def test_lerp_threading(self):
for nthr in range(16):
f = Gimenez(npol=500, nthr=nthr, lerp=True)(self.z, self.k, self.u[0])
npt.assert_array_almost_equal(f, self.f_ref[:,0], decimal=4)
for i in range(2,4):
f = Gimenez(npol=500, nthr=nthr, lerp=True)(self.z, self.k, self.u[0:i])
npt.assert_array_almost_equal(f, self.f_ref[:,0:i], decimal=4)
def __init__(self, lcdata, tmodel=None, **kwargs):
self.tmodel = tmodel or Gimenez()
self.lcdata = lcdata
self.group = None
self.pid_ar = None
self.pid_ld = None
self.pid_private = None
## Precalculate the likelihood constants
## =====================================
self.lln = -0.5 * self.lcdata.npt * m.log(2 * m.pi)
## Define priors
## =============
pr = kwargs.get('priors', {})
self.priors = []
self.priors.extend([
UP(0.2, 5.0, 'e', 'Error multiplier')
for i in range(self.lcdata.npb)
])
self.priors.extend([
UP(0.0, 0.01, 'c', 'Contamination') for i in range(self.lcdata.npb)
])
self.ps = PriorSet(self.priors)
self.np = len(self.priors)
self.pv_start_idx = None
import numpy as np
import matplotlib.pyplot as pl
from pytransit.gimenez import Gimenez
k, t0, p, a, i, e, w = 0.1, 1., 4., 8., 0.5*np.pi, 0.01, 0.5*np.pi
t = np.linspace(0.9,1.1,500)
u = np.array([[0.04*iu, 0.025*iu, 0.01*iu] for iu in range(10)])
c = np.linspace(0,0.9,10)
m = Gimenez(lerp=False, npol=100, nldc=3)
f = m.evaluate(t, k, u, t0, p, a, i, e, w, c=c)
fig,ax = pl.subplots(1,1,figsize=(7,7))
ax.plot(t,f + np.arange(u.shape[0])*0.00, 'k');
pl.setp(ax, ylim=(0.9875,1.011), yticks=[], xlabel='Time [d]', ylabel='Normalised flux',
title='Light curve for a set of multiple limb darkening coefficients');
fig.tight_layout()
pl.show()
def test_lerp_npol(self):
f = Gimenez(npol=100, nthr=1, lerp=True)(self.z, self.k, self.u[0])
npt.assert_array_almost_equal(f,self.f_ref[:,0], decimal=4)
for i in range(2,4):
f = Gimenez(npol=100, nthr=1, lerp=True)(self.z, self.k, self.u[0:i])
npt.assert_array_almost_equal(f,self.f_ref[:,0:i], decimal=4)
def test_nolerp_basic(self):
f = Gimenez(npol=500, nthr=1, lerp=False)(self.z, self.k, self.u[0])
npt.assert_array_almost_equal(f, self.f_ref[:,0])
for i in range(2,4):
f = Gimenez(npol=500, nthr=1, lerp=False)(self.z, self.k, self.u[0:i])
npt.assert_array_almost_equal(f, self.f_ref[:,0:i])
import numpy as np
import matplotlib.pyplot as pl
from pytransit.gimenez import Gimenez
z = np.linspace(1e-7, 1.5, 1000)
u = np.array([0.3, 0.1])
k = 0.10
npols = [800, 500, 200, 100, 50, 20, 10, 5]
I = [Gimenez(npol, 2)(z, k, u) for npol in npols]
fig, ax = pl.subplots(1, 1)
la = 1 - np.linspace(0, 0.75, len(npols))
ax.plot(I[0], c='0.0')
for i in range(1, len(npols)):
print '\tN {:3d} -- Max deviation {:6.4f} ppm'.format(
npols[i], 1e3 * np.abs(I[0] - I[i]).max())
ax.plot(I[i], c='0', alpha=la[i])
ax.set_ylim(0.988, 1.001)
pl.show()
import numpy as np
import matplotlib.pyplot as pl
from pytransit.gimenez import Gimenez
k, t0, p, a, i, e, w = 0.1, 1., 4., 8., 0.5 * np.pi, 0.01, 0.5 * np.pi
t = np.linspace(0.9, 1.1, 500)
u = np.array([[0.04 * iu, 0.025 * iu, 0.01 * iu] for iu in range(10)])
c = np.linspace(0, 0.9, 10)
m = Gimenez(lerp=False, npol=100, nldc=3)
f = m.evaluate(t, k, u, t0, p, a, i, e, w, c=c)
fig, ax = pl.subplots(1, 1, figsize=(7, 7))
ax.plot(t, f + np.arange(u.shape[0]) * 0.00, 'k')
pl.setp(ax,
ylim=(0.9875, 1.011),
yticks=[],
xlabel='Time [d]',
ylabel='Normalised flux',
title='Light curve for a set of multiple limb darkening coefficients')
fig.tight_layout()
pl.show()