def save_master(self, data, outfile=None, raw_files=None, steps=None, overwrite=True, extensions=None, names=None):
"""
Args:
data (dict): wv_calib dict
outfile (str, optional):
raw_files: Not used
steps: Not used
overwrite: Not used
extensions: Not used
names: Not used
Returns:
"""
_outfile = self.ms_name if outfile is None else outfile
if os.path.exists(_outfile) and (not overwrite):
msgs.warn("This file already exists. Use overwrite=True to overwrite it")
return
#
msgs.info("Saving master {0:s} frame as:".format(self.frametype) + msgs.newline() + _outfile)
# Wavelength fit(s)
# jsonify has the annoying property that it modifies the objects when it jsonifies them so make a copy,
# which converts lists to arrays, so we make a copy
data_for_json = copy.deepcopy(data)
gddict = linetools.utils.jsonify(data_for_json)
linetools.utils.savejson(_outfile, gddict, easy_to_read=True, overwrite=True)
# Finish
msgs.info("Master {0:s} frame saved successfully:".format(self.frametype) + msgs.newline() + _outfile)
return
def key_list_allowed(v, allowed):
""" Check that a keyword argument is a list. Set the
appropriate type of the list based on the supplied values.
Then, check that each value in the list is also in the
supplied 'allowed' list.
Parameters
----------
v : str
value of a keyword argument
allowed : list
list of allowed values that v can take
Returns
-------
v : list
A value used by the settings dictionary
"""
ll = inspect.currentframe().f_back.f_code.co_name.split('_')
func_name = "'" + " ".join(ll) + "'"
v = key_list(v)
for ll in v:
if ll not in allowed:
msgs.error("The allowed list does not include: {0:s}".format(ll) +
msgs.newline() + "Please choose one of the following:" +
msgs.newline() + ", ".join(allowed) + msgs.newline() +
"for the argument of {0:s}".format(func_name))
return v
def check_deprecated(v, deprecated, upper=False):
""" Check if a keyword argument is deprecated.
Parameters
----------
v : str
value of a keyword argument
deprecated : list
list of deprecated values that v might be
upper : bool (optional)
If True, the allowed list is expected to contain only
uppercase strings. If False, the allowed list is expected
to contain only lowercase strings.
Returns
-------
v : str
A string used by the settings dictionary
"""
ll = inspect.currentframe().f_back.f_code.co_name.split('_')
func_name = "'" + " ".join(ll) + "'"
if upper:
v = v.upper()
else:
v = v.lower()
if v in deprecated:
msgs.error("The argument of {0:s} is deprecated.".format(func_name) +
msgs.newline() + "Please choose one of the following:" +
msgs.newline() + ", ".join(deprecated))
return
def read_coaddfile(ifile):
"""
Read a ``PypeIt`` coadd1d file, akin to a standard PypeIt file.
The top is a config block that sets ParSet parameters. The name of the
spectrograph is required.
Args:
ifile (:obj:`str`):
Name of the coadd file
Returns:
:obj:`tuple`: Three objects are returned: a :obj:`list` with the
configuration entries used to modify the relevant
:class:`~pypeit.par.parset.ParSet` parameters, a :obj:`list` the names
of spec1d files to be coadded, and a :obj:`list` with the object IDs
aligned with each of the spec1d files.
