337 lines
1.8 MiB
Plaintext
337 lines
1.8 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": "d26a4f5b",
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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": "6c91f3a4",
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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": "c1e013c1",
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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": "e4ac61e5",
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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 = 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": null,
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"id": "8496f4af",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axs = plt.subplots(13,2,figsize=(20, 60), sharey=True)\n",
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"\n",
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"for j,k in zip(range(1,105),numxalph):\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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" r = int((j-1)/2)%13\n",
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" c = (j-1)%2\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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" f_short = shorten(f)\n",
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" \n",
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" idx = (f > 100) | (f == 0)\n",
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" f=f[idx]\n",
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" t=ex_letter['Millis']-ex_letter['Millis'][0]\n",
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" t=t[idx]\n",
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" axs[r][c].title.set_text(f'{letter}')\n",
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" axs[r][c].plot(t, f)\n",
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"plt.savefig('./ten_force_entries_all_alphs.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": 6,
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"id": "d62de263",
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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 648x5616 with 78 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"user_count = 1\n",
|
||
|
|
"\n",
|
||
|
|
"fig, axs = plt.subplots(26, 3, figsize=(3*3, 3*26), sharey=True)\n",
|
||
|
|
" \n",
|
||
|
|
"col = 'Acc1'\n",
|
||
|
|
"\n",
|
||
|
|
"for j,k in zip(range(1,27), range(65,91)):\n",
|
||
|
|
" num = j\n",
|
||
|
|
" letter = chr(k)\n",
|
||
|
|
" filename = f'{num}{letter}.csv'\n",
|
||
|
|
