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13
book/_config.yml
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@ -3,11 +3,22 @@ author: Caspar Jungbacker
logo: images/TUDelft_logo_rgb.png
execute:
execute_notebooks: auto
execute_notebooks: 'off'
launch_buttons:
thebe: true
sphinx:
extra_extensions:
- sphinx_inline_tabs
- jupyterlite_sphinx
- sphinx.ext.imgconverter
- download_link_replacer
- jupyterbook_patches
config:
jupyterlite_config: jupyterlite-config.json
html_js_files:
- https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
thebe_config:
use_thebe_lite: true
exclude_patterns: ["**/_*.yml", "**/*.md", "**/*.ipynb"]

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book/_toc.yml
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@ -23,20 +23,38 @@ parts:
sections:
- file: workflows/git-merge-conflict
- file: workflows/gitlab
- file: workflows/github
- caption: Features
chapters:
- file: basic-features/overview
sections:
- file: basic-features/recipes.md
- file: features/overview
sections:
- file: features/hypothesis
- file: features/remove_from_publish
- file: features/recipes.md
- file: features/grasple.md
- file: features/custom_toc.md
- file: features/custom_download.md
- file: features/h5p.md
- file: features/live_code.md
sections:
- file: features/widgets.ipynb
- file: features/exercise_checking.ipynb
- file: features/jupyterlite/jupyterlite
sections:
- file: features/jupyterlite/configuration
- file: features/jupyterlite/including-jupyterlite
- file: features/h5p.md
- caption: Examples
chapters:
- file: examples/overview
- file: examples/overview
- file: examples/h5p_examples.md
- file: examples/theory_questions.md
- file: examples/interactive_templates.md
sections:
- file: examples/example_gumbel.ipynb
- file: examples/example_quiz_interactive.ipynb
- file: examples/coding_theory_widgets.ipynb
- file: examples/parametric_questions.ipynb

0
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Programming assignment\n",
"\n",
"This page shows how you can convert the classical Jupyter Notebook assignment to a jupyter book format with live coding. A widget has been implemented to check the answers, of which the data is included in a separate python file. The page automatically loads and a custom download page has been added\n",
"\n",
"```{custom_download_link} ./example_gumbel_stripped.ipynb\n",
":replace_default: \"True\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## Gumbel Distribution Exercise\n",
"\n",
"Imagine you are concerned with the concentration of an airborne contaminant, $X$, measured in ppm. A previous benchmark study estimates that there is a 10% and 1% chance, respectively, of exceeding 4 and 10 ppm, respectively. You have been asked by the regulatory agency to estimate the contaminant concentration with 0.1% probability of being exceeded. Prior studies suggest that a Gumbel distribution can be used to model contaminant concentration, given by the CDF:\n",
"\n",
"$$\n",
"F_X(x) = \\exp{\\Bigg( -\\exp{\\Big(-\\frac{x-\\mu}{\\beta}\\Big)}\\Bigg)}\n",
"$$\n",
"\n",
"Using the cell blocks below as a guide (and also to check your analysis): Task 1) find the parameters of a Gumbel distribution that matches the information provided above, then, Tasks 2-3) use it to estimate the concentration with exceedance probability 0.1%.\n",
"\n",
"To complete this assignment, you can use `numpy`, `matplotlib` and from the `math` library, `log` and `e` (these are imported for you when you inialize the notebook)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import micropip\n",
"await micropip.install(\"ipywidgets\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from math import log, e\n",
"import numpy as np\n",
"from example_gumbel import check_example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 0:** fill in the appropriate fitting points:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"auto-execute-page"
]
},
"outputs": [],
"source": [
"x_1 = _\n",
"p_1 = _\n",
"x_2 = _\n",
"p_2 = _"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 1:** derive the distribution parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def gumbel_2_points(x_1, p_1, x_2, p_2): \n",
" \"\"\"Compute Gumbel distribution parameters from two points of the CDF.\n",
" Arguments:\n",
" x_1 (float): point one\n",
" p_1 (float): cumulative probability for point 1\n",
" x_2 (float): point two\n",
" p_2 (float): cumulative probability for point 2\n",
" \"\"\"\n",
"\n",
" # YOUR CODE GOES HERE #\n",
" beta = _\n",
" mu = _\n",
" #######################\n",
"\n",
" return mu, beta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cells below will print your parameter values and create a plot to help confirm you have the right implementation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"def plot_distribution(mu, beta,\n",
" x_1, p_1, x_2, p_2):\n",
" plt.title(\"Gumbel Distribution, $1-F_X(x)$\")\n",
" plt.xlabel(\"Contaminant Concentration, $X$ [ppm]\")\n",
" plt.ylabel(\"Exceedance Probability [--]\")\n",
" plt.grid(color='black', linestyle='-', linewidth=0.3)\n",
" x_axis = np.arange(0, 20, 0.1)\n",
" plt.plot(x_axis, np.vectorize(gumbel_distribution)(x_axis), linewidth=2)\n",
" plt.plot(x_1, 1 - p_1, 'ro')\n",
" plt.annotate(\"Point 1\", (x_1 + 0.4, 1 - p_1 + 0.02))\n",
" plt.plot(x_2, 1 - p_2, 'ro')\n",
" plt.annotate(\"Point 2\", (x_2 + 0.4, 1 - p_2 + 0.001))\n",
" plt.yscale(\"log\") \n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mu, beta = gumbel_2_points(x_1, p_1, x_2, p_2)\n",
"\n",
"print(f\"Your mu: {mu:0.5f}\\nYour beta: {beta:0.5f}\")\n",
"\n",
"gumbel_distribution = lambda x: 1 - e**(-e**(-(x - mu)/beta))\n",
"\n",
"plot_distribution(mu, beta, x_1, p_1, x_2, p_2)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 2:** write a function to solve for the random variable value (it will be tested for you with the Check answer button, along with the distribution parameters)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def find_x_with_probability_p(p): \n",
" \"\"\" Compute point in the gumbel distribution for which the CDF is p\n",
" Use the variables mu and beta defined above!\n",
" Hint: they have been defined globally, so you don't need to \n",
" include them as arguments.\n",
" \"\"\"\n",
" # YOUR CODE GOES HERE #\n",
" x = _\n",
" #######################\n",
"\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 3:** use the function `find_x_with_probability_p` to evaluate the random variable value with exceedance probabiltiy of 0.001."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE GOES HERE #\n",
"x = _\n",
"#######################\n",
"\n",
"print(f\"The value of x with probability of exceedance 0.001 os {x:0.5f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_example(globals())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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book/examples/example_gumbel.py Normal file
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@ -0,0 +1,61 @@
from grading import check
from math import isclose
tolerance = 0.001
# Using rel_tol is more reliable in the face of aliasing, but to fit the instructions give we use abs_total
check_float = lambda a, b: isclose(a, b, rel_tol=0, abs_tol=tolerance)
# We have to pass in the globals object since we need to have access to changes as well
@check
def check_example(glob):
mu, beta = glob["mu"], glob["beta"]
if (check_float(mu, -1.7461520011511888) and check_float(beta, 2.553428469837263)):
print(f"Part 1: you got the parameters right, well done! (checked with tolerance {tolerance})")
else:
print(f"Part 1: your parameters are incorrect. (checked with tolerance {tolerance})")
print(f" Other parts won't be graded until these are fixed. ")
return
function = glob["find_x_with_probability_p"]
test_inputs = [(1 - 0.001, 15.891029744351862), (1 - 0.2, 2.0838374640878863), (1 - 0.9, -3.875794191615308)]
failed = []
for x, out in test_inputs:
result = function(x)
if not check_float(result, out):
failed.append((x, out, result))
if len(failed) == 0:
print(f"Part 2: Well done, your inverse function is correct! (checked with tolerance {tolerance})")
else:
print(f"Part 2: Your function failed some tests. Keep in mind the tolerance is {tolerance}")
print(" --> Failed inputs:")
for case in failed:
print(f"{case[0]} gave {case[2]}, expected {case[1]}")
print(f" Other parts won't be graded until these are fixed.")
