Source code for PyDynamic.uncertainty.propagate_DFT

# -*- coding: utf-8 -*-

"""
The :mod:`PyDynamic.uncertainty.propagate_DFT` module implements methods for the propagation of uncertainties in the
application of the DFT, inverse DFT, deconvolution and multiplication in the frequency domain, transformation from
amplitude and phase to real and imaginary parts and vice versa.

The correspoding scientific publications is
	S. Eichstädt und V. Wilkens
	GUM2DFT — a software tool for uncertainty evaluation of transient signals in the frequency domain.
	*Measurement Science and Technology*, 27(5), 055001, 2016.
	[DOI: `10.1088/0957-0233/27/5/055001 <http://dx.doi.org/10.1088/0957-0233/27/5/055001>`_]

This module contains the following functions:
* GUM_DFT: Calculation of the DFT of the time domain signal x and propagation of the squared uncertainty Ux
	associated with the time domain sequence x to the real and imaginary parts of the DFT of x
* GUM_iDFT: GUM propagation of the squared uncertainty UF associated with the DFT values F through the
	inverse DFT
* GUM_DFTfreq: Return the Discrete Fourier Transform sample frequencies
* DFT_transferfunction: Calculation of the transfer function H = Y/X in the frequency domain with X being the Fourier transform
	of the system's input signal and Y that of the output signal
* DFT_deconv: Deconvolution in the frequency domain
* DFT_multiply: Multiplication in the frequency domain
* AmpPhase2DFT: Transformation from magnitude and phase to real and imaginary parts
* DFT2AmpPhase: Transformation from real and imaginary parts to magnitude and phase
* AmpPhase2Time: Transformation from amplitude and phase to time domain
* Time2AmpPhase: Transformation from time domain to amplitude and phase

"""

import warnings

import numpy as np
from scipy import sparse

__all__ = ['GUM_DFT','GUM_iDFT', 'GUM_DFTfreq', 'DFT_transferfunction', 'DFT_deconv', 'DFT_multiply', 'AmpPhase2DFT', 'DFT2AmpPhase', 'AmpPhase2Time', 'Time2AmpPhase']

def apply_window(x,Ux,window):
	"""Apply a time domain window to the signal x of equal length and propagate uncertainties.
	This is an internal helper function.

	Args:
		x: vector of time domain signal values of shape (N,)
		Ux: covariance matrix of shape (N,N) associated with x or noise variance as float
		window: vector of time domain window (same length as x)
	Returns:
		xw,Uxw
	"""
	assert(len(x)==len(window))
	if not isinstance(Ux,float):
		assert(Ux.shape[0]==Ux.shape[1] and Ux.shape[0]==len(x))
	xw = x.copy()*window
	if isinstance(Ux,float):
		Uxw = Ux*window**2
	else:
		Uxw = prod(window,prod(Ux,window))
	return xw,Uxw

def prod(A,B):
	"""Calculate the matrix-vector product, or vector-matrix product that corresponds to diag(A)*B or A*diag(B),
	respectively; depending	on which of A,B is the matrix and which the vector.
	This is an internal helper function.

	"""
	if len(A.shape)==1 and len(B.shape)==2:  # A is the vector and B the matrix
		C = np.zeros_like(B)
		for k in range(C.shape[0]):
			C[k,:] = A[k]*B[k,:]
		return C
	elif len(A.shape)==2 and len(B.shape)==1: # A is the matrix and B the vector
		C = np.zeros_like(A)
		for k in range(C.shape[1]):
			C[:,k] = A[:,k]*B[k]
		return C
	else:
		raise ValueError("Wrong dimension of inputs")

