def main():
eventname = sys.argv[1]
cfile = sys.argv[2]
# Load the POET event object (up through p5)
event_chk = me.loadevent(eventname + "_p5c")
event_pht = me.loadevent(eventname + "_pht")
event_ctr = me.loadevent(eventname + "_ctr", load=['data', 'uncd'])
data = event_ctr.data
uncd = event_ctr.uncd
phase = event_chk.phase[0]
phot = event_pht.fp.aplev[0]
photerr = event_pht.fp.aperr[0]
# Default to 3x3 box of pixels
avgcentx = np.floor(np.average(event_pht.fp.x) + 0.5)
avgcenty = np.floor(np.average(event_pht.fp.y) + 0.5)
avgcent = [avgcenty, avgcentx]
pixels = []
for i in range(3):
for j in range(3):
pixels.append([avgcenty - 1 + i, avgcentx - 1 + j])
phat, dP = zf.zen_init(data, pixels)
npix = len(pixels)
necl = 6 #number of eclipse parameters
#photerr = photerr/np.sqrt(np.mean(phot))
#phot = phot/np.mean(phot)
# FINDME: This is the general structure we need for MC3, but names/numbers
# are subject to change
allp, bp = mc3.mcmc(phot, photerr, func=zf.zen,
indparams=[phase, phat, npix], cfile=cfile)
return
# Alternatively, edit the paths from this script to adjust to your
# working directory.
# demo01.py:
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.append("../MCcubed/src/")
import mccubed as mc3
# Get function to model (and sample):
sys.path.append("../MCcubed/examples/models/")
from quadratic import quad
# Create a synthetic dataset:
x = np.linspace(0, 10, 100) # Independent model variable
p0 = 3, -2.4, 0.5 # True-underlying model parameters
y = quad(p0, x) # Noiseless model
uncert = np.sqrt(np.abs(y)) # Data points uncertainty
error = np.random.normal(0, uncert) # Noise for the data
data = y + error # Noisy data set
# Fit the quad polynomial coefficients:
params = np.array([ 20.0, -2.0, 0.1]) # Initial guess of fitting params.
# Run the MCMC:
allp, bp = mc3.mcmc(data, uncert, func=quad, indparams=[x],
params=params, numit=3e4, burnin=100)
walk = 'demc'
grtest = True
burnin = 100
plots = True
savefile = 'output_ex1.npy'
savemodel = 'output_model.npy'
# Run the MCMC:
allp, bp = mc3.mcmc(data,
uncert,
func,
indparams,
params,
pmin,
pmax,
stepsize,
numit=numit,
nchains=nchains,
walk=walk,
grtest=grtest,
burnin=burnin,
plots=plots,
savefile=savefile,
savemodel=savemodel)
# Evaluate and plot:
y0 = quad(params, x) # Initial guess values
y1 = quad(bp, x) # MCMC best fitting values
plt.figure(10)
plt.clf()
plt.plot(x, y, "-k", label='true')
# If func does not require additional arguments define indparams as:
# indparams=[], or simple leave it undefined in the mcmc call.
# MCMC setup:
numit = 3e4
nchains = 10
walk = 'demc'
grtest = True
burnin = 100
plots = True
savefile = 'output_ex1.npy'
savemodel = 'output_model.npy'
# Run the MCMC:
allp, bp = mc3.mcmc(data, uncert, func, indparams,
params, pmin, pmax, stepsize,
numit=numit, nchains=nchains, walk=walk, grtest=grtest,
burnin=burnin, plots=plots, savefile=savefile, savemodel=savemodel)
# Evaluate and plot:
y0 = quad(params, x) # Initial guess values
y1 = quad(bp, x) # MCMC best fitting values
plt.figure(10)
plt.clf()
plt.plot(x, y, "-k", label='true')
plt.errorbar(x, data, yerr=uncert, fmt=".b", label='data')
plt.plot(x, y0, "-g", label='Initial guess')
plt.plot(x, y1, "-r", label='MCMC best fit')
plt.legend(loc="best")
plt.xlabel("X")
# Optimization:
leastsq = True # Least-squares minimization prior to the MCMC
chisqscale = False # Scale the data uncertainties such red.chisq = 1
# Convergence:
grtest = True # Calculate the GR convergence test
grexit = False # Stop the MCMC after two successful GR
# File outputs:
logfile = 'MCMC.log' # Save the MCMC screen outputs to file
savefile = 'MCMC_sample.npy' # Save the MCMC parameters sample to file
savemodel = 'MCMC_models.npy' # Save the MCMC evaluated models to file
plots = True # Generate best-fit, trace, and posterior plots
# Correlated-noise assessment:
wlike = False # Use Carter & Winn's Wavelet-likelihood method
rms = False # Compute the time-averaging test and plot
# Run the MCMC:
# posterior is the parameters' posterior distribution
# bestp is the array of best fitting parameters
posterior, besttp = mc3.mcmc(data=data, uncert=uncert,
func=func, indparams=indparams,
params=params, pmin=pmin, pmax=pmax, stepsize=stepsize,
prior=prior, priorlow=priorlow, priorup=priorup,
leastsq=leastsq, chisqscale=chisqscale, mpi=mpi,
numit=numit, nchains=nchains, walk=walk, burnin=burnin,
grtest=grtest, grexit=grexit, wlike=wlike, logfile=logfile,
plots=plots, savefile=savefile, savemodel=savemodel, rms=rms)
nchains = 10 # Number of parallel chains
burnin = 100 # Number of burned-in samples per chain
thinning = 1 # Thinning factor for outputs
# Optimization:
leastsq = True # Least-squares minimization prior to the MCMC
chisqscale = False # Scale the data uncertainties such red.chisq = 1
# Convergence:
grtest = True # Calculate the GR convergence test
grexit = False # Stop the MCMC after two successful GR
logfile = 'MCMC.log' # Save the MCMC screen outputs to file
savefile = 'MCMC_sample.npy' # Save the MCMC parameters sample to file
savemodel = 'MCMC_models.npy' # Save the MCMC evaluated models to file
plots = True # Generate best-fit, trace, and posterior plots
# Correlated-noise assessment:
wlike = False # Use Carter & Winn's Wavelet-likelihood method.
rms = False # Compute the time-averaging test and plot
# Run the MCMC:
posterior, bestp = mc3.mcmc(data=data, func=func, indparams=indparams,
params=params,
leastsq=leastsq, chisqscale=chisqscale, mpi=mpi,
numit=numit, nchains=nchains, walk=walk, burnin=burnin,
grtest=grtest, grexit=grexit, wlike=wlike, logfile=logfile,
plots=plots, savefile=savefile, savemodel=savemodel, rms=rms)