"""Contains basic functions for analyzing the results of reconstructions The functions in this module are designed to work either with pytorch tensors or numpy arrays, so they can be used either directly after reconstructions on the attributes of the models themselves, or after-the-fact once the data has been stored in numpy arrays. """ import torch as t import numpy as np from CDTools.tools import image_processing as ip from scipy import fftpack from scipy import linalg as sla from scipy import special __all__ = ['orthogonalize_probes', 'standardize', 'synthesize_reconstructions', 'calc_consistency_prtf', 'calc_deconvolved_cross_correlation', 'calc_frc', 'calc_vn_entropy', 'calc_top_mode_fraction', 'calc_rms_error', 'calc_fidelity', 'calc_generalized_rms_error'] def orthogonalize_probes(probes, density_matrix=None, keep_transform=False, normalize=False): """Orthogonalizes a set of incoherently mixing probes The strategy is to define a reduced orthogonal basis that spans all of the retrieved probes, and then build the density matrix defined by the probes in that basis. After diagonalization, the eigenvectors can be recast into the original basis and returned By default, it assumes that the set of probes are defined just as standard incoherently mixing probe modes, and orthogonalizes them. However, if a density matrix is explicitly given, it will instead consider the problem of extracting the eigenbasis of the matrix probes * denstity_matrix * probes^dagger, where probes is the column matrix of the given probe functions. This latter problem arises in the generalization of the probe mixing model, and reduces to the simpler case when the density matrix is equal to the identity matrix If the parameter "keep_transform" is set, the function will additionally return the matrix A such that A * ortho_probes^dagger = probes^dagger If the parameter "normalize" is False (as is the default), the variation in intensities in the probe modes will be kept in the probe modes, as is natural for a purely incoherent model. If it is set to "True", the returned probe modes will all be normalized instead. Parameters ---------- probes : array An l x n x m complex array representing a stack of probes density_matrix : np.array An optional l x l density matrix further elaborating on the state keep_transform : bool Default False, whether to return the map from probes to ortho_probes normalize : bool Default False, whether to normalize the probe modes Returns ------- ortho_probes: array An l x n x m complex array representing a stack of probes """ try: probes = probes.detach().cpu().numpy() send_to_torch = True except: send_to_torch = False # We can do the orthogonalization with an SVD, so first we have to # reshape the final two dimensions (the image shape) into a single # vectorized dimension. This matrix is probes^dagger, hence the # conjugation probes_mat = probes.reshape(probes.shape[0], probes.shape[1]*probes.shape[2]) if density_matrix is None: density_matrix = np.eye(probes.shape[0]) # next we want to extract the eigendecomposition of the density matrix # itself w,v = sla.eigh(density_matrix) w = w[::-1] v = v[:,::-1] # We do this just to avoid total failure when the density # matrix is not positive definite. # In most cases (such as when rho is generated directly from some other # matrix A such that rho=A A^dagger), w should never have any negative # entries. w = np.maximum(w,0) B_dagger = np.dot(np.diag(np.sqrt(w)), v.conj().transpose()) #u,s,vh = np.linalg.svd(np.dot(B_dagger,probes_mat), full_matrices=False) u,s,vh = sla.svd(np.dot(B_dagger,probes_mat), full_matrices=False) if normalize: ortho_probes = vh.reshape(probes.shape[0], probes.shape[1], probes.shape[2]) B_dagger_inv = np.linalg.pinv(B_dagger) A = np.dot(B_dagger_inv,np.dot(u,np.diag(s))) #A_dagger = np.dot(np.linalg.pinv(np.diag(s)), # np.dot(np.transpose(u).conj(),B_dagger)) else: ortho_probes = np.dot(np.diag(s),vh).reshape(probes.shape[0], probes.shape[1], probes.shape[2]) B_dagger_inv = np.linalg.pinv(B_dagger) A = np.dot(B_dagger_inv,u) #A_dagger = np.dot(np.transpose(u).conj(),B_dagger) if send_to_torch: ortho_probes = t.as_tensor(np.stack(ortho_probes)) A = t.as_tensor(A) if keep_transform: return ortho_probes, A#_dagger else: return ortho_probes def standardize(probe, obj, obj_slice=None, correct_ramp=False): """Standardizes a probe and object to prepare them for comparison There are a number of ambiguities in the definition of a ptychographic reconstruction. This function makes an explicit choice for each ambiguity to allow comparisons between independent reconstructions without confusing these ambiguities for real differences between the reconstructions. The ambiguities and standardizations are: 1) a. Probe and object can be scaled inversely to one another b. So we set the probe intensity to an average per-pixel value of 1 2) a. The probe and object can aquire equal and opposite phase ramps b. So we set the centroid of the FFT of the probe to zero frequency 3) a. The probe and object can each acquire an arbitrary overall phase b. So we set the phase of the sum of all values of both the probe and object to 0 When dealing with the properties of the object, a slice is used by default as the edges of the object often are dominated by unphysical noise. The default slice is from 3/8 to 5/8 of the way across. If the probe is actually a stack of incoherently mixing probes, then the dominant probe mode (assumed to be the first in the list) is used, but all the probes are updated with the same factors. Parameters ---------- probe : array A complex array storing a retrieved probe or stack of incoherently mixed probes obj : array A complex array storing a retrieved object obj_slice : slice Optional, a slice to take from the object for calculating normalizations correct_ramp : bool Default False, whether to correct for the relative phase ramps Returns ------- standardized_probe : array The standardized probe standardized_obj : array The standardized object """ # First, we normalize the probe intensity to a fixed value. probe_np = False if isinstance(probe, np.ndarray): probe = t.as_tensor(probe, dtype=t.complex64) probe_np = True obj_np = False if isinstance(obj, np.ndarray): obj = t.as_tensor(obj,dtype=t.complex64) obj_np = True # If this is a single probe and not a stack of probes if len(probe.shape) == 2: probe = probe[None,...] single_probe = True else: single_probe = False normalization = t.sqrt(t.mean(t.abs(probe[0])**2)) probe = probe / normalization obj = obj * normalization # Default slice of the object to use for alignment, etc. if obj_slice is None: obj_slice = np.s_[(obj.shape[0]//8)*3:(obj.shape[0]//8)*5, (obj.shape[1]//8)*3:(obj.shape[1]//8)*5] if correct_ramp: # Need to check if this is actually working and, if not, why not center_freq = ip.centroid(t.abs(t.fft.fftshift(t.fft.fft2(probe[0]), dim=(-1,-2)))**2) center_freq -= t.div(t.tensor(probe[0].shape,dtype=t.float32),2,rounding_mode='floor') center_freq /= t.as_tensor(probe[0].shape,dtype=t.float32) Is, Js = np.mgrid[:probe[0].shape[0],:probe[0].shape[1]] probe_phase_ramp = t.exp(2j * np.pi * (center_freq[0] * t.tensor(Is).to(t.float32) + center_freq[1] * t.tensor(Js).to(t.float32))) probe = probe * t.conj(probe_phase_ramp) Is, Js = np.mgrid[:obj.shape[0],:obj.shape[1]] obj_phase_ramp = t.exp(2j*np.pi * (center_freq[0] * t.tensor(Is).to(t.float32) + center_freq[1] * t.tensor(Js).to(t.float32))) obj = obj * obj_phase_ramp # Then, we set them to consistent absolute phases obj_angle = t.angle(t.sum(obj[obj_slice])) obj = obj * t.exp(-1j*obj_angle) for i in range(probe.shape[0]): probe_angle = t.angle(t.sum(probe[i])) probe[i] = probe[i] * t.exp(-1j*probe_angle) if single_probe: probe = probe[0] if probe_np: probe = probe.detach().cpu().numpy() if obj_np: obj = obj.detach().cpu().numpy() return probe, obj def synthesize_reconstructions(probes, objects, use_probe=False, obj_slice=None, correct_ramp=False): """Takes a collection of reconstructions and outputs a single synthesized probe and object The function first standardizes the sets of probes and objects using the standardize function, passing through the relevant options. Then it calculates the closest overlap of subsequent frames to subpixel precision and uses a sinc interpolation to shift all the probes and objects to a common frame. Then the images are