def matprod(M,V,W,return_as_matrix=True):
	"""Calculate the matrix-matrix-matrix product (V1,V2)M(W1,W2) for V=(V1,V2)
	and W=(W1,W2). M can be sparse, one-dimensional or a full (quadratic) matrix.
	This is an internal helper function.
	"""
	if len(M.shape)==2:
		assert(M.shape[0]==M.shape[1])
	assert(M.shape[0]==V.shape[0])
	assert(V.shape==W.shape)
	N = V.shape[0]//2
	v1 = V[:N]; v2 = V[N:]
	w1 = W[:N]; w2 = W[N:]
	if isinstance(M,sparse.dia_matrix):
		nrows = M.shape[0]
		offset= M.offsets
		diags = M.data
		A = diags[0][:N]
		B = diags[1][offset[1]:nrows+offset[1]]
		D = diags[0][N:]
		return np.diag(v1*A*w1 + v2*B*w1 + v1*B*w2 + v2*D*w2)
	elif len(M.shape)==1:
		A = M[:N]
		D = M[N:]
		if return_as_matrix:
			return np.diag(v1*A*w1 + v2*D*w2)
		else:
			return np.r_[v1*A*w1 + v2*D*w2]
	else:
		A = M[:N,:N]
		B = M[:N,N:]
		D = M[N:,N:]
		return prod(v1,prod(A,w1)) + prod(v2,prod(B.T,w1)) + prod(v1,prod(B,w2)) + prod(v2,prod(D,w2))




