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import pytest
import warnings
import numpy as np
import astropy.units as u
# from astropy.modeling import models, fitting
from ..analysis_utilities import stack_spectra, fourier_shift, stack_cube
from .utilities import generate_gaussian_cube, gaussian
from ..utils import BadVelocitiesWarning
def test_shift():
amp = 1
v0 = 0 * u.m / u.s
sigma = 8
spectral_axis = np.arange(-50, 51) * u.m / u.s
true_spectrum = gaussian(spectral_axis.value,
amp, v0.value, sigma)
# Shift is an integer, so rolling is equivalent
rolled_spectrum = np.roll(true_spectrum, 10)
shift_spectrum = fourier_shift(true_spectrum, 10)
np.testing.assert_allclose(shift_spectrum,
rolled_spectrum,
rtol=1e-4)
# With part masked
masked_spectrum = true_spectrum.copy()
mask = np.abs(spectral_axis.value) <= 30
masked_spectrum[~mask] = np.nan
rolled_mask = np.roll(mask, 10)
rolled_masked_spectrum = rolled_spectrum.copy()
rolled_masked_spectrum[~rolled_mask] = np.nan
shift_spectrum = fourier_shift(masked_spectrum, 10)
np.testing.assert_allclose(shift_spectrum,
rolled_masked_spectrum,
rtol=1e-4)
def test_stacking(use_dask):
'''
Use a set of identical Gaussian profiles randomly offset to ensure the
shifted spectrum has the correct properties.
'''
amp = 1.
v0 = 0. * u.km / u.s
sigma = 8.
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, use_dask=use_dask)
true_spectrum = gaussian(test_cube.spectral_axis.value,
amp, v0.value, sigma)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=False)
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
# Now fit a Gaussian to the mean stacked profile.
# fit_vals = fit_gaussian(stacked.spectral_axis.value, stacked.value)[0]
# np.testing.assert_allclose(fit_vals, np.array([amp, v0.value, sigma]),
# atol=1e-3)
# The stacked spectrum should have the same spectral axis
np.testing.assert_allclose(stacked.spectral_axis.value,
test_cube.spectral_axis.value)
def test_cube_stacking(use_dask):
'''
Test passing a list of cubes
This test simply averages two copies of the same thing.
A more thorough test might be to verify that cubes with different frequency
supports also yield good results.
'''
amp = 1.
sigma = 8.
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, use_dask=use_dask)
test_cube1 = test_cube.with_spectral_unit(u.GHz, rest_value=1*u.GHz, velocity_convention='radio')
test_cube2 = test_cube.with_spectral_unit(u.GHz, rest_value=2*u.GHz, velocity_convention='radio')
vmin = -10*u.km/u.s
vmax = 10*u.km/u.s
# Stack two cubes
stacked = stack_cube([test_cube1, test_cube2], linelist=[1.,2.]*u.GHz,
vmin=vmin, vmax=vmax, average=np.nanmean,
convolve_beam=None, return_cutouts=False)
np.testing.assert_allclose(stacked.filled_data[:],
test_cube.spectral_slab(vmin, vmax).filled_data[:])
# Stack one cube with two frequencies, one that's out of band
stacked = stack_cube(test_cube1, linelist=[1.,2.]*u.GHz,
vmin=vmin, vmax=vmax, average=np.nanmean,
convolve_beam=None, return_cutouts=False)
np.testing.assert_allclose(stacked.filled_data[:],
test_cube.spectral_slab(vmin, vmax).filled_data[:])
# TODO: add tests of multiple lines in the same cube
# (this requires a different test cube setup)
def test_stacking_badvels(use_dask):
'''
Regression test for #493: don't include bad velocities when stacking
'''
amp = 1.
v0 = 0. * u.km / u.s
sigma = 8.
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, use_dask=use_dask)
true_spectrum = gaussian(test_cube.spectral_axis.value,
amp, v0.value, sigma)
test_vels[12,11] = 500*u.km/u.s
with pytest.warns(BadVelocitiesWarning,
match='Some velocities are outside the allowed range and will be'):
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=False)
# Calculate residuals (the one bad value shouldn't have caused a problem)
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
def test_stacking_reversed_specaxis(use_dask):
'''
Use a set of identical Gaussian profiles randomly offset to ensure the
shifted spectrum has the correct properties.
'''
amp = 1.
v0 = 0. * u.km / u.s
sigma = 8.
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, spec_scale=-1. * u.km / u.s, use_dask=use_dask)
true_spectrum = gaussian(test_cube.spectral_axis.value,
amp, v0.value, sigma)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=False)
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
# The stacked spectrum should have the same spectral axis
np.testing.assert_allclose(stacked.spectral_axis.value,
test_cube.spectral_axis.value)
def test_stacking_wpadding(use_dask):
'''
Use a set of identical Gaussian profiles randomly offset to ensure the
shifted spectrum has the correct properties.
