def __init__(self, fitsfile, ID = 0, quiet = False, clobber = False, cadence = 'lc', **kwargs):
'''
'''
# Read kwargs
self.ID = ID
self._season = 12
self.mission = 'k2'
self.clobber = clobber
self.cadence = cadence
# Initialize preliminary logging
if not quiet:
screen_level = logging.DEBUG
else:
screen_level = logging.CRITICAL
everest.utils.InitLog(None, logging.DEBUG, screen_level, False)
# Load
self.fitsfile = fitsfile
self.model_name = pyfits.getheader(self.fitsfile, 1)['MODEL']
self._weights = None
self.load_fits()
python类getheader()的实例源码
def split_galex_observations(self, original_path, path_fuv, path_nuv):
"""
This function ...
:param original_path:
:param path_fuv:
:param path_nuv:
:return:
"""
# Loop over the files in the path
for path, name in fs.files_in_path(original_path, extension="fits", returns=["path", "name"]):
# Get header
header = getheader(path)
# Check band
band = header["BAND"]
if band == 1: shutil.copy(path, path_fuv)
elif band == 2: shutil.copy(path, path_nuv)
else: raise RuntimeError("Invalid band: " + str(band))
# -----------------------------------------------------------------
def from_file(cls, path):
"""
This function ...
:param path:
:return:
"""
# Get the header and flatten it (remove references to third axis)
header = fits.getheader(path)
header["NAXIS"] = 2
if "NAXIS3" in header: del header["NAXIS3"]
for key in header:
if "PLANE" in key: del header[key]
return cls(header)
# -----------------------------------------------------------------
def from_file(cls, path):
"""
This function ...
:param path:
:return:
"""
# Get the header and flatten it (remove references to third axis)
header = fits.getheader(path)
header["NAXIS"] = 2
if "NAXIS3" in header: del header["NAXIS3"]
for key in header:
if "PLANE" in key: del header[key]
return cls(header)
# -----------------------------------------------------------------
cumulative_distributions.py 文件源码
项目：DR1_analysis
作者: GBTAmmoniaSurvey
项目源码
文件源码
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def regrid_h2(projDir='/media/DATAPART/projects/GAS/testing/',
region_list = ['L1688','NGC1333','B18','OrionA'],
file_extension='DR1_rebase3',
herDir = '/media/DATAPART/projects/GAS/otherData/herschel_ayushi/',
herFile_list=['OphL1688','perseus','Tau_B18','orionA-N']):
for region_i in range(len(region_list)):
region = region_list[region_i]
herFilename = herFile_list[region_i]
herColFits = herDir+'{0}/Colden_temp/{1}_colden_masked.fits'.format(region,herFilename)
nh3ImFits = projDir + '{0}/{0}_NH3_11_{1}_mom0_QA_trim.fits'.format(region,file_extension)
h2_hdu = fits.open(herColFits)
nh3_hdr = fits.getheader(nh3ImFits)
new_h2 = FITS_tools.hcongrid.hcongrid(h2_hdu[0].data,h2_hdu[0].header,nh3_hdr)
new_h2_hdu = fits.PrimaryHDU(new_h2,nh3_hdr)
new_h2_hduList = fits.HDUList([new_h2_hdu])
new_h2_hduList.writeto('nh2_regridded/{0}_NH2_regrid.fits',clobber=True)
def getGLEAMPhase(filename):
"""
copied from ngamsGLEAM_VO_JobPlugin
"""
fileId = os.path.basename(filename)
hdrs = pyfits.getheader(filename)
gleam_phase = 1
getf_frmfn = 0
if 'ORIGIN' in hdrs:
if 'WSClean' in hdrs['ORIGIN']:
gleam_phase = 2
else:
getf_frmfn = 1
if (getf_frmfn == 1 and fileId.split('_v')[1].split('.')[0] == '2'): # filename pattern is brittle, only use it if no fits header key: ORIGIN
gleam_phase = 2
return gleam_phase
def _create_reductions(self):
'''
Detect and create valid reductions in path
'''
print('Create reductions from available data')
wpath = os.walk(self._path)
for w in wpath:
subs = w[1]
if 'raw' in subs:
# if directory has a raw/ sub-directory, make sure it
# has FITS files and that they are from a valid
# sub-system
reduction_path = w[0]
fits_files = glob.glob(os.path.join(reduction_path, 'raw', '*.fits'))
if len(fits_files) != 0:
hdr = fits.getheader(fits_files[0])
try:
arm = hdr['HIERARCH ESO SEQ ARM']
if arm == 'IRDIS':
instrument = 'IRDIS'
reduction = IRDIS.ImagingReduction(reduction_path)
self._IRDIS_reductions.append(reduction)
elif arm == 'IFS':
instrument = 'IFS'
reduction = IFS.Reduction(reduction_path)
self._IFS_reductions.append(reduction)
else:
raise NameError('Unknown arm {0}'.format(arm))
except:
continue
print(reduction_path)
print(' ==> {0}, {1} files'.format(instrument, len(fits_files)))
print()
# merge all reductions into a single list
self._reductions = self._IFS_reductions + self._IRDIS_reductions
def get_frame_names(path):
"""
This function ...
