Agregando Taller 1
This commit is contained in:
@@ -6,3 +6,4 @@ services:
|
||||
volumes:
|
||||
- ./extensions.json:/home/extensions.json
|
||||
- ./custom-scripts:/usr/bin/custom-scripts
|
||||
- ./proyectos:/home/vscode/proyectos
|
||||
23
proyectos/taller-1/README.md
Normal file
23
proyectos/taller-1/README.md
Normal file
@@ -0,0 +1,23 @@
|
||||
# Try Jupyter, powered by JupyterLite
|
||||
|
||||
[](https://jupyter.org/try-jupyter)
|
||||
|
||||
A tour of Jupyter and IPython, powered by JupyterLite.
|
||||
|
||||
## ✨ Try it in your browser ✨
|
||||
|
||||
➡️ **https://jupyter.org/try-jupyter**
|
||||
|
||||
Clicking the link above should load a JupyterLab environment running in your browser. Open the Introductory notebook at `notebooks/Intro.ipynb` to get started.
|
||||
|
||||
## About this repository
|
||||
|
||||
This is a demonstration repository meant for use at `try.jupyter.org`. It uses the [JupyterLite project](https://jupyterlite.readthedocs.io/en/latest/) to embed a self-contained Jupyter environment in the browser, along with many popular packages in scientific computing.
|
||||
|
||||
It uses GitHub pages to serve the JupyterLite bundle, and is accessible at https://jupyter.org/try-jupyter.
|
||||
|
||||
### How to edit these notebooks
|
||||
|
||||
The notebooks in this repository are written with [JupyterLite kernels](https://jupyterlite.readthedocs.io/en/latest/kernels/index.html), so if you edit them locally, you will likely over-write the kernel information with your local kernels.
|
||||
As such, the easiest way to make edits to them is to do so [**via the Try Jupyter Page**](https://jupyter.org/try-jupyter).
|
||||
Make the edits you wish at that URL, then download the notebook and replace the one in a repository locally.
|
||||
1088
proyectos/taller-1/data/Museums_in_DC.geojson
Normal file
1088
proyectos/taller-1/data/Museums_in_DC.geojson
Normal file
File diff suppressed because it is too large
Load Diff
BIN
proyectos/taller-1/data/audio.wav
Normal file
BIN
proyectos/taller-1/data/audio.wav
Normal file
Binary file not shown.
54
proyectos/taller-1/data/bar.vl.json
Normal file
54
proyectos/taller-1/data/bar.vl.json
Normal file
@@ -0,0 +1,54 @@
|
||||
{
|
||||
"data": {
|
||||
"values": [
|
||||
{
|
||||
"a": "A",
|
||||
"b": 28
|
||||
},
|
||||
{
|
||||
"a": "B",
|
||||
"b": 55
|
||||
},
|
||||
{
|
||||
"a": "C",
|
||||
"b": 43
|
||||
},
|
||||
{
|
||||
"a": "D",
|
||||
"b": 91
|
||||
},
|
||||
{
|
||||
"a": "E",
|
||||
"b": 81
|
||||
},
|
||||
{
|
||||
"a": "F",
|
||||
"b": 53
|
||||
},
|
||||
{
|
||||
"a": "G",
|
||||
"b": 19
|
||||
},
|
||||
{
|
||||
"a": "H",
|
||||
"b": 87
|
||||
},
|
||||
{
|
||||
"a": "I",
|
||||
"b": 52
|
||||
}
|
||||
]
|
||||
},
|
||||
"description": "A simple bar chart with embedded data.",
|
||||
"encoding": {
|
||||
"x": {
|
||||
"field": "a",
|
||||
"type": "ordinal"
|
||||
},
|
||||
"y": {
|
||||
"field": "b",
|
||||
"type": "quantitative"
|
||||
}
|
||||
},
|
||||
"mark": "bar"
|
||||
}
|
||||
8
proyectos/taller-1/data/fasta-example.fasta
Normal file
8
proyectos/taller-1/data/fasta-example.fasta
Normal file
@@ -0,0 +1,8 @@
|
||||
>SEQUENCE_1
|
||||
MTEITAAMVKELRESTGAGMMDCKNALSETNGDFDKAVQLLREKGLGKAAKKADRLAAEG
|
||||
LVSVKVSDDFTIAAMRPSYLSYEDLDMTFVENEYKALVAELEKENEERRRLKDPNKPEHK
|
||||
IPQFASRKQLSDAILKEAEEKIKEELKAQGKPEKIWDNIIPGKMNSFIADNSQLDSKLTL
|
||||
MGQFYVMDDKKTVEQVIAEKEKEFGGKIKIVEFICFEVGEGLEKKTEDFAAEVAAQL
|
||||
>SEQUENCE_2
|
||||
SATVSEINSETDFVAKNDQFIALTKDTTAHIQSNSLQSVEELHSSTINGVKFEEYLKSQI
|
||||
ATIGENLVVRRFATLKAGANGVVNGYIHTNGRVGVVIAAACDSAEVASKSRDLLRQICMH
|
||||
151
proyectos/taller-1/data/iris.csv
Normal file
151
proyectos/taller-1/data/iris.csv
Normal file
@@ -0,0 +1,151 @@
|
||||
sepal_length,sepal_width,petal_length,petal_width,species
|
||||
5.1,3.5,1.4,0.2,se
|