summed. Parameters ---------- probes : list(array) A list of probes or stacks of probe modes objects : list(array) A list of objects use_probe : bool Default False, whether to use the probe or object for alignment obj_slice : slice Optional, A slice of the object to use for alignment and normalization correct_ramp : bool Default False, whether to correct for a relative phase ramp in the probe and object Returns ------- synth_probe : array The synthesized probe synth_obj : array The synthesized object obj_stack : list(array) A list of standardized objects, for further processing """ # This should be cleaned up so it accepts anything array_like probe_np = False if isinstance(probes[0], np.ndarray): probes = [t.as_tensor(probe,dtype=t.complex64) for probe in probes] probe_np = True obj_np = False if isinstance(objects[0], np.ndarray): objects = [t.as_tensor(obj,dtype=t.complex64) for obj in objects] obj_np = True obj_shape = np.min(np.array([obj.shape for obj in objects]),axis=0) objects = [obj[:obj_shape[0],:obj_shape[1]] for obj in objects] if obj_slice is None: obj_slice = np.s_[(objects[0].shape[0]//8)*3:(objects[0].shape[0]//8)*5, (objects[0].shape[1]//8)*3:(objects[0].shape[1]//8)*5] synth_probe, synth_obj = standardize(probes[0].clone(), objects[0].clone(), obj_slice=obj_slice,correct_ramp=correct_ramp) obj_stack = [synth_obj] for i, (probe, obj) in enumerate(zip(probes[1:],objects[1:])): probe, obj = standardize(probe.clone(), obj.clone(), obj_slice=obj_slice,correct_ramp=correct_ramp) if use_probe: shift = ip.find_shift(synth_probe[0],probe[0], resolution=50) else: shift = ip.find_shift(synth_obj[obj_slice],obj[obj_slice], resolution=50) obj = ip.sinc_subpixel_shift(obj,np.array(shift)) if len(probe.shape) == 3: probe = t.stack([ip.sinc_subpixel_shift(p,tuple(shift)) for p in probe],dim=0) else: probe = ip.sinc_subpixel_shift(probe,tuple(shift)) synth_probe = synth_probe + probe synth_obj = synth_obj + obj obj_stack.append(obj) # If there only was one image try: i except: i = -1 if probe_np: synth_probe = synth_probe.numpy() if obj_np: synth_obj = synth_obj.numpy() obj_stack = [obj.numpy() for obj in obj_stack] return synth_probe/(i+2), synth_obj/(i+2), obj_stack def calc_consistency_prtf(synth_obj, objects, basis, obj_slice=None,nbins=None): """Calculates a PRTF between each the individual objects and a synthesized one The consistency PRTF at any given spatial frequency is defined as the ratio between the intensity of any given reconstruction and the intensity of a synthesized or averaged reconstruction at that spatial frequency. Typically, the PRTF is averaged over spatial frequencies with the same magnitude. Parameters ---------- synth_obj : array The synthesized object in the numerator of the PRTF objects : list(array) A list of objects or diffraction patterns for the denomenator of the PRTF basis : array The basis for the reconstruction array to allow output in physical unit obj_slice : slice Optional, a slice of the objects to use for calculating the PRTF nbins : int Optional, number of bins to use in the histogram. Defaults to a sensible value Returns ------- freqs : array The frequencies for the PRTF PRTF : array The values of the PRTF """ obj_np = False if isinstance(objects[0], np.ndarray): objects = [t.as_tensor(obj, dtype=t.complex64) for obj in objects] obj_np = True if isinstance(synth_obj, np.ndarray): synth_obj = t.as_tensor(synth_obj, dtype=t.complex64) if isinstance(basis, t.Tensor): basis = basis.detach().cpu().numpy() if obj_slice is None: obj_slice = np.s_[(objects[0].shape[0]//8)*3:(objects[0].shape[0]//8)*5, (objects[0].shape[1]//8)*3:(objects[0].shape[1]//8)*5] if nbins is None: nbins = np.max(synth_obj[obj_slice].shape) // 4 synth_fft = (t.abs(t.fft.fftshift(t.fft.fft2(synth_obj[obj_slice]), dim=(-1,-2)))**2).numpy() di = np.linalg.norm(basis[:,0]) dj = np.linalg.norm(basis[:,1]) i_freqs = fftpack.fftshift(fftpack.fftfreq(synth_fft.shape[0],d=di)) j_freqs = fftpack.fftshift(fftpack.fftfreq(synth_fft.shape[1],d=dj)) Js,Is = np.meshgrid(j_freqs,i_freqs) Rs = np.sqrt(Is**2+Js**2) synth_ints, bins = np.histogram(Rs,bins=nbins,weights=synth_fft) prtfs = [] for obj in objects: obj = obj[obj_slice] single_fft = (t.abs(t.fft.fftshift(t.fft.fft2(obj), dim=(-1,-2)))**2).numpy() single_ints, bins = np.histogram(Rs,bins=nbins,weights=single_fft) prtfs.append(synth_ints/single_ints) prtf = np.mean(prtfs,axis=0) if not obj_np: bins = t.Tensor(bins) prtf = t.Tensor(prtf) return bins[:-1], prtf def calc_deconvolved_cross_correlation(im1, im2, im_slice=None): """Calculates a cross-correlation between two images with their autocorrelations deconvolved. This is formally defined as the inverse Fourier transform of the normalized product of the Fourier transforms of the two images. It results in a kernel, whose characteristic size is related to the exactness of the possible alignment between the two images, on top of a random background Parameters ---------- im1 : array The first image, as a complex or real valued array im2 : array The first image, as a complex or real valued array im_slice : slice Default is from 3/8 to 5/8 across the image, a slice to use in the processing. Returns ------- corr : array The complex-valued deconvolved cross-correlation, in real space """ im_np = False if isinstance(im1, np.ndarray): im1 = t.as_tensor(im1) im_np = True if isinstance(im2, np.ndarray): im2 = t.as_tensor(im2) im_np = True if im_slice is None: im_slice = np.s_[(im1.shape[0]//8)*3:(im1.shape[0]//8)*5, (im1.shape[1]//8)*3:(im1.shape[1]//8)*5] cor_fft = t.fft.fft2(im1[im_slice]) * \ t.conj(t.fft.fft2(im2[im_slice])) # Not sure if this is more or less stable than just the correlation # maximum - requires some testing cor = t.fft.ifft2(cor_fft / t.abs(cor_fft)) if im_np: cor = cor.numpy() return cor def calc_frc(im1, im2, basis, im_slice=None, nbins=None, snr=1., limit='side'): """Calculates a Fourier ring correlation between two images This function requires an input of a basis to allow for FRC calculations to be related to physical units. Like other analysis functions, this can take input in numpy or pytorch, and will return output in the respective format. Parameters ---------- im1 : array The first image, a complex or real valued array im2 : array The first image, a complex or real valued array basis : array The basis for the images, defined as is standard for datasets im_slice : slice Default is from 3/8 to 5/8 across the image, a slice to use in the processing. nbins : int Number of bins to break the FRC up into snr : float The signal to noise ratio (for the combined information in both images) to return a threshold curve for. limit : str Default is 'side'. What is the highest frequency to calculate the FRC to? If 'side', it chooses the side of the Fourier transform, if 'corner' it goes fully to the corner. Returns ------- freqs : array The frequencies associated with each FRC value FRC : array The FRC values threshold : array The threshold curve for comparison """ im_np = False if isinstance(im1, np.ndarray): im1 = t.as_tensor(im1) im_np = True if isinstance(im2, np.ndarray): im2 = t.as_tensor(im2) im_np = True if isinstance(basis, np.ndarray): basis = t.tensor(basis) if im_slice is None: im_slice = np.s_[(im1.shape[0]//8)*3:(im1.shape[0]//8)*5, (im1.shape[1]//8)*3:(im1.shape[1]//8)*5] if nbins is None: nbins = np.max(im1[im_slice].shape) // 8 f1 = t.fft.fftshift(t.fft.fft2(im1[im_slice]),dim=(-1,-2)) f2 = t.fft.fftshift(t.fft.fft2(im2[im_slice]),dim=(-1,-2)) cor_fft = f1 * t.conj(f2) F1 = t.abs(f1)**2 F2 = t.abs(f2)**2 di = np.linalg.norm(basis[:,0]) dj = np.linalg.norm(basis[:,1]) i_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[0],d=di)) j_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[1],d=dj)) Js,Is = np.meshgrid(j_freqs,i_freqs) Rs = np.sqrt(Is**2+Js**2) if limit.lower().strip() == 'side': max_i = np.max(i_freqs) max_j = np.max(j_freqs) frc_range = [0, max(max_i,max_j)] elif limit.lower().strip() == 'corner': frc_range = [0, np.max(Rs)] else: raise ValueError('Invalid FRC limit: choose "side" or "corner"') numerator, bins = np.histogram(Rs, bins=nbins, range=frc_range, weights=cor_fft.numpy()) denominator_F1, bins = np.histogram(Rs, bins=nbins, range=frc_range, weights=F1.detach().cpu().numpy()) denominator_F2, bins = np.histogram(Rs, bins=nbins, range=frc_range, weights=F2.detach().cpu().numpy()) n_pix, bins = np.histogram(Rs, bins=nbins, range=frc_range) n_pix = n_pix / 4 # This is for an apodized image, apodized with a hann window frc = np.abs(numerator) / np.sqrt(denominator_F1*denominator_F2) # This moves from combined-image SNR to single-image SNR snr /= 2 # NOTE: I should update this to produce lots of different threshold curves # sigma, 2sigma, 3sigma, traditional FRC 1-bit, my better one, n_pix, etc. threshold = (snr + (2 * np.sqrt(snr) + 1) / np.sqrt(n_pix)) / \ (1 + snr + (2 * np.sqrt(snr)) / np.sqrt(n_pix)) my_threshold = np.sqrt(snr**2 + (2*snr**2 + 2*snr + 1)/n_pix) / \ np.sqrt(snr**2 + 2 * snr + 1 + 2*snr**2 / n_pix) twosigma_threshold = 2/ np.sqrt(n_pix) if not im_np: bins = t.tensor(bins) frc = t.tensor(frc) threshold = t.tensor(threshold) return bins[:-1], frc, threshold def calc_vn_entropy(matrix): """Calculates the Von Neumann entropy of a density matrix Will either accept a single matrix, or a stack of matrices. Matrices are assumed to be Hermetian and positive definite, to be well-formed density matrices Parameters ---------- matrix : np.array The nxn matrix or lxnxn stack of matrices to calculate the entropy of Returns ------- entropy: float or np.array The entropy or entropies of the arrays """ if len(matrix.shape) == 3: # Get the eigenvalues eigs = [np.linalg.eigh(mat)[0] for mat in matrix] # Normalize them to match standard density matrix form eigs = [eig / np.sum(eig) for eig in eigs] # And calculate the VN entropy! entropies = [-np.sum(special.xlogy(eig,eig)) for eig in eigs] return np.array(entropies) else: eig = np.linalg.eigh(matrix)[0] entropy = -np.sum(special.xlogy(eig,eig))/np.sum(eig) return entropy def calc_top_mode_fraction(matrix): """Calculates the fraction of total power in the top mode of a density matrix Will either accept a single matrix, or a stack of matrices. Matrices are assumed to be Hermetian and positive definite, to be well-formed density matrices Parameters ---------- matrix : np.array The nxn matrix or lxnxn stack of matrices to work from Returns ------- entropy: float or np.array The fraction of power in the top mode of each matrix """ if len(matrix.shape) == 3: # Get the eigenvalues eigs = [np.linalg.eigh(mat)[0] for mat in matrix] # Normalize them to match standard density matrix form fractions = [np.max(eig) / np.sum(eig) for eig in eigs] return np.array(fractions) else: eig = np.linalg.eigh(matrix)[0] fraction = np.max(eig) / np.sum(eig) return fraction def calc_rms_error(field_1, field_2, align_phases=True, normalize=False, dims=2): """Calculates the root-mean-squared error between two complex wavefields The formal definition of this function is: output = norm * sqrt(mean(abs(field_1 - gamma * field_2)**2)) Where norm is an optional normalization factor, and gamma is an optional phase factor which is appropriate when the wavefields suffer from a global phase degeneracy as is often the case in diffractive imaging. The normalization is defined as the square root of the total intensity contained in field_1, which is appropriate when field_1 represents a known ground truth: norm = sqrt(mean(abs(field_1)**2)) The phase offset is an analytic expression for the phase offset which will minimize the RMS error between the two wavefields: gamma = exp(1j * angle(sum(field_1 * conj(field_2)))) This implementation is stable even in cases where field_1 and field_2 are completely orthogonal. In the definitions above, the field_n are n-dimensional wavefields. The dimensionality of the wavefields can be altered via the dims argument, but the default is 2 for a 2D wavefield. Parameters ---------- field_1 : array The first complex-valued field field_2 : array The second complex-valued field align_phases : bool Default is True, whether to account for a global phase offset normalize : bool Default is False, whether to normalize to the intensity of field_1 dims : (int or tuple of python:ints) Default is 2, the number of final dimensions to reduce over. Returns ------- rms_error : float or t.Tensor The RMS error, or tensor of RMS errors, depending on the dim argument """ sumdims = tuple(d - dims for d in range(dims)) if align_phases: # Keepdim allows us