def mr_convert(self, meas):
"""Runs conversion from mass to radius"""
if self.config_parameters["mass_radius"] == "mrexo":
return pfm(meas, predict="radius", dataset="kepler")[0]
elif self.config_parameters["mass_radius"] == "otegi":
return self.otegi_mr(meas, "radius")
def syssim_pers(self, per, rad, P, M_star):
"""Generates probability of periods using clustered periods from He, Ford, and Ragozzine (2019)"""
sigmap = 0.2
Np = len(per)
PR = [per[i] / per[i - 1] for i in range(1, len(per))]
bools = []
for i in range(len(PR)):
if PR[i] > spst.lognorm.ppf(0.95, Np * sigmap):
bools.append(1)
else:
bools.append(0)
def calc_bools_dict_value(bl):
kt_planets = [p for p in range(1, len(bl) + 2)]
kt_cluster = {}
kt_cluster_count = 0
for i in range(0, len(bl)):
if bl[i] == 0:
if kt_planets[i] not in [
v for sl in kt_cluster.values() for v in sl
]:
kt_cluster_count += 1
kt_cluster[kt_cluster_count] = [
kt_planets[i], kt_planets[i + 1]
]
else:
found_in_key = [
k for (k, v) in kt_cluster.items()
if kt_planets[i] in v
][0]
kt_cluster[found_in_key].append(kt_planets[i + 1])
else:
if kt_planets[i] not in [
v for sl in kt_cluster.values() for v in sl
]:
kt_cluster_count += 1
kt_cluster[kt_cluster_count] = [kt_planets[i]]
kt_cluster_count += 1
kt_cluster[kt_cluster_count] = [kt_planets[i + 1]]
else:
kt_cluster_count += 1
kt_cluster[kt_cluster_count] = [kt_planets[i + 1]]
return len(kt_cluster), list(kt_cluster.values())
Nc, Np = calc_bools_dict_value(bools)
if Nc == 1:
Pcb = 0
Pcbs = []
fPcb = []
for i in range(len(P)):
test = 0
for k in range(len(per)):
test += spst.lognorm.pdf(per[k] / P[i], len(per) * sigmap)
Pcbs.append(P[i])
fPcb.append(test)
Pcb = round(Pcbs[np.where(fPcb == max(fPcb))[0][0]], 1)
else:
Pcb = []
for i in range(Nc):
Pcbs = []
fPcb = []
for k in range(len(P)):
test = 0
for j in Np[i]:
test += spst.lognorm.pdf(per[j - 1] / P[k],
len(Np[i]) * sigmap)
Pcbs.append(P[k])
fPcb.append(test)
Pcb.append(Pcbs[np.where(fPcb == max(fPcb))[0][0]])
Pcb = np.array([round(Pcb[i], 1) for i in range(len(Pcb))])
Pip = np.linspace(math.sqrt(min(P) / max(P)),
math.sqrt(max(P) / min(P)), 1001)
fPip = []
for i in range(Nc):
fPip.append(
spst.lognorm.pdf(Pip, (len(per) if Nc == 1 else len(Np[i])) *
sigmap))
fP = np.zeros(len(P))
fD = np.zeros(len(P))
for i in range(len(fP)):
f = []
for j in range(Nc):
f.append(
np.interp(P[i], (Pcb if Nc == 1 else Pcb[j]) * Pip,
fPip[j]))
fP[i] = max(f)
m = np.zeros(len(per))
for k in range(len(per)):
if self.config_parameters["mass_radius"] == "mrexo":
m[k] = pfm(measurement=rad[k],
predict='mass',
dataset='kepler')[0]
elif self.config_parameters["mass_radius"] == "otegi":
m[k] = self.otegi_mr(rad[k], "mass")
m2 = np.mean(m)
GMfp213 = (self.G * M_star * self.M_sun / (4 * math.pi**2))**(1 / 3)
dc = 8
rats = [math.sqrt(per[k + 1] / per[k]) for k in range(len(per) - 1)]
k = 0
for i in range(len(P)):
if k < len(per) - 1 and P[i] > per[k] * rats[k] and P[i] < per[k +
1]:
k += 1
a1 = GMfp213 * (P[i] * self.seconds_per_day if P[i] < per[k] else
per[k] * self.seconds_per_day)**(2 / 3)
a2 = GMfp213 * (per[k] * self.seconds_per_day if P[i] < per[k] else
P[i] * self.seconds_per_day)**(2 / 3)
fD[i] = 2 * (a2 - a1) / (a2 + a1) * (
(m[k] + m2) * self.M_earth /
(3 * M_star * self.M_sun))**(-1 / 3)
if fD[i] < dc:
fP[i] = 0
Du = np.arange(0, max(fD) + 1)
fDu = np.zeros(len(Du))
trap = np.trapz(fP, P)
cdfp = np.zeros(len(fP))
for i in range(len(fD)):
j = int(fD[i])
fDu[j] += fP[i] / trap
for i in range(len(fP)):
cdfP[i] = np.trapz(fP[:i + 1], P[:i + 1]) / trap
return fP / trap, fDu, cdfP
def epos_pers(self, p0, per, rad, P, M_star):
"""Generates probability of periods using dimensionless spacing in period ratios from EPOS (Mulders et al. 2018)"""
fP = np.zeros(len(P))
cdfP = np.zeros(len(P))
fD = np.zeros(len(P))
ind = 0
for i in range(len(P)):
if P[i] < p0:
fP[i] = ((P[i] / 12)**1.6 if P[i] < 12 else (P[i] / 12)**-0.9)
else:
ind = i
break
logD = -0.9
sigma = 0.41
PRgrid = np.logspace(0, 1)
with np.errstate(divide='ignore'):
Dgrid = np.log(2. * (PRgrid**(2. / 3.) - 1.) /
(PRgrid**(2. / 3.) + 1.))
