In this example, we will see how to sample from priors and likelihoods given as Python functions, and how to dynamically define new parameters. This time, we will start from the interpreter and then learn how to create a pure yaml input file with the same information.

## From a Python interpreter¶

Our likelihood will be a gaussian ring centred at 0 with radius 1. We define it with the following Python function and add it to the information dictionary like this:

import numpy as np
from scipy import stats

def gauss_ring_logp(x, y):
return stats.norm.logpdf(np.sqrt(x**2+y**2), loc=1, scale=0.2)

info = {"likelihood": {"ring": gauss_ring_logp}}


Note

NB: external likelihood and priors (as well as internal ones) must return log-probabilities.

cobaya will automatically recognise x and y (or whatever parameter names of your choice) as the input parameters of that likelihood, which we have named ring. Let’s define a prior for them:

from collections import OrderedDict as odict

info["params"] = odict([
["x", {"prior": {"min": -2, "max": 2}, "ref": 1, "proposal": 0.2}],
["y", {"prior": {"min": -2, "max": 2}, "ref": 0, "proposal": 0.2}]])


Now, let’s assume that we want to track the radius of the ring, whose posterior will be approximately gaussian, and the angle, whose posterior will be uniform. We can define them as function of known input parameters:

get_r = lambda x,y: np.sqrt(x**2+y**2)
get_theta = lambda x,y: np.arctan(y/x)

info["params"]["r"] = {"derived": get_r}
info["params"]["theta"] = {"derived": get_theta, "latex": r"\theta",
"min": -np.pi/2, "max": np.pi/2}


Now, we add the sampler information and run. Notice the high number of samples requested for just two dimensions, in order to map the curving posterior accurately:

info["sampler"] = {
"mcmc": {"burn_in": 500, "max_samples": 10000}}

from cobaya.run import run
updated_info, products = run(info)


And now we plot the posterior for x, y, the radius and the angle:

%matplotlib inline
from getdist.mcsamples import MCSamplesFromCobaya
import getdist.plots as gdplt

gdsamples = MCSamplesFromCobaya(updated_info, products["sample"])
gdplot = gdplt.getSubplotPlotter(width_inch=5)
gdplot.triangle_plot(gdsamples, ["x", "y"], filled=True)
gdplot = gdplt.getSubplotPlotter(width_inch=5)
gdplot.plots_1d(gdsamples, ["r", "theta"], nx=2)  Now let’s assume that we are only interested in the region x>y. We can add this constraint as an external prior, in a similar way the external likelihood was added. The logprior for this can be added simply as:

info["prior"] = {"xGTy": lambda x,y: np.log(x>y)}


(Notice that in Python the numerical value of True and False are respectively 0 and 1. This will print a single Warning, since $$log(0)$$ is not finite, but cobaya has no problem dealing with infinities.)

Let’s run with the same configuration and analyse the output:

updated_info_xGTy, products_xGTy = run(info)

gdsamples_xGTy = MCSamplesFromCobaya(
updated_info_xGTy, products_xGTy["sample"])
gdplot = gdplt.getSubplotPlotter(width_inch=5)
gdplot.triangle_plot(gdsamples_xGTy, ["x", "y"], filled=True) ### Alternative: r and theta defined inside the likelihood function¶

Custom likelihoods also allow for the definition of derived parameters. In this example, it would make sense for r and theta to be computed inside the likelihood. To do that, we would redefine the likelihood as (see details at External likelihoods: how to quickly define your own):

# List available derived parameters in the default value of the _derived keyword
def gauss_ring_logp_with_derived(x, y, _derived=["r", "theta"]):
r = np.sqrt(x**2+y**2)
# Assuming _derived is passed at runtime as a dictionary to be filled
_derived["r"] = r
_derived["theta"] = np.arctan(y/x)
return stats.norm.logpdf(r, loc=1, scale=0.2)

info_alt = {"likelihood": {"ring": gauss_ring_logp_with_derived}}


And remove the definition (but not the mention!) of r and theta in the params block:

info_alt["params"] = odict([
["x", {"prior": {"min": -2, "max": 2}, "ref": 1, "proposal": 0.2}],
["y", {"prior": {"min": -2, "max": 2}, "ref": 0, "proposal": 0.2}],
["r", None],
["theta", {"latex": r"\theta"}]])
info_alt["prior"] = {"xGTy": lambda x,y: np.log(x>y)}


### Even better: sampling from r and theta directly¶

The posterior on the radius and the angle is a gaussian times a uniform, much simpler than that on x and y. So we should probably sample on r and theta instead, and we would get a more accurate result with the same number of samples, since now we don’t have the problem of having to go around the ring.

