def test_meanify():
if __name__ == '__main__':
rtol = 4.e-1
atol = 5.e-2
bin_spacing = 30 # arcsec
else:
rtol = 1.e-1
atol = 3.e-2
bin_spacing = 150 # arcsec
psf_file = 'test_mean_*.piff'
average_file = 'average.fits'
psfs_list = sorted(glob.glob(os.path.join('output', 'test_mean_*.piff')))
config0 = {
'output': {
'file_name': psfs_list,
},
'hyper': {
'file_name': 'output/' + average_file,
}
}
config1 = {
'output': {
'file_name': psf_file,
'dir': 'output',
},
'hyper': {
'file_name': average_file,
'dir': 'output',
'bin_spacing': bin_spacing,
'statistic': 'mean',
'params_fitted': [0, 2]
}
}
config2 = {
'output': {
'file_name': psf_file,
'dir': 'output',
},
'hyper': {
'file_name': average_file,
'dir': 'output',
'bin_spacing': bin_spacing,
'statistic': 'median',
}
}
for config in [config0, config1, config2]:
piff.meanify(config)
## test if found initial average
average = fitsio.read(os.path.join('output', average_file))
params0 = make_average(coord=average['COORDS0'][0] / 0.26, gp=False)
keys = ['hlr', 'g1', 'g2']
for i, key in enumerate(keys):
if config == config1 and i == 1:
np.testing.assert_allclose(np.zeros(
len(average['PARAMS0'][0][:, i])),
average['PARAMS0'][0][:, i],
rtol=0,
atol=0)
else:
np.testing.assert_allclose(params0[key],
average['PARAMS0'][0][:, i],
rtol=rtol,
atol=atol)
## gaussian process testing of meanify
np.random.seed(68)
x = np.random.uniform(0, 2048, size=1000)
y = np.random.uniform(0, 2048, size=1000)
coord = np.array([x, y]).T
average = make_average(coord=coord)
stars = params_to_stars(average, noise=0.0, rng=None)
stars_training = stars[:900]
stars_validation = stars[900:]
fit_hyp = ['none', 'isotropic']
for fit in fit_hyp:
gp = piff.GPInterp(kernel="0.009 * RBF(300.*0.26)",
optimizer=fit,
white_noise=1e-5,
average_fits='output/average.fits')
gp.initialize(stars_training)
gp.solve(stars_training)
stars_interp = gp.interpolateList(stars_validation)
params_interp = np.array([s.fit.params for s in stars_interp])
params_validation = np.array([s.fit.params for s in stars_validation])
params_training = np.array([s.fit.params for s in stars_training])
np.testing.assert_allclose(params_interp,
params_validation,
rtol=rtol,
atol=atol)
def test_invalid():
psf_file = 'test_mean_*.piff'
average_file = 'average.fits'
psfs_list = os.path.join('output', 'test_mean_*.piff')
if __name__ == '__main__':
logger = piff.config.setup_logger(verbose=2)
else:
logger = piff.config.setup_logger(
log_file='output/test_invalid_config.log')
config = {
'output': {
'file_name': psfs_list,
},
'hyper': {
'file_name': 'output/' + average_file,
'bin_spacing': 30,
'statistic': 'mean',
'params_fitted': [0, 2]
}
}
# Both output and hyper are required
with np.testing.assert_raises(ValueError):
piff.meanify(config={'output': config['output']}, logger=logger)
with np.testing.assert_raises(ValueError):
piff.meanify(config={'hyper': config['hyper']}, logger=logger)
# Both require file_name
with np.testing.assert_raises(ValueError):
piff.meanify(config={
'output': config['output'],
'hyper': {}
},
logger=logger)
with np.testing.assert_raises(ValueError):
piff.meanify(config={
'hyper': config['hyper'],
'output': {}
},
logger=logger)
# Invalid statistic
config['hyper']['statistic'] = 'invalid'
with np.testing.assert_raises(ValueError):
piff.meanify(config=config, logger=logger)
config['hyper']['statistic'] = 'mean'
# Invalid params_fitted
config['hyper']['params_fitted'] = 0
with np.testing.assert_raises(TypeError):
piff.meanify(config=config, logger=logger)
config['hyper']['params_fitted'] = [0, 2]
# Invalid file_name
config['output']['file_name'] = []
with np.testing.assert_raises(ValueError):
piff.meanify(config=config, logger=logger)
config['output']['file_name'] = os.path.join('output', 'invalid_*.piff')
with np.testing.assert_raises(ValueError):
piff.meanify(config=config, logger=logger)
config['output']['file_name'] = 7
with np.testing.assert_raises(ValueError):
piff.meanify(config=config, logger=logger)
config['output']['file_name'] = psfs_list
def test_meanify():
if __name__ == '__main__':
rtol = 1.e-1
atol = 2.e-2
bin_spacing = 30 # arcsec
else:
rtol = 1.e-1
atol = 3.e-2
bin_spacing = 150 # arcsec
psf_file = 'test_mean_*.piff'
average_file = 'average.fits'
psfs_list = sorted(glob.glob(os.path.join('output', 'test_mean_*.piff')))
config0 = {
'output' : {
'file_name' : psfs_list,
},
'hyper' : {
'file_name' : 'output/'+average_file,
}}
config1 = {
'output' : {
'file_name' : psf_file,
'dir': 'output',
},
'hyper' : {
'file_name' : average_file,
'dir': 'output',
'bin_spacing' : bin_spacing,
'statistic' : 'mean',
'params_fitted': [0, 2]
}}
config2 = {
'output' : {
'file_name' : psf_file,
'dir': 'output',
},
'hyper' : {
'file_name' : average_file,
'dir': 'output',
'bin_spacing' : bin_spacing,
'statistic' : 'median',
}}
for config in [config0, config1, config2]:
piff.meanify(config)
## test if found initial average
average = fitsio.read(os.path.join('output',average_file))
params0 = make_average(coord=average['COORDS0'][0] / 0.26, gp=False)
keys = ['hlr', 'g1', 'g2']
for i,key in enumerate(keys):
if config == config1 and i == 1:
np.testing.assert_allclose(np.zeros(len(average['PARAMS0'][0][:,i])),
average['PARAMS0'][0][:,i], rtol=0, atol=0)
else:
np.testing.assert_allclose(params0[key], average['PARAMS0'][0][:,i],
rtol=rtol, atol=atol)
## gaussian process testing of meanify
np.random.seed(68)
x = np.random.uniform(0, 2048, size=1000)
y = np.random.uniform(0, 2048, size=1000)
coord = np.array([x,y]).T
average = make_average(coord=coord)
stars = params_to_stars(average, noise=0.0, rng=None)
stars_training = stars[:900]
stars_validation = stars[900:]
fit_hyp = [False, True]
for fit in fit_hyp:
gp = piff.GPInterp2pcf(kernel="0.009 * RBF(300.*0.26)",
optimize=fit_hyp, white_noise=1e-5, average_fits='output/average.fits')
gp.initialize(stars_training)
gp.solve(stars_training)
stars_interp = gp.interpolateList(stars_validation)
params_interp = np.array([s.fit.params for s in stars_interp])
params_validation = np.array([s.fit.params for s in stars_validation])
params_training = np.array([s.fit.params for s in stars_training])
np.testing.assert_allclose(params_interp, params_validation, rtol=rtol, atol=atol)