def basis(self, type, M, X_star=None):
if type is 'monomial':
if X_star is None:
Phi = np.zeros((np.shape(self.X)[0], (M + 1)))
for c in range(0, np.shape(Phi)[1]):
Phi[:, c] = self.X[:, 0].T**c
else:
Phi = np.zeros((np.shape(X_star)[0], (M + 1)))
for c in range(0, np.shape(Phi)[1]):
Phi[:, c] = X_star[:, 0].T**c
if type is 'fourier':
if X_star is None:
Phi = np.zeros((np.shape(self.X)[0], (M + 1) * 2))
for c in range(0, np.shape(Phi)[1], 2):
Phi[:, c] = np.sin(int(c / 2) * np.pi * self.X[:, 0].T)
Phi[:, c + 1] = np.cos(int(c / 2) * np.pi * self.X[:, 0].T)
else:
Phi = np.zeros((np.shape(X_star)[0], (M + 1) * 2))
for c in range(0, np.shape(Phi)[1], 2):
Phi[:, c] = np.sin(int(c / 2) * np.pi * X_star[:, 0].T)
Phi[:, c + 1] = np.cos(int(c / 2) * np.pi * X_star[:, 0].T)
if type is 'legendre':
from numpy.polynomial import Legendre
if X_star is None:
Phi = Legendre.basis(M)(self.X)
else:
Phi = Legendre.basis(M)(X_star)
return Phi
def dg_interior_flux_matrix(p):
"""Calculate the interior flux matrix for the standard DG advection equations
There is an analytical expression I could use for this too.
See my thesis page 74.
"""
F = np.zeros((p + 1, p + 1))
for i in range(0, p + 1):
dli = L.basis(i).deriv()
for j in range(0, p + 1):
lj = L.basis(j)
F[i, j] = basis.integrate_legendre_product(dli, lj)
return F
def dg_interior_flux_matrix(p):
"""Calculate the interior flux matrix for the standard DG advection equations
There is an analytical expression I could use for this too.
See my thesis page 74.
"""
F = np.zeros((p + 1, p + 1))
for i in range(0, p + 1):
dli = L.basis(i).deriv()
for j in range(0, p + 1):
lj = L.basis(j)
F[i, j] = basis.integrate_legendre_product(dli, lj)
return F
def icb_interface_flux_matrix(p, K, T):
"""The interface flux matrices for the ICB schemes, we use upwinding
to get the fluxes. Denote T the translation necessary to
evaluate the flux from the other cell.
"""
# Enhanced polynomial degree
phat = p + len(K)
G0 = smp.zeros(p + 1, phat + 1)
G1 = smp.zeros(p + 1, phat + 1)
for i in range(0, p + 1):
for j in range(0, phat + 1):
li = L.basis(i)
lj = L.basis(j)
G0[i, j] = leg.legval(-1, li.coef) * \
leg.legval(1, lj.coef) * (T**-1)
G1[i, j] = leg.legval(1, li.coef) * leg.legval(1, lj.coef)
# Enhancement matrix
A, Ainv, B, Binv = enhance.enhancement_matrices(p, K)
# Using the enhanced function in the flux (see notes 21/4/15)
BL = smp.zeros(phat + 1, phat + 1)
BR = smp.zeros(phat + 1, phat + 1)
for i in range(p + 1):
li = L.basis(i)
BL[i, i] = 1 # basis.integrate_legendre_product(li,li)
BR[i, i] = 1 # BL[i,i]
for i, k in enumerate(K):
lk = L.basis(k)
int_lklk = basis.integrate_legendre_product(lk, lk)
BL[i + p + 1, i + p + 1] = T # * int_lklk
BR[i + p + 1, i + p + 1] = (T**(-1)) # * int_lklk
# reduction matrix
R = smp.zeros(phat + 1, p + 1)
for i in range(p + 1):
R[i, i] = 1
for i, k in enumerate(K):
for j in range(p + 1):
R[i + p + 1, j] = auxf.delta(k, j)
# Convert to sympy matrices
G0 = smp.Matrix(G0) * smp.Matrix(Ainv) * BL * R
G1 = smp.Matrix(G1) * smp.Matrix(Ainv) * BL * R
return G0, G1
def icb_interface_flux_matrix(p, K, T):
"""The interface flux matrices for the ICB schemes, we use upwinding
to get the fluxes. Denote T the translation necessary to
evaluate the flux from the other cell.
