diff --git a/AUTHORS.md b/AUTHORS.md
new file mode 100644
index 0000000000000000000000000000000000000000..7310ea5685ebeb50928c8019adab340c20df78ac
--- /dev/null
+++ b/AUTHORS.md
@@ -0,0 +1,5 @@
+Development Leads
+-----------------
+
+* Rutger van Haasteren (@vhaasteren) <https://github.com/vhaasteren>
+
diff --git a/README.md b/README.md
index 3c09eebcce48c0a88af6a41e60b2483f060cffb0..92e8846f6b8af6b1ff6d5b5abb13158bce88fb1b 100644
--- a/README.md
+++ b/README.md
@@ -1,93 +1,31 @@
# Hierarchical Bayesian Models
+## Introduction
-## Getting started
+This is the repository to create rudimentary Hierarchical Models with Enterprise. At the moment, this is just a workaround, and this is not even an installable package. Also, there will be bugs.
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+## How to use:
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+Just look at the code for now
-## Add your files
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+## TODO
-```
-cd existing_repo
-git remote add origin https://gitlab.nanograv.org/rutger.vhaasteren/hierarchical-bayesian-models.git
-git branch -M main
-git push -uf origin main
-```
+- [ ] [Port to Enterprise](https://github.com/nanograv/enterprise) or [Enterprise_Extensions](https://github.com/nanograv/enterprise_extensions)
+- [ ] Write documentation
+- [ ] Create better models
+- [ ] Figure out what to do
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.nanograv.org/rutger.vhaasteren/hierarchical-bayesian-models/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+For this project, and create a PR?
## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+- Rutger van Haasteren
## License
-For open source projects, say how it is licensed.
+MIT licence
## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+This thing was just started
diff --git a/prior_wrapper.py b/prior_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..06f40379d49e9a94419ad94ea1bfba3c835754d3
--- /dev/null
+++ b/prior_wrapper.py
@@ -0,0 +1,622 @@
+import numpy as np
+import scipy.stats as sstats
+import scipy.linalg as sl
+import re
+
+class kumaraswamy_distribution(sstats.rv_continuous):
+ """Kumaraswamy distribution like for scipy"""
+ def _pdf(self, x, a, b):
+ # Adding a condition to ensure x is within the [0,1] interval
+ return np.where((x >= 0) & (x <= 1), a * b * x**(a-1) * (1 - x**a)**(b-1), 0)
+
+ def _cdf(self, x, a, b):
+ return np.where((x >= 0) & (x <= 1), 1 - (1 - x**a)**b, 0)
+
+ def _ppf(self, q, a, b):
+ return (1 - (1 - q)**(1.0/b))**(1.0/a)
+
+def get_sigmoid_transformed_distribution(distribution, lower=0, upper=1, name='sigmoid-transformed-distribution'):
+ """Class factory for a sigmoid-transformed scipy distribution"""
+
+ def forward(x):
+ return np.log( (x-lower) / (upper-x))
+
+ def backward(p):
+ num = (upper-lower) * np.exp(p)
+ den = 1 + np.exp(p)
+ return lower + num/den
+
+ def dpdx(x):
+ num = (x-lower) + (upper-x)
+ den = (x-lower) * (upper-x)
+ return num / den
+
+ def log_dpdx(x):
+ num = (x-lower) + (upper-x)
+ den = (x-lower) * (upper-x)
+ return np.log(num) - np.log(den)
+
+ def dxdp(p):
+ num = 1 + np.exp(p)
+ return (upper-lower) * (1/num - 1/(num**2))
+
+
+ class sigmoid_transformed_distribution(sstats.rv_continuous):
+
+ def _pdf(self, x, **kwargs):
+ dist = distribution(**kwargs)
+ p = forward(x)
+ return np.exp(dist.logpdf(p) + log_dpdx(x))
+
+ def _logpdf(self, x, **kwargs):
+ dist = distribution(**kwargs)
+ p = forward(x)
+ return dist.logpdf(p) + log_dpdx(x)
+
+ def _pdf(self, x, **kwargs):
+ return np.exp(self._logpdf(x, **kwargs))
+
+ def _logcdf(self, x, **kwargs):
+ dist = distribution(**kwargs)
+ p = forward(x)
+ return dist.logcdf(p)