x = glob["x"]
if check_float(x, 15.891029744351862):
print(f"Part 3: you got the value of x right, well done! (checked with tolerance {tolerance})")
else:
print(f"Part 3: your value of x is incorrect. (checked with tolerance {tolerance})")
return
"""
Task 0:
x_1 = 4
p_1 = 1 - .1
x_2 = 10
p_2 = 1 - .01
Task 1:
beta = -(x_2 - x_1) / log(log(p_2) / log(p_1))
mu = x_1 + beta * log(-log(p_1))
Task 2:
x = mu - beta * log(-log(p))
Task 3:
x = find_x_with_probability_p(1 - 0.001)
"""

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book/examples/example_gumbel_stripped.ipynb Normal file
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@ -0,0 +1,203 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## Gumbel Distribution Exercise\n",
"\n",
"Imagine you are concerned with the concentration of an airborne contaminant, $X$, measured in ppm. A previous benchmark study estimates that there is a 10% and 1% chance, respectively, of exceeding 4 and 10 ppm, respectively. You have been asked by the regulatory agency to estimate the contaminant concentration with 0.1% probability of being exceeded. Prior studies suggest that a Gumbel distribution can be used to model contaminant concentration, given by the CDF:\n",
"\n",
"$$\n",
"F_X(x) = \\exp{\\Bigg( -\\exp{\\Big(-\\frac{x-\\mu}{\\beta}\\Big)}\\Bigg)}\n",
"$$\n",
"\n",
"Using the cell blocks below as a guide (and also to check your analysis): Task 1) find the parameters of a Gumbel distribution that matches the information provided above, then, Tasks 2-3) use it to estimate the concentration with exceedance probability 0.1%.\n",
"\n",
"To complete this assignment, you can use `numpy`, `matplotlib` and from the `math` library, `log` and `e`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from math import log, e\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 0:** fill in the appropriate fitting points:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"x_1 = _\n",
"p_1 = _\n",
"x_2 = _\n",
"p_2 = _"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 1:** derive the distribution parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def gumbel_2_points(x_1, p_1, x_2, p_2): \n",
" \"\"\"Compute Gumbel distribution parameters from two points of the CDF.\n",
" Arguments:\n",
" x_1 (float): point one\n",
" p_1 (float): cumulative probability for point 1\n",
" x_2 (float): point two\n",
" p_2 (float): cumulative probability for point 2\n",
" \"\"\"\n",
"\n",
" # YOUR CODE GOES HERE #\n",
" beta = _\n",
" mu = _\n",
" #######################\n",
"\n",
" return mu, beta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cells below will print your parameter values and create a plot to help confirm you have the right implementation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def plot_distribution(mu, beta,\n",
" x_1, p_1, x_2, p_2):\n",
" plt.title(\"Gumbel Distribution, $1-F_X(x)$\")\n",
" plt.xlabel(\"Contaminant Concentration, $X$ [ppm]\")\n",
" plt.ylabel(\"Exceedance Probability [--]\")\n",
" plt.grid(color='black', linestyle='-', linewidth=0.3)\n",
" x_axis = np.arange(0, 20, 0.1)\n",
" plt.plot(x_axis, np.vectorize(gumbel_distribution)(x_axis), linewidth=2)\n",
" plt.plot(x_1, 1 - p_1, 'ro')\n",
" plt.annotate(\"Point 1\", (x_1 + 0.4, 1 - p_1 + 0.02))\n",
" plt.plot(x_2, 1 - p_2, 'ro')\n",
" plt.annotate(\"Point 2\", (x_2 + 0.4, 1 - p_2 + 0.001))\n",
" plt.yscale(\"log\") \n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mu, beta = gumbel_2_points(x_1, p_1, x_2, p_2)\n",
"\n",
"print(f\"Your mu: {mu:0.5f}\\nYour beta: {beta:0.5f}\")\n",
"\n",
"gumbel_distribution = lambda x: 1 - e**(-e**(-(x - mu)/beta))\n",
"\n",
"plot_distribution(mu, beta, x_1, p_1, x_2, p_2)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 2:** write a function to solve for the random variable value (it will be tested for you with the Check answer button, along with the distribution parameters)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def find_x_with_probability_p(p): \n",
" \"\"\" Compute point in the gumbel distribution for which the CDF is p\n",
" Use the variables mu and beta defined above!\n",
" Hint: they have been defined globally, so you don't need to \n",
" include them as arguments.\n",
" \"\"\"\n",
" # YOUR CODE GOES HERE #\n",
" x = _\n",
" #######################\n",
"\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Task 3:** use the function `find_x_with_probability_p` to evaluate the random variable value with exceedance probabiltiy of 0.001."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# YOUR CODE GOES HERE #\n",
"x = _\n",
"#######################\n",
"\n",
"print(f\"The value of x with probability of exceedance 0.001 os {x:0.5f}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

359
book/examples/example_quiz_interactive.ipynb Normal file
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@ -0,0 +1,359 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Combining theory with interactive questions and extensive coding assignment\n",
"This page shows an example of how to combine theory, quizzes and code on one page. With headings and boxes, the interactive parts can be indicated (for now only if part of one cell). Automatic execution has been activated and a custom ipynb.file has replaced the download button.\n",
"\n",
"```{custom_download_link} ./example_quiz_interactive_stripped.ipynb\n",
":replace_default: \"True\"\n",
"```\n",
"\n",
"\n",
"## Weighted least-squares estimation\n",
"In ordinary least-squares estimation, we assume that all observations are equally important. In many cases this is not realistic, as observations may be obtained by different measurement systems, or under different circumstances. We want our methodology for least-squares estimation to be able to take this into account.\n",
"\n",
"We achieve this goal by introducing a weight matrix in the minimization problem:\n",
"\n",
"$$\n",
"\\underset{\\mathrm{x}}{\\min} {\\mathrm{(y-Ax)^T W(y-Ax)}}\n",
"$$\n",
"\n",
"In the unweighted least-squares approach, we arrive at the normal equation by pre-multiplying both sides of $\\mathrm{y=Ax}$ with the transpose of the design matrix $\\mathrm{A^T}$:\n",
"\n",
"$$ \n",
"\\mathrm{y=Ax} \\; \\rightarrow \\; \\mathrm{A^T\\; y = A^T\\; A x }\n",
"$$\n",
"\n",
"In the weighted least-squares approach, we now need to add weight matrix $W$ to this pre-multiplication factor, i.e., $ \\mathrm{A^T W}$, to obtain the normal equation:\n",
"\n",
"$$ \n",
"\\mathrm{y=Ax} \\; \\rightarrow \\; \\mathrm{A^T W \\; y = A^TW \\; A x}\n",
"$$\n",
"\n",
"The normal matrix is now defined as $\\mathrm{N=A^T W A}$. From this, assuming that the normal matrix $N$ is invertible (non-singular) we find the weighted least-squares estimate $ \\mathrm{\\hat{x}} $, \n",
"\n",
"$$ \n",
"\\begin{align*}\n",
"\\mathrm{\\hat{x}} &= \\mathrm{(A^T W A)^{-1} A^T W y} \\\\\n",
" &= \\arg \\underset{\\mathrm{x}}{\\min} {\\mathrm{(y-Ax)^T W(y-Ax)}}\n",
"\\end{align*}\n",
