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Improve tests by checking the notebook actually run #277

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Improve tests
martinRenou Sep 2, 2025
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Linter
martinRenou Sep 2, 2025
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Make a notebook fail for testing
martinRenou Sep 2, 2025
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Try failing on exec failure
martinRenou Sep 2, 2025
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Try
martinRenou Sep 2, 2025
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Try another approach
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Dumb debug
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212 changes: 153 additions & 59 deletions ui-tests/notebooks/Lorenz.ipynb
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Original file line number Diff line number Diff line change
@@ -1,155 +1,249 @@
{
"metadata": {
"kernelspec": {
"name": "xpython (env-python)",
"display_name": "Python 3.13 (XPython) [env-python]",
"language": "python"
},
"language_info": {
"codemirror_mode": {
"name": "python",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8"
}
},
"nbformat_minor": 4,
"nbformat": 4,
"cells": [
{
"cell_type": "markdown",
"source": "# The Lorenz Differential Equations",
"metadata": {}
"metadata": {},
"source": [
"# The Lorenz Differential Equations"
]
},
{
"cell_type": "markdown",
"source": "Before we start, we import some preliminary libraries.",
"metadata": {}
"metadata": {},
"source": [
"Before we start, we import some preliminary libraries."
]
},
{
"cell_type": "code",
"source": "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy import integrate\n\nfrom ipywidgets import interactive",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from scipy import integrate\n",
"\n",
"from ipywidgets import interactive"
]
},
{
"cell_type": "markdown",
"source": "We will also define the actual solver and plotting routine.",
"metadata": {}
"metadata": {},
"source": [
"We will also define the actual solver and plotting routine."
]
},
{
"cell_type": "code",
"source": "def solve_lorenz(sigma=10.0, beta=8./3, rho=28.0):\n \"\"\"Plot a solution to the Lorenz differential equations.\"\"\"\n\n max_time = 4.0\n N = 30\n\n fig = plt.figure(1)\n ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n ax.axis('off')\n\n # prepare the axes limits\n ax.set_xlim((-25, 25))\n ax.set_ylim((-35, 35))\n ax.set_zlim((5, 55))\n\n def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n x, y, z = x_y_z\n return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n\n # Choose random starting points, uniformly distributed from -15 to 15\n np.random.seed(1)\n x0 = -15 + 30 * np.random.random((N, 3))\n\n # Solve for the trajectories\n t = np.linspace(0, max_time, int(250*max_time))\n x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n for x0i in x0])\n\n # choose a different color for each trajectory\n colors = plt.cm.viridis(np.linspace(0, 1, N))\n\n for i in range(N):\n x, y, z = x_t[i,:,:].T\n lines = ax.plot(x, y, z, '-', c=colors[i])\n plt.setp(lines, linewidth=2)\n angle = 104\n ax.view_init(30, angle)\n plt.show()\n\n return t, x_t",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"def solve_lorenz(sigma=10.0, beta=8./3, rho=28.0):\n",
" \"\"\"Plot a solution to the Lorenz differential equations.\"\"\"\n",
"\n",
" max_time = 4.0\n",
" N = 30\n",
"\n",
" fig = plt.figure(1)\n",
" ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
" ax.axis('off')\n",
"\n",
" # prepare the axes limits\n",
" ax.set_xlim((-25, 25))\n",
" ax.set_ylim((-35, 35))\n",
" ax.set_zlim((5, 55))\n",
"\n",
" def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n",
" \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
" x, y, z = x_y_z\n",
" return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
"\n",
" # Choose random starting points, uniformly distributed from -15 to 15\n",
" np.random.seed(1)\n",
" x0 = -15 + 30 * np.random.random((N, 3))\n",
"\n",
" # Solve for the trajectories\n",
" t = np.linspace(0, max_time, int(250*max_time))\n",
" x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
" for x0i in x0])\n",
"\n",
" # choose a different color for each trajectory\n",
" colors = plt.cm.viridis(np.linspace(0, 1, N))\n",
"\n",
" for i in range(N):\n",
" x, y, z = x_t[i,:,:].T\n",
" lines = ax.plot(x, y, z, '-', c=colors[i])\n",
" plt.setp(lines, linewidth=2)\n",
" angle = 104\n",
" ax.view_init(30, angle)\n",
" plt.show()\n",
"\n",
" return t, x_t"
]
},
{
"cell_type": "markdown",
"source": "We explore the Lorenz system of differential equations:\n\n$$\n\\begin{aligned}\n\\dot{x} & = \\sigma(y-x) \\\\\n\\dot{y} & = \\rho x - y - xz \\\\\n\\dot{z} & = -\\beta z + xy\n\\end{aligned}\n$$\n\nLet's change (\\\\(\\sigma\\\\), \\\\(\\beta\\\\), \\\\(\\rho\\\\)) with ipywidgets and examine the trajectories.",
"metadata": {}
"metadata": {},
"source": [
"We explore the Lorenz system of differential equations:\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"\\dot{x} & = \\sigma(y-x) \\\\\n",
"\\dot{y} & = \\rho x - y - xz \\\\\n",
"\\dot{z} & = -\\beta z + xy\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"Let's change (\\\\(\\sigma\\\\), \\\\(\\beta\\\\), \\\\(\\rho\\\\)) with ipywidgets and examine the trajectories."
