def test_gen_angles(self):
"""
Test generation of orientation angles.
We expect long. and periapse to be uniformly distributed and
inclination to be sinusoidally distributed.
Test method: chi squares
"""
pp = PlanetPopulation(**self.spec)
x = 10000
I, O, w = pp.gen_angles(x)
#O & w are expected to be uniform
for param,param_range in zip([O,w],[pp.Orange,pp.wrange]):
h = np.histogram(param,100,density=True)
chi2 = scipy.stats.chisquare(h[0],[1.0/np.diff(param_range.value)[0]]*len(h[0]))
self.assertGreater(chi2[1], 0.95)
#I is expected to be sinusoidal
hI = np.histogram(I.to(u.rad).value,100,density=True)
Ix = np.diff(hI[1])/2.+hI[1][:-1]
Ip = np.sin(Ix)/2
Ichi2 = scipy.stats.chisquare(hI[0],Ip)
assert(Ichi2[1] > 0.95)
def test_gen_angles(self):
"""
Test generation of orientation angles.
We expect long. and periapse to be uniformly distributed and
inclination to be sinusoidally distributed.
Test method: chi squares
"""
pp = PlanetPopulation(**self.spec)
x = 100000
I, O, w = pp.gen_angles(x)
#O & w are expected to be uniform
for param, param_range in zip([O, w], [pp.Orange, pp.wrange]):
h = np.histogram(param, 100, density=True)
chi2 = scipy.stats.chisquare(
h[0], [1.0 / np.diff(param_range.value)[0]] * len(h[0]))
self.assertGreater(chi2[1], 0.95)
#I is expected to be sinusoidal
hI = np.histogram(I.to(u.rad).value, 100, density=True)
Ix = np.diff(hI[1]) / 2. + hI[1][:-1]
Ip = np.sin(Ix) / 2
Ichi2 = scipy.stats.chisquare(hI[0], Ip)
assert (Ichi2[1] > 0.95)
def test_gen_plan_params(self):
"""
Test generation of planet orbital and phyiscal properties.
We expect eccentricity and albedo to be uniformly distributed
and sma and radius to be log-uniform
Test method: chi squares
"""
pp = PlanetPopulation(**self.spec)
x = 10000
a, e, p, Rp = pp.gen_plan_params(x)
#expect e and p to be uniform
for param,param_range in zip([e,p],[pp.erange,pp.prange]):
h = np.histogram(param,100,density=True)
chi2 = scipy.stats.chisquare(h[0],[1.0/np.diff(param_range)[0]]*len(h[0]))
assert(chi2[1] > 0.95)
#expect a and Rp to be log-uniform
for param,param_range in zip([a.value,Rp.value],[pp.arange.value,pp.Rprange.value]):
h = np.histogram(param,100,density=True)
hx = np.diff(h[1])/2.+h[1][:-1]
hp = 1.0/(hx*np.log(param_range[1]/param_range[0]))
chi2 = scipy.stats.chisquare(h[0],hp)
assert(chi2[1] > 0.95)
def test_gen_plan_params(self):
"""
Test generation of planet orbital and phyiscal properties.
We expect eccentricity and albedo to be uniformly distributed
and sma and radius to be log-uniform
Test method: chi squares
"""
pp = PlanetPopulation(**self.spec)
x = 10000
a, e, p, Rp = pp.gen_plan_params(x)
#expect e and p to be uniform
for param, param_range in zip([e, p], [pp.erange, pp.prange]):
h = np.histogram(param, 100, density=True)
chi2 = scipy.stats.chisquare(
h[0], [1.0 / np.diff(param_range)[0]] * len(h[0]))
assert (chi2[1] > 0.95)
#expect a and Rp to be log-uniform
for param, param_range in zip([a.value, Rp.value],
[pp.arange.value, pp.Rprange.value]):
h = np.histogram(param, 100, density=True)
hx = np.diff(h[1]) / 2. + h[1][:-1]
hp = 1.0 / (hx * np.log(param_range[1] / param_range[0]))
chi2 = scipy.stats.chisquare(h[0], hp)
assert (chi2[1] > 0.95)
def __init__(self, eta=0.1, **specs):
specs['eta'] = eta
specs['arange'] = [0.95, 1.67]
specs['erange'] = [0,0]
