### Complete Analysis Example - GWB Search

Source: https://context7.com/nanograv/enterprise/llms.txt

This section provides a comprehensive example demonstrating the setup and execution of a gravitational wave background (GWB) search analysis using the Enterprise library.

```APIDOC
## Complete Analysis Example - GWB Search

### Description
A complete example showing how to set up and run a gravitational wave background analysis.

### Method
Loading pulsar data, defining noise models, and setting up the PTA for analysis.

### Endpoint
N/A (Script Example)

### Parameters
None (Illustrative example)

### Request Example
```python
import numpy as np
from enterprise.pulsar import Pulsar
from enterprise.signals import signal_base, white_signals, gp_signals, gp_priors, utils, parameter, selections
from enterprise.signals.selections import Selection

# Load pulsar data
import glob
parfiles = sorted(glob.glob('data/*.par'))
timfiles = sorted(glob.glob('data/*.tim'))
psrs = [Pulsar(p, t) for p, t in zip(parfiles, timfiles)]

# Calculate time span for frequency basis
tmin = min(psr.toas.min() for psr in psrs)
tmax = max(psr.toas.max() for psr in psrs)
Tspan = tmax - tmin

# White noise with per-backend EFAC/EQUAD
selection = Selection(selections.by_backend)
efac = parameter.Uniform(0.5, 1.5)
equad = parameter.Uniform(-10, -5)
ef = white_signals.MeasurementNoise(efac=efac, log10_t2equad=equad, selection=selection)

# ECORR
ecorr = parameter.Uniform(-10, -5)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr, selection=selection)

# Intrinsic red noise per pulsar
log10_A_rn = parameter.Uniform(-20, -11)
gamma_rn = parameter.Uniform(0, 7)
pl_rn = gp_priors.powerlaw(log10_A=log10_A_rn, gamma=gamma_rn)
rn = gp_signals.FourierBasisGP(spectrum=pl_rn, components=30, Tspan=Tspan, name='red_noise')
```

### Response
#### Success Response (200)
N/A (Illustrative example)

#### Response Example
(Further steps in a GWB analysis would involve combining these signals into a PTA, defining the GWB model, and running inference.)
```

--------------------------------

### Example Parameter Initialization Output

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/usage.ipynb

Illustrates the expected output when printing the parameters of an initialized timing model. This shows the parameter names, including the pulsar designation, and their associated prior distributions.

```text
["B1855+09_efac":Uniform(0.5,5),
 "B1855+09_gamma":Uniform(1,7),
 "B1855+09_log10_A":Uniform(-18,-12),
 "B1855+09_log10_ecorr":Uniform(-10,-5),
 "B1855+09_log10_equad":Uniform(-10,-5)]
```

--------------------------------

### Get Initial Sampled Parameters for Enterprise PTA

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Generates a dictionary of initial parameter values by sampling from their defined priors for the enterprise PTA. This dictionary is used to start the MCMC chain.

```python
xs = {par.name: par.sample() for par in pta.params}
print(xs)

```

--------------------------------

### Clone Enterprise Repository and Set Up Remotes

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This set of commands guides users through forking the enterprise repository, cloning their fork locally, and setting up the official repository as an 'upstream' remote for pulling updates.

```shell
# Clone your forked repository
$ git clone git@github.com:your_name_here/enterprise.git
# Navigate into the cloned directory
$ cd enterprise/
# Add the official nanograv/enterprise repository as an upstream remote
$ git remote add upstream https://github.com/nanograv/enterprise.git
```

--------------------------------

### Run PTMCMC Sampler

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Executes the PTMCMC sampler for a specified number of steps (N). Initializes the sampler with starting parameters (x0) and specifies weights for different sampling algorithms (SCAM, AM, DE).

```python
# sampler for N steps
N = 1000000
x0 = np.hstack(p.sample() for p in pta.params)
sampler.sample(x0, N, SCAMweight=30, AMweight=15, DEweight=50, )
```

--------------------------------

### Setup Simple Noise Model in enterprise

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This Python snippet demonstrates setting up a basic noise model for a single pulsar using enterprise. It defines parameters and priors for EFAC and red noise (power-law), sets up corresponding signals (MeasurementNoise, FourierBasisGP), and combines them with a TimingModel into a full PTA object.

```python
import enterprise.signals.signal_utils as utils
import enterprise.signals.gp_signals as gp_signals
import enterprise.signals.white_signals as white_signals
from enterprise.core import parameter
from enterprise.core import signal_base

##### 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])])
```

