def _from_File(self, fname):
"""_from_File -- load the content of a FITS or HDF file
Parameters
----------
fname: str
filename (incl. path) to read from
"""
# load_seds - load wavelength and seds
if self._get_type(fname) == "fits":
with pyfits.open(fname) as f:
self.seds = f[0].data[:-1]
self.lamb = f[0].data[-1]
self.grid = Table(fname)
elif self._get_type(fname) == "hdf":
with HDFStore(fname, mode="r") as s:
self.seds = s["/seds"].read()
self.lamb = s["/lamb"].read()
try:
self.cov_diag = s["/covdiag"].read()
except Exception:
self.cov_diag = None
try:
self.cov_offdiag = s["/covoffdiag"].read()
except Exception:
self.cov_offdiag = None
self.grid = Table(fname, tablename="/grid")
self._header = self.grid.header
def _load_grid(self, fname):
"""load_grid - load grid table"""
# load_seds - load wavelength and seds
if self._grid is None:
if self._get_type(fname) == "fits":
self._grid = Table(self.fname)
elif self._get_type(fname) == "hdf":
self._grid = Table(self.fname, tablename="/grid")
def _load_table_(self, source):
t = Table(self.source)
data = {}
for k in list(t.keys()):
data[k] = t[k]
# Alias columns
data["logM"] = log10(np.asarray(data["M_ini"]))
data["logg"] = np.asarray(data["logG"])
data["logT"] = np.asarray(data["logTe"])
data["logL"] = np.asarray(data["logL/Lo"])
data["logA"] = np.asarray(data["log(age/yr)"])
# clean columns
data.pop("log(age/yr)")
data.pop("M_ini")
data.pop("logG")
data.pop("logTe")
data.pop("logL/Lo")
self.data = Table(data, name="Isochrone from %s" % self.name)
def _from_HDFBackend(self, b):
"""_from_HDFBackend -- convert from HDFBackend
Parameters
----------
b: GridBackend or sub class
backend to convert from
"""
self.lamb = b.lamb.read()
self.seds = b.seds.read()
self.grid = Table(b.grid.read())
self._filters = b._filters[:]
self._header = b.header
self._aliases = b._aliases
def make_extinguished_grid(
spec_grid,
filter_names,
extLaw,
avs,
rvs,
fAs=None,
av_prior_model={"name": "flat"},
rv_prior_model={"name": "flat"},
fA_prior_model={"name": "flat"},
chunksize=0,
add_spectral_properties_kwargs=None,
absflux_cov=False,
filterLib=None,
):
"""
Extinguish spectra and extract an SEDGrid through given series of filters
(all wavelengths in stellar SEDs and filter response functions are assumed
to be in Angstroms)
Parameters
----------
spec_grid: string or grid.SpectralGrid
if string:
spec_grid is the filename to the grid file with stellar spectra
the backend to load this grid will be the minimal invasive: 'HDF'
if possible, 'cache' otherwise.
if not a string, expecting the corresponding SpectralGrid instance
(backend already setup)
filter_names: list
list of filter names according to the filter lib
Avs: sequence
Av values to iterate over
av_prior_model: list
list including prior model name and parameters
Rvs: sequence
Rv values to iterate over
rv_prior_model: list
list including prior model name and parameters
fAs: sequence (optional)
f_A values to iterate over
f_A can be omitted if the extinction Law does not use it or allow
fixed values
fA_prior_model: list
list including prior model name and parameters
chunksize: int, optional (default=0)
number of extinction model variations to generate at each cycle.
Note that this means len(spec_grid * chunksize)
If default <= 0, all models will be returned at once.
filterLib: str
full filename to the filter library hd5 file
add_spectral_properties_kwargs: dict
keyword arguments to call :func:`add_spectral_properties` at each
iteration to add model properties from the spectra into the grid
property table
asbflux_cov: boolean
set to calculate the absflux covariance matrices for each model
(can be very slow!!! But it is the right thing to do)
Returns
-------
g: grid.SpectralGrid
final grid of reddened SEDs and models
"""
# Check inputs
# ============
# get the stellar grid (no dust yet)
# if string is provided try to load the most memory efficient backend
# otherwise use a cache-type backend (load only when needed)
if isinstance(spec_grid, str):
ext = spec_grid.split(".")[-1]
if ext in ["hdf", "hd5", "hdf5"]:
g0 = SpectralGrid(spec_grid, backend="hdf")
else:
g0 = SpectralGrid(spec_grid, backend="cache")
else:
helpers.type_checker("spec_grid", spec_grid, SpectralGrid)
g0 = spec_grid
# Tag fA usage
if fAs is None:
with_fA = False
else:
with_fA = True
# get the min/max R(V) values necessary for the grid point definition
min_Rv = min(rvs)
max_Rv = max(rvs)
# Create the sampling mesh
# ========================
# basically the dot product from all input 1d vectors
# setup interation over the full dust parameter grid
if with_fA:
