def test_gaussian():
pts = np.linspace(0, 10, 100)
profile = generate_gaussian_profile(pts)
params, errors, _, _, fail = \
gauss_model(pts, profile, np.ones_like(pts), 1.0)
# Check the amplitude
npt.assert_allclose(params[0], 2.5, atol=0.01)
# Width
npt.assert_allclose(params[1], 3.0, atol=0.01)
# Background
npt.assert_allclose(params[2], 0.5, atol=0.02)
def test_gaussian():
pts = np.linspace(0, 10, 100)
profile = generate_gaussian_profile(pts)
params, errors, _, _, fail = \
gauss_model(pts, profile, np.ones_like(pts), 1.0)
# Check the amplitude
npt.assert_allclose(params[0], 2.5, atol=0.01)
# Width
npt.assert_allclose(params[1], 3.0, atol=0.01)
# Background
npt.assert_allclose(params[2], 0.5, atol=0.02)
def test_radial_profile_output(theta):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0, background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=20)
params, errors, _, _, fail = \
gauss_model(dist, radprof, np.ones_like(dist), 1.0)
npt.assert_allclose(params[:-1], [1.0, 10.0, 0.0], atol=1e-1)
def test_radial_profile_output(theta):
model, skeleton = generate_filament_model(width=10.0,
amplitude=1.0,
background=0.0)
dist_transform = nd.distance_transform_edt((~skeleton).astype(np.int))
dist, radprof, weights, unbin_dist, unbin_radprof = \
radial_profile(model, dist_transform, dist_transform,
((1, 1), (model.shape[0] // 2, model.shape[1] // 2)),
img_scale=1.0, auto_cut=False, max_distance=20)
params, errors, _, _, fail = \
gauss_model(dist, radprof, np.ones_like(dist), 1.0)
npt.assert_allclose(params[:-1], [1.0, 10.0, 0.0], atol=1e-1)
def fit_skeleton_width(skel_array, intensity_array, pixscale, beam_width,
max_dist=50):
'''
Reproduce the width fitting of FilFinder, but simplified for a single
super-structure.
Parameters
----------
pixscale : float
Conversion of pixels to pc.
beam_width : float
Beam size in pc.
max_dist : float or int
Maximum radial distance in PIXELS.
'''
dist_arr = nd.distance_transform_edt(~skel_array)
# bins = np.linspace(dist_arr.min(), max_dist,
# int(np.sqrt(np.isfinite(dist_arr).sum())))
del_bin = 0.25 * (beam_width / pixscale)
bins = np.arange(dist_arr.min(), max_dist + del_bin,
del_bin)
vals, bin_edges, bin_num = \
binned_statistic(dist_arr.ravel(), intensity_array.ravel(),
bins=bins,
statistic=np.nanmean)
variances = \
binned_statistic(dist_arr.ravel(), intensity_array.ravel(),
bins=bins,
statistic=np.nanvar)[0]
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width / 2
# weights = np.array([(bin_num == i).sum() for i in range(len(bins))])
# weights = weights[weights > 0]
# Convert the bin_centers to pc
bin_centers *= pixscale
# Nans are returned when nothing usable in a bin. Remove these before
# fitting
bin_centers = bin_centers[np.isfinite(vals)]
variances = variances[np.isfinite(vals)]
vals = vals[np.isfinite(vals)]
weights = 1 / variances
profile = np.array([bin_centers, vals, np.sqrt(variances)])
# Now fit to a gaussian
fit, err, model, names, fail = \
gauss_model(bin_centers, vals, weights, beam_width)
if fail:
warn("Fail flag was raised. Check the output")
# raise ValueError("Fit failed.")
return fit, err, model, profile