" for i in range(0, user_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",
|
||
|
|
" \n",
|
||
|
|
" x = ex_letter[f'{col} X']\n",
|
||
|
|
" y = ex_letter[f'{col} Y']\n",
|
||
|
|
" z = ex_letter[f'{col} Z']\n",
|
||
|
|
" f = ex_letter[f'Force']\n",
|
||
|
|
" t=ex_letter['Millis']-ex_letter['Millis'][0]\n",
|
||
|
|
" temp_axs = axs[j-1]\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, x)\n",
|
||
|
|
" temp_axs[1].plot(t, y)\n",
|
||
|
|
" temp_axs[2].plot(t, z)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, f)\n",
|
||
|
|
" temp_axs[1].plot(t, f)\n",
|
||
|
|
" temp_axs[2].plot(t, f)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].title.set_text(f'{letter}: x')\n",
|
||
|
|
" temp_axs[1].title.set_text(f'{letter}: y')\n",
|
||
|
|
" temp_axs[2].title.set_text(f'{letter}: z')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.savefig('./u1_Acc_all_alphs.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"id": "12151db8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 648x5616 with 78 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"user_count = 1\n",
|
||
|
|
"\n",
|
||
|
|
"fig, axs = plt.subplots(26, 3, figsize=(3*3, 3*26), sharey=True)\n",
|
||
|
|
" \n",
|
||
|
|
"col = 'Gyro'\n",
|
||
|
|
"\n",
|
||
|
|
"for j,k in zip(range(1,27), range(65,91)):\n",
|
||
|
|
" num = j\n",
|
||
|
|
" letter = chr(k)\n",
|
||
|
|
" filename = f'{num}{letter}.csv'\n",
|
||
|
|
" for i in range(0, user_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",
|
||
|
|
" \n",
|
||
|
|
" x = ex_letter[f'{col} X']\n",
|
||
|
|
" y = ex_letter[f'{col} Y']\n",
|
||
|
|
" z = ex_letter[f'{col} Z']\n",
|
||
|
|
" f = ex_letter[f'Force']\n",
|
||
|
|
" t=ex_letter['Millis']-ex_letter['Millis'][0]\n",
|
||
|
|
" temp_axs = axs[j-1]\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, x)\n",
|
||
|
|
" temp_axs[1].plot(t, y)\n",
|
||
|
|
" temp_axs[2].plot(t, z)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, f)\n",
|
||
|
|
" temp_axs[1].plot(t, f)\n",
|
||
|
|
" temp_axs[2].plot(t, f)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].title.set_text(f'{letter}: x')\n",
|
||
|
|
" temp_axs[1].title.set_text(f'{letter}: y')\n",
|
||
|
|
" temp_axs[2].title.set_text(f'{letter}: z')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.savefig('./u1_Gyro_all_alphs.