Fix typos, grammar issues, and UK English compliance

Spelling fixes across all notebooks and markdown files:
- instanteated/instanted -> instantiated
- shorthanded -> shorthand
- extremelly -> extremely
- homogenous -> homogeneous
- lenghts -> lengths
- reacheable -> reachable
- programatically -> programmatically
- pseudo-invese -> pseudo-inverse
- embbed -> embed
- milisecond -> millisecond
- pratice -> practice
- simultenously -> simultaneously
- forth -> fourth (ordinal number context)
- pre-requisites -> prerequisites

Grammar fixes:
- can computed -> can be computed
- operations matter -> operations matters
- a list of list -> a list of lists

UK English compliance:
- behavior -> behaviour
- modeled -> modelled
- modeling -> modelling

Author: openhands-agent

README.md

@@ -62,8 +62,8 @@ The content shown in this work is based on my own expert knowledge (and interpre
 
 ## Contents
 
-- [The Basics of Kinematic Modeling and Control of Serial-link Manipulators Using `numpy`](basic_lessons/README.md).            
-- [Kinematic modeling and control of serial-link robotic manipulators using `dqrobotics`: From zero to hero.](other/dqrobotics).
+- [The Basics of Kinematic Modelling and Control of Serial-link Manipulators Using `numpy`](basic_lessons/README.md).            
+- [Kinematic modelling and control of serial-link robotic manipulators using `dqrobotics`: From zero to hero.](other/dqrobotics).
 
 
 

basic_lessons/README.md

@@ -1,4 +1,4 @@
-# The Basics of Kinematic Modeling and Control of Serial-link Manipulators Using `numpy`
+# The Basics of Kinematic Modelling and Control of Serial-link Manipulators Using `numpy`
 
 In this five-lesson tutorial, we start from the very basics of scalar and matricial operations in Python using `numpy`,
 all the way until the basics of kinematic control. Until kinematic control, most is based on [@spong2020robot].