to broadcast the result correctly when we # multiply by the fields gamma = t.exp(1j * t.angle(t.sum(field_1 * t.conj(field_2), dim=sumdims, keepdim=True))) else: gamma = 1 if normalize: norm = 1 / t.mean(t.abs(field_1)**2, dim=sumdims) else: norm = 1 difference = field_1 - gamma * field_2 return t.sqrt(norm * t.mean(t.abs(difference)**2, dim=sumdims)) def calc_fidelity(fields_1, fields_2, dims=2): """Calculates the fidelity between two density matrices The fidelity is a comparison metric between two density matrices (i.e. mutual coherence functions) that extends the idea of the overlap to incoherent light. As a reminder, the overlap between two fields is: overlap = abs(sum(field_1 * field_2))**2 Whereas the fidelity is defined as: fidelity = trace(sqrt(sqrt(dm_1) dm_2 sqrt(dm_1)))**2 where dm_n refers to the density matrix encoded by fields_n such that dm_n = fields_n fields_.conjtranspose(), sqrt refers to the matrix square root, and is the matrix product. This is not a practical implementation, however, as it is not feasible to explicitly construct the matrices dm_1 and dm_2 in memory. Therefore, we take advantage of the alternate definition based directly on the fields_ parameter: fidelity = sum(svdvals(fields_1 fields_2.conjtranspose()))**2 In the definitions above, the fields_n are regarded as collections of wavefields, where each wavefield is by default 2-dimensional. The dimensionality of the wavefields can be altered via the dims argument, but the fields_n arguments must always have at least one more dimension than the dims argument. Any additional dimensions are treated as batch dimensions. Parameters ---------- fields_1 : array The first set of complex-valued field modes fields_2 : array The second set of complex-valued field modes dims : int Default is 2, the number of final dimensions to reduce over. Returns ------- fidelity : float or t.Tensor The fidelity, or tensor of fidelities, depending on the dim argument """ fields_1 = t.as_tensor(fields_1) fields_2 = t.as_tensor(fields_2) mult = fields_1.unsqueeze(-dims-2) * fields_2.unsqueeze(-dims-1).conj() sumdims = tuple(d - dims for d in range(dims)) mat = t.sum(mult,dim=sumdims) # Because I think this is the nuclear norm squared, I would like to swap # Out the definition for this, but I need to test it before swapping. # It also probably makes sense to implement sqrt_fidelity separately # because that's more important #return t.linalg.matrix_norm(mat, ord='nuc')**2 # I think this is just the nuclear norm. svdvals = t.linalg.svdvals(mat) return t.sum(svdvals, dim=-1)**2 def calc_generalized_rms_error(fields_1, fields_2, normalize=False, dims=2): """Calculates a generalization of the root-mean-squared error between two complex wavefields This function calculates an generalization of the RMS error which uses the concept of fidelity to extend it to capture the error between incoherent wavefields, defined as a mode decomposition. The extension has several nice properties, in particular: 1) For coherent wavefields, it precisely matches the RMS error including a correction for the global phase degeneracy (align_phases=True) 2) All mode decompositions of either field that correspond to the same density matrix / mutual coherence function will produce the same output 3) The error will only be zero when comparing mode decompositions that correspond to the same density matrix. 4) Due to (2), one need not worry about the ordering of the modes, properly orthogonalizing the modes, and it is even possible to compare mode decompositions with different numbers of modes. The formal definition of this function is: output = norm * sqrt(mean(abs(fields_1)**2) + mean(abs(fields_2)**2) - 2 * sqrt(fidelity(fields_1,fields_2))) Where norm is an optional normalization factor, and the fidelity is defined based on the mean, rather than the sum, to match the convention for the root *mean* squared error. The normalization is defined as the square root of the total intensity contained in fields_1, which is appropriate when fields_1 represents a known ground truth: norm = sqrt(mean(abs(fields_1)**2)) In the definitions above, the fields_n are regarded as collections of wavefields, where