223 lines
3.0 MiB
Plaintext
223 lines
3.0 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": "30758151",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from math import isqrt\n",
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"delim = ';'\n",
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"\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": "1365458a",
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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 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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"\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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" thresh = 50\n",
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" isOver = False\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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" \n",
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" except:\n",
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" continue\n",
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" #arcmax arcmin numpy.where , von hinten laufen und thresh\n",
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" f = ex_letter['Force'] \n",
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" \n",
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" f_over_T = 0\n",
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" \n",
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" for a in range ( 0, len(f) ):\n",
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" if(f[a]>thresh):\n",
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" f_over_T = a\n",
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" isOver = True\n",
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" break\n",
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" f_Short = []\n",
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" f_final = []\n",
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" for x in range(f_over_T-3, len(f)):\n",
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" f_Short.append(f[x])\n",
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" \n",
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" isOver= False\n",
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" \n",
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" for y in range ((len(f_Short)-1),0, -1):\n",
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" if(f_Short[y]> thresh):\n",
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" f_over_T = y\n",
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" isOver = True\n",
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" break\n",
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" for z in range(0, f_over_T+3):\n",
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" f_final.append(f_Short[z])\n",
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" \n",
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" # print (len(f_Short), \" \", len(f_final))\n",
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" \n",
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" isOver = False\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(f_final)\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": 3,
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"id": "cef98b83",
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"metadata": {},
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"outputs": [],
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"source": [
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"count = 10\n",
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"\n",
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"numxalph = np.array(np.meshgrid(range(65,91), range(0,4)))[0].flatten() # I swear there must be a more efficient method to this..."
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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": "86f1c9b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def shorten(npList):\n",
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" temp = npList\n",
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" thresh = 100\n",
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" temp_over_T = 0\n",
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" isOver = False\n",
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" temp_short = []\n",
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" temp_final = []\n",
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" \n",
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" for a in range (0, len(temp) ):\n",
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" if(temp[a]>thresh):\n",
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" temp_over_T = a\n",
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" isOver = True\n",
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" break\n",
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" \n",
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" for x in range(temp_over_T-3, len(temp)):\n",
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" temp_short.append(f[x])\n",
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" \n",
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" isOver = False\n",
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" \n",
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" for y in range ((len(temp_short)-1),0, -1): \n",
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" if(temp_short[y] > thresh):\n",
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" temp_over_T = y\n",
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" isOver = True\n",
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" break\n",
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" \n",
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" for z in range(0, temp_over_T+1):\n",
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" temp_final.append(temp_short[z])\n",
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" \n",
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" temp_final.append(0)\n",
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" return temp_final\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": 5,
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"id": "ab518f47",
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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 1440x4320 with 26 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
" f_short = shorten(f)\n",
|
||
|
|
" \n",
|
||
|
|
" idx = (f > 100) | (f == 0)\n",
|
||
|
|
" f=f[idx]\n",
|
||
|
|
" t=ex_letter['Millis']-ex_letter['Millis'][0]\n",
|
||
|
|
" t=t[idx]\n",
|
||
|
|
" axs[r][c].title.set_text(f'{letter}')\n",
|
||
|
|
" axs[r][c].plot(f_short)\n",
|
||
|
|
"plt.savefig('./ten_force_entries_all_alphs.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "a0c1292b",
|
||
|
|
"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
|
||
|
|
}
|