def read_data(path, dtype=np.float64, bin_factor=None, **kargs):
"""
Read SOHO / STEREO data files and output a Data instance
Input :
path : path of the data set
dtype : cast of the data array
kargs : arguments of the data filtering
"""
if not os.path.isdir(path):
raise ValueError('Directory does not exist')
# read files
fnames = os.listdir(path)
files = [pyfits.fitsopen(os.path.join(path, fname))[0] for fname in fnames]
files = filter_files(files, **kargs)
fits_arrays = list()
for f in files:
fits_array = fa.hdu2fitsarray(f)
if bin_factor is not None:
fits_array = fits_array.bin(bin_factor)
fits_array.header['RSUN'] /= bin_factor
update_header(fits_array)
fits_arrays.append(fits_array)
data = fa.infoarrays2infoarray(fits_arrays)
data = data.astype(dtype)
return data
def __new__(subtype,
shape=None,
data=None,
file=None,
ext=0,
dtype=float,
buffer=None,
offset=0,
strides=None,
order=None,
header=None):
# various inputs
if shape is not None:
obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset,
strides, order)
elif data is not None:
obj = np.array(data).view(subtype)
elif file is not None:
fits = pyfits.fitsopen(file)
obj = fits[ext].data.view(subtype)
header = fits[ext].header
dtype = get_dtype(header)
# ensure minimal header is setup
if header is None:
header = pyfits.PrimaryHDU(obj).header
if isinstance(header, dict):
header = dict2header(header)
obj.header = header
enforce_minimal_header(obj)
return obj
def processImage(fn,d,obs_id=None,threshold=2.7,pr=False,ds9cat=None,bysegments=False,stars=[]):
"""Process image, print its FWHM. Works with multi extension images.
"""
ff = pyfits.fitsopen(fn)
rts2 = rts2comm.Rts2Comm()
if d:
d.set('file mosaicimage iraf ' + fn)
sexcols = ['X_IMAGE','Y_IMAGE','MAG_BEST','FLAGS','CLASS_STAR','FWHM_IMAGE','A_IMAGE','B_IMAGE','EXT_NUMBER','FLUX_BEST','BACKGROUND']
c = sextractor.Sextractor(sexcols,threshold=threshold,sexconfig='/home/observer/findfwhm/Sextractor/focus.sex',starnnw='/home/observer/findfwhm/Sextractor/default.nnw')
c.runSExtractor(fn)
c.sortObjects(2)
for st in stars:
# append distance - none and star number - to star list
st.append(None)
st.append(None)
# dump Sextractor to DS9 catalogue
if ds9cat:
cat = open(ds9cat,'w')
cat.write('\t'.join(sexcols) + '\n')
for x in c.objects:
cat.write('\t'.join(map(lambda y:str(y),x)) + '\n')
cat.close()
seg_fwhms = map(lambda x:FWHM(),range(0,len(ff)+1))
for x in c.objects:
if pr:
print '\t'.join(map(lambda y:str(y),x))
segnum = int(x[8])
for st in stars:
if st[0] == segnum:
dist = math.sqrt((x[0]-st[1])**2+(x[1]-st[2])**2)
if st[4] is None or st[4] > dist:
st[4] = dist
st[5] = x
if x[3] == 0 and x[4] != 0:
if d:
d.set('regions','tile {0}\nimage; circle {1} {2} 10 # color=green'.format(segnum,x[0],x[1]))
seg_fwhms[0].addFWHM(x[5],x[6],x[7])
seg_fwhms[segnum].addFWHM(x[5],x[6],x[7])
elif d:
d.set('regions','# tile {0}\nphysical; point {1} {2} # point = x 5 color=red'.format(segnum,x[0],x[1]))
# average results
map(FWHM.average,seg_fwhms)
# default suffix
defsuffix = '_KCAM'
try:
defsuffix = '_' + ff[0].header['CCD_NAME']
except KeyError,er:
pass
def read_fits_array(filename, ext=0):
"""Reads a fits file and output a FitsArray
"""
fits = pyfits.fitsopen(filename)
header = fits[ext].header
dtype = get_dtype(header)
data = fits[ext].data.astype(dtype)
fits_array = FitsArray(data=data, header=header, dtype=dtype)
return fits_array
def update_header(filename, ext, rec, val):
"""
Update value in an fits header
"""
fits = pyfits.fitsopen(filename, "update")
for e in ext:
for r, v in zip(rec, val):
fits[e].header.update(r, v)
fits.flush()
def read_fits_array(filename, ext=0):
"""Reads a fits file and output a FitsArray
"""
fits = pyfits.fitsopen(filename)
header = fits[ext].header
dtype = get_dtype(header)
data = fits[ext].data.astype(dtype)
fits_array = FitsArray(data=data, header=header, dtype=dtype)
return fits_array
def update_header(filename, ext, rec, val):
"""
Update value in an fits header
"""
fits = pyfits.fitsopen(filename, "update")
for e in ext:
for r, v in zip(rec, val):
fits[e].header.update(r, v)
fits.flush()
def median(of,files): """Computes normalized median of files.""" f = pyfits.fitsopen(files[0]) d = numpy.empty([len(files),len(f[0].data),len(f[0].data[0])]) avrg = numpy.mean(f[0].data) d[0] = f[0].data / avrg for x in range(1,len(files)): f = pyfits.fitsopen(files[x]) avrg = numpy.mean(f[0].data) d[x] = f[0].data / avrg if (os.path.exists(of)): print "removing " + of os.unlink(of) f = pyfits.open(of,mode='append') m = numpy.median(d,axis=0) i = pyfits.PrimaryHDU(data=m) f.append(i) print 'writing %s of mean: %f std: %f median: %f' % (of,numpy.mean(m), numpy.std(m), numpy.median(numpy.median(m))) f.close()
def mrdfits(filename, extension=1, transpose=False, verbose=False):
"""
Read a an extended fits file.
