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@seahawk1986
Created February 12, 2021 08:59
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"import io\n",
"\n",
"from collections import defaultdict\n",
"from typing import List, Mapping, TextIO, Tuple\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"example_a = io.StringIO(\"\"\"00901,test,0050,DataLength\n",
"\"header\",0000000040,\"lines\"\n",
"\"test\",1.85,\"M\"\n",
"\"Cycle\",\"Value1\",\"Value2\",\"Value3\",\"Value4\"\n",
"1,00:00:01,0000001201, 0, 1\n",
"1,00:00:01,0000001201, 1, 2\n",
"\"\"\", newline='')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"example_b = io.StringIO(\"\"\"\"Cycle\",\"Value1\",\"Value2\",\"Value3\",\"Value4\"\n",
"1,00:00:01,0000001201, 0, 1\n",
"1,00:00:01,0000001201, 1, 2\n",
"\"\"\", newline='')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def load_data(f: TextIO) -> Tuple[pd.DataFrame, Mapping[str, str]]:\n",
" pos = 0\n",
" metadata = defaultdict(list)\n",
" reader = csv.reader(f)\n",
" for row in reader:\n",
" if row[0] in (\"test\", \"header\"):\n",
" metadata[row[0]].extend([row[1], row[2]])\n",
" elif row[0] == \"Cycle\":\n",
" f.seek(pos)\n",
" break\n",
" pos = f.tell()\n",
" data = pd.read_csv(f, skipinitialspace=True)\n",
" f.seek(0)\n",
" return data, metadata"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def plot_data(data: pd.DataFrame, metadata: Mapping[str, List[str]]=None) -> None:\n",
" fig, ax = plt.subplots()\n",
" plt.plot(data[\"Value3\"], data[\"Value4\"])\n",
" if metadata:\n",
" for i, (k, v) in enumerate(metadata.items()): # Schreibe Daten in Grafik\n",
" ax.text(0.98, 0.05+i*0.04, f\"{k}: {' '.join(v)}\",\n",
" verticalalignment='bottom', horizontalalignment='right',\n",
" transform=ax.transAxes,\n",
" color='slategrey', alpha=.5, fontsize=10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data, metadata = load_data(example_a)\n",
"plot_data(data, metadata)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data, metadata = load_data(example_b)\n",
"plot_data(data, metadata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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