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Commit e49e6471 authored by Martin Reinecke's avatar Martin Reinecke
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revert multifrequency plotting for the moment

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demos/D4PO.py deleted 100644 → 0
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# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
import nifty5 as ift
import numpy as np
def get_RG_instrument():
pointings = 1000*np.random.binomial(1, 0.001, position_space.shape)
psf = np.zeros_like(pointings)
psf[0, 0] = 1.
sigmas_sensitivity = np.arange(0.1, 0.5, 0.4/energy_space.shape[0])
sigmas_psf = np.arange(0.005, 0.0125, 0.0075/energy_space.shape[0])
total_psf = np.empty(htp.domain.shape)
total_psf_sens = np.empty(htp.domain.shape)
sensitivity = np.empty(total_space.shape)
sensitivity.T[:] = pointings
kernel = hp_space.get_k_length_array()
for i in range(energy_space.shape[0]):
smoother = hp_space.get_fft_smoothing_kernel_function(sigmas_psf[i])
smoother_sens = hp_space.get_fft_smoothing_kernel_function(
sigmas_sensitivity[i])
total_psf.T[i] = np.round(smoother(kernel).val, 20)
total_psf_sens.T[i] = np.round(smoother_sens(kernel).val, 20)
sensitivity = ift.Field.from_global_data(total_space, sensitivity)
total_psf = ift.Field.from_global_data(htp.domain, total_psf)
total_psf_sens = ift.Field.from_global_data(htp.domain, total_psf_sens)
HT = ift.HartleyOperator(htp.domain, position_space, space=0)
K = ift.DiagonalOperator(total_psf)
KK = ift.DiagonalOperator(total_psf_sens)
sensitivity = (HT @ KK @ HT.inverse)(sensitivity)
E = ift.DiagonalOperator(sensitivity)
PSF = HT @ K @ HT.inverse
G = ift.GeometryRemover(total_space)
return G @ E @ PSF
def get_PsfOp():
# kernel = get_kernel()
h_space_spatial = position_space.get_default_codomain()
sigmas_psf = np.arange(0.02, 0.04, 0.02/energy_space.shape[0])
total_psf = np.empty(htp.domain.shape)
kernel = hp_space.get_k_length_array()
for i in range(energy_space.shape[0]):
smoother = hp_space.get_fft_smoothing_kernel_function(sigmas_psf[i])
total_psf.T[i] = np.round(smoother(kernel).val, 20)
total_psf = ift.Field.from_global_data(htp.domain, total_psf)
K = ift.DiagonalOperator(total_psf)
# gau = h_space_spatial.get_fft_smoothing_kernel_function(0.01)
# k_l_a = h_space_spatial.get_k_length_array()
# kernel = gau(k_l_a).val
scaling = 4*np.pi/12/position_space.nside**2
he_space = ift.DomainTuple.make((h_space_spatial, energy_space))
Scaling = ift.ScalingOperator(scaling, he_space)
HT = ift.HarmonicTransformOperator(
he_space, target=position_space, space=0)
PI = ift.ScalingOperator(4*np.pi, HT.target)
# multi_kernel = np.empty(he_space.shape)
# multi_kernel.T[:] = kernel
# multi_kernel = ift.Field(he_space,val=multi_kernel)
# Kernel = ift.DiagonalOperator(multi_kernel)
return PI @ HT @ K @ Scaling @ HT.adjoint
class ReverseOuterProduct(ift.LinearOperator):
"""Performs the pointwise outer product of two fields.
FIXME Update this docstring. What does this class do as opposed to
ift.OuterProduct? Can we merge them?