[docs]def GUM_DFT(x,Ux,N=None,window=None,CxCos=None,CxSin=None,returnC=False,mask=None): """Calculation of the DFT of the time domain signal x and propagation of the squared uncertainty Ux associated with the time domain sequence x to the real and imaginary parts of the DFT of x. Parameters ---------- x : numpy.ndarray of shape (M,) vector of time domain signal values Ux : numpy.ndarray covariance matrix associated with x, shape (M,M) or noise variance as float N : int, optional length of time domain signal for DFT; N>=len(x) window : numpy.ndarray, optional of shape (M,) vector of the time domain window values CxCos : numpy.ndarray, optional cosine part of sensitivity matrix CxSin : numpy.ndarray, optional sine part of sensitivity matrix returnC : bool, optional if true, return sensitivity matrix blocks for later use mask: ndarray of dtype bool calculate DFT values and uncertainties only at those frequencies where mask is `True` Returns ------- F : numpy.ndarray vector of complex valued DFT values or of its real and imaginary parts UF : numpy.ndarray covariance matrix associated with real and imaginary part of F References ---------- * Eichstädt and Wilkens [Eichst2016]_ """ L=0 if isinstance(window,np.ndarray): x,Ux = apply_window(x,Ux,window) # apply the chose window for the application of the FFT if isinstance(N,int): L = N - len(x) assert(L>=0) x = np.r_[x.copy(),np.zeros(L,)] # zero-padding N = len(x) if np.mod(N,2) == 0: # N is even M = N+2 else: M = N+1 if isinstance(mask, np.ndarray): F = np.fft.rfft(x)[mask] # calculation of best estimate F = np.r_[np.real(F), np.imag(F)] # in real,imag format in accordance with GUM S2 warnings.warn("In a future release, because of issues with the current version, \nthe handling of masked DFT arrays will be changed to use numpy masked arrays.",DeprecationWarning) else: F = np.fft.rfft(x) # calculation of best estimate F = np.r_[np.real(F),np.imag(F)] # in real,imag format in accordance with GUM S2 mask = np.ones(len(F)//2, dtype=bool) Nm = 2*np.sum(mask) beta = 2*np.pi*np.arange(N-L)/N # for simplified calculation of sensivities Cxkc = lambda k: np.cos(k*beta)[np.newaxis,:] # sensitivity matrix wrt cosinus part Cxks = lambda k: -np.sin(k*beta)[np.newaxis,:] # sensitivity matrix wrt sinus part if isinstance(Ux,float): UF = np.zeros(Nm) km = 0 for k in range(M//2): # Block cos/cos if mask[k]: UF[km] = np.sum(Ux*Cxkc(k)**2) km += 1 km = 0 for k in range(M//2): # Block sin/sin if mask[k]: UF[Nm//2+km] = np.sum(Ux*Cxks(k)**2) km += 1 else: # general method if len(Ux.shape)==1: Ux = np.diag(Ux) if not isinstance(CxCos,np.ndarray): CxCos = np.zeros((Nm//2,N-L)) CxSin = np.zeros((Nm//2,N-L)) km = 0 for k in range(M//2): if mask[k]: CxCos[km,:] = Cxkc(k) CxSin[km,:] = Cxks(k) km += 1 UFCC = np.dot(CxCos,np.dot(Ux,CxCos.T)) UFCS = np.dot(CxCos,np.dot(Ux,CxSin.T)) UFSS = np.dot(CxSin,np.dot(Ux,CxSin.T)) try: UF = np.vstack((np.hstack((UFCC,UFCS)),np.hstack((UFCS.T,UFSS)))) # stack together full cov matrix except MemoryError: print("Could not put covariance matrix together due to memory constraints.") print("Returning the three blocks (A,B,C) such that U = [[A,B],[B.T,C]] instead.") UF = (UFCC,UFCS,UFSS) # return blocks only because of lack of memory if returnC: return F,UF,{"CxCos":CxCos,"CxSin":CxSin} # return sensivities if requested else: return F,UF
