Overhaul (#34)

* fix: `find_object()`Attribute Error

Initializing a synapse with a tag for src or dst neuron group, requires
tag seach to obtain the src or dst object.
e.g. SynapseGroup(src='pop1', dst=pop2, ...)
this search calls `find_objects()` for all objects, Therefore since
a recoder haven't yet been initialized there's no attribute `variables`

* style: each setting as argument for network

* doc: fix missing keyword and new setup method

* fix: test cuda device

* feat: default  for parameter

* relase: bump version to 0.1.3

HISTORY.rst

@@ -2,11 +2,22 @@
 History
 =======
 
+0.1.3 (2023-08-16)
+
+* BREAKING CHANGE: `Network` no longer accept settings. Individual setting are now argument for Network.
+* Bugg fixes.
+
+
+0.1.2 (2023-06-14)
+
+* `tensor` method for NetworkObject
+
+
 0.1.1 (2023-05-26)
 ------------------
 
 * Every NetworkObject can have a recorder behavior.
-* Netowrk settings accept "index" entry.
+* Network settings accept "index" entry.
 * Bug fixes and general improvement.
 
 

docs/tutorial.rst

@@ -12,7 +12,7 @@ Just like ``PymoNNto``, each ``Network`` in ``PymoNNtorch`` is composed of ``Neu
     ng = NeuronGroup(net=net, size=1000, behavior={})
     syn = SynapseGroup(net=net, src=ng, dst=ng, tag='GLUTAMATE')
 
-So far, ``ng`` has been added to network ``net`` and synaptic connection has been defined to connect ``ng`` to itself, i.e. both afferent and efferent synapses of ``ng`` are ``syn``. By default, each network and its components are created on CPU and the data type of any tensor inside the objects is set to ``torch.float32``. Pass an argument ``settings`` to the ``Network`` to change these default setups. ``settings`` is a dictionary with keys ``device`` and ``dtype`` which indicate the device and data type of everything within the network, respectively.
+So far, ``ng`` has been added to network ``net`` and synaptic connection has been defined to connect ``ng`` to itself, i.e. both afferent and efferent synapses of ``ng`` are ``syn``. By default, each network and its components are created on CPU and the data type of any tensor inside the objects is set to ``torch.float32``. To change these settings on creation, simply, fill the arguments of the network with your desired device and dtype.
 
 To have a functioning network, we can write ``Behavior`` (s) for different network objects to define dynamics and attributes for them. To do so, we can proceed as follows: ::
 
@@ -29,7 +29,7 @@ To have a functioning network, we can write ``Behavior`` (s) for different netwo
             firing = neurons.voltage >= self.threshold
             neurons.spike = firing.byte()
             neurons.voltage[firing] = 0.0 # reset
-            
+
             neurons.voltage *= 0.9 # voltage decay
             neurons.voltage += neurons.vector(mode="uniform", density=0.1)
 
@@ -52,7 +52,7 @@ Note that each behavior is given an index upon being assigned to a network objec
                 neurons.voltage += [email protected]() / synapse.src.size * 10
 
 Now, assume we have defined ``ng`` by::
-    
+
     ng = NeuronGroup(net=net,
                      size=1000,
                      behavior={
@@ -74,11 +74,11 @@ In most simulations, we need to keep track of variables through time. To do so,
 
     net = Network()
     ng = NeuronGroup(net=net,
-                    size=1000, 
+                    size=1000,
                     behavior={
                         1: BasicBehavior(),
                         2: InputBehavior(),
-                        9: Recorder(['voltage', 'mean(voltage)']),
+                        9: Recorder(['voltage', 'torch.mean(voltage)']),
                         10: EventRecorder(['spike'])
                     })
     SynapseGroup(ng, ng, net, tag='GLUTAMATE')

pymonntorch/NetworkBehavior/Recorder/Recorder.py

@@ -54,6 +54,7 @@ def __init__(
             tag=tag,
             **kwargs,
         )
+        self.variables = {}
 
     def initialize(self, object):
         super().initialize(object)
@@ -71,7 +72,6 @@ def initialize(self, object):
         self.auto_annotate = self.parameter("auto_annotate", True)
         self.counter = 0
         self.new_data_available = False
-        self.variables = {}
         self.compiled = {}
 
         self.add_variables(variables)

pymonntorch/NetworkCore/Behavior.py

@@ -131,7 +131,7 @@ def check_unused_attrs(self):
     def parameter(
         self,
         key,
-        default,
+        default=None,
         object=None,
         do_not_diversify=False,
         search_other_behaviors=False,

pymonntorch/NetworkCore/Network.py

@@ -6,9 +6,6 @@
 from pymonntorch.NetworkCore.Behavior import Behavior
 from pymonntorch.NetworkCore.SynapseGroup import *
 
