Merge remote-tracking branch 'origin/spring_cleaning' into jax

neurolib/models/model.py

@@ -70,7 +70,7 @@ def get_bold_variable(self, variables):
         default_index = self.state_vars.index(self.default_output)
         return variables[default_index]
 
-    def simulateBold(self, bold_variable, append=False):
+    def simulateBold(self, bold_variable, append=True):
         """Gets the default output of the model and simulates the BOLD model.
         Adds the simulated BOLD signal to outputs.
         """
@@ -192,7 +192,7 @@ def run(
         The model can be run in three different ways:
         1) `model.run()` starts a new run.
         2) `model.run(chunkwise=True)` runs the simulation in chunks of length `chunksize`.
-        3) `mode.run(continue_run=True)` continues the simulation of a previous run.
+        3) `mode.run(continue_run=True)` continues the simulation of a previous run. This has no effect during the first run.
 
         :param inputs: list of inputs to the model, must have the same order as model.input_vars. Note: no sanity check is performed for performance reasons. Take care of the inputs yourself.
         :type inputs: list[np.ndarray|]
@@ -202,9 +202,9 @@ def run(
         :type chunksize: int, optional
         :param bold: simulate BOLD signal (only for chunkwise integration), defaults to False
         :type bold: bool, optional
-        :param append_outputs: append new and chunkwise outputs to the outputs attribute, defaults to False. Note: BOLD outputs are always appended
+        :param append_outputs: append new and chunkwise outputs to the outputs attribute, defaults to False. Note: BOLD outputs are always appended.
         :type append_outputs: bool, optional
-        :param continue_run: continue a simulation by using the initial values from a previous simulation
+        :param continue_run: continue a simulation by using the initial values from a previous simulation. This has no effect during the first run.
         :type continue_run: bool
         """
         self.initializeRun(initializeBold=bold)
@@ -325,8 +325,8 @@ def storeOutputsAndStates(self, t, variables, append=False):
 
     def setInitialValuesToLastState(self):
         """Reads the last state of the model and sets the initial conditions to that state for continuing a simulation."""
-        if not hasattr(self, "t"):
-            raise ValueError("You tried using continue_run=True on the first run.")
+        if not all([sv in self.state for sv in self.state_vars]):
+            return
         for iv, sv in zip(self.init_vars, self.state_vars):
             # if state variables are one-dimensional (in space only)
             if (self.state[sv].ndim == 0) or (self.state[sv].ndim == 1):

neurolib/models/multimodel/model.py

@@ -59,8 +59,6 @@ def __init__(self, model_instance):
         self.boldInitialized = False
         self.params["sampling_dt"] = self.params["sampling_dt"] or self.params["dt"]
 
-        self.start_t = 0.0
-
         logging.info(f"{self.name}: Model initialized.")
 
     def _set_model_params(self):
@@ -201,13 +199,11 @@ def integrate(self, append_outputs=False, simulate_bold=False, noise_input=None)
 
         # bold simulation after integration
         if simulate_bold and self.boldInitialized:
-            self.simulateBold(result[self.default_output].values.T, append=append_outputs)
+            self.simulateBold(result[self.default_output].values.T, append=True)
 
     def setInitialValuesToLastState(self):
-        if not hasattr(self, "t"):
-            raise ValueError("You tried using continue_run=True on the first run.")
-        # set start t for next run for the last value now
-        self.start_t = self.t[-1]
+        if not self.state:
+            return
         new_initial_state = np.zeros((self.model_instance.initial_state.shape[0], self.maxDelay + 1))
         total_vars_counter = 0
         for node_idx, node_vars in enumerate(self.state_vars):
@@ -217,12 +213,6 @@ def setInitialValuesToLastState(self):
         # set initial state
         self.model_instance.initial_state = new_initial_state
 
-    def clearModelState(self):
-        # set start_t to zero again
-        self.start_t = 0.0
-        # `clearModelState` as per base class
-        super().clearModelState()
-
     def integrateChunkwise(self, chunksize, bold, append_outputs):
         raise NotImplementedError("for now...")
 

tests/test_models.py

@@ -260,7 +260,6 @@ def test_init(self):
         self.assertEqual(model.model_instance, fhn_net)
         self.assertTrue(isinstance(model.params, star_dotdict))
         self.assertTrue(model.integration is None)
-        self.assertEqual(model.start_t, 0.0)
         self.assertEqual(model.num_noise_variables, 4)
         self.assertEqual(model.num_state_variables, 4)
         max_delay = int(DELAY / model.params["dt"])
@@ -352,7 +351,6 @@ def test_continue_run_node(self):
         self.assertAlmostEqual(model.t[0] - last_t, model.params["dt"] / 1000.0)
         # assert start_t is reset to 0, when continue_run=False
         model.run()
-        self.assertEqual(model.start_t, 0.0)
 
     def test_continue_run_network(self):
         DELAY = 13.0
@@ -371,7 +369,6 @@ def test_continue_run_network(self):
         self.assertAlmostEqual(model.t[0] - last_t, model.params["dt"] / 1000.0)
         # assert start_t is reset to 0, when continue_run=False
         model.run()
-        self.assertEqual(model.start_t, 0.0)
 
 
 if __name__ == "__main__":