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utils.py
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utils.py
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from scipy.stats import gamma,norm,beta,truncnorm
import numpy as np
def transform_uniform(x,a,b):
return a + (b-a)*x
def transform_loguniform(x,a,b):
la=np.log(a)
lb=np.log(b)
return np.exp(la + x*(lb-la))
def transform_normal(x,mu,sigma):
return norm.ppf(x,loc=mu,scale=sigma)
def transform_beta(x,a,b):
return beta.ppf(x,a,b)
def transform_exponential(x,a=1.):
return gamma.ppf(x, a)
def transform_truncated_normal(x,mu,sigma,a=0.,b=1.):
ar, br = (a - mu) / sigma, (b - mu) / sigma
return truncnorm.ppf(x,ar,br,loc=mu,scale=sigma)
def readlc(fname):
fin = open(fname,'r')
ts = np.array([])
fs = np.array([])
ferrs = np.array([])
instruments = np.array([])
# Arguments of an optional linear model. This will save the regression matrix "X" in a model of the form X*theta = y, where theta
# are the coefficients:
lm_arguments = {}
# This will save a True or False for each instrument --- True if there are
# inputs and therefore we want a linear model, False if not:
lm_boolean = {}
instrument_names = []
while True:
line = fin.readline()
if line != '':
all_vals = line.split()
t,f,ferr,instrument = all_vals[0:4]
lm_variables = all_vals[4:]
ts = np.append(ts,np.double(t))
fs = np.append(fs,np.double(f))
ferrs = np.append(ferrs,np.double(ferr))
instruments = np.append(instruments,instrument.split()[0])
if instrument.split()[0] not in instrument_names:
instrument_names.append(instrument.split()[0])
if len(lm_variables)>0:
lm_arguments[instrument.split()[0]] = np.array([])
lm_boolean[instrument.split()[0]] = True
else:
lm_boolean[instrument.split()[0]] = False
if lm_boolean[instrument.split()[0]]:
if len(lm_arguments[instrument.split()[0]]) == 0:
lm_arguments[instrument.split()[0]] = np.array(lm_variables).astype(np.double)
else:
lm_arguments[instrument.split()[0]] = np.vstack((lm_arguments[instrument.split()[0]],\
np.array(lm_variables).astype(np.double)))
else:
break
# Identify instrument indeces:
indexes = {}
for instrument in instrument_names:
indexes[instrument] = np.where(instruments == instrument)[0]
return ts,fs,ferrs,instruments,indexes,len(instrument_names),instrument_names,lm_boolean,lm_arguments
def readeparams(fname,RV=False):
fin = open(fname,'r')
GPDictionary = {}
ftime = True
while True:
line = fin.readline()
if line != '':
if line[0] != '#':
vector = line.split()
if RV:
variables = vector
if ftime:
GPDictionary['variables'] = np.double(np.array(variables))
ftime = False
else:
GPDictionary['variables'] = np.vstack((GPDictionary['variables'],np.double(np.array(variables))))
else:
variables,instrument = vector[:-1],vector[-1].split()[0]
if instrument in GPDictionary.keys():
GPDictionary[instrument]['variables'] = np.vstack((GPDictionary[instrument]['variables'],np.double(np.array(variables))))
else:
GPDictionary[instrument] = {}
GPDictionary[instrument]['variables'] = np.double(np.array(variables))
else:
break
return GPDictionary
def readpriors(priorname):
"""
This function takes either a string or a dict and spits out information about the prior. If a string, it
reads a prior file. If a dict, it assumes the input dictionary has already defined all the variables and
distributions and simply spits out information about the system (e.g., number of transiting planets, RV
planets, etc.)
