Upload demo-slides.ipynb
Browse files- demo-slides.ipynb +260 -0
demo-slides.ipynb
ADDED
|
@@ -0,0 +1,260 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "raw",
|
| 5 |
+
"id": "833c8e20",
|
| 6 |
+
"metadata": {
|
| 7 |
+
"slideshow": {
|
| 8 |
+
"slide_type": "skip"
|
| 9 |
+
}
|
| 10 |
+
},
|
| 11 |
+
"source": [
|
| 12 |
+
"---\n",
|
| 13 |
+
"title: 🚀 Demo notebook\n",
|
| 14 |
+
"description: Simple notebook with widgets demo\n",
|
| 15 |
+
"output: slides\n",
|
| 16 |
+
"show-code: False\n",
|
| 17 |
+
"params:\n",
|
| 18 |
+
" name:\n",
|
| 19 |
+
" input: text\n",
|
| 20 |
+
" label: What is your name?\n",
|
| 21 |
+
" value: Piotr\n",
|
| 22 |
+
" mu: \n",
|
| 23 |
+
" input: slider\n",
|
| 24 |
+
" label: X-data mean\n",
|
| 25 |
+
" value: 0\n",
|
| 26 |
+
" min: -5\n",
|
| 27 |
+
" max: 5\n",
|
| 28 |
+
" sigma:\n",
|
| 29 |
+
" input: numeric\n",
|
| 30 |
+
" label: X-data sigma\n",
|
| 31 |
+
" value: 1\n",
|
| 32 |
+
" min: 0\n",
|
| 33 |
+
" max: 3\n",
|
| 34 |
+
" step: 0.01\n",
|
| 35 |
+
" points:\n",
|
| 36 |
+
" input: select\n",
|
| 37 |
+
" label: How many points?\n",
|
| 38 |
+
" value: 100\n",
|
| 39 |
+
" choices: [50, 100, 200, 500, 1000]\n",
|
| 40 |
+
" ---"
|
| 41 |
+
]
|
| 42 |
+
},
|
| 43 |
+
{
|
| 44 |
+
"cell_type": "code",
|
| 45 |
+
"execution_count": 7,
|
| 46 |
+
"id": "192c65e7",
|
| 47 |
+
"metadata": {
|
| 48 |
+
"slideshow": {
|
| 49 |
+
"slide_type": "skip"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"outputs": [],
|
| 53 |
+
"source": [
|
| 54 |
+
"from IPython.display import Markdown as md\n",
|
| 55 |
+
"from matplotlib import pyplot as plt\n",
|
| 56 |
+
"from random import gauss\n",
|
| 57 |
+
"plt.rcParams.update({'font.size': 22})"
|
| 58 |
+
]
|
| 59 |
+
},
|
| 60 |
+
{
|
| 61 |
+
"cell_type": "markdown",
|
| 62 |
+
"id": "c418de47",
|
| 63 |
+
"metadata": {
|
| 64 |
+
"slideshow": {
|
| 65 |
+
"slide_type": "slide"
|
| 66 |
+
}
|
| 67 |
+
},
|
| 68 |
+
"source": [
|
| 69 |
+
"<center>\n",
|
| 70 |
+
" <h1> 🚀 Interactive slides from notebook 👋 </h1>\n",
|
| 71 |
+
" <br /><br />\n",
|
| 72 |
+
" by Piotr Płoński\n",
|
| 73 |
+
"</center>"
|
| 74 |
+
]
|
| 75 |
+
},
|
| 76 |
+
{
|
| 77 |
+
"cell_type": "code",
|
| 78 |
+
"execution_count": 2,
|
| 79 |
+
"id": "05963627",
|
| 80 |
+
"metadata": {
|
| 81 |
+
"slideshow": {
|
| 82 |
+
"slide_type": "skip"
|
| 83 |
+
}
|
| 84 |
+
},
|
| 85 |
+
"outputs": [],
|
| 86 |
+
"source": [
|
| 87 |
+
"name = \"Piotr\"\n",
|
| 88 |
+
"mu = 0\n",
|
