def table(self, filter_homogenous=False):
"""Return a text table for this datagroup. See :meth:`output`.
.. note:: This method currently returns a list of strings. In the future, it will return an :class:`astropy.table.Table` object containing only the desired header keywords.
"""
from astropy.table import Table, Column
if filter_homogenous:
groups = [ group for group in self if not isinstance(group, ListFITSDataGroup) ]
list_groups = []
else:
groups = [ group for group in self if not isinstance(group, ListFITSDataGroup) ]
list_groups = [ group for group in self if isinstance(group, ListFITSDataGroup) ]
groups.sort(key=lambda g : g.name)
result = Table([ group.keylist for group in groups ], names=map(str, self.keywords))
name_column = Column(name=str("Name"), data=[ group.name for group in groups ])
result.add_column(name_column, index=0)
number_column = Column(name=str("N"), data=[ len(group) for group in groups ])
result.add_column(number_column)
for lgroup in list_groups:
result.add_row({"Name":lgroup.name, "N":len(lgroup)})
return result
def get_feature_importances(data_table, obs_metadata, lines_table, use_con_flux=False):
feature_importances_list = []
X_colnames = None
for line_name, line_wavelength in lines_table['source', 'wavelength_target']:
subset = data_table[(data_table['source'] == line_name) & (data_table['wavelength_target'] == line_wavelength)]
X, y, labels = get_X_and_y(subset, obs_metadata, use_con_flux)
if X_colnames is None:
X_colnames = X.colnames
params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,
'learning_rate': 0.01, 'loss': 'lad'}
clf = ensemble.GradientBoostingRegressor(**params)
X = ndarrayidze(X)
# Scaling is optional, but I think I'm going to do it (for now) for all methods,
# just in comparing between valued here and with e.g. ICA there are fewer diffs
X = skpp.scale(X)
y = skpp.scale(y)
clf.fit(X, y)
feature_importances_list.append(clf.feature_importances_)
fi = np.array(feature_importances_list)
fi_table = Table(fi, names = X_colnames)
fi_table.add_column(lines_table['source'])
fi_table.add_column(lines_table['wavelength_target'])
return fi_table
def print_table(fname):
tab = pickle.load(open(fname))
data = Table()
data.add_column(
Column(data=processes_pretty, name='process')
)
for rowname, row in zip([t[0] for t in tab], tab):
data.add_column(
Column(data=row[1:], name=rownames[rowname])
)
data[rownames[rowname]].format = '%.0f'
class MyLatex(ascii.Latex):
def __init__(self, **kwargs):
ascii.Latex.__init__(self, **kwargs)
#self.header.data = ['process'] + order
self.latex['header_start'] = '\hline'
self.latex['header_end'] = '\hline'
self.latex['preamble'] = r'\begin{center}'
self.latex['tablefoot'] = r'\end{center}'
self.latex['data_end'] = r'\hline'
self.header.comment = r"\%Generated by src/yields/print_tables.py"
print r"% BEGIN AUTOGENERATED, NOT FOR CHANGING"
print r"% Generated by src/yields/print_tables.py on " + str(datetime.datetime.utcnow())
print r"% Hostname: " + socket.gethostname() + " User: "******"% Input file: " + fname
ascii.write(data, Writer=MyLatex, latexdict = {'col_align':'|c|cccccc|c|'})
print r"% END AUTOGENERATED"
def test_load_lc_fits():
dt = 0.1
n_seg = 5
n_seconds = 100
output_file = 'out.fits'
ref_lc = np.arange(0, n_seconds, dt)
ci_lc = np.array(np.array_split(np.arange(0, n_seconds*n_seg, dt), n_seg))
n_bins = len(ref_lc)
lightcurves = Table()
lightcurves.add_column(Column(name='REF', data=ref_lc.T/dt))
lightcurves.add_column(Column(name='CI', data=ci_lc.T/dt))
lightcurves.meta['N_BINS'] = n_bins
lightcurves.meta['DT'] = dt
lightcurves.meta['N_SEG'] = n_seg
lightcurves.meta['NSECONDS'] = n_seconds
lightcurves.write(output_file, format='fits', overwrite=True)
ref, ci, meta = spec.load_lc_fits(output_file, counts_type=False)
remove(output_file)
assert np.all(ref_lc == ref)
assert np.all(ci_lc == ci)
assert meta['N_BINS'] == n_bins
assert meta['DT'] == dt
assert meta['N_SEG'] == n_seg
assert meta['NSECONDS'] == n_seconds
def do_surf_phot(img_list=imglist, aperture_radii=np.arange(1,20,1)*u.arcsec):
''' Do aperture photometry on a list of images in a bunch of apertures
input: img_list: list of images
aperture_list: apertures to use (same for all)
output: astropy table with photomety for all
'''
# make an empty table to hold the output
rcol = Column(aperture_radii.value, name=('r_arcsec'))
phot_tab = Table([rcol])
# loop over the images
for (img_num,img_name) in enumerate(img_list):
img = fits.open(img_name)
photlist = multi_ap_phot(img, glx_ctr, aperture_radii, sb=True)
# calibrate photmetry: TODO
# obs_val = out_tab['aperture_sum'][0] * out_tab['aperture_sum'].unit
# cal_phot = calib_phot(obs_val, img, output_units='MJy')
phot_tab.add_column(Column(photlist, name=img_name))
# done loop over images
return(phot_tab)
def test_read_write_memory(tmpdir):
with h5py.File('test', 'w', driver='core', backing_store=False) as output_file:
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.write(output_file, path='the_table')
t2 = Table.read(output_file, path='the_table')
assert np.all(t2['a'] == [1, 2, 3])
def test_from_table_without_mask():
from astropy.table import Table, Column
t = Table()
c = Column(data=[1, 2, 3], name='a')
t.add_column(c)
output = io.BytesIO()
t.write(output, format='votable')
def _get_uncertainties(self, star_group_size):
"""
Retrieve uncertainties on fitted parameters from the fitter
object.
Parameters
----------
star_group_size : int
Number of stars in the given group.
Returns
-------
unc_tab : `~astropy.table.Table`
Table which contains uncertainties on the fitted parameters.
The uncertainties are reported as one standard deviation.
"""
unc_tab = Table()
for param_name in self.psf_model.param_names:
if not self.psf_model.fixed[param_name]:
unc_tab.add_column(Column(name=param_name + "_unc",
data=np.empty(star_group_size)))
if 'param_cov' in self.fitter.fit_info.keys():
if self.fitter.fit_info['param_cov'] is not None:
k = 0
n_fit_params = len(unc_tab.colnames)
for i in range(star_group_size):
unc_tab[i] = np.sqrt(np.diag(
self.fitter.fit_info['param_cov'])
)[k: k + n_fit_params]
k = k + n_fit_params
return unc_tab
def test_write_nopath(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
with pytest.raises(ValueError) as exc:
t1.write(test_file)
assert exc.value.args[0] == "table path should be set via the path= argument"
def test_read_nopath(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.write(test_file, path='the_table')
t2 = Table.read(test_file)
assert np.all(t1['a'] == t2['a'])
def test_write_invalid_path(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
with pytest.raises(ValueError) as exc:
t1.write(test_file, path='test/')
assert exc.value.args[0] == "table path should end with table name, not /"
def get_table(self, **kwargs):
D = self.lineLum(**kwargs)
# remap names to match RADEX
name_mapping = {'upper':'upperlevel',
'lower':'lowerlevel',
'freq':'frequency',}
names = D[0].keys()
T = Table(names=[name_mapping[n]
if n in name_mapping
else n
for n in names],
dtype=[type(D[0][k]) for k in names])
for row in D:
T.add_row([row[k] for k in names])
T.add_column(Column(name='upperlevelpop',
data=self.upperlevelpop,
dtype='float'))
T.add_column(Column(name='lowerlevelpop',
data=self.lowerlevelpop,
dtype='float'))
return T
def save_fluxes(model_fluxes, model_parameters, filters, names, filename,
directory=OUT_DIR, out_format='ascii.commented_header'):
"""Save fluxes and associated parameters into a table.
Parameters
----------
model_fluxes: RawArray
Contains the fluxes of each model.
model_parameters: RawArray
Contains the parameters associated to each model.
filters: list
Contains the filter names.
names: List
Contains the parameters names.
filename: str
Name under which the file should be saved.
directory: str
Directory under which the file should be saved.
out_format: str
Format of the output file
"""
out_fluxes = np.ctypeslib.as_array(model_fluxes[0])
out_fluxes = out_fluxes.reshape(model_fluxes[1])
out_params = np.ctypeslib.as_array(model_parameters[0])
out_params = out_params.reshape(model_parameters[1])
out_table = Table(np.hstack((out_fluxes, out_params)),
names=filters + list(names))
out_table.add_column(Column(np.arange(model_fluxes[1][0]), name='id'),
index=0)
out_table.write("{}/{}".format(directory, filename), format=out_format)
def compute_Q_analytical(component_table):
"""Compute Q factors analytically.
"""
q_table = Table()
q_table.add_column(Column(data=component_table['Name'], name='Q_AB'))
Q_all_list = ['All others']
for j in range(len(component_table)):
# Get parameters A
row_A = component_table[j]
x_A, y_A, sigma_A, N_A = row_A['GLON'], row_A['GLAT'], row_A['Sigma'], row_A['Norm']
# Compute Q_factor all others
components_all = [[row['GLON'], row['GLAT'], row['Sigma'], row['Norm']] for row in component_table if not row == row_A]
Q_All = compute_Q_from_components([[x_A, y_A, sigma_A, N_A]], components_all)
Q_all_list.append(Q_All)
# Compute Q factors pairwise
Q_AB_list = np.zeros(len(component_table))
for i, row_B in enumerate(component_table):
# Get parameters B
x_B, y_B, sigma_B, N_B = row_B['GLON'], row_B['GLAT'], row_B['Sigma'], row_B['Norm']
# Compute Q_factor
Q_AB = Q_factor_analytical(sigma_A, sigma_B, x_A, y_A, x_B, y_B)
Q_AB_list[i] = Q_AB
q_table.add_column(Column(data=Q_AB_list, name=row_A['Name']))
q_table.add_row(Q_all_list)
return q_table
def ions(self, iZion, Ej=0., skip_null=False):
"""
Generate a Table of columns and so on
Restrict to those systems where flg_clm > 0
Parameters
----------
iZion : tuple
Z, ion e.g. (6,4) for CIV
Ej : float [1/cm]
Energy of the lower level (0. is resonance)
skip_null : boolean (False)
Skip systems without an entry, else pad with zeros
Returns
-------
Table of values for the Survey
"""
if len(self.abs_sys()[0]._ionN) == 0:
raise IOError("ionN table not set. Use fill_ionN")
#
keys = [u'name', ] + self.abs_sys()[0]._ionN.keys()
t = Table(self.abs_sys()[0]._ionN[0:1]).copy() # Avoids mixin trouble
t.add_column(Column(['dum'], name='name', dtype='<U32'))
t = t[keys]
if 'Ej' not in keys:
warnings.warn("Ej not in your ionN table. Ignoring. Be careful..")
# Loop on systems (Masked)
for abs_sys in self.abs_sys():
# Grab
if 'Ej' in keys:
mt = ((abs_sys._ionN['Z'] == iZion[0])
& (abs_sys._ionN['ion'] == iZion[1])
& (abs_sys._ionN['Ej'] == Ej))
else:
mt = ((abs_sys._ionN['Z'] == iZion[0])
& (abs_sys._ionN['ion'] == iZion[1]))
if np.sum(mt) == 1:
irow = abs_sys._ionN[mt]
# Cut on flg_clm
if irow['flag_N'] > 0:
row = [abs_sys.name] + [irow[key] for key in keys[1:]]
t.add_row(row) # This could be slow
else:
if skip_null is False:
row = [abs_sys.name] + [0 for key in keys[1:]]
t.add_row(row)
elif np.sum(mt) == 0:
if skip_null is False:
row = [abs_sys.name] + [0 for key in keys[1:]]
t.add_row( row )
continue
else:
raise ValueError("Multple entries")
# Return
return t[1:]
def test_read_write_existing_overwrite(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
h5py.File(test_file, 'w').close() # create empty file
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.write(test_file, path='the_table', overwrite=True)
t2 = Table.read(test_file, path='the_table')
assert np.all(t2['a'] == [1, 2, 3])
def test_read_invalid_path(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.write(test_file, path='the_table')
with pytest.raises(OSError) as exc:
Table.read(test_file, path='test/')
assert exc.value.args[0] == "Path test/ does not exist"
def test_read_write_existing_table(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.write(test_file, path='the_table')
with pytest.raises(OSError) as exc:
t1.write(test_file, path='the_table', append=True)
assert exc.value.args[0] == "Table the_table already exists"
def test_read_write_existing(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
h5py.File(test_file, 'w').close() # create empty file
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
with pytest.raises(OSError) as exc:
t1.write(test_file, path='the_table')
assert exc.value.args[0].startswith("File exists:")
def test_write_wrong_type():
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
with pytest.raises(TypeError) as exc:
t1.write(1212, path='path/to/data/the_table', format='hdf5')
assert exc.value.args[0] == ('output should be a string '
'or an h5py File or Group object')
def get_asol(info=None):
"""
Get aspect solution DY, DZ, DTHETA values sampled at 1 ksec intervals
during all science observations.
:param info: dict of processing information and outputs
:returns: aspect solution data (Table)
"""
start = Time(opt.start)
stop = Time(opt.stop)
h5_file = os.path.join(opt.data_root, 'aimpoint_asol_values.h5')
logger.info('Reading asol file {}'.format(h5_file))
h5 = tables.openFile(h5_file)
asol = Table(h5.root.data[:])
h5.close()
bad = (asol['dy'] == 0.0) & (asol['dz'] == 0.0)
asol = asol[~bad]
year = Column(Time(asol['time'], format='cxcsec').decimalyear, name='year')
asol.add_column(year, index=0)
asol.sort('time')
# Include only points between --start and --stop
i0, i1 = np.searchsorted(asol['time'], [start.cxcsec, stop.cxcsec])
asol = asol[i0:i1]
# Exclude from 10ksec before to 3 days after any normal sun or safe sun intervals.
normal_suns = events.normal_suns()
safe_suns = events.safe_suns()
normal_suns.interval_pad = 10000, 86400 * 3
safe_suns.interval_pad = 10000, 86400 * 3
exclude_intervals = (normal_suns | safe_suns).intervals(asol['time'][0], asol['time'][-1])
ok = np.ones(len(asol), dtype=np.bool)
for date0, date1 in exclude_intervals:
logger.info('Excluding asol values from {} to {} (normal/safe sun)'.format(date0, date1))
i0, i1 = np.searchsorted(asol['time'], Time([date0, date1], format='yday').cxcsec)
ok[i0:i1] = False
asol = asol[ok]
if info is not None:
info['last_ctime'] = Time(asol['time'][-1], format='cxcsec').datetime.ctime()
info['last_obsid'] = asol['obsid'][-1]
for date, jump in AIMPOINT_JUMPS.items():
jump_date = Time(date)
if jump_date < stop:
# Make the mean of the "before" interval match the mean of the "after" interval.
i0 = np.searchsorted(asol['time'], jump_date.cxcsec)
asol['dy'][:i0] -= jump['d_dy'] / 20.0
asol['dz'][:i0] -= jump['d_dz'] / 20.0
# Capture info about jump
info.setdefault('aimpoint_jumps', {})[date] = jump
logger.info('Applying aimpoint jump of {} at {}'.format(jump, date))
return asol
def test_add_masked_row_to_masked_table_mapping4(self):
# When adding values to a masked table, if the mask is specified as a
# dict, then keys in values should match keys in mask
t = Table(masked=True)
t.add_column(MaskedColumn(name='a', data=[1], mask=[0]))
t.add_column(MaskedColumn(name='b', data=[4], mask=[1]))
with pytest.raises(ValueError) as exc:
t.add_row({'b': 5}, mask={'a': True})
assert exc.value.args[0] == 'keys in mask should match keys in vals'
def createBiomassTable(species, biomass, inputCount):
if inputCount == ():
t = Table([tuple(species), biomass], names = ('species', 'biomass')) #simple table
return t
else:
t = Table([tuple(species), biomass[:, 0]], names = ('species', 'biomass input1'))
for col in range(inputCount - 1):
t.add_column(Column(biomass[:, col + 1], name = 'biomass input%d' %(col + 2))) #simple table + more columns
return t
def test_write_csv(tmpdir):
'''If properly registered, filename should be sufficient to specify format
#3189
'''
t = Table()
t.add_column(Column(name='a', data=[1, 2, 3]))
t.add_column(Column(name='b', data=['a', 'b', 'c']))
path = str(tmpdir.join("data.csv"))
t.write(path)
def csv_per_order(infile, col, outfile):
'''Rewrite one column in ``aeffRfromraygrid`` to csv file
Turn one vector-valued (all orders in one cell) column into a
csv table with one entry per cell.