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"id": "812b1b32",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAABC/CAYAAABYGACaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd5xjd33v/9dHdXrb3ry7thfba2NcFhfAFDtgQwiGBAjdl5A4F5wbUu4jgZBfSEJIAikQLgRCgm9MCeAACQ6Ba4wxxYDLupdlvbu2t/fpVe37++MczWhmpBlJo5GOpPfz8ZjHOTo60nw1+9F3P+fbjjnnEBEREQmqUK0LICIiIrIQJSsiIiISaEpWREREJNCUrIiIiEigKVkRERGRQFOyIiIiIoGmZEVEREQCTclKHTCzH5jZgJnFa10WkXIohqURKI5rR8lKwJnZFuAqwAGvqW1pREqnGJZGoDiuLSUrwfcO4B7gX4Ebin2Rmb3AzE6Z2Sb/8fP8K4Jzl6eYIgWVG8OfMrO/m3PsNjP73coWT6Qo5cbxH5jZaM5P0sz+dZnK2LBMy+0Hm5ntBf4euBfvi7LROXfcf+4fAZxz7ynw2g8DVwK/CNwH/JNz7pPVKLdIVrkxbGaXAf/pn58xs5XAAWBr9vUi1bKUujjnPTb5r3+Xc+47y1vixqJkJcDM7EXAXcA659wpM/s5XsLxsSJfH8X7UsWAw8Arnf7BpYoqEMO7gN92zt1hZr8FvMo596plLLLIPEuNY/89WoG7gVudcx9ZpqI2LHUDBdsNwHedc6f8x/9GCc2PzrkkXpPlBcDfKVGRGlhSDAO3AG/z998GfKGCZRMp1lLjGOBzwG4lKuVRy0pA+Vn4MSAMjPqH40APcJFz7pEi3mMD8AhwG7ADeL5zbmpZCiwyR4VieCPwOPAS4EfAWufcxLIUWCSPCsXx+4DXA1cpfsujlpXgei2QBrYDF/k/5wE/xhvotSAzM7xWlc8B7wKOAh9ajoKKFPBalhDDAM65Q8D9eC0qX1dFLzXwWpZWF78S+G3gdYrf8ilZCa4bgP/rnDvgnDuW/QE+CbzVzCJm9hkz+0yB1/82sBr4//zun3cC7zSzq6pTfJElx3DWLcBzUReQ1MZS4/hXgVXArpwZQYvFvMyhbiARCTQzezHwRWCzxl2JNCe1rIhIYPkz2t4L/IsSFZHmpWRFRALJzM4DBoF1wMdrWhgRqSl1A4mIiEigqWVFREREAk3JioiIiARapNYFWMjKlSvdli1bal0MaRAPPPDAKefcqmr/XsWxVIpiWBpBOXEc6GRly5Yt7Ny5s9bFkAZhZvtr8XsVx1IpimFpBOXE8aLdQGbWYmb3mdkjZvaEmf2Zf3yrmd1rZnvN7KtmFvOPx/3He/3nt+S81/v947vN7NpSCysiIiLNp5gxK1PA1c655+EtM3ydmV0BfAT4mHPubGAAb0l3/O2Af/xj/nmY2XbgTcD5wHXAP5pZuIKfRURERBrQosmK82Rv3hT1fxxwNfA1//gtePdPALjef4z//DX+fWquB77inJtyzj0D7AUuq8SHEBERkcZV1GwgMwub2cPACeAOYB8w6JxL+accAjb4+xuAgwD+80PAitzjeV4j9eauv4T7/rnWpRAp365vwW2/XetSiJRvYD/c8hqYHK51SZZdUcmKcy7tnLsI2IjXGnLuchXIzG40s51mtvPkyZPL9WtkqX74Efj2/651KQJLcVwHvvpWePCWxc9rUorhOnDXX8IzP4Td36l1SZZdSeusOOcGgbuAK4EeM8vOJtoIHPb3DwObAPznu4HTucfzvCb3d3zWObfDObdj1aqqz9ATqQjFsdQ7xXAdMP+/cJepbTmqoJjZQKvMrMffbwVeDuzCS1pe7592A/BNf/82/zH+89/3b0B2G/Amf7bQVmAbcF+FPoeIiEhzMfO2TZCsFLPOyjrgFn/mTgi41Tn3LTN7EviKmf0F8BDwOf/8zwFfMLO9QD/eDCCcc0+Y2a3Ak0AKuMk5l67sxxEREWkSSlZmOOceBS7Oc/xp8szmcc5NAm8o8F4fBj5cejFFRJaJczOVvkg9UTeQiEiTaIKKXhpV87SsKFlpZkPzxjeL1JexU5CaWtp7ZNQbLTU0NQITg+W9Vi0r0vCe+RF8bDs8eVutSyJSnnQS/uYs+OZNS3ufTGrxc0SWy8efC3+7rbzXKlmRhpedl3/88dqWQ6RcRx72to99bcHTFqVkRWolnYSJAUgnynu9khVpeCd3e9vk+NLeZ/jI0ssiUo5nf+xtuzctfF4h2VuT7buzMuURKVU24QZIjJX++o413vap272B4g1MyUqzOuzf7n3s9NLe5+/PW3pZRMpxyI/hcifyrL3A2/77/2iK5colgA7lLDU2XkZd3LHa2+67s+FXY1ay0qymRrxtYqS25RAp15SfYEyNLnxeIX1nzeynk0svj0ippnLq37LiOKc15cSuJRcnyJSsNKNMeqaPc6rMZKWlu3LlESlHNsGYGimvCdxyqr8m6POXAModq1JOXZwbt+oGkoaTexVZTj8pQKSlMmURKVcmObMtp2Ukt6LXIFuphVl1cRktK7kJSkvX0ssTYEpWmlEm5wtS7ij0bEXftmLp5REpR3qJcaxkRWotN+6WmnCvOHvp5QkwJSvNKPdLkVpishKOL708IuWYVdGXE8c5V6UZjVmRGlhywp0Tww0+7krJSjNa6hcEZpIVXZFKreTG7pJbVrSKrdRARWO4setiJSvNqCLdQH5G3+BfEAmwWS2EZSy5n3tVqjiWWqho62Bjx7CSlWaUreQjLUpWpH5lUjMDvcvq72+eJnQJqHRyJobLSrjVsiKNLFsxR9vKvwmcuoGk1tJJL4YB0qropQ5lcmNYM9oWomSlGWW7gWIdS7iiVMuK1Fg64cVwdr9UGrMitZZOLjGG1Q0kjSyboMTa81+RHn3UW6DoJ/9QOJnJVvRqPpdayaS8GIb5s