specs['prange'] = [0.2,0.2]
specs['Rprange'] = [0.61905858602731,1.4]
specs['Mprange'] = [1,1]
specs['scaleOrbits'] = True
PlanetPopulation.__init__(self, **specs)
def __init__(self, eta=0.1, **specs):
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['erange'] = [0,0]
specs['Rprange'] = [1,1]
specs['Mprange'] = [1,1]
specs['prange'] = [0.367,0.367]
specs['scaleOrbits'] = True
PlanetPopulation.__init__(self, **specs)
def __init__(self, eta=0.1, **specs):
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['erange'] = [0,0]
specs['prange'] = [0.367,0.367]
specs['Rprange'] = [1,1]
specs['Mprange'] = [1,1]
specs['scaleOrbits'] = True
PlanetPopulation.__init__(self, **specs)
def __init__(self, eta=0.1, erange=[0.,0.9], constrainOrbits=True, **specs):
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['erange'] = erange
specs['Rprange'] = [1,1]
specs['Mprange'] = [1,1]
specs['prange'] = [0.367,0.367]
specs['scaleOrbits'] = True
specs['constrainOrbits'] = constrainOrbits
PlanetPopulation.__init__(self, **specs)
def __init__(self,
smaknee=30,
esigma=0.175 / np.sqrt(np.pi / 2.),
prange=[0.083, 0.882],
Rprange=[1, 22.6],
**specs):
specs['prange'] = prange
specs['Rprange'] = Rprange
PlanetPopulation.__init__(self, **specs)
# calculate norm for sma distribution with decay point (knee)
self.smaknee = float(smaknee)
ar = self.arange.to('AU').value
# sma distribution without normalization
tmp_dist_sma = lambda x, s0=self.smaknee: x**(-0.62) * np.exp(-(x / s0)
**2)
self.smanorm = integrate.quad(tmp_dist_sma, ar[0], ar[1])[0]
# calculate norm for eccentricity Rayleigh distribution
self.esigma = float(esigma)
er = self.erange
self.enorm = np.exp(-er[0]**2/(2.*self.esigma**2)) \
- np.exp(-er[1]**2/(2.*self.esigma**2))
# define Kepler radius distribution
Rs = np.array([1, 1.4, 2.0, 2.8, 4.0, 5.7, 8.0, 11.3, 16,
22.6]) #Earth Radii
Rvals85 = np.array([
0.1555, 0.1671, 0.1739, 0.0609, 0.0187, 0.0071, 0.0102, 0.0049,
0.0014
])
#sma of 85 days
a85 = ((85. * u.day / 2. / np.pi)**2 * u.solMass * const.G)**(1. / 3.)
# sma of 0.8 days (lower limit of Fressin et al 2012)
a08 = ((0.8 * u.day / 2. / np.pi)**2 * u.solMass * const.G)**(1. / 3.)
fac1 = integrate.quad(tmp_dist_sma,
a08.to('AU').value,
a85.to('AU').value)[0]
Rvals = integrate.quad(tmp_dist_sma, ar[0],
ar[1])[0] * (Rvals85 / fac1)
Rvals[5:] *= 2.5 #account for longer orbital baseline data
self.Rs = Rs
self.Rvals = Rvals
self.eta = np.sum(Rvals)
# populate outspec with attributes specific to KeplerLike1
self._outspec['smaknee'] = self.smaknee
self._outspec['esigma'] = self.esigma
self._outspec['eta'] = self.eta
self.dist_albedo_built = None
def __init__(self, eta=0.1, erange=[0.,0.9], constrainOrbits=True, **specs):
specs['erange'] = erange
specs['constrainOrbits'] = constrainOrbits
# specs being modified in EarthTwinHabZone1
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['Rprange'] = [1,1]
specs['Mprange'] = [1,1]
specs['prange'] = [0.367,0.367]
specs['scaleOrbits'] = True
PlanetPopulation.__init__(self, **specs)
def __init__(self, smaknee=30, esigma=0.25, **specs):
specs['prange'] = [0.083, 0.882]
specs['Rprange'] = [1, 22.6]
PlanetPopulation.__init__(self, **specs)
assert (smaknee >= self.arange[0].to('AU').value) and \
(smaknee <= self.arange[1].to('AU').value), \
"sma knee value must be in sma range."