"""
# Read in the pypeit reduction file
msgs.info('Loading the coadd1d file')
lines = par.util._read_pypeit_file_lines(ifile)
is_config = np.ones(len(lines), dtype=bool)
# Parse the fluxing block
spec1dfiles = []
objids_in = []
s, e = par.util._find_pypeit_block(lines, 'coadd1d')
if s >= 0 and e < 0:
msgs.error("Missing 'coadd1d end' in {0}".format(ifile))
elif (s < 0) or (s==e):
msgs.error("Missing coadd1d read or [coadd1d] block in in {0}. Check the input format for the .coadd1d file".format(ifile))
else:
for ctr, line in enumerate(lines[s:e]):
prs = line.split(' ')
spec1dfiles.append(prs[0])
if ctr == 0 and len(prs) != 2:
msgs.error('Invalid format for .coadd1d file.' + msgs.newline() +
'You must have specify a spec1dfile and objid on the first line of the coadd1d block')
if len(prs) > 1:
objids_in.append(prs[1])
is_config[s-1:e+1] = False
# Chck the sizes of the inputs
nspec = len(spec1dfiles)
if len(objids_in) == 1:
objids = nspec*objids_in
elif len(objids_in) == nspec:
objids = objids_in
else:
msgs.error('Invalid format for .flux file.' + msgs.newline() +
'You must specify a single objid on the first line of the coadd1d block,' + msgs.newline() +
'or specify am objid for every spec1dfile in the coadd1d block.' + msgs.newline() +
'Run pypeit_coadd_1dspec --help for information on the format')
# Construct config to get spectrograph
cfg_lines = list(lines[is_config])
# Return
return cfg_lines, spec1dfiles, objids
def load_ordloc(fname):
# Load the files
mstrace_bname, mstrace_bext = os.path.splitext(fname)
lname = mstrace_bname + "_ltrace" + mstrace_bext
rname = mstrace_bname + "_rtrace" + mstrace_bext
# Load the order locations
ltrace = np.array(fits.getdata(lname, 0), dtype=np.float)
msgs.info("Loaded left order locations for frame:" + msgs.newline() +
fname)
rtrace = np.array(fits.getdata(rname, 0), dtype=np.float)
msgs.info("Loaded right order locations for frame:" + msgs.newline() +
fname)
return ltrace, rtrace
def main(args):
""" Runs fluxing steps
"""
# Load the file
config_lines, spec1dfiles, sensfiles_in = read_fluxfile(args.flux_file)
# Read in spectrograph from spec1dfile header
header = fits.getheader(spec1dfiles[0])
spectrograph = load_spectrograph(header['PYP_SPEC'])
# Parameters
spectrograph_def_par = spectrograph.default_pypeit_par()
par = pypeitpar.PypeItPar.from_cfg_lines(
cfg_lines=spectrograph_def_par.to_config(),
merge_with=config_lines)
# Write the par to disk
print("Writing the parameters to {}".format(args.par_outfile))
par.to_config(args.par_outfile)
# Chck the sizes of the inputs
nspec = len(spec1dfiles)
if len(sensfiles_in) == 1:
# Use one sens file for each spec1d
sensfiles = nspec * sensfiles_in
elif len(sensfiles_in) == nspec:
# One sensfile specified / spec1d
sensfiles = sensfiles_in
elif len(sensfiles_in
) == 0 and par['fluxcalib']['use_archived_sens'] == True:
# No sensfile specified, but an archived sensfunc can be used.
sf_archive = SensFileArchive.get_instance(spectrograph.name)
sensfiles = nspec * [
sf_archive.get_archived_sensfile(spec1dfiles[0])
]
else:
msgs.error(
'Invalid format for .flux file.' + msgs.newline() +
'You must specify a single sensfile on the first line of the flux block,'
+ msgs.newline() +
'or specify a sensfile for every spec1dfile in the flux block,'
+ msgs.newline() +
'or specify "use_archived_sens = True" to use an archived sensfile.'
+ msgs.newline() +
'Run pypeit_flux_calib --help for information on the format')
# Instantiate
FxCalib = fluxcalibrate.FluxCalibrate.get_instance(
spec1dfiles, sensfiles, par=par['fluxcalib'], debug=args.debug)
msgs.info('Flux calibration complete')
return 0
def save(self, outfile=None, overwrite=True):
"""
Save the bias master data.
Args:
outfile (:obj:`str`, optional):
Name for the output file. Defaults to
:attr:`file_path`.
