def __init__(self,m_ini,age,feh,m_act,logL,Teff,logg,mags,tri=None,
minage=None, maxage=None, ext_table=False):
"""Warning: if tri object not provided, this will be very slow to be created.
"""
self.minage = age.min()
self.maxage = age.max()
self.minmass = m_act.min()
self.maxmass = m_act.max()
self.minfeh = feh.min()
self.maxfeh = feh.max()
self.ext_table = ext_table
if minage is not None:
logging.warning("minage and maxage keywords are deprecated." + \
"Use instead the .set_bounds(age=(lo, hi)) attribute of StarModel.")
self.minage = minage
if maxage is not None:
logging.warning("minage and maxage keywords are deprecated." + \
"Use instead the .set_bounds(age=(lo, hi)) attribute of StarModel.")
self.maxage = maxage
L = 10**logL
if tri is None:
points = np.zeros((len(m_ini),3))
points[:,0] = m_ini
points[:,1] = age
points[:,2] = feh
fn = interpnd(points,m_act)
self.tri = fn.tri
else:
self.tri = tri
self.mass = interpnd(self.tri,m_act)
self._data = {'mass':m_act,
'logL':logL,
'logg':logg,
'logTeff':np.log10(Teff),
'mags':mags}
self._props = ['mass', 'logL', 'logg', 'logTeff']
self.bands = mags.keys()
self._mag = {band:interpnd(self.tri,mags[band]) for band in self.bands}
d = {}
for b in self._mag.keys():
d[b] = self._mag_fn(b)
self.mag = d
def build_interpfn(ks=None,nz=100,return_vals=False,filename='cosi_pdf_grid.h5',
kmax=300, save=False):
"""
"""
if ks is None:
ks = np.concatenate((np.logspace(-2,0,100),np.linspace(1.1,kmax+1,300)))
nk = len(ks)
zs = np.linspace(0,0.9999,nz)
vals = np.zeros(nk*nz)
allks = vals*0
allzs = vals*0
i=0
for k in ks:
for z in zs:
vals[i] = cosi_pdf(z,k)
allks[i] = k
allzs[i] = z
i += 1
pts = np.array([allzs,allks]).T
if save:
df = pd.DataFrame({'z':allzs, 'k':allks, 'val': vals})
df.to_hdf(filename,'grid')
if return_vals:
return pts,vals
else:
return interpnd(pts,vals)
def _prop(self, prop, *args):
if prop not in self._props:
raise ValueError('Cannot call this function with {}.'.format(prop))
attr = '_{}'.format(prop)
if not hasattr(self, attr):
setattr(self, attr, interpnd(self.tri, self._data[prop]))
fn = getattr(self, attr)
return fn(*args)
def cosi_pdf_fn(filename=resource_filename('obliquity','data/cosi_pdf_grid.h5'),
recalc=False,**kwargs):
if recalc:
return build_interpfn(**kwargs)
else:
df = pd.read_hdf(filename,'grid')
pts = np.array([df['z'],df['k']]).T
vals = np.array(df['val'])
return interpnd(pts,vals)
def func(*x):
x = np.atleast_1d(x).squeeze()
values = interpnd(points=self.grid.coord_vectors,
values=inp.data.reshape(self.grid.shape,
order=self.order),
method='nearest',
xi=x,
fill_value=None) # Allow points outside
return values[0] if values.shape == (1,) else values
def __init__(self,age,m_ini,m_act,logL,Teff,logg,mags,fehs=None):
self.minage = age.min()
self.maxage = age.max()
self.minmass = m_act.min()
self.maxmass = m_act.max()
self.bands = []
for band in mags:
self.bands.append(band)
L = 10**logL
#R = sqrt(G*m_act*MSUN/10**logg)/RSUN
if fehs is None:
points = array([m_ini,age]).T
self.is3d = False
else:
points = array([m_ini,age,fehs]).T
self.is3d = True
if self.is3d:
self.feh = lambda m,age,feh: feh
else:
self.feh = lambda m,age: self.isofeh
self.M = interpnd(points,m_act)
self.tri = self.M.tri
#self.R = interpnd(points,R)
self.logL = interpnd(self.tri,logL)
self.logg = interpnd(self.tri,logg)
