def fits_writer(data,
fitsheader,
colnames,
desc,
filename="evtname.fits",
binary=False):
'''
This function takes in several data arrays and lists of strings to produce
a FITS data table. The table is saved as 'filename'.
Parameters:
-----------
data: list
A list of numpy 1D array objects.
fitsheader: PyFITS .header object
Header to be included in the FITS file.
colnames: string list
Each entry is one column name.
desc: string list
Each entry is the description for a column.
filename: string
The FITS table will be saved to the (optional) filename.
binary: boolean
Determines type of table. Choose True for binary, False for
ascii. Default is False.
Returns:
-------
None
Notes:
-----
All string lists MUST be lists, even if only containing one element.
'fitsheader' should be fitsfile.header when the file 'fitsfile.fits' is
read into Python.
Example:
-------
>>> import numpy as np
>>> import irsa
>>>
>>> data = [ np.arange(10), np.arange(10.) ]
>>> header = fitsfile.header
>>> colnames = ['col1','col2']
>>> desc = ['first column', 'second column']
>>> irsa.fits_writer(data, header, colnames, desc)
>>>
Revisions:
----------
2011-07-13: [email protected] initial version
2012-12-18: [email protected] changed width calculation method to
stop underestimating the width
'''
# Define constants
allnum = '0123456789' # integers to be stripped from string
padding = 3 # number of extra spaces in each column
# declen = 10 # max number of decimal places
# intform = 'I6' # integer column format
# floatform = 'F18.7' # float column format
# doubleform = 'D18.7' # double column format
# charform = 'S16' # character column format
filename = filename + '.fits'
# Split each value into a whole number and a decimal, then get the length
# of each. Store the largest whole number and decimal lengths per column.
# Finally, combine largest whole number and decimal lengths to get a column
# width. Keep column width and the decimal length for use in FITS
# formatting code.
maxlen = np.zeros(len(data), dtype='int64') # Maximum column length
maxdec = np.zeros(len(data), dtype='int64') # Maximum decimal length
maxpredec = np.zeros(len(data),
dtype='int64') # Largest whole number per column
maxpostdec = np.zeros(len(data),
dtype='int64') # Largest decimal per column
for column in range(len(data)):
for row in range(len(data[column])):
# Check if value is a float
# print(data[column][row])
if str(data[column].dtype).rstrip(allnum) == 'float':
# Check if float is a nan/inf.
# Can't split by '.' if it is a nan/inf!
if np.isnan(data[column][row]):
predec = len('nan')
postdec = 0
elif np.isinf(data[column][row]):
predec = len('inf')
postdec = 0
else:
predec = len(str(data[column][row]).split('.')[0]) + 1
postdec = len(str(data[column][row]).split('.')[1])
else:
predec = len(str(data[column][row]))
postdec = 0
# Store the largest whole number and decimal number lengths per
if maxpredec[column] < predec: # column.
maxpredec[column] = predec
if maxpostdec[column] < postdec:
maxpostdec[column] = postdec
# Add the two lengths together to get a column width.
maxlen[column] = maxpredec[column] + maxpostdec[column]
maxdec[column] = maxpostdec[column] # Keep the decimal width for later
# Load data into list of column objects
fitscol = []
for column in range(len(data)):
colname = colnames[column]
coldata = data[column]
# Get number of decimal places. If zero, do not include in 'length'.
if maxdec[column] == 0:
length = str(maxlen[column] + padding)
else:
length = str(maxlen[column] + padding) + '.' + str(maxdec[column])
# Determine the data type of the column
if str(coldata.dtype).rstrip(allnum) == 'int':
coltype = 'I' + length
elif str(coldata.dtype).rstrip(allnum) == 'float' and binary == False:
coltype = 'F' + length
elif str(coldata.dtype).rstrip(allnum) == 'float' and binary == True:
coltype = 'E' + length
elif str(coldata.dtype).rstrip(allnum) == '|S':
coltype = 'A' + length
else:
coltype = 'F' + length
col = fits.Column(name=colname, format=coltype, array=coldata)
fitscol.append(col)
# Generate a table HDU object
if binary == False:
tbhdu = fits.TableHDU.from_columns(fitscol)
else:
tbhdu = fits.BinTableHDU.from_columns(fitscol)
# Add comments to the table header
tbheader = tbhdu.header
for column in range(len(data)):
tbnum = column + 1 # the first column is 1, not 0
ttype = 'TTYPE' + str(tbnum)
# Update existing 'TTYPE' key with comment from 'desc'
tbheader[ttype] = (tbheader[ttype], desc[column])
# Make FITS file using 'fitsheader' and 'tbhdu'
hdu = fits.PrimaryHDU(None, fitsheader)
tbhdulist = fits.HDUList([hdu, tbhdu])
# try:
tbhdulist.writeto(filename)
# except:
# print('Format error occurred while writing file. Attempting to fix.')
# tbhdulist[0].verify('fix')
# tbhdulist[1].verify('fix')
# tbhdulist.writeto(filename)
return
def make_bg(GZ='.gz'):
"""
Make average background images with object masks
"""
files = glob.glob('ibhm*flt.seg.fits')
PATH = '/research/HST/GRISM/3DHST/COSMOS/RAW/'
PATH = '/3DHST/Spectra/Work/COSMOS/RAW/'
fp = open('COSMOS.g141.list')
files = fp.readlines()
fp.close()
for i in range(len(flt_files)):
files[i] = files[i][:-1].replace('msk','flt')
files = glob.glob('ib37*flt.seg.fits')
PATH = '/research/HST/GRISM/3DHST/GOODS-N/RAW/'
#### Direct flat-field
flat = pyfits.open(IREF+'/uc721143i_pfl.fits')[1].data[5:-5,5:-5]
flat[flat <= 0] = 5
flat[flat > 5] = 5
#### Candels
os.chdir('/Users/gbrammer/CANDELS/Flats/')
files = np.array(glob.glob('ib*flt.seg.fits'))
PATH = '/Users/gbrammer/CANDELS/UDS/RAW/'
info = catIO.Readfile(PATH+'../PREP_FLT/files.info')
files = files[info.filter == 'F125W']
flat = pyfits.open(IREF+'/uc72113qi_pfl.fits')[1].data[5:-5,5:-5]
NF = len(files)
idx = np.arange(NF)
X = np.zeros((NF,1014.**2))
## Otherwise get it from "show_profile" above
test = idx > -10
for j,i in enumerate(idx):
if ~test[i]:
continue
#
fi = files[i].replace('.seg','')
if not os.path.exists(fi.replace('flt','flt.seg')+GZ):
continue
#
if os.path.exists(fi+'.mask.reg'):
continue
#
print('%d %s' %(i, files[i]))
flt = pyfits.open(PATH+fi+'.gz')
flt[1].data *= flat
### Segmentation mask
masked = pyfits.open(fi.replace('flt','flt.seg')+GZ)[0].data == 0
### DQ mask, hot pixels and the "death star"
dq_ok = (flt[3].data & (4+32+16)) == 0
#
ok = masked & np.isfinite(flt[1].data) & (dq_ok)
flt[1].data /= np.median(flt[1].data[ok])
flt[1].data[(ok == False)] = 0
X[j,:] = flt[1].data.flatten()
#### Average
nsum = np.sum(X != 0, axis=0).reshape(1014,1014)
avg = np.sum(X, axis=0).reshape(1014,1014)/nsum
### Fill empty pixels with no input images
sky = avg
x,y = np.where((np.isfinite(sky) == False) | (sky/flat > 1.15))
NX = len(x)
pad = 1
for i in range(NX):
xi = x[i]
yi = y[i]
sub = sky[xi-pad:xi+pad+2,yi-pad:yi+pad+2]
if (np.sum(sub) != 0.0):
sky[xi,yi] = np.median(sub[np.isfinite(sub)])
still_bad = (np.isfinite(sky) == False) | (sky <= 0.01)
sky[still_bad] = flat[still_bad]
# bad_flat = (flat < 0.5)
# sky[bad_flat] = flat[bad_flat]
im_sky = pyfits.PrimaryHDU(data=sky)
im_n = pyfits.ImageHDU(data=nsum)
im = pyfits.HDUList([im_sky, im_n])
im.writeto('sky.fits', clobber=True)
#### for DIRECT flat
flatim = pyfits.open(IREF+'/uc721143i_pfl.fits')
flatim[1].data[5:-5,5:-5] = sky
flatim[3].data[5:-5,5:-5] = nsum
'TELESCOP' : 'BURSTCUBE',
'INSTRUME' : 'BURSTCUBE',
'TRIGTIME' : 387128425.854846,
'OBJECT' : 'TESTOBJ',
'RADECSYS' : 'FK5',
'EQUINOX' : 2000.0,
'RA_OBJ' : 30.0000,
'DEC_OBJ' : -15.000,
'ERR_RAD' : 3.000,
'TSTART' : 387128290.1728720,
'TSTOP' : 387128904.582618,
'TRIGTIME' : 387128425.854846,
'HDUCLASS' : 'OGIP',
'HDUCLAS1' : 'EVENTS'}
primaryheader = fits.Header(primarykeywords)
# Construct the output HDUs
primary_hdu = fits.PrimaryHDU(header=primaryheader)
ebounds_hdu = bintablehdu_constructor(channels,('CHANNEL','E_MIN','E_MAX'), ('short','float','float'),'EBOUNDS')
events_hdu = bintablehdu_constructor(eventpairs,('TIME','PHA'), ('double','short'),'EVENTS')
gti_hdu = bintablehdu_constructor([[tstart,tstop]],('START','STOP'), ('double','double'), 'GTI')
hdulist = fits.HDUList([primary_hdu,ebounds_hdu,events_hdu,gti_hdu])
# Add additional keywords to headers
# Write out the tte file
hdulist.writeto(outfile)
def main():
import sys
import fitsio
from astropy.io import fits
import itertools
import numpy as np
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
#sys.path.insert(0, '/global/cscratch1/sd/alsina/alpha-beta-gamma/code/src')
args = parse_args()
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
namesout = ['TAU0', 'TAU2', 'TAU5']
if args.xim:
args.tausjnfile = '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/alltausm_jk.fits'
alldata = fitsio.read(args.tausjnfile, ext=1)
names = ['BIN1', 'BIN2', 'ANGBIN', 'VALUE', 'ANG']
forms = ['i4', 'i4', 'i4', 'f8', 'f8']
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
a = [i for i in range(0, nrows)]
b = [j for j in range(0, nrows)]
bin_pairs = []
for p in itertools.product(a, b):
bin_pairs.append(p)
for nam in namesout:
corr = []
covmat = np.zeros(shape=(nrows, nrows))
for i, j in bin_pairs:
bin1 = (alldata['ANGBIN'] == i)
bin2 = (alldata['ANGBIN'] == j)
a = alldata[nam][bin1]
b = alldata[nam][bin2]
covmat[i, j] = np.cov(a, b)[0][1]
if (i == j):
corr.append(np.mean(alldata[nam][bin1]))
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
covmathdu = fits.ImageHDU(covmat, name='COVMAT')
hdul.insert(1, covmathdu)
angarray = alldata['THETA'][alldata['JKR'] == 0]
valuearray = np.array(corr)
bin1array = np.array([-999] * nrows)
bin2array = np.array([-999] * nrows)
angbinarray = np.arange(nrows)
array_list = [bin1array, bin2array, angbinarray, valuearray, angarray]
for array, name in zip(array_list, names):
outdata[name] = array
corrhdu = fits.BinTableHDU(outdata, name=nam)
hdul.insert(2, corrhdu)
if args.xim:
hdul.writeto(outpath + nam + 'm.fits', clobber=True)
else:
hdul.writeto(outpath + nam + 'p.fits', clobber=True)
import numpy as np
from astropy.io import fits
source = 'DSv'
fnum = 10
detectnum = 5
home = f'/blue/adamginsburg/d.jeff/SgrB2DSreorg/field{fnum}/CH3OH/'
sourcepath = home + f'{source}/200K_field10originals_z0_000190713_5-6mhzwidth_stdfixes/'
print(f'Accessing images in {sourcepath}')
transitionimg = fits.open(
sourcepath + "texmap_3transmask_3sigma_allspw_withnans_weighted.fits")[0]
teximg = fits.open(sourcepath +
"texmap_3sigma_allspw_withnans_weighted.fits")[0]
transitionmap = transitionimg.data
texmap = teximg.data
print('Masking temperature map')
maskedarr = np.ma.masked_where(transitionmap < detectnum, texmap)
maskedmap = maskedarr.filled(fill_value=np.nan)
print('Creating new fits file')
maskedhdu = fits.PrimaryHDU(maskedmap)
maskedhdu.header = teximg.header
maskedfits = fits.HDUList(maskedhdu)
maskedoutpath = sourcepath + f"texmap_{detectnum}transmask_3sigma_allspw_withnans_weighted.fits"
print(f'Saving at {maskedoutpath}')
maskedfits.writeto(maskedoutpath)
print('Done')
def mkmaster(instrument, fn_dict, mtype, fmin=5, master_dir='./'):
"""
PURPOSE:
Make master calibration frames (bias, dark, flat)
* currently no outlier rejection other than median combine
INPUT:
instrument - instrument name defined in instrument_dict (ex. 'ratir')
fn_dict - dictionary output by choose_calib() containing organized
fits file names. can also provide file name of pickled dictionary.
