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rabernat/numpy_arrays.ipynb

Created September 18, 2014 01:17
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numpy_arrays.ipynb
{
"worksheets": [
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{
"metadata": {},
"cell_type": "heading",
"source": "Can you read this?",
"level": 1
},
{
"metadata": {},
"cell_type": "code",
"input": "# namespace\nprint dir()",
"prompt_number": 1,
"outputs": [
{
"output_type": "stream",
"text": "['In', 'Out', '_', '__', '___', '__builtin__', '__builtins__', '__doc__', '__name__', '__package__', '_dh', '_i', '_i1', '_ih', '_ii', '_iii', '_oh', '_sh', 'exit', 'get_ipython', 'quit', 'readline', 'rlcompleter']\n",
"stream": "stdout"
}
],
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"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "whos",
"prompt_number": 2,
"outputs": [
{
"output_type": "stream",
"text": "Interactive namespace is empty.\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "import numpy as np",
"prompt_number": 1,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a = np.array([6,1,7,8,0,0,4,2,3,7])",
"prompt_number": 2,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "from IPython.display import Image\nImage(url='http://docs.scipy.org/doc/numpy/_images/threefundamental.png')",
"prompt_number": 3,
"outputs": [
{
"text": "<IPython.core.display.Image at 0x103823810>",
"html": "<img src=\"http://docs.scipy.org/doc/numpy/_images/threefundamental.png\"/>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 3
}
],
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"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.dtype",
"prompt_number": 4,
"outputs": [
{
"text": "dtype('int64')",
"output_type": "pyout",
"metadata": {},
"prompt_number": 4
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.shape",
"prompt_number": 5,
"outputs": [
{
"text": "(10,)",
"output_type": "pyout",
"metadata": {},
"prompt_number": 5
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "b = a.view(dtype=np.float)\nb",
"prompt_number": 9,
"outputs": [
{
"text": "array([ 2.96439388e-323, 4.94065646e-324, 3.45845952e-323,\n 3.95252517e-323, 0.00000000e+000, 0.00000000e+000,\n 1.97626258e-323, 9.88131292e-324, 1.48219694e-323,\n 3.45845952e-323])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 9
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c = np.array([[2,1,4],[9,1,7]], dtype=np.float)",
"prompt_number": 10,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c",
"prompt_number": 11,
"outputs": [
{
"text": "array([[ 2., 1., 4.],\n [ 9., 1., 7.]])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 11
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c.shape",
"prompt_number": 12,
"outputs": [
{
"text": "(2, 3)",
"output_type": "pyout",
"metadata": {},
"prompt_number": 12
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a[1]",
"prompt_number": 13,
"outputs": [
{
"text": "1",
"output_type": "pyout",
"metadata": {},
"prompt_number": 13
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c[1]",
"prompt_number": 14,
"outputs": [
{
"text": "array([ 9., 1., 7.])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 14
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c[1,0]",
"prompt_number": 15,
"outputs": [
{
"text": "9.0",
"output_type": "pyout",
"metadata": {},
"prompt_number": 15
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c[:,0]",
"prompt_number": 16,
"outputs": [
{
"text": "array([ 2., 9.])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 16
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c[1,:]",
"prompt_number": 17,
"outputs": [
{
"text": "array([ 9., 1., 7.])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 17
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "c[1,:2]",
"prompt_number": 18,
"outputs": [
{
"text": "array([ 9., 1.])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 18
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "a.repeat(3)",
"prompt_number": 20,
"outputs": [
{
"text": "array([6, 6, 6, 1, 1, 1, 7, 7, 7, 8, 8, 8, 0, 0, 0, 0, 0, 0, 4, 4, 4, 2, 2,\n 2, 3, 3, 3, 7, 7, 7])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 20
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "np.tile(a,3)",
"prompt_number": 22,
"outputs": [
{
"text": "array([6, 1, 7, 8, 0, 0, 4, 2, 3, 7, 6, 1, 7, 8, 0, 0, 4, 2, 3, 7, 6, 1, 7,\n 8, 0, 0, 4, 2, 3, 7])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 22
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "r = np.arange(100)\nr",
"prompt_number": 23,
"outputs": [
{
"text": "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 23
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "other_r = np.arange(99,100,0.02)\nprint other_r",
"prompt_number": 24,
"outputs": [
{
