Remove v3 attrs from simulation stub (#796)

* Remove v3 attrs from simulation stub

* Update simulation.rst

* Update parameter_sweep.py

* Update simulation.rst

* fix formatting

docs/simulation/simulation.rst

@@ -4,17 +4,13 @@
 Simulating networks
 ###################
 
-.. The BSB manages simulations by deferring as soon as possible to the simulation backends.
-.. Each simulator has good reasons to make their design choices, befitting of their
-.. simulation paradigm. These choices lead to divergence in how simulations are described,
-.. and each simulator has their own niche functions. This means that if you are already
-.. familiar with a simulator, writing simulation config should feel familiar, on top of that
-.. the BSB is able to offer you access to each simulator's full set of features. The downside
-.. is that you're required to write a separate simulation config block per backend.
-..
-.. Now, let's get started.
-
-Simulations can be run through the CLI tool, or through the ``bsb`` library for more
+Simulations can be run through the CLI:
+
+.. code-block:: bash
+
+  bsb simulate my_network.hdf5 my_sim_name
+
+or through the ``bsb`` library for more
 control. When using the CLI, the framework sets up a "hands off" simulation workflow:
 
 * Read the network file
@@ -24,13 +20,7 @@ control. When using the CLI, the framework sets up a "hands off" simulation work
 * Run the simulation
 * Collect all the output
 
-.. code-block:: bash
-
-  bsb simulate my_network.hdf5 my_sim_name
-
-When you use the library, you can set up more complex workflows. For example a parameter
-sweep that loops and modifies the release probability of the AMPA synapse in the
-cerebellar granule cell:
+When you use the library, you can set up more complex workflows, such as parameter sweeps:
 
 .. literalinclude:: ../../examples/simulation/parameter_sweep.py
   :language: python
@@ -247,21 +237,18 @@ NEURON
 Cell models
 -----------
 
-A cell model is described by loading external ``arborize.CellModel`` classes:
+By default the NEURON adapter uses an ``ArborizedCellModel``, which loads
+external ``arborize`` definition to instantiate cells.
 
 .. code-block:: json
 
   {
     "cell_models": {
       "cell_type_A": {
-        "model": "dbbs_models.GranuleCell",
-        "record_soma": true,
-        "record_spikes": true
+        "model": "dbbs_models.GranuleCell"
       },
       "cell_type_B": {
-        "model": "dbbs_models.PurkinjeCell",
-        "record_soma": true,
-        "record_spikes": true
+        "model": "dbbs_models.PurkinjeCell"
       }
     }
   }
@@ -290,11 +277,9 @@ can be referenced by name:
 Devices
 -------
 
-In NEURON an assortment of devices is provided by the BSB to send input, or
-record output.
+Devices send input, or record output.
 
-In addition to voltage and spike recording we'll place a spike generator and a
-voltage clamp:
+Here we'll place a spike generator and a voltage clamp:
 
 .. code-block:: json
 

examples/simulation/parameter_sweep.py

@@ -1,6 +1,3 @@
-# A module with cerebellar cell models
-import dbbs_models
-
 # A module to read HDF5 data
 import h5py
 
@@ -9,35 +6,24 @@
 
 from bsb.core import from_storage
 
-# Read the network file
-network = from_storage("my_network.hdf5")
-
 
+# This decorator runs each call to the function in isolation
+# on a separate process. Does not work with MPI.
 @nrnsub.isolate
 def sweep(param):
-    # Get an adapter to the simulation
-    adapter = network.create_adapter("my_sim_name")
-    # Modify the parameter to sweep
-    dbbs_models.GranuleCell.synapses["AMPA"]["U"] = param
-    # Prepare simulator & instantiate all the cells and connections
-    simulation = adapter.prepare()
+    # Open the network file
+    network = from_storage("my_network.hdf5")
 
-    # (Optionally perform more custom operations before the simulation here.)
+    # Here you can set whatever simulation parameter you want.
+    network.simulations.my_sim.devices.my_stim.rate = param
 
     # Run the simulation
-    adapter.simulate(simulation)
-
-    # (Optionally perform more operations or even additional simulation steps here.)
-
-    # Collect all results in an HDF5 file and get the path to it.
-    result_file = adapter.collect_output()
-    return result_file
+    results = network.run_simulation("my_sim")
+    # Tjese are the recorded spiketrains and signals
+    print(results.spiketrains)
+    print(results.analogsignals)
 
 
 for i in range(11):
     # Sweep parameter from 0 to 1 in 0.1 increments
-    result_file = sweep(i / 10)
-
-    # Analyze each run's results here
-    with h5py.File(result_file, "r") as results:
-        print("What did I record?", list(results["recorders"].keys()))
+    sweep(i / 10)