+
+ def _cdf(self, x, **kwargs):
+ return np.exp(self._logcdf(x, **kwargs))
+
+ def _ppf(self, u, **kwargs):
+ dist = distribution(**kwargs)
+ p = dist.ppf(u)
+ return backward(p)
+
+ return sigmoid_transformed_distribution(name=name)
+
+# Create an instance of the Kumaraswamy distribution
+kumaraswamy = kumaraswamy_distribution(name='kumaraswamy')
+
+
+class IntervalTransform(object):
+ """Stand-alone interval Transform class"""
+ def __init__(self, lower=0, upper=1):
+ self._lower = lower
+ self._upper = upper
+
+ def forward(self, x):
+ return np.log( (x-self._lower) / (self._upper-x))
+
+ def backward(self, p):
+ num = (self._upper-self._lower) * np.exp(p)
+ den = 1 + np.exp(p)
+ return self._lower + num/den
+
+ def dpdx(self, x):
+ num = (x-self._lower) + (self._upper-x)
+ den = (x-self._lower) * (self._upper-x)
+ return num / den
+
+ def log_dpdx(self, x):
+ num = (x-self._lower) + (self._upper-x)
+ den = (x-self._lower) * (self._upper-x)
+ return np.log(num) - np.log(den)
+
+ def dxdp(self, p):
+ num = 1 + np.exp(p)
+ return (self._upper-self._lower) * (1/num - 1/(num**2))
+
+ def log_dxdp(self, p):
+ num = 1 + np.exp(p)
+ return np.log(self._upper-self._lower) + np.log(1/num - 1/(num**2))
+
+def ptmcmc_to_inferencedata(chain, params):
+ """Inferencedata, used by Arviz, can be created from a chain like this"""
+ import arviz as az
+ import xarray as xr
+ samples = chain.reshape(1, -1, chain.shape[1])
+
+ datasets = {}
+ for i, name in enumerate(params):
+ data_array = xr.DataArray(samples[:, :, i], dims=("chain", "draw"), name=name)
+ datasets[name] = data_array
+ dataset = xr.merge(datasets.values())
+ return az.convert_to_inference_data(dataset)
+
+def inferencedata_to_ptmcmc(idata):
+ """InferenceData to ptmcmc chain"""
+
+ # Get parameter names
+ param_names = list(idata.posterior.data_vars)
+
+ # Get MCMC chains
+ posterior = idata.posterior
+ chains = {var_name: posterior[var_name].values for var_name in param_names}
+
+ return chains, param_names
+
+def log_sum_exp(log_prior1, log_prior2):
+ """Take the log of two exponential sums, stable numerically"""
+ max_log_prior = np.maximum(log_prior1, log_prior2)
+ log_prior_diff = np.abs(log_prior1 - log_prior2)
+
+ # Compute the sum of exponentials with numerical stability
+ log_sum_exp_result = max_log_prior + np.log(1 + np.exp(-log_prior_diff))
+
+ return log_sum_exp_result
+
+def log_weighted_sum_exp(log_prior1, log_prior2, f):
+ max_log_prior = np.maximum(log_prior1, log_prior2)
+ #log_prior_diff = np.abs(log_prior1 - log_prior2)
+
+ # Compute the sum of exponentials with numerical stability
+ log_sum_exp_result = max_log_prior + np.log(f * np.exp(log_prior1 - max_log_prior) + (1 - f) * np.exp(log_prior2 - max_log_prior))
+
+ return log_sum_exp_result
+
+def ptapar_offsets(pta):
+ """Some parameters in Enterprise are an array. Get the offsets"""
+ sizes = [1 if p.size is None else p.size for p in pta.params]
+ return np.hstack([[0], np.cumsum(sizes)[:-1]])
+
+def ptapar_mapping(pta):
+ """Create a mapping between arrays, and enterprise parameters"""
+ sizes = [1 if p.size is None else p.size for p in pta.params]
+ offsets = np.hstack([[0], np.cumsum(sizes)])
+
+ ptapar_to_array = [np.arange(offsets[ii],offsets[ii+1]) for ii in range(len(pta.params))]
+ array_to_ptapar = np.array([ii for (ii, inds) in enumerate(ptapar_to_array) for _ in inds])
+
+ return ptapar_to_array, array_to_ptapar
+
+
+class BoundedMvNormalPlHierarchicalPrior(object):
+ """Class to represent a Bounded MvNormal hierarchical prior for Enterprise Powerlaw Signals"""
+
+ def __init__(self, pta, log_amplitude_regexp, gamma_regexp, ind_offset=0, gamma_lower=0, gamma_upper=7, name='MvNormal'):