Parameters
---------
field: Field,
domain: DomainTuple, the domain of the input field
---------
"""
def __init__(self, field, domain):
self._domain = domain
self._field = field
self._target = ift.DomainTuple.make(
tuple(sub_d for sub_d in domain._dom + field.domain._dom))
self._capability = self.TIMES | self.ADJOINT_TIMES
def apply(self, x, mode):
self._check_input(x, mode)
if mode == self.TIMES:
return ift.Field.from_global_data(
self._target,
np.multiply.outer(x.to_global_data(),
self._field.to_global_data()))
axes = len(self._field.shape)
return ift.Field.from_global_data(
self._domain,
np.tensordot(x.to_global_data(), self._field.to_global_data(),
axes))
def _default_powerspace(space):
return ift.PowerSpace(space.get_default_codomain())
if __name__ == '__main__':
# FIXME description of the tutorial
np.random.seed(41)
np.seterr(all='raise')
# position_space = ift.RGSpace([128,128])
position_space = ift.HPSpace(64)
energy_space = ift.RGSpace([4])
total_space = ift.DomainTuple.make([position_space, energy_space])
# Set up amplitude models
A_p = ift.SLAmplitude(
target=_default_powerspace(position_space),
n_pix=64,
a=3,
k0=0.4,
sm=-3,
sv=1,
im=3,
iv=1,
keys=['tau_p', 'phi_p'])
# FIXME Have corrected iv=0 to iv=1 here
A_e = ift.SLAmplitude(
target=_default_powerspace(energy_space),
n_pix=8,
a=3,
k0=0.4,
sm=-2,
sv=1,
im=1,
iv=1,
keys=['tau_e', 'phi_e'])
# FIXME zero_mode False is not supported any more
A_eu = ift.SLAmplitude(
target=_default_powerspace(energy_space),
n_pix=8,
a=0.3,
k0=0.4,
sm=-4,
sv=1,
im=-5,
iv=1,
keys=['tau_eu', 'phi_eu'])
correlated_field = ift.MfCorrelatedField(total_space, [A_p, A_e])
# Build the model for a correlated signal
hp_space = position_space.get_default_codomain()
he_space = energy_space.get_default_codomain()
hpe_space = ift.DomainTuple.make([hp_space, he_space])
hte = ift.HarmonicTransformOperator(hpe_space, space=1)
htp = ift.HarmonicTransformOperator(
hte.target, target=position_space, space=0)
ht = htp @ hte
# EXPERIMENTAL
peu_space = A_eu.target[0]
Peu = ift.PowerDistributor(he_space, peu_space)
one_p = ift.full(hp_space, val=1.)
one_u = ift.full(energy_space, val=1.)
OP_e = ift.OuterProduct(one_p, ift.DomainTuple.make(he_space))
OP_u = ReverseOuterProduct(one_u, ift.DomainTuple.make(position_space))
PA_eu = OP_e(Peu(A_eu))
#
scale = ift.ScalingOperator(1e-16, ht.target)
off = ift.OffsetOperator(ift.full(ht.target, 0.))
xi_u = ift.FieldAdapter(hpe_space, "xi_u")
u_spectra = (off((ht(PA_eu*xi_u)))).absolute()
u = ift.InverseGammaOperator(position_space, 1.0, 1e-3)(ift.FieldAdapter(
position_space, 'u'))
points = u_spectra*OP_u(u)
signal = ift.exp(correlated_field) + points
# Build line-of-sight response
G = ift.GeometryRemover(signal.target)
# exposure = get_3D_exposure()
# exposure = ift.Field.from_global_data(signal.target,exposure)
# E = ift.DiagonalOperator(exposure)
# PSF = get_PsfOp()
R = G # @ PSF