||||
4.9,3,1.4,0.2,setosa
|
||||
4.7,3.2,1.3,0.2,setosa
|
||||
4.6,3.1,1.5,0.2,setosa
|
||||
5,3.6,1.4,0.2,setosa
|
||||
5.4,3.9,1.7,0.4,setosa
|
||||
4.6,3.4,1.4,0.3,setosa
|
||||
5,3.4,1.5,0.2,setosa
|
||||
4.4,2.9,1.4,0.2,setosa
|
||||
4.9,3.1,1.5,0.1,setosa
|
||||
5.4,3.7,1.5,0.2,setosa
|
||||
4.8,3.4,1.6,0.2,setosa
|
||||
4.8,3,1.4,0.1,setosa
|
||||
4.3,3,1.1,0.1,setosa
|
||||
5.8,4,1.2,0.2,setosa
|
||||
5.7,4.4,1.5,0.4,setosa
|
||||
5.4,3.9,1.3,0.4,setosa
|
||||
5.1,3.5,1.4,0.3,setosa
|
||||
5.7,3.8,1.7,0.3,setosa
|
||||
5.1,3.8,1.5,0.3,setosa
|
||||
5.4,3.4,1.7,0.2,setosa
|
||||
5.1,3.7,1.5,0.4,setosa
|
||||
4.6,3.6,1,0.2,setosa
|
||||
5.1,3.3,1.7,0.5,setosa
|
||||
4.8,3.4,1.9,0.2,setosa
|
||||
5,3,1.6,0.2,setosa
|
||||
5,3.4,1.6,0.4,setosa
|
||||
5.2,3.5,1.5,0.2,setosa
|
||||
5.2,3.4,1.4,0.2,setosa
|
||||
4.7,3.2,1.6,0.2,setosa
|
||||
4.8,3.1,1.6,0.2,setosa
|
||||
5.4,3.4,1.5,0.4,setosa
|
||||
5.2,4.1,1.5,0.1,setosa
|
||||
5.5,4.2,1.4,0.2,setosa
|
||||
4.9,3.1,1.5,0.1,setosa
|
||||
5,3.2,1.2,0.2,setosa
|
||||
5.5,3.5,1.3,0.2,setosa
|
||||
4.9,3.1,1.5,0.1,setosa
|
||||
4.4,3,1.3,0.2,setosa
|
||||
5.1,3.4,1.5,0.2,setosa
|
||||
5,3.5,1.3,0.3,setosa
|
||||
4.5,2.3,1.3,0.3,setosa
|
||||
4.4,3.2,1.3,0.2,setosa
|
||||
5,3.5,1.6,0.6,setosa
|
||||
5.1,3.8,1.9,0.4,setosa
|
||||
4.8,3,1.4,0.3,setosa
|
||||
5.1,3.8,1.6,0.2,setosa
|
||||
4.6,3.2,1.4,0.2,setosa
|
||||
5.3,3.7,1.5,0.2,setosa
|
||||
5,3.3,1.4,0.2,setosa
|
||||
7,3.2,4.7,1.4,versicolor
|
||||
6.4,3.2,4.5,1.5,versicolor
|
||||
6.9,3.1,4.9,1.5,versicolor
|
||||
5.5,2.3,4,1.3,versicolor
|
||||
6.5,2.8,4.6,1.5,versicolor
|
||||
5.7,2.8,4.5,1.3,versicolor
|
||||
6.3,3.3,4.7,1.6,versicolor
|
||||
4.9,2.4,3.3,1,versicolor
|
||||
6.6,2.9,4.6,1.3,versicolor
|
||||
5.2,2.7,3.9,1.4,versicolor
|
||||
5,2,3.5,1,versicolor
|
||||
5.9,3,4.2,1.5,versicolor
|
||||
6,2.2,4,1,versicolor
|
||||
6.1,2.9,4.7,1.4,versicolor
|
||||
5.6,2.9,3.6,1.3,versicolor
|
||||
6.7,3.1,4.4,1.4,versicolor
|
||||
5.6,3,4.5,1.5,versicolor
|
||||
5.8,2.7,4.1,1,versicolor
|
||||
6.2,2.2,4.5,1.5,versicolor
|
||||
5.6,2.5,3.9,1.1,versicolor
|
||||
5.9,3.2,4.8,1.8,versicolor
|
||||
6.1,2.8,4,1.3,versicolor
|
||||
6.3,2.5,4.9,1.5,versicolor
|
||||
6.1,2.8,4.7,1.2,versicolor
|
||||
6.4,2.9,4.3,1.3,versicolor
|
||||
6.6,3,4.4,1.4,versicolor
|
||||
6.8,2.8,4.8,1.4,versicolor
|
||||
6.7,3,5,1.7,versicolor
|
||||
6,2.9,4.5,1.5,versicolor
|
||||
5.7,2.6,3.5,1,versicolor
|
||||
5.5,2.4,3.8,1.1,versicolor
|
||||
5.5,2.4,3.7,1,versicolor
|
||||
5.8,2.7,3.9,1.2,versicolor
|
||||
6,2.7,5.1,1.6,versicolor
|
||||
5.4,3,4.5,1.5,versicolor
|
||||
6,3.4,4.5,1.6,versicolor
|
||||
6.7,3.1,4.7,1.5,versicolor
|
||||
6.3,2.3,4.4,1.3,versicolor
|
||||
5.6,3,4.1,1.3,versicolor
|
||||
5.5,2.5,4,1.3,versicolor
|
||||
5.5,2.6,4.4,1.2,versicolor
|
||||
6.1,3,4.6,1.4,versicolor
|
||||
5.8,2.6,4,1.2,versicolor
|
||||
5,2.3,3.3,1,versicolor
|
||||
5.6,2.7,4.2,1.3,versicolor
|
||||
5.7,3,4.2,1.2,versicolor
|
||||
5.7,2.9,4.2,1.3,versicolor
|
||||
6.2,2.9,4.3,1.3,versicolor
|
||||
5.1,2.5,3,1.1,versicolor
|
||||
5.7,2.8,4.1,1.3,versicolor
|
||||
6.3,3.3,6,2.5,virginica
|
||||
5.8,2.7,5.1,1.9,virginica
|
||||
7.1,3,5.9,2.1,virginica
|
||||
6.3,2.9,5.6,1.8,virginica
|
||||
6.5,3,5.8,2.2,virginica
|
||||
7.6,3,6.6,2.1,virginica
|
||||
4.9,2.5,4.5,1.7,virginica
|
||||
7.3,2.9,6.3,1.8,virginica
|
||||
6.7,2.5,5.8,1.8,virginica
|
||||
7.2,3.6,6.1,2.5,virginica
|
||||
6.5,3.2,5.1,2,virginica
|
||||
6.4,2.7,5.3,1.9,virginica
|
||||
6.8,3,5.5,2.1,virginica
|
||||
5.7,2.5,5,2,virginica
|
||||
5.8,2.8,5.1,2.4,virginica
|
||||
6.4,3.2,5.3,2.3,virginica
|
||||
6.5,3,5.5,1.8,virginica
|
||||
7.7,3.8,6.7,2.2,virginica
|
||||
7.7,2.6,6.9,2.3,virginica
|
||||
6,2.2,5,1.5,virginica
|
||||
6.9,3.2,5.7,2.3,virginica
|
||||
5.6,2.8,4.9,2,virginica
|
||||