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
"""
# Some checks
if self.pypeitImage is None:
msgs.warn('No MasterBias to save!')
return
if not self.pypeitImage.validate():
msgs.warn('MasterBias is not a proper image.')
return
# Proceed
_outfile = self.master_file_path if outfile is None else outfile
# Check if it exists
if os.path.exists(_outfile) and not overwrite:
msgs.warn('Master file exists: {0}'.format(_outfile) + msgs.newline()
+ 'Set overwrite=True to overwrite it.')
return
# Save
hdr = self.build_master_header(steps=self.process_steps, raw_files=self.file_list)
self.pypeitImage.write(_outfile, hdr=hdr, iext='BIAS')
msgs.info('Master frame written to {0}'.format(_outfile))
def get_headarr(self, inp, strict=True):
"""
Read the header data from all the extensions in the file.
Args:
inp (:obj:`str`, `astropy.io.fits.HDUList`_):
Name of the file to read or the previously opened HDU list.
strict (:obj:`bool`, optional):
Function will fault if :func:`fits.getheader` fails to read
any of the headers. Set to False to report a warning and
continue.
Returns:
:obj:`list`: A list of `astropy.io.fits.Header`_ objects with the
extension headers.
"""
# Faster to open the whole file and then assign the headers,
# particularly for gzipped files (e.g., DEIMOS)
if isinstance(inp, str):
try:
hdu = io.fits_open(inp)
except:
if strict:
msgs.error('Problem opening {0}.'.format(inp))
else:
msgs.warn(
'Problem opening {0}.'.format(inp) + msgs.newline() +
'Proceeding, but should consider removing this file!')
return ['None'] * 999 # self.numhead
else:
hdu = inp
return [hdu[k].header for k in range(len(hdu))]
def apply_spectral_flexure(self, shift, sky_spec):
"""
Apply interpolation with the flexure dict
Args:
shift (float):
additive spectral flexure in pixels
sky_spec (`linetools.spectra.xspectrum1d.XSpectrum1D`_):
Sky Spectrum
Returns:
`linetools.spectra.xspectrum1d.XSpectrum1D`_: New sky
spectrum (mainly for QA)
"""
# Simple interpolation to apply
# Apply
for attr in ['BOX', 'OPT']:
if self[attr + '_WAVE'] is not None:
msgs.info(
"Applying flexure correction to {0:s} extraction for object:"
.format(attr) + msgs.newline() +
"{0:s}".format(str(self.NAME)))
self[attr + '_WAVE'] = flexure.flexure_interp(
shift, self[attr + '_WAVE']).copy()
# Shift sky spec too
twave = flexure.flexure_interp(shift,
sky_spec.wavelength.value) * units.AA
new_sky = xspectrum1d.XSpectrum1D.from_tuple((twave, sky_spec.flux))
# Save - since flexure may have been applied/calculated twice, this needs to be additive
self.update_flex_shift(shift, flex_type='local')
# Return
return new_sky
def load_master(self, filename, force=False):
# Does the master file exist?
if not os.path.isfile(filename):
# msgs.warn("No Master frame found of type {:s}: {:s}".format(self.frametype, filename))
msgs.warn("No Master frame found of {:s}".format(filename))
if force:
msgs.error("Crashing out because reduce-masters-force=True:" + msgs.newline() + filename)
return None
else:
# msgs.info("Loading a pre-existing master calibration frame of type: {:}".format(self.frametype) + " from filename: {:}".format(filename))
msgs.info("Loading a pre-existing master calibration frame of SENSFUNC from filename: {:}".format(filename))
hdu = fits.open(filename)
norder = hdu[0].header['NORDER']
sens_dicts = {}
for iord in range(norder):
head = hdu[iord + 1].header
tbl = hdu['SENSFUNC-ORDER{0:04}'.format(iord)].data
sens_dict = {}
sens_dict['wave'] = tbl['WAVE']
sens_dict['sensfunc'] = tbl['SENSFUNC']
for key in ['wave_min', 'wave_max', 'exptime', 'airmass', 'std_file', 'std_ra', 'std_dec',
'std_name', 'cal_file', 'ech_orderindx']:
try:
sens_dict[key] = head[key.upper()]
except:
pass
sens_dicts[str(iord)] = sens_dict
sens_dicts['norder'] = norder
return sens_dicts
def get_headarr(self, filename, strict=True):
"""
Read the header data from all the extensions in the file.
