[DATALAD RUNCMD] run codespell throughout but ignore fail

=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w || :",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^

doc/conf.py

@@ -118,7 +118,7 @@
         ("GUI", "gui/index"),
         ("API", "api"),
         ("Glossary", "glossary"),
-        ("Whats new", "whats_new"),
+        ("What's new", "whats_new"),
         ("GitHub", "https://github.com/jonescompneurolab/hnn-core", True)
     ],
     'bootswatch_theme': "yeti"

doc/gui/tutorial_erp.ipynb

@@ -210,7 +210,7 @@
    "id": "0030deba",
    "metadata": {},
    "source": [
-    "You can also view all cell connectivity paramters, i.e., weight and connectivity probability, in the `Cell connectivity` tab:"
+    "You can also view all cell connectivity parameters, i.e., weight and connectivity probability, in the `Cell connectivity` tab:"
    ]
   },
   {
@@ -291,7 +291,7 @@
    "id": "6c7c3da2",
    "metadata": {},
    "source": [
-    "After simulation, you can immediately find simulation results (dipoles) at the right side of the GUI. In the figures below, the thin gray traces are dipole signals from individual trials while the green trace is the average ERP. In the left panel, the aggregated dipole data is plotted while in the right panel layer-specific dipole data are also shwon so you can check the contribution per layer."
+    "After simulation, you can immediately find simulation results (dipoles) at the right side of the GUI. In the figures below, the thin gray traces are dipole signals from individual trials while the green trace is the average ERP. In the left panel, the aggregated dipole data is plotted while in the right panel layer-specific dipole data are also shown so you can check the contribution per layer."
    ]
   },
   {

doc/roadmap.rst

@@ -37,7 +37,7 @@ Timeline Overview
 This roadmap timeline outlines the major short-term and longer-term 
 goals for HNNs. The short term goals will entail a substantial reorganization of the
 HNN code and creation of an API to facilitate HNN expansions, community contribution,
-and integration with other relevant open-source platforms (e.g. MNE-Python, NetPyNE). To this end, in March 2021, we released the first version of the HNN-core repository. HNN-core contains improved versions of HNN’s non-GUI components following best practices in open-source software design, with unit testing and continuous integration, along with initial API and documentation for command-line coding. We will adopt similar best practices to develop a new HNN-GUI and several new HNN features, including the ability to simulate and visualize LFP/CSD and to use improved parameter estimation procedures. Our process will be to develop all new features in HNN-core, with  API and examples of use followed, when applicable, by integration into the HNN-GUI with correspoding GUI-based tutorials on our website. Longer-term goals include integration with the related modeling software MNE-Python and NetPyNe, the development of a web-based interface with ability for simultaneous GUI and Command Line Interface (CLI), and extension to multi-area simulations. 
+and integration with other relevant open-source platforms (e.g. MNE-Python, NetPyNE). To this end, in March 2021, we released the first version of the HNN-core repository. HNN-core contains improved versions of HNN’s non-GUI components following best practices in open-source software design, with unit testing and continuous integration, along with initial API and documentation for command-line coding. We will adopt similar best practices to develop a new HNN-GUI and several new HNN features, including the ability to simulate and visualize LFP/CSD and to use improved parameter estimation procedures. Our process will be to develop all new features in HNN-core, with  API and examples of use followed, when applicable, by integration into the HNN-GUI with corresponding GUI-based tutorials on our website. Longer-term goals include integration with the related modeling software MNE-Python and NetPyNe, the development of a web-based interface with ability for simultaneous GUI and Command Line Interface (CLI), and extension to multi-area simulations. 
 
 Short-Term Goals
 --------------------------
@@ -54,7 +54,7 @@ This reorganization will entail continued improvements within the HNN-core repos
 