dustpriors = PriorWeightsDust(avs, av_prior_model, rvs, rv_prior_model,
fAs, fA_prior_model)
it = np.nditer(np.ix_(avs, rvs, fAs))
niter = np.size(avs) * np.size(rvs) * np.size(fAs)
npts, pts = _make_dust_fA_valid_points_generator(it, min_Rv, max_Rv)
# Pet the user
print("""number of initially requested points = {0:d}
number of valid points = {1:d} (based on restrictions in R(V)
versus f_A plane)
""".format(niter, npts))
if npts == 0:
raise AttributeError("No valid points")
else:
dustpriors = PriorWeightsDust(avs, av_prior_model, rvs, rv_prior_model,
[1.0], fA_prior_model)
it = np.nditer(np.ix_(avs, rvs))
npts = np.size(avs) * np.size(rvs)
pts = ((float(ak), float(rk)) for ak, rk in it)
# Generate the Grid
# =================
N0 = len(g0.grid)
N = N0 * npts
if chunksize <= 0:
print("Generating a final grid of {0:d} points".format(N))
else:
print("Generating a final grid of {0:d} points in {1:d}" +
" pieces".format(N, int(float(N0) / chunksize + 1.0)))
if chunksize <= 0:
chunksize = npts
if add_spectral_properties_kwargs is not None:
nameformat = add_spectral_properties_kwargs.pop("nameformat",
"{0:s}") + "_wd"
for chunk_pts in helpers.chunks(pts, chunksize):
# iter over chunks of models
# setup chunk outputs
cols = {"Av": np.empty(N, dtype=float), "Rv": np.empty(N, dtype=float)}
if with_fA:
cols["Rv_A"] = np.empty(N, dtype=float)
cols["f_A"] = np.empty(N, dtype=float)
keys = list(g0.keys())
for key in keys:
cols[key] = np.empty(N, dtype=float)
n_filters = len(filter_names)
_seds = np.empty((N, n_filters), dtype=float)
if absflux_cov:
n_offdiag = ((n_filters**2) - n_filters) / 2
_cov_diag = np.empty((N, n_filters), dtype=float)
_cov_offdiag = np.empty((N, n_offdiag), dtype=float)
for count, pt in enumerate(tqdm(chunk_pts, desc="SED grid")):
if with_fA:
Av, Rv, f_A = pt
dust_prior_weight = dustpriors.get_weight(Av, Rv, f_A)
Rv_MW = extLaw.get_Rv_A(Rv, f_A)
r = g0.applyExtinctionLaw(extLaw,
Av=Av,
Rv=Rv,
f_A=f_A,
inplace=False)
# add extra "spectral bands" if requested
if add_spectral_properties_kwargs is not None:
r = add_spectral_properties(
r,
nameformat=nameformat,
filterLib=filterLib,
**add_spectral_properties_kwargs)
temp_results = r.getSEDs(filter_names, filterLib=filterLib)
# adding the dust parameters to the models
cols["Av"][N0 * count:N0 * (count + 1)] = Av
cols["Rv"][N0 * count:N0 * (count + 1)] = Rv
cols["f_A"][N0 * count:N0 * (count + 1)] = f_A
cols["Rv_A"][N0 * count:N0 * (count + 1)] = Rv_MW
else:
Av, Rv = pt
dust_prior_weight = dustpriors.get_weight(Av, Rv, 1.0)
r = g0.applyExtinctionLaw(extLaw, Av=Av, Rv=Rv, inplace=False)
if add_spectral_properties_kwargs is not None:
r = add_spectral_properties(
r,
nameformat=nameformat,
filterLib=filterLib,
**add_spectral_properties_kwargs)
temp_results = r.getSEDs(filter_names, filterLib=filterLib)
# adding the dust parameters to the models
cols["Av"][N0 * count:N0 * (count + 1)] = Av
cols["Rv"][N0 * count:N0 * (count + 1)] = Rv
# get new attributes if exist
for key in list(temp_results.grid.keys()):
if key not in keys:
k1 = N0 * count
k2 = N0 * (count + 1)
cols.setdefault(key, np.empty(
N, dtype=float))[k1:k2] = temp_results.grid[key]
# compute the fractional absflux covariance matrices
if absflux_cov:
absflux_covmats = calc_absflux_cov_matrices(
r, temp_results, filter_names)
_cov_diag[N0 * count:N0 * (count + 1)] = absflux_covmats[0]
_cov_offdiag[N0 * count:N0 * (count + 1)] = absflux_covmats[1]
# assign the extinguished SEDs to the output object
_seds[N0 * count:N0 * (count + 1)] = temp_results.seds[:]
# copy the rest of the parameters
for key in keys:
cols[key][N0 * count:N0 * (count + 1)] = g0.grid[key]
# multiply existing prior weights by the dust prior weight
cols["weight"][N0 * count:N0 * (count + 1)] *= dust_prior_weight
cols["prior_weight"][N0 * count:N0 *
(count + 1)] *= dust_prior_weight
if count == 0:
cols["lamb"] = temp_results.lamb[:]
_lamb = cols.pop("lamb")
# free the memory of temp_results
# del temp_results
# del tempgrid
# Ship
if absflux_cov:
g = SpectralGrid(
_lamb,
seds=_seds,
cov_diag=_cov_diag,
cov_offdiag=_cov_offdiag,
grid=Table(cols),
backend="memory",
)
else:
g = SpectralGrid(_lamb,
seds=_seds,
grid=Table(cols),
backend="memory")
g.grid.header["filters"] = " ".join(filter_names)
yield g
def apply_distance_grid(specgrid, distances, redshift=0):
"""
Distances are applied to the spectral grid by copying the grid and
applying a scaling factor.
Parameters
----------
project: str
project name
specgrid: grid.SpectralGrid object
spectral grid to transform
distances: list of float
Distances at which models should be shifted
0 means absolute magnitude.