basic_lessons/lesson1_exercise_answers.ipynb

@@ -154,4 +154,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

basic_lessons/lesson1_tutorial.ipynb

@@ -4,23 +4,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# L1 A quick Python refresher\n",
-    "\n",
-    "*License: CC-BY-NC-SA 4.0*\n",
-    "\n",
-    "*Author: Murilo M. Marinho (murilo.marinho@manchester.ac.uk)*\n",
-    "\n",
-    "### Pre-requisites\n",
-    "The user of this notebook is expected to have prior knowledge in\n",
-    "- Basic Python [[Tutorial]](https://docs.python.org/3/tutorial/index.html)\n",
-    "- Numpy \n",
-    "    - [[Tutorial: basics for beginners]](https://numpy.org/doc/stable/user/absolute_beginners.html)\n",
-    "    - [[Tutorial: for MATLAB users]](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html)\n",
-    "- Jupyter Notebook Basics [[Tutorial]](https://docs.jupyter.org/en/latest/)\n",
-    "\n",
-    "### I found an issue\n",
-    "Thank you! Please report it at https://github.com/MarinhoLab/OpenExecutableBooksRobotics/issues\n",
-    "\n",
+    "# L1 A quick Python refresher",
+    "",
+    "*License: CC-BY-NC-SA 4.0*",
+    "",
+    "*Author: Murilo M. Marinho (murilo.marinho@manchester.ac.uk)*",
+    "",
+    "### Prerequisites",
+    "The user of this notebook is expected to have prior knowledge in",
+    "- Basic Python [[Tutorial]](https://docs.python.org/3/tutorial/index.html)",
+    "- Numpy ",
+    "    - [[Tutorial: basics for beginners]](https://numpy.org/doc/stable/user/absolute_beginners.html)",
+    "    - [[Tutorial: for MATLAB users]](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html)",
+    "- Jupyter Notebook Basics [[Tutorial]](https://docs.jupyter.org/en/latest/)",
+    "",
+    "### I found an issue",
+    "Thank you! Please report it at https://github.com/MarinhoLab/OpenExecutableBooksRobotics/issues",
+    "",
     "### Latex Macros"
    ]
   },
@@ -309,19 +309,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### n-th root\n",
-    "The nth root,\n",
-    "\n",
-    "$$c= \\sqrt[n]{a}, n \\in \\mathbb{N},$$\n",
-    "\n",
-    "does not seem to have a shorthanded version in Python, but can computed through simple properties such as\n",
-    "\n",
-    "$$c = \\sqrt[n]{a} = a^{\\frac{1}{n}} = e^{\\frac{ln(a)}{n}}.$$\n",
-    "\n",
-    "For example, suppose that\n",
-    "\n",
-    "$$n = 3.$$ \n",
-    "\n",
+    "### n-th root",
+    "The nth root,",
+    "",
+    "$$c= \\sqrt[n]{a}, n \\in \\mathbb{N},$$",
+    "",
+    "does not seem to have a shorthand version in Python, but can be computed through simple properties such as",
+    "",
+    "$$c = \\sqrt[n]{a} = a^{\\frac{1}{n}} = e^{\\frac{ln(a)}{n}}.$$",
+    "",
+    "For example, suppose that",
+    "",
+    "$$n = 3.$$ ",
+    "",
     "Then,"
    ]
   },
@@ -471,12 +471,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Instantiating vectors\n",
-    "A row vector can be instanteated from a list of list. For instance, for \n",
-    "$$\\myvec{v} = \\left[\\begin{array}{ccc}\n",
-    "         1 & 2 \n",
-    "        \\end{array}\\right],\n",
-    "$$ \n",
+    "### Instantiating vectors",
+    "A row vector can be instantiated from a list of lists. For instance, for ",
+    "$$\\myvec{v} = \\left[\\begin{array}{ccc}",
+    "         1 & 2 ",
+    "        \\end{array}\\right],",
+    "$$ ",
     "we have"
    ]
   },
@@ -504,11 +504,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A column vector can be instanted from a list of singleton *lists*. For instance, for \n",
-    "$$\\myvec{u} = \\left[\\begin{array}{ccc}\n",
-    "         1 \\\\\n",
-    "         2\n",
-    "        \\end{array}\\right],$$ \n",
+    "A column vector can be instantiated from a list of singleton *lists*. For instance, for ",
+    "$$\\myvec{u} = \\left[\\begin{array}{ccc}",
+    "         1 \\\\",
+    "         2",
+    "        \\end{array}\\right],$$ ",
     "we have"
    ]
   },
@@ -886,18 +886,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Diagonal matrices\n",
-    "\n",
-    "Diagonal matrices get increasingly sparse with size, so it is important to have shorthanded commands for creating them. For instance, suppose that we have the following diagonal matrix\n",
-    "\n",
-    "$$\\mymatrix{D} = \\left[\\begin{array}{ccc}\n",
-    "         1 & 0 & 0 \\\\\n",
-    "         0 & 2 & 0 \\\\\n",
-    "         0 & 0 & 3   \n",
-    "        \\end{array}\\right] ,\n",
-    "$$\n",
-    "\n",
-    "this can be instanteated in `numpy` with"
+    "### Diagonal matrices",
+    "",
+    "Diagonal matrices get increasingly sparse with size, so it is important to have shorthand commands for creating them. For instance, suppose that we have the following diagonal matrix",
+    "",
+    "$$\\mymatrix{D} = \\left[\\begin{array}{ccc}",
+    "         1 & 0 & 0 \\\\",
+    "         0 & 2 & 0 \\\\",
+    "         0 & 0 & 3   ",
+    "        \\end{array}\\right] ,",
+    "$$",
+    "",
+    "this can be instantiated in `numpy` with"
    ]
   },
   {
@@ -925,18 +925,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Identity matrix\n",
-    "\n",
-    "Among frequently used diagonal matrices, the identity matrix appears frequently. For instance, \n",
-    "\n",
-    "$$ \\mymatrix{I}_3 = \\left[\\begin{array}{ccc}\n",
-    "         1 & 0 & 0 \\\\\n",
-    "         0 & 1 & 0 \\\\\n",
-    "         0 & 0 & 1   \n",
-    "        \\end{array}\\right],\n",
-    "$$ \n",
-    "\n",
-    "can be instanteated in `numpy` with"
+    "### Identity matrix",
+    "",
+    "Among frequently used diagonal matrices, the identity matrix appears frequently. For instance, ",
+    "",
+    "$$ \\mymatrix{I}_3 = \\left[\\begin{array}{ccc}",
+    "         1 & 0 & 0 \\\\",
+    "         0 & 1 & 0 \\\\",
+    "         0 & 0 & 1   ",
+    "        \\end{array}\\right],",
+    "$$ ",
+    "",
+    "can be instantiated in `numpy` with"
    ]
   },
   {
@@ -1077,4 +1077,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

basic_lessons/lesson2_exercise_answers.ipynb

@@ -228,7 +228,9 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "`H_c` is *not* the same as `H_d`. This indicates that the order of operations matter. That is, sequential pose transformations are not commutative."
+   "source": [
+    "`H_c` is *not* the same as `H_d`. This indicates that the order of operations matters. That is, sequential pose transformations are not commutative."
+   ]
   },
   {
    "metadata": {},
@@ -329,4 +331,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}