mtype - type of master frame. should be either 'flat', 'dark' or 'bias'
fmin - minimum number of files needed to make a master frame
EXAMPLE:
mkmaster('ratir', fn_dict=output from choose_calib(), mtype = bias, dark or flat name)
FUTURE IMPROVEMENTS:
- Better outlier rejection
"""
instrum = instrument_dict[instrument]
# check if input is a file name
if type(fn_dict) is str:
if fn_dict.split('.')[-1] == 'p':
af.print_bold("Loading pickled dictionary from file.")
fn_dict = pickle.load(open(fn_dict, 'rb'))
else:
af.print_err("Invalid pickle file extension detected. Exiting...")
return
# check for valid mtype
if mtype not in [instrum.flatname, instrum.biasname, instrum.darkname]:
af.print_err(
"Error: valid arguments for mtype are {}, {} and {}. Exiting...".
format(instrum.flatname, instrum.biasname, instrum.darkname))
return
bands = fn_dict.keys()
sorttype = 'BAND'
if mtype in [instrum.biasname, instrum.darkname]:
sorttype = 'CAMERA'
d = os.getcwd().split('/')[-1] # name of current directory
af.print_head("\nMaking master {} frame in {}:".format(mtype, d))
# work on FITs files for specified photometric bands
for band in bands:
# print current band
af.print_under("\n{:^50}".format('{} {}'.format(band, sorttype)))
first_file = fn_dict[band][0]
ind_C = first_file.index('C')
cam = first_file[ind_C:ind_C + 2]
cam_i = int(cam[1])
# check if required files are present
if instrum.has_cam_bias(cam_i):
mbias_fn = '{}_{}.fits'.format(instrum.biasname, cam)
else:
mbias_fn = None
if instrum.has_cam_dark(cam_i):
mdark_fn = '{}_{}.fits'.format(instrum.darkname, cam)
else:
mdark_fn = None
if mtype is not instrum.biasname:
if mbias_fn is not None:
if not os.path.exists(mbias_fn):
af.print_err(
'Error: {} not found. Move master bias file to working directory to proceed.'
.format(mbias_fn))
continue
if mdark_fn is not None:
if (mtype is
instrum.flatname) and (not os.path.exists(mdark_fn)):
af.print_err(
'Error: {} not found. Move master dark file to working directory to proceed.'
.format(mdark_fn))
continue
# check dictionary entries
fns = fn_dict[band]
if len(fns) < fmin:
if len(fns) == 0:
af.print_err(
'Error: no frames available to make master {} for {} {}.'.
format(mtype, band, sorttype.lower()))
continue
else:
temp = raw_input(
af.bcolors.WARNING +
"Only {} frames available to make master {} for {} {}. Continue? (y/n): "
.format(len(fns), mtype, band, sorttype.lower()) +
af.bcolors.ENDC)
if temp.lower() != 'y' and temp.lower() != 'yes':
af.print_warn("Skipping {}...".format(band))
continue
# load calibration data
hdu = pf.PrimaryHDU()
filter_arr = [] # to check that all frames used the same filter
exptime_arr = [
] # to check that all frames have the same exposure time (where relevant)
data_arr = []
i = 0
for fn in fns:
print fn
hdu.header[FITS_IN_KEY(
i)] = fn # add flat fn to master flat header
hdulist = pf.open(fn)
data_arr.append(hdulist[0].data)
filter_arr.append(hdulist[0].header['FILTER'])
exptime_arr.append(hdulist[0].header['EXPTIME'])
i += 1
data_arr = np.array(data_arr, dtype=np.float)
# check that frames match
for i in range(len(fns) - 1):
if (filter_arr[i + 1] != filter_arr[0]) and (mtype is
instrum.flatname):
af.print_err(
"Error: cannot combine flat frames with different filters. Skipping {} {}..."
.format(band, sorttype.lower()))
continue
if (exptime_arr[i + 1] != exptime_arr[0]) and (mtype is
instrum.darkname):
af.print_err(
"Error: cannot combine dark frames with different exposure times. Skipping {} {}..."
.format(band, sorttype.lower()))
continue
if instrum.flatname:
hdu.header['FILTER'] = filter_arr[
0] # add filter keyword to master frame
if instrum.darkname:
hdu.header['EXPTIME'] = exptime_arr[
0] # add exposure time keyword to master frame
# add CAMERA header keyword
hdu.header['CAMERA'] = cam_i # add camera keyword to master frame
# crop bias frames
if mtype is instrum.biasname:
data_arr = data_arr[(np.s_[:], instrum.slice(cam)[0],
instrum.slice(cam)[1])]
# crop dark frames and perform calculations
elif mtype is instrum.darkname:
data_arr = data_arr[(np.s_[:], instrum.slice(cam)[0],
instrum.slice(cam)[1])]
data_arr = (data_arr - pf.getdata(mbias_fn)
) / hdu.header['EXPTIME'] # calculate dark current
# crop flat frames and perform calculations
elif mtype is instrum.flatname:
if mbias_fn is not None:
mbd = pf.getdata(mbias_fn)
mbd = mbd
if mdark_fn is not None:
mdd = pf.getdata(mdark_fn)
mdd = mdd
if instrum.is_cam_split(cam_i):
pass # split data is already cropped
else:
data_arr = data_arr[(np.s_[:], instrum.slice(cam)[0],
instrum.slice(cam)[1])]
for i in range(len(exptime_arr)):
if mbias_fn is not None:
data_arr[i] -= mbd
if mdark_fn is not None:
data_arr[i] -= mdd * exptime_arr[i]
data_arr[i] /= np.median(data_arr[i])
# make master frame
master = af.imcombine(data_arr, type='median').astype(np.float)
# add master to hdu
if mtype is instrum.flatname:
hdu.data = master / np.median(master) # normalize master flat
else:
hdu.data = master
# save master to fits
hdulist = pf.HDUList([hdu])
try:
hdulist.writeto('{}{}_{}.fits'.format(master_dir, mtype, band),
clobber=True)
except IOError:
os.mkdir(master_dir)
hdulist.writeto('{}{}_{}.fits'.format(master_dir, mtype, band),
clobber=True)
col8 = fits.Column(name='RWAV', format='D', array=np.array(a8))
col9 = fits.Column(name='ZFLUX',
format='PD()',
array=np.array(a9, dtype=np.object))
col10 = fits.Column(name='ZAND',
format='PI()',
array=np.array(a10, dtype=np.object))
col11 = fits.Column(name='ZINV',
format='PD()',
array=np.array(a11, dtype=np.object))
col12 = fits.Column(name='ZWAV', format='D', array=np.array(a12))
col13 = fits.Column(name='SPECTYPE', format='I', array=np.array(a13))
col14 = fits.Column(name='TRUEZ', format='D', array=np.array(a14))
col15 = fits.Column(name='TARGETID', format='I', array=np.array(a15))
cols = fits.ColDefs([
col1, col2, col3, col4, col5, col6, col7, col8, col9, col10, col11,
col12, col13, col14, col15
])
tbhdu = fits.BinTableHDU.from_columns(cols)
prihdr = fits.Header()
prihdr['FIBER'] = C_Fiber
prihdr['OBJNO'] = len(a5)
prihdu = fits.PrimaryHDU(header=prihdr)
file_dir = desidir + slash + np.str(C_Fiber) + slash
os.mkdir(file_dir)
file_name = file_dir + "New" + np.str(C_Fiber)
print(file_name)
thdulist = fits.HDUList([prihdu, tbhdu])
thdulist.writeto(file_name)
i = i + 1
def to_fits(self, header=None, energy_unit='TeV', **kwargs):
"""
Convert RM to FITS HDU list format.
Parameters
----------
header : `~astropy.io.fits.Header`
Header to be written in the fits file.
energy_unit : str
Unit in which the energy is written in the HDU list
Returns
-------
hdulist : `~astropy.io.fits.HDUList`
RMF in HDU list format.
Notes
-----
For more info on the RMF FITS file format see:
http://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/summary/cal_gen_92_002_summary.html
"""
self._e_reco = self._e_reco.to(energy_unit)
self._e_true = self._e_true.to(energy_unit)
# Cannot use table_to_fits here due to variable length array
# http://docs.astropy.org/en/v1.0.4/io/fits/usage/unfamiliar.html
table = self.to_table()
cols = table.columns
c0 = fits.Column(name=cols[0].name, format='E', array=cols[0],
unit='{}'.format(cols[0].unit))
c1 = fits.Column(name=cols[1].name, format='E', array=cols[1],
unit='{}'.format(cols[1].unit))
c2 = fits.Column(name=cols[2].name, format='I', array=cols[2])
c3 = fits.Column(name=cols[3].name, format='PI()', array=cols[3])
c4 = fits.Column(name=cols[4].name, format='PI()', array=cols[4])
c5 = fits.Column(name=cols[5].name, format='PE()', array=cols[5])
hdu = fits.BinTableHDU.from_columns([c0, c1, c2, c3, c4, c5])
if header is None:
header = hdu.header
header['EXTNAME'] = 'MATRIX', 'name of this binary table extension'
header['TELESCOP'] = 'DUMMY', 'Mission/satellite name'
header['INSTRUME'] = 'DUMMY', 'Instrument/detector'
header['FILTER'] = '', 'Filter information'
header['CHANTYPE'] = 'PHA', 'Type of channels (PHA, PI etc)'
header['HDUCLASS'] = 'OGIP', 'Organisation devising file format'
header['HDUCLAS1'] = 'RESPONSE', 'File relates to response of instrument'
header['HDUCLAS2'] = 'RSP_MATRIX', 'Keyword information for Caltools Software.'