'''
amp = 1.
sigma = 8.
v0 = 0. * u.km / u.s
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(shape=shape, amp=amp, sigma=sigma, noise=noise, use_dask=use_dask)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=True)
true_spectrum = gaussian(stacked.spectral_axis.value,
amp, v0.value, sigma)
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
# Now fit a Gaussian to the mean stacked profile.
# fit_vals = fit_gaussian(stacked.spectral_axis.value, stacked.value)[0]
# np.testing.assert_allclose(fit_vals, np.array([amp, 0.0, sigma]),
# atol=1e-3)
# The spectral axis should be padded by ~25% on each side
stack_shape = int(test_cube.shape[0] * 1.5)
# This is rounded, so the shape could be +/- 1
assert (stacked.size == stack_shape) or (stacked.size == stack_shape - 1) \
or (stacked.size == stack_shape + 1)
def test_padding_direction(use_dask):
amp = 1.
sigma = 8.
v0 = 0. * u.km / u.s
noise = None
shape = (100, 2, 2)
vel_surface = np.array([[0, 5], [5, 10]])
test_cube, test_vels = \
generate_gaussian_cube(shape=shape, amp=amp, sigma=sigma, noise=noise,
vel_surface=vel_surface, use_dask=use_dask)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=True)
true_spectrum = gaussian(stacked.spectral_axis.value,
amp, v0.value, sigma)
# now check that the stacked spectral axis is right
# (all shifts are negative, so vmin < -50 km/s, should be -60?)
assert stacked.spectral_axis.min() == -60*u.km/u.s
assert stacked.spectral_axis.max() == 49*u.km/u.s
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
def test_stacking_woffset(use_dask):
'''
Use a set of identical Gaussian profiles randomly offset to ensure the
shifted spectrum has the correct properties.
Make sure the operations aren't affected by absolute velocity offsets
'''
amp = 1.
sigma = 8.
v0 = 100. * u.km / u.s
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(shape=shape, amp=amp, sigma=sigma, noise=noise,
v0=v0.value, use_dask=use_dask)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=True)
true_spectrum = gaussian(stacked.spectral_axis.value,
amp, v0.value, sigma)
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= 1e-3
# The spectral axis should be padded by ~25% on each side
stack_shape = int(test_cube.shape[0] * 1.5)
# This is rounded, so the shape could be +/- 1
assert (stacked.size == stack_shape) or (stacked.size == stack_shape - 1) \
or (stacked.size == stack_shape + 1)
def test_stacking_shape_failure(use_dask):
"""
Regression test for #466
"""
amp = 1.
v0 = 0. * u.km / u.s
sigma = 8.
noise = None
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, use_dask=use_dask)
# make the test_vels array the wrong shape
test_vels = test_vels[:-1, :-1]
with pytest.raises(ValueError) as exc:
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=False)
assert 'Velocity surface map does not match' in exc.value.args[0]
test_vels = np.ones(shape[1:], dtype='float') + np.nan
with pytest.raises(ValueError) as exc:
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False, pad_edges=False)
assert "velocity_surface contains no finite values" in exc.value.args[0]
def test_stacking_noisy(use_dask):
# Test stack w/ S/N of 0.2
# This is cheating b/c we know the correct peak velocities, but serves as
# a good test that the stacking is working.
amp = 1.
sigma = 8.
v0 = 0 * u.km / u.s
noise = 5.0
shape = (100, 25, 25)
test_cube, test_vels = \
generate_gaussian_cube(amp=amp, sigma=sigma, noise=noise,
shape=shape, use_dask=use_dask)
# Stack the spectra in the cube
stacked = \
stack_spectra(test_cube, test_vels, v0=v0,
stack_function=np.nanmean,
xy_posns=None, num_cores=1,
chunk_size=-1,
progressbar=False,
pad_edges=True)
true_spectrum = gaussian(stacked.spectral_axis.value,
amp, v0.value, sigma)
# Calculate residuals
resid = np.abs(stacked.value - true_spectrum)
assert np.std(resid) <= noise / np.sqrt(shape[1] * shape[2])
# Now fit a Gaussian to the mean stacked profile.
# fit_vals, fit_errs = fit_gaussian(stacked.spectral_axis.value,
# stacked.value)
# Check that the fit is consistent with the true values within 1-sigma err
# for fit_val, fit_err, true_val in zip(fit_vals, fit_errs,
# [amp, v0.value, sigma]):
# np.testing.assert_allclose(fit_val, true_val,
# atol=fit_err)
# def fit_gaussian(vels, data):
# g_init = models.Gaussian1D()
# fit_g = fitting.LevMarLSQFitter()
# g_fit = fit_g(g_init, vels, data)
# cov = fit_g.fit_info['param_cov']
# if cov is None:
# cov = np.zeros((3, 3)) * np.nan
# parvals = g_fit.parameters
# parerrs = np.sqrt(np.diag(cov))
# return parvals, parerrs
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