| 89 |
+
"sigma = 1\n",
|
| 90 |
+
"points = 100"
|
| 91 |
+
]
|
| 92 |
+
},
|
| 93 |
+
{
|
| 94 |
+
"cell_type": "code",
|
| 95 |
+
"execution_count": 15,
|
| 96 |
+
"id": "bf1faf4b",
|
| 97 |
+
"metadata": {
|
| 98 |
+
"slideshow": {
|
| 99 |
+
"slide_type": "slide"
|
| 100 |
+
}
|
| 101 |
+
},
|
| 102 |
+
"outputs": [
|
| 103 |
+
{
|
| 104 |
+
"data": {
|
| 105 |
+
"text/markdown": [
|
| 106 |
+
"# Welcome Piotr! 😀\n",
|
| 107 |
+
"\n",
|
| 108 |
+
"This presentation is interactive. You can change parameters on the left sidebar. \n",
|
| 109 |
+
"Please click `Run` to recompute the presentation with new values. \n",
|
| 110 |
+
"\n",
|
| 111 |
+
"How does it work?\n",
|
| 112 |
+
"\n",
|
| 113 |
+
"- The presentation was created in Jupyter Notebook and is converted to slides with [reveal.js](https://github.com/hakimel/reveal.js/) package.\n",
|
| 114 |
+
"- The interactive widgets are constructed by [Mercury](https://github.com/mljar/mercury) based on YAML config\n",
|
| 115 |
+
"- The presentation is served in HuggingFace Spaces!\n"
|
| 116 |
+
],
|
| 117 |
+
"text/plain": [
|
| 118 |
+
"<IPython.core.display.Markdown object>"
|
| 119 |
+
]
|
| 120 |
+
},
|
| 121 |
+
"execution_count": 15,
|
| 122 |
+
"metadata": {},
|
| 123 |
+
"output_type": "execute_result"
|
| 124 |
+
}
|
| 125 |
+
],
|
| 126 |
+
"source": [
|
| 127 |
+
"md(f\"\"\"# Welcome {name}! 😀\n",
|
| 128 |
+
"\n",
|
| 129 |
+
"This presentation is interactive. You can change parameters on the left sidebar. \n",
|
| 130 |
+
"Please click `Run` to recompute the presentation with new values. \n",
|
| 131 |
+
"\n",
|
| 132 |
+
"How does it work?\n",
|
| 133 |
+
"\n",
|
| 134 |
+
"- The presentation was created in Jupyter Notebook and is converted to slides with [reveal.js](https://github.com/hakimel/reveal.js/) package.\n",
|
| 135 |
+
"- The interactive widgets are constructed by [Mercury](https://github.com/mljar/mercury) based on YAML config\n",
|
| 136 |
+
"- The presentation is served in HuggingFace Spaces!\n",
|
| 137 |
+
"\"\"\")"
|
| 138 |
+
]
|
| 139 |
+
},
|
| 140 |
+
{
|
| 141 |
+
"cell_type": "markdown",
|
| 142 |
+
"id": "0f44ba3a",
|
| 143 |
+
"metadata": {
|
| 144 |
+
"slideshow": {
|
| 145 |
+
"slide_type": "slide"
|
| 146 |
+
}
|
| 147 |
+
},
|
| 148 |
+
"source": [
|
| 149 |
+
"# Let's generate some data 💻"
|
| 150 |
+
]
|
| 151 |
+
},
|
| 152 |
+
{
|
| 153 |
+
"cell_type": "code",
|
| 154 |
+
"execution_count": 4,
|
| 155 |
+
"id": "5354c942",
|
| 156 |
+
"metadata": {