"$$\n",
"\n",
"We also find the derived estimate $ \\mathrm{\\hat{y}} $ and $ \\mathrm{\\hat{\\epsilon}} $:\n",
"\n",
"$$\n",
"\\mathrm{\\hat{y} = A \\hat{x} = A (A^T W A )^{-1} A^T W y}\n",
"$$\n",
"\n",
"$$\n",
" \\mathrm{\\hat{\\epsilon} = y - \\hat{y}= y - A \\hat{x} = y-A (A^T W A )^{-1} A^T W y = (I- A(A^T W A )^{-1} A^T W) y}\n",
"$$\n",
"\n",
"### Quiz question\n",
":::{card}\n",
"<iframe src=\"https://tudelft.h5p.com/content/1292046737060674407/embed\" aria-label=\"WLS_1\" width=\"1088\" height=\"637\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" allow=\"autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *\"></iframe><script src=\"https://tudelft.h5p.com/js/h5p-resizer.js\" charset=\"UTF-8\"></script>\n",
":::\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Video\n",
"```{eval-rst}\n",
".. raw:: html\n",
"\n",
" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/iJmkkz37EuU\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
"```\n",
"\n",
"### Discussion on the weight matrix\n",
"The weight matrix $\\mathrm{W}$ expresses the (relative) weights between the observations. It is always a square matrix. The size of the weight matrix depends on the number of observations, $m$. The size of the weight matrix is $m\\times m$. \n",
"\n",
"If it is a unit matrix ($\\mathrm{W=I}$), this implies that all observations have equal weight. Note that in this case the equations are equal to the ordinary least-squares solution. \n",
"\n",
"If it is a diagonal matrix, with different values on the diagonal, this implies that entries with a higher value correspond to the observations which are considered to be of more importance. If the weight matrix has non-zero elements on the off-diagonal positions, this implies that (some) observations are correlated.\n",
"\n",
"### Weighted least-squares estimator: properties\n",
"\n",
"Until now, we have looked at the weighted least-squares solution of a single *realization* of the observations, where generally we assume that it is a realization of the *random* observable vector $Y$, since measurements are affected by random errors. As such it follows the the weighted least-squares *estimator* is given by:\n",
"\n",
"$$\n",
"\\hat{X} = \\mathrm{(A^T W A )^{-1} A^T W} Y\n",
"$$\n",
"\n",
"This estimator has two important properties: it is *linear* and *unbiased*.\n",
"\n",
"The linearity property is due to the fact that $\\hat{X}$ is a linear function of the observables $Y$.\n",
"\n",
"The unbiased property means that the expectation of $\\hat{X}$ is equal to the true (but unknown) $\\mathrm{x}$. This can be shown as follows:\n",
"\n",
"$$\n",
"\\mathbb{E}(\\hat{X}) = \\mathrm{(A^T W A )^{-1} A^T W} \\mathbb{E}(Y) = \\mathrm{(A^T W A )^{-1} A^T W Ax = x}\n",
"$$\n",
"\n",
"This a very desirable property. It applies that if we would repeat the measurements many times to obtain a new estimate, the *average of the estimated* values would be equal to the truy values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
":::{card}\n",
"\n",
"You have a time series of 8 measurements and fit a model assuming a linear trend (constant velocity).\n",
"\n",
"Times of observations, observed values and number of observations are given as the variables `t`, `y` and `m`, as well as `numpy` and `matplotlib.pyplot` abbreviated as `np` and `plt`\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"# this will print all float arrays with 3 decimal places\n",
"import numpy as np\n",
"float_formatter = \"{:.2f}\".format\n",
"np.set_printoptions(formatter={'float_kind':float_formatter})\n",
"\n",
"import micropip\n",
"await micropip.install(\"ipywidgets\")\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"import operator\n",
"\n",
"def check_answer(variable_name, expected, comparison = operator.eq):\n",
" output = widgets.Output()\n",
" button = widgets.Button(description=\"Check answer\")\n",
" def _inner_check(button):\n",
" with output:\n",
" if comparison(globals()[variable_name], expected):\n",
" output.outputs = [{'name': 'stdout', 'text': 'Correct!', 'output_type': 'stream'}]\n",
" else:\n",
" output.outputs = [{'name': 'stdout', 'text': 'Incorrect!', 'output_type': 'stream'}]\n",
" button.on_click(_inner_check)\n",
" display(button, output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"# times of observation [months]\n",
"t = np.arange(8)\n",
"\n",
"# observed heights [m]\n",
"y = [1.39, 1.26, 1.48, 4.03, 5.89, 5.14, 6.47, 7.64]\n",
"\n",
"# number of observations\n",
"m = len(t)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{card}\n",
"Fill in the correct $\\mathrm{A}$-matrix.\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"auto-execute-page"
]
},
"outputs": [],
"source": [
"# design matrix\n",
"A = np.column_stack((np.ones(m), t))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_answer(\"A\", np.column_stack((np.ones(m), t)), np.array_equiv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{card}\n",
"Define the weight matrix for $\\mathrm{W}=I_m$\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"W_1 = 1 #added to have initial input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Weight matrix for case 1\n",
"W_1 = ?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_answer(\"W_1\",np.eye(m), np.array_equiv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{card}\n",
"Define the weight matrix with the weight of first 3 observations is five times as large as the rest\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"W_2 = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Weight matrix for case 2\n",
"W_2 = ?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"# Weight matrix for case 2\n",
"w=5\n",
"W_2_ans = np.eye(m)\n",
"W_2_ans[0,0] = w\n",
"W_2_ans[1,1] = w\n",
"W_2_ans[2,2] = w\n",
"check_answer(\"W_2\",W_2_ans, np.array_equiv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
":::{card}\n",
"Define the weight matrix with the weight of 4th and 5th observation is five times as large as the rest\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Weight matrix for case 3\n",
"W_3 = ?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

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@ -0,0 +1,221 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Weighted least-squares estimation\n",
"In ordinary least-squares estimation, we assume that all observations are equally important. In many cases this is not realistic, as observations may be obtained by different measurement systems, or under different circumstances. We want our methodology for least-squares estimation to be able to take this into account.\n",
"\n",
"We achieve this goal by introducing a weight matrix in the minimization problem:\n",
"\n",
"$$\n",
"\\underset{\\mathrm{x}}{\\min} {\\mathrm{(y-Ax)^T W(y-Ax)}}\n",
"$$\n",
"\n",
"In the unweighted least-squares approach, we arrive at the normal equation by pre-multiplying both sides of $\\mathrm{y=Ax}$ with the transpose of the design matrix $\\mathrm{A^T}$:\n",