"output_type": "stream",
"text": "[ 99. 99.02 99.04 99.06 99.08 99.1 99.12 99.14 99.16 99.18\n 99.2 99.22 99.24 99.26 99.28 99.3 99.32 99.34 99.36 99.38\n 99.4 99.42 99.44 99.46 99.48 99.5 99.52 99.54 99.56 99.58\n 99.6 99.62 99.64 99.66 99.68 99.7 99.72 99.74 99.76 99.78\n 99.8 99.82 99.84 99.86 99.88 99.9 99.92 99.94 99.96 99.98]\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "linspc = np.linspace(2,5,99)\nlinspc",
"prompt_number": 26,
"outputs": [
{
"text": "array([ 2. , 2.03061224, 2.06122449, 2.09183673, 2.12244898,\n 2.15306122, 2.18367347, 2.21428571, 2.24489796, 2.2755102 ,\n 2.30612245, 2.33673469, 2.36734694, 2.39795918, 2.42857143,\n 2.45918367, 2.48979592, 2.52040816, 2.55102041, 2.58163265,\n 2.6122449 , 2.64285714, 2.67346939, 2.70408163, 2.73469388,\n 2.76530612, 2.79591837, 2.82653061, 2.85714286, 2.8877551 ,\n 2.91836735, 2.94897959, 2.97959184, 3.01020408, 3.04081633,\n 3.07142857, 3.10204082, 3.13265306, 3.16326531, 3.19387755,\n 3.2244898 , 3.25510204, 3.28571429, 3.31632653, 3.34693878,\n 3.37755102, 3.40816327, 3.43877551, 3.46938776, 3.5 ,\n 3.53061224, 3.56122449, 3.59183673, 3.62244898, 3.65306122,\n 3.68367347, 3.71428571, 3.74489796, 3.7755102 , 3.80612245,\n 3.83673469, 3.86734694, 3.89795918, 3.92857143, 3.95918367,\n 3.98979592, 4.02040816, 4.05102041, 4.08163265, 4.1122449 ,\n 4.14285714, 4.17346939, 4.20408163, 4.23469388, 4.26530612,\n 4.29591837, 4.32653061, 4.35714286, 4.3877551 , 4.41836735,\n 4.44897959, 4.47959184, 4.51020408, 4.54081633, 4.57142857,\n 4.60204082, 4.63265306, 4.66326531, 4.69387755, 4.7244898 ,\n 4.75510204, 4.78571429, 4.81632653, 4.84693878, 4.87755102,\n 4.90816327, 4.93877551, 4.96938776, 5. ])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 26
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "x = np.linspace(-np.pi, np.pi, 100)\nf = np.sin(x)",
"prompt_number": 28,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "from matplotlib import pyplot as plt\n%matplotlib inline",
"prompt_number": 29,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.plot(x, f)",
"prompt_number": 30,
"outputs": [
{
"text": "[<matplotlib.lines.Line2D at 0x10bc38d90>]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 30
},
{
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pyIvYQR29BcXHxzN9+nTS09O9+mzZX3/9lRYtWrBx40YCAgJMxxGxJHX0bmro\n0KEcPnyYpUuXmo5i1HvvvccDDzygIi9iJyu1i+roz7NkyRLGjh3Ltm3bvPLGGkVFRdxyyy3k5ORw\nww03mI4jYlnq6N1Yv379uPrqq/nss89MRzHilVdeYfTo0SryIg6gjt7CMjIyCA0NZffu3V51g40t\nW7bQt29f9uzZo3vqipRDHb2bCwoKolu3bnzwwQemo7hMSUkJzz//PG+88YaKvIiDqKO3uP3799Oh\nQweys7Np2LCh6ThOt3jxYl566SVycnKoWrWq6TgilleRjl6F3g2MGzeOgoIC/vGPf5iO4lS///47\nbdu2JTo6mpCQENNxRNyCCr2HOHbsGK1ateKLL74gKCjIdBynefPNN8nJyWHBggWmo4i4DRV6DzJv\n3jzeeecdNm/eTLVq1UzHcbh9+/Zx66236haBIpWkxVgPEhoayvXXX8/EiRNNR3GKZ599lmeffVZF\nXsQJ1NG7kfz8fLp27crmzZs9qiAuWrToXyeHVa9e3XQcEbeiqRsP9Pbbb5OZmUliYqJHXAfn0KFD\n3HjjjcybN4/u3bubjiPidlToPdDJkyfp0KEDr7zyCkOGDDEdx26RkZFcfvnlTJkyxXQUEbekQu+h\nNm/ezD333MPWrVupX7++6TiXbPXq1QwfPpwdO3Zw1VVXmY4j4pa0GOuhbr31Vh577DEeeeQR3PXJ\n8dChQ4wYMYLo6GgVeREnU0fvpv744w+6dOnCk08+yahRo0zHqZSSkhIefvhhrrvuOiZNmmQ6johb\n09SNh9uxYwd33HEHaWlptG7d2nScCouPj+f9999n06ZNXHHFFabjiLg1FXovEBsby8cff0xmZiY1\natQwHadc+fn5BAYGsmrVKtq3b286jojbU6H3AiUlJURERHDZZZcRGxtrOs5FHTt2jK5duzJ69Gge\nf/xx03FEPIIKvZc4duwYnTp14rnnniMqKsp0nDKdm5evWbMmsbGxHnEOgIgVVKTQ6zqwHsDPz4/E\nxES6d+9Os2bNuOOOO0xH+i/jx4+noKDA6294LmKClX7j1NHbafXq1QwePJj09HRatGhhOs6/LFiw\ngGeeeYYNGzZ4xTX1RVxJ++i9zJ133snf//53+vbty48//mg6DgBr1qzhiSeeICkpSUVexBBN3XiY\nyMhIfvjhB3r27Elqaip169Y1liU7O5vQ0FASEhK4+eabjeUQ8XYq9B5o3LhxnDx5kpCQEFavXs01\n11zj8gz7tmDKAAAFIUlEQVSZmZnce++9fPrpp5ZcMxDxJpq68VBvvfUWISEhBAcHc+DAAZeOnZaW\nRr9+/YiJiWHgwIEuHVtE/psKvYfy8fHh/fffJywsjKCgIHJyclwy7vz58xk4cCBz586lf//+LhlT\nRC5Ou268QEJCAk899RSffvqp0zrs06dP88orr5CQkMCiRYvo0KGDU8YRkf/k7F03DwI7gTPAxX6r\newC5QB7wlB3jySUKDQ1l6dKl/O1vfyMyMpJjx4459Ovv3buXu+++my1btpCVlaUiL2Ix9hT67cAA\nIK2c4yYCjwJ3AaOBv9gxpnGpqammI5SrrIydO3dm69atlJSUcNNNN7Fw4UK7L3F86tQp3n33XTp3\n7kzv3r1Zvnw5f/lLxf973eF7CcrpaMrpevYU+t3AnnKOqVX6Mg3YD6wAutgxpnHu8J9/oYxXXnkl\nsbGxTJs2jbfffpugoCBSUlI4c+ZMpb7+iRMnmDx5Mi1atCAtLY2srCxefPFFqlat3CYud/hegnI6\nmnK6nrMXYzthe0I4ZxfQ1cljSjlCQkLYvHkzo0eP5uWXX6ZJkyaMGzeO9PR0jh8/XubnHD16lK++\n+orIyEgaN27M6tWrmTt3LsuXL6dJkyYu/heISGWU14KtBK4v4/0vA0scH0dcpUqVKoSFhREWFkZO\nTg7x8fE8//zz7NixA39/f2rVqkWNGjU4ceIE+/bt4/Dhw3Tr1o1+/frx8ssv07RpU9P/BBGpIEfs\nulkDjAG2lPGxWkAqcEvp258AyUBSGcfmAwEOyCMi4k32As2cPcga4NaLfHwrtp03jbFN47j1YqyI\niDcZABQDvwM/AstL31+f/+zYb8e2vTIfeNqVAUVERERExIAxwFnA9Vfiqpi3gG1ANjAHqGM2zgV9\ngO0vqS3ABMCqd+Gu6Il3prjDCX9xwEFs57ZYWUNsU707sa3dDTGapmzVgY3Yfr83AM+ajVMuX2zT\n4261OaYhtsXaQqxb6K887/XXgDdNBSlHCLbts1WAGGCU2TgX1Apoga0AWLHQn1tjaoR115i6Y9vw\nYPVCfz1w7nrVfwEK+M/fJ6uoUfrycmAHLljotMNzwD+AxRc7yGoXNfsIeNF0iHIcLX1ZFagJ/J/B\nLBezEttfRmeBFGxrJVZUkRPvTHGXE/7SgUOmQ1TAj9g6ZYCfsXX2Hc3FuaATpS/9sP2enzSY5WIa\nAPcAM3CjO0zdBxwAXHOZRfv8HdsP7W3AeMNZKiIKN/vTziJ0wp/zNAPaApmmg5ShCrbp2YPAZGyb\nTqzoY+AFbM3cRbn6xiMXOgHrFWAscPd57zN5Zc3yThR7BVux/zvwHubm8SpyQttr2P4Kme+qUGXQ\niXdyviuBBGy/N2Wfim3WWeAmbFvClwHfYJvCs5J+wE/YcgWbjVJxN2J79iwsfZwC9gHXGsxUEe2w\nLdhY1XBsP6TVDeeoCCvO0dfiP3/BPwH6GspSnsZYf44eoBq2KbC/mg5SQeOBx0yHKMM72P7SKAR+\nwPaEGW800SWw8mJs89KXVbF9s626ptAb2xyoVXcF/Vl5J96Z4i4n/DXG+oXeB1sx+sh0kIv4C3B1\n6et1sE0l1zMXp0Jux03/Mi7AuoV+AbZfqEzgfaC22TgXlIdtAXFr6WOq2TgXdKET76zCHU74mwt8\nj23RsBgYYTbOBd2GbVokm3//XPY2mui/tcO2JXkbtk0MEWbjVMjtlLPrRkRERERERERERERERERE\nRERERERERERERETEY/w/DaCBtyH72CIAAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0x10bc0e850>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "!curl -LO http://bit.ly/argo_prof",
"prompt_number": 31,
"outputs": [
{
"output_type": "stream",
"text": " % Total % Received % Xferd Average Speed Time Time Time Current\r\n Dload Upload Total Spent Left Speed\r\n\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 218 100 218 0 0 7901 0 --:--:-- --:--:-- --:--:-- 8074\r\n",
"stream": "stdout"
},
{
"output_type": "stream",
"text": "\r 0 0 0 4695 0 0 26503 0 --:--:-- --:--:-- --:--:-- 26503\r\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data = np.genfromtxt('argo_prof', delimiter=',', skiprows=1)\nprint data",
"prompt_number": 34,
"outputs": [
{
"output_type": "stream",
"text": "[[ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 0.00000000e+00 1.37310000e+01 3.58370000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.00000000e+00 1.37340000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.00000000e+00 1.37370000e+01 3.58330000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.00000000e+00 1.37360000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.00000000e+00 1.37350000e+01 3.58360000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.00000000e+00 1.37360000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.00000000e+00 1.37370000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.00000000e+00 1.37370000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.00000000e+00 1.37380000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.00000000e+00 1.37390000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.00000000e+01 1.37400000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.50000000e+01 1.37390000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.60000000e+01 1.37380000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.50000000e+01 1.37330000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.60000000e+01 1.37230000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.60000000e+01 1.37160000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.50000000e+01 1.37160000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.50000000e+01 1.37180000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.60000000e+01 1.37190000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.60000000e+01 1.37200000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.06000000e+02 1.37220000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.15000000e+02 1.37230000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.25000000e+02 1.37240000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.36000000e+02 1.37250000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.46000000e+02 1.37260000e+01 3.58350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.56000000e+02 1.37270000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.66000000e+02 1.37280000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.76000000e+02 1.37270000e+01 3.58340000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.86000000e+02 1.37230000e+01 3.58330000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.96000000e+02 1.36980000e+01 3.58280000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.13000000e+02 1.35880000e+01 3.58060000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.38000000e+02 1.34020000e+01 3.57740000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.62000000e+02 1.32130000e+01 3.57410000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 2.88000000e+02 1.29860000e+01 3.57090000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.13000000e+02 1.28330000e+01 3.56880000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.38000000e+02 1.26910000e+01 3.56690000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.64000000e+02 1.23830000e+01 3.56370000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 3.88000000e+02 1.21910000e+01 3.56200000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.13000000e+02 1.20970000e+01 3.56140000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.38000000e+02 1.19450000e+01 