Total Complexity | 82 |
Total Lines | 1867 |
Duplicated Lines | 2.04 % |
Changes | 0 |
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
Complex classes like gammapy.datasets.tests.test_map often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
1 | # Licensed under a 3-clause BSD style license - see LICENSE.rst |
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2 | import json |
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3 | import pytest |
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4 | import numpy as np |
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5 | from numpy.testing import assert_allclose |
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6 | import astropy.units as u |
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7 | from astropy.coordinates import SkyCoord |
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8 | from astropy.table import Table |
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9 | from regions import CircleSkyRegion |
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10 | from gammapy.catalog import SourceCatalog3FHL |
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11 | from gammapy.data import GTI |
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12 | from gammapy.datasets import Datasets, MapDataset, MapDatasetOnOff |
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13 | from gammapy.datasets.map import RAD_AXIS_DEFAULT |
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14 | from gammapy.irf import ( |
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15 | EDispKernelMap, |
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16 | EDispMap, |
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17 | EffectiveAreaTable2D, |
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18 | EnergyDependentMultiGaussPSF, |
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19 | EnergyDispersion2D, |
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20 | PSFMap, |
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21 | ) |
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22 | from gammapy.makers.utils import make_map_exposure_true_energy, make_psf_map |
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23 | from gammapy.maps import HpxGeom, Map, MapAxis, RegionGeom, WcsGeom, WcsNDMap |
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24 | from gammapy.maps.io import JsonQuantityEncoder |
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25 | from gammapy.modeling import Fit |
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26 | from gammapy.modeling.models import ( |
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27 | DiskSpatialModel, |
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28 | FoVBackgroundModel, |
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29 | GaussianSpatialModel, |
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30 | Models, |
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31 | PointSpatialModel, |
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32 | PowerLawSpectralModel, |
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33 | SkyModel, |
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34 | ) |
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35 | from gammapy.utils.testing import mpl_plot_check, requires_data, requires_dependency |
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36 | |||
37 | |||
38 | @pytest.fixture |
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39 | def geom_hpx(): |
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40 | axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3) |
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41 | |||
42 | energy_axis_true = MapAxis.from_energy_bounds( |
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43 | "1 TeV", "10 TeV", nbin=4, name="energy_true" |
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44 | ) |
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45 | |||
46 | geom = HpxGeom.create(nside=32, axes=[axis], frame="galactic") |
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47 | |||
48 | return {"geom": geom, "energy_axis_true": energy_axis_true} |
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49 | |||
50 | |||
51 | @pytest.fixture |
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52 | def geom_hpx_partial(): |
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53 | axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3) |
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54 | |||
55 | energy_axis_true = MapAxis.from_energy_bounds( |
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56 | "1 TeV", "10 TeV", nbin=4, name="energy_true" |
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57 | ) |
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58 | |||
59 | geom = HpxGeom.create( |
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60 | nside=32, axes=[axis], frame="galactic", region="DISK(110.,75.,10.)" |
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61 | ) |
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62 | |||
63 | return {"geom": geom, "energy_axis_true": energy_axis_true} |
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64 | |||
65 | |||
66 | @pytest.fixture |
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67 | def geom(): |
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68 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=2) |
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69 | return WcsGeom.create( |
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70 | skydir=(266.40498829, -28.93617776), |
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71 | binsz=0.02, |
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72 | width=(2, 2), |
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73 | frame="icrs", |
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74 | axes=[axis], |
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75 | ) |
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76 | |||
77 | |||
78 | @pytest.fixture |
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79 | def geom1(): |
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80 | e_axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=20) |
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81 | t_axis = MapAxis.from_bounds(0, 10, 2, name="time", unit="s") |
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82 | return WcsGeom.create( |
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83 | skydir=(266.40498829, -28.93617776), |
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84 | binsz=0.02, |
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85 | width=(3, 2), |
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86 | frame="icrs", |
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87 | axes=[e_axis, t_axis], |
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88 | ) |
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89 | |||
90 | |||
91 | @pytest.fixture |
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92 | def geom_etrue(): |
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93 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3, name="energy_true") |
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94 | return WcsGeom.create( |
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95 | skydir=(266.40498829, -28.93617776), |
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96 | binsz=0.02, |
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97 | width=(2, 2), |
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98 | frame="icrs", |
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99 | axes=[axis], |
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100 | ) |
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101 | |||
102 | |||
103 | @pytest.fixture |
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104 | def geom_image(): |
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105 | energy = np.logspace(-1.0, 1.0, 2) |
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106 | axis = MapAxis.from_edges(energy, name="energy", unit=u.TeV, interp="log") |
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107 | return WcsGeom.create( |
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108 | skydir=(0, 0), binsz=0.02, width=(2, 2), frame="galactic", axes=[axis] |
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109 | ) |
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110 | |||
111 | |||
112 | def get_exposure(geom_etrue): |
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113 | filename = ( |
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114 | "$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits" |
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115 | ) |
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116 | aeff = EffectiveAreaTable2D.read(filename, hdu="EFFECTIVE AREA") |
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117 | |||
118 | exposure_map = make_map_exposure_true_energy( |
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119 | pointing=SkyCoord(1, 0.5, unit="deg", frame="galactic"), |
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120 | livetime=1 * u.hr, |
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121 | aeff=aeff, |
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122 | geom=geom_etrue, |
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123 | ) |
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124 | return exposure_map |
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125 | |||
126 | |||
127 | def get_psf(): |
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128 | filename = ( |
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129 | "$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits" |
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130 | ) |
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131 | psf = EnergyDependentMultiGaussPSF.read(filename, hdu="POINT SPREAD FUNCTION") |
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132 | |||
133 | geom = WcsGeom.create( |
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134 | skydir=(0, 0), |
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135 | frame="galactic", |
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136 | binsz=2, |
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137 | width=(2, 2), |
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138 | axes=[RAD_AXIS_DEFAULT, psf.axes["energy_true"]], |
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139 | ) |
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140 | |||
141 | return make_psf_map( |
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142 | psf=psf, |
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143 | pointing=SkyCoord(0, 0.5, unit="deg", frame="galactic"), |
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144 | geom=geom, |
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145 | exposure_map=Map.from_geom(geom.squash("rad"), unit="cm2 s"), |
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146 | ) |
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147 | |||
148 | |||
149 | @requires_data() |
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150 | def get_edisp(geom, geom_etrue): |
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151 | filename = "$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_020136.fits.gz" |
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152 | edisp2d = EnergyDispersion2D.read(filename, hdu="EDISP") |
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153 | energy = geom.axes["energy"].edges |
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154 | energy_true = geom_etrue.axes["energy_true"].edges |