"\n",
"$$ \n",
"\\mathrm{y=Ax} \\; \\rightarrow \\; \\mathrm{A^T\\; y = A^T\\; A x }\n",
"$$\n",
"\n",
"In the weighted least-squares approach, we now need to add weight matrix $W$ to this pre-multiplication factor, i.e., $ \\mathrm{A^T W}$, to obtain the normal equation:\n",
"\n",
"$$ \n",
"\\mathrm{y=Ax} \\; \\rightarrow \\; \\mathrm{A^T W \\; y = A^TW \\; A x}\n",
"$$\n",
"\n",
"The normal matrix is now defined as $\\mathrm{N=A^T W A}$. From this, assuming that the normal matrix $N$ is invertible (non-singular) we find the weighted least-squares estimate $ \\mathrm{\\hat{x}} $, \n",
"\n",
"$$ \n",
"\\begin{align*}\n",
"\\mathrm{\\hat{x}} &= \\mathrm{(A^T W A)^{-1} A^T W y} \\\\\n",
" &= \\arg \\underset{\\mathrm{x}}{\\min} {\\mathrm{(y-Ax)^T W(y-Ax)}}\n",
"\\end{align*}\n",
"$$\n",
"\n",
"We also find the derived estimate $ \\mathrm{\\hat{y}} $ and $ \\mathrm{\\hat{\\epsilon}} $:\n",
"\n",
"$$\n",
"\\mathrm{\\hat{y} = A \\hat{x} = A (A^T W A )^{-1} A^T W y}\n",
"$$\n",
"\n",
"$$\n",
" \\mathrm{\\hat{\\epsilon} = y - \\hat{y}= y - A \\hat{x} = y-A (A^T W A )^{-1} A^T W y = (I- A(A^T W A )^{-1} A^T W) y}\n",
"$$\n",
"\n",
"### Quiz question\n",
"<iframe src=\"https://tudelft.h5p.com/content/1292046737060674407/embed\" aria-label=\"WLS_1\" width=\"1088\" height=\"637\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\" allow=\"autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *\"></iframe><script src=\"https://tudelft.h5p.com/js/h5p-resizer.js\" charset=\"UTF-8\"></script>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Video\n",
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/iJmkkz37EuU\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
"\n",
"### Discussion on the weight matrix\n",
"The weight matrix $\\mathrm{W}$ expresses the (relative) weights between the observations. It is always a square matrix. The size of the weight matrix depends on the number of observations, $m$. The size of the weight matrix is $m\\times m$. \n",
"\n",
"If it is a unit matrix ($\\mathrm{W=I}$), this implies that all observations have equal weight. Note that in this case the equations are equal to the ordinary least-squares solution. \n",
"\n",
"If it is a diagonal matrix, with different values on the diagonal, this implies that entries with a higher value correspond to the observations which are considered to be of more importance. If the weight matrix has non-zero elements on the off-diagonal positions, this implies that (some) observations are correlated.\n",
"\n",
"### Weighted least-squares estimator: properties\n",
"\n",
"Until now, we have looked at the weighted least-squares solution of a single *realization* of the observations, where generally we assume that it is a realization of the *random* observable vector $Y$, since measurements are affected by random errors. As such it follows the the weighted least-squares *estimator* is given by:\n",
"\n",
"$$\n",
"\\hat{X} = \\mathrm{(A^T W A )^{-1} A^T W} Y\n",
"$$\n",
"\n",
"This estimator has two important properties: it is *linear* and *unbiased*.\n",
"\n",
"The linearity property is due to the fact that $\\hat{X}$ is a linear function of the observables $Y$.\n",
"\n",
"The unbiased property means that the expectation of $\\hat{X}$ is equal to the true (but unknown) $\\mathrm{x}$. This can be shown as follows:\n",
"\n",
"$$\n",
"\\mathbb{E}(\\hat{X}) = \\mathrm{(A^T W A )^{-1} A^T W} \\mathbb{E}(Y) = \\mathrm{(A^T W A )^{-1} A^T W Ax = x}\n",
"$$\n",
"\n",
"This a very desirable property. It applies that if we would repeat the measurements many times to obtain a new estimate, the *average of the estimated* values would be equal to the truy values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise\n",
"\n",
"You have a time series of 8 measurements and fit a model assuming a linear trend (constant velocity)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# times of observation [months]\n",
"t = np.arange(8)\n",
"\n",
"# observed heights [m]\n",
"y = [1.39, 1.26, 1.48, 4.03, 5.89, 5.14, 6.47, 7.64]\n",
"\n",
"# number of observations\n",
"m = len(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fill in the correct $\\mathrm{A}$-matrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# design matrix\n",
"A = np.column_stack((np.ones(m), t))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the weight matrix for $\\mathrm{W}=I_m$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Weight matrix for case 1\n",
"W_1 = ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the weight matrix with the weight of first 3 observations is five times as large as the rest"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Weight matrix for case 2\n",
"W_2 = ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the weight matrix with the weight of 4th and 5th observation is five times as large as the rest"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Weight matrix for case 3\n",
"W_3 = ?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

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# H5p
This page shows some examples of H5p questions.
Some best-practices on the use of H5p:
- To startup your H5p-account, first login to H5p via Brightspace (follow instructions as under [https://www.tudelft.nl/teaching-support/educational-tools/h5p](https://www.tudelft.nl/teaching-support/educational-tools/h5p)). After that, direct login is possible via [tudelft.h5p.com](tudelft.h5p.com)
- Place your H5p-elements in a shared folder in H5p.
- Disable the display options "Toolbar Below Content" and "Display author's name to public (anonymous users) (only relevant when content's status is set to Public )" for each
## Complex fill in the blanks
This can be used to evaluate numerical answers:
<iframe src="https://tudelft.h5p.com/content/1292010272694757307/embed" aria-label="Example MUDE" width="1088" height="637" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
## Dialog cards
This can be used to ask open questions:
<iframe src="https://tudelft.h5p.com/content/1292050206829217497/embed" aria-label="PA1 File Contents" width="1088" height="637" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
## Question set
This can be used to create a set of Multiple Choice, Drag and Drop, Fill in the blanks, True/False questions and Image choice questions:
<iframe src="https://tudelft.h5p.com/content/1292062157562749767/embed" aria-label="quiz_uniform_motion" width="1088" height="637" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
## True/False Question
<iframe src="https://tudelft.h5p.com/content/1292061623388939257/embed" aria-label="quiz_blue_vs_LS" width="1088" height="637" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>

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# Templates for interactive coding / non-coding elements
These pages show examples of how to use interactive coding / non-coding elements in a Jupyter Book.
