Merge pull request #27 from TeachBooks/draft_book

Draft book

Author: SandraVerhagen

book/_toc.yml

@@ -52,11 +52,11 @@ parts:
         # - file: notebook_5_figures/PythonNotebook5_matplotlib.ipynb
         - file: exercise_notebooks/exercise_notebook_5_figures.ipynb
 
-  #   - file: notebook_6_numpy/PythonNotebook6_first_page.ipynb
-  #     sections:
-  #       - file: notebook_6_numpy/PythonNotebook6_vectors_and_plotting_with_numpy.ipynb
-  #       - file: notebook_6_numpy/PythonNotebook6_loading_data.ipynb
-  #       - file: exercise_notebooks/exercise_notebook_6_numpy.ipynb
+    - file: notebook_6_numpy/PythonNotebook6_first_page.ipynb
+      sections:
+        - file: notebook_6_numpy/PythonNotebook6_vectors_and_plotting_with_numpy.ipynb
+        - file: notebook_6_numpy/PythonNotebook6_loading_data.ipynb
+        - file: exercise_notebooks/exercise_notebook_6_numpy.ipynb
 
   #   - file: notebook_7_files/PythonNotebook7_first_page.ipynb
   #     sections:

book/exercise_notebooks/exercise_notebook_6_numpy.ipynb

@@ -55,10 +55,14 @@
     "\n",
     "You can do this by shifting the x-coordinates of your bars, and setting the bar width using the `width=` argument to the bar graph function.\n",
     "\n",
-    "* copy your plotting code from the last notebook. \n",
-    "* make a numpy array of the month list.\n",
-    "* now you can easily add or subtract a number from the whole array.\n",
-    "* make a plot where the bars for the different regions don't overlap.\n",
+    "Let's see how it works...\n",
+    "\n",
+    "* first copy your plotting code from the last exercise notebook. \n",
+    "* use the `width=` argument of the <code>plt.bar</code> function to change the width of the bars.\n",
+    "* now make a numpy array of the month list.\n",
+    "* now you can easily add or subtract a single number from this array, and in this way you can shift the x-values for the different countries a little to the left or right when plotting the bars for that country.\n",
+    "\n",
+    "Try out: make a plot where the bars for the different regions don't overlap.\n",
     "\n",
     "</div></div>"
    ]
@@ -78,18 +82,17 @@
    "source": [
     "<div class=\"alert alert-block alert-info\"><b>Exercise 6.3</b><br><br><div style=\"text-align: justify\">\n",
     "\n",
-    "* Create a 4x2 matrix `m` filled with different values \n",
+    "In linear algebra you will learn about matrices, which are basically 2D arrays (a vector being the 1D equivalent): an $m\\times n$ matrix has $m$ rows and $n$ columns.\n",
     "\n",
-    "4x2 means four rows and two columns.\n",
-    "You can use `np.array()`, and pass it nested lists,\n",
-    "or you can use `np.zeros((rows, columns))` and fill in the values afterwards.\n",
-    "Here `rows` and `columns` are the number of rows and columns you want. Note the double parentheses. The reason is that `(rows, columns)` is a tuple,\n",
-    "and the `np.zeros()` function takes this tuple as it's argument.\n",
+    "* Create a 4x2 matrix `M` filled with different values you may choose yourself\n",
     "\n",
+    "You can use `np.array()`, and pass it nested lists (lists within a list),\n",
+    "or you can use `M = np.zeros((rows, columns))` and fill in the values afterwards by assigning values to the individual elements.\n",
+    "Here `rows` and `columns` are the number of rows and columns you want. Note the double parentheses. The reason is that `(rows, columns)` is a tuple, and the `np.zeros()` function takes this tuple as it's argument to create a matrix filled with zeros of the required dimensions.\n",
     "\n",
-    "* Print the shape of your matrix.\n",
+    "* Check the shape of your matrix, using `M.shape`\n",
     "\n",
-    "* Try the `transpose()` function on your array (hint: `m.transpose()`). What did it do?\n",
+    "* Try the transpose() function on your array (hint: `M.transpose()`). What did it do?\n",
     "\n",
     "</div></div>"
    ]
@@ -142,18 +145,18 @@
    "source": [
     "<div class=\"alert alert-block alert-info\"><b>Exercise 6.5 Plot the RICO input data</b><br><br><div style=\"text-align: justify\">\n",
     "\n",
-    "* split the matrix into separate variables for each column    \n",
+    "* load the data\n",
+    "* create a separate array (with their own name) for each column    \n",
     "* use subplots to plot several quantities side by side:\n",
     "    ```\n",
     "    fig, axes = plt.subplots(nrows=1, ncols=5, sharey=True, figsize=(13,4))\n",
     "    ```\n",
+    "* `figsize=(13,4)` to set the figure size in inches (width, height) at some assumed dots-per-inch value. It's included here to make the plot a bit wider, so that the labels don't overlap. Change the values a bit to see what happens.\n",
     "* to plot in the first subplot, use `axes[0].plot(...)`\n",
     "* to set x and y labels, use `axes[0].set_ylabel(...)`\n",
-    "* use the y axis of the plot for the hight above ground\n",
-    "  \n",
-    "    \n",
-    "* `sharey=True` makes the plots share the y axis\n",
-    "* `figsize=(13,4)` sets the figure size in inches (width, height) at some assumed dots-per-inch value. It's included here to make the plot a bit wider, so that the labels don't overlap. \n",
+    "* use the y-axis of the plot for the height above ground, in this way it is the same for all subplots \n",
+    "* ... and that's why we used `sharey=True` we tell matplotlib that the y-axis is to be shared\n",
+    "\n",
     "\n",
     "</div></div>"
    ]
@@ -170,7 +173,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -184,7 +187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.2"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -203,11 +206,6 @@
    "latex_user_defs": false,
    "report_style_numbering": false,
    "user_envs_cfg": false
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "1fe2f2b718b1108b9c4176932db8a0ead471245140baaa21ea96a4066683e6b2"
-   }
   }
  },
  "nbformat": 4,