7.7,2.8,6.7,2,virginica
|
||||
6.3,2.7,4.9,1.8,virginica
|
||||
6.7,3.3,5.7,2.1,virginica
|
||||
7.2,3.2,6,1.8,virginica
|
||||
6.2,2.8,4.8,1.8,virginica
|
||||
6.1,3,4.9,1.8,virginica
|
||||
6.4,2.8,5.6,2.1,virginica
|
||||
7.2,3,5.8,1.6,virginica
|
||||
7.4,2.8,6.1,1.9,virginica
|
||||
7.9,3.8,6.4,2,virginica
|
||||
6.4,2.8,5.6,2.2,virginica
|
||||
6.3,2.8,5.1,1.5,virginica
|
||||
6.1,2.6,5.6,1.4,virginica
|
||||
7.7,3,6.1,2.3,virginica
|
||||
6.3,3.4,5.6,2.4,virginica
|
||||
6.4,3.1,5.5,1.8,virginica
|
||||
6,3,4.8,1.8,virginica
|
||||
6.9,3.1,5.4,2.1,virginica
|
||||
6.7,3.1,5.6,2.4,virginica
|
||||
6.9,3.1,5.1,2.3,virginica
|
||||
5.8,2.7,5.1,1.9,virginica
|
||||
6.8,3.2,5.9,2.3,virginica
|
||||
6.7,3.3,5.7,2.5,virginica
|
||||
6.7,3,5.2,2.3,virginica
|
||||
6.3,2.5,5,1.9,virginica
|
||||
6.5,3,5.2,2,virginica
|
||||
6.2,3.4,5.4,2.3,virginica
|
||||
5.9,3,5.1,1.8,virginica
|
||||
|
BIN
proyectos/taller-1/data/marie.png
Normal file
BIN
proyectos/taller-1/data/marie.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 106 KiB |
BIN
proyectos/taller-1/data/matplotlib.png
Normal file
BIN
proyectos/taller-1/data/matplotlib.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
120
proyectos/taller-1/notebooks/Intro.ipynb
Normal file
120
proyectos/taller-1/notebooks/Intro.ipynb
Normal file
File diff suppressed because one or more lines are too long
249
proyectos/taller-1/notebooks/Lorenz.ipynb
Normal file
249
proyectos/taller-1/notebooks/Lorenz.ipynb
Normal file
@@ -0,0 +1,249 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# The Lorenz Differential Equations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Before we start, we import some preliminary libraries."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"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",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We will also define the actual solver and plotting routine."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"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",
|
||||
"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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"w=interactive(solve_lorenz,sigma=(0.0,50.0),rho=(0.0,50.0))\n",
|
||||
"w"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For the default set of parameters, we see the trajectories swirling around two points, called attractors. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t, x_t = w.result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"w.kwargs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xyz_avg = x_t.mean(axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xyz_avg.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Creating histograms of the average positions (across different trajectories) show that, on average, the trajectories swirl about the attractors."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.hist(xyz_avg[:,0])\n",
|
||||
"plt.title('Average $x(t)$');"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.hist(xyz_avg[:,1])\n",
|
||||
"plt.title('Average $y(t)$');"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python (XPython)",
|
||||
"language": "python",
|
||||
"name": "xpython"
|
||||
},
|
||||
"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
|
||||
}
|
||||
403
proyectos/taller-1/notebooks/cpp-smallpt.ipynb
Normal file
403
proyectos/taller-1/notebooks/cpp-smallpt.ipynb
Normal file
File diff suppressed because one or more lines are too long
588
proyectos/taller-1/notebooks/cpp-third-party-libs.ipynb
Normal file
588
proyectos/taller-1/notebooks/cpp-third-party-libs.ipynb
Normal file
@@ -0,0 +1,588 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "472d1671-a068-4ce5-afae-076586f0b1b3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<center>\n",
|
||||