[docs]def GUM_iDFT(F,UF,Nx=None,Cc=None,Cs=None,returnC=False): """GUM propagation of the squared uncertainty UF associated with the DFT values F through the inverse DFT The matrix UF is assumed to be for real and imaginary part with blocks: UF = [[u(R,R), u(R,I)],[u(I,R),u(I,I)]] and real and imaginary part obtained from calling rfft (DFT for real-valued signal) Parameters ---------- F : np.ndarray of shape (2M,) vector of real and imaginary parts of a DFT result UF: np.ndarray of shape (2M,2M) covariance matrix associated with real and imaginary parts of F Nx: int, optional number of samples of iDFT result Cc: np.ndarray, optional cosine part of sensitivities Cs: np.ndarray, optional sine part of sensitivities returnC: if true, return sensitivity matrix blocks Returns ------- x: np.ndarry vector of time domain signal values Ux: np.ndarray covariance matrix associated with x References ---------- * Eichstädt and Wilkens [Eichst2016]_ """ N = UF.shape[0]-2 if Nx is None: Nx = N else: assert(Nx<=UF.shape[0]-2) beta = 2*np.pi*np.arange(Nx)/N # calculate inverse DFT x = np.fft.irfft(F[:N//2+1]+1j*F[N//2+1:])[:Nx] if not isinstance(Cc,np.ndarray):# calculate sensitivities Cc = np.zeros((Nx,N//2+1)) Cc[:,0] = 1.0; Cc[:,-1] = np.cos(np.pi*np.arange(Nx)) for k in range(1,N//2): Cc[:,k] = 2*np.cos(k*beta) if not isinstance(Cs,np.ndarray): Cs = np.zeros((Nx,N//2+1)) Cs[:,0] = 0.0; Cs[:,-1] = -np.sin(np.pi*np.arange(Nx)) for k in range(1,N//2): Cs[:,k] = -2*np.sin(k*beta) # calculate blocks of uncertainty matrix if len(UF.shape)==2: RR = UF[:N//2+1,:N//2+1] RI = UF[:N//2+1, N//2+1:] II = UF[N//2+1:,N//2+1:] # propagate uncertainties Ux = np.dot(Cc,np.dot(RR,Cc.T)) Ux = Ux + 2*np.dot(Cc,np.dot(RI,Cs.T)) Ux = Ux + np.dot(Cs,np.dot(II,Cs.T)) else: RR = UF[:N//2+1] II = UF[N//2+1:] Ux = np.dot(Cc,prod(RR,Cc.T)) + np.dot(Cs,prod(II,Cs.T)) if returnC: return x,Ux/N**2,{"Cc":Cc,"Cs":Cs} else: return x,Ux/N**2
[docs]def GUM_DFTfreq(N, dt=1): """Return the Discrete Fourier Transform sample frequencies Parameters ---------- N: int window length dt: float sample spacing (inverse of sampling rate) Returns ------- f: ndarray Array of length ``n//2 + 1`` containing the sample frequencies See also -------- `mod`::numpy.fft.rfftfreq """ return np.fft.rfftfreq(N, dt)
[docs]def DFT2AmpPhase(F,UF,keep_sparse=False, tol=1.0, return_type="separate"): """Transformation from real and imaginary parts to magnitude and phase Calculate the matrix U_AP = [[U1,U2],[U2^T,U3]] associated with magnitude and phase of the vector F=[real,imag] with associated covariance matrix U_F=[[URR,URI],[URI^T,UII]] Parameters ---------- F: np.ndarray of shape (2M,) vector of real and imaginary parts of a DFT result UF: np.ndarray of shape (2M,2M) covariance matrix associated with F keep_sparse: bool, optional if true then UAP will be sparse if UF is one-dimensional tol: float, optional lower bound for A/uF below which a warning will be issued concerning unreliable results return_type: str, optional If "separate" then magnitude and phase are returned as seperate arrays. Otherwise the array [A, P] is returned Returns ------- If `return_type` is `separate`: A: np.ndarray vector of magnitude values P: np.ndarray vector of phase values in radians, in the range [-pi, pi] UAP: np.ndarray covariance matrix associated with (A,P) Otherwise: AP: np.ndarray vector of magnitude and phase values UAP: np.ndarray covariance matrix associated with AP """ # calculate inverse DFT N = len(F)-2 R = F[:N//2+1]; I = F[N//2+1:] A = np.sqrt(R**2+I**2) # absolute value P = np.arctan2(I,R) # phase value if len(UF.shape)==1: uF = 0.5*(np.sqrt(UF[:N//2+1])+np.sqrt(UF[N//2+1:])) # uncertainty of real,imag else: uF = 0.5*(np.sqrt(np.diag(UF[:N//2+1,:N//2+1]))+ np.sqrt(np.diag(UF[N//2+1:,N//2+1:]))) if np.any(A/uF < tol): print('DFT2AmpPhase Warning\n Some amplitude values are below the defined threshold.') print('The GUM formulas may become unreliable and a Monte Carlo approach is recommended instead.') print('The actual minimum value of A/uF is %.2e and the threshold is %.2e'%((A/uF).min(), tol)) aR = R/A # sensitivities aI = I/A pR = -I/A**2 pI = R/A**2 if len(UF.shape)==1: # uncertainty calculation of zero correlation URR = UF[:N//2+1] UII = UF[N//2+1:] U11 = URR*aR**2 + UII*aI**2 U12 = aR*URR*pR + aI*UII*pI U22 = URR*pR**2 + UII*pI**2 UAP = sparse.diags([np.r_[U11,U22],U12,U12],[0,N//2+1,-(N//2+1)]) if not keep_sparse: UAP = UAP.toarray() else: # uncertainty calculation for full covariance URR = UF[:N//2+1,:N//2+1] URI = UF[:N//2+1,N//2+1:] UII = UF[N//2+1:,N//2+1:] U11 = prod(aR,prod(URR,aR)) + prod(aR,prod(URI,aI)) + prod(aI,prod(URI.T,aR)) + prod(aI,prod(UII,aI)) U12 = prod(aR,prod(URR,pR)) + prod(aI,prod(URI,pI)) + prod(aI,prod(URI.T,pR)) + prod(aI,prod(UII,pI)) U22 = prod(pR,prod(URR,pR)) + prod(pR,prod(URI,pI)) + prod(pI,prod(URI.T,pR)) + prod(pI,prod(UII,pI)) UAP = np.vstack((np.hstack((U11,U12)),np.hstack((U12.T,U22)))) if return_type == "separate": return A,P,UAP # amplitude and phase as separate variables else: return np.r_[A,P], UAP
[docs]def AmpPhase2DFT(A,P,UAP,keep_sparse=False): """Transformation from magnitude and phase to real and imaginary parts Calculate the vector F=[real,imag] and propagate the covariance matrix UAP associated with [A, P] Parameters ---------- A: np.ndarray of shape (N,) vector of magnitude values P: np.ndarray of shape (N,) vector of phase values (in radians) UAP: np.ndarray of shape (2N,2N) covariance matrix associated with (A,P) or vector of squared standard uncertainties [u^2(A),u^2(P)] keep_sparse: bool, optional whether to transform sparse matrix to numpy array or not Returns ------- F: np.ndarray vector of real and imaginary parts of DFT result UF: np.ndarray covariance matrix associated with F """ assert(len(A.shape)==1) assert(A.shape==P.shape) assert(UAP.shape==(2*len(A),2*len(A)) or UAP.shape==(2*len(A),)) F = np.r_[A*np.cos(P),A*np.sin(P)] # calculation of best estimate # calculation of sensitivities CRA = np.cos(P) CRP = -A*np.sin(P) CIA = np.sin(P) CIP = A*np.cos(P) # assignment of uncertainty blocks in UAP N = len(A) if UAP.shape==(2*N,): # zero correlation; just standard deviations Ua = UAP[:N] Up = UAP[N:] U11 = CRA*Ua*CRA + CRP*Up*CRP U12 = CRA*Ua*CIA + CRP*Up*CIP U22 = CIA*Ua*CIA + CIP*Up*CIP UF = sparse.diags([np.r_[U11,U22],U12,U12],[0,N,-N]) if not keep_sparse: UF = UF.toarray() else: if isinstance(UAP,sparse.dia_matrix): nrows = 2*N offset= UAP.offsets diags = UAP.data Uaa = diags[0][:N] Uap = diags[1][offset[1]:nrows+offset[1]] Upp = diags[0][N:] U11 = Uaa*CRA**2 + CRP*Uap*CRA + CRA*Uap*CRP + Upp*CRP**2 U12 = CRA*Uaa*CIA + CRP*Uap*CIA + CRA*Uap*CIA + CRP*Upp*CIP U22 = Uaa*CIA**2 + CIP*Uap*CIA + CIA*Uap*CIP + Upp*CIP**2 UF = sparse.diags([np.r_[U11,U22],U12,U12],[0,N,-N]) # default is sparse if not keep_sparse: UF = UF.toarray() # fall back to non-sparse else: Uaa = UAP[:N,:N] Uap = UAP[:N,N:] Upp = UAP[N:,N:] U11 = prod(CRA,prod(Uaa,CRA)) + prod(CRP,prod(Uap.T,CRA)) + prod(CRA,prod(Uap,CRP)) + prod(CRP,prod(Upp,CRP)) U12 = prod(CRA,prod(Uaa,CIA)) + prod(CRP,prod(Uap.T,CIA)) + prod(CRA,prod(Uap,CIA)) + prod(CRP,prod(Upp,CIP)) U22 = prod(CIA,prod(Uaa,CIA)) + prod(CIP,prod(Uap.T,CIA)) + prod(CIA,prod(Uap,CIP)) + prod(CIP,prod(Upp,CIP)) UF = np.vstack((np.hstack((U11,U12)),np.hstack((U12.T,U22)))) # stack together the full covariance matrix return F, UF
[docs]def Time2AmpPhase(x,Ux): """Transformation from time domain to amplitude and phase Parameters ---------- x: np.ndarray of shape (N,) time domain signal Ux: np.ndarray of shape (N,N) squared uncertainty associated with x Returns ------- A: np.ndarray amplitude values P: np.ndarray phase values UAP: np.ndarray covariance matrix associated with [A,P] """ F,UF = GUM_DFT(x,Ux) # propagate to DFT domain A,P,UAP = DFT2AmpPhase(F,UF) # propagate to amplitude and phase return A,P,UAP