#define sma distribution
self.smaknee = float(smaknee)
self.smadist1 = lambda x, s0=self.smaknee: x**(-0.62) * np.exp(-(
(x / s0)**2))
self.smanorm = integrate.quad(self.smadist1,
self.arange.min().to('AU').value,
self.arange.max().to('AU').value)[0]
self.smadist = lambda x, s0=self.smaknee: (x**(-0.62) * np.exp(-(
(x / s0)**2))) / self.smanorm
#define Rayleigh eccentricity distribution
self.esigma = float(esigma)
self.edist1 = lambda x, sigma=self.esigma: (x / sigma**2) * np.exp(
-x**2 / (2. * sigma**2))
self.enorm = integrate.quad(self.edist1, self.erange.min(),
self.erange.max())[0]
self.edist = lambda x, sigma=self.esigma: (
(x / sigma**2) * np.exp(-x**2 / (2.0 * sigma**2))) / self.enorm
#define Kepler radius distribution
Rs = np.array([1, 1.4, 2.0, 2.8, 4.0, 5.7, 8.0, 11.3, 16,
22.6]) #Earth Radii
Rvals85 = np.array([
0.1555, 0.1671, 0.1739, 0.0609, 0.0187, 0.0071, 0.0102, 0.0049,
0.0014
])
a85 = (((85 * u.day / 2 / np.pi)**2 * const.M_sun *
const.G)**(1. / 3)).to('AU') #sma of 85 days
fac1 = integrate.quad(self.smadist, 0, a85.value)[0]
Rvals = integrate.quad(
self.smadist, 0,
self.arange[1].to('AU').value)[0] * (Rvals85 / fac1)
Rvals[5:] *= 2.5 #account for longer orbital baseline data
self.Rs = Rs
self.Rvals = Rvals
self.eta = np.sum(Rvals)
self.dist_albedo_built = False
self.dist_radius_built = False
def __init__(self,
eta=0.1,
erange=[0., 0.9],
constrainOrbits=True,
**specs):
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['erange'] = erange
specs['Rprange'] = [1, 1]
specs['Mprange'] = [1, 1]
specs['prange'] = [0.367, 0.367]
specs['scaleOrbits'] = True
specs['constrainOrbits'] = constrainOrbits
PlanetPopulation.__init__(self, **specs)
def __init__(self, eta=1, arange=[0.7*np.sqrt(0.83),1.5*np.sqrt(0.83)], erange=[0.,0.35], constrainOrbits=False, **specs):
#eta is probability of planet occurance in a system. I set this to 1
specs['erange'] = erange
specs['constrainOrbits'] = constrainOrbits
pE = 0.26 # From Brown 2005 #0.33 listed in paper but q=0.26 is listed in the paper in the figure
# specs being modified in JupiterTwin
specs['eta'] = eta
specs['arange'] = arange #*u.AU
specs['Rprange'] = [1.,1.] #*u.earthRad
#specs['Mprange'] = [1*MpEtoJ,1*MpEtoJ]
specs['prange'] = [pE,pE]
specs['scaleOrbits'] = True
self.pE = pE
PlanetPopulation.__init__(self, **specs)
def __init__(self,
eta=0.1,
erange=[0., 0.9],
constrainOrbits=True,
**specs):
specs['erange'] = erange
specs['constrainOrbits'] = constrainOrbits
# specs being modified in EarthTwinHabZone1
specs['eta'] = eta
specs['arange'] = [0.7, 1.5]
specs['Rprange'] = [1, 1]
specs['Mprange'] = [1, 1]
specs['prange'] = [0.367, 0.367]
specs['scaleOrbits'] = True
PlanetPopulation.__init__(self, **specs)
def __init__(self, eta=1, erange=[0.,0.048], constrainOrbits=True, **specs):
#eta is probability of planet occurance in a system. I set this to 1
specs['erange'] = erange
specs['constrainOrbits'] = constrainOrbits
aEtoJ = 5.204
RpEtoJ = 11.209
MpEtoJ = 317.83
pJ = 0.538# 0.538 from nssdc.gsfc.nasa.gov
# specs being modified in JupiterTwin
specs['eta'] = eta
specs['arange'] = [1*aEtoJ,1*aEtoJ]#0.7*aEtoJ, 1.5*aEtoJ]
specs['Rprange'] = [1*RpEtoJ,1*RpEtoJ]
specs['Mprange'] = [1*MpEtoJ,1*MpEtoJ]
specs['prange'] = [pJ,pJ]
specs['scaleOrbits'] = True
self.RpEtoJ = RpEtoJ
self.pJ = pJ
PlanetPopulation.__init__(self, **specs)
def __init__(self, smaknee=30, esigma=0.175/np.sqrt(np.pi/2.),
prange=[0.083, 0.882], Rprange=[1, 22.6], **specs):
specs['prange'] = prange
specs['Rprange'] = Rprange
PlanetPopulation.__init__(self, **specs)
# calculate norm for sma distribution with decay point (knee)
self.smaknee = float(smaknee)
ar = self.arange.to('AU').value
# sma distribution without normalization
tmp_dist_sma = lambda x,s0=self.smaknee: x**(-0.62)*np.exp(-(x/s0)**2)
self.smanorm = integrate.quad(tmp_dist_sma, ar[0], ar[1])[0]
# calculate norm for eccentricity Rayleigh distribution
self.esigma = float(esigma)
er = self.erange
self.enorm = np.exp(-er[0]**2/(2.*self.esigma**2)) \
- np.exp(-er[1]**2/(2.*self.esigma**2))
# define Kepler radius distribution
Rs = np.array([1,1.4,2.0,2.8,4.0,5.7,8.0,11.3,16,22.6]) #Earth Radii
Rvals85 = np.array([0.1555,0.1671,0.1739,0.0609,0.0187,0.0071,0.0102,0.0049,0.0014])
#sma of 85 days
a85 = ((85.*u.day/2./np.pi)**2*u.solMass*const.G)**(1./3.)