self.logTe = interpnd(self.tri,log10(Teff))
def Teff_fn(*pts):
return 10**self.logTe(*pts)
#self.Teff = lambda *pts: 10**self.logTe(*pts)
self.Teff = Teff_fn
def R_fn(*pts):
return sqrt(G*self.M(*pts)*MSUN/10**self.logg(*pts))/RSUN
#self.R = lambda *pts: sqrt(G*self.M(*pts)*MSUN/10**self.logg(*pts))/RSUN
self.R = R_fn
self.mag = {}
for band in self.bands:
self.mag[band] = interpnd(self.tri,mags[band])
def write_tri(filename=os.path.join(ISOCHRONES, 'dartmouth.tri')):
df = DartmouthModelGrid(['g']).df
N = len(df)
pts = np.zeros((N, 3))
pts[:, 0] = np.array(df['MMo'])
pts[:, 1] = np.array(df['age'])
pts[:, 2] = np.array(df['feh'])
gmags = np.array(df['g'])
gfn = interpnd(pts, gmags)
with open(filename, 'wb') as f:
pickle.dump(gfn.tri, f)
def write_tri(df=MASTERDF,outfile=TRI_FILE):
N = len(df)
pts = np.zeros((N,3))
pts[:,0] = np.array(df['M_ini'])
pts[:,1] = np.array(df['age'])
pts[:,2] = np.array(df['feh'])
Jmags = np.array(df['J'])
Jfn = interpnd(pts,Jmags)
f = open(outfile,'wb')
pickle.dump(Jfn.tri,f)
f.close()
def write_tri(filename=os.path.join(ISOCHRONES,'yapsi.tri')):
df = YAPSIModelGrid(['V']).df
N = len(df)
pts = np.zeros((N,3))
pts[:,0] = np.array(df['mass'])
pts[:,1] = np.array(df['age'])
pts[:,2] = np.array(df['feh'])
mags = np.array(df['V'])
fn = interpnd(pts,mags)
with open(filename,'wb') as f:
pickle.dump(fn.tri,f)
def write_tri(df=MASTERDF, outfile=TRI_FILE):
N = len(df)
pts = np.zeros((N, 3))
pts[:, 0] = np.array(df['M_ini'])
pts[:, 1] = np.array(df['age'])
pts[:, 2] = np.array(df['feh'])
Jmags = np.array(df['J'])
Jfn = interpnd(pts, Jmags)
f = open(outfile, 'wb')
pickle.dump(Jfn.tri, f)
f.close()
def write_tri(df=MASTERDF, outfile=TRI_FILE):
N = len(df)
pts = np.zeros((N, 3))
pts[:, 0] = np.array(df['mini'])
pts[:, 1] = np.array(df['logage'])
pts[:, 2] = np.array(df['feh'])
Rs = np.array(df['radius'])
Rfn = interpnd(pts, Rs)
f = open(outfile, 'wb')
pickle.dump(Rfn.tri, f)
f.close()
def write_tri(df=MASTERDF,outfile=TRI_FILE):
N = len(df)
pts = np.zeros((N,3))
pts[:,0] = np.array(df['mini'])
pts[:,1] = np.array(df['logage'])
pts[:,2] = np.array(df['feh'])
Rs = np.array(df['radius'])
Rfn = interpnd(pts,Rs)
f = open(outfile,'wb')
pickle.dump(Rfn.tri,f)
f.close()
def __init__(self,
m_ini,
age,
feh,
m_act,
logL,
Teff,
logg,
mags,
tri=None):
"""Warning: if tri object not provided, this will be very slow to be created.
"""
self.minage = age.min()
self.maxage = age.max()
self.minmass = m_act.min()
self.maxmass = m_act.max()
self.minfeh = feh.min()
self.maxfeh = feh.max()
L = 10**logL
if tri is None:
points = np.zeros((len(m_ini), 2))
points[:, 0] = m_ini
points[:, 1] = age
self.mass = interpnd(points, m_act)
self.tri = self.mass.tri
else:
self.tri = tri
self.mass = interpnd(self.tri, m_act)
self.logL = interpnd(self.tri, logL)
self.logg = interpnd(self.tri, logg)
self.logTeff = interpnd(self.tri, np.log10(Teff))
def Teff_fn(*pts):
return 10**self.logTeff(*pts)
self.Teff = Teff_fn
def R_fn(*pts):
return np.sqrt(
G * self.mass(*pts) * MSUN / 10**self.logg(*pts)) / RSUN
self.radius = R_fn
self.bands = []
for band in mags.keys():
self.bands.append(band)
self.mag = {
band: interpnd(self.tri, mags[band])
for band in self.bands
}
def write_tri(df=MASTERDF, outfile=TRI_FILE):
"""Writes the Delanuay triangulation of the models to file. Takes a few minutes, so beware.