Try to emulate mrdfits.pro
Arguments
---------
filename: string
extension: int
transpose: bool
verbose: bool
Returns
-------
A dictionary of arrays
"""
if verbose:
print 'extension = ', extension
# load data
fits = pyfits.fitsopen(filename)
hdu = fits[extension]
data = hdu.data
if not (isinstance(hdu, pyfits.BinTableHDU)
or isinstance(hdu, pyfits.TableHDU)):
# this is an "image"
return data
# else this is a table (ASCII or Binary)
header = hdu.header
# create dictionnary
mydict = dict.fromkeys(data._names)
# beware order is not kept
# fill the dictionnary
for i, name in enumerate(data._names):
mydim = "tdim{0}".format(i + 1)
if header.has_key(mydim):
myshape = header[mydim]
# n = [int(i) for i in header.strip('()').split(',')]
exec('myshape = ' + header[mydim])
# need to reverse shape due to FITS convention
# (quick axis first in FITS and last in Python)
myshape = myshape[::-1]
mydict[name] = data.field(name).reshape(myshape)
if transpose:
mydict[name] = mydict[name].transpose()
else:
mydict[name] = data.field(name).ravel()
#
# check
if verbose:
for k, v in mydict.iteritems():
print k, v[0:1]
#
return mydict
def mrdfits(filename, extension=1, transpose=False, verbose=False):
"""
Read a an extended fits file.
Try to emulate mrdfits.pro
Arguments
---------
filename: string
extension: int
transpose: bool
verbose: bool
Returns
-------
A dictionary of arrays
"""
if verbose:
print 'extension = ', extension
# load data
fits = pyfits.fitsopen(filename)
hdu = fits[extension]
data = hdu.data
if not (isinstance(hdu, pyfits.BinTableHDU)
or isinstance(hdu, pyfits.TableHDU)):
# this is an "image"
return data
# else this is a table (ASCII or Binary)
header = hdu.header
# create dictionnary
mydict = dict.fromkeys(data._names)
# beware order is not kept
# fill the dictionnary
for i, name in enumerate(data._names):
mydim = "tdim{0}".format(i+1)
if header.has_key(mydim):
myshape = header[mydim]
# n = [int(i) for i in header.strip('()').split(',')]
exec('myshape = ' + header[mydim])
# need to reverse shape due to FITS convention
# (quick axis first in FITS and last in Python)
myshape = myshape[::-1]
mydict[name] = data.field(name).reshape(myshape)
if transpose:
mydict[name] = mydict[name].transpose()
else:
mydict[name] = data.field(name).ravel()
#
# check
if verbose:
for k, v in mydict.iteritems():
print k, v[0:1]
#
return mydict
def fitsfilter(filenames, test_str, ext=0):
"""
Filter a list of fits filenames checking a test string.
Arguments:
----------
filenames: (list of strings)
list of string corresponding to fits filenames.
test_str: (string)
a test string of the form :
(a[NAXIS] == 3 and a[NAXIS1] == 1024) or a[NAXIS2] == 2
Returns:
--------
out_files: (list of strings)
list of string corresponding to fits filenames verifying the test.
Notes:
------
The use of
"""
out_files = list()
# parse filenames
for f in filenames:
test = True
try:
a = pyfits.fitsopen(f)[ext].header
# file does not exist
except IOError:
test = False
# extension does not exist
except IndexError:
test = False
if "a" in locals():
try:
test_str = add_quotes_to_str(test_str)
test_str = add_quotes_to_keys(test_str)
exec_str = " ".join(("if", "not", "(" + test_str + "):", "test = False"))
exec (exec_str)
# one of the key does not exist
except KeyError:
test = False
if test:
out_files.append(f)
return out_files
def file_to_config(filename, config_filename=None):
"""
Convert info from header into configuration for srt inversion.
Arguments
---------
filename (str):
The filename of the fits file (output of srt).
config_filename (str, optional):
If provided, the config is saved into this file.
Returns
-------
config (ConfigParser.RawConfigParser instance): A configuration
instance, only if config_filename is not provided.
"""
import pyfits
import ConfigParser
# read data
h = dict(pyfits.fitsopen(filename)[0].header)
# convert to dictionaries
obj_params, data_params, opt_params, mask_params = params_from_header(h)
# generate config
config = ConfigParser.RawConfigParser()
# define sections
config.add_section("object")
config.add_section("data")
config.add_section("masking")
config.add_section("optimization")
# fill in sections
for k in obj_params:
config.set("object", k, obj_params[k])
for k in data_params:
config.set("data", k, data_params[k])
for k in opt_params:
config.set("optimization", k, opt_params[k])
for k in mask_params:
config.set("masking", k, mask_params[k])
if config_filename is not None:
fp = file(config_filename, "w")
config.write(fp)
fp.close()
return config
def file_to_config(filename, config_filename=None):
"""
Convert info from header into configuration for srt inversion.
Arguments
---------
filename (str):
The filename of the fits file (output of srt).
config_filename (str, optional):
If provided, the config is saved into this file.
Returns
-------
config (ConfigParser.RawConfigParser instance): A configuration
instance, only if config_filename is not provided.