--------------------------------

### Install Local Enterprise Fork with Pip

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This command installs the 'enterprise' package in editable mode from the local source code. This is typically done after cloning the repository and setting up the development environment, allowing changes in the source code to be immediately reflected.

```shell
# Install the local enterprise fork in editable mode
$ pip install -e .
```

--------------------------------

### Get Initial Parameters for MCMC Sampling

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This Python code generates an initial parameter dictionary for MCMC sampling. It iterates through the parameters defined in the PTA object and samples a starting value for each using their respective sample() methods. This dictionary is used to initialize the sampler.

```python
xs = {par.name: par.sample() for par in pta.params}
print(xs)
```

--------------------------------

### Define Enterprise Parameter with Uniform Prior

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/usage.ipynb

Demonstrates how to define a signal parameter in Enterprise with a specified prior distribution. This example creates an 'efac' parameter with a uniform prior ranging from 0.5 to 5. Abstract parameter classes allow for flexible initialization with different names.

```python
# lets define an efac parameter with a uniform prior from [0.5, 5]
efac = parameter.Uniform(0.5, 5)
```

--------------------------------

### Setup Sampler for PTA Analysis (Python)

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This Python code initializes the MCMC sampler for the PTA analysis. It generates initial parameter values, determines the parameter space dimension, and creates an initial jump covariance matrix. Jump groups are defined to optimize the sampling process, particularly for red noise parameters.

```python
# 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/')
```

--------------------------------

### Install local enterprise fork

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Installs the local development version of the enterprise project into the active Python environment using pip. This command assumes dependencies are already installed, potentially via conda.

```shell
$ pip install -e .
```

--------------------------------

### Initialize and activate enterprise virtual environment

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Initializes a Python virtual environment named '.enterprise' and activates it. This is an alternative method for setting up the development environment if dependencies were not installed via conda.

```shell
$ make init
$ source .enterprise/bin/activate
```

--------------------------------

### Python Class Factory Example

Source: https://github.com/nanograv/enterprise/blob/master/docs/data.md

Demonstrates a Python class factory, a function that returns a class. This allows for 'global' parameters to be passed to a class without needing them during instantiation. The example shows how arguments passed to the factory are accessible within the class instances.

```python
def A(farg1, farg2):
    class A(object):

        def __init__(self, iarg):
            self.iarg = iarg

        def print_info(self):
            print('Object instance {}\nInstance argument: {}\nFunction args: {} {}
'.format(
                self, self.iarg, farg1, farg2))
    return A
```

```python
# define class A with arguments that can be seen within the class
a = A('arg1', 'arg2')

# instantiate 2 instances of class A with different arguments
a1 = a('iarg1')
a2 = a('iarg2')

# call print_info method
a1.print_info()
a2.print_info()
```

--------------------------------

### Complete Gravitational Wave Background Search Analysis in Python

Source: https://context7.com/nanograv/enterprise/llms.txt

Provides a comprehensive example of setting up and running a gravitational wave background (GWB) search analysis. This includes loading pulsar data, calculating time span, defining white noise parameters (EFAC, EQUAD, ECORR), modeling intrinsic red noise, and setting up the PTA object for analysis.

```python
import numpy as np
from enterprise.pulsar import Pulsar
from enterprise.signals import signal_base, white_signals, gp_signals, gp_priors, utils, parameter, selections
from enterprise.signals.selections import Selection
import glob

# Load pulsar data
parfiles = sorted(glob.glob('data/*.par'))
timfiles = sorted(glob.glob('data/*.tim'))
psrs = [Pulsar(p, t) for p, t in zip(parfiles, timfiles)]

# Calculate time span for frequency basis
tmin = min(psr.toas.min() for psr in psrs)
tmax = max(psr.toas.max() for psr in psrs)
Tspan = tmax - tmin

# White noise with per-backend EFAC/EQUAD
selection = Selection(selections.by_backend)
efac = parameter.Uniform(0.5, 1.5)
equad = parameter.Uniform(-10, -5)
ef = white_signals.MeasurementNoise(efac=efac, log10_t2equad=equad, selection=selection)

# ECORR
ecorr = parameter.Uniform(-10, -5)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr, selection=selection)

# Intrinsic red noise per pulsar
log10_A_rn = parameter.Uniform(-20, -11)
gamma_rn = parameter.Uniform(0, 7)
pl_rn = gp_priors.powerlaw(log10_A=log10_A_rn, gamma=gamma_rn)
rn = gp_signals.FourierBasisGP(spectrum=pl_rn, components=30, Tspan=Tspan, name='red_noise')
```