Typically no need to do this unless you can't download the .tri file for some reason...
"""
N = len(df)
pts = np.zeros((N, 3))
pts[:, 0] = np.array(df['M/Mo'])
pts[:, 1] = np.log10(np.array(df['age']) * 1e9)
pts[:, 2] = np.array(df['feh'])
Jmags = np.array(df['J'])
Jfn = interpnd(pts, Jmags)
f = open(outfile, 'wb')
pickle.dump(Jfn.tri, f)
f.close()
def write_tri(df=MASTERDF, outfile=TRI_FILE):
"""Writes the Delanuay triangulation of the models to file. Takes a few minutes, so beware.
Typically no need to do this unless you can't download the .tri file for some reason...
"""
N = len(df)
pts = np.zeros((N,3))
pts[:,0] = np.array(df['M/Mo'])
pts[:,1] = np.log10(np.array(df['age'])*1e9)
pts[:,2] = np.array(df['feh'])
Jmags = np.array(df['J'])
Jfn = interpnd(pts,Jmags)
f = open(outfile,'wb')
pickle.dump(Jfn.tri,f)
f.close()
def read_Pdelta(root=MAIN_PATH+'matter_power/',**kwargs):
"""This is an interpolation in k and z, for array P_delta(k,z) output by CAMB.
Input parameters for this function are the root where the power spectra arrays are stored, plus kwargs.
This returns result in [cm^3 comoving]. It takes z, and k[1/Mpc comoving].
Note1: Array of redshifts corresponding to the grid P must be stored in the location root/zs.txt.
Note2: This also relies on particular file naming (regulated by CAMB), which can be adjusted in the function."""
zs = np.loadtxt('%s/zs.txt' % root)
all_Pdeltas = []
for i,z in enumerate(zs):
ks,Ps = np.loadtxt('%s/_matterpower_%i.dat' % (root,i),unpack=True)
ks *= h0
Ps *= (Mpc_in_cm/h0)**3
all_Pdeltas.append(Ps)
all_Pdeltas = np.array(all_Pdeltas).T
Zs,Ks = np.meshgrid(zs,ks)
points = np.array([Zs.ravel(),Ks.ravel()]).T
return interpnd(points,all_Pdeltas.ravel(),**kwargs)
def __init__(self,m_ini,age,feh,m_act,logL,Teff,logg,mags,tri=None,
minage=None, maxage=None):
"""Warning: if tri object not provided, this will be very slow to be created.