" <h1>Interactive Demos of C++ Libraries in the Browser</h1>\n",
|
||||
" <h3>Powered by Xeus-Cpp-Lite and WebAssembly</h3>\n",
|
||||
"</center>\n",
|
||||
"\n",
|
||||
"This notebook demonstrates several powerful C++ libraries running entirely in the browser using **xeus-cpp-lite**. The examples span symbolic computation, numerical analysis, interactive visualization, and more — all compiled to WebAssembly and ready to run in a JupyterLite environment.\n",
|
||||
"\n",
|
||||
"**Featured libraries:**\n",
|
||||
"\n",
|
||||
"1. **Scientific Computation (Symbolic & Numeric)**: `boost`, `symengine`, `openblas`\n",
|
||||
"2. **Array based Computation through Xtensor-stack**: `xtl`, `xtensor`, `xsimd`, `xtensor-blas`\n",
|
||||
"3. **Utilities & Miscellaneous**: `nlohmann_json`, and others"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c5f00d53-7cc9-43fb-82c7-c69b631e7b92",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Scientific Computation (Symbolic & Numeric)\n",
|
||||
"\n",
|
||||
"Let’s begin with powerful C++ libraries for symbolic math, high-precision arithmetic, and efficient numerical computation using BLAS and LAPACK routines."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "7ef2ba09-bcc8-41e2-b503-3b21e60fdd3e",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Product of 98745636214564698 * 7459874565236544789 = \n",
|
||||
"736630060025131838840151335215258722"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#include <boost/multiprecision/cpp_int.hpp>\n",
|
||||
"using namespace boost::multiprecision;\n",
|
||||
"using namespace std;\n",
|
||||
"\n",
|
||||
"int128_t boost_product(long long A, long long B)\n",
|
||||
"{\n",
|
||||
" int128_t ans = (int128_t)A * B;\n",
|
||||
" return ans;\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"long long first = 98745636214564698;\n",
|
||||
"long long second = 7459874565236544789;\n",
|
||||
"cout << \"Product of \" << first << \" * \" << second << \" = \\n\"\n",
|
||||
" << boost_product(first, second);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "d8e235a1-5d41-43f8-8908-9f76b4c6c1d7",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#include <symengine/expression.h>\n",
|
||||
"\n",
|
||||
"using namespace SymEngine;\n",
|
||||
"\n",
|
||||
"Expression x(\"x\");\n",
|
||||
"auto ex = pow(x + sqrt(Expression(2)), 6);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "82912670-8abf-4364-8bfb-b55e206fb153",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/latex": [
|
||||
"$\\left(x + \\sqrt{2}\\right)^6$"
|
||||
],
|
||||
"text/plain": [
|
||||
"(x + sqrt(2))**6"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#include <xcpp/xdisplay.hpp>\n",
|
||||
"xcpp::display(ex);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "f738315b-f1ed-48b8-ae71-250b3667c694",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/latex": [
|
||||
"$8 + 24 \\sqrt{2} x + 40 \\sqrt{2} x^3 + 6 \\sqrt{2} x^5 + 60 x^2 + 30 x^4 + x^6$"
|
||||
],
|
||||
"text/plain": [
|
||||
"8 + 24*sqrt(2)*x + 40*sqrt(2)*x**3 + 6*sqrt(2)*x**5 + 60*x**2 + 30*x**4 + x**6"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"auto expanded_ex = expand(ex);\n",
|
||||
"xcpp::display(expanded_ex);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "f25ec9b7-951e-4494-b932-64d71a601127",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"LU Decomposition (OpenBLAS dgetrf_)\n",
|
||||
"info: 0\n",
|
||||
"\n",
|
||||
"Factorized Matrix (A after dgetrf_):\n",
|
||||
" 3.000000 6.000000 10.000000\n",
|
||||
" 0.333333 2.000000 3.666667\n",
|
||||
" 0.666667 0.500000 -0.500000\n",
|
||||
"\n",
|
||||
"Pivot Indices:\n",
|
||||
"3 3 3 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#include <iostream>\n",
|
||||
"#include <iomanip>\n",
|
||||
"\n",
|
||||
"typedef int INTEGER;\n",