"""
# Enhanced polynomial degree
phat = p + len(K)
G0 = smp.zeros(p + 1, phat + 1)
G1 = smp.zeros(p + 1, phat + 1)
for i in range(0, p + 1):
for j in range(0, phat + 1):
li = L.basis(i)
lj = L.basis(j)
G0[i, j] = leg.legval(-1, li.coef) * \
leg.legval(1, lj.coef) * (T**-1)
G1[i, j] = leg.legval(1, li.coef) * leg.legval(1, lj.coef)
# Enhancement matrix
A, Ainv, B, Binv = enhance.enhancement_matrices(p, K)
# Using the enhanced function in the flux (see notes 21/4/15)
BL = smp.zeros(phat + 1, phat + 1)
BR = smp.zeros(phat + 1, phat + 1)
for i in range(p + 1):
li = L.basis(i)
BL[i, i] = 1 # basis.integrate_legendre_product(li,li)
BR[i, i] = 1 # BL[i,i]
for i, k in enumerate(K):
lk = L.basis(k)
int_lklk = basis.integrate_legendre_product(lk, lk)
BL[i + p + 1, i + p + 1] = T # * int_lklk
BR[i + p + 1, i + p + 1] = (T**(-1)) # * int_lklk
# reduction matrix
R = smp.zeros(phat + 1, p + 1)
for i in range(p + 1):
R[i, i] = 1
for i, k in enumerate(K):
for j in range(p + 1):
R[i + p + 1, j] = auxf.delta(k, j)
# Convert to sympy matrices
G0 = smp.Matrix(G0) * smp.Matrix(Ainv) * BL * R
G1 = smp.Matrix(G1) * smp.Matrix(Ainv) * BL * R
return G0, G1
def regress_poly(degree, data, remove_mean=True, axis=-1):
''' returns data with degree polynomial regressed out.
Be default it is calculated along the last axis (usu. time).
If remove_mean is True (default), the data is demeaned (i.e. degree 0).
If remove_mean is false, the data is not.
'''
IFLOG.debug('Performing polynomial regression on data of shape ' +
str(data.shape))
datashape = data.shape
timepoints = datashape[axis]
# Rearrange all voxel-wise time-series in rows
data = data.reshape((-1, timepoints))
# Generate design matrix
X = np.ones((timepoints, 1)) # quick way to calc degree 0
for i in range(degree):
polynomial_func = Legendre.basis(i + 1)
value_array = np.linspace(-1, 1, timepoints)
X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
# Calculate coefficients
betas = np.linalg.pinv(X).dot(data.T)
# Estimation
if remove_mean:
datahat = X.dot(betas).T
else: # disregard the first layer of X, which is degree 0
datahat = X[:, 1:].dot(betas[1:, ...]).T
regressed_data = data - datahat
# Back to original shape
return regressed_data.reshape(datashape)
def regress_poly(degree, data, remove_mean=True, axis=-1):
''' returns data with degree polynomial regressed out.
Be default it is calculated along the last axis (usu. time).
If remove_mean is True (default), the data is demeaned (i.e. degree 0).
If remove_mean is false, the data is not.
'''
IFLOG.debug('Performing polynomial regression on data of shape ' + str(data.shape))
datashape = data.shape
timepoints = datashape[axis]
# Rearrange all voxel-wise time-series in rows
data = data.reshape((-1, timepoints))
# Generate design matrix
X = np.ones((timepoints, 1)) # quick way to calc degree 0
for i in range(degree):
polynomial_func = Legendre.basis(i + 1)
value_array = np.linspace(-1, 1, timepoints)
X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
# Calculate coefficients
betas = np.linalg.pinv(X).dot(data.T)
# Estimation
if remove_mean:
datahat = X.dot(betas).T
else: # disregard the first layer of X, which is degree 0
datahat = X[:, 1:].dot(betas[1:, ...]).T
regressed_data = data - datahat
# Back to original shape
return regressed_data.reshape(datashape)
def evaluate_basis_gauss(self):
"""Evaluate the basis at the Gaussian quadrature nodes.