"""
self.minage = age.min()
self.maxage = age.max()
self.minmass = m_act.min()
self.maxmass = m_act.max()
self.minfeh = feh.min()
self.maxfeh = feh.max()
if minage is not None:
self.minage = minage
if maxage is not None:
self.maxage = maxage
L = 10**logL
if tri is None:
points = np.zeros((len(m_ini),3))
points[:,0] = m_ini
points[:,1] = age
points[:,2] = feh
self.mass = interpnd(points,m_act)
self.tri = self.mass.tri
else:
self.tri = tri
self.mass = interpnd(self.tri,m_act)
self.logL = interpnd(self.tri,logL)
self.logg = interpnd(self.tri,logg)
self.logTeff = interpnd(self.tri,np.log10(Teff))
def Teff_fn(*pts):
return 10**self.logTeff(*pts)
self.Teff = Teff_fn
def R_fn(*pts):
return np.sqrt(G*self.mass(*pts)*MSUN/10**self.logg(*pts))/RSUN
self.radius = R_fn
self.bands = []
for band in mags.keys():
self.bands.append(band)
self.mag = {band:interpnd(self.tri,mags[band]) for band in self.bands}
class ObservedThePlot(ThePlot):
f_mv_sat = fits['f(mv_sat)']
po = np.load(files['p_obs'])
po_ms_z = po['ho'].sum(axis=2) / po['ha'].sum(axis=2)
po_ms_z = np.where(np.isnan(po_ms_z), 0, po_ms_z)
po_ms_i = interpnd(
((po['ms'][1:] + po['ms'][:-1]) / 2, (po['z'][1:] + po['z'][:-1]) / 2),
po_ms_z,
bounds_error=False,
fill_value=0)
def __init__(self, z, ms, mv):
self.dms = ms[1] - ms[0]
self.z = z
self.po_ms = self.po_ms_i(np.array((ms, [self.z] * len(ms))).T)
mv = mv.reshape((-1, 1))
pars = self.f_mv_sat.best_values.copy()
pars['x0'] = mv + pars['x0']
self.f = np.power(10, self.f_mv_sat.model.func(mv, **pars))
def p_mv0_mv(self, f_mv):
ret = f_mv * self.f
ret = ret / ret.sum()
return np.where(np.isnan(ret), 0, ret)
def fn(self, mv, p, f_mv):
p_mv0_mv = self.p_mv0_mv(f_mv.ravel())
fn = ((p_mv0_mv / p_mv0_mv.sum(axis=1, keepdims=True))[:, :,
np.newaxis] *
(p / p.sum(axis=1, keepdims=True))[np.newaxis, :, :] *
self.po_ms[np.newaxis, np.newaxis, :])
return np.nansum(fn, axis=(1, 2)).reshape(mv.shape)
def N(self, mv, a, N12, p, f):
return self.fn(mv, p, f) * super(ObservedThePlot, self).N(
mv, a, N12, p, f)
else:
print(
'{} is not a symlink. Not removing file. Please run this again with symlinked files'
.format(fin[izip]))
else:
print('LUT is okay for :', fin, '. Doing nothing.')
sys.exit(1)
else:
npt = len(zzn)
coordn = np.hstack((np.array(xxn).reshape(npt, 1),
np.array(yyn).reshape(npt, 1)))
noise = np.array(zzn).reshape(npt, 1)
print(
'Start 2D interpolation on noiseLut. This may take a while....'
)
interpfn2 = interpnd(coordn, noise)
noiseIntrp = interpfn2(fullX, fullY)
prevNoise = noiseToRead
if inps.oc:
ocfile = os.path.join(
odir, 'IW{0}'.format(swath),
'noise-iw' + str(swath) + '.cal')
write2flt(noiseIntrp, ylist, xlist, ocfile)
write2xml(noiseIntrp, ylist[0], xlist[0], 1, 1,
ocfile)
print('Writing noise calibration file to ', ocfile)
#### Radio Calibration
if radioToRead != prevRadio:
### Locate radiometric calibration xml file
if radioToRead.startswith('/vsizip'): # read from zip file
import pickle
from .isochrone import Isochrone
#DATADIR = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
DATADIR = os.getenv('ISOCHRONES',
os.path.expanduser(os.path.join('~','.isochrones')))
if not os.path.exists(DATADIR):
os.mkdir(DATADIR)
MASTERFILE = '{}/dartmouth.h5'.format(DATADIR)
TRI_FILE = '{}/dartmouth.tri'.format(DATADIR)
MAXAGES = np.load(resource_filename('isochrones','data/dartmouth_maxages.npz'))
MAXAGE = interpnd(MAXAGES['points'], MAXAGES['maxages'])
def _download_h5():
"""
Downloads HDF5 file containing Dartmouth grids from Zenodo.