--------------------------------

### Import Enterprise Libraries and Setup

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/data.ipynb

Imports necessary libraries for enterprise, including numpy, matplotlib, and specific enterprise modules like Pulsar and parameter. It also sets up matplotlib for inline plotting and retina display.

```python
% matplotlib inline
%config InlineBackend.figure_format = 'retina'

from __future__ import division

import numpy as np
import matplotlib.pyplot as plt

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

datadir = enterprise.__path__[0] + '/datafiles/ng9/'
```

--------------------------------

### Setup PTA Model for GWB Analysis (Python)

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This Python code sets up a PTA model for analyzing the Gravitational Wave Background (GWB) across 36 pulsars. It defines white noise parameters, red noise using a powerlaw spectrum, and the GWB signal with Hellings and Downs spatial correlations. The model is then compiled and used to initialize the PTA object.

```python
# 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])
```

--------------------------------

### Python Parameter Class Factory Usage

Source: https://github.com/nanograv/enterprise/blob/master/docs/data.md

Illustrates the use of the Parameter class factory in enterprise for defining signal parameters with prior distributions. It shows how to create a Uniform prior, instantiate it with a name, and access its methods like getting the PDF and sampling.

```python
# lets define an efac parameter with a uniform prior from [0.5, 5]
efac = parameter.Uniform(0.5, 5)
print(efac)
```

```python
# initialize efac parameter with name "efac_1"
efac1 = efac('efac_1')
print(efac1)

# return parameter name
print(efac1.name)

# get pdf at a point (log pdf is access)
print(efac1.get_pdf(1.3), efac1.get_logpdf(1.3))

# return 5 samples from this prior distribution
print(efac1.sample(n=5))
```

--------------------------------

### Setup PTMCMCSampler for enterprise PTA

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This snippet configures the PTMCMCSampler for use with an enterprise PTA object. It determines the parameter space dimension, sets up an initial covariance matrix, defines jump groups for correlated parameters, and initializes the sampler with the PTA's log-likelihood and log-prior functions.

```python
import numpy as np
from PTMCMCSampler.sampler import Sampler as ptmcmc

# 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/')

```

--------------------------------

### Initialize and Activate Enterprise Development Environment (Manual)

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This sequence of commands is for manually setting up a Python 3 virtual environment for local enterprise development. It first initializes the environment and then activates it, making the local fork of enterprise available.

```shell
# Initialize the development environment (creates .enterprise directory)
$ make init
# Activate the Python 3 virtual environment
$ source .enterprise/bin/activate
```

--------------------------------

### Install enterprise dependencies and development version

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Installs the 'enterprise-pulsar' package using conda, then removes it to allow for the use of a local development version. Finally, it installs a conditional coverage plugin for tests.

```shell
$ conda install -c conda-forge enterprise-pulsar
$ conda remove enterprise-pulsar --force
$ pip install coverage-conditional-plugin
```

--------------------------------

### Initialize Linear Timing Model (Python)

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/usage.ipynb

Sets up the linear timing model as a Gaussian Process with very large variances. This approach is equivalent to marginalizing over the linear timing model coefficients assuming uniform priors, and is implemented using enterprise's TimingModel class.

```python
# timing model as GP (no parameters)
tm = gs.TimingModel()
```

--------------------------------

### Initialize and Print Sine Wave Function

Source: https://github.com/nanograv/enterprise/blob/master/docs/data.md

Demonstrates the initialization of a sine wave function using `enterprise.parameter.Uniform` for logarithmic amplitude and frequency, and then prints the resulting function object. This shows the basic structure of an enterprise function.

```python
sw_function = sine_wave(log10_A=parameter.Uniform(-10,-5), log10_f=parameter.Uniform(-9, -7))
print(sw_function)
```

