def handle_create(outname=None, filelist=[], plot_filt=False):
"""Create standard star correction. Units are erg/s/cm2/Ang
Read in the std-correction vector for each standard star, massage
the correction (see below) and output an ensemble correction.
Args:
outname (str): name for resulting correction array (def: atm-corr.npy)
filelist (list): list of standard star extractions (e.g. sp_STD-\*.npy)
plot_filt (bool): set to plot intermediate filtered steps (def: False)
Returns:
dict: A dictionary containing the ensemble correction and some
details::
{ "nm": wavelengths (nm),
"maxnm": maximum wavelength,
"correction": the correction spectrum,
"doc": "Correct ph/10 m/nm to erg/s/cm2/ang",
"Nspec": number of spectra used,
"correction_std": STDev of ensemble corrections,
"outname": outname (see Parameters),
"files": filelist (see Parameters),
"when": timestamp string,
"user": user who ran the process }
Note:
Writes a \*.npy file with the resulting correction
"""
if outname is None:
outname = 'atm-corr.npy'
ll = Wavelength.fiducial_spectrum()
corrs = []
corr_vals = []
legend = [
"corr",
]
filt_legend = ["orig"]
maxnm = 915.
for ifile in filelist:
""" Filename is sp_STD-nnnnn_obs*.npy """
# Try to read input file
try:
data = np.load(ifile)[0]
except:
raise Exception("Not able to load %s. " % ifile)
# Check quality of extraction
if data["quality"] > 0:
print "Bad std extraction in %s" % ifile
continue
# Check for calculated correction
if "std-correction" not in data.keys():
print "No std-correction vector in %s" % ifile
continue
correction = data['std-correction']
# What was the maximum wavelength?
if "std-maxnm" in data.keys():
if data['std-maxnm'] > maxnm:
maxnm = data['std-maxnm']
# Convert file named sp_STD-nnnn_obs* to a name compaitable
# with Standards.py. First strip of "sp_STD-"
pred = ifile[7:]
pred = pred.split("_")[0]
legend.append(pred)
pred = pred.lower().replace("+", "").replace("-", "_")
print ifile, pred, pred in Stds.Standards
# Are we in our list of standard stars?
if pred not in Stds.Standards:
print(
"File named '%s' is reduced to '%s' and no such standard "
"seems to exist." % (ifile, pred))
continue
# Record median correction in ROI
roi = (ll > 600) & (ll < 850)
corr_vals.append(np.median(correction[roi]))
# Normalize each correction by the median value
correction /= corr_vals[-1]
corrs.append(correction)
# END: for ifile in filelist:
# Rescale each correction and scale to erg/s/cm2/Ang
corrs = np.array(corrs)
erg_s_cm2_ang = corrs * np.median(corr_vals) * 1e-16
# Take the median of the correction vectors
the_corr = scipy.stats.nanmedian(erg_s_cm2_ang, 0)
# Fit red end unless we are calibrated out there
if not np.isfinite(the_corr).all() and maxnm < 1000.0:
print "Fitting red end"
# Fit polynomial to extrapolate correction
redend = (ll > 880) & (ll < maxnm)
ss = np.poly1d(np.polyfit(ll[redend], the_corr[redend], 2))
# Insert extrapolation back into correction vector
redend = (ll > maxnm)
the_corr[redend] = ss(ll[redend])
# Plot data, if requested
if plot_filt:
pl.figure(2)
pl.clf()
pl.grid(True)
pl.ylim(1e-20, 1e-15)
pl.semilogy(ll, the_corr, linewidth=1)
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
"""
# Now clean over the Balmer series (skip this for now)
balmers = [656.3, 486.1, 434.0, 410.2, 397.0]
#balmers = [656.3, 486.1, 434.0]
for balmer in balmers:
