print(result.samples.parameter_lists[10][:])
print(result.samples.log_likelihood_list[10])
"""
We can also use it to get a model instance of the `median_pdf` model, which is the model where each parameter is
the value estimated from the probability distribution of parameter space.
"""
mp_instance = result.samples.median_pdf_instance
print()
print("Median PDF Model:\n")
print("Centre = ", mp_instance.centre)
print("Intensity = ", mp_instance.intensity)
print("Sigma = ", mp_instance.sigma)
"""
The Probability Density Functions (PDF's) of the results can be plotted using the Emcee's visualization
tool `corner.py`, which is wrapped via the `EmceePlotter` object.
The PDF shows the 1D and 2D probabilities estimated for every parameter after the model-fit. The two dimensional
figures can show the degeneracies between different parameters, for example how increasing $\sigma$ and decreasing
the intensity $I$ can lead to similar likelihoods and probabilities.
"""
emcee_plotter = aplt.EmceePlotter(samples=result.samples)
emcee_plotter.corner()
"""
The PDF figure above can be seen to have labels for all parameters, whereby sigma appears as a sigma symbol, the
intensity is `I`, and centre is `x`. This is set via the config file `config/notation/label.ini`. When you write
your own model-fitting code with PyAutoFit, you can update this config file so your PDF's automatically have the
correct labels.
we'll come back to the `Samples` objects in tutorial 6!
"""
"""
We now pass the samples to a `EmceePlotter` which will allow us to use emcee's in-built plotting libraries to
make figures.
The emcee readthedocs describes fully all of the methods used below
- https://emcee.readthedocs.io/en/stable/user/sampler/
The plotter wraps the `corner` method of the library `corner.py` to make corner plots of the PDF:
- https://corner.readthedocs.io/en/latest/index.html
In all the examples below, we use the `kwargs` of this function to pass in any of the input parameters that are
described in the API docs.
"""
emcee_plotter = aplt.EmceePlotter(samples=samples)
"""
The `corner` method produces a triangle of 1D and 2D PDF's of every parameter in the model fit.
"""
emcee_plotter.corner(
bins=20,
range=None,
color="k",
hist_bin_factor=1,
smooth=None,
smooth1d=None,
label_kwargs=None,
titles=None,
show_titles=False,
title_fmt=".2f",
title_kwargs=None,