--------------------------------

### Install and Manage Enterprise Dependencies with Conda

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This code sequence installs the 'enterprise-pulsar' package using Conda, then immediately removes it to allow for local development versions. It also installs a conditional coverage plugin for testing.

```shell
# Install enterprise-pulsar from conda-forge channel
$ conda install -c conda-forge enterprise-pulsar
# Remove the installed version to use local development version
$ conda remove enterprise-pulsar --force
# Install a plugin for conditional test coverage
$ pip install coverage-conditional-plugin
```

--------------------------------

### Python Function Structure Example

Source: https://github.com/nanograv/enterprise/blob/master/docs/data.md

Demonstrates the enterprise Function structure, which converts standard Python functions into objects that can interact with Pulsar data and enterprise Parameters. The example shows a sine wave function decorated with @signal_base.function.

```python
@signal_base.function
def sine_wave(toas, log10_A=-7, log10_f=-8):
    return 10**log10_A * np.sin(2*np.pi*toas*10**log10_f)
```

```python
# treat it just as a standard function with a vector input
sw = sine_wave(np.array([1,2,3]), log10_A=-8, log10_f=-7.5)
print(sw)
```

--------------------------------

### Initialize and Use Enterprise Parameter

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/usage.ipynb

Initializes an abstract Enterprise parameter with a specific name ('efac_1') and demonstrates its methods. This includes retrieving the parameter name, calculating the probability density function (PDF) and log PDF at a given point, and sampling from the prior distribution.

```python
# initialize efac parameter with name "efac_1"
efac1 = efac('efac_1')

# return parameter name
print(efac1.name)

# get pdf at a point (log pdf is access)
print(efac1.get_pdf(1.3), efac1.get_logpdf(1.3))

# return 5 samples from this prior distribution
print(efac1.sample(size=5))
```

--------------------------------

### Run Enterprise Tests and Documentation Builds

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

These commands are used to verify local changes before committing. 'make test' runs the project's test suite, and 'make docs' builds the project's documentation, ensuring that code changes adhere to project standards and that documentation is correctly formatted.

```shell
# Run the test suite
$ make test
# Build the documentation
$ make docs
```

--------------------------------

### Initialize MCMC Sampler

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Sets up the PTMCMC sampler for a PTA analysis. It defines the parameter space, initial covariance matrix, and jump groups for efficient sampling.

```python
# 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/')
```

--------------------------------

### Clone and set up enterprise repository

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Clones the enterprise repository from GitHub, navigates into the directory, and sets the upstream remote to the main nanograv/enterprise repository for pulling updates.

```shell
$ git clone git@github.com:your_name_here/enterprise.git
$ cd enterprise/
$ git remote add upstream https://github.com/nanograv/enterprise.git
```

--------------------------------

### Get Par and Tim Files for Pulsar Data

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Retrieves the par and tim files for the open MDC1 dataset. These files contain pulsar timing information necessary for analysis.

```python
parfiles = sorted(glob.glob(datadir + '/*.par'))
timfiles = sorted(glob.glob(datadir + '/*.tim'))

```

--------------------------------

### Set Up Signal Components

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Configures various signal components for the PTA model, including white noise (MeasurementNoise, EquadNoise, EcorrKernelNoise), red noise (FourierBasisGP with powerlaw spectrum), gravitational wave background (FourierBasisGP with powerlaw spectrum), and a timing model. Dependencies include 'white_signals', 'utils', 'gp_signals', and 'signal_base'.

```python
# white noise
ef = white_signals.MeasurementNoise(efac=efac, selection=selection)
eq = white_signals.EquadNoise(log10_equad=equad, selection=selection)
ec = white_signals.EcorrKernelNoise(log10_ecorr=ecorr, selection=selection)

# 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 (no spatial correlations)
cpl = utils.powerlaw(log10_A=log10_A_gw, gamma=gamma_gw)
gw = gp_signals.FourierBasisGP(spectrum=cpl, components=30, Tspan=Tspan)

# timing model
tm = gp_signals.TimingModel()

# full model is sum of components
model = ef + eq + ec + rn + tm + gw

# intialize PTA
pta = signal_base.PTA([model(psr) for psr in psrs])
```