+ """This is a Hierarchical prior component for use with the EnterpriseWrapper
+
+ This class represents a single MvGaussian hyper-prior on a set of parameters. It was greated for power-law parameters, for which one of the two parameters has a bounded interval: gamma has [0,7] as its domain
+
+ The parameters are selected through regular expressions. For powerlaw noise, you would do:
+ log_amplitude_regexp = r"_red_noise_log10_A$"
+ gamma_regexp = r"_red_noise_gamma$"
+
+ For DM variations you would do:
+ log_amplitude_regexp = r"_dm_gp_log10_A$"
+ gamma_regexp = r"_dm_gp_gamma$"
+ """
+
+ self._pta = pta
+ self._la_pattern = re.compile(log_amplitude_regexp)
+ self._g_pattern = re.compile(gamma_regexp)
+
+ # Select the relevant parameters of Enterprise
+ la_param_names = list(filter(self._la_pattern.search, pta.param_names))
+ g_param_names = list(filter(self._g_pattern.search, pta.param_names))
+
+ # Parameter masks
+ self._la_msk = np.array([pn in la_param_names for pn in pta.param_names], dtype=bool)
+ self._g_msk = np.array([pn in g_param_names for pn in pta.param_names], dtype=bool)
+
+ self._la_inds = np.where(self._la_msk)[0]
+ self._g_inds = np.where(self._g_msk)[0]
+ self._ind_offset = ind_offset
+ self._ind_hyper = np.arange(ind_offset, ind_offset+self.hyper_ndim())
+ self._ind_level1 = np.sort(np.hstack([self._la_inds, self._g_inds], dtype=int))
+
+ if len(self._la_inds) != len(self._g_inds):
+ raise ValueError("Unequal number of amplitude / gamma parameters")
+
+ self._gamma_transform = IntervalTransform(lower=gamma_lower, upper=gamma_upper)
+ self._gamma_lower = gamma_lower
+ self._gamma_upper = gamma_upper
+ self._npsrs = len(self._la_inds)
+ self._name = name
+
+ self.set_hyperpriors()
+
+ def hyper_ndim(self):
+ """The number of hyperparameters of this prior class"""
+ return 5
+
+ def set_hyperpriors(self):
+ """Set the hyper parameter priors"""
+ self._mu_amp = sstats.uniform(loc=-20, scale=7)
+ #self._mu_gamma = sstats.uniform(loc=0, scale=7)
+ self._mu_gamma = sstats.uniform(loc=-4, scale=8)
+ self._L_amp = sstats.uniform(loc=0.03, scale=3.47)
+ self._L_gamma = sstats.uniform(loc=0.03, scale=3.47)
+ self._L_12 = sstats.uniform(loc=-1.5, scale=3)
+
+ self._hyper_dists = [
+ self._mu_amp,
+ self._mu_gamma,
+ self._L_amp,
+ self._L_gamma,
+ self._L_12,
+ ]
+
+ self.hyperparameter_names = [
+ f"{self._name}_mu_amp",
+ f"{self._name}_mu_gamma",
+ f"{self._name}_L_A",
+ f"{self._name}_L_gamma",
+ f"{self._name}_L_12",
+ ]
+
+ def get_hyper_pars(self, x):
+ """Get only the hyperparameters"""
+ return np.array(x)[self._ind_hyper]
+
+ def get_mu_L(self, x):
+ """Get the mu and L of the prior"""
+ (mu1, mu2, L1, L2, L12) = self.get_hyper_pars(x)
+
+ L = np.array([[L1, 0],[L12, L2]]) # The Cholesky decomposition
+ mu = np.array([mu1, mu2])
+ return mu, L
+
+ def forward(self, x):
+ """Transform the gamma parameters to their transformed state"""
+ p = np.copy(x)
+ p[self._g_inds] = self._gamma_transform.forward(p[self._g_inds])
+ return p
+
+ def backward(self, p):
+ """Transform the gamma parameters back to their original state"""
+ x = np.copy(p)
+ x[self._g_inds] = self._gamma_transform.backward(x[self._g_inds])
+ return x
+
+ def log_dpdx(self, x):
+ """The Jacobian of the gamma transform"""
+ return np.sum([self._gamma_transform.log_dpdx(x[gi]) for gi in self._g_inds])
+
+ def log_hyperprior(self, x):
+ """The hyperprior log-prior"""
+ hyper_pars = self.get_hyper_pars(x)
+
+ return np.sum([dd.logpdf(pp) for (dd, pp) in zip(self._hyper_dists, hyper_pars)])
+
+ def sample(self):
+ """Draw a random sample from this prior"""
+ x0 = np.zeros(self._ind_offset + self.hyper_ndim())
+