phi will be used to transform Legendre solution coefficients
to the solution evaluated at the Gaussian quadrature nodes.
dphi_w will be used for the interior flux integral
"""
phi = np.zeros((len(self.x), self.N_s))
dphi_w = np.zeros((len(self.x), self.N_s))
for n in range(self.N_s):
# Get the Legendre polynomial of order n and its gradient
l = L.basis(n)
dl = l.deriv()
# Evaluate the basis at the Gaussian nodes
phi[:, n] = leg.legval(self.x, l.coef)
# Evaluate the gradient at the Gaussian nodes and multiply by the
# weights
dphi_w[n, :] = leg.legval(self.x, dl.coef) * self.w
return phi, dphi_w
def evaluate_basis_gauss(self):
"""Evaluate the basis at the Gaussian quadrature nodes.
phi will be used to transform Legendre solution coefficients
to the solution evaluated at the Gaussian quadrature nodes.
dphi_w will be used for the interior flux integral
"""
phi = np.zeros((len(self.x), self.N_s))
dphi_w = np.zeros((len(self.x), self.N_s))
for n in range(self.N_s):
# Get the Legendre polynomial of order n and its gradient
l = L.basis(n)
dl = l.deriv()
# Evaluate the basis at the Gaussian nodes
phi[:, n] = leg.legval(self.x, l.coef)
# Evaluate the gradient at the Gaussian nodes and multiply by the
# weights
dphi_w[n, :] = leg.legval(self.x, dl.coef) * self.w
return phi, dphi_w
def enhancement_matrices(solution_order, modes):
"""Returns the enhancement matrices (and their inverse)
Returns A and inv(A) where A \hat{u} = [uL;some_modes_of(uR)]
B and inv(B) where B \hat{u} = [uR;some_modes_of(uL)]
Note: this is slightly different than what I do in
icb_functions.py (called by advection.py) where the right hand
side contains the normalization factors (i.e A x = b where b =
uL_i \int \phi_i \phi_i dx). Here I put \int \phi_i \phi_i dx into
A and B (denoted norm down in the code below).
"""
# Enhanced solution order
order = solution_order + len(modes)
# Submatrices to build the main matrix later
a = np.diag(np.ones(solution_order + 1))
b = np.zeros((solution_order + 1, len(modes)))
cl = np.zeros((len(modes), order + 1))
cr = np.zeros((len(modes), order + 1))
# Loop on the modes we are keeping in the neighboring cell
# (the right cell)
for i, mode in enumerate(modes):
# Loop on the enhancement basis
for j in range(order + 1):
# Basis function in the right cell
l1 = L.basis(mode)
# Enhanced basis function extending into the right cell (or left
# cell)
ll = basis.shift_legendre_polynomial(L.basis(j), 2)
lr = basis.shift_legendre_polynomial(L.basis(j), -2)
# Inner product for the left and right enhancements
norm = basis.integrate_legendre_product(l1, l1)
cl[i, j] = basis.integrate_legendre_product(l1, ll) / norm
cr[i, j] = basis.integrate_legendre_product(l1, lr) / norm
# Put the matrices together
A = np.vstack((np.hstack((a, b)), cl))
B = np.vstack((np.hstack((a, b)), cr))
return A, np.linalg.inv(A), B, np.linalg.inv(B)
def enhancement_matrices(solution_order, modes):
"""Returns the enhancement matrices (and their inverse)
Returns A and inv(A) where A \hat{u} = [uL;some_modes_of(uR)]
B and inv(B) where B \hat{u} = [uR;some_modes_of(uL)]
Note: this is slightly different than what I do in
icb_functions.py (called by advection.py) where the right hand
side contains the normalization factors (i.e A x = b where b =
uL_i \int \phi_i \phi_i dx). Here I put \int \phi_i \phi_i dx into
A and B (denoted norm down in the code below).