Expecting pc units
redshift: float
Redshift to which wavelengths should be shifted
Default is 0 (rest frame)
"""
g0 = specgrid
# Current length of the grid
N0 = len(g0.grid)
N = N0 * len(distances)
# Make singleton list if a single distance is given
if not hasattr(distances, "__iter__"):
_distances = [distances]
else:
_distances = distances
# Add distance column if multiple distances are specified
cols = {}
cols["distance"] = np.empty(N, dtype=float)
# Existing columns
keys0 = list(g0.keys())
for key in keys0:
cols[key] = np.empty(N, dtype=float)
n_sed_points = g0.seds.shape[1]
new_seds = np.empty((N, n_sed_points), dtype=float)
for count, distance in enumerate(tqdm(_distances, desc="Distance grid")):
# The range where the current distance points will live
distance_slice = slice(N0 * count, N0 * (count + 1))
# The seds default to 10 pc.
# Therefore, scale them with (d / (10 pc))**(-2).
distance_pc = distance.to(units.pc).value
new_seds[distance_slice, :] = g0.seds / (0.1 * distance_pc)**2
# Fill in the distance in the distance column
cols["distance"][distance_slice] = distance_pc
# Copy the old columns
for key in keys0:
cols[key][distance_slice] = g0.grid[key]
# apply redshift
g0.lamb = g0.lamb * (1.0 + redshift)
# New object
g = SpectralGrid(g0.lamb,
seds=new_seds,
grid=Table(cols),
backend="memory")
return g
def trim_models(
sedgrid,
sedgrid_noisemodel,
obsdata,
sed_outname,
noisemodel_outname,
sigma_fac=3.0,
n_detected=4,
inFlux=True,
trunchen=False,
):
"""
For a given set of observations, there will be models that are so
bright or faint that they will always have ~0 probability of fitting
the data. This program trims those models out of the SED grid
so that time is not spent calculating model points that are always
zero probability.
Parameters
----------
sedgrid: grid.SEDgrid instance
model grid
sedgrid_noisemodel: beast noisemodel instance
noise model data
obsdata: Observation object instance
observation catalog
sed_outname: str
name for output sed file
noisemodel_outname: str
name for output noisemodel file
sigma_fac: float
factor for trimming the upper and lower range of grid so that
the model range cuts off sigma_fac above and below the brightest
and faintest models, respectively (default: 3.)
n_detected: int
minimum number of bands where ASTs yielded a detection for
a given model, if fewer detections than n_detected this model
gets eliminated (default: 4)
inFlux: boolean
if true data are in fluxes (default: True)
trunchen: boolean
if true use the trunchen noise model (default: False)
"""
# Store the brigtest and faintest fluxes in each band (for data and asts)
n_filters = len(obsdata.filters)
min_data = np.zeros(n_filters)
max_data = np.zeros(n_filters)
min_models = np.zeros(n_filters)
max_models = np.zeros(n_filters)
for k, filtername in enumerate(obsdata.filters):
sfiltname = obsdata.data.resolve_alias(filtername)
if inFlux:
min_data[k] = np.amin(obsdata.data[sfiltname] *
obsdata.vega_flux[k])
max_data[k] = np.amax(obsdata.data[sfiltname] *
obsdata.vega_flux[k])
else:
min_data[k] = np.amin(10**(-0.4 * obsdata.data[sfiltname]) *
obsdata.vega_flux[k])
max_data[k] = np.amax(10**(-0.4 * obsdata.data[sfiltname]) *
obsdata.vega_flux[k])
min_models[k] = np.amin(sedgrid.seds[:, k])
max_models[k] = np.amax(sedgrid.seds[:, k])
# first remove all models that have any band with fluxes below the
# faintest ASTs run
# when the noisemodel was computed, models with fluxes below the
# faintest ASTs were tagged with a negative error/uncertainty
# identify the models that have been detected in enough bands
# the idea here is that if the ASTs are not measured that means
# that *none* were recovered and this implies
# that no model with these values would be recovered and thus the
# probability should always be zero
model_unc = sedgrid_noisemodel.root.error[:]
above_ast = model_unc > 0
sum_above_ast = np.sum(above_ast, axis=1)
indxs, = np.where(sum_above_ast >= n_detected)
# cache the noisemodel values
model_bias = sedgrid_noisemodel.root.bias[:]
model_unc = np.fabs(sedgrid_noisemodel.root.error[:])
model_compl = sedgrid_noisemodel.root.completeness[:]
if trunchen:
model_q_norm = sedgrid_noisemodel.root.q_norm[:]
model_icov_diag = sedgrid_noisemodel.root.icov_diag[:]
model_icov_offdiag = sedgrid_noisemodel.root.icov_offdiag[:]
if len(indxs) <= 0:
raise ValueError("no models are brighter than the minimum ASTs run")
n_ast_indxs = len(indxs)
# Find models with fluxes (with margin) between faintest and brightest data
for k in range(n_filters):
print("working on filter # = ", k)