"""
input_dict = False
if type(priorname) == str:
fin = open(priorname)
priors = {}
else:
counter = -1
priors = priorname
input_dict = True
all_parameters = priors.keys()
n_allkeys = len(all_parameters)
n_transit = 0
n_rv = 0
n_params = 0
numbering_transit = np.array([])
numbering_rv = np.array([])
while True:
if not input_dict:
line = fin.readline()
else:
# Dummy variable so we enter the while:
line = 'nc'
counter += 1
if line != '':
if line[0] != '#':
if not input_dict:
out = line.split()
parameter,prior_name,vals = line.split()
parameter = parameter.split()[0]
prior_name = prior_name.split()[0]
vals = vals.split()[0]
priors[parameter] = {}
else:
param = all_parameters[counter]
parameter,prior_name = param,priors[param]['distribution'],
pvector = parameter.split('_')
# Check if parameter/planet is from a transiting planet:
if pvector[0] == 'r1' or pvector[0] == 'p':
pnumber = int(pvector[1][1:])
numbering_transit = np.append(numbering_transit,pnumber)
n_transit += 1
# Check if parameter/planet is from a RV planet:
if pvector[0] == 'K':
pnumber = int(pvector[1][1:])
numbering_rv = np.append(numbering_rv,pnumber)
n_rv += 1
#if parameter == 'r1_p'+str(n_transit+1) or parameter == 'p_p'+str(n_transit+1):
# numbering_transit = np.append(numbering_transit,n_transit+1)
# n_transit += 1
#if parameter == 'K_p'+str(n_rv+1):
# numbering_rv = np.append(numbering_rv,n_rv+1)
# n_rv += 1
if prior_name.lower() == 'fixed':
if not input_dict:
priors[parameter]['type'] = prior_name.lower()
priors[parameter]['value'] = np.double(vals)
priors[parameter]['cvalue'] = np.double(vals)
else:
n_params += 1
if not input_dict:
priors[parameter]['type'] = prior_name.lower()
if priors[parameter]['type'] != 'truncatednormal':
v1,v2 = vals.split(',')
priors[parameter]['value'] = [np.double(v1),np.double(v2)]
else:
v1,v2,v3,v4 = vals.split(',')
priors[parameter]['value'] = [np.double(v1),np.double(v2),np.double(v3),np.double(v4)]
priors[parameter]['cvalue'] = 0.
else:
break
if input_dict:
if counter == n_allkeys-1:
break
if not input_dict:
return priors,n_transit,n_rv,numbering_transit.astype('int'),numbering_rv.astype('int'),n_params
else:
return n_transit,n_rv,numbering_transit.astype('int'),numbering_rv.astype('int'),n_params
def get_phases(t,P,t0):
"""
Given input times, a period (or posterior dist of periods)
and time of transit center (or posterior), returns the
phase at each time t.
"""
if type(t) is not float:
phase = ((t - np.median(t0))/np.median(P)) % 1
ii = np.where(phase>=0.5)[0]
phase[ii] = phase[ii]-1.0
else:
phase = ((t - np.median(t0))/np.median(P)) % 1
if phase>=0.5:
phase = phase - 1.0
return phase
def get_quantiles(dist,alpha = 0.68, method = 'median'):
"""
get_quantiles function
DESCRIPTION
This function returns, in the default case, the parameter median and the error%
credibility around it. This assumes you give a non-ordered
distribution of parameters.
OUTPUTS
Median of the parameter,upper credibility bound, lower credibility bound
"""
ordered_dist = dist[np.argsort(dist)]
param = 0.0
# Define the number of samples from posterior
nsamples = len(dist)
nsamples_at_each_side = int(nsamples*(alpha/2.)+1)
if(method == 'median'):
med_idx = 0
if(nsamples%2 == 0.0): # Number of points is even
med_idx_up = int(nsamples/2.)+1
med_idx_down = med_idx_up-1
param = (ordered_dist[med_idx_up]+ordered_dist[med_idx_down])/2.
return param,ordered_dist[med_idx_up+nsamples_at_each_side],\
ordered_dist[med_idx_down-nsamples_at_each_side]
else:
med_idx = int(nsamples/2.)
param = ordered_dist[med_idx]
return param,ordered_dist[med_idx+nsamples_at_each_side],\
ordered_dist[med_idx-nsamples_at_each_side]
def bin_data(x,y,n_bin):
x_bins = []
y_bins = []
y_err_bins = []
for i in range(0,len(x),n_bin):
x_bins.append(np.median(x[i:i+n_bin-1]))
y_bins.append(np.median(y[i:i+n_bin-1]))
y_err_bins.append(np.sqrt(np.var(y[i:i+n_bin-1]))/np.sqrt(len(y[i:i+n_bin-1])))
return np.array(x_bins),np.array(y_bins),np.array(y_err_bins)
def writepp(fout,posteriors):
if 'pu' in posteriors:
pu = posteriors['pu']
pl = posteriors['pl']
Ar = (pu - pl)/(2. + pl + pu)
fout.write('# {0:18} \t \t {1:12} \t \t {2:12} \t \t {3:12}\n'.format('Parameter Name','Median','Upper 68 CI','Lower 68 CI'))
for pname in posteriors['posterior_samples'].keys():
if pname != 'unnamed' and pname != 'loglike':
val,valup,valdown = get_quantiles(posteriors['posterior_samples'][pname])