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155 | edisp_kernel = edisp2d.to_edisp_kernel( |
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156 | offset="1.2 deg", energy=energy, energy_true=energy_true |
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157 | ) |
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158 | edisp = EDispKernelMap.from_edisp_kernel(edisp_kernel) |
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159 | return edisp |
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160 | |||
161 | |||
162 | @pytest.fixture |
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163 | def sky_model(): |
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164 | spatial_model = GaussianSpatialModel( |
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165 | lon_0="0.2 deg", lat_0="0.1 deg", sigma="0.2 deg", frame="galactic" |
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166 | ) |
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167 | spectral_model = PowerLawSpectralModel( |
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168 | index=3, amplitude="1e-11 cm-2 s-1 TeV-1", reference="1 TeV" |
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169 | ) |
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170 | return SkyModel( |
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171 | spatial_model=spatial_model, spectral_model=spectral_model, name="test-model" |
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172 | ) |
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173 | |||
174 | |||
175 | def get_map_dataset(geom, geom_etrue, edisp="edispmap", name="test", **kwargs): |
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176 | """Returns a MapDataset""" |
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177 | # define background model |
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178 | background = Map.from_geom(geom) |
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179 | background.data += 0.2 |
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180 | |||
181 | psf = get_psf() |
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182 | exposure = get_exposure(geom_etrue) |
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183 | |||
184 | e_reco = geom.axes["energy"] |
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185 | e_true = geom_etrue.axes["energy_true"] |
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186 | |||
187 | if edisp == "edispmap": |
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188 | edisp = EDispMap.from_diagonal_response(energy_axis_true=e_true) |
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189 | data = exposure.get_spectrum(geom.center_skydir).data |
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190 | edisp.exposure_map.data = np.repeat(data, 2, axis=-1) |
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191 | elif edisp == "edispkernelmap": |
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192 | edisp = EDispKernelMap.from_diagonal_response( |
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193 | energy_axis=e_reco, energy_axis_true=e_true |
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194 | ) |
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195 | data = exposure.get_spectrum(geom.center_skydir).data |
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196 | edisp.exposure_map.data = np.repeat(data, 2, axis=-1) |
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197 | else: |
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198 | edisp = None |
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199 | |||
200 | # define fit mask |
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201 | center = SkyCoord("0.2 deg", "0.1 deg", frame="galactic") |
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202 | circle = CircleSkyRegion(center=center, radius=1 * u.deg) |
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203 | mask_fit = geom.region_mask([circle]) |
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204 | |||
205 | models = FoVBackgroundModel(dataset_name=name) |
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206 | |||
207 | return MapDataset( |
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208 | models=models, |
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209 | exposure=exposure, |
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210 | background=background, |
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211 | psf=psf, |
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212 | edisp=edisp, |
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213 | mask_fit=mask_fit, |
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214 | name=name, |
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215 | **kwargs, |
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216 | ) |
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217 | |||
218 | |||
219 | def test_map_dataset_name(): |
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220 | with pytest.raises(ValueError, match="of type '<class 'int'>"): |
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221 | _ = MapDataset(name=6353) |
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222 | |||
223 | with pytest.raises(ValueError, match="of type '<class 'list'>"): |
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224 | _ = MapDataset(name=[1233, "234"]) |
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225 | |||
226 | |||
227 | @requires_data() |
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228 | def test_map_dataset_str(sky_model, geom, geom_etrue): |
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229 | dataset = get_map_dataset(geom, geom_etrue) |
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230 | |||
231 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
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232 | dataset.models = [sky_model, bkg_model] |
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233 | |||
234 | dataset.counts = dataset.npred() |
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235 | dataset.mask_safe = dataset.mask_fit |
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236 | assert "MapDataset" in str(dataset) |
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237 | assert "(frozen)" in str(dataset) |
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238 | assert "background" in str(dataset) |
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239 | |||
240 | dataset.mask_safe = None |
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241 | assert "MapDataset" in str(dataset) |
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242 | |||
243 | |||
244 | def test_map_dataset_str_empty(): |
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245 | dataset = MapDataset() |
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246 | assert "MapDataset" in str(dataset) |
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247 | |||
248 | |||
249 | @requires_data() |
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250 | def test_fake(sky_model, geom, geom_etrue): |
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251 | """Test the fake dataset""" |
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252 | dataset = get_map_dataset(geom, geom_etrue) |
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253 | |||
254 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
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255 | dataset.models = [sky_model, bkg_model] |
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256 | |||
257 | npred = dataset.npred() |
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258 | assert np.all(npred.data >= 0) # npred must be positive |
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259 | dataset.counts = npred |
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260 | real_dataset = dataset.copy() |
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261 | dataset.fake(314) |
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262 | |||
263 | assert real_dataset.counts.data.shape == dataset.counts.data.shape |
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264 | assert_allclose(real_dataset.counts.data.sum(), 9525.299054, rtol=1e-5) |
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265 | assert_allclose(dataset.counts.data.sum(), 9711) |
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266 | |||
267 | |||
268 | @requires_data() |
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269 | def test_different_exposure_unit(sky_model, geom): |
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270 | energy_range_true = np.logspace(2, 4, 3) |
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271 | axis = MapAxis.from_edges( |
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272 | energy_range_true, name="energy_true", unit="GeV", interp="log" |
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273 | ) |
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274 | geom_gev = geom.to_image().to_cube([axis]) |
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275 | dataset = get_map_dataset(geom, geom_gev, edisp="None") |
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276 | |||
277 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
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278 | dataset.models = [sky_model, bkg_model] |
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279 | |||
280 | npred = dataset.npred() |
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281 | |||
282 | assert_allclose(npred.data[0, 50, 50], 6.086019, rtol=1e-2) |
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283 | |||
284 | |||
285 | @pytest.mark.parametrize(("edisp_mode"), ["edispmap", "edispkernelmap"]) |
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286 | @requires_data() |
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287 | def test_to_spectrum_dataset(sky_model, geom, geom_etrue, edisp_mode): |
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288 | |||
289 | dataset_ref = get_map_dataset(geom, geom_etrue, edisp=edisp_mode) |
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290 | |||
291 | bkg_model = FoVBackgroundModel(dataset_name=dataset_ref.name) |
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292 | dataset_ref.models = [sky_model, bkg_model] |
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293 | |||
294 | dataset_ref.counts = dataset_ref.npred_background() * 0.0 |
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295 | dataset_ref.counts.data[1, 50, 50] = 1 |
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296 | dataset_ref.counts.data[1, 60, 50] = 1 |
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297 | |||
298 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
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299 | dataset_ref.gti = gti |
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300 | on_region = CircleSkyRegion(center=geom.center_skydir, radius=0.05 * u.deg) |
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301 | spectrum_dataset = dataset_ref.to_spectrum_dataset(on_region) |
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302 | spectrum_dataset_corrected = dataset_ref.to_spectrum_dataset( |
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303 | on_region, containment_correction=True |
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304 | ) |
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305 | mask = np.ones_like(dataset_ref.counts, dtype="bool") |
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306 | mask[1, 40:60, 40:60] = False |
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307 | dataset_ref.mask_safe = Map.from_geom(dataset_ref.counts.geom, data=mask) |
