def main(spec, out, freqf):
"""
"""
specs = glob.glob(spec)
# Sort the SBs
crrls.natural_sort(specs)
for s in specs:
## Determine the subband name
#try:
#sb = re.findall('SB\d+', s)[0]
#except IndexError:
#print "Could not find SB number."
#print "Will use SB???"
#sb = 'SB???'
data = np.loadtxt(s, comments='#')
freq = data[:,0]*freqf
tb = data[:,1]
data[:,0] = data[:,0]/1e6
# write the processed spectrum
#np.savetxt('{0}_{1}.ascii'.format(basename, sb), data)
tbtable = Table([freq, tb],
names=['FREQ MHz',
'Tb Jy/BEAM'])
ascii.write(tbtable, out, format='commented_header')
def writefiles(tiles, fnbase, overwrite=False):
from astropy.io import fits
from astropy.io import ascii
from matplotlib.mlab import rec_drop_fields
from astropy import table
fits.writeto(fnbase+'.fits', tiles, overwrite=overwrite)
hdulist = fits.open(fnbase+'.fits', mode='update')
hdulist[1].header['EXTNAME'] = 'TILES'
hdulist.close()
tilestab = table.Table(
rec_drop_fields(tiles, ['brightra', 'brightdec', 'brightvtmag']))
metadata = {'tileid': ('', 'Unique tile ID'),
'ra': ('deg', 'Right ascension'),
'dec': ('deg', 'Declination'),
'pass': ('', 'DESI layer'),
'in_desi': ('', '1=within DESI footprint; 0=outside'),
'ebv_med':('mag', 'Median Galactic E(B-V) extinction in tile'),
'airmass':('', 'Airmass if observed at hour angle 15 deg'),
'star_density':('deg^-2', 'median number density of Gaia stars brighter than 19.5 mag in tile'),
'exposefac':('', 'Multiplicative exposure time factor from airmass and E(B-V)'),
'program':('', 'DARK, GRAY, BRIGHT, or EXTRA'),
'obsconditions':('', '1 for DARK, 2 for GRAY, 4 for BRIGHT, 0 for EXTRA'),
'brightra':('deg', 'RAs of 3 brightest Tycho-2 stars in tile'),
'brightdec':('deg', 'Decs of 3 brightest Tycho-2 stars in tile'),
'brightvtmag':('mag', 'V_T magnitudes of 3 brightest Tycho-2 stars in tile'),
'centerid':('', 'Unique tile ID of pass 0 tile corresponding to this tile'),
}
from astropy import units as u
unitdict = {'': None, 'deg': u.deg, 'mag': u.mag, 'deg^-2': 1/u.mag/u.mag}
for name in tilestab.dtype.names:
tilestab[name].unit = unitdict[metadata[name][0]]
tilestab[name].description = metadata[name][1]
ascii.write(tilestab, fnbase+'.ecsv', format='ecsv', overwrite=overwrite)
def bsnip_edits():
bsnip = glob.glob ('../../data/bsnip/*.flm')
bsnip_sn = []
b = []
v = []
for i in range (len(bsnip)):
bsnip[i] = bsnip[i].split('-')[0]
bsnip[i] = bsnip[i].split('/')
bsnip[i] = bsnip[i][len(bsnip[i])-1]
if i > 0:#removes redundant sn names
if bsnip[i-1] != bsnip[i]:
bsnip_sn.append(bsnip[i])
cut_index = 0
flag = False
for i in range(len(bsnip_sn)):
if (not flag):
print "passing",bsnip_sn[i]
#certain cases for specfic sn names
if bsnip_sn[i] == 'sn2007s1':#want to start at first changed sn
print "start with",len(bsnip_sn),"sn"
print "START at index:",i
cut_index = i
flag = True #start here
if(flag):
print "looking at",bsnip_sn[i]
if bsnip_sn[i] == "sn2007s1":
bsnip_sn[i] = "SNF20071021-000"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s1":
bsnip_sn[i] = "SNF20080514-002"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008r3":
bsnip_sn[i] = "sn1989a"###doesn't work
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s3":
bsnip_sn[i] = "SNF20080825-006"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s4":
bsnip_sn[i] = "sn1989a"###can't find correct name
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s5":
bsnip_sn[i] = "SNF20080909-030"
print "changed to",bsnip_sn[i]
if bsnip_sn[i] == "sn2008s8":
bsnip_sn[i] = "sn1989a"###look for correct names
print "changed to",bsnip_sn[i]
ext = IrsaDust.get_extinction_table(bsnip_sn[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
print "stopped successfullly?...cutting table"
del bsnip_sn[:cut_index]
param = Table([bsnip_sn,b,v])
print param
ascii.write(param,'bsnip_extinc_added.dat')
def csv_generator(headers, keywords, filename):
"""
Pulls headers and keywords from SQL query to make a csv table.
Parameters
----------
headers : class (<class 'sqlalchemy.engine.result.RowProxy'>)
All of the header information returned from SQL query.
keywords : list
A list of column names returned from SQL query.
filename : str
The names of CSV output file.
Returns
-------
None
Outputs
-------
t : CSV file
A CSV files that contains all of the queried header information.
"""
datarows = []
for item in headers:
datarows.append(item)
print(type(item))
t = Table(rows = datarows, names = keywords, meta = {'Name':'COS HEADER TABLE'})
ascii.write(t, filename + '.csv', format='csv')
def test_rdb_write_types():
dat = ascii.read(['a b c d', '1 1.0 cat 2.1'],
Reader=ascii.Basic)
out = StringIO()
ascii.write(dat, out, Writer=ascii.Rdb)
outs = out.getvalue().splitlines()
assert_equal(outs[1], 'N\tN\tS\tN')
def print_results(args, colnames, stats):
colnames = colnames + ['n_samples']
colvals = [[] for _ in xrange(len(colnames))]
kvs = sorted(rtk.itr.iter_nested_d(stats))
for keys, val in kvs:
i = 0
for k in keys:
colvals[i].append(k)
i += 1
n_results_cols = len(val[0])
for j in xrange(n_results_cols):
colvals[i].append(round(mean(args, map(rtk.itr.ig(j), val)), 3))
i += 1
colvals[i].append(round(std(args, map(rtk.itr.ig(j), val)), 3))
i += 1
colvals[i].append(len(val))
ascii.write(colvals,
Writer=ascii.FixedWidthTwoLine,
names=colnames,
bookend=True,
delimiter='|',
delimiter_pad=' ')
def query_bsnip():
#Creates extinction.dat from bsnip data
#list of files
bsnip = glob.glob ('../../data/bsnip/*.flm')
bsnip_sn = []
for i in range (len(bsnip)):
bsnip[i] = bsnip[i].split('-')[0]
bsnip[i] = bsnip[i].split('/')
bsnip[i] = bsnip[i][len(bsnip[i])-1]
if i > 0:#removes redundant sn names
if bsnip[i-1] != bsnip[i]:
bsnip_sn.append(bsnip[i])
b = []
v = []
for i in range(len(bsnip_sn)):
print "looking at",bsnip_sn[i]
#certain cases for specfic sn names
if bsnip_sn[i] == 'sn2007s1':
print "WARNING"
del bsnip_sn[i:]
break
ext = IrsaDust.get_extinction_table(bsnip_sn[i])
print ext
print ext[1][0],ext[1][3]
print ext[2][0],ext[2][3]
b.append(ext[1][3])
v.append(ext[2][3])
#makes table in format 'sn','B','V'
param = Table([bsnip_sn,b,v])
ascii.write(param,'bsnip_extinc.dat')
def concatenate_line_tables(filelist,outtablefile='compiledVPoutputs.dat'):
'''
Compiles the output from several fitting runs into a single table
Parameters
----------
filelist : list of strings or str
This should be a list containing the names of VP input files or a string referring to a file simply
listing the input files.
See joebvpfit.readpars for details of file format
outtablefile : str
Name of compiled model parameter output file
'''
if isinstance(filelist, str):
lstarr=np.genfromtxt(filelist,dtype=None)
listofiles=lstarr.tolist()
else:
listofiles=filelist
tabs = []
for i, ff in enumerate(listofiles):
tabs.append(ascii.read(ff))
bigpartable = vstack(tabs)
ascii.write(bigpartable, output=outtablefile, delimiter='|') # write out compiled table
def csv_generator(headers,keywords,path,filename):
"""
Pulls headers and keywords from SQL query to make a csv table.
Parameters
----------
headers : class (<class 'sqlalchemy.engine.result.RowProxy'>)
All of the header information returned from SQL query.
keywords : list
A list of column names returned from SQL query.
filename : str
The names of CSV output file.
Returns
-------
None
Outputs
-------
t : CSV file
A CSV files that contains all of the queried header information.
"""
#try:
if filename.endswith('.txt'):
form = 'ascii'
else:
form = 'csv'
datarows = []
for item in headers:
datarows.append(item)
t = Table(rows = datarows, names = keywords, meta = {'Name':'COS HEADER TABLE'})
ascii.write(t,os.path.join(path,filename),format=form)
#except:
print('Cannot handle files that end with {}'.format(filename.split('.')[1]))
def generate_count_table(hsts, fnout=None, maglims=[21, 20.5, 20], outercutrad=-90,remove_cached=True):
from astropy.io import ascii
from astropy import table
targetingkwargs = []
for m in maglims:
targetingkwargs.append({'faintlimit': m, 'outercutrad': outercutrad, 'colname': str(m)})
tab = count_targets(hsts, targetingkwargs=targetingkwargs, remove_cached=remove_cached)
for m in maglims:
satcnt = []
for hs in hsts:
satcnt.append(count_mw_sats(hs, m))
tab.add_column(table.Column(name='nsat_' + str(m), data=satcnt))
for m in maglims:
nsatstr = 'nsat_' + str(m)
ntargstr = 'ntarg_' + str(m)
tab.add_column(table.Column(name='ntargpersat_' + str(m), data=tab[ntargstr] / tab[nsatstr]))
if fnout:
ascii.write(tab, fnout)
return tab
def write_metadata(targname, header_data, qldb_rootnames):
'''
Writes an output file containing the relationships between the columns,
rootnames, and times of observation.
'''
# Build dictionary containing data to send to output
out_dict = {}
out_dict['orig_columns'] = [header_data['col'][i] for i in
range(len(header_data)) if header_data['rootname'][i]
in qldb_rootnames]
out_dict['new_columns'] = [i for i in range(len(out_dict['orig_columns']))]
out_dict['rootnames'] = [header_data['rootname'][i] for i in
range(len(header_data)) if header_data['rootname'][i]
in qldb_rootnames]
# Query database for DATE-OBS and TIME-OBS for each rootname
out_dict['date_obs'], out_dict['time_obs'] = \
query_for_times(out_dict['rootnames'])
# Write results to text file
outfile = '{}_metadata.dat'.format(targname)
ascii.write([
out_dict['orig_columns'],
out_dict['new_columns'],
out_dict['rootnames'],
out_dict['date_obs'],
out_dict['time_obs']],
outfile,
names=['orig column', 'new column', 'rootname', 'DATE-OBS',
'TIME-OBS'])
def write_and_correct_latex_table(table, filename, caption, begin=0, end=30,
**kwargs):
# 1. Convert all the column headers to colhead { old_name }
for colname in table.colnames:
table[colname].name = "\\colhead{ %s }" % colname
# 2. Prepare the latexdict
header_start = ('\\tabletypesize{\\scriptsize}' +
'\n\\rotate\n' +
('\\tablecaption{ %s }\n' % caption) +
'\\tablewidth{0pt}\n\n')
data_start = r'\startdata'
data_end = r'\enddata'
latexdict = {
'header_start': header_start+r'\tablehead{',
'header_end': '}',
'data_start': data_start,
'data_end': data_end
}
latexdict.update(kwargs)
# 3. Write the table as a {table} and {tabular} thing
ascii.write(
table[begin:end], filename,
Writer = ascii.Latex,
latexdict = latexdict,
)
# 4. Convert it from {table}+{tabular} environment to {deluxetable}
convert_tabletabular_to_deluxetable(filename)
def write_ascii(self, filename, **kwargs):
'''
Read a table from an ASCII file using asciitable
Optional Keyword Arguments:
Writer - Writer class (default= Basic)
delimiter - column delimiter string
write_comment - string defining a comment line in table
quotechar - one-character string to quote fields containing special characters
formats - dict of format specifiers or formatting functions
names - list of names corresponding to each data column
include_names - list of names to include in output (default=None selects all names)
exclude_names - list of names to exlude from output (applied after include_names)
See the asciitable documentation at http://cxc.harvard.edu/contrib/asciitable/ for more details.
'''
if 'overwrite' in kwargs:
overwrite = kwargs.pop('overwrite')
else:
overwrite = False
if type(filename) is str and os.path.exists(filename):
if overwrite:
os.remove(filename)
else:
raise Exception("File exists: %s" % filename)
asciitable.write(self.data, filename, **kwargs)
def test_roundtrip_masked(fmt_name_class):
"""
Round trip a simple masked table through every writable format and confirm
that reading back gives the same result.
"""
fmt_name, fmt_cls = fmt_name_class
if not getattr(fmt_cls, '_io_registry_can_write', True):
return
# Skip tests for fixed_width or HTML without bs4
if ((fmt_name == 'html' and not HAS_BEAUTIFUL_SOUP)
or fmt_name == 'fixed_width'):
return
t = simple_table(masked=True)
out = StringIO()
fast = fmt_name in ascii.core.FAST_CLASSES
try:
ascii.write(t, out, format=fmt_name, fast_writer=fast)
except ImportError: # Some failed dependency, e.g. PyYAML, skip test
return
# No-header formats need to be told the column names
kwargs = {'names': t.colnames} if 'no_header' in fmt_name else {}
t2 = ascii.read(out.getvalue(), format=fmt_name, fast_reader=fast, guess=False, **kwargs)
assert t.colnames == t2.colnames
for col, col2 in zip(t.itercols(), t2.itercols()):
assert col.dtype.kind == col2.dtype.kind
assert np.all(col == col2)
def update_html(filename, roles):
"""
Replaces the Roles table in the HTML file with the new values in the
``roles`` table.