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format(pname,val,usigma,dsigma))
if pname.split('_')[0] == 'r2':
par,planet = pname.split('_')
r1 = posteriors['posterior_samples']['r1_'+planet]
r2 = posteriors['posterior_samples']['r2_'+planet]
b,p = np.zeros(len(r1)),np.zeros(len(r1))
for i in range(len(r1)):
if r1[i] > Ar:
b[i],p[i] = (1+pl)*(1. + (r1[i]-1.)/(1.-Ar)),\
(1-r2[i])*pl + r2[i]*pu
else:
b[i],p[i] = (1. + pl) + np.sqrt(r1[i]/Ar)*r2[i]*(pu-pl),\
pu + (pl-pu)*np.sqrt(r1[i]/Ar)*(1.-r2[i])
val,valup,valdown = get_quantiles(p)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('p_'+planet,val,usigma,dsigma))
val,valup,valdown = get_quantiles(b)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('b_'+planet,val,usigma,dsigma))
# Calculate inclination:
if 'ecosomega_'+planet in posteriors['posterior_samples']:
iplanet = planet[1:]
ecc = np.sqrt(posteriors['posterior_samples']['ecosomega_p'+str(iplanet)]**2+posteriors['posterior_samples']['esinomega_p'+str(iplanet)]**2)
omega = np.arctan2(posteriors['posterior_samples']['esinomega_p'+str(iplanet)],\
posteriors['posterior_samples']['ecosomega_p'+str(iplanet)])
elif 'secosomega_'+planet in posteriors['posterior_samples']:
iplanet = planet[1:]
ecc = posteriors['posterior_samples']['secosomega_p'+str(iplanet)]**2+posteriors['posterior_samples']['sesinomega_p'+str(iplanet)]**2
omega = np.arctan2(posteriors['posterior_samples']['sesinomega_p'+str(iplanet)],\
posteriors['posterior_samples']['secosomega_p'+str(iplanet)])
elif 'ecc_'+planet in posteriors['posterior_samples']:
ecc = posteriors['posterior_samples']['ecc_'+planet]
omega = posteriors['posterior_samples']['omega_'+planet]*np.pi/180.
else:
ecc = 0.
omega = 90.
ecc_factor = (1. + ecc*np.sin(omega))/(1. - ecc**2)
if 'rho' in posteriors['posterior_samples']:
G = 6.67408e-11
a = ((posteriors['posterior_samples']['rho']*G*((posteriors['posterior_samples']['P_'+planet]*24.*3600.)**2))/(3.*np.pi))**(1./3.)
else:
a = posteriors['posterior_samples']['a_'+planet]
inc_inv_factor = (b/a)*ecc_factor
inc = np.arccos(inc_inv_factor)*180./np.pi
val,valup,valdown = get_quantiles(inc)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('inc_'+planet,val,usigma,dsigma))
if pname.split('_')[0] == 'P':
if 'rho' in posteriors['posterior_samples']:
par,planet = pname.split('_')
G = 6.67408e-11
a = ((posteriors['posterior_samples']['rho']*G*((posteriors['posterior_samples']['P_'+planet]*24.*3600.)**2))/(3.*np.pi))**(1./3.)
val,valup,valdown = get_quantiles(a)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('a_'+planet,val,usigma,dsigma))
if pname.split('_')[0] == 'ecosomega':
par,planet = pname.split('_')
iplanet = planet[1:]
ecc = np.sqrt(posteriors['posterior_samples']['ecosomega_p'+str(iplanet)]**2+posteriors['posterior_samples']['esinomega_p'+str(iplanet)]**2)
omega = np.arctan2(posteriors['posterior_samples']['esinomega_p'+str(iplanet)],\
posteriors['posterior_samples']['ecosomega_p'+str(iplanet)])*(180/np.pi)
val,valup,valdown = get_quantiles(ecc)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('ecc_'+planet,val,usigma,dsigma))
idx = np.where(omega>0.)[0]
val,valup,valdown = get_quantiles(omega[idx])
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('omega_'+planet,val,usigma,dsigma))
if pname.split('_')[0] == 'secosomega':
par,planet = pname.split('_')
iplanet = planet[1:]
ecc = posteriors['posterior_samples']['secosomega_p'+str(iplanet)]**2+posteriors['posterior_samples']['sesinomega_p'+str(iplanet)]**2
omega = np.arctan2(posteriors['posterior_samples']['sesinomega_p'+str(iplanet)],\
posteriors['posterior_samples']['secosomega_p'+str(iplanet)])*(180/np.pi)
val,valup,valdown = get_quantiles(ecc)
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('ecc_'+planet,val,usigma,dsigma))
idx = np.where(omega>0.)[0]
val,valup,valdown = get_quantiles(omega[idx])
usigma = valup-val
dsigma = val - valdown
fout.write('{0:18} \t \t {1:.10f} \t \t {2:.10f} \t \t {3:.10f}\n'.format('omega_'+planet,val,usigma,dsigma))
fout.close()
from astropy.time import Time as APYTime
def convert_time(conv_string,t):
input_t,output_t = conv_string.split('->')
if input_t != output_t:
tobj = APYTime(t, format = 'jd', scale = input_t)
# print('new_t = tobj.'+output_t+'.jd')
# exec('new_t = tobj.'+output_t+'.jd')
if output_t == 'utc':
return tobj.utc.jd
else:
return t
# return new_t
else:
return t