--------------------------------

### Initialize Parameter Caches

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Initializes parameter caches by sampling from the prior and evaluating the likelihood and prior. This step is optional as caches are filled on the first sampler call, but explicit evaluation can be useful for debugging or pre-computation.

```python
xs = {par.name: par.sample() for par in pta.params}
print pta.get_lnlikelihood(xs);
print pta.get_lnprior(xs);
```

--------------------------------

### Define Parameter Priors with ENTERPRISE

Source: https://context7.com/nanograv/enterprise/llms.txt

Illustrates how to define parameters with various prior distributions using the `enterprise.signals.parameter` module. This includes Uniform, Normal, LinearExp, Truncated Normal, Constant, and Vector parameters, along with sampling from multiple parameters.

```python
from enterprise.signals import parameter
from enterprise.signals.parameter import sample

# Uniform prior
efac = parameter.Uniform(0.5, 1.5)
efac_param = efac('B1855+09_efac')
print(f"Parameter name: {efac_param.name}")
print(f"Sample: {efac_param.sample()}")
print(f"PDF at 1.0: {efac_param.get_pdf(1.0)}")
print(f"Log PDF: {efac_param.get_logpdf(1.0)}")

# Normal prior (Gaussian)
log10_A = parameter.Normal(mu=-15, sigma=2)
gamma = parameter.Normal(mu=4.33, sigma=0.5)

# LinearExp prior (uniform in linear space, log parameter)
log10_equad = parameter.LinearExp(-10, -5)

# Truncated Normal
bounded_param = parameter.TruncNormal(mu=0, sigma=1, pmin=-2, pmax=2)

# Constant parameter (fixed value)
efac_fixed = parameter.Constant(1.0)

# Vector parameter (multiple values)
jup_orb = parameter.Uniform(-0.05, 0.05, size=6)('jup_orb_elements')

# Sample from multiple parameters consistently
params = [efac('efac'), log10_A('log10_A'), gamma('gamma')]
sampled = sample(params)
print(f"Sampled parameters: {sampled}")
```

--------------------------------

### Import Enterprise Libraries (Python)

Source: https://github.com/nanograv/enterprise/blob/master/tests/data.ipynb

Imports necessary libraries from the Enterprise package and its dependencies, including numpy, matplotlib, and specific modules for pulsar data, signal parameters, and utility functions. This setup is crucial for subsequent data analysis and modeling.

```python
% matplotlib inline
%config InlineBackend.figure_format = 'retina'

from __future__ import division

import numpy as np
import matplotlib.pyplot as plt

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 tests.enterprise_test_data import datadir
from enterprise_test_data import datadir
```

--------------------------------

### Load Pulsar Object from Feather File

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/usage.ipynb

Loads a Pulsar object from a previously saved feather file. This process does not require 'libstempo' or 'PINT' to be installed, making it efficient for accessing stored pulsar data. The loaded object can then be used for further analysis within Enterprise.

```python
psr = Pulsar(datadir + f"/{psr_name}_NANOGrav_9yv1.t2.feather")
```

--------------------------------

### Set Up MCMC Sampler

Source: https://github.com/nanograv/enterprise/blob/master/docs/nano9.md

Configures and sets up the PTMCMC sampler for Bayesian inference. It defines the dimensionality of the parameter space, the initial jump covariance matrix, and groups parameters for efficient sampling. The output directory for chain files is also specified.

```python
# 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))))
groups.extend([[36]])

sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior, cov, groups=groups,
                 outDir='chains/nano_9_gwb/')
```

--------------------------------

### Get Pulsar Timing Data Files

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This snippet uses the glob module to find all .par and .tim files in a specified directory. These files are essential for loading pulsar timing data into the enterprise package. The files are sorted to ensure consistent ordering.

```python
import glob

parfiles = sorted(glob.glob(datadir + '/*.par'))
timfiles = sorted(glob.glob(datadir + '/*.tim'))
```

--------------------------------

### Examine MCMC Chain Output

Source: https://github.com/nanograv/enterprise/blob/master/docs/mdc.md

This Python snippet loads the MCMC chain data from a text file and prepares it for analysis. It reads the chain, sorts the parameter names, and calculates the burn-in period. This data can then be used for visualization and parameter estimation, for example, using corner plots.

```python
import corner

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);
```

