144 lines
16 KiB
Plaintext
144 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d741705b",
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"metadata": {},
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"source": [
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"# Boxplot Funktion (r_boxplot)\n",
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"\n",
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"Boxplot Funktion, weil GGB scheiße ist\n",
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"\n",
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"## Funktions Signatur\n",
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"\n",
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"```python\n",
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"def r_boxplot(arr: List[float], label: str) -> None:\n",
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"```"
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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": null,
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"id": "280820ff",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 600x250 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"from typing import List\n",
|
|
"\n",
|
|
"def r_boxplot(arr: List[float], label: str) -> None:\n",
|
|
" data = np.asarray(arr, dtype=float)\n",
|
|
" p = np.percentile(data, [0, 25, 50, 75, 100])\n",
|
|
"\n",
|
|
" fig, ax = plt.subplots(figsize=(6, 2.5))\n",
|
|
" ax.boxplot(data, positions=[1], vert=False, patch_artist=True, widths=0.6)\n",
|
|
"\n",
|
|
" labels = [\"0%\", \"25%\", \"50%\", \"75%\", \"100%\"]\n",
|
|
" for val, lab in zip(p, labels):\n",
|
|
" ax.axvline(val, color=\"#ff0000\", linestyle=\"--\", linewidth=0.8, alpha=0.9)\n",
|
|
"\n",
|
|
" ax.text(0.5, 1.15, label, transform=ax.transAxes,\n",
|
|
" ha=\"center\", va=\"bottom\", fontsize=10, fontweight=\"bold\", color=\"black\")\n",
|
|
"\n",
|
|
" ax.set_yticks([1])\n",
|
|
" ax.xaxis.grid(True, linestyle=\"--\", alpha=0.3)\n",
|
|
"\n",
|
|
" fig.tight_layout()\n",
|
|
" fig.subplots_adjust(top=0.78)\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"def implementation() -> None:\n",
|
|
" # r_boxplot([1, 2, 3, 4, 5], \"Example Boxplot\")\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\": \n",
|
|
" implementation()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4ea5f68b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# r_stddev (Standardabweichung)\n",
|
|
"\n",
|
|
"Gibt die Standardabweichung als float zurück\n",
|
|
"\n",
|
|
"## Funktions Signatur\n",
|
|
"\n",
|
|
"```python\n",
|
|
"def r_stddev(lst: list, r_dtype : Union[int, float]) -> Union[int, float]:\n",
|
|
"```\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "a3d44ce2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Standard Deviation with a ddof of 0 (just normal stddev): 1.4142135623730951\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"from typing import Union\n",
|
|
"\n",
|
|
"def r_stddev(lst: list, r_dtype : Union[int, float], r_stichprobe: Union[int, float]) -> Union[int, float]:\n",
|
|
" r_arr = np.asarray(lst)\n",
|
|
"\n",
|
|
" return np.std(\n",
|
|
" r_arr,\n",
|
|
" dtype=r_dtype,\n",
|
|
" ddof=r_stichprobe\n",
|
|
" )\n",
|
|
"\n",
|
|
"def implemenentation():\n",
|
|
" print(\"Standard Deviation with a ddof of 0 (just normal stddev): \" + \n",
|
|
" str(r_stddev([1, 2, 3, 4, 5], r_dtype=float, r_stichprobe=0))\n",
|
|
" )\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" implemenentation()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv (3.13.13)",
|
|
"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": "3.13.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|