"""
import pyfits
import ConfigParser
# read data
h = dict(pyfits.fitsopen(filename)[0].header)
# convert to dictionaries
obj_params, data_params, opt_params, mask_params = params_from_header(h)
# generate config
config = ConfigParser.RawConfigParser()
# define sections
config.add_section("object")
config.add_section("data")
config.add_section("masking")
config.add_section("optimization")
# fill in sections
for k in obj_params:
config.set("object", k, obj_params[k])
for k in data_params:
config.set("data", k, data_params[k])
for k in opt_params:
config.set("optimization", k, opt_params[k])
for k in mask_params:
config.set("masking", k, mask_params[k])
if config_filename is not None:
fp = file(config_filename, "w")
config.write(fp)
fp.close()
return config
def read_data(path, dtype=np.float64, bin_factor=None, **kargs):
"""
Read SOHO / STEREO data files and output a Data instance
Input :
path : path of the data set
dtype : cast of the data array
kargs : arguments of the data filtering
"""
if not os.path.isdir(path):
raise ValueError('Directory does not exist')
# read files
fnames = os.listdir(path)
files = list()
for fname in fnames:
try:
files.append(pyfits.fitsopen(os.path.join(path, fname))[0])
except (IOError):
pass
files = filter_files(files, **kargs)
if len(files) == 0:
print("No file matching.")
return None
for i, f in enumerate(files):
fits_array = fa.hdu2fitsarray(f)
if bin_factor is not None:
fits_array = fits_array.bin(bin_factor)
update_header(fits_array)
fits_array = fits_array.T
if i == 0:
data = fa.InfoArray(fits_array.shape + (len(files), ),
header=[
dict(fits_array.header),
])
data[..., i] = fits_array
if i != 0:
data.header.append(dict(fits_array.header))
# ensure coherent data type
data = data.astype(dtype)
for i in xrange(data.shape[-1]):
data.header[i]['BITPIX'] = fa.bitpix_inv[dtype.__name__]
return data
def filtersort(of,files):
# sort by filters..
flats = {}
for x in files:
f = pyfits.fitsopen(x)
filt = f[0].header['FILTER']
try:
flats[filt].append(x)
except KeyError:
flats[filt] = [x]
print '\rflats ',
for y in flats.keys():
print '%s : %03d' % (y,len(flats[y])),
for x in flats.keys():
print ''
print 'processing filter %s with %d images' % (x,len(flats[x]))
median(of + x + '.fits', flats[x])
def show_fits(filename, ext=None, outfile=None):
# imports
import matplotlib.pylab as mp
import pyfits
# open file
fits = pyfits.fitsopen(filename)
if ext is None:
# display the first image-like extension
for e in xrange(len(fits)):
a = fits[e].data
if a.ndim == 2:
break
else:
a = fits[ext].data
mp.imshow(a)
if outfile is None:
mp.show()
else:
mp.savefig(outfile)
def show_fits(filename, ext=None, outfile=None):
# imports
import matplotlib.pylab as mp
import pyfits
# open file
fits = pyfits.fitsopen(filename)
if ext is None:
# display the first image-like extension
for e in xrange(len(fits)):
a = fits[e].data
if a.ndim == 2:
break
else:
a = fits[ext].data
mp.imshow(a)
if outfile is None:
mp.show()
else:
mp.savefig(outfile)
def read_data(path, dtype=np.float64, bin_factor=None, **kargs):
"""
Read SOHO / STEREO data files and output a Data instance
Input :
path : path of the data set
dtype : cast of the data array
kargs : arguments of the data filtering
"""
if not os.path.isdir(path):
raise ValueError('Directory does not exist')
# read files
fnames = os.listdir(path)
files = list()
for fname in fnames:
try:
files.append(pyfits.fitsopen(os.path.join(path, fname))[0])
except(IOError):
pass
files = filter_files(files, **kargs)
if len(files) == 0:
print("No file matching.")
return None
for i, f in enumerate(files):
fits_array = fa.hdu2fitsarray(f)
if bin_factor is not None:
fits_array = fits_array.bin(bin_factor)
update_header(fits_array)
fits_array = fits_array.T
if i == 0:
data = fa.InfoArray(fits_array.shape + (len(files),), header=[dict(fits_array.header),])
data[..., i] = fits_array
if i != 0:
data.header.append(dict(fits_array.header))
# ensure coherent data type
data = data.astype(dtype)
for i in xrange(data.shape[-1]):
data.header[i]['BITPIX'] = fa.bitpix_inv[dtype.__name__]
return data
def print_header(filename, ext=None, rec=None):
"""
Print header to standard output.
Arguments
---------
fits : string
The filename of a fits file.
ext : optional int (default: None)
The extension number. If not given, print all extensions.
rec: optional string (default: None)
Record names to look up. If not given, print all records.
Returns
-------
Nothing. Prints header to standard output.
"""
# load file
try:
fits = pyfits.fitsopen(filename)
except (IOError):
print("File " + filename + " not a fits file or does not exist.")
return
# check extension
n_ext = len(fits)
if len(ext) == 0:
ext = xrange(n_ext)
# print headers
for e in ext:
if isinstance(ext, xrange) and not n_ext == 1:
print("-" * LINE_WIDTH)
print("Extension number " + str(e))
print("-" * LINE_WIDTH)
# check records
replace_rec = False
if len(rec) == 0:
crec = fits[e].header.keys()
else:
crec = rec
for r in crec:
print(r + " " * (KEY_WIDTH - len(r)) + '\t' +
str(fits[e].header[r]))
def print_header(filename, ext=None, rec=None):
"""
Print header to standard output.
Arguments
---------
fits : string
The filename of a fits file.
ext : optional int (default: None)
The extension number. If not given, print all extensions.
rec: optional string (default: None)
Record names to look up. If not given, print all records.
