def spline_int(x, y, a, b):
from scipy.interpolate import splrep as bspline
from scipy.interpolate import splint as integ
list_assert(x, 'x')
list_assert(y, 'y')
numeric_assert(a, 'a')
numeric_assert(b, 'b')
bspline_obj = bspline(x, y)
int_spline_val = integ(a, b, bspline_obj)
return (int_spline_val)
def spline_vals(xnew, x, y, der):
from scipy.interpolate import interp1d as spln
from scipy.interpolate import splrep as bspline
from scipy.interpolate import splev as deriv
list_assert(xnew, 'xnew')
list_assert(x, 'x')
list_assert(y, 'y')
int_assert(der, 'der')
assert der >= 0, "Error: 'der' must be an integer greater than or equal to zero"
spline = spln(x, y, kind='cubic')
spline_vals = spline(xnew)
if (der == 0):
return (spline_vals)
if (der > 0):
bspline_obj = bspline(x, y)
der_spline_vals = deriv(xnew, bspline_obj, der)
return (der_spline_vals)
def get_limb_coeff(Tstar,loggstar,filter='Kp',plot=False,network=None,limb='quad',interp='linear'):
from scipy.interpolate import griddata
import pylab as pl
from mpl_toolkits.mplot3d import axes3d, Axes3D
from scipy.interpolate import RectBivariateSpline as bspline
global ldc1func,ldc2func,ldc3func,ldc4func
# Account for gap in look up tables between 4800 and 5000K
if (Tstar > 4800 and Tstar <= 4900):
Tstar = 4800
if (Tstar > 4900 and Tstar < 5000):
Tstar = 5000
# Choose proper file to read
if limb == 'nlin':
skiprows = 49
filtcol = 8
metcol = 9
mercol = 10
col1 = 4
col2 = 5
col3 = 6
col4 = 7
if (Tstar <= 4800):
file = 'Claret_cool_nlin.dat'
if (Tstar >= 5000):
file = 'Claret_hot_nlin.dat'
else:
skiprows = 58
filtcol = 4
metcol = 5
mercol = 6
col1 = 9
col2 = 10
col3 = 11
col4 = 12
if (Tstar <= 4800):
file = 'Claret_cool.dat'
if (Tstar >= 5000):
file = 'Claret_hot.dat'
if network == 'doug':
path = '/home/douglas/Astronomy/Resources/'
if network == 'astro':
path = '/home/jswift/Mdwarfs/'
if network == None:
path = '/Users/jonswift/Astronomy/Exoplanets/TransitFits/'
limbdata = np.loadtxt(path+file,dtype='string', delimiter='|',skiprows=skiprows)
logg = limbdata[:,0].astype(np.float).flatten()
Teff = limbdata[:,1].astype(np.float).flatten()
Z = limbdata[:,2].astype(np.float).flatten()
xi = limbdata[:,3].astype(np.float).flatten()
filt = np.char.strip(limbdata[:,filtcol].flatten())
method = limbdata[:,metcol].flatten()
avec = limbdata[:,col1].astype(np.float).flatten()
bvec = limbdata[:,col2].astype(np.float).flatten()
cvec = limbdata[:,col3].astype(np.float).flatten()
dvec = limbdata[:,col4].astype(np.float).flatten()
# Select out the limb darkening coefficients
# inds = np.where((filt == 'Kp') & (Teff == 3000) & (logg == 5.0) & (method == 'L'))
idata, = np.where((filt == filter) & (method == 'L'))
npts = idata.size
uTeff = np.unique(Teff[idata])
ulogg = np.unique(logg[idata])
# agrid0 = np.zeros((len(uTeff),len(ulogg)))
# for i in np.arange(len(uTeff)):
# for ii in np.arange(len(ulogg)):
# ind, = np.where((Teff[idata] == uTeff[i]) & (logg[idata] == ulogg[ii]))
# val = avec[idata[ind]]
# if len(val) > 0:
# agrid0[i,ii] = val[0]
# else:
# pass #pdb.set_trace()
locs = np.zeros(2*npts).reshape(npts,2)