--------------------------------

### Set up development environment with conda

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Creates a conda environment named 'ent_dev' with specified Python and development tools, then activates it. This environment is used for running tests and developing the enterprise project.

```shell
$ conda create -n ent_dev -y -c conda-forge python=3.9 black=22.3.0 flake8 sphinx_rtd_theme pytest-cov
$ conda activate ent_dev
```

--------------------------------

### Set Up Enterprise Analysis Model Parameters (Python)

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Configures the analysis model by setting white noise parameters to constant values derived from the noise files using `enterprise.signals.selections.Selection`. It also defines priors for red noise and GWB amplitude parameters using a `LinearExp` prior and fixes the GWB spectral index. Spatial correlations are omitted by using a single parameter instance for all pulsars.

```python
# 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)

# selection class to break white noise by backend
selection = selections.Selection(selections.by_backend)

##### parameters and priors #####

# white noise parameters

```

--------------------------------

### Configure PTMCMC Sampler

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Sets up the PTMCMC sampler, defining the dimensionality of the parameter space, likelihood and prior functions, initial jump covariance matrix, and jump groups. The output directory for chains is also specified.

```python
# 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))))
groups.extend([[36]])

sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior, cov, groups=groups, 
                 outDir='chains/nano_9_gwb/')
```

--------------------------------

### Deterministic Signals - Physical Models

Source: https://context7.com/nanograv/enterprise/llms.txt

Implements deterministic signals, which are predictable waveforms subtracted from residuals. Examples include physical ephemeris corrections for solar system bodies and custom waveforms like continuous gravitational waves or sinusoidal signals. These signals can be defined using physical models or custom functions decorated with `@function`.

```python
from enterprise.signals import deterministic_signals, parameter, utils
from enterprise.signals.parameter import function
import numpy as np

# Physical ephemeris signal for solar system ephemeris corrections
eph = deterministic_signals.PhysicalEphemerisSignal(
    frame_drift_rate=parameter.Uniform(-1e-9, 1e-9)('frame_drift_rate'),
    d_jupiter_mass=parameter.Normal(0, 1.55e-11)('d_jupiter_mass'),
    d_saturn_mass=parameter.Normal(0, 8.17e-12)('d_saturn_mass'),
    d_uranus_mass=parameter.Normal(0, 5.72e-11)('d_uranus_mass'),
    d_neptune_mass=parameter.Normal(0, 7.96e-11)('d_neptune_mass'),
    jup_orb_elements=parameter.Uniform(-0.05, 0.05, size=6)('jup_orb'),
    use_epoch_toas=True
)

# Custom deterministic signal using @function decorator
@function
def sine_wave(toas, log10_A=-7, log10_f=-8):
    """Simple sinusoidal signal."""
    return 10**log10_A * np.sin(2 * np.pi * toas * 10**log10_f)

# Create signal with parameter priors
sw = sine_wave(
    log10_A=parameter.Uniform(-10, -5),
    log10_f=parameter.Uniform(-9, -7)
)
sine_signal = deterministic_signals.Deterministic(sw, name='sine')

# Continuous gravitational wave (example custom waveform)
@function
def cw_delay(toas, pos, pdist, cos_gwtheta=0, gwphi=0, log10_fgw=-8,
             log10_h=-15, phase0=0, psi=0, cos_inc=0):
    """Simplified CW waveform (actual implementation is more complex)."""
    fgw = 10**log10_fgw
    h = 10**log10_h
    # Simplified - actual CW waveform includes antenna patterns, etc.
    return h * np.cos(2 * np.pi * fgw * toas + phase0)

```

--------------------------------

### Set up Bayesian model parameters and signals (Python)

Source: https://github.com/nanograv/enterprise/blob/master/docs/nano9.md

This Python code configures parameters for a Bayesian analysis using the 'enterprise' package. It defines white noise parameters as constants, red noise parameters with LinearExp and Uniform priors, and gravitational wave (GW) parameters with LinearExp and Constant priors. It also initializes GW parameters to be common across all pulsars and sets the GW spectral index to a constant value.

```python
# 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)

# selection class to break white noise by backend
selection = selections.Selection(selections.by_backend)

##### parameters and priors #####

# white noise parameters
# since we are fixing these to values from the noise file we set
# them as constant parameters
efac = parameter.Constant()
equاد = parameter.Constant()
ecorr = parameter.Constant()

# red noise parameters
log10_A = parameter.LinearExp(-20,-12)
gamma = parameter.Uniform(0,7)

# GW parameters (initialize with names here to use parameters in common across pulsars)
log10_A_gw = parameter.LinearExp(-18,-12)('log10_A_gw')
gamma_gw = parameter.Constant(4.33)('gamma_gw')

##### Set up signals #####