|
||||
"typedef double DOUBLE;\n",
|
||||
"\n",
|
||||
"extern \"C\" {\n",
|
||||
" int dgetrf_(const INTEGER* m, const INTEGER* n, DOUBLE* a,\n",
|
||||
" const INTEGER* lda, INTEGER* ipiv, INTEGER* info);\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"const INTEGER m = 3, n = 3, lda = 3;\n",
|
||||
"DOUBLE A[9] = {\n",
|
||||
" 1.0, 2.0, 3.0,\n",
|
||||
" 4.0, 5.0, 6.0,\n",
|
||||
" 7.0, 8.0, 10.0\n",
|
||||
"}; // Column-major layout\n",
|
||||
"\n",
|
||||
"INTEGER ipiv[3];\n",
|
||||
"INTEGER info;\n",
|
||||
"\n",
|
||||
"dgetrf_(&m, &n, A, &lda, ipiv, &info);\n",
|
||||
"\n",
|
||||
"// Output\n",
|
||||
"std::cout << std::fixed << std::setprecision(6);\n",
|
||||
"std::cout << \"LU Decomposition (OpenBLAS dgetrf_)\\n\";\n",
|
||||
"std::cout << \"info: \" << info << \"\\n\\n\";\n",
|
||||
"\n",
|
||||
"std::cout << \"Factorized Matrix (A after dgetrf_):\\n\";\n",
|
||||
"for (int i = 0; i < m; ++i) {\n",
|
||||
" for (int j = 0; j < n; ++j)\n",
|
||||
" std::cout << std::setw(10) << A[i + j * lda];\n",
|
||||
" std::cout << \"\\n\";\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"std::cout << \"\\nPivot Indices:\\n\";\n",
|
||||
"for (int i = 0; i < std::min(m, n); ++i)\n",
|
||||
" std::cout << ipiv[i] << \" \";\n",
|
||||
"std::cout << \"\\n\";\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6b7e64a2-546c-47a7-b4ed-95e98febd785",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Array based Computation through Xtensor-stack\n",
|
||||
"\n",
|
||||
"Explore NumPy-style array computing in C++ with Xtensor, Xtensor-BLAS, and Xsimd — enabling high-performance numerical and SIMD-accelerated workflows."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "6496d5e7-d1e5-4437-be87-bb26b48ce735",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table style='border-style:solid;border-width:1px;'><tbody><tr><td style='font-family:monospace;' title='0'><pre> 7.</pre></td></tr><tr><td style='font-family:monospace;' title='1'><pre> 11.</pre></td></tr><tr><td style='font-family:monospace;' title='2'><pre> 14.</pre></td></tr></tbody></table>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#include \"xtensor/containers/xarray.hpp\"\n",
|
||||
"#include \"xtensor/io/xio.hpp\"\n",
|
||||
"#include \"xtensor/views/xview.hpp\"\n",
|
||||
"\n",
|
||||
"xt::xarray<double> arr1 = {{1.0, 2.0, 3.0}, {2.0, 5.0, 7.0}, {2.0, 5.0, 7.0}};\n",
|
||||
"xt::xarray<double> arr2{5.0, 6.0, 7.0};\n",
|
||||
"\n",
|
||||
"xcpp::display(xt::view(arr1, 1) + arr2);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "c748227d-4083-4c19-8f07-a449d41e67fe",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table style='border-style:solid;border-width:1px;'><tbody><tr><td style='font-family:monospace;' title='(0, 0)'><pre>1</pre></td><td style='font-family:monospace;' title='(0, 1)'><pre>2</pre></td><td style='font-family:monospace;' title='(0, 2)'><pre>3</pre></td></tr><tr><td style='font-family:monospace;' title='(1, 0)'><pre>4</pre></td><td style='font-family:monospace;' title='(1, 1)'><pre>5</pre></td><td style='font-family:monospace;' title='(1, 2)'><pre>6</pre></td></tr><tr><td style='font-family:monospace;' title='(2, 0)'><pre>7</pre></td><td style='font-family:monospace;' title='(2, 1)'><pre>8</pre></td><td style='font-family:monospace;' title='(2, 2)'><pre>9</pre></td></tr></tbody></table>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xt::xarray<int> arr = {1, 2, 3, 4, 5, 6, 7, 8, 9};\n",
|
||||
"arr.reshape({3, 3});\n",
|
||||
"\n",
|
||||
"xcpp::display(arr);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "74298554-9964-4633-bff3-e67d03a1797d",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#include <iostream>\n",
|
||||
"#include \"xtensor-blas/xlinalg.hpp\"\n",
|
||||
"\n",
|
||||