"""
with open(filename) as fh:
lines = fh.readlines()
idx0, idx1 = get_table_location(lines, 'astropy-roles')
orig_indent = get_indent(lines[idx0])
outlines = lines[:idx0]
# Plug in the roles table
roles_out = StringIO()
clean_kwargs = {'tags': ['a', 'span', 'sup', 'br'],
'attributes': {'a': ['href'],
'*': ['style']},
'styles': ['color', 'font-style']}
ascii.write(roles, roles_out, format='html',
htmldict={'table_id': 'astropy-roles',
'raw_html_cols': ['Role', 'Lead', 'Deputy'],
'raw_html_clean_kwargs': clean_kwargs})
roles_lines = roles_out.getvalue().splitlines()
ridx0, ridx1 = get_table_location(roles_lines, 'astropy-roles')
roles_indent = get_indent(roles_lines[ridx0])
indent = orig_indent - roles_indent
newlines = [' ' * indent + str(line) + os.linesep for line in roles_lines[ridx0:ridx1 + 1]]
outlines += newlines
outlines += lines[idx1 + 1:]
return outlines
def main(numIter=1000,screenSize=1024,
numDiameter=20,minDiameter=0.1,maxDiameter=30,
radialOrders=(1,)):
"""Run a set of simulations for different interferometer parameters"""
results=[]
for radialOrder in radialOrders:
for dr0 in np.logspace(np.log10(minDiameter),np.log10(maxDiameter),
numDiameter):
diameter=ChoosePupilDiameter(dr0,minR0=6.0)
r=VisibilityStats(numTelescope=2,
pupilSize=diameter,
r0=float(diameter)/dr0,
radialOrder=radialOrder,
numIter=numIter,
screenSize=screenSize)
r.update({'d/r0':dr0,
'radialOrder':radialOrder,
'numIter':numIter,
'pupilSize':diameter,
'screenSize':screenSize,})
results.append(r)
results=Table(results,names=("radialOrder","d/r0","Vsq","stdVsq","fibrePspec","stdFibrePspec","couple","stdCouple","numIter","pupilSize", "screenSize"))
ascii.write(results,time.strftime("tmp%y%m%d-%H%M.dat"),
format='fixed_width',
bookend=False,
delimiter=None,
formats={"d/r0":"%8.4f",
"Vsq":"%10.4g",
"stdVsq":"%10.4g",
"fibrePspec":"%10.4g",
"stdFibrePspec":"%10.4g",
"couple":"%10.4g",
"stdCouple":"%10.4g"})
def construct_standard_star_table(stars, write_to=results_dir):
mwo_dict = dict()
apo_dict = dict()
for star in stars:
mwo_dict[star.name.upper()] = []
apo_dict[star.name.upper()] = []
names = list(mwo_dict.keys())
for star_name in names:
all_obs_this_star = [star for star in stars if star.name.upper() == star_name]
mwo_dict[star_name] = combine_measurements([s.s_mwo for s in all_obs_this_star])
apo_dict[star_name] = combine_measurements([s.s_apo for s in all_obs_this_star])
sp_types = []
s_mwo = []
s_apo = []
n_obs = []
for star in names:
s_mwo.append(mwo_dict[star].to_latex())
s_apo.append(apo_dict[star].to_latex())
n_obs.append(apo_dict[star].meta)
customSimbad = Simbad()
customSimbad.add_votable_fields('sptype')
sp_type = customSimbad.query_object(star)['SP_TYPE'][0]
sp_types.append(sp_type)
print('N_stars = {0}'.format(len(names)))
print('N_spectra = {0}'.format(sum(n_obs)))
print('N_sptype=G = {0}'.format(len([spt for spt in sp_types if spt.startswith(b'G')])))
print('N_sptype=K = {0}'.format(len([spt for spt in sp_types if spt.startswith(b'K')])))
print('N_sptype=M = {0}'.format(len([spt for spt in sp_types if spt.startswith(b'M')])))
# for star in stars:
# names.append(star.name.upper())
# customSimbad = Simbad()
# customSimbad.add_votable_fields('sptype')
# sp_type = customSimbad.query_object(star.name)['SP_TYPE'][0]
# sp_types.append(sp_type)
#
# s_mwo.append(star.s_mwo.to_latex())
# s_apo.append(star.s_apo.to_latex())
standard_table = Table([names, sp_types, s_mwo, s_apo, n_obs],
names=['Star', 'Sp.~Type', '$S_{MWO}$', '$S_{APO}$', '$N$'])
standard_table.sort(keys='$S_{MWO}$')
latexdict = dict(col_align='l l c c c', preamble=r'\begin{center}',
tablefoot=r'\end{center}',
caption=r'Stars observed to calibrate the $S$-index '
r'(see Section~\ref{sec:def_s_index}). \label{tab:cals}',
data_start=r'\hline')
# output_path,
ascii.write(standard_table, format='latex', latexdict=latexdict,
output='cal_stars.tex')
def generate_test_data():
alpha = 4.0
beta = 3.0
sigsqr = 0.5
# GMM with 3 components for xi
xi = np.random.normal(loc=1.0, scale=1.0, size=9)
xi = np.concatenate([xi, np.random.normal(loc=2.0, scale=1.5, size=20)])
xi = np.concatenate([xi, np.random.normal(loc=3.0, scale=0.5, size=30)])
eta = np.random.normal(loc=alpha+beta*xi, scale=np.sqrt(sigsqr))
# Let's mix in some weird measurement uncertainties:
xsig = 0.25 * np.sin(np.arange(len(xi))) + 0.5
ysig = 0.25 * np.cos(np.arange(len(eta)))**2 + 0.5
x = np.random.normal(loc=xi, scale=xsig)
y = np.random.normal(loc=eta, scale=ysig)
# And put in zero uncertainty in a few of these.
wzx = np.random.choice(np.arange(len(xi)), size=5, replace=False)
xsig[wzx] = 0.0
wzy = np.random.choice(np.arange(len(eta)), size=5, replace=False)
ysig[wzy] = 0.0
# And censor all the ydata less than 10, unless the yerr is 0
w10 = (y < 10) & (ysig != 0)
y[w10] = 10
delta = np.ones((len(x),), dtype=int) # should really be bool, but ints are easier
delta[w10] = 0
out = Table([x, y, xsig, ysig, delta], names=['x', 'y', 'xsig', 'ysig', 'delta'])
import astropy.io.ascii as ascii
ascii.write(out, 'test.dat')
def test_columns_names_with_formats(formats, issues_warning):
"""Test the fix for #4508."""
t = table.Table([[1, 2, 3], [4.1, 5.2, 6.3]])
with catch_warnings(AstropyWarning) as ASwarn:
out = StringIO()
ascii.write(t, out, formats=formats)
assert (issues_warning == (len(ASwarn) == 1))
def test_latex_units():
"""
Check to make sure that Latex and AASTex writers attempt to fall
back on the **unit** attribute of **Column** if the supplied
**latexdict** does not specify units.
"""
t = table.Table([table.Column(name='date', data=['a', 'b']),
table.Column(name='NUV exp.time', data=[1, 2])])
latexdict = copy.deepcopy(ascii.latexdicts['AA'])
latexdict['units'] = {'NUV exp.time': 's'}
out = StringIO()
expected = '''\
\\begin{table}{cc}
\\tablehead{\\colhead{date} & \\colhead{NUV exp.time}\\\\ \\colhead{ } & \\colhead{s}}
\\startdata
a & 1 \\\\
b & 2
\\enddata
\\end{table}
'''.replace('\n', os.linesep)
ascii.write(t, out, format='aastex', latexdict=latexdict)
assert out.getvalue() == expected
# use unit attribute instead
t['NUV exp.time'].unit = units.s
t['date'].unit = units.yr
out = StringIO()
ascii.write(t, out, format='aastex', latexdict=ascii.latexdicts['AA'])
assert out.getvalue() == expected.replace(
'colhead{s}', r'colhead{$\mathrm{s}$}').replace(
'colhead{ }', r'colhead{$\mathrm{yr}$}')
def write_skycoord_table(data, cube_ref, **kwargs):
"""
Writes out a text file with flattened coordinates of the cube
stacked with input array data. Additional arguments are passed
to astropy's text writing function.
TODO: add a useful `names` keyword?
See astropy.io.ascii.write docstring for more info.
Parameters
----------
data : array-like structure of the same xy-grid as cube_ref.
cube_ref : a cube file to get the coordinate grid from.
"""
from astropy.table import Table
from astropy.io import ascii
from spectral_cube import SpectralCube
cube = SpectralCube.read(cube_ref)
flat_coords = [cube.spatial_coordinate_map[i].flatten() for i in [1,0]]
# TODO: finish this up for multiple components
#n_repeat = np.prod(np.array(data).shape)%np.prod(cube.shape[1:])+1
table = Table(np.vstack(flat_coords +
[np.array(xy_slice).flatten() for xy_slice in data]).T)
ascii.write(table, **kwargs)
def add_stars(slit_num, ax_stis, ax_wfc3, im_stis, im_wfc3, cenwave):
'''
#ask if any stars need to be added
'''
tbdata_current = ascii.read(get_filename(slit_num, cenwave))
if slit_num == 1:
tbdata_previous = None
tbdata_next = ascii.read(get_filename(slit_num+1, cenwave))
elif slit_num == 17:
tbdata_next = None
tbdata_previous = ascii.read(get_filename(slit_num -1, cenwave))
else:
tbdata_previous = ascii.read(get_filename(slit_num -1, cenwave))
tbdata_next = ascii.read(get_filename(slit_num+1, cenwave))
ax_stis.set_ylim(900, 1040)
ax_wfc3.set_ylim(900, 1040)
pyplot.draw()
to_do_flag = raw_input('Would you like to adjust the contrast (c), enter a star to be added to this slit (a), move down the slit (m), or move to next slit (q) ? ')
while to_do_flag != 'q':
if to_do_flag == 'c':
set_contrast(im_stis)
set_contrast(im_wfc3)
elif to_do_flag == 'a':
tbdata_current = add_star_to_slit(slit_num, tbdata_current, tbdata_previous, tbdata_next)
elif to_do_flag == 'm':
ax_stis = move_down_slit(ax_stis)
ax_wfc3 = move_down_slit(ax_wfc3)
to_do_flag = raw_input('Would you like to adjust the contrast (c), enter a star to be added to this slit (a), move down the slit (m), or finish (q) ? ')
if slit_num < 10:
shutil.copyfile('slit0{}_{}_phot.dat'.format(int(slit_num), cenwave), 'slit{}_{}_phot_no_split.dat'.format(int(slit_num), cenwave))
ascii.write(tbdata_current, 'slit0{}_{}_phot.dat'.format(int(slit_num), cenwave))
else:
shutil.copyfile('slit{}_{}_phot.dat'.format(int(slit_num), cenwave), 'slit{}_{}_phot_no_split.dat'.format(int(slit_num), cenwave))
ascii.write(tbdata_current, 'slit{}_{}_phot.dat'.format(int(slit_num), cenwave))
def save_reals(reals, path, zrange=None, rad=0):
fpath, zrang, radfac = (0, 0, 0)
if path.endswith('fits'):
asc_out = path[:-4]+'asc'
fpath = True
elif path.endswith('asc'):
asc_out = path
if zrange!=None:
zstring = '_z%s-%s'%(zrange[0],zrange[1])
asc_out = asc_out[:-4]+zstring+'.asc'
zrang = True
colnames = ['# RA','DEC','z']
if rad & any((reals.T[:2]>2*np.pi).flatten()):
reals.T[:2] = reals.T[:2] * (np.pi/180)
radfac = True
if fpath|zrang|radfac:
try:
print('saving reals.asc..')
ascii.write(reals, asc_out, names=colnames, delimiter='\t', overwrite=1)
except ValueError:
print('..with weights..')
colnames += ['weights']
ascii.write(reals, asc_out, names=colnames, delimiter='\t', overwrite=1)
else:
print('nothing changed, not re-saving reals')
def read_rename(filename, rename_dict=None, date_format=None,
remove_language=False):
"""Update columns names and their data if they are dates from csv files
inputs
------
filename : str
name of the file that we want to modify
rename_dict : dict
dictionary with the old and new values as key/values respectively
date_format : dict
dictionary with the field names and the format in which they date is in
the file
remove_languages : boolean
whether the language column want to be removed
"""
data = ascii.read(filename, delimiter=';')
if rename_dict:
for k, v in rename_dict.items():
data[k].name = v
if date_format:
for k, v in date_format.items():
column_i = [datetime.datetime.strptime(x, v) for x in data[k]]
data.remove_column(k)
data.add_column(Column(data=column_i, name=k))
if ('language' in data.keys()) and remove_language:
data.remove_column('language')
ascii.write(data, filename, format='csv', delimiter=';', overwrite=True)
def datafile(obj_names,centroids,tcentroids,outfile):
names = ['File Name','x-centroid','y-centroid','x-aligned','y-aligned']
data = [obj_names,centroids[:,0],centroids[:,1],tcentroids[:,0],tcentroids[:,1]]
table = Table(data, names=names)
ascii.write(table, outfile, formats={'x-centroid': '%.3f',\
'y-centroid': '%.3f','x-aligned': '%.3f','y-aligned': '%.3f'})
return
def save_imlist(self):
"""Save selected image filename(s) to a plain text file.
If no image selected, no output is generated.
This can be re-implemented by sub-class if a different
output format is needed.