'''
tab = Table.read(infile)
outtab = Table(tab[col], names=['order_{0}'.format(o) for o in orders_from_meta(tab.meta)])
outtab.add_column(tab['wave'], index=0)
outtab.write(outfile, format='ascii.csv', overwrite=True)
def test_rename_masked_column(self):
t = Table(masked=True)
t.add_column(MaskedColumn(name='a', data=[1, 2, 3], mask=[0, 1, 0]))
t['a'].fill_value = 42
t.rename_column('a', 'b')
assert t.masked
assert np.all(t['b'] == np.array([1, 2, 3]))
assert np.all(t['b'].mask == np.array([0, 1, 0], bool))
assert t['b'].fill_value == 42
assert t.colnames == ['b']
def test_add_masked_row_to_masked_table_mismatch(self):
t = Table(masked=True)
t.add_column(MaskedColumn(name='a', data=[1], mask=[0]))
t.add_column(MaskedColumn(name='b', data=[4], mask=[1]))
with pytest.raises(TypeError) as exc:
t.add_row([2, 5], mask={'a': 1, 'b': 0})
assert exc.value.args[0] == "Mismatch between type of vals and mask"
with pytest.raises(TypeError) as exc:
t.add_row({'b': 5, 'a': 2}, mask=[1, 0])
assert exc.value.args[0] == "Mismatch between type of vals and mask"
def test_fail_meta_serialize(tmpdir):
test_file = str(tmpdir.join('test.hdf5'))
t1 = Table()
t1.add_column(Column(name='a', data=[1, 2, 3]))
t1.meta['f'] = str
with pytest.raises(Exception) as err:
t1.write(test_file, path='the_table', serialize_meta=True)
assert "cannot represent an object: <class 'str'>" in str(err)
def test_add_masked_row_to_masked_table_mapping1(self):
t = Table(masked=True)
t.add_column(MaskedColumn(name='a', data=[1], mask=[0]))
t.add_column(MaskedColumn(name='b', data=[4], mask=[1]))
t.add_row({'b': 5, 'a': 2}, mask={'a': 1, 'b': 0})
t.add_row({'a': 3, 'b': 6}, mask={'b': 1, 'a': 0})
assert t.masked
assert np.all(np.array(t['a']) == np.array([1, 2, 3]))
assert np.all(t['a'].mask == np.array([0, 1, 0], bool))
assert np.all(np.array(t['b']) == np.array([4, 5, 6]))
assert np.all(t['b'].mask == np.array([1, 0, 1], bool))
def unify_qso_catalog_mq(qsos):
qsos = Table(qsos)
qsos.rename_column('name','NAME_OLD')
qsos.rename_column("z", 'redshift')
qsos.add_column(Column(name='objid_mq', data=np.arange(len(qsos))+1))
qsos.rename_column("FUV", 'mag_fuv')
# unify name
name = [give_name(ra,dec) for ra,dec in zip(qsos['ra_d'],qsos['dec_d'])]
qsos['name'] = name
return qsos
def mk_meta(files,
ztbl,
fname=False,
stype='QSO',
skip_badz=False,
mdict=None,
parse_head=None,
debug=False,
chkz=False,
mtbl_file=None,
verbose=False,
specdb=None,
sdb_key=None,
**kwargs):
""" Generate a meta Table from an input list of files
Parameters
----------
files : list
List of FITS files
ztbl : Table
Table of redshifts. Must include RA, DEC, ZEM, ZEM_SOURCE
Used for RA/DEC if fname=False; then requires SPEC_FILE too
fname : bool, optional
Attempt to parse RA/DEC from the file name
Format must be
SDSSJ######(.##)+/-######(.#)[x]
where x cannot be a #. or +/-
stype : str, optional
Description of object type (e.g. 'QSO', 'Galaxy', 'SN')
specdb : SpecDB, optional
Database object to grab ID values from
Requires sdb_key
sdb_key : str, optional
ID key in SpecDB object
skip_badz : bool, optional
Skip spectra without a parseable redshift (using the Myers catalog)
parse_head : dict, optional
Parse header for meta info with this dict
mdict : dict, optional
Input meta data in dict form e.g. mdict=dict(INSTR='ESI')
chkz : bool, optional
If any sources have no parseable redshift, hit a set_trace
mtbl_file : str
Filename of input meta table. Current allowed extensions are _meta.ascii or _meta.fits
and they must be readable by Table.read(). The values in this table will overwrite
any others generated. Table must include a SPEC_FILE column to link meta data
Returns
-------
meta : Table
Meta table
"""
if specdb is not None:
if sdb_key is None:
raise IOError("Must specify sdb_key if you are passing in specdb")
Rdicts = defs.get_res_dicts()
#
coordlist = []
snames = []
for ifile in files:
sname = ifile.split('/')[-1]
snames.append(sname)
if fname:
# Starting index
if 'SDSSJ' in ifile:
i0 = ifile.find('SDSSJ') + 4
else:
i0 = ifile.rfind('J') + 1
# Find end (ugly)
for ii in range(i0 + 1, 99999):
if ifile[ii] in ('0', '1', '2', '3', '4', '5', '6', '7', '8',
'9', '.', '+', '-'):
continue
else:
i1 = ii
break
# Deal with .fits
if ifile[i1 - 1] == '.':
i1 -= 1
# Get coord
try:
coord = ltu.radec_to_coord(ifile[i0:i1])
except (UnboundLocalError, ValueError):
pdb.set_trace()
else:
mt = np.where(ztbl['SPEC_FILE'] == sname)[0]
if len(mt) != 1:
raise IndexError("NO MATCH FOR {:s}".format(sname))
coord = ltu.radec_to_coord((ztbl['RA'][mt], ztbl['DEC'][mt]))[0]
coordlist.append(coord)
ras = np.array([coord.ra.degree for coord in coordlist])
decs = np.array([coord.dec.degree for coord in coordlist])
coords = SkyCoord(ra=ras, dec=decs, unit='deg')
# Generate maindb Table
#maindb, tkeys = spbu.start_maindb(private=True)
# Fill
meta = Table()
meta['RA_GROUP'] = coords.ra.deg
meta['DEC_GROUP'] = coords.dec.deg
meta['STYPE'] = [str(stype)] * len(meta)
zem, zsource = spzu.zem_from_radec(meta['RA_GROUP'], meta['DEC_GROUP'],
ztbl, **kwargs)
badz = zem <= 0.
if np.sum(badz) > 0:
if skip_badz:
warnings.warn(
"Skipping {:d} entries without a parseable redshift".format(
np.sum(badz)))
else:
if chkz: # Turn this on to hit a stop instead of an Exception
pdb.set_trace()
else:
raise ValueError(
"{:d} entries without a parseable redshift".format(
np.sum(badz)))
meta['zem_GROUP'] = zem
meta['sig_zem'] = 0. # Need to add
meta['flag_zem'] = zsource
# Cut
meta = meta[~badz]
# specdb IDs
if sdb_key is not None:
meta[sdb_key] = [-9999] * len(meta)
if sdb_key not in meta.keys():
meta[sdb_key] = [-9999] * len(meta)
c_igmsp = SkyCoord(ra=specdb.qcat.cat['RA'],
dec=specdb.qcat.cat['DEC'],
unit='deg')
c_new = SkyCoord(ra=meta['RA_GROUP'],
dec=meta['DEC_GROUP'],
unit='deg')
# Find new sources
idx, d2d, d3d = match_coordinates_sky(c_new, c_igmsp, nthneighbor=1)
cdict = defs.get_cat_dict()
mtch = d2d < cdict['match_toler']
meta[sdb_key][mtch] = specdb.qcat.cat[sdb_key][idx[mtch]]
# Stack (primarily as a test)
'''
try:
maindb = vstack([maindb,meta], join_type='exact')
except:
pdb.set_trace()
'''
# SPEC_FILE
meta['SPEC_FILE'] = np.array(files)[~badz]
root_names = np.array(snames)[~badz]
# Try Header?
if parse_head is not None:
# Setup to store
plist = {}
for key in parse_head.keys():
plist[key] = []
# Loop on files
for sfile in meta['SPEC_FILE']:
if verbose:
print('Parsing {:s}'.format(sfile))
try:
head = fits.open(sfile)[0].header
except FileNotFoundError: # Try for compressed
head = fits.open(sfile + '.gz')[0].header
for key, item in parse_head.items():
# R
if key == 'R':
if parse_head[key] is True:
try:
plist[key].append(spbu.set_resolution(head))
except ValueError:
if mdict is not None:
try:
plist[key].append(mdict['R'])
except KeyError:
pdb.set_trace()
else:
pdb.set_trace()
plist[key].append(0.)
else:
raise ValueError("Set something else for R")
elif key == 'DATE-OBS':
if 'MJD' in item:
tval = Time(head[item],
format='mjd',
out_subfmt='date')
else:
tval = Time(head[item].replace('/', '-'),
format='isot',
out_subfmt='date')
plist[key].append(tval.iso)
else:
plist[key].append(head[item])
# INSTRUMENT SPECIFIC
try:
instr = head['INSTRUME']
except KeyError:
instr = 'none'
if 'LRIS' in instr:
if 'DISPERSER' not in plist.keys():
plist['DISPERSER'] = []
plist['INSTR'] = []
plist['R'] = []
try:
det = head['DETECTOR']
except KeyError:
if head['OUTFILE'] == 'lred':
det = 'LRIS-R'
else:
det = 'LRIS-B'
if 'LRIS-R' in det:
plist['DISPERSER'].append(head['GRANAME'])
plist['INSTR'].append('LRISr')
else:
plist['DISPERSER'].append(head['GRISNAME'])
plist['INSTR'].append('LRISb')
# Resolution
res = Rdicts[plist['INSTR'][-1]][plist['DISPERSER'][-1]]
try:
sname = head['SLITNAME']
except KeyError:
swidth = 1.
else:
swidth = defs.slit_width(sname, LRIS=True)
plist['R'].append(res / swidth)
# Finish
for key in plist.keys():
meta[key] = plist[key]
# mdict
if mdict is not None:
for key, item in mdict.items():
meta[key] = [item] * len(meta)
# EPOCH
if 'EPOCH' not in meta.keys():
warnings.warn("EPOCH not defined. Filling with 2000.")
meta['EPOCH'] = 2000.
# GROUP ID
meta['GROUP_ID'] = np.arange(len(meta)).astype(int)
# Fill in empty columns with warning
mkeys = meta.keys()
req_clms = defs.get_req_clms(sdb_key=sdb_key)
for clm in req_clms:
if clm not in mkeys:
if clm not in ['NPIX', 'WV_MIN', 'WV_MAX']: # File in ingest_spec
warnings.warn(
"Meta Column {:s} not defined. Filling with DUMMY".format(
clm))
if clm == 'DATE-OBS':
meta[clm] = ['9999-1-1'] * len(meta)
else:
meta[clm] = ['DUMMY'] * len(meta)
# Input meta table
if mtbl_file is not None:
# Read
if '_meta.ascii' in mtbl_file:
imtbl = Table.read(mtbl_file, format='ascii')
elif '_meta.fits' in mtbl_file:
imtbl = Table.read(mtbl_file)
else:
raise IOError(
"Input meta table must have either an ascii or fits extension")
# Check length
if len(imtbl) != len(meta):
raise IOError(
"Input meta table must have same length as self-generated one")
# Check for SPEC_FILE
if 'SPEC_FILE' not in imtbl.keys():
raise ValueError("Input meta table must include SPEC_FILE column")
# Loop to get indices
idx = []
for row in imtbl:
imt = np.where(root_names == row['SPEC_FILE'])[0]
if len(imt) == 0:
print("No match to spec file {:s}. Will ignore".format(
row['SPEC_FILE']))
elif len(imt) == 1:
idx.append(imt[0])
else:
raise ValueError(
"Two entries with the same SPEC_FILE. Something went wrong.."
