def test_ptt_basis(verbose=False, plot=False,
tiptiltonly=True, pistononly=False,
rings=2):
""" Test that we can create randomly-pistoned, tipped, and tilted segments, and then
re-determine the amounts of those deviations. """
segment_ptt_basis = zernike.Segment_PTT_Basis(rings=rings)
# Make some random aberrations
random_ptt = np.random.randn(segment_ptt_basis.nsegments*3)
if tiptiltonly:
for i in range(segment_ptt_basis.nsegments):
random_ptt[i*3] = 0
elif pistononly:
for i in range(segment_ptt_basis.nsegments):
random_ptt[i*3+1] = 0
random_ptt[i*3+2] = 0
else:
# make the pistons small compared to the tips & tilts
for i in range(segment_ptt_basis.nsegments):
random_ptt[i*3] *= 1e-3
# Generate an OPD with those aberrations
ptted_opd = zernike.opd_from_zernikes(basis=segment_ptt_basis, coeffs=random_ptt, outside=0)
# Perform a fit to measure them
results = zernike.opd_expand_segments(ptted_opd,
basis=segment_ptt_basis,
aperture=segment_ptt_basis.aperture(),
nterms=segment_ptt_basis.nsegments*3,
verbose=verbose)
# Generate another OPD to show the measurements
ptted_v2 = zernike.opd_from_zernikes(basis=segment_ptt_basis, coeffs=results, outside=0)
if verbose:
print(random_ptt)
print(results)
if plot:
plt.subplot(121)
ax = plt.imshow(ptted_opd)
plt.title("Randomly generated OPD")
plt.subplot(122)
ax2 = plt.imshow(ptted_v2, norm=ax.norm)
plt.title("Reproduced from fit coefficients")
# adjust tolerances for the ones that are precisely zero - allow larger atol since rtol doesn't help there.
wz = np.where(random_ptt ==0)
wnz = np.where(random_ptt !=0)
assert np.allclose(random_ptt[wnz], results[wnz])
assert np.allclose(random_ptt[wz], results[wz], atol=1e-6)
return random_ptt, results, ptted_opd, ptted_v2
def _opd_from_coeff(self, coeff, basis, mask):
"""Generate OPD image from a set of coefficients, basis function, and mask"""
npix = mask.shape[0]
return zernike.opd_from_zernikes(coeff,
basis=basis,
npix=npix,
outside=0)
def test_piston_basis(verbose=False):
""" Test that we can create randomly-pistoned segments, and then
re-determine the amounts of those pistons.
This tests both the segment piston baseis, and the segment basis
decomposition function.
"""
segment_piston_basis = zernike.Segment_Piston_Basis(rings=2)
random_pistons = np.random.randn(18)
pistoned_opd = zernike.opd_from_zernikes(basis=segment_piston_basis,
coeffs=random_pistons,
outside=0)
aperture = segment_piston_basis.aperture()
#aperture = np.asarray(pistoned_opd != 0, dtype=int)
for border_pad in [None, 5]:
results = zernike.opd_expand_segments(pistoned_opd,
basis=segment_piston_basis,
aperture=aperture,
nterms=18,
verbose=verbose,
ignore_border=border_pad)
if verbose:
print(random_pistons)
print(results)
assert np.allclose(random_pistons, results)
return random_pistons, results, pistoned_opd
def test_opd_from_zernikes():
coeffs = [0,0.1, 0.4, 2, -0.3]
opd = zernike.opd_from_zernikes(coeffs, npix=256)
outcoeffs = zernike.opd_expand(opd, nterms=len(coeffs))
# only compare on indices 1-3 to avoid divide by zero on piston
diffs = np.abs(np.asarray(coeffs[1:5]) - np.asarray(outcoeffs[1:5]))/np.asarray(coeffs[1:5])
max_diff = np.max(diffs )
assert max_diff < 2e-3, "recovered coefficients from opd_expand differ more than expected"
def test_opd_from_zernikes():
coeffs = [0,0.1, 0.4, 2, -0.3]
opd = zernike.opd_from_zernikes(coeffs, npix=256)
outcoeffs = zernike.opd_expand(opd, nterms=len(coeffs))
# only compare on indices 1-3 to avoid divide by zero on piston
diffs = np.abs(np.asarray(coeffs[1:5]) - np.asarray(outcoeffs[1:5]))/np.asarray(coeffs[1:5])
max_diff = np.max(diffs )
assert max_diff < 2e-3, "recovered coefficients from opd_expand differ more than expected"
def test_opd_from_zernikes():
coeffs = [0, 0.1, 0.4, 2, -0.3]
opd = zernike.opd_from_zernikes(coeffs, npix=256)
outcoeffs = zernike.opd_expand(opd, nterms=len(coeffs))
diffs = np.abs(np.asarray(coeffs) -
np.asarray(outcoeffs)) / np.asarray(coeffs)
diffs[0] = 0 # ignore divide by zero on piston
max_diff = np.max(diffs)
assert max_diff < 2e-3, "recovered coefficients from opd_expand differ more than expected"
def test_opd_from_zernikes():
coeffs = [0,0.1, 0.4, 2, -0.3]
opd = zernike.opd_from_zernikes(coeffs, npix=256)
outcoeffs = zernike.opd_expand(opd, nterms=len(coeffs))
diffs = np.abs(np.asarray(coeffs) - np.asarray(outcoeffs))/np.asarray(coeffs)
diffs[0] = 0 # ignore divide by zero on piston
max_diff = np.max(diffs )
assert max_diff < 2e-3, "recovered coefficients from opd_expand differ more than expected"
def test_piston_basis(verbose=False):
""" Test that we can create randomly-pistoned segments, and then
re-determine the amounts of those pistons.
