From 4aaa8cda38810f25464518a86f994fc2d9071f8d Mon Sep 17 00:00:00 2001
From: Georg Reich <georgreich99@gmail.com>
Date: Mon, 11 Mar 2024 17:40:36 +0100
Subject: [PATCH] remove unused `self.start_t`, fix BOLD append for multimodel

---
 neurolib/models/model.py            |  2 +-
 neurolib/models/multimodel/model.py | 12 +-----------
 tests/test_models.py                |  3 ---
 3 files changed, 2 insertions(+), 15 deletions(-)

diff --git a/neurolib/models/model.py b/neurolib/models/model.py
index e50b0764..464ce5df 100644
--- a/neurolib/models/model.py
+++ b/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.
         """
diff --git a/neurolib/models/multimodel/model.py b/neurolib/models/multimodel/model.py
index 88231c7c..d8948b53 100644
--- a/neurolib/models/multimodel/model.py
+++ b/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]
         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...")
 
diff --git a/tests/test_models.py b/tests/test_models.py
index ec765172..850564cc 100644
--- a/tests/test_models.py
+++ b/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__":