Args:
filename (:obj:`str`):
Name of the file to read.
strict (:obj:`bool`, optional):
Function will fault if :func:`fits.getheader` fails to
read any of the headers. Set to False to report a
warning and continue.
Returns:
list: Returns a list of :attr:`numhead` :obj:`fits.Header`
objects with the extension headers.
"""
# Faster to open the whole file and then assign the headers,
# particularly for gzipped files (e.g., DEIMOS)
try:
hdu = fits.open(filename)
except:
if strict:
msgs.error('Problem opening {0}.'.format(filename))
else:
msgs.warn('Problem opening {0}.'.format(filename) + msgs.newline()
+ 'Proceeding, but should consider removing this file!')
return ['None']*self.numhead
return [ hdu[k].header for k in range(self.numhead) ]
def save_wavelength_calibration(outfile, wv_calib, overwrite=True):
"""
Save a wavelength solution to a file.
Args:
outfile (:obj:`str`):
Name for the output file.
wv_calib (:obj:`dict`):
Dictionary with the wavelength solution. TODO: Document
the format of this dictionary!
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
"""
# Check if it exists
if os.path.exists(outfile) and not overwrite:
# TODO: Should this throw an error instead?
msgs.warn('File exists: {0}'.format(outfile) + msgs.newline()
+ 'Set overwrite=True to overwrite it.')
return
# jsonify has the annoying property that it modifies the objects
# when it jsonifies them so make a copy, which converts lists to
# arrays, so we make a copy
data_for_json = copy.deepcopy(wv_calib)
gddict = linetools.utils.jsonify(data_for_json)
linetools.utils.savejson(outfile, gddict, easy_to_read=True, overwrite=True)
def compound_meta(self, headarr: List[fits.Header], meta_key: str):
"""
Methods to generate meta in a more complex manner than simply
reading from the header.
mjd is converted from UTSHUT header
dispangle is parsed from ANGLE header
Args:
headarr: List[fits.Header]
List of headers
meta_key: str
Returns:
value:
"""
if meta_key == 'mjd':
return Time(headarr[0]['UTSHUT']).mjd
elif meta_key == 'dispangle':
try:
return Angle(headarr[0]['ANGLE']).deg
except Exception as e:
msgs.warn("Could not read dispangle from header:" + msgs.newline() + str(headarr[0]['ANGLE']))
raise e
else:
return None
def save(self, outfile=None, overwrite=True):
"""
Save the alignment traces to a master frame.
Args:
outfile (:obj:`str`, optional):
Name of the output file. Defaults to :attr:`master_file_path`.
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
"""
_outfile = self.master_file_path if outfile is None else outfile
# Check if it exists
if os.path.exists(_outfile) and not overwrite:
msgs.warn('Master file exists: {0}'.format(_outfile) +
msgs.newline() + 'Set overwrite=True to overwrite it.')
return
# Report and save
prihdr = self.build_master_header(steps=self.steps)
# - Add the binning
prihdr['BINNING'] = (self.binning, 'PypeIt: Binning')
# - Add the detector number
prihdr['DET'] = (self.det, 'PypeIt: Detector')
# - Add the tracing parameters
self.par.to_header(prihdr)
# Set the data and extension names
data = [self.align_dict['alignments']]
extnames = ['ALIGNMENTS']
# Write the output to a fits file
save.write_fits(prihdr, data, _outfile, extnames=extnames)
msgs.info('Master frame written to {0}'.format(_outfile))
def compound_meta(self, headarr: List[fits.Header], meta_key: str):
"""
Methods to generate metadata requiring interpretation of the header
data, instead of simply reading the value of a header card.
