92 lines
18 KiB
Plaintext
92 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[12.5, 14.3, 16.4, 19.0, 22.1, 25.8, 30.3, 35.7, 42.1, 49.9, 59.3, 70.6, 84.2, 100.5, 120.1, 143.8]\n"
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]
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},
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{
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"data": {
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"image/png": 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",
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"<Figure size 640x480 with 1 Axes>"
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]
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|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"xs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
|
|
"# xs = [0, 1, 2, 3, 4]\n",
|
|
"\n",
|
|
"ys = []\n",
|
|
"current_y = 0\n",
|
|
"max_x = float(max(xs))\n",
|
|
"base = 16.0\n",
|
|
"length = 1.40\n",
|
|
"start_offset = 0.125\n",
|
|
"for x in xs:\n",
|
|
" current_y = base**(x/max_x) / base * length\n",
|
|
" ys.append(current_y)\n",
|
|
"\n",
|
|
"offset = start_offset - ys[0]\n",
|
|
"for i in range(0, len(ys)):\n",
|
|
" ys[i] += offset\n",
|
|
" ys[i] *= 1000\n",
|
|
" ys[i] = round(ys[i])\n",
|
|
" ys[i] /= 10\n",
|
|
"\n",
|
|
"plt.plot(xs, ys,'o')\n",
|
|
"plt.grid()\n",
|
|
"print(ys)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "my_python_env",
|
|
"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": "3.11.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|