3.56050000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.63000000e+02 1.17240000e+01 3.55890000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 4.87000000e+02 1.15630000e+01 3.55750000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.13000000e+02 1.14370000e+01 3.55660000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.38000000e+02 1.12330000e+01 3.55350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.63000000e+02 1.10530000e+01 3.55190000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 5.88000000e+02 1.08860000e+01 3.54980000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.14000000e+02 1.06490000e+01 3.54620000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.38000000e+02 1.06040000e+01 3.54730000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.63000000e+02 1.04570000e+01 3.54690000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 6.88000000e+02 1.03200000e+01 3.54620000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.13000000e+02 1.00310000e+01 3.54430000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.38000000e+02 9.73100000e+00 3.54300000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.63000000e+02 9.54900000e+00 3.54300000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 7.88000000e+02 9.37800000e+00 3.54300000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.13000000e+02 9.08800000e+00 3.54150000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.38000000e+02 8.80100000e+00 3.53990000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.64000000e+02 8.51900000e+00 3.53920000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 8.88000000e+02 8.36700000e+00 3.53940000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.13000000e+02 8.31900000e+00 3.54270000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.38000000e+02 8.44100000e+00 3.54960000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.63000000e+02 8.37500000e+00 3.55060000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 9.88000000e+02 8.19300000e+00 3.54950000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.01300000e+03 7.95000000e+00 3.54640000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.03800000e+03 7.42200000e+00 3.53860000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.06300000e+03 6.91800000e+00 3.53090000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.08800000e+03 6.56600000e+00 3.52550000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.11300000e+03 6.13000000e+00 3.51910000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.13900000e+03 5.78100000e+00 3.51460000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.16300000e+03 5.59800000e+00 3.51290000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.18800000e+03 5.47100000e+00 3.51170000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.21300000e+03 5.40100000e+00 3.51100000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.23800000e+03 5.30700000e+00 3.50990000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.26300000e+03 5.20100000e+00 3.50870000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.28800000e+03 5.09100000e+00 3.50740000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.31300000e+03 4.93100000e+00 3.50560000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.33800000e+03 4.76000000e+00 3.50370000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.36300000e+03 4.64500000e+00 3.50250000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.38800000e+03 4.57300000e+00 3.50180000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.41300000e+03 4.49600000e+00 3.50100000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.43800000e+03 4.42600000e+00 3.50020000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.46300000e+03 4.35900000e+00 3.49950000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.48800000e+03 4.30200000e+00 3.49890000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.51300000e+03 4.23500000e+00 3.49830000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.53800000e+03 4.17000000e+00 3.49770000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.56300000e+03 4.11300000e+00 3.49710000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.58800000e+03 4.07200000e+00 3.49670000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.61300000e+03 4.04400000e+00 3.49650000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.63800000e+03 4.00100000e+00 3.49610000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.66300000e+03 3.97500000e+00 3.49600000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.68800000e+03 3.94300000e+00 3.49580000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.71300000e+03 3.90600000e+00 3.49550000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.73800000e+03 3.85900000e+00 