def interp_midMVq(
Pk=5.0,
kappa=np.inf,
struct="pp",
sampling=1,
KDE_qz_sampling=101j,
do_single_spec=True,
KDE_pickle_loc=None,
error=0.05,
nproc=1,
diags=["[NII]/[SII]+;[OIII]/[SII]+"],
):
# 1: get the intermediate data points from the MV shell script
# Zs are identical, but at least we have changed the Qs
# Where are we ?
this_dir = os.path.dirname(__file__)
fn = os.path.join(this_dir, "grid_QZ_midQs_pp_GCZO_Pk50_kinf.csv")
metadata = pyqz.pyqz_tools.get_MVphotogrid_metadata(fn)
data = np.loadtxt(fn, comments="c", delimiter=",", skiprows=4)
# Build 'Pseudo' line fluxes - and then assume a 10% error
Hb = np.ones_like(data[:, 0])
Oiii = 10 ** data[:, metadata["columns"].index("[OIII]/Hb")]
Oiip = 10 ** data[:, metadata["columns"].index("[OII]+/Hb")]
Nii = Oiip * 10 ** data[:, metadata["columns"].index("[NII]/[OII]+")]
Siip = 1.0 / 10 ** data[:, metadata["columns"].index("[NII]/[SII]+")] * Nii
Ha = 1.0 / 10 ** data[:, metadata["columns"].index("[NII]/Ha")] * Nii
all_fluxes = np.zeros((len(Hb), 12))
for i in range(len(Hb)):
all_fluxes[i, 0] = 1.0
all_fluxes[i, 1] = all_fluxes[i, 0] * error
all_fluxes[i, 2] = Oiii[i]
all_fluxes[i, 3] = all_fluxes[i, 2] * error
all_fluxes[i, 4] = Oiip[i]
all_fluxes[i, 5] = all_fluxes[i, 4] * error
all_fluxes[i, 6] = Nii[i]
all_fluxes[i, 7] = all_fluxes[i, 6] * error
all_fluxes[i, 8] = Siip[i]
all_fluxes[i, 9] = all_fluxes[i, 8] * error
all_fluxes[i, 10] = Ha[i]
all_fluxes[i, 11] = all_fluxes[i, 10] * error
# Launch the interpolation
nspec = 16 # 16 on grid,14 = grid edge
if do_single_spec:
specs = np.reshape(all_fluxes[nspec, :], (1, 12))
else:
specs = np.ones((24, 12)) * np.reshape(all_fluxes[nspec, :], (1, 12))
results = pyqz.get_global_qz(
specs,
[
"Hb",
"stdHb",
"[OIII]",
"std[OIII]",
"[OII]+",
"std[OII]+",
"[NII]",
"std[NII]",
"[SII]+",
"std[SII]+",
"Ha",
"stdHa",
],
diags,
Pk=5.0,
kappa=np.inf,
struct="pp",
KDE_pickle_loc=KDE_pickle_loc,
sampling=sampling,
KDE_qz_sampling=KDE_qz_sampling,
KDE_method="multiv",
srs=400,
nproc=nproc,
verbose=True,
)
return (
np.abs(results[0][0, results[1].index("<LogQ>")] / data[nspec, metadata["columns"].index("LogQ")] - 1.0) < 0.01
and np.abs(
results[0][0, results[1].index("<LogQ{KDE}>")] / data[nspec, metadata["columns"].index("LogQ")] - 1.0
)
< 0.01
and np.abs(
results[0][0, results[1].index("<Tot[O]+12>")] / data[nspec, metadata["columns"].index("Tot[O]+12")] - 1.0
)
< 0.01
and np.abs(
results[0][0, results[1].index("<Tot[O]+12{KDE}>")] / data[nspec, metadata["columns"].index("Tot[O]+12")]
- 1.0
)
< 0.01
)
def get_bad_global_qz(Pk=5.0, kappa=np.inf, struct="pp", sampling=1, nproc=1, error=0.05, KDE_pickle_loc=None):
# 1: get the intermediate data points from the MV shell script
# Zs are identical, but at least we have chnaged the Qs
# Where are we ?
this_dir = os.path.dirname(__file__)
fn = os.path.join(this_dir, "grid_QZ_midQs_pp_GCZO_Pk50_kinf.csv")
metadata = pyqz.pyqz_tools.get_MVphotogrid_metadata(fn)
data = np.loadtxt(fn, comments="c", delimiter=",", skiprows=4)
# Build 'Pseudo' line fluxes
Hb = np.ones_like(data[:, 0])
Oiii = 10 ** data[:, metadata["columns"].index("[OIII]/Hb")]
Oiip = 10 ** data[:, metadata["columns"].index("[OII]+/Hb")]
Nii = Oiip * 10 ** data[:, metadata["columns"].index("[NII]/[OII]+")]
Siip = 1.0 / 10 ** data[:, metadata["columns"].index("[NII]/[SII]+")] * Nii
Ha = 1.0 / 10 ** data[:, metadata["columns"].index("[NII]/Ha")] * Nii
all_fluxes = np.zeros((len(Hb), 12))
for i in range(len(Hb)):
all_fluxes[i, 0] = 1.0
all_fluxes[i, 1] = all_fluxes[i, 0] * error
all_fluxes[i, 2] = Oiii[i] * 1e3 # Make this an obviously bad line.
all_fluxes[i, 3] = all_fluxes[i, 2] * error
all_fluxes[i, 4] = Oiip[i] / 1e3 # Idem
all_fluxes[i, 5] = all_fluxes[i, 4] * error
all_fluxes[i, 6] = Nii[i]
all_fluxes[i, 7] = all_fluxes[i, 6] * error
all_fluxes[i, 8] = Siip[i]
all_fluxes[i, 9] = all_fluxes[i, 8] * error
all_fluxes[i, 10] = Ha[i]
all_fluxes[i, 11] = all_fluxes[i, 10] * error
# Launch the interpolation
nspec = 16 # 16 on grid,14 = grid edge
results = pyqz.get_global_qz(
np.reshape(all_fluxes[nspec, :], (1, 12)),
[
"Hb",
"stdHb",
"[OIII]",
"std[OIII]",
"[OII]+",
"std[OII]+",
"[NII]",
"std[NII]",
"[SII]+",
"std[SII]+",
"Ha",
"stdHa",
],
["[NII]/[SII]+;[OIII]/Hb", "[NII]/[OII]+;[OIII]/[SII]+"],
Pk=5.0,
kappa=np.inf,
struct="pp",
KDE_pickle_loc=KDE_pickle_loc,
sampling=1,
KDE_qz_sampling=201j,
KDE_method="multiv",
srs=400,
nproc=nproc,
verbose=True,
)
return np.isnan(results[0][0, results[1].index("<LogQ>")])