|
| 157 |
+
"slideshow": {
|
| 158 |
+
"slide_type": "skip"
|
| 159 |
+
}
|
| 160 |
+
},
|
| 161 |
+
"outputs": [],
|
| 162 |
+
"source": [
|
| 163 |
+
"# random data from gaussian distribution\n",
|
| 164 |
+
"data_x = [gauss(mu, sigma) for _ in range(int(points))]\n",
|
| 165 |
+
"data_y = [gauss(0, 1) for _ in range(int(points))]"
|
| 166 |
+
]
|
| 167 |
+
},
|
| 168 |
+
{
|
| 169 |
+
"cell_type": "code",
|
| 170 |
+
"execution_count": 10,
|
| 171 |
+
"id": "2710f410",
|
| 172 |
+
"metadata": {
|
| 173 |
+
"slideshow": {
|
| 174 |
+
"slide_type": "fragment"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"outputs": [
|
| 178 |
+
{
|
| 179 |
+
"data": {
|
| 180 |
+
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsQAAAFzCAYAAAA5ch/TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xt8VNW5//HvY7jEIyESjYpADOJdwKNE1JYjKCoVvCBCRaroTwEFlHrqpagHi1YtttIjVRQveClqVRCtCgp4gaJFSWjrhVqFn0YRTAUDBKiEi8/5YyYpCQnMJHNJsj7v12tee2b22ms/M+6Erytr723uLgAAACBUe6S7AAAAACCdCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAhas1TvcN999/X8/PxU7xYAAACBWbJkyRp3z91du5QH4vz8fBUVFaV6twAAAAiMmX0RSzumTAAAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIIWVyA2s/Zmdq+ZfWJm35nZZjNbZmZTzOzgZBUJAAAAJEvMgdjMjpX0oaSrJP2HpDmSXpO0p6QrJL1vZj9IRpEAAABAssQzQjxZ0t6SHpZ0sLv3d/f+kjpKelRSK0kPJL5EAAAAIHliCsRmlinppOjLX7j71op10ef/E33Z1cz+I7ElAgAAAMkT653qtkvaFkP7TZK+q1dFAACg0SsvL1dpaak2bNig7du3p7scNHIZGRnKyspSTk6OWrZsmfD+YwrE7r7VzN6Q1EfSrWY2umKU2MyaS/pltOlUd/eEVwkAABqN8vJyffnll2rTpo3y8/PVvHlzmVm6y0Ij5e7aunWrysrK9OWXXyovLy/hoTjWEWJJGqXISXTDJZ1pZkXR94+X1EbSPZJuSGh1AACg0SktLVWbNm207777prsUNAFmphYtWlQeT6WlpWrbtm1C9xHzSXXu/pmkH0h6VVJ7Sf2jj3aS/i5p4Y5ziwEAQJg2bNig1q1bp7sMNEGtW7fWhg0bEt5vzCPE0UuqzZRUJulcSX+OrvqhpImSnjezX7j7bTVsO0LSCEnKy8urb80AkDb5Y2fVedviCf0SWAnQcG3fvl3NmzdPdxlogpo3b56UOemxXmVib0kvSsqS9CN3f8nd10Qff5T0I0VOphtnZodW397dH3L3AncvyM3NTWT9AACgAWLOMJIhWcdVrFMm+knKlfRudOpEFe6+XNJ7iow490pYdQAAAECSxRqIK+Y5rN9Fm3XRZU7dywEAAABSK9ZAvCq67Ba9zFoV0fe6RV9+nojCAAAAmqI5c+bIzLT33nvrq6++qrXd0KFDZWbq1y9x5x+0b99eZrbL/YYo1pPqXpX0L0VGiv/XzK5193JJMrOWilxyrYOktZLmJKNQAADQdNTnBNV0SORJsX369NGwYcP0yCOPaPjw4Xr11Vd3avPSSy9p2rRp2nvvvfXwww8nbN+J9Prrr+v0009X79699frrr6e7nHqJaYTY3b9R5DrE2yWNlvSZmb1sZi8rMiJ8paRySZe5+66mVQAAAARv4sSJ6tChg1577TU98sgjVdaVlpbqiiuukCRNmjRJBx54YDpKDEo81yF+QlJ3SdMkbZF0evTxnaSpko5z9xeTUSQAAEBT0rp168ogfO2112rFihWV60aPHq2SkhKdffbZGjp0aLpKDErMgViS3P0v7j7U3Tu6e2b00cndh7n735NVJAAAQFNzxhlnaPjw4SorK9OwYcMkSTNnztQzzzyjnJwcPfjgg3Xq9/PPP9fFF1+s/fbbT3vuuaeOPvpo3X333bu8fu9HH32kcePG6aSTTtKBBx6