:param path:
:return:
"""
# Load the header
header = fits.getheader(path)
# Get the number of planes
nplanes = headers.get_number_of_frames(header)
# Initialize a dictionary to contain the frame names and corresponding descriptions
frames = dict()
# Look at the properties of each plane
for i in range(nplanes):
# Get name and description of plane
name, description, plane_type = headers.get_frame_name_and_description(header, i, always_call_first_primary=False)
if plane_type == "frame": frames[name] = description
# Return the frames with their name and description
return frames
# -----------------------------------------------------------------
def get_mask_names(path):
"""
This function ...
:param path:
:return:
"""
from ..tools import headers
# Load the header
header = fits.getheader(path)
# Get the number of planes
nplanes = headers.get_number_of_frames(header)
# Initialize a dictionary to contain the mask names and corresponding descriptions
masks = dict()
# Look at the properties of each plane
for i in range(nplanes):
# Get name and description of plane
name, description, plane_type = headers.get_frame_name_and_description(header, i, always_call_first_primary=False)
if plane_type == "mask": masks[name] = description
# Return the masks with their name and description
return masks
# -----------------------------------------------------------------
def get_frame_names(path):
"""
This function ...
:param path:
:return:
"""
# Load the header
header = fits.getheader(path)
# Get the number of planes
nplanes = headers.get_number_of_frames(header)
# Initialize a dictionary to contain the frame names and corresponding descriptions
frames = dict()
# Look at the properties of each plane
for i in range(nplanes):
# Get name and description of plane
name, description, plane_type = headers.get_frame_name_and_description(header, i, always_call_first_primary=False)
if plane_type == "frame": frames[name] = description
# Return the frames with their name and description
return frames
# -----------------------------------------------------------------
def get_mask_names(path):
"""
This function ...
:param path:
:return:
"""
from ..tools import headers
# Load the header
header = fits.getheader(path)
# Get the number of planes
nplanes = headers.get_number_of_frames(header)
# Initialize a dictionary to contain the mask names and corresponding descriptions
masks = dict()
# Look at the properties of each plane
for i in range(nplanes):
# Get name and description of plane
name, description, plane_type = headers.get_frame_name_and_description(header, i, always_call_first_primary=False)
if plane_type == "mask": masks[name] = description
# Return the masks with their name and description
return masks
# -----------------------------------------------------------------
def regrid_h2(nh3_image,h2_image):
# Edit to write out regridded image - glue won't work if files not on same grid
h2fits = fits.open(h2_image)
nh3_hdr = fits.getheader(nh3_image)
new_h2 = FITS_tools.hcongrid.hcongrid(h2fits[0].data,h2fits[0].header,nh3_hdr)
new_h2_hdu = fits.PrimaryHDU(new_h2,nh3_hdr)
return new_h2_hdu
def wcs(galaxy):
return WCS(fits.getheader(galaxy.cubepath, 1))
def _getPlateFromFile(self):
''' Initialize a Plate from a Cube/RSS File'''
# Load file
try:
self._hdr = fits.getheader(self.filename, 1)
self.plateid = int(self._hdr['PLATEID'])
except Exception as e:
raise MarvinError('Could not initialize via filename: {0}'
.format(e))
else:
self.data_origin = 'file'
self._makePdict()
def get_vsini(file_list, vsini_filename=VSINI_FILE):
"""
Get the vsini for every fits file in file_list. Uses the OBJECT keyword and a pre-tabulated vsini table.
This is really only useful for my project...