# Get upper and lower values for the models given the noise model
# sigma_fac defaults to 3.
model_val = sedgrid.seds[indxs, k] + model_bias[indxs, k]
model_down = model_val - sigma_fac * model_unc[indxs, k]
model_up = model_val + sigma_fac * model_unc[indxs, k]
nindxs, = np.where((model_up >= min_data[k])
& (model_down <= max_data[k]))
if len(nindxs) > 0:
indxs = indxs[nindxs]
if len(indxs) == 0:
raise ValueError("no models that are within the data range")
print("number of original models = ", len(sedgrid.seds[:, 0]))
print("number of ast trimmed models = ", n_ast_indxs)
print("number of trimmed models = ", len(indxs))
# Save the grid
print("Writing trimmed sedgrid to disk into {0:s}".format(sed_outname))
cols = {}
for key in list(sedgrid.grid.keys()):
cols[key] = sedgrid.grid[key][indxs]
# New column to save the index of the model in the full grid
cols["fullgrid_idx"] = indxs.astype(int)
g = SpectralGrid(sedgrid.lamb,
seds=sedgrid.seds[indxs],
grid=Table(cols),
backend="memory")
filternames = obsdata.filters
g.grid.header["filters"] = " ".join(filternames)
# trimmed grid name
g.writeHDF(sed_outname)
# save the trimmed noise model
print("Writing trimmed noisemodel to disk into {0:s}".format(
noisemodel_outname))
with tables.open_file(noisemodel_outname, "w") as outfile:
outfile.create_array(outfile.root, "bias", model_bias[indxs])
outfile.create_array(outfile.root, "error", model_unc[indxs])
outfile.create_array(outfile.root, "completeness", model_compl[indxs])
if trunchen:
outfile.create_array(outfile.root, "q_norm", model_q_norm[indxs])
outfile.create_array(outfile.root, "icov_diag",
model_icov_diag[indxs])
outfile.create_array(outfile.root, "icov_offdiag",
model_icov_offdiag[indxs])
def _get_continuous_isochrone(self, *args, **kwargs):
""" Return a resampled isochrone accounting for variations
useful for continuous sampling
"""
# define the maximum allowable difference between points
dm = kwargs.pop("dm", 0.01)
dt = kwargs.pop("dt", 0.01)
dl = kwargs.pop("dl", 0.01)
iso = self._get_isochrone(*args, **kwargs)
logT, logg, logL, logM = (iso["logT"], iso["logg"], iso["logL"],
iso["logM"])
# compute vector of discrete derivaties for each quantity
# and the final number of points
npts = (np.abs(np.divide(np.diff(logM), dm))).astype(int)
npts += (np.abs(np.divide(np.diff(logT), dt))).astype(int)
npts += (np.abs(np.divide(np.diff(logL), dl))).astype(int)
idx = np.hstack([[0], np.cumsum(npts + 1)])
# set up vectors for storage
ntot = (npts + 1).sum()
newm = np.empty(ntot, dtype=float)
newdm = np.empty(ntot, dtype=float)
newt = np.empty(ntot, dtype=float)
newg = np.empty(ntot, dtype=float)
newl = np.empty(ntot, dtype=float)
for i in range(len(npts)):
a, b = idx[i], idx[i] + npts[i] + 1
if npts[i] > 0:
# construct new 1d grids in each dimension, being careful
# about endpoints
# append them to storage vectors
newm[a:b] = np.linspace(logM[i],
logM[i + 1],
npts[i] + 1,
endpoint=False)
newt[a:b] = np.linspace(logT[i],
logT[i + 1],
npts[i] + 1,
endpoint=False)
newg[a:b] = np.linspace(logg[i],
logg[i + 1],
npts[i] + 1,
endpoint=False)
newl[a:b] = np.linspace(logL[i],
logL[i + 1],
npts[i] + 1,
endpoint=False)
newdm[a:b] = (np.ones(npts[i] + 1) * (logM[i + 1] - logM[i]) /
(npts[i] + 1))
else:
# if the maximumum allowable difference is small,
# then just store the good point
newm[a] = logM[i]
newt[a] = logT[i]
newg[a] = logg[i]
newl[a] = logL[i]
newdm[a] = logM[i + 1] - logM[i]
# tack on the last point on the grid, as the loop is one element short
newm[-1] = logM[-1]
newt[-1] = logT[-1]
newg[-1] = logg[-1]
newl[-1] = logL[-1]
newdm[-1] = logM[-1] - logM[-2]
table = Table(
dict(logM=newm, logT=newt, logg=newg, logL=newl, dlogm=newdm))
for k in list(iso.header.keys()):
table.header[k] = iso.header[k]
table.header["NAME"] = "Resampled " + table.header["NAME"]
table.header["dlogT"] = dt
table.header["dlogM"] = dm
table.header["dlogg"] = dl
return table
def _get_t_isochrone(self,
age,
metal=None,
FeH=None,
masses=None,
*args,
**kwargs):
""" Retrieve isochrone from the original source
internal use to adapt any library
"""
# make sure unit is in years and then only give the value (no units)
_age = int(units.Quantity(age, units.year).value)
# if hasUnit(age):
# _age = int(age.to('yr').magnitude)
# else:
# _age = int(age * inputUnit.to('yr').magnitude)
_logA = np.log10(_age)
assert (metal
is not None) | (FeH is not None), "Need a chemical par. value."
if (metal is not None) & (FeH is not None):
print("Warning: both Z & [Fe/H] provided, ignoring [Fe/H].")
if metal is None:
metal = self.FeHtometal(FeH)
if self.interpolation():
# Do the actual nd interpolation
# Maybe already exists?
if (metal in self.Z) & (_age in self.ages):
t = self.selectWhere(
"*",
"(round(Z, 6) == {0}) & (round(logA, 6) == {1})".format(
metal, _logA),
)
if t.nrows > 0:
return t
# apparently not
# find 2 closest metal values
ca1 = self.ages <= _age
ca2 = self.ages > _age
cz1 = self.Z <= metal
cz2 = self.Z > metal
if metal in self.Z:
# perfect match in metal, need to find ages
if _age in self.ages:
return self.selectWhere(
"*",
"(round(Z, 6) == {0}) & (round(logA, 6) == {1})".