header['HDUVERS '] = '1.3.0', 'Version of file format'
# Obsolet RMF headers, included for the benefit of old software
header['RMFVERSN'] = '1992a', 'Obsolete'
header['HDUVERS1'] = '1.1.0', 'Obsolete'
header['HDUVERS2'] = '1.2.0', 'Obsolete'
for key, value in kwargs.items():
header[key] = value
header['DETCHANS'] = self._e_reco.nbins, 'Total number of detector PHA channels'
header['TLMIN4'] = 0, 'First legal channel number'
# E_reco nbins - 1
header['TLMAX4'] = len(self._e_reco) - 2, 'Highest legal channel number'
numgrp, numelt = 0, 0
#for val, val2 in zip(hdu.data['N_GRP'], hdu.data['N_CHAN']):
# numgrp += np.sum(val)
# numelt += np.sum(val2)
#header['NUMGRP'] = numgrp, 'Total number of channel subsets'
#header['NUMELT'] = numelt, 'Total number of response elements'
header['LO_THRES'] = self.pdf_threshold, 'Lower probability density threshold for matrix'
hdu.header = header
hdu2 = self._e_reco.to_ebounds()
prim_hdu = fits.PrimaryHDU()
return fits.HDUList([prim_hdu, hdu, hdu2])
return hdu_list
def make_savefile(anchor_fits, simname, haloname, simdir, DD, ds, ad):
#a = fits.open(fits_name)
'''
mss = a['STAR_MASS'].data
id_s = a['STARS_ID'].data
xs, ys, zs = a['STARS_GAL_POSITION'].data
vxs, vys, vzs = a['STARS_GAL_VELOCITY'].data
ep_s = a['STARS_EPSILON_FIXED'].data
xs_box, ys_box, zs_box = a['STARS_BOX_POSITION'].data
vxs_box, vys_box, vzs_box = a['STARS_BOX_VELOCITY'].data
'''
DDname = 'DD%.4i' % DD
#ds = yt.load('%s/%s/%s/%s/%s'%(simdir, haloname, simname, DDname, DDname))
#ad = ds.all_data()
#def _stars(pfilter, data): return data[(pfilter.filtered_type, "particle_type")] == 2
#yt.add_particle_filter("stars",function=_stars, filtered_type='all',requires=["particle_type"])
#ds.add_particle_filter('stars')
mss = ad['stars', 'particle_mass'].in_units('Msun')
xs_box = ad['stars', 'particle_position_x'].in_units('kpc')
ys_box = ad['stars', 'particle_position_y'].in_units('kpc')
zs_box = ad['stars', 'particle_position_z'].in_units('kpc')
vxs_box = ad['stars', 'particle_velocity_x'].in_units('km/s')
vys_box = ad['stars', 'particle_velocity_y'].in_units('km/s')
vzs_box = ad['stars', 'particle_velocity_z'].in_units('km/s')
id_s = ad['stars', 'particle_index']
hdus = []
prim_hdu = fits.PrimaryHDU()
hdus.append(prim_hdu)
for sat_n in arange(shape(anchor_fits)[0]):
anchor_ids = anchor_fits[sat_n]
gd_indices = []
for anch_id in anchor_ids:
match = where(id_s == anch_id)[0]
if len(match) > 0: gd_indices.append(int(match))
gd_indices = array(gd_indices)
if len(gd_indices) > 10:
print 'more than 10 anchor stars found for sat %i in DD%.4i' % (
sat_n, DD)
anchor_mss = mss[gd_indices]
anchor_xs_box = xs_box[gd_indices]
anchor_ys_box = ys_box[gd_indices]
anchor_zs_box = zs_box[gd_indices]
anchor_vxs_box = vxs_box[gd_indices]
anchor_vys_box = vys_box[gd_indices]
anchor_vzs_box = vzs_box[gd_indices]
anchor_R = sqrt(anchor_xs_box**2. + anchor_ys_box**2. +
anchor_zs_box**2.)
hist_R, r_edges = np.histogram(anchor_R.value,
weights=anchor_mss.value,
bins=arange(
min(anchor_R.value) - 20,
max(anchor_R.value) + 20, 10))
Rmid = np.mean(
[r_edges[argmax(hist_R)], r_edges[argmax(hist_R) + 1]])
good = where(abs(anchor_R.value - Rmid) < 5)[0]
anchor_xs_box_avg, _ = weighted_avg_and_std(anchor_xs_box,
weights=anchor_mss,
good=good)
anchor_ys_box_avg, _ = weighted_avg_and_std(anchor_ys_box,
weights=anchor_mss,
good=good)
anchor_zs_box_avg, _ = weighted_avg_and_std(anchor_zs_box,
weights=anchor_mss,
good=good)
anchor_vxs_box_avg, _ = weighted_avg_and_std(anchor_vxs_box,
weights=anchor_mss,
good=good)
anchor_vys_box_avg, _ = weighted_avg_and_std(anchor_vys_box,
weights=anchor_mss,
good=good)
anchor_vzs_box_avg, _ = weighted_avg_and_std(anchor_vzs_box,
weights=anchor_mss,
good=good)
box_avg = [
anchor_xs_box_avg, anchor_ys_box_avg, anchor_zs_box_avg,
anchor_vxs_box_avg, anchor_vys_box_avg, anchor_vzs_box_avg
]
cols1 = fits.ColDefs([
fits.Column(name='box_avg', array=box_avg, format='D'),
fits.Column(name='anchor_mss ',
array=anchor_mss,
format='D'),
fits.Column(name='anchor_xs_box ',
array=anchor_xs_box,
format='D'),
fits.Column(name='anchor_ys_box ',
array=anchor_ys_box,
format='D'),
fits.Column(name='anchor_zs_box ',
array=anchor_zs_box,
format='D'),
fits.Column(name='anchor_vxs_box ',
array=anchor_vxs_box,
format='D'),
fits.Column(name='anchor_vys_box ',
array=anchor_vys_box,
format='D'),
fits.Column(name='anchor_vzs_box ',
array=anchor_vzs_box,
format='D'),
fits.Column(name='ids_used_avg', array=good, format='I'),
])
else:
print 'less than 10 anchor stars found for sat %i in DD%.4i' % (
sat_n, DD)
cols1 = fits.ColDefs([
fits.Column(name='box_avg ', array=None, format='0D'),
fits.Column(name='anchor_mss ', array=None, format='0D'),
fits.Column(name='anchor_xs_box ', array=None, format='0D'),
fits.Column(name='anchor_ys_box ', array=None, format='0D'),
fits.Column(name='anchor_zs_box ', array=None, format='0D'),
fits.Column(name='anchor_vxs_box ', array=None, format='0D'),
fits.Column(name='anchor_vys_box ', array=None, format='0D'),
fits.Column(name='anchor_vzs_box ', array=None, format='0D'),
fits.Column(name='ids_used_avg', array=None, format='0D'),
])
hdus.append(
fits.BinTableHDU.from_columns(cols1, name='SAT_%.2i' % sat_n))
hdus_fits = fits.HDUList(hdus)
hdus_fits.writeto(
'/nobackupp2/rcsimons/foggie_momentum/anchor_files/%s_DD%.4i_anchorprops.fits'
% (simname, DD),
overwrite=True)
def to_hdulist(self):
hdu = table_to_fits_table(self.to_table())
prim_hdu = fits.PrimaryHDU()
return fits.HDUList([prim_hdu, hdu])
hdulist = pyfits.open(inputfile)
outlist = [pyfits.PrimaryHDU()]
for i in range(1, len(hdulist)):
try:
extname = hdulist[i].header['EXTNAME']
delete_this_one = False
for ext_to_delete in sys.argv[3:]:
if (extname == ext_to_delete):
delete_this_one = True
break
if (delete_this_one):
print("Deleting extension %s ..." % (extname))
continue
except:
pass
print("Keeping extension %s ..." % (extname))
outlist.append(hdulist[i])
print("writing")
hdu_out = pyfits.HDUList(outlist)
hdu_out.writeto(outputfile, overwrite=True)
itertools.repeat(wvs4broadening),
itertools.repeat(planet_convspec_broadsampling),
itertools.repeat(A0_spec),
itertools.repeat(phoenix_A0_func),
itertools.repeat(A0_rv), itertools.repeat(A0_baryrv),
itertools.repeat(science_baryrv),
itertools.repeat(c_kms), itertools.repeat(cutoff),
itertools.repeat(rv_list)))
for vsini_id, out in enumerate(outputs_list):
_fluxout, _dAICout, _logpostout = out
fluxout[:, :, vsini_id, :] = _fluxout
dAICout[:, vsini_id, :] = _dAICout
logpostout[:, vsini_id, :] = _logpostout
if save:
hdulist = pyfits.HDUList()
hdulist.append(pyfits.PrimaryHDU(data=fluxout))
hdulist.append(pyfits.ImageHDU(data=dAICout))
hdulist.append(pyfits.ImageHDU(data=logpostout))
hdulist.append(pyfits.ImageHDU(data=vsini_list))
hdulist.append(pyfits.ImageHDU(data=rv_list))
if combined:
if not os.path.exists(os.path.join(sciencedir, "out")):
os.makedirs(os.path.join(sciencedir, "out"))
out = os.path.join(sciencedir, "out",
"flux_and_posterior.fits")
else:
if not os.path.exists(
os.path.join(os.path.dirname(sciencefilename),
"out")):
os.makedirs(
data[i3:i4,i3:i4] = data[i3:i4,i3:i4] * wfec # FFT ft = pyfftw.interfaces.numpy_fft.fft2(data) fts = np.fft.fftshift(ft) # 結果を入射光子数(電子数)の重みを付けて加算する。 i1 = int(N/2-N0/2) i2 = int(N/2+N0/2) NPm = (NP[i] + NP[i+1])/2 image = image + NPm*(fts.real[i1:i2,i1:i2]**2 + fts.imag[i1:i2,i1:i2]**2) # 最後に規格化しておく、値は1秒あたりのelectron数になるように s = np.sum(image) image = image/s * Ntot * Stel # Save hdu = fits.PrimaryHDU(image) hdu.header['NTOT'] = Ntot # total flux/m2/sec hdu.header['APTFILE'] = ahdr['APTFILE'] # telescope.json hdu.header['EPD'] = ahdr['EPD'] # pupil diameter (mm) hdu.header['COBS'] = ahdr['COBS'] # central obscuration ratio hdu.header['STYPE'] = ahdr['STYPE'] # Spider type hdu.header['TSP'] = ahdr['TSP'] # spider thickness (mm) hdu.header['STEL'] = ahdr['STEL'] # Area of telescope aperture (m2) hdu.header['SPRES'] = args['-s'] # spectral responce hdu.header['M'] = M # Number of FFT cells per wavelength in um hdulist = fits.HDUList([hdu]) hdulist.writeto(args['-p'],overwrite=True)
pixel_scale = hdu[0].header['PIXSCALE'] # arcsec/pix
flux__xl = hdu['SCI'].data / flux_cal
eflux__xl = np.sqrt(hdu['VAR'].data) / flux_cal
flux_rf__xl = flux__xl * (1. + z)
eflux_rf__xl = eflux__xl * (1. + z)
SN_rf__xl = flux_rf__xl / eflux_rf__xl
cpix = flux_rf__xl.sum(axis=1).argmax()
flag__xl = hdu['DQ'].data
dist__x = np.abs([(i - cpix) * pixel_scale for i in range(Npix)])
iS = np.argsort(dist__x)
for i in range(Npix):
i_spectra = iS[i]
print i, iS[i]
with open('spectra/ngc3081_545_spec%04d_%04d.txt' % (i, i_spectra),
'w') as f:
for i_l in range(Nl):
f.write(
'%.1f %e %e %e\n' %
(l_rf[i_l], flux_rf__xl[i_spectra, i_l],
eflux_rf__xl[i_spectra, i_l], flag__xl[i_spectra, i_l]))
if save_fits:
hduout = fits.HDUList()
hduout.append(fits.PrimaryHDU(header=hdu[0].header))
hduout.append(fits.ImageHDU(data=wl, name='L_RF'))
hduout.append(fits.ImageHDU(data=flux_rf__xl, name='F_RF__XL'))
hduout.append(fits.ImageHDU(data=eflux_rf__xl, name='ERR_F_RF__XL'))
hduout.append(fits.ImageHDU(data=flag__xl, name='FLAG__XL'))
hduout.append(fits.ImageHDU(data=dist__x, name='DIST__X'))
hduout.writeto('ngc3081_545_vardq_extract_spectra.fits', clobber=True)
fileout = 'Tb_reso_' + str(reso) + 'km.s-1_' + "Tmin_" + str(Tk_lim_inf) + "_Tmax_" + str(Tk_lim_sup) + '_ROHSA.fits'
fileout_thin = 'Tb_reso_' + str(reso) + 'km.s-1_' + "Tmin_" + str(Tk_lim_inf) + "_Tmax_" + str(Tk_lim_sup) + '_ROHSA_thin.fits'
# Write PPV cube
hdu0 = fits.PrimaryHDU(Tb)
hdu0.header['COMMENT'] = 'Brightness Temperature Tb'
hdu0.header['NAXIS'] = 3
hdu0.header['NAXIS1'] = Tb.shape[1]
hdu0.header['NAXIS2'] = Tb.shape[2]
hdu0.header['NAXIS3'] = len(u)
hdu0.header['CTYPE3'] = 'v [km.s-1]'
hdu0.header['CRVAL3'] = u[40]
hdu0.header['CDELT3'] = reso
hdu0.header['CRPIX3'] = 40
hdu0.header['BUNIT'] = 'K'
hdulist = fits.HDUList([hdu0])
hdulist.writeto(path_out + fileout, overwrite=True)
# Write PPV cube thin limit
hdu0 = fits.PrimaryHDU(Tb_thin)
hdu0.header['COMMENT'] = 'Brightness Temperature Tb'
hdu0.header['NAXIS'] = 3
hdu0.header['NAXIS1'] = Tb_thin.shape[1]
hdu0.header['NAXIS2'] = Tb_thin.shape[2]
hdu0.header['NAXIS3'] = len(u)
hdu0.header['CTYPE3'] = 'v [km.s-1]'
hdu0.header['CRVAL3'] = u[40]
hdu0.header['CDELT3'] = reso
hdu0.header['CRPIX3'] = 40
hdu0.header['BUNIT'] = 'K'
hdulist = fits.HDUList([hdu0])
def convert_tscube_old(infile, outfile):
"""Convert between old and new TSCube formats."""