#pl.figure(3)
line_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.01)[0])
broad_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.04)[0])
broad_ll = list(broad_ROI)
broad_ll.sort()
#pl.plot(ll[broad_ll], the_corr[broad_ll])
around_line_ROI = list(broad_ROI - line_ROI)
#pl.plot(ll[around_line_ROI], the_corr[around_line_ROI],'^')
fit = np.poly1d(np.polyfit(ll[around_line_ROI],
the_corr[around_line_ROI], 5))
to_fix = list(line_ROI)
#the_corr[to_fix] = fit(ll[to_fix])
#pl.plot(ll[to_fix], the_corr[to_fix])
#pl.show()
"""
# Plot intermediate correction
if plot_filt:
pl.semilogy(ll, the_corr * 4., linewidth=2)
filt_legend.append("Balmer*4")
# Filter correction to remove spectral features and leave response alone
the_corr = scipy.signal.savgol_filter(the_corr, 9, 5)
# Plot intermediate correction
if plot_filt:
pl.semilogy(ll, the_corr * 2., linewidth=2)
filt_legend.append("Filtered*2")
pl.semilogy(ll, the_corr, linewidth=2)
filt_legend.append("Filtered")
pl.legend(filt_legend)
pl.show()
# Plot data
pl.figure(1)
pl.clf()
pl.grid(True)
pl.ylim(1e-20, 1e-15)
pl.semilogy(ll, the_corr, linewidth=4)
for ix, e in enumerate(erg_s_cm2_ang):
pl.semilogy(ll, e * corr_vals[ix] / np.mean(corr_vals))
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
pl.legend(legend)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
pl.savefig("Standard_Correction.pdf")
print("Mean cor: %10.3g, Sigma cor: %10.3g" %
(np.mean(corr_vals) * 1e-16, np.std(corr_vals) * 1e-16))
maxnm = np.min([maxnm, np.max(ll)])
print "Max nm: %7.2f" % maxnm
# Construct result
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
res = {
"nm": ll,
"maxnm": maxnm,
"correction": the_corr,
"doc": "Correct ph/10 m/nm to erg/s/cm2/ang",
"Nspec": len(corrs),
"correction_std": np.nanstd(erg_s_cm2_ang, 0),
"outname": outname,
"files": filelist,
"when": '%s' % datetime.datetime.now(),
"user": os.getlogin()
}
np.save(outname, [res])
return res
def handle_create(outname=None, filelist=[]):
''' Create standard star correction. Units are erg/s/cm2/Ang '''
if outname is None: outname = 'atm-corr.npy'
ll = Wavelength.fiducial_spectrum()
spectra = []
corrs = []
corr_vals = []
for file in filelist:
''' Filename is STD-nnnnn_obs* '''
try:
data = np.load(file)[0]
except:
raise Exception(
"Not passed a spectrum file, the file should start with spectrum_"
)
# Convert file named sp_STD-nnnn_obs* to a name compaitable
# with Standards.py
pred = file.lstrip("spectrum_STD-")
pred = pred.lstrip("sp_STD-")
pred = pred.split("_")[0]
pred = pred.lower().replace("+", "").replace("-", "_")
print file, pred, pred in SS.Standards
if pred not in SS.Standards:
print "File named '%s' is reduced to '%s' and no such standard seems to exist." % (
file, pred)
continue
ff = interp1d(data['nm'], data['ph_10m_nm'], bounds_error=False)
spectra.append(ff(ll))
# Now process the standard
standard = SS.Standards[pred]
wav = standard[:, 0] / 10.0
flux = standard[:, 1]
std_ff = interp1d(wav, flux, bounds_error=False, fill_value=np.nan)
correction = std_ff(ll) / ff(ll)
ROI = (ll > 600) & (ll < 850)
corr_vals.append(np.median(correction[ROI]))
correction /= corr_vals[-1]
corrs.append(correction)
corrs = np.array(corrs)
erg_s_cm2_ang = corrs * np.median(corr_vals) * 1e-16
roi = (ll > 900) & (ll < 1000)
the_corr = scipy.stats.nanmedian(erg_s_cm2_ang, 0)
