Coverage for tests/test_semiEmpiricalPrior.py : 84%

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# # LSST Data Management System # # Copyright 2008-2016 AURA/LSST. # # This product includes software developed by the # LSST Project (http://www.lsst.org/). # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the LSST License Statement and # the GNU General Public License along with this program. If not, # see <https://www.lsstcorp.org/LegalNotices/>. #
except ImportError: scipy = None
# a prior with broad ramps and non-zero slope; broad ramps makes evaluating numerical # derivatives easier, and we want to do that to check the analytic ones ("d_eta1", float), ("d_eta2", float), ("d_lnR", float), ("d2_eta1_eta1", float), ("d2_eta1_eta2", float), ("d2_eta1_lnR", float), ("d2_eta2_eta2", float), ("d2_eta2_lnR", float), ("d2_lnR_lnR", float)]) os.path.realpath(__file__)), "data", "SEP.txt"), dtype=dtype)
numpy.array([row["eta1"], row["eta2"], row["lnR"]]), self.amplitudes, grad[:3], grad[3:], hess[:3, :3], hess[3:, 3:], hess[:3, 3:] )
numpy.array([row["eta1"], row["eta2"], row["lnR"]]), self.amplitudes, grad[:3], grad[3:], hess[:3, :3], hess[3:, 3:], hess[:3, 3:] )
b = numpy.broadcast(eta1, eta2, lnR) p = numpy.zeros(b.shape, dtype=lsst.meas.modelfit.Scalar) for i, (eta1i, eta2i, lnRi) in enumerate(b): p.flat[i] = self.prior.evaluate(numpy.array([eta1i, eta2i, lnRi]), self.amplitudes) return p
lsst.utils.tests.init() unittest.main() |