Args:
headarr (:obj:`list`):
List of `astropy.io.fits.Header`_ objects.
meta_key (:obj:`str`):
Metadata keyword to construct.
Returns:
object: Metadata value read from the header(s).
"""
if meta_key == 'mjd':
return Time(headarr[0]['UTSHUT']).mjd
elif meta_key == 'dispangle':
try:
return Angle(headarr[0]['ANGLE'].lower()).deg
except Exception as e:
msgs.warn("Could not read dispangle from header:" +
msgs.newline() + str(headarr[0]['ANGLE']))
raise e
else:
return None
def func_vander(x, func, deg, minx=None, maxx=None):
if func == "polynomial":
return np.polynomial.polynomial.polyvander(x, deg)
elif func == "legendre":
if minx is None or maxx is None:
if np.size(x) == 1:
xmin, xmax = -1.0, 1.0
else:
xmin, xmax = np.min(x), np.max(x)
else:
xmin, xmax = minx, maxx
xv = 2.0 * (x-xmin)/(xmax-xmin) - 1.0
return np.polynomial.legendre.legvander(xv, deg)
elif func == "chebyshev":
if minx is None or maxx is None:
if np.size(x) == 1:
xmin, xmax = -1.0, 1.0
else:
xmin, xmax = np.min(x), np.max(x)
else:
xmin, xmax = minx, maxx
xv = 2.0 * (x-xmin)/(xmax-xmin) - 1.0
return np.polynomial.chebyshev.chebvander(xv, deg)
else:
msgs.error("Fitting function '{0:s}' is not implemented yet" + msgs.newline() +
"Please choose from 'polynomial', 'legendre', 'chebyshev'")
def load_master(self, filename):
"""
Load a full (all slit) wv_calib dict
Includes converting the JSON lists of particular items into ndarray
Fills self.wv_calib and self.par
Args:
filename (str): Master file
Returns:
dict or None: self.wv_calib
"""
# Does the master file exist?
if not os.path.isfile(filename):
msgs.warn("No Master frame found of type {:s}: {:s}".format(self.frametype, filename))
return None, None
else:
msgs.info("Loading Master {0:s} frame:".format(self.frametype) + msgs.newline() + filename)
self.wv_calib = linetools.utils.loadjson(filename)
# Recast a few items as arrays
for key in self.wv_calib.keys():
if key in ['steps', 'par']: # This isn't really necessary
continue
for tkey in self.wv_calib[key].keys():
if isinstance(self.wv_calib[key][tkey], list):
self.wv_calib[key][tkey] = np.array(self.wv_calib[key][tkey])
# parset
if 'par' in self.wv_calib.keys():
self.par = self.wv_calib['par'].copy()
return self.wv_calib, None
def _read_pypeit_file_lines(ifile):
"""
General parser for a pypeit file.
- Checks that the file exists.
- Reads all the lines in the file
- Removes comments, empty lines, and replaces special characters.
Applies to settings, setup, and user-level reduction files.
Args:
ifile (str): Name of the file to parse.
Returns:
:obj:`numpy.ndarray`: Returns a list of the valid lines in the
files.
"""
# Check the files
if not os.path.isfile(ifile):
msgs.error('The filename does not exist -' + msgs.newline() + ifile)
# Read the input lines and replace special characters
with open(ifile, 'r') as f:
lines = np.array([l.replace('\t', ' ').replace('\n', ' ').strip() \
for l in f.readlines()])
# Remove empty or fully commented lines
lines = lines[np.array([len(l) > 0 and l[0] != '#' for l in lines])]
# Remove appended comments and return
return np.array([l.split('#')[0] for l in lines])
def save(self, outfile=None, overwrite=True):
"""
Save the wavelength calibration data to a master frame.