if not np.isfinite(the_corr).all():
# Fit polynomial to extrapolate correction
redend = (ll > 800) & (ll < 915)
ss = np.poly1d(np.polyfit(ll[redend], the_corr[redend], 2))
redend = (ll > 910)
the_corr[redend] = ss(ll[redend])
# Now clean over the Balmer series
balmers = [656.3, 486.1, 434.0, 410.2, 397.0]
for balmer in balmers:
eps = balmer * 0.02
line_ROI = sets.Set(np.where(np.abs((ll - balmer) / balmer) < 0.03)[0])
broad_ROI = sets.Set(
np.where(np.abs((ll - balmer) / balmer) < 0.06)[0])
around_line_ROI = list(broad_ROI - line_ROI)
fit = np.poly1d(
np.polyfit(ll[around_line_ROI], the_corr[around_line_ROI], 1))
to_fix = list(line_ROI)
the_corr[to_fix] = fit(ll[to_fix])
the_corr = scipy.signal.savgol_filter(the_corr, 3, 1)
# Plot data
pl.figure(1)
pl.clf()
pl.grid(True)
pl.ylim(1e-20, 1e-15)
pl.semilogy(ll, the_corr, linewidth=4)
for ix, e in enumerate(erg_s_cm2_ang):
pl.semilogy(ll, e * corr_vals[ix] / np.mean(corr_vals))
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
pl.savefig("Standard_Correction.pdf")
print np.mean(corr_vals) * 1e-16, np.std(corr_vals) * 1e-16
# Construct result
res = {
"nm": ll,
"correction": the_corr,
"doc": "Correct ph/10 m/nm to erg/2/cm2/ang",
"Nspec": len(corrs),
"correction_std": np.nanstd(erg_s_cm2_ang, 0),
"outname": outname,
"files": filelist,
"when": '%s' % datetime.datetime.now(),
"user": os.getlogin()
}
np.save(outname, [res])
return res
def handle_create(outname=None, filelist=[], plot_filt=False):
"""Create standard star correction. Units are erg/s/cm2/Ang
Read in the std-correction vector for each standard star, massage
the correction (see below) and output an ensemble correction.
Args:
outname (str): name for resulting correction array (def: atm-corr.npy)
filelist (list): list of standard star extractions (e.g. sp_STD-\*.npy)
plot_filt (bool): set to plot intermediate filtered steps (def: False)
Returns:
dict: A dictionary containing the ensemble correction and some
details::
{ "nm": wavelengths (nm),
"maxnm": maximum wavelength,
"correction": the correction spectrum,
"doc": "Correct ph/10 m/nm to erg/s/cm2/ang",
"Nspec": number of spectra used,
"correction_std": STDev of ensemble corrections,
"outname": outname (see Parameters),
"files": filelist (see Parameters),
"when": timestamp string,
"user": user who ran the process }
Note:
Writes a \*.npy file with the resulting correction
"""
if outname is None:
outname = 'atm-corr.npy'
ll = Wavelength.fiducial_spectrum()
corrs = []
corr_vals = []
legend = ["corr", ]
filt_legend = ["orig"]
maxnm = 915.
for ifile in filelist:
""" Filename is sp_STD-nnnnn_obs*.npy """
# Try to read input file
try:
data = np.load(ifile)[0]
except:
raise Exception("Not able to load %s. " % ifile)
# Check quality of extraction
if data["quality"] > 0:
print "Bad std extraction in %s" % ifile
continue
# Check for calculated correction
if "std-correction" not in data.keys():
print "No std-correction vector in %s" % ifile
continue
correction = data['std-correction']
# What was the maximum wavelength?
if "std-maxnm" in data.keys():
if data['std-maxnm'] > maxnm:
maxnm = data['std-maxnm']
# Convert file named sp_STD-nnnn_obs* to a name compaitable
# with Standards.py. First strip of "sp_STD-"
pred = ifile[7:]
pred = pred.split("_")[0]
legend.append(pred)
pred = pred.lower().replace("+", "").replace("-", "_")