"""
# Enhanced solution order
order = solution_order + len(modes)
# Submatrices to build the main matrix later
a = np.diag(np.ones(solution_order + 1))
b = np.zeros((solution_order + 1, len(modes)))
cl = np.zeros((len(modes), order + 1))
cr = np.zeros((len(modes), order + 1))
# Loop on the modes we are keeping in the neighboring cell
# (the right cell)
for i, mode in enumerate(modes):
# Loop on the enhancement basis
for j in range(order + 1):
# Basis function in the right cell
l1 = L.basis(mode)
# Enhanced basis function extending into the right cell (or left
# cell)
ll = basis.shift_legendre_polynomial(L.basis(j), 2)
lr = basis.shift_legendre_polynomial(L.basis(j), -2)
# Inner product for the left and right enhancements
norm = basis.integrate_legendre_product(l1, l1)
cl[i, j] = basis.integrate_legendre_product(l1, ll) / norm
cr[i, j] = basis.integrate_legendre_product(l1, lr) / norm
# Put the matrices together
A = np.vstack((np.hstack((a, b)), cl))
B = np.vstack((np.hstack((a, b)), cr))
return A, np.linalg.inv(A), B, np.linalg.inv(B)
def dg_interface_flux_matrix(p, T):
"""The interface flux matrices, we use upwinding to get the fluxes
Denote T the translation necessary to evaluate the flux from the
other cell.
There are also analytical expressions for these. See my thesis page 73.
"""
G0 = smp.zeros(p + 1)
G1 = smp.zeros(p + 1)
for i in range(0, p + 1):
for j in range(0, p + 1):
li = L.basis(i)
lj = L.basis(j)
G0[i, j] = leg.legval(-1, li.coef) * \
leg.legval(1, lj.coef) * (T**-1)
G1[i, j] = leg.legval(1, li.coef) * leg.legval(1, lj.coef)
return G0, G1
def dg_interface_flux_matrix(p, T):
"""The interface flux matrices, we use upwinding to get the fluxes
Denote T the translation necessary to evaluate the flux from the
other cell.
There are also analytical expressions for these. See my thesis page 73.
"""
G0 = smp.zeros(p + 1)
G1 = smp.zeros(p + 1)
for i in range(0, p + 1):
for j in range(0, p + 1):
li = L.basis(i)
lj = L.basis(j)
G0[i, j] = leg.legval(-1, li.coef) * \
leg.legval(1, lj.coef) * (T**-1)
G1[i, j] = leg.legval(1, li.coef) * leg.legval(1, lj.coef)
return G0, G1
def test_integrate_legendre_product(self):
"""Is the integral of the product of two Legendre polynomials correct
"""
# Given a Legendre Polynomial: L(x) = 0.5*(3x^2 -1)
l1 = L.basis(2)
# Evaluate L(x+2)
l2 = basis.shift_legendre_polynomial(l1, 2)
# The integral of l1*l2 over [-1,1] = 0.4
self.assertAlmostEqual(basis.integrate_legendre_product(l1, l2), 0.4)
def test_integrate_legendre_product(self):
"""Is the integral of the product of two Legendre polynomials correct
"""
# Given a Legendre Polynomial: L(x) = 0.5*(3x^2 -1)
l1 = L.basis(2)
# Evaluate L(x+2)
l2 = basis.shift_legendre_polynomial(l1, 2)
# The integral of l1*l2 over [-1,1] = 0.4
self.assertAlmostEqual(basis.integrate_legendre_product(l1, l2), 0.4)
def test_shift_legendre_polynomial(self):
"""Is the shifting of Legendre polynomials correct"""
# Given a Legendre Polynomial: L(x) = 0.5*(3x^2 -1)
l = L.basis(2)
# Evaluate L(x+2)
ls = basis.shift_legendre_polynomial(l, 2)
# This should be equal to 5.5 + 6x + 1.5 x^2
npt.assert_array_almost_equal(ls.convert(
kind=P).coef, np.array([5.5, 6, 1.5]), decimal=13)
def genNoise(self,grid,maxNoiseOrder):
"""Noise is a matrix of Legendre polynomials of 0<order<maxNoiseOrder
