Pycomex

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

* Added ``pycomex.experiment.ArchivedExperiment`` which makes it possible to load an arbitrary experiment
instance from the archived folder and use it much like it is possible from within ``analysis.py``
* Added ``pycomex.experiment.ExperimentRegistry`` which can be used to load an experiment base path and
automatically discover all the (nested) namespace folders within which contain actual experiment run
archives.
* Added ``pycomex.experiment.NamespaceFolder`` which represents and allows to work with namespace
folders, for example by easily getting the ``ArchivedExperiment`` instance according to an experiment
run (numeric) index.
* Added ``psutil`` to dependencies to implement hardware resource monitoring as an additional feature
when printing the intermediate status of the experiment run with ``Experiment.status()``

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

* Fixed bug that ``e.info()`` could not be used inside the ``analysis.py`` file
* Decided to add ``numpy`` and ``matplotlib`` to the dependencies after all. Originally I did not want to
include them because I don't strictly need them and they are quite big packages. But honestly, what kind
of computational experiment works without those two nowadays?
* Renamed the template files with better naming scheme
* Updated readme

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

* Added ``Experiment.commit_json`` to directly store dict data as json file artifacts for the experiment
records
* Improved the ``analysis.py`` templating for experiments
* Using the context manager ``Experiment.analysis`` within the experiment file can be used to not only
directly execute the analysis right after the experiment is completed but also all the code within
that context managers content block is copied into the analysis template of that run and it will
work as it is
* This is due to the fact, that ``Experiment`` now automatically realizes if it is being imported
from a ``snapshot.py`` within an existing record folder. In that case it populates internal fields
such as ``Experiment.data`` by loading the persistent file artifact.
* Added ``examples/analysis.py`` which documents / explains the previously mentioned process

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

* Now it is possible to commit matplotlib Figures directly to the experiment with ``Experiment.commit_fig``
* File artifact paths are now automatically tracked as metadata
* Added a default template ``annotations.rst`` to be rendered for each experiment which provides a
boilerplate starting point for additional thoughts to be added

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

* Every experiment file now has a command line interface realized with ``argparse``
* It is possible to pass in either a .JSON or a .PY file which are able to modify the default
experiment parameters defined in the experiment file
* It is possible to retrieve the destination path when invoking an experiment file over the command line
* A copy of the actual experiment file is copied as a snapshot to the experiment record folder
* It is possible to define additional jinja templates which are rendered as additional files into the
experiment record folder
* One default template which is rendered this way is "analysis.py" module, which provides a boilerplate
starting point for further analysis on the experiment results.

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

* Added the "poetry_bumpversion" plugin https://github.com/monim67/poetry-bumpversion to update the version
strings in all the relevant files
* Using "black" for code formatting
* Using "flake9" for linting