print ifile, pred, pred in Stds.Standards
# Are we in our list of standard stars?
if pred not in Stds.Standards:
print("File named '%s' is reduced to '%s' and no such standard "
"seems to exist." % (ifile, pred))
continue
# Record median correction in ROI
roi = (ll > 600) & (ll < 850)
corr_vals.append(np.median(correction[roi]))
# Normalize each correction by the median value
correction /= corr_vals[-1]
corrs.append(correction)
# END: for ifile in filelist:
# Rescale each correction and scale to erg/s/cm2/Ang
corrs = np.array(corrs)
erg_s_cm2_ang = corrs * np.median(corr_vals) * 1e-16
# Take the median of the correction vectors
the_corr = scipy.stats.nanmedian(erg_s_cm2_ang, 0)
# Fit red end unless we are calibrated out there
if not np.isfinite(the_corr).all() and maxnm < 1000.0:
print "Fitting red end"
# Fit polynomial to extrapolate correction
redend = (ll > 880) & (ll < maxnm)
ss = np.poly1d(np.polyfit(ll[redend], the_corr[redend], 2))
# Insert extrapolation back into correction vector
redend = (ll > maxnm)
the_corr[redend] = ss(ll[redend])
# Plot data, if requested
if plot_filt:
pl.figure(2)
pl.clf()
pl.grid(True)
pl.ylim(1e-20, 1e-15)
pl.semilogy(ll, the_corr, linewidth=1)
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
"""
# Now clean over the Balmer series (skip this for now)
balmers = [656.3, 486.1, 434.0, 410.2, 397.0]
#balmers = [656.3, 486.1, 434.0]
for balmer in balmers:
#pl.figure(3)
line_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.01)[0])
broad_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.04)[0])
broad_ll = list(broad_ROI)
broad_ll.sort()
#pl.plot(ll[broad_ll], the_corr[broad_ll])
around_line_ROI = list(broad_ROI - line_ROI)
#pl.plot(ll[around_line_ROI], the_corr[around_line_ROI],'^')
fit = np.poly1d(np.polyfit(ll[around_line_ROI],
the_corr[around_line_ROI], 5))
to_fix = list(line_ROI)
#the_corr[to_fix] = fit(ll[to_fix])
#pl.plot(ll[to_fix], the_corr[to_fix])
#pl.show()
"""
# Plot intermediate correction
if plot_filt:
pl.semilogy(ll, the_corr * 4., linewidth=2)
filt_legend.append("Balmer*4")
# Filter correction to remove spectral features and leave response alone
the_corr = scipy.signal.savgol_filter(the_corr, 9, 5)
# Plot intermediate correction
if plot_filt:
pl.semilogy(ll, the_corr * 2., linewidth=2)
filt_legend.append("Filtered*2")
pl.semilogy(ll, the_corr, linewidth=2)
filt_legend.append("Filtered")
pl.legend(filt_legend)
pl.show()
# Plot data
pl.figure(1)
pl.clf()
pl.grid(True)
pl.ylim(1e-20, 1e-15)
pl.semilogy(ll, the_corr, linewidth=4)
for ix, e in enumerate(erg_s_cm2_ang):
pl.semilogy(ll, e * corr_vals[ix] / np.mean(corr_vals))
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
pl.legend(legend)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
pl.savefig("Standard_Correction.pdf")
print("Mean cor: %10.3g, Sigma cor: %10.3g" %
(np.mean(corr_vals) * 1e-16, np.std(corr_vals) * 1e-16))
maxnm = np.min([maxnm, np.max(ll)])
print "Max nm: %7.2f" % maxnm
# Construct result
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
res = {
"nm": ll,
"maxnm": maxnm,
"correction": the_corr,
"doc": "Correct ph/10 m/nm to erg/s/cm2/ang",
"Nspec": len(corrs),
"correction_std": np.nanstd(erg_s_cm2_ang, 0),
"outname": outname,
"files": filelist,
"when": '%s' % datetime.datetime.now(),
"user": os.getlogin()
}
np.save(outname, [res])
return res
def handle_create(outname=None, filelist=[]):