"""
imlist = self.get_selected_paths()
if len(imlist) == 0:
s = 'No image selected!'
self.logger.error(s)
self.update_status(s)
return
fname = Widgets.SaveDialog(
title='Save image list', selectedfilter='*.txt').get_path()
if not fname: # Cancel
return
if os.path.exists(fname):
s = '{0} will be overwritten'.format(fname)
self.logger.warn(s)
ascii.write(
[imlist], fname, names=['IMAGE'], format='commented_header')
s = 'Image list saved'
self.logger.info(s)
self.update_status(s)
def GetTeXTable(self,tableName="params.tex"):
marg = self.anal.get_stats()["marginals"]
tmp = []
for params,val,err in zip(self.parameters,self.bestFit,marg):
err = err["1sigma"]
print "\t%s:\t%.2f\t+%.2f -%.2f"%(params,val,err[1]-val,val-err[0])
tmp.append(['%s'%params,'%.2f'%val,'%.2f'%(err[1]-val),'%.2f'%(val-err[0])])
data = {}
for t in tmp:
data[t[0]]=["$%s^{+%s}_{-%s}$"%(t[1],t[2],t[3])]
ascii.write(data,output=tableName,Writer=ascii.Latex,latexdict={'preamble': r'\begin{center}','tablefoot': r'\end{center}','tabletype': 'table*','header_end': '\\hline \\hline \\hspace{3mm}','caption':self.modName})
def DoPhotometryofNight(PC,night):
""" Does photometry of all images in the night as well as create the final magnitude catlog table """
with open(os.path.join(PC.OUTDIR,night,PC.OUTFITSFILELIST),'r') as outfitsfilelist:
# Skip blank lines and Commented out lines with #
imgfilterlist = [tuple(imageLINE.rstrip().split()) for imageLINE in outfitsfilelist
if ((imageLINE.strip() is not '') and (imageLINE[0] !='#'))]
for OutFinalImage,Filtr in imgfilterlist:
MagTable = RunPhotometryofImage(OutFinalImage,Filtr)
## Append differential 2MASS magnitude of all sources to table
TableHeaders = ['ra','dec','mag','magerror','Qflag']#filter,epoch,ImgFile
filter_col = Column(name='Filter', data=[Filtr]*len(MagTable))
epoch_col = Column(name='Epoch', data=[epoch]*len(MagTable))
imgfile_col = Column(name='ImgFile', data=[OutFinalImage]*len(MagTable))
TableToOutput = MagTable[TableHeaders]
TableToOutput.add_columns([filter_col, epoch_col, imgfile_col])
# Now append the Full output table also to an ascii file.
OutputTableFilename = os.path.join(PC.OUTDIR,PC.OUTPUTFILE)
try :
PreviousFullTable = ascii.read(OutputTableFilename,delimiter=',',
format='commented_header',fill_values=('--','0'))
except IOError :
logger.info('No previous photometry output found, hence we will be creating'
' a new file {0}.'.format(OutputTableFilename))
OutputTableToWrite = TableToOutput
else :
OutputTableToWrite = table.vstack([PreviousFullTable,TableToOutput], join_type='outer')
# Writing the final appended table
ascii.write(OutputTableToWrite, OutputTableFilename,delimiter=',',format='commented_header')
logger.info("Photometry of {0} over.".format(OutFinalImage))
def readPadova(filename,request,outfile,req_age):
'''
Program to read a Padova isochrone file and output desired values as a txt file table
Args:
Filename: file containing isochrone data identified as string
List of values: Identified by column header as a list of strings, input None if no request
Output filename: string, appended with .txt
Requested age: Float, given as log(age/yr)
Returns:
Txt file containing the generated table
'''
colnames = ['Z','log(age/yr)','M_ini','M_act','logL/Lo','logTe','logG','mbol','U','B','V','R','I','J','H','K','int_IMF','stage']
data = ascii.read(filename,names = colnames)
req_cols = []
#assigns default table values if None is given
if request != None:
req_cols = request
else:
req_cols = ['Z','log(age/yr)','logTe','mbol','int_IMF','stage']
#defines the output table as consisting of the desired, or default, columns
output = Table()
for colname in req_cols:
output[colname] = data[colname]
#removes undesirable ages from the table
if req_age != None:
selection = np.where(data['log(age/yr)']==req_age)
output = output[selection]
ascii.write(output,outfile+'.txt')
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"
for col in t.columns:
if col in ['neighbors']:
t[col] = [[round(cc, 4) for cc in c] for c in t[col]]
if col in ['diff', 'ivar']:
todel.append(col)
for col in todel:
del (t[col])
ncols = 0
for tup in t.dtype.descr:
ncols += 1
if len(tup) == 2 and tup[1][1] == 'f':
t[tup[0]] = [round(c, 4) for c in t[tup[0]]]
ascii.write(
t,
sys.argv[2],
names=[r"\texttt{{{}}}".format(n.replace("_", r"\_")) for n in t.columns],
format='aastex',
overwrite=True,
formats={},
col_align="l" * ncols)
#for col in t.columns:
# if col in ['neighbors', 'weights', 'diff', 'err', 'loss']:
# t[col] = [np.array_str(c, precision=3) for c in t[col]]
#
#df = t.to_pandas()
#with open(sys.argv[2], 'w') as f:
# f.write(df.to_latex(index=False, na_rep="",
# float_format="{:0.2f}".format))
def make_validation_table(fitspath: str, vmin_4363SN=3, vmin_5007SN=100,
vmax_4363sig=1.6, rlmin_4363SN=3,
rlmax_4363sig=1.6, rlmin_5007SN=100):
"""
This function creates a validation table for a given binning set.
The validation table contains a OIII4363 detection column where 1.0
means detection, 0.5 means non-detection with reliable OIII5007, and
0.0 means unreliable non-detection. This function will be run every
time the analysis is completed and will create a validation table
for every analysis.
Usage:
valid_table.make_validation_table(fitspath, bin_type_str)
:param fitspath: Full file path where the input file is and where the
output file will be placed.
:param vmin_4363SN: int. minimum OIII4363 S/N for valid detection
:param vmin_5007SN: int. minimum OIII5007 S/N for valid detection
:param vmax_4363sig: int. maximum OIII4363 sigma for valid detection
:param rlmin_4363SN: int. minimum OIII4363 S/N for robust limit
:param rlmax_4363sig: int. maximum OIII4363 sigma for robust limit
:param rlmin_5007SN: int. minimum OIII5007 S/N for robust limit
Outputs:
fitspath + 'bin_validation.tbl'
Validation table containing bin IDs; number of galaxies in each bin;
and column indicating OIII4363 detection/non-detection,
OIII4363_Flux_Observed, OIII4363_S/N
"""
bin_table = asc.read(fitspath + filename_dict['bin_info'])
em_table = asc.read(fitspath + filename_dict['bin_fit'])
bin_ID = em_table['bin_ID'].data
raw_OIII4363 = em_table['OIII_4363_Flux_Observed'].data
O_4363_SN = em_table['OIII_4363_S/N'].data
O_4363_sigma = em_table['OIII_4363_Sigma'].data
O_5007_SN = em_table['OIII_5007_S/N'].data
N_stack = bin_table['N_stack'].data
Hgamma_SN = em_table['HGAMMA_S/N'].data
Hgamma = em_table['HGAMMA_Flux_Observed'].data
detection = np.zeros(len(bin_ID))
OIII4363 = np.zeros(len(bin_ID))
up_limit = (Hgamma/Hgamma_SN) * 3
valid_stacks_idx = np.where((O_4363_SN >= vmin_4363SN) &
(O_5007_SN > vmin_5007SN) &
(O_4363_sigma < vmax_4363sig))[0]
reliable_5007_stacks = np.where((O_4363_sigma < rlmax_4363sig) &
(O_5007_SN > rlmin_5007SN))[0]
wide_line_valid = np.where((O_4363_SN >= rlmin_4363SN) &
(O_5007_SN > rlmin_5007SN) &
(O_4363_sigma >= rlmax_4363sig))[0]
detection[reliable_5007_stacks] = 0.5
detection[wide_line_valid] = 0.5
detection[valid_stacks_idx] = 1
print(detection)
for ii in range(len(OIII4363)):
if detection[ii] == 1:
OIII4363[ii] = raw_OIII4363[ii]
if detection[ii] == 0.5:
OIII4363[ii] = up_limit[ii]
if detection[ii] == 0:
OIII4363[ii] = up_limit[ii]
ver_tab = fitspath + filename_dict['bin_valid']
tab1 = Table([bin_ID, N_stack, detection, OIII4363, O_4363_SN],
names=valid_table_names0)
asc.write(tab1, ver_tab, format='fixed_width_two_line')
def main():
fullcat = ID.inputCatalogue(runtime)
fullcat.runInput(data)
fullcat.clean = args.clean
fullcat.suppress = args.suppress
fullcat.progress = args.progress
fullcat.saveprogress = args.saveprogress
fullcat.multiop = args.multiop
fullcat.latex = args.latex
## create a temp file for all object files
uf.checkpath('%sstackertemp/' % fullcat.outloc)
if uf.checkkeys(data, 'BinYN'):
binyn = data["BinYN"]
else:
binyn = raw_input(
'\nWould you like to stack in various bins? [y/n] ').lower()
if binyn == 'y':
bincatalogue = BD.binnedCatalogue(fullcat)
try:
bincatalogue.determineBins(data)
except (SystemExit, KeyboardInterrupt):
logger.error('Early exit', exc_info=False)
uf.exit(fullcat)
except:
logging.warning(
"Couldn't determine the catalogue for the different bins:",
exc_info=True)
uf.earlyexit(fullcat)
if fullcat.uncert == 'y':
repeat = 2
else:
repeat = 1
counter = 0
# print(bincatalogue.catalogue)
while counter < repeat:
mccount = counter
for m in range(0, len(bincatalogue.binpartindicies) - 1):
if counter > 0:
bincatalogue = pck.load(
open(
'%sstackertemp/catalogue_obj.pkl' % fullcat.outloc,
'rb'))
else:
h = 0
try:
bincatalogue.createCatalogue(bincatalogue.binpartindicies,
bincatalogue.binnedtable, m,
counter)
except KeyboardInterrupt:
logger.error('Early exit', exc_info=False)
uf.earlyexit(fullcat)
raise sys.exit()
except SystemExit:
logger.error('Early exit', exc_info=False)
uf.earlyexit(fullcat)
raise sys.exit()
except:
logger.error(
'Exception occurred trying to create the binned catalogue.',
exc_info=True)
uf.earlyexit(fullcat)
raise sys.exit()
if len(bincatalogue.catalogue) < 2:
logger.info('Bin %i has too few objects' % (m + 1))
pass
else:
try:
catalogue = pipeline(bincatalogue, config, data, 'bin',
m + 1, mccount)
if counter == 0:
bincatalogue = bincatalogue + catalogue
else:
pass
except TypeError:
logger.error('Problem with bin %i. Skipping' % (m + 1))
if (counter == 0):
bincatalogue.outcatalogue[
'Stellar Mass'].unit = bincatalogue.fullcatalogue[
'Stellar Mass'].unit
bincatalogue.outcatalogue[
'Other Data'].unit = bincatalogue.fullcatalogue[
'Other Data'].unit
bincatalogue.outcatalogue[
'Integrated Flux'].unit = astun.Jy * astun.km / astun.s
pck.dump(
bincatalogue,
open(
'%sstackertemp/catalogue_obj.pkl' %
bincatalogue.outloc, 'wb'))
catalogue = uf.maskTable(bincatalogue.outcatalogue)
if len(catalogue) > 1:
catalogue = asttab.unique(catalogue, keys='Object ID')
else:
pass
catalogue.sort('Bin Number')
astasc.write(
catalogue,
bincatalogue.outloc +
'Stacked_Catalogue_%s.csv' % bincatalogue.origruntime,
format='ecsv')
fullcat.updateconfig(data, bincatalogue.fullmaxgalw,
bincatalogue.fullrebinstatus,
bincatalogue.fullsmoothtype,
bincatalogue.fullsmoothwin,
bincatalogue.fullfuncnum,
bincatalogue.fulloptnum)
else:
pass
saveprogress = None
counter += 1
else:
fullcat.catalogue.add_column(
asttab.Column(name='Bin Number',
data=np.ones(len(fullcat.catalogue), dtype=int)))
fullcat = pipeline(fullcat, config, data, 'normal')
try:
fullcat.updateconfig(data, fullcat.maxgalw.value,
fullcat.rebinstatus, fullcat.smoothtype,
fullcat.smoothwin, fullcat.funcnum,
fullcat.optnum)
fullcat.outcatalogue['Stellar Mass'].unit = fullcat.catalogue[
'Stellar Mass'].unit
fullcat.outcatalogue['Other Data'].unit = fullcat.catalogue[
'Other Data'].unit
fullcat.outcatalogue[
'Integrated Flux'].unit = astun.Jy * astun.km / astun.s
catalogue = uf.maskTable(fullcat.outcatalogue)
catalogue = asttab.unique(catalogue, keys='Object ID')
catalogue.sort('Bin Number')
astasc.write(catalogue,
fullcat.outloc +
'Stacked_Catalogue_%s.csv' % fullcat.runtime,
format='ecsv')
logging.info('Written Stacked Catalogue to file.')