]
},
{
"cell_type": "code",
"source": "w=interactive(solve_lorenz,sigma=(0.0,50.0),rho=(0.0,50.0))\nw",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"w=interactive(solve_lorenz,sigma=(0.0,50.0),rho=(0.0,50.0))\n",
"w"
]
},
{
"cell_type": "markdown",
"source": "For the default set of parameters, we see the trajectories swirling around two points, called attractors. ",
"metadata": {}
"metadata": {},
"source": [
"For the default set of parameters, we see the trajectories swirling around two points, called attractors. "
]
},
{
"cell_type": "markdown",
"source": "The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:",
"metadata": {}
"metadata": {},
"source": [
"The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:"
]
},
{
"cell_type": "code",
"source": "t, x_t = w.result",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"t, x_t = w.result"
]
},
{
"cell_type": "code",
"source": "w.kwargs",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"w.kwargs"
]
},
{
"cell_type": "markdown",
"source": "After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in \\\\(x\\\\), \\\\(y\\\\) and \\\\(z\\\\).",
"metadata": {}
"metadata": {},
"source": [
"After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in \\\\(x\\\\), \\\\(y\\\\) and \\\\(z\\\\)."
]
},
{
"cell_type": "code",
"source": "xyz_avg = x_t.mean(axis=1)",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"xyz_avg = x_t.mean(axis=1)"
]
},
{
"cell_type": "code",
"source": "xyz_avg.shape",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"xyz_avg.shape"
]
},
{
"cell_type": "markdown",
"source": "Creating histograms of the average positions (across different trajectories) show that, on average, the trajectories swirl about the attractors.",
"metadata": {}
"metadata": {},
"source": [
"Creating histograms of the average positions (across different trajectories) show that, on average, the trajectories swirl about the attractors."
]
},
{
"cell_type": "code",
"source": "from matplotlib import pyplot as plt\n%matplotlib inline",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"source": "plt.hist(xyz_avg[:,0])\nplt.title('Average $x(t)$');",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"plt.hist(xyz_avg[:,0])\n",
"plt.title('Average $x(t)$');"
]
},
{
"cell_type": "code",
"source": "plt.hist(xyz_avg[:,1])\nplt.title('Average $y(t)$');",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"execution_count": null
"source": [
"plt.hist(xyz_avg[:,1])\n",
"plt.title('Average $y(t)$');"
]
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.13 (XPython) [env-python]",
"language": "python",
"name": "xpython (env-python)"
},
"language_info": {
"codemirror_mode": {
"name": "python",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
23 changes: 0 additions & 23 deletions ui-tests/notebooks/cpp.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -76,29 +76,6 @@
"std::cout << \"some output\" << std::endl;"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9622f20f-5925-4544-a97b-aada3a14209a",
"metadata": {
"trusted": true,
"vscode": {
"languageId": "c++"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"some error\n"
]
}
],
"source": [
"std::cerr << \"some error\" << std::endl;"
]
},
{
"cell_type": "code",
"execution_count": 3,
Expand Down
31 changes: 0 additions & 31 deletions ui-tests/notebooks/r.ipynb
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Expand Up @@ -165,13 +165,6 @@
"## Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some of the following R Code was pulled from https://www.datacamp.com/doc/r/graphics-with-ggplot2, and updated to use newer `ggplot2` APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -413,30 +406,6 @@
"print(hsv_color)\n",
"print(lab_color)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"trusted": true,
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"trusted": true,
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
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