You can make use of:
- Admonitions
- JupyterQuiz
- H5p
- Grasple-exercises
- Interactive widgets
- Live coding exercise checking with widgets
- Any combination of the above
The following templates are given:
```{tableofcontents}
```

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# Overview
This part will include examples for jupyter book pages with features
This chapter includes examples for jupyter book pages with features. Here is an overview of the different examples:
- H5p: examples of H5p questions
- Combining theory with interactive questions
- Templates for interactive coding / non-coding elements:
- Programming assignment: Example of how to convert a Jupyter notebook to Jupyter Book with live coding
- Combining theory with interactive questions and extensive coding assignment: Example how to combine theory, quizzes and code on one page
- Combining theory with interactive questions: Example how to combine theory with questions, so there's no coding involved.
- Parametric question: Example for an equation to be checked on equivalence with the correct answer

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parametric question\n",
"\n",
"On this page an example is given for parametric questions, in which an equation is checked on equivalence with the correct answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"from sympy import pi, latex\n",
"from sympy.printing.mathml import mathml\n",
"\n",
"import operator\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, Latex, display_jpeg, Math, Markdown\n",
"sp.init_printing(use_latex=True) \n",
"\n",
"check_equation = lambda eq1, eq2: sp.simplify(eq1-eq2) == 0\n",
"\n",
"def check_answer(variable_name, expected, comparison = operator.eq):\n",
" output = widgets.Output()\n",
" button = widgets.Button(description=\"Check answer\")\n",
" def _inner_check(button):\n",
" with output:\n",
" if comparison(globals()[variable_name], expected):\n",
" output.outputs = [{'name': 'stdout', 'text': 'Correct!',\n",
" 'output_type': 'stream'}]\n",
" else:\n",
" output.outputs = [{'name': 'stdout', 'text': 'Incorrect!',\n",
" 'output_type': 'stream'}]\n",
" button.on_click(_inner_check)\n",
" display(button, output)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises on Taylor expansion\n",
"\n",
"This page shows some exercises on calculation Taylor expansion. If you reload this page, you'll get new values.\n",
"\n",
"### Exercise 1\n",
"\n",
"Calculate the taylor series expension of:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"x, y = sp.symbols('x, y')\n",
"a_1 = sp.Integer(np.random.randint(2,6))\n",
"b_1 = sp.Integer(np.random.randint(-10,10))\n",
"c_1 = sp.Integer(np.random.randint(-5,5))\n",
"eq1_original = a_1 * x**2 + b_1*x\n",
"eq1_correct = sp.series(eq1_original,x,c_1)\n",
"eq1_answer = 0\n",
"display(eq1_original)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"around:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"display(sp.Eq(x,c_1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fill in your answer and run the cell before clicking 'Check answer'. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"auto-execute-page",
"disable-download-page"
]
},
"outputs": [],
"source": [
"eq1_answer = "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_answer(\"eq1_answer\",eq1_correct, check_equation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Calculate the taylor series expension of:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"a_2 = sp.Integer(np.random.randint(1,7))\n",
"c_2 = sp.Integer(np.random.randint(-5,5))\n",
"eq2_original = a_2*sp.tan(x)\n",
"display(eq2_original)\n",
"eq2_correct = sp.series(eq2_original,x,c_2*sp.pi,3).removeO()\n",
"#display(eq2_correct)\n",
"eq2_answer = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"around:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"display(sp.Eq(x,c_2*sp.pi))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"discard any $O(x^3)$ terms.\n",
"\n",
"Fill in your answer and run the cell before clicking 'Check answer'. Furthermore, use `pi` for $\\pi$:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eq2_answer = "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_answer(\"eq2_answer\",eq2_correct, check_equation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 3\n",
"\n",
"Calculate the taylor series expension of:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"a_3 = sp.Integer(np.random.randint(1,10))\n",
"c_3 = sp.Integer(np.random.randint(-1,1))\n",
"eq3_original = a_3 / (1 - x)\n",
"display(eq3_original)\n",
"eq3_correct = sp.series(eq3_original,x,c_3,3).removeO()\n",
"#display(eq3_correct)\n",
"eq3_answer = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"around:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"display(sp.Eq(x,c_3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"discard any $O(x^3)$ terms.\n",
"\n",
"Fill in your answer and run the cell before clicking 'Check answer':"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eq3_answer ="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"check_answer(\"eq3_answer\",eq3_correct, check_equation)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"nbformat_minor": 2
}

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# Combining theory with interactive questions
This page shows you how to combine theory with questions, so there's no coding involved.
```{index} Truss structures
```
## Truss structures
Truss structures are structures assembled of straight elements connected in nodes. These elements partly create triangles, which are of importance for its deformability. Of course, the structures you'll design shouldn't deform, so it's crucial to understand this topic.
### Examples truss structures
Examples of these structures are commonly found in rail bridges like the one below:
```{figure} theory_questions_images/pic0.jpg
:width: 400
```
Furthermore, these structures are commonly found in building as well, both invisible and visible:
```{figure} theory_questions_images/pic1.jpg
:width: 400
```
### Modelling truss structures
Truss structures are modelled as rigid bars (so elements which cannot deform) connected by hinges (so elements can rotate with respect to one each other). In our model, hinges are indicate with a circle, and bars with a line. For example, the structure you've seen in the second example (with two diagonal bars removed) is modelled as follows:
```{figure} theory_questions_images/Truss1.svg
```
Now that you've been introduced to truss structures, answer the following question:
:::{card} Test Yourself
<iframe src="https://tudelft.h5p.com/content/1291910926067816717/embed" aria-label="Modelling truss structures" width="1088" height="200" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
:::
### Mechanisms
A mechanism is a structure which can deform as a whole, while the individual elements don't deform. This deformation doesn't have to be large. An example of a mechanism is a rectangular truss structure, it can deform as shown below:
```{figure} theory_questions_images/rectangle.svg
```
A triangle cannot deform:
```{figure} theory_questions_images/triangle.svg
```
For structures which consists of partly triangles, partly quadrilaterals, it is not directly clear whether the structure can deform. To identify whether the truss structure is a mechanism, you can try to move each node with respect to the nodes with which it is connected:
```{figure} theory_questions_images/gifje.gif
:width: 300px
```
An example of a truss structure of partly triangles, partly quadrilaterals, which is not a mechanism is the following:
```{figure} theory_questions_images/structure3.svg
```
Now that you've been introduced to mechanisms, answer the following questions:
:::{card} Test Yourself
<iframe src="https://tudelft.h5p.com/content/1291910957230324507/embed" aria-label="Truss structures and mechanism" width="1088" height="300 " frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
:::
:::{card} Test Yourself
<iframe src="https://tudelft.h5p.com/content/1291910962551764997/embed" aria-label="Truss structures and mechanims 2" width="1088" height="300" frameborder="0" allowfullscreen="allowfullscreen" allow="autoplay *; geolocation *; microphone *; camera *; midi *; encrypted-media *"></iframe><script src="https://tudelft.h5p.com/js/h5p-resizer.js" charset="UTF-8"></script>
:::

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# Page download options
You can control the download option of the book in two ways:
- Disabling downloading using cell tags `disable-download-page`
- Add / replace download link with custom link using `spinhx-download-link-replacer`
## Disabling download
If you add `disable-download-page` as a cell tag to a cell in a python notebook, the download button ({fa}`download`) will disappear from the topright corner. The cell tag can be added to any code cell in the notebook. This function might be handy if your page includes code which you don't want the students to see. Be aware that this also removes the option to download a PDF of the page.