inhdulist = fits.open(infile)
# If already in the new-style format just write and exit
if 'DLOGLIKE_SCAN' in inhdulist['SCANDATA'].columns.names:
if infile != outfile:
inhdulist.writeto(outfile, clobber=True)
return
# Get stuff out of the input file
nrows = inhdulist['SCANDATA']._nrows
nebins = inhdulist['EBOUNDS']._nrows
npts = inhdulist['SCANDATA'].data.field('NORMSCAN').shape[1] / nebins
emin = inhdulist['EBOUNDS'].data.field('e_min') / 1E3
emax = inhdulist['EBOUNDS'].data.field('e_max') / 1E3
eref = np.sqrt(emin * emax)
dnde_emin = inhdulist['EBOUNDS'].data.field('E_MIN_FL')
dnde_emax = inhdulist['EBOUNDS'].data.field('E_MAX_FL')
index = np.log(dnde_emin / dnde_emax) / np.log(emin / emax)
flux = PowerLaw.eval_flux(emin, emax, [dnde_emin, index], emin)
eflux = PowerLaw.eval_eflux(emin, emax, [dnde_emin, index], emin)
dnde = PowerLaw.eval_dnde(np.sqrt(emin * emax), [dnde_emin, index], emin)
ts_map = inhdulist['PRIMARY'].data.reshape((nrows))
ok_map = inhdulist['TSMAP_OK'].data.reshape((nrows))
n_map = inhdulist['N_MAP'].data.reshape((nrows))
errp_map = inhdulist['ERRP_MAP'].data.reshape((nrows))
errn_map = inhdulist['ERRN_MAP'].data.reshape((nrows))
err_map = np.ndarray((nrows))
m = errn_map > 0
err_map[m] = 0.5 * (errp_map[m] + errn_map[m])
err_map[~m] = errp_map[~m]
ul_map = n_map + 2.0 * errp_map
ncube = np.rollaxis(inhdulist['N_CUBE'].data, 0, 3).reshape(
(nrows, nebins))
errpcube = np.rollaxis(inhdulist['ERRPCUBE'].data, 0, 3).reshape(
(nrows, nebins))
errncube = np.rollaxis(inhdulist['ERRNCUBE'].data, 0, 3).reshape(
(nrows, nebins))
tscube = np.rollaxis(inhdulist['TSCUBE'].data, 0, 3).reshape(
(nrows, nebins))
nll_cube = np.rollaxis(inhdulist['NLL_CUBE'].data, 0, 3).reshape(
(nrows, nebins))
ok_cube = np.rollaxis(inhdulist['TSCUBE_OK'].data, 0, 3).reshape(
(nrows, nebins))
ul_cube = ncube + 2.0 * errpcube
m = errncube > 0
errcube = np.ndarray((nrows, nebins))
errcube[m] = 0.5 * (errpcube[m] + errncube[m])
errcube[~m] = errpcube[~m]
norm_scan = inhdulist['SCANDATA'].data.field('NORMSCAN').reshape(
(nrows, npts, nebins)).swapaxes(1, 2)
nll_scan = inhdulist['SCANDATA'].data.field('NLL_SCAN').reshape(
(nrows, npts, nebins)).swapaxes(1, 2)
# Adjust the "EBOUNDS" hdu
columns = inhdulist['EBOUNDS'].columns
columns.add_col(
fits.Column(name=str('e_ref'),
format='E',
array=eref * 1E3,
unit='keV'))
columns.add_col(
fits.Column(name=str('ref_flux'),
format='D',
array=flux,
unit='ph / (cm2 s)'))
columns.add_col(
fits.Column(name=str('ref_eflux'),
format='D',
array=eflux,
unit='MeV / (cm2 s)'))
columns.add_col(
fits.Column(name=str('ref_dnde'),
format='D',
array=dnde,
unit='ph / (MeV cm2 s)'))
columns.change_name('E_MIN_FL', str('ref_dnde_e_min'))
columns.change_unit('ref_dnde_e_min', 'ph / (MeV cm2 s)')
columns.change_name('E_MAX_FL', str('ref_dnde_e_max'))
columns.change_unit('ref_dnde_e_max', 'ph / (MeV cm2 s)')
columns.change_name('NPRED', str('ref_npred'))
hdu_e = fits.BinTableHDU.from_columns(columns, name='EBOUNDS')
# Make the "FITDATA" hdu
columns = fits.ColDefs([])
columns.add_col(fits.Column(name=str('fit_ts'), format='E', array=ts_map))
columns.add_col(
fits.Column(name=str('fit_status'), format='E', array=ok_map))
columns.add_col(fits.Column(name=str('fit_norm'), format='E', array=n_map))
columns.add_col(
fits.Column(name=str('fit_norm_err'), format='E', array=err_map))
columns.add_col(
fits.Column(name=str('fit_norm_errp'), format='E', array=errp_map))
columns.add_col(
fits.Column(name=str('fit_norm_errn'), format='E', array=errn_map))
hdu_f = fits.BinTableHDU.from_columns(columns, name='FITDATA')
# Make the "SCANDATA" hdu
columns = fits.ColDefs([])
columns.add_col(
fits.Column(name=str('ts'),
format='%iE' % nebins,
array=tscube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('bin_status'),
format='%iE' % nebins,
array=ok_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm'),
format='%iE' % nebins,
array=ncube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_ul'),
format='%iE' % nebins,
array=ul_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_err'),
format='%iE' % nebins,
array=errcube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_errp'),
format='%iE' % nebins,
array=errpcube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_errn'),
format='%iE' % nebins,
array=errncube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('loglike'),
format='%iE' % nebins,
array=nll_cube,
dim=str('(%i)' % nebins)))
columns.add_col(
fits.Column(name=str('norm_scan'),
format='%iE' % (nebins * npts),
array=norm_scan,
dim=str('(%i,%i)' % (npts, nebins))))
columns.add_col(
fits.Column(name=str('dloglike_scan'),
format='%iE' % (nebins * npts),
array=nll_scan,
dim=str('(%i,%i)' % (npts, nebins))))
hdu_s = fits.BinTableHDU.from_columns(columns, name='SCANDATA')
hdulist = fits.HDUList(
[inhdulist[0], hdu_s, hdu_f, inhdulist["BASELINE"], hdu_e])
hdulist['SCANDATA'].header['UL_CONF'] = 0.95
hdulist.writeto(outfile, clobber=True)
return hdulist
def modelSpec(lib='GK',
teff=4500,
logg=2.5,
metals=0.,
cfe=0.,
nfe=0.,
afe=0.,
vmicro=2.,
dr=None,
rmHDU1=True,
rmHDU2=True):
"""
NAME:
modelSpec
PURPOSE:
download a model spectrum file
INPUT:
lib= ('GK') spectral library
teff= (4500) grid-point Teff
logg= (2.5) grid-point logg
metals= (0.) grid-point metallicity
cfe= (0.) grid-point carbon-enhancement
nfe= (0.) grid-point nitrogen-enhancement
afe= (0.) grid-point alpha-enhancement
vmicro= (2.) grid-point microturbulence
dr= return the path corresponding to this data release
rmHUD1= (True) if True, rm the first (v. large) HDU with the high-resolution model spectrum
rmHDU2= (True) if True, rm the second (quite large) HDU with the model spectrum convolved with the LSF
OUTPUT:
(none; just downloads)
HISTORY:
2015-01-20 - Written - Bovy (IAS)
"""
if dr is None: dr = path._default_dr()
# First make sure the file doesn't exist
filePath = path.modelSpecPath(lib=lib,
teff=teff,
logg=logg,
metals=metals,
cfe=cfe,
nfe=nfe,
afe=afe,
vmicro=vmicro,
dr=dr)
if os.path.exists(filePath): return None
# Create the file path
downloadPath = filePath.replace(
os.path.join(path._APOGEE_DATA, _dr_string(dr)), _base_url(dr=dr))
_download_file(downloadPath, filePath, dr, verbose=True)
# Post-processing of the file, removing the big first HDU or the first two for local storage
if rmHDU1 or rmHDU2:
# Open the file, need to use astropy's fits reader, bc the file has issues
import astropy.io.fits as apyfits
from astropy.utils.exceptions import AstropyUserWarning
import warnings
warnings.filterwarnings('ignore', category=AstropyUserWarning)
hdulist = apyfits.open(filePath)
if rmHDU1:
hdu = apyfits.PrimaryHDU(numpy.zeros((2, 2)))
else:
hdu = hdulist[0].copy()
inp = [hdu]
if rmHDU2:
hdu2 = apyfits.ImageHDU(numpy.zeros((2, 2)))
else:
hdu2 = hdulist[1].copy()
inp.append(hdu2)
inp.extend(hdulist[2:])
newHdulist = apyfits.HDUList(inp)
# Fix any issues in the headers
for ii in range(5):
if '[A/M]' in newHdulist[ii].header:
newHdulist[ii].header['AM'] = newHdulist[ii].header.pop(
'[A/M]', None)
if '[C/M]' in newHdulist[ii].header:
newHdulist[ii].header['CM'] = newHdulist[ii].header.pop(
'[C/M]', None)
if '[N/M]' in newHdulist[ii].header:
newHdulist[ii].header['NM'] = newHdulist[ii].header.pop(
'[N/M]', None)
# Overwrite file
newHdulist.writeto(filePath, clobber=True, output_verify='silentfix')
return None
def _build_tpf(self,
cube_fits,
img_cube,
uncert_cube,
cutout_wcs_dict,
aperture,
verbose=True):
"""
Building the cutout target pixel file (TPF) and formatting it to match TESS pipeline TPFs.
Paramters
---------
cube_fits : `~astropy.io.fits.hdu.hdulist.HDUList`
The cube hdu list.
img_cube : `numpy.array`
The untransposed image cutout array
uncert_cube : `numpy.array`
The untransposed uncertainty cutout array
cutout_wcs_dict : dict
Dictionary of wcs keyword/value pairs to be added to each array
column in the cutout table header.
aperture : `numpy.array`
The aperture array (an array the size of a single cutout
that is 1 where there is image data and 0 where there isn't)
verbose : bool
Optional. If true intermediate information is printed.