# R = get_RG_instrument()
# Build signal response model and model likelihood
signal_response = R(signal).clip(min=1e-15)
# Specify noise
data_space = R.target
# Generate mock data
MOCK_POSITION = ift.from_random('normal', signal_response.domain)
rate = signal_response(MOCK_POSITION)
data = np.random.poisson(np.abs(rate.to_global_data().astype(np.float64)))
data = ift.Field.from_global_data(data_space, data)
# Set up model likelihood
likelihood = ift.PoissonianEnergy(data)(signal_response)
# Set up minimization and inversion schemes
ic_sampling = ift.GradientNormController(iteration_limit=50)
ic_newton = ift.GradInfNormController(
name='Newton', tol=1e-7, iteration_limit=10)
minimizer = ift.NewtonCG(ic_newton)
# Build model Hamiltonian
H = ift.Hamiltonian(likelihood, ic_sampling)
INITIAL_POSITION = 0.1*ift.from_random('normal', H.domain)
position = INITIAL_POSITION
plo = ift.Plot()
for i in range(9):
plo.add(signal(ift.from_random('normal', signal.domain)))
plo.output(name='prior_samples.png', nx=3, ny=3)
# Number of samples used to estimate the KL
N_samples = 3
for i in range(5):
KL = ift.KL_Energy(position, H, N_samples, gen_mirrored_samples=True)
KL, convergence = minimizer(KL)
position = KL.position
plo = ift.Plot()
plo.add(G.adjoint_times(data), title='data')
plo.add(
G.adjoint_times(signal_response(MOCK_POSITION)),
title='signal response')
plo.add(signal(MOCK_POSITION), title='true signal')
plo.add(signal(position), title='signal')
plo.add(
ift.exp(correlated_field.force(MOCK_POSITION)),
title='truediffuse')
plo.add(ift.exp(correlated_field.force(position)), title='diffuse')
plo.add(points.force(position), title='points')
plo.add(signal(position + KL.samples[0]), title='sample')
plo.output(name='signal.pdf', nx=2, ny=4, xsize=15, ysize=20)
nifty5/plot.py
View file @ e49e6471
......@@ -56,281 +56,6 @@ def _mollweide_helper(xsize):
return res, mask, theta, phi
def _rgb_data(spectral_cube):
def _eye_sensitivity(energy_bins, spacing=None):
from scipy.ndimage import zoom
rgb_high=[[9.85234460e-03, 6.13406271e-04, 0.00000000e+00],
[1.04118690e-02, 6.49048870e-04, 0.00000000e+00],
[1.09713934e-02, 6.84691469e-04, 0.00000000e+00],
[1.15309178e-02, 7.20334068e-04, 0.00000000e+00],
[1.20904422e-02, 7.55976668e-04, 0.00000000e+00],
[1.29039276e-02, 8.08680908e-04, 0.00000000e+00],
[1.37595706e-02, 8.64217381e-04, 0.00000000e+00],
[1.46152136e-02, 9.19753853e-04, 0.00000000e+00],
[1.54708566e-02, 9.75290326e-04, 0.00000000e+00],
[1.63264996e-02, 1.03082680e-03, 0.00000000e+00],
[1.71821426e-02, 1.08636327e-03, 0.00000000e+00],
[1.81667722e-02, 1.15137696e-03, 0.00000000e+00],
[1.93698228e-02, 1.23243904e-03, 0.00000000e+00],
[2.05728734e-02, 1.31350111e-03, 0.00000000e+00],
[2.17759241e-02, 1.39456318e-03, 0.00000000e+00],
