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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Analyzing Open MDC data
In this tutorial we will use enterprise to analyze open MDC dataset 1.
Step1:  Get par and tim files
The first step in the process is getting the open MDC1 par and tim files in the tests directory.
Step2:  Load pulsars into Pulsar objects
enterprise uses a specific Pulsar object to store all of the relevant pulsar information (i.e. TOAs, residuals, error bars, flags, etc) from the timing package. Eventually enterprise will support both PINT and tempo2; however, for the moment it only supports tempo2 through the libstempo package. This object is then used to initalize Signals that define the generative model for the pulsar residuals. This is in keeping with the overall enterprise philosophy that the pulsar data should be as loosley coupled as possible to the pulsar model.
Step3:  Setup and run a simple noise model on a single pulsar
Here we will demonstrate how to do a simple noise run on a single pulsar. In this analysis we will simply model the noise via a single EFAC parameter and a power-law red noise process.
Set up model
Here we see the basic enterprise model building steps
Step4:  We can see which parameters we are going to be searching over with
Step5:  Get initial parameters
We will start our MCMC chain at a random point in parameter space. We accomplish this by setting up a parameter dictionary using the name and sample methods for each Parameter.
Step6:  Note that the rest of the analysis here is dependent on the sampling method and not on enterprise itself.
Set up sampler
Here we are making use of the PTMCMCSampler package for sampling. For this sampler, as in many others, it requires a function to compute the log-likelihood and log-prior given a vector of parameters. Here, these are supplied by PTA as pta.get_lnlikelihood and  pta.get_lnprior.
Step7:  Sample!
Step8:  Examine chain output
We see here that we have indeed recovered the injected values!
Step9:  Run full PTA GWB analysis
Here we will use the full 36 pulsar PTA to conduct a search for the GWB. In this analysis we fix the EFAC=1 for simplicity (and since we already know the answer!). This shows an example of how to use Constant parameters in enterprise. 
Here you notice some of the simplicity of enterprise. For the most part, setting up the model for the full PTA is identical to that for one pulsar. In this case the only differences are that we are specifying the timespan to use when setting the GW and red noise frequencies and we are including a FourierBasisCommonGP signal, which models the GWB spectrum and spatial correlations.
After this setup, the rest is nearly identical to the single pulsar run above.
Step10:  Set up sampler
Step11:  Plot output
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Python Code:
% matplotlib inline
%config InlineBackend.figure_format = 'retina'
%load_ext line_profiler
from __future__ import division
import numpy as np
import glob
import matplotlib.pyplot as plt
import scipy.linalg as sl
import enterprise
from enterprise.pulsar import Pulsar
import enterprise.signals.parameter as parameter
from enterprise.signals import utils
from enterprise.signals import signal_base
from enterprise.signals import selections
from enterprise.signals.selections import Selection
from enterprise.signals import white_signals
from enterprise.signals import gp_signals
import corner
from PTMCMCSampler.PTMCMCSampler import PTSampler as ptmcmc
datadir = enterprise.__path__[0] + '/datafiles/mdc_open1/'
Explanation: Analyzing Open MDC data
In this tutorial we will use enterprise to analyze open MDC dataset 1.
End of explanation
parfiles = sorted(glob.glob(datadir + '/*.par'))
timfiles = sorted(glob.glob(datadir + '/*.tim'))
Explanation: Get par and tim files
The first step in the process is getting the open MDC1 par and tim files in the tests directory.
End of explanation
psrs = []
for p, t in zip(parfiles, timfiles):
    psr = Pulsar(p, t)
    psrs.append(psr)
Explanation: Load pulsars into Pulsar objects
enterprise uses a specific Pulsar object to store all of the relevant pulsar information (i.e. TOAs, residuals, error bars, flags, etc) from the timing package. Eventually enterprise will support both PINT and tempo2; however, for the moment it only supports tempo2 through the libstempo package. This object is then used to initalize Signals that define the generative model for the pulsar residuals. This is in keeping with the overall enterprise philosophy that the pulsar data should be as loosley coupled as possible to the pulsar model.
End of explanation
##### parameters and priors #####
# Uniform prior on EFAC
efac = parameter.Uniform(0.1, 5.0)
# red noise parameters 
# Uniform in log10 Amplitude and in spectral index
log10_A = parameter.Uniform(-18,-12)
gamma = parameter.Uniform(0,7)
##### Set up signals #####
# white noise
ef = white_signals.MeasurementNoise(efac=efac)
# red noise (powerlaw with 30 frequencies)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(spectrum=pl, components=30)
# timing model
tm = gp_signals.TimingModel()
# full model is sum of components
model = ef + rn + tm 
# initialize PTA
pta = signal_base.PTA([model(psrs[0])])
Explanation: Setup and run a simple noise model on a single pulsar
Here we will demonstrate how to do a simple noise run on a single pulsar. In this analysis we will simply model the noise via a single EFAC parameter and a power-law red noise process.