## Add/replace download link
The download link {fa}`download` --> {fa}`file`{guilabel}`.ipynb` can be replaced by using the following code in any markdown / notebook file:
````md
```{custom_download_link} <link_target>
:text: "Custom text"
:replace_default: "True"
```
````
An explanation of the options of this command is given on https://gitlab.tudelft.nl/mude/sphinx-download-link-replacer.
A potential application of this functionality is when creating a page in which students have to do some coding. Downloading the page allows the student to save their work and work with their local environment. However, the original source file might include code (jupyterbook formatting, widgets, answers) which is not necessary for students. You can make a copy of the notebook file without these elements and replace the {fa}`file`{guilabel}`.ipynb` link to this custom notebook file. Furthermore, you can add any additional data as an additional {fa}`file`{guilabel}`.zip` file.
### Installation instructions
To make use of this functionality, you have to install the `download_link_replace` package by running the following line in your command line:
```
pip install git+https://gitlab.tudelft.nl/mude/sphinx-download-link-replacer
```
Furthermore, you should add the following line to `requirements.txt`
```
download_link_replacer @ git+https://gitlab.tudelft.nl/mude/sphinx-download-link-replacer
```
and to `_config.yml`:
```
sphinx:
extra_extensions:
- download_link_replacer
```

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# Exclude parts of book from published book
A new feature is added to enable testing of all interactive features on the web server (i.e., in the draft book) while preventing students from seeing it. Pages in the draft book (`main` branch) can be manually stripped out of the table of contents when a merge request from `main` to `publish` is made using a tag `REMOVE-FROM-PUBLISH`. Pages marked with this feature are still visible in the draft book and books build from source on personal computers. The tag is applied as follows:
```
format: jb-book
root: intro
parts:
- caption: ...
chapters:
- file: ...
...
# START REMOVE-FROM-PUBLISH
- file: files_to_remove
# END REMOVE-FROM-PUBLISH
```
There is no limit to the number of stripped sections, they can be sequential and indentation does not matter. The action is implemented in shell script `build-book.sh` and is executed at run time (see line in script [here](https://gitlab.tudelft.nl/mude/book/-/blob/main/build-book.sh#L13)).
```{note}
The `REMOVE-FROM-PUBLISH` feature was developed to retain testing of all interactive features on the web server and we recommended incorporating this in all books that rely on these features. It avoids problems associated with building the book on various platforms and virtual environments, rather than the actualy webserver where the book will be deployed.
```

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise checking using check-answer button\n",
"\n",
"Using the python-enabled interactivity, exercise checking can be intuitively incorporated using a widgets which checks the value of certain variables with respect to the answer. These examples show different approaches to achieving this goal. \n",
"\n",
"A function has been made to check the value of a variable. As input values it takes the variable name, the expected value (correct answer) and how these values should be compared. You need to add this function to each page, including the imports, and add the `thebe-remove-input-init` tag to the cell.\n",
"```python\n",
"import micropip\n",
"await micropip.install(\"ipywidgets\")\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"import operator\n",
"\n",
"def check_answer(variable_name, expected, comparison = operator.eq):\n",
" output = widgets.Output()\n",
" button = widgets.Button(description=\"Check answer\")\n",
" def _inner_check(button):\n",
" with output:\n",
" if comparison(globals()[variable_name], expected):\n",
" output.outputs = [{'name': 'stdout', 'text': 'Correct!', 'output_type': 'stream'}]\n",
" else:\n",
" output.outputs = [{'name': 'stdout', 'text': 'Incorrect!', 'output_type': 'stream'}]\n",
" button.on_click(_inner_check)\n",
" display(button, output)\n",
"```\n",
"\n",
"To check a float value, you could use `math.isclose` as a function. For checking the equivalence of arrays, you might consider `numpy.array_equiv`\n",
"\n",
"You can choose to include the correct answers in the same notebook. If students download the page and you didn't specify a custom url, they can find the correct answers. You can also place the correct answers in a seperate .py file which you import in the notebook.\n",
"\n",
"## Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import micropip\n",
"await micropip.install(\"ipywidgets\")\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"import operator\n",
"\n",
"def check_answer(variable_name, expected, comparison = operator.eq):\n",
" output = widgets.Output()\n",
" button = widgets.Button(description=\"Check answer\")\n",
" def _inner_check(button):\n",
" with output:\n",
" if comparison(globals()[variable_name], expected):\n",
" output.outputs = [{'name': 'stdout', 'text': 'Correct!', 'output_type': 'stream'}]\n",
" else:\n",
" output.outputs = [{'name': 'stdout', 'text': 'Incorrect!', 'output_type': 'stream'}]\n",
" button.on_click(_inner_check)\n",
" display(button, output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-init",
"auto-execute-page"
]
},
"outputs": [],
"source": [
"# This example has the user type in the answer as a Python variable, but they need to run the cell to update the answer checked!\n",
"pi = 3.14"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"import math\n",
"check_answer(\"pi\", 3.14, math.isclose)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

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# Embedding Grasple question
# Grasple questions
[Grasple](https://app.grasple.com/) gives you an embed code for each exercise which looks like this, which can be added cirectly to your markdown-file.
```html

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# Adding `JupyterLite` to a page
# How to add `JupyterLite` to a page
There are three different `JupyterLite` directives that you can include in your web page:

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# Gallery
On this page, you can find some example usage of `JupyterLite`. The reader is invited to contribute to this gallery with their own interesting examples.
## Interactive plot
```{eval-rst}
.. retrolite:: interactive-wave.ipynb
:width: 100%
:height: 600px
```

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# Interactive elements with JupyterLite-Sphinx
# JupyterLite-Sphinx: Interactive elements
By popular demand: how to include interactive pieces of code in your Jupyter Book. We can do this by using the [JupyterLite](https://github.com/jupyterlite) app, which allows you to run a Jupyter Lab session and execute Python code entirely in-browser. Jupyterlite can be embedded in a Jupyter Book with the [`JupyterLite-Sphinx`](https://github.com/jupyterlite/jupyterlite-sphinx) extension.

