def test_simpSample(self):
""" Test simple rejection sampler
Test method: use KS-statistic for two continuous distributions
and ensure that generated values correctly correlate with each one
"""
#uniform dist
ulim = [0,1]
ufun = lambda x: 1.0/np.diff(ulim)
n = int(1e5)
usample = statsFun.simpSample(ufun,n,ulim[0],ulim[1])
self.assertGreaterEqual(usample.min(), ulim[0])
self.assertLessEqual(usample.max(), ulim[1])
nlim = [-10,10]
nfun = lambda x: np.exp(-x**2./2.0)/np.sqrt(2.0*np.pi)
nsample = statsFun.simpSample(nfun,n,nlim[0],nlim[1])
self.assertGreaterEqual(nsample.min(), nlim[0])
self.assertLessEqual(nsample.min(), nlim[1])
self.assertGreaterEqual(scipy.stats.kstest(usample,'uniform')[1],0.01,'Uniform sample does not look uniform.')
self.assertGreaterEqual(scipy.stats.kstest(nsample,'norm')[1],0.01,'Normal sample does not look normal.')
self.assertLessEqual(scipy.stats.kstest(nsample,'uniform')[1],0.01,'Normal sample looks too uniform.')
self.assertLessEqual(scipy.stats.kstest(usample,'norm')[1],0.01,'Uniform sample looks too normal.')
def test_simpSample_trivial(self):
""" Test simple rejection sampler with trivial inputs
Test method: set up sampling with equal upper and lower bounds
"""
ulim = [0,1]
ufun = lambda x: 1.0/np.diff(ulim)
n = 10000
sample = statsFun.simpSample(ufun,n,0.5,0.5)
self.assertEqual(len(sample),n)
self.assertTrue(np.all(sample == 0.5))
def gen_eccen(self, n):
"""Generate eccentricity values
Rayleigh distribution, as in Kipping et. al (2013)
Args:
n (integer):
Number of samples to generate
Returns:
e (ndarray):
Planet eccentricity
"""
n = self.gen_input_check(n)
e = statsFun.simpSample(self.edist, n, self.erange[0],self.erange[1])
return e
def gen_eccen(self, n):
"""Generate eccentricity values
Rayleigh distribution, as in Kipping et. al (2013)
Args:
n (integer):
Number of samples to generate
Returns:
e (ndarray):
Planet eccentricity
"""
n = self.gen_input_check(n)
e = statsFun.simpSample(self.edist, n, self.erange[0], self.erange[1])
return e
def gen_sma(self, n):
"""Generate semi-major axis values in AU
Samples a power law distribution with exponential turn-off
determined by class attribute smaknee
Args:
n (integer):
Number of samples to generate
Returns:
a (astropy Quantity array):
Semi-major axis in units of AU
"""
n = self.gen_input_check(n)
a = statsFun.simpSample(self.smadist, n, self.arange[0].to('AU').value,\
self.arange[1].to('AU').value)*u.AU
return a
def gen_sma(self, n):
"""Generate semi-major axis values in AU
Samples a power law distribution with exponential turn-off
determined by class attribute smaknee
Args:
n (integer):
Number of samples to generate
Returns:
astropy Quantity array:
Semi-major axis values in units of AU
"""
n = self.gen_input_check(n)
ar = self.arange.to('AU').value
a = statsFun.simpSample(self.dist_sma, n, ar[0], ar[1]) * u.AU
return a
def gen_mass(self,n):
"""Generate planetary mass values in Earth mass
The mass is determined by sampling the RV mass distribution from
Cumming et al. 2010
Args:
n (integer):
Number of samples to generate
Returns:
Mp (astropy Quantity array):
Planet mass values in units of Earth mass
"""
n = self.gen_input_check(n)
# unitless mass range
Mpr = self.Mprange.to('earthMass').value
Mp = statsFun.simpSample(self.dist_mass, n, Mpr[0], Mpr[1])*u.earthMass
return Mp
def gen_mass(self,n):
"""Generate planetary mass values in kg
The mass is determined by sampling the RV mass distribution from
Cumming et al. 2010
Args:
n (integer):
Number of samples to generate
Returns:
Mp (astropy Quantity array):
Planet mass in units of kg
"""
n = self.gen_input_check(n)
Mmin = (self.Mprange[0]/const.M_jup).decompose().value
Mmax = (self.Mprange[1]/const.M_jup).decompose().value
Mp = statsFun.simpSample(self.massdist, n, Mmin, Mmax)*const.M_jup
return Mp
def gen_mass(self,n):
"""Generate planetary mass values in kg
The mass is determined by sampling the RV mass distribution from
Cumming et al. 2010
Args:
n (integer):
Number of samples to generate
Returns:
Mp (astropy Quantity array):
Planet mass in units of kg
"""
n = self.gen_input_check(n)
Mmin = (self.Mprange[0]/const.M_jup).decompose().value
Mmax = (self.Mprange[1]/const.M_jup).decompose().value
Mp = statsFun.simpSample(self.massdist, n, Mmin, Mmax)*const.M_jup
return Mp
def gen_mass(self, n):
"""Generate planetary mass values in Earth mass
The mass is determined by sampling the RV mass distribution from
Cumming et al. 2010
Args:
n (integer):
Number of samples to generate
Returns:
Mp (astropy Quantity array):
Planet mass values in units of Earth mass
"""
n = self.gen_input_check(n)
# unitless mass range
Mpr = self.Mprange.to('earthMass').value
Mp = statsFun.simpSample(self.dist_mass, n, Mpr[0],
Mpr[1]) * u.earthMass
return Mp