Returns
-------
response : `~astropy.io.fits.HDUList`
Target pixel file HDU list
"""
# The primary hdu is just the main header, which is the same
# as the one on the cube file
primary_hdu = cube_fits[0]
self._update_primary_header(primary_hdu.header)
cols = list()
# Adding the cutouts
tform = str(img_cube[0].size) + "E"
dims = str(img_cube[0].shape[::-1])
empty_arr = np.zeros(img_cube.shape)
# Adding the Time relates columns
cols.append(
fits.Column(name='TIME',
format='D',
unit='BJD - 2457000, days',
disp='D14.7',
array=(cube_fits[2].columns['TSTART'].array +
cube_fits[2].columns['TSTOP'].array) / 2))
cols.append(
fits.Column(name='TIMECORR',
format='E',
unit='d',
disp='E14.7',
array=cube_fits[2].columns['BARYCORR'].array))
# Adding CADENCENO as zeros b/c we don't have this info
cols.append(
fits.Column(name='CADENCENO',
format='J',
disp='I10',
array=empty_arr[:, 0, 0]))
# Adding counts (-1 b/c we don't have data)
cols.append(
fits.Column(name='RAW_CNTS',
format=tform.replace('E', 'J'),
unit='count',
dim=dims,
disp='I8',
array=empty_arr - 1,
null=-1))
# Adding flux and flux_err (data we actually have!)
cols.append(
fits.Column(name='FLUX',
format=tform,
dim=dims,
unit='e-/s',
disp='E14.7',
array=img_cube))
cols.append(
fits.Column(name='FLUX_ERR',
format=tform,
dim=dims,
unit='e-/s',
disp='E14.7',
array=uncert_cube))
# Adding the background info (zeros b.c we don't have this info)
cols.append(
fits.Column(name='FLUX_BKG',
format=tform,
dim=dims,
unit='e-/s',
disp='E14.7',
array=empty_arr))
cols.append(
fits.Column(name='FLUX_BKG_ERR',
format=tform,
dim=dims,
unit='e-/s',
disp='E14.7',
array=empty_arr))
# Adding the quality flags
cols.append(
fits.Column(name='QUALITY',
format='J',
disp='B16.16',
array=cube_fits[2].columns['DQUALITY'].array))
# Adding the position correction info (zeros b.c we don't have this info)
cols.append(
fits.Column(name='POS_CORR1',
format='E',
unit='pixel',
disp='E14.7',
array=empty_arr[:, 0, 0]))
cols.append(
fits.Column(name='POS_CORR2',
format='E',
unit='pixel',
disp='E14.7',
array=empty_arr[:, 0, 0]))
# Adding the FFI_FILE column (not in the pipeline tpfs)
cols.append(
fits.Column(name='FFI_FILE',
format='38A',
unit='pixel',
array=cube_fits[2].columns['FFI_FILE'].array))
# making the table HDU
table_hdu = fits.BinTableHDU.from_columns(cols)
table_hdu.header['EXTNAME'] = 'PIXELS'
table_hdu.header['INHERIT'] = True
# Adding the wcs keywords to the columns and removing from the header
self._add_column_wcs(table_hdu.header, cutout_wcs_dict)
# Adding the extra image keywords
self._add_img_kwds(table_hdu.header)
# Building the aperture HDU
aperture_hdu = fits.ImageHDU(data=aperture)
aperture_hdu.header['EXTNAME'] = 'APERTURE'
self._add_aperture_wcs(aperture_hdu.header, cube_fits[2].header)
aperture_hdu.header['INHERIT'] = True
cutout_hdu_list = fits.HDUList([primary_hdu, table_hdu, aperture_hdu])
self._apply_header_inherit(cutout_hdu_list)
return cutout_hdu_list
def average_adjacent_obs(obslist, tharlist, folder):
obslist_jd = array(obslist[1])
for i in range(len(tharlist[0]) - 1):
obsindx = []
objname = []
for j in range(len(obslist[1])):
fitsname = os.path.basename(obslist[0][j])
print fitsname
### return spectra taken between these two thars
if obslist[1][j] > tharlist[1][i] and obslist[1][j] < tharlist[1][
i + 1] and os.path.exists(folder + "/temp/" + fitsname +
".spec.pkl"):
print 'in the loop', j
objname.append(pyfits.getheader(obslist[0][j])["OBJECT"])
obsindx.append(j)
print 'obsindx', obsindx
if len(obsindx) > 0:
objectlist = unique(objname)
print objectlist
for obj in objectlist:
spectrum_list = []
background_list = []
thar_list = []
spectrum_noflat_list = []
background_noflat_list = []
header_list = []
for i in range(len(
objname)): ### read in all the spectra of that object
if objname[i] == obj:
fitsname = os.path.basename(obslist[0][obsindx[i]])
spec = pickle.load(
open(folder + "/temp/" + fitsname + ".spec.pkl",
"rb"))
header_list.append(
pyfits.getheader(obslist[0][obsindx[i]]))
spectrum_list.append(spec[0])
background_list.append(spec[1])
thar_list.append(spec[2])
spectrum_noflat_list.append(spec[3])
background_noflat_list.append(spec[4])
header, fitsTIME = combine_header(header_list)
print obj, len(spectrum_list)
if len(spectrum_list) == 1:
spectrum_master, background_master, thar_master, spectrum_noflat_master, background_noflat_master = spectrum_list[
0], background_list[0], thar_list[
0], spectrum_noflat_list[
0], background_noflat_list[0]
else:
spectrum_master,background_master,thar_master,spectrum_noflat_master,background_noflat_master = [],[],[],[],[]
for order in range(len(spectrum_list[0])):
order_specs = []
for s in background_list:
order_specs.append(s[order])
#plt.plot(s[order])
#plt.show()
bk_summed = sum(array(order_specs), axis=0)
background_master.append(bk_summed)
order_specs = []
for s in spectrum_list:
order_specs.append(s[order])
spec_summed = combine_order(array(order_specs))
spectrum_master.append(spec_summed) #-bk_summed)
order_specs = []
for s in thar_list:
order_specs.append(s[order])
spec_summed = mean(array(order_specs), axis=0)
thar_master.append(spec_summed)
order_specs = []
for s in background_noflat_list:
order_specs.append(s[order])
bk_summed = sum(array(order_specs), axis=0)
background_noflat_master.append(bk_summed)
order_specs = []
for s in spectrum_noflat_list:
order_specs.append(s[order])
spec_summed = combine_order(array(order_specs))
spectrum_noflat_master.append(
spec_summed) #-bk_summed)
### perform checks on spectrum for nan and infs
def nanchecks(spec):
for i in range(len(spec)):
mask = spec[i] != spec[i]
mask += abs(spec[i]) == inf
try:
print i, len(spec[i]), spec[i], mask, ~any(mask)
indx = arange(len(spec[i]))
if sum(mask) > 0 and sum(mask) < len(spec[i]):
print 'sum(mask)', sum(mask)
for j in indx[mask]:
adjacent_indx = abs(indx - j)
adjacent_indx_mask = adjacent_indx > 0
adjacent_indx_mask *= adjacent_indx < min(
adjacent_indx[invert(
mask)]) + 10 # this line crashes
fixval = nanmean(
spec[i][adjacent_indx_mask])
if fixval != fixval or abs(fixval) == inf:
fixval = nanmedian(spec[i])
spec[i][j] = fixval
elif sum(mask) == len(spec[i]):
print 'sum(mask)', sum(mask), 'setting to 0'
spec[i] = zeros(len(spec[i]))
except:
print 'len0', 'setting to 0'
spec[i] = zeros(2042) # HARDCODED
print
return spec
spectrum_master = nanchecks(spectrum_master)
background_master = nanchecks(background_master)
thar_master = nanchecks(thar_master)
spectrum_noflat_master = nanchecks(spectrum_noflat_master)
background_noflat_master = nanchecks(background_noflat_master)
hduspec = pyfits.PrimaryHDU(array(spectrum_master),
header=header)
hdubk = pyfits.ImageHDU(array(background_master))
hduthar = pyfits.ImageHDU(array(thar_master))
hduspec_nf = pyfits.ImageHDU(array(spectrum_noflat_master))
hdubk_nf = pyfits.ImageHDU(array(background_noflat_master))
hdulist = pyfits.HDUList(
[hduspec, hdubk, hduthar, hduspec_nf, hdubk_nf])
output_name = folder + "/temp/" + "ANU23e_" + obj + "_" + fitsTIME + ".fits"
os.system("rm " + output_name)
hdulist.writeto(output_name, overwrite=True)
print('DONE creating fits files.')
f_date_iso=f_date_start.strftime('%Y-%m-%dT%H:%M:%S.%f')
hdu_dark = fits.PrimaryHDU(dark*s1c_spcal_day_coef)
hdu_dark.header['DATE-OBS']=f_date_iso
hdu_dark.header['BITPIX']=-64
hdu_dark.header['EXPTIME']=f_exp
hdu_dark.header['PROJ-TYP']='TAN'
hdu_dark.header['AMCAL-A0']=az0
hdu_dark.header['AMCAL-H0']=alt0
hdu_dark.header['AMCAL-A']=str(a)
hdu_dark.header['AMCAL-B']=str(b)
hdu_dark.header['AMCAL-C']=str(c)
hdu_dark.header['AMCAL-D']=str(d)
hdu_dark.header['MED-WID1']=avr_width1
hdulist_dark = fits.HDUList([hdu_dark])
hdulist_dark.writeto(spath+fn.split('/')[-1].split('.')[-2]+"_dark.fit",overwrite=True)
hdulist = fits.open(fn,ignore_missing_end=True)
img=hdulist[0].data.astype('float')
#~ img=ss.medfilt((img-masterdark.astype('float'))/masterflat.astype('float') - dark,kernel_size=avr_width2) * s1c_spcal_day_coef
img=ss.medfilt((img-masterdark.astype('float'))/masterflat.astype('float') - dark,kernel_size=avr_width2) * 2.20
hdu_light = fits.PrimaryHDU(img)
hdu_light.header['DATE-OBS']=f_date_iso
hdu_light.header['BITPIX']=-64
hdu_light.header['EXPTIME']=f_exp
hdu_light.header['PROJ-TYP']='TAN'
hdu_light.header['AMCAL-A0']=az0
hdu_light.header['AMCAL-H0']=alt0
hdu_light.header['AMCAL-A']=str(a)
('PHS_OFFS', 0.0, 'Phase offset of bin 0 for gated data'))
table_hdu.header.append(
('NBITS', 8, 'Nr of bits/datum (SEARCH mode "X" data, else 1)'))
table_hdu.header.append(
('NSUBOFFS', nsuboffs, 'Subint offset (Contiguous SEARCH-mode files)'))
table_hdu.header.append(
('NCHAN', chnum / Fdsamp, 'Number of channels/sub-bands in this file'))
table_hdu.header.append(
('CHAN_BW', chan_bw * Fdsamp * 1.0, '[MHz] Channel/sub-band width'))
table_hdu.header.append(
('NCHNOFFS', 0, 'Channel/sub-band offset for split files'))
table_hdu.header.append(('NSBLK', nsblk, 'Samples/row (SEARCH mode, else 1)'))
table_hdu.header.append(
('extname', 'subint ', 'name of this binary table extension'))
hdulist2 = pyfits.HDUList([hdu0, table_hdu])
outname1 = fileroot + '_4pols_' + sys.argv[1] + '_' + sys.argv[
2] + '_' + sys.argv[3] + '_' + sys.argv[4] + '.fits'
rmcomm1 = 'rm -f ' + outname1
os.system(rmcomm1)
print "++++++++++++++++++++++++++++++++++++++++++++++=="
print "out File header:"
hdutmp = hdulist2[1]
datatmp = hdutmp.data
float_datatmp = np.array(datatmp['DATA'])
print float_datatmp.shape
print "++++++++++++++++++++++++++++++++++++++++++++++=="
hdulist2.writeto(outname1)
def to_fits(self):
primary_hdu = fits.PrimaryHDU()
image_hdu = fits.ImageHDU(self.data, self.header)
hdulist = fits.HDUList([primary_hdu, image_hdu])
return hdulist
def write_to_fits(d, ofile, name=None, hdr=None, overwrite=False, checksum=True):
"""
Write the provided object to a fits file.
This is either a convenience wrapper for :func:`write_to_hdu`
that adds a primary HDU and writes the result to the provided
file, or a convenience wrapper for an already formed
`astropy.io.fits.HDUList`_ passed as (``d``).
If the provided file name includes the '.gz' extension, the file
is first written using `astropy.io.fits.HDUList.writeto`_ and
then compressed using :func:`compress_file`.
.. note::
Compressing the file is generally slow, but following the
two-step process of running
`astropy.io.fits.HDUList.writeto`_ and then
:func:`compress_file` is generally faster than having
`astropy.io.fits.HDUList.writeto`_ do the compression,
particularly for files with many extensions (or at least this
was true in the past).