except (SystemExit, KeyboardInterrupt):
logger.error('Early exit', exc_info=False)
uf.exit(fullcat)
except:
logging.warning("Struggled to save the catalogue files.",
exc_info=True)
uf.earlyexit(fullcat)
## inform the user that the stacking has finished
if fullcat.suppress != 'hide':
outloc = uf.bashfriendlypath(fullcat.outloc)
os.system('open ' + outloc)
else:
pass
print('\nStacker has finished.\n')
uf.exit(fullcat)
return
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
savepath = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
#################################################################
catalogues = [
'Mr19_age_distribution_matching_mock', 'sm9.8_age_matching_mock'
]
catalogue = sys.argv[1]
f = h5py.File(filepath + catalogue + '.hdf5', 'r')
GC = f.get(catalogue)
GC = np.array(GC)
#make new catalogue and copy over values from original catalogue
dtype = GC.dtype.descr
for d in dtype:
print d
dtype = np.dtype(dtype)
GC_new = np.recarray((len(GC), ), dtype=dtype)
GC_new.fill(0.0)
GC_new = np.array(GC, copy=True)
#identify central galaxies, host haloes
centrals = np.where(
GC['ID_host'] == -1)[0] #indices of the central galaxies
print 'number of centrals, host haloes:', len(centrals)
satellites = np.where(
GC['ID_host'] != -1)[0] #indices of the central galaxies
print 'number of satellites, sub-haloes:', len(satellites)
#define mass bins, and which central are in each mass bin
mass_bins = np.arange(8.0, 16.0, 0.1) #log mass bins
#group histogram by log(host_mass)
mass_hist, bins = np.histogram(GC['M_host'][centrals], bins=mass_bins)
#indices of groups in log(host_mass) bins
mass_bin_ind = np.digitize(GC['M_host'][centrals], bins=mass_bins)
#go through each mass bin
for i in range(0, len(mass_bins) - 1):
print i, 'mass bin:', mass_bins[i], mass_bins[i + 1]
ind = np.where(mass_bin_ind == i +
1)[0] #indices of host haloes in this mass bin
if len(ind) > 0: #if there are any haloes in the mass bin
print 'number of groups:', len(ind)
ids = GC['ID_halo'][centrals[ind]]
sat_galaxy_members = np.in1d(
GC['ID_host'], ids) #satellite galaxies in the mass bin
sat_galaxy_members = np.where(sat_galaxy_members)[
0] #indicies of galaxies
cen_galaxy_members = np.in1d(
GC['ID_halo'], ids) #central galaxies in the mass bin
cen_galaxy_members = np.where(cen_galaxy_members)[
0] #indicies of galaxies
galaxy_members = np.hstack(
(sat_galaxy_members, cen_galaxy_members))
print 'number of galaxies:', len(galaxy_members)
satellite_members = np.where(
GC['ID_host'][galaxy_members] != -1)[0] #satellites
satellite_members = galaxy_members[
satellite_members] #indices of satellites
central_members = np.where(
GC['ID_host'][galaxy_members] == -1)[0] #centrals
central_members = galaxy_members[
central_members] #indices of centrals
print 'number of centrals:', len(central_members)
print 'number of satellites:', len(satellite_members)
print 'check:', len(central_members) + len(
satellite_members) == len(galaxy_members)
#shuffle list of host haloes in mass bin
shuffle = np.random.permutation(
np.arange(0, len(central_members), 1))
shuffled_central_members = central_members[shuffle]
unshuffle = np.arange(0, len(central_members), 1)
#shuffle centrals --> leave halo props alone, change gal props
#GC_new['M_r,0.1'][central_members] = GC['M_r,0.1'][shuffled_central_members]
#GC_new['g-r'][central_members] = GC['g-r'][shuffled_central_members]
#GC_new['M_star'][central_members] = GC['M_star'][shuffled_central_members]
#shuffle satellite systems --> leave gal props alone, change halo props
for i in range(0, len(satellite_members)):
print "\r", i,
sys.stdout.flush()
old_host_ID = GC['ID_host'][
satellite_members[i]] #old host halo ID
old_host_ind = np.where((GC['ID_halo'] == old_host_ID
))[0] #index of old host central
new_host_ind = np.where(
shuffled_central_members == old_host_ind)[
0] #location in central members list
new_host_ind = central_members[new_host_ind] #new host index
#assign a new host properties
GC_new['ID_host'][
satellite_members[i]] = GC['ID_halo'][new_host_ind]
GC_new['M_host'][
satellite_members[i]] = GC['M_host'][new_host_ind]
GC_new['M_vir'][
satellite_members[i]] = GC['M_vir'][new_host_ind]
GC_new['R200'][satellite_members[i]] = GC['R200'][new_host_ind]
#calculate satellite positions
x_new_cen = GC['x'][new_host_ind]
y_new_cen = GC['y'][new_host_ind]
z_new_cen = GC['z'][new_host_ind]
x_old_cen = GC['x'][old_host_ind]
y_old_cen = GC['y'][old_host_ind]
z_old_cen = GC['z'][old_host_ind]
GC_new['x'][satellite_members[i]] = GC_new['x'][
satellite_members[i]] - x_old_cen + x_new_cen
GC_new['y'][satellite_members[i]] = GC_new['y'][
satellite_members[i]] - y_old_cen + y_new_cen
GC_new['z'][satellite_members[i]] = GC_new['z'][
satellite_members[i]] - z_old_cen + z_new_cen
#calculate satellite velocities
Vx_new_cen = GC['Vx'][new_host_ind]
Vy_new_cen = GC['Vy'][new_host_ind]
Vz_new_cen = GC['Vz'][new_host_ind]
Vx_old_cen = GC['Vx'][old_host_ind]
Vy_old_cen = GC['Vy'][old_host_ind]
Vz_old_cen = GC['Vz'][old_host_ind]
GC_new['Vx'][satellite_members[i]] = GC_new['Vx'][
satellite_members[i]] - Vx_old_cen + Vx_new_cen
GC_new['Vy'][satellite_members[i]] = GC_new['Vy'][
satellite_members[i]] - Vy_old_cen + Vy_new_cen
GC_new['Vz'][satellite_members[i]] = GC_new['Vz'][
satellite_members[i]] - Vz_old_cen + Vz_new_cen
print ' '
#Fix any boundary condition issues, Lbox=250 Mpc
fix = np.where(GC_new['x'] < 0.0)[0]
GC_new['x'][fix] = 250.0 - np.absolute(GC_new['x'][fix])
fix = np.where(GC_new['y'] < 0.0)[0]
GC_new['y'][fix] = 250.0 - np.absolute(GC_new['y'][fix])
fix = np.where(GC_new['z'] < 0.0)[0]
GC_new['z'][fix] = 250.0 - np.absolute(GC_new['z'][fix])
fix = np.where(GC_new['x'] > 250.0)[0]
GC_new['x'][fix] = GC_new['x'][fix] - 250.0
fix = np.where(GC_new['y'] > 250.0)[0]
GC_new['y'][fix] = GC_new['y'][fix] - 250.0
fix = np.where(GC_new['z'] > 250.0)[0]
GC_new['z'][fix] = GC_new['z'][fix] - 250.0
catalogue = catalogue + '_satsys_shuffle'
print 'saving hdf5 version of the catalogue...'
filename = catalogue
print filename
f = h5py.File(savepath + filename + '.hdf5', 'w')
dset = f.create_dataset(catalogue, data=GC_new)
f.close()
print 'saving ascii version of the catalogue...'
print filename
data_table = table.table.Table(data=GC_new)
ascii.write(data_table, savepath + filename + '.dat')
print data_table
n1=3.0,
n2=1.5,
Xe=0.7,
Ye=0.3)
r, rho, u, P, M = nps.joinPolytropes(r1, rho1, M1, K1, n1, r2, rho2,
M2, K2, n2)
else:
r, rho, u, P, M = nps.Get_Polytrope(M_star, R_star, n1)
# ===================================================================
# this Table is used later to create the SPH particle distribution
dat = Table([r, rho, u, P, M], names=('r', 'rho', 'u', 'P', 'M'))
ascii.write(dat, 'PolyStar_cgs.dat')
if scale_to_units:
dat_solar = Table([
r / DistUnit, rho / DensUnit, u / E_perMassUnit, P / P_Unit,
M / MassUnit
],
names=('r', 'rho', 'u', 'P', 'M'))
ascii.write(dat_solar, 'PolyStar_scaled.dat')
else:
# print np.shape(r), np.shape(rho), np.shape(u), np.shape(P), np.shape(M)
dat = Table([r, rho, u, P, M], names=('r', 'rho', 'u', 'P', 'M'))
ascii.write(dat, 'StarProfile_cgs.dat')
def pick_positions(filename, separation, refimage=None):
"""
Assigns postions to fake star list generated by pick_models
INPUTS:
-------
filename: string
Name of AST list generated by pick_models
separation: float
Minimum pixel separation between AST and star in photometry
catalog provided in the datamodel.
refimage: Name of the reference image. If supplied, the method will use the
reference image header to convert from RA and DEC to X and Y.
OUTPUTS:
--------
Ascii table that replaces [filename] with a new version of [filename] that contains the necessary
position columns for running the ASTs though DOLPHOT
"""
noise = 3.0 #Spreads the ASTs in a circular annulus of 3 pixel width instead of all being
#precisely [separation] from an observed star.
catalog = datamodel.get_obscat(datamodel.obsfile, datamodel.filters)
colnames = catalog.data.columns
if 'X' or 'x' in colnames:
if 'X' in colnames:
x_positions = catalog.data['X'][:]
y_positions = catalog.data['Y'][:]
if 'x' in colnames:
x_positions = catalog.data['x'][:]
y_positions = catalog.data['y'][:]
else:
if refimage:
if 'RA' or 'ra' in colnames:
if 'RA' in colnames:
ra_positions = catalog.data['RA'][:]
dec_positions = catalog.data['DEC'][:]
if 'ra' in colnames:
ra_positions = catalog.data['ra'][:]
dec_positions = catalog.data['dec'][:]
else:
raise RuntimeError(
"Your catalog does not supply X, Y or RA, DEC information for spatial AST distribution"
)
else:
raise RuntimeError(
"You must supply a Reference Image to determine spatial AST distribution."
)
wcs = WCS(refimage)
x_positions, y_positions = wcs.all_world2pix(ra_positions,
dec_positions, 0)
astmags = ascii.read(filename)
n_asts = len(astmags)
# keep is defined to ensure that no fake stars are put outside of the image boundaries
keep = (x_positions > np.min(x_positions) + separation + noise) & (x_positions < np.max(x_positions) - separation - noise) & \
(y_positions > np.min(y_positions) + separation + noise) & (y_positions < np.max(y_positions) - separation - noise)
x_positions = x_positions[keep]
y_positions = y_positions[keep]
ncat = len(x_positions)
ind = np.random.random(n_asts) * ncat
ind = ind.astype('int')
# Here we generate the circular distribution of ASTs surrounding random observed stars
separation = np.random.random(n_asts) * noise + separation
theta = np.random.random(n_asts) * 2.0 * np.pi
xvar = separation * np.cos(theta)
yvar = separation * np.sin(theta)
new_x = x_positions[ind] + xvar
new_y = y_positions[ind] + yvar
column1 = 0 * new_x
column2 = column1 + 1
column1 = Column(name='zeros', data=column1.astype('int'))
column2 = Column(name='ones', data=column2.astype('int'))
column3 = Column(name='X', data=new_x, format='%.2f')
column4 = Column(name='Y', data=new_y, format='%.2f')
astmags.add_column(column1, 0)
astmags.add_column(column2, 1)
astmags.add_column(column3, 2)
astmags.add_column(column4, 3)
ascii.write(astmags, filename, overwrite=True)
def makeobslog(path,root=''):
"""Make a journal of an observation night.
Parameters
----------
path : string
path where data are located.
root : string, optional
root of files to journal.
Default is '', i.e. all files will be journaled.
Returns
-------
None.
"""
# set file name and pattern
fileout = path+ 'journal' + root + '.log'
pattern = path + root + '*.fit*'
# make list of files from pattern
listfile = glob.glob(pattern)
nfile = len(listfile)
# check number of files
if (nfile==0):
msg = '*** WARNING: there is no file of type: '+pattern
t120.log.error(msg)
raise IOError(msg)
t120.log.info('There are '+str(nfile)+' files of type '+pattern)
# get common prefix to all files
prefix = os.path.commonprefix(listfile)
# create output table:
# method 1: obsolete and dumb... make a first dummy row so astropy.table.QTable knows 'FILTER' is a string
#data = QTable([[' '],[' '],
# [0.0],[0.0],[0.0],[0.0],[' '],[0.0]],
# names=['FILE','TARGET','JD','RA (deg)','DEC (deg)','EXP (s)','FILTER','AIRMASS'])
# method 2: declare types
data = QTable(dtype=[object,object,float,float,float,float,object,float]
names=['FILE','TARGET','JD','RA (deg)','DEC (deg)','EXP (s)','FILTER','AIRMASS'])
# loop over the files
for imgfile in listfile:
hdul = fits.open(imgfile)
hdr = hdul[0].header
target_name = hdr['OBJECT']#.strip().upper().replace(' ','')
# JD, the center coordinate (RA, Dec), exposure time, filter, airmass.
JD = hdr['JD']
try:
RAcenter = hdr['CRVAL1']
DEcenter = hdr['CRVAL2']
except:
t120.log.warning('There is no CRVAL keyword in file '+imgfile)
skycoo = SkyCoord(hdr['OBJCTRA'],hdr['OBJCTDEC'], unit=(u.hourangle, u.deg))
RAcenter = skycoo.ra.to('deg').value
DEcenter = skycoo.dec.to('deg').value
exptime = hdr['EXPOSURE']
filtr = hdr['FILTER']
airmass = hdr['AIRMASS']
data.add_row([imgfile.replace(prefix,'').lstrip(),target_name,JD,RAcenter,DEcenter,exptime,filtr,airmass])
t120.log.debug('File='+imgfile+' TARGET'+target_name+' JD='+str(JD)+' RA='+str(RAcenter)+' DE='+str(DEcenter)+
' exp='+str(exptime)+' fil='+str(filtr)+' airmass='+str(airmass))
# remove first row : useful if method 1 is used: obsolete and dumb...
#data.remove_row(0)
# save log in file
ascii.write(data,fileout,format='fixed_width',delimiter=' ',formats={'JD': '%18.12f'},overwrite=True)
t120.log.info('There are '+str(len(listfile))+' data saved in '+fileout)
return
filetype = args.filetype
r, rIn, rOut = args.radius
ap_corr = args.ap_corr
ch, pix = args.ch_px
rejection = args.outlier
nRun = args.run
tName = args.target
comments = args.comments
#----------------------------
#Generating & writing data tables to csv files
res, data, prob = run(crdFormat, aor_crd, filetype, r, rIn, rOut, ap_corr,
int(ch), pix, rejection)
ascii.write(res,
'../Reduction_Data_&_Logs/%s_aor_data.csv' % nRun,
delimiter=',',
overwrite=True)
ascii.write(data,
'../Reduction_Data_&_Logs/%s_img_data.csv' % nRun,
delimiter=',',
overwrite=True)
#Writing a txt file that keeps log about this reduction run
#----------------------------------------------------------
log = open('../Reduction_Data_&_Logs/run%s_log.txt' % nRun, 'w')
log.write("Date Reduced : %s \n" % datetime.now().isoformat())
log.write("Input Parameters : %s \n" % str(vars(args)))
log.write("Instrument : IRAC Channel %i \n" % int(ch))
log.write("File Type : %s \n" % filetype.upper())
log.write("Target : %s \n" % tName)
log.write("Radius Used : r %i, rIn %i, rOut %i \n" % (r, rIn, rOut))
outname = typex + "_Exp35Exp56.csv"
tab1 = ascii.read(filex[0])
tab2 = ascii.read(filex[1])
del tab1["type"]
del tab2["type"]
tab1["count"].name = "count1"
tab2["count"].name = "count2"
newtab = join(tab1, tab2, keys="shRNA", join_type="inner")
total_count = [
x + y for index, (x, y) in enumerate(
zip(list(newtab["count1"]), list(newtab["count2"])))
]
newtab["count"] = total_count
del newtab["count1"]
del newtab["count2"]
ascii.write(newtab, outname, format="csv", overwrite=True)
#----- Filter out bench contaminants
wk_dir = "/Volumes/Yolanda/CRF_Screen/InVivo/1_1_Norm/20190516/Exp35Exp56Combined"
os.chdir(wk_dir)
# Contaminant: CDK9, Ccnt1
# Off target shRNA: Cd19
cont_list = ["CDK9", "Ccnt1", "Cd19"]
def flt_ct(inFile):
outName = inFile.replace(".csv", "flt-ct.csv")
with open(inFile, 'r') as fin:
with open(outName, 'w') as fout:
rfin = csv.reader(fin, delimiter=",")
wfout = csv.writer(fout, delimiter=",")
def bundle_definition(file_in, ifu=1, path='./', diagnose=False, pilot=False):
"""
Make a definition file containing a schematic of a fibre bundle
There is some duplication in this code, as it includes a test for
two different methods to plot the so-called bundle definition file.