Set up model
Here we see the basic enterprise model building steps:
Define parameters and priors (This makes use of the Parameter class factory)
Set up the signals making use of the Signal class factories. 
Define the model by summing the individual Signal classes.
Define a PTA by initializing the signal model with a Pulsar object.
Notice that powerlaw is uses as a Function here.
End of explanation
print(pta.params)
Explanation: We can see which parameters we are going to be searching over with:
End of explanation
xs = {par.name: par.sample() for par in pta.params}
print(xs)
Explanation: Get initial parameters
We will start our MCMC chain at a random point in parameter space. We accomplish this by setting up a parameter dictionary using the name and sample methods for each Parameter.
End of explanation
# dimension of parameter space
ndim = len(xs)
# initial jump covariance matrix
cov = np.diag(np.ones(ndim) * 0.01**2)
# set up jump groups by red noise groups 
ndim = len(xs)
groups  = [range(0, ndim)]
groups.extend([[1,2]])
# intialize sampler
sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior, cov, groups=groups, 
                 outDir='chains/mdc/open1/')
Explanation: Note that the rest of the analysis here is dependent on the sampling method and not on enterprise itself.
Set up sampler
Here we are making use of the PTMCMCSampler package for sampling. For this sampler, as in many others, it requires a function to compute the log-likelihood and log-prior given a vector of parameters. Here, these are supplied by PTA as pta.get_lnlikelihood and  pta.get_lnprior.
End of explanation
# sampler for N steps
N = 100000
x0 = np.hstack(p.sample() for p in pta.params)
sampler.sample(x0, N, SCAMweight=30, AMweight=15, DEweight=50)
Explanation: Sample!
End of explanation
chain = np.loadtxt('chains/mdc/open1/chain_1.txt')
pars = sorted(xs.keys())
burn = int(0.25 * chain.shape[0])
truths = [1.0, 4.33, np.log10(5e-14)]
corner.corner(chain[burn:,:-4], 30, truths=truths, labels=pars);
Explanation: Examine chain output
We see here that we have indeed recovered the injected values!
End of explanation
# find the maximum time span to set GW frequency sampling
tmin = [p.toas.min() for p in psrs]
tmax = [p.toas.max() for p in psrs]
Tspan = np.max(tmax) - np.min(tmin)
##### parameters and priors #####
# white noise parameters
# in this case we just set the value here since all efacs = 1 
# for the MDC data
efac = parameter.Constant(1.0)
# red noise parameters 
log10_A = parameter.Uniform(-18,-12)
gamma = parameter.Uniform(0,7)
##### Set up signals #####
# white noise
ef = white_signals.MeasurementNoise(efac=efac)
# red noise (powerlaw with 30 frequencies)
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(spectrum=pl, components=30, Tspan=Tspan)
# gwb
# We pass this signal the power-law spectrum as well as the standard 
# Hellings and Downs ORF
orf = utils.hd_orf()
crn = gp_signals.FourierBasisCommonGP(pl, orf, components=30, name='gw', Tspan=Tspan)
# timing model
tm = gp_signals.TimingModel()
# full model is sum of components
model = ef + rn + tm  + crn
# initialize PTA
pta = signal_base.PTA([model(psr) for psr in psrs])
Explanation: Run full PTA GWB analysis
Here we will use the full 36 pulsar PTA to conduct a search for the GWB. In this analysis we fix the EFAC=1 for simplicity (and since we already know the answer!). This shows an example of how to use Constant parameters in enterprise. 
Here you notice some of the simplicity of enterprise. For the most part, setting up the model for the full PTA is identical to that for one pulsar. In this case the only differences are that we are specifying the timespan to use when setting the GW and red noise frequencies and we are including a FourierBasisCommonGP signal, which models the GWB spectrum and spatial correlations.
After this setup, the rest is nearly identical to the single pulsar run above.
End of explanation
# initial parameters
xs = {par.name: par.sample() for par in pta.params}
# dimension of parameter space
ndim = len(xs)
# initial jump covariance matrix
cov = np.diag(np.ones(ndim) * 0.01**2)
# set up jump groups by red noise groups 
ndim = len(xs)
groups  = [range(0, ndim)]
groups.extend(map(list, zip(range(0,ndim,2), range(1,ndim,2))))
sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior, cov, groups=groups, 
                 outDir='chains/mdc/open1_gwb/')
# sampler for N steps
N = 100000
x0 = np.hstack(p.sample() for p in pta.params)
sampler.sample(x0, N, SCAMweight=30, AMweight=15, DEweight=50)
Explanation: Set up sampler
End of explanation
chain = np.loadtxt('chains/mdc/open1_gwb/chain_1.txt')
pars = sorted(xs.keys())
burn = int(0.25 * chain.shape[0])
corner.corner(chain[burn:,-6:-4], 40, labels=pars[-2:], smooth=True, truths=[4.33, np.log10(5e-14)]);
Explanation: Plot output
End of explanation
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