Philipp Arras committed Jan 09, 2019 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ``````# 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 GNU General Public License # along with this program. If not, see . # # Copyright(C) 2013-2019 Max-Planck-Society # # NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik. import numpy as np import pytest `````` Philipp Arras committed Jan 13, 2019 20 ``````from numpy.testing import assert_ `````` Philipp Arras committed Jan 09, 2019 21 22 23 `````` import nifty5 as ift `````` Reimar H Leike committed Jan 23, 2019 24 ``````from ..common import list2fixture `````` Philipp Arras committed Jan 09, 2019 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 `````` pmp = pytest.mark.parametrize space = list2fixture([ ift.GLSpace(15), ift.RGSpace(64, distances=.789), ift.RGSpace([32, 32], distances=.789) ]) space1 = space seed = list2fixture([4, 78, 23]) def _make_linearization(type, space, seed): np.random.seed(seed) S = ift.ScalingOperator(1., space) s = S.draw_sample() if type == "Constant": return ift.Linearization.make_const(s) elif type == "Variable": return ift.Linearization.make_var(s) raise ValueError('unknown type passed') def testBasics(space, seed): var = _make_linearization("Variable", space, seed) model = ift.ScalingOperator(6., var.target) ift.extra.check_value_gradient_consistency(model, var.val) @pmp('type1', ['Variable', 'Constant']) @pmp('type2', ['Variable']) def testBinary(type1, type2, space, seed): dom1 = ift.MultiDomain.make({'s1': space}) dom2 = ift.MultiDomain.make({'s2': space}) `````` Philipp Arras committed Jan 13, 2019 58 59 60 61 `````` # FIXME Remove this? _make_linearization(type1, dom1, seed) _make_linearization(type2, dom2, seed) `````` Philipp Arras committed Jan 09, 2019 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 `````` dom = ift.MultiDomain.union((dom1, dom2)) select_s1 = ift.ducktape(None, dom, "s1") select_s2 = ift.ducktape(None, dom, "s2") model = select_s1*select_s2 pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) model = select_s1 + select_s2 pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) model = select_s1.scale(3.) pos = ift.from_random("normal", dom1) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) model = ift.ScalingOperator(2.456, space)(select_s1*select_s2) pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) `````` Philipp Arras committed Jan 13, 2019 78 `````` model = ift.sigmoid(2.456*(select_s1*select_s2)) `````` Philipp Arras committed Jan 09, 2019 79 80 81 82 83 `````` pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) pos = ift.from_random("normal", dom) model = ift.OuterProduct(pos['s1'], ift.makeDomain(space)) ift.extra.check_value_gradient_consistency(model, pos['s2'], ntries=20) `````` Martin Reinecke committed Jan 24, 2019 84 `````` model = select_s1**2 `````` Reimar H Leike committed Jan 23, 2019 85 86 `````` pos = ift.from_random("normal", dom1) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) `````` Martin Reinecke committed Jan 24, 2019 87 `````` model = select_s1.clip(-1, 1) `````` Reimar H Leike committed Jan 23, 2019 88 89 `````` pos = ift.from_random("normal", dom1) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) `````` Philipp Arras committed Jan 09, 2019 90 91 92 93 94 95 96 97 98 `````` if isinstance(space, ift.RGSpace): model = ift.FFTOperator(space)(select_s1*select_s2) pos = ift.from_random("normal", dom) ift.extra.check_value_gradient_consistency(model, pos, ntries=20) def testModelLibrary(space, seed): # Tests amplitude model and coorelated field model np.random.seed(seed) `````` Philipp Arras committed Jan 13, 2019 99 `````` domain = ift.PowerSpace(space.get_default_codomain()) `````` Philipp