This can be removed.
Adapted to new IFU object input. Kept old input (still appropriate
for Pilot Sample data).
"""
if pilot:
# Follow old input style, appropriate for RSS files from Pilot Sample.
# Open file and mask single IFU
hdu = pf.open(path + file_in)
fibtab = hdu[2].data # binary table containing fibre information
mask_ifu = fibtab.field('PROBENAME') == ifu # index the selected IFU
fibtab = fibtab[mask_ifu] # and mask fibre data
nfib = len(fibtab) # count the number of fibres
fib1 = np.where(fibtab['FIBNUM'] == 1)[0] # identify the central fibre
if not pilot:
myIFU = utils.IFU(file_in, ifu, flag_name=False)
nfib = len(myIFU.n)
# get true angular separation (a) between each fibre and Fib1
# ra and dec separations will then be cos(a), sin(a)
offset_ra = np.zeros(nfib, dtype='double')
offset_dec = np.zeros(nfib, dtype='double')
for i in range(nfib):
if pilot:
ra1 = np.radians(fibtab['FIB_MRA'][fib1])
ra_fib = np.radians(fibtab['FIB_MRA'][i])
dec1 = np.radians(fibtab['FIB_MDEC'][fib1])
dec_fib = np.radians(fibtab['FIB_MDEC'][i])
if not pilot:
ra1 = np.radians(myIFU.xpos[np.where(myIFU.n == 1)])
dec1 = np.radians(myIFU.ypos[np.where(myIFU.n == 1)])
ra_fib = np.radians(myIFU.xpos[i])
dec_fib = np.radians(myIFU.ypos[i])
# Angular distance
cosA = np.cos(np.pi/2-dec1) * np.cos(np.pi/2-dec_fib) + \
np.sin(np.pi/2-dec1) * np.sin(np.pi/2-dec_fib) * np.cos(ra1-ra_fib)
# DEC offset
cos_dRA = np.cos(np.pi/2-dec1) * np.cos(np.pi/2-dec1) + \
np.sin(np.pi/2-dec1) * np.sin(np.pi/2-dec1) * np.cos(ra1-ra_fib)
# RA offset
cos_dDEC = np.cos(np.pi/2-dec1) * np.cos(np.pi/2-dec_fib) + \
np.sin(np.pi/2-dec1) * np.sin(np.pi/2-dec_fib) * np.cos(ra1-ra1)
# Sign check; trig collapses everything to a single quadrant, so need
# to check which I am on:
if (ra_fib >= ra1) and (dec_fib >= dec1): # 1. quadrant (+, +)
offset_ra[i] = np.degrees(np.arccos(cos_dRA[0]))
offset_dec[i] = np.degrees(np.arccos(cos_dDEC[0]))
if (ra_fib <= ra1) and (dec_fib >= dec1): # 2. quadrant (-, +)
offset_ra[i] = np.negative(np.degrees(np.arccos(cos_dRA[0])))
offset_dec[i] = np.degrees(np.arccos(cos_dDEC[0]))
if (ra_fib <= ra1) and (dec_fib <= dec1): # 3. quadrant (-, -)
offset_ra[i] = np.negative(np.degrees(np.arccos(cos_dRA[0])))
offset_dec[i] = np.negative(np.degrees(np.arccos(cos_dDEC[0])))
if (ra_fib >= ra1) and (dec_fib <= dec1): # 4. quadrant (+, -)
offset_ra[i] = np.degrees(np.arccos(cos_dRA[0]))
offset_dec[i] = np.negative(np.degrees(np.arccos(cos_dDEC[0])))
# Write a dictionary of relative RA, DEC lists
datatab = {
'RA': offset_ra,
'DEC': offset_dec
} # proper, spherical trig, sky-projected
"""
datatab2 = {'RA': fibtab['FIB_MRA'] - fibtab['FIB_MRA'][fib1],
'DEC': fibtab['FIB_MDEC'] - fibtab['FIB_MDEC'][fib1]} # simple
"""
# Write to file
file_out = './bundle' + str(ifu) + '.bdf'
tab.write(datatab, file_out, names=['RA', 'DEC']) # need 'names' in order
# And test the positioning:
if diagnose == True:
ctr = [0.0, 0.0]
fig = plt.gcf()
fig.clf()
ax = fig.add_subplot(111)
axis = [
-8. / 3600. + ctr[0], 8. / 3600. + ctr[0], -8. / 3600. + ctr[1],
8. / 3600. + ctr[1]
]
plt.axis(axis)
plt.title('Bundle ' + str(ifu))
plt.xlabel('RA Offset [degrees]')
plt.ylabel('DEC Offset [degrees]')
ax.set_aspect('equal')
for i in range(61):
circ = patches.Circle(
(datatab['RA'][i] + ctr[0], datatab['DEC'][i] + ctr[1]),
0.8 / 3600.,
edgecolor='none',
facecolor='cyan',
alpha=.5)
ax.add_patch(circ)
"""
circ2 = patches.Circle((datatab2['RA'][i] + ctr[0],
datatab2['DEC'][i] + ctr[1]), 0.8/3600.,
edgecolor='none', facecolor='cyan',alpha=.5)
ax.add_patch(circ2)
"""
big_circ = patches.Circle(ctr,
7.5 / 3600.,
edgecolor='green',
facecolor='none',
lw=3)
#ax.add_patch(big_circ)
plt.savefig('/Users/iraklis/Desktop/bundle.pdf', transparent=True)
plt.show()
def cat_trans_im(incat, dbfile, geomap_infile, refcat, clustname, septol,
**kwargs):
'''This routine reads in a FITS catalog, writes a temporary output
ASCII catalog, then transforms this catalog using geoxytran. It
then appends these new coordinates as new columns to the original catalog.
It is usually run by hand after the geomap solution has been derived.
INPUT
incat: the input SExtractor catalog
dbfile: the geomap database file
geomap_infile: used as the database record
refcat: the original astrometric reference catalog
clustname: the name of the cluster
septol: the maximum separation allowed for a match in pixels
OPTIONAL KEYWORDS
xmin, xmax, ymin, ymax. These are the limits over which the
transform was originally computed. If these are given then it
uses those limits to color the points in the ra and decdiff plots.
If one is given, all must be given.
'''
#read in GMOS sextractor photometry catalog with (x,y) coordinates
cat_dat = ascii.read(incat)
#read in original astrometric catalog
ref_dat = ascii.read(refcat)
xref = np.array(ref_dat['X_IMAGE'])
yref = np.array(ref_dat['Y_IMAGE'])
#tmp coordinate files for geoxytran
tmpin = 'tmp_geoxytran_im_in'
tmpout = 'tmp_geoxytran_im_out'
#make a new name for the transformed file
newcat = incat.replace('.sexcat', '.trans.cat')
if os.path.isfile(tmpout) is True:
cmdstr = 'rm ' + tmpout
os.system(cmdstr)
cmdstr = 'rm ' + newcat
os.system(cmdstr)
#output temporary ASCII file with coordinates
fo = open(tmpin, "w")
fo.write("# x y\n")
for i, val in enumerate(cat_dat['X_IMAGE']):
fo.write('{} {}\n'.format(cat_dat['X_IMAGE'][i],
cat_dat['Y_IMAGE'][i]))
fo.close()
iraf.geoxytran(tmpin, tmpout, dbfile, geomap_infile, direction="backward",\
xcolumn=1, ycolumn = 2)
#read in ascii output file
trans_dat = ascii.read(tmpout)
xtrans = np.array(trans_dat['x'])
ytrans = np.array(trans_dat['y'])
#replace the old RAs and DECs with new RAs and DECs in place
tcat_dat = cat_dat
tcat_dat['X_IMAGE'] = xtrans
tcat_dat['Y_IMAGE'] = ytrans
#write the new catalog file with the transformed coordinates
ascii.write(tcat_dat, newcat, format='commented_header')
#match new catalog against original catalog
mfile = "allcat_im_match.txt"
(xrefm,yrefm,xtransm,ytransm, translims) = cmti.cat_im_match(xref, yref, \
xtrans, ytrans, septol, \
matchfile = mfile)
#make a plot of the residuals
allcattrans_plotfile = 'allcat_trans.' + clustname + '_coordiff_im.pdf'
#passes ra and dec limits if they are defined to find source
#outside of ra and dec lims. Assumes that if one keyword is givenSpARCS0035_GMOS_z.v0.sexcat'
#that all are given
if 'xmin' in kwargs.keys():
cmti.match_diff_im_plot(xrefm,yrefm,xtransm,ytransm, plotfile = allcattrans_plotfile, \
xmin = kwargs['xmin'], xmax = kwargs['xmax'], \
ymin = kwargs['ymin'], ymax = kwargs['ymax'])
else:
cmti.match_diff_im_plot(xrefm,
yrefm,
xtransm,
ytransm,
plotfile=allcattrans_plotfile)
## Define posiciones X e Y a partir de los datos entregados por SExtractor.
sex_x = sex_aux2['X_IMAGE']
sex_y = sex_aux2['Y_IMAGE']
sex_mag = sex_aux2['MAG_ISO']
# Conversion a coordenadas de CMOS.
sex_x1 = (sex_x - 512) * 0.00270 # queda en mm centrada en (0,0)
sex_y1 = (sex_y - 512) * 0.00270 # queda en mm centrada en (0,0)
os.chdir(current_path)
# Guarda las columnas X, Y y MAG del resultado de Sextractor.
ascii.write([sex_x1, sex_y1, sex_mag],
'tmp/sext',
delimiter='\t',
format='no_header',
formats={
'col0': '% 15.10f',
'col1': '% 15.10f',
'col2': '% 15.10f'
})
###Read Source extractor
with open('tmp/sext') as f:
content = f.readlines()
content = [x.strip() for x in content]
x_sext_list = []
y_sext_list = []
ID_sext_list = []
groupcount,
np.median(cat['RA'][sel]),
np.median(cat['DEC'][sel]),
np.median(cat['wave'][sel]),
np.median(cat['sn'][sel]),
np.std(cat['sn'][sel]),
np.max(cat['nalines'][sel]),
np.max(cat['ncat0'][sel]),
np.max(cat['ngood'][sel])
])
# if np.size(sel) > 3: detectlist.append([groupcount,cat['ID'][sel]])
groupcount += 1
#save output table
ascii.write(cat, 'cosdeep_group_info.dat', overwrite=True)
sel2 = np.where((cat['nobs'] > 1))
nuniq = np.size(np.unique(cat['multi_idx'][sel2]))
print "number of unique objects found in 2 or more shots is", nuniq
outputarr = Table(rows=output,
names=('multi_idx', 'ra', 'dec', 'wave', 'sn', 'snrms',
'nalines', 'nobs', 'ngood'),
dtype=('i4', 'f8', 'f8', 'f8', 'f8', 'f8', 'i4', 'i4', 'i4'))
#first plot completeness
nbin = 20
snarray = np.arange(nbin) * 2
ra2 = radians(catRA[i])
dec2 = radians(catDec[i])
# Angular distance equation
angSep = math.acos(
math.sin(dec2) * math.sin(dec1) + math.cos(dec2) * math.cos(dec1) *
math.cos(ra2 - ra1)) * (180 / math.pi) * 3600
angDist.append(angSep)
#if (catID[i] == 350):
#print ("350",angDist[i])
print(angDist)
print("CATID", len(catID))
print("ANGDIST", len(angDist))
print(
"-------------------------------------------------------------------------"
)
#Write galaxy ID, galaxy RA, and galaxy Dec to ascii table to read into RunGrismFiles program
RaDecData = Table(
[catID, catRA, catDec, angDist],
names=['Galaxy ID', 'Galaxy RA', 'Galaxy Dec', 'Angular Distance'])
ascii.write(RaDecData, 'RaDecData.dat', format='fixed_width', overwrite=True)
print("RaDecData.dat file written")
"""
file = ascii.read('RaDecData.dat', delimiter="|")
print (file['col2'])
print (file['col3'])
print (file['col4'])
print (file['col5'])
"""
path = sys.argv[1]
if path[-1] != '/':
path = path + '/'
filenames = glob(path + '*panstarrs_radec.csv')
clustername = filenames[0].split('_')[0]
# filename = pathfile.split('/')[-1]
# filename_nosuffix = filename.split('.')[0]
# path = pathfile[: - len(filename)]
panstarrs_file = path + clustername + '_panstarrs_radec.csv'
allwise_file = path + clustername + '_allwise_radec.txt'
panstarrs = ascii.read(panstarrs_file)
allwise = ascii.read(allwise_file)
# t0 = ascii.read(pathfile)
p1 = panstarrs['raMean', 'decMean']
a1 = allwise['ra', 'dec']
p1.add_column(np.full(len(p1), 2), name='size')
a1.add_column(np.full(len(a1), 2), name='size')
p1.rename_columns(('raMean', 'decMean'), ('ra', 'dec'))
a1.meta = {}
ascii.write(p1, output=path + clustername + '_panstarrs_radec_for_extinction_upload.csv', delimiter=',', format='basic', overwrite=True)
ascii.write(a1, output=path + clustername + '_allwise_radec_for_extinction_upload.csv', delimiter=',', format='basic', overwrite=True)
def save_star(detectid):
try:
lae = complete_lae_tab[complete_lae_tab["detectid"]==detectid]
lae_ra, lae_dec = lae["ra"], lae["dec"]
lae_coords = SkyCoord(ra = lae_ra*u.deg, dec = lae_dec*u.deg)
rs = lae_coords.separation(shot_coords).arcsec
mask = rs <= 50
if len(mask[mask]) < 10:
print("{} is empty.".format(detectid))
return 0
rs = rs[mask]
spec_here = ffskysub[mask]
err_here = spec_err[mask]
mask_7_here = mask_7[mask]
mask_10_here = mask_10[mask]
lae_ra = shot_tab["ra"][mask]
lae_dec = shot_tab["dec"][mask]
order = np.argsort(rs)
rs = rs[order]
spec_here = spec_here[order]
err_here = err_here[order]
mask_7_here = mask_7_here[order]
mask_10_here = mask_10_here[order]
lae_ra = lae_ra[order]
lae_dec = lae_dec[order]
wlhere = (def_wave > 4550) & (def_wave <= 4650)
weights = err_here[:,wlhere] ** (-2)
weights_sum = np.nansum(weights, axis=1)
flux_mean = np.nansum(spec_here[:,wlhere]*weights, axis=1) / weights_sum
flux_mean_error = 1./np.sqrt(weights_sum)
mask = (flux_mean != 0) & (flux_mean_error != 0)
rs_0 = rs[mask][:] #/ u.arcsec
#rs_0 = rs_0.decompose()
flux_mean = flux_mean[mask].data[:]
flux_mean_error = flux_mean_error[mask].data[:]
mask_7_here_0 = mask_7_here[mask]
mask_10_here_0 = mask_10_here[mask]
lae_ra_0 = lae_ra[mask]
lae_dec_0 = lae_dec[mask]
tab = {"r": rs_0,
"ra": lae_ra_0,
"dec": lae_dec_0,
"flux": flux_mean,
"sigma": flux_mean_error,
"mask_7": mask_7_here_0,
"mask_10": mask_10_here_0}
save_file = os.path.join(basedir, f"radial_profiles/stars_skymask/star_{detectid}.dat")
ascii.write(tab, save_file)
print("Wrote to "+save_file)
except Exception as e:
print("{} failed: ".format(detectid))
print(e)
return 0
return 1
object_properties['RA'] = ra_dd
object_properties['DEC'] = dec_dd
# Consistency check
print(bcolors.OKGREEN + "\nIs the object in the image footprint?" +
bcolors.ENDC)
try:
x_exp, y_exp = fits_tools.sky2xy(args.fits, RA=ra_dd, DEC=dec_dd)
object_properties['X_EXP'] = x_exp
object_properties['Y_EXP'] = y_exp
filename_science_xy = args.outdir + object_properties['OBJECT'][
0] + '_xy.cat'
ascii.write(np.array([x_exp, y_exp]).T,
filename_science_xy,
format='no_header',
overwrite=True)
# print('Coordinates = %s, %s' %(x_exp, y_exp))
print('Yes.')