 -   Following best practices in open-source software design, including continuous integration testing, 
     to develop HNN-core. HNN-core will contain clean and reorganized code, and separate all components that 
-    interact directly with the NEURON simulator (e.g. cell and network intantiation, external drives, etc..), 
+    interact directly with the NEURON simulator (e.g. cell and network instantiation, external drives, etc..), 
     from those that pertain to post-processing data analysis and plotting functions (e.g. spectra lanalysis). 
     **COMPLETED FEB 2021** 
 -   Convert installation procedures to PIP. **COMPLETED FEB 2021** 
@@ -98,7 +98,7 @@ domain over which the predictions will be tested is local field potential (LFP)
 across the cortical layers and the associated current source density (CSD) profiles.  
 We will develop a method to simulate and visualize LFP/CSD across the cortical layers 
 and to statistically compare model simulations to recorded data. These components will 
-be developed in HNN-core, with correponding API and examples of use, followed by integration 
+be developed in HNN-core, with corresponding API and examples of use, followed by integration 
 into the HNN-GUI, with corresponding GUI based tutorials on the HNN website, in the following steps:
 
 - Develop code in HNN-core to simulate and visualize LFP/CSD from cellular 

doc/whats_new.rst

@@ -240,7 +240,7 @@ Changelog
 
 - Add method for setting in-plane cell distances and layer separation in the network :func:`~hnn_core.Network.set_cell_positions`, by `Christopher Bailey`_ in `#370 <https://github.com/jonescompneurolab/hnn-core/pull/370>`_
 
-- External drives API now accepts probability argument for targetting subsets of cells,
+- External drives API now accepts probability argument for targeting subsets of cells,
   by `Nick Tolley`_ in :gh:`416`
 
 Bug
@@ -275,7 +275,7 @@ API
   :func:`~hnn_core.Network.add_connection`, by `Nick Tolley`_ in :gh:`276`
 
 - Remove :class:`~hnn_core.L2Pyr`, :class:`~hnn_core.L5Pyr`, :class:`~hnn_core.L2Basket`,
-  and :class:`~hnn_core.L5Basket` classes in favor of instantation through functions and
+  and :class:`~hnn_core.L5Basket` classes in favor of instantiation through functions and
   a more consistent :class:`~hnn_core.Cell` class by `Mainak Jas`_ in  :gh:`322`
 
 - Remove parameter ``distribution`` in :func:`~hnn_core.Network.add_bursty_drive`.

examples/howto/optimize_evoked.py

@@ -107,7 +107,7 @@
 #
 # First, we define a function that will tell the optimization routine how to
 # modify the network drive parameters. The function will take in the Network
-# object with no attached drives, and a dictionary of the paramters we wish to
+# object with no attached drives, and a dictionary of the parameters we wish to
 # optimize.
 
 

examples/howto/plot_record_extracellular_potentials.py

@@ -39,7 +39,7 @@
 net.set_cell_positions(inplane_distance=30.)
 
 ###############################################################################
-# Extracellular recordings require specifying the electrode postions. It can be
+# Extracellular recordings require specifying the electrode positions. It can be
 # useful to visualize the cells of the network to decide on the placement of
 # each electrode.
 net.plot_cells()

examples/workflows/plot_simulate_beta.py

@@ -6,7 +6,7 @@
 This example demonstrates how event related potentials (ERP) are modulated
 by prestimulus beta events. Specifically, this example reproduces Figure 5
 from Law et al. 2021 [1]_. To be consistent with the publication, the default
-network connectivity is altered. These modfications demonstrate a potential
+network connectivity is altered. These modifications demonstrate a potential
 mechanism by which transient beta activity in the neocortex can suppress
 the perceptibility of sensory input. This suppression depends on the timing
 of the beta event, and the incoming sensory information.
@@ -171,7 +171,7 @@ def add_beta_drives(net, beta_start):
 # occurs exclusively at 50 ms, the peak of the gaussian distributed proximal
 # and distal inputs. This spiking activity leads to sustained GABAb mediated
 # inhibition of the L2 and L5 pyrmaidal cells. One effect of this inhibition
-# is an assymetric beta event with a long positive tail.
+# is an asymmetric beta event with a long positive tail.
 import matplotlib.pyplot as plt
 import numpy as np
 fig, axes = plt.subplots(4, 1, sharex=True, figsize=(7, 7),

hnn_core/cell.py

@@ -268,7 +268,7 @@ class Cell:
         Stores the tree representation of a cell.
         Root is the 0 end of 'soma'. Nodes are a tuple (sec_name, node_pos)
         where sec_name is the name of the section and node_pos is the 0 end
-        or 1 end. The data structure is the adjacency list represetation of a
+        or 1 end. The data structure is the adjacency list representation of a
         tree. The keys of the dict are the parent nodes. The value is the
         list of nodes (children nodes) connected to the parent node.
 
