def test_function_values_have_not_changed(): with h5py.File(_get_data_file_path("past_1D_values.h5"), "r") as f: eval_x = f["eval_values"][()] for key in _known_functions: this_function = _known_functions[key] # Test only the power law of XSpec, which is the only one we know we can test at 1 keV if key.find("XS") == 0 or (key in _multiplicative_models): # An XSpec model OR EBLattenuation function. Test it only if it's a power law (the others might need other parameters during # initialization) continue if key.find("TemplateModel") == 0: # The TemplateModel function has its own test continue # if key.find("Synchrotron")==0: # Naima Synchtron function should have its own test # continue if this_function._n_dim == 1: print("testing %s ..." % key) func = this_function() new_values = np.atleast_1d(func(eval_x)) with h5py.File(_get_data_file_path("past_1D_values.h5"), "r") as f: if key not in f.keys(): print( "the function %s does not exist in the past data. You must run a script to add it" % key) else: old_values = f[key][()] npt.assert_almost_equal(new_values, old_values)
def test_xspec_table_model(): test_table = _get_data_file_path("tests/test_xspec_table_model.fits") xtm = XSPECTableModel(test_table) xtm.to_table_model('xspectm_test', 'xspec model', overwrite=True)
def test_table_conversion(): old_table_file = _get_data_file_path("tests/old_table.h5") p = Path.home() / ".astromodels" / "data" / "old_table.h5" shutil.copy(old_table_file, p) # convert the table convert_old_table_model("old_table") # now load the old table old_table = TemplateModel("old_table") # should be the same as in test test = TemplateModel("__test") xx = np.logspace(1, 3, 50) npt.assert_almost_equal(test(xx), old_table(xx)) p.unlink()
def build_tbabs_arg(ene): file_name = _get_data_file_path(Path("xsect/xsect_tbabs_wilm.fits")) fxs = fits.open(file_name) dxs = fxs[1].data xsect_ene = dxs["ENERGY"] xsect_val = dxs["SIGMA"] return np.interp(ene, xsect_ene, xsect_val)
def _get_xsect_table(model, abund_table): """ contructs the abundance table from the values given """ assert model in _abs_tables, "the model %s does not exist" % model assert abund_table in _abs_tables[model], ("the table %s does not exist" % abund_table) path_to_xsect = _get_data_file_path( os.path.join( "xsect", "xsect_%s_%s.fits" % (model, _abs_tables[model][abund_table]))) fxs = fits.open(path_to_xsect) dxs = fxs[1].data xsect_ene = dxs["ENERGY"] xsect_val = dxs["SIGMA"] return xsect_ene, xsect_val
# import sys import h5py from astromodels.functions.function import _known_functions from astromodels.functions.priors import * from astromodels.utils.data_files import _get_data_file_path eval_x = np.logspace(-1,3, 10) _multiplicative_models = ["PhAbs", "TbAbs", "WAbs", "APEC", "VAPEC"] input = int(sys.argv[-1]) file_path = _get_data_file_path("past_1D_values.h5") if input == 0: # do not regenerate only add with h5py.File(file_path, "r") as f: already_known_functions = list(f.keys()) flag = "a" elif input == 1: already_known_functions = [] flag = "w"
def _setup(self): tablepath = _get_data_file_path("dark_matter/gammamc_dif.dat") self._data = np.loadtxt(tablepath) """ Mapping between the channel codes and the rows in the gammamc file 1 : 8, # ee 2 : 6, # mumu 3 : 3, # tautau 4 : 1, # bb 5 : 