3.49510000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.76300000e+03 3.81600000e+00 3.49480000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.78800000e+03 3.77200000e+00 3.49440000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.81300000e+03 3.72700000e+00 3.49410000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.83800000e+03 3.69900000e+00 3.49390000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.86300000e+03 3.65800000e+00 3.49370000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.88800000e+03 3.62800000e+00 3.49350000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.91300000e+03 3.59100000e+00 3.49330000e+01]\n [ 6.90097200e+06 1.12542000e+05 3.98900000e+01 -2.75940000e+01\n 1.93600000e+03 3.57700000e+00 3.49330000e+01]]\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print data.shape\nprint data.dtype",
"prompt_number": 35,
"outputs": [
{
"output_type": "stream",
"text": "(100, 7)\nfloat64\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "data_rec = np.genfromtxt('argo_prof', delimiter=',', names=True)\ndata_rec",
"prompt_number": 36,
"outputs": [
{
"text": "array([(6900972.0, 112542.0, 39.89, -27.594, 0.0, 13.731, 35.837),\n (6900972.0, 112542.0, 39.89, -27.594, 1.0, 13.734, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 2.0, 13.737, 35.833),\n (6900972.0, 112542.0, 39.89, -27.594, 3.0, 13.736, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 4.0, 13.735, 35.836),\n (6900972.0, 112542.0, 39.89, -27.594, 5.0, 13.736, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 6.0, 13.737, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 7.0, 13.737, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 8.0, 13.738, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 9.0, 13.739, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 10.0, 13.74, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 15.0, 13.739, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 26.0, 13.738, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 35.0, 13.733, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 46.0, 13.723, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 56.0, 13.716, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 65.0, 13.716, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 75.0, 13.718, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 86.0, 13.719, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 96.0, 13.72, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 106.0, 13.722, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 115.0, 13.723, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 125.0, 13.724, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 136.0, 13.725, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 146.0, 13.726, 35.835),\n (6900972.0, 112542.0, 39.89, -27.594, 156.0, 13.727, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 166.0, 13.728, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 176.0, 13.727, 35.834),\n (6900972.0, 112542.0, 39.89, -27.594, 186.0, 13.723, 35.833),\n (6900972.0, 112542.0, 39.89, -27.594, 196.0, 13.698, 35.828),\n (6900972.0, 112542.0, 39.89, -27.594, 213.0, 13.588, 35.806),\n (6900972.0, 112542.0, 39.89, -27.594, 238.0, 13.402, 35.774),\n (6900972.0, 112542.0, 39.89, -27.594, 262.0, 13.213, 35.741),\n (6900972.0, 112542.0, 39.89, -27.594, 288.0, 12.986, 35.709),\n (6900972.0, 112542.0, 39.89, -27.594, 313.0, 12.833, 35.688),\n (6900972.0, 112542.0, 39.89, -27.594, 338.0, 12.691, 35.669),\n (6900972.0, 112542.0, 39.89, -27.594, 364.0, 12.383, 35.637),\n (6900972.0, 112542.0, 39.89, -27.594, 388.0, 12.191, 35.62),\n (6900972.0, 112542.0, 39.89, -27.594, 413.0, 12.097, 35.614),\n (6900972.0, 112542.0, 39.89, -27.594, 438.0, 11.945, 35.605),\n (6900972.0, 112542.0, 39.89, -27.594, 463.0, 11.724, 35.589),\n (6900972.0, 112542.0, 39.89, -27.594, 487.0, 11.563, 35.575),\n (6900972.0, 112542.0, 39.89, -27.594, 513.0, 11.437, 35.566),\n (6900972.0, 112542.0, 39.89, -27.594, 538.0, 11.233, 35.535),\n (6900972.0, 112542.0, 39.89, -27.594, 563.0, 11.053, 35.519),\n (6900972.0, 112542.0, 39.89, -27.594, 588.0, 10.886, 35.498),\n (6900972.0, 112542.0, 39.89, -27.594, 614.0, 10.649, 35.462),\n (6900972.0, 112542.0, 39.89, -27.594, 638.0, 10.604, 35.473),\n (6900972.0, 112542.0, 39.89, -27.594, 663.0, 10.457, 35.469),\n (6900972.0, 112542.0, 39.89, -27.594, 688.0, 10.32, 35.462),\n (6900972.0, 112542.0, 39.89, -27.594, 713.0, 10.031, 35.443),\n (6900972.0, 112542.0, 39.89, -27.594, 738.0, 9.731, 35.43),\n (6900972.0, 112542.0, 39.89, -27.594, 763.0, 9.549, 35.43),\n (6900972.0, 112542.0, 39.89, -27.594, 788.0, 9.378, 35.43),\n (6900972.0, 112542.0, 39.89, -27.594, 813.0, 9.088, 35.415),\n (6900972.0, 112542.0, 39.89, -27.594, 838.0, 8.801, 35.399),\n (6900972.0, 112542.0, 39.89, -27.594, 864.0, 8.519, 35.392),\n (6900972.0, 112542.0, 39.89, -27.594, 888.0, 8.367, 35.394),\n (6900972.0, 112542.0, 39.89, -27.594, 913.0, 8.319, 35.427),\n (6900972.0, 112542.0, 39.89, -27.594, 938.0, 8.441, 35.496),\n (6900972.0, 112542.0, 39.89, -27.594, 963.0, 8.375, 35.506),\n (6900972.0, 112542.0, 39.89, -27.594, 988.0, 8.193, 35.495),\n (6900972.0, 112542.0, 39.89, -27.594, 1013.0, 7.95, 35.464),\n (6900972.0, 112542.0, 39.89, -27.594, 1038.0, 7.422, 35.386),\n (6900972.0, 112542.0, 39.89, -27.594, 1063.0, 6.918, 35.309),\n (6900972.0, 112542.0, 39.89, -27.594, 1088.0, 6.566, 35.255),\n (6900972.0, 112542.0, 39.89, -27.594, 1113.0, 6.13, 35.191),\n (6900972.0, 112542.0, 39.89, -27.594, 1139.0, 5.781, 35.146),\n (6900972.0, 112542.0, 39.89, -27.594, 1163.0, 5.598, 35.129),\n (6900972.0, 112542.0, 39.89, -27.594, 1188.0, 5.471, 35.117),\n (6900972.0, 112542.0, 39.89, -27.594, 1213.0, 5.401, 35.11),\n (6900972.0, 112542.0, 39.89, -27.594, 1238.0, 5.307, 35.099),\n (6900972.0, 112542.0, 39.89, -27.594, 1263.0, 5.201, 35.087),\n (6900972.0, 112542.0, 39.89, -27.594, 1288.0, 5.091, 35.074),\n (6900972.0, 112542.0, 39.89, -27.594, 1313.0, 4.931, 35.056),\n (6900972.0, 112542.0, 39.89, -27.594, 1338.0, 4.76, 35.037),\n (6900972.0, 112542.0, 39.89, -27.594, 1363.0, 4.645, 35.025),\n (6900972.0, 112542.0, 39.89, -27.594, 1388.0, 4.573, 35.018),\n (6900972.0, 112542.0, 39.89, -27.594, 1413.0, 4.496, 35.01),\n (6900972.0, 112542.0, 39.89, -27.594, 1438.0, 4.426, 35.002),\n (6900972.0, 112542.0, 39.89, -27.594, 1463.0, 4.359, 34.995),\n (6900972.0, 112542.0, 39.89, -27.594, 1488.0, 4.302, 34.989),\n (6900972.0, 112542.0, 39.89, -27.594, 1513.0, 4.235, 34.983),\n (6900972.0, 112542.0, 39.89, -27.594, 1538.0, 4.17, 34.977),\n (6900972.0, 112542.0, 39.89, -27.594, 1563.0, 4.113, 34.971),\n (6900972.0, 112542.0, 39.89, -27.594, 1588.0, 4.072, 34.967),\n (6900972.0, 112542.0, 39.89, -27.594, 1613.0, 4.044, 34.965),\n (6900972.0, 112542.0, 39.89, -27.594, 1638.0, 4.001, 34.961),\n (6900972.0, 112542.0, 39.89, -27.594, 1663.0, 3.975, 34.96),\n (6900972.0, 112542.0, 39.89, -27.594, 1688.0, 3.943, 34.958),\n (6900972.0, 112542.0, 39.89, -27.594, 1713.0, 3.906, 34.955),\n (6900972.0, 112542.0, 39.89, -27.594, 1738.0, 3.859, 34.951),\n (6900972.0, 112542.0, 39.89, -27.594, 1763.0, 3.816, 34.948),\n (6900972.0, 112542.0, 39.89, -27.594, 1788.0, 3.772, 34.944),\n (6900972.0, 112542.0, 39.89, -27.594, 1813.0, 3.727, 34.941),\n (6900972.0, 112542.0, 39.89, -27.594, 1838.0, 3.699, 34.939),\n (6900972.0, 112542.0, 39.89, -27.594, 1863.0, 3.658, 34.937),\n (6900972.0, 112542.0, 39.89, -27.594, 1888.0, 3.628, 34.935),\n (6900972.0, 112542.0, 39.89, -27.594, 1913.0, 3.591, 34.933),\n (6900972.0, 112542.0, 39.89, -27.594, 1936.0, 3.577, 34.933)], \n dtype=[('PLATFORM', '<f8'), ('ARGOS_ID', '<f8'), ('LATITUDE', '<f8'), ('LONGITUDE', '<f8'), ('PRES', '<f8'), ('TEMP', '<f8'), ('SAL', '<f8')])",
"output_type": "pyout",
"metadata": {},
"prompt_number": 36
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print data_rec.shape\nprint data_rec.dtype",
"prompt_number": 37,
"outputs": [
{
"output_type": "stream",
"text": "(100,)\n[('PLATFORM', '<f8'), ('ARGOS_ID', '<f8'), ('LATITUDE', '<f8'), ('LONGITUDE', '<f8'), ('PRES', '<f8'), ('TEMP', '<f8'), ('SAL', '<f8')]\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "S = data_rec['SAL']\nT = data_rec['TEMP']\nP = data_rec['PRES']",
"prompt_number": 38,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print type(data)\nprint type(data_rec)",
"prompt_number": 39,
"outputs": [
{
"output_type": "stream",
"text": "<type 'numpy.ndarray'>\n<type 'numpy.ndarray'>\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.plot(T, -P)",
"prompt_number": 41,
"outputs": [
{
"text": "[<matplotlib.lines.Line2D at 0x10c24f7d0>]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 41
},
{
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WyHXxxOLzA8C3bt/vwHrjDwIOuW3PBtq5PSbbdf0IUBeo6oFsIh7jdDpZu3YtsbGxNG/e\nnF27dvHll18yZ84clYLYyrVGDCkUP9//IrDsKo8pboGjuBXjCm7bK1zxuKsukkyaNOni9cjISCIj\nI692V5Fycfz4cebPn8+sWbMoLCzk6aefZtasWdx2222mo4mfcjgcOByOG358WeyV9CXwHNaiMkBv\n4BFgjOv7TKATUIC16NzUtf054AzwDtbaw3rgE6AW8DmX1h/caa8kMaqwsJCdO3eSkZFBZmYmGRkZ\nbN68ma5duzJy5Eg6d+6sT1wTr2PqM5/df+Am4A2gIVYJFGGVAljrErHAv7EWnJ91bd8IDMAqhD8C\nX5dRLpEbdvr0abKysi4WQGZmJllZWdStW5c2bdoQGhrKmDFjeOCBB6hTp47puCJlpjR/2vTB2uOo\nNnAcyAAuHLEzBhgNnAOGA+tc21tiLTLXBBYBL7i2Vwbewxpp5GOVx4FifqZGDOIRP/7442UFkJGR\nQX5+PsHBwRdLoE2bNrRu3Zpbb73VdFyREtEpMUSu4YcffmDLli2XXY4ePUpoaOjFAggNDaVly5ZU\nrap9IMT+VAwibg4ePHjxzT89PZ0tW7bw008/ERYWRnh4+MWvQUFBVKyoM8SIb1IxiN8qKChg3bp1\npKWlXSyDkydPEh4eflkJNG3aVCUgfkXFIH7D6XTyzTffsHLlSpKTk9m8eTMRERG0bdv2Ygk0adJE\newmJ31MxiE87fvw4//73vy+WQZUqVYiKiiIqKorIyEgCAwNNRxTxOioG8SmnT59m8+bNrFu3jlWr\nVpGRkUHHjh3p0aMHUVFR3HvvvRoRiFyDikFs7cCBA3z11VcXL9988w333XcfHTp0oGvXrnTu3Jlq\n1aqZjiliKyoGsY3CwkKys7MvK4Jjx47x4IMP0qFDBzp06MD999+vIhApJRWDeLX8/Hw+++wzVq5c\nyYYNG6hbt+5lRRAcHKw9hkTKmIpBvMr58+fZsGEDy5Yt47PPPuPo0aP07NmT6OhoOnXqpFNJiJQD\nFYMYd/ToUZKTk/nss89ITk6mcePG9OrVi169ehEeHq4RgUg5UzGIMampqbz88sukpqYSGRlJr169\n6NmzJ/Xr1zcdTcSvmTq7qvixrVu3MnHiRLZt28Zf//pXPvnkEwICAkzHEpEbpDG93LDc3Fzi4uLo\n3r07Xbt2JScnhyFDhqgURGxOxSA3ZO7cubRr145WrVqRm5vLM888ozORivgITSVJiZw9e5ZnnnmG\ntWvXsm7dOlq0aGE6koiUMRWDXLf9+/fTp08fGjRowKZNm6hevbrpSCLiAZpKkuty/vx5+vXrxyOP\nPMJHH32kUhDxYSoGuS6TJ0/m5ptv5pVXXtFJ60R8nN3+h+s4BgM2btxI7969SU9Pp0GDBqbjiEgJ\nlfQ4Bo0Y5FdlZ2fz+OOPM3fuXJWCiJ9QMchV5efn0717d9544w169+5tOo6IlBMVgxRrz549dO3a\nlRdeeIEBAwaYjiMi5UjFIL+wY8cOOnXqxJgxYxg5cqTpOCJSznQcg1wmMzOTqKgoXnvtNQYPHmw6\njogYoGKQi9avX8+TTz7JjBkz6Nu3r+k4ImKIppIEgE8//ZQnnniCDz74QKUg4udUDEJCQgLDhw9n\n+fLldO3a1XQcETFMU0l+7MCBAzz77LNs2bKFNWvW0KxZM9ORRMQLaMTgh4qKipg1axYhISE0bdqU\nrVu3qhRE5CKNGPzMtm3bGDFiBBUqVOCLL77gvvvuMx1JRLyMRgx+4scff2TUqFE88sgjxMfHs27d\nOpWCiBRLxeDjCgsLmTlzJi1btqRChQp89913DB8+nIoV9asXkeJpKsmHZWVlMXDgQGrWrElKSgoh\nISGmI4mIDejPRh/1+eef06VLF8aOHcsXX3yhUhCR66YRgw9KSEjghRde4OOPP6ZTp06m44iIzagY\nfMzbb7/N9OnTWbt2LcHBwabjiIgN6RPcfEhaWhrR0dFs2rSJxo0bm44jIl5Cn+Dmp06cOEFcXBwz\nZsxQKYhIqWjE4AOcTifx8fFUqlSJhIQE03FExMuUdMSgNQYfMHv2bDIyMti4caPpKCLiAzRisLm0\ntDSioqL46quvdL4jESmW1hj8yIkTJ4iNjeXdd99VKYhImdGIwcaGDRtGYWGh1hVE5FdpjcFPLF26\nlNWrV7N161bTUUTEx2jEYEOHDx8mJCSExMREOnbsaDqOiHi5ko4YVAw2FBsbS/369Zk6darpKCJi\nA5pK8nGffPIJmZmZ/OMf/zAdRUR8VGn2SuoLbAcKgTC37Y2B00CG6/Ku220tgHQgH3jVbXtlYC6w\nB3AA9UqRy2edOXOGsWPHMmvWLAICAkzHEREfVZpiyAL6AGuLuS0XaOO6jHTbPhWYDEQAnYH7Xdv7\nALdiFUcyMLEUuXzWW2+9RXh4OJGRkaajiIgPK81U0nc38JhgYLHrehLQFkhzff0AOAXMBlaVIpdP\n+v7775k6daqObhYRj/PUAW5NgEzgPaC1a1sQcMjtPtlAO9f1B1zfAxwB6gJVPZTNdpxOJ0OHDmXs\n2LHcc889puOIiI+71oghheLn+18Ell3lMfuBu4GjQBQwHwjhlyviFQCn2/UKV9xWrEmTJl28HhkZ\n6RfTKnPmzOHIkSOMHz/edBQRsQGHw4HD4bjhx5fF7qpfAs9hLSoXJx3oh7XukA80dW1/DjgDvIO1\n9rAe+ASoBXzOpfUHd363u+revXsJCwvD4XDQqlUr03FExIZMnSvJ/QfWBm5yXQ8DArBKAax1iVjX\nffoAFybMNwIDgFuAPwJfl1EuW3M6nQwfPpwxY8aoFESk3JSmGPoAe7HWCZYDK13bOwNbsdYYXgSG\nuz3mf4BxwGZgHdbCM1gjhePAt0AP4H9LkctnLFiwgH379jFu3DjTUUTEj+jIZy91/Phxmjdvzqef\nfkpERITpOCJiYzolho8YP348hw8fZu7cuaajiIjNqRh8QH5+Pg888ABZWVnceeedpuOIiM3pg3p8\nwPTp0xk+fLhKQUSM0IjBy5w8eZKGDRuSkZFBw4YNTccRER+gEYPNJSYm8uCDD6oURMQYFYOXWbp0\nKbGxsaZjiIgf01SSFzl37hx16tQhNzeXOnXqmI4jIj5CU0k25nA4aNasmUpBRIxSMXiRmTNnMnTo\nUNMxRMTPaSrJS+zatYuIiAj27NnDLbfcYjqOiPgQTSXZ1Lx58xgwYIBKQUSMUzF4iZSUFKKjo03H\nEBHRVJI3OHHiBPXq1ePQoUNUq1bNdBwR8TGaSrKh1atX07ZtW5WCiHgFFYMXSEpKok+fPqZjiIgA\nmkoy7ueff6ZevXps3bqVBg0amI4jIj5IU0k2s3LlSpo3b65SEBGvoWIw7P333yc+Pt50DBGRizSV\nZNAPP/xAUFAQe/bs4bbbbjMdR0R8lKaSbGTGjBnExMSoFETEq2jEYEhBQQFNmzYlNTWVe++913Qc\nEfFhGjHYxNy5c4mMjFQpiIjX0YjBgMLCQpo1a8YHH3xA+/btTccRER+nEYMNrFixgttvv5127dqZ\njiIi8gsqBgOmT5/O6NGjL7S4iIhXsds7k+2nkrKysujevTu7d++mSpUqpuOIiB/QVJKXmzZtGqNG\njVIpiIjX0oihHB08eJAWLVqQk5ND7dq1TccRET+hEYMXe+edd4iJiVEpiIhX04ihnJw+fZpGjRqx\nfv16mjVrZjqOiPgRjRi81Jo1awgODlYpiIjXUzGUk+TkZKKiokzHEBG5JhVDOXA6naxYsYLu3bub\njiIick0qhnKwdu1aKlWqRFhYmOkoIiLXpGIoBzNnzmTEiBE60llEbMFu71S22yvp6NGjNG7cWB/G\nIyLGaK8kL5OcnMxDDz2kUhAR21AxeNiyZcvo3bu36RgiItdNU0kedPr0aerXr88333zDXXfdZTqO\niPgpTSV5kaVLlxIREaFSEBFbUTF40Lx58xg0aJDpGCIiJaKpJA8pKiqievXq/Pe//6VGjRqm44iI\nH9NUkpfYu3cvt912m0pBRGxHxeAhOTk5BAcHm44hIlJimkrykFOnTnH48GEaNmxoOoqI+LmSTiWp\nGEREfFx5rjG8AXwLpAPTgAC3254BdgLZQEe37S1c988HXnXbXhmYC+wBHEC9UuQSEZFSKE0xfA60\nAu4HbgHiXNvvAEYCXYCngbfdHjMVmAxEAJ1djwXoA9yKVRzJwMRS5LIth8NhOoJH6fXZly+/NvD9\n11dSpSmGFKDIdVmF9UYP0Bbrzf17YA3W8CXQdVswsBj4EUhy3ffCYz4ATgGz3bb7FV//x6nXZ1++\n/NrA919fSZXVXknDgGWu6w9gTTFdsAPrjT4IOOS2PRto5/aYbNf1I0BdoGoZZRMRkRKodI3bUyh+\nvv9FLhXBX4ECYInr++IWOIpbMa7gtr3CFY+z26K4iIi4PAV8Bdzstq03MN3t+0yguut6vtv254BR\nrutTsdYZAGoBaVf5eblYZaKLLrroosv1X3IpJz2A7cDtV2yvC3wHNAQisfZCumAFEAvUBtZzafG5\nH/Ax1iL2n4EZngotIiKesxNr99IM1+Vdt9vGYDVUNtDJbXtLrKLYBfxft+2VgQSsBWsH2l1VRERE\nRERK4m7gS6ypKweXjpnwJTdhjbyWXeuONnQL8D6Qw+V7o/mKYUAqsAXrYE+7SwAOAllu26oDS7FG\n9f/i0i7odlTc6/u1A3btprjXd8FzWIcY1CrXRB5SDwh1Xa+NtYhd/ep3t6X/AywAPjUdxAOmAK9g\n7aRQCetgRl9RC2tq9Bas3b9XAN2NJiq9TkAbLn9jGQf8HWs38hnA/xjIVVaKe31dsX5/FYH/Bwwx\nkKusFPf6wPoDOxnr36tPFMOVlgEPmw5RhhoA/8Z6Tb44YsjE3n+B/ZoAYDdwF1Y5OLCOy7G7xlz+\nxvIRl/44C+PS7ul21Zji/6IG+B0wr/yieERjfvn6lgAhXEcx2PG020FYp+LYZDpIGXoLeB5riOdr\nGmCNFGYCG4HxXL57s92dxjr1y27gANbu2770b/OCCKy9DXF99YXyuxr3A3Z9xWPAf4Bt13NnuxVD\ndaxTaowFThrOUlZ6YR0RnoFvHth3M9AMa3fkSKxS72cyUBmrg1V6LbH+SmsP9DQZyEN88d9mca48\nYNcXVMM6KPklt20+8/usjHXivmdNByljrwF7sYZ3/8UqPLsPY6/kfoqUKGChqSAe0BNY5Pb901gn\nirS7xlw+FfEx1rw1QDjW1JKdNeaXUy1P8csDdu2qMZde32+wFqN3uS4/Y41w7zARrCxVwHqzfNN0\nEA/rjO8NYcFaUG+LNUKdgb0X9q5UA+uYnVpYC7OfYp1Z2O4aU/zicwDwDvZefIZfvr6rHbBrV425\n+hqKzyw+d8Saf8/k0gF1PYwm8ozO+OZeSc2Ar7F+f1OwFml9yVNYZxLejLX3ld2maK+0ENgPnMUa\nzQ7Gt3ZXvfD6zmG9vj/w6wfs2k1xvz93+fhIMYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI/5\n/7/3R4ktEUE5AAAAAElFTkSuQmCC\n",
