[DATALAD RUNCMD] run codespell throughout fixing typo automagically

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

000402/MICrONS/demo/000402_microns_demo.ipynb

@@ -1068,7 +1068,7 @@
     "The two photon data contains 50000 imaging volumes collected at ~8.6 Hz, with six fields per volume.\n",
     "The center of the volume was placed at the junction of primary visual cortex (VISp) and three higher visual areas, lateromedial area (VISlm), rostrolateral area (VISrl) and anterolateral area (VISal).\n",
     "\n",
-    "The imaging data for each field is stored in a `TwoPhotonSeries` object which can be accessed from `nwbfile.acqusition`.\n",
+    "The imaging data for each field is stored in a `TwoPhotonSeries` object which can be accessed from `nwbfile.acquisition`.\n",
     "\n",
     "The information about the imaging plane can accessed as `nwbfile.acquisition[\"TwoPhotonSeries1\"].imaging_plane` or from `nwbfile.imaging_planes[\"ImagingPlane1\"]`."
    ]

000409/IBL/03_analysis_Imbizo_2023.ipynb

@@ -63,11 +63,11 @@
     "\n",
     "#### Encoding 🔛\n",
     "If you were to rely on a single unit to predict the choice value, which one would you chose, and what would be the magnitude of the difference between its response to left or right choices?\n",
-    "- Note: This analysis contatenates the activity across trials\n",
+    "- Note: This analysis concatenates the activity across trials\n",
     "\n",
     "#### Manifold 📈\n",
     "Using all units in a given brain region, when exactly would you be able to differentiate between the left or right choice in our time window of interest?\n",
-    "- Note: This analysis contatenates the activity across trials\n",
+    "- Note: This analysis concatenates the activity across trials\n",
     "\n",
     "#### Decoding ⏭\n",
     "For a given trial, if you were a downstream neuron receiving the activity from all units in a given region as input, would you be able to differentiate left versus right choices?\n",
@@ -1240,7 +1240,7 @@
     "\n",
     "Now, we will study **_when_ the neural activity representing left or right choices differs** before the first movement.\n",
     "\n",
-    "We will use a dimentionality approach (Manifold) to measure the distance between the left and right choice representation during the time window of interest."
+    "We will use a dimensionality approach (Manifold) to measure the distance between the left and right choice representation during the time window of interest."
    ]
   },
   {
@@ -1985,7 +1985,7 @@
     "We list below a few continuous variables:\n",
     "\n",
     "- the `wheel speed` and `position`\n",
-    "- the `paw positionn`     (loading documentation [here](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_video_data.html))  TODO CHANGE FOR NWB DOC\n",
+    "- the `paw position`     (loading documentation [here](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_video_data.html))  TODO CHANGE FOR NWB DOC\n",
     "- the `motion energy`     (loading documentation [here](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_video_data.html))\n",
     "\n",
     "Using e.g. linear regression, you can try to decode the values of these continuous variables using neural data.\n",
@@ -2015,7 +2015,7 @@
     "\n",
     "Use the output from the video segmentation algorithm (loading documentation [here](https://int-brain-lab.github.io/iblenv/notebooks_external/loading_video_data.html)), e.g. the paw position (`paw_R`) to see if there are any `stereotypical movements`.\n",
     "\n",
-    "Use an `unsupervised classifer` method to segregate different kinds of movement the animals makes, and assess whether there are particular patterns of occurrence.\n",
+    "Use an `unsupervised classifier` method to segregate different kinds of movement the animals makes, and assess whether there are particular patterns of occurrence.\n",
     "\n",
     "Assess whether these stereotypical movements can be `decoded` from the neural activity using the techniques of this tutorial (Encoding or Decoding).\n"
    ]

tutorials/cosyne_2020/NWB_tutorial_2019.ipynb

@@ -141,7 +141,7 @@
    "metadata": {},
    "source": [
     "## Electrodes table\n",
-    "Extracellular electrodes are stored in a `electrodes`, which is a `DynamicTable`. `electrodes` has several required fields: x, y, z, impedance, location, filtering, and electrode_group. Here, we also demonstate how to add optional columns to a table by adding the `'label'` column.<img src=\"images/electrodes_table.png\" width=\"300\">"
+    "Extracellular electrodes are stored in a `electrodes`, which is a `DynamicTable`. `electrodes` has several required fields: x, y, z, impedance, location, filtering, and electrode_group. Here, we also demonstrate how to add optional columns to a table by adding the `'label'` column.<img src=\"images/electrodes_table.png\" width=\"300\">"
    ]
   },
   {

tutorials/cosyne_2020/NWB_tutorial_2019.m

@@ -69,7 +69,7 @@
 % Extracellular |electrodes| are stored in a electrodes, which is a 
 % |DynamicTable|. |electrodes| has several required fields: x, y, z, 
 % impedance, location, filtering, and electrode_group. Here, we also 
-% demonstate how to add optional columns to a table by adding the |'label'|
+% demonstrate how to add optional columns to a table by adding the |'label'|
 % column.
 
 shank_channels = [3, 4];