logger.info('Is the object in the image footprint: Passed')
except:
msg = 'Object (RA, DEC = {ra}, {dec}) is not in the image footprint. Check object coordinates and FITS file.'.format(
ra=args.ra, dec=args.dec)
print(bcolors.FAIL + msg + bcolors.ENDC)
logger.error(msg)
sys.exit()
# If sextractor is run in dual image mode, process reference image first
lamout = np.linspace(lam.min(), lam.max(), len(lam))
zpaux = np.zeros([len(uorder), len(lamout)])
for i in range(len(uorder)):
f = interp1d(lam[order == uorder[i]],
zplam[order == uorder[i]],
bounds_error=False,
fill_value=0)
zpaux[i, :] = f(lamout)
zpout = np.zeros(len(lamout))
for i in range(len(lamout)):
zpout[i] = np.amax(zpaux[:, i])
# ASUMMING READ MODE = FAST (GAIN = 1.12)
zpout = zpout + 2.5 * np.log10(1.2)
ascii.write([lamout, zpout],
fileout,
format='commented_header',
names=['lambda', 'zeropoint'],
formats={
'lambda': '%.3f',
'zeropoint': '%.2f'
})
#plt.plot(lamout, zpaux[0,:])
plt.plot(lamout, zpout)
plt.show()
def pick_models_toothpick_style(
sedgrid_fname,
filters,
N_fluxes,
min_N_per_flux,
outfile=None,
outfile_params=None,
bins_outfile=None,
bright_cut=None,
):
"""
Creates a fake star catalog from a BEAST model grid. The chosen seds
are optimized for the toothpick model, by working with a given
number of flux bins, and making sure that every flux bin is covered
by at least a given number of models (for each filter individually,
which is how the toothpick model works).
Parameters
----------
sedgrid_fname: string
BEAST model grid from which the models are picked (hdf5 file)
filters: list of string
Names of the filters, to be used as columns of the output table
N_fluxes: integer
The number of flux bins into which the dynamic range of the
model grid in each filter is divided
min_N_per_flux: integer
Minimum number of model seds that need to fall into each bin
outfile: string
Output path for the models (optional). If this file already
exists, the chosen seds are loaded from this file instead.
outfile_params: string (default=None)
If a file name is given, the physical parameters associated with
each model will be written to disk
bins_outfile: string
Output path for a file containing the flux bin limits for each
filter, and the number of samples for each (optional)
bright_cut: list of float
List of magnitude limits for each filter (won't sample model
SEDs that are too bright)
Returns
-------
sedsMags: astropy Table
A table containing the selected model seds (columns are named
after the filters)
"""
if outfile is not None and os.path.isfile(outfile):
print(
"{} already exists. Will attempt to load SEDs for ASTs from there."
.format(outfile))
t = Table.read(outfile, format="ascii")
return t
with Vega() as v:
vega_f, vega_flux, lambd = v.getFlux(filters)
modelsedgrid = SEDGrid(sedgrid_fname)
sedsMags = -2.5 * np.log10(modelsedgrid.seds[:] / vega_flux)
Nseds = sedsMags.shape[0]
Nf = sedsMags.shape[1]
idxs = np.arange(Nseds)
# Check if logL=-9.999 model points sliently sneak through
if min(modelsedgrid.grid["logL"]) < -9:
warnings.warn("There are logL=-9.999 model points in the SED grid!")
print("Excluding those SED models from selecting input ASTs")
idxs = np.where(modelsedgrid.grid["logL"] > -9)[0]
sedsMags = sedsMags[idxs]
# Set up a number of flux bins for each filter
maxes = np.amax(sedsMags, axis=0)
mins = np.amin(sedsMags, axis=0)
bin_edges = np.zeros((N_fluxes + 1, Nf)) # indexed on [fluxbin, nfilters]
for f in range(Nf):
bin_edges[:, f] = np.linspace(mins[f], maxes[f], N_fluxes + 1)
bin_mins = bin_edges[:-1, :]
bin_maxs = bin_edges[1:, :]
if not len(bin_mins) == len(bin_maxs) == N_fluxes:
raise AssertionError()
bin_count = np.zeros((N_fluxes, Nf))
chosen_idxs = []
counter = 0
successes = 0
include_mask = np.full(idxs.shape, True, dtype=bool)
chunksize = 100000
while True:
counter += 1
# pick some random models
rand_idx = np.random.choice(idxs[include_mask], size=chunksize)
randomseds = sedsMags[rand_idx, :]
# Find in which bin each model belongs, for each filter
fluxbins = np.zeros(randomseds.shape, dtype=int)
for fltr in range(Nf):
fluxbins[:, fltr] = np.digitize(randomseds[:, fltr],
bin_maxs[:, fltr])
# Clip in place (models of which the flux is equal to the max
# are assigned bin nr N_fluxes. Move these down to bin nr
# N_fluxes - 1)
np.clip(fluxbins, a_min=0, a_max=N_fluxes - 1, out=fluxbins)
add_these = np.full((len(rand_idx)), False, dtype=bool)
for r in range(len(rand_idx)):
# If any of the flux bins that this model falls into does
# not have enough samples yet, add it to the list of model
# spectra to be output
if (bin_count[fluxbins[r, :], range(Nf)] < min_N_per_flux).any():
bin_count[fluxbins[r, :], range(Nf)] += 1
successes += 1
add_these[r] = True
# If all these bins are full...
else:
# ... do not include this model again, since we will reject it
# anyway.
include_mask[idxs == rand_idx] = False
# Add the approved models
chosen_idxs.extend(rand_idx[add_these])
# If some of the randomly picked models were not added
if not add_these.any():
# ... check if we have enough samples everywhere, or if all
# the models have been exhausted (and hence the bins are
# impossible to fill).
enough_samples = (bin_count.flatten() >= min_N_per_flux).all()
still_models_left = include_mask.any()
if enough_samples or not still_models_left:
break
if not counter % 10:
print("Sampled {} models. {} successfull seds. Ratio = {}".format(
counter * chunksize, successes,
successes / counter / chunksize))
# Gather the selected model seds in a table
sedsMags = Table(sedsMags[chosen_idxs, :], names=filters)
if outfile is not None:
ascii.write(
sedsMags,
outfile,
overwrite=True,
formats={k: "%.5f"
for k in sedsMags.colnames},
)
# if chosen, save the corresponding model parameters
if outfile_params is not None:
grid_dict = {}
for key in list(modelsedgrid.grid.keys()):
grid_dict[key] = modelsedgrid.grid[key][chosen_idxs]
grid_dict["sedgrid_indx"] = chosen_idxs
ast_params = Table(grid_dict)
ast_params.write(outfile_params, overwrite=True)
if bins_outfile is not None:
bin_info_table = Table()
col_bigarrays = [bin_mins, bin_maxs, bin_count]
col_basenames = ["bin_mins_", "bin_maxs_", "bin_count_"]
for fltr, filter_name in enumerate(filters):
for bigarray, basename in zip(col_bigarrays, col_basenames):
bin_info_table.add_column(
Column(bigarray[:, fltr], name=basename + filter_name))
ascii.write(bin_info_table, bins_outfile, overwrite=True)
return sedsMags
def pick_models(
sedgrid_fname,
filters,
mag_cuts,
Nfilter=3,
N_stars=70,
Nrealize=20,
outfile=None,
outfile_params=None,
bright_cut=None,
vega_fname=None,
ranseed=None,
):
"""Creates a fake star catalog from a BEAST model grid
Parameters
----------
sedgrid_fname: string
BEAST model grid from which the models are picked (hdf5 file)
filters: list of string
Names of the filters
mag_cuts: list
List of magnitude limits for each filter
Nfilter: Integer
In how many filters, you want a fake star to be brighter
than the limit (mag_cut) (default = 3)
N_stars: Integer
Number of stellar models picked per a single log(age)
(default=70)
Nrealize: Integer
Number of realization of each models (default = 20)
outfile: str
If a file name is given, the selected models will be written to
disk
outfile_params: str
If a file name is given, the physical parameters associated with
each model will be written to disk
bright_cut: list of float
Same as mag_cuts, but for the bright end
vega_fname: str
filename of vega file
ranseed : int
used to set the seed to make the results reproducable
useful for testing
Returns
-------
astropy Table of selected models
- and optionally -
ascii file: A list of selected models, written to 'outfile'
fits file: the corresponding physical parameters, written to 'outfile_params'
"""
with Vega(source=vega_fname) as v: # Get the vega fluxes
vega_f, vega_flux, lamb = v.getFlux(filters)
modelsedgrid = SEDGrid(sedgrid_fname)
# Convert to Vega mags
sedsMags = -2.5 * np.log10(modelsedgrid.seds[:] / vega_flux)
# make sure Nfilters isn't larger than the total number of filters
if Nfilter > len(filters):
Nfilter = len(filters)
# Select the models above the magnitude limits in N filters
idxs = mag_limits(sedsMags,
mag_cuts,
Nfilter=Nfilter,
bright_cut=bright_cut)
cols = {}
for key in list(modelsedgrid.grid.keys()):
cols[key] = modelsedgrid.grid[key][idxs]
grid_cut = Table(cols)
# Sample the model grid uniformly
prime_params = np.column_stack(
(grid_cut["logA"], grid_cut["M_ini"], grid_cut["Av"]))
search_age = np.unique(prime_params[:, 0])
N_sample = N_stars
model_ind = [] # indices for the model grid
ast_params = grid_cut[[]] # the corresponding model parameters
# set the random seed - mainly for testing
if not None:
np.random.seed(ranseed)
for iage in search_age:
(tmp, ) = np.where(prime_params[:, 0] == iage)
new_ind = np.random.choice(tmp, N_sample)
model_ind.append(new_ind)
[ast_params.add_row(grid_cut[new_ind[i]]) for i in range(len(new_ind))]
index = np.repeat(idxs[np.array(model_ind).reshape((-1))], Nrealize)
sedsMags = Table(sedsMags[index, :], names=filters)
if outfile is not None:
ascii.write(
sedsMags,
outfile,
overwrite=True,
formats={k: "%.5f"
for k in sedsMags.colnames},
)
if outfile_params is not None:
ast_params.write(outfile_params, overwrite=True)
return sedsMags
def supplement_ast(
sedgrid_fname,
filters,
nAST=1000,
existingASTfile=None,
outASTfile=None,
outASTfile_params=None,
mag_cuts=None,
color_cuts=None,
):
"""
Creates an additional fake star catalog from a BEAST model grid
that fulfills the customized conditions to supplement input ASTs.
If the existing input AST parameter file is given, already selected
models will be excluded from this process. The input artificial
stars are picked randomly from the remaining models.
Parameters
----------
sedgrid_fname: string
BEAST model grid from which the models are picked (hdf5 file)
filters: list of string
Names of the filters
nAST: int
Number of unique additional ASTs per source density bin
existingASTfile: string (optional, default=None)
Name of the existing input AST parameter file. If not None,
the models that were already listed in the existing list Will
be removed by default
outASTfile: string (optional, default=None)
Output file name for the chosen models
outASTfile_params: string (optional, default=None)
If a file name is given, the physical parameters associated with
each model will be written to disk
mag_cut: dictionary (optional, default=None)
Dictionary of bright and faint magnitude limits for given filters.
The way to specify the cuts is by updating the "ast_suppl_maglimit" key
in the beast_settings file. This is a dictionary that includes information
for the magnitude cuts as a function of the filters included in observation.
For example, for a field observed with HST_WFC3_F336W, HST_WFC3_F475W,
and HST_WFC3_F814W, to set a magnitude range limit of 16<HST_WFC3_F475W<28 mag,
and 15<HST_WFC3_F814W<27 mag you need to set the following within the beast_settings file:
# specify that the ast_supplement mode should be on
ast_supplement = True
# initialize and populate the dictionary of desired magnitude limits
ast_suppl_maglimits = {}
# the magntidue limits are defined by the filter and a list of the limits in magnitudes
ast_suppl_maglimits["HST_WFC3_F475W"] = [16,28]
ast_suppl_maglimits["HST_WFC3_F814W"] = [15,27]
# set the key word
ast_suppl_maglimit = ast_suppl_maglimits
color_cut: dictionary (optional, default=None)
Dictionary of red color limits for given filters.