[2.29789747e-02, 1.47562526e-03, 0.00000000e+00],
[2.41820254e-02, 1.55668733e-03, 0.00000000e+00],
[2.53874406e-02, 1.63793709e-03, 0.00000000e+00],
[2.70586835e-02, 1.75616085e-03, 0.00000000e+00],
[2.87299264e-02, 1.87438460e-03, 0.00000000e+00],
[3.04011692e-02, 1.99260836e-03, 0.00000000e+00],
[3.20724121e-02, 2.11083211e-03, 0.00000000e+00],
[3.37436550e-02, 2.22905587e-03, 0.00000000e+00],
[3.54148979e-02, 2.34727962e-03, 0.00000000e+00],
[3.75526858e-02, 2.50677171e-03, 0.00000000e+00],
[3.98413542e-02, 2.67960994e-03, 0.00000000e+00],
[4.21300226e-02, 2.85244818e-03, 0.00000000e+00],
[4.44186909e-02, 3.02528642e-03, 0.00000000e+00],
[4.67073593e-02, 3.19812466e-03, 0.00000000e+00],
[4.89960277e-02, 3.37096290e-03, 0.00000000e+00],
[5.17840545e-02, 3.59459245e-03, 0.00000000e+00],
[5.48783544e-02, 3.84937399e-03, 0.00000000e+00],
[5.79726543e-02, 4.10415554e-03, 0.00000000e+00],
[6.10669543e-02, 4.35893709e-03, 0.00000000e+00],
[6.41612542e-02, 4.61371864e-03, 0.00000000e+00],
[6.72555541e-02, 4.86850018e-03, 0.00000000e+00],
[7.09715657e-02, 5.20598062e-03, 0.00000000e+00],
[7.51114716e-02, 5.59984666e-03, 0.00000000e+00],
[7.92513775e-02, 5.99371269e-03, 0.00000000e+00],
[8.33912835e-02, 6.38757873e-03, 0.00000000e+00],
[8.75311894e-02, 6.78144477e-03, 0.00000000e+00],
[9.16710953e-02, 7.17531081e-03, 0.00000000e+00],
[9.67048710e-02, 7.69960083e-03, 0.00000000e+00],
[1.02165218e-01, 8.28613156e-03, 0.00000000e+00],
[1.07625564e-01, 8.87266229e-03, 0.00000000e+00],
[1.13085911e-01, 9.45919302e-03, 0.00000000e+00],
[1.18546257e-01, 1.00457237e-02, 0.00000000e+00],
[1.24006604e-01, 1.06322545e-02, 0.00000000e+00],
[1.30860134e-01, 1.14285362e-02, 0.00000000e+00],
[1.37932889e-01, 1.22578234e-02, 0.00000000e+00],
[1.45005644e-01, 1.30871106e-02, 0.00000000e+00],
[1.52078399e-01, 1.39163978e-02, 0.00000000e+00],
[1.59151155e-01, 1.47456850e-02, 0.00000000e+00],
[1.66454559e-01, 1.56214122e-02, 0.00000000e+00],
[1.75033740e-01, 1.67540109e-02, 0.00000000e+00],
[1.83612920e-01, 1.78866096e-02, 0.00000000e+00],
[1.92192101e-01, 1.90192083e-02, 0.00000000e+00],
[2.00771282e-01, 2.01518070e-02, 0.00000000e+00],
[2.09350463e-01, 2.12844057e-02, 0.00000000e+00],
[2.18438958e-01, 2.27487266e-02, 0.00000000e+00],
[2.27958699e-01, 2.44939217e-02, 0.00000000e+00],
[2.37478439e-01, 2.62391168e-02, 0.00000000e+00],
[2.46998180e-01, 2.79843119e-02, 0.00000000e+00],
[2.56517920e-01, 2.97295070e-02, 0.00000000e+00],
[2.66089164e-01, 3.14923971e-02, 0.00000000e+00],
[2.77541802e-01, 3.39016757e-02, 0.00000000e+00],
[2.88994440e-01, 3.63109543e-02, 0.00000000e+00],
[3.00447079e-01, 3.87202330e-02, 0.00000000e+00],
[3.11899717e-01, 4.11295116e-02, 0.00000000e+00],
[3.23352355e-01, 4.35387902e-02, 0.00000000e+00],
[3.36149249e-01, 4.64923760e-02, 0.00000000e+00],