"xt::xtensor<double, 2> M = {{1.5, 0.5}, {0.7, 1.0}};"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "22dadb84-d39e-4028-a919-febd64f4a60d",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Matrix rank: 2\n",
|
||||
"Matrix inverse: \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table style='border-style:solid;border-width:1px;'><tbody><tr><td style='font-family:monospace;' title='(0, 0)'><pre> 0.869565</pre></td><td style='font-family:monospace;' title='(0, 1)'><pre>-0.434783</pre></td></tr><tr><td style='font-family:monospace;' title='(1, 0)'><pre>-0.608696</pre></td><td style='font-family:monospace;' title='(1, 1)'><pre> 1.304348</pre></td></tr></tbody></table>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Eigen values: \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<table style='border-style:solid;border-width:1px;'><tbody><tr><td style='font-family:monospace;' title='0'><pre> 1.892262+0.i</pre></td></tr><tr><td style='font-family:monospace;' title='1'><pre> 0.607738+0.i</pre></td></tr></tbody></table>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"std::cout << \"Matrix rank: \" << xt::linalg::matrix_rank(M) << std::endl;\n",
|
||||
"std::cout << \"Matrix inverse: \" << std::endl;\n",
|
||||
"xcpp::display(xt::linalg::inv(M));\n",
|
||||
"std::cout << \"Eigen values: \" << std::endl;\n",
|
||||
"xcpp::display(xt::linalg::eigvals(M));"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "d8e9e9e7-5def-4d77-b459-afb8002282ae",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(3.300000, 7.700000, 12.100000, 16.500000)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#include <xsimd/xsimd.hpp>\n",
|
||||
"\n",
|
||||
"namespace xs = xsimd;\n",
|
||||
"\n",
|
||||
"// Define two SIMD float vectors using the wasm backend\n",
|
||||
"xs::batch<float, xs::wasm> X = {1.2f, 3.4f, 5.6f, 7.8f};\n",
|
||||
"xs::batch<float, xs::wasm> Y = {2.1f, 4.3f, 6.5f, 8.7f};\n",
|
||||
"\n",
|
||||
"// Perform SIMD addition\n",
|
||||
"xs::batch<float, xs::wasm> Z = X + Y;\n",
|
||||
"\n",
|
||||
"// Print the result\n",
|
||||
"std::cout << Z << std::endl;"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0f0978c4-7ec4-4615-894a-c6558503d851",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Miscallaneous\n",
|
||||
"\n",
|
||||
"Feel free to explore other useful C++ libraries in the browser, like nlohmann_json, pugixml, xtl etc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "2ce27d4d-43f7-464c-a611-5400c3cdb735",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#include \"nlohmann/json.hpp\"\n",
|
||||
"\n",
|
||||
"nlohmann::json jsonObject;\n",
|
||||
"\n",
|
||||
"jsonObject[\"name\"] = \"John Doe\";\n",
|
||||
"jsonObject[\"age\"] = 30;\n",
|
||||
"jsonObject[\"is_student\"] = false;\n",
|
||||
"jsonObject[\"skills\"] = {\"C++\", \"Python\", \"JavaScript\"};\n",
|
||||
"\n",
|
||||
"std::cout << jsonObject.dump(4) << std::endl;"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "36b1b0f7-00d9-47bf-8543-985e27088d07",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "b2647bb5-0c2e-4f12-b0ed-0c0f5c7ed65c",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "07f19b34aac74f97947e4e9b068ebe5f",
|
||||
"version_major": 2,
|
||||
"version_minor": 1
|
||||
},
|
||||
"text/plain": [
|
||||
"A Jupyter widget with unique id: 07f19b34aac74f97947e4e9b068ebe5f"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "0f3e4dcb-5d9f-479b-b568-8d7625683946",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "1eed2b39-b632-440c-9d3d-c8d377bc504c",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "4d1797e5-3ce1-4737-9561-a87a7e82cce1",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "e34c45cc453240cebac33426cd7c448d",
|
||||
"version_major": 2,
|
||||
"version_minor": 1
|
||||
},
|
||||
"text/plain": [
|
||||