Additionally 60Hz sine and cosine waves are added to account for the DC component of EEG
grid -- Grid to be used for timing information
maxNoiseOrder--Maximum order of noise to be considered"""
if self.grid() is not None and self.noiseOrders() is not None:
logger.info( 'Generating noise matrix')
legpoly = np.array([Legendre.basis(i)(np.arange(len(grid.times()))) for i in self.noiseOrders()]).T #Polynomials
sw = np.sin(60 * np.arange(len(grid.times())) * 2 * np.pi / float(grid.fs())) # Sine for AC component
cw = np.cos(60 * np.arange(len(grid.times())) * 2 * np.pi / float(grid.fs())) # Cosine for AC component
legpoly = np.column_stack((legpoly, sw, cw))
return pd.DataFrame(legpoly,index=self.grid().times())
def test_shift_legendre_polynomial(self):
"""Is the shifting of Legendre polynomials correct"""
# Given a Legendre Polynomial: L(x) = 0.5*(3x^2 -1)
l = L.basis(2)
# Evaluate L(x+2)
ls = basis.shift_legendre_polynomial(l, 2)
# This should be equal to 5.5 + 6x + 1.5 x^2
npt.assert_array_almost_equal(ls.convert(kind=P).coef,
np.array([5.5, 6, 1.5]),
decimal=13)
def genNoise(self, grid, maxNoiseOrder):
"""Noise is a matrix of Legendre polynomials of 0<order<maxNoiseOrder
Additionally 60Hz sine and cosine waves are added to account for the DC component of EEG
grid -- Grid to be used for timing information
maxNoiseOrder--Maximum order of noise to be considered"""
if self.grid() is not None and self.noiseOrders() is not None:
logger.info('Generating noise matrix')
legpoly = np.array([
Legendre.basis(i)(np.arange(len(grid.times())))
for i in self.noiseOrders()
]).T #Polynomials
sw = np.sin(60 * np.arange(len(grid.times())) * 2 * np.pi /
float(grid.fs())) # Sine for AC component
cw = np.cos(60 * np.arange(len(grid.times())) * 2 * np.pi /
float(grid.fs())) # Cosine for AC component
legpoly = np.column_stack((legpoly, sw, cw))
return pd.DataFrame(legpoly, index=self.grid().times())
def regress_poly(degree, data, remove_mean=True, axis=-1):
"""
Returns data with degree polynomial regressed out.
:param bool remove_mean: whether or not demean data (i.e. degree 0),
:param int axis: numpy array axes along which regression is performed
"""
timepoints = data.shape[0]
# Generate design matrix
X = np.ones((timepoints, 1)) # quick way to calc degree 0
for i in range(degree):
polynomial_func = Legendre.basis(i + 1)
value_array = np.linspace(-1, 1, timepoints)
X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
non_constant_regressors = X[:, :-1] if X.shape[1] > 1 else np.array([])
betas = np.linalg.pinv(X).dot(data)
if remove_mean:
datahat = X.dot(betas)
else: # disregard the first layer of X, which is degree 0
datahat = X[:, 1:].dot(betas[1:, ...])
regressed_data = data - datahat
return regressed_data, non_constant_regressors
def approx_legendre_poly(Moments):
n_moments = Moments.shape[0]-1
exp_coef = (np.zeros((1)))
# For method description see, for instance:
# Chapter 3 of "The Problem of Moments", James Alexander Shohat, Jacob David Tamarkin
for i in range(n_moments+1):
p = Legendre.basis(i).convert(window = [0.0,1.0], kind=Polynomial)
q = (2*i+1)*np.sum(Moments[0:(i+1)]*p.coef)
pq = (p.coef*q)
exp_coef = polynomial.polyadd(exp_coef, pq)
expansion = Polynomial(exp_coef)
return expansion
def regress_poly(degree, data, remove_mean=True, axis=-1):
"""
Returns data with degree polynomial regressed out.
