==============================
MultiAnalysisSamplesDict class
==============================
`pesummary` is able to store multiple sets of posterior samples in a custom
`MultiAnalysisSamplesDict` class. This `MultiAnalysisSamplesDict` class is
inherited from the core `dict` class and includes extra properties for returning
comparison statistics between samples. The posterior samples for each analysis
is stored in a `pesummary.utils.samples_dict.SamplesDict` object. For details
about this class see `SamplesDict class <SamplesDict.html>`_
Initializing the MultiAnalysisSamplesDict class
-----------------------------------------------
The `MultiAnalysisSamplesDict` is initialized with either an array of
parameters and samples for each analysis, or a dictionary containing the label
as key, and posterior samples stored as the items,
.. code-block:: python
>>> from pesummary.utils.samples_dict import MultiAnalysisSamplesDict
>>> parameters = ["a", "b", "c"]
>>> samples = [
... [[1,2,3,4,5,6], [6,5,4,3,2,1], [1,3,5,2,4,6]],
... [[1,2,3,4,5,6], [6,5,4,3,2,1], [1,3,5,2,4,6]]
... ]
>>> samplesdict = MultiAnalysisSamplesDict(
... parameters, samples, labels=["analysis_1", "analysis_2"]
... )
>>> print(samplesdict.keys())
dict_keys(['analysis_1', 'analysis_2'])
>>> print(samplesdict["analysis_1"])
idx a b c
0 1.000000 6.000000 1.000000
1 2.000000 5.000000 3.000000
2 3.000000 4.000000 5.000000
3 4.000000 3.000000 2.000000
4 5.000000 2.000000 4.000000
5 6.000000 1.000000 6.000000
>>> data = {
... "analysis_1": {
... "a": [1,2,3,4,5,6,7,8],
... "b": [8,7,6,5,4,3,2,1],
... },
... "analysis_2": {
... "b": [10,20,30,40,50,60,70,80],
... "c": [1,4,8,2,3,5,6,7]
... }
... }
>>> samplesdict = MultiAnalysisSamplesDict(data)
>>> print(samplesdict.keys())
dict_keys(['analysis_1', 'analysis_2'])
>>> print(samplesdict["analysis_2"])
idx b c
0 10.000000 1.000000
1 20.000000 4.000000
2 30.000000 8.000000
3 40.000000 2.000000
. . .
. . .
6 70.000000 6.000000
7 80.000000 7.000000
Alternatively, it may be initialized with a dictionary containing the path to
multiple result files containing posterior samples and a label for each analysis,
.. code-block:: python
>>> samplesdict = MultiAnalysisSamplesDict.from_files(
... {
... "analysis_1": "path_to_analysis_1.hdf5",
... "analysis_2": "path_to_analysis_2.dat"
... }
... )
This `classmethod` simply calls the `read function <read.html>`_ for each file
and initializes the class with the parameters and samples that are already
stored.
Finally, you may also initialize this class with `SamplesDict <SamplesDict.html>`_
instances,
.. code-block:: python
>>> from pesummary.io import read
>>> f = read("path_to_analysis_1.hdf5")
>>> g = read("path_to_analysis_2.dat")
>>> samplesdict = MultiAnalysisSamplesDict(
... {"analysis_1": f.samples_dict, "analysis_2": g.samples_dict}
... )
After initializing the class, you may select a subset of columns by running:
.. code-block:: python
>>> samplesdict[["analysis_1", "analysis_2"]]
Using the MultiAnalysisSamplesDict properties
---------------------------------------------
Below we show some of the useful properties of the `MultiAnalysisSamplesDict`
class. For full details see the doc string,
.. code-block:: python
>>> samplesdict.total_number_of_samples
16
>>> samplesdict.minimum_number_of_samples
8
Using the MultiAnalysisSamplesDict functions
--------------------------------------------
The `MultiAnalysisSamplesDict` class also has functions to try and make it
easier to manipulate and compare the stored analyses,
.. code-block:: python
>>> samplesdict.js_divergence("b")
0.55198
>>> samplesdict.ks_statistic("b")
0.00016
The `MultiAnalysisSamplesDict` class also provides the ability to plot the
posterior samples directly. This can be achieved through the `plot()` method.
For example, if we want to make a comparison plot, comparing `analysis_1`
and `analysis_2` samples and plot them as a KDE, we can run,
.. code-block:: python
>>> import matplotlib.pyplot as plt
>>> parameter = "a"
>>> fig = samplesdict.plot(parameter, type="hist", labels=["analysis_1", "analysis_2"], kde=True)
>>> plt.show()
or if we wanted to make a corner plot which compares a subset of the
`analysis_1` and `analysis_2` samples, we can run,
.. code-block:: python
>>> import matplotlib.pyplot as plt
>>> parameters = ["a", "b"]
>>> fig = samplesdict.plot(type="corner", labels=["analysis_1", "analysis_2"], parameters=parameters)
>>> plt.show()
To see the full list of available plots, you can run:
.. code-block:: python
>>> samplesdict.available_plots