format(metal, _logA),
)
elif (True in ca1) & (True in ca2):
# bracket on _age: closest values
a1, a2 = (
np.log10(max(self.ages[ca1])),
np.log10(min(self.ages[ca2])),
)
iso = self.selectWhere(
"*",
"(Z == 0.02) & ( (abs(logA - {0}) < 1e-4) | (abs(logA - {1}) < 1e-4 ) )"
.format(a1, a2),
)
if masses is None:
_logM = np.unique(iso["logM"])
else:
_logM = masses
# define interpolator
points = np.array([self[k]
for k in "logA logM Z".split()]).T
values = np.array(
[self[k] for k in list(self.data.keys())]).T
_ifunc = interpolate.LinearNDInterpolator(points, values)
pts = np.array([(_logA, logMk, metal) for logMk in _logM])
r = _ifunc(pts)
return Table(r)
else:
raise Exception("Age not covered by the isochrones")
elif (True in cz1) & (True in cz2):
# need to find closest Z
pass
return
else:
# find the closest match
_Z = self.Z[((metal - self.Z)**2).argmin()]
# _logA = np.log10(self.ages[((_age - self.ages) ** 2).argmin()])
_logA = self.logages[((np.log10(_age) - self.logages)**2).argmin()]
tab = self.data.selectWhere(
"*", "(round(Z, 6) == {0}) & (round(logA,6) == {1})".format(
_Z, _logA))
# mass selection
if masses is not None:
# masses are expected in logM for interpolation
# if masses.max() > 2.3:
# _m = np.log10(masses)
# else:
_m = masses
data_logM = tab["logM"][:]
# refuse extrapolation!
# ind = np.where(_m <= max(data_logM))
data = {}
for kn in list(tab.keys()):
data[kn] = interp(_m,
data_logM,
tab[kn],
left=np.nan,
right=np.nan)
return Table(data)
def _load_table_(self, source):
self.data = Table(self.source).selectWhere("*", "isfinite(logA)")
class ezIsoch(Isochrone):
""" Trying to make something that is easy to manipulate
This class is basically a proxy to a table (whatever format works best)
and tries to keep things coherent.
"""
def __init__(self, source, interp=False):
super().__init__()
self.name = "<auto>"
self.source = source
self._load_table_(self.source)
# round because of precision noise
self.logages = np.unique(np.round(self.data["logA"], 6))
self.ages = np.round(10**self.logages)
self.Z = np.unique(np.round(self.data["Z"], 6))
self.interpolation(interp)
def selectWhere(self, *args, **kwargs):
return self.data.selectWhere(*args, **kwargs)
def interpolation(self, b=None):
if b is not None:
if hasattr(self, "interp"):
print("Do not use interpolation yet, at your own risks!!")
self.interp = bool(b)
else:
return self.interp
def _load_table_(self, source):
self.data = Table(self.source).selectWhere("*", "isfinite(logA)")
def __getitem__(self, key):
return self.data[key]
def _get_t_isochrone(self,
age,
metal=None,
FeH=None,
masses=None,
*args,
**kwargs):
""" Retrieve isochrone from the original source
internal use to adapt any library
"""
# make sure unit is in years and then only give the value (no units)
_age = int(units.Quantity(age, units.year).value)
# if hasUnit(age):
# _age = int(age.to('yr').magnitude)
# else:
# _age = int(age * inputUnit.to('yr').magnitude)
_logA = np.log10(_age)
assert (metal
is not None) | (FeH is not None), "Need a chemical par. value."
if (metal is not None) & (FeH is not None):
print("Warning: both Z & [Fe/H] provided, ignoring [Fe/H].")
if metal is None:
metal = self.FeHtometal(FeH)
if self.interpolation():
# Do the actual nd interpolation
# Maybe already exists?
if (metal in self.Z) & (_age in self.ages):
t = self.selectWhere(
"*",
"(round(Z, 6) == {0}) & (round(logA, 6) == {1})".format(
metal, _logA),
)
if t.nrows > 0:
return t
# apparently not
# find 2 closest metal values
ca1 = self.ages <= _age
ca2 = self.ages > _age
cz1 = self.Z <= metal
cz2 = self.Z > metal
if metal in self.Z:
# perfect match in metal, need to find ages
if _age in self.ages:
return self.selectWhere(
"*",
"(round(Z, 6) == {0}) & (round(logA, 6) == {1})".
format(metal, _logA),
)
elif (True in ca1) & (True in ca2):
# bracket on _age: closest values
a1, a2 = (
np.log10(max(self.ages[ca1])),
np.log10(min(self.ages[ca2])),
)
iso = self.selectWhere(
"*",
"(Z == 0.02) & ( (abs(logA - {0}) < 1e-4) | (abs(logA - {1}) < 1e-4 ) )"
.format(a1, a2),
)
if masses is None:
_logM = np.unique(iso["logM"])
else:
_logM = masses
# define interpolator
points = np.array([self[k]
for k in "logA logM Z".split()]).T
values = np.array(
[self[k] for k in list(self.data.keys())]).T
_ifunc = interpolate.LinearNDInterpolator(points, values)
pts = np.array([(_logA, logMk, metal) for logMk in _logM])
r = _ifunc(pts)
return Table(r)
else:
raise Exception("Age not covered by the isochrones")
elif (True in cz1) & (True in cz2):
# need to find closest Z
pass
return
else:
# find the closest match
_Z = self.Z[((metal - self.Z)**2).argmin()]
# _logA = np.log10(self.ages[((_age - self.ages) ** 2).argmin()])
_logA = self.logages[((np.log10(_age) - self.logages)**2).argmin()]
tab = self.data.selectWhere(
"*", "(round(Z, 6) == {0}) & (round(logA,6) == {1})".format(
_Z, _logA))
# mass selection
if masses is not None:
# masses are expected in logM for interpolation
# if masses.max() > 2.3:
# _m = np.log10(masses)
# else:
_m = masses
data_logM = tab["logM"][:]