''' Create standard star correction. Units are erg/s/cm2/Ang '''
if outname is None: outname='atm-corr.npy'
ll = Wavelength.fiducial_spectrum()
spectra = []
corrs = []
corr_vals =[]
for file in filelist:
''' Filename is STD-nnnnn_obs* '''
try: data = np.load(file)[0]
except:
raise Exception("Not passed a spectrum file, the file should start with spectrum_")
# Convert file named sp_STD-nnnn_obs* to a name compaitable
# with Standards.py
pred = file.lstrip("spectrum_STD-")
pred = pred.lstrip("sp_STD-")
pred = pred.split("_")[0]
pred = pred.lower().replace("+","").replace("-","_")
print file, pred, pred in SS.Standards
if pred not in SS.Standards:
print "File named '%s' is reduced to '%s' and no such standard seems to exist." % (file, pred)
continue
ff = interp1d(data['nm'], data['ph_10m_nm'], bounds_error=False)
spectra.append(ff(ll))
# Now process the standard
standard = SS.Standards[pred]
wav = standard[:,0]/10.0
flux = standard[:,1]
std_ff = interp1d(wav, flux, bounds_error=False, fill_value=np.nan)
correction = std_ff(ll)/ff(ll)
ROI = (ll > 600) & (ll < 850)
corr_vals.append(np.median(correction[ROI]))
correction /= corr_vals[-1]
corrs.append(correction)
corrs = np.array(corrs)
erg_s_cm2_ang = corrs * np.median(corr_vals) * 1e-16
roi = (ll > 900) & (ll < 1000)
the_corr = scipy.stats.nanmedian(erg_s_cm2_ang,0)
if not np.isfinite(the_corr).all():
# Fit polynomial to extrapolate correction
redend = (ll > 800) & (ll < 915)
ss = np.poly1d(np.polyfit(ll[redend], the_corr[redend], 2))
redend = (ll>910)
the_corr[redend] = ss(ll[redend])
# Now clean over the Balmer series
balmers = [656.3, 486.1, 434.0, 410.2, 397.0]
for balmer in balmers:
eps = balmer * 0.02
line_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.03)[0])
broad_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.06)[0])
around_line_ROI = list(broad_ROI - line_ROI)
fit = np.poly1d(np.polyfit(ll[around_line_ROI],
the_corr[around_line_ROI], 1))
to_fix = list(line_ROI)
the_corr[to_fix] = fit(ll[to_fix])
the_corr = scipy.signal.savgol_filter(the_corr, 3, 1)
# Plot data
pl.figure(1)
pl.clf()
pl.grid(True)
pl.ylim(1e-20,1e-15)
pl.semilogy(ll, the_corr, linewidth=4)
for ix,e in enumerate(erg_s_cm2_ang):
pl.semilogy(ll, e*corr_vals[ix]/np.mean(corr_vals))
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
pl.savefig("Standard_Correction.pdf")
print np.mean(corr_vals) * 1e-16, np.std(corr_vals)*1e-16
# Construct result
res = {"nm": ll,
"correction": the_corr,
"doc": "Correct ph/10 m/nm to erg/2/cm2/ang",
"Nspec": len(corrs),
"correction_std": np.nanstd(erg_s_cm2_ang,0),
"outname": outname,
"files": filelist,
"when": '%s' % datetime.datetime.now(),
"user": os.getlogin()
}
np.save(outname, [res])
return res
def handle_create(outname=None, filelist=[], plot_filt=False):
''' Create standard star correction. Units are erg/s/cm2/Ang '''
if outname is None: outname='atm-corr.npy'
ll = Wavelength.fiducial_spectrum()
corrs = []
corr_vals =[]
legend=["corr",]
filt_legend=["orig"]
maxnm = 915.
for file in filelist:
''' Filename is sp_STD-nnnnn_obs*.npy '''
# Try to read input file
try: data = np.load(file)[0]
except:
raise Exception("Not passed a spectrum file, the file should start with sp_")
# Check for calculated correction
if "std-correction" not in data.keys():
print "No std-correction vector in %s" % file
continue
correction = data['std-correction']