The way to specify the cuts is by updating the "ast_suppl_colorlimit" key
in the beast_settings file. This is a dictionary that includes information
for the color cuts as a function of the filters included in observation.
For example, for a field observed with HST_WFC3_F336W, HST_WFC3_F475W,
and HST_WFC3_F814W, to set a color range limit of HST_WFC3_F475W-HST_WFC3_F814W<6,
HST_WFC3_F336W-HST_WFC3_F475W<5 and HST_WFC3_F336W-HST_WFC3_F814W<4, you need
to set the following within the beast_settings file:
# specify that the ast_supplement mode should be on
ast_supplement = True
# initialize the dictionary of desired magnitude limits
ast_suppl_colorlimits = {}
# the color limits are defined by the first filter in the color (e.g, X for X-Y),
# and the input is a list including the second filter (e.g., Y for X-Y) and the
# color limit in magnitudes
ast_suppl_colorlimits["HST_WFC3_F475W"] = [["HST_WFC3_F814W",6]]
ast_suppl_colorlimits["HST_WFC3_F336W"] = [["HST_WFC3_F475W",5], ["HST_WFC3_F814W",4]]
# set the key word
ast_suppl_colorlimit = ast_suppl_colorlimits
Returns
-------
sedsMags: astropy Table
A table containing the selected model seds (columns are named
after the filters)
"""
with Vega() as v:
vega_f, vega_flux, lambd = v.getFlux(filters)
modelsedgrid = SEDGrid(sedgrid_fname)
# Convert to Vega mags
sedsMags = -2.5 * np.log10(modelsedgrid.seds[:] / vega_flux)
Nseds = sedsMags.shape[0]
sedsIndx = np.arange(Nseds)
if existingASTfile is not None and os.path.isfile(existingASTfile):
print("{} exists. Will attempt to load SEDs for ASTs from there \
and remove those SEDs from the SED grid".format(existingASTfile))
print("existing AST file", existingASTfile)
t = Table.read(existingASTfile, format="fits")
sedsMags = np.delete(sedsMags, t["sedgrid_indx"], axis=0)
sedsIndx = np.delete(sedsIndx, t["sedgrid_indx"])
Nseds = sedsMags.shape[0]
# Apply selection conditions if supplied
# Just magnitude cuts
print("mag_cuts", mag_cuts)
print("color_cuts", color_cuts)
if mag_cuts is not None:
cond = np.ones(Nseds, dtype=bool)
for key in list(mag_cuts.keys()):
idx_filter = [i for i, iflt in enumerate(filters) if key in iflt]
bright_cut = mag_cuts[key][0]
faint_cut = mag_cuts[key][1]
tmp_cond = np.logical_and(
(sedsMags[:, idx_filter] >= bright_cut),
(sedsMags[:, idx_filter] <= faint_cut),
)
if color_cuts is not None:
if key in color_cuts:
for limit in color_cuts[key]:
idx_color_filter = [
i for i, iflt in enumerate(filters)
if limit[0] in iflt
]
tmp_cond = np.logical_and(
tmp_cond,
(sedsMags[:, idx_filter] -
sedsMags[:, idx_color_filter] <= limit[1]),
)
cond = np.logical_and(cond, tmp_cond.ravel())
sedsMags = sedsMags[cond, :]
sedsIndx = sedsIndx[cond]
# Randomly select models
# Supplementing ASTs does not need to follow
# the toothpick-way selection
chosen_idxs = np.random.choice(len(sedsIndx), nAST)
sedsIndx = sedsIndx[chosen_idxs]
# Gather the selected model seds in a table
sedsMags = Table(sedsMags[chosen_idxs, :], names=filters)
if outASTfile is not None:
ascii.write(
sedsMags,
outASTfile,
overwrite=True,
formats={k: "%.5f"
for k in sedsMags.colnames},
)
# if chosen, save the corresponding model parameters
if outASTfile_params is not None:
grid_dict = {}
for key in list(modelsedgrid.grid.keys()):
grid_dict[key] = modelsedgrid.grid[key][sedsIndx]
grid_dict["sedgrid_indx"] = sedsIndx
ast_params = Table(grid_dict)
ast_params.write(outASTfile_params, overwrite=True)
return sedsMags
def DoAll():
data = ascii.read('M83-Comb1_4mags.txt')
m1, m2, m3, m4, ra, dec = data['m1'], data['m2'], data['m3'], data[
'm4'], data['ra'], data['dec']
del data
# HST UVIS-1/IR Vega zero points in Jy, and corresponding lambda_eff
fo = np.array([4196.2, 2439.35, 1738.4,
1138.06]) # F438W, F814W, F110W, F160W
wv1 = np.array([0.43151, 0.790114, 1.102969, 1.523589]) * 1e-4 # cm
# Flux per freq bin in ergs/s/cm2/Hz
m = np.array([m1, m2, m3, m4]).T
f_nu = 10**(m / (-2.5)) * fo * 1e-23
# log of Counts per lambda bin in photons/s/cm2/cm
N_lam = np.log10(f_nu / (wv1 * h))
wv1 = np.log10(wv1)
del m, f_nu
# Effective Area * Relative Bandpass, and lambda_pivot for the notional ZYH filters + F606-like f_1
Arel = np.array([0.2107, 0.5663, 0.5856, 0.5686]) * 1e4 # m^2 > cm^2
wv2 = np.array([0.6342, 0.8758, 1.0671, 1.5909]) * 1e-4 # um > cm
wv2 = np.log10(wv2)
tmp = np.array([
np.polyfit(wv1[0:2], [N_lam[:, 0][i], N_lam[:, 1][i]], 1)
for i in range(N_lam.shape[0])
])
A, B = tmp[:, 0], tmp[:, 1]
N0 = 10**(A * wv2[0] + B) * 10**wv2[0] * Arel[0]
del tmp, A, B
tmp = np.array([
np.polyfit(wv1[1:3], [N_lam[:, 1][i], N_lam[:, 2][i]], 1)
for i in range(N_lam.shape[0])
])
A, B = tmp[:, 0], tmp[:, 1]
N1, N2 = 10**(A * wv2[1] + B) * 10**wv2[1] * Arel[1], 10**(
A * wv2[2] + B) * 10**wv2[2] * Arel[2]
del tmp, A, B
tmp = np.array([
np.polyfit(wv1[2:4], [N_lam[:, 2][i], N_lam[:, 3][i]], 1)
for i in range(N_lam.shape[0])
])
A, B = tmp[:, 0], tmp[:, 1]
N3 = 10**(A * wv2[3] + B) * 10**wv2[3] * Arel[3]
del tmp, A, B
id = np.arange(ra.size) + 1
ones = np.ones_like(id)
typ = np.repeat(np.array(['point']), id.size)
cmnt = np.repeat(np.array(['comment']), id.size)
tab0 = [id, ra, dec, N0, typ, ones, ones, ones, ones, cmnt]
tab1 = [id, ra, dec, N1, typ, ones, ones, ones, ones, cmnt]
tab2 = [id, ra, dec, N2, typ, ones, ones, ones, ones, cmnt]
tab3 = [id, ra, dec, N3, typ, ones, ones, ones, ones, cmnt]
nms = ('id', 'ra', 'dec', 'flux', 'type', 'n', 're', 'phi', 'ratio',
'notes')
fmt = {'id':'%10d', 'ra':'%10.5f', 'dec':'%10.5f', 'flux':'%15.5f', 'type':'%8s', \
'n':'%8.1f', 're':'%8.1f', 'phi':'%8.1f', 'ratio':'%8.1f', 'notes':'%8s'}
t0 = Table(tab0, names=nms)
t1 = Table(tab1, names=nms)
t2 = Table(tab2, names=nms)
t3 = Table(tab3, names=nms)
ascii.write(t0,
'list0.Comb1.tbl',
format='fixed_width',
delimiter='',
formats=fmt)
ascii.write(t1,
'list1.Comb1.tbl',
format='fixed_width',
delimiter='',
formats=fmt)
ascii.write(t2,
'list2.Comb1.tbl',
format='fixed_width',
delimiter='',
formats=fmt)
ascii.write(t3,
'list3.Comb1.tbl',
format='fixed_width',
delimiter='',
formats=fmt)
def td1_estimate(td1_hdu):
alpha = td1_hdu[1].data['ra']
delta = td1_hdu[1].data['dec']
nuv_flux = td1_hdu[1].data['flux_2365_a']
fuv_flux = td1_hdu[1].data['flux_1565_a']
# NUV
refined_set = [(al, de, nf) for al, de, nf in zip(alpha, delta, nuv_flux)
if (cc.ra.value - 5) <= al <= (cc.ra.value + 5) and
(cc.dec.value - 5) <= de <= (cc.dec.value + 5)]
nalpha, ndelta, nuv_flux = zip(*refined_set)
confined_set = [
nf for al, de, nf in zip(nalpha, ndelta, nuv_flux)
if cel_separation(al, de) <= field_radius[instrument] * u.arcsec
]
# If list is empty, normal value need to be taken.
if len(confined_set) == 0:
confined_set.append(flux_norm)
nd = sorted(confined_set)[-1]
flux, ta, tb, tc, td, te = td1_countnuv(nd)
nuv_res = Table([[flux], [ta], [tb], [tc], [td], [te]],
names=('flux_2365_a', 'silica', 'b4', 'b13', 'b15', 'n2'),
meta={'name': 'NUV counts'})
#nuv_res['flux_2365_a'].format = '
nuv_res['silica'].format = '4.1f'
nuv_res['b4'].format = '4.1f'
nuv_res['b13'].format = '4.1f'
nuv_res['b15'].format = '4.1f'
nuv_res['n2'].format = '4.1f'
print('\n\n### NUV\n\n{}\n'.format(nuv_res))
# To select NUV safe filters.
nuv_filter_dict = {
0: 'Silica',
1: 'NUV-B4',
2: 'NUV-B13',
3: 'NUV-B15',
4: 'NUV-N2'
}
i = 0
nuv_safe = []
for Filter in zip(*nuv_res['silica', 'b4', 'b13', 'b15', 'n2']):
if sum(np.array(Filter) > 1500) == 0:
nuv_safe.append(nuv_filter_dict[i])
if i == 0:
if sum(np.array(Filter) > 1133) == 0:
nuv_safe.append('NUV-grating')
i = i + 1
nuv_declaration = 'Safe filters in NUV: {}'.format(nuv_safe)
print('\n\n{}\n'.format(nuv_declaration))
# To write to file.
nuv_table = 'NUV_td1-nd-int.txt'
ascii.write(nuv_res, nuv_table, format='csv', overwrite=True)
with open('safe_NUV_filters.txt', 'w') as safe_file:
safe_file.write(nuv_declaration)
# FUV
refined_set = [(al, de, ff) for al, de, ff in zip(alpha, delta, fuv_flux)
if (cc.ra.value - 5) <= al <= (cc.ra.value + 5) and
(cc.dec.value - 5) <= de <= (cc.dec.value + 5)]
nalpha, ndelta, fuv_flux = zip(*refined_set)
confined_set = [
ff for al, de, ff in zip(nalpha, ndelta, fuv_flux)
if cel_separation(al, de) <= field_radius[instrument] * u.arcsec
]
# If list is empty, normal value need to be taken.
if len(confined_set) == 0:
confined_set.append(flux_norm)
fd = sorted(confined_set)[-1]
flux, ta, tb, tc, td = td1_countfuv(fd)
fuv_res = Table([[flux], [ta], [tb], [tc], [td]],
names=('flux_1565_a', 'caf2', 'baf2', 'sapphire',
'silica'),
meta={'name': 'NUV counts'})
#fuv_res['flux_1565_a'].format = '
fuv_res['caf2'].format = '4.1f'
fuv_res['baf2'].format = '4.1f'
fuv_res['sapphire'].format = '4.1f'
fuv_res['silica'].format = '4.1f'
print('\n### FUV \n\n{}\n\n'.format(fuv_res))
# To select FUV safe filters.
fuv_filter_dict = {0: 'CaF2', 1: 'BaF2', 2: 'Sapphire', 3: 'Silica'}
j = 0
fuv_safe = []
for Filter in zip(*fuv_res['caf2', 'baf2', 'sapphire', 'silica']):
if sum(np.array(Filter) > 1500) == 0:
fuv_safe.append(fuv_filter_dict[j])
if j == 0:
if sum(np.array(Filter) > 892) == 0:
fuv_safe.append('FUV-grating')
j = j + 1
fuv_declaration = 'Safe filters in FUV: {}'.format(fuv_safe)
print('\n\n{}\n'.format(fuv_declaration))
# To write to file.
fuv_table = 'FUV_td1-fd-int.txt'
ascii.write(fuv_res, fuv_table, format='csv', overwrite=True)
with open('safe_FUV_filters.txt', 'w') as safe_file:
safe_file.write(fuv_declaration)
ext.ypos = ycen
ext.alpha = 0.8
bgnd.c0 = 0.04
freeze(ext.ellip, ext.theta, ext.xpos, ext.ypos)
# freeze(ext.xpos,ext.ypos)
show_model()
set_stat(
"cstat"
) #CStat - A maximum likelihood function (XSPEC implementation of Cash); the background must be fitted
set_method("neldermead")
fit()
covariance()
image_data()
image_resid(tile=True, newframe=True)
image_source_component(ext, tile=True, newframe=True)
save_model("model.fits")
save_resid("resid.fits")
c = get_covariance_results()
# out = numpy.array([c.parvals,c.parmins, c.parmaxes])
# out[:,1] = out[:,1]*pixscale
at.write(numpy.array([c.parvals, c.parmins, c.parmaxes]),
"beta2d.txt",
names=c.parnames)
exit
i = 0
nuv_safe = []
for Filter in zip(*nuv_res['silica', 'b4', 'b13', 'b15', 'n2']):
if sum(np.array(Filter) > 1500) == 0:
nuv_safe.append(nuv_filter_dict[i])
if i == 0:
if sum(np.array(Filter) > 1133) == 0:
nuv_safe.append('NUV-grating')
i = i + 1
nuv_declaration = 'Safe filters in NUV: {}'.format(nuv_safe)
print('\n\n{}\n'.format(nuv_declaration))
# To write to file.
nuv_table = 'NUV_' + catalogue.replace('.fits.gz', '-nd-int.txt')
ascii.write(nuv_res, nuv_table, format='csv', overwrite=True)
with open('safe_NUV_filters.txt', 'w') as safe_file:
safe_file.write(nuv_declaration)