Args:
d (:obj:`dict`, :obj:`list`, `numpy.ndarray`_, `astropy.table.Table`_, `astropy.io.fits.HDUList`_):
Object to write to the HDU. See :func:`write_to_hdu`.
ofile (:obj:`str`):
File name (path) for the fits file.
name (:obj:`str`, optional):
Name for the extension with the data. If None, the
extension is not given a name. However, if the input
object is a dictionary, see :func:`dict_to_hdu` for how
the name will overwrite any dictionary keyword associated
with the data to write.
hdr (`astropy.io.fits.Header`_, optional):
Base-level header to use for *all* HDUs.
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
checksum (:obj:`bool`, optional):
Passed to `astropy.io.fits.HDUList.writeto`_ to add the
DATASUM and CHECKSUM keywords fits header(s).
"""
if os.path.isfile(ofile) and not overwrite:
raise FileExistsError('File already exists; to overwrite, set overwrite=True.')
# Determine if the file should be compressed
_ofile = ofile[:ofile.rfind('.')] if ofile.split('.')[-1] == 'gz' else ofile
_hdr = initialize_header() if hdr is None else hdr.copy()
# Construct the hdus and write the fits file.
fits.HDUList(d if isinstance(d, fits.HDUList) else
[fits.PrimaryHDU(header=_hdr)] + [write_to_hdu(d, name=name, hdr=_hdr)]
).writeto(_ofile, overwrite=True, checksum=checksum)
# Compress the file if the output filename has a '.gz' extension;
# this is slow but still faster than if you have astropy.io.fits do
# it directly
# TODO: use pypmsgs?
if _ofile is not ofile:
print('Compressing file: {0}'.format(_ofile))
compress_file(_ofile, overwrite=True)
print('File written to: {0}'.format(ofile))
def reduce_rawdata():
"""Reduce the Subaru/HDS spectra.
"""
# read obslog and config
config = load_config('HDS\S*\.cfg$')
logtable = load_obslog('\S*\.obslog$', fmt='astropy')
# extract keywords from config file
section = config['data']
rawpath = section.get('rawpath')
section = config['reduce']
midpath = section.get('midpath')
odspath = section.get('odspath')
figpath = section.get('figpath')
mode = section.get('mode')
fig_format = section.get('fig_format')
oned_suffix = section.get('oned_suffix')
ncores = section.get('ncores')
# create folders if not exist
if not os.path.exists(figpath): os.mkdir(figpath)
if not os.path.exists(odspath): os.mkdir(odspath)
if not os.path.exists(midpath): os.mkdir(midpath)
# determine number of cores to be used
if ncores == 'max':
ncores = os.cpu_count()
else:
ncores = min(os.cpu_count(), int(ncores))
############ count different setups #############
setup_lst = {}
for logitem in logtable:
setup = logitem['setup']
objtype = logitem['objtype']
binning = logitem['binning']
if (setup, binning) not in setup_lst:
setup_lst[(setup, binning)] = {}
if objtype not in setup_lst[(setup, binning)]:
setup_lst[(setup, binning)][objtype] = 0
setup_lst[(setup, binning)][objtype] += 1
object_setup_lst = []
for (setup, binning), objtype_lst in sorted(setup_lst.items()):
print('Setup: {} Binning: {}'.format(setup, binning))
count_total = 0
for objtype, count in sorted(objtype_lst.items()):
print(' - {:10s}: {:3d} Frames'.format(objtype, count))
count_total += count
if objtype == 'OBJECT':
object_setup_lst.append((setup, binning))
print(' - {:10s}: {:3d} Frames'.format('Total', count_total))
object_setup_lst = list(set(object_setup_lst))
# loop over different setups and binnings
for sel_setup, sel_binning in object_setup_lst:
print('Selected setup={}; selected binning={}'.format(
sel_setup, sel_binning))
############### parse bias #################
bias_filter = lambda item: item['setup']==sel_setup \
and item['binning']==sel_binning \
and item['objtype']=='BIAS' \
and item['object']=='BIAS' \
and item['nsat_1']<100 \
and item['q95_1']<10000
bias_file = config['reduce.bias'].get('bias_file')
if mode == 'debug' and os.path.exists(bias_file):
pass
else:
bias_data_lst1 = []
bias_data_lst2 = []
bias_card_lst = []
logitem_lst = list(filter(bias_filter, logtable))
# get the number of bias images
n_bias = len(logitem_lst)
if n_bias == 0:
pass
fmt_str = (
' - {:>5s} {:12s} {:12s} {:<7s} {:<7s} {:1s}I2 {:>7}'
' {:<7s} {:5}' # setup, binning
' {:>7} {:>7} {:>5} {:>5}' # nsat_1, nsat_2, q95_1, q95_2
)
head_str = fmt_str.format('FID', 'fileid1', 'fileid2', 'objtype',
'object', '', 'exptime', 'setup',
'binning', 'nsat_1', 'nsat_2', 'q95_1',
'q95_2')
print(head_str)
for ifile, logitem in enumerate(logitem_lst):
fname1 = '{}.fits'.format(logitem['fileid1'])
fname2 = '{}.fits'.format(logitem['fileid2'])
filename1 = os.path.join(rawpath, fname1)
filename2 = os.path.join(rawpath, fname2)
data1, head1 = fits.getdata(filename1, header=True)
data2, head2 = fits.getdata(filename2, header=True)
data1 = parse_image(data1, head1)
data2 = parse_image(data2, head2)
string = fmt_str.format('[{:d}]'.format(logitem['frameid']),
logitem['fileid1'], logitem['fileid2'],
logitem['objtype'], logitem['object'],
logitem['i2'], logitem['exptime'],
logitem['setup'], logitem['binning'],
logitem['nsat_1'], logitem['nsat_2'],
logitem['q95_1'], logitem['q95_2'])
print(print_wrapper(string, logitem))
bias_data_lst1.append(data1)
bias_data_lst2.append(data2)
# append the file information
prefix = 'HIERARCH GAMSE BIAS FILE {:03d}'.format(ifile + 1)
card = (prefix + ' FILEID1', logitem['fileid1'])
bias_card_lst.append(card)
card = (prefix + ' FILEID2', logitem['fileid2'])
bias_card_lst.append(card)
prefix = 'HIERARCH GAMSE BIAS '
bias_card_lst.append((prefix + 'NFILE', n_bias))
# combine bias images
bias_data_lst1 = np.array(bias_data_lst1)
bias_data_lst2 = np.array(bias_data_lst2)
combine_mode = 'mean'
cosmic_clip = section.getfloat('cosmic_clip')
maxiter = section.getint('maxiter')
maskmode = (None, 'max')[n_bias >= 3]
bias_combine1 = combine_images(
bias_data_lst1,
mode=combine_mode,
upper_clip=cosmic_clip,
maxiter=maxiter,
maskmode=maskmode,
ncores=ncores,
)
bias_combine2 = combine_images(
bias_data_lst2,
mode=combine_mode,
upper_clip=cosmic_clip,
maxiter=maxiter,
maskmode=maskmode,
ncores=ncores,
)
bias_card_lst.append((prefix + 'COMBINE_MODE', combine_mode))
bias_card_lst.append((prefix + 'COSMIC_CLIP', cosmic_clip))
bias_card_lst.append((prefix + 'MAXITER', maxiter))
bias_card_lst.append((prefix + 'MASK_MODE', str(maskmode)))
# create the hdu list to be saved
hdu_lst = fits.HDUList()
# create new FITS Header for bias
head = fits.Header()
# pack new card list into header and bias_card_lst
for card in bias_card_lst:
head.append(card)
head['HIERARCH GAMSE FILECONTENT 0'] = 'BIAS COMBINED'
hdu_lst.append(fits.PrimaryHDU(data=bias_combine1, header=head))
hdu_lst.append(fits.ImageHDU(data=bias_combine2, header=head))
# write to FITS file
hdu_lst.writeto(bias_file, overwrite=True)
message = 'Bias image written to "{}"'.format(bias_file)
logger.info(message)
print(message)
############### find flat groups #################
flat_file_str = config['reduce.flat'].get('flat_file')
flat_file = flat_file.format(sel_setup, sel_binning)
if mode == 'debug' and os.path.exists(flat_file):
continue
# pass
else:
filterfunc = lambda item: item['setup']==sel_setup \
and item['binning']==sel_binning \
and item['objtype']=='FLAT' \
and item['object']=='FLAT'
logitem_lst = list(filter(filterfunc, logtable))
fmt_str = (
' - {:>5s} {:12s} {:12s} {:<7s} {:<7s} {:1s}I2 {:>7}'
' {:<7s} {:5} {:8}' # setup, binning, slitsize
' {:>7} {:>7} {:>5} {:>5}' # nsat_1, nsat_2, q95_1, q95_2
)
head_str = fmt_str.format('FID', 'fileid1', 'fileid2', 'objtype',
'object', '', 'exptime', 'setup',
'binning', 'slitsize', 'nsat_1',
'nsat_2', 'q95_1', 'q95_2')
for logitem in logtable:
objtype = logitem['objtype']
objname = logitem['object']
def write_uvfits(self,
filename,
spoof_nonessential=False,
write_lst=True,
force_phase=False,
run_check=True,
check_extra=True,
run_check_acceptability=True):
"""
Write the data to a uvfits file.
Parameters
----------
filename : str
The uvfits file to write to.
spoof_nonessential : bool
Option to spoof the values of optional UVParameters that are not set
but are required for uvfits files.
write_lst : bool
Option to write the LSTs to the metadata (random group parameters).
force_phase : bool
Option to automatically phase drift scan data to zenith of the first
timestamp.
run_check : bool
Option to check for the existence and proper shapes of parameters
before writing the file.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of parameters before
writing the file.
"""
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
if self.phase_type == 'phased':
pass
elif self.phase_type == 'drift':
if force_phase:
print('The data are in drift mode and do not have a '
'defined phase center. Phasing to zenith of the first '
'timestamp.')
phase_time = Time(self.time_array[0], format='jd')
self.phase_to_time(phase_time)
else:
raise ValueError('The data are in drift mode. '
'Set force_phase to true to phase the data '
'to zenith of the first timestamp before '
'writing a uvfits file.')
else:
raise ValueError('The phasing type of the data is unknown. '
'Set the phase_type to drift or phased to '
'reflect the phasing status of the data')
if self.Nfreqs > 1:
freq_spacing = self.freq_array[0, 1:] - self.freq_array[0, :-1]
if not np.isclose(np.min(freq_spacing),
np.max(freq_spacing),
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[1]):
raise ValueError(
'The frequencies are not evenly spaced (probably '
'because of a select operation). The uvfits format '
'does not support unevenly spaced frequencies.')
if not np.isclose(freq_spacing[0],
self.channel_width,
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[1]):
raise ValueError(
'The frequencies are separated by more than their '
'channel width (probably because of a select operation). '
'The uvfits format does not support frequencies '
'that are spaced by more than their channel width.')
freq_spacing = freq_spacing[0]
else:
freq_spacing = self.channel_width
if self.Npols > 1:
pol_spacing = np.diff(self.polarization_array)
if np.min(pol_spacing) < np.max(pol_spacing):
raise ValueError(
'The polarization values are not evenly spaced (probably '
'because of a select operation). The uvfits format '
'does not support unevenly spaced polarizations.')
pol_spacing = pol_spacing[0]
else:
pol_spacing = 1
for p in self.extra():
param = getattr(self, p)
if param.name in self.uvfits_required_extra:
if param.value is None:
if spoof_nonessential:
# spoof extra keywords required for uvfits
if isinstance(param, uvp.AntPositionParameter):
param.apply_spoof(self, 'Nants_telescope')
else:
param.apply_spoof()
setattr(self, p, param)
else:
raise ValueError(
'Required attribute {attribute} '
'for uvfits not defined. Define or '
'set spoof_nonessential to True to '
'spoof this attribute.'.format(attribute=p))
# check for unflagged data with nsample = 0. Warn if any found
wh_nsample0 = np.where(self.nsample_array == 0)
if np.any(~self.flag_array[wh_nsample0]):
warnings.warn('Some unflagged data has nsample = 0. Flags and '
'nsamples are combined in uvfits files such that '
'these data will appear to be flagged.')