"text": "<matplotlib.figure.Figure at 0x10bc4e990>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print T.mean()",
"prompt_number": 42,
"outputs": [
{
"output_type": "stream",
"text": "9.35006\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print T.std()",
"prompt_number": 43,
"outputs": [
{
"output_type": "stream",
"text": "3.98860484084\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print T.max(), T.min()",
"prompt_number": 44,
"outputs": [
{
"output_type": "stream",
"text": "13.74 3.577\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "b = -2e-4*T + 1e-3*S",
"prompt_number": 45,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.plot(b, -P)",
"prompt_number": 47,
"outputs": [
{
"text": "[<matplotlib.lines.Line2D at 0x10c2e44d0>]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 47
},
{
"png": 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QQLt27cyOKBajInGlIpGAc+bMGTIzM8nIyCA5OZnIyEgSExPp1KmT2dHEIlQkrlQkEtAu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\n/x8+f9Qxp+g+hQAAAABJRU5ErkJggg==\n",
"text": "<matplotlib.figure.Figure at 0x10bc20250>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "#!pip install gsw\nimport gsw",
"prompt_number": 49,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "sigma0 = gsw.sigma0(S,T)",
"prompt_number": 50,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.plot(sigma0, -P)",
"prompt_number": 53,
"outputs": [
{
"text": "[<matplotlib.lines.Line2D at 0x10d52bb50>]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 53
},
{
"png": 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"text": "<matplotlib.figure.Figure at 0x10d1ca0d0>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "fig = plt.figure(figsize=(10,5))\nplt.subplot(1,2,1)\nplt.plot(T, -P, 'r', linewidth=2, linestyle=':')\nplt.xlabel('Temperature')\nplt.ylabel('Pressure')\nplt.title('ARGO Profile')\nplt.subplot(1,2,2)\nplt.plot(S, -P, 'b', linewidth=2, linestyle=':')\nplt.xlabel('Salinity')\n#plt.ylabel('Pressure')\nplt.yticks([])\nplt.title('ARGO Profile')\nplt.tight_layout()\nplt.savefig('my_first_python_figure.pdf')",
"prompt_number": 63,
"outputs": [
{
"png": 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"text": "<matplotlib.figure.Figure at 0x10e108bd0>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "!open my_first_python_figure.pdf",
"prompt_number": 62,
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "markdown",
"source": "fig = plt.figure(figsize=(8,4))\nax1 = fig.add_subplot(1,2,1)\nax1.plot(T, -P, 'r')\nax2 = fig.add_subplot(1,2,2)\nax2.plot(S, -P, 'b')\n\nax1.set_title('Temperature')\nax2.set_title('Salinity')\nax2.yaxis.set_ticklabels([])"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We have\n$ T(z) $\n\nLet's start by making\n$ T_{2D}(z,t) = T(z) f(t) $\n\nwhere\n$ f(t) = \\sin(a t) $"
},
{
"metadata": {},
"cell_type": "code",
"input": "time = np.arange(0,7,1/24.)\nf = np.sin(2*np.pi*time)\nplt.plot(time, f)",
"prompt_number": 74,
"outputs": [
{
"text": "[<matplotlib.lines.Line2D at 0x10f3f1dd0>]",
"output_type": "pyout",
"metadata": {},
"prompt_number": 74
},
{
"png": 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/Gp39p6ambE/NrblE79bwb2pqytdAAXf9gwh0t4feup6oEIy/7u3vlv/q6qrv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\n",
"text": "<matplotlib.figure.Figure at 0x10d57c850>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "T_hourly = T[:,np.newaxis] * f[np.newaxis,:]\nprint T_hourly.shape",
"prompt_number": 82,
"outputs": [
{
"output_type": "stream",
"text": "(100, 168)\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "print T[np.newaxis, np.newaxis, np.newaxis,:].shape\nprint f[:,np.newaxis].shape",
"prompt_number": 79,
"outputs": [
{
"output_type": "stream",
"text": "(1, 1, 1, 100)\n(168, 1)\n",
"stream": "stdout"
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.pcolormesh(time, -P, T_hourly)",
"prompt_number": 83,
"outputs": [
{
"text": "<matplotlib.collections.QuadMesh at 0x10fc73850>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 83
},
{
"png": 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"text": "<matplotlib.figure.Figure at 0x10fbf08d0>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "plt.contourf(time, -P, T_hourly, 30, cmap='RdBu_r')",
"prompt_number": 84,
"outputs": [
{
"text": "<matplotlib.contour.QuadContourSet instance at 0x10fcf0560>",
"output_type": "pyout",
"metadata": {},
"prompt_number": 84
},
{
"png": 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"text": "<matplotlib.figure.Figure at 0x10fc56590>",
"output_type": "display_data",
"metadata": {}
}
],
"language": "python",
"trusted