"A Jupyter widget with unique id: e34c45cc453240cebac33426cd7c448d"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d26a70d2-9dc6-4ca0-b974-2cfd4249a8a7",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "e1fdb4b5-384a-42a1-80c1-ba2bcd515e01",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{\n",
|
||||
" \"age\": 30,\n",
|
||||
" \"is_student\": false,\n",
|
||||
" \"name\": \"John Doe\",\n",
|
||||
" \"skills\": [\n",
|
||||
" \"C++\",\n",
|
||||
" \"Python\",\n",
|
||||
" \"JavaScript\"\n",
|
||||
" ]\n",
|
||||
"}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ed69c0cd-c65a-47d7-9ee2-13502953e876",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "c++"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "C++23",
|
||||
"language": "cpp",
|
||||
"name": "xcpp23"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": "text/x-c++src",
|
||||
"file_extension": ".cpp",
|
||||
"mimetype": "text/x-c++src",
|
||||
"name": "C++",
|
||||
"version": "23"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
873
proyectos/taller-1/notebooks/cpp.ipynb
Normal file
873
proyectos/taller-1/notebooks/cpp.ipynb
Normal file
File diff suppressed because one or more lines are too long
276
proyectos/taller-1/notebooks/r.ipynb
Normal file
276
proyectos/taller-1/notebooks/r.ipynb
Normal file
@@ -0,0 +1,276 @@
|
||||
{
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "xr",
|
||||
"display_name": "R 4.4.3 (xr)",
|
||||
"language": "R"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": "",
|
||||
"file_extension": "R",
|
||||
"mimetype": "text/x-R",
|
||||
"name": "R",
|
||||
"nbconvert_exporter": "",
|
||||
"pygments_lexer": "",
|
||||
"version": "4.4.3"
|
||||
}
|
||||
},
|
||||
"nbformat_minor": 4,
|
||||
"nbformat": 4,
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": "# XEUS-R",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "R.version",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": "## Internal packages",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "library(stats)\n\nset.seed(123)\nx <- rnorm(100)\ny <- 2 * x + rnorm(100)\n\n# Linear regression\nmodel <- lm(y ~ x)\nsummary(model)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "A <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2)\n\n# Singular value decomposition\nsvd_result <- La.svd(A)\n\nprint(svd_result)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "A <- matrix(c(4, 1, 1, 3), nrow = 2, byrow = TRUE)\n\n# Eigen decomposition\neigen_result <- eigen(A)\n\nprint(eigen_result$values)\n\nprint(eigen_result$vectors)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Example positive-definite matrix\nA <- matrix(c(4, 1, 1, 3), nrow = 2, byrow = TRUE)\n\n# Cholesky decomposition\nchol_result <- chol(A)\n\nprint(chol_result)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "B <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2)\nC <- matrix(c(7, 8, 9, 10, 11, 12), nrow = 3, ncol = 2)\n\n# Perform matrix multiplication using BLAS\nresult <- crossprod(B, C)\n\nprint(result)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Fibonacci sequence\nfibonacci_loop <- function(n) {\n a <- 0\n b <- 1\n\n for (i in 1:n) {\n fib <- a\n cat(fib, \"\\n\")\n a <- b\n b <- fib + b\n }\n return(a)\n}\n\nfibonacci_loop(10)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": "## Plotting",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": "Some of the following R Code was pulled from https://www.statmethods.net/advgraphs/ggplot2.html, and updated to use newer `ggplot2` APIs.",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# ggplot2 examples\nlibrary(ggplot2)",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# create factors with value labels\nmtcars$gear <- factor(mtcars$gear,levels=c(3,4,5),\n \tlabels=c(\"3gears\",\"4gears\",\"5gears\"))\nmtcars$am <- factor(mtcars$am,levels=c(0,1),\n \tlabels=c(\"Automatic\",\"Manual\"))\nmtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8),\n labels=c(\"4cyl\",\"6cyl\",\"8cyl\"))",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Kernel density plots for mpg\n# grouped by number of gears (indicated by color)\nggplot(mtcars, aes(x = mpg, fill = gear)) +\n geom_density(alpha = 0.5) +\n labs(\n title = \"Distribution of Gas Milage\",\n x = \"Miles Per Gallon\",\n y = \"Density\",\n fill = \"Gears\"\n )",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Scatterplot of mpg vs. hp for each combination of gears and cylinders\n# in each facet, transmission type is represented by shape and color\nggplot(mtcars, aes(x = hp, y = mpg, shape = am, color = am)) +\n geom_point(size = 3) +\n facet_grid(gear ~ cyl) +\n labs(\n x = \"Horsepower\",\n y = \"Miles per Gallon\",\n shape = \"Transmission\",\n color = \"Transmission\"\n )",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Separate regressions of mpg on weight for each number of cylinders\nggplot(mtcars, aes(x = wt, y = mpg, color = cyl)) +\n geom_point() +\n geom_smooth(method = \"lm\", formula = y ~ x) +\n labs(\n title = \"Regression of MPG on Weight\",\n x = \"Weight\",\n y = \"Miles per Gallon\",\n color = \"Cylinders\"\n )",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "# Boxplots of mpg by number of gears\n# observations (points) are overlayed and jittered\nggplot(mtcars, aes(x = gear, y = mpg, fill = gear)) +\n geom_boxplot() +\n geom_jitter(width = 0.2) +\n labs(\n title = \"Mileage by Gear Number\",\n x = \"\",\n y = \"Miles per Gallon\",\n fill = \"Gears\"\n )",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": "## Other R Packages",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "plaintext"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "library(RColorBrewer)\npalette <- brewer.pal(8, \"Set3\")\nprint(palette)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "library(crayon)\n\n# Create colorful text\nred_text <- red(\"This is red text\")\ngreen_text <- green(\"This is green text\")\nblue_text <- blue(\"This is blue text\")\nbold_text <- bold(\"This is bold text\")\nunderline_text <- underline(\"This is underlined text\")\n\ncat(red_text, \"\\n\")\ncat(green_text, \"\\n\")\ncat(blue_text, \"\\n\")\ncat(bold_text, \"\\n\")\ncat(underline_text, \"\\n\")",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "library(htmltools)\n\n# Create HMTL elements\ntitle <- tags$title(\"My HTML Page\")\nheader <- tags$h1(\"Hello there!\")\nparagraph <- tags$p(\"This is a paragraph created using htmltools.\")\nlist_items <- tags$ul(tags$li(\"Item 1\"), tags$li(\"Item 2\"), tags$li(\"Item 3\"))\n\n# Combine elements into an HTML document\nhtml_doc <- tags$html(\n tags$head(title),\n tags$body(\n header,\n paragraph,\n list_items\n )\n)\n\nprint(html_doc)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "library(farver)\n\n# Define a color in RBG space\nrgb_color <- matrix(c(255, 0, 0), ncol = 3)\n\n# Convert RGB to HSV\nhsv_color <- convert_colour(rgb_color, \"rgb\", \"hsv\")\n\n# Convert RGB to LAB\nlab_color <- convert_colour(rgb_color, \"rgb\", \"lab\")\n\nprint(rgb_color)\nprint(hsv_color)\nprint(lab_color)",
|
||||
"metadata": {
|
||||
"trusted": true,
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "",
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "r"
|
||||
},
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": "",
|
||||
"metadata": {
|
||||
"trusted": true
|
||||
},
|
||||
"outputs": [],
|
||||
"execution_count": null
|
||||
}
|
||||
]
|
||||
}
|
||||
666
proyectos/taller-1/notebooks/sqlite.ipynb
Normal file
666
proyectos/taller-1/notebooks/sqlite.ipynb
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user