"""
#url = 'http://zenodo.org/record/12800/files/dartmouth.h5'
url = 'http://zenodo.org/record/15843/files/dartmouth.h5'
import urllib
print('Downloading Dartmouth stellar model data (should happen only once)...')
if os.path.exists(MASTERFILE):
os.remove(MASTERFILE)
urllib.urlretrieve(url,MASTERFILE)
def _download_tri():
import numpy as np
from scipy.optimize import newton
from scipy.interpolate import LinearNDInterpolator as interpnd
except ImportError:
np, newton, interpnd = (None, None, None)
on_rtd = True
if not on_rtd:
DATAFOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
Es = np.load(os.path.join(DATAFOLDER,'Etable.npy'))
eccs = np.load(os.path.join(DATAFOLDER,'Etable_eccs.npy'))
Ms = np.load(os.path.join(DATAFOLDER,'Etable_Ms.npy'))
ECCS,MS = np.meshgrid(eccs,Ms)
points = np.array([MS.ravel(),ECCS.ravel()]).T
EFN = interpnd(points,Es.ravel())
else:
DATAFOLDER, Es, eccs, Ms, ECCS, MS = (None, None, None, None, None, None)
points, EFN = (None, None)
def Efn(Ms,eccs):
"""
Returns Eccentric anomaly, interpolated from pre-computed grid of M, ecc
Instantaneous solution of Kepler's equation!
Works for ``-2*np.pi < Ms < 2*np.pi`` and ``eccs <= 0.97``
:param Ms: (``float`` or array-like)
Mean anomaly
if not on_rtd:
import pandas as pd
else:
pd = None
import pickle
from ..isochrone import Isochrone, FastIsochrone
from ..config import ISOCHRONES
TRI_FILE = '{}/dartmouth.tri'.format(ISOCHRONES)
if not on_rtd:
MAXAGES = np.load(
resource_filename('isochrones', 'data/dartmouth_maxages.npz'))
MAXAGE = interpnd(MAXAGES['points'], MAXAGES['maxages'])
from .grid import DartmouthModelGrid
TRI = None
class Dartmouth_Isochrone(Isochrone):
"""Dotter (2008) Stellar Models, at solar a/Fe and He abundances.
:param bands: (optional)
List of desired photometric bands. Default list of bands is
``['B','V','g','r','i','z','J','H','K','W1','W2','W3','Kepler']``.
Model grids are obtained from `here <http://stellar.dartmouth.edu/models/>`_
"""
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 5 Gyr, feh=-1.5')
jj = (a == 5) * (feh == -2.5)
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 5 Gyr, feh=-2.5')
jj = (a == 5) * (feh == -2.0)
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 5 Gyr, feh=-2.0')
jj = (a == 12) * (feh == -2.0)
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 12 Gyr, feh=-2.0')
jj = (a == 12) * (feh == -2.5)
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 12 Gyr, feh=-2.5')
jj = (a == 12) * (feh == -1.5)
p.plot(V[jj] - I[jj], I[jj], '-', lw=2, label='age = 12 Gyr, feh=-1.5')
#tri = np.load('asd.tri.npy')
iV = interpnd(pts, V[j])
iI = interpnd(iV.tri, I[j])
np.save('asd.tri', iV.tri)
FEH = -2.5
AGE = 5.0
ms = np.arange(1, 300, 0.1) ## actually eep
for feh in [-1.75, -2.25]:
p.plot(iV(ms, AGE, feh) - iI(ms, AGE, feh),
iI(ms, AGE, feh),
'k:',
label='age = {:.2f} Gyr, feh={:.2f}'.format(AGE, feh))
FEH = -2.5
AGE = 12.0
ms = np.arange(1, 300, 0.1) ## actually eep
def __init__(self,u1=0.394,u2=0.261,pmin=0.007,pmax=2,nps=200,nzs=200,zmax=None):
self.u1 = u1
self.u2 = u2
self.pmin = pmin
self.pmax = pmax
if zmax is None:
zmax = 1+pmax
self.zmax = zmax
self.nps = nps
ps = np.logspace(np.log10(pmin),np.log10(pmax),nps)
if pmax < 0.5:
zs = np.concatenate([np.array([0]),ps-1e-10,ps,np.arange(pmax,1-pmax,0.01),
np.arange(1-pmax,zmax,0.005)])
elif pmax < 1:
zs = np.concatenate([np.array([0]),ps-1e-10,ps,np.arange(1-pmax,zmax,0.005)])
else:
zs = np.concatenate([np.array([0]),ps-1e-10,ps,np.arange(pmax,zmax,0.005)])
self.nzs = np.size(zs)
#zs = linspace(0,zmax,nzs)
#zs = concatenate([zs,ps,ps+1e-10])
mu0s = np.zeros((np.size(ps),np.size(zs)))
lambdads = np.zeros((np.size(ps),np.size(zs)))
etads = np.zeros((np.size(ps),np.size(zs)))
fs = np.zeros((np.size(ps),np.size(zs)))
for i,p0 in enumerate(ps):
f,res = occultquad(zs,u1,u2,p0,return_components=True)
mu0s[i,:] = res[0]
lambdads[i,:] = res[1]
etads[i,:] = res[2]
fs[i,:] = f
P,Z = np.meshgrid(ps,zs)
points = np.array([P.ravel(),Z.ravel()]).T
self.mu0 = interpnd(points,mu0s.T.ravel())
##need to make two interpolation functions for lambdad
## b/c it's strongly discontinuous at z=p
mask = (Z<P)
pointmask = points[:,1] < points[:,0]
w1 = np.where(mask)
w2 = np.where(~mask)
wp1 = np.where(pointmask)
wp2 = np.where(~pointmask)
self.lambdad1 = interpnd(points[wp1],lambdads.T[w1].ravel())
self.lambdad2 = interpnd(points[wp2],lambdads.T[w2].ravel())
def lambdad(p,z):
#where p and z are exactly equal, this will return nan....