:param bool remove_mean: whether or not demean data (i.e. degree 0),
:param int axis: numpy array axes along which regression is performed
"""
IFLOGGER.debug('Performing polynomial regression on data of shape %s',
str(data.shape))
datashape = data.shape
timepoints = datashape[axis]
# Rearrange all voxel-wise time-series in rows
data = data.reshape((-1, timepoints))
# Generate design matrix
X = np.ones((timepoints, 1)) # quick way to calc degree 0
for i in range(degree):
polynomial_func = Legendre.basis(i + 1)
value_array = np.linspace(-1, 1, timepoints)
X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
non_constant_regressors = X[:, :-1] if X.shape[1] > 1 else np.array([])
# Calculate coefficients
betas = np.linalg.pinv(X).dot(data.T)
# Estimation
if remove_mean:
datahat = X.dot(betas).T
else: # disregard the first layer of X, which is degree 0
datahat = X[:, 1:].dot(betas[1:, ...]).T
regressed_data = data - datahat
# Back to original shape
return regressed_data.reshape(datashape), non_constant_regressors
def regress_poly(degree, data, remove_mean=True, axis=-1):
"""
Returns data with degree polynomial regressed out.
:param bool remove_mean: whether or not demean data (i.e. degree 0),
:param int axis: numpy array axes along which regression is performed
"""
IFLOGGER.debug('Performing polynomial regression on data of shape %s',
str(data.shape))
datashape = data.shape
timepoints = datashape[axis]
# Rearrange all voxel-wise time-series in rows
data = data.reshape((-1, timepoints))
# Generate design matrix
X = np.ones((timepoints, 1)) # quick way to calc degree 0
for i in range(degree):
polynomial_func = Legendre.basis(i + 1)
value_array = np.linspace(-1, 1, timepoints)
X = np.hstack((X, polynomial_func(value_array)[:, np.newaxis]))
non_constant_regressors = X[:, :-1] if X.shape[1] > 1 else np.array([])
# Calculate coefficients
betas = np.linalg.pinv(X).dot(data.T)
# Estimation
if remove_mean:
datahat = X.dot(betas).T
else: # disregard the first layer of X, which is degree 0
datahat = X[:, 1:].dot(betas[1:, ...]).T
regressed_data = data - datahat
# Back to original shape
return regressed_data.reshape(datashape), non_constant_regressors
def fit_legendres_images(images,
centers,
lg_inds,
rad_inds,
maxPixel,
rotate=0,
image_stds=None,
image_counts=None,
image_nanMaps=None,
image_weights=None,
chiSq_fit=False,
rad_range=None):
"""
Fits legendre polynomials to an array of single images (3d) or a list/array of
an array of scan images, possible dimensionality:
1) [NtimeSteps, image_rows, image_cols]
2) [NtimeSteps (list), Nscans, image_rows, image_cols]
"""
if image_counts is None:
image_counts = []
for im in range(len(images)):
image_counts.append(np.ones_like(images[im]))
image_counts[im][np.isnan(images[im])] = 0
if chiSq_fit and (image_stds is None):
print("If using the chiSq fit you must supply image_stds")
return None
if image_stds is None:
image_stds = []
for im in range(len(images)):
image_stds.append(np.ones_like(images[im]))
image_stds[im][np.isnan(images[im])] = 0
with_scans = len(images[0].shape) + 1 >= 4
img_fits = [[] for x in range(len(images))]
img_covs = [[] for x in range(len(images))]
for rad in range(maxPixel):
if rad_range is not None:
if rad < rad_range[0] or rad >= rad_range[1]:
continue
if rad % 25 == 0:
print("Fitting radius {}".format(rad))
pixels, nans, angles = [], [], []
all_angles = np.arctan2(rad_inds[rad][1].astype(float),
rad_inds[rad][0].astype(float))
all_angles[all_angles < 0] += 2 * np.pi
all_angles = np.mod(all_angles + rotate, 2 * np.pi)
all_angles[all_angles > np.pi] -= 2 * np.pi
if np.sum(np.mod(lg_inds, 2)) == 0:
all_angles[np.abs(all_angles) > np.pi / 2.] -= np.pi * np.sign(
all_angles[np.abs(all_angles) > np.pi / 2.])