)
idx = np.array(idx)
# Loop on keys
for key in imtbl.keys():
# Skip?
if key in ['SPEC_FILE']:
continue
if key in meta.keys():
pdb.set_trace()
else:
# Add Column
meta.add_column(imtbl[key][idx])
# Return
if debug:
meta[['RA_GROUP', 'DEC_GROUP', 'SPEC_FILE']].pprint(max_width=120)
pdb.set_trace()
return meta
def test_cutout_tool_correctness(self):
# Construct image:
image_hdu = self.construct_test_image()
# Construct catalog
ra = [0] * u.deg # Center pixel
dec = [45] * u.deg # Center pixel
ids = ["target_1"]
# Cutout should be 3 by 3:
cutout_width = cutout_height = [3.0] * u.pix
catalog = Table(
data=[ids, ra, dec, cutout_width, cutout_height],
names=['id', 'ra', 'dec', 'cutout_width', 'cutout_height'])
# Call cutout tool and extract the first cutout:
cutout = cutouts_from_fits(image_hdu, catalog)[0]
# check if values are correct:
w_orig = WCS(image_hdu.header)
w_new = WCS(cutout.header)
for x_new, x_orig in enumerate(range(3, 6)):
for y_new, y_orig in enumerate(range(3, 6)):
coords_orig = SkyCoord.from_pixel(x_orig,
y_orig,
w_orig,
origin=0)
coords_new = SkyCoord.from_pixel(x_new, y_new, w_new, origin=0)
assert_almost_equal(image_hdu.data[x_orig][y_orig],
cutout.data[x_new][y_new])
assert_almost_equal(coords_orig.ra.value, coords_new.ra.value)
assert_almost_equal(coords_orig.dec.value,
coords_new.dec.value)
# Test for rotation:
pa = [180] * u.deg
catalog.add_column(pa, name="cutout_pa")
# Call cutout tool:
cutout = cutouts_from_fits(image_hdu, catalog)[0]
# check if values are correct:
w_orig = WCS(image_hdu.header)
w_new = WCS(cutout.header)
for x_new, x_orig in enumerate(range(5, 2, -1)):
for y_new, y_orig in enumerate(range(5, 2, -1)):
coords_orig = SkyCoord.from_pixel(x_orig,
y_orig,
w_orig,
origin=0)
coords_new = SkyCoord.from_pixel(x_new, y_new, w_new, origin=0)
assert_almost_equal(image_hdu.data[x_orig][y_orig],
cutout.data[x_new][y_new])
assert_almost_equal(coords_orig.ra.value, coords_new.ra.value)
assert_almost_equal(coords_orig.dec.value,
coords_new.dec.value)
def fit_SB1_gp(time, rv,rv_err, initial_params, bounds, priors, flux_weighted=False, plot_guess=True,
freeze_parameters=['r1', 'k', 'sbratio', 'b', 'q', \
'Pdot', 'T_ld', 'M_ld',\
'Logg_ld', 'dV0'], emcee_fit=False, draws =1000):
print('\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('RV fitting procedure with Gaussian Processes')
print('S. Gill ([email protected])')
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n')
mean_model = _SB1_Model(**initial_params, bounds=bounds)
time_upsampled = np.linspace(min(time), max(time), len(time) * 1000)
if plot_guess:
print('Plotting initial Guess. . .')
plt.plot(time_upsampled,mean_model.get_value(time_upsampled), 'r--', linewidth=2, \
label='Starting guess', alpha = 0.7)
plt.errorbar(time, rv, yerr=rv_err, fmt='ko')
plt.legend()
plt.show()
return
######################
# Initiate the kernel
######################
#kernel = (terms.RealTerm(log_a=-9.77, log_c=1.91) )
kernel = (terms.Matern32Term(log_sigma=-2, log_rho=2))
########################
# Initiate the GP model
########################
gp = celerite.GP(kernel, mean=mean_model, fit_mean=True)
########################################
# Freeze parameter we do ot wish to fit
########################################
for i in freeze_parameters:
gp.freeze_parameter('mean:{}'.format(i))
#############################################
# Compute and calculate the initial log like
#############################################
gp.compute(time, rv_err)
print('Minimisation of lightcurve with GP')
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print("{:>20}: {:.3f}".format('Initial log-like', gp.log_likelihood(rv)))
##################
# Now optimise
##################
initial_params = gp.get_parameter_vector()
bounds_ = gp.get_parameter_bounds()
soln = minimize(neg_log_like,
initial_params,
method="L-BFGS-B",
bounds=bounds_,
args=(rv, gp))
###################
# Print the output
###################
print("{:>20}: {:.3f} ({} iterations)".format('Final log-like', -soln.fun,
soln.nit))
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
#for i in range(len(gp.get_parameter_names())):
# print('{:.3f} {}'.format(soln.x[i], gp.get_parameter_names()[i]))
############################################################################
# Now get the best model as a fictionary so we can put it back in and plot
############################################################################
best_params = {}
for i in range(len(gp.parameter_names)):
if gp.parameter_names[i][:5] == 'mean:':
best_params[gp.parameter_names[i][5:]] = gp.parameter_vector[i]
print('{:>12}: {:.3f}'.format(gp.parameter_names[i][5:],
gp.parameter_vector[i]))
#########################
# Now get the best model
#########################
plt.cla()
plt.errorbar(time, rv, yerr=rv_err, fmt='ko', alpha=0.3)
# Plot the best model (just to be sure)
mean_model_better = _SB1_Model(**best_params, bounds=bounds)
plt.plot(time,mean_model_better.get_value(time), 'b--', linewidth=2, \
label='Model selected')
# Plot the variance with upsampled time series
mu, var = gp.predict(rv, time_upsampled, return_var=True)
std = np.sqrt(abs(var))
color = "#ff7f0e"
plt.fill_between( time_upsampled, \
mu+std, \
mu-std, color=color, alpha=0.8, edgecolor="none", label = 'Best fit')
plt.xlabel('Time')
plt.ylabel('RV [km/s]')
plt.legend()
plt.show()
if emcee_fit:
initial = np.array(soln.x)
ndim, nwalkers = len(initial), 32
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
log_probability,
args=(gp, rv, priors, flux_weighted),
threads=8)
print("Running burn-in...")
p0 = initial + 1e-5 * np.random.randn(nwalkers, ndim)
burn_in = draws
width = 30
start_time = time_.time()
i, result = [], []
for i, result in enumerate(sampler.sample(p0, iterations=burn_in)):
n = int((width + 1) * float(i) / burn_in)
delta_t = time_.time(
) - start_time # time to do float(i) / n_steps % of caluculations
time_incr = delta_t / (float(i + 1) / burn_in
) # seconds per increment
time_left = time_incr * (1 - float(i) / burn_in)
m, s = divmod(time_left, 60)
h, m = divmod(m, 60)
sys.stdout.write('\r[{0}{1}] {2}% - {3}h:{4}m:{5:.2f}s'.format(
'#' * n, ' ' * (width - n), 100 * float(i) / burn_in, h, m, s))
names = gp.get_parameter_names()
cols = mean_model.get_parameter_names()
inds = np.array([names.index("mean:" + k) for k in cols])
samples = sampler.chain[:, int(np.floor(draws * 0.75)):, :].reshape(
(-1, ndim))
print(samples.shape, cols)
corner.corner(samples, labels=gp.get_parameter_names())
plt.show()
########################
# Now save to fits file
########################
from astropy.table import Table, Column
chain_file = 'rv_fit.fits'
try:
os.remove(chain_file)
except:
pass
t = Table(sampler.flatchain, names=gp.get_parameter_names())
t.add_column(Column(sampler.flatlnprobability, name='loglike'))
indices = np.mgrid[0:nwalkers, 0:burn_in]
step = indices[1].flatten()
walker = indices[0].flatten()
t.add_column(Column(step, name='step'))
t.add_column(Column(walker, name='walker'))
t.write(chain_file)
def get_simspec(simspecfile, log=None, nspec=None):
''' Get the simspec object
The simspec table holds the "truth" spectra and the intrinsic properties
of each object (redshift, noiseless photometry, [OII] flux, etc.).
(Input spectra to simulate with pixsim.)
http://desidatamodel.readthedocs.io/en/latest/DESI_SPECTRO_SIM/PIXPROD/NIGHT/simspec-EXPID.html
Parameters:
simspecfile (str):
The filename of the input simspec file
'''
minwave = 3533.
maxwave = 9913.1
stepwave = 0.2
scale = 1.e17
exptime = 5. # typical BOSS exposure time in s
if simspecfile:
if log:
log.info('Reading input file {}'.format(args.simspec))
# create SimSpec object
simspec = lvmsim.io.read_simspec(args.simspec)
# number of spectra to simulate from SimSpec
sim_nspec = simspec.nspec
# get the spectra and wavelengths arrays for different flavors
if simspec.flavor == 'arc':
# - TODO: do we need quickgen to support arcs? For full pipeline
# - arcs are used to measure PSF but aren't extracted except for
# - debugging.
# - TODO: if we do need arcs, this needs to be redone.
# - conversion from phot to flux doesn't include throughput,
# - and arc lines are rebinned to nearest 0.2 A.
# Create full wavelength and flux arrays for arc exposure
wave_b = np.array(simspec.wave['b'])
wave_r = np.array(simspec.wave['r'])
wave_z = np.array(simspec.wave['z'])
phot_b = np.array(simspec.phot['b'][0])
phot_r = np.array(simspec.phot['r'][0])
phot_z = np.array(simspec.phot['z'][0])
sim_wave = np.concatenate((wave_b, wave_r, wave_z))
sim_phot = np.concatenate((phot_b, phot_r, phot_z))
wavelengths = np.arange(minwave, maxwave, stepwave)
phot = np.zeros(len(wavelengths))
for i in range(len(sim_wave)):
wavelength = sim_wave[i]
flux_index = np.argmin(abs(wavelength - wavelengths))
phot[flux_index] = sim_phot[i]
# Convert photons to flux: following specter conversion method
dw = np.gradient(wavelengths)
fibarea = const.pi * (1.07e-2 /
2)**2 # cross-sectional fiber area in cm^2
hc = scale * const.h * const.c # convert to erg A
spectra = (hc * exptime * fibarea * dw * phot) / wavelengths
else:
wavelengths = simspec.wave['brz']
spectra = simspec.flux
# check there's enough spectra to simulate from what we ask for
if sim_nspec < nspec:
log.info("Only {} spectra in input file".format(sim_nspec))
nspec = sim_nspec
else:
# Initialize the output truth table.
spectra = []
wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10))
truth['FLUX'] = np.zeros((args.nspec, npix))
truth['WAVE'] = wavelengths
jj = list()
for thisobj in set(true_objtype):
ii = np.where(true_objtype == thisobj)[0]
nobj = len(ii)
truth['OBJTYPE'][ii] = thisobj
if log:
log.info('Generating {} template'.format(thisobj))
# Generate the templates
if thisobj == 'ELG':
elg = lvmsim.templates.ELG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = elg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_elg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'LRG':
lrg = lvmsim.templates.LRG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = lrg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_lrg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'QSO':
qso = lvmsim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(
nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = lvmsim.templates.BGS(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = bgs.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_bgs,
rmagrange=args.rmagrange_bgs,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'STD':
fstd = lvmsim.templates.FSTD(wave=wavelengths)
flux, tmpwave, meta1 = fstd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = lvmsim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = lvmsim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'WD':
wd = lvmsim.templates.WD(wave=wavelengths)
flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'SKY':
flux = np.zeros((nobj, npix))
meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32)))
elif thisobj == 'TEST':
flux = np.zeros((args.nspec, npix))
indx = np.where(wave > 5800.0 - 1E-6)[0][0]
ref_integrated_flux = 1E-10
ref_cst_flux_density = 1E-17
single_line = (np.arange(args.nspec) % 2 == 0).astype(
np.float32)
continuum = (np.arange(args.nspec) % 2 == 1).astype(np.float32)
for spec in range(nspec):
flux[spec, indx] = single_line[
spec] * ref_integrated_flux / np.gradient(wavelengths)[
indx] # single line
flux[spec] += continuum[
spec] * ref_cst_flux_density # flat continuum
meta1 = Table(
dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32),
LINE=wave[indx] *
np.ones(args.nspec, dtype=np.float32),
LINEFLUX=single_line * ref_integrated_flux,
CONSTFLUXDENSITY=continuum * ref_cst_flux_density))
else:
raise RuntimeError('Unknown object type')
# Pack it in.
truth['FLUX'][ii] = flux
meta = vstack([meta, meta1])
jj.append(ii.tolist())
# Sanity check on units; templates currently return ergs, not 1e-17 ergs...
# assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6)
# Sort the metadata table.
jj = sum(jj, [])
meta_new = Table()
for k in range(nspec):
index = int(np.where(np.array(jj) == k)[0])
meta_new = vstack([meta_new, meta[index]])
meta = meta_new
# Add TARGETID and the true OBJTYPE to the metadata table.
meta.add_column(
Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE'))
meta.add_column(Column(targetids, name='TARGETID'))
# Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z
meta.rename_column('REDSHIFT', 'TRUEZ')
return spectra, wavelengths, nspec
def ref_fk4_no_e_fk4(fnout='fk4_no_e_fk4.csv'):
"""
Accuracy tests for the FK4 (with no E-terms of aberration) to/from FK4
conversion, with arbitrary equinoxes and epoch of observation.
"""
import os
import numpy as np
import starlink.Ast as Ast
from astropy.table import Table, Column
np.random.seed(12345)
N = 200
# Sample uniformly on the unit sphere. These will be either the FK4
# coordinates for the transformation to FK5, or the FK5 coordinates for the
# transformation to FK4.
ra = np.random.uniform(0., 360., N)
dec = np.degrees(np.arcsin(np.random.uniform(-1., 1., N)))
# Generate random observation epoch and equinoxes
obstime = [
"B{0:7.2f}".format(x) for x in np.random.uniform(1950., 2000., N)
]
ra_fk4ne, dec_fk4ne = [], []
ra_fk4, dec_fk4 = [], []
for i in range(N):
# Set up frames for AST
frame_fk4ne = Ast.SkyFrame(
'System=FK4-NO-E,Epoch={epoch},Equinox=B1950'.format(
epoch=obstime[i]))
frame_fk4 = Ast.SkyFrame(
'System=FK4,Epoch={epoch},Equinox=B1950'.format(epoch=obstime[i]))
# FK4 to FK4 (no E-terms)
frameset = frame_fk4.convert(frame_fk4ne)
coords = np.degrees(
frameset.tran([[np.radians(ra[i])], [np.radians(dec[i])]]))
ra_fk4ne.append(coords[0, 0])
dec_fk4ne.append(coords[1, 0])
# FK4 (no E-terms) to FK4
frameset = frame_fk4ne.convert(frame_fk4)
coords = np.degrees(
frameset.tran([[np.radians(ra[i])], [np.radians(dec[i])]]))
ra_fk4.append(coords[0, 0])
dec_fk4.append(coords[1, 0])
# Write out table to a CSV file
t = Table()
t.add_column(Column(name='obstime', data=obstime))
t.add_column(Column(name='ra_in', data=ra))
t.add_column(Column(name='dec_in', data=dec))
t.add_column(Column(name='ra_fk4ne', data=ra_fk4ne))
t.add_column(Column(name='dec_fk4ne', data=dec_fk4ne))
t.add_column(Column(name='ra_fk4', data=ra_fk4))
t.add_column(Column(name='dec_fk4', data=dec_fk4))
f = open(fnout, 'wb')
f.write("# This file was generated with the {0} script, and the reference "
"values were computed using AST\n".format(
os.path.basename(__file__)))
t.write(f, format='ascii', delimiter=',')
def get_acq_table(obsid):
manvrs = events.manvrs.filter(obsid=obsid)
if not len(manvrs):
return None
manvr = manvrs[0]
start_time = DateTime(manvr.acq_start).secs
stop_time = start_time + (60 * 5)
acq_data = fetch.MSIDset(msids + slot_msids, start_time, stop_time)
vals = Table([acq_data[col].vals for col in msids], names=msids)
for field in slot_msids:
vals.add_column(Column(name=field, data=acq_data[field].vals))
times = Table([acq_data['AOACASEQ'].times], names=['time'])
def compress_data(data, dtime):
return data[data['AOREPEAT'] == '0 '], dtime[data['AOREPEAT'] == '0 ']
vals, times = compress_data(vals, times)
# Get the catalog for the stars
# This is used both to map ACQID to the right slot and
# to get the star positions to estimate deltas later
timeline_at_acq = mica.starcheck.starcheck.get_timeline_at_date(manvr.start)
mp_dir = None if (timeline_at_acq is None) else timeline_at_acq['mp_dir']
starcheck = mica.starcheck.get_starcheck_catalog(int(obsid), mp_dir=mp_dir)
if 'cat' not in starcheck:
raise ValueError('No starcheck catalog found for {}'.format(obsid))
catalog = Table(starcheck['cat'])
catalog.sort('idx')
# Filter the catalog to be just acquisition stars
catalog = catalog[(catalog['type'] == 'ACQ') | (catalog['type'] == 'BOT')]
slot_for_pos = [cat_row['slot'] for cat_row in catalog]
pos_for_slot = dict([(slot, idx) for idx, slot in enumerate(slot_for_pos)])
# Also, save out the starcheck index for each slot for later
index_for_slot = dict([(cat_row['slot'], cat_row['idx']) for cat_row in catalog])
# Estimate the offsets from the expected catalog positions
dy, dz = deltas_vs_obc_quat(vals, times['time'], catalog)
for slot in range(0, 8):
vals.add_column(Column(name='dy{}'.format(slot), data=dy[slot].data))
vals.add_column(Column(name='dz{}'.format(slot), data=dz[slot].data))
cat_entry = catalog[catalog['slot'] == slot][0]
dmag = vals['AOACMAG{}'.format(slot)] - cat_entry['mag']
vals.add_column(Column(name='dmag{}'.format(slot), data=dmag.data))
# make a list of dicts of the table
simple_data = []
kalm_start = None
for drow, trow in zip(vals, times):
if (kalm_start is None) and (drow['AOACASEQ'] == 'KALM'):
kalm_start = trow['time']
if (kalm_start is not None) and (trow['time'] > kalm_start + 5):
continue
slot_data = {'slots': [],
'time': trow['time'],
'aorfstr1_slot': slot_for_pos[int(drow['AORFSTR1'])],
'aorfstr2_slot': slot_for_pos[int(drow['AORFSTR2'])],
}
for m in msids:
slot_data[m] = drow[m]
for slot in range(0, 8):
row_dict = {'slot': slot,
'catpos': pos_for_slot[slot],
'index': index_for_slot[slot]}
for col in per_slot:
if col not in ['AOACQID']:
row_dict[col] = drow['{}{}'.format(col, slot)]
for col in ['dy', 'dz', 'dmag']:
row_dict[col] = drow['{}{}'.format(col, slot)]
row_dict['POS_ACQID'] = drow['AOACQID{}'.format(pos_for_slot[slot])]
slot_data['slots'].append(row_dict)
simple_data.append(slot_data)
return simple_data
sds_val = np.array(ap_summed['aperture_sum'])
sds_val = np.flip(sds_val)
#%%
"""
Data save
"""
Savepath = Path('/home/astro_02/AO2019-2/2019-10-24/Specdata')
#%%
table = Table()
wave_length = Column(data=sds_wvlt, name='Wavelength')
Value = Column(data=sds_val, name='Value')
table.add_column(wave_length, index=0)
table.add_column(Value, index=1)
table.write(Savepath / 'NGC676_01.csv', format='ascii.csv', overwrite=True)
#%%
# Object만 바꿔서 저장용
#%%
Savepath = Path('/home/astro_02/AO2019-2/2019-10-24/Specdata')
DISPAXIS = 2 # 1 = line = python_axis_1 // 2 = column = python_axis_0
FONTSIZE = 12 # Change it on your computer if you wish.