-SxD = False
-DxS = True
-
 
 class Network(NetworkObject):
     """This is the class to construct a neural network.
@@ -20,26 +17,41 @@ class Network(NetworkObject):
         NeuronGroups (list): List of all NeuronGroups in the network.
         SynapseGroups (list): List of all SynapseGroups in the network.
         behavior (list or dict): List of all network-specific behaviors.
-        def_dtype (type): Floating point precision of tensors. Defaults to `torch.float32`; can be changed via setttings with "dtype" key.
-        device (string): The device to allocate tensors' memory on. Defaults to `'cpu'`; can be changed via setttings with "device" key.
-        transposed_synapse_matrix_mode (bool): If `True`, in the matrix created by synapse, each row corresponds to a neuron from the source neuron group. Defaults to `False`; can be changed via settings with the "synapse_mode" key, which can have `DxS` and `SxD` as possible values.
-        index_neurons (bool): If True, the `id` attribute for neuron groups refers to neuron indices. Defaults to `True`; can be changed via setttings with "index" key.
+        def_dtype (type): Floating point precision of tensors. Defaults to `torch.float32`.
+        device (string): The device to allocate tensors on. Defaults to `'cpu'`.
+        transposed_synapse_matrix_mode (bool): If `True`, in the matrix created by synapse, each row corresponds to a neuron from the source neuron group. Defaults to `False`.
+        index_neurons (bool): If True, the `id` attribute for neuron groups refers to neuron indices. Defaults to `True`.
     """
 
-    def __init__(self, tag=None, behavior=None, settings=None):
+    def __init__(
+        self,
+        tag=None,
+        behavior=None,
+        dtype=torch.float32,
+        device="cpu",
+        synapse_mode="SxD",
+        index=True,
+    ):
         """Initialize the network.
 
         Args:
             tag (str): Tag to add to the network. It can also be a comma-separated string of multiple tags.
             behavior (list or dict): List or dictionary of behaviors. If a dictionary is used, the keys must be integers.
-            settings (dict): Dictionary of network-wide settings, e.g. `dtype`, `synapse_mode`, `device` and `index`.
-                             `dtype`: Floating point precision of tensors. Defaults to `torch.float32`.
-                             `synapse_mode`: If `SxD`, rows of matrix created by a synapse referes to the source neuron group and columns referes to the Destination neurpn group. Possible values `DxS` and `SxD`.
-                             `device`: The device to allocate tensors' data on. Defaults to `'cpu'`.
-                             `index`: If True, create an indexing tensor as `id` attribute for each neuron group.
+            dtype (torch.dtype): Floating point precision of tensors.
+            synapse_mode (string): If `SxD`, rows of matrix created by a synapse referes to the source neuron group and columns referes to the Destination neurpn group. Possible values `DxS` and `SxD`.
+            device (string): The device to allocate tensors' data on. Defaults to `'cpu'`.
+            index (bool): If True, create an indexing tensor as `id` attribute for each neuron group.
         """
-        settings = settings if settings is not None else {}
-        self.apply_settings(settings)
+        self.device = device
+        self._def_dtype = dtype
+        self.index_neurons = index
+        if synapse_mode not in ["SxD", "DxS"]:
+            print(
+                f"""warning: synapse_mode should be either "SxD" or "DxS". using "DxS"."""
+            )
+        self.transposed_synapse_matrix_mode = (
+            False if synapse_mode == "DxS" else synapse_mode == "SxD"
+        )
 
         self.NeuronGroups = []
         self.SynapseGroups = []
@@ -52,14 +64,6 @@ def __init__(self, tag=None, behavior=None, settings=None):
 
         super().__init__(tag, self, behavior, device=self.device)
 
-    def apply_settings(self, settings):
-        self.device = settings.setdefault("device", "cpu")
-        self._def_dtype = settings.setdefault("dtype", torch.float32)
-        self.index_neurons = settings.setdefault("index", True)
-        self.transposed_synapse_matrix_mode = (
-            settings.setdefault("synapse_mode", DxS) != DxS
-        )
-
     def set_behaviors(self, tag, enabled):
         """Set behaviors of specific tag to be enabled or disabled.
 

setup.py

@@ -49,6 +49,6 @@
     test_suite="tests",
     tests_require=test_requirements,
     url="https://github.com/cnrl/PymoNNtorch",
-    version="0.1.2",
+    version="0.1.3",
     zip_safe=False,
 )

tests/test_pymonntorch.py

@@ -106,7 +106,7 @@ def forward(self, neurons):
                     synapse.W @ synapse.src.spike.float() / synapse.src.size * 10
                 )
 
-    net = Network(settings={"device": "cuda"})
+    net = Network(device="cuda")
     ng = NeuronGroup(
         net=net,
         size=100,