angles = np.unique(np.abs(all_angles))
ang_sort_inds = np.argsort(angles)
angles = angles[ang_sort_inds]
Nangles = angles.shape[0]
if len(angles) == len(all_angles):
do_merge = False
else:
do_merge = True
mi_rows, mi_cols, mi_data = [], [], []
pr, pc, pv = [], [], []
for ia, ang in enumerate(angles):
inds = np.where(np.abs(all_angles) == ang)[0]
mi_rows.append(np.ones_like(inds) * ia)
mi_cols.append(inds)
mi_rows, mi_cols = np.concatenate(mi_rows), np.concatenate(mi_cols)
merge_indices = csr_matrix(
(np.ones_like(mi_rows), (mi_rows, mi_cols)),
shape=(len(angles), len(all_angles)))
for im in range(len(images)):
if with_scans:
angs_tile = np.tile(angles, (images[im].shape[0], 1))
scn_inds, row_inds, col_inds = [], [], []
for isc in range(images[im].shape[0]):
scn_inds.append(
np.ones(rad_inds[rad][0].shape[0], dtype=int) * isc)
row_inds.append(rad_inds[rad][0] + centers[im][isc, 0])
col_inds.append(rad_inds[rad][1] + centers[im][isc, 1])
scn_inds = np.concatenate(scn_inds)
row_inds = np.concatenate(row_inds)
col_inds = np.concatenate(col_inds)
img_pixels = np.reshape(
copy(images[im][scn_inds, row_inds, col_inds]),
(images[im].shape[0], -1))
img_counts = np.reshape(
copy(image_counts[im][scn_inds, row_inds, col_inds]),
(images[im].shape[0], -1))
img_stds = np.reshape(
copy(image_stds[im][scn_inds, row_inds, col_inds]),
(images[im].shape[0], -1))
if image_nanMaps is not None:
img_pixels[np.reshape(
image_nanMaps[im][scn_inds, row_inds, col_inds],
(images[im].shape[0], -1)).astype(bool)] = np.nan
img_counts[np.reshape(
image_nanMaps[im][scn_inds, row_inds, col_inds],
(images[im].shape[0], -1)).astype(bool)] = 0
if image_weights is not None:
img_weights = np.reshape(
copy(image_weights[im][scn_inds, row_inds, col_inds]),
(images[im].shape[0], -1))
else:
angs_tile = np.expand_dims(angles, 0)
row_inds = rad_inds[rad][0] + centers[im, 0]
col_inds = rad_inds[rad][1] + centers[im, 1]
img_pixels = np.reshape(copy(images[im][row_inds, col_inds]),
(1, -1))
img_counts = np.reshape(
copy(image_counts[im][row_inds, col_inds]), (1, -1))
img_stds = np.reshape(copy(image_stds[im][row_inds, col_inds]),
(1, -1))
if image_nanMaps is not None:
img_pixels[np.reshape(
image_nanMaps[im][row_inds, col_inds],
(1, -1)).astype(bool)] = np.nan
img_counts[np.reshape(
image_nanMaps[im][row_inds, col_inds],
(1, -1)).astype(bool)] = 0
if image_weights is not None:
img_weights = np.reshape(
copy(image_weights[im][row_inds, col_inds]), (1, -1))
img_pix = img_pixels * img_counts
img_var = img_counts * (img_stds**2)
img_pix[np.isnan(img_pixels)] = 0
img_var[np.isnan(img_pixels)] = 0
if do_merge:
img_pixels[np.isnan(img_pixels)] = 0
img_pix = np.transpose(merge_indices.dot(
np.transpose(img_pix)))
img_var = np.transpose(merge_indices.dot(
np.transpose(img_var)))
img_counts = np.transpose(
merge_indices.dot(np.transpose(img_counts)))
if image_weights is not None:
print("Must fill this in, don't forget std option")
sys.exit(0)
else:
img_pix = img_pix[:, ang_sort_inds]
img_var = img_var[:, ang_sort_inds]
img_counts = img_counts[:, ang_sort_inds]
img_pix /= img_counts
img_var /= img_counts
Nnans = np.sum(np.isnan(img_pix), axis=-1)
ang_inds = np.where(img_counts > 0)
arr_inds = np.concatenate(
[np.arange(Nangles - Nn) for Nn in Nnans])
img_pixels = np.zeros_like(img_pix)
img_vars = np.zeros_like(img_var)
img_angs = np.zeros_like(img_pix)
img_dang = np.zeros_like(img_pix)
img_pixels[ang_inds[0][:-1], arr_inds[:-1]] =\
(img_pix[ang_inds[0][:-1], ang_inds[1][:-1]] + img_pix[ang_inds[0][1:], ang_inds[1][1:]])/2.