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308 | spectrum_dataset_mask = dataset_ref.to_spectrum_dataset(on_region) |
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309 | |||
310 | assert np.sum(spectrum_dataset.counts.data) == 1 |
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311 | assert spectrum_dataset.data_shape == (2, 1, 1) |
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312 | assert spectrum_dataset.background.geom.axes[0].nbin == 2 |
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313 | assert spectrum_dataset.exposure.geom.axes[0].nbin == 3 |
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314 | assert spectrum_dataset.exposure.unit == "m2s" |
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315 | |||
316 | energy_axis = geom.axes["energy"] |
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317 | assert ( |
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318 | spectrum_dataset.edisp.get_edisp_kernel(energy_axis=energy_axis) |
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319 | .axes["energy"] |
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320 | .nbin |
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321 | == 2 |
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322 | ) |
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323 | assert ( |
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324 | spectrum_dataset.edisp.get_edisp_kernel(energy_axis=energy_axis) |
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325 | .axes["energy_true"] |
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326 | .nbin |
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327 | == 3 |
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328 | ) |
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329 | |||
330 | assert_allclose(spectrum_dataset.edisp.exposure_map.data[1], 3.069227e09, rtol=1e-5) |
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331 | assert np.sum(spectrum_dataset_mask.counts.data) == 0 |
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332 | assert spectrum_dataset_mask.data_shape == (2, 1, 1) |
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333 | assert spectrum_dataset_corrected.exposure.unit == "m2s" |
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334 | |||
335 | assert_allclose(spectrum_dataset.exposure.data[1], 3.070884e09, rtol=1e-5) |
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336 | assert_allclose(spectrum_dataset_corrected.exposure.data[1], 2.05201e09, rtol=1e-5) |
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337 | |||
338 | |||
339 | @requires_data() |
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340 | def test_energy_range(sky_model, geom1, geom_etrue): |
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341 | sky_coord1 = SkyCoord(266.5, -29.3, unit="deg") |
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342 | region1 = CircleSkyRegion(sky_coord1, 0.5 * u.deg) |
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343 | mask1 = geom1.region_mask([region1]) & geom1.energy_mask(1 * u.TeV, 7 * u.TeV) |
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344 | sky_coord2 = SkyCoord(266.5, -28.7, unit="deg") |
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345 | region2 = CircleSkyRegion(sky_coord2, 0.5 * u.deg) |
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346 | mask2 = geom1.region_mask([region2]) & geom1.energy_mask(2 * u.TeV, 8 * u.TeV) |
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347 | mask3 = geom1.energy_mask(3 * u.TeV, 6 * u.TeV) |
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348 | |||
349 | mask_safe = Map.from_geom(geom1, data=(mask1 | mask2 | mask3).data) |
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350 | dataset = get_map_dataset(geom1, geom_etrue, edisp=None, mask_safe=mask_safe) |
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351 | energy = geom1.axes["energy"].edges.value |
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352 | |||
353 | e_min, e_max = dataset.energy_range_safe |
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354 | assert_allclose(e_min.get_by_coord((265, -28, 0)), energy[15]) |
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355 | assert_allclose(e_max.get_by_coord((265, -28, 5)), energy[17]) |
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356 | assert_allclose(e_min.get_by_coord((sky_coord1.ra, sky_coord1.dec, 6)), energy[10]) |
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357 | assert_allclose(e_max.get_by_coord((sky_coord1.ra, sky_coord1.dec, 1)), energy[18]) |
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358 | assert_allclose(e_min.get_by_coord((sky_coord2.ra, sky_coord2.dec, 2)), energy[14]) |
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359 | assert_allclose(e_max.get_by_coord((sky_coord2.ra, sky_coord2.dec, 7)), energy[19]) |
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360 | assert_allclose(e_min.get_by_coord((266.5, -29, 8)), energy[10]) |
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361 | assert_allclose(e_max.get_by_coord((266.5, -29, 3)), energy[19]) |
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362 | |||
363 | e_min, e_max = dataset.energy_range_fit |
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364 | assert_allclose(e_min.get_by_coord((265, -28, 0)), np.nan) |
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365 | assert_allclose(e_max.get_by_coord((265, -28, 5)), np.nan) |
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366 | assert_allclose(e_min.get_by_coord((266, -29, 4)), energy[0]) |
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367 | assert_allclose(e_max.get_by_coord((266, -29, 9)), energy[20]) |
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368 | |||
369 | e_min, e_max = dataset.energy_range |
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370 | assert_allclose(e_min.get_by_coord((266, -29, 4)), energy[15]) |
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371 | assert_allclose(e_max.get_by_coord((266, -29, 9)), energy[17]) |
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372 | |||
373 | mask_zeros = Map.from_geom(geom1, data=np.zeros_like(mask_safe)) |
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374 | e_min, e_max = dataset._energy_range(mask_zeros) |
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375 | assert_allclose(e_min.get_by_coord((266.5, -29, 8)), np.nan) |
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376 | assert_allclose(e_max.get_by_coord((266.5, -29, 3)), np.nan) |
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377 | |||
378 | e_min, e_max = dataset._energy_range() |
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379 | assert_allclose(e_min.get_by_coord((265, -28, 0)), energy[0]) |
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380 | assert_allclose(e_max.get_by_coord((265, -28, 5)), energy[20]) |
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381 | |||
382 | |||
383 | @requires_data() |
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384 | def test_info_dict(sky_model, geom, geom_etrue): |
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385 | dataset = get_map_dataset(geom, geom_etrue) |
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386 | |||
387 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
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388 | dataset.models = [sky_model, bkg_model] |
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389 | |||
390 | dataset.counts = dataset.npred() |
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391 | info_dict = dataset.info_dict() |
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392 | |||
393 | assert_allclose(info_dict["counts"], 9526, rtol=1e-3) |
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394 | assert_allclose(info_dict["background"], 4000.0005, rtol=1e-3) |
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395 | assert_allclose(info_dict["npred_background"], 4000.0, rtol=1e-3) |
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396 | assert_allclose(info_dict["excess"], 5525.756, rtol=1e-3) |
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397 | assert_allclose(info_dict["exposure_min"].value, 8.32e8, rtol=1e-3) |
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398 | assert_allclose(info_dict["exposure_max"].value, 1.105e10, rtol=1e-3) |
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399 | assert info_dict["exposure_max"].unit == "m2 s" |
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400 | assert info_dict["name"] == "test" |
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401 | |||
402 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
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403 | dataset.gti = gti |
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404 | info_dict = dataset.info_dict() |
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405 | assert_allclose(info_dict["counts"], 9526, rtol=1e-3) |
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406 | assert_allclose(info_dict["background"], 4000.0005, rtol=1e-3) |
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407 | assert_allclose(info_dict["npred_background"], 4000.0, rtol=1e-3) |
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408 | assert_allclose(info_dict["sqrt_ts"], 74.024180, rtol=1e-3) |
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409 | assert_allclose(info_dict["excess"], 5525.756, rtol=1e-3) |
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410 | assert_allclose(info_dict["ontime"].value, 3600) |
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411 | |||
412 | assert info_dict["name"] == "test" |
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413 | |||
414 | # try to dump as json |
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415 | result = json.dumps(info_dict, cls=JsonQuantityEncoder) |
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416 | assert "counts" in result |
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417 | |||
418 | |||
419 | def get_fermi_3fhl_gc_dataset(): |
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420 | counts = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts-cube.fits.gz") |
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421 | background = Map.read( |
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422 | "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background-cube.fits.gz" |
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423 | ) |
||
424 | bkg_model = FoVBackgroundModel(dataset_name="fermi-3fhl-gc") |
||
425 | |||
426 | exposure = Map.read( |
||
427 | "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure-cube.fits.gz" |
||
428 | ) |
||
429 | return MapDataset( |
||
430 | counts=counts, |
||
431 | background=background, |
||
432 | models=[bkg_model], |
||
433 | exposure=exposure, |
||
434 | name="fermi-3fhl-gc", |
||
435 | ) |
||
436 | |||
437 | |||
438 | @requires_data() |
||
439 | def test_resample_energy_3fhl(): |
||
440 | dataset = get_fermi_3fhl_gc_dataset() |
||
441 | |||
442 | new_axis = MapAxis.from_edges([10, 35, 100] * u.GeV, interp="log", name="energy") |
||
443 | grouped = dataset.resample_energy_axis(energy_axis=new_axis) |
||
444 | |||
445 | assert grouped.counts.data.shape == (2, 200, 400) |
||
446 | assert grouped.counts.data[0].sum() == 28581 |
||
447 | assert_allclose( |
||
448 | grouped.npred_background().data.sum(axis=(1, 2)), |
||
449 | [25074.366386, 2194.298612], |
||
450 | rtol=1e-5, |
||
451 | ) |
||
452 | assert_allclose(grouped.exposure.data, dataset.exposure.data, rtol=1e-5) |
||
453 | |||
454 | axis = grouped.counts.geom.axes[0] |
||
455 | npred = dataset.npred() |
