2022-08-01 09:52:56 +01:00

78 lines
14 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create a time linspace - up to 8 pi\n",
"t = np.linspace(0, 8 * np.pi, 1000)\n",
"# use this to make the idealised pid response graph, with a little overshoot and undershoot\n",
"error = np.sin(t) * 0.125\n",
"for n in range(1, 63, 2):\n",
" error = np.sin(t / n)+ error\n",
"plt.plot(t, error)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.4 ('.venv': venv)",
"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": "3.7.4"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "97190208b4a6552dc9493965a1caf8d39c9d1897fe9495f2fe88af90c5a07bd6"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}