[3.49819910e-01, 4.97997613e-02, 0.00000000e+00],
[3.63490571e-01, 5.31071466e-02, 0.00000000e+00],
[3.77161232e-01, 5.64145319e-02, 0.00000000e+00],
[3.90831894e-01, 5.97219172e-02, 0.00000000e+00],
[4.04905270e-01, 6.33547279e-02, 0.00000000e+00],
[4.20027945e-01, 6.78354525e-02, 0.00000000e+00],
[4.35150619e-01, 7.23161770e-02, 0.00000000e+00],
[4.50273293e-01, 7.67969016e-02, 0.00000000e+00],
[4.65395968e-01, 8.12776261e-02, 0.00000000e+00],
[4.80491754e-01, 8.58174413e-02, 0.00000000e+00],
[4.94910505e-01, 9.18451595e-02, 0.00000000e+00],
[5.09329255e-01, 9.78728776e-02, 0.00000000e+00],
[5.23748005e-01, 1.03900596e-01, 0.00000000e+00],
[5.38166755e-01, 1.09928314e-01, 0.00000000e+00],
[5.52585506e-01, 1.15956032e-01, 0.00000000e+00],
[5.67615464e-01, 1.23568964e-01, 9.47898839e-07],
[5.82723782e-01, 1.31385129e-01, 2.01732317e-06],
[5.97832099e-01, 1.39201294e-01, 3.08674750e-06],
[6.12940417e-01, 1.47017459e-01, 4.15617183e-06],
[6.28048735e-01, 1.54833624e-01, 5.22559616e-06],
[6.43098813e-01, 1.64307246e-01, 5.84877257e-06],
[6.58136078e-01, 1.74145478e-01, 6.37378263e-06],
[6.73173344e-01, 1.83983711e-01, 6.89879268e-06],
[6.88210609e-01, 1.93821943e-01, 7.42380273e-06],
[7.03247875e-01, 2.03660175e-01, 7.94881278e-06],
[7.17546471e-01, 2.15436756e-01, 8.72244917e-06],
[7.31700230e-01, 2.27593405e-01, 9.54483582e-06],
[7.45853990e-01, 2.39750055e-01, 1.03672225e-05],
[7.60007749e-01, 2.51906704e-01, 1.11896091e-05],
[7.74161508e-01, 2.64063354e-01, 1.20119958e-05],
[7.86497696e-01, 2.78333181e-01, 1.32818969e-05],
[7.98704366e-01, 2.92753591e-01, 1.45836873e-05],
[8.10911036e-01, 3.07174002e-01, 1.58854778e-05],
[8.23117706e-01, 3.21594412e-01, 1.71872683e-05],
[8.35186518e-01, 3.36208819e-01, 1.85747438e-05],
[8.46139602e-01, 3.52393309e-01, 2.06556971e-05],
[8.57092687e-01, 3.68577799e-01, 2.27366504e-05],
[8.68045771e-01, 3.84762289e-01, 2.48176037e-05],
[8.78998855e-01, 4.00946779e-01, 2.68985570e-05],
[8.89215651e-01, 4.17579320e-01, 2.94434547e-05],
[8.98143940e-01, 4.34995949e-01, 3.28002551e-05],
[9.07072229e-01, 4.52412578e-01, 3.61570554e-05],
[9.16000518e-01, 4.69829208e-01, 3.95138558e-05],
[9.24928807e-01, 4.87245837e-01, 4.28706562e-05],
[9.31880728e-01, 5.04651340e-01, 4.76834190e-05],
[9.37955635e-01, 5.22051906e-01, 5.31422653e-05],
[9.44030542e-01, 5.39452472e-01, 5.86011115e-05],
[9.50105449e-01, 5.56853038e-01, 6.40599578e-05],
[9.55892875e-01, 5.74294183e-01, 6.98501401e-05],
[9.58948160e-01, 5.92120984e-01, 7.87892608e-05],
[9.62003444e-01, 6.09947784e-01, 8.77283815e-05],
[9.65058728e-01, 6.27774585e-01, 9.66675021e-05],
[9.68114013e-01, 6.45601386e-01, 1.05606623e-04],
[9.72354205e-01, 6.64398627e-01, 1.17838668e-04],
[9.77489728e-01, 6.83929146e-01, 1.32558884e-04],
[9.82625251e-01, 7.03459664e-01, 1.47279100e-04],
