Merge pull request #126 from neurolib-dev/improvement/minimal_model_i…

…mplementation

Minimal custom model implementation

README.md

@@ -81,6 +81,7 @@ Example [IPython Notebooks](examples/) on how to use the library can be found in
 
 - [Example 0.0](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-0-aln-minimal.ipynb) - Basic use of the `aln` model
 - [Example 0.3](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-0.3-fhn-minimal.ipynb) - Fitz-Hugh Nagumo model `fhn` on a brain network
+- [Example 0.6](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-0.6-custom-model.ipynb) - Minimal example of how to implement your own model in `neurolib`
 - [Example 1.2](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-1.2-brain-network-exploration.ipynb) - Parameter exploration of a brain network and fitting to BOLD data
 - [Example 2.0](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-2-evolutionary-optimization-minimal.ipynb) - A simple example of the evolutionary optimization framework 
 

neurolib/models/model.py

@@ -14,6 +14,8 @@ def __init__(self, integration, params):
         if hasattr(self, "name"):
             if self.name is not None:
                 assert isinstance(self.name, str), f"Model name is not a string."
+        else:
+            self.name = "Noname"
 
         assert integration is not None, "Model integration function not given."
         self.integration = integration
@@ -33,6 +35,10 @@ def __init__(self, integration, params):
 
         assert isinstance(self.default_output, str), "`default_output` must be a string."
 
+        # if no output_vars is set, it will be replaced by state_vars
+        if not hasattr(self, "output_vars"):
+            self.output_vars = self.state_vars
+
         # create output and state dictionary
         self.outputs = dotdict({})
         self.state = dotdict({})
@@ -301,7 +307,8 @@ def clearModelState(self):
         self.state = dotdict({})
         self.outputs = dotdict({})
         # reinitialize bold model
-        self.initializeBold()
+        if self.params.get("bold"):
+            self.initializeBold()
 
     def storeOutputsAndStates(self, t, variables, append=False):
         """Takes the simulated variables of the integration and stores it to the appropriate model output and state object.
@@ -397,18 +404,24 @@ def getMaxDelay(self):
         :return: maxmimum delay of the model in units of dt
         :rtype: int
         """
-        dt = self.params["dt"]
-        Dmat = self.params["lengthMat"]
+        dt = self.params.get("dt")
+        Dmat = self.params.get("lengthMat")
 
-        if "signalV" in self.params:
-            signalV = self.params["signalV"]
+        if Dmat is not None:
+            # divide Dmat by signalV
+            signalV = self.params.get("signalV") or 0
             if signalV > 0:
                 Dmat = Dmat / signalV
             else:
+                # if signalV is 0, eliminate delays
                 Dmat = Dmat * 0.0
 
-        Dmat_ndt = np.around(Dmat / dt)  # delay matrix in multiples of dt
-        max_global_delay = int(np.amax(Dmat_ndt))
+        # only if Dmat and dt exist, a global max delay can be computed
+        if Dmat is not None and dt is not None:
+            Dmat_ndt = np.around(Dmat / dt)  # delay matrix in multiples of dt
+            max_global_delay = int(np.amax(Dmat_ndt))
+        else:
+            max_global_delay = 0
         return max_global_delay
 
     def setStateVariables(self, name, data):