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{
"metadata": {
"name": "",
"signature": "sha256:7cf05771095db26c82daea63b3c748f552eaf7c87e811ca85e1b82487121dd0e"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"\n",
"import datetime\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import IPython.display\n",
"\n",
"import mosquitto\n",
"\n",
"\n",
"class UpdatingPlot:\n",
" \n",
" def __init__(self):\n",
" self.timestamps = []\n",
" self.data = []\n",
" self.max_len = 10\n",
" self.redraw_graph()\n",
"\n",
" def update(self, data):\n",
" self.timestamps.append(datetime.datetime.now())\n",
" self.data.append(data)\n",
" if len(self.data) > self.max_len:\n",
" self.timestamps = self.timestamps[-self.max_len:]\n",
" self.data = self.data[-self.max_len:]\n",
" self.redraw_graph()\n",
"\n",
" def redraw_graph(self):\n",
" plt.cla()\n",
" plt.title(\"Temperature\")\n",
" plt.axis(ymin=0, ymax=35)\n",
" plt.plot_date(self.timestamps, self.data, 'bo-')\n",
" IPython.display.clear_output(wait=True)\n",
" IPython.display.display(plt.gcf())\n",
"\n",
"\n",
"my_plot = UpdatingPlot()\n",
"\n",
"def on_connect(client, *args, **kwargs):\n",
" client.subscribe('data/#')\n",
"\n",
"def on_message(client, userdata, msg):\n",
" print(msg.topic+\" \"+str(msg.payload))\n",
" if 'here/temperature' in msg.topic:\n",
" try:\n",
" temp = float(msg.payload)\n",
" except ValueError:\n",
" temp = None\n",
" else:\n",
" my_plot.update(temp)\n",
" \n",
"client = mosquitto.Mosquitto()\n",
"client.on_connect = on_connect\n",
"client.on_message = on_message\n",
"\n",
"client.connect('inside')\n",
"\n",
"client.loop_start()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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/RlRbzJjhz9PZs7MbZ1PyIfEvBzbjSz2/cs79OuV9JX5p1vTp8OMfw6xZUUci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\nKPGLiBQZJX4RkSLz/wFpN6fB2hXLIQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f21135636d8>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#client.loop_stop()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f2113441860>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
|