Returns
-------
Nothing. Prints header to standard output.
"""
# load file
try:
fits = pyfits.fitsopen(filename)
except(IOError):
print("File " + filename + " not a fits file or does not exist.")
return
# check extension
n_ext = len(fits)
if len(ext) == 0:
ext = xrange(n_ext)
# print headers
for e in ext:
if isinstance(ext, xrange) and not n_ext==1:
print("-" * LINE_WIDTH)
print("Extension number " + str(e))
print("-" * LINE_WIDTH)
# check records
replace_rec = False
if len(rec) == 0:
crec = fits[e].header.keys()
else:
crec = rec
for r in crec:
print(r + " " * (KEY_WIDTH - len(r)) +'\t' + str(fits[e].header[r]))
def __new__(subtype, shape=None, data=None, file=None, ext=0, dtype=float, buffer=None, offset=0,
strides=None, order=None, header=None):
# various inputs
if shape is not None:
obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset,
strides, order)
elif data is not None:
obj = np.array(data).view(subtype)
elif file is not None:
fits = pyfits.fitsopen(file)
obj = fits[ext].data.view(subtype)
header = fits[ext].header
dtype = get_dtype(header)
# ensure minimal header is setup
if header is None:
header = pyfits.PrimaryHDU(obj).header
if isinstance(header, dict):
header = dict2header(header)
obj.header = header
enforce_minimal_header(obj)
return obj
#!/usr/bin/python
# Test focusing infrastrucure
import focusing
import sys
import pyfits
from scipy import *
from pylab import *
tries = {}
for fn in sys.argv[1:]:
ff = pyfits.fitsopen(fn)
tries[float(ff[0].header['FOC_POS'])]=fn
f = focusing.Focusing()
b,ftype = f.findBestFWHM(tries)
# for way off-focus, low S/N images..
#b,ftype = f.findBestFWHM(tries,min_stars=10,filterGalaxies=False,threshold=5)
print b
f.plotFit(b,ftype)
def __init__(self, todfile, invnttfile, mapmaskfile, convert, ndetectors,
missing_value=None, comm_tod=None):
# Get information from files
nslices, status = tmf.madmap1_nslices(invnttfile, ndetectors)
if status != 0: raise RuntimeError()
npixels_per_sample, nsamples, ncorrelations, status = tmf.madmap1_info(\
todfile, invnttfile, convert, ndetectors, nslices)
if status != 0: raise RuntimeError()
m=re.search(r'(?P<filename>.*)\[(?P<extname>\w+)\]$', mapmaskfile)
if m is None:
mask = pyfits.fitsopen(mapmaskfile)[0].data
else:
filename = m.group('filename')
extname = m.group('extname')
mask = pyfits.fitsopen(filename)[extname].data
if mask is None:
raise IOError('HDU '+mapmaskfile+' has no data.')
mapmask = np.zeros(mask.shape, dtype='int8')
if missing_value is None:
mapmask[mask != 0] = 1
elif np.isnan(missing_value):
mapmask[np.isnan(mask)] = 1
elif np.isinf(missing_value):
mapmask[np.isinf(mask)] = 1
else:
mapmask[mask == missing_value] = 1
# Store instrument information
self.instrument = Instrument('Unknown', (ndetectors,))
# Store observation information
class MadMap1ObservationInfo(object):
pass
self.info = MadMap1ObservationInfo()
self.info.todfile = todfile
self.info.invnttfile = invnttfile
self.info.ncorrelations = ncorrelations
self.info.npixels_per_sample = npixels_per_sample
self.info.mapmaskfile = mapmaskfile
self.info.convert = convert
self.info.missing_value = missing_value
self.info.mapmask = mapmask
# Store slice information
self.slice = np.recarray(nslices, dtype=[('nsamples_all', int),
('invnttfile', 'S256')])
self.slice.nsamples_all = nsamples
self.slice.nfinesamples = nsamples
self.slice.invnttfile = [invnttfile+'.'+str(i) for i in range(nslices)]
# Store pointing information
self.pointing = np.recarray(np.sum(nsamples), [('removed', np.bool_)])
self.pointing.removed = False
# Store communicator information
self.comm_tod = comm_tod or var.comm_tod
if self.comm_tod.Get_size() > 1:
raise NotImplementedError('The parallelisation of the TOD is not ' \
'implemented')
#labels, nlabels = ndi.measurements.label(roi)
# select biggest region
#region_size = [np.sum(labels == i) for i in xrange(1, nlabels)]
#biggest_index = np.where(region_size == np.max(region_size))[0][0] + 1
#roi = labels == biggest_index
roi = np.ones((192, 192))
# intiat list of radial profiles
phot_tables = []
pix_tables = []
p = []
datas = []
headers = []
for i, filename in enumerate(filenames):
# read data
p.append(pyfits.fitsopen(filename)[0])
datas.append(np.ma.MaskedArray(p[-1].data.T, mask=(1 - roi)))
if i==0:
# backprojection normalize
datas[-1] /= 8
headers.append(p[-1].header)
# run radial_profile
radii, phot_table, pix_table = radial_profile(datas[-1], headers[-1],
center, incl, pa)
phot_tables.append(phot_table)
pix_tables.append(pix_table)
# compute background
bkg_roi = np.zeros(datas[0].shape, dtype=np.bool8)
bkg_roi[0:40, 0:50] = 1.
bkg_roi[0:50, 150:-1] = 1.