This is largely a wrapper for
:func:`pypeit.core.wavecal.waveio.save_wavelength_calibration`.
Args:
outfile (:obj:`str`, optional):
Name for the output file. Defaults to
:attr:`file_path`.
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
"""
_outfile = self.master_file_path if outfile is None else outfile
# Check if it exists
if os.path.exists(_outfile) and not overwrite:
msgs.warn('Master file exists: {0}'.format(_outfile) +
msgs.newline() + 'Set overwrite=True to overwrite it.')
return
# Report and save
# jsonify has the annoying property that it modifies the objects
# when it jsonifies them so make a copy, which converts lists to
# arrays, so we make a copy
data_for_json = copy.deepcopy(self.wv_calib)
gddict = linetools.utils.jsonify(data_for_json)
linetools.utils.savejson(_outfile,
gddict,
easy_to_read=True,
overwrite=True)
msgs.info('Master frame written to {0}'.format(_outfile))
def save(self, outfile=None, overwrite=True):
"""
Save the wavelength calibration data to a master frame.
This is largely a wrapper for
:func:`pypeit.core.wavecal.waveio.save_wavelength_calibration`.
Args:
outfile (:obj:`str`, optional):
Name for the output file. Defaults to
:attr:`file_path`.
overwrite (:obj:`bool`, optional):
Overwrite any existing file.
"""
_outfile = self.file_path if outfile is None else outfile
# Check if it exists
if os.path.exists(_outfile) and not overwrite:
msgs.warn('Master file exists: {0}'.format(_outfile) +
msgs.newline() + 'Set overwrite=True to overwrite it.')
return
# Report and save
waveio.save_wavelength_calibration(_outfile,
self.wv_calib,
overwrite=overwrite)
msgs.info('Master frame written to {0}'.format(_outfile))
def load_master(self, filename, exten=0, force=False):
"""
Load the master frame
Args:
filename (str): Master file
exten (int, optinal):
force (bool, optional):
Returns:
dict: Tilts dict
"""
# Does the master file exist?
if not os.path.isfile(filename):
msgs.warn("No Master frame found of type {:s}: {:s}".format(self.frametype, filename))
if force:
msgs.error("Crashing out because reduce-masters-force=True:" + msgs.newline() + filename)
return None, None
else:
msgs.info("Loading a pre-existing master calibration frame of type: {:}".format(self.frametype) + " from filename: {:}".format(filename))
hdu = fits.open(filename)
head0 = hdu[0].header
tilts = hdu[0].data
coeffs = hdu[1].data
slitcen = hdu[2].data
spat_order = hdu[3].data
spec_order = hdu[4].data
tilts_dict = {'tilts':tilts, 'coeffs':coeffs, 'slitcen':slitcen,
'func2d':hdu[0].header['FUNC2D'], 'nslit':hdu[0].header['NSLIT'],
'spat_order':spat_order, 'spec_order':spec_order}
return tilts_dict, head0
def key_none_allowed_filename(v, allowed):
""" Check if a keyword argument is set to None. If not,
check that its value is in the 'allowed' list. Finally,
assume that the supplied value is the name of a filename.
Parameters
----------
v : str
value of a keyword argument
allowed : list
list of allowed values that v can take
Returns
-------
v : None, str
A value used by the settings dictionary
"""
if v.lower() == "none":
v = None
elif v.lower() in allowed:
for i in allowed:
if v.lower() == i:
v = i
break
else:
msgs.info("Assuming the following is the name of a file:" +
msgs.newline() + v)
return v
def get_headarr(self, filename, strict=True):
"""
Read the header data from all the extensions in the file.
Args:
filename (:obj:`str`):
Name of the file to read.
strict (:obj:`bool`, optional):
Function will fault if :func:`fits.getheader` fails to
read any of the headers. Set to False to report a
warning and continue.
Returns:
list: Returns a list of :attr:`numhead` :obj:`fits.Header`
objects with the extension headers.