```

--------------------------------

### Set Up Enterprise Signal Components for Noise Model

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Configures the signal components for the noise model, including white noise (MeasurementNoise), red noise (FourierBasisGP with powerlaw spectrum), and a timing model. These components are then combined.

```python
##### 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])])

```

--------------------------------

### Use Custom Backends and Apply Selection in Python

Source: https://context7.com/nanograv/enterprise/llms.txt

Shows how to utilize custom backends for selection and apply a specific selection method (by_backend) for measurement noise parameters like EFAC. This is useful for applying different noise properties to different backends.

```python
from enterprise.signals import white_signals, parameter, selections
from enterprise.signals.selections import Selection

# Using custom backends
custom_sel = Selection(selections.custom_backends(['GUPPI', 'PUPPI']))

# Apply selection to white noise
efac_by_backend = white_signals.MeasurementNoise(
    efac=parameter.Uniform(0.5, 1.5),
    selection=Selection(selections.by_backend)
)
```

--------------------------------

### Instantiate and Use Class Factory Objects in Python

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/data.ipynb

Shows how to use the 'A' class factory. It first defines the factory with arguments, then instantiates two objects of the returned class with different arguments, and finally calls a method on these instances.

```python
# define class A with arguments that can be seen within the class
a = A('arg1', 'arg2')

# instantiate 2 instances of class A with different arguments
a1 = a('iarg1')
a2 = a('iarg2')

# call print_info method
a1.print_info()
a2.print_info()
```

--------------------------------

### Set Up PTMCMCSampler for Enterprise PTA

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Initializes the PTMCMCSampler for analyzing the enterprise PTA. It configures the sampler with the parameter space dimension, log-likelihood and log-prior functions, initial covariance matrix, and jump groups.

```python
# 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/')

```

--------------------------------

### Load and Access Pulsar Data with ENTERPRISE

Source: https://context7.com/nanograv/enterprise/llms.txt

Demonstrates loading pulsar data from par/tim files using tempo2 or PINT, and accessing various pulsar attributes. It also shows how to load multiple pulsars and save/load data using the feather format for efficient I/O.

```python
import numpy as np
from enterprise.pulsar import Pulsar

# Load pulsar from par and tim files using tempo2
psr = Pulsar('path/to/pulsar.par', 'path/to/pulsar.tim')

# Or specify timing package explicitly
psr = Pulsar('pulsar.par', 'pulsar.tim', timing_package='pint')

# Access pulsar attributes
print(f"Pulsar name: {psr.name}")
print(f"Number of TOAs: {len(psr.toas)}")
print(f"Residuals (s): {psr.residuals}")
print(f"TOA errors (s): {psr.toaerrs}")
print(f"Frequencies (MHz): {psr.freqs}")
print(f"Design matrix shape: {psr.Mmat.shape}")
print(f"Sky position (theta, phi): {psr.theta}, {psr.phi}")
print(f"Distance (kpc): {psr.pdist}")
print(f"Backend flags: {psr.backend_flags}")

# Load multiple pulsars
import glob
parfiles = sorted(glob.glob('datadir/*.par'))
timfiles = sorted(glob.glob('datadir/*.tim'))
psrs = [Pulsar(p, t) for p, t in zip(parfiles, timfiles)]

# Save/load using feather format for fast I/O
psr.to_feather('pulsar.feather', noisedict={'B1855+09_efac': 1.0})
psr_loaded = Pulsar('pulsar.feather')
```

--------------------------------

### Create and Switch to a New Git Branch for Development

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This command creates a new Git branch for local development and immediately switches to it. This is a standard practice for isolating new features or bug fixes from the main codebase.

```shell
# Create a new branch and switch to it
$ git checkout -b name-of-your-bugfix-or-feature
```