#!/usr/bin/env python
import getopt, sys, os
import numpy as np
import pyfits
from pylab import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid.inset_locator import mark_inset
#fname_ext = '/home/nbarbey/data/csh/output/ngc6946_cross_robust.fits'
fname_ext = sys.argv[1]
fname = fname_ext.split('.')[0]
out_fname = fname + '.png'
print('displaying ' + fname)
title_str = fname.split(os.sep)[-1]
t = np.flipud(pyfits.fitsopen(fname_ext)[0].data.T)
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
#imshow(t , interpolation="nearest")
#imshow((t - t.min())) ** .25, interpolation="nearest")
tt = t ** .25
tt[np.isnan(tt)] = 0
extent = [0., 192., 0., 192.]
ax.imshow(tt, extent=extent, interpolation="nearest")
tzoom = tt[135:155, 80:100,]
axins = zoomed_inset_axes(ax, 2, loc=3) # zoom = 6
extent = [80., 100., 192. - 155., 192. - 135, ]
im = axins.imshow(tzoom, extent=extent, interpolation="nearest")
im.set_clim([tt.min(), tt.max()])
def __init__(self,
todfile,
invnttfile,
mapmaskfile,
convert,
ndetectors,
missing_value=None,
comm_tod=None):
# Get information from files
nslices, status = tmf.madmap1_nslices(invnttfile, ndetectors)
if status != 0: raise RuntimeError()
npixels_per_sample, nsamples, ncorrelations, status = tmf.madmap1_info(\
todfile, invnttfile, convert, ndetectors, nslices)
if status != 0: raise RuntimeError()
m = re.search(r'(?P<filename>.*)\[(?P<extname>\w+)\]$', mapmaskfile)
if m is None:
mask = pyfits.fitsopen(mapmaskfile)[0].data
else:
filename = m.group('filename')
extname = m.group('extname')
mask = pyfits.fitsopen(filename)[extname].data
if mask is None:
raise IOError('HDU ' + mapmaskfile + ' has no data.')
mapmask = np.zeros(mask.shape, dtype='int8')
if missing_value is None:
mapmask[mask != 0] = 1
elif np.isnan(missing_value):
mapmask[np.isnan(mask)] = 1
elif np.isinf(missing_value):
mapmask[np.isinf(mask)] = 1
else:
mapmask[mask == missing_value] = 1
# Store instrument information
self.instrument = Instrument('Unknown', (ndetectors, ))
# Store observation information
class MadMap1ObservationInfo(object):
pass
self.info = MadMap1ObservationInfo()
self.info.todfile = todfile
self.info.invnttfile = invnttfile
self.info.ncorrelations = ncorrelations
self.info.npixels_per_sample = npixels_per_sample
self.info.mapmaskfile = mapmaskfile
self.info.convert = convert
self.info.missing_value = missing_value
self.info.mapmask = mapmask
# Store slice information
self.slice = np.recarray(nslices,
dtype=[('nsamples_all', int),
('invnttfile', 'S256')])
self.slice.nsamples_all = nsamples
self.slice.nfinesamples = nsamples
self.slice.invnttfile = [
invnttfile + '.' + str(i) for i in range(nslices)
]
# Store pointing information
self.pointing = np.recarray(np.sum(nsamples), [('removed', np.bool_)])
self.pointing.removed = False
# Store communicator information
self.comm_tod = comm_tod or var.comm_tod
if self.comm_tod.Get_size() > 1:
raise NotImplementedError('The parallelisation of the TOD is not ' \
'implemented')
#!/usr/bin/env python
import pyfits
from pylab import matplotlib
from matplotlib.pyplot import *
from numpy import *
import getopt, sys, os
fname_ext = sys.argv[1]
fname = fname_ext.split(".")[0]
out_fname = fname + ".png"
print("displaying " + fname)
title_str = fname.split(os.sep)[-1]
t = pyfits.fitsopen(fname_ext)[0].data
h = figure()
# imshow(flipud(t.T) , interpolation="nearest")
# imshow(flipud(t.T) ** .25, interpolation="nearest")
imshow(flipud((t - t.min()).T) ** 0.25, interpolation="nearest")
colorbar()
title(title_str)
xlabel("Right Ascension")
ylabel("Declination")
show()
h.savefig(out_fname)
#!/usr/bin/python
# test xy2wcs - compares what we get using ds9 and xy2wcs algorithm
from rts2.astrometry import xy2wcs
import sys
import ds9
import pyfits
import re
d=ds9.ds9('XY2WCS')
for x in sys.argv[1:]:
ff = pyfits.fitsopen(x,'readonly')
fh=ff[0].header
ff.close()
d.set('file {0}'.format(x))
d.set('regions system wcs')
xmax = fh['NAXIS1']
ymax = fh['NAXIS2']
# pixels to check
xy = [[0,0],[0,ymax],[xmax,0],[xmax,ymax],[xmax/2.0,ymax/2.0]]
pmatch = re.compile('point\(([^,]*),([^)]*)\)')
for p in xy:
d.set('regions delete all')
d.set('regions','image; point {0} {1} # point=cross'.format(p[0],p[1]))
r=d.get('regions')
radec = xy2wcs(p[0],p[1],fh)
for l in r.split('\n'):
match = pmatch.match(l)
if match:
#!/usr/bin/python
# test xy2wcs - compares what we get using ds9 and xy2wcs algorithm
from rts2.astrometry import xy2wcs
import sys
import ds9
import pyfits
import re
d = ds9.ds9('XY2WCS')
for x in sys.argv[1:]:
ff = pyfits.fitsopen(x, 'readonly')
fh = ff[0].header
ff.close()
d.set('file {0}'.format(x))
d.set('regions system wcs')
xmax = fh['NAXIS1']
ymax = fh['NAXIS2']
# pixels to check
xy = [[0, 0], [0, ymax], [xmax, 0], [xmax, ymax], [xmax / 2.0, ymax / 2.0]]
pmatch = re.compile('point\(([^,]*),([^)]*)\)')
for p in xy:
d.set('regions delete all')
d.set('regions',
'image; point {0} {1} # point=cross'.format(p[0], p[1]))
r = d.get('regions')
radec = xy2wcs(p[0], p[1], fh)
for l in r.split('\n'):
match = pmatch.match(l)
#!/usr/bin/python
# Test focusing infrastrucure
import focusing
import sys
import pyfits
from scipy import *
from pylab import *
tries = {}
for fn in sys.argv[1:]:
ff = pyfits.fitsopen(fn)
tries[float(ff[0].header['FOC_POS'])] = fn
f = focusing.Focusing()
b, ftype = f.findBestFWHM(tries)