:param file_list:
:return:
"""
vsini = pd.read_csv(vsini_filename, sep='|', skiprows=8)[1:]
vsini_dict = {}
prim_vsini = []
for fname in file_list:
root = fname.split('/')[-1][:9]
if root in vsini_dict:
prim_vsini.append(vsini_dict[root])
else:
header = fits.getheader(fname)
star = header['OBJECT']
try:
v = vsini.loc[vsini.Identifier.str.strip() == star]['vsini(km/s)'].values[0]
prim_vsini.append(float(v) * 0.8)
vsini_dict[root] = float(v) * 0.8
except IndexError:
logging.warn('No vsini found for star {}! No primary star removal will be attempted!'.format(star))
prim_vsini.append(None)
return prim_vsini
def has_pixelscale(fits_file):
"""
Find pixel scale keywords in FITS file
Parameters
----------
fits_file: str
Path to a FITS image file
"""
header = pyfits.getheader(fits_file)
return [key
for key in PIXSCL_KEYS
if key in list(header.keys())]
def test_write(self, tmpdir, fname, precision, ans):
f = str(tmpdir.join(fname))
self.sp.writefits(f, precision=precision)
hdr = fits.getheader(f, ext=1)
assert hdr['tform2'].lower() == ans
def zoom_fits(fitsfile, scalefactor, preserve_bad_pixels=True, **kwargs):
"""
This function is used to zoom in on a FITS image by interpolating using scipy.ndimage.interpolation.zoom.
It takes the following arguments:
:param fitsfile: the FITS file name
:param scalefactor: the zoom factor along all axes
:param preserve_bad_pixels: try to set NaN pixels to NaN in the zoomed image. Otherwise, bad pixels will be set to
zero.
:param kwargs:
:return:
"""
# Get the data array and the header of the FITS file
arr = pyfits.getdata(fitsfile)
h = pyfits.getheader(fitsfile)
h['CRPIX1'] = (h['CRPIX1']-1)*scalefactor + scalefactor/2. + 0.5
h['CRPIX2'] = (h['CRPIX2']-1)*scalefactor + scalefactor/2. + 0.5
if 'CD1_1' in h:
for ii in (1,2):
for jj in (1,2):
k = "CD%i_%i" % (ii,jj)
if k in h: # allow for CD1_1 but not CD1_2
h[k] = h[k]/scalefactor
elif 'CDELT1' in h:
h['CDELT1'] = h['CDELT1']/scalefactor
h['CDELT2'] = h['CDELT2']/scalefactor
bad_pixels = np.isnan(arr) + np.isinf(arr)
arr[bad_pixels] = 0
upscaled = scipy.ndimage.zoom(arr,scalefactor,**kwargs)
if preserve_bad_pixels:
bp_up = scipy.ndimage.zoom(bad_pixels,scalefactor,mode='constant',cval=np.nan,order=0)
upscaled[bp_up] = np.nan
up_hdu = pyfits.PrimaryHDU(data=upscaled, header=h)
return up_hdu
# -----------------------------------------------------------------
def zoom_fits(fitsfile, scalefactor, preserve_bad_pixels=True, **kwargs):
"""
This function is used to zoom in on a FITS image by interpolating using scipy.ndimage.interpolation.zoom.
It takes the following arguments:
:param fitsfile: the FITS file name
:param scalefactor: the zoom factor along all axes
:param preserve_bad_pixels: try to set NaN pixels to NaN in the zoomed image. Otherwise, bad pixels will be set to
zero.
:param kwargs:
:return:
"""
# Get the data array and the header of the FITS file
arr = pyfits.getdata(fitsfile)
h = pyfits.getheader(fitsfile)
h['CRPIX1'] = (h['CRPIX1']-1)*scalefactor + scalefactor/2. + 0.5
h['CRPIX2'] = (h['CRPIX2']-1)*scalefactor + scalefactor/2. + 0.5
if 'CD1_1' in h:
for ii in (1,2):
for jj in (1,2):
k = "CD%i_%i" % (ii,jj)
if k in h: # allow for CD1_1 but not CD1_2
h[k] = h[k]/scalefactor
elif 'CDELT1' in h:
h['CDELT1'] = h['CDELT1']/scalefactor
h['CDELT2'] = h['CDELT2']/scalefactor
bad_pixels = np.isnan(arr) + np.isinf(arr)
arr[bad_pixels] = 0
upscaled = scipy.ndimage.zoom(arr,scalefactor,**kwargs)
if preserve_bad_pixels:
bp_up = scipy.ndimage.zoom(bad_pixels,scalefactor,mode='constant',cval=np.nan,order=0)
upscaled[bp_up] = np.nan
up_hdu = pyfits.PrimaryHDU(data=upscaled, header=h)
return up_hdu
# -----------------------------------------------------------------
