116 lines
12 KiB
Plaintext
116 lines
12 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import common\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"time, pos = common.import_csv_data('w_data.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.collections.PathCollection at 0x7f7fb0145390>"
|
|
]
|
|
},
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"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": [
|
|
"plt.scatter(pos[0], -pos[1], s=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'pos' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-2-85aa0823d467>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'pos' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"pos[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"216"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": []
|
|
},
|
|
{
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|