2, # tt 6 : 7, # gg 7 : 4, # ww 8 : 5, # zz 9 : 0, # cc 10 : 10, # uu 11 : 11, # dd 12 : 9, # ss """ channel_index_mapping = { 1: 8, # ee 2: 6, # mumu 3: 3, # tautau 4: 1, # bb 5: 2, # tt 6: 7, # gg 7: 4, # ww 8: 5, # zz 9: 0, # cc 10: 10, # uu 11: 11, # dd 12: 9, # ss } # Number of decades in x = log10(E/M) ndec = 10.0 xedge = np.linspace(0, 1.0, 251) self._x = 0.5 * (xedge[1:] + xedge[:-1]) * ndec - ndec ichan = channel_index_mapping[int(self.channel.value)] # These are the mass points self._mass = np.array([2.0, 4.0, 6.0, 8.0, 10.0, 25.0, 50.0, 80.3, 91.2, 100.0, 150.0, 176.0, 200.0, 250.0, 350.0, 500.0, 750.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 1E4]) self._dn = self._data.reshape((12, 24, 250)) self._dn_interp = RegularGridInterpolator([self._mass, self._x], self._dn[ichan, :, :], bounds_error=False, fill_value=None) if self.mass.value > 10000: print("Warning: DMFitFunction only appropriate for masses <= 10 TeV") print("To model DM from 2 GeV < mass < 1 PeV use DMSpectra")
def _setup(self): # Get and open the two data files tablepath_h = _get_data_file_path("dark_matter/dmSpecTab.npy") self._data_h = np.load(tablepath_h) tablepath_f = _get_data_file_path("dark_matter/gammamc_dif.dat") self._data_f = np.loadtxt(tablepath_f) """ Mapping between the channel codes and the rows in the gammamc file dmSpecTab.npy created to match this mapping too 1 : 8, # ee 2 : 6, # mumu 3 : 3, # tautau 4 : 1, # bb 5 : 2, # tt 6 : 7, # gg 7 : 4, # ww 8 : 5, # zz 9 : 0, # cc 10 : 10, # uu 11 : 11, # dd 12 : 9, # ss """ channel_index_mapping = { 1: 8, # ee 2: 6, # mumu 3: 3, # tautau 4: 1, # bb 5: 2, # tt 6: 7, # gg 7: 4, # ww 8: 5, # zz 9: 0, # cc 10: 10, # uu 11: 11, # dd 12: 9, # ss } # Number of decades in x = log10(E/M) ndec = 10.0 xedge = np.linspace(0, 1.0, 251) self._x = 0.5 * (xedge[1:] + xedge[:-1]) * ndec - ndec ichan = channel_index_mapping[int(self.channel.value)] # These are the mass points in GeV self._mass_h = np.array([50., 61.2, 74.91, 91.69, 112.22, 137.36, 168.12, 205.78, 251.87, 308.29, 377.34, 461.86, 565.31, 691.93, 846.91, 1036.6, 1268.78, 1552.97, 1900.82, 2326.57, 2847.69, 3485.53, 4266.23, 5221.81, 6391.41, 7823.0, 9575.23, 11719.94, 14345.03, 17558.1, 21490.85, 26304.48, 32196.3, 39407.79, 48234.54, 59038.36, 72262.07, 88447.7, 108258.66, 132506.99, 162186.57, 198513.95, 242978.11, 297401.58, 364015.09, 445549.04, 545345.37, 667494.6, 817003.43, 1000000.]) # These are the mass points in GeV self._mass_f = np.array([2.0, 4.0, 6.0, 8.0, 10.0, 25.0, 50.0, 80.3, 91.2, 100.0, 150.0, 176.0, 200.0, 250.0, 350.0, 500.0, 750.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 1E4]) self._mass = np.append(self._mass_f, self._mass_h[27:]) self._dn_f = self._data_f.reshape((12, 24, 250)) # Is this really used? self._dn_h = self._data_h self._dn = np.zeros((12, len(self._mass), 250)) self._dn[:, 0:24, :] = self._dn_f self._dn[:, 24:, :] = self._dn_h[:, 27:, :] self._dn_interp = RegularGridInterpolator([self._mass, self._x], self._dn[ichan, :, :], bounds_error=False, fill_value=None) if self.channel.value in [1, 6, 7] and self.mass.value > 10000.: print("ERROR: currently spectra for selected channel and mass not implemented.") print("Spectra for channels ['ee','gg','WW'] currently not available for mass > 10 TeV")