[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 ^^^

completed_tutorials/02-Calcium Imaging Imported Tables.ipynb

@@ -732,7 +732,7 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beginning of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
     "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]

completed_tutorials/03-Calcium Imaging Computed Tables.ipynb

@@ -770,7 +770,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are multiple ways to perform the segementation. To keep it simple, we just detect the cells by setting up the threshold on the average image."
+    "There are multiple ways to perform the segmentation. To keep it simple, we just detect the cells by setting up the threshold on the average image."
    ]
   },
   {
@@ -1027,7 +1027,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We would like to perform the segmentation for a **combination** of `AverageFrame`s and different set of paremeters of `threshold` and `size_cutoff` values. To do this while still taking advantage of the `make` and `populate` logic, you would want to define a table to house parameters for segmentation in a `Lookup` table!"
+    "We would like to perform the segmentation for a **combination** of `AverageFrame`s and different set of parameters of `threshold` and `size_cutoff` values. To do this while still taking advantage of the `make` and `populate` logic, you would want to define a table to house parameters for segmentation in a `Lookup` table!"
    ]
   },
   {
@@ -1160,7 +1160,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Computed` table is labeled as a pink oval and the `Part` table is bare text. We see that `Segmentation` is a `Computed` table that depends on **both AverageFrame and SegmentationParam**. Finally, let's go ahead and implement the `make` method for the `Segmenation` table. "
+    "The `Computed` table is labeled as a pink oval and the `Part` table is bare text. We see that `Segmentation` is a `Computed` table that depends on **both AverageFrame and SegmentationParam**. Finally, let's go ahead and implement the `make` method for the `Segmentation` table. "
    ]
   },
   {
@@ -1342,7 +1342,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And for the part table `Segmenation.Roi`, there was an additional primary key attribute `roi_idx`:`"
+    "And for the part table `Segmentation.Roi`, there was an additional primary key attribute `roi_idx`:`"
    ]
   },
   {
@@ -1721,7 +1721,7 @@
     }
    ],
    "source": [
-    "# ENTER YOUR CODE! - populate the Segmenation table for real!\n",
+    "# ENTER YOUR CODE! - populate the Segmentation table for real!\n",
     "Segmentation.populate()"
    ]
   },
@@ -2177,7 +2177,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can simply delete the unwanted paramter from the `SegmentationParam` table, and let DataJoint cascade the deletion:"
+    "We can simply delete the unwanted parameter from the `SegmentationParam` table, and let DataJoint cascade the deletion:"
    ]
   },
   {

completed_tutorials/04-Electrophysiology Imported Tables.ipynb

@@ -523,7 +523,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convetion!"
+    "Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convention!"
    ]
   },
   {
@@ -977,7 +977,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So this particular file contains a NumPy array of size 1 x 1000. This represents a (simulated) recording of raw electric activity from neuron(s) (1st dimension) over 1000 time bins (2nd dimesion)."
+    "So this particular file contains a NumPy array of size 1 x 1000. This represents a (simulated) recording of raw electric activity from neuron(s) (1st dimension) over 1000 time bins (2nd dimension)."
    ]
   },
   {
@@ -1068,7 +1068,7 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beginning of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
     "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]

completed_tutorials/05-Electrophysiology Computed Tables.ipynb

@@ -3333,7 +3333,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can simply delete the unwanted paramter from the `SpikeDetectionParam` table, and let DataJoint cascade the deletion:"
+    "We can simply delete the unwanted parameter from the `SpikeDetectionParam` table, and let DataJoint cascade the deletion:"
    ]
   },
   {

short_tutorials/University.ipynb

@@ -490,7 +490,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Millenials\n",
+    "# Millennials\n",
     "millennials = Student & 'date_of_birth between \"1981-01-01\" and \"1996-12-31\"'"
    ]
   },
@@ -519,7 +519,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Millenials who have never enrolled\n",
+    "# Millennials who have never enrolled\n",
     "millennials - Enroll"
    ]
   },

tutorials/01-DataJoint Basics.ipynb

@@ -51,7 +51,7 @@
     "If you visit the [documentation for DataJoint](https://docs.datajoint.io/introduction/Data-pipelines.html), we define a data pipeline as follows:\n",
     "> A data pipeline is a sequence of steps (more generally a directed acyclic graph) with integrated storage at each step. These steps may be thought of as nodes in a graph.\n",
     "\n",
-    "While this is an accurate description, it may not be the most intuitive definition. Put succinctly, a data pipeline is a listing or a \"map\" of various \"things\" that you work with in a project, with line connecting things to each other to indicate their dependecies. The \"things\" in a data pipeline tends to be the *nouns* you find when describing a project. The \"things\" may include anything from mouse, experimenter, equipment, to experiment session, trial, two-photon scans, electric activities, to receptive fields, neuronal spikes, to figures for a publication! A data pipeline gives you a framework to:\n",
+    "While this is an accurate description, it may not be the most intuitive definition. Put succinctly, a data pipeline is a listing or a \"map\" of various \"things\" that you work with in a project, with line connecting things to each other to indicate their dependencies. The \"things\" in a data pipeline tends to be the *nouns* you find when describing a project. The \"things\" may include anything from mouse, experimenter, equipment, to experiment session, trial, two-photon scans, electric activities, to receptive fields, neuronal spikes, to figures for a publication! A data pipeline gives you a framework to:\n",
     "\n",
     "1. define these \"things\" as tables in which you can store the information about them\n",
     "2. define the relationships (in particular the dependencies) between the \"things\"\n",

