163 lines
2.0 MiB
Plaintext
163 lines
2.0 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "877ecd7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n"
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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": 2,
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"id": "c7b1f50c",
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"metadata": {},
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"outputs": [],
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"source": [
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"delim = ';'"
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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": 3,
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"id": "c906f335",
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"metadata": {
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"tags": []
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},
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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": [
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"<Figure size 1080x5616 with 130 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from math import isqrt\n",
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"\n",
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"count = 5\n",
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"\n",
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"plt_in_row = 5\n",
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"\n",
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"fig, axs = plt.subplots(26, plt_in_row, figsize=(3*plt_in_row, 3*26), sharey=True)\n",
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" \n",
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"for j,k in zip(range(1,27), range(65,91)):\n",
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" num = j\n",
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" letter = chr(k)\n",
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" filename = f'{num}{letter}.csv'\n",
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" for i in range(0, count):\n",
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" path = f'/opt/iui-datarelease1-sose2021/{i}/split_letters_csv/{filename}'\n",
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" try:\n",
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" ex_letter = pd.read_csv(path, delim)\n",
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" except:\n",
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" continue\n",
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" f = ex_letter['Force']\n",
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" temp_axs = axs[j-1][i%plt_in_row]\n",
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" temp_axs.title.set_text(f'{letter}')\n",
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" temp_axs.plot(ex_letter['Time']-ex_letter['Time'][0], f)\n",
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" \n",
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"plt.savefig('./single_first_five.png')"
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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": 4,
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"id": "89662e4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"from math import isqrt\n",
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"\n",
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"count = 100\n",
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"\n",
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"numxalph = np.array(np.meshgrid(range(65,91), range(0,4)))[0].flatten()"
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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": 5,
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"id": "28c993cc",
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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": [
|
||
|
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"<Figure size 1440x4320 with 26 Axes>"
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]
|
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},
|
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"metadata": {
|
||
|
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"needs_background": "light"
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||
|
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},
|
||
|
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"output_type": "display_data"
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||
|
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}
|
||
|
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],
|
||
|
|
"source": [
|
||
|
|
"fig, axs = plt.subplots(13,2,figsize=(20, 60), sharey=True)\n",
|
||
|
|
"\n",
|
||
|
|
"for j,k in zip(range(1,105),numxalph):\n",
|
||
|
|
" num = j\n",
|
||
|
|
" letter = chr(k)\n",
|
||
|
|
" filename = f'{num}{letter}.csv'\n",
|
||
|
|
" r = int((j-1)/2)%13\n",
|
||
|
|
" c = (j-1)%2\n",
|
||
|
|
" for i in range(0, count):\n",
|
||
|
|
" path = f'/opt/iui-datarelease1-sose2021/{i}/split_letters_csv/{filename}'\n",
|
||
|
|
" try:\n",
|
||
|
|
" ex_letter = pd.read_csv(path, delim)\n",
|
||
|
|
" except:\n",
|
||
|
|
" continue\n",
|
||
|
|
" f = ex_letter['Force']\n",
|
||
|
|
" idx = (f > 100) | (f == 0)\n",
|
||
|
|
" f=f[idx]\n",
|
||
|
|
" t=ex_letter['Time']-ex_letter['Time'][0]\n",
|
||
|
|
" t=t[idx]\n",
|
||
|
|
" axs[r][c].title.set_text(f'{letter}')\n",
|
||
|
|
" axs[r][c].plot(f)\n",
|
||
|
|
"plt.savefig('./all_entries.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "6bf9cbc7",
|
||
|
|
"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": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|