each wavefield is by default 2-dimensional. The dimensionality of the wavefields can be altered via the dims argument, but the fields_n arguments must always have at least one more dimension than the dims argument. Any additional dimensions are treated as batch dimensions. Parameters ---------- fields_1 : array The first set of complex-valued field modes fields_2 : array The second set of complex-valued field modes normalize : bool Default is False, whether to normalize to the intensity of fields_1 dims : (int or tuple of python:ints) Default is 2, the number of final dimensions to reduce over. Returns ------- rms_error : float or t.Tensor The generalized RMS error, or tensor of generalized RMS errors, depending on the dim argument """ fields_1 = t.as_tensor(fields_1) fields_2 = t.as_tensor(fields_2) npix = t.prod(t.as_tensor(fields_1.shape[-dims:],dtype=t.int32)) sumdims = tuple(d - dims - 1 for d in range(dims+1)) fields_1_intensity = t.sum(t.abs(fields_1)**2,dim=sumdims) / npix fields_2_intensity = t.sum(t.abs(fields_2)**2,dim=sumdims) / npix fidelity = calc_fidelity(fields_1, fields_2, dims=dims) / npix**2 result = fields_1_intensity + fields_2_intensity - 2 * t.sqrt(fidelity) if normalize: result /= fields_1_intensity return t.sqrt(result) def calc_generalized_frc(fields_1, fields_2, basis, im_slice=None, nbins=None, snr=1.): """Calculates a Fourier ring correlation between two images This function requires an input of a basis to allow for FRC calculations to be related to physical units. Like other analysis functions, this can take input in numpy or pytorch, and will return output in the respective format. Parameters ---------- im1 : array The first image, a complex or real valued array im2 : array The first image, a complex or real valued array basis : array The basis for the images, defined as is standard for datasets im_slice : slice Default is from 3/8 to 5/8 across the image, a slice to use in the processing. nbins : int Number of bins to break the FRC up into snr : float The signal to noise ratio (for the combined information in both images) to return a threshold curve for. Returns ------- freqs : array The frequencies associated with each FRC value FRC : array The FRC values threshold : array The threshold curve for comparison """ im_np = False if isinstance(fields_1, np.ndarray): fields_1 = t.as_tensor(fields_) im_np = True if isinstance(fields_2, np.ndarray): fields_2 = t.as_tensor(fields_2) im_np = True if isinstance(basis, np.ndarray): basis = t.tensor(basis) if im_slice is None: im_slice = np.s_[(im1.shape[0]//8)*3:(im1.shape[0]//8)*5, (im1.shape[1]//8)*3:(im1.shape[1]//8)*5] if nbins is None: nbins = np.max(fields_1[...,im_slice].shape[-2:]) // 4 f1 = t.fft.fftshift(t.fft.fft2(im1[im_slice]),dim=(-1,-2)) f2 = t.fft.fftshift(t.fft.fft2(im2[im_slice]),dim=(-1,-2)) cor_fft = f1 * t.conj(f2) F1 = t.abs(f1)**2 F2 = t.abs(f2)**2 di = np.linalg.norm(basis[:,0]) dj = np.linalg.norm(basis[:,1]) i_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[0],d=di)) j_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[1],d=dj)) Js,Is = np.meshgrid(j_freqs,i_freqs) Rs = np.sqrt(Is**2+Js**2) # This line is used to get a set of bins that matches the logic # used by np.histogram, so that this function will match the choices # of bin edges that comes from the non-generalized version. This also # gets us the count on the number of pixels per bin so we can calculate # the threshold curve n_pix, bins = np.histogram(Rs,bins=nbins) frc = [] for i in range(len(bins)-1): mask = t.logical_and(Rs=bins[i]) masked_f1 = f1 * mask[...,:,:] masked_f2 = f2 * mask[...,:,:] numerator = t.sqrt(calc_fidelity(masked_f1, masked_f2)) denominator_f1 = t.sqrt(calc_fidelity(masked_f1, masked_f1)) denominator_f2 = t.sqrt(calc_fidelity(masked_f2, masked_f2)) frc.append(numerator / t.sqrt((denominator_f1 * denominator_f2))) frc = np.array(frc) # This moves from combined-image SNR to single-image SNR snr /= 2 threshold = (snr + (2 * snr + 1) / np.sqrt(n_pix)) / \ (1 + snr + (2 * np.sqrt(snr)) / np.sqrt(n_pix)) if not im_np: bins = t.tensor(bins) frc = t.tensor(frc) threshold = t.tensor(threshold) return bins[:-1], frc, threshold