Arras committed Jan 15, 2019 100 101 `````` model = ift.SLAmplitude(target=domain, n_pix=4, a=.5, k0=2, sm=3, sv=1.5, im=1.75, iv=1.3) `````` Philipp Arras committed Jan 13, 2019 102 `````` assert_(isinstance(model, ift.Operator)) `````` Philipp Arras committed Jan 09, 2019 103 104 105 106 107 108 109 110 111 `````` S = ift.ScalingOperator(1., model.domain) pos = S.draw_sample() ift.extra.check_value_gradient_consistency(model, pos, ntries=20) model2 = ift.CorrelatedField(space, model) S = ift.ScalingOperator(1., model2.domain) pos = S.draw_sample() ift.extra.check_value_gradient_consistency(model2, pos, ntries=20) `````` Philipp Arras committed Jan 14, 2019 112 113 114 115 116 117 `````` domtup = ift.DomainTuple.make((space, space)) model3 = ift.MfCorrelatedField(domtup, [model, model]) S = ift.ScalingOperator(1., model3.domain) pos = S.draw_sample() ift.extra.check_value_gradient_consistency(model3, pos, ntries=20) `````` Philipp Arras committed Jan 09, 2019 118 119 120 121 122 123 `````` def testPointModel(space, seed): S = ift.ScalingOperator(1., space) pos = S.draw_sample() alpha = 1.5 q = 0.73 `````` Philipp Arras committed Jan 09, 2019 124 `````` model = ift.InverseGammaOperator(space, alpha, q) `````` Philipp Arras committed Jan 09, 2019 125 126 `````` # FIXME All those cdfs and ppfs are not very accurate ift.extra.check_value_gradient_consistency(model, pos, tol=1e-2, ntries=20) `````` Martin Reinecke committed Jan 09, 2019 127 `````` `````` Philipp Arras committed Jan 13, 2019 128 `````` `````` Philipp Frank committed Jan 16, 2019 129 ``````@pmp('target', [ `````` Martin Reinecke committed Jan 24, 2019 130 131 132 `````` ift.RGSpace(64, distances=.789, harmonic=True), ift.RGSpace([32, 32], distances=.789, harmonic=True), ift.RGSpace([32, 32, 8], distances=.789, harmonic=True) `````` Philipp Arras committed Jan 13, 2019 133 ``````]) `````` Martin Reinecke committed Jan 09, 2019 134 135 136 ``````@pmp('causal', [True, False]) @pmp('minimum_phase', [True, False]) @pmp('seed', [4, 78, 23]) `````` Philipp Frank committed Jan 16, 2019 137 138 139 140 141 142 143 144 145 146 147 ``````def testDynamicModel(target, causal, minimum_phase, seed): dct = { 'target': target, 'harmonic_padding': None, 'sm_s0': 3., 'sm_x0': 1., 'key': 'f', 'causal': causal, 'minimum_phase': minimum_phase } model, _ = ift.dynamic_operator(**dct) `````` Martin Reinecke committed Jan 09, 2019 148 149 150 `````` S = ift.ScalingOperator(1., model.domain) pos = S.draw_sample() # FIXME I dont know why smaller tol fails for 3D example `````` Philipp Arras committed Jan 13, 2019 151 `````` ift.extra.check_value_gradient_consistency(model, pos, tol=1e-5, ntries=20) `````` Philipp Frank committed Jan 16, 2019 152 `````` if len(target.shape) > 1: `````` Philipp Arras committed Jan 13, 2019 153 `````` dct = { `````` Philipp Frank committed Jan 16, 2019 154 `````` 'target': target, `````` Philipp Arras committed Jan 13, 2019 155 156 157 158 159 160 161 162 163 164 `````` 'harmonic_padding': None, 'sm_s0': 3., 'sm_x0': 1., 'key': 'f', 'lightcone_key': 'c', 'sigc': 1., 'quant': 5, 'causal': causal, 'minimum_phase': minimum_phase } `````` Philipp Frank committed Jan 16, 2019 165 166 167 `````` dct['lightcone_key'] = 'c' dct['sigc'] = 1. dct['quant'] = 5 `````` Philipp Arras committed Jan 13, 2019 168 `````` model, _ = ift.dynamic_lightcone_operator(**dct) `````` Martin Reinecke committed Jan 09, 2019 169 170 171 `````` S = ift.ScalingOperator(1., model.domain) pos = S.draw_sample() # FIXME I dont know why smaller tol fails for 3D example `````` Philipp Arras committed Jan 13, 2019 172 173 `````` ift.extra.check_value_gradient_consistency( model, pos, tol=1e-5, ntries=20)``````