This tests both the segment
"""
segment_piston_basis = zernike.Segment_Piston_Basis(rings=2)
random_pistons = np.random.randn(18)
pistoned_opd = zernike.opd_from_zernikes(basis=segment_piston_basis, coeffs=random_pistons, outside=0)
aperture = segment_piston_basis.aperture()
#aperture = np.asarray(pistoned_opd != 0, dtype=int)
results = zernike.opd_expand_segments(pistoned_opd, basis=segment_piston_basis, aperture=aperture, nterms=18, verbose=verbose)
if verbose:
print(random_pistons)
print(results)
assert np.allclose(random_pistons, results)
return random_pistons, results, pistoned_opd
def test_ptt_basis(verbose=False,
plot=False,
tiptiltonly=True,
pistononly=False,
rings=2):
""" Test that we can create randomly-pistoned, tipped, and tilted segments, and then
re-determine the amounts of those deviations. """
segment_ptt_basis = zernike.Segment_PTT_Basis(rings=rings)
# Make some random aberrations
random_ptt = np.random.randn(segment_ptt_basis.nsegments * 3)
if tiptiltonly:
for i in range(segment_ptt_basis.nsegments):
random_ptt[i * 3] = 0
elif pistononly:
for i in range(segment_ptt_basis.nsegments):
random_ptt[i * 3 + 1] = 0
random_ptt[i * 3 + 2] = 0
else:
# make the pistons small compared to the tips & tilts
for i in range(segment_ptt_basis.nsegments):
random_ptt[i * 3] *= 1e-3
# Generate an OPD with those aberrations
ptted_opd = zernike.opd_from_zernikes(basis=segment_ptt_basis,
coeffs=random_ptt,
outside=0)
# Perform a fit to measure them
results = zernike.opd_expand_segments(
ptted_opd,
basis=segment_ptt_basis,
aperture=segment_ptt_basis.aperture(),
nterms=segment_ptt_basis.nsegments * 3,
verbose=verbose)
# Generate another OPD to show the measurements
ptted_v2 = zernike.opd_from_zernikes(basis=segment_ptt_basis,
coeffs=results,
outside=0)
if verbose:
print(random_ptt)
print(results)
if plot:
plt.subplot(121)
ax = plt.imshow(ptted_opd)
plt.title("Randomly generated OPD")
plt.subplot(122)
ax2 = plt.imshow(ptted_v2, norm=ax.norm)
plt.title("Reproduced from fit coefficients")
# adjust tolerances for the ones that are precisely zero - allow larger atol since rtol doesn't help there.
wz = np.where(random_ptt == 0)
wnz = np.where(random_ptt != 0)
assert np.allclose(random_ptt[wnz], results[wnz])
assert np.allclose(random_ptt[wz], results[wz], atol=1e-6)
return random_ptt, results, ptted_opd, ptted_v2
from poppy import zernike
from model.src.coherent import construct_SA1_wavefront
import numpy as np
from wpg.wpg_uti_wf import calculate_fwhm
wfr = construct_SA1_wavefront(1000, 1000, 12, 0.1)
ii = wfr.get_intensity()[:,:,0]
ph = wfr.get_phase()[:,:,0]
aperture = np.ones(ii.shape)
aperture[np.where(ii < ii.max()/10)] = 0
zc = zernike.opd_expand(ph, aperture = aperture)
opd = zernike.opd_from_zernikes(zc)
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.bar(np.linspace(0,len(zc), len(zc)),zc)
ax1.set_xticks(np.linspace(0+1,len(zc)+1, len(zc)+1))
ax1.set_xticklabels(labels = ["$Z_{}$".format(int(z)) for z in np.linspace(0+1,len(zc)+1, len(zc)+1)])
plt.show()
for i in range(15):
if i == 0:
pass
else:
print("index: {},".format(i-1), "j = {}".format(i), zernike.zern_name(i))