rcParams.update({'font.size': FONTSIZE})
COMPIMAGE = ppdpath / 'Comp-master.fits' # Change directory if needed!
LINE_FITTER = LevMarLSQFitter()
def write(self,filename=None,max_rows=None,format="ascii.latex",column_format="{0:.3f}",**kwargs):
"""
Outputs the points that make up the design in a nicely formatted table
:param filename: name of the file to which the table will be saved; if None the contents will be printed
:type filename: str. or file descriptor
:param max_rows: maximum number of rows in the table, if smaller than the number of points the different chunks are hstacked (useful if there are too many rows for display)
:type max_rows: int.
:param format: passed to the Table.write astropy method
:type format: str.
:param column_format: format specifier for the numerical values in the Table
:type column_format: str.
:param kwargs: the keyword arguments are passed to astropy.Table.write method
:type kwargs: dict.
:returns: the Table instance with the design parameters
"""
#Check that there is something to save
assert hasattr(self,"points"),"There are no points in your design yet!"
names = [ self.label[p] for p in self.parameters ]
if (max_rows is None) or (max_rows>=len(self)):
#Build the table
design_table = Table(self.values,names=names)
#Add the number column to the left
design_table.add_column(Column(data=range(1,len(self)+1),name=r"$N$"),index=0)
else:
#Figure out the splitting
num_chunks = len(self) // max_rows
if len(self)%max_rows!=0:
num_chunks+=1
#Construct the list of tables to hstack
design_table = list()
#Cycle through the chunks and create the sub-tables
for n in range(num_chunks-1):
columns = self.values[n*max_rows:(n+1)*max_rows]
#Build the sub-table
design_table.append(Table(columns,names=names))
#Add the number column to the left
design_table[-1].add_column(Column(data=range(n*max_rows+1,(n+1)*max_rows+1),name=r"$N$"),index=0)
#Create the last sub-table
columns = self.values[(num_chunks-1)*max_rows:]
design_table.append(Table(columns,names=names))
design_table[-1].add_column(Column(data=range((num_chunks-1)*max_rows+1,len(self)+1),name=r"$N$"),index=0)
#hstack in a single table
design_table = hstack(design_table)
#Tune the format
for colname in design_table.colnames:
if not design_table.dtype[colname]==np.int:
design_table[colname].format = column_format
#Write the table or return it
if filename is not None:
design_table.write(filename,format=format,**kwargs)
return None
else:
return design_table
#y_err = d[1].data['jackknifStackErrors'][sel]
N_spec = d[1].data['NspectraPerPixel'][sel]
N_spectra[index] = int(n.median(N_spec))
hduSPM = fits.open(path_2_spFly)
table_entry_1 = get_table_entry_full(hduSPM[1])
table_entry_2 = get_table_entry_full(hduSPM[2])
table_all[index] = n.hstack((table_entry_1, table_entry_2))
newDat = n.transpose(table_all)
headers = " Chabrier_MILES_age_lightW Chabrier_MILES_age_lightW_up_1sig Chabrier_MILES_age_lightW_low_1sig Chabrier_MILES_age_lightW_up_2sig Chabrier_MILES_age_lightW_low_2sig Chabrier_MILES_age_lightW_up_3sig Chabrier_MILES_age_lightW_low_3sig Chabrier_MILES_metallicity_lightW Chabrier_MILES_metallicity_lightW_up_1sig Chabrier_MILES_metallicity_lightW_low_1sig Chabrier_MILES_metallicity_lightW_up_2sig Chabrier_MILES_metallicity_lightW_low_2sig Chabrier_MILES_metallicity_lightW_up_3sig Chabrier_MILES_metallicity_lightW_low_3sig Chabrier_MILES_age_massW Chabrier_MILES_age_massW_up_1sig Chabrier_MILES_age_massW_low_1sig Chabrier_MILES_age_massW_up_2sig Chabrier_MILES_age_massW_low_2sig Chabrier_MILES_age_massW_up_3sig Chabrier_MILES_age_massW_low_3sig Chabrier_MILES_metallicity_massW Chabrier_MILES_metallicity_massW_up_1sig Chabrier_MILES_metallicity_massW_low_1sig Chabrier_MILES_metallicity_massW_up_2sig Chabrier_MILES_metallicity_massW_low_2sig Chabrier_MILES_metallicity_massW_up_3sig Chabrier_MILES_metallicity_massW_low_3sig Chabrier_MILES_total_mass Chabrier_MILES_stellar_mass Chabrier_MILES_living_stars_mass Chabrier_MILES_remnant_mass Chabrier_MILES_remnant_mass_in_whitedwarfs Chabrier_MILES_remnant_mass_in_neutronstars Chabrier_MILES_remnant_mass_blackholes Chabrier_MILES_mass_of_ejecta Chabrier_MILES_total_mass_up_1sig Chabrier_MILES_total_mass_low_1sig Chabrier_MILES_total_mass_up_2sig Chabrier_MILES_total_mass_low_2sig Chabrier_MILES_total_mass_up_3sig Chabrier_MILES_total_mass_low_3sig Chabrier_MILES_spm_EBV Chabrier_MILES_nComponentsSSP Chabrier_MILES_total_mass_ssp_0 Chabrier_MILES_stellar_mass_ssp_0 Chabrier_MILES_living_stars_mass_ssp_0 Chabrier_MILES_remnant_mass_ssp_0 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_0 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_0 Chabrier_MILES_remnant_mass_in_blackholes_ssp_0 Chabrier_MILES_mass_of_ejecta_ssp_0 Chabrier_MILES_log_age_ssp_0 Chabrier_MILES_metal_ssp_0 Chabrier_MILES_SFR_ssp_0 Chabrier_MILES_weightMass_ssp_0 Chabrier_MILES_weightLight_ssp_0 Chabrier_MILES_total_mass_ssp_1 Chabrier_MILES_stellar_mass_ssp_1 Chabrier_MILES_living_stars_mass_ssp_1 Chabrier_MILES_remnant_mass_ssp_1 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_1 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_1 Chabrier_MILES_remnant_mass_in_blackholes_ssp_1 Chabrier_MILES_mass_of_ejecta_ssp_1 Chabrier_MILES_log_age_ssp_1 Chabrier_MILES_metal_ssp_1 Chabrier_MILES_SFR_ssp_1 Chabrier_MILES_weightMass_ssp_1 Chabrier_MILES_weightLight_ssp_1 Chabrier_MILES_total_mass_ssp_2 Chabrier_MILES_stellar_mass_ssp_2 Chabrier_MILES_living_stars_mass_ssp_2 Chabrier_MILES_remnant_mass_ssp_2 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_2 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_2 Chabrier_MILES_remnant_mass_in_blackholes_ssp_2 Chabrier_MILES_mass_of_ejecta_ssp_2 Chabrier_MILES_log_age_ssp_2 Chabrier_MILES_metal_ssp_2 Chabrier_MILES_SFR_ssp_2 Chabrier_MILES_weightMass_ssp_2 Chabrier_MILES_weightLight_ssp_2 Chabrier_MILES_total_mass_ssp_3 Chabrier_MILES_stellar_mass_ssp_3 Chabrier_MILES_living_stars_mass_ssp_3 Chabrier_MILES_remnant_mass_ssp_3 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_3 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_3 Chabrier_MILES_remnant_mass_in_blackholes_ssp_3 Chabrier_MILES_mass_of_ejecta_ssp_3 Chabrier_MILES_log_age_ssp_3 Chabrier_MILES_metal_ssp_3 Chabrier_MILES_SFR_ssp_3 Chabrier_MILES_weightMass_ssp_3 Chabrier_MILES_weightLight_ssp_3 Chabrier_MILES_total_mass_ssp_4 Chabrier_MILES_stellar_mass_ssp_4 Chabrier_MILES_living_stars_mass_ssp_4 Chabrier_MILES_remnant_mass_ssp_4 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_4 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_4 Chabrier_MILES_remnant_mass_in_blackholes_ssp_4 Chabrier_MILES_mass_of_ejecta_ssp_4 Chabrier_MILES_log_age_ssp_4 Chabrier_MILES_metal_ssp_4 Chabrier_MILES_SFR_ssp_4 Chabrier_MILES_weightMass_ssp_4 Chabrier_MILES_weightLight_ssp_4 Chabrier_MILES_total_mass_ssp_5 Chabrier_MILES_stellar_mass_ssp_5 Chabrier_MILES_living_stars_mass_ssp_5 Chabrier_MILES_remnant_mass_ssp_5 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_5 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_5 Chabrier_MILES_remnant_mass_in_blackholes_ssp_5 Chabrier_MILES_mass_of_ejecta_ssp_5 Chabrier_MILES_log_age_ssp_5 Chabrier_MILES_metal_ssp_5 Chabrier_MILES_SFR_ssp_5 Chabrier_MILES_weightMass_ssp_5 Chabrier_MILES_weightLight_ssp_5 Chabrier_MILES_total_mass_ssp_6 Chabrier_MILES_stellar_mass_ssp_6 Chabrier_MILES_living_stars_mass_ssp_6 Chabrier_MILES_remnant_mass_ssp_6 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_6 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_6 Chabrier_MILES_remnant_mass_in_blackholes_ssp_6 Chabrier_MILES_mass_of_ejecta_ssp_6 Chabrier_MILES_log_age_ssp_6 Chabrier_MILES_metal_ssp_6 Chabrier_MILES_SFR_ssp_6 Chabrier_MILES_weightMass_ssp_6 Chabrier_MILES_weightLight_ssp_6 Chabrier_MILES_total_mass_ssp_7 Chabrier_MILES_stellar_mass_ssp_7 Chabrier_MILES_living_stars_mass_ssp_7 Chabrier_MILES_remnant_mass_ssp_7 Chabrier_MILES_remnant_mass_in_whitedwarfs_ssp_7 Chabrier_MILES_remnant_mass_in_neutronstars_ssp_7 Chabrier_MILES_remnant_mass_in_blackholes_ssp_7 Chabrier_MILES_mass_of_ejecta_ssp_7 Chabrier_MILES_log_age_ssp_7 Chabrier_MILES_metal_ssp_7 Chabrier_MILES_SFR_ssp_7 Chabrier_MILES_weightMass_ssp_7 Chabrier_MILES_weightLight_ssp_7 Chabrier_ELODIE_age_lightW Chabrier_ELODIE_age_lightW_up_1sig Chabrier_ELODIE_age_lightW_low_1sig Chabrier_ELODIE_age_lightW_up_2sig Chabrier_ELODIE_age_lightW_low_2sig Chabrier_ELODIE_age_lightW_up_3sig Chabrier_ELODIE_age_lightW_low_3sig Chabrier_ELODIE_metallicity_lightW Chabrier_ELODIE_metallicity_lightW_up_1sig Chabrier_ELODIE_metallicity_lightW_low_1sig Chabrier_ELODIE_metallicity_lightW_up_2sig Chabrier_ELODIE_metallicity_lightW_low_2sig Chabrier_ELODIE_metallicity_lightW_up_3sig Chabrier_ELODIE_metallicity_lightW_low_3sig Chabrier_ELODIE_age_massW Chabrier_ELODIE_age_massW_up_1sig Chabrier_ELODIE_age_massW_low_1sig Chabrier_ELODIE_age_massW_up_2sig Chabrier_ELODIE_age_massW_low_2sig Chabrier_ELODIE_age_massW_up_3sig Chabrier_ELODIE_age_massW_low_3sig Chabrier_ELODIE_metallicity_massW Chabrier_ELODIE_metallicity_massW_up_1sig Chabrier_ELODIE_metallicity_massW_low_1sig Chabrier_ELODIE_metallicity_massW_up_2sig Chabrier_ELODIE_metallicity_massW_low_2sig Chabrier_ELODIE_metallicity_massW_up_3sig Chabrier_ELODIE_metallicity_massW_low_3sig Chabrier_ELODIE_total_mass Chabrier_ELODIE_stellar_mass Chabrier_ELODIE_living_stars_mass Chabrier_ELODIE_remnant_mass Chabrier_ELODIE_remnant_mass_in_whitedwarfs Chabrier_ELODIE_remnant_mass_in_neutronstars Chabrier_ELODIE_remnant_mass_blackholes Chabrier_ELODIE_mass_of_ejecta Chabrier_ELODIE_total_mass_up_1sig Chabrier_ELODIE_total_mass_low_1sig Chabrier_ELODIE_total_mass_up_2sig Chabrier_ELODIE_total_mass_low_2sig Chabrier_ELODIE_total_mass_up_3sig Chabrier_ELODIE_total_mass_low_3sig Chabrier_ELODIE_spm_EBV Chabrier_ELODIE_nComponentsSSP Chabrier_ELODIE_total_mass_ssp_0 Chabrier_ELODIE_stellar_mass_ssp_0 Chabrier_ELODIE_living_stars_mass_ssp_0 Chabrier_ELODIE_remnant_mass_ssp_0 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_0 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_0 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_0 Chabrier_ELODIE_mass_of_ejecta_ssp_0 Chabrier_ELODIE_log_age_ssp_0 Chabrier_ELODIE_metal_ssp_0 Chabrier_ELODIE_SFR_ssp_0 Chabrier_ELODIE_weightMass_ssp_0 Chabrier_ELODIE_weightLight_ssp_0 Chabrier_ELODIE_total_mass_ssp_1 Chabrier_ELODIE_stellar_mass_ssp_1 Chabrier_ELODIE_living_stars_mass_ssp_1 Chabrier_ELODIE_remnant_mass_ssp_1 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_1 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_1 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_1 Chabrier_ELODIE_mass_of_ejecta_ssp_1 Chabrier_ELODIE_log_age_ssp_1 Chabrier_ELODIE_metal_ssp_1 Chabrier_ELODIE_SFR_ssp_1 Chabrier_ELODIE_weightMass_ssp_1 Chabrier_ELODIE_weightLight_ssp_1 Chabrier_ELODIE_total_mass_ssp_2 Chabrier_ELODIE_stellar_mass_ssp_2 Chabrier_ELODIE_living_stars_mass_ssp_2 Chabrier_ELODIE_remnant_mass_ssp_2 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_2 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_2 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_2 Chabrier_ELODIE_mass_of_ejecta_ssp_2 Chabrier_ELODIE_log_age_ssp_2 Chabrier_ELODIE_metal_ssp_2 Chabrier_ELODIE_SFR_ssp_2 