@@ -306,7 +306,7 @@ class Cell:
         Stores the tree representation of a cell.
         Root is the 0 end of 'soma'. Nodes are a tuple (sec_name, node_pos)
         where sec_name is the name of the section and node_pos is the 0 end
-        or 1 end. The data structure is the adjacency list represetation of a
+        or 1 end. The data structure is the adjacency list representation of a
         tree. The keys of the dict are the parent nodes. The value is the
         list of nodes (children nodes) connected to the parent node.
 

hnn_core/cell_response.py

@@ -27,7 +27,7 @@ class CellResponse(object):
     spike_types : list (n_trials,) of list (n_spikes,) of float, shape | None
         Each element of the outer list is a trial.
         The inner list contains the type of spike (e.g., evprox1
-        or L2_pyramidal) that occured at the corresonding time stamp.
+        or L2_pyramidal) that occurred at the corresponding time stamp.
         Each gid corresponds to a type via Network().gid_ranges.
     times : numpy array | None
         Array of time points for samples in continuous data.
@@ -48,7 +48,7 @@ class CellResponse(object):
     spike_types : list (n_trials,) of list (n_spikes,) of float, shape
         Each element of the outer list is a trial.
         The inner list contains the type of spike (e.g., evprox1
-        or L2_pyramidal) that occured at the corresonding time stamp.
+        or L2_pyramidal) that occurred at the corresponding time stamp.
         Each gid corresponds to a type via Network::gid_ranges.
     vsec : list (n_trials,) of dict, shape
         Each element of the outer list is a trial.

hnn_core/cells_default.py

@@ -312,7 +312,7 @@ def _exp_g_at_dist(x, zero_val, exp_term, offset):
     zero_val : float | int
         Value of function when x = 0
     exp_term : float | int
-        Mutiplier of x in the exponent
+        Multiplier of x in the exponent
     offset: float |int
         Offset value added to output
 

hnn_core/externals/bayesopt.py

@@ -58,7 +58,7 @@ def bayes_opt(func, x0, cons, acquisition, maxfun=200,
     cons : list of tuples
         Parameter constraints in solver-specific format.
     acquisition : func
-        Acquisiton function we want to use to find query points.
+        Acquisition function we want to use to find query points.
     maxfun : int, optional
         Maximum number of function evaluations. The default is 200.
     debug : bool, optional

hnn_core/extracellular.py

@@ -90,7 +90,7 @@ def _get_laminar_z_coords(electrode_positions):
         raise ValueError(
             'Electrode contacts are incompatible with laminar profiling '
             'in a neocortical column. Make sure the '
-            'electrode postions are equispaced, colinear, and projecting '
+            'electrode positions are equispaced, colinear, and projecting '
             'along the z-axis.')
     else:
         return np.array(electrode_positions)[:, 2], z_delta

hnn_core/gui/_viz_manager.py

@@ -746,7 +746,7 @@ def _simulate_edit_figure(self, fig_name, ax_name, simulation_name,
             fig_name : str
                 The figure name shown in the GUI, e.g., 'Figure 1'.
             ax_name : str
-                Axis name shwon in the left side of GUI, like, 'ax0'.
+                Axis name shown in the left side of GUI, like, 'ax0'.
             simulation_name : str
                 The name of simulation you want to visualize
             plot_type : str

hnn_core/gui/gui.py

@@ -431,7 +431,7 @@ def compose(self, return_layout=True):
             self._connectivity_out,
         ])
 
-        # accordians to group local-connectivity by cell type
+        # accordions to group local-connectivity by cell type
         connectivity_boxes = [
             VBox(slider) for slider in self.connectivity_widgets]
         connectivity_names = (

hnn_core/mpi_child.py

@@ -133,7 +133,7 @@ def run(self, net, tstop, dt, n_trials):
             # only rank 0 has data that should be sent back to MPIBackend
             sim_data.append(single_sim_data)
 
-        # flush output buffers from all ranks (any errors or status mesages)
+        # flush output buffers from all ranks (any errors or status messages)
         sys.stdout.flush()
         sys.stderr.flush()