Of course, in principle we would modify the likelihood to take r and theta instead of x and y. But let us assume that this is not easy or even not possible.

Still, this can be done in a simple way at the level of the parameters, i.e. without needing to modify the parameters that the likelihood takes, as explained in Defining parameters dynamically. In essence:

• We give a prior to the parameters over which we want to sample, here r and theta, and signal that they are not understood by the likelihood by giving them the property drop: True.
• We define the parameters taken by the likelihood, here x and y, as functions of the parameters we want to sample over, here r and theta. By default, their values will be saved to the chain files.

[We start from the original example, not the one with theta and r as derived parameters.]

from copy import deepcopy
info_rtheta = deepcopy(info)
info_rtheta["params"] = odict([
["r", {"prior": {"min": 0, "max": 2}, "ref": 1,
"proposal": 0.5, "drop": True}],
["theta", {"prior": {"min": -0.75*np.pi, "max": np.pi/4}, "ref": 0,
"proposal": 0.5, "latex": r"\theta", "drop": True}],
["x", "lambda r,theta: r*np.cos(theta)"],
["y", "lambda r,theta: r*np.sin(theta)"]])
# The x>y condition is already incorporated in the prior of theta
info_rtheta["prior"].pop("xGTy")


## From the shell¶

To run the example above in from the shell, we could just save all the Python code above in a text file and run it with python [file_name]. To get the sampling results as text output, we would add to the info dictionary some output prefix, e.g. info["output"] = "chains/ring".

But there a small complication: cobaya would fail at the time of dumping a copy of the information dictionary, since there is no way to dump a pure Python function to pure-text yaml in a reproducible manner. To solve that, for functions that can be written in a single line, we simply write it lambda form and wrap it in quotation marks, e.g. for r that would be "lambda x,y: np.sqrt(x**2+y**2)". Inside this lambdas, you can use np for numpy and stats for scipy.stats.

More complex functions must be saved to a separate file and imported on the fly. In the example above, let’s assume that we have saved the definition of the gaussian ring likelihood (which could actually be written in a single line anyway), to a file called my_likelihood in the same folder as the Python script. In that case, we would load the likelihood as

# Notice the use of single vs double quotes
info = {"likelihood": {"ring": "import_module('my_likelihood').ring"}}


With those changes, we would be able to run out Python script from the shell (with MPI, if desired) and have the chains saved where requested. We could also have incorporated those text definitions into a yaml file, that we could call with cobaya-run:

likelihood:
ring: import_module('my_likelihood').gauss_ring_logp

params:
x:
prior: {min: -2, max: 2}
ref: 1
proposal: 0.2
y:
prior: {min: -2, max: 2}
ref: 0
proposal: 0.2
r:
derived: 'lambda x,y: np.sqrt(x**2+y**2)'
theta:
derived: 'lambda x,y: np.arctan(y/x)'
latex: \theta

prior:
xGTy: 'lambda x,y: np.log(x>y)'

sampler:
mcmc:

output: chains/ring


Note

Notice that we keep the quotes around the definition of the lambda functions, or yaml would get confused by the :.

If we would like to sample on theta and r instead, our input file would be:

likelihood:
ring: import_module('my_likelihood').gauss_ring_logp

params:
r:
prior: {min: 0, max: 2}
ref: 1
proposal: 0.5
drop: True
theta:
prior: {min: -2.3562, max: 0.7854}
ref: 0
proposal: 0.5
latex: \theta
drop: True
x: 'lambda r,theta: r*np.cos(theta)'
y: 'lambda r,theta: r*np.sin(theta)'

sampler:
mcmc:

output: chains/ring