Chabrier_ELODIE_weightMass_ssp_2 Chabrier_ELODIE_weightLight_ssp_2 Chabrier_ELODIE_total_mass_ssp_3 Chabrier_ELODIE_stellar_mass_ssp_3 Chabrier_ELODIE_living_stars_mass_ssp_3 Chabrier_ELODIE_remnant_mass_ssp_3 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_3 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_3 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_3 Chabrier_ELODIE_mass_of_ejecta_ssp_3 Chabrier_ELODIE_log_age_ssp_3 Chabrier_ELODIE_metal_ssp_3 Chabrier_ELODIE_SFR_ssp_3 Chabrier_ELODIE_weightMass_ssp_3 Chabrier_ELODIE_weightLight_ssp_3 Chabrier_ELODIE_total_mass_ssp_4 Chabrier_ELODIE_stellar_mass_ssp_4 Chabrier_ELODIE_living_stars_mass_ssp_4 Chabrier_ELODIE_remnant_mass_ssp_4 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_4 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_4 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_4 Chabrier_ELODIE_mass_of_ejecta_ssp_4 Chabrier_ELODIE_log_age_ssp_4 Chabrier_ELODIE_metal_ssp_4 Chabrier_ELODIE_SFR_ssp_4 Chabrier_ELODIE_weightMass_ssp_4 Chabrier_ELODIE_weightLight_ssp_4 Chabrier_ELODIE_total_mass_ssp_5 Chabrier_ELODIE_stellar_mass_ssp_5 Chabrier_ELODIE_living_stars_mass_ssp_5 Chabrier_ELODIE_remnant_mass_ssp_5 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_5 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_5 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_5 Chabrier_ELODIE_mass_of_ejecta_ssp_5 Chabrier_ELODIE_log_age_ssp_5 Chabrier_ELODIE_metal_ssp_5 Chabrier_ELODIE_SFR_ssp_5 Chabrier_ELODIE_weightMass_ssp_5 Chabrier_ELODIE_weightLight_ssp_5 Chabrier_ELODIE_total_mass_ssp_6 Chabrier_ELODIE_stellar_mass_ssp_6 Chabrier_ELODIE_living_stars_mass_ssp_6 Chabrier_ELODIE_remnant_mass_ssp_6 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_6 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_6 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_6 Chabrier_ELODIE_mass_of_ejecta_ssp_6 Chabrier_ELODIE_log_age_ssp_6 Chabrier_ELODIE_metal_ssp_6 Chabrier_ELODIE_SFR_ssp_6 Chabrier_ELODIE_weightMass_ssp_6 Chabrier_ELODIE_weightLight_ssp_6 Chabrier_ELODIE_total_mass_ssp_7 Chabrier_ELODIE_stellar_mass_ssp_7 Chabrier_ELODIE_living_stars_mass_ssp_7 Chabrier_ELODIE_remnant_mass_ssp_7 Chabrier_ELODIE_remnant_mass_in_whitedwarfs_ssp_7 Chabrier_ELODIE_remnant_mass_in_neutronstars_ssp_7 Chabrier_ELODIE_remnant_mass_in_blackholes_ssp_7 Chabrier_ELODIE_mass_of_ejecta_ssp_7 Chabrier_ELODIE_log_age_ssp_7 Chabrier_ELODIE_metal_ssp_7 Chabrier_ELODIE_SFR_ssp_7 Chabrier_ELODIE_weightMass_ssp_7 Chabrier_ELODIE_weightLight_ssp_7"
t = Table()
for data_array, head in zip(newDat, headers.split()):
t.add_column(Column(name=head, format='D', data=data_array))
for id_col, (col_chi2,
col_ndof) in enumerate(zip(n.transpose(chi2), n.transpose(ndof))):
t.add_column(
Column(name=hdu_header_prefix[id_col] + "chi2",
format='D',
data=col_chi2))
t.add_column(
Column(name=hdu_header_prefix[id_col] + "ndof",
format='D',
data=col_ndof))
t.add_column(Column(name="z_min", format='D', data=th_min))
t.add_column(Column(name="z_max", format='D', data=th_max))
t.add_column(Column(name="radial_bin", format='D', data=cz_sel))
# printed output
print()
print()
print((' Target: ' + target_name))
print()
print()
print(("Luminosity\t\t" + str(round(luminosity))))
print(("Optical depth\t\t" + str(grid_outputs[model_index]['odep'])))
print(("Expansion velocity (scaled)\t" + str(round(scaled_vexp, 2))))
print(("Mass loss (scaled)\t\t" + str("%.2E" % float(scaled_mdot))))
# saves results csv file
file_a = Table(np.array(latex_array),
names=('source', 'L', 'vexp_predicted', 'teff', 'tinner',
'odep', 'mdot'),
dtype=('S16', 'int32', 'f8', 'int32', 'int32', 'f8', 'f8'))
file_a.write('../fitting_results.csv', format='csv', overwrite=True)
# saves plotting file
file_b = Table(np.array(follow_up_array))
file_b.add_column(Column(follow_up_index, name='index'), index=0)
file_b.add_column(Column(follow_up_normilazation, name='norm'), index=0)
file_b.add_column(Column(targets, name='data_file'), index=0)
file_b.add_column(Column(follow_up_names, name='target_name'), index=0)
file_b.remove_columns(('c', 'd', 'e', 'f', 'g', 'h', 'i'))
file_b.write('../fitting_plotting_outputs.csv', format='csv', overwrite=True)
end = time.time()
print('Time:' + str((end - start) / 60) + 'minutes')
def run_LS(self, minf=1/12.5, maxf=1/0.1, spp=50):
""" Runs LS fit for each light curve.
Parameters
----------
minf : float, optional
The minimum frequency to search in the LS routine. Default = 1/20.
maxf : float, optional
The maximum frequency to search in the LS routine. Default = 1/0.1.
spp : int, optional
The number of samples per peak. Default = 50.
Attributes
----------
LS_results : astropy.table.Table
"""
def per_orbit(t, f):
nonlocal maxf, spp
minf = 1/(t[-1]-t[0])
if minf > 1/12.0:
minf = 1/12.0
freq, power = LombScargle(t, f).autopower(minimum_frequency=minf,
maximum_frequency=maxf,
samples_per_peak=spp)
arg = np.argmax(power)
per = 1.0/freq
popt = self.fit_LS_peak(per, power, arg)
## SEARCHES & MASKS RESONANCES OF THE BEST-FIT PERIOD
perlist = per[arg] * np.array([0.5, 1.0, 2.0, 4.0, 8.0])
remove_res = np.zeros(len(per))
maskreg = int(spp/1.5)
for p in perlist:
where = np.where( (per <= p))[0]
if len(where) > 0:
ind = int(where[0])
if ind-maskreg > 0 and ind<len(per)-maskreg:
remove_res[int(ind-maskreg):int(ind+maskreg)] = 1
elif ind < maskreg:
remove_res[0:int(maskreg)] = 1
elif ind > len(per)-maskreg:
remove_res[int(len(per)-maskreg):len(per)] = 1
if perlist[1] == 1/minf:
remove_res[0:int(spp/2)] = 1
rr = remove_res == 0
arg1 = np.argmax(power[rr])
## REDOS PERIOD ROUTINE FOR SECOND HIGHEST PEAK
if arg1 == len(per[rr]):
arg1 = int(arg1-3)
popt2 = self.fit_LS_peak(per[rr], power[rr], arg1)
maxpower = power[arg]
secpower = power[rr][arg1]
bestperiod = per[arg]
secbperiod = per[rr][arg1]
bestwidth = popt[0]
return bestperiod, secbperiod, maxpower, secpower, bestwidth
tab = Table()
periods = np.zeros(len(self.IDs))
stds = np.zeros(len(self.IDs))
peak_power = np.zeros(len(self.IDs))
periods2 = np.zeros(len(self.IDs))
peak_power2 = np.zeros(len(self.IDs))
orbit_flag = np.zeros(len(self.IDs))
orbit_flag1 = np.zeros(len(self.IDs))
orbit_flag2 = np.zeros(len(self.IDs))
for i in tqdm(range(len(self.flux)), desc="Finding most likely periods"):
time, flux, flux_err = self.time[i], self.flux[i], self.flux_err[i]
# SPLITS BY ORBIT
diff = np.diff(time)
brk = np.where(diff >= np.nanmedian(diff)+14*np.nanstd(diff))[0]
if len(brk) > 1:
brk_diff = brk - (len(time)/2)
try:
brk_diff = np.where(brk_diff<0)[0][-1]
except IndexError:
brk_diff = np.argmin(brk_diff)
brk = np.array([brk[brk_diff]], dtype=int)
# DEFINITELY TRIMS OUT EARTHSHINE MOFO
t1, f1 = time[:brk[0]], flux[:brk[0]]#[300:-500], flux[:brk[0]]#[300:-500]
t2, f2 = time[brk[0]:], flux[brk[0]:]#[800:-200], flux[brk[0]:]#[800:-200]
o1_params = per_orbit(t1, f1)
o2_params = per_orbit(t2, f2)
both = np.array([o1_params[0], o2_params[0]])
avg_period = np.nanmedian(both)
flag1 = self.assign_flag(o1_params[0], o1_params[2], o1_params[-1],
avg_period, o1_params[-2], t1[-1]-t1[0])
flag2 = self.assign_flag(o2_params[0], o2_params[2], o2_params[-1],
avg_period, o2_params[-2], t2[-1]-t2[0])
if np.abs(o1_params[1]-avg_period) < 0.5 and np.abs(o2_params[1]-avg_period)<0.5:
flag1 = flag2 = 0.0
if flag1 != 0 and flag2 != 0:
orbit_flag[i] = 1.0
else:
orbit_flag[i] = 0.0
periods[i] = np.nanmedian([o1_params[0], o2_params[0]])
orbit_flag1[i] = flag1
orbit_flag2[i] = flag2
stds[i] = o1_params[-1]
peak_power[i] = o1_params[2]
periods2[i] = o2_params[0]
peak_power2[i] = o1_params[-2]
tab.add_column(Column(self.IDs, 'Target_ID'))
tab.add_column(Column(periods, name='period_days'))
tab.add_column(Column(periods2, name='secondary_period_days'))
tab.add_column(Column(stds, name='gauss_width'))
tab.add_column(Column(peak_power, name='max_power'))
tab.add_column(Column(peak_power2, name='secondary_max_power'))
tab.add_column(Column(orbit_flag, name='orbit_flag'))
tab.add_column(Column(orbit_flag1, name='oflag1'))
tab.add_column(Column(orbit_flag2, name='oflag2'))
tab = self.averaged_per_sector(tab)
self.LS_results = tab
def nstar(self, image, star_groups):
"""
Fit, as appropriate, a compound or single model to the given
``star_groups``. Groups are fitted sequentially from the
smallest to the biggest. In each iteration, ``image`` is
subtracted by the previous fitted group.
Parameters
----------
image : numpy.ndarray
Background-subtracted image.
star_groups : `~astropy.table.Table`
This table must contain the following columns: ``id``,
``group_id``, ``x_0``, ``y_0``, ``flux_0``. ``x_0`` and
``y_0`` are initial estimates of the centroids and
``flux_0`` is an initial estimate of the flux. Additionally,
columns named as ``<param_name>_0`` are required if any
other parameter in the psf model is free (i.e., the
``fixed`` attribute of that parameter is ``False``).
Returns
-------
result_tab : `~astropy.table.Table`
Astropy table that contains photometry results.
image : numpy.ndarray
Residual image.
"""
result_tab = Table()
for param_tab_name in self._pars_to_output.keys():
result_tab.add_column(Column(name=param_tab_name))
unc_tab = Table()
for param, isfixed in self.psf_model.fixed.items():
if not isfixed:
unc_tab.add_column(Column(name=param + "_unc"))
y, x = np.indices(image.shape)
star_groups = star_groups.group_by('group_id')
for n in range(len(star_groups.groups)):
group_psf = get_grouped_psf_model(self.psf_model,
star_groups.groups[n],
self._pars_to_set)
usepixel = np.zeros_like(image, dtype=bool)
for row in star_groups.groups[n]:
usepixel[overlap_slices(large_array_shape=image.shape,
small_array_shape=self.fitshape,
position=(row['y_0'], row['x_0']),
mode='trim')[0]] = True
fit_model = self.fitter(group_psf, x[usepixel], y[usepixel],
image[usepixel])
param_table = self._model_params2table(fit_model,
len(star_groups.groups[n]))
result_tab = vstack([result_tab, param_table])
if 'param_cov' in self.fitter.fit_info.keys():
unc_tab = vstack([
unc_tab,
self._get_uncertainties(len(star_groups.groups[n]))
])
try:
from astropy.nddata.utils import NoOverlapError
except ImportError:
raise ImportError("astropy 1.1 or greater is required in "
"order to use this class.")
# do not subtract if the fitting did not go well
try:
image = subtract_psf(image,
self.psf_model,
param_table,
subshape=self.fitshape)
except NoOverlapError:
pass
if 'param_cov' in self.fitter.fit_info.keys():
result_tab = hstack([result_tab, unc_tab])
return result_tab, image
## stuff for later
galaxy_name = sys.argv[1]
#ptype = sys.argv[2]
ptype = 'coarse_final/'
from astropy.io import fits
from astropy.table import Table, Column
arr = [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],
[0.0], [0.0], [0.0], [0.0], [0.0]]
varnames = ['mass', 'logzsol', 'tau', 'tage', 'dust2']
coltit = [ty + par for par in varnames for ty in ['low ', 'best ', 'high ']]
table = Table(arr, names=(coltit))
obname = Column(name='objid', data=['J0000+0000'])
table.add_column(obname, index=0)
table.remove_row(0)
# In [10]
def lnprobfn(theta):
"""Given a parameter vector, a dictionary of observational data
and a model object, return the ln of the posterior.
This requires that an sps object (and if using spectra
and gaussian processes, a GP object) be instantiated.