# for way off-focus, low S/N images..
#b,ftype = f.findBestFWHM(tries,min_stars=10,filterGalaxies=False,threshold=5)
print b
f.plotFit(b, ftype)
#!/usr/bin/env python
import numpy as np
import pyfits
import os
import fht
import pylab as pl
from mpl_toolkits.axes_grid import AxesGrid
# data
datadir = os.getenv('CSH_DATA')
filename = datadir + '/../lowfreq/1342182424_blue_level0Frames.fits'
data = pyfits.fitsopen(filename)[1].data[..., 50000:60000]
idx = 2000
im0 = data[..., idx].copy()
m = np.mean(data, axis=-1)
for i in xrange(data.shape[-1]):
data[..., i] -= m
im = data[..., idx]
im_fht = fht.fht(im0)
im_fht[0, 0] = 0.
# figure
fig = pl.figure()
pl.gray()
grid = AxesGrid(fig, 111, nrows_ncols=(3, 1),
axes_pad=0.0,
share_all=True,
# cbar_mode="each",
class TestFailure(Exception): pass
tamasis.var.verbose = True
path = os.path.abspath(os.path.dirname(__file__)) + '/data/madmap1/'
obs = MadMap1Observation(path+'todSpirePsw_be', path+'invnttSpirePsw_be',
path+'madmapSpirePsw.fits[coverage]', 'big_endian',
135, missing_value=np.nan)
obs.instrument.name = 'SPIRE/PSW'
tod = obs.get_tod(unit='Jy/beam')
projection = Projection(obs)
packing = Unpacking(obs.info.mapmask, field=np.nan).T
map_naive = mapper_naive(tod, projection)
map_naive = mapper_naive(tod, projection*packing)
map_ref = pyfits.fitsopen(path+'naivemapSpirePsw.fits')['image'].data
if any_neq(map_naive,map_ref): raise TestFailure('mapper_naive madcap 1')
map_naive_1d = mapper_naive(tod, projection)
map_naive_2d = packing.T(map_naive_1d)
if any_neq(map_naive_2d, map_ref): raise TestFailure('mapper_naive madcap 2')
packing = Unpacking(obs.info.mapmask).T
#M = 1 / map_naive.coverage
#M[~np.isfinite(M)] = np.max(M[np.isfinite(M)])
#map_rlsw1 = mapper_rls(tod, projection*packing, padding.T * fft.T * invNtt * fft * padding, hyper=0, tol=1.e-5, M=M, solver=cg)
#print('Elapsed time: ' + str(map_rlsw1.header['time']))
M = Diagonal(packing(1/map_naive.coverage))
if np.any(~np.isfinite(M.data)):
def processImage(fn,
d,
obs_id=None,
threshold=2.7,
pr=False,
ds9cat=None,
bysegments=False,
stars=[]):
"""Process image, print its FWHM. Works with multi extension images.
"""
ff = pyfits.fitsopen(fn)
rts2 = rts2comm.Rts2Comm()
if d:
d.set('file mosaicimage iraf ' + fn)
sexcols = [
'X_IMAGE', 'Y_IMAGE', 'MAG_BEST', 'FLAGS', 'CLASS_STAR', 'FWHM_IMAGE',
'A_IMAGE', 'B_IMAGE', 'EXT_NUMBER', 'FLUX_BEST', 'BACKGROUND'
]
c = sextractor.Sextractor(
sexcols,
threshold=threshold,
sexconfig='/home/observer/findfwhm/Sextractor/focus.sex',
starnnw='/home/observer/findfwhm/Sextractor/default.nnw')
c.runSExtractor(fn)
c.sortObjects(2)
for st in stars:
# append distance - none and star number - to star list
st.append(None)
st.append(None)
# dump Sextractor to DS9 catalogue
if ds9cat:
cat = open(ds9cat, 'w')
cat.write('\t'.join(sexcols) + '\n')
for x in c.objects:
cat.write('\t'.join(map(lambda y: str(y), x)) + '\n')
cat.close()
seg_fwhms = map(lambda x: FWHM(), range(0, len(ff) + 1))
for x in c.objects:
if pr:
print '\t'.join(map(lambda y: str(y), x))
segnum = int(x[8])
for st in stars:
if st[0] == segnum:
dist = math.sqrt((x[0] - st[1])**2 + (x[1] - st[2])**2)
if st[4] is None or st[4] > dist:
st[4] = dist
st[5] = x
if x[3] == 0 and x[4] != 0:
if d:
d.set(
'regions',
'tile {0}\nimage; circle {1} {2} 10 # color=green'.format(
segnum, x[0], x[1]))
seg_fwhms[0].addFWHM(x[5], x[6], x[7])
seg_fwhms[segnum].addFWHM(x[5], x[6], x[7])
elif d:
d.set(
'regions',
'# tile {0}\nphysical; point {1} {2} # point = x 5 color=red'.