# sma of 0.8 days (lower limit of Fressin et al 2012)
a08 = ((0.8*u.day/2./np.pi)**2*u.solMass*const.G)**(1./3.)
fac1 = integrate.quad(tmp_dist_sma, a08.to('AU').value, a85.to('AU').value)[0]
Rvals = integrate.quad(tmp_dist_sma, ar[0], ar[1])[0]*(Rvals85/fac1)
Rvals[5:] *= 2.5 #account for longer orbital baseline data
self.Rs = Rs
self.Rvals = Rvals
self.eta = np.sum(Rvals)
# populate outspec with attributes specific to KeplerLike1
self._outspec['smaknee'] = self.smaknee
self._outspec['esigma'] = self.esigma
self._outspec['eta'] = self.eta
self.dist_albedo_built = None
def __init__(self,
eta=1,
erange=[0., 0.048],
constrainOrbits=True,
**specs):
#eta is probability of planet occurance in a system. I set this to 1
specs['erange'] = erange
specs['constrainOrbits'] = constrainOrbits
aEtoJ = 5.204
RpEtoJ = 11.209
MpEtoJ = 317.83
pJ = 0.538 # 0.538 from nssdc.gsfc.nasa.gov
# specs being modified in JupiterTwin
specs['eta'] = eta
specs['arange'] = [1 * aEtoJ, 1 * aEtoJ] #0.7*aEtoJ, 1.5*aEtoJ]
specs['Rprange'] = [1 * RpEtoJ, 1 * RpEtoJ]
specs['Mprange'] = [1 * MpEtoJ, 1 * MpEtoJ]
specs['prange'] = [pJ, pJ]
specs['scaleOrbits'] = True
self.RpEtoJ = RpEtoJ
self.pJ = pJ
PlanetPopulation.__init__(self, **specs)
def __init__(self, smaknee=30, esigma=0.25, **specs):
specs['prange'] = [0.083,0.882]
specs['Rprange'] = [1,22.6]
PlanetPopulation.__init__(self, **specs)
assert (smaknee >= self.arange[0].to('AU').value) and \
(smaknee <= self.arange[1].to('AU').value), \
"sma knee value must be in sma range."