"""
headarr = ['None'] * self.numhead
for k in range(self.numhead):
try:
headarr[k] = fits.getheader(filename, ext=k)
except:
if strict:
msgs.error("Header error in extension {0} in {1}.".format(
k, filename))
else:
msgs.warn('Bad header in extension {0} in {1}'.format(
k, filename) + msgs.newline() +
'Proceeding on the hopes this was a ' +
'calibration file, otherwise consider removing.')
return headarr
def key_none_allowed(v, allowed):
""" Check if a keyword argument is set to None. If not,
check that its value is in the 'allowed' list.
Parameters
----------
v : str
value of a keyword argument
allowed : list
list of allowed values that v can take
Returns
-------
v : None, str
A value used by the settings dictionary
"""
ll = inspect.currentframe().f_back.f_code.co_name.split('_')
func_name = "'" + " ".join(ll) + "'"
if v.lower() == "none":
v = None
elif v.lower() in allowed:
for i in allowed:
if v.lower() == i:
v = i
break
else:
msgs.error("The argument of {0:s} must be one of".format(func_name) +
msgs.newline() + ", ".join(allowed))
return v
def key_allowed(v, allowed, upper=False):
""" Check that a keyword argument is in an allowed list of parameters.
Parameters
----------
v : str
value of a keyword argument
allowed : list
list of allowed values that v can take
upper : bool (optional)
If True, the allowed list is expected to contain only
uppercase strings. If False, the allowed list is expected
to contain only lowercase strings.
Returns
-------
v : str
A string used by the settings dictionary
"""
ll = inspect.currentframe().f_back.f_code.co_name.split('_')
func_name = "'" + " ".join(ll) + "'"
if upper:
v = v.upper()
else:
v = v.lower()
if v not in allowed:
msgs.error("The argument of {0:s} must be one of".format(func_name) +
msgs.newline() + ", ".join(allowed))
if v.lower() == "none":
v = None
return v
def load_tree(polygon=4, numsearch=20):
"""
Load a KDTree of ThAr patterns that is stored on disk
Parameters
----------
polygon : int
Number of sides to the polygon used in pattern matching:
- polygon=3 --> trigon (two anchor lines and one floating line)
- polygon=4 --> tetragon (two anchor lines and two floating lines)
- polygon=5 --> pentagon (two anchor lines and three floating lines)
numsearch : int
Number of consecutive detected lines used to generate a pattern.
For example, if numsearch is 4, then for a trigon, the following
patterns will be generated (assuming line #1 is the left
anchor):
- 1 2 3 (in this case line #3 is the right anchor)
- 1 2 4 (in this case line #4 is the right anchor)
- 1 3 4 (in this case line #4 is the right anchor)
Returns
-------
file_load : KDTree instance
The KDTree containing the patterns
index : ndarray
For each pattern in the KDTree, this array stores the
corresponding index in the linelist
"""
# TODO: Can we save these as fits files instead?
# TODO: Please don't use imports within functions
import pickle
filename = os.path.join(
data.Paths.linelist,
f'ThAr_patterns_poly{polygon}_search{numsearch}.kdtree')
fileindx = os.path.join(
data.Paths.linelist,
f'ThAr_patterns_poly{polygon}_search{numsearch}.index.npy')
try:
file_load = pickle.load(open(filename, 'rb'))
index = np.load(fileindx)
except FileNotFoundError:
msgs.info(
'The requested KDTree was not found on disk' + msgs.newline() +
'please be patient while the ThAr KDTree is built and saved to disk.'