format(segnum, x[0], x[1]))
# average results
map(FWHM.average, seg_fwhms)
# default suffix
defsuffix = '_KCAM'
try:
defsuffix = '_' + ff[0].header['CCD_NAME']
except KeyError, er:
pass
#!/usr/bin/env python
import os
import numpy as np
import pyfits
version = '1.0.3'
filename = os.path.join(os.getenv('HOME'), 'projets', 'tamasis-' + version,
'pacs', 'data', 'PCalPhotometer_BadPixelMask_FM_v5.fits')
# save a backup
os.system('cp ' + filename + ' ' + filename + '.bak')
# load file
fits = pyfits.fitsopen(filename)
# put zeros into data
for f in fits[1:]:
f.data = np.zeros(f.data.shape)
# remove old file
os.system('rm -f ' + filename)
# save file
fits.writeto(filename)
data_path + 'ngc6946_cross_robust_noc.fits',
data_path + 'ngc6946_cross_robust_ca.fits',
data_path + 'ngc6946_cross_robust_cs.fits',
data_path + 'ngc6946_cross_robust.fits',
]
letters = ['a', 'b', 'c', 'd', 'e']
letters = ['(' + l + ')' for l in letters]
markers = ['-o', '-v', '-s', '-p', '-*']
out_fname = data_path + 'ngc6946_cross_robust_line_cut_grid' + '.png'
# create grid
# figure
fig = plt.figure()
labels = ['PL', 'PLI', 'ACI', 'HCI', 'NOC']
labels = ['(' + l + ')' for l in labels]
# loop on each map
for i, fname in enumerate(fname_ext):
data = np.flipud(pyfits.fitsopen(fname)[0].data.T)
if i == 0:
data /= 8.
data[np.isnan(data)] = 0
plt.plot(linear_cut(data, xmin=153, xmax=136, ymin=85, ymax=95, N=20),
markers[i],
label=labels[i])
plt.legend()
plt.show()
fig.savefig(out_fname)
# cleanup
shutil.rmtree(self.odir)
return ret
if __name__ == '__main__':
if len(sys.argv) <= 2:
print 'Usage: %s <odir> <fits filename> <xoffs> <yoffs> <origname> <obs_id> <imgid>' % (sys.argv[0])
sys.exit(1)
a = imgAstrometryScript(sys.argv[2],sys.argv[1])
ra = dec = None
ff=pyfits.fitsopen(sys.argv[2],'readonly')
ra=ff[0].header['RA']
dec=ff[0].header['DEC']
object=ff[0].header['OBJECT']
ff.close()
xoffs = 0
yoffs = 0
if len(sys.argv) >= 5:
xoffs = float(sys.argv[3])
yoffs = float(sys.argv[4])
num = -1
try:
fn = os.path.basename(sys.argv[5])
]
letters = ['a', 'b', 'c', 'd', 'e']
letters = ['(' + l + ')' for l in letters]
out_fname = data_path + 'ngc6946_cross_robust_maps_grid' + '.png'
# create grid
# figure
#plt.gray()
fig = plt.figure()
grid = AxesGrid(fig, 111, nrows_ncols=(3, 2),
axes_pad=0.0,
share_all=True,
)
# loop on each map
for i, fname in enumerate(fname_ext):
data = np.flipud(pyfits.fitsopen(fname)[0].data.T)
if i == 0:
data /= 8.
data = data ** .25
data[np.isnan(data)] = 0
extent = [0., 192., 0., 192.]
im = grid[i].imshow(data, extent=extent, interpolation="nearest")
grid[i].text(10, 170, letters[i], fontsize=20, color="white")
data_zoom = data[135:155, 80:100,]
axins = zoomed_inset_axes(grid[i], 2, loc=3) # zoom = 6
extent = [80., 100., 192. - 155., 192. - 135, ]
im2 = axins.imshow(data_zoom, extent=extent, interpolation="nearest")
im2.set_clim([data.min(), data.max()])
plt.xticks(visible=False)
plt.yticks(visible=False)
im.set_clim([0., .3])
#!/usr/bin/env python
import os
import pyfits
from tamasis import *
import lo
from csh import *
# define data set
datadir = os.getenv('CSH_DATA')
ids = ['1342184518', '1342184519', '1342184596', '1342184597',
'1342184598', '1342184599']
filenames = [os.path.join(datadir, id_str + '_blue_PreparedFrames.fits')
for id_str in ids]
pacs = PacsObservation(filename=filenames,
fine_sampling_factor=1, keep_bad_detectors=False)
# get header from altieri maps
header = pyfits.fitsopen('/mnt/herschel1/mapmaking/nbarbey/Abell2218_altieri/' +
'a2218_red_Map.v2.2.sci.fits')[0].header
# data
tod = pacs.get_tod()
# remove bad pixels (by updating mask !)
#tod = remove_bad_pixels(tod)
# deglitching
projection = Projection(pacs, header=header, resolution=3., npixels_per_sample=6)
tod.mask = deglitch_l2mad(tod, projection)
# model
masking = Masking(tod.mask)
model = masking * projection
# remove drift
tod = filter_median(tod, length=49)
# first map
backmap = model.transpose(tod)
P = lo.aslinearoperator(model.aslinearoperator())
#!/usr/bin/env python
import numpy as np
import pyfits
import os
import fht
import pylab as pl
from mpl_toolkits.axes_grid import AxesGrid
# data
datadir = os.getenv('CSH_DATA')
filename = datadir + '/../lowfreq/1342182424_blue_level0Frames.fits'
data = pyfits.fitsopen(filename)[1].data[..., 50000:60000]
idx = 2000
im0 = data[..., idx].copy()
m = np.mean(data, axis=-1)
for i in xrange(data.shape[-1]):
data[..., i] -= m
im = data[..., idx]
im_fht = fht.fht(im0)
im_fht[0, 0] = 0.