+ x_hyper = np.array([d.ppf(np.random.rand()) for d in self._hyper_dists])
+ x0[self._ind_hyper] = x_hyper
+
+ mu, L = self.get_mu_L(x0)
+ uag = np.random.randn(2, self._npsrs)
+ xag = mu[:,None] + np.dot(L, uag)
+
+ x0[self._la_inds] = xag[0,:]
+ x0[self._g_inds] = np.clip(xag[1,:], self._gamma_lower+0.001, self._gamma_upper-0.001)
+
+ return x0[np.concatenate([self._ind_level1, self._ind_hyper])]
+
+ def get_level1_parameter_mask(self):
+ return np.logical_or(self._la_msk, self._g_msk)
+
+ def get_parameter_inds(self):
+ return np.sort(np.hstack([self._ind_level1, self._ind_hyper]))
+
+ def get_hyperparameter_names(self):
+ return self.hyperparameter_names
+
+ def log_prior(self, x):
+ """The full prior, including all HBM levels, for this component"""
+ x_gammas = x[self._g_inds]
+ if np.any(x_gammas <= self._gamma_lower) or np.any(x_gammas >= self._gamma_upper):
+ return -np.inf
+
+ p = self.forward(x)
+ mu, L = self.get_mu_L(x)
+
+ amps = p[self._la_inds]
+ gammas = p[self._g_inds]
+ pag = np.vstack([amps, gammas])
+ uag = sl.solve_triangular(L, pag - mu[:,None], trans=0, lower=True)
+
+ quad = -0.5 * np.sum(uag**2, axis=0)
+ norm = - np.sum(np.log(np.diag(L))) - np.log(2*np.pi)
+ log_prior = np.sum(quad + norm)
+ log_jacobian = self.log_dpdx(x)
+ log_hyperprior = self.log_hyperprior(x)
+
+ return log_prior + log_jacobian + log_hyperprior
+
+ def get_draw_from_priors(self, full_log_prior):
+ """Create prior draw functions for PTMCMC"""
+
+ def draw_from_mvn_prior_hyper(x, iter, beta):
+ """Draw a new hyperparameter"""
+ q = x.copy()
+
+ # Select random parameter to jump in & propose
+ ind = np.random.randint(0, self.hyper_ndim())
+ ind_off = ind + self._ind_offset
+ prior_dist = self._hyper_dists[int(ind)]
+ q[ind_off] = prior_dist.rvs()
+
+ # Use only the hyper-prior here, as that's what we draw from
+ # The full prior is conditional on these parameters, so it
+ # would otherwise change a lot
+ x_logp = prior_dist.logpdf(x[ind_off])
+ q_logp = prior_dist.logpdf(q[ind_off])
+
+ lqxy = x_logp - q_logp
+
+ return q, float(lqxy)
+
+ def draw_from_mvn_prior_low(x, iter, beta):
+ """Draw a new low-level parameter from the conditional MvGaussian"""
+ q = x.copy()
+
+ # Go to transformed coordinates
+ qp = self.forward(q)
+
+ # Whiten the parameters
+ mu, L = self.get_mu_L(x)
+ amps = qp[self._la_inds]
+ gammas = qp[self._g_inds]
+ pag = np.vstack([amps, gammas])
+ uag = sl.solve_triangular(L, pag - mu[:,None], trans=0, lower=True)
+
+ # Draw a random element from uag to update
+ n_total = np.prod(uag.shape)
+ random_index = np.random.choice(n_total)
+ indices = np.unravel_index(random_index, uag.shape)
+
+ # Draw a new value for this parameter
+ uag[indices] = np.random.randn()
+ pag = mu[:,None] + np.dot(L, uag)
+
+ # Transform back to original coordinates
+ qp[self._la_inds] = pag[0,:]
+ qp[self._g_inds] = pag[1,:]
+ q = self.backward(qp)
+
+ x_logp = full_log_prior(x)
+ q_logp = full_log_prior(q)
+
+ lqxy = x_logp - q_logp
+
+ return q, float(lqxy)
+
+ return draw_from_mvn_prior_hyper, draw_from_mvn_prior_low
+ #return (draw_from_mvn_prior_low,)
+
+class BoundedTwoComponentMvNormalPlHierarchicalPrior(BoundedMvNormalPlHierarchicalPrior):
+ """Same as BoundedMvNormalPlHierarchicalPrior, but with two populations"""
+
+ def __init__(self, pta, log_amplitude_regexp, gamma_regexp, ind_offset=0, gamma_lower=0, gamma_upper=7, name='TwoComponentMvNormal'):
+ super().__init__(
+ pta,
+ log_amplitude_regexp,
+ gamma_regexp,
+ ind_offset,
+ gamma_lower=gamma_lower,
+ gamma_upper=gamma_upper,
+ name=name
+ )
+
+ def hyper_ndim(self):
+ return 11
+
+ def set_hyperpriors(self):
+ super().set_hyperpriors()
+
+ # Perhaps this should be the Beta(0.5, 0.5) distribution?
