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@phsheth
Created February 3, 2018 05:06
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import serial\n",
"import time\n",
"import string\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import style\n",
"import matplotlib.animation as animation\n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"while True:\n",
" sensordata = np.genfromtxt('dtop.csv', delimiter=',', unpack= True, dtype=int)\n",
" lightsensor = (sensordata[0])\n",
" datetimestr = np.array([sensordata[1:7]])\n",
" datetimestr = datetimestr.T\n",
" datetime_new = []\n",
" datetime_new = [datetime.datetime(*x) for x in datetimestr] #this is the most pythonic method of converting variables to datetime format\n",
" plt.plot(datetime_new,lightsensor, label = \"Light Sensor\")\n",
" plt.title(\"Light Sensor Data\")\n",
" plt.grid()\n",
" plt.legend()\n",
" plt.xlabel(\"Date Time\")\n",
" plt.ylabel(\"Light Sensor Output\")\n",
" plt.savefig('dtop.png')\n",
" client = dropbox.client.DropboxClient('HIDDEN KEY')\n",
" print 'linked account: ', client.account_info()\n",
" f = open('DTOP.png', 'rb')\n",
" response = client.put_file('/Apps/dtopfiles/dtop.png', f, overwrite = True)\n",
" print('uploaded!')\n",
" time.sleep(1800)\n",
" plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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Ie+62FkyiiD20aIz31fXYXJ2BU0XkKaAAeEBVZwKtgGlB22W7aQAbyqT3L+/AIjICGAGQ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\nNWbBxBhjTI1ZMDHGGFNj/x+q8WzOUwxhNAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x92b03c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(datetime_new,lightsensor, label = \"Light Sensor\")\n",
"plt.title(\"Light Sensor Data\")\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.xlabel(\"Date Time\")\n",
"plt.ylabel(\"Light Sensor Output\")\n",
"plt.show()"
]
},
{
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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