[9.87760774e-01, 7.22990182e-01, 1.61999316e-04],
[9.92509943e-01, 7.42196432e-01, 1.77817359e-04],
[9.94261297e-01, 7.58886608e-01, 2.02153721e-04],
[9.96012651e-01, 7.75576783e-01, 2.26490084e-04],
[9.97764004e-01, 7.92266959e-01, 2.50826447e-04],
[9.99515358e-01, 8.08957134e-01, 2.75162810e-04],
[9.99065878e-01, 8.23559025e-01, 3.11123629e-04],
[9.97791085e-01, 8.37377810e-01, 3.51443619e-04],
[9.96516292e-01, 8.51196595e-01, 3.91763610e-04],
[9.95241498e-01, 8.65015380e-01, 4.32083600e-04],
[9.93237351e-01, 8.77470091e-01, 4.83190079e-04],
[9.90172100e-01, 8.87940271e-01, 5.49989336e-04],
[9.87106849e-01, 8.98410452e-01, 6.16788594e-04],
[9.84041597e-01, 9.08880632e-01, 6.83587852e-04],
[9.80871738e-01, 9.19166847e-01, 7.57052109e-04],
[9.77122292e-01, 9.28433790e-01, 8.67444063e-04],
[9.73372847e-01, 9.37700733e-01, 9.77836018e-04],
[9.69623402e-01, 9.46967676e-01, 1.08822797e-03],
[9.65873957e-01, 9.56234619e-01, 1.19861993e-03],
[9.59779661e-01, 9.61388103e-01, 1.37907137e-03],
[9.53593770e-01, 9.66380906e-01, 1.56225950e-03],
[9.47407879e-01, 9.71373709e-01, 1.74544764e-03],
[9.41221987e-01, 9.76366512e-01, 1.92863578e-03],
[9.35862130e-01, 9.81296800e-01, 2.20723373e-03],
[9.30666137e-01, 9.86214687e-01, 2.50475852e-03],
[9.25470143e-01, 9.91132574e-01, 2.80228330e-03],
[9.20274150e-01, 9.96050461e-01, 3.09980808e-03],
[9.11776049e-01, 9.96814428e-01, 3.53678623e-03],
[9.02278626e-01, 9.96321289e-01, 4.01596737e-03],
[8.92781202e-01, 9.95828150e-01, 4.49514851e-03],
[8.83283779e-01, 9.95335011e-01, 4.97432965e-03],
[8.70357183e-01, 9.90138809e-01, 5.66803016e-03],
[8.56351813e-01, 9.83463085e-01, 6.42921569e-03],
[8.42346443e-01, 9.76787360e-01, 7.19040121e-03],
[8.28341073e-01, 9.70111636e-01, 7.95158673e-03],
[8.14214130e-01, 9.61934330e-01, 9.03521277e-03],
[8.00059079e-01, 9.53409844e-01, 1.01933900e-02],
[7.85904028e-01, 9.44885358e-01, 1.13515672e-02],
[7.71748976e-01, 9.36360872e-01, 1.25097445e-02],
[7.54803293e-01, 9.23647801e-01, 1.42317318e-02],
[7.37618069e-01, 9.10575194e-01, 1.60021149e-02],
[7.20432846e-01, 8.97502586e-01, 1.77724979e-02],
[7.02994012e-01, 8.83987616e-01, 1.96135712e-02],
[6.82848697e-01, 8.65751846e-01, 2.22090338e-02],
[6.62703383e-01, 8.47516077e-01, 2.48044965e-02],
[6.42558069e-01, 8.29280307e-01, 2.73999591e-02],
[6.21021574e-01, 8.08629266e-01, 3.03177497e-02],
[5.96317925e-01, 7.82479626e-01, 3.39693509e-02],
[5.71614275e-01, 7.56329987e-01, 3.76209521e-02],
[5.46910626e-01, 7.30180347e-01, 4.12725533e-02],
[5.21885772e-01, 7.02842258e-01, 4.56468260e-02],
[4.96626730e-01, 6.74637674e-01, 5.05479963e-02],
[4.71367687e-01, 6.46433091e-01, 5.54491667e-02],
[4.46108644e-01, 6.18228507e-01, 6.03503370e-02],
[4.22732390e-01, 5.90585713e-01, 6.78261191e-02],
[3.99556150e-01, 5.63002600e-01, 7.55754081e-02],
[3.76379909e-01, 5.35419486e-01, 8.33246972e-02],