||
456 | npred_grouped = grouped.npred() |
||
457 | assert_allclose(npred.resample_axis(axis=axis).data.sum(), npred_grouped.data.sum()) |
||
458 | |||
459 | |||
460 | @requires_data() |
||
461 | def test_to_image_3fhl(): |
||
462 | dataset = get_fermi_3fhl_gc_dataset() |
||
463 | |||
464 | dataset_im = dataset.to_image() |
||
465 | |||
466 | assert dataset_im.counts.data.sum() == dataset.counts.data.sum() |
||
467 | assert_allclose(dataset_im.npred_background().data.sum(), 28548.625, rtol=1e-5) |
||
468 | assert_allclose(dataset_im.exposure.data, dataset.exposure.data, rtol=1e-5) |
||
469 | |||
470 | npred = dataset.npred() |
||
471 | npred_im = dataset_im.npred() |
||
472 | assert_allclose(npred.data.sum(), npred_im.data.sum()) |
||
473 | |||
474 | |||
475 | def test_to_image_mask_safe(): |
||
476 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=2) |
||
477 | geom = WcsGeom.create( |
||
478 | skydir=(0, 0), binsz=0.5, width=(1, 1), frame="icrs", axes=[axis] |
||
479 | ) |
||
480 | dataset = MapDataset.create(geom) |
||
481 | |||
482 | # Check map_safe handling |
||
483 | data = np.array([[[False, True], [True, True]], [[False, False], [True, True]]]) |
||
484 | dataset.mask_safe = WcsNDMap.from_geom(geom=geom, data=data) |
||
485 | |||
486 | dataset_im = dataset.to_image() |
||
487 | assert dataset_im.mask_safe.data.dtype == bool |
||
488 | |||
489 | desired = np.array([[False, True], [True, True]]) |
||
490 | assert (dataset_im.mask_safe.data == desired).all() |
||
491 | |||
492 | # Check that missing entries in the dataset do not break |
||
493 | dataset_copy = dataset.copy() |
||
494 | dataset_copy.exposure = None |
||
495 | dataset_im = dataset_copy.to_image() |
||
496 | assert dataset_im.exposure is None |
||
497 | |||
498 | dataset_copy = dataset.copy() |
||
499 | dataset_copy.counts = None |
||
500 | dataset_im = dataset_copy.to_image() |
||
501 | assert dataset_im.counts is None |
||
502 | |||
503 | |||
504 | @requires_data() |
||
505 | def test_downsample(): |
||
506 | dataset = get_fermi_3fhl_gc_dataset() |
||
507 | |||
508 | downsampled = dataset.downsample(2) |
||
509 | |||
510 | assert downsampled.counts.data.shape == (11, 100, 200) |
||
511 | assert downsampled.counts.data.sum() == dataset.counts.data.sum() |
||
512 | assert_allclose( |
||
513 | downsampled.npred_background().data.sum(axis=(1, 2)), |
||
514 | dataset.npred_background().data.sum(axis=(1, 2)), |
||
515 | rtol=1e-5, |
||
516 | ) |
||
517 | assert_allclose(downsampled.exposure.data[5, 50, 100], 3.318082e11, rtol=1e-5) |
||
518 | |||
519 | with pytest.raises(ValueError): |
||
520 | dataset.downsample(2, axis_name="energy") |
||
521 | |||
522 | |||
523 | def test_downsample_energy(geom, geom_etrue): |
||
524 | # This checks that downsample and resample_energy_axis give identical results |
||
525 | counts = Map.from_geom(geom, dtype="int") |
||
526 | counts += 1 |
||
527 | mask = Map.from_geom(geom, dtype="bool") |
||
528 | mask.data[1:] = True |
||
529 | counts += 1 |
||
530 | exposure = Map.from_geom(geom_etrue, unit="m2s") |
||
531 | edisp = EDispKernelMap.from_gauss(geom.axes[0], geom_etrue.axes[0], 0.1, 0.0) |
||
532 | dataset = MapDataset( |
||
533 | counts=counts, |
||
534 | exposure=exposure, |
||
535 | mask_safe=mask, |
||
536 | edisp=edisp, |
||
537 | ) |
||
538 | dataset_downsampled = dataset.downsample(2, axis_name="energy") |
||
539 | dataset_resampled = dataset.resample_energy_axis(geom.axes[0].downsample(2)) |
||
540 | |||
541 | assert dataset_downsampled.edisp.edisp_map.data.shape == (3, 1, 1, 2) |
||
542 | assert_allclose( |
||
543 | dataset_downsampled.edisp.edisp_map.data[:, :, 0, 0], |
||
544 | dataset_resampled.edisp.edisp_map.data[:, :, 0, 0], |
||
545 | ) |
||
546 | |||
547 | |||
548 | @requires_data() |
||
549 | def test_map_dataset_fits_io(tmp_path, sky_model, geom, geom_etrue): |
||
550 | dataset = get_map_dataset(geom, geom_etrue) |
||
551 | |||
552 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
||
553 | dataset.models = [sky_model, bkg_model] |
||
554 | |||
555 | dataset.counts = dataset.npred() |
||
556 | dataset.mask_safe = dataset.mask_fit |
||
557 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
558 | dataset.gti = gti |
||
559 | |||
560 | hdulist = dataset.to_hdulist() |
||
561 | |||
562 | actual = [hdu.name for hdu in hdulist] |
||
563 | |||
564 | desired = [ |
||
565 | "PRIMARY", |
||
566 | "COUNTS", |
||
567 | "COUNTS_BANDS", |
||
568 | "EXPOSURE", |
||
569 | "EXPOSURE_BANDS", |
||
570 | "BACKGROUND", |
||
571 | "BACKGROUND_BANDS", |
||
572 | "EDISP", |
||
573 | "EDISP_BANDS", |
||
574 | "EDISP_EXPOSURE", |
||
575 | "EDISP_EXPOSURE_BANDS", |
||
576 | "PSF", |
||
577 | "PSF_BANDS", |
||
578 | "PSF_EXPOSURE", |
||
579 | "PSF_EXPOSURE_BANDS", |
||
580 | "MASK_SAFE", |
||
581 | "MASK_SAFE_BANDS", |
||
582 | "MASK_FIT", |
||
583 | "MASK_FIT_BANDS", |
||
584 | "GTI", |
||
585 | ] |
||
586 | |||
587 | assert actual == desired |
||
588 | |||
589 | dataset.write(tmp_path / "test.fits") |
||
590 | |||
591 | dataset_new = MapDataset.read(tmp_path / "test.fits") |
||
592 | |||
593 | assert dataset_new.name == "test" |
||
594 | |||
595 | assert dataset_new.mask.data.dtype == bool |
||
596 | |||
597 | assert_allclose(dataset.counts.data, dataset_new.counts.data) |
||
598 | assert_allclose( |
||
599 | dataset.npred_background().data, dataset_new.npred_background().data |
||
600 | ) |
||
601 | assert_allclose(dataset.edisp.edisp_map.data, dataset_new.edisp.edisp_map.data) |
||
602 | assert_allclose(dataset.psf.psf_map.data, dataset_new.psf.psf_map.data) |
||
603 | assert_allclose(dataset.exposure.data, dataset_new.exposure.data) |
||
604 | assert_allclose(dataset.mask_fit.data, dataset_new.mask_fit.data) |
||
605 | assert_allclose(dataset.mask_safe.data, dataset_new.mask_safe.data) |
||
606 | |||
607 | assert dataset.counts.geom == dataset_new.counts.geom |
||
608 | assert dataset.exposure.geom == dataset_new.exposure.geom |
||
609 | |||
610 | assert_allclose(dataset.exposure.meta["livetime"], 1 * u.h) |
||
611 | assert dataset.npred_background().geom == dataset_new.npred_background().geom |
||
612 | assert dataset.edisp.edisp_map.geom == dataset_new.edisp.edisp_map.geom |
||
613 | |||
614 | assert_allclose( |
||
615 | dataset.gti.time_sum.to_value("s"), dataset_new.gti.time_sum.to_value("s") |
||
616 | ) |
||
617 | |||
618 | # To test io of psf and edisp map |
||
619 | stacked = MapDataset.create(geom) |
||
620 | stacked.write(tmp_path / "test-2.fits", overwrite=True) |
||
621 | stacked1 = MapDataset.read(tmp_path / "test-2.fits") |
||
622 | assert stacked1.psf.psf_map is not None |
||
623 | assert stacked1.psf.exposure_map is not None |
||
624 | assert stacked1.edisp.edisp_map is not None |
||
625 | assert stacked1.edisp.exposure_map is not None |
||
626 | assert stacked.mask.data.dtype == bool |
||
627 | |||
628 | assert_allclose(stacked1.psf.psf_map, stacked.psf.psf_map) |
||
629 | assert_allclose(stacked1.edisp.edisp_map, stacked.edisp.edisp_map) |
||
630 | |||
631 | |||
632 | @requires_data() |
||
633 | def test_map_fit(sky_model, geom, geom_etrue): |
||
634 | dataset_1 = get_map_dataset(geom, geom_etrue, name="test-1") |
||
635 | dataset_2 = get_map_dataset(geom, geom_etrue, name="test-2") |
||
636 | datasets = Datasets([dataset_1, dataset_2]) |
||
637 | |||
638 | models = Models(datasets.models) |
||
639 | models.insert(0, sky_model) |
||
640 | |||
641 | models["test-1-bkg"].spectral_model.norm.value = 0.5 |
||
642 | models["test-model"].spatial_model.sigma.frozen = True |
||
643 | |||
644 | datasets.models = models |
||
645 | dataset_2.counts = dataset_2.npred() |
||
646 | dataset_1.counts = dataset_1.npred() |
||
647 | |||
648 | models["test-1-bkg"].spectral_model.norm.value = 0.49 |
||
649 | models["test-2-bkg"].spectral_model.norm.value = 0.99 |
||
650 | |||
651 | fit = Fit() |
||
652 | result = fit.run(datasets=datasets) |
||
653 | |||
654 | assert result.success |
||
655 | assert "minuit" in repr(result) |
||
656 | |||
657 | npred = dataset_1.npred().data.sum() |
||
658 | assert_allclose(npred, 7525.790688, rtol=1e-3) |
||
659 | assert_allclose(result.total_stat, 21625.845714, rtol=1e-3) |
||
660 | |||
661 | pars = models.parameters |
||
662 | assert_allclose(pars["lon_0"].value, 0.2, rtol=1e-2) |
||
663 | assert_allclose(pars["lon_0"].error, 0.002244, rtol=1e-2) |
||
664 | |||
665 | assert_allclose(pars["index"].value, 3, rtol=1e-2) |
||
666 | assert_allclose(pars["index"].error, 0.0242, rtol=1e-2) |
||
667 | |||
668 | assert_allclose(pars["amplitude"].value, 1e-11, rtol=1e-2) |
||
669 | assert_allclose(pars["amplitude"].error, 4.216e-13, rtol=1e-2) |
||
670 | |||
671 | # background norm 1 |
||
672 | assert_allclose(pars[8].value, 0.5, rtol=1e-2) |
||
673 | assert_allclose(pars[8].error, 0.015811, rtol=1e-2) |
||
674 | |||
675 | # background norm 2 |
||
676 | assert_allclose(pars[11].value, 1, rtol=1e-2) |
||
677 | assert_allclose(pars[11].error, 0.02147, rtol=1e-2) |
||
678 | |||
679 | # test mask_safe evaluation |
||
680 | dataset_1.mask_safe = geom.energy_mask(energy_min=1 * u.TeV) |
||
681 | dataset_2.mask_safe = geom.energy_mask(energy_min=1 * u.TeV) |
||
682 | |||
683 | stat = datasets.stat_sum() |
||
684 | assert_allclose(stat, 14823.772744, rtol=1e-5) |
||
685 | |||
686 | region = sky_model.spatial_model.to_region() |
||
687 | |||
688 | initial_counts = dataset_1.counts.copy() |
||
689 | with mpl_plot_check(): |
||
690 | dataset_1.plot_residuals(kwargs_spectral=dict(region=region)) |
||
691 | |||
692 | # check dataset has not changed |
||
693 | assert initial_counts == dataset_1.counts |
||
694 | |||
695 | # test model evaluation outside image |
||
696 | dataset_1.models[0].spatial_model.lon_0.value = 150 |
||
697 | dataset_1.npred() |
||
698 | assert not dataset_1.evaluators["test-model"].contributes |
||
699 | |||
700 | |||
701 | @requires_data() |
||
702 | def test_map_fit_linked(sky_model, geom, geom_etrue): |
||
703 | dataset_1 = get_map_dataset(geom, geom_etrue, name="test-1") |
||
704 | dataset_2 = get_map_dataset(geom, geom_etrue, name="test-2") |
||
705 | datasets = Datasets([dataset_1, dataset_2]) |
||
706 | |||
707 | models = Models(datasets.models) |
||
708 | models.insert(0, sky_model) |
||
709 | sky_model2 = sky_model.copy(name="test-model-2") |
||
710 | sky_model2.spectral_model.index = sky_model.spectral_model.index |
||
711 | sky_model2.spectral_model.reference = sky_model.spectral_model.reference |
||
712 | |||
713 | models.insert(0, sky_model2) |
||
714 | |||
715 | models["test-1-bkg"].spectral_model.norm.value = 0.5 |
||
716 | models["test-model"].spatial_model.sigma.frozen = True |
||
717 | |||
718 | datasets.models = models |
||
719 | dataset_2.counts = dataset_2.npred() |
||
720 | dataset_1.counts = dataset_1.npred() |
||
721 | |||
722 | models["test-1-bkg"].spectral_model.norm.value = 0.49 |
||
723 | models["test-2-bkg"].spectral_model.norm.value = 0.99 |
||
724 | |||
725 | fit = Fit() |
||
726 | result = fit.run(datasets=datasets) |
||
727 | |||
728 | assert result.success |
||
729 | assert "minuit" in repr(result) |
||
730 | |||
731 | assert sky_model2.parameters["index"] is sky_model.parameters["index"] |
||
732 | assert sky_model2.parameters["reference"] is sky_model.parameters["reference"] |
||
733 | |||
734 | assert len(datasets.models.parameters.unique_parameters) == 20 |
||
735 | assert datasets.models.covariance.shape == (22, 22) |
||
736 | |||
737 | |||
738 | @requires_data() |
||
739 | def test_map_fit_one_energy_bin(sky_model, geom_image): |
||
740 | energy_axis = geom_image.axes["energy"] |
||
741 | geom_etrue = geom_image.to_image().to_cube([energy_axis.copy(name="energy_true")]) |
||
742 | |||
743 | dataset = get_map_dataset(geom_image, geom_etrue) |
||
744 | |||
745 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
||