[docs]def AmpPhase2Time(A,P,UAP): """Transformation from amplitude and phase to time domain GUM propagation of covariance matrix UAP associated with DFT amplitude A and phase P to the result of the inverse DFT. Uncertainty UAP is assumed to be given for amplitude and phase with blocks: UAP = [[u(A,A), u(A,P)],[u(P,A),u(P,P)]] Parameters ---------- A: np.ndarray of shape (N,) vector of amplitude values P: np.ndarray of shape (N,) vector of phase values (in rad) UAP: np.ndarray of shape (2N,2N) covariance matrix associated with [A,P] Returns ------- x: np.ndarray vector of time domain values Ux: np.ndarray covariance matrix associated with x """ N = UAP.shape[0]-2 assert(np.mod(N,2)==0) beta = 2*np.pi*np.arange(N)/N # calculate inverse DFT F = A*np.exp(1j*P) x = np.fft.irfft(F) Pf = np.r_[P,-P[-2:0:-1]] # phase values to take into account symmetric part Cc = np.zeros((N,N//2+1)) # sensitivities wrt cosinus part Cc[:,0] = np.cos(P[0]); Cc[:,-1] = np.cos(P[-1]+np.pi*np.arange(N)) for k in range(1,N//2): Cc[:,k] = 2*np.cos(Pf[k]+k*beta) Cs = np.zeros((N,N//2+1)) # sensitivities wrt sinus part Cs[:,0] = -A[0]*np.sin(P[0]); Cs[:,-1] = -A[-1]*np.sin(P[-1]+np.pi*np.arange(N)) for k in range(1,N//2): Cs[:,k] = -A[k]*2*np.sin(Pf[k]+k*beta) # calculate blocks of uncertainty matrix if len(UAP.shape)==1: AA = UAP[:N//2+1] PP = UAP[N//2+1:] Ux = np.dot(Cc,prod(AA,Cc.T)) + np.dot(Cs,prod(PP,Cs.T)) else: if isinstance(UAP,sparse.dia_matrix): nrows = UAP.shape[0] n = nrows//2 offset= UAP.offsets diags = UAP.data AA = diags[0][:n] AP = diags[1][offset[1]:nrows+offset[1]] PP = diags[0][n:] Ux = np.dot(Cc,prod(AA,Cc.T)) + 2*np.dot(Cc,prod(AP,Cs.T)) + np.dot(Cs,prod(PP,Cs.T)) else: AA = UAP[:N//2+1,:N//2+1] AP = UAP[:N//2+1, N//2+1:] PP = UAP[N//2+1:,N//2+1:] # propagate uncertainties Ux = np.dot(Cc,np.dot(AA,Cc.T)) + 2*np.dot(Cc,np.dot(AP,Cs.T)) + np.dot(Cs,np.dot(PP,Cs.T)) return x,Ux/N**2
# for backward compatibility GUMdeconv = lambda H, Y, UH, UY: DFT_deconv(H, Y, UH, UY)
[docs]def DFT_transferfunction(X, Y, UX, UY): """Calculation of the transfer function H = Y/X in the frequency domain with X being the Fourier transform of the system's input signal and Y that of the output signal. Parameters ---------- X: np.ndarray real and imaginary parts of the system's input signal Y: np.ndarray real and imaginary parts of the system's output signal UX: np.ndarray covariance matrix associated with X UY: np.ndarray covariance matrix associated with Y Returns ------- H: np.ndarray real and imaginary parts of the system's frequency response UH: np.ndarray covariance matrix associated with H This function uses `DFT_deconv`. """ return DFT_deconv(X, Y, UX, UY)
[docs]def DFT_deconv(H, Y, UH, UY): """Deconvolution in the frequency domain GUM propagation of uncertainties for the deconvolution X = Y/H with Y and H being the Fourier transform of the measured signal and of the system's impulse response, respectively. This function returns the covariance matrix as a tuple of blocks if too large for complete storage in memory. Parameters ---------- H: np.ndarray of shape (2M,) real and imaginary parts of frequency response values (N an even integer) Y: np.ndarray of shape (2M,) real and imaginary parts of DFT values UH: np.ndarray of shape (2M,2M) covariance matrix associated with H UY: np.ndarray of shape (2M,2M) covariance matrix associated with Y Returns ------- X: np.ndarray of shape (2M,) real and imaginary parts of DFT values of deconv result UX: np.ndarray of shape (2M,2M) covariance matrix associated with real and imaginary part of X References ---------- * Eichstädt and Wilkens [Eichst2016]_ """ assert(len(H)==len(Y)) if len(UY.shape)==2: assert (UH.shape == (len(H), len(H))) assert(UH.shape==UY.shape) N = UH.shape[0]-2 else: assert(len(UH)==len(H)) assert(len(UY)==len(Y)) N = len(UH)-2 assert(np.mod(N,2)==0) # real and imaginary parts of system and signal rH, iH = H[:N//2+1], H[N//2+1:] rY, iY = Y[:N//2+1], Y[N//2+1:] Yc = Y[:N//2+1] + 1j*Y[N//2+1:] Hc = H[:N//2+1] + 1j*H[N//2+1:] X = np.r_[np.real(Yc/Hc),np.imag(Yc/Hc)] # sensitivities norm = rH**2+iH**2 RY = np.r_[rH/norm,iH/norm] IY = np.r_[-iH/norm,rH/norm] RH = np.r_[(-rY*rH**2+rY*iH**2-2*iY*iH*rH)/norm**2, (iY*rH**2-iH*iH**2-2*rY*rH*iH)/norm**2] IH = np.r_[(-iY*rH**2+iY*iH**2+2*rY*iH*rH)/norm**2, (-rY*rH**2+rY*iH**2-2*iY*rH*iH)/norm**2] # calculate blocks of uncertainty matrix URRX = matprod(UY,RY,RY) + matprod(UH,RH,RH) URIX = matprod(UY,RY,IY) + matprod(UH,RH,IH) UIIX = matprod(UY,IY,IY) + matprod(UH,IH,IH) try: UX = np.vstack((np.hstack((URRX,URIX)),np.hstack((URIX.T,UIIX)))) except MemoryError: print("Could not put covariance matrix together due to memory constraints.") print("Returning the three blocks (A,B,C) such that U = [[A,B],[B.T,C]] instead.") UX = (URRX,URIX,UIIX) return X,UX
[docs]def DFT_multiply(Y, F, UY, UF=None): """Multiplication in the frequency domain GUM uncertainty propagation for multiplication in the frequency domain, where the second factor F may have an associated uncertainty. This method can be used, for instance, for the application of a low-pass filter in the frequency domain or the application of deconvolution as a multiplication with an inverse of known uncertainty. Parameters ---------- Y: np.ndarray of shape (2M,) real and imaginary parts of the first factor F: np.ndarray of shape (2M,) real and imaginary parts of the second factor UY: np.ndarray either shape (2M,) or shape (2M,2M) covariance matrix or squared uncertainty associated with Y UF: np.ndarray of shape (2M,2M) covariance matrix associated with F (optional), default is None Returns ------- YF: np.ndarray of shape (2M,) the product of Y and F UYF: np.ndarray of shape (2M,2M) the uncertainty associated with YF """ assert(len(Y)==len(F)) def calcU(A, UB): # uncertainty propagation for A*B with B uncertain (helper function) n = len(A) RA = A[:n//2] IA = A[n//2:] if isinstance(UB, float): # simpler calculation if only single uncertainty uRR = RA * UB * RA + IA * UB * IA uRI = RA * UB * IA - IA * UB * RA uII = IA * UB * IA + RA * UB * RA elif len(UB.shape)==1: # simpler calculation if no correlation UBRR = UB[:n//2] UBII = UB[n//2:] uRR = RA*UBRR*RA + IA*UBII*IA uRI = RA*UBRR*IA - IA*UBII*RA uII = IA*UBRR*IA + RA*UBII*RA else: # full calculation because of full input covariance UBRR = UB[:n//2,:n//2] UBRI = UB[:n//2,n//2:] UBII = UB[n//2:,n//2:] uRR = prod(RA, prod(UBRR, RA)) - prod(IA,prod(UBRI.T, RA)) - prod(RA, prod(UBRI,IA)) + prod(IA,prod(UBII,IA)) uRI = prod(RA, prod(UBRR, IA)) - prod(IA,prod(UBRI.T, IA)) + prod(RA, prod(UBRI,RA)) - prod(IA,prod(UBII,RA)) uII = prod(IA, prod(UBRR, IA)) + prod(RA,prod(UBRI.T, IA)) + prod(IA, prod(UBRI,RA)) + prod(RA,prod(UBII,RA)) return uRR, uRI, uII N = len(Y) RY = Y[:N//2]; IY = Y[N//2:] # decompose into block matrix RF = F[:N//2]; IF = F[N//2:] # decompose into block matrix YF = np.r_[RY*RF - IY*IF, RY*IF + IY*RF] # apply product rule if not isinstance(UF, np.ndarray): # second factor is known exactly UYRR, UYRI, UYII = calcU(F, UY) UYF = np.vstack((np.hstack((UYRR, UYRI)), np.hstack((UYRI.T, UYII)))) # stack together covariance matrix else: # both factors are uncertain URR_Y, URI_Y, UII_Y = calcU(F, UY) URR_F, URI_F, UII_F = calcU(Y, UF) URR = URR_Y + URR_F URI = URI_Y + URI_F UII = UII_Y + UII_F UYF = np.vstack((np.hstack((URR, URI)), np.hstack((URI.T, UII)))) # stack together covariance matrix return YF, UYF