[3.54150919e-01, 5.08679668e-01, 9.15660981e-02],
[3.34345779e-01, 4.84097693e-01, 1.01066727e-01],
[3.14540639e-01, 4.59515718e-01, 1.10567355e-01],
[2.94735499e-01, 4.34933743e-01, 1.20067983e-01],
[2.77694238e-01, 4.13550010e-01, 1.30415519e-01],
[2.61791044e-01, 3.93483202e-01, 1.41111781e-01],
[2.45887851e-01, 3.73416393e-01, 1.51808043e-01],
[2.30724201e-01, 3.54217289e-01, 1.63258477e-01],
[2.18819558e-01, 3.38841970e-01, 1.78032377e-01],
[2.06914916e-01, 3.23466651e-01, 1.92806277e-01],
[1.95010274e-01, 3.08091332e-01, 2.07580177e-01],
[1.85839746e-01, 2.96264763e-01, 2.28113511e-01],
[1.77786849e-01, 2.85888824e-01, 2.51001139e-01],
[1.69733951e-01, 2.75512886e-01, 2.73888768e-01],
[1.61966598e-01, 2.65368567e-01, 2.98662491e-01],
[1.54926257e-01, 2.55813966e-01, 3.28238311e-01],
[1.47885916e-01, 2.46259366e-01, 3.57814131e-01],
[1.40845574e-01, 2.36704765e-01, 3.87389952e-01],
[1.34384065e-01, 2.27560262e-01, 4.24364167e-01],
[1.27986519e-01, 2.18461077e-01, 4.62155940e-01],
[1.21588973e-01, 2.09361892e-01, 4.99947714e-01],
[1.15488374e-01, 2.00339219e-01, 5.38691241e-01],
[1.09616987e-01, 1.91375605e-01, 5.78169419e-01],
[1.03745600e-01, 1.82411992e-01, 6.17647598e-01],
[9.78980944e-02, 1.73260478e-01, 6.54076290e-01],
[9.21142738e-02, 1.63607897e-01, 6.82373018e-01],
[8.63304532e-02, 1.53955317e-01, 7.10669746e-01],
[8.05663926e-02, 1.44323302e-01, 7.38641004e-01],
[7.55830929e-02, 1.35503863e-01, 7.53752198e-01],
[7.05997933e-02, 1.26684424e-01, 7.68863392e-01],
[6.56164936e-02, 1.17864985e-01, 7.83974586e-01],
[6.22882135e-02, 1.11589324e-01, 8.05751119e-01],
[5.93247626e-02, 1.05874409e-01, 8.28996946e-01],
[5.63613116e-02, 1.00159494e-01, 8.52242773e-01],
[5.41909435e-02, 9.58527781e-02, 8.80230772e-01],
[5.24365127e-02, 9.22846018e-02, 9.10705835e-01],
[5.06820820e-02, 8.87164255e-02, 9.41180899e-01],
[4.90109904e-02, 8.51181293e-02, 9.61647451e-01],
[4.74127283e-02, 8.14935115e-02, 9.73367629e-01],
[4.58144663e-02, 7.78688937e-02, 9.85087806e-01],
[4.42430778e-02, 7.42331402e-02, 9.91441525e-01],
[4.27043214e-02, 7.05838651e-02, 9.91278831e-01],
[4.11655651e-02, 6.69345899e-02, 9.91116137e-01],
[3.94185919e-02, 6.30133732e-02, 9.79110351e-01],
[3.73708609e-02, 5.86993522e-02, 9.49997875e-01],
[3.53231300e-02, 5.43853312e-02, 9.20885399e-01],
[3.32966294e-02, 5.01302108e-02, 8.89766895e-01],
[3.13012767e-02, 4.59615062e-02, 8.55705260e-01],
[2.93059241e-02, 4.17928016e-02, 8.21643626e-01],
[2.73987252e-02, 3.79727389e-02, 7.83328843e-01],
[2.56039108e-02, 3.45971493e-02, 7.39591852e-01],
[2.38090964e-02, 3.12215596e-02, 6.95854862e-01],
[2.21391780e-02, 2.81738767e-02, 6.51344383e-01],
[2.05887070e-02, 2.54397958e-02, 6.06094160e-01],
[1.90382361e-02, 2.27057149e-02, 5.60843937e-01],
[1.73497807e-02, 2.01171617e-02, 5.08432174e-01],