oFi1aaL/99lO/fv00d+7cndr36NFDp59+uiTpjTfekJlVPk477bQqtdx5553q1auXOnTooJYtWyonJ0ennnqqnnnmmTp9vmSI59bNAAAASKCJEydqzpw5mjt3ru68805NmjRJkvS73/2uTrcn/uijj9SzZ0+VlpYqLy9Pp556qkpLS3XTTTepsLCw1u3uvvtu/f73v9cRRxyhY445RllZWfrss880e/ZszZ49W5MmTdKYMWMq2/ft21d77bWX5s6dq7Zt2+qMM86oXHf00UdXPn/iiSd066236pBDDtGRRx6pH/7wh1qxYoUWLFigt956S4sXL9Zvf/vbuD9nopm7p3SHBQUFXlRUlNJ9AkCicKc6YPc+/vhjHXnkkbtsE/JJddVVnJxWoX///nrhhRfi7sfddeyxx+r999/XpZdeqoceeqjyjoEffvihTj31VK1Zs0aStGLFCrVv375y27feeksHH3ywDjrooCp9Llq0SH369FF5ebmKi4urhPRYTqpbvHixsrKydjoePvnkE/Xu3VsrV65UUVGRunXrVuP2NYnl+KpgZkvcvWB37eKaMgEAAIDEOu2001RQEMlszZo10/3331+nfubPn6/3339fbdq00e9+97sqt8/u0qWLbrrpplq3PeWUU3YKw5J00kknaeTIkdqyZYteeumluGvq3r17jeH18MMP18033yxJmjFjRtz9JhpTJgAAANLo+eefV8Vfz7dt26YZM2bo6quvjrufBQsWSJLOOeccZWVl7bT+4osv1s9+9rNaty8rK9OsWbP0/vvv69tvv9XWrVslSZ9++mmVZbw2b96s1157TUVFRVq9erXKy8slSatWrapXv4lEIAYAAEiTNWvWaNSoUZKkMWPG6N5779WNN96os846Sx07dqzS9s4779wpPGZkZGjq1KmSVHmzjerbVdh3333VqlUrbdy4cad1M2fO1LBhw7R27dpaay0rK4v9g0W9/fbbGjx4sFauXJnQfhONKRMAAABpMmrUKH3zzTcaOHCgJk2apCuvvFKbNm3S5Zdfrurnec2ePVtPPPHETo/6+uKLLzRkyBCtW7dON998sz788EOVlZVp+/btcndNnjxZknaqZ3c2btyoAQMGaOXKlRoxYoSWLFmidevWVfY7a9asOvWbDARiAACANJg+fbqmT5+u3NzcynnDv/71r3XQQQfprbfe0pQpU6q0f/vtt+XuVR7btm2rXN+uXTtJUnFxcY37W7NmTY2jwy+//LLKy8v14x//WLfffrs6d+6srKws7bFHJCYuX768Tp9v/vz5Wr16tU444QQ9+OCDOu6445SdnV3vfpOBQAwAAJBiq1ev1ujRoyVJkydPVm5uriSpVatWlbdqvuGGG/TFF1/E3GfPnj0lRW77XFPwfeqpp2rcrrS0VJLUoUOHndZt3rxZM2fOrHG7Fi1aSFKVUB5rv+6uP/zhDzVulw4EYgAAgBQbOXKkVq9erUGDBmnQoEFV1p1++ukaNmyYNm7cqOHDh8fc5ymnnKIuXbqotLRU11xzTZWgunTpUt1xxx01bnfEEUdIioxYf/PNN5Xvl5eXa/To0bWG8ooR6WXLltV404+KfufNm6dly5ZVvv/999/rF7/4hd59992YP1uyEYgBAABS6Nlnn9Xzzz+v3Nzcyvm51U2cOFHt27fXvHnzKkeMd8fMNG3aNLVp00ZTp07VIYccogsvvFB9+vTRcccdp169elWG2B31799fXbt21RdffKFDDz1U55xzjgYNGqSOHTtq+vTptV7xolOnTuratatWrVqlrl27aujQoRo2bFjljTa6d++uM888U+vXr1eXLl105plnavDgwTrkkEM0YcIEXX/99TF+Y8lHIAYAAEiRb775RldddZWkqlMlqmvdurUeeughSdJ1112nFStWxNT/Mccco8LCQg0ZMkSbNm3SCy+8oC+//FK33nprrVMUWrRooYULF+q6667T/vvvr7lz5+qdd97RKaecoiVLlqhr16617u/FF1/UwIEDtWbNGj311FOaOnWqZs+eXbn+hRde0B133KFOnTpp/vz5evPNN9WlSxe98847Ve5ul27cqQ4A4sCd6oDdi+dOYkC8uFMdAAAAkGAEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAAKdKvXz+ZmS677LJdtluwYIH22GMPZWVl6fPPP6/3fgcPHiwz0zPPPFPvvpqiZukuAAAABGh8droriM/49Qnp5uGHH1bnzp312GOPaeDAgerbt+9ObTZt2qTLLrtM7q67775bHTt2TMi+E2Xz5s3ac8891bJlS23evDnd5SQEI8QAAAApcuCBB+ree++VJA0fPlzr1q3bqc3YsWP12Wef6YwzztAVV1yR6hKDRCAGAABIoZ/85CcaMGCAVq1apTFjxlRZt2DBAk2ePFnZ2dmaOnVqmioMD1MmAAC7lD92Vp23LZ7QL4GVAE3HlClTtHDhQk2bNk0DBw7UOeecU2WqxKRJk9S+ffu4+tywYYNuv/12Pffcc1q1apX2339/9e/fX7fddlut25SUlOjpp5/Wa6+9pk8//VQlJSXKzMzUUUcdpUsuuUTDhw/XHnv8e/x07NixuuuuuyRJ5eXlMrPKdTtOoVi/fr3+8Ic/aPbs2Vq6dKm+/vprZWRk6LDDDtOPf/xjXXPNNWrZsmVcny+ZCMQAAAAplpubqwceeEADBw7UFVdcoR49euiWW27RZ599prPPPluXXHJJXP2VlZWpZ8+e+tvf/qbs7Gz17dtXZqZp06Zp3rx5tc5DfuWVV3TttdcqLy9PnTp10oknnqiSkhItWrRIixYt0ptvvqlnn322sn23bt100UUX6cknn9Qee+yhiy++uHJd8+bNK58XFhZq5MiROuCAA3TYYYepe/fuWr16td577z2NHTtWr7zyit544w21aNEizm8uOQjEAAAAaXD++edryJAhevrpp9W3b18tXrxY++yzjx5++OG4+7rxxhv1t7/9Td26ddOcOXO0zz77SJJKS0v1ox/9SK+++mqN25144okqLCxUQUFBlfdXrlypPn366LnnntOQIUN07rnnSpIGDRqks88+W08++aSaN2+uxx9/vMZ+Dz30UM2fP18nn3xylVHk0tJSDRo0SG+++aYeeOAB/fSnP437syYDc4gBAADS5N5771Xbtm313nvvyd01efJk7b///nH1UVZWpkcffVSSdN9991WGYUnKycnR5MmTa922c+fOO4VhSWrXrp1+9atfSZJmzJgRVz2SdNBBB6lnz55VwnBFPffcc0+d+00WRogBAADSJCcnRzfddJOuvvpqdevWTRdccEHcfSxevFibN2+unPJQ3fHHH6/DDz9cn3zySY3bb926Va+//rree+89lZSUqLy8XO5eeQWMTz/9NO6aJMnd9ac//Ulvv/22Vq5cqe+++07urm3bttWr32QgEAMAAKRRq1atqiyrmz59umbN2vnk1nHjxqlTp0766quvJGmX1yvOz8+vMRAvXbpU5513npYtW1brtmVlZbusvyarVq1S//79VVhYmNB+k4VADAAA0IAtWbJETzzxxE7vX3nllerUqVOd+/3+++81YMAALVu2TOeff76uvfZaHXHEEWrdurUyMjL0wQcf6JhjjpG7x933JZdcosLCQvXq1Uu33HKLunTpouzsbDVv3lxlZWXKzs6uU7/JQiAGAABowCZMmKAJEybUur5du3aSpOLi4lrb1LTugw8+0KeffqoOHTroueeeq3J5NUlavnx5nepdu3Zt5RUkXnnlFe21114J6TeZOKkOAACgETvhhBOUmZmp5cuXa/HixTutX7JkSY3TJUpLSyVFAnX1MCxJTz31VI37q7i82vbt22tcv3btWrm7cnJydgrDu+o3nQjEAAAAjVjr1q0rr1t81VVXVQZdKRJOR48eXeN2hx9+uMxMf/nLX/Tuu+9WWTdlyhTNnDmzxu0yMjJ0wAEHaNu2bTXOPW7fvr1atWqlkpKSnfp46aWXdN9998X1+VKBQAwAANDI3XXXXeratasKCwvVqVMnDRgwQAMGDNDBBx+sdevW6cwzz9xpm3bt2unyyy/Xli1b1KNHD5122mm68MILddRRR2nUqFG68cYba93feeedJ0k6+eSTNXjwYA0bNkwjR46UJLVo0UJjx46VFLnWco8ePTRkyBAdf/zxOvfcc3Xdddcl4RuoHwIxAABAI5edna2FCxfq+uuvV3Z2tmbNmqXCwkJdeOGF+vOf/6zWrVvXuN2UKVM0efJkde7cWYsWLdKcOXPUoUMHzZ07V0OHDq11f7/5zW80ZswYZWZmaubMmZo6daoee+yxyvU333yznnnmGXXv3l0ffPCBZs2apczMTD377LMaN25cwj9/fVmqz/ArKCjwoqKilO4TABIlf+zOlz6KVfGEfgmsJHVC/Myon48//lhHHnlkustAExXP8WVmS9x95zuPVMMIMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAABIuFTf+AthSNZxRSAGAAAJlZGRoa1bt6a7DDRBW7duVUZGRsL7JRADAICEysrKUllZWbrLQBNUVlamrKyshPdLIAYAAAmVk5OjtWvXas2aNdqyZQvTJ1Av7q4tW7ZozZo1Wrt2rXJychK+j2YJ7xEAAAStZcuWysvLU2lpqYqLi7V9+/Z0l4RGLiMjQ1lZWcrLy1PLli0T3j+BGAAAJFzLli3Vtm1btW3bNt2lALvFlAkAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIIWdyA2sz3N7AYzKzSzdWb2LzP73Mymm9kPk1EkAAAAkCxxXWXCzDpKmivpEElfS3pL0jZJB0nqL+l9Se8kuEYAAAAgaWIOxGa2l6R5kg6WNFbS3e6+fYf1+0jaJ+EVAgAAAEkUzwjx/0jqJOk+d7+r+kp3/1bSt4kqDAAAAEiFmOYQm1kLScOjL3+bvHIAAACA1Ip1hLibItMhVrr752Z2nKTzJO0n6Z+S5rr720mqEQAAAEiaWANxl+hypZndLenaauvHmdmLki5y900Jqw4AAABIslgvu5YTXR6rSBi+R5ErTbSRdK6klYpcZeL+RBcIAAAAJFOsI8QVwbm5pCfd/b93WPeSma2StFjSxWZ2m7v//x03NrMRkkZIUl5eXj1LBoAmbnx2PbZdn7g6ACAQsY4Qb9jh+cPVV7p7kaQlkkxSzxrWP+TuBe5ekJubW6dCAQAAgGSINRB/XsvzmtocUPdyAAAAgNSKNRD/dYfntd18Y9/ocmPdywEAAABSK6ZA7O4rJb0Xfdm7+nozayPpuOjLosSUBgAAACRfrCPEknRHdHmTmRVUvGlmmZIekJStyDziRYkrDwAAAEiumG/d7O4vm9lERS679mcze1eRWzV3l3SgIpdeu9DdPSmVAgAAAEkQzwix3P06SedLeluRm3X0lfQvRW7nfKy7L0t4hQAAAEASxTxCXMHdZ0qamYRaAAAAgJSLa4QYAAAAaGoIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKA1S3cBQEOTP3ZWzG2LM4fUfUfj19d928ZofHY9tg3su6qH2o7f4gn9UlxJitTjuMrf/HSt65L6fdXnZ6Fe+22YP0fx/M6trske10g5RogBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaHUOxGZ2p5l59HFdIosCAAAAUqVOgdjMjpd0gyRPbDkAAABAasUdiM2spaQnJP1T0h8TXhEAAACQQnUZIb5N0pGSrpS0PrHlAAAAAKkVVyA2sxMkXSvpaXd/OTklAQAAAKkTcyA2s0xFpkqUSvpp0ioCAAAAUqhZHG3vkHS4pMHuviZJ9QAAAAApFVMgNrMfSLpG0ovu/my8OzGzEZJGSFJeXl68mwPYjfyxs+q8bfGEfgmspAEan13nTfM3P13nbYszh9RQS527q7f6HCNNVY3/jSqM383G4xvhKTT1+FnY3efl+EJjt9spE2a2p6THJZVJGlWXnbj7Q+5e4O4Fubm5dekCAAAASIpYRojvlHSopMvc/esk1wMAAACkVCyB+DxJ30u6xMwuqbbuiOhypJmdJWm5uw9LZIEAAABAMsV6Ut0eknruYv3B0cfe9a4IAAAASKHdziF293x3t5oeilyGTZKuj773n8ktFwAAAEisutypDgAAAGgyCMQAAAAIGoEYAAAAQYvnTnU7cfdLJV2akEoAAACANGCEGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAELRm6S4ATdD47Dptlr/56crnxRP6JaqahquO31NNijPrU0fsTXf8bxSv+tWYuO+qqSvOHJLuEqoaH0ub9cmuIvECOybzx86q03b1OR7r8/smbepzXDTGn4MmhBFiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACFpMgdjMmptZbzObaGZFZlZmZlvMbKWZzTCzXkmuEwAAAEiKZjG26ylpXvR5iaQ/Sdok6ShJ50s638x+6e63JL5EAAAAIHlinTLxvaTnJZ3s7m3d/Sx3v8Ddu0gaLGm7pHFmdkqyCgUAAACSIaZA7O5vuvtAd19Yw7pnJT0efXlRAmsDAAAAki5RJ9X9Nbpsn6D+AAAAgJRIVCA+NLr8OkH9AQAAAClR70BsZgdIujT68vn69gcAAACkUr0CsZk1k/SkpGxJb7j7ywmpCgAAAEiRWC+7VpspknpLWqFdnFBnZiMkjZCkvLy8eu6yHsZnp2m/6+uxbT1qrs9+06A4c8i/X4xPWxkqzkzfvoEQ5Y+dVeP7xRP6pbgSAKGq8wixmU2SdLki1yXu7e4ltbV194fcvcDdC3Jzc+u6SwAAACDh6hSIzWyipDGSVisShpcltCoAAAAgReIOxGb2a0k/k/StpNPc/e8JrwoAAABIkbgCsZlNkHS9pLWSTnf3D5JSFQAAAJAiMQdiM7td0s8lrVMkDP91N5sAAAAADV5MV5kws3Mk3Rx9uVzS1WZWU9N/uPuEBNUGAAAAJF2sl13L2eF5QfRRkwWSCMQAAABoNGIKxO7+uKTHk1oJAAAAkAb1vnUzAAAA0JgRiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEFrlu4CQpE/dladtivOrP8+iyf0q3snwA6KM4eku4SUCu3zpkut3/P4lJaBBiamn7/xSdjx+PVJ6DSW/WanZ7/1kL/56ZjaNYYcwggxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABI1ADAAAgKARiAEAABA0AjEAAACCRiAGAABA0AjEAAAACBqBGAAAAEEjEAMAACBoBGIAAAAEjUAMAACAoBGIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgkYgBgAAQNAIxAAAAAgagRgAAABBIxADAAAgaARiAAAABC3uQGxmQ8xsoZmtN7ONZlZkZqPNjHANAACARieuEGtmkyU9JalA0kJJ8yQdJuk+STMIxQAAAGhsYg6wZna+pFGSSiR1dfez3P08SYdK+ljSeZKuTkqVAAAAQJLEM6J7Y3T5c3dfVvGmu/9T0sjoy7GMEgMAAKAxiSm8mll7Sd0kbZE0vfp6d18gaaWkAySdmMgCAQAAgGSKdTT32Ohyqbt/V0ubwmptAQAAgAYv1kDcMbr8YhdtvqzWFgAAAGjwYg3EraLLTbtoszG6zKp7OQAAAEBqNUvFTsxshKQR0ZcbzeyTVOy3gdhXt9qaum5s9dr1WZE+7qpXJ0itfSXV+XhBUDhWEI96Hi9n1Wmr+v0blia3NsqqEymOYyW24yLNOeSgWBrFGogrRn/32kWbilHkDdVXuPtDkh6KcV9NipkVuXtBuutA48DxglhxrCAeHC+IVajHSqxTJoqjy12l7A7V2gIAAAANXqyB+K/R5dFmtmctbY6v1hYAAABo8GIKxO6+QtJfJLWQNKj6ejNQNi5hAAAFcklEQVTrKam9InexW5TIApuAIKeKoM44XhArjhXEg+MFsQryWDF3j62h2UBFbspRIum/3H159P39JL0l6ShJ17j7pCTVCgAAACRczIFYkszsfkVu07xZ0uuStkrqLam1pBclDXT37UmoEwAAAEiKWOcQS5LcfZSknygyfaKnpD6Slku6StL5hOHdM7PDzey/zew1M/vazLaa2XozW2Rm15hZy3TXiIbBzPYys5+Y2T1m9o6ZbTIzN7NX0l0b0sfMhpjZwujvjY1mVmRmo80srt/naLqi/8781MyeNLN/mNn30d8dA9NdGxoWM2tuZr3NbGL0d0mZmW0xs5VmNsPMeqW7xlSJa4QY9WdmX0lqp8goe5GkryTtL+kkSZmKnJR4mruXpq1INAhm9p+q+STVWe5et4uColEzs8mSRiny++MN/fuvdFmSXlDkr3Tfp69CNARmdo+kn9awapC7z0h1PWi4zOw0SfOiL0skLVHkJmxHSeocff+X7n5LGspLKUYUUu8TSZdLynX3/3L3C939VElHSloq6VhJ/5vOAtFgbJD0qCIB6ARJV6a3HKSTmZ2vyLFQIqmru5/l7udJOlTSx5LOk3R1GktEw/GRpN9IukDSIZIWpLccNGDfS3pe0snu3jb6e+UCd+8iabCk7ZLGmdkpaa0yBRghbkDMrIekhYqM/mS7+5Y0l4QGxMwulfSYGCEOkpkVSeom6RJ3/321dT0lzVckLLdjlBg7MrP5ikxzZIQYcTGzRxQZxHvU3S9Pdz3JxAhxw1Lx5/FMSfuksxAADYeZtVckDG9R5Go/Vbj7AkkrJR0g6cTUVgegCavIJe3TWkUKEIgblkOjyy2SmEMMoMKx0eVSd/+uljaF1doCQH1V5JKv01pFChCIG5ax0eUr7l6e1koANCQdo8svdtHmy2ptAaDOzOwASZdGXz6fxlJSgkDcQETnh14g6V+SbkpvNQAamFbR5aZdtNkYXWYluRYATZyZNZP0pKRsSW+4+8tpLinpmqW7gMbEzH4t6Zw6bNrb3Vfuot/ekh6U5JKucPdP6lgiGohkHSsAAKTAFEUu6bhC0kVpriUlCMTxOVDS4XXYrnltK6JXlvijpBaSxrj7k3WsDQ1Lwo8VBK1i9HevXbSpGEXekORaADRhZjZJkStLlCgySFOS5pJSgikTcXD3i9zd6vAorqk/M/uBpNmK/CN3g7vfm8rPg+RJ9LGC4BVHlwftok2Ham0BIC5mNlHSGEmrFQnDy9JcUsoQiNPEzE6U9Joi8/3+x91/k+aSADRcFZc+OtrM9qylzfHV2gJAzKJT/X4m6VtF7pj79zSXlFIE4jQws+6S5igShse7+x1pLglAA+buKyT9RZGpVYOqr4/emKO9In/iXJTa6gA0dmY2QdL1ktZKOt3dP0hzSSlHIE4xMyuQNFdSa0XuD35rmksC0Dj8Krq8y8wOqXjTzPaTdH/05QTuUgcgHmZ2u6SfS1qnSBgO8q9M3Lo5xcysVFIbRQ68P+6i6XXuviY1VaGhMrMXJLWNvsyVdLAix86OVyL5pbvPSnVtSD0zu1/SSEVu7/66pK2KnAneWtKLkga6+/b0VYiGwMyO07//J0mSjlLkL5LLtMNNn9yduxoGzszO0b+zSJGkpbU0/Ye7T0hNVelBIE4xM4v1C+/ICVYws2Lt+kQqSfp/7v548qtBQ2BmQySNltRFUoakf0h6VNIDjA5Dksysl6S3dtfO3S351aAhi94D4bEYmi5w917JrSa9CMQAAAAIGnOIAQAAEDQCMQAAAIJGIAYAAEDQCMQAAAAIGoEYAAAAQSMQAwAAIGgEYgAAAASNQAwAAICgEYgBAAAQNAIxAAAAgvZ/iQD3uUinRmAAAAAASUVORK5CYII=\n",