locs[:,0] = Teff[idata].flatten()
locs[:,1] = logg[idata].flatten()
vals = np.zeros(npts)
vals[:] = avec[idata]
agrid = np.zeros((len(uTeff),len(ulogg)))
for i in np.arange(len(uTeff)):
for ii in np.arange(len(ulogg)):
eval = np.array([uTeff[i],ulogg[ii]]).reshape(1,2)
val = griddata(locs,vals,eval,method='cubic')
if len(val) > 0:
agrid[i,ii] = val[0]
else:
pass #pdb.set_trace()
ldc1func = bspline(uTeff, ulogg, agrid, kx=1, ky=1, s=0)
aval = ldc1func(Tstar,loggstar)[0][0]
if plot:
plt.figure(1)
plt.clf()
plt.imshow(agrid,interpolation='none',
extent=[np.min(ulogg),np.max(ulogg),np.min(uTeff),np.max(uTeff)],
aspect=1./1000,vmin=np.min(agrid),vmax=np.max(agrid))
plt.colorbar()
#------------------------------
# Second coefficient
#------------------------------
vals = np.zeros(npts)
vals[:] = bvec[idata]
bgrid = np.zeros((len(uTeff),len(ulogg)))
for i in np.arange(len(uTeff)):
for ii in np.arange(len(ulogg)):
eval = np.array([uTeff[i],ulogg[ii]]).reshape(1,2)
val = griddata(locs,vals,eval,method='cubic')
if len(val) > 0:
bgrid[i,ii] = val[0]
else:
pass
ldc2func = bspline(uTeff, ulogg, bgrid, kx=1, ky=1, s=0)
bval = ldc2func(Tstar,loggstar)[0][0]
if plot:
plt.figure(2)
plt.clf()
plt.imshow(bgrid,interpolation='none',
extent=[np.min(ulogg),np.max(ulogg),np.min(uTeff),np.max(uTeff)],
aspect=1./1000,vmin=np.min(bgrid),vmax=np.max(bgrid))
plt.colorbar()
#------------------------------
# Third coefficient
#------------------------------
vals = np.zeros(npts)
vals[:] = cvec[idata]
cgrid = np.zeros((len(uTeff),len(ulogg)))
for i in np.arange(len(uTeff)):
for ii in np.arange(len(ulogg)):
eval = np.array([uTeff[i],ulogg[ii]]).reshape(1,2)
val = griddata(locs,vals,eval,method='cubic')
if len(val) > 0:
cgrid[i,ii] = val[0]
else:
pass
ldc3func = bspline(uTeff, ulogg, cgrid, kx=1, ky=1, s=0)
cval = ldc3func(Tstar,loggstar)[0][0]
if plot:
plt.figure(3)
plt.clf()
plt.imshow(cgrid,interpolation='none',
extent=[np.min(ulogg),np.max(ulogg),np.min(uTeff),np.max(uTeff)],
aspect=1./1000,vmin=np.min(cgrid),vmax=np.max(cgrid))
plt.colorbar()
#------------------------------
# Fourth coefficient
#------------------------------
vals = np.zeros(npts)
vals[:] = dvec[idata]
dgrid = np.zeros((len(uTeff),len(ulogg)))
for i in np.arange(len(uTeff)):
for ii in np.arange(len(ulogg)):
eval = np.array([uTeff[i],ulogg[ii]]).reshape(1,2)
val = griddata(locs,vals,eval,method='cubic')
if len(val) > 0:
dgrid[i,ii] = val[0]
else:
pass
ldc4func = bspline(uTeff, ulogg, dgrid, kx=1, ky=1, s=0)
dval = ldc4func(Tstar,loggstar)[0][0]
if plot:
plt.figure(4)
plt.clf()
plt.imshow(dgrid,interpolation='none',
extent=[np.min(ulogg),np.max(ulogg),np.min(uTeff),np.max(uTeff)],
aspect=1./1000,vmin=np.min(dgrid),vmax=np.max(dgrid))
plt.colorbar()
if limb == 'quad':
return aval, bval
if limb == 'sqrt':
return cval, dval
if limb == 'nlin':
return aval, bval, cval, dval