def tst_holy(name, spec_file, lines, wv_cen, disp, score, fidx, test='semi_brute', debug=False):
# Favored parameters (should match those in the defaults)
siglev=20.
#min_ampl=1000.
min_ampl=200.
min_match = 10
# Load spectrum
exten = spec_file.split('.')[-1]
if exten == 'json':
with open(test_arc_path+spec_file,'r') as f:
pypit_fit = json.load(f)
# Allow for new PYPIT output
if '0' in pypit_fit.keys():
pypit_fit = pypit_fit['0']
# Continue
spec = np.array(pypit_fit['spec'])
elif exten == 'hdf5':
hdf = h5py.File(test_arc_path+spec_file,'r')
spec = hdf['arcs/{:d}/spec'.format(fidx)].value
elif exten == 'ascii':
tbl = Table.read(test_arc_path+spec_file, format='ascii')
spec = tbl['flux'].data
else:
pdb.set_trace()
# Run
outroot = outdir+name
#if spec_file == 'deimos_830G_r_PYPIT.json':
# debug=True
if test == 'semi_brute':
best_dict, final_fit = grail.semi_brute(spec, lines, wv_cen, disp, debug=debug,
min_ampl=min_ampl, min_nmatch=min_match, outroot=outroot)
elif test == 'general':
best_dict, final_fit = grail.general(spec, lines, debug=debug,
min_ampl=min_ampl, outroot=outroot)
else:
pdb.set_trace()
# Score
grade = 'PASSED'
if final_fit['rms'] > score['rms']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed RMS!!".format(name))
if len(final_fit['xfit']) < score['nxfit']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed N xfit!!".format(name))
if best_dict['nmatch'] < score['nmatch']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed N match!!".format(name))
# Warn
return grade, best_dict, final_fit
def main(pargs=None):
""" Run
Parameters
----------
args
Returns
-------
"""
import numpy as np
from arclines import io as arcl_io
from arclines.holy import utils as arch_utils
from arclines.holy.grail import semi_brute
from arclines.holy import patterns as arch_patt
from arclines.holy import fitting as arch_fit
if pargs.outroot is None:
pargs.outroot = 'tmp_matches'
# Defaults
min_ampl = (pargs.min_ampl if (pargs.min_ampl is not None) else 100.)
# Load spectrum
spec = arcl_io.load_spectrum(pargs.spectrum)
# Arc lines
lines = pargs.lines.split(',')
# Call brute
semi_brute(spec,
lines,
pargs.wvcen,
pargs.disp,
min_ampl=min_ampl,
debug=pargs.debug,
outroot=pargs.outroot,
do_fit=pargs.fit,
verbose=True)
if pargs.debug:
pdb.set_trace()
def test_holy(name, spec_file, lines, wv_cen, disp, score, fidx):
# Favored parameters (should match those in the defaults)
siglev = 20.
min_ampl = 1000.
min_match = 10
# Load spectrum
exten = spec_file.split('.')[-1]
if exten == 'json':
with open(test_arc_path + spec_file, 'r') as f:
pypit_fit = json.load(f)
spec = np.array(pypit_fit['spec'])
elif exten == 'hdf5':
hdf = h5py.File(test_arc_path + spec_file, 'r')
spec = hdf['arcs/{:d}/spec'.format(fidx)].value
else:
pdb.set_trace()
# Run
outroot = outdir + name
best_dict, final_fit = grail.semi_brute(spec,
lines,
wv_cen,
disp,
siglev=siglev,
min_ampl=min_ampl,
min_nmatch=10,
outroot=outroot)
# Score
grade = 'PASSED'
if final_fit['rms'] > score['rms']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed RMS!!".format(name))
if len(final_fit['xfit']) < score['nxfit']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed N xfit!!".format(name))
if best_dict['nmatch'] < score['nmatch']:
grade = 'FAILED'
warnings.warn("Solution for {:s} failed N match!!".format(name))
# Warn
return grade, best_dict, final_fit
def calib_with_arclines(slf, det, get_poly=False, use_method="general"):
"""Simple calibration algorithm for longslit wavelengths
Uses slf._arcparam to guide the analysis
Parameters
----------
get_poly : bool, optional
Pause to record the polynomial pix = b0 + b1*lambda + b2*lambda**2
Returns
-------
final_fit : dict
Dict of fit info
"""
from arclines.holy.grail import basic, semi_brute, general
# Parameters (just for convenience)
aparm = slf._arcparam[det - 1]
# Extract the arc
msgs.work("Detecting lines")
tampl, tcent, twid, w, satsnd, spec = detect_lines(slf, det,
slf._msarc[det - 1])
if use_method == "semi-brute":
best_dict, final_fit = semi_brute(spec,
aparm['lamps'],
aparm['wv_cen'],
aparm['disp'],
fit_parm=aparm,
min_ampl=aparm['min_ampl'])
elif use_method == "basic":
stuff = basic(spec, aparm['lamps'], aparm['wv_cen'], aparm['disp'])
status, ngd_match, match_idx, scores, final_fit = stuff
else:
# Now preferred
best_dict, final_fit = general(spec,
aparm['lamps'],
fit_parm=aparm,
min_ampl=aparm['min_ampl'])
arqa.arc_fit_qa(slf, final_fit)
#
return final_fit
def main(pargs=None):
""" Run
Parameters
----------
args
Returns
-------
"""
import numpy as np
from matplotlib import pyplot as plt
from linetools import utils as ltu
from arclines import io as arcl_io
from arclines.holy import utils as arch_utils
from arclines.holy.grail import general, semi_brute
from arclines.holy import patterns as arch_patt
from arclines.holy import fitting as arch_fit
if pargs.outroot is None:
pargs.outroot = 'tmp_matches'
# Defaults
# Load spectrum
spec = arcl_io.load_spectrum(pargs.spectrum)
if pargs.show_spec:
plt.clf()
ax = plt.gca()
ax.plot(spec)
plt.show()
# Arc lines
lines = pargs.lines.split(',')
# Call brute
if pargs.brute:
best_dict, final_fit = semi_brute(spec,
lines,
pargs.wvcen,
pargs.disp,
min_ampl=pargs.min_ampl,
debug=pargs.debug,
outroot=pargs.outroot,
do_fit=pargs.fit,
verbose=True)
#best_dict, final_fit = grail.semi_brute(spec, lines, wv_cen, disp, siglev=siglev,
# min_ampl=min_ampl, min_nmatch=min_match, outroot=outroot)
else:
best_dict, final_fit = general(spec,
lines,
do_fit=pargs.fit,
verbose=True,
debug=pargs.debug,
min_ampl=pargs.min_ampl,
outroot=pargs.outroot)
if pargs.debug:
pdb.set_trace()
if pargs.fit:
ltu.savejson(pargs.outroot + '_fit.json',
ltu.jsonify(final_fit),
easy_to_read=True,
overwrite=True)