|
| 181 |
+
"text/plain": [
|
| 182 |
+
"<Figure size 864x432 with 1 Axes>"
|
| 183 |
+
]
|
| 184 |
+
},
|
| 185 |
+
"metadata": {
|
| 186 |
+
"needs_background": "light"
|
| 187 |
+
},
|
| 188 |
+
"output_type": "display_data"
|
| 189 |
+
}
|
| 190 |
+
],
|
| 191 |
+
"source": [
|
| 192 |
+
"plt.figure(figsize=(12,6))\n",
|
| 193 |
+
"_ = plt.hist(data_x, bins=40, label=\"X-data\")\n",
|
| 194 |
+
"_ = plt.hist(data_y, bins=40, label=\"Y-data\")\n",
|
| 195 |
+
"_ = plt.legend()"
|
| 196 |
+
]
|
| 197 |
+
},
|
| 198 |
+
{
|
| 199 |
+
"cell_type": "code",
|
| 200 |
+
"execution_count": 23,
|
| 201 |
+
"id": "e21caf2a",
|
| 202 |
+
"metadata": {
|
| 203 |
+
"slideshow": {
|
| 204 |
+
"slide_type": "subslide"
|
| 205 |
+
}
|
| 206 |
+
},
|
| 207 |
+
"outputs": [
|
| 208 |
+
{
|
| 209 |
+
"data": {
|
| 210 |
+
"image/png": "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\n",
|
| 211 |
+
"text/plain": [
|
| 212 |
+
"<Figure size 864x432 with 1 Axes>"
|
| 213 |
+
]
|
| 214 |
+
},
|
| 215 |
+
"metadata": {
|
| 216 |
+
"needs_background": "light"
|
| 217 |
+
},
|
| 218 |
+
"output_type": "display_data"
|
| 219 |
+
}
|
| 220 |
+
],
|
| 221 |
+
"source": [
|
| 222 |
+
"plt.figure(figsize=(12,6))\n",
|
| 223 |
+
"_ = plt.plot(data_x, data_y, 'o')\n",
|
| 224 |
+
"_ = plt.title(\"Random points scatter plot\")\n",
|
| 225 |
+
"_ = plt.hlines(0, min(data_x), max(data_x), color=\"lightgray\")\n",
|
| 226 |
+
"_ = plt.vlines(0, min(data_y), max(data_y), color=\"lightgray\")"
|
| 227 |
+
]
|
| 228 |
+
},
|
| 229 |
+
{
|
| 230 |
+
"cell_type": "code",
|
| 231 |
+
"execution_count": null,
|
| 232 |
+
"id": "23b60e04",
|
| 233 |
+
"metadata": {},
|
| 234 |
+
"outputs": [],
|
| 235 |
+
"source": []
|
| 236 |
+
}
|
| 237 |
+
],
|
| 238 |
+
"metadata": {
|
| 239 |
+
"celltoolbar": "Slideshow",
|
| 240 |
+
"kernelspec": {
|
| 241 |
+
"display_name": "Python 3",
|
| 242 |
+
"language": "python",
|
| 243 |
+
"name": "python3"
|
| 244 |
+
},
|
| 245 |
+
"language_info": {
|
| 246 |
+
"codemirror_mode": {
|
| 247 |
+
"name": "ipython",
|
| 248 |
+
"version": 3
|
| 249 |
+
},
|
| 250 |
+
"file_extension": ".py",
|
| 251 |
+
"mimetype": "text/x-python",
|
| 252 |
+
"name": "python",
|
| 253 |
+
"nbconvert_exporter": "python",
|
| 254 |
+
"pygments_lexer": "ipython3",
|
| 255 |
+
"version": "3.6.5"
|
| 256 |
+
}
|
| 257 |
+
},
|
| 258 |
+
"nbformat": 4,
|
| 259 |
+
"nbformat_minor": 5
|
| 260 |
+
}
|