+ self._fraction = sstats.uniform(loc=0.1, scale=0.8)
+ self._hyper_dists = self._hyper_dists + self._hyper_dists + [self._fraction]
+
+ self.hyperparameter_names = [
+ f"{self._name}_mu1_amp",
+ f"{self._name}_mu1_gamma",
+ f"{self._name}_L1_A",
+ f"{self._name}_L1_gamma",
+ f"{self._name}_L1_12",
+ f"{self._name}_mu2_amp",
+ f"{self._name}_mu2_gamma",
+ f"{self._name}_L2_A",
+ f"{self._name}_L2_gamma",
+ f"{self._name}_L2_12",
+ f"{self._name}_CF",
+ ]
+
+ def get_all_mu_L(self, x):
+ """Get the mu and L of the prior"""
+ (mu1_1, mu1_2, L1_1, L1_2, L1_12, mu2_1, mu2_2, L2_1, L2_2, L2_12, CF) = self.get_hyper_pars(x)
+
+ L1 = np.array([[L1_1, 0],[L1_12, L1_2]]) # The Cholesky decomposition
+ mu1 = np.array([mu1_1, mu1_2])
+
+ L2 = np.array([[L2_1, 0],[L2_12, L2_2]]) # The Cholesky decomposition
+ mu2 = np.array([mu2_1, mu2_2])
+ return mu1, L1, mu2, L2, CF
+
+ def get_mu_L(self, x):
+ """Get the mu and L for a random component (with prob CF)
+
+ This is done, so that we can keep the following functions from superclass
+ - get_draw_from_priors
+ - sample
+ """
+
+ mu1, L1, mu2, L2, CF = self.get_all_mu_L(x)
+
+ if np.random.rand() <= CF:
+ return mu1, L1
+
+ else:
+ return mu2, L2
+
+ def log_prior(self, x):
+ """The full prior, including all HBM levels, for this component"""
+
+ x_gammas = x[self._g_inds]
+ if np.any(x_gammas <= self._gamma_lower) or np.any(x_gammas >= self._gamma_upper):
+ return -np.inf
+
+ # Hyper parameter quantities
+ p = self.forward(x)
+ mu1, L1, mu2, L2, CF = self.get_all_mu_L(x)
+
+ # Demand that mu1 > mu2 (gamma), so that there is no mode confusion?
+ if mu1[1] > mu2[1]:
+ return -np.inf
+
+ # Amplitudes & Gammas
+ amps = p[self._la_inds]
+ gammas = p[self._g_inds]
+ pag = np.vstack([amps, gammas])
+
+ # Mode 1 & 2 Gaussian components
+ uag1 = sl.solve_triangular(L1, pag - mu1[:,None], trans=0, lower=True)
+ quad1 = -0.5 * np.sum(uag1**2, axis=0)
+ norm1 = - np.sum(np.log(np.diag(L1))) - np.log(2*np.pi)
+ uag2 = sl.solve_triangular(L2, pag - mu2[:,None], trans=0, lower=True)
+ quad2 = -0.5 * np.sum(uag2**2, axis=0)
+ norm2 = - np.sum(np.log(np.diag(L2))) - np.log(2*np.pi)
+ log_prior1 = np.sum(quad1 + norm1)
+ log_prior2 = np.sum(quad2 + norm2)
+
+ log_prior = log_weighted_sum_exp(log_prior1, log_prior2, CF)
+ log_jacobian = self.log_dpdx(x)
+ log_hyperprior = self.log_hyperprior(x)
+
+ return log_prior + log_jacobian + log_hyperprior
+
+
+class EnterpriseWrapper(object):
+ """Class to wrap an Enterprise pta object to allow for Hierarchical Priors"""
+
+ def __init__(self, pta, hyper_regexps={}):
+ """Initialize the Enterprise Wrapper
+
+ param hyper_regexps: dict of dictionaries
+ {'rn_noise': {'log10_amp': 'regexp',
+ 'gamma': 'regexp',
+ 'prior': BoundedMvNormalPlHierarchicalPrior}}
+
+ """
+ self._pta = pta
+ self._ndim_level1 = len(pta.param_names)
+ self._ndim_level2 = 0
+ self.hyper_priors = []
+
+ for noise_component_name, noise_component in hyper_regexps.items():
+ prior_class = noise_component.get('prior', BoundedMvNormalPlHierarchicalPrior)
+ prior = prior_class(pta,
+ noise_component['log10_amp'],
+ noise_component['gamma'],
+ ind_offset=self._ndim_level1+self._ndim_level2,