9pO7YXEONz3zzB0uMAbaDaQ1Fg6ORPDc+vikePez1O3w/6f5X99bsKdbuxkJVLrAkgNTCcrbV6Fn8lAyM9bn/wm3PoOWHOBd0fmeBfseOf895j+krjZrxepllkVfU6ykhiDT14K6y+GIw/Bzv8L7/np/Nc3URO6BFQm6dXDMP/C7++eM/vxnw7Nf30TxbD+h2lGmZyWFZhd0R9+wNsef9zbFrors9NVqdTYrIo+56r06KPe9shD3na4QMvKrKtSxbDUQDoJ4RiEIrPHDxa9/lXz1MNKVppRtmLuWOttT+2GP+2GJ2+DU3tmn1toAK7LgIW9/QbP6CWAsve3ysbw6An49IvgW783k3BnFbqPlXMz9wfSmBWphXTSS1TaVngxfPsH4P9cCieeKO712YTbQg1fDytZaUbZDHztBd72ka962598HA7thFXnQSjqHRs9kf89XMa7IoCG/5JIAGUT7pXP8WL16KNw/DHY+TkvWYm2ec/BzGyLuVxmZgVmxbDUQsZvWVn5HDj5c/jZJ+H0Xjh4n/f8GS+YOXcyXzeQ37ISjjd8wq1kpRllK/o1frLy5H9620waxk7Ajl+DDxyD1du9L1BebiZZafCBXRJA2YQ72gIrt8Gu/5p57shDcPYvwE33wY53wcAz+Styl4FwdPb7iVRTOunF4Kpz4eTumeN774S2lfDOb8OvfM47dvKp+a+fTlaiDd+VqWSlGU13A62G9lUzffrDR7xt51oIR2DthbO/QLlmVfRKVqTKsjEcinpXpcOHZp4bOwVd68HMG2SbmoSBZ/O/j2JYaimT8rqBVp0DiZGZ40cegs51XgxvuMQ7dnLX/NdP36Mt2vAxrGSlGWXmVPRZY36XT8RvGl99LowcgYnB2a+fzubVDSQ1kk1WwpHZMQxepZ+NzdXnedt8LYSzuoEauwldAiqd8BKNuTE8dgIifgz3bIZIK5woEMOYV5c3eD2sZKUZZbttwtH8/flTfoa/8hxve3rf7Odzmx5BTehSfdmYC8egpWv+80/9P2+b/U9g7sBx8JMVf/WGBm9Cl4DKzgbKF8PZgeKhMKw8G07niWGc1/oSiihZkQa
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 648x5616 with 78 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"\n",
|
||
|
|
"user_count = 1\n",
|
||
|
|
"\n",
|
||
|
|
"fig, axs = plt.subplots(26, 3, figsize=(3*3, 3*26), sharey=True)\n",
|
||
|
|
" \n",
|
||
|
|
"col = 'Mag'\n",
|
||
|
|
"\n",
|
||
|
|
"for j,k in zip(range(1,27), range(65,91)):\n",
|
||
|
|
" num = j\n",
|
||
|
|
" letter = chr(k)\n",
|
||
|
|
" filename = f'{num}{letter}.csv'\n",
|
||
|
|
" for i in range(0, user_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",
|
||
|
|
" \n",
|
||
|
|
" x = ex_letter[f'{col} X']\n",
|
||
|
|
" y = ex_letter[f'{col} Y']\n",
|
||
|
|
" z = ex_letter[f'{col} Z']\n",
|
||
|
|
" f = ex_letter[f'Force']\n",
|
||
|
|
" t=ex_letter['Millis']-ex_letter['Millis'][0]\n",
|
||
|
|
" temp_axs = axs[j-1]\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, x)\n",
|
||
|
|
" temp_axs[1].plot(t, y)\n",
|
||
|
|
" temp_axs[2].plot(t, z)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].plot(t, f)\n",
|
||
|
|
" temp_axs[1].plot(t, f)\n",
|
||
|
|
" temp_axs[2].plot(t, f)\n",
|
||
|
|
" \n",
|
||
|
|
" temp_axs[0].title.set_text(f'{letter}: x')\n",
|
||
|
|
" temp_axs[1].title.set_text(f'{letter}: y')\n",
|
||
|
|
" temp_axs[2].title.set_text(f'{letter}: z')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.savefig('./u1_Mag_all_alphs.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"id": "8eddc349",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"exit()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|