# What was the maximum wavelength?
if "std-maxnm" in data.keys():
if data['std-maxnm'] > maxnm: maxnm = data['std-maxnm']
# Convert file named sp_STD-nnnn_obs* to a name compaitable
# with Standards.py
pred = file.lstrip("sp_STD-")
pred = pred.split("_")[0]
legend.append(pred)
pred = pred.lower().replace("+","").replace("-","_")
print file, pred, pred in SS.Standards
if pred not in SS.Standards:
print "File named '%s' is reduced to '%s' and no such standard seems to exist." % (file, pred)
continue
# Record median correction in ROI
ROI = (ll > 600) & (ll < 850)
corr_vals.append(np.median(correction[ROI]))
# Normalize each correction by the median value
correction /= corr_vals[-1]
corrs.append(correction)
# END: for file in filelist:
# Rescale each correction and scale to erg/s/cm2/Ang
corrs = np.array(corrs)
erg_s_cm2_ang = corrs * np.median(corr_vals) * 1e-16
# Take the median of the correction vectors
the_corr = scipy.stats.nanmedian(erg_s_cm2_ang,0)
# Fit red end unless we are calibrated out there
if not np.isfinite(the_corr).all() and maxnm < 1000.0:
print "Fitting red end"
# Fit polynomial to extrapolate correction
redend = (ll > 880) & (ll < maxnm)
ss = np.poly1d(np.polyfit(ll[redend], the_corr[redend], 2))
redend = (ll>maxnm)
the_corr[redend] = ss(ll[redend])
# Plot data, if requested
if plot_filt:
pl.figure(2)
pl.clf()
pl.grid(True)
pl.ylim(1e-20,1e-15)
pl.semilogy(ll, the_corr, linewidth=1)
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
'''
# Now clean over the Balmer series (skip this for now)
balmers = [656.3, 486.1, 434.0, 410.2, 397.0]
#balmers = [656.3, 486.1, 434.0]
for balmer in balmers:
#pl.figure(3)
line_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.01)[0])
broad_ROI = sets.Set(np.where(np.abs((ll-balmer)/balmer) < 0.04)[0])
broad_ll = list(broad_ROI)
broad_ll.sort()
#pl.plot(ll[broad_ll], the_corr[broad_ll])
around_line_ROI = list(broad_ROI - line_ROI)
#pl.plot(ll[around_line_ROI], the_corr[around_line_ROI],'^')
fit = np.poly1d(np.polyfit(ll[around_line_ROI],
the_corr[around_line_ROI], 5))
to_fix = list(line_ROI)
#the_corr[to_fix] = fit(ll[to_fix])
#pl.plot(ll[to_fix], the_corr[to_fix])
#pl.show()
'''
if plot_filt:
pl.semilogy(ll, the_corr*4., linewidth=2)
filt_legend.append("Balmer*4")
# Filter correction to remove spectral features and leave response alone
the_corr = scipy.signal.savgol_filter(the_corr, 9, 5)
if plot_filt:
pl.semilogy(ll, the_corr*2., linewidth=2)
filt_legend.append("Filtered*2")
pl.semilogy(ll, the_corr, linewidth=2)
filt_legend.append("Filtered")
pl.legend(filt_legend)
pl.show()
# Plot data
pl.figure(1)
pl.clf()
pl.grid(True)
pl.ylim(1e-20,1e-15)
pl.semilogy(ll, the_corr, linewidth=4)
for ix,e in enumerate(erg_s_cm2_ang):
pl.semilogy(ll, e*corr_vals[ix]/np.mean(corr_vals))
pl.xlabel("Wavelength [nm]")
pl.ylabel("Correction [erg/s/cm cm/Ang]")
pl.title("Correct ph/10 m/nm to erg/s/cm2/Ang")
pl.legend(legend)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
pl.savefig("Standard_Correction.pdf")
print "Mean cor: %10.3g, Sigma cor: %10.3g" % (np.mean(corr_vals) * 1e-16, np.std(corr_vals)*1e-16)
maxnm = np.min([maxnm,np.max(ll)])
print "Max nm: %7.2f" % maxnm
# Construct result
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
res = {"nm": ll,
"maxnm": maxnm,
"correction": the_corr,
"doc": "Correct ph/10 m/nm to erg/2/cm2/ang",
"Nspec": len(corrs),
"correction_std": np.nanstd(erg_s_cm2_ang,0),
"outname": outname,
"files": filelist,
"when": '%s' % datetime.datetime.now(),
"user": os.getlogin()
}
np.save(outname, [res])
return res
