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Forked from DanielJMaher/gist:8738221
Created January 31, 2014 18:55
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{
"metadata": {
"name": ""
},
"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import requests\n",
"import matplotlib\n",
"matplotlib.rcParams['figure.figsize'] = (17, 7)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"minlon = \"-80.0\"\n",
"minlat = \"38.5\"\n",
"maxlon = \"0.0\"\n",
"maxlat = \"44.5\"\n",
"start = \"2014-01-21 15:45:07Z\"\n",
"end = \"2014-01-30 15:45:07Z\"\n",
"\n",
"payload = {\n",
" \"MinLon\" : minlon,\n",
" \"MinLat\" : minlat, \n",
" \"MaxLon\" : maxlon, \n",
" \"MaxLat\" : maxlat, \n",
" \"Start\" : start, \n",
" \"End\" : end\n",
" }\n",
"\n",
"url = \"http://staging.coastmap.com/sldmb\" \n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r=requests.get(url, params=payload)\n",
"r.raise_for_status()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import json\n",
"data=json.loads(r.text)\n",
"data=data[u'usp_buoydataboxandhoursResults'][u'usp_buoydataboxandhoursResult']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"{u'AltSensor': u'0',\n",
" u'BuoyNum': u'38796',\n",
" u'Cycle': u'3',\n",
" u'DataDate': u'2014-01-23 12:30:00Z',\n",
" u'DataID': u'4083543',\n",
" u'FOM': u'2',\n",
" u'Lat': u'41.345712',\n",
" u'Lon': u'-72.096354',\n",
" u'Quality': u'G ',\n",
" u'SST': u'19.9500007629395'}"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pandas.DataFrame.from_records(data, index=['DataDate', 'BuoyNum'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>AltSensor</th>\n",
" <th>Cycle</th>\n",
" <th>DataID</th>\n",
" <th>FOM</th>\n",
" <th>Lat</th>\n",
" <th>Lon</th>\n",
" <th>Quality</th>\n",
" <th>SST</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DataDate</th>\n",
" <th>BuoyNum</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-01-23 12:30:00Z</th>\n",
" <th>38796</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4083543</td>\n",
" <td> 2</td>\n",
" <td> 41.345712</td>\n",
" <td> -72.096354</td>\n",
" <td> G </td>\n",
" <td> 19.9500007629395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-23 12:00:00Z</th>\n",
" <th>38796</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4083543</td>\n",
" <td> 0</td>\n",
" <td> 41.345907</td>\n",
" <td> -72.096256</td>\n",
" <td> G </td>\n",
" <td> -1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-23 14:00:00Z</th>\n",
" <th>38796</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4083543</td>\n",
" <td> 3</td>\n",
" <td> 41.346828</td>\n",
" <td> -72.096525</td>\n",
" <td> G </td>\n",
" <td> 20.5499992370605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-23 13:30:00Z</th>\n",
" <th>38796</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4083543</td>\n",
" <td> 2</td>\n",
" <td> 41.345884</td>\n",
" <td> -72.096268</td>\n",
" <td> G </td>\n",
" <td> 20.3999996185303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-23 13:00:00Z</th>\n",
" <th>38796</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4083543</td>\n",
" <td> 1</td>\n",
" <td> 41.345786</td>\n",
" <td> -72.09617</td>\n",
" <td> G </td>\n",
" <td> -1000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
" AltSensor Cycle DataID FOM Lat \\\n",
"DataDate BuoyNum \n",
"2014-01-23 12:30:00Z 38796 0 3 4083543 2 41.345712 \n",
"2014-01-23 12:00:00Z 38796 0 3 4083543 0 41.345907 \n",
"2014-01-23 14:00:00Z 38796 0 3 4083543 3 41.346828 \n",
"2014-01-23 13:30:00Z 38796 0 3 4083543 2 41.345884 \n",
"2014-01-23 13:00:00Z 38796 0 3 4083543 1 41.345786 \n",
"\n",
" Lon Quality SST \n",
"DataDate BuoyNum \n",
"2014-01-23 12:30:00Z 38796 -72.096354 G 19.9500007629395 \n",