)
from pypeit.core.wavecal import kdtree_generator
file_load, index = kdtree_generator.main(polygon,
numsearch=numsearch,
verbose=True,
ret_treeindx=True,
outname=filename)
return file_load, index
def insufficient_frame_error(frametype):
msgs.error('Insufficient {0} frames found. Include more frames, '.format(
frametype) + 'reduce the required amount by setting' + msgs.newline() +
'[calibrations]' + msgs.newline() +
' [[{0}frame]]'.format(frametype) + msgs.newline() +
' number = XX' + msgs.newline() +
'in the pypeit file, or specify a specific' +
'pixelflat file by setting' + msgs.newline() +
'[calibrations]' + msgs.newline() +
' [[{0}frame]]'.format(frametype) + msgs.newline() +
' useframe = XX' + msgs.newline() + 'in the pypeit file')
def func_der(coeffs, func, nderive=1):
if func == "polynomial":
return np.polynomial.polynomial.polyder(coeffs, m=nderive)
elif func == "legendre":
return np.polynomial.legendre.legder(coeffs, m=nderive)
elif func == "chebyshev":
return np.polynomial.chebyshev.chebder(coeffs, m=nderive)
else:
msgs.error("Functional derivative '{0:s}' is not implemented yet"+msgs.newline() +
"Please choose from 'polynomial', 'legendre', 'chebyshev'")
def key_keyword(v, force_format=True):
""" Check that a keyword argument satisfies the form required
for specifying a header keyword.
Parameters
----------
v : str
value of a keyword argument
force_format : bool
If True, v can only have the format of a header keyword.
If False, v can take the form of a header keyword, or a
user-specified value. In the latter case, the boolean
variable 'valid' is returned so the parent function
knows if the supplied value of v should be dealt with as
a manual entry or as a header keyword.
Returns
-------
v : str
A value used by the settings dictionary
valid : bool
Is the input a valid header keyword format
"""
ll = inspect.currentframe().f_back.f_code.co_name.split('_')
func_name = "'" + " ".join(ll) + "'"
if v.lower() == "none":
v = None
else:
valid = is_keyword(v)
if not valid and force_format:
msgs.error(
"The argument of {0:s} must be of the form:".format(
func_name) + msgs.newline() + "##.NAME" + msgs.newline() +
"where ## is the fits extension (see command: fits headext##),"
+ msgs.newline() + "and NAME is the header keyword name")
elif valid and force_format:
return v
else:
return v, valid
return v
def get_pc(data, k, tol=0.0, maxiter=20, nofix=False, noortho=False):
p = data.shape[0]
if p == 0:
msgs.error("You need to supply more components in the PCA")
#n = np.size(data)/p
if k > p:
debugger.set_trace()
msgs.error(
"The number of principal components must be less than or equal" +
msgs.newline() + "to the order of the fitting function")
# Set the initial conditions
eigv = np.zeros((p, k))
eigv[:k, :k] = np.identity(k)
niter = 0
diff = tol * 2.0 + 1.0
while (niter < maxiter) and (diff > tol):
hidden = np.dot(np.dot(np.linalg.inv(np.dot(eigv.T, eigv)), eigv.T),
data)
oldeigv = eigv.copy()
eigv = np.dot(
data, np.dot(hidden.T, np.linalg.inv(np.dot(hidden, hidden.T))))
if tol > 0.0:
diff = 0.0
for i in range(k):
diff += np.abs(
1.0 - np.sum(oldeigv[:, i] * eigv[:, i]) /
np.sqrt(np.sum(oldeigv[:, i]**2) * np.sum(eigv[:, i]**2)))
niter += 1
# Orthonormalize?
if not noortho:
for b in range(k):
# Orthogonalize
for bp in range(b):
dot = np.sum(eigv[:, b] * eigv[:, bp])
eigv[:, b] -= dot * eigv[:, bp]
# Normalize
dot = np.sum(eigv[:, b]**2)
dot = 1.0 / np.sqrt(dot)
eigv[:, b] *= dot
# Project variables onto new coordinates?
if not nofix:
hidden = np.dot(eigv.T, data)
eval_hidden, evec_hidden = do_pca(hidden.T, cov=True)
eigv = np.dot(eigv, evec_hidden.T)
hidden = np.dot(eigv.T, data)
return eigv, hidden