"""
#print('lnprobfn loves pizza')
# Calculate prior probability and exit if not within prior
lnp_prior = model.prior_product(theta)
if not np.isfinite(lnp_prior):
def main(args):
# Set up the logger
if args.verbose:
log = get_logger(DEBUG)
else:
log = get_logger()
# Make sure all necessary environment variables are set
setup_envs()
# Initialize random number generator to use.
np.random.seed(args.seed)
random_state = np.random.RandomState(args.seed)
# Derive spectrograph number from nstart if needed
if args.spectrograph is None:
args.spectrograph = args.nstart / args.n_fibers
# Read fibermapfile to get object type, night and expid
fibermap, objtype, night, expid = get_fibermap(args.fibermap,
log=log,
nspec=args.nspec)
# Initialize the spectral simulator
log.info("Initializing SpecSim with config {}".format(args.config))
lvmparams = load_lvmparams(config=args.config, telescope=args.telescope)
qsim = get_simulator(args.config, num_fibers=1, params=lvmparams)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = lvmsim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
# - TODO: do we need quickgen to support arcs? For full pipeline
# - arcs are used to measure PSF but aren't extracted except for
# - debugging.
# - TODO: if we do need arcs, this needs to be redone.
# - conversion from phot to flux doesn't include throughput,
# - and arc lines are rebinned to nearest 0.2 A.
# Create full wavelength and flux arrays for arc exposure
wave_b = np.array(simspec.wave['b'])
wave_r = np.array(simspec.wave['r'])
wave_z = np.array(simspec.wave['z'])
phot_b = np.array(simspec.phot['b'][0])
phot_r = np.array(simspec.phot['r'][0])
phot_z = np.array(simspec.phot['z'][0])
sim_wave = np.concatenate((wave_b, wave_r, wave_z))
sim_phot = np.concatenate((phot_b, phot_r, phot_z))
wavelengths = np.arange(3533., 9913.1, 0.2)
phot = np.zeros(len(wavelengths))
for i in range(len(sim_wave)):
wavelength = sim_wave[i]
flux_index = np.argmin(abs(wavelength - wavelengths))
phot[flux_index] = sim_phot[i]
# Convert photons to flux: following specter conversion method
dw = np.gradient(wavelengths)
exptime = 5. # typical BOSS exposure time in s
fibarea = const.pi * (1.07e-2 /
2)**2 # cross-sectional fiber area in cm^2
hc = 1.e17 * const.h * const.c # convert to erg A
spectra = (hc * exptime * fibarea * dw * phot) / wavelengths
else:
wavelengths = simspec.wave['brz']
spectra = simspec.flux
if nspec < args.nspec:
log.info("Only {} spectra in input file".format(nspec))
args.nspec = nspec
else:
# Initialize the output truth table.
spectra = []
wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10))
truth['FLUX'] = np.zeros((args.nspec, npix))
truth['WAVE'] = wavelengths
jj = list()
for thisobj in set(true_objtype):
ii = np.where(true_objtype == thisobj)[0]
nobj = len(ii)
truth['OBJTYPE'][ii] = thisobj
log.info('Generating {} template'.format(thisobj))
# Generate the templates
if thisobj == 'ELG':
elg = lvmsim.templates.ELG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = elg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_elg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'LRG':
lrg = lvmsim.templates.LRG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = lrg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_lrg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'QSO':
qso = lvmsim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(
nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = lvmsim.templates.BGS(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = bgs.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_bgs,
rmagrange=args.rmagrange_bgs,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'STD':
fstd = lvmsim.templates.FSTD(wave=wavelengths)
flux, tmpwave, meta1 = fstd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = lvmsim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = lvmsim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'WD':
wd = lvmsim.templates.WD(wave=wavelengths)
flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'SKY':
flux = np.zeros((nobj, npix))
meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32)))
elif thisobj == 'TEST':
flux = np.zeros((args.nspec, npix))
indx = np.where(wave > 5800.0 - 1E-6)[0][0]
ref_integrated_flux = 1E-10
ref_cst_flux_density = 1E-17
single_line = (np.arange(args.nspec) % 2 == 0).astype(
np.float32)
continuum = (np.arange(args.nspec) % 2 == 1).astype(np.float32)
for spec in range(args.nspec):
flux[spec, indx] = single_line[
spec] * ref_integrated_flux / np.gradient(wavelengths)[
indx] # single line
flux[spec] += continuum[
spec] * ref_cst_flux_density # flat continuum
meta1 = Table(
dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32),
LINE=wave[indx] *
np.ones(args.nspec, dtype=np.float32),
LINEFLUX=single_line * ref_integrated_flux,
CONSTFLUXDENSITY=continuum * ref_cst_flux_density))
else:
log.fatal('Unknown object type {}'.format(thisobj))
sys.exit(1)
# Pack it in.
truth['FLUX'][ii] = flux
meta = vstack([meta, meta1])
jj.append(ii.tolist())
# Sanity check on units; templates currently return ergs, not 1e-17 ergs...
# assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6)
# Sort the metadata table.
jj = sum(jj, [])
meta_new = Table()
for k in range(args.nspec):
index = int(np.where(np.array(jj) == k)[0])
meta_new = vstack([meta_new, meta[index]])
meta = meta_new
# Add TARGETID and the true OBJTYPE to the metadata table.
meta.add_column(
Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE'))
meta.add_column(Column(targetids, name='TARGETID'))
# Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z
meta.rename_column('REDSHIFT', 'TRUEZ')
# ---------- end simspec
# explicitly set location on focal plane if needed to support airmass
# variations when using specsim v0.5
if qsim.source.focal_xy is None:
qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm'))
# Set simulation parameters from the simspec header or lvmparams
bright_objects = ['bgs', 'mws', 'bright', 'BGS', 'MWS', 'BRIGHT_MIX']
gray_objects = ['gray', 'grey']
if args.simspec is None:
object_type = objtype
flavor = None
elif simspec.flavor == 'science':
object_type = None
flavor = simspec.header['PROGRAM']
else:
object_type = None
flavor = simspec.flavor
log.warning(
'Maybe using an outdated simspec file with flavor={}'.format(
flavor))
# Set airmass
if args.airmass is not None:
qsim.atmosphere.airmass = args.airmass
elif args.simspec and 'AIRMASS' in simspec.header:
qsim.atmosphere.airmass = simspec.header['AIRMASS']
else:
qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2
# Set site location
if args.location is not None:
qsim.observation.observatory = args.location
else:
qsim.observation.observatory = 'APO'
# Set exptime
if args.exptime is not None:
qsim.observation.exposure_time = args.exptime * u.s
elif args.simspec and 'EXPTIME' in simspec.header:
qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s
elif objtype in bright_objects:
qsim.observation.exposure_time = lvmparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = lvmparams['exptime_dark'] * u.s
# Set Moon Phase
if args.moon_phase is not None:
qsim.atmosphere.moon.moon_phase = args.moon_phase
elif args.simspec and 'MOONFRAC' in simspec.header:
qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC']
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_phase = 0.7
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_phase = 0.1
else:
qsim.atmosphere.moon.moon_phase = 0.5
# Set Moon Zenith
if args.moon_zenith is not None:
qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg
elif args.simspec and 'MOONALT' in simspec.header:
qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_zenith = 30 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_zenith = 80 * u.deg
else:
qsim.atmosphere.moon.moon_zenith = 100 * u.deg
# Set Moon - Object Angle
if args.moon_angle is not None:
qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg
elif args.simspec and 'MOONSEP' in simspec.header:
qsim.atmosphere.moon.separation_angle = simspec.header[
'MOONSEP'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.separation_angle = 50 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
else:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
# Initialize per-camera output arrays that will be saved
waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {}
maxbin = 0
nmax = args.nspec
for camera in qsim.instrument.cameras:
# Lookup this camera's resolution matrix and convert to the sparse format used in lvmspec.
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(),
[args.nspec, 1, 1])
waves[camera.name] = (camera.output_wavelength.to(
u.Angstrom).value.astype(np.float32))
nwave = len(waves[camera.name])
maxbin = max(maxbin, len(waves[camera.name]))
nobj = np.zeros((nmax, 3, maxbin)) # object photons
nsky = np.zeros((nmax, 3, maxbin)) # sky photons
nivar = np.zeros((nmax, 3, maxbin)) # inverse variance (object+sky)
cframe_observedflux = np.zeros(
(nmax, 3, maxbin)) # calibrated object flux
cframe_ivar = np.zeros(
(nmax, 3, maxbin)) # inverse variance of calibrated object flux
cframe_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to calibrated flux
sky_ivar = np.zeros((nmax, 3, maxbin)) # inverse variance of sky
sky_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to sky only
frame_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to nobj+nsky
trueflux[camera.name] = np.empty(
(args.nspec, nwave)) # calibrated flux
noisyflux[camera.name] = np.empty(
(args.nspec, nwave)) # observed flux with noise
obsivar[camera.name] = np.empty(
(args.nspec, nwave)) # inverse variance of flux
if args.simspec:
dw = np.gradient(simspec.wave[camera.name])
else:
sflux = np.empty((args.nspec, npix))
# - Check if input simspec is for a continuum flat lamp instead of science
# - This does not convolve to per-fiber resolution
if args.simspec:
if simspec.flavor == 'flat':
log.info("Simulating flat lamp exposure")
for i, camera in enumerate(qsim.instrument.cameras):
channel = camera.name
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
dw = np.gradient(simspec.wave[channel])
meanspec = resample_flux(
waves[channel], simspec.wave[channel],
np.average(simspec.phot[channel] / dw, axis=0))
fiberflat = random_state.normal(loc=1.0,
scale=1.0 / np.sqrt(meanspec),
size=(nspec, num_pixels))
ivar = np.tile(meanspec, [nspec, 1])
mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32)
for kk in range((args.nspec + args.nstart - 1) //
args.n_fibers + 1):
camera = channel + str(kk)
outfile = lvmspec.io.findfile('fiberflat', night, expid,
camera)
start = max(args.n_fibers * kk, args.nstart)
end = min(args.n_fibers * (kk + 1), nmax)
if (args.spectrograph <= kk):
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, kk, start, end))
ff = FiberFlat(waves[channel],
fiberflat[start:end, :],
ivar[start:end, :],
mask[start:end, :],
meanspec,
header=dict(CAMERA=camera))
write_fiberflat(outfile, ff)
filePath = lvmspec.io.findfile("fiberflat", night, expid,
camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
scale = 1e-17
fluxunits = scale * u.erg / (u.s * u.cm**2 * u.Angstrom)
for j in range(args.nspec):
thisobjtype = objtype[j]
sys.stdout.flush()
if flavor == 'arc':
qsim.source.update_in('Quickgen source {0}'.format, 'perfect',
wavelengths * u.Angstrom,
spectra * fluxunits)
else:
qsim.source.update_in('Quickgen source {0}'.format(j),
thisobjtype.lower(),
wavelengths * u.Angstrom,
spectra[j, :] * fluxunits)
qsim.source.update_out()
qsim.simulate()
qsim.generate_random_noise(random_state)
for i, output in enumerate(qsim.camera_output):
assert output['observed_flux'].unit == 1e17 * fluxunits
# Extract the simulation results needed to create our uncalibrated
# frame output file.
num_pixels = len(output)
nobj[j, i, :num_pixels] = output['num_source_electrons'][:, 0]
nsky[j, i, :num_pixels] = output['num_sky_electrons'][:, 0]
nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:, 0]
# Get results for our flux-calibrated output file.
cframe_observedflux[
j, i, :num_pixels] = 1e17 * output['observed_flux'][:, 0]
cframe_ivar[
j,
i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:, 0]
# Fill brick arrays from the results.
camera = output.meta['name']
trueflux[camera][j][:] = 1e17 * output['observed_flux'][:, 0]
noisyflux[camera][j][:] = 1e17 * (
output['observed_flux'][:, 0] +
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:,
0]
# Use the same noise realization in the cframe and frame, without any
# additional noise from sky subtraction for now.
frame_rand_noise[
j, i, :num_pixels] = output['random_noise_electrons'][:, 0]
cframe_rand_noise[j, i, :num_pixels] = 1e17 * (
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
# The sky output file represents a model fit to ~40 sky fibers.
# We reduce the variance by a factor of 25 to account for this and
# give the sky an independent (Gaussian) noise realization.
sky_ivar[
j,
i, :num_pixels] = 25.0 / (output['variance_electrons'][:, 0] -
output['num_source_electrons'][:, 0])
sky_rand_noise[j, i, :num_pixels] = random_state.normal(
scale=1.0 / np.sqrt(sky_ivar[j, i, :num_pixels]),
size=num_pixels)
armName = {"b": 0, "r": 1, "z": 2}
for channel in 'brz':
# Before writing, convert from counts/bin to counts/A (as in Pixsim output)
# Quicksim Default:
# FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang]
# COUNTS_OBJ - object counts in 0.5 Ang bin
# COUNTS_SKY - sky counts in 0.5 Ang bin
num_pixels = len(waves[channel])
dwave = np.gradient(waves[channel])
nobj[:, armName[channel], :num_pixels] /= dwave
frame_rand_noise[:, armName[channel], :num_pixels] /= dwave
nivar[:, armName[channel], :num_pixels] *= dwave**2
nsky[:, armName[channel], :num_pixels] /= dwave
sky_rand_noise[:, armName[channel], :num_pixels] /= dwave
sky_ivar[:, armName[channel], :num_pixels] /= dwave**2
# Now write the outputs in DESI standard file system. None of the output file can have more than args.n_fibers spectra
# Looping over spectrograph
for ii in range((args.nspec + args.nstart - 1) // args.n_fibers + 1):
start = max(args.n_fibers * ii,
args.nstart) # first spectrum for a given spectrograph
end = min(args.n_fibers * (ii + 1),
nmax) # last spectrum for the spectrograph
if (args.spectrograph <= ii):
camera = "{}{}".format(channel, ii)
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, ii, start, end))
num_pixels = len(waves[channel])
# Write frame file
framefileName = lvmspec.io.findfile("frame", night, expid,
camera)
frame_flux = nobj[start:end, armName[channel], :num_pixels] + \
nsky[start:end, armName[channel], :num_pixels] + \
frame_rand_noise[start:end, armName[channel], :num_pixels]
frame_ivar = nivar[start:end, armName[channel], :num_pixels]
# required for slicing the resolution metric, resolusion matrix has (nspec, ndiag, wave)
# for example if nstart =400, nspec=150: two spectrographs:
# 400-499=> 0 spectrograph, 500-549 => 1
sh1 = frame_flux.shape[0]
if (args.nstart == start):
resol = resolution[channel][:sh1, :, :]
else:
resol = resolution[channel][-sh1:, :, :]
# must create lvmspec.Frame object
frame = Frame(waves[channel],
frame_flux,
frame_ivar,
resolution_data=resol,
spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor))
lvmspec.io.write_frame(framefileName, frame)
framefilePath = lvmspec.io.findfile("frame", night, expid,
camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName = lvmspec.io.findfile("cframe", night, expid,
camera)
cframeFlux = cframe_observedflux[start:end, armName[channel], :num_pixels] + \
cframe_rand_noise[start:end, armName[channel], :num_pixels]
cframeIvar = cframe_ivar[start:end,
armName[channel], :num_pixels]
# must create lvmspec.Frame object
cframe = Frame(waves[channel],
cframeFlux,
cframeIvar,
resolution_data=resol,
spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor))
lvmspec.io.frame.write_frame(cframeFileName, cframe)
cframefilePath = lvmspec.io.findfile("cframe", night, expid,
camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName = lvmspec.io.findfile("sky", night, expid, camera)
skyflux = nsky[start:end, armName[channel], :num_pixels] + \
sky_rand_noise[start:end, armName[channel], :num_pixels]
skyivar = sky_ivar[start:end, armName[channel], :num_pixels]
skymask = np.zeros(skyflux.shape, dtype=np.uint32)
# must create lvmspec.Sky object
skymodel = SkyModel(waves[channel],
skyflux,
skyivar,
skymask,
header=dict(CAMERA=camera))
lvmspec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath = lvmspec.io.findfile("sky", night, expid, camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile = lvmspec.io.findfile("calib", night, expid,
camera)
flux = cframe_observedflux[start:end,
armName[channel], :num_pixels]
phot = nobj[start:end, armName[channel], :num_pixels]
calibration = np.zeros_like(phot)
jj = (flux > 0)
calibration[jj] = phot[jj] / flux[jj]