746 | dataset.models = [sky_model, bkg_model] |
||
747 | |||
748 | sky_model.spectral_model.index.value = 3.0 |
||
749 | sky_model.spectral_model.index.frozen = True |
||
750 | dataset.models[f"{dataset.name}-bkg"].spectral_model.norm.value = 0.5 |
||
751 | |||
752 | dataset.counts = dataset.npred() |
||
753 | |||
754 | # Move a bit away from the best-fit point, to make sure the optimiser runs |
||
755 | sky_model.parameters["sigma"].value = 0.21 |
||
756 | dataset.models[f"{dataset.name}-bkg"].parameters["norm"].frozen = True |
||
757 | |||
758 | fit = Fit() |
||
759 | result = fit.run(datasets=[dataset]) |
||
760 | |||
761 | assert result.success |
||
762 | |||
763 | npred = dataset.npred().data.sum() |
||
764 | assert_allclose(npred, 16538.124036, rtol=1e-3) |
||
765 | assert_allclose(result.total_stat, -34844.125047, rtol=1e-3) |
||
766 | |||
767 | pars = sky_model.parameters |
||
768 | |||
769 | assert_allclose(pars["lon_0"].value, 0.2, rtol=1e-2) |
||
770 | assert_allclose(pars["lon_0"].error, 0.001689, rtol=1e-2) |
||
771 | |||
772 | assert_allclose(pars["sigma"].value, 0.2, rtol=1e-2) |
||
773 | assert_allclose(pars["sigma"].error, 0.00092, rtol=1e-2) |
||
774 | |||
775 | assert_allclose(pars["amplitude"].value, 1e-11, rtol=1e-2) |
||
776 | assert_allclose(pars["amplitude"].error, 8.127593e-14, rtol=1e-2) |
||
777 | |||
778 | |||
779 | def test_create(): |
||
780 | # tests empty datasets created |
||
781 | rad_axis = MapAxis(nodes=np.linspace(0.0, 1.0, 51), unit="deg", name="rad") |
||
782 | e_reco = MapAxis.from_edges( |
||
783 | np.logspace(-1.0, 1.0, 3), name="energy", unit=u.TeV, interp="log" |
||
784 | ) |
||
785 | e_true = MapAxis.from_edges( |
||
786 | np.logspace(-1.0, 1.0, 4), name="energy_true", unit=u.TeV, interp="log" |
||
787 | ) |
||
788 | geom = WcsGeom.create(binsz=0.02, width=(2, 2), axes=[e_reco]) |
||
789 | empty_dataset = MapDataset.create( |
||
790 | geom=geom, energy_axis_true=e_true, rad_axis=rad_axis |
||
791 | ) |
||
792 | |||
793 | assert empty_dataset.counts.data.shape == (2, 100, 100) |
||
794 | |||
795 | assert empty_dataset.exposure.data.shape == (3, 100, 100) |
||
796 | |||
797 | assert empty_dataset.psf.psf_map.data.shape == (3, 50, 10, 10) |
||
798 | assert empty_dataset.psf.exposure_map.data.shape == (3, 1, 10, 10) |
||
799 | |||
800 | assert isinstance(empty_dataset.edisp, EDispKernelMap) |
||
801 | assert empty_dataset.edisp.edisp_map.data.shape == (3, 2, 10, 10) |
||
802 | assert empty_dataset.edisp.exposure_map.data.shape == (3, 1, 10, 10) |
||
803 | assert_allclose(empty_dataset.edisp.edisp_map.data.sum(), 300) |
||
804 | |||
805 | assert_allclose(empty_dataset.gti.time_delta, 0.0 * u.s) |
||
806 | |||
807 | |||
808 | def test_create_with_migra(tmp_path): |
||
809 | # tests empty datasets created |
||
810 | migra_axis = MapAxis(nodes=np.linspace(0.0, 3.0, 51), unit="", name="migra") |
||
811 | rad_axis = MapAxis(nodes=np.linspace(0.0, 1.0, 51), unit="deg", name="rad") |
||
812 | e_reco = MapAxis.from_edges( |
||
813 | np.logspace(-1.0, 1.0, 3), name="energy", unit=u.TeV, interp="log" |
||
814 | ) |
||
815 | e_true = MapAxis.from_edges( |
||
816 | np.logspace(-1.0, 1.0, 4), name="energy_true", unit=u.TeV, interp="log" |
||
817 | ) |
||
818 | geom = WcsGeom.create(binsz=0.02, width=(2, 2), axes=[e_reco]) |
||
819 | empty_dataset = MapDataset.create( |
||
820 | geom=geom, energy_axis_true=e_true, migra_axis=migra_axis, rad_axis=rad_axis |
||
821 | ) |
||
822 | |||
823 | empty_dataset.write(tmp_path / "test.fits") |
||
824 | |||
825 | dataset_new = MapDataset.read(tmp_path / "test.fits") |
||
826 | |||
827 | assert isinstance(empty_dataset.edisp, EDispMap) |
||
828 | assert empty_dataset.edisp.edisp_map.data.shape == (3, 50, 10, 10) |
||
829 | assert empty_dataset.edisp.exposure_map.data.shape == (3, 1, 10, 10) |
||
830 | assert_allclose(empty_dataset.edisp.edisp_map.data.sum(), 5000) |
||
831 | |||
832 | assert_allclose(empty_dataset.gti.time_delta, 0.0 * u.s) |
||
833 | |||
834 | assert isinstance(dataset_new.edisp, EDispMap) |
||
835 | assert dataset_new.edisp.edisp_map.data.shape == (3, 50, 10, 10) |
||
836 | |||
837 | |||
838 | def test_stack(sky_model): |
||
839 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=3) |
||
840 | geom = WcsGeom.create( |
||
841 | skydir=(266.40498829, -28.93617776), |
||
842 | binsz=0.05, |
||
843 | width=(2, 2), |
||
844 | frame="icrs", |
||
845 | axes=[axis], |
||
846 | ) |
||
847 | axis_etrue = MapAxis.from_energy_bounds( |
||
848 | "0.1 TeV", "10 TeV", nbin=5, name="energy_true" |
||
849 | ) |
||
850 | geom_etrue = WcsGeom.create( |
||
851 | skydir=(266.40498829, -28.93617776), |
||
852 | binsz=0.05, |
||
853 | width=(2, 2), |
||
854 | frame="icrs", |
||
855 | axes=[axis_etrue], |
||
856 | ) |
||
857 | |||
858 | edisp = EDispKernelMap.from_diagonal_response( |
||
859 | energy_axis=axis, energy_axis_true=axis_etrue, geom=geom |
||
860 | ) |
||
861 | edisp.exposure_map.quantity = ( |
||
862 | 1e0 * u.m**2 * u.s * np.ones(edisp.exposure_map.data.shape) |
||
863 | ) |
||
864 | |||
865 | bkg1 = Map.from_geom(geom) |
||
866 | bkg1.data += 0.2 |
||
867 | |||
868 | cnt1 = Map.from_geom(geom) |
||
869 | cnt1.data = 1.0 * np.ones(cnt1.data.shape) |
||
870 | |||
871 | exp1 = Map.from_geom(geom_etrue) |
||
872 | exp1.quantity = 1e7 * u.m**2 * u.s * np.ones(exp1.data.shape) |
||
873 | |||
874 | mask1 = Map.from_geom(geom) |
||
875 | mask1.data = np.ones(mask1.data.shape, dtype=bool) |
||
876 | mask1.data[0][:][5:10] = False |
||
877 | dataset1 = MapDataset( |
||
878 | counts=cnt1, |
||
879 | background=bkg1, |
||
880 | exposure=exp1, |
||
881 | mask_safe=mask1, |
||
882 | mask_fit=mask1, |
||
883 | name="dataset-1", |
||
884 | edisp=edisp, |
||
885 | meta_table=Table({"OBS_ID": [0]}), |
||
886 | ) |
||
887 | |||
888 | bkg2 = Map.from_geom(geom) |
||
889 | bkg2.data = 0.1 * np.ones(bkg2.data.shape) |
||
890 | |||
891 | cnt2 = Map.from_geom(geom) |
||
892 | cnt2.data = 1.0 * np.ones(cnt2.data.shape) |
||
893 | |||
894 | exp2 = Map.from_geom(geom_etrue) |
||
895 | exp2.quantity = 1e7 * u.m**2 * u.s * np.ones(exp2.data.shape) |
||
896 | |||
897 | mask2 = Map.from_geom(geom) |
||
898 | mask2.data = np.ones(mask2.data.shape, dtype=bool) |
||
899 | mask2.data[0][:][5:10] = False |
||
900 | mask2.data[1][:][10:15] = False |
||
901 | |||
902 | dataset2 = MapDataset( |
||
903 | counts=cnt2, |
||
904 | background=bkg2, |
||
905 | exposure=exp2, |
||
906 | mask_safe=mask2, |
||
907 | mask_fit=mask2, |
||
908 | name="dataset-2", |
||
909 | edisp=edisp, |
||
910 | meta_table=Table({"OBS_ID": [1]}), |
||
911 | ) |
||
912 | |||
913 | background_model2 = FoVBackgroundModel(dataset_name="dataset-2") |
||
914 | background_model1 = FoVBackgroundModel(dataset_name="dataset-1") |
||
915 | |||
916 | dataset1.models = [background_model1, sky_model] |
||
917 | dataset2.models = [background_model2, sky_model] |
||
918 | |||
919 | stacked = MapDataset.from_geoms(**dataset1.geoms) |
||
920 | stacked.stack(dataset1) |
||
921 | stacked.stack(dataset2) |
||
922 | |||
923 | stacked.models = [sky_model] |
||
924 | npred_b = stacked.npred() |
||
925 | |||
926 | assert_allclose(npred_b.data.sum(), 1459.985035, 1e-5) |
||
927 | assert_allclose(stacked.npred_background().data.sum(), 1360.00, 1e-5) |
||
928 | assert_allclose(stacked.counts.data.sum(), 9000, 1e-5) |
||
929 | assert_allclose(stacked.mask_safe.data.sum(), 4600) |
||
930 | assert_allclose(stacked.mask_fit.data.sum(), 4600) |
||
931 | assert_allclose(stacked.exposure.data.sum(), 1.6e11) |
||
932 | |||
933 | assert_allclose(stacked.meta_table["OBS_ID"][0], [0, 1]) |
||
934 | |||
935 | |||
936 | @requires_data() |
||
937 | def test_npred(sky_model, geom, geom_etrue): |
||
938 | dataset = get_map_dataset(geom, geom_etrue) |
||
939 | |||
940 | pwl = PowerLawSpectralModel() |
||
941 | gauss = GaussianSpatialModel( |
||
942 | lon_0="0.0 deg", lat_0="0.0 deg", sigma="0.5 deg", frame="galactic" |
||
943 | ) |
||
944 | model1 = SkyModel(pwl, gauss, name="m1") |
||
945 | |||
946 | bkg = FoVBackgroundModel(dataset_name=dataset.name) |
||
947 | dataset.models = [bkg, sky_model, model1] |
||
948 | |||
949 | assert_allclose( |
||
950 | dataset.npred_signal(model_name=model1.name).data.sum(), 150.7487, rtol=1e-3 |
||
951 | ) |
||
952 | assert dataset._background_cached is None |
||
953 | assert_allclose(dataset.npred_background().data.sum(), 4000.0, rtol=1e-3) |
||
954 | assert_allclose(dataset._background_cached.data.sum(), 4000.0, rtol=1e-3) |
||
955 | |||
956 | assert_allclose(dataset.npred().data.sum(), 9676.047906, rtol=1e-3) |
||
957 | assert_allclose(dataset.npred_signal().data.sum(), 5676.04790, rtol=1e-3) |
||
958 | |||
959 | bkg.spectral_model.norm.value = 1.1 |
||
960 | assert_allclose(dataset.npred_background().data.sum(), 4400.0, rtol=1e-3) |
||
961 | assert_allclose(dataset._background_cached.data.sum(), 4400.0, rtol=1e-3) |
||
962 | |||
963 | with pytest.raises( |
||
964 | KeyError, |
||
965 | match="m2", |
||
966 | ): |
||
967 | dataset.npred_signal(model_name="m2") |
||
968 | |||
969 | |||
970 | def test_stack_npred(): |
||
971 | pwl = PowerLawSpectralModel() |
||
972 | gauss = GaussianSpatialModel(sigma="0.2 deg") |
||
973 | model = SkyModel(pwl, gauss) |
||
974 | |||
975 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=5) |
||
976 | axis_etrue = MapAxis.from_energy_bounds( |
||
977 | "0.1 TeV", "10 TeV", nbin=11, name="energy_true" |
||
978 | ) |
||
979 | |||
980 | geom = WcsGeom.create( |
||
981 | skydir=(0, 0), |
||
982 | binsz=0.05, |
||
983 | width=(2, 2), |
||
984 | frame="icrs", |
||
985 | axes=[axis], |
||
986 | ) |
||
987 | |||
988 | dataset_1 = MapDataset.create( |
||
989 | geom, |
||
990 | energy_axis_true=axis_etrue, |
||
991 | name="dataset-1", |
||
992 | gti=GTI.create("0 min", "30 min"), |
||
993 | ) |
||
994 | dataset_1.psf = None |
||
995 | dataset_1.exposure.data += 1 |
||
996 | dataset_1.mask_safe = geom.energy_mask(energy_min=1 * u.TeV) |
||
997 | dataset_1.background.data += 1 |
||
998 | |||
999 | bkg_model_1 = FoVBackgroundModel(dataset_name=dataset_1.name) |
||
1000 | dataset_1.models = [model, bkg_model_1] |
||
1001 | |||
1002 | dataset_2 = MapDataset.create( |
||
1003 | geom, |
||
1004 | energy_axis_true=axis_etrue, |
||
1005 | name="dataset-2", |
||
1006 | gti=GTI.create("30 min", "60 min"), |
||
1007 | ) |
||
1008 | dataset_2.psf = None |
||
1009 | dataset_2.exposure.data += 1 |
||
1010 | dataset_2.mask_safe = geom.energy_mask(energy_min=0.2 * u.TeV) |
||
1011 | dataset_2.background.data += 1 |
||
1012 | |||
1013 | bkg_model_2 = FoVBackgroundModel(dataset_name=dataset_2.name) |
||
1014 | dataset_2.models = [model, bkg_model_2] |
||
1015 | |||
1016 | npred_1 = dataset_1.npred() |
||
1017 | npred_1.data[~dataset_1.mask_safe.data] = 0 |
||
1018 | npred_2 = dataset_2.npred() |
||
1019 | npred_2.data[~dataset_2.mask_safe.data] = 0 |
||
1020 | |||
1021 | stacked_npred = Map.from_geom(geom) |
||
1022 | stacked_npred.stack(npred_1) |
||
1023 | stacked_npred.stack(npred_2) |
||
1024 | |||
1025 | stacked = MapDataset.create(geom, energy_axis_true=axis_etrue, name="stacked") |
||
1026 | stacked.stack(dataset_1) |
||
1027 | stacked.stack(dataset_2) |
||
1028 | |||
1029 | npred_stacked = stacked.npred() |
||
1030 | |||
1031 | assert_allclose(npred_stacked.data, stacked_npred.data) |
||
1032 | |||
1033 | |||
1034 | def to_cube(image): |
||
1035 | # introduce a fake energy axis for now |
||
1036 | axis = MapAxis.from_edges([1, 10] * u.TeV, name="energy") |
||
1037 | geom = image.geom.to_cube([axis]) |
||
1038 | return WcsNDMap.from_geom(geom=geom, data=image.data) |
||
1039 | |||
1040 | |||
1041 | @pytest.fixture |
||
1042 | def images(): |
||
1043 | """Load some `counts`, `counts_off`, `acceptance_on`, `acceptance_off" images""" |
||
1044 | filename = "$GAMMAPY_DATA/tests/unbundled/hess/survey/hess_survey_snippet.fits.gz" |
