From 6436e20a4cfe81384ee23961e56bb8d6e01bbf69 Mon Sep 17 00:00:00 2001
From: Georg Reich <georgreich99@gmail.com>
Date: Tue, 5 Mar 2024 19:38:36 +0100
Subject: [PATCH] remove duplicate arguments

---
 neurolib/models/model.py            | 16 +++++-----------
 neurolib/models/multimodel/model.py | 11 +++--------
 tests/test_autochunk.py             |  4 ++--
 3 files changed, 10 insertions(+), 21 deletions(-)

diff --git a/neurolib/models/model.py b/neurolib/models/model.py
index 4e2ee5a4..e50b0764 100644
--- a/neurolib/models/model.py
+++ b/neurolib/models/model.py
@@ -180,12 +180,10 @@ def initializeRun(self, initializeBold=False):
 
     def run(
         self,
-        inputs=None,
         chunkwise=False,
         chunksize=None,
         bold=False,
-        append=False,
-        append_outputs=None,
+        append_outputs=False,
         continue_run=False,
     ):
         """
@@ -204,15 +202,11 @@ def run(
         :type chunksize: int, optional
         :param bold: simulate BOLD signal (only for chunkwise integration), defaults to False
         :type bold: bool, optional
-        :param append: append the chunkwise outputs to the outputs attribute, defaults to False. Note: BOLD outputs are always appended
-        :type append: bool, optional
+        :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
         :type continue_run: bool
         """
-        # TODO: legacy argument support
-        if append_outputs is not None:
-            append = append_outputs
-
         self.initializeRun(initializeBold=bold)
 
         # if a previous run is not to be continued clear the model's state
@@ -225,7 +219,7 @@ def run(
         chunkwise = chunkwise if chunksize is None else True
 
         if chunkwise is False:
-            self.integrate(append_outputs=append, simulate_bold=bold)
+            self.integrate(append_outputs=append_outputs, simulate_bold=bold)
 
         else:
             if chunksize is None:
@@ -235,7 +229,7 @@ def run(
             # and whether sampling_dt is compatible with duration and chunksize
             self.checkChunkwise(chunksize)
 
-            self.integrateChunkwise(chunksize=chunksize, bold=bold, append_outputs=append)
+            self.integrateChunkwise(chunksize=chunksize, bold=bold, append_outputs=append_outputs)
 
         # check if there was a problem with the simulated data
         self.checkOutputs()
diff --git a/neurolib/models/multimodel/model.py b/neurolib/models/multimodel/model.py
index 262bd691..88231c7c 100644
--- a/neurolib/models/multimodel/model.py
+++ b/neurolib/models/multimodel/model.py
@@ -126,17 +126,12 @@ def run(
         chunkwise=False,
         chunksize=None,
         bold=False,
-        append=False,
-        append_outputs=None,
+        append_outputs=False,
         continue_run=False,
         noise_input=None,
     ):
         self._update_model_params()
 
-        # TODO: legacy argument support
-        if append_outputs is not None:
-            append = append_outputs
-
         # if a previous run is not to be continued clear the model's state
         if continue_run:
             self.setInitialValuesToLastState()
@@ -146,7 +141,7 @@ def run(
         self.initializeRun(initializeBold=bold)
 
         if chunkwise is False:
-            self.integrate(append_outputs=append, simulate_bold=bold, noise_input=noise_input)
+            self.integrate(append_outputs=append_outputs, simulate_bold=bold, noise_input=noise_input)
 
         else:
             if chunksize is None:
@@ -156,7 +151,7 @@ def run(
             if bold and not self.boldInitialized:
                 logging.warn(f"{self.name}: BOLD model not initialized, not simulating BOLD. Use `run(bold=True)`")
                 bold = False
-            self.integrateChunkwise(chunksize=chunksize, bold=bold, append_outputs=append)
+            self.integrateChunkwise(chunksize=chunksize, bold=bold, append_outputs=append_outputs)
 
         # check if there was a problem with the simulated data
         self.checkOutputs()
diff --git a/tests/test_autochunk.py b/tests/test_autochunk.py
index 29f82ab9..8249be41 100644
--- a/tests/test_autochunk.py
+++ b/tests/test_autochunk.py
@@ -38,7 +38,7 @@ def single_node_test(self, model):
                 # chunkwise run
                 m2 = model()
                 m2.params = pars_bak.copy()
-                m2.run(chunkwise=True, chunksize=chunksize, append=True)
+                m2.run(chunkwise=True, chunksize=chunksize, append_outputs=True)
                 # check
                 self.assertTupleEqual(m1.output.shape, m2.output.shape)
                 difference = np.sum(np.abs(m1.output - m2.output))
@@ -61,7 +61,7 @@ def network_test(self, model):
                     # chunkwise run
                     m2 = model(Cmat=ds.Cmat, Dmat=ds.Dmat)
                     m2.params = pars_bak.copy()
-                    m2.run(chunkwise=True, chunksize=chunksize, append=True)
+                    m2.run(chunkwise=True, chunksize=chunksize, append_outputs=True)
                     # check
                     self.assertTupleEqual(m1.output.shape, m2.output.shape)
                     difference = np.sum(np.abs(m1.output - m2.output))