# - TODO: what should calibivar be?
# - For now, model it as the noise of combining ~10 spectra
calibivar = 10 / cframe_ivar[start:end,
armName[channel], :num_pixels]
# mask=(1/calibivar>0).astype(int)??
mask = np.zeros(calibration.shape, dtype=np.uint32)
# write flux calibration
fluxcalib = FluxCalib(waves[channel], calibration, calibivar,
mask)
write_flux_calibration(calibVectorFile, fluxcalib)
calibfilePath = lvmspec.io.findfile("calib", night, expid,
camera)
log.info("Wrote file {}".format(calibfilePath))
decintpix, 0,
psf_in[0].data.shape[0] - 1)
# Yes, it's bonkers that the wcs is the other way round compared to the fits file
psf_ratio = psf_in[0].data[decclip, raclip]
# Get the PSF information
a_in = fits.open(aimage)
b_in = fits.open(bimage)
pa_in = fits.open(paimage)
a = a_in[0].data[decclip, raclip]
b = b_in[0].data[decclip, raclip]
pa = pa_in[0].data[decclip, raclip]
# Scale the peak fluxes
if options.scaleint:
data['int_flux'] /= psf_ratio
else:
data['peak_flux'] *= psf_ratio
vot = Table(data)
# Add the PSF columns
vot.add_column(Column(data=a, name='a_psf'))
vot.add_column(Column(data=b, name='b_psf'))
vot.add_column(Column(data=pa, name='pa_psf'))
# description of this votable
vot.description = "Corrected for position-dependent PSF variation"
print "Writing to " + output
writetoVO(vot, output)
pos = np.array([position[0] * np.ones(n),
position[1] * np.ones(n),
position[2] * np.ones(n),
np.ones(n)])
#print 'Positions:'
#print pos
dir = np.array([dir[0] * np.ones(n) + dir_diff[0] * np.arange(0, n),
dir[1] * np.ones(n) + dir_diff[1] * np.arange(0, n),
dir[2] * np.ones(n) + dir_diff[2] * np.arange(0, n),
np.zeros(n)])
if dir_diff != [0, 0, 0]:
print 'Directions:'
print dir
photons.add_column(Column(name='pos', data=pos.T))
photons.add_column(Column(name='dir', data=dir.T))
photons.add_column(Column(name='polarization', data=polarization_vectors(dir.T, photons['polangle'])))
return photons
def fullRotation(exp_time, offset = 0, ):
'''The source and first mirror are rotated around 360 degrees, and the simulation is run at 10 degree intervals.
'''
counts = np.zeros(36)
for i in np.arange(0, 360, 10):
print 'angle: ' + str(i)
photons = sourceMirrorDetector(sourceAngle = i, time = float(exp_time), sourceOffset = offset, detOffset = offset)
p = photons[photons['probability'] > 0].copy()
#p['probability'] *= 100
def _json_to_table(json_obj):
"""
Takes a JSON object as returned from a MAST microservice request and turns it into an `~astropy.table.Table`.
Parameters
----------
json_obj : dict
A MAST microservice response JSON object (python dictionary)
Returns
-------
response : `~astropy.table.Table`
"""
data_table = Table(masked=True)
if not all(x in json_obj.keys() for x in ['info', 'data']):
raise KeyError("Missing required key(s) 'data' and/or 'info.'")
# determine database type key in case missing
type_key = 'type' if json_obj['info'][0].get('type') else 'db_type'
# for each item in info, store the type and column name
for idx, col, col_type, ignore_value in \
[(idx, x['name'], x[type_key], "NULL") for idx, x in enumerate(json_obj['info'])]:
# if default value is NULL, set ignore value to None
if ignore_value == "NULL":
ignore_value = None
# making type adjustments
if col_type == "char" or col_type == "STRING":
col_type = "str"
ignore_value = "" if (ignore_value is None) else ignore_value
elif col_type == "boolean" or col_type == "BINARY":
col_type = "bool"
elif col_type == "unsignedByte":
col_type = np.ubyte
elif col_type == "int" or col_type == "short" or col_type == "long" or col_type == "NUMBER":
# int arrays do not admit Non/nan vals
col_type = np.int64
ignore_value = -999 if (ignore_value is None) else ignore_value
elif col_type == "double" or col_type == "float" or col_type == "DECIMAL":
# int arrays do not admit Non/nan vals
col_type = np.float64
ignore_value = -999 if (ignore_value is None) else ignore_value
elif col_type == "DATETIME":
col_type = "str"
ignore_value = "" if (ignore_value is None) else ignore_value
# Make the column list (don't assign final type yet or there will be errors)
# Step through data array of values
col_data = np.array([x[idx] for x in json_obj['data']], dtype=object)
if ignore_value is not None:
col_data[np.where(np.equal(col_data, None))] = ignore_value
# no consistant way to make the mask because np.equal fails on ''
# and array == value fails with None
if col_type == 'str':
col_mask = (col_data == ignore_value)
else:
col_mask = np.equal(col_data, ignore_value)
# add the column
data_table.add_column(
MaskedColumn(col_data.astype(col_type), name=col, mask=col_mask))
return data_table
def ref_galactic_fk4(fnout='galactic_fk4.csv'):
"""
Accuracy tests for the ICRS (with no E-terms of aberration) to/from FK5
conversion, with arbitrary equinoxes and epoch of observation.
"""
import os
import numpy as np
import starlink.Ast as Ast
from astropy.table import Table, Column
np.random.seed(12345)
N = 200
# Sample uniformly on the unit sphere. These will be either the ICRS
# coordinates for the transformation to FK5, or the FK5 coordinates for the
# transformation to ICRS.
lon = np.random.uniform(0., 360., N)
lat = np.degrees(np.arcsin(np.random.uniform(-1., 1., N)))
# Generate random observation epoch and equinoxes
obstime = [
"B{0:7.2f}".format(x) for x in np.random.uniform(1950., 2000., N)
]
equinox_fk4 = [
"J{0:7.2f}".format(x) for x in np.random.uniform(1975., 2025., N)
]
lon_gal, lat_gal = [], []
ra_fk4, dec_fk4 = [], []
for i in range(N):
# Set up frames for AST
frame_gal = Ast.SkyFrame(
'System=Galactic,Epoch={epoch}'.format(epoch=obstime[i]))
frame_fk4 = Ast.SkyFrame(
'System=FK4,Epoch={epoch},Equinox={equinox_fk4}'.format(
epoch=obstime[i], equinox_fk4=equinox_fk4[i]))
# ICRS to FK5
frameset = frame_gal.convert(frame_fk4)
coords = np.degrees(
frameset.tran([[np.radians(lon[i])], [np.radians(lat[i])]]))
ra_fk4.append(coords[0, 0])
dec_fk4.append(coords[1, 0])
# FK5 to ICRS
frameset = frame_fk4.convert(frame_gal)
coords = np.degrees(
frameset.tran([[np.radians(lon[i])], [np.radians(lat[i])]]))
lon_gal.append(coords[0, 0])
lat_gal.append(coords[1, 0])
# Write out table to a CSV file
t = Table()
t.add_column(Column(name='equinox_fk4', data=equinox_fk4))
t.add_column(Column(name='obstime', data=obstime))
t.add_column(Column(name='lon_in', data=lon))
t.add_column(Column(name='lat_in', data=lat))
t.add_column(Column(name='ra_fk4', data=ra_fk4))
t.add_column(Column(name='dec_fk4', data=dec_fk4))
t.add_column(Column(name='lon_gal', data=lon_gal))
t.add_column(Column(name='lat_gal', data=lat_gal))
f = open(fnout, 'wb')
f.write("# This file was generated with the {0} script, and the reference "
"values were computed using AST\n".format(
os.path.basename(__file__)))
t.write(f, format='ascii', delimiter=',')
fint=np.append(ynew2,ynew1[i1]) fint=np.sort(fint)[::-1] finn=np.append(xnew2,xnew1[i1]) finn=np.sort(finn)[::-1] fint=np.reshape(fint,(np.shape(fint)[0],1)) finn=np.reshape(finn,(np.shape(finn)[0],1)) fX3=np.concatenate((fint, finn), axis=1) fX3=fX3[fX3[:, 0].argsort()] mytck3,myu3=itp.splprep([kx3,ky3],k=1) xnew3,ynew3=itp.splev(np.linspace(0,1,550),mytck3) #----------------------------------------------------------------------------------------------------------# #----------------------------------------------------------------------------------------------------------# # Exporting the points of the fit to a text file tab1=Table() tab2=Table() tab1.add_column(Column(data=fX3[:, 0],name='Latitude')) tab1.add_column(Column(data=fX3[:, 1],name='Longitude')) tab2.add_column(Column(data=ynew3,name='Latitude')) tab2.add_column(Column(data=xnew3,name='Longitude')) sys.stdout = open(output_file_path1, 'w') tab1.pprint(max_lines=-1, max_width=-1) sys.stdout = open(output_file_path2, 'w') tab2.pprint(max_lines=-1, max_width=-1) #----------------------------------------------------------------------------------------------------------#
def test_write_html(tmpdir):
t = Table()
t.add_column(Column(name='a', data=[1, 2, 3]))
t.add_column(Column(name='b', data=['a', 'b', 'c']))
path = str(tmpdir.join("data.html"))
t.write(path, format='html')
def savestats(
basepath="/bio/web/secure/adamginsburg/ALMA-IMF/October31Release",
suffix='image.tt0',
filetype=".fits"):
if 'October31' in basepath:
stats = assemble_stats(f"{basepath}/*/*/*_12M_*.{suffix}*{filetype}",
ditch_suffix=f".{suffix[:-1]}")
else:
# extra layer: bsens, cleanest, etc
stats = assemble_stats(f"{basepath}/*/*/*/*_12M_*.{suffix}*{filetype}",
ditch_suffix=f".{suffix[:-1]}")
with open(f'{basepath}/tables/metadata_{suffix}.json', 'w') as fh:
json.dump(stats, fh, cls=MyEncoder)
requested = get_requested_sens()
meta_keys = [
'region', 'band', 'array', 'selfcaliter', 'robust', 'suffix', 'bsens',
'pbcor', 'filename'
]
stats_keys = [
'bmaj',
'bmin',
'bpa',
'peak',
'sum',
'fluxsum',
'sumgt3sig',
'sumgt5sig',
'mad',
'mad_sample',
'std_sample',
'peak/mad',
'psf_secondpeak',
'psf_secondpeak_radius',
'psf_secondpeak_sidelobefraction',
]
req_keys = ['B3_res', 'B3_sens', 'B6_res', 'B6_sens']
req_keys_head = ['Req_Res', 'Req_Sens']
rows = []
for entry in stats:
band = entry['meta']['band']
requested_this = requested[requested['Field'] == entry['meta']
['region']]
if len(requested_this) == 0:
print(f"Skipped {entry['meta']['region']}")
continue
rows += [[entry['meta'][key] for key in meta_keys] + [
entry['stats'][key] if key in entry['stats'] else np.nan
for key in stats_keys
] + [requested_this[key][0] for key in req_keys if band in key]]
tbl = Table(rows=rows, names=meta_keys + stats_keys + req_keys_head)
# do some QA
tbl.add_column(
Column(name='SensVsReq', data=tbl['mad'] * 1e3 / tbl['Req_Sens']))
tbl.add_column(
Column(name='BeamVsReq',
data=(tbl['bmaj'] * tbl['bmin'])**0.5 / tbl['Req_Res']))
tbl.write(f'{basepath}/tables/metadata_{suffix}.ecsv', overwrite=True)
tbl.write(f'{basepath}/tables/metadata_{suffix}.html',
format='ascii.html',
overwrite=True)
tbl.write(f'{basepath}/tables/metadata_{suffix}.tex', overwrite=True)
tbl.write(f'{basepath}/tables/metadata_{suffix}.js.html',
format='jsviewer')
return tbl
fdic['dark'][float(hdr['EXPTIME'])].append(filename)
elif hdr['OBJECT'].lower().startswith('bias'):
fdic['bias'].append(filename)
elif hdr['OBJECT'].lower().startswith('flat'):
fdic['flat'].append(filename)
elif hdr['OBJECT'].lower().startswith('comp'):
fdic['comp'].append(filename)
else:
fdic['objt'].append(filename)
obnames.append(hdr["OBJECT"])
# table에 이름 column 추가
fnames = Column(data=fnames, name='FILE')
table.add_column(fnames, index=0)
table.sort('FILE')
# table 폴더를 따로 만들어 저장함
tablepath = Path(newfitspath / 'Headertable')
if not tablepath.exists():
tablepath.mkdir()
else:
print("Table 저장 폴더가 이미 있어서 새로 만들지 않음")
table.write(tablepath / 'Headersumm.csv', format='ascii.csv', overwrite=True)
#%%
"""Preprocess용 Table 나누기"""
biastab = table[(table['OBJECT'] == 'Bias')]
# exptime 가짓 수
darkdic = {}
def mark(tv, stars=None, rad=3, auto=False, color='m', new=False, exit=False):
""" Interactive mark stars on TV, or recenter current list
Args :
tv : TV instance from which user will mark stars
stars = : existing star table
auto= (bool) : if True, recentroid from existing position
radius= (int): radius to use for centroiding and for size of circles (default=3)
color= (char) : color for circles (default='m')
"""
# clear display and mark current star list( if not new)
if new: tv.tvclear()
try:
dateobs = Time(tv.hdr['DATE-OBS'], format='fits')
except:
dateobs = None
#try: exptime=tv.hdr['EXPTIME']
#except: exptime=None
#try: filt=tv.hdr['FILTER']
#except: filt=None
cards = ['EXPTIME', 'FILTER', 'AIRMASS']
types = ['f4', 'S', 'f4']
if stars == None:
stars = Table(names=('id', 'x', 'y'), dtype=('i4', 'f4', 'f4'))
stars['x'].info.format = '.2f'
stars['y'].info.format = '.2f'
if dateobs is not None:
stars.add_column(Column([], name='MJD', dtype=('f8')))
stars['MJD'].info.format = '.6f'
#if exptime is not None :
# stars.add_column(Column([],name='EXPTIME',dtype=('f4')))
#if filt is not None :
# stars.add_column(Column([],name='FILTER',dtype=('S')))
for icard, card in enumerate(cards):
try:
stars.add_column(Column([], name=card, dtype=(types[icard])))
except:
pass
else:
if auto:
# with auto option, recentroid and update from current header
for star in stars:
x, y = centroid(tv.img, star['x'], star['y'], rad)
star['x'] = x
star['y'] = y
if dateobs is not None: star['MJD'] = dateobs.mjd
#if exptime is not None : star['EXPTIME'] = exptime
#if filt is not None : star['FILTER'] = filt
for icard, card in enumerate(cards):
try:
star[card] = tv.hdr[card]
except:
pass
# display stars
for star in stars:
tv.tvcirc(star['x'], star['y'], rad, color=color)
if exit: return stars
istar = len(stars) + 1
while True:
key, x, y = tv.tvmark()
if key == 'q' or key == 'e': break
if key == 'i':
# add at nearest integer pixel
x = round(x)
y = round(y)
elif key == 'c':
# centroid around marked position
x, y = centroid(tv.img, x, y, rad)
# add blank row, recognizing that we may have added other columns
stars.add_row()
stars[len(stars) - 1]['id'] = istar
stars[len(stars) - 1]['x'] = x
stars[len(stars) - 1]['y'] = y