weights_array = self.nsample_array * \
np.where(self.flag_array, -1, 1)
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
data_array = np.conj(self.data_array[:, np.newaxis,
np.newaxis, :, :, :, np.newaxis])
weights_array = weights_array[:, np.newaxis, np.newaxis, :, :, :,
np.newaxis]
# uvfits_array_data shape will be (Nblts,1,1,[Nspws],Nfreqs,Npols,3)
uvfits_array_data = np.concatenate(
[data_array.real, data_array.imag, weights_array], axis=6)
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
uvw_array_sec = -1 * self.uvw_array / const.c.to('m/s').value
# jd_midnight = np.floor(self.time_array[0] - 0.5) + 0.5
tzero = np.float32(self.time_array[0])
# uvfits convention is that time_array + relevant PZERO = actual JD
# We are setting PZERO4 = float32(first time of observation)
time_array = np.float32(self.time_array - np.float64(tzero))
int_time_array = self.integration_time
baselines_use = self.antnums_to_baseline(self.ant_1_array,
self.ant_2_array,
attempt256=True)
# Set up dictionaries for populating hdu
# Note that uvfits antenna arrays are 1-indexed so we add 1
# to our 0-indexed arrays
group_parameter_dict = {
'UU ': uvw_array_sec[:, 0],
'VV ': uvw_array_sec[:, 1],
'WW ': uvw_array_sec[:, 2],
'DATE ': time_array,
'BASELINE': baselines_use,
'ANTENNA1': self.ant_1_array + 1,
'ANTENNA2': self.ant_2_array + 1,
'SUBARRAY': np.ones_like(self.ant_1_array),
'INTTIM ': int_time_array
}
pscal_dict = {
'UU ': 1.0,
'VV ': 1.0,
'WW ': 1.0,
'DATE ': 1.0,
'BASELINE': 1.0,
'ANTENNA1': 1.0,
'ANTENNA2': 1.0,
'SUBARRAY': 1.0,
'INTTIM ': 1.0
}
pzero_dict = {
'UU ': 0.0,
'VV ': 0.0,
'WW ': 0.0,
'DATE ': tzero,
'BASELINE': 0.0,
'ANTENNA1': 0.0,
'ANTENNA2': 0.0,
'SUBARRAY': 0.0,
'INTTIM ': 0.0
}
if write_lst:
# lst is a non-standard entry (it's not in the AIPS memo)
# but storing it can be useful (e.g. can avoid recalculating it on read)
# need to store it in 2 parts to get enough accuracy
# angles in uvfits files are stored in degrees, so first convert to degrees
lst_array_deg = np.rad2deg(self.lst_array)
lst_array_1 = np.float32(lst_array_deg)
lst_array_2 = np.float32(lst_array_deg - np.float64(lst_array_1))
group_parameter_dict['LST '] = lst_array_1
pscal_dict['LST '] = 1.0
pzero_dict['LST '] = 0.0
# list contains arrays of [u,v,w,date,baseline];
# each array has shape (Nblts)
parnames_use = ['UU ', 'VV ', 'WW ', 'DATE ']
if (np.max(self.ant_1_array) < 255 and np.max(self.ant_2_array) < 255):
# if the number of antennas is less than 256 then include both the
# baseline array and the antenna arrays in the group parameters.
# Otherwise just use the antenna arrays
parnames_use.append('BASELINE')
parnames_use += ['ANTENNA1', 'ANTENNA2', 'SUBARRAY', 'INTTIM ']
if write_lst:
parnames_use.append('LST ')
group_parameter_list = [
group_parameter_dict[parname] for parname in parnames_use
]
if write_lst:
# add second LST array part
parnames_use.append('LST ')
group_parameter_list.append(lst_array_2)
hdu = fits.GroupData(uvfits_array_data,
parnames=parnames_use,
pardata=group_parameter_list,
bitpix=-32)
hdu = fits.GroupsHDU(hdu)
for i, key in enumerate(parnames_use):
hdu.header['PSCAL' + str(i + 1) + ' '] = pscal_dict[key]
hdu.header['PZERO' + str(i + 1) + ' '] = pzero_dict[key]
# ISO string of first time in self.time_array
hdu.header['DATE-OBS'] = Time(self.time_array[0],
scale='utc',
format='jd').isot
hdu.header['CTYPE2 '] = 'COMPLEX '
hdu.header['CRVAL2 '] = 1.0
hdu.header['CRPIX2 '] = 1.0
hdu.header['CDELT2 '] = 1.0
# Note: This axis is called STOKES to comply with the AIPS memo 117
# However, this confusing because it is NOT a true Stokes axis,
# it is really the polarization axis.
hdu.header['CTYPE3 '] = 'STOKES '
hdu.header['CRVAL3 '] = self.polarization_array[0]
hdu.header['CRPIX3 '] = 1.0
hdu.header['CDELT3 '] = pol_spacing
hdu.header['CTYPE4 '] = 'FREQ '
hdu.header['CRVAL4 '] = self.freq_array[0, 0]
hdu.header['CRPIX4 '] = 1.0
hdu.header['CDELT4 '] = freq_spacing
hdu.header['CTYPE5 '] = 'IF '
hdu.header['CRVAL5 '] = 1.0
hdu.header['CRPIX5 '] = 1.0
hdu.header['CDELT5 '] = 1.0
hdu.header['CTYPE6 '] = 'RA'
hdu.header['CRVAL6 '] = self.phase_center_ra_degrees
hdu.header['CTYPE7 '] = 'DEC'
hdu.header['CRVAL7 '] = self.phase_center_dec_degrees
hdu.header['BUNIT '] = self.vis_units
hdu.header['BSCALE '] = 1.0
hdu.header['BZERO '] = 0.0
hdu.header['OBJECT '] = self.object_name
hdu.header['TELESCOP'] = self.telescope_name
hdu.header['LAT '] = self.telescope_location_lat_lon_alt_degrees[0]
hdu.header['LON '] = self.telescope_location_lat_lon_alt_degrees[1]
hdu.header['ALT '] = self.telescope_location_lat_lon_alt[2]
hdu.header['INSTRUME'] = self.instrument
hdu.header['EPOCH '] = float(self.phase_center_epoch)
if self.phase_center_frame is not None:
hdu.header['PHSFRAME'] = self.phase_center_frame
if self.x_orientation is not None:
hdu.header['XORIENT'] = self.x_orientation
if self.blt_order is not None:
blt_order_str = ', '.join(self.blt_order)
hdu.header['BLTORDER'] = blt_order_str
for line in self.history.splitlines():
hdu.header.add_history(line)
# end standard keywords; begin user-defined keywords
for key, value in self.extra_keywords.items():
# header keywords have to be 8 characters or less
if len(str(key)) > 8:
warnings.warn(
'key {key} in extra_keywords is longer than 8 '
'characters. It will be truncated to 8 as required '
'by the uvfits file format.'.format(key=key))
keyword = key[:8].upper()
if isinstance(value, (dict, list, np.ndarray)):
raise TypeError('Extra keyword {keyword} is of {keytype}. '
'Only strings and numbers are '
'supported in uvfits.'.format(
keyword=key, keytype=type(value)))
if keyword == 'COMMENT':
for line in value.splitlines():
hdu.header.add_comment(line)
else:
hdu.header[keyword] = value
# ADD the ANTENNA table
staxof = np.zeros(self.Nants_telescope)
# 0 specifies alt-az, 6 would specify a phased array
mntsta = np.zeros(self.Nants_telescope)
# beware, X can mean just about anything
poltya = np.full((self.Nants_telescope), 'X', dtype=np.object_)
polaa = [90.0] + np.zeros(self.Nants_telescope)
poltyb = np.full((self.Nants_telescope), 'Y', dtype=np.object_)
polab = [0.0] + np.zeros(self.Nants_telescope)
col1 = fits.Column(name='ANNAME',
format='8A',
array=self.antenna_names)
# AIPS memo #117 says that antenna_positions should be relative to
# the array center, but in a rotated ECEF frame so that the x-axis
# goes through the local meridian.
longitude = self.telescope_location_lat_lon_alt[1]
rot_ecef_positions = uvutils.rotECEF_from_ECEF(self.antenna_positions,
longitude)
col2 = fits.Column(name='STABXYZ',
format='3D',
array=rot_ecef_positions)
# convert to 1-indexed from 0-indexed indicies
col3 = fits.Column(name='NOSTA',
format='1J',
array=self.antenna_numbers + 1)
col4 = fits.Column(name='MNTSTA', format='1J', array=mntsta)
col5 = fits.Column(name='STAXOF', format='1E', array=staxof)
col6 = fits.Column(name='POLTYA', format='1A', array=poltya)
col7 = fits.Column(name='POLAA', format='1E', array=polaa)
# col8 = fits.Column(name='POLCALA', format='3E', array=polcala)
col9 = fits.Column(name='POLTYB', format='1A', array=poltyb)
col10 = fits.Column(name='POLAB', format='1E', array=polab)
# col11 = fits.Column(name='POLCALB', format='3E', array=polcalb)
# note ORBPARM is technically required, but we didn't put it in
col_list = [col1, col2, col3, col4, col5, col6, col7, col9, col10]
if self.antenna_diameters is not None:
col12 = fits.Column(name='DIAMETER',
format='1E',
array=self.antenna_diameters)
col_list.append(col12)
cols = fits.ColDefs(col_list)
ant_hdu = fits.BinTableHDU.from_columns(cols)
ant_hdu.header['EXTNAME'] = 'AIPS AN'
ant_hdu.header['EXTVER'] = 1
# write XYZ coordinates if not already defined
ant_hdu.header['ARRAYX'] = self.telescope_location[0]
ant_hdu.header['ARRAYY'] = self.telescope_location[1]
ant_hdu.header['ARRAYZ'] = self.telescope_location[2]
ant_hdu.header['FRAME'] = 'ITRF'
ant_hdu.header['GSTIA0'] = self.gst0
ant_hdu.header['FREQ'] = self.freq_array[0, 0]
ant_hdu.header['RDATE'] = self.rdate
ant_hdu.header['UT1UTC'] = self.dut1
ant_hdu.header['TIMSYS'] = self.timesys
if self.timesys != 'UTC':
raise ValueError(
'This file has a time system {tsys}. '
'Only "UTC" time system files are supported'.format(
tsys=self.timesys))