--------------------------------

### Run Enterprise Unit Tests with Python

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

This command executes the unit tests for the enterprise module using Python's built-in unittest framework. Ensure you have the project structure set up correctly for the tests to be discovered and run.

```python
python -m unittest tests.test_enterprise
```

--------------------------------

### Create and Activate Conda Environment for Enterprise Development

Source: https://github.com/nanograv/enterprise/blob/master/docs/contributing.md

This snippet demonstrates how to create a new Conda environment named 'ent_dev' with specific Python and development dependencies, and then activate it. This environment is crucial for setting up enterprise for local development and running tests.

```shell
# Create a virtual environment with development dependencies
$ conda create -n ent_dev -y -c conda-forge python=3.9 black=22.3.0 flake8 sphinx_rtd_theme pytest-cov
# Activate the environment
$ conda activate ent_dev
```

--------------------------------

### Build and Evaluate PTA Models in Python

Source: https://context7.com/nanograv/enterprise/llms.txt

Illustrates the process of constructing a Pulsar Timing Array (PTA) object by defining various signal components (white noise, red noise, timing model) and combining them. It covers loading pulsars, defining parameters, creating the model, initializing the PTA, and evaluating likelihood and prior.

```python
from enterprise.signals import signal_base, gp_signals, white_signals, parameter, gp_priors
from enterprise.pulsar import Pulsar
import numpy as np

# Load pulsars
psrs = [Pulsar(f'psr{i}.par', f'psr{i}.tim') for i in range(3)]

# Define signal components
efac = parameter.Uniform(0.5, 1.5)
log10_A = parameter.Uniform(-18, -12)
gamma = parameter.Uniform(0, 7)

ef = white_signals.MeasurementNoise(efac=efac)
pl = gp_priors.powerlaw(log10_A=log10_A, gamma=gamma)
rn = gp_signals.FourierBasisGP(spectrum=pl, components=30)
tm = gp_signals.TimingModel()

# Combine signals into model
model = ef + rn + tm

# Create PTA by initializing model with each pulsar
pta = signal_base.PTA([model(psr) for psr in psrs])

# Access PTA properties
print(f"Parameters: {pta.param_names}")
print(f"Number of pulsars: {len(pta.pulsars)}")
print(f"Pulsar names: {pta.pulsars}")

# Generate parameter dictionary from array
x0 = np.array([p.sample() for p in pta.params])
params = pta.map_params(x0)

# Evaluate likelihood and prior
lnlike = pta.get_lnlikelihood(params)
lnprior = pta.get_lnprior(params)
print(f"Log likelihood: {lnlike}")
print(f"Log prior: {lnprior}")

# Get model summary
summary = pta.summary(include_params=True)
print(summary)

# Set constant/default parameters
noise_dict = {
    'B1855+09_efac': 1.0,
    'B1937+21_efac': 1.1
}
pta.set_default_params(noise_dict)

# Access individual pulsar models
for name, sc in pta.items():
    print(f"Pulsar {name}: {len(sc.signals)} signals")

# Get residuals and basis matrices
residuals = pta.get_residuals()
bases = pta.get_basis(params)
delays = pta.get_delay(params)
```

--------------------------------

### Run enterprise tests and documentation checks

Source: https://github.com/nanograv/enterprise/blob/master/CONTRIBUTING.rst

Executes the project's test suite and documentation formatting checks. This ensures that code changes are functional and documentation is correctly formatted before committing.

```shell
$ make test
$ make docs
```

--------------------------------

### Load and Prepare Chain Data

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/nano9.ipynb

Loads the MCMC chain data from a file, sorts the parameter names, and determines the burn-in period for analysis. Assumes the chain is stored in 'chain_1.txt' within the specified output directory.

```python
chain = np.loadtxt('chains/nano_9_gwb/chain_1.txt)
pars = sorted(xs.keys())
burn = int(0.25 * chain.shape[0])
```

--------------------------------

### Define Noise Model Parameters and Priors in Enterprise

Source: https://github.com/nanograv/enterprise/blob/master/docs/_static/notebooks/mdc.ipynb

Sets up parameters and their prior distributions for a noise model in enterprise. This includes EFAC for white noise and parameters for a power-law red noise process.

```python
##### 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)

```