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# Python-enabled interactivity
Our book has been enable to run Python code live in the browser (thanks Max!). This page contains some installation instructions and the other sections show how to use this functionality to create interactive figures and feedback on code
## Setting up Python live coding
To set up the Python live coding you need to add our [own sphinx-thebe extension](https://gitlab.tudelft.nl/interactivetextbooks-citg/extensions/sphinx-thebe) to your book. This extensions doesn't rely on a 3rd party like Binder and it supports local python execution and custom features. Therefore, you need to add some lines to `requirements.txt` and `_config.yml`
For `requirements.txt` add the following lines:
```
--extra-index-url https://gitlab.tudelft.nl/api/v4/projects/11239/packages/pypi/simple
sphinx-thebe == 0.2.1000
```
This will download the correct version of the sphinx extension when the book is build on the server (which loads the required packages from `requirements.txt`)
Afterwards, this sphinx extension needs to be enabled in your book. This can be done by adding the following lines to `_config.yml`:
```
launch_buttons:
thebe: true
sphinx:
html_js_files:
- https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
thebe_config:
use_thebe_lite: true
exclude_patterns: ["**/_*.yml", "**/*.md", "**/*.ipynb"]
```
By `launch_buttons` you initiate the {fa}`rocket` --> {guilabel}`Live Code` on the top right corner of every page generated from a `.ipynb` file. The `html_js_files` link calls for some required javascript files. `use_thebe_lite` makes sure you initiate our adapted sphinx thebe extensions. The `exclude_patterns` makes sure you import all files so that they can be accessed from your code, except for the files matching the patterns shown.
## Instructions: local build
To test the fuctionality, you have to run a local server, otherwise the interactivity doens't work. So, you cannot just open the `index.html` file any more. This can be done with the following steps:
1. build the book by running ` jupyter book build <directory of your book>`. If you are using Windows you should use the Git Bash terminal. Set this as your default in VS Code, then open it using "New Terminal." If you have not already done so, you will need to allow Git Bash to use your Python libraries by running the command `conda init bash`.
2. Set up a server by running `python -m http.server 8000`.
3. Open the book by going to [http://localhost:8000/](http://localhost:8000/) in your browser. You may need to reload the page once the rebuild is complete. You can do this on Chrome by right-clicking somewhere on the page, select "Inspect", open the "Network" tab, then reload with `CTRL+R`. Or try `ctrl`+`F5`
4. Go to any page that is created from a Jupyter notebook
If you push to main, you can test the interactivity on the book-draft website as well.
## Custom cell tags
With the extended functionality of live code, additional cell tags have been developed to be added to cells in any .ipynb file, next to the [existing tags] of the jupyter book (https://jupyterbook.org/en/stable/content/metadata.html):
- `disable-execution-cell` disables the ability to execute the cell when thebe is activated. This might be useful if you notebook file includes interactivity for only a part of the coding cells
- `disable-execution-page` disables the ability to execute the entire page. This might be useful if you use python code for creating a page, but the code is not part of the actual content of the book.
- `auto-execute-page` automatically starts the thebe live coding functionality whenever the page is opened. This is particularly useful if you're using widgets on a page. At this moment, this tags doesn't work in combination with `thebe-remove-input-init`
- `thebe-init` directly starts running this cell as soon as the thebe live coding functionality starts.
- `thebe-remove-input-init`, as `thebe-init`, starts the cell directly as soon as the thebe live coding functionality starts. However, the input is not shown to the students (in static and interactive mode). The original `remove-input` tag will not work as it deletes the entire cell, so it can never be executed.

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# CEG-book Features
```{warning} Under Construction!
This page will provide a list of some commonly used book features, and sub-pages will provide additional information, specifically:
This chapter provides a list of commonly used book features together with sub-pages that provide additional information on:
- the purpose
- how to install them
- how to use them
- where to find the source code
```
Overview of features covered:
- Interaction on the website: Adding comments and feedback to content on the website
- Grasple Questions: Explanation how to embed Grasple questions in the book.
- Adding interactive h5p elements: Embedding interactive HTML-blocks in the book
- Python-enabled interactivity: Run Python code live in the browser
- Widgets
- Exercise checking using check-answer button
- JupyterLite-Sphinx: Including interactive pieces of content in the book.
- Configuration
- How to add `JupyterLite` to a page
- Exclude parts of book from published book
- Page download options
To see examples for these features, go to the Example chapter.
The {guilabel}`books-CEG` label indicates a feature developed by us.

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4
book/features/remove_from_publish.md
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(remove_from_publish)=
# Remove From Publish
Describe me.

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Widgets\n",
"\n",
"If you publish widgets in a jupyter book they might (depending on your implementation) require an active python kernel for the output to be interactive. Using the [thebe-integration](live_code.md), this is possible. Note: You can circumvent the use of a kernel by using packages which don't need such a kernel, for example the [non-kernel-based widgets using Plotly](interactive_element)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please note:\n",
"- You might want to hide the output before the thebe has been activated.\n",
"- You can hide the code by adding a custom-made tag: `thebe-remove-input-init`.\n",
"- You can automatically start the thebe functionality when you add `auto-execute-page` to a cell. \n",
"\n",
"## Example\n",
"The code below shows an example of the strain energy in a structure and work done by a force for different trial functions. Because of the actual python code, the plot 'refreshes' upon updating a widgets. This can be avoided by updating the current figure instead of plotting a new figure. Remember, first press {fa}`rocket` --> {guilabel}`Live Code` and then wait until all cells are executed:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init",