+ gamma_lower=0,
+ gamma_upper=7,
+ name=noise_component_name
+ )
+
+ self._ndim_level2 += prior.hyper_ndim()
+
+ self.hyper_priors.append(prior)
+
+ self._ndim = self._ndim_level1 + self._ndim_level2
+ self._ptapar_to_array, self._array_to_ptapar = ptapar_mapping(self._pta)
+
+ def param_names(self):
+ """All parameter names of whole HBM"""
+ param_names_orig = self._pta.param_names
+
+ param_names_hyper = [hp for prior in self.hyper_priors for hp in prior.hyperparameter_names]
+
+ return param_names_orig + param_names_hyper
+
+ def hbm_level1_indices(self):
+ """All the level1 (not flat, not level2) indices"""
+ level1_indices = []
+
+ for prior in self.hyper_priors:
+ level1_indices = np.concatenate([level1_indices, np.where(prior.get_level1_parameter_mask())[0]])
+
+ return np.sort(np.hstack(level1_indices))
+
+ def hbm_level2_indices(self):
+ """All HBM level2 indices"""
+ level2_indices = []
+
+ for prior in self.hyper_priors:
+ level2_indices = np.concatenate([level2_indices, prior._ind_hyper])
+
+ return np.sort(np.hstack(level2_indices))
+
+ def get_nohbm_indices(self):
+ """All indices that are not multi-level"""
+ all_inds = set(np.arange(self._ndim))
+
+ return np.array(list(all_inds - set(self.hbm_level1_indices()) - set(self.hbm_level2_indices())))
+
+ def sample_orig(self):
+ """Sample from the original Enterprise prior, not the Hierarchical one"""
+ x0_orig = np.hstack([p.sample() for p in self._pta.params])
+ return x0_orig
+
+ def sample(self):
+ """Sample randomly from the HBM prior"""
+ x = np.zeros(self._ndim)
+ x_orig = self.sample_orig()
+ x[:len(x_orig)] = x_orig # Some will be overwritten
+
+ while True:
+ for prior in self.hyper_priors:
+ x_prior = prior.sample()
+ x[prior.get_parameter_inds()] = x_prior
+
+ if np.isfinite(self.log_prior(x)):
+ return x
+
+ return x
+
+ def get_low_level_pars(self, x):
+ """Return only the low-level parameters. Includes *flat* parameters"""
+ return x[:self._ndim_level1]
+
+ def log_prior(self, x):
+ """Full hierarchical log-prior"""
+ logp = np.sum([self._pta.params[self._array_to_ptapar[ii]].get_logpdf(x[ii]) for ii in self.get_nohbm_indices()])
+
+ for prior in self.hyper_priors:
+ logp += prior.log_prior(x)
+
+ return logp
+
+ def log_likelihood(self, x):
+ """Log-likelihood as defined by Enterprise"""
+ try:
+ logp = self._pta.get_lnlikelihood(self.get_low_level_pars(x))
+ except (ValueError, OverflowError):
+ logp = -np.inf
+
+ return logp
+
+ def get_draw_from_prior_functions(self):
+ """Create a list of prior draw functions for PTMCMC"""
+
+ def draw_from_prior_flat(x, iter, beta):
+ """Draw from flat (non-multi-level) parameters"""
+ q = x.copy()
+
+ ind = np.random.choice(self.get_nohbm_indices())
+ q[ind] = np.atleast_1d(self._pta.params[self._array_to_ptapar[ind]].sample())[0]
+
+ x_logp = self.log_prior(x)
+ q_logp = self.log_prior(q)
+
+ lqxy = x_logp - q_logp
+
+ return q, float(lqxy)
+
+ prior_functions = [draw_from_prior_flat]
+
+ for prior in self.hyper_priors:
+ for draw_function in prior.get_draw_from_priors(self.log_prior):
+ prior_functions.append(draw_function)
+
+ return prior_functions