Pyrates

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- Added experimental support for multiple source variables per edge

- edges can either have multiple input variable from the same input
node, or
- they can have additional (“modulating”) input from any node in the
network

- Added experimental support for Fortran code creation backend
- Edge delays can now be transformed into delay distributions via
convoluted Gamma-Kernels based on differential equation using a mean
and spread parameter for the delay
- various performance improvements

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- Allow to initialise CircuitTemplate with instances of
``EdgeTemplate`` instead of a template path, previous behaviour is
unaffected.
- Fix writing graph to the file by passing ``_format`` along until the
end

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- updated tensorflow dependency to >=2.0, fixes some dependency
problems
- Improved cluster distribution system, available under
``pyrates.utility.grid_search``
- New feature: model optimization with genetic algorithms, available
under ``pyrates.utility.genetic_algorithm``
- Miscellaneous bug fixes

---

~~~~~

- removed version ID numbers of operator/node instances in the
intermediate representation. I.e. a node label ``mynode`` was
previously renamed to ``mynode.0`` and will now keep it’s original
label.
- moved all functionality of ComputeGraph into CircuitIR, which is now
the main interface for the backend.

- ``CircuitIR`` now has a ``.compile`` method that performs all
vectorization and transformation into the computable backend form.

- vectorization will transform all nodes into instances of
``VectorizedNodeIR`` that have labels like ``vector_nodeX`` with X
being a integer index. The map between old nodes and vectorized nodes
with respective index is saved in the ``label_map`` dictionary
attribute of the ``CircuitIR``
- When adding input or sampling output of a network with multiple
stacked levels of circuits, you can now use ``all`` to get all nodes
within that particular level. For example
``mysubcircuit1/all/mynode`` will get all nodes with label ``mynode``
that are in one level of sub-circuits below ``mysubcircuit``.
- Tensorflow support now relies on the current 2.0 release candidate
``tensorflow-2.0-rc``
- Added optional install requirements via ``extras_require`` in
setup.py