||
1045 | return { |
||
1046 | "counts": to_cube(WcsNDMap.read(filename, hdu="ON")), |
||
1047 | "counts_off": to_cube(WcsNDMap.read(filename, hdu="OFF")), |
||
1048 | "acceptance": to_cube(WcsNDMap.read(filename, hdu="ONEXPOSURE")), |
||
1049 | "acceptance_off": to_cube(WcsNDMap.read(filename, hdu="OFFEXPOSURE")), |
||
1050 | "exposure": to_cube(WcsNDMap.read(filename, hdu="EXPGAMMAMAP")), |
||
1051 | "background": to_cube(WcsNDMap.read(filename, hdu="BACKGROUND")), |
||
1052 | } |
||
1053 | |||
1054 | |||
1055 | def test_npred_psf_after_edisp(): |
||
1056 | energy_axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3) |
||
1057 | energy_axis_true = MapAxis.from_energy_bounds( |
||
1058 | "0.8 TeV", "15 TeV", nbin=6, name="energy_true" |
||
1059 | ) |
||
1060 | |||
1061 | geom = WcsGeom.create(width=4 * u.deg, binsz=0.02, axes=[energy_axis]) |
||
1062 | dataset = MapDataset.create(geom=geom, energy_axis_true=energy_axis_true) |
||
1063 | dataset.background.data += 1 |
||
1064 | dataset.exposure.data += 1e12 |
||
1065 | dataset.mask_safe.data += True |
||
1066 | dataset.psf = PSFMap.from_gauss( |
||
1067 | energy_axis_true=energy_axis_true, sigma=0.2 * u.deg |
||
1068 | ) |
||
1069 | |||
1070 | model = SkyModel( |
||
1071 | spectral_model=PowerLawSpectralModel(), |
||
1072 | spatial_model=PointSpatialModel(), |
||
1073 | name="test-model", |
||
1074 | ) |
||
1075 | |||
1076 | model.apply_irf["psf_after_edisp"] = True |
||
1077 | |||
1078 | bkg_model = FoVBackgroundModel(dataset_name=dataset.name) |
||
1079 | dataset.models = [bkg_model, model] |
||
1080 | |||
1081 | npred = dataset.npred() |
||
1082 | assert_allclose(npred.data.sum(), 129553.858658) |
||
1083 | |||
1084 | |||
1085 | def get_map_dataset_onoff(images, **kwargs): |
||
1086 | """Returns a MapDatasetOnOff""" |
||
1087 | mask_geom = images["counts"].geom |
||
1088 | mask_data = np.ones(images["counts"].data.shape, dtype=bool) |
||
1089 | mask_safe = Map.from_geom(mask_geom, data=mask_data) |
||
1090 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1091 | energy_axis = mask_geom.axes["energy"] |
||
1092 | energy_axis_true = energy_axis.copy(name="energy_true") |
||
1093 | |||
1094 | psf = PSFMap.from_gauss( |
||
1095 | energy_axis_true=energy_axis_true, sigma=0.2 * u.deg, geom=mask_geom.to_image() |
||
1096 | ) |
||
1097 | |||
1098 | edisp = EDispKernelMap.from_diagonal_response( |
||
1099 | energy_axis=energy_axis, energy_axis_true=energy_axis_true, geom=mask_geom |
||
1100 | ) |
||
1101 | |||
1102 | return MapDatasetOnOff( |
||
1103 | counts=images["counts"], |
||
1104 | counts_off=images["counts_off"], |
||
1105 | acceptance=images["acceptance"], |
||
1106 | acceptance_off=images["acceptance_off"], |
||
1107 | exposure=images["exposure"], |
||
1108 | mask_safe=mask_safe, |
||
1109 | psf=psf, |
||
1110 | edisp=edisp, |
||
1111 | gti=gti, |
||
1112 | name="MapDatasetOnOff-test", |
||
1113 | **kwargs, |
||
1114 | ) |
||
1115 | |||
1116 | |||
1117 | @requires_data() |
||
1118 | @pytest.mark.parametrize("lazy", [False, True]) |
||
1119 | def test_map_dataset_on_off_fits_io(images, lazy, tmp_path): |
||
1120 | dataset = get_map_dataset_onoff(images) |
||
1121 | dataset.meta_table = Table(data=[[1.0 * u.h], [111]], names=["livetime", "obs_id"]) |
||
1122 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1123 | dataset.gti = gti |
||
1124 | |||
1125 | hdulist = dataset.to_hdulist() |
||
1126 | actual = [hdu.name for hdu in hdulist] |
||
1127 | |||
1128 | desired = [ |
||
1129 | "PRIMARY", |
||
1130 | "COUNTS", |
||
1131 | "COUNTS_BANDS", |
||
1132 | "EXPOSURE", |
||
1133 | "EXPOSURE_BANDS", |
||
1134 | "EDISP", |
||
1135 | "EDISP_BANDS", |
||
1136 | "EDISP_EXPOSURE", |
||
1137 | "EDISP_EXPOSURE_BANDS", |
||
1138 | "PSF", |
||
1139 | "PSF_BANDS", |
||
1140 | "PSF_EXPOSURE", |
||
1141 | "PSF_EXPOSURE_BANDS", |
||
1142 | "MASK_SAFE", |
||
1143 | "MASK_SAFE_BANDS", |
||
1144 | "GTI", |
||
1145 | "META_TABLE", |
||
1146 | "COUNTS_OFF", |
||
1147 | "COUNTS_OFF_BANDS", |
||
1148 | "ACCEPTANCE", |
||
1149 | "ACCEPTANCE_BANDS", |
||
1150 | "ACCEPTANCE_OFF", |
||
1151 | "ACCEPTANCE_OFF_BANDS", |
||
1152 | ] |
||
1153 | |||
1154 | assert actual == desired |
||
1155 | |||
1156 | dataset.write(tmp_path / "test.fits") |
||
1157 | |||
1158 | if lazy: |
||
1159 | with pytest.raises(NotImplementedError): |
||
1160 | dataset_new = MapDatasetOnOff.read(tmp_path / "test.fits", lazy=lazy) |
||
1161 | else: |
||
1162 | dataset_new = MapDatasetOnOff.read(tmp_path / "test.fits", lazy=lazy) |
||
1163 | assert dataset_new.name == "MapDatasetOnOff-test" |
||
1164 | assert dataset_new.mask.data.dtype == bool |
||
1165 | assert dataset_new.meta_table["livetime"] == 1.0 * u.h |
||
1166 | assert dataset_new.meta_table["obs_id"] == 111 |
||
1167 | |||
1168 | assert_allclose(dataset.counts.data, dataset_new.counts.data) |
||
1169 | assert_allclose(dataset.counts_off.data, dataset_new.counts_off.data) |
||
1170 | assert_allclose(dataset.acceptance.data, dataset_new.acceptance.data) |
||
1171 | assert_allclose(dataset.acceptance_off.data, dataset_new.acceptance_off.data) |
||
1172 | assert_allclose(dataset.exposure.data, dataset_new.exposure.data) |
||
1173 | assert_allclose(dataset.mask_safe, dataset_new.mask_safe) |
||
1174 | |||
1175 | assert np.all(dataset.mask_safe.data == dataset_new.mask_safe.data) |
||
1176 | assert dataset.mask_safe.geom == dataset_new.mask_safe.geom |
||
1177 | assert dataset.counts.geom == dataset_new.counts.geom |
||
1178 | assert dataset.exposure.geom == dataset_new.exposure.geom |
||
1179 | |||
1180 | assert_allclose( |
||
1181 | dataset.gti.time_sum.to_value("s"), dataset_new.gti.time_sum.to_value("s") |
||
1182 | ) |
||
1183 | |||
1184 | assert dataset.psf.psf_map == dataset_new.psf.psf_map |
||
1185 | assert dataset.psf.exposure_map == dataset_new.psf.exposure_map |
||
1186 | assert dataset.edisp.edisp_map == dataset_new.edisp.edisp_map |
||
1187 | assert dataset.edisp.exposure_map == dataset_new.edisp.exposure_map |
||
1188 | |||
1189 | |||
1190 | @requires_data() |
||
1191 | def test_map_datasets_on_off_fits_io(images, tmp_path): |
||
1192 | dataset = get_map_dataset_onoff(images) |
||
1193 | Datasets([dataset]).write(tmp_path / "test.yaml") |
||
1194 | datasets = Datasets.read(tmp_path / "test.yaml", lazy=False) |
||
1195 | with pytest.raises(NotImplementedError): |
||
1196 | datasets = Datasets.read(tmp_path / "test.yaml", lazy=True) |
||
1197 | |||
1198 | dataset_new = datasets[0] |
||
1199 | |||
1200 | assert dataset.name == dataset_new.name |
||
1201 | assert_allclose(dataset.counts.data, dataset_new.counts.data) |
||
1202 | assert_allclose(dataset.counts_off.data, dataset_new.counts_off.data) |
||
1203 | assert_allclose(dataset.acceptance.data, dataset_new.acceptance.data) |
||
1204 | assert_allclose(dataset.acceptance_off.data, dataset_new.acceptance_off.data) |
||
1205 | assert_allclose(dataset.exposure.data, dataset_new.exposure.data) |
||
1206 | assert_allclose(dataset.mask_safe, dataset_new.mask_safe) |
||
1207 | |||
1208 | |||
1209 | def test_create_onoff(geom): |
||
1210 | # tests empty datasets created |
||
1211 | |||
1212 | migra_axis = MapAxis(nodes=np.linspace(0.0, 3.0, 51), unit="", name="migra") |
||
1213 | rad_axis = MapAxis(nodes=np.linspace(0.0, 1.0, 51), unit="deg", name="rad") |
||
1214 | energy_axis = geom.axes["energy"].copy(name="energy_true") |
||
1215 | |||
1216 | empty_dataset = MapDatasetOnOff.create(geom, energy_axis, migra_axis, rad_axis) |
||
1217 | |||
1218 | assert_allclose(empty_dataset.counts.data.sum(), 0.0) |
||
1219 | assert_allclose(empty_dataset.counts_off.data.sum(), 0.0) |
||
1220 | assert_allclose(empty_dataset.acceptance.data.sum(), 0.0) |
||
1221 | assert_allclose(empty_dataset.acceptance_off.data.sum(), 0.0) |
||
1222 | |||
1223 | assert empty_dataset.psf.psf_map.data.shape == (2, 50, 10, 10) |
||
1224 | assert empty_dataset.psf.exposure_map.data.shape == (2, 1, 10, 10) |
||
1225 | |||
1226 | assert empty_dataset.edisp.edisp_map.data.shape == (2, 50, 10, 10) |
||
1227 | assert empty_dataset.edisp.exposure_map.data.shape == (2, 1, 10, 10) |
||
1228 | |||
1229 | assert_allclose(empty_dataset.edisp.edisp_map.data.sum(), 3333.333333) |
||
1230 | |||
1231 | assert_allclose(empty_dataset.gti.time_delta, 0.0 * u.s) |
||
1232 | |||
1233 | |||
1234 | @requires_data() |
||
1235 | def test_map_dataset_onoff_str(images): |
||
1236 | dataset = get_map_dataset_onoff(images) |
||
1237 | assert "MapDatasetOnOff" in str(dataset) |
||
1238 | |||
1239 | |||
1240 | @requires_data() |
||
1241 | def test_stack_onoff(images): |
||
1242 | dataset = get_map_dataset_onoff(images) |
||
1243 | stacked = dataset.copy() |
||
1244 | stacked.stack(dataset) |
||
1245 | |||
1246 | assert_allclose(stacked.counts.data.sum(), 2 * dataset.counts.data.sum()) |
||
1247 | assert_allclose(stacked.counts_off.data.sum(), 2 * dataset.counts_off.data.sum()) |
||
1248 | assert_allclose( |
||
1249 | stacked.acceptance.data.sum(), dataset.data_shape[1] * dataset.data_shape[2] |
||
1250 | ) |
||
1251 | assert_allclose(np.nansum(stacked.acceptance_off.data), 2.925793e08, rtol=1e-5) |
||
1252 | assert_allclose(stacked.exposure.data, 2.0 * dataset.exposure.data) |
||
1253 | |||
1254 | |||
1255 | def test_dataset_cutout_aligned(geom): |
||
1256 | dataset = MapDataset.create(geom) |
||
1257 | |||
1258 | kwargs = {"position": geom.center_skydir, "width": 1 * u.deg} |
||
1259 | geoms = {name: geom.cutout(**kwargs) for name, geom in dataset.geoms.items()} |
||
1260 | |||
1261 | cutout = MapDataset.from_geoms(**geoms, name="cutout") |
||
1262 | |||
1263 | assert dataset.counts.geom.is_aligned(cutout.counts.geom) |
||
1264 | assert dataset.exposure.geom.is_aligned(cutout.exposure.geom) |
||
1265 | assert dataset.edisp.edisp_map.geom.is_aligned(cutout.edisp.edisp_map.geom) |
||
1266 | assert dataset.psf.psf_map.geom.is_aligned(cutout.psf.psf_map.geom) |
||
1267 | |||
1268 | |||
1269 | def test_stack_onoff_cutout(geom_image): |
||
1270 | # Test stacking of cutouts |
||
1271 | energy_axis_true = MapAxis.from_energy_bounds( |
||
1272 | "1 TeV", "10 TeV", nbin=3, name="energy_true" |
||
1273 | ) |
||
1274 | |||
1275 | dataset = MapDatasetOnOff.create(geom_image, energy_axis_true=energy_axis_true) |
||
1276 | dataset.gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1277 | |||
1278 | kwargs = {"position": geom_image.center_skydir, "width": 1 * u.deg} |
||
1279 | geoms = {name: geom.cutout(**kwargs) for name, geom in dataset.geoms.items()} |
||
1280 | |||
1281 | dataset_cutout = MapDatasetOnOff.from_geoms(**geoms, name="cutout-dataset") |
||
1282 | dataset_cutout.gti = GTI.create( |
||
1283 | [0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00" |
||
1284 | ) |
||
1285 | dataset_cutout.mask_safe.data += True |
||
1286 | dataset_cutout.counts.data += 1 |
||
1287 | dataset_cutout.counts_off.data += 1 |
||
1288 | dataset_cutout.exposure.data += 1 |
||
1289 | |||
1290 | dataset.stack(dataset_cutout) |
||
1291 | |||
1292 | assert_allclose(dataset.counts.data.sum(), 2500) |
||
1293 | assert_allclose(dataset.counts_off.data.sum(), 2500) |
||
1294 | assert_allclose(dataset.alpha.data.sum(), 0) |
||
1295 | assert_allclose(dataset.exposure.data.sum(), 7500) |
||
1296 | assert dataset_cutout.name == "cutout-dataset" |
||
1297 | |||
1298 | |||
1299 | def test_datasets_io_no_model(tmpdir): |
||
1300 | axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=2) |
||
1301 | geom = WcsGeom.create(npix=(5, 5), axes=[axis]) |
||
1302 | dataset_1 = MapDataset.create(geom, name="dataset_1") |
||
1303 | dataset_2 = MapDataset.create(geom, name="dataset_2") |
||
1304 | |||
1305 | datasets = Datasets([dataset_1, dataset_2]) |
||
1306 | |||
1307 | datasets.write(filename=tmpdir / "datasets.yaml") |
||
1308 | |||
1309 | filename_1 = tmpdir / "dataset_1.fits" |
