def drive_fit(expid, params={}, weights={}, skip_fit=False):
params_default = {'analytic': True, 'number_sample': 0, 'verbose': True,
'num_bins': 1}
params_default.update(param_default_kils(expid))
params_default.update(params)
params = params_default
model = Digestor().digest_directory(
params['data_directory'],
file_type=params['data_name'])
if params['number_sample'] > 0:
rows = np.random.choice(model.index.values,
params['number_sample'], replace=False)
model = model.ix[rows]
# guess rzero
rzero = r0_guess(model['e0'].min())
rzeros_float = np.array([0.08 + 0.01 * i for i in xrange(15)])
rzeros = ['{0:.2f}'.format(0.08 + 0.01 * i) for i in xrange(15)]
# always want the rzero one above so that we underestimate it
# note that this also implies that we want a dc component for e0 in
# addition to e1 and e2
rzero_i = np.searchsorted(rzeros_float, rzero)
rzero_key = rzeros[rzero_i]
model['rzero'] = rzero
# get the PSF_Interpolator
PSF_Interpolator = Mesh_Interpolator(mesh_name=params['mesh_name'],
directory=params['mesh_directory'])
if params['analytic']:
PSF_Evaluator = Zernike_Evaluator(*np.load(params['analytic_coeffs']).item()[rzero_key])
WF = DECAM_Analytic_Wavefront(rzero=rzero,
PSF_Interpolator=PSF_Interpolator,
PSF_Evaluator=PSF_Evaluator,
num_bins=params['num_bins'], model=model)
else:
WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator,
num_bins=params['num_bins'],
model=model)
misalignment = translate_misalignment_to_arguments({})
# fix some keys
pop_keys = ['rzero', 'delta1', 'delta2', 'zeta1', 'zeta2']
for pop_key in pop_keys:
_ = misalignment.pop(pop_key)
if skip_fit:
return WF
minuit, chi2, plane = do_fit(WF, misalignment=misalignment, verbose=params['verbose'], weights=weights)
return minuit, chi2, plane, WF
# data wavefront will be either analytic or decam_model_wavefront with a model
# paraemter passed in that is the data of the assessed fits files
if __name__ == '__main__':
import matplotlib.pyplot as plt
from WavefrontPSF.psf_interpolator import Mesh_Interpolator
from WavefrontPSF.wavefront import Wavefront
from WavefrontPSF.digestor import Digestor
from WavefrontPSF.analytic_interpolator import DECAM_Analytic_Wavefront, r0_guess
from WavefrontPSF.donutengine import DECAM_Model_Wavefront
model = Digestor().digest_directory(
'/Users/cpd/Projects/WavefrontPSF/meshes/00253794/',
file_type='_selpsfcat.fits')
# guess rzero
model['rzero'] = r0_guess(model['e0'].min())
mesh_directory = '/Users/cpd/Projects/WavefrontPSF/meshes/Science-20140212s2-v1i2'
mesh_name = 'Science-20140212s2-v1i2_All'
PSF_Interpolator = Mesh_Interpolator(mesh_name=mesh_name, directory=mesh_directory)
#from astropy.stats import mad_std
WF = DECAM_Model_Wavefront(PSF_Interpolator=PSF_Interpolator,
num_bins=1,
model=model)#, reducer=mad_std)
# WF.plot_field('e0')
# naieve = WF(WF.data)
# WF.plot_colormap(naieve, 'x', 'y', 'e0', num_bins=1)
