Release 0.9.4

Author: Andrew Davison

doc/download.txt

@@ -5,7 +5,7 @@ Downloads
 Source distributions
 --------------------
 
-The `latest stable version of PyNN`_ (0.9.3) may be downloaded from the
+The `latest stable version of PyNN`_ (0.9.4) may be downloaded from the
 `Python Package Index`_. This is recommended for
 anyone using PyNN for the first time.
 

doc/installation.txt

@@ -7,7 +7,7 @@ install and run PyNN on Windows, but this has not been tested.
 
 Installing PyNN requires:
 
-    * Python (version 2.7, 3.3-3.6)
+    * Python (version 2.7, 3.3-3.7)
     * a recent version of the NumPy_ package
     * the lazyarray_ package
     * the Neo_ package (>= 0.5.0)
@@ -31,8 +31,8 @@ The easiest way to get PyNN is to use pip_::
 If you would prefer to install manually, :doc:`download the latest
 source distribution <download>`, then run the setup script, e.g.::
 
-    $ tar xzf PyNN-0.9.3.tar.gz
-    $ cd PyNN-0.9.3
+    $ tar xzf PyNN-0.9.4.tar.gz
+    $ cd PyNN-0.9.4
     $ python setup.py install
 
 This will install it to your Python :file:`site-packages` directory, and may

doc/release_notes.txt

@@ -6,6 +6,7 @@ Release notes
 .. toctree::
    :maxdepth: 1
 
+   releases/0.9.4.txt
    releases/0.9.3.txt
    releases/0.9.2.txt
    releases/0.9.1.txt

doc/releases/0.9.4.txt

@@ -0,0 +1,33 @@
+========================
+PyNN 0.9.4 release notes
+========================
+
+March 22nd 2019
+
+Welcome to PyNN 0.9.4!
+
+
+SONATA
+-------
+
+SONATA_ is a data format for representing/storing data-driven spiking neuronal network models, experimental protocols
+(injecting spikes, currents) and simulation outputs.
+
+In the network representation, all connections are represented explicity, as in PyNN’s
+:class:`FromFileConnector` and :class:`FromListConnector`.
+
+A PyNN model/simulation script can be exported in SONATA format,
+and a SONATA model/simulation can be read and executed through PyNN
+provided the cell types used in the model are compatible with PyNN,
+i.e. they must be point neurons.
+
+For more information on working with the SONATA format, see :ref:`sec-sonata`.
+
+
+Bug fixes and performance improvements
+--------------------------------------
+
+A `small number of bugs`_ have been fixed, and the documentation clarified in a few places.
+
+.. _SONATA: https://github.com/AllenInstitute/sonata
+.. _`small number of bugs`: https://github.com/NeuralEnsemble/PyNN/issues?q=milestone%3A0.9.4+is%3Aclosed

examples/simpleRandomNetwork.py

@@ -37,7 +37,7 @@
 print("[%d] Creating populations" % node)
 n_spikes = int(2 * tstop * input_rate / 1000.0)
 spike_times = numpy.add.accumulate(rng.next(n_spikes, 'exponential',
-                                            {'beta': 1000.0 / input_rate}, mask_local=False))
+                                            {'beta': 1000.0 / input_rate}))
 
 input_population = sim.Population(10, sim.SpikeSourceArray(spike_times=spike_times), label="input")
 output_population = sim.Population(20, sim.IF_curr_exp(**cell_params), label="output")

pyNN/__init__.py

@@ -69,7 +69,7 @@
 :license: CeCILL, see LICENSE for details.
 """
 
-__version__ = '0.9.3'
+__version__ = '0.9.4'
 __all__ = ["common", "random", "nest", "neuron", "brian",
            "recording", "errors", "space", "descriptions",
-           "standardmodels", "parameters", "core"]
+           "standardmodels", "parameters", "core", "serialization"]

setup.py

@@ -87,7 +87,7 @@ def find(self, command):
 
 setup(
     name="PyNN",
-    version="0.9.3",
+    version="0.9.4",
     packages=['pyNN', 'pyNN.nest', 'pyNN.neuron',
                 'pyNN.brian', 'pyNN.common', 'pyNN.mock', 'pyNN.neuroml',
                 'pyNN.recording', 'pyNN.standardmodels', 'pyNN.descriptions',
@@ -120,6 +120,7 @@ def find(self, command):
                  'Programming Language :: Python :: 3.4',
                  'Programming Language :: Python :: 3.5',
                  'Programming Language :: Python :: 3.6',
+                 'Programming Language :: Python :: 3.7',
                  'Topic :: Scientific/Engineering'],
     cmdclass={'build_py': build},
     install_requires=['numpy>=1.8.2', 'lazyarray>=0.3.2', 'neo>=0.5.2',