"2014-01-23 12:00:00Z 38796 -72.096256 G -1000 \n",
"2014-01-23 14:00:00Z 38796 -72.096525 G 20.5499992370605 \n",
"2014-01-23 13:30:00Z 38796 -72.096268 G 20.3999996185303 \n",
"2014-01-23 13:00:00Z 38796 -72.09617 G -1000 "
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.index[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"(u'2014-01-23 12:30:00Z', u'38796')"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.dtypes"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 62,
"text": [
"AltSensor object\n",
"Cycle object\n",
"DataID object\n",
"FOM object\n",
"Lat object\n",
"Lon object\n",
"Quality object\n",
"SST object\n",
"dtype: object"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['SST'] = df['SST'].astype(float)\n",
"df['SST'] = df['SST'].map(lambda x: np.nan if x < -100 else x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['SST']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"DataDate BuoyNum\n",
"2014-01-23 12:30:00Z 38796 19.950001\n",
"2014-01-23 12:00:00Z 38796 NaN\n",
"2014-01-23 14:00:00Z 38796 20.549999\n",
"2014-01-23 13:30:00Z 38796 20.400000\n",
"2014-01-23 13:00:00Z 38796 NaN\n",
"2014-01-23 14:30:00Z 38796 20.850000\n",
"2014-01-23 16:00:00Z 38796 21.450001\n",
"2014-01-23 15:30:00Z 38796 21.299999\n",
"2014-01-23 15:00:00Z 38796 21.000000\n",
"2014-01-23 18:00:00Z 38796 21.900000\n",
"2014-01-23 17:30:00Z 38796 21.900000\n",
"2014-01-23 17:00:00Z 38796 NaN\n",
"2014-01-23 18:30:00Z 38796 22.049999\n",
"2014-01-23 19:30:00Z 38796 22.350000\n",
"2014-01-23 19:00:00Z 38796 NaN\n",
"...\n",
"2014-01-30 08:00:00Z 38796 NaN\n",
"2014-01-30 07:30:00Z 38796 NaN\n",
"2014-01-30 09:30:00Z 38796 19.799999\n",
"2014-01-30 09:00:00Z 38796 19.799999\n",
"2014-01-30 10:00:00Z 38796 19.799999\n",
"2014-01-30 11:00:00Z 38796 19.950001\n",
"2014-01-30 10:30:00Z 38796 19.799999\n",
"2014-01-30 12:30:00Z 38796 20.700001\n",
"2014-01-30 12:00:00Z 38796 NaN\n",
"2014-01-30 14:30:00Z 38796 21.600000\n",
"2014-01-30 14:00:00Z 38796 21.450001\n",
"2014-01-30 13:30:00Z 38796 NaN\n",
"2014-01-30 13:00:00Z 38796 NaN\n",
"2014-01-30 15:30:00Z 38796 22.049999\n",
"2014-01-30 15:00:00Z 38796 NaN\n",
"Name: SST, Length: 246, dtype: float64"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['SST'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
"<matplotlib.axes.AxesSubplot at 0x10776c250>"
]
},
{
"metadata": {},
"output_type": "display_data",
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WJLBrQzJ6qkgIuX8Q4SLuERrfYj6bte+zT39lMlQl9Mebb0q33ZZ/m29x31vD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\nRERERFRnXEggIiIiIiIiojr7f9HDNLBciUVvAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10777fcd0>"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Lat'] = df['Lat'].astype(float)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Lat'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1075ee750>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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VKkD16tE522GXkQCwT0J5Zy5tYEYCEVHsmEsbmJFAZrm5UipaWMjUbidZswZ4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"text": [
"<matplotlib.figure.Figure at 0x1075ea5d0>"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Lat'].head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 75,
"text": [
"DataDate BuoyNum\n",
"2014-01-23 12:30:00Z 38796 41.345712\n",
"2014-01-23 12:00:00Z 38796 41.345907\n",
"2014-01-23 14:00:00Z 38796 41.346828\n",
"2014-01-23 13:30:00Z 38796 41.345884\n",
"2014-01-23 13:00:00Z 38796 41.345786\n",
"Name: Lat, dtype: float64"
]
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ok"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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