def genPlot(path, teff, logg, metals, am, nm, cm):
# Get the model
mspec= ferre.interpolate(teff,logg,metals,am,nm,cm)
# Plot the model
splot.waveregions(mspec,
labelID='[N/M]=' + str(nm) + ', [C/M]=' + str(cm),
labelTeff=teff,
labellogg=logg,
labelmetals=metals,
labelafe=am)
# Format and save the file
fig = plot.gcf()
fig.set_size_inches(30.0, 15.0, forward=True)
plot.draw()
if not os.path.exists('model_plots/' + path):
os.makedirs('model_plots/' + path)
plot.savefig('model_plots/' + path + '/' +
str(teff) + '_' + str(logg) + '_'
+ str(metals) + '_' + str(am) + '_'
+ str(nm) + '_' + str(cm) + '.png',
format='png', dpi=300)
plot.clf()
plot.close('all');
def binaryModelGen(locationID, apogeeID, params, visit, ipf=None, ipg=None, plot=True): ''' Interpolates the spectra of the individual stars in the binary using some initial parameters and their velocity difference. :param locationID: The location ID of the binary. :param apogeeID: The apogee ID of the binary. :param params: The paramters to test against the observed data. format: [ [Teff1, ...], [logg1, ...], [metals1, ...], [am1, ...], [nm1, ...], [cm1, ...]] :param visit: The visit to test against. :param ip: The interpolator instance that keeps running ferre. Should be faster to keep using this instead. (default=None) :param plot: If true plots each component in the binary in 'plots/model_gen' (default=True) :returns: The binary model flux and the maximum value from the cross correlation function between the modeled totalFlux and the continuum-normalized spectrum. (totalFlux, max) ''' # Generate models (flux1, flux2) if (np.logical_and(ipf == None, ipg == None)): # todo: put in condition for both gk and f libraries mspecs = ferre.interpolate(params[0], params[1], params[2], params[3], params[4], params[5]) else: if (params[0][0] > 6000.): mspec1 = ipf(params[0][0], params[1][0], params[2][0], params[3][0], params[4][0], params[5][0]) else: mspec1 = ipg(params[0][0], params[1][0], params[2][0], params[3][0], params[4][0], params[5][0]) if (params[0][1] > 6000.): mspec2 = ipf(params[0][1], params[1][1], params[2][1], params[3][1], params[4][1], params[5][1]) else: mspec2 = ipg(params[0][1], params[1][1], params[2][1], params[3][1], params[4][1], params[5][1]) mspecs = [ mspec1, mspec2 ] for mspec in mspecs: mspec[np.isnan(mspec)] = 0. # Calculate deltaV RVs = getRVs(locationID, apogeeID, visit) # Generate the wavelength grid restLambda = splot.apStarWavegrid() # Calculates wavelength grid for the second star with a doppler shift shiftLambda = [restLambda * (1. + rv / (const.c / 1000.)) for rv in RVs] # The fluxes of both stars shiftedFlux = np.array([np.interp(restLambda, shiftLambda[i], mspecs[i]) for i in range(len(shiftLambda))]) # The combined flux of the stars in the modeled binary (params[7][1] defined as flux ratio) totalFlux = (shiftedFlux[0] + (shiftedFlux[1] * params[6][1])) / 2.0 # Make the plots if (plot == True): BinPlot.plotDeltaVCheck(locationID, apogeeID, visit, [[ restLambda, mspecs[0], 'blue', 'rest model specA' ], [ restLambda, mspecs[1], 'green', 'rest model specB' ], [ restLambda, shiftedFlux[0], 'orange', 'shift model specA' ], [ restLambda, shiftedFlux[1], 'purple', 'shift model specB' ]], [params[0][0], params[0][1]], 'Binary Model Gen - Prelim. Proc.', folder='model_gen'); return totalFlux
[data["TEFF"][0], 5500.0, 3500.0], # effective temperature
[data["LOGG"][0], data["LOGG"][0], data["LOGG"][0]], # surface gravity
[data["FEH"][0], data["FEH"][0], data["FEH"][0]], # metal abundance
[data["ALPHA"][0], data["ALPHA"][0], data["ALPHA"][0]], # α element
[0.0, 0.0, 0.0], # nitrogen relative abundance
[data["CARBON"][0], data["CARBON"][0], data["CARBON"][0]],
] # carbon relative abundance
if params[0][0] < 3500.0:
params[0][0] = 3500.0
if params[0][0] >= 6000.0:
params[0][0] = 5500.0
if params[1][0] >= 5.0:
params[1][0] = params[1][2] = params[1][1] = 4.9
# Generate models (flux1, flux2)
mspecs = ferre.interpolate(params[0], params[1], params[2], params[3], params[4], params[5])
# for mspec in mspecs:
# mspec[np.where(np.isnan(mspec))] = 0.0
# prep obs spec
"""aspec= np.reshape(spec,(1,len(spec)))
aspecerr= np.reshape(specerr,(1,len(specerr)))
cont= spec / continuum.fit(aspec,aspecerr,type='aspcap')[0]"""
"""spec[np.where(np.isnan(spec))] = 0.0
cont = spec / max(spec)"""
# Generate the wavelength grid
restLambda = splot.apStarWavegrid()
ccfs = []
velocityShift = 0
for i in range(len(nan_vals_spec) - 1)
if nan_vals_spec[i + 1] != nan_vals_spec[i] + 1
]
print(nan_ranges_spec)
f.write(
"{0}\t{1}\t{2}\t{3}\t{4}".format(
locationID,
apogeeID,
"[" + str(nan_ranges_spec[0][0]) + "," + str(nan_ranges_spec[0][1]) + "]",
"[" + str(nan_ranges_spec[1][0]) + "," + str(nan_ranges_spec[1][1]) + "]",
"[" + str(nan_ranges_spec[2][0]) + "," + str(nan_ranges_spec[2][1]) + "]\n",
)
)
mspec = ferre.interpolate(4500.0, 4.7, 0.0, 0.0, 0.0, 0.0)
nan_vals_mspec = np.where(np.isnan(mspec))[0]
nan_ranges_mspec = [
(nan_vals_mspec[i] + 1, nan_vals_mspec[i + 1])
for i in range(len(nan_vals_mspec) - 1)
if nan_vals_mspec[i + 1] != nan_vals_mspec[i] + 1
]
f.write(
"{0}\t{1}\t{2}\t{3}\t{4}".format(
"model",
"\t\t\t\t",
"[" + str(nan_ranges_mspec[0][0]) + "," + str(nan_ranges_mspec[0][1]) + "]",
"[" + str(nan_ranges_mspec[1][0]) + "," + str(nan_ranges_mspec[1][1]) + "]",
"[" + str(nan_ranges_mspec[2][0]) + "," + str(nan_ranges_mspec[2][1]) + "]\n",
)
)