||
1310 | assert filename_1.exists() |
||
1311 | |||
1312 | filename_2 = tmpdir / "dataset_2.fits" |
||
1313 | assert filename_2.exists() |
||
1314 | |||
1315 | |||
1316 | @requires_data() |
||
1317 | def test_map_dataset_on_off_to_spectrum_dataset(images): |
||
1318 | dataset = get_map_dataset_onoff(images) |
||
1319 | |||
1320 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1321 | dataset.gti = gti |
||
1322 | |||
1323 | on_region = CircleSkyRegion( |
||
1324 | center=dataset.counts.geom.center_skydir, radius=0.1 * u.deg |
||
1325 | ) |
||
1326 | |||
1327 | spectrum_dataset = dataset.to_spectrum_dataset(on_region) |
||
1328 | |||
1329 | assert spectrum_dataset.counts.data[0] == 8 |
||
1330 | assert spectrum_dataset.data_shape == (1, 1, 1) |
||
1331 | assert spectrum_dataset.counts_off.data[0] == 33914 |
||
1332 | assert_allclose(spectrum_dataset.alpha.data[0], 0.0002143, atol=1e-7) |
||
1333 | |||
1334 | excess_map = images["counts"] - images["background"] |
||
1335 | excess_true = excess_map.get_spectrum(on_region, np.sum).data[0] |
||
1336 | |||
1337 | excess = spectrum_dataset.excess.data[0] |
||
1338 | assert_allclose(excess, excess_true, rtol=1e-3) |
||
1339 | |||
1340 | assert spectrum_dataset.name != dataset.name |
||
1341 | |||
1342 | |||
1343 | @requires_data() |
||
1344 | def test_map_dataset_on_off_to_spectrum_dataset_weights(): |
||
1345 | e_reco = MapAxis.from_bounds(1, 10, nbin=3, unit="TeV", name="energy") |
||
1346 | |||
1347 | geom = WcsGeom.create( |
||
1348 | skydir=(0, 0), width=(2.5, 2.5), binsz=0.5, axes=[e_reco], frame="galactic" |
||
1349 | ) |
||
1350 | counts = Map.from_geom(geom) |
||
1351 | counts.data += 1 |
||
1352 | counts_off = Map.from_geom(geom) |
||
1353 | counts_off.data += 2 |
||
1354 | acceptance = Map.from_geom(geom) |
||
1355 | acceptance.data += 1 |
||
1356 | acceptance_off = Map.from_geom(geom) |
||
1357 | acceptance_off.data += 4 |
||
1358 | |||
1359 | weights = Map.from_geom(geom, dtype="bool") |
||
1360 | weights.data[1:, 2:4, 2] = True |
||
1361 | |||
1362 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1363 | |||
1364 | dataset = MapDatasetOnOff( |
||
1365 | counts=counts, |
||
1366 | counts_off=counts_off, |
||
1367 | acceptance=acceptance, |
||
1368 | acceptance_off=acceptance_off, |
||
1369 | mask_safe=weights, |
||
1370 | gti=gti, |
||
1371 | ) |
||
1372 | |||
1373 | on_region = CircleSkyRegion( |
||
1374 | center=dataset.counts.geom.center_skydir, radius=1.5 * u.deg |
||
1375 | ) |
||
1376 | |||
1377 | spectrum_dataset = dataset.to_spectrum_dataset(on_region) |
||
1378 | |||
1379 | assert_allclose(spectrum_dataset.counts.data[:, 0, 0], [0, 2, 2]) |
||
1380 | assert_allclose(spectrum_dataset.counts_off.data[:, 0, 0], [0, 4, 4]) |
||
1381 | assert_allclose(spectrum_dataset.acceptance.data[:, 0, 0], [0, 0.08, 0.08]) |
||
1382 | assert_allclose(spectrum_dataset.acceptance_off.data[:, 0, 0], [0, 0.32, 0.32]) |
||
1383 | assert_allclose(spectrum_dataset.alpha.data[:, 0, 0], [0, 0.25, 0.25]) |
||
1384 | |||
1385 | |||
1386 | @requires_data() |
||
1387 | def test_map_dataset_on_off_cutout(images): |
||
1388 | dataset = get_map_dataset_onoff(images) |
||
1389 | gti = GTI.create([0 * u.s], [1 * u.h], reference_time="2010-01-01T00:00:00") |
||
1390 | dataset.gti = gti |
||
1391 | |||
1392 | cutout_dataset = dataset.cutout( |
||
1393 | images["counts"].geom.center_skydir, ["1 deg", "1 deg"] |
||
1394 | ) |
||
1395 | |||
1396 | assert cutout_dataset.counts.data.shape == (1, 50, 50) |
||
1397 | assert cutout_dataset.counts_off.data.shape == (1, 50, 50) |
||
1398 | assert cutout_dataset.acceptance.data.shape == (1, 50, 50) |
||
1399 | assert cutout_dataset.acceptance_off.data.shape == (1, 50, 50) |
||
1400 | assert cutout_dataset.name != dataset.name |
||
1401 | |||
1402 | |||
1403 | def test_map_dataset_on_off_fake(geom): |
||
1404 | rad_axis = MapAxis(nodes=np.linspace(0.0, 1.0, 51), unit="deg", name="rad") |
||
1405 | energy_true_axis = geom.axes["energy"].copy(name="energy_true") |
||
1406 | |||
1407 | empty_dataset = MapDatasetOnOff.create(geom, energy_true_axis, rad_axis=rad_axis) |
||
1408 | empty_dataset.acceptance.data = 1.0 |
||
1409 | empty_dataset.acceptance_off.data = 10.0 |
||
1410 | |||
1411 | empty_dataset.acceptance_off.data[0, 50, 50] = 0 |
||
1412 | background_map = Map.from_geom(geom, data=1) |
||
1413 | empty_dataset.fake(background_map, random_state=42) |
||
1414 | |||
1415 | assert_allclose(empty_dataset.counts.data[0, 50, 50], 0) |
||
1416 | assert_allclose(empty_dataset.counts.data.mean(), 0.99445, rtol=1e-3) |
||
1417 | assert_allclose(empty_dataset.counts_off.data.mean(), 10.00055, rtol=1e-3) |
||
1418 | |||
1419 | |||
1420 | @requires_data() |
||
1421 | def test_map_dataset_on_off_to_image(): |
||
1422 | axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV") |
||
1423 | geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis]) |
||
1424 | |||
1425 | counts = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1426 | counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1427 | acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1428 | acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1429 | acceptance_off *= 2 |
||
1430 | |||
1431 | dataset = MapDatasetOnOff( |
||
1432 | counts=counts, |
||
1433 | counts_off=counts_off, |
||
1434 | acceptance=acceptance, |
||
1435 | acceptance_off=acceptance_off, |
||
1436 | ) |
||
1437 | image_dataset = dataset.to_image() |
||
1438 | |||
1439 | assert image_dataset.counts.data.shape == (1, 10, 10) |
||
1440 | assert image_dataset.acceptance_off.data.shape == (1, 10, 10) |
||
1441 | assert_allclose(image_dataset.acceptance, 2) |
||
1442 | assert_allclose(image_dataset.acceptance_off, 4) |
||
1443 | assert_allclose(image_dataset.counts_off, 2) |
||
1444 | assert image_dataset.name != dataset.name |
||
1445 | |||
1446 | # Try with a safe_mask |
||
1447 | mask_safe = Map.from_geom(geom, data=np.ones((2, 10, 10), dtype="bool")) |
||
1448 | mask_safe.data[0] = 0 |
||
1449 | dataset.mask_safe = mask_safe |
||
1450 | image_dataset = dataset.to_image() |
||
1451 | |||
1452 | assert_allclose(image_dataset.acceptance, 1) |
||
1453 | assert_allclose(image_dataset.acceptance_off, 2) |
||
1454 | assert_allclose(image_dataset.counts_off, 1) |
||
1455 | |||
1456 | |||
1457 | def test_map_dataset_geom(geom, sky_model): |
||
1458 | e_true = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=5, name="energy_true") |
||
1459 | dataset = MapDataset.create(geom, energy_axis_true=e_true) |
||
1460 | dataset.counts = None |
||
1461 | dataset.background = None |
||
1462 | |||
1463 | npred = dataset.npred() |
||
1464 | assert npred.geom == geom |
||
1465 | |||
1466 | dataset.mask_safe = None |
||
1467 | dataset.mask_fit = None |
||
1468 | |||
1469 | with pytest.raises(ValueError): |
||
1470 | dataset._geom |
||
1471 | |||
1472 | |||
1473 | @requires_data() |
||
1474 | def test_names(geom, geom_etrue, sky_model): |
||
1475 | m = Map.from_geom(geom) |
||
1476 | m.quantity = 0.2 * np.ones(m.data.shape) |
||
1477 | background_model1 = FoVBackgroundModel(dataset_name="test") |
||
1478 | assert background_model1.name == "test-bkg" |
||
1479 | |||
1480 | c_map1 = Map.from_geom(geom) |
||
1481 | c_map1.quantity = 0.3 * np.ones(c_map1.data.shape) |
||
1482 | |||
1483 | model1 = sky_model.copy() |
||
1484 | assert model1.name != sky_model.name |
||
1485 | model1 = sky_model.copy(name="model1") |
||
1486 | assert model1.name == "model1" |
||
1487 | model2 = sky_model.copy(name="model2") |
||
1488 | |||
1489 | dataset1 = MapDataset( |
||
1490 | counts=c_map1, |
||
1491 | models=Models([model1, model2, background_model1]), |
||
1492 | exposure=get_exposure(geom_etrue), |
||
1493 | background=m, |
||
1494 | name="test", |
||
1495 | ) |
||
1496 | |||
1497 | dataset2 = dataset1.copy() |
||
1498 | assert dataset2.name != dataset1.name |
||
1499 | assert dataset2.models is None |
||
1500 | |||
1501 | dataset2 = dataset1.copy(name="dataset2") |
||
1502 | |||
1503 | assert dataset2.name == "dataset2" |
||
1504 | assert dataset2.models is None |
||
1505 | |||
1506 | |||
1507 | def test_stack_dataset_dataset_on_off(): |
||
1508 | axis = MapAxis.from_edges([1, 10] * u.TeV, name="energy") |
||
1509 | geom = WcsGeom.create(width=1, axes=[axis]) |
||
1510 | |||
1511 | gti = GTI.create([0 * u.s], [1 * u.h]) |
||
1512 | |||
1513 | dataset = MapDataset.create(geom, gti=gti) |
||
1514 | dataset_on_off = MapDatasetOnOff.create(geom, gti=gti) |
||
1515 | dataset_on_off.mask_safe.data += True |
||
1516 | |||
1517 | dataset_on_off.acceptance_off += 5 |
||
1518 | dataset_on_off.acceptance += 1 |
||
1519 | dataset_on_off.counts_off += 1 |
||
1520 | dataset.stack(dataset_on_off) |
||
1521 | |||
1522 | assert_allclose(dataset.npred_background().data, 0.166667, rtol=1e-3) |
||
1523 | |||
1524 | |||
1525 | @requires_data() |
||
1526 | def test_info_dict_on_off(images): |
||
1527 | dataset = get_map_dataset_onoff(images) |
||
1528 | info_dict = dataset.info_dict() |
||
1529 | assert_allclose(info_dict["counts"], 4299, rtol=1e-3) |
||
1530 | assert_allclose(info_dict["excess"], -22.52295, rtol=1e-3) |
||
1531 | assert_allclose(info_dict["exposure_min"].value, 1.739467e08, rtol=1e-3) |
||
1532 | assert_allclose(info_dict["exposure_max"].value, 3.4298378e09, rtol=1e-3) |
||
1533 | assert_allclose(info_dict["npred"], 4321.518, rtol=1e-3) |
||
1534 | assert_allclose(info_dict["counts_off"], 20407510.0, rtol=1e-3) |
||
1535 | assert_allclose(info_dict["acceptance"], 4272.7075, rtol=1e-3) |
||
1536 | assert_allclose(info_dict["acceptance_off"], 20175596.0, rtol=1e-3) |
||
1537 | assert_allclose(info_dict["alpha"], 0.00021176, rtol=1e-3) |
||
1538 | assert_allclose(info_dict["ontime"].value, 3600) |
||
1539 | |||
1540 | |||
1541 | def test_slice_by_idx(): |
||
1542 | axis = MapAxis.from_energy_bounds("0.1 TeV", "10 TeV", nbin=17) |
||
1543 | axis_etrue = MapAxis.from_energy_bounds( |
||
1544 | "0.1 TeV", "10 TeV", nbin=31, name="energy_true" |
||
1545 | ) |
||
1546 | |||
1547 | geom = WcsGeom.create( |
||
1548 | skydir=(0, 0), |
||
1549 | binsz=0.5, |
||
1550 | width=(2, 2), |
||
1551 | frame="icrs", |
||
1552 | axes=[axis], |
||
1553 | ) |
||
1554 | dataset = MapDataset.create(geom=geom, energy_axis_true=axis_etrue, binsz_irf=0.5) |
||
1555 | |||
1556 | slices = {"energy": slice(5, 10)} |
||
1557 | sub_dataset = dataset.slice_by_idx(slices) |
||
1558 | |||
1559 | assert sub_dataset.counts.geom.data_shape == (5, 4, 4) |
||
1560 | assert sub_dataset.mask_safe.geom.data_shape == (5, 4, 4) |
||
1561 | assert sub_dataset.npred_background().geom.data_shape == (5, 4, 4) |
||
1562 | assert sub_dataset.exposure.geom.data_shape == (31, 4, 4) |
||
1563 | assert sub_dataset.edisp.edisp_map.geom.data_shape == (31, 5, 4, 4) |
||
1564 | assert sub_dataset.psf.psf_map.geom.data_shape == (31, 66, 4, 4) |
||
1565 | |||
1566 | axis = sub_dataset.counts.geom.axes["energy"] |
||
1567 | assert_allclose(axis.edges[0].value, 0.387468, rtol=1e-5) |
||
1568 | |||
1569 | slices = {"energy_true": slice(5, 10)} |
||
1570 | sub_dataset = dataset.slice_by_idx(slices) |
||
1571 | |||
1572 | assert sub_dataset.counts.geom.data_shape == (17, 4, 4) |
||
1573 | assert sub_dataset.mask_safe.geom.data_shape == (17, 4, 4) |
||
1574 | assert sub_dataset.npred_background().geom.data_shape == (17, 4, 4) |
||
1575 | assert sub_dataset.exposure.geom.data_shape == (5, 4, 4) |
||
1576 | assert sub_dataset.edisp.edisp_map.geom.data_shape == (5, 17, 4, 4) |
||
1577 | assert sub_dataset.psf.psf_map.geom.data_shape == (5, 66, 4, 4) |
||
1578 | |||
1579 | axis = sub_dataset.counts.geom.axes["energy"] |
||
1580 | assert_allclose(axis.edges[0].value, 0.1, rtol=1e-5) |
||
1581 | |||
1582 | axis = sub_dataset.exposure.geom.axes["energy_true"] |
||
1583 | assert_allclose(axis.edges[0].value, 0.210175, rtol=1e-5) |
