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")