# refuse extrapolation!
# ind = np.where(_m <= max(data_logM))
data = {}
for kn in list(tab.keys()):
data[kn] = interp(_m,
data_logM,
tab[kn],
left=np.nan,
right=np.nan)
return Table(data)
def _get_isochrone(self,
age,
metal=None,
FeH=None,
masses=None,
*args,
**kwargs):
""" Retrieve isochrone from the original source
internal use to adapt any library
"""
# make sure unit is in years and then only give the value (no units)
_age = int(units.Quantity(age, units.year).value)
# if hasUnit(age):
# _age = int(age.to('Myr').magnitude)
# else:
# _age = int(age * inputUnit.to('Myr').magnitude)
assert (metal
is not None) | (FeH is not None), "Need a chemical par. value."
if (metal is not None) & (FeH is not None):
print("Warning: both Z & [Fe/H] provided, ignoring [Fe/H].")
if metal is None:
metal = self.FeHtometal(FeH)
assert metal in self.Z, "Metal %f not find in %s" % (metal, self.Z)
# node = self.data.getNode('/Z' + str(metal)[2:])
data = {}
if age in self.ages:
# no interpolation, isochrone already in the file
t = self.data.getNode("/Z" + str(metal)[2:] + "/a" + str(_age))
for kn in t.colnames:
data[kn] = t.col(kn)
else:
# interpolate between isochrones
d = (self.ages - float(age))**2
a1, a2 = np.sort(self.ages[np.argsort(d)[:2]] * 1e-6)
# print "Warning: Interpolation between %d and %d Myr" % (a1, a2)
r = np.log10(_age / a1) / np.log10(a2 / a1)
t1 = self.data.getNode("/Z" + str(metal)[2:] + "/a" + str(int(a1)))
t2 = self.data.getNode("/Z" + str(metal)[2:] + "/a" + str(int(a2)))
stop = min(t1.nrows, t2.nrows)
for kn in t1.colnames:
y2 = t2.col(kn)[:stop]
y1 = t1.col(kn)[:stop]
data[kn] = y2 * r + y1 * (1.0 - r)
del y1, y2
# mass selection
if masses is not None:
# masses are expected in logM for interpolation
if masses.max() > 2.3:
_m = np.log10(masses)
else:
_m = masses
data_logM = data["logM"][:]
for kn in data:
data[kn] = interp(_m, data_logM, data[kn])
table = Table(data, name="Isochrone from %s" % self.name)
table.header["metal"] = metal
table.header["time"] = _age * 1e6
return table
def _get_isochrone(self,
age,
metal=None,
FeH=None,
masses=None,
*args,
**kwargs):
""" Retrieve isochrone from the original source
internal use to adapt any library
"""
# make sure unit is in years and then only give the value (no units)
_age = int(units.Quantity(age, units.year).value)
# if hasUnit(age):
# _age = int(age.to('yr').magnitude)
# else:
# _age = int(age * inputUnit.to('yr').magnitude)
assert (metal
is not None) | (FeH is not None), "Need a chemical par. value."
if (metal is not None) & (FeH is not None):
print("Warning: both Z & [Fe/H] provided, ignoring [Fe/H].")
if metal is None:
metal = self.FeHtometal(FeH)
assert metal in self.Z, "Metal %f not find in %s" % (metal, self.Z)
data = {}
t = self.data.selectWhere("*", "(Z == _z)", condvars={"_z": metal})
if _age in self.ages:
# no interpolation, isochrone already in the file
t = t.selectWhere("*",
"(logA == _age)",
condvars={"_age": log10(_age)})
for kn in list(t.keys()):
data[kn] = np.asarray(t[kn])
else:
# interpolate between isochrones
d = (self.ages - float(_age))**2
a1, a2 = self.ages[np.argsort(d)[:2]]
# print "Warning: Interpolation between %d and %d Myr" % (a1, a2)
r = np.log10(_age / a1) / np.log10(a2 / a1)
t1 = t.selectWhere("*",
"logA == _age",
condvars={"_age": log10(a1)})
t2 = t.selectWhere("*",
"logA == _age",
condvars={"_age": log10(a2)})
stop = min(t1.nrows, t2.nrows)
for kn in list(t1.keys()):
y2 = t2[kn][:stop]
y1 = t1[kn][:stop]
data[kn] = y2 * r + y1 * (1.0 - r)
del y1, y2
# mass selection
if masses is not None:
# masses are expected in logM for interpolation
if masses.max() > 2.3:
_m = np.log10(masses)
else:
_m = masses
data_logM = data["logM"][:]
for kn in data:
data[kn] = interp(_m, data_logM, data[kn])
del t
table = Table(data, name="Isochrone from %s" % self.name)
table.header["metal"] = metal
table.header["time"] = _age
return table
class CacheBackend(GridBackend):
"""CacheBackend -- Load content from a file only when needed
The key idea is to be able to load the content only at the first query
Currently the grid attribute is an eztable.Table object as it was before.