||
1584 | |||
1585 | |||
1586 | def test_plot_residual_onoff(): |
||
1587 | axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV") |
||
1588 | geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis]) |
||
1589 | |||
1590 | counts = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1591 | counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1592 | acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1593 | acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1594 | acceptance_off *= 2 |
||
1595 | |||
1596 | dataset = MapDatasetOnOff( |
||
1597 | counts=counts, |
||
1598 | counts_off=counts_off, |
||
1599 | acceptance=acceptance, |
||
1600 | acceptance_off=acceptance_off, |
||
1601 | ) |
||
1602 | with mpl_plot_check(): |
||
1603 | dataset.plot_residuals_spatial() |
||
1604 | |||
1605 | |||
1606 | def test_to_map_dataset(): |
||
1607 | axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV") |
||
1608 | geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis]) |
||
1609 | |||
1610 | counts = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1611 | counts_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1612 | acceptance = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1613 | acceptance_off = Map.from_geom(geom, data=np.ones((2, 10, 10))) |
||
1614 | acceptance_off *= 2 |
||
1615 | |||
1616 | dataset_onoff = MapDatasetOnOff( |
||
1617 | counts=counts, |
||
1618 | counts_off=counts_off, |
||
1619 | acceptance=acceptance, |
||
1620 | acceptance_off=acceptance_off, |
||
1621 | ) |
||
1622 | |||
1623 | dataset = dataset_onoff.to_map_dataset(name="ds") |
||
1624 | |||
1625 | assert dataset.name == "ds" |
||
1626 | assert_allclose(dataset.npred_background().data.sum(), 100) |
||
1627 | assert isinstance(dataset, MapDataset) |
||
1628 | assert dataset.counts == dataset_onoff.counts |
||
1629 | |||
1630 | |||
1631 | def test_downsample_onoff(): |
||
1632 | axis = MapAxis.from_energy_bounds(1, 10, 4, unit="TeV") |
||
1633 | geom = WcsGeom.create(npix=(10, 10), binsz=0.05, axes=[axis]) |
||
1634 | |||
1635 | counts = Map.from_geom(geom, data=np.ones((4, 10, 10))) |
||
1636 | counts_off = Map.from_geom(geom, data=np.ones((4, 10, 10))) |
||
1637 | acceptance = Map.from_geom(geom, data=np.ones((4, 10, 10))) |
||
1638 | acceptance_off = Map.from_geom(geom, data=np.ones((4, 10, 10))) |
||
1639 | acceptance_off *= 2 |
||
1640 | |||
1641 | dataset_onoff = MapDatasetOnOff( |
||
1642 | counts=counts, |
||
1643 | counts_off=counts_off, |
||
1644 | acceptance=acceptance, |
||
1645 | acceptance_off=acceptance_off, |
||
1646 | ) |
||
1647 | |||
1648 | downsampled = dataset_onoff.downsample(2, axis_name="energy") |
||
1649 | |||
1650 | assert downsampled.counts.data.shape == (2, 10, 10) |
||
1651 | assert downsampled.counts.data.sum() == dataset_onoff.counts.data.sum() |
||
1652 | assert downsampled.counts_off.data.sum() == dataset_onoff.counts_off.data.sum() |
||
1653 | assert_allclose(downsampled.alpha.data, 0.5) |
||
1654 | |||
1655 | |||
1656 | @requires_data() |
||
1657 | def test_source_outside_geom(sky_model, geom, geom_etrue): |
||
1658 | dataset = get_map_dataset(geom, geom_etrue) |
||
1659 | dataset.edisp = get_edisp(geom, geom_etrue) |
||
1660 | |||
1661 | models = dataset.models |
||
1662 | model = SkyModel( |
||
1663 | PowerLawSpectralModel(), |
||
1664 | DiskSpatialModel(lon_0=276.4 * u.deg, lat_0=-28.9 * u.deg, r_0=10 * u.deg), |
||
1665 | ) |
||
1666 | |||
1667 | assert not geom.to_image().contains(model.position)[0] |
||
1668 | dataset.models = models + [model] |
||
1669 | dataset.npred() |
||
1670 | model_npred = dataset.evaluators[model.name].compute_npred().data |
||
1671 | assert np.sum(np.isnan(model_npred)) == 0 |
||
1672 | assert np.sum(~np.isfinite(model_npred)) == 0 |
||
1673 | assert np.sum(model_npred) > 0 |
||
1674 | |||
1675 | |||
1676 | # this is a regression test for an issue found, where the model selection fails |
||
1677 | @requires_data() |
||
1678 | def test_source_outside_geom_fermi(): |
||
1679 | dataset = MapDataset.read( |
||
1680 | "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz", format="gadf" |
||
1681 | ) |
||
1682 | |||
1683 | catalog = SourceCatalog3FHL() |
||
1684 | source = catalog["3FHL J1637.8-3448"] |
||
1685 | |||
1686 | dataset.models = source.sky_model() |
||
1687 | npred = dataset.npred() |
||
1688 | |||
1689 | assert_allclose(npred.data.sum(), 28548.63, rtol=1e-4) |
||
1690 | |||
1691 | |||
1692 | def test_region_geom_io(tmpdir): |
||
1693 | axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=1) |
||
1694 | geom = RegionGeom.create("icrs;circle(0, 0, 0.2)", axes=[axis]) |
||
1695 | |||
1696 | dataset = MapDataset.create(geom, name="geom-test") |
||
1697 | |||
1698 | filename = tmpdir / "test.fits" |
||
1699 | dataset.write(filename) |
||
1700 | |||
1701 | dataset = MapDataset.read(filename, format="gadf") |
||
1702 | |||
1703 | assert dataset.name == "geom-test" |
||
1704 | assert isinstance(dataset.counts.geom, RegionGeom) |
||
1705 | assert isinstance(dataset.edisp.edisp_map.geom, RegionGeom) |
||
1706 | assert isinstance(dataset.psf.psf_map.geom, RegionGeom) |
||
1707 | |||
1708 | |||
1709 | def test_dataset_mixed_geom(tmpdir): |
||
1710 | energy_axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=3) |
||
1711 | energy_axis_true = MapAxis.from_energy_bounds( |
||
1712 | "1 TeV", "10 TeV", nbin=7, name="energy_true" |
||
1713 | ) |
||
1714 | |||
1715 | rad_axis = MapAxis.from_bounds(0, 1, nbin=10, name="rad", unit="deg") |
||
1716 | |||
1717 | geom = WcsGeom.create(npix=5, axes=[energy_axis]) |
||
1718 | geom_exposure = WcsGeom.create(npix=5, axes=[energy_axis_true]) |
||
1719 | |||
1720 | geom_psf = RegionGeom.create( |
||
1721 | "icrs;circle(0, 0, 0.2)", axes=[rad_axis, energy_axis_true] |
||
1722 | ) |
||
1723 | |||
1724 | geom_edisp = RegionGeom.create( |
||
1725 | "icrs;circle(0, 0, 0.2)", axes=[energy_axis, energy_axis_true] |
||
1726 | ) |
||
1727 | |||
1728 | dataset = MapDataset.from_geoms( |
||
1729 | geom=geom, geom_exposure=geom_exposure, geom_psf=geom_psf, geom_edisp=geom_edisp |
||
1730 | ) |
||
1731 | |||
1732 | filename = tmpdir / "test.fits" |
||
1733 | dataset.write(filename) |
||
1734 | |||
1735 | dataset = MapDataset.read(filename, format="gadf") |
||
1736 | |||
1737 | assert isinstance(dataset.counts.geom, WcsGeom) |
||
1738 | assert isinstance(dataset.exposure.geom, WcsGeom) |
||
1739 | assert isinstance(dataset.background.geom, WcsGeom) |
||
1740 | |||
1741 | assert isinstance(dataset.psf.psf_map.geom.region, CircleSkyRegion) |
||
1742 | assert isinstance(dataset.edisp.edisp_map.geom.region, CircleSkyRegion) |
||
1743 | |||
1744 | geom_psf_reco = RegionGeom.create( |
||
1745 | "icrs;circle(0, 0, 0.2)", axes=[rad_axis, energy_axis] |
||
1746 | ) |
||
1747 | |||
1748 | dataset = MapDataset.from_geoms( |
||
1749 | geom=geom, |
||
1750 | geom_exposure=geom_exposure, |
||
1751 | geom_psf=geom_psf_reco, |
||
1752 | geom_edisp=geom_edisp, |
||
1753 | ) |
||
1754 | assert dataset.psf.tag == "psf_map_reco" |
||
1755 | |||
1756 | |||
1757 | @requires_data() |
||
1758 | def test_map_dataset_region_geom_npred(): |
||
1759 | dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz") |
||
1760 | |||
1761 | pwl = PowerLawSpectralModel() |
||
1762 | point = PointSpatialModel(lon_0="0 deg", lat_0="0 deg", frame="galactic") |
||
1763 | model_1 = SkyModel(pwl, point, name="model-1") |
||
1764 | |||
1765 | pwl = PowerLawSpectralModel(amplitude="1e-11 TeV-1 cm-2 s-1") |
||
1766 | gauss = GaussianSpatialModel( |
||
1767 | lon_0="0.3 deg", lat_0="0.3 deg", sigma="0.5 deg", frame="galactic" |
||
1768 | ) |
||
1769 | model_2 = SkyModel(pwl, gauss, name="model-2") |
||
1770 | |||
1771 | dataset.models = [model_1, model_2] |
||
1772 | |||
1773 | region = RegionGeom.create("galactic;circle(0, 0, 0.4)").region |
||
1774 | npred_ref = dataset.npred().to_region_nd_map(region) |
||
1775 | |||
1776 | dataset_spec = dataset.to_region_map_dataset(region) |
||
1777 | dataset_spec.models = [model_1, model_2] |
||
1778 | |||
1779 | npred = dataset_spec.npred() |
||
1780 | |||
1781 | assert_allclose(npred_ref.data, npred.data, rtol=1e-2) |
||
1782 | |||
1783 | |||
1784 | View Code Duplication | @requires_dependency("healpy") |
|
|
|||
1785 | def test_map_dataset_create_hpx_geom(geom_hpx): |
||
1786 | |||
1787 | dataset = MapDataset.create(**geom_hpx, binsz_irf=10 * u.deg) |
||
1788 | |||
1789 | assert isinstance(dataset.counts.geom, HpxGeom) |
||
1790 | assert dataset.counts.data.shape == (3, 12288) |
||
1791 | |||
1792 | assert isinstance(dataset.background.geom, HpxGeom) |
||
1793 | assert dataset.background.data.shape == (3, 12288) |
||
1794 | |||
1795 | assert isinstance(dataset.exposure.geom, HpxGeom) |
||
1796 | assert dataset.exposure.data.shape == (4, 12288) |
||
1797 | |||
1798 | assert isinstance(dataset.edisp.edisp_map.geom, HpxGeom) |
||
1799 | assert dataset.edisp.edisp_map.data.shape == (4, 3, 768) |
||
1800 | |||
1801 | assert isinstance(dataset.psf.psf_map.geom, HpxGeom) |
||
1802 | assert dataset.psf.psf_map.data.shape == (4, 66, 768) |
||
1803 | |||
1804 | |||
1805 | View Code Duplication | @requires_dependency("healpy") |
|
1806 | def test_map_dataset_create_hpx_geom_partial(geom_hpx_partial): |
||
1807 | |||
1808 | dataset = MapDataset.create(**geom_hpx_partial, binsz_irf=2 * u.deg) |
||
1809 | |||
1810 | assert isinstance(dataset.counts.geom, HpxGeom) |
||
1811 | assert dataset.counts.data.shape == (3, 90) |
||
1812 | |||
1813 | assert isinstance(dataset.background.geom, HpxGeom) |
||
1814 | assert dataset.background.data.shape == (3, 90) |
||
1815 | |||
1816 | assert isinstance(dataset.exposure.geom, HpxGeom) |
||
1817 | assert dataset.exposure.data.shape == (4, 90) |
||
1818 | |||
1819 | assert isinstance(dataset.edisp.edisp_map.geom, HpxGeom) |
||
1820 | assert dataset.edisp.edisp_map.data.shape == (4, 3, 24) |
||
1821 | |||
1822 | assert isinstance(dataset.psf.psf_map.geom, HpxGeom) |
||
1823 | assert dataset.psf.psf_map.data.shape == (4, 66, 24) |
||
1824 | |||
1825 | |||
1826 | @requires_dependency("healpy") |
||
1827 | def test_map_dataset_stack_hpx_geom(geom_hpx_partial, geom_hpx): |
||
1828 | |||
1829 | dataset_all = MapDataset.create(**geom_hpx, binsz_irf=5 * u.deg) |
||
1830 | |||
1831 | gti = GTI.create(start=0 * u.s, stop=30 * u.min) |
||
1832 | dataset_cutout = MapDataset.create(**geom_hpx_partial, binsz_irf=5 * u.deg, gti=gti) |
||
1833 | dataset_cutout.counts.data += 1 |
||
1834 | dataset_cutout.background.data += 1 |
||
1835 | dataset_cutout.exposure.data += 1 |
||
1836 | dataset_cutout.mask_safe.data[...] = True |
||
1837 | |||
1838 | dataset_all.stack(dataset_cutout) |
||
1839 | |||
1840 | assert_allclose(dataset_all.counts.data.sum(), 3 * 90) |
||
1841 | assert_allclose(dataset_all.background.data.sum(), 3 * 90) |
||
1842 | assert_allclose(dataset_all.exposure.data.sum(), 4 * 90) |
||
1843 | |||
1844 | |||
1845 | @requires_data() |
||
1846 | @requires_dependency("healpy") |
||
1847 | def test_map_dataset_hpx_geom_npred(geom_hpx_partial): |
||
1848 | hpx_geom = geom_hpx_partial["geom"] |
||
1849 | hpx_true = hpx_geom.to_image().to_cube([geom_hpx_partial["energy_axis_true"]]) |
||
1850 | dataset = get_map_dataset(hpx_geom, hpx_true, edisp="edispkernelmap") |
||
1851 | |||
1852 | pwl = PowerLawSpectralModel() |
||
1853 | point = PointSpatialModel(lon_0="110 deg", lat_0="75 deg", frame="galactic") |
||
1854 | sky_model = SkyModel(pwl, point) |
||
1855 | |||
1856 | dataset.models = [sky_model] |
||
1857 | |||
1858 | assert_allclose(dataset.npred().data.sum(), 54, rtol=1e-3) |
||
1859 | |||
1860 | |||
1861 | @requires_data() |
||
1862 | def test_peek(images): |
||
1863 | dataset = get_map_dataset_onoff(images) |
||
1864 | |||
1865 | with mpl_plot_check(): |
||
1866 | dataset.peek() |
||
1867 |