode import svdcore as lib
# Need vector subspace to be at least as large as the number of EOFs extracted.
if subspace < num: subspace = num
# Run the single-pass guess to seed the first iteration
guess_eof, guess_eig, guess_pc = EOF_guess (x, subspace, iaxis, weight=weight, out=None)
# Convert NaNs to zeros so they don't screw up the matrix operations
guess_eof = fill (guess_eof, 0)
x, time, space = prep(var=x, iaxis=iaxis, weight=weight, out=out)
del iaxis
eofshape = (subspace,) + space.shape
pcshape = time.shape + (subspace,)
pcs = np.empty(pcshape,dtype='d')
oldeofs = np.empty(eofshape,dtype='d')
# Seed with initial guess (in the weighted space)
neweofs = apply_weights (guess_eof, weight=weight).get()
neweofs = np.array(neweofs, dtype='d') # so we can write
# neweofs = np.random.rand(*eofshape)
# Workspace for smaller representative matrix
work1 = np.empty([subspace,subspace], dtype='d')
work2 = np.empty([subspace,subspace], dtype='d')
NX = space.size
# Variance accumulation (on first iteration only)
variance = 0.0
for iter_num in range(1,max_iter+1):
print('iter_num: %d'%iter_num)
neweofs, oldeofs = oldeofs, neweofs
# Reset the accumulation arrays for the next approximations
neweofs[()] = 0
# Apply the covariance matrix
for inview in View(x.axes).loop_mem():
X = np.ascontiguousarray(inview.get(x), dtype='d')
assert X.size >= space.size, "spatial pattern is too large"
nt = inview.shape[0]
time_offset = inview.slices[0].start
ier = lib.build_eofs (subspace, nt, NX, X, oldeofs,
neweofs, pcs[time_offset,...])
assert ier == 0
# Compute variance?
if iter_num == 1:
variance += (X**2).sum()
# Useful dot products
lib.dot(subspace, NX, oldeofs, neweofs, work1)
lib.dot(subspace, NX, neweofs, neweofs, work2)
# Compute surrogate matrix (using all available information from this iteration)
A, residues, rank, s = np.linalg.lstsq(work1,work2,rcond=1e-30)
# Eigendecomposition on surrogate matrix
w, P = np.linalg.eig(A)
# Sort by eigenvalue
S = np.argsort(w)[::-1]
w = w[S]
print(w)
# assert P.dtype.name == 'float64', P.dtype.name
P = np.ascontiguousarray(P[:,S], dtype='d')
# Translate the surrogate eigenvectors to an estimate of the true eigenvectors
lib.transform(subspace, NX, P, neweofs)
# Normalize
lib.normalize (subspace, NX, neweofs)
# # verify orthogonality
# for i in range(num):
# print [np.dot(neweofs[i,...].flatten(), neweofs[j,...].flatten()) for j in range(num)]
if np.allclose(oldeofs[:num,...],neweofs[:num,...], atol=0):
print('converged after %d iterations'%iter_num)
break
assert iter_num != max_iter, "no convergence"
# Wrap as pygeode vars, and return
# Only need some of the eofs for output (the rest might not have even converged yet)
eof = neweofs[:num]
pc = pcs[...,:num].transpose()
# Extract the eigenvalues
# (compute magnitude of pc arrays)
#TODO: keep eigenvalues as a separate variable in the iteration loop
eig = np.array([sqrt( (pc[i,...]**2).sum() ) for i in range(pc.shape[0]) ])
pc = np.dot(np.diag(1/eig), pc)
return finalize (x, time, space, eof, eig, pc, variance, weight=weight, out=out)