#define sma distribution
self.smaknee = float(smaknee)
self.smadist1 = lambda x,s0=self.smaknee: x**(-0.62)*np.exp(-((x/s0)**2))
self.smanorm = integrate.quad(self.smadist1, self.arange.min().to('AU').value, self.arange.max().to('AU').value)[0]
self.smadist = lambda x,s0=self.smaknee: (x**(-0.62)*np.exp(-((x/s0)**2)))/self.smanorm
#define Rayleigh eccentricity distribution
self.esigma = float(esigma)
self.edist1 = lambda x,sigma=self.esigma: (x/sigma**2)*np.exp(-x**2/(2.*sigma**2))
self.enorm = integrate.quad(self.edist1, self.erange.min(), self.erange.max())[0]
self.edist = lambda x,sigma=self.esigma: ((x/sigma**2)*np.exp(-x**2/(2.0*sigma**2)))/self.enorm
#define Kepler radius distribution
Rs = np.array([1,1.4,2.0,2.8,4.0,5.7,8.0,11.3,16,22.6]) #Earth Radii
Rvals85 = np.array([0.1555, 0.1671, 0.1739, 0.0609, 0.0187, 0.0071, 0.0102, 0.0049, 0.0014])
a85 = (((85*u.day/2/np.pi)**2*const.M_sun*const.G)**(1./3)).to('AU') #sma of 85 days
fac1 = integrate.quad(self.smadist,0,a85.value)[0]
Rvals = integrate.quad(self.smadist,0,self.arange[1].to('AU').value)[0]*(Rvals85/fac1)
Rvals[5:] *= 2.5 #account for longer orbital baseline data
self.Rs = Rs
self.Rvals = Rvals
self.eta = np.sum(Rvals)
self.dist_albedo_built = False
self.dist_radius_built = False
def test_checkranges(self):
"""
Test that check ranges is doing what it should do
"""
pp = PlanetPopulation(arange=[10, 1], **self.spec)
self.assertTrue(pp.arange[0].value == 1)
self.assertTrue(pp.arange[1].value == 10)
with self.assertRaises(AssertionError):
pp = PlanetPopulation(prange=[-1, 1], **self.spec)
with self.assertRaises(AssertionError):
pp = PlanetPopulation(erange=[-1, 1], **self.spec)
with self.assertRaises(AssertionError):
pp = PlanetPopulation(arange=[0, 1], **self.spec)
with self.assertRaises(AssertionError):
pp = PlanetPopulation(Rprange=[0, 1], **self.spec)
with self.assertRaises(AssertionError):
pp = PlanetPopulation(Mprange=[0, 1], **self.spec)
def __init__(self, starMass=1., occDataPath=None, esigma=0.175/np.sqrt(np.pi/2.), **specs):
PlanetPopulation.__init__(self, **specs)
self.starMass = starMass * u.M_sun
self.occDataPath = occDataPath
self.esigma = float(esigma)
er = self.erange
self.enorm = np.exp(-er[0]**2/(2.*self.esigma**2)) \
- np.exp(-er[1]**2/(2.*self.esigma**2))
self.dist_sma_built = None
self.dist_radius_built = None
self.dist_albedo_built = None
# check occDataPath
if self.occDataPath is None:
classpath = os.path.split(inspect.getfile(self.__class__))[0]
filename = 'NominalOcc_Radius.csv'
self.occDataPath = os.path.join(classpath, filename)
assert os.path.exists(self.occDataPath), "occurrence rate table not found at {}".format(self.occDataPath)
# load data
occData = dict(ascii.read(self.occDataPath))
self.aData = "a_min" in occData
self.RData = "R_min" in occData
# load occurrence rates
eta2D = []
for tmp in occData['Occ']:
eta2D.append(tmp)
eta2D = np.array(eta2D)
# load semi-major axis or period
if self.aData:
amin = []
for tmp in occData['a_min']:
amin.append(tmp)
amin = np.array(amin)
amax = []
for tmp in occData['a_max']:
amax.append(tmp)
amax = np.array(amax)
self.smas = np.hstack((np.unique(amin),np.unique(amax)[-1]))
len1 = len(self.smas) - 1
self.arange = np.array([self.smas[0],self.smas[-1]])*u.AU
else:
Pmin = []
for tmp in occData['P_min']:
Pmin.append(tmp)
Pmin = np.array(Pmin)
Pmax = []
for tmp in occData['P_max']:
Pmax.append(tmp)
Pmax = np.array(Pmax)
self.Ps = np.hstack((np.unique(Pmin),np.unique(Pmax)[-1]))
len1 = len(self.Ps) -1
self.arange = ((const.G*self.starMass*(np.array([self.Ps[0],self.Ps[-1]])*u.day)**2/4./np.pi**2)**(1./3.)).to('AU')
# load radius or mass
if self.RData:
Rmin = []
for tmp in occData['R_min']:
Rmin.append(tmp)
Rmin = np.array(Rmin)
Rmax = []
for tmp in occData['R_max']:
Rmax.append(tmp)
Rmax = np.array(Rmax)