# To select FUV safe filters.
print('\n### FUV \n\n{}\n\n'.format(fuv_res))
fuv_filter_dict = {0: 'CaF2', 1: 'BaF2', 2: 'Sapphire', 3: 'Silica'}
j = 0
fuv_safe = []
for Filter in zip(*fuv_res['caf2', 'baf2', 'sapphire', 'silica']):
if sum(np.array(Filter) > 1500) == 0:
fuv_safe.append(fuv_filter_dict[j])
if j == 0:
if sum(np.array(Filter) > 892) == 0:
fuv_safe.append('FUV-grating')
def stpars(n_ms, n_rg, feh, afe, age, logg_cn=3, fig=False, iso='DARTMOUTH'):
#---------------------------------
# SET OUTPUT FILENAME
#---------------------------------
fileout = set_stpars_filename(n_ms, n_rg, feh, afe, age, logg_cn)
#---------------------------------
# ISOCHRONE [FE/H]-INTERPOLATION
#---------------------------------
isofile = gettracks(feh, afe, age, iso=iso)
if iso.upper() == 'DARTMOUTH':
print('Utilizing the DARTMOUTH isochrones')
if iso.upper() == 'PADOVA':
print('Utilizing the Padova isochrones')
#---------------------------------
# READ ISOCHRONE
#---------------------------------
t = np.loadtxt(isofile)
F0 = 1.021e-20 #Vega flux in erg cm-2 s-1 Hz-1
pc = 3.086e18 # parsec in cm
if iso.upper() == 'DARTMOUTH':
isoteff = 10**t[:, 2]
isologg = t[:, 3]
isomass = t[:, 1]
isologL = t[:, 4]
if iso.upper() == 'PADOVA':
isoteff = 10**t[:, 6]
isologg = t[:, 7]
isomass = t[:, 2]
isologL = t[:, 5]
isoHFlux = 10**(-t[:, 30] / 2.5) * F0
isologLH = np.log10(isoHFlux * 4 * np.pi * (10 * pc)**2)
#---------------------------------
# GET STELLAR PARAMETERS
#---------------------------------
logg_lim = max(isologg[isoteff == max(isoteff)])
min_teff_ms = min(isoteff[isologg >= logg_lim])
min_teff_rg = min(isoteff[isologg < logg_lim])
delta_teff_ms = (max(isoteff) - min_teff_ms) / n_ms
delta_teff_rg = (max(isoteff) - min_teff_rg) / n_rg
teff_grid = np.linspace(1, n_ms + n_rg + 1, n_ms + n_rg + 1)
phase = list(itertools.repeat("ms", n_ms + n_rg + 1))
for i in range(n_ms):
teff_grid[i] = min_teff_ms + i * delta_teff_ms
teff_grid[n_ms] = max(isoteff)
j = 0
for i in range(n_ms + n_rg, n_ms, -1):
teff_grid[i] = min_teff_rg + j * delta_teff_rg
phase[i] = "rgb"
j += 1
index_lim = np.where(teff_grid == max(isoteff))[0][0]
logg_grid = np.zeros(len(teff_grid))
mass_grid = np.zeros(len(teff_grid))
lumi_grid = np.zeros(len(teff_grid))
for i in range(len(teff_grid)):
if i <= index_lim:
xxxx = isologg >= logg_lim
else:
xxxx = isologg < logg_lim
temp = abs(isoteff[xxxx] - teff_grid[i])
teff_grid_temp = isoteff[xxxx]
condition = np.where(temp == min(temp))[0]
teff_grid[i] = teff_grid_temp[condition][0]
logg_grid_temp = isologg[xxxx]
logg_grid[i] = logg_grid_temp[condition][0]
mass_grid_temp = isomass[xxxx]
mass_grid[i] = mass_grid_temp[condition][0]
lumi_grid_temp = isologLH[xxxx]
lumi_grid[i] = lumi_grid_temp[condition][0]
#
# logg_grid[i] = isologg[(xxxx) and np.where(temp == min(temp))][0]
# mass_grid[i] = isomass[(xxxx) and np.where(temp == min(temp))][0]
# lumi_grid[i] = isologL[(xxxx) and np.where(temp == min(temp))][0]
if logg_grid[i] <= logg_cn:
phase[i] = "rgb_cn"
isoteffgrid = teff_grid
isologggrid = logg_grid
isomassgrid = mass_grid
isologLgrid = lumi_grid
#---------------------------------
# WRITE OUTPUT FILE
#---------------------------------
exists = os.path.isfile('./Stellar_pars')
if exists is False:
os.system("mkdir Stellar_pars")
ascii.write([isoteffgrid, isologggrid, isomassgrid, isologLgrid, phase],
fileout,
names=['#Teff/k', 'logg', 'Mass/Msun', 'logL/Lsun', 'phase'],
overwrite=True)
#---------------------------------
# PLOT ISOCHRONE
#---------------------------------
# plt.figure()
# plt.plot()
# lines(iso.teff, iso.logg, lwd = 2, col = 'red')
# points(iso.teff.grid, iso.logg.grid, col = 'red', pch = 19, cex = 1.4)
isoplot = plt.plot(isoteff, isologg)
isogridplot = plt.plot(isoteffgrid, isologggrid, 'o')
plt.xlabel('Temperature (K)')
plt.ylabel('log g (dex)')
plt.xlim([2000, 8000])
plt.ylim([-1, 6])
plt.gca().invert_xaxis()
plt.gca().invert_yaxis()
plt.legend(
(('Isochrone [Fe/H] = ' + str(feh) + ', [a/Fe] = ' + str(afe) +
', Age = ' + str(age) + ' Gyr'), ('Selected Stellar Parameters')),
loc='upper left',
fontsize='small')
# plt.text(4750,0.5,('Isochrone [Fe/H] = ' + str(feh) + '\n[a/Fe] = '+ str(afe) \
# + '\nAge = ' + str(age) + ' Gyr'))
plt.show()
#---------------------------------
# PRINT SOME INFORMATION
#---------------------------------
print('Isochrone: [Fe/H] = ' + str(feh))
print(' [a/Fe] = ' + str(afe))
print(' Age = ' + str(age) + 'Gyr')
print('STELLAR PARAMETERS OF: ' + str(n_ms + n_rg + 1) + 'STARS IN THE')
print('OUTPUT FILE: ' + fileout)
lst_id = []
lst_field = []
lst_ra = []
lst_dec = []
lst_z = []
lst_lmass = []
lst_uv = []
lst_vj = []
#getting the necessary data for lists, as well as calculating uv and vj#
for i in range(len(data_color)):
gal = data_color[i]
gal_info = data_info[i]
uv = -2.5 * np.log10(gal['L153'] / gal['L155'])
vj = -2.5 * np.log10(gal['L155'] / gal['L161'])
lst_id.append(gal_info['id'])
lst_field.append(gal_info['field'])
lst_ra.append(gal_info['ra'])
lst_dec.append(gal_info['dec'])
lst_z.append(gal_info['z_peak'])
lst_lmass.append(gal_info['lmass'])
lst_uv.append(uv)
lst_vj.append(vj)
#writing table#
table = Table(
[lst_id, lst_field, lst_ra, lst_dec, lst_z, lst_lmass, lst_uv, lst_vj],
names=['id', 'field', 'ra', 'dec', 'z', 'lmass', 'uv', 'vj'])
ascii.write(table, 'color_values.dat')
ew4 = []
ew5 = []
for i in ew_list:
ew1.append(np.mean(np.array(i[0])))
ew2.append(np.mean(np.array(i[1])))
ew3.append(np.mean(np.array(i[2])))
ew4.append(np.mean(np.array(i[3])))
ew5.append(np.mean(np.array(i[4])))
cont1 = []
cont2 = []
cont3 = []
cont4 = []
cont5 = []
for i in cont_lst:
cont1.append(np.mean(np.array(i[0])))
cont2.append(np.mean(np.array(i[1])))
cont3.append(np.mean(np.array(i[2])))
cont4.append(np.mean(np.array(i[3])))
cont5.append(np.mean(np.array(i[4])))
exp_data_ew = Table(
[date_list, ew1, ew2, ew3, ew4, ew5],
names=['date', 'HeI4 ew', 'CaII ew', 'FeII3 ew', 'OI2 ew', 'CI ew'])
ascii.write(exp_data_ew, sne_name + '.4.ew.txt', overwrite=True)
exp_data_cont = Table(
[date_list, cont1, cont2, cont3, cont4, cont5],
names=['date', 'HeI4 cont', 'CaII cont', 'FeII3 cont', 'OI2 cont', 'CI'])
ascii.write(exp_data_cont, sne_name + '.4.cont.txt', overwrite=True)
def main():
# get simulation information
if len(sys.argv) > 1:
sim_name = sys.argv[1]
snapnum = int(sys.argv[2])
shape_type = sys.argv[3]
sample_name = sys.argv[4]
else:
sim_name = 'TNG300-1' # full physics high-res run
snapnum = 99 # z=0
shape_type = 'reduced' # non-reduced, reduced, iterative
sample_name = 'sample_3'
# load a test halo catalog
from halotools.sim_manager import CachedHaloCatalog
halocat = CachedHaloCatalog(simname='bolplanck',
halo_finder='rockstar',
redshift=0.0,
dz_tol=0.1,
version_name='halotools_v0p4')
from halotools.empirical_models import HodModelFactory
# define the central occupatoion model
from halotools.empirical_models import TrivialPhaseSpace, Zheng07Cens
cens_occ_model = Zheng07Cens()
cens_prof_model = TrivialPhaseSpace()
# define the satellite occupation model
from halotools.empirical_models import Zheng07Sats
from halotools.empirical_models import NFWPhaseSpace, SubhaloPhaseSpace
from intrinsic_alignments.ia_models.anisotropic_nfw_phase_space import AnisotropicNFWPhaseSpace
sats_occ_model = Zheng07Sats()
#sats_prof_model = AnisotropicNFWPhaseSpace()
sats_prof_model = SubhaloPhaseSpace('satellites',
np.logspace(10.5, 15.2, 15))
# define the alignment models
from intrinsic_alignments.ia_models.ia_model_components import CentralAlignment,\
RadialSatelliteAlignment, MajorAxisSatelliteAlignment, HybridSatelliteAlignment
central_orientation_model = CentralAlignment()
satellite_orientation_model = RadialSatelliteAlignment()
if sample_name == 'sample_1':
cens_occ_model.param_dict['logMmin'] = 12.54
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 12.68
sats_occ_model.param_dict['logM1'] = 13.48
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.755
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.279
elif sample_name == 'sample_2':
cens_occ_model.param_dict['logMmin'] = 11.93
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 12.05
sats_occ_model.param_dict['logM1'] = 12.85
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.64
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.084
elif sample_name == 'sample_3':
cens_occ_model.param_dict['logMmin'] = 11.61
cens_occ_model.param_dict['sigma_logM'] = 0.26
sats_occ_model.param_dict['alpha'] = 1.0
sats_occ_model.param_dict['logM0'] = 11.8
sats_occ_model.param_dict['logM1'] = 12.6
central_orientation_model.param_dict[
'central_alignment_strength'] = 0.57172919
satellite_orientation_model.param_dict[
'satellite_alignment_strength'] = 0.01995
# combine model components
model_instance = HodModelFactory(
centrals_occupation=cens_occ_model,
centrals_profile=cens_prof_model,
satellites_occupation=sats_occ_model,
satellites_profile=sats_prof_model,
centrals_orientation=central_orientation_model,
satellites_orientation=satellite_orientation_model,
model_feature_calling_sequence=('centrals_occupation',
'centrals_profile',
'satellites_occupation',
'satellites_profile',
'centrals_orientation',
'satellites_orientation'))
from intrinsic_alignments.utils.jackknife_observables import jackknife_ed_3d
from halotools.mock_observables.alignments import ed_3d
rbins = np.logspace(-1, 1.5, 15)
rbin_centers = (rbins[:-1] + rbins[1:]) / 2.0
N = 10
ed = np.zeros((N, len(rbins) - 1))
for i in range(0, N):
# populate mock catalog
model_instance.populate_mock(halocat)
print("number of galaxies: ", len(model_instance.mock.galaxy_table))
mock = model_instance.mock.galaxy_table
# galaxy coordinates and orientations
coords = np.vstack((mock['x'], mock['y'], mock['z'])).T
orientations = np.vstack(
(mock['galaxy_axisA_x'], mock['galaxy_axisA_y'],
mock['galaxy_axisA_z'])).T
rbins = np.logspace(-1, 1.5, 15)
rbin_centers = (rbins[:-1] + rbins[1:]) / 2.0
ed[i, :] = ed_3d(coords,
orientations,
coords,
rbins,
period=halocat.Lbox)
err = np.std(ed, axis=0)
ed = np.mean(ed, axis=0)
# save measurements
fpath = fpath = PROJECT_DIRECTORY + 'modelling_illustris/data/'
fname = sim_name + '_' + str(snapnum) + '-' + sample_name + '_model_ed.dat'
ascii.write([rbin_centers, ed, err],
fpath + fname,
names=['r', 'ed', 'err'],
overwrite=True)
N = 1000
np.random.seed(123)
cos = np.array([np.random.normal(.0, .3) for i in range(10000)])
cos0 = [c for c in cos if c >= 0. and c <= 1.]
cos = np.array([np.random.normal(1., .3) for i in range(10000)])
cos1 = [c for c in cos if c >= 0. and c <= 1.]
plt.hist(cos0, color='b', alpha=.8)
plt.hist(cos1, color='g', alpha=.8)
plt.show()
cos, y, ecdf, yfit, d_yfit, a2 = fits(cos0)
print('a2=', a2)
ascii.write(Table(np.column_stack([cos, ecdf(cos), y, yfit, d_yfit])),
'../data/fits_ones',
names=['cos', 'ecdf', 'y', 'yfit', 'd_yfit'],
overwrite=True)
plt.step(cos, y, 'b-')
plt.plot(cos, yfit, 'k--')
cos, y, ecdf, yfit, d_yfit, a2 = fits(cos1)
print('a2=', a2)
ascii.write(Table(np.column_stack([cos, ecdf(cos), y, yfit, d_yfit])),
'../data/fits_zeros',
names=['cos', 'ecdf', 'y', 'yfit', 'd_yfit'],
overwrite=True)
plt.step(cos, y, 'g-')
plt.plot(cos, yfit, 'k--')
plt.show()
# %%