p = np.atleast_1d(p)
z = np.atleast_1d(z)
l1 = self.lambdad1(p,z)
l2 = self.lambdad2(p,z)
bad1 = np.isnan(l1)
l1[np.where(bad1)]=0
l2[np.where(~bad1)]=0
return l1*~bad1 + l2*bad1
self.lambdad = lambdad
#self.lambdad = interpnd(points,lambdads.T.ravel())
self.etad = interpnd(points,etads.T.ravel())
self.fn = interpnd(points,fs.T.ravel())
if not on_rtd:
import astropy.constants as const
AU = const.au.cgs.value
RSUN = const.R_sun.cgs.value
MSUN = const.M_sun.cgs.value
REARTH = const.R_earth.cgs.value
MEARTH = const.M_earth.cgs.value
G = const.G.cgs.value
DAY = 86400.
DATAFOLDER = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
LDDATA = np.recfromtxt('{}/keplerld.dat'.format(DATAFOLDER),names=True)
LDOK = ((LDDATA.teff < 10000) & (LDDATA.logg > 2.0) & (LDDATA.feh > -2))
LDPOINTS = np.array([LDDATA.teff[LDOK],LDDATA.logg[LDOK]]).T
U1FN = interpnd(LDPOINTS,LDDATA.u1[LDOK])
U2FN = interpnd(LDPOINTS,LDDATA.u2[LDOK])
else:
const, AU, RSUN, MSUN = (None, None, None, None)
REARTH, MEARTH, DAY = (None, None, None)
DATAFOLDER = None
LDDATA, LDOK, LDPOINTS, U1FN, U2FN = (None, None, None, None, None)
def ldcoeffs(teff,logg=4.5,feh=0):
"""
Returns limb-darkening coefficients in Kepler band.
"""
teffs = np.atleast_1d(teff)
loggs = np.atleast_1d(logg)
import astropy.constants as const
AU = const.au.cgs.value
RSUN = const.R_sun.cgs.value
MSUN = const.M_sun.cgs.value
REARTH = const.R_earth.cgs.value
MEARTH = const.M_earth.cgs.value
G = const.G.cgs.value
DAY = 86400.
DATAFOLDER = os.path.abspath(
os.path.join(os.path.dirname(__file__), 'data'))
LDDATA = np.recfromtxt('{}/keplerld.dat'.format(DATAFOLDER), names=True)
LDOK = ((LDDATA.teff < 10000) & (LDDATA.logg > 2.0) & (LDDATA.feh > -2))
LDPOINTS = np.array([LDDATA.teff[LDOK], LDDATA.logg[LDOK]]).T
U1FN = interpnd(LDPOINTS, LDDATA.u1[LDOK])
U2FN = interpnd(LDPOINTS, LDDATA.u2[LDOK])
else:
const, AU, RSUN, MSUN = (None, None, None, None)
REARTH, MEARTH, DAY = (None, None, None)
DATAFOLDER = None
LDDATA, LDOK, LDPOINTS, U1FN, U2FN = (None, None, None, None, None)
MAXSLOPE = 30
def ldcoeffs(teff, logg=4.5, feh=0):
"""
Returns limb-darkening coefficients in Kepler band.