img_vars[ang_inds[0][:-1], arr_inds[:-1]] =\
(img_var[ang_inds[0][:-1], ang_inds[1][:-1]] + img_var[ang_inds[0][1:], ang_inds[1][1:]])/2.
img_angs[ang_inds[0][:-1], arr_inds[:-1]] =\
(angs_tile[ang_inds[0][:-1], ang_inds[1][:-1]] + angs_tile[ang_inds[0][1:], ang_inds[1][1:]])/2.
img_dang[ang_inds[0][:-1], arr_inds[:-1]] =\
(angs_tile[ang_inds[0][1:], ang_inds[1][1:]] - angs_tile[ang_inds[0][:-1], ang_inds[1][:-1]])
for isc in range(Nnans.shape[0]):
# Using angle midpoint => one less angle => Nnans[isc]+1
img_pixels[isc, -1 * (Nnans[isc] + 1):] = 0
img_vars[isc, -1 * (Nnans[isc] + 1):] = 0
img_angs[isc, -1 * (Nnans[isc] + 1):] = 0
img_dang[isc, -1 * (Nnans[isc] + 1):] = 0
if image_weights is not None:
print("Must fill this in and check below")
sys.exit(0)
elif chiSq_fit:
img_weights = 1. / img_vars
img_weights[img_vars == 0] = 0
else:
img_weights = np.ones_like(img_pixels)
img_weights *= np.sin(img_angs) * img_dang
lgndrs = []
for lg in lg_inds:
lgndrs.append(Legendre.basis(lg)(np.cos(img_angs)))
lgndrs = np.transpose(np.array(lgndrs), (1, 0, 2))
empty_scan = np.sum(img_weights.astype(bool), -1) < 2
overlap = np.einsum('bai,bi,bci->bac',
lgndrs[np.invert(empty_scan)],
img_weights[np.invert(empty_scan)],
lgndrs[np.invert(empty_scan)],
optimize='greedy')
empty_scan[np.invert(empty_scan)] = (np.linalg.det(overlap) == 0.0)
if np.any(empty_scan):
fit = np.ones((img_pixels.shape[0], len(lg_inds))) * np.nan
cov = np.ones(
(img_pixels.shape[0], len(lg_inds), len(lg_inds))) * np.nan
if np.any(np.invert(empty_scan)):
img_pixels = img_pixels[np.invert(empty_scan)]
img_weights = img_weights[np.invert(empty_scan)]
img_vars = img_vars[np.invert(empty_scan)]
lgndrs = lgndrs[np.invert(empty_scan)]
fit[np.invert(empty_scan)], cov[np.invert(empty_scan)] =\
normal_eqn_vects(lgndrs, img_pixels, img_weights, img_vars)
else:
fit, cov = normal_eqn_vects(lgndrs, img_pixels, img_weights,
img_vars)
img_fits[im].append(np.expand_dims(fit, 1))
img_covs[im].append(np.expand_dims(cov, 1))
Nscans = None
for im in range(len(img_fits)):
img_fits[im] = np.concatenate(img_fits[im], 1)
img_covs[im] = np.concatenate(img_covs[im], 1)
if Nscans is None:
Nscans = img_fits[im].shape[0]
elif Nscans != img_fits[im].shape[0]:
Nscans = -1
if Nscans > 0:
img_fits = np.array(img_fits)
img_covs = np.array(img_covs)
if with_scans:
return img_fits, img_covs
else:
return img_fits[:, 0, :, :], img_covs[:, 0, :, :]