"""
def __init__(self, fname, *args, **kwargs):
"""__init__
Parameters
----------
fname: str
FITS or HD5 file containing the grid
"""
super(CacheBackend, self).__init__()
self.fname = fname
self._type = self._get_type(fname)
self.clear()
def clear(self, attrname=None):
"""clear current cache
Parameters
----------
attrname: str in [lamb, filters, grid, header, lamb, seds]
if provided clear only one attribute
else all cache will be erased
"""
if attrname is None:
self._seds = None
self._lamb = None
self._filters = None
self._grid = None
self._header = None
else:
setattr(self, "_{0}".format(attrname), None)
def _load_seds(self, fname):
"""load_seds - load seds"""
if self._seds is None:
if self._get_type(fname) == "fits":
with pyfits.open(self.fname) as f:
self._seds = f[0].data[:-1]
elif self._get_type(fname) == "hdf":
with HDFStore(self.fname, mode="r") as s:
self._seds = s["/seds"].read()
def _load_lamb(self, fname):
"""load_seds - load wavelength"""
if self._lamb is None:
if self._get_type(fname) == "fits":
with pyfits.open(self.fname) as f:
self._lamb = f[0].data[-1]
elif self._get_type(fname) == "hdf":
with HDFStore(self.fname, mode="r") as s:
self._lamb = s["/lamb"].read()
def _load_grid(self, fname):
"""load_grid - load grid table"""
# load_seds - load wavelength and seds
if self._grid is None:
if self._get_type(fname) == "fits":
self._grid = Table(self.fname)
elif self._get_type(fname) == "hdf":
self._grid = Table(self.fname, tablename="/grid")
def _load_filters(self, fname):
"""load_filters -- load only filters"""
if self._filters is None:
if self._type == "fits":
with pyfits.open(self.fname) as f:
self._filters = f[1].header.get(
"FILTERS", None) or f[1].header.get("filters", None)
if self._filters is not None:
self._filters = self._filters.split()
elif self._type == "hdf":
self._filters = self.header.get(
"FILTERS", None) or self.header.get("filters", None)
if self._filters is not None:
self._filters = self._filters.split()
@property
def seds(self):
"""seds - load in cache if needed """
self._load_seds(self.fname)
return self._seds
@seds.setter
def seds(self, value):
""" replace seds value """
self._seds = value
@property
def lamb(self):
"""lamb - load in cache if needed """
self._load_lamb(self.fname)
return self._lamb
@lamb.setter
def lamb(self, value):
""" replace seds value """
self._lamb = value
@property
def grid(self):
"""grid - load in cache if needed """
self._load_grid(self.fname)
return self._grid
@grid.setter
def grid(self, value):
""" replace seds value """
self._grid = value
@property
def header(self):
"""header - load in cache if needed """
self._load_grid(self.fname)
return self._grid.header
@header.setter
def header(self, value):
""" replace seds value """
self._header = value
@property
def filters(self):
"""filters - load in cache if needed """
self._load_filters(self.fname)
return self._filters
@filters.setter
def filters(self, value):
""" replace seds value """
self._filters = value
def keys(self):
""" return column names when possible, avoid loading when possible """
if hasattr(self._grid, "coldescrs"):
return list(self._grid.coldescrs.keys())
elif hasattr(self._grid, "keys"):
return list(self._grid.keys())
elif hasattr(self.grid, "keys"):
return list(self.grid.keys())
else:
return []
def writeFITS(self, fname, *args, **kwargs):
"""write -- export to fits file
Parameters
----------
fname: str
filename (incl. path) to export to
"""
if (self.lamb is not None) & (self.seds is not None) & (self.grid
is not None):
if not isinstance(self.grid, Table):
raise TypeError("Only eztables.Table are supported so far")
r = numpy.vstack([self.seds, self.lamb])
pyfits.writeto(fname, r, **kwargs)
del r
if getattr(self, "filters", None) is not None:
if "FILTERS" not in list(self.grid.header.keys()):
self.grid.header["FILTERS"] = " ".join(self.filters)
self.grid.write(fname, append=True)
def writeHDF(self, fname, append=False, *args, **kwargs):
"""write -- export to HDF file
Parameters
----------
fname: str
filename (incl. path) to export to
append: bool, optional (default False)
if set, it will append data to each Array or Table
"""
if (self.lamb is not None) & (self.seds is not None) & (self.grid
is not None):
if not isinstance(self.grid, Table):
raise TypeError("Only eztables.Table are supported so far")
with HDFStore(fname, mode="a") as hd:
if not append:
hd["/seds"] = self.seds[:]
hd["/lamb"] = self.lamb[:]
else:
try:
node = hd.get_node("/seds")
node.append(self.seds[:])
except Exception:
hd["/seds"] = self.seds[:]
hd["/lamb"] = self.lamb[:]
if getattr(self, "filters", None) is not None:
if "FILTERS" not in list(self.grid.header.keys()):
self.grid.header["FILTERS"] = " ".join(self.filters)
self.grid.write(fname, tablename="grid", append=True)
def copy(self):
""" implement a copy method """
g = CacheBackend(self.fname)
g._aliases = copy.deepcopy(self._aliases)
if self._grid is not None:
g._grid = copy.deepcopy(self._grid)
if self._seds is not None:
g._seds = copy.deepcopy(self._seds)
if self._lamb is not None:
g._lamb = copy.deepcopy(self._lamb)
if self._header is not None:
g._header = copy.deepcopy(self._header)
if self._filters is not None:
g._filters = copy.deepcopy(self._filters)
return g
class MemoryBackend(GridBackend):
""" Instanciate an grid object that has no physical storage
Helps to create new grids on the fly. Because it deriveds from
ModelGrid, this can be exported on disk too.