ant_hdu.header['ARRNAM'] = self.telescope_name
ant_hdu.header['NO_IF'] = self.Nspws
ant_hdu.header['DEGPDY'] = self.earth_omega
# ant_hdu.header['IATUTC'] = 35.
# set mandatory parameters which are not supported by this object
# (or that we just don't understand)
ant_hdu.header['NUMORB'] = 0
# note: Bart had this set to 3. We've set it 0 after aips 117. -jph
ant_hdu.header['NOPCAL'] = 0
ant_hdu.header['POLTYPE'] = 'X-Y LIN'
# note: we do not support the concept of "frequency setups"
# -- lists of spws given in a SU table.
ant_hdu.header['FREQID'] = -1
# if there are offsets in images, this could be the culprit
ant_hdu.header['POLARX'] = 0.0
ant_hdu.header['POLARY'] = 0.0
ant_hdu.header['DATUTC'] = 0 # ONLY UTC SUPPORTED
# we always output right handed coordinates
ant_hdu.header['XYZHAND'] = 'RIGHT'
# ADD the FQ table
# skipping for now and limiting to a single spw
# write the file
hdulist = fits.HDUList(hdus=[hdu, ant_hdu])
hdulist.writeto(filename, overwrite=True)
indx = eLIER & np.invert((log[1].data['PLATE-IFU'] == '8146-3702')
| (log[1].data['PLATE-IFU'] == '8158-3703'))
mass = log[1].data['MASS_ELL_PETRO'][indx][good_data]
center_mass = np.median(mass)
low_mass = mass <= center_mass
high_mass = mass > center_mass
aligned_low = deltaPA[low_mass] < alignthreshold
misaligned_low = np.invert(aligned_low)
aligned_high = deltaPA[high_mass] < alignthreshold
misaligned_high = np.invert(aligned_high)
hdul = fits.HDUList()
if stack_number == 2:
hdul.append(fits.PrimaryHDU(wave))
# =============================================================================
#
# =============================================================================
if stack_number == 1:
plate = log[1].data['PLATE'][indx][good_data][low_mass][aligned_low]
ifu = log[1].data['IFUDESIGN'][indx][good_data][low_mass][aligned_low]
if stack_number == 2:
plate = log[1].data['PLATE'][indx][good_data][low_mass][misaligned_low]
ifu = log[1].data['IFUDESIGN'][indx][good_data][low_mass][
misaligned_low]
if stack_number == 3:
def condense_pdf1d_files(bname, cur_dir, out_dir, n_sources):
# useful default for the "failure" case of all negative values and log spacing requested
# n_bins_default = 50
# get all the files
pdf1d_files = sorted(glob.glob(cur_dir + "*_pdf1d.fits"))
# loop over the pdf1d files, accumulating the 1d pdfs and bins
with fits.open(pdf1d_files[0]) as hdu:
cond_pdf1d_name = [
hdu[i].header["EXTNAME"] for i in range(1, len(hdu))
]
n_qnames = len(cond_pdf1d_name)
pdf1d_vals = [[] for i in range(n_qnames)]
pdf1d_bins = [[] for i in range(n_qnames)]
for i, cur_pdf1d in enumerate(pdf1d_files):
hdulist = fits.open(cur_pdf1d)
for k in range(n_qnames):
pdf1d_histo = hdulist[k + 1].data
n_cur_source, n_bins = pdf1d_histo.shape
n_cur_source -= 1
# copy the 1D PDFs
pdf1d_vals[k].append(pdf1d_histo[0:n_cur_source, :])
# copy the bin values
pdf1d_bins[k].append(pdf1d_histo[-1, :])
# condense the info into arrays with dimensions [n_stars, max(n_bins), 2]
tot_stars = np.sum(np.array([i.shape[0] for i in pdf1d_vals[0]]))
hdulist = fits.HDUList([fits.PrimaryHDU()])
for k, qname in enumerate(cond_pdf1d_name):
max_bin_length = np.max(
[len(pdf1d_bins[k][i]) for i in range(len(pdf1d_bins[k]))])
# initialize array with NaNs
cond_data = np.zeros((tot_stars, max_bin_length, 2)) + np.nan
# fill in the array
curr_star = 0
for i in range(len(pdf1d_bins[k])):
n_bin = len(pdf1d_bins[k][i])
n_star = pdf1d_vals[k][i].shape[0]
cond_data[curr_star:curr_star + n_star, 0:n_bin,
0] = pdf1d_vals[k][i]
cond_data[curr_star:curr_star + n_star, 0:n_bin,
1] = pdf1d_bins[k][i]
curr_star += n_star
chdu = fits.PrimaryHDU(cond_data)
chdu.header.set("XTENSION", "IMAGE")
chdu.header.set("EXTNAME", qname)
hdulist.append(chdu)
# write the 1D PDFs
hdulist.writeto(out_dir + "/" + bname + "_pdf1d.fits", overwrite=True)
def to_hdulist(self,
hdu=None,
hdu_bands=None,
sparse=False,
format="gadf"):
"""Convert to `~astropy.io.fits.HDUList`.
Parameters
----------
hdu : str
Name or index of the HDU with the map data.
hdu_bands : str
Name or index of the HDU with the BANDS table.
sparse : bool
Sparsify the map by only writing pixels with non-zero
amplitude.
format : {'gadf', 'fgst-ccube','fgst-template'}
FITS format convention.
Returns
-------
hdu_list : `~astropy.io.fits.HDUList`
"""
if sparse:
hdu = "SKYMAP" if hdu is None else hdu.upper()
else:
hdu = "PRIMARY" if hdu is None else hdu.upper()
if sparse and hdu == "PRIMARY":
raise ValueError(
"Sparse maps cannot be written to the PRIMARY HDU.")
if format in ["fgst-ccube", "fgst-template"]:
if self.geom.axes[0].name != "energy" or len(self.geom.axes) > 1:
raise ValueError(
"All 'fgst' formats don't support extra axes except for energy."
)
if self.geom.axes:
hdu_bands_out = self.geom.to_bands_hdu(hdu=hdu_bands,
hdu_skymap=hdu,
format=format)
hdu_bands = hdu_bands_out.name
else:
hdu_bands = None
hdu_out = self.to_hdu(hdu=hdu, hdu_bands=hdu_bands, sparse=sparse)
hdu_out.header["META"] = json.dumps(self.meta)
hdu_out.header["BUNIT"] = self.unit.to_string("fits")
if hdu == "PRIMARY":
hdulist = [hdu_out]
else:
hdulist = [fits.PrimaryHDU(), hdu_out]
if self.geom.axes:
hdulist += [hdu_bands_out]
return fits.HDUList(hdulist)
c=sigma*(sqrt(-2.*log(x)))*(cos(2.*pi*y))
if '-s' in sys.argv and str(data1[j][i]) == 'nan':
data1[j][i]=sky+c
elif str(data2[j][i]) == 'nan':
data1[j][i]=data1[j][i]+c
if '-b' in sys.argv:
for j in range(2,ny-2,1):
for i in range(2,nx-2,1):
if str(data2[j][i]) == 'nan':
avg=data1[j-1][i-1]+data1[j][i-1]+data1[j+1][i-1]
avg=avg+data1[j-1][i]+data1[j][i]+data1[j+1][i]
avg=avg+data1[j-1][i+1]+data1[j][i+1]+data1[j+1][i+1]
data1[j][i]=avg/9.
if '-s' not in sys.argv and '-c' not in sys.argv:
file=pyfits.open(filename)
data2=file[0].data
data3=data2-data1
if os.path.isfile(filename.split('.')[0]+'.stars'): os.remove(filename.split('.')[0]+'.stars')
fitsobj=pyfits.HDUList()
hdu=pyfits.PrimaryHDU()
hdu.header=file[0].header
if '-s' in sys.argv or '-c' in sys.argv:
hdu.data=data1
else:
hdu.data=data3
fitsobj.append(hdu)
fitsobj.writeto(filename.split('.')[0]+'.stars')
def nirspec_cube_pars(tmpdir_factory):
""" Set up the nirspec cube pars reference file """
filename = tmpdir_factory.mktemp('cube_pars')
filename = str(filename.join('nirspec_cube_pars.fits'))
hdu0 = fits.PrimaryHDU()
hdu0.header['REFTYPE'] = 'CUBEPAR'
hdu0.header['INSTRUME'] = 'NIRSPEC'
hdu0.header['MODELNAM'] = 'FM'
hdu0.header['DETECTOR'] = 'N/A'
hdu0.header['EXP_TYPE'] = 'NRS_IFU'
hdu0.header['BAND'] = 'N/A'
hdu0.header['CHANNEL'] = 'N/A'
# make the first extension
disp = np.array([
'PRISM', 'G140M', 'G140M', 'G140H', 'G140H', 'G235M', 'G235H', 'G395M',
'G395H'
])
filt = np.array([
'CLEAR', 'F070LP', 'F100LP', 'F070LP', 'F100LP', 'F170LP', 'F170LP',
'F290LP', 'F290LP'
])
spsize = np.array([0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10])
wsamp = np.array(
[0.005, 0.001, 0.0006, 0.0002, 0.0002, 0.001, 0.0004, 0.0017, 0.0007])
wmin = np.array([0.6, 0.7, 0.97, 0.7, 0.97, 1.66, 1.66, 2.87, 2.87])
wmax = np.array([5.3, 1.27, 1.89, 1.27, 1.89, 3.17, 3.17, 5.27, 5.27])
col1 = fits.Column(name='DISPERSER', format='5A', array=disp)
col2 = fits.Column(name='FILTER', format='6A', array=filt)
col3 = fits.Column(name='WAVEMIN', format='E', array=wmin, unit='micron')
col4 = fits.Column(name='WAVEMAX', format='E', array=wmax, unit='micron')
col5 = fits.Column(name='SPAXELSIZE',
format='E',
array=spsize,
unit='arcsec')
col6 = fits.Column(name='SPECTRALSTEP',
format='D',
array=wsamp,
unit='micron')
hdu1 = fits.BinTableHDU.from_columns([col1, col2, col3, col4, col5, col6])
hdu1.header['EXTNAME'] = 'CUBEPAR'
# make the second extension
disp = np.array([
'PRISM', 'G140M', 'G140M', 'G140H', 'G140H', 'G235M', 'G235H', 'G395M',
'G395H'
])
filt = np.array([
'CLEAR', 'F070LP', 'F100LP', 'F070LP', 'F100LP', 'F170LP', 'F170LP',
'F290LP', 'F290LP'
])
roispat = np.array(
[0.201, 0.202, 0.203, 0.204, 0.205, 0.206, 0.207, 0.208, 0.209])
roispec = np.array(
[0.011, 0.0012, 0.0012, 0.0004, 0.0004, 0.002, 0.0008, 0.003, 0.0012])
power = np.array([2, 2, 2, 2, 2, 2, 2, 2, 2])
softrad = np.array([0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01])
col1 = fits.Column(name='DISPERSER', format='5A', array=disp)
col2 = fits.Column(name='FILTER', format='6A', array=filt)
col3 = fits.Column(name='ROISPATIAL',
format='E',
array=roispat,
unit='arcsec')
col4 = fits.Column(name='ROISPECTRAL',
format='E',
array=roispec,
unit='micron')
col5 = fits.Column(name='POWER', format='I', array=power)
col6 = fits.Column(name='SOFTRAD',
format='E',
array=softrad,
unit='arcsec')
hdu2 = fits.BinTableHDU.from_columns([col1, col2, col3, col4, col5, col6])
hdu2.header['EXTNAME'] = 'CUBEPAR_MSM'
# make the third extension
# Define the multiextension wavelength solution
finalwave = np.arange(0.6, 5.3, 0.1)
nelem = len(finalwave)
# Linear relation of spatial roi with wavelength
roispat = np.ones(nelem) * 0.2
# Linear relation of spectral roi with wavelength
roispec = np.ones(nelem) * 0.01
# Power is 2 at all wavelengths
power = np.ones(nelem, dtype=int) * 2
# Softening radius is 0.01 at all wavelengths
softrad = np.ones(nelem) * 0.01
col1 = fits.Column(name='WAVELENGTH',
format='D',
array=finalwave,
unit='micron')
col2 = fits.Column(name='ROISPATIAL',
format='E',
array=roispat,
unit='arcsec')
col3 = fits.Column(name='ROISPECTRAL',
format='E',
array=roispec,
unit='micron')
col4 = fits.Column(name='POWER', format='I', array=power)
col5 = fits.Column(name='SOFTRAD',
format='E',
array=softrad,
unit='arcsec')
hdu3 = fits.BinTableHDU.from_columns([col1, col2, col3, col4, col5])
hdu3.header['EXTNAME'] = 'MULTICHAN_PRISM_MSM'
# make the 4th extension
# Define the multiextension wavelength solution
finalwave = np.arange(0.7, 7.7, 0.1)
nelem = len(finalwave)
# Linear relation of spatial roi with wavelength
roispat = np.ones(nelem) * 0.2
# Linear relation of spectral roi with wavelength
roispec = np.ones(nelem) * 0.01
# Power is 2 at all wavelengths
power = np.ones(nelem, dtype=int) * 2
# Softening radius is 0.01 at all wavelengths
softrad = np.ones(nelem) * 0.01
col1 = fits.Column(name='WAVELENGTH',
format='D',
array=finalwave,
unit='micron')
col2 = fits.Column(name='ROISPATIAL',
format='E',
array=roispat,
unit='arcsec')
col3 = fits.Column(name='ROISPECTRAL',
format='E',
array=roispec,
unit='micron')
col4 = fits.Column(name='POWER', format='I', array=power)
col5 = fits.Column(name='SOFTRAD',
format='E',
array=softrad,
unit='arcsec')
hdu4 = fits.BinTableHDU.from_columns([col1, col2, col3, col4, col5])
hdu4.header['EXTNAME'] = 'MULTICHAN_MED_MSM'
hdu = fits.HDUList([hdu0, hdu1, hdu2, hdu3, hdu4])
hdu.writeto(filename, overwrite=True)
return filename