"""
teffs = np.atleast_1d(teff)
def __init__(self,
u1=0.394,
u2=0.261,
pmin=0.007,
pmax=2,
nps=200,
nzs=200,
zmax=None):
self.u1 = u1
self.u2 = u2
self.pmin = pmin
self.pmax = pmax
if zmax is None:
zmax = 1 + pmax
self.zmax = zmax
self.nps = nps
ps = np.logspace(np.log10(pmin), np.log10(pmax), nps)
if pmax < 0.5:
zs = np.concatenate([
np.array([0]), ps - 1e-10, ps,
np.arange(pmax, 1 - pmax, 0.01),
np.arange(1 - pmax, zmax, 0.005)
])
elif pmax < 1:
zs = np.concatenate([
np.array([0]), ps - 1e-10, ps,
np.arange(1 - pmax, zmax, 0.005)
])
else:
zs = np.concatenate(
[np.array([0]), ps - 1e-10, ps,
np.arange(pmax, zmax, 0.005)])
self.nzs = np.size(zs)
#zs = linspace(0,zmax,nzs)
#zs = concatenate([zs,ps,ps+1e-10])
mu0s = np.zeros((np.size(ps), np.size(zs)))
lambdads = np.zeros((np.size(ps), np.size(zs)))
etads = np.zeros((np.size(ps), np.size(zs)))
fs = np.zeros((np.size(ps), np.size(zs)))
for i, p0 in enumerate(ps):
f, res = occultquad(zs, u1, u2, p0, return_components=True)
mu0s[i, :] = res[0]
lambdads[i, :] = res[1]
etads[i, :] = res[2]
fs[i, :] = f
P, Z = np.meshgrid(ps, zs)
points = np.array([P.ravel(), Z.ravel()]).T
self.mu0 = interpnd(points, mu0s.T.ravel())
##need to make two interpolation functions for lambdad
## b/c it's strongly discontinuous at z=p
mask = (Z < P)
pointmask = points[:, 1] < points[:, 0]
w1 = np.where(mask)
w2 = np.where(~mask)
wp1 = np.where(pointmask)
wp2 = np.where(~pointmask)
self.lambdad1 = interpnd(points[wp1], lambdads.T[w1].ravel())
self.lambdad2 = interpnd(points[wp2], lambdads.T[w2].ravel())
def lambdad(p, z):
#where p and z are exactly equal, this will return nan....
p = np.atleast_1d(p)
z = np.atleast_1d(z)
l1 = self.lambdad1(p, z)
l2 = self.lambdad2(p, z)
bad1 = np.isnan(l1)
l1[np.where(bad1)] = 0
l2[np.where(~bad1)] = 0
return l1 * ~bad1 + l2 * bad1
self.lambdad = lambdad
#self.lambdad = interpnd(points,lambdads.T.ravel())
self.etad = interpnd(points, etads.T.ravel())
self.fn = interpnd(points, fs.T.ravel())
def foo(*args, **kwargs):
pass
return foo
if not on_rtd:
DATAFOLDER = os.path.abspath(
os.path.join(os.path.dirname(__file__), 'data'))
Es = np.load(os.path.join(DATAFOLDER, 'Etable.npy'))
eccs = np.load(os.path.join(DATAFOLDER, 'Etable_eccs.npy'))
Ms = np.load(os.path.join(DATAFOLDER, 'Etable_Ms.npy'))
ECCS, MS = np.meshgrid(eccs, Ms)
points = np.array([MS.ravel(), ECCS.ravel()]).T
EFN = interpnd(points, Es.ravel())
else:
DATAFOLDER, Es, eccs, Ms, ECCS, MS = (None, None, None, None, None, None)
points, EFN = (None, None)
def Efn(Ms, eccs):
"""
Returns Eccentric anomaly, interpolated from pre-computed grid of M, ecc
Instantaneous solution of Kepler's equation!
Works for ``-2*np.pi < Ms < 2*np.pi`` and ``eccs <= 0.97``
:param Ms: (``float`` or array-like)
Mean anomaly