[1.55765090e-02, 1.76180613e-02, 4.51618362e-01],
[1.38032374e-02, 1.51189609e-02, 3.94804551e-01],
[1.21218105e-02, 1.29757867e-02, 3.41210630e-01],
[1.04688473e-02, 1.09429181e-02, 2.88614589e-01],
[8.81588406e-03, 8.91004948e-03, 2.36018548e-01],
[7.40566041e-03, 7.40847074e-03, 1.95596905e-01],
[6.01017453e-03, 5.93914888e-03, 1.55914421e-01],
[4.69529289e-03, 4.56170905e-03, 1.18703848e-01],
[3.81412524e-03, 3.67866644e-03, 9.47940869e-02],
[2.93295759e-03, 2.79562384e-03, 7.08843254e-02],
[2.20646541e-03, 2.07474126e-03, 5.17641069e-02],
[1.70753686e-03, 1.59243385e-03, 3.96904214e-02],
[1.20860831e-03, 1.11012644e-03, 2.76167359e-02],
[8.89261197e-04, 8.07902163e-04, 2.01786925e-02],
[6.52132098e-04, 5.88125582e-04, 1.48629913e-02],
[4.15003000e-04, 3.68349000e-04, 9.54729000e-03]]
rgb_high = np.array(rgb_high)
# if spacing != None:
# spacing = np.arange(0, 1, 1 / energy_bins)
rgb = zoom(rgb_high.T,(1,energy_bins/len(rgb_high.T[0])))
return np.clip(rgb,1e-15, rgb.max())
rgb = _eye_sensitivity(spectral_cube.shape[-1])
rgb_data = np.tensordot(spectral_cube, rgb, axes=[-1, -1])
rgb_data = np.log(rgb_data)
rgb_data -= rgb_data.min()
rgb_data /= rgb_data.max()
return rgb_data
def _find_closest(A, target):
# A must be sorted
idx = np.clip(A.searchsorted(target), 1, len(A)-1)
......@@ -505,16 +230,8 @@ def _plot2D(f, ax, **kwargs):
dom = f.domain
if len(dom) > 2:
raise ValueError("DomainTuple can have at most two entries.")
# check for multifrequency plotting
have_rgb = False
if len(dom) == 2:
if (not isinstance(dom[1], RGSpace)) or len(dom[1].shape) != 1:
raise TypeError("need 1D RGSpace as second domain")
rgb = _rgb_data(f.to_global_data())
have_rgb = True
if len(dom) > 1:
raise ValueError("DomainTuple must have exactly one entry.")
label = kwargs.pop("label", None)
......@@ -525,49 +242,35 @@ def _plot2D(f, ax, **kwargs):
ax.set_xlabel(kwargs.pop("xlabel", ""))
ax.set_ylabel(kwargs.pop("ylabel", ""))
dom = dom[0]
if not have_rgb:
cmap = kwargs.pop("colormap", plt.rcParams['image.cmap'])
cmap = kwargs.pop("colormap", plt.rcParams['image.cmap'])
if isinstance(dom, RGSpace):
nx, ny = dom.shape
dx, dy = dom.distances
if have_rgb:
im = ax.imshow(
rgb, extent=[0, nx*dx, 0, ny*dy], origin="lower", **norm)
else:
im = ax.imshow(
f.to_global_data().T, extent=[0, nx*dx, 0, ny*dy],
vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
cmap=cmap, origin="lower", **norm)
plt.colorbar(im)
im = ax.imshow(
f.to_global_data().T, extent=[0, nx*dx, 0, ny*dy],
vmin=kwargs.get("zmin"), vmax=kwargs.get("zmax"),
cmap=cmap, origin="lower", **norm)
plt.colorbar(im)
_limit_xy(**kwargs)
return
elif isinstance(dom, (HPSpace, GLSpace)):
import pyHealpix
xsize = 800
res, mask, theta, phi = _mollweide_helper(xsize)
if have_rgb:
res = np.full(shape=res.shape+(3,), fill_value=1., dtype=np.float64)