self.Rs = np.hstack((np.unique(Rmin), np.unique(Rmax)[-1]))
len2 = len(self.Rs) - 1
self.Rprange = np.array([self.Rs[0],self.Rs[-1]])*u.R_earth
# maximum from data sheets provided
self.Mprange = np.array([0.08, 4768.])*u.earthMass
else:
Mmin = []
for tmp in occData['M_min']:
Mmin.append(tmp)
Mmin = np.array(Mmin)
Mmax = []
for tmp in occData['M_max']:
Mmax.append(tmp)
Mmax = np.array(Mmax)
self.Ms = np.hstack((np.unique(Mmin), np.unique(Mmax)[-1]))
len2 = len(self.Ms) - 1
self.Mprange = np.array([self.Ms[0],self.Ms[-1]])*u.M_earth
self.Rprange = np.array([1.008*0.08**(0.279), 17.739*(0.225*u.M_jup.to('earthMass'))**(-0.044)])*u.earthRad
if self.constrainOrbits:
self.rrange = self.arange
else:
self.rrange = np.array([self.arange[0].to('AU').value*(1.0-self.erange[1]),
self.arange[1].to('AU').value*(1.0+self.erange[1])])*u.AU
# reshape eta2D array
self.eta2D = eta2D.reshape((len1,len2)).T
self.eta = self.eta2D.sum()
def __init__(self,
starMass=1.,
occDataPath=None,
esigma=0.175 / np.sqrt(np.pi / 2.),
**specs):
PlanetPopulation.__init__(self, **specs)
self.starMass = starMass * u.M_sun
self.occDataPath = occDataPath
self.esigma = float(esigma)
er = self.erange
self.enorm = np.exp(-er[0]**2 / (2. * self.esigma**2)) - np.exp(
-er[1]**2 / (2. * self.esigma**2))
self.dist_sma_built = None
self.dist_radius_built = None
self.dist_albedo_built = None
# check occDataPath
if self.occDataPath is None:
classpath = os.path.split(inspect.getfile(self.__class__))[0]
filename = 'NominalOcc_Radius.csv'
self.occDataPath = os.path.join(classpath, filename)
assert os.path.exists(
self.occDataPath), "occurrence rate table not found at {}".format(
self.occDataPath)
# load data
occData = dict(ascii.read(self.occDataPath))
self.aData = "a_min" in occData
self.RData = "R_min" in occData
# load occurrence rates
eta2D = []
for tmp in occData['Occ']:
eta2D.append(tmp)
eta2D = np.array(eta2D)
# load semi-major axis or period
if self.aData:
amin = []
for tmp in occData['a_min']:
amin.append(tmp)
amin = np.array(amin)
amax = []
for tmp in occData['a_max']:
amax.append(tmp)
amax = np.array(amax)
self.smas = np.hstack((np.unique(amin), np.unique(amax)[-1]))
len1 = len(self.smas) - 1
self.arange = np.array([self.smas[0], self.smas[-1]]) * u.AU
else:
Pmin = []
for tmp in occData['P_min']:
Pmin.append(tmp)
Pmin = np.array(Pmin)
Pmax = []
for tmp in occData['P_max']:
Pmax.append(tmp)
Pmax = np.array(Pmax)
self.Ps = np.hstack((np.unique(Pmin), np.unique(Pmax)[-1]))
len1 = len(self.Ps) - 1
self.arange = ((const.G * self.starMass *
(np.array([self.Ps[0], self.Ps[-1]]) * u.day)**2 /
4. / np.pi**2)**(1. / 3.)).to('AU')
# load radius or mass
if self.RData:
Rmin = []
for tmp in occData['R_min']:
Rmin.append(tmp)
Rmin = np.array(Rmin)
Rmax = []
for tmp in occData['R_max']:
Rmax.append(tmp)
Rmax = np.array(Rmax)
self.Rs = np.hstack((np.unique(Rmin), np.unique(Rmax)[-1]))
len2 = len(self.Rs) - 1
self.Rprange = np.array([self.Rs[0], self.Rs[-1]]) * u.R_earth
# maximum from data sheets provided
self.Mprange = np.array([0.08, 4768.]) * u.earthMass
else:
Mmin = []
for tmp in occData['M_min']:
Mmin.append(tmp)
Mmin = np.array(Mmin)
Mmax = []
for tmp in occData['M_max']:
Mmax.append(tmp)
Mmax = np.array(Mmax)
self.Ms = np.hstack((np.unique(Mmin), np.unique(Mmax)[-1]))
len2 = len(self.Ms) - 1
self.Mprange = np.array([self.Ms[0], self.Ms[-1]]) * u.M_earth
self.Rprange = np.array([
1.008 * 0.08**(0.279), 17.739 *
(0.225 * u.M_jup.to('earthMass'))**(-0.044)
]) * u.earthRad
if self.constrainOrbits:
self.rrange = self.arange
else:
self.rrange = np.array([
self.arange[0].to('AU').value *
(1.0 - self.erange[1]), self.arange[1].to('AU').value *
(1.0 + self.erange[1])
]) * u.AU
# reshape eta2D array
self.eta2D = eta2D.reshape((len1, len2))
self.eta = self.eta2D.sum()