tv.tvcirc(x, y, rad, color=color)
if dateobs is not None:
stars[len(stars) - 1]['MJD'] = dateobs.mjd
for icard, card in enumerate(cards):
try:
stars[len(stars) - 1][card] = tv.hdr[card]
except:
pass
#if exptime is not None :
# stars[len(stars)-1]['EXPTIME'] = exptime
#if filt is not None :
# stars[len(stars)-1]['FILTER'] = filt
istar += 1
return stars
if get_quality(qa_txt, nline=47)==1: over_masked[i]='O'
if get_quality(qa_txt, nline=20)==1: fov[i]='V'
if get_quality(qa_txt, nline=19)==1: multiple[i]='M'
if get_quality(qa_txt, nline=18)==1: bright_star[i]='B'
if get_quality(qa_txt, nline=17)==1: uncertain[i]='U'
note[i]= read_note(qa_txt)
#####################################################################
myTable = Table()
myTable.add_column(Column(data=pgc_, name='pgc'))
myTable.add_column(Column(data=ra_, name='ra', format='%0.4f'))
myTable.add_column(Column(data=dec_, name='dec', format='%0.4f'))
myTable.add_column(Column(data=l_, name='gl', format='%0.4f'))
myTable.add_column(Column(data=b_, name='gb', format='%0.4f'))
myTable.add_column(Column(data=sgl_, name='sgl', format='%0.4f'))
myTable.add_column(Column(data=sgb_, name='sgb', format='%0.4f'))
myTable.add_column(Column(data=d25_, name='d25', format='%0.2f'))
myTable.add_column(Column(data=b_a_, name='b_a', format='%0.2f'))
myTable.add_column(Column(data=pa_, name='pa', format='%0.1f'))
myTable.add_column(Column(data=ty_, name='ty', format='%0.1f'))
myTable.add_column(Column(data=type_, name='type'))
myTable.add_column(Column(data=sdss_, name='sdss'))
myTable.add_column(Column(data=alfa100, name='alfa100'))
myTable.add_column(Column(data=QA, name='QA_sdss'))
myTable.add_column(Column(data=QA_wise, name='QA_wise'))
def quickcat(tilefiles,
targets,
truth,
zcat=None,
obsconditions=None,
perfect=False):
"""
Generates quick output zcatalog
Args:
tilefiles : list of fiberassign tile files that were observed
targets : astropy Table of targets
truth : astropy Table of input truth with columns TARGETID, TRUEZ, and TRUETYPE
zcat (optional): input zcatalog Table from previous observations
obsconditions (optional): Table or ndarray with observing conditions from surveysim
perfect (optional): if True, treat spectro pipeline as perfect with input=output,
otherwise add noise and zwarn!=0 flags
Returns:
zcatalog astropy Table based upon input truth, plus ZERR, ZWARN,
NUMOBS, and TYPE columns
"""
#- convert to Table for easier manipulation
if not isinstance(truth, Table):
truth = Table(truth)
#- Count how many times each target was observed for this set of tiles
print('{} QC Reading {} tiles'.format(asctime(), len(tilefiles)))
nobs = Counter()
targets_in_tile = {}
tileids = list()
for infile in tilefiles:
fibassign, header = fits.getdata(infile,
'FIBER_ASSIGNMENTS',
header=True)
tile_id = header['TILEID']
tileids.append(tile_id)
ii = (fibassign['TARGETID'] != -1) #- targets with assignments
nobs.update(fibassign['TARGETID'][ii])
targets_in_tile[tile_id] = fibassign['TARGETID'][ii]
#- Trim obsconditions to just the tiles that were observed
if obsconditions is not None:
ii = np.in1d(obsconditions['TILEID'], tileids)
if np.any(ii == False):
obsconditions = obsconditions[ii]
assert len(obsconditions) > 0
#- Sort obsconditions to match order of tiles
#- This might not be needed, but is fast for O(20k) tiles and may
#- prevent future surprises if code expects them to be row aligned
tileids = np.array(tileids)
if (obsconditions is not None) and \
(np.any(tileids != obsconditions['TILEID'])):
i = np.argsort(tileids)
j = np.argsort(obsconditions['TILEID'])
k = np.argsort(i)
obsconditions = obsconditions[j[k]]
assert np.all(tileids == obsconditions['TILEID'])
#- Trim truth down to just ones that have already been observed
print('{} QC Trimming truth to just observed targets'.format(asctime()))
obs_targetids = np.array(list(nobs.keys()))
iiobs = np.in1d(truth['TARGETID'], obs_targetids)
truth = truth[iiobs]
targets = targets[iiobs]
#- Construct initial new z catalog
print('{} QC Constructing new redshift catalog'.format(asctime()))
newzcat = Table()
newzcat['TARGETID'] = truth['TARGETID']
if 'BRICKNAME' in truth.dtype.names:
newzcat['BRICKNAME'] = truth['BRICKNAME']
else:
newzcat['BRICKNAME'] = np.zeros(len(truth), dtype=(str, 8))
#- Copy TRUETYPE -> SPECTYPE so that we can change without altering original
newzcat['SPECTYPE'] = truth['TRUESPECTYPE'].copy()
#- Add ZERR and ZWARN
print('{} QC Adding ZERR and ZWARN'.format(asctime()))
nz = len(newzcat)
if perfect:
newzcat['Z'] = truth['TRUEZ'].copy()
newzcat['ZERR'] = np.zeros(nz, dtype=np.float32)
newzcat['ZWARN'] = np.zeros(nz, dtype=np.int32)
else:
# get the observational conditions for the current tilefiles
if obsconditions is None:
obsconditions = get_median_obsconditions(tileids)
# get the redshifts
z, zerr, zwarn = get_observed_redshifts(targets, truth,
targets_in_tile, obsconditions)
newzcat['Z'] = z #- update with noisy redshift
newzcat['ZERR'] = zerr
newzcat['ZWARN'] = zwarn
#- Add numobs column
print('{} QC Adding NUMOBS column'.format(asctime()))
newzcat.add_column(Column(name='NUMOBS', length=nz, dtype=np.int32))
for i in range(nz):
newzcat['NUMOBS'][i] = nobs[newzcat['TARGETID'][i]]
#- Merge previous zcat with newzcat
print('{} QC Merging previous zcat'.format(asctime()))
if zcat is not None:
#- don't modify original
#- Note: this uses copy on write for the columns to be memory
#- efficient while still letting us modify a column if needed
zcat = zcat.copy()
#- targets that are in both zcat and newzcat
repeats = np.in1d(zcat['TARGETID'], newzcat['TARGETID'])
#- update numobs in both zcat and newzcat
ii = np.in1d(newzcat['TARGETID'], zcat['TARGETID'][repeats])
orig_numobs = zcat['NUMOBS'][repeats].copy()
new_numobs = newzcat['NUMOBS'][ii].copy()
zcat['NUMOBS'][repeats] += new_numobs
newzcat['NUMOBS'][ii] += orig_numobs
#- replace only repeats that had ZWARN flags in original zcat
#- replace in new
replace = repeats & (zcat['ZWARN'] != 0)
jj = np.in1d(newzcat['TARGETID'], zcat['TARGETID'][replace])
zcat[replace] = newzcat[jj]
#- trim newzcat to ones that shouldn't override original zcat
discard = np.in1d(newzcat['TARGETID'], zcat['TARGETID'])
newzcat = newzcat[~discard]
#- Should be non-overlapping now
assert np.all(np.in1d(zcat['TARGETID'], newzcat['TARGETID']) == False)
#- merge them
newzcat = vstack([zcat, newzcat])
#- check for duplicates
targetids = newzcat['TARGETID']
assert len(np.unique(targetids)) == len(targetids)
#- Metadata for header
newzcat.meta['EXTNAME'] = 'ZCATALOG'
print('{} QC done'.format(asctime()))
return newzcat
# Build the gorup ID number
groupID = i + 1
# Count the number of images in this group
numberOfImages = groupEndInd - groupStartInd
# Build the list of ID numbers for THIS group and append it to the full list
thisGroupID = numberOfImages * [groupID]
groupIDs.extend(thisGroupID)
# Fill in the final entry
groupIDs.append(groupID)
# Store the groupID number in the reducedFileIndex
groupIDcolumn = Column(name='GROUP_ID', data=groupIDs)
reducedFileIndex.add_column(groupIDcolumn, index=2)
# Now remove any GROUPS with less than 8 images
groupIndex = reducedFileIndex.group_by('GROUP_ID')
goodGroupInds = []
groupInds = groupIndex.groups.indices
for startInd, endInd in zip(groupInds[:-1], groupInds[+1:]):
# Count the number of images in this group and test if it's any good.
if (endInd - startInd) >= 8:
goodGroupInds.extend(range(startInd, endInd))
# Cull the reducedFileIndex to only include viable groups
goodGroupInds = np.array(goodGroupInds)
reducedFileIndex = reducedFileIndex[goodGroupInds]
# Match a dither type for each group ("ABBA" or "HEX")
import matplotlib.pyplot as plt
from astropy.io import ascii
from astropy.table import Table
import numpy as np
from scipy import spatial
import seaborn
lbox=205.
basePath = '/home/fede/TNG300-1/output/'
fields = ['SubhaloPos','SubhaloMass']
subgroups = il.groupcat.loadSubhalos(basePath,99,fields=fields)
gxs = Table(subgroups['SubhaloPos'],names=['x','y','z'],dtype=['float64','float64','float64'])
col_m = Table.Column(subgroups['SubhaloMass'],name='mass',dtype='float64')
gxs.add_column(col_m)
gxs = gxs[(np.log10(gxs['mass'])>-1.)&(np.log10(gxs['mass'])<3.)]
for col in ['x','y','z']:
gxs[col]/=1000.
gxs.remove_row(np.where(gxs['y']==205.0)[0][0])
gxs.remove_row(np.where(gxs['x']<0.)[0][0])
tree = spatial.cKDTree(data=np.column_stack((gxs['x'],gxs['y'],gxs['z'])),boxsize=lbox)
voids = ascii.read('../data/tng300-1_voids.dat',names=['r','x','y','z','vx','vy','vz','deltaint_1r','maxdeltaint_2-3r','log10Poisson','Nrecenter'])
#%%
# N=[]
# for i in range(len(voids)):
# if i%1000==0: print(i)
def _do_photometry(self, param_tab, n_start=1):
"""
Helper function which performs the iterations of the photometry
process.
Parameters
----------
param_names : list
Names of the columns which represent the initial guesses.
For example, ['x_0', 'y_0', 'flux_0'], for intial guesses on
the center positions and the flux.
n_start : int
Integer representing the start index of the iteration. It
is 1 if init_guesses are None, and 2 otherwise.
Returns
-------
output_table : `~astropy.table.Table` or None
Table with the photometry results, i.e., centroids and
fluxes estimations and the initial estimates used to start
the fitting process.
"""
output_table = Table()
self._define_fit_param_names()
for (init_parname, fit_parname) in zip(self._pars_to_set.keys(),
self._pars_to_output.keys()):
output_table.add_column(Column(name=init_parname))
output_table.add_column(Column(name=fit_parname))
sources = self.finder(self._residual_image)
n = n_start
while (sources is not None
and (self.niters is None or n <= self.niters)):
positions = np.transpose(
(sources['xcentroid'], sources['ycentroid']))
apertures = CircularAperture(positions, r=self.aperture_radius)
sources['aperture_flux'] = aperture_photometry(
self._residual_image, apertures)['aperture_sum']
init_guess_tab = Table(names=['id', 'x_0', 'y_0', 'flux_0'],
data=[
sources['id'], sources['xcentroid'],
sources['ycentroid'],
sources['aperture_flux']
])
self._get_additional_columns(sources, init_guess_tab)
for param_tab_name, param_name in self._pars_to_set.items():
if param_tab_name not in (['x_0', 'y_0', 'flux_0']):
init_guess_tab.add_column(
Column(name=param_tab_name,
data=(getattr(self.psf_model, param_name) *
np.ones(len(sources)))))
star_groups = self.group_maker(init_guess_tab)
table, self._residual_image = super().nstar(
self._residual_image, star_groups)
star_groups = star_groups.group_by('group_id')
table = hstack([star_groups, table])
table['iter_detected'] = n * np.ones(table['x_fit'].shape,
dtype=np.int32)
output_table = vstack([output_table, table])
# do not warn if no sources are found beyond the first iteration
with warnings.catch_warnings():
warnings.simplefilter('ignore', NoDetectionsWarning)
sources = self.finder(self._residual_image)
n += 1
return output_table
def _fits_summary(self, header_keywords):
"""
Generate a summary table of keywords from FITS headers.
Parameters
----------
header_keywords : list of str or '*'
Keywords whose value should be extracted from FITS headers or '*'
to extract all.
"""
if not self.files:
return None
# Make sure we have a list...for example, in python 3, dict.keys()
# is not a list.
original_keywords = list(header_keywords)
# Get rid of any duplicate keywords, also forces a copy.
header_keys = set(original_keywords)
header_keys.add('file')
file_name_column = MaskedColumn(name='file', data=self.files)
if not header_keys or (header_keys == {'file'}):
summary_table = Table(masked=True)
summary_table.add_column(file_name_column)
return summary_table
summary_dict = None
missing_marker = None
for file_name in file_name_column.tolist():
file_path = path.join(self.location, file_name)
try:
# Note: summary_dict is an OrderedDict, so should preserve
# the order of the keywords in the FITS header.
summary_dict = self._dict_from_fits_header(
file_path,
input_summary=summary_dict,
missing_marker=missing_marker)
except IOError as e:
logger.warning('unable to get FITS header for file %s: %s.',
file_path, e)
continue
summary_table = Table(summary_dict, masked=True)
for column in summary_table.colnames:
summary_table[column].mask = [
v is missing_marker for v in summary_table[column].tolist()
]
self._set_column_name_case_to_match_keywords(header_keys,
summary_table)
missing_columns = header_keys - set(summary_table.colnames)
missing_columns -= {'*'}
length = len(summary_table)
for column in missing_columns:
all_masked = MaskedColumn(name=column,
data=np.zeros(length),
mask=np.ones(length))
summary_table.add_column(all_masked)
if '*' not in header_keys:
# Rearrange table columns to match order of keywords.
# File always comes first.
header_keys -= {'file'}
original_order = ['file'] + sorted(header_keys,
key=original_keywords.index)
summary_table = summary_table[original_order]
if not summary_table.masked:
summary_table = Table(summary_table, masked=True)
return summary_table
def test_from_table_without_mask():
t = Table()
c = Column(data=[1, 2, 3], name='a')
t.add_column(c)
output = io.BytesIO()
t.write(output, format='votable')