tutorials/02-Calcium Imaging Imported Tables.ipynb

@@ -342,7 +342,7 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beginning of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
     "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]

tutorials/03-Calcium Imaging Computed Tables.ipynb

@@ -250,7 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are multiple ways to perform the segementation. To keep it simple, we just detect the cells by setting up the threshold on the average image."
+    "There are multiple ways to perform the segmentation. To keep it simple, we just detect the cells by setting up the threshold on the average image."
    ]
   },
   {
@@ -397,7 +397,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We would like to perform the segmentation for a **combination** of `AverageFrame`s and different set of paremeters of `threshold` and `size_cutoff` values. To do this while still taking advantage of the `make` and `populate` logic, you would want to define a table to house parameters for segmentation in a `Lookup` table!"
+    "We would like to perform the segmentation for a **combination** of `AverageFrame`s and different set of parameters of `threshold` and `size_cutoff` values. To do this while still taking advantage of the `make` and `populate` logic, you would want to define a table to house parameters for segmentation in a `Lookup` table!"
    ]
   },
   {
@@ -506,7 +506,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Computed` table is labeled as a pink oval and the `Part` table is bare text. We see that `Segmentation` is a `Computed` table that depends on **both AverageFrame and SegmentationParam**. Finally, let's go ahead and implement the `make` method for the `Segmenation` table. "
+    "The `Computed` table is labeled as a pink oval and the `Part` table is bare text. We see that `Segmentation` is a `Computed` table that depends on **both AverageFrame and SegmentationParam**. Finally, let's go ahead and implement the `make` method for the `Segmentation` table. "
    ]
   },
   {
@@ -597,7 +597,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And for the part table `Segmenation.Roi`, there was an additional primary key attribute `roi_idx`:`"
+    "And for the part table `Segmentation.Roi`, there was an additional primary key attribute `roi_idx`:`"
    ]
   },
   {
@@ -693,7 +693,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ENTER YOUR CODE! - populate the Segmenation table for real!\n"
+    "# ENTER YOUR CODE! - populate the Segmentation table for real!\n"
    ]
   },
   {
@@ -804,7 +804,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can simply delete the unwanted paramter from the `SegmentationParam` table, and let DataJoint cascade the deletion:"
+    "We can simply delete the unwanted parameter from the `SegmentationParam` table, and let DataJoint cascade the deletion:"
    ]
   },
   {

tutorials/04-Electrophysiology Imported Tables.ipynb

@@ -190,7 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convetion!"
+    "Let's take the first key, and generate the file name that corresponds to this session. Remember the `data_{mouse_id}_{session_date}.npy` filename convention!"
    ]
   },
   {
@@ -267,7 +267,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So this particular file contains a NumPy array of size 1 x 1000. This represents a (simulated) recording of raw electric activity from neuron(s) (1st dimension) over 1000 time bins (2nd dimesion)."
+    "So this particular file contains a NumPy array of size 1 x 1000. This represents a (simulated) recording of raw electric activity from neuron(s) (1st dimension) over 1000 time bins (2nd dimension)."
    ]
   },
   {
@@ -345,7 +345,7 @@
    "source": [
     "In DataJoint, the tier of the table indicates **the nature of the data and the data source for the table**. So far we have encountered two table tiers: `Manual` and `Imported`, and we will encounter the two other major tiers in this session. \n",
     "\n",
-    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beggining of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
+    "DataJoint tables in `Manual` tier, or simply **Manual tables** indicate that its contents are **manually** entered by either experimenters or a recording system, and its content **do not depend on external data files or other tables**. This is the most basic table type you will encounter, especially as the tables at the beginning of the pipeline. In the Diagram, `Manual` tables are depicted by green rectangles.\n",
     "\n",
     "On the other hand, **Imported tables** are understood to pull data (or *import* data) from external data files, and come equipped with functionalities to perform this importing process automatically, as we will see shortly! In the Diagram, `Imported` tables are depicted by blue ellipses."
    ]

tutorials/05-Electrophysiology Computed Tables.ipynb

@@ -1030,7 +1030,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can simply delete the unwanted paramter from the `SpikeDetectionParam` table, and let DataJoint cascade the deletion:"
+    "We can simply delete the unwanted parameter from the `SpikeDetectionParam` table, and let DataJoint cascade the deletion:"
    ]
   },
   {