Dgrid[0] = -4
pdfPR = spst.norm(logD, sigma).pdf(Dgrid)
cdfPR = spst.norm(logD, sigma).cdf(Dgrid)
j = 0
for i in range(ind, len(fP)):
if j < len(per) - 1 and P[i] > per[j + 1]:
j += 1
fP[i] = np.interp(P[i] / per[j], PRgrid, pdfPR)
if j != len(per) - 1:
fP[i] *= np.interp(per[j + 1] / P[i], PRgrid, pdfPR)
m = np.zeros(len(per))
for k in range(len(per)):
if self.config_parameters["mass_radius"] == "mrexo":
m[k] = pfm(measurement=rad[k],
predict='mass',
dataset='kepler')[0]
elif self.config_parameters["mass_radius"] == "otegi":
m[k] = self.otegi_mr(rad[k], "mass")
m2 = np.mean(m)
GMfp213 = (self.G * M_star * self.M_sun / (4 * math.pi**2))**(1 / 3)
dc = 8
rats = [math.sqrt(per[k + 1] / per[k]) for k in range(len(per) - 1)]
k = 0
for i in range(len(P)):
if k < len(per) - 1 and P[i] > per[k] * rats[k] and P[i] < per[k +
1]:
k += 1
a1 = GMfp213 * (P[i] * self.seconds_per_day if P[i] < per[k] else
per[k] * self.seconds_per_day)**(2 / 3)
a2 = GMfp213 * (per[k] * self.seconds_per_day if P[i] < per[k] else
P[i] * self.seconds_per_day)**(2 / 3)
fD[i] = 2 * (a2 - a1) / (a2 + a1) * (
(m[k] + m2) * self.M_earth /
(3 * M_star * self.M_sun))**(-1 / 3)
if fD[i] < dc:
fP[i] = 0
Du = np.arange(0, max(fD) + 1)
fDu = np.zeros(len(Du))
trap = np.trapz(fP, P)
for i in range(len(fD)):
j = int(fD[i])
fDu[j] += fP[i] / trap
for i in range(len(cdfP)):
cdfP[i] = np.trapz(fP[:i + 1], P[:i + 1]) / trap
return fP / trap, fDu, cdfP
def epos_pers(self, p0, per, rad, P, M_star):
"""Generates probability of periods using dimensionless spacing in period ratios from EPOS (Mulders et al. 2018)"""
fP = np.zeros(len(P))
fD = np.zeros(len(P))
ind = 0
for i in range(len(P)):
if P[i] < p0:
fP[i] = ((P[i] / 12)**1.6 if P[i] < 12 else (P[i] / 12)**-0.9)
else:
ind = i
break
logD = -0.9
sigma = 0.41
PRgrid = np.logspace(0, 1)
with np.errstate(divide='ignore'):
Dgrid = np.log(2. * (PRgrid**(2. / 3.) - 1.) /
(PRgrid**(2. / 3.) + 1.))
Dgrid[0] = -4
pdfP = spst.norm(logD, sigma).pdf(Dgrid)
cdfP = spst.norm(logD, sigma).cdf(Dgrid)
for i in range(ind, len(fP)):
for j in range(len(per)):
if P[i] > per[j]:
fP[i] = np.interp(P[i] / per[j], PRgrid, pdfP)
if j < len(per) - 1 and P[i] < per[j + 1]:
fP[i] *= np.interp(per[j + 1] / P[i], PRgrid, pdfP)
elif j < len(per) - 1 and P[i] >= per[j + 1]:
fP[i] = 0
m = np.zeros(len(per))
for k in range(len(per)):
if self.config_parameters["mass_radius"] == "mrexo":
m[k] = pfm(measurement=rad[k],
predict='mass',
dataset='kepler')[0]
elif self.config_parameters["mass_radius"] == "otegi":
m[k] = self.otegi_mr(rad[k], "mass")
for i in range(len(P)):
for k in range(len(per)):
m1 = m[k]
m2 = np.mean(m)
dc = 8
a1 = self.K3(P[i], M_star) if P[i] < per[k] else self.K3(
per[k], M_star)
a2 = self.K3(per[k], M_star) if P[i] < per[k] else self.K3(
P[i], M_star)
fD[i] = 2 * (a2 - a1) / (a2 + a1) * (
(m1 + m2) * const.M_earth.cgs.value /
(3 * M_star * const.M_sun.cgs.value))**(-1 / 3)
if fD[i] < dc:
fP[i] = 0
Du = np.arange(0, max(fD) + 1)
fDu = np.zeros(len(Du))
for i in range(len(fD)):
j = int(fD[i])
fDu[j] += fP[i] / np.trapz(fP, P)
return fP / np.trapz(fP, P), fDu