# figure
fig = pl.figure()
pl.gray()
grid = AxesGrid(
fig,
111,
nrows_ncols=(3, 1),
#!/usr/bin/env python
import pyfits
from pylab import matplotlib
from matplotlib.pyplot import *
from numpy import *
import getopt, sys, os
fname_ext = sys.argv[1]
fname = fname_ext.split('.')[0]
out_fname = fname + '.png'
print('displaying ' + fname)
title_str = fname.split(os.sep)[-1]
t = pyfits.fitsopen(fname_ext)[0].data
h = figure()
#imshow(flipud(t.T) , interpolation="nearest")
#imshow(flipud(t.T) ** .25, interpolation="nearest")
imshow(flipud((t - t.min()).T) ** .25, interpolation="nearest")
colorbar()
title(title_str)
xlabel('Right Ascension')
ylabel('Declination')
show()
h.savefig(out_fname)
#labels, nlabels = ndi.measurements.label(roi)
# select biggest region
#region_size = [np.sum(labels == i) for i in xrange(1, nlabels)]
#biggest_index = np.where(region_size == np.max(region_size))[0][0] + 1
#roi = labels == biggest_index
roi = np.ones((192, 192))
# intiat list of radial profiles
phot_tables = []
pix_tables = []
p = []
datas = []
headers = []
for i, filename in enumerate(filenames):
# read data
p.append(pyfits.fitsopen(filename)[0])
datas.append(np.ma.MaskedArray(p[-1].data.T, mask=(1 - roi)))
if i == 0:
# backprojection normalize
datas[-1] /= 8
headers.append(p[-1].header)
# run radial_profile
radii, phot_table, pix_table = radial_profile(datas[-1], headers[-1],
center, incl, pa)
phot_tables.append(phot_table)
pix_tables.append(pix_table)
# compute background
bkg_roi = np.zeros(datas[0].shape, dtype=np.bool8)
bkg_roi[0:40, 0:50] = 1.
bkg_roi[0:50, 150:-1] = 1.
path = os.path.abspath(os.path.dirname(__file__)) + '/data/madmap1/'
obs = MadMap1Observation(path + 'todSpirePsw_be',
path + 'invnttSpirePsw_be',
path + 'madmapSpirePsw.fits[coverage]',
'big_endian',
135,
missing_value=np.nan)
obs.instrument.name = 'SPIRE/PSW'
tod = obs.get_tod(unit='Jy/beam')
projection = Projection(obs)
packing = Unpacking(obs.info.mapmask, field=np.nan).T
map_naive = mapper_naive(tod, projection)
map_naive = mapper_naive(tod, projection * packing)
map_ref = pyfits.fitsopen(path + 'naivemapSpirePsw.fits')['image'].data
if any_neq(map_naive, map_ref): raise TestFailure('mapper_naive madcap 1')
map_naive_1d = mapper_naive(tod, projection)
map_naive_2d = packing.T(map_naive_1d)
if any_neq(map_naive_2d, map_ref): raise TestFailure('mapper_naive madcap 2')
packing = Unpacking(obs.info.mapmask).T
#M = 1 / map_naive.coverage
#M[~np.isfinite(M)] = np.max(M[np.isfinite(M)])
#map_rlsw1 = mapper_rls(tod, projection*packing, padding.T * fft.T * invNtt * fft * padding, hyper=0, tol=1.e-5, M=M, solver=cg)
#print('Elapsed time: ' + str(map_rlsw1.header['time']))
M = Diagonal(packing(1 / map_naive.coverage))
if np.any(~np.isfinite(M.data)):
# define data set
datadir = os.getenv('CSH_DATA')
ids = [
'1342184518', '1342184519', '1342184596', '1342184597', '1342184598',
'1342184599'
]
filenames = [
os.path.join(datadir, id_str + '_blue_PreparedFrames.fits')
for id_str in ids
]
pacs = PacsObservation(filename=filenames,
fine_sampling_factor=1,
keep_bad_detectors=False)
# get header from altieri maps
header = pyfits.fitsopen(
'/mnt/herschel1/mapmaking/nbarbey/Abell2218_altieri/' +
'a2218_red_Map.v2.2.sci.fits')[0].header
# data
tod = pacs.get_tod()
# remove bad pixels (by updating mask !)
#tod = remove_bad_pixels(tod)
# deglitching
projection = Projection(pacs,
header=header,
resolution=3.,
npixels_per_sample=6)
tod.mask = deglitch_l2mad(tod, projection)
# model
masking = Masking(tod.mask)
model = masking * projection
# remove drift
shutil.rmtree(self.odir)
return ret
if __name__ == '__main__':
if len(sys.argv) <= 2:
print 'Usage: %s <odir> <fits filename> <xoffs> <yoffs> <origname> <obs_id> <imgid>' % (
sys.argv[0])
sys.exit(1)
a = imgAstrometryScript(sys.argv[2], sys.argv[1])
ra = dec = None
ff = pyfits.fitsopen(sys.argv[2], 'readonly')
ra = ff[0].header['RA']
dec = ff[0].header['DEC']
object = ff[0].header['OBJECT']
ff.close()
xoffs = 0
yoffs = 0
if len(sys.argv) >= 5:
xoffs = float(sys.argv[3])
yoffs = float(sys.argv[4])
num = -1
try:
fn = os.path.basename(sys.argv[5])