"""
def __init__(
self,
lamb,
seds=None,
grid=None,
cov_diag=None,
cov_offdiag=None,
header={},
aliases={},
):
"""__init__
Parameters
----------
lamb: ndarray or GridBackend subclass
if ndarray: wavelength of the SEDs (requires seds and grid
arguments)
if backend: ref to the given grid
seds: ndarray[dtype=float, ndim=2]
array of seds
grid: eztable.Table
table of properties associated to each sed
header: dict
if provided, update the grid table header
aliases:
if provided, update the grid table aliases
"""
super(MemoryBackend, self).__init__()
# read from various formats
if isinstance(lamb, HDFBackend):
self._fromHDFBackend(lamb)
elif isNestedInstance(lamb, GridBackend):
self._from_GridBackend(lamb)
elif isinstance(lamb, basestring):
self._from_File(lamb)
else:
if (seds is None) | (grid is None):
raise ValueError("Wrong number of arguments")
self.lamb = lamb
self.seds = seds
self.grid = grid
if (cov_diag is not None) & (cov_offdiag is not None):
print("including cov diag and offdiag")
self.cov_diag = cov_diag
self.cov_offdiag = cov_offdiag
else:
self.cov_diag = None
self.cov_offdiag = None
# update header
if self._header is None:
self._header = header
else:
for k, v in list(header.items()):
self.grid.header[k] = v
# update aliases
self._aliases.update(aliases)
self.fname = ":memory:"
@property
def filters(self):
"""filters"""
r = self._header.get("filters", None) or self._header.get(
"FILTERS", None)
if r is not None:
r = r.split()
return r
@property
def header(self):
return self._header
def _from_File(self, fname):
"""_from_File -- load the content of a FITS or HDF file
Parameters
----------
fname: str
filename (incl. path) to read from
"""
# load_seds - load wavelength and seds
if self._get_type(fname) == "fits":
with pyfits.open(fname) as f:
self.seds = f[0].data[:-1]
self.lamb = f[0].data[-1]
self.grid = Table(fname)
elif self._get_type(fname) == "hdf":
with HDFStore(fname, mode="r") as s:
self.seds = s["/seds"].read()
self.lamb = s["/lamb"].read()
try:
self.cov_diag = s["/covdiag"].read()
except Exception:
self.cov_diag = None
try:
self.cov_offdiag = s["/covoffdiag"].read()
except Exception:
self.cov_offdiag = None
self.grid = Table(fname, tablename="/grid")
self._header = self.grid.header
def writeFITS(self, fname, *args, **kwargs):
"""write -- export to fits file
Parameters
----------
fname: str
filename (incl. path) to export to
"""
if (self.lamb is not None) & (self.seds is not None) & (self.grid
is not None):
if not isinstance(self.grid, Table):
raise TypeError("Only eztables.Table are supported so far")
r = numpy.vstack([self.seds, self.lamb])
pyfits.writeto(fname, r, **kwargs)
if getattr(self, "filters", None) is not None:
if "FILTERS" not in list(self.grid.header.keys()):
self.grid.header["FILTERS"] = " ".join(self.filters)
self.grid.write(fname, append=True)
def writeHDF(self, fname, append=False, *args, **kwargs):
"""write -- export to HDF file
Parameters
----------
fname: str
filename (incl. path) to export to
append: bool, optional (default False)
if set, it will append data to each Array or Table
"""
if (self.lamb is not None) & (self.seds is not None) & (self.grid
is not None):
if not isinstance(self.grid, Table):
raise TypeError("Only eztables.Table are supported so far")
with HDFStore(fname, mode="a") as hd:
if not append:
hd["/seds"] = self.seds[:]
hd["/lamb"] = self.lamb[:]
if self.cov_diag is not None:
hd["/covdiag"] = self.cov_diag[:]
if self.cov_offdiag is not None:
hd["/covoffdiag"] = self.cov_offdiag[:]
else:
try:
node = hd.get_node("/seds")
node.append(self.seds[:])
except Exception:
hd["/seds"] = self.seds[:]
hd["/lamb"] = self.lamb[:]
if self.cov_diag is not None:
hd["/covdiag"] = self.cov_diag[:]
if self.cov_offdiag is not None:
hd["/covoffdiag"] = self.cov_offdiag[:]
if getattr(self, "filters", None) is not None:
if "FILTERS" not in list(self.grid.header.keys()):
self.grid.header["FILTERS"] = " ".join(self.filters)
self.grid.write(fname, tablename="grid", append=True)
def copy(self):
""" implement a copy method """
g = MemoryBackend(
copy.deepcopy(self.lamb),
seds=copy.deepcopy(self.seds),
grid=copy.deepcopy(self.grid),
cov_diag=copy.deepcopy(self.cov_diag),
cov_offdiag=copy.deepcopy(self.cov_offdiag),
)
g._filters = copy.deepcopy(self._filters)
g._header = copy.deepcopy(self._header)
g._aliases = copy.deepcopy(self._aliases)
return g