book/exercise_notebooks/exercise_notebook_6_numpy.zip

@@ -34,17 +34,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": [
-     "theme-remove-input-init"
-    ]
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from pathlib import Path\n",
     "\n",
-    "rico_dataset_path = Path.cwd().parent / \"data\" / \"rico.txt\"\n",
+    "rico_dataset_path = Path.cwd().parent / \"data/rico.txt\"\n",
     "\n",
     "rico = np.loadtxt(rico_dataset_path)"
    ]
@@ -53,6 +49,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Note that the file in this case is save in a folder <code>data</code> within the parent directory.\n",
+    "\n",
     "What did we just load?"
    ]
   },
@@ -62,8 +60,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(type(rico))\n",
-    "print(rico.shape)"
+    "print('The type of rico is:', type(rico))\n",
+    "print('The number of rows and columns is equal to:',rico.shape)"
    ]
   },
   {
@@ -95,11 +93,11 @@
     "* look at the top of the file. You will see two lines starting with `#` followed by lines of numbers.\n",
     "\n",
     "* lines starting with `#` are considered as comments, and are ignored by Numpy.\n",
-    "Those lines describe the columns of the file.\n",
+    "Those lines describe what is in the columns of the file.\n",
     "\n",
     "* the following lines with numbers are loaded into the numpy array. You can compare the first few rows from the file with the array content you printed above.\n",
     "\n",
-    "As you probably know, numbers like `2.0000e+01` are written in scientific notation, this one means `2 * 10**1 = 20`\n"
+    "As you probably know, numbers like `2.0000e+01` are written in scientific notation, this one means $2 \\times 10^1 = 20$\n"
    ]
   },
   {
@@ -142,9 +140,9 @@
    "source": [
     "## 6.2.1 Surface and color plots\n",
     "\n",
-    "Next you will learn some more plotting with matplotlib, namely to plot a function of two variables, f(x,y). Either as a 2D image with colors showing the value, or as a surface in 3D where z = f(x,y).  \n",
+    "Next you will learn some more plotting with matplotlib, namely to plot a function of two variables, $f(x,y)$. Either as a 2D image with colors showing the value, or as a surface in 3D where $z = f(x,y)$.  \n",
     "\n",
-    "First try the folloring example showing `np.meshgrid()`. Meshgrid is used to create 2D arrays X and Y, where X contains an x-coordinate and Y contains an y-coordinate."
+    "First try the folloring example showing `np.meshgrid()`. Meshgrid is used to create 2D arrays <code>X</code> and <code>Y</code>, with x- and y-coordinates, respectively."
    ]
   },
   {
@@ -252,7 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### After this Notebook you should be able to:\n",
+    "#### After this Chapter you should be able to:\n",
     "\n",
     "- know the basics of how to use `numpy`\n",
     "- be able to import and perform calculations with datasets\n",
@@ -263,7 +261,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -277,7 +275,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.2"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

book/notebook_6_numpy/PythonNotebook6_loading_data.ipynb

@@ -42,7 +42,7 @@
    "source": [
     "Now functions in numpy are accessible as `np.array()` for example.\n",
     "\n",
-    "The part `as np` is not necessary, but commonly done for slightly shorter code than typing `numpy.array()` every time (just like we imported `matplotlib.pyplot` as `plt` last week).\n"
+    "The part `as np` is not necessary, but commonly done for slightly shorter code than typing `numpy.array()` every time (just like we imported `matplotlib.pyplot` as `plt` before).\n"
    ]
   },
   {
@@ -219,7 +219,12 @@
     "```{admonition} Attention\n",
     ":class: danger\n",
     "\n",
-    "Choose `N` carefully. `np.arange(0, 1, 0.1)` yields an array with 10 numbers: `[0, 0.1, 0.2, ..., 0.9]`. `np.linspace(0, 1, 10)` also yields an array with 10 numbers, but different ones: `[0, 0.1111111, 0.22222222, ..., 0.88888889, 1]`. This is the difference between including and excluding the end point. \n",
+    "Note the differences! \n",
+    "\n",
+    "With `np.arange(0, 1, 0.1)` you create an array starting at 0 with step size 0.1 (defined by the last number); the end point is not included: in this case the array has 10 elements `[0, 0.1, 0.2, ..., 0.9]`. \n",
+    "\n",
+    "With `np.linspace(0, 1, 10)` you create an array with 10 elements (defined by the last number), but since in this case the end point is included, it yields `[0, 0.1111111, 0.22222222, ..., 0.88888889, 1]`. \n",
+    "\n",
     "+++\n",
     "```"
    ]
@@ -245,7 +250,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note we used `np.cos()`, which is similar to `math.cos()` but can work on numpy arrays.\n",
+    "Note we used `np.cos()`, which is similar to `math.cos()` but allows to use a numpy array as input, while the `math` equivalent can only work with single variables (scalars).\n",
     "\n",
     "```{admonition} Attention\n",
     ":class: danger\n",
@@ -287,7 +292,9 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "plt.plot(x, y1)"
+    "plt.plot(x, y1,'.')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('cos(x)')                                                                                                                              "
    ]
   },
   {
@@ -305,7 +312,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(x, y1)\n",
+    "plt.plot(x, y1,'.-')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('cos(x)') \n",
     "plt.savefig('cosplot.png')"
    ]
   },
@@ -315,7 +324,7 @@
    "source": [
     "Matplotlib will now save your plot in the file you specified. The file will be placed in the same folder / directory as your notebook. You can also specify the directory that you would want the image to be saved: check out the documentation of the `savefig` function [here](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html). \n",
     "\n",
-    "*Note that in this interactive environment, saving the file won't work---this is when you're working locally.*\n",
+    "*Note that in this interactive online environment, saving the file won't work.*\n",
     "\n",
     "Matplotlib can handle several formats: .png (an image), .pdf (vector graphics, small files and often looks good), .svg (also vector graphics, can be edited in for example Inkscape)\n",
     "\n",
@@ -325,7 +334,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -339,7 +348,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.2"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,