"auto-execute-page"
]
},
"outputs": [],
"source": [
"import sympy as sp\n",
"F = 10\n",
"EI = 20e3\n",
"L = 3\n",
"\n",
"# Polynomial trial function\n",
"x, C1, C2, C3, C4, F, u0= sp.symbols('x, C1, C2, C3, C4, F, u0')\n",
"w = C1*x**3+C2*x**2+C3*x + C4\n",
"eq1 = sp.Eq(w.subs(x,0),0)\n",
"eq2 = sp.Eq(w.diff(x).subs(x,0),0)\n",
"eq3 = sp.Eq((-w.diff(x,2)*EI).subs(x,L),0)\n",
"eq4 = sp.Eq(w.subs(x,L),u0)\n",
"sol = sp.solve((eq1, eq2,eq3,eq4),(C1,C2,C3,C4))\n",
"w_sol = sp.nsimplify(w.subs(sol))\n",
"w_numpy_polynomial = sp.lambdify((u0,x),w_sol)\n",
"\n",
"M_sol_polynomial = sp.nsimplify(EI * w_sol.diff(x,2))\n",
"\n",
"Ev_polynomial = sp.integrate(sp.nsimplify(M_sol_polynomial**2/(EI*2)),(x,0,3))\n",
"\n",
"Ev_numpy_polynomial = sp.lambdify(u0,Ev_polynomial)\n",
"\n",
"V_polynomial = Ev_polynomial - 10 * u0\n",
"V_numpy_polynomial = sp.lambdify(u0,V_polynomial)\n",
"\n",
"#cosine trial function\n",
"w_sol = u0*sp.sin(x/12*2*sp.pi-sp.pi/2)+u0\n",
"w_numpy_sin = sp.lambdify((u0,x),w_sol)\n",
"\n",
"M_sol_sin = sp.nsimplify(EI * w_sol.diff(x,2))\n",
"\n",
"Ev_sin = sp.integrate(sp.nsimplify(M_sol_sin**2/(EI*2)),(x,0,3))\n",
"\n",
"Ev_numpy_sin = sp.lambdify(u0,Ev_sin)\n",
"\n",
"V_sin = Ev_sin - 10 * u0\n",
"V_numpy_sin = sp.lambdify(u0,V_sin)\n",
"\n",
"#sin trial function\n",
"w_sol = 2*u0*(sp.cosh(x/sp.pi))-2*u0\n",
"w_numpy_cosh = sp.lambdify((u0,x),w_sol)\n",
"\n",
"M_sol_cosh = sp.nsimplify(EI * w_sol.diff(x,2))\n",
"\n",
"Ev_cosh = sp.integrate(sp.nsimplify(M_sol_cosh**2/(EI*2)),(x,0,3))\n",
"\n",
"Ev_numpy_cosh = sp.lambdify(u0,Ev_cosh)\n",
"\n",
"V_cosh = Ev_cosh - 10 * u0\n",
"V_numpy_cosh = sp.lambdify(u0,V_cosh)\n",
"\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"from ipywidgets import widgets, interact\n",
"\n",
"def func(u,trial_function):\n",
" fig, axs = plt.subplots(2, 2, figsize=(10, 6))\n",
"\n",
" x = np.linspace(0,3,100)\n",
" if trial_function == 'Polynomial':\n",
" w_numpy = w_numpy_polynomial\n",
" Ev_numpy = Ev_numpy_polynomial\n",
" V_numpy = V_numpy_polynomial\n",
" elif trial_function == 'cos':\n",
" w_numpy = w_numpy_sin\n",
" Ev_numpy = Ev_numpy_sin\n",
" V_numpy = V_numpy_sin\n",
" elif trial_function == 'cosh':\n",
" w_numpy = w_numpy_cosh\n",
" Ev_numpy = Ev_numpy_cosh\n",
" V_numpy = V_numpy_cosh\n",
" axs[0,0].plot(x,w_numpy(u,x))\n",
" axs[0,0].set_xlim([-0.2,3.2])\n",
" axs[0,0].set_ylim([-0.003,0.01])\n",
" axs[0,0].invert_yaxis()\n",
" axs[0,0].annotate(text='', xy=(3,w_numpy(u,3)), xytext=(3,0), arrowprops=dict(arrowstyle='fancy'))\n",
" axs[0,0].text(3.1,w_numpy(u,3),'$u_0$')\n",
" axs[0,0].axis('off')\n",
" axs[0,1].axis('off')\n",
" axs[0,1].set_xlim([-0.2,3.2])\n",
" axs[0,1].set_ylim([-0.003,0.01])\n",
" axs[0,1].annotate(text='', xy=(1.5,w_numpy(u,3)), xytext=(1.5,w_numpy(u,3)-0.003), arrowprops=dict(arrowstyle='simple'))\n",
" axs[0,1].invert_yaxis()\n",
" x_axis= ['$E_v$','$A_F$','$E_v - A_F$']\n",
" y_axis = [Ev_numpy(u),w_numpy(u,3)*10,Ev_numpy(u)-w_numpy(u,3)*10]\n",
" axs[1,0].bar(x_axis,y_axis,color=('blue','green','orange'))\n",
" axs[1,0].set_ylim([-0.03,0.1])\n",
" axs[1,0].set_yticklabels([])\n",
" axs[1,0].set_yticks([])\n",
" \n",
" u_range=np.linspace(-0.0015,0.01,100)\n",
" axs[1,1].plot(u_range,Ev_numpy(u_range),label='$E_v$',color='blue')\n",
" axs[1,1].plot(u_range,w_numpy(u_range,3)*10,label='$F_A$',color='green')\n",
" axs[1,1].plot(u_range,V_numpy(u_range),label='$E_v - A_F$',color='orange')\n",
" axs[1,1].legend()\n",
" axs[1,1].plot(u,Ev_numpy(u),'o',color='blue')\n",
" axs[1,1].plot(u,w_numpy(u,3)*10,'o',color='green')\n",
" axs[1,1].plot(u,Ev_numpy(u)-w_numpy(u,3)*10,'o',color='orange')\n",
" axs[1,1].set_ylim([-0.03,0.1])\n",
" axs[1,1].set_xlim([-0.0015,0.01])\n",
" axs[1,1].set_xlabel('$u_0$')\n",
" axs[1,1].set_xticks([])\n",
" axs[1,1].set_xticklabels([])\n",
" axs[1,1].set_yticks([])\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The graph will appear below in interactive mode:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"thebe-remove-input-init"
]
},
"outputs": [],
"source": [
"interact(func, u = widgets.FloatSlider(min=-0.001, max=0.01, value=0, step=0.0002, description=\"Displacement u_0\",readout_format='.4f',style= {'description_width': '180px'},layout = {'width': '400px'}),\n",
" trial_function = widgets.ToggleButtons(options=['Polynomial', 'cos', 'cosh']));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# GitHub template
To get started making your Jupyter Book with our functionalities, fork our template book https://github.com/TeachBooks/template :
![alt text](../images/image.png)
Fill in a repository name, this name will be used in the future url of your book:
![alt text](../images/image-1.png)
Set up the build of your website using GitHub pages by selecting under `Settings` - `Pages` - `Build and deployment` - `Source` - `GitHub Actions`:
![alt text](../images/image-2.png)
Enable the predefined workflow to publish your book under `Actions` - `I understand my workflows, go ahead an enable them`:
![alt text](../images/image-3.png)
Edit a file under `Code` - `book` - A file of your choice - `Edit in place`, in the example below `book/intro.md` is edited:
![alt text](../images/image-5.png)
Make your edit and `Commit changes` to `main` branch:
![alt text](../images/image-6.png)
![alt text](../images/image-7.png)
Wait a few minutes for the website to be deployed, if you're curious, have a look at the progress under `Actions` - `All wokflows`:
![alt text](../images/image-8.png)
When the workflow has finished, visit your build book at https://<username>.github.io/<repository_name> (case sensitive). For our example it is: https://dummystudent-Tom.github.io/My_own_book:
![alt text](../images/image-9.png)

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book/workflows/overview.md
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```{warning} Under Construction!
This page will provide a workflow how to collaborate in Git and GitLab for different uses types. Every team might have a different workflow but this workflow is shown as an example and is used in the MUDE course in CEG.
This page will provide a workflow how to collaborate in Git and GitLab for different uses types. Every team might have a different workflow but this workflow is shown as an example and is used in the MUDE course in CEG.
An alternative is to use Github and Github pages to publish your book.

8
requirements.txt
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jupyter-book
sphinx-inline-tabs
jupyterlite-xeus-python
jupyterlite-sphinx
jupyterlite-sphinx
--extra-index-url https://gitlab.tudelft.nl/api/v4/projects/11239/packages/pypi/simple
sphinx-thebe == 0.2.1000
--extra-index-url https://gitlab.tudelft.nl/api/v4/projects/17983/packages/pypi/simple
jupyterbook_patches == 0.1.2
download_link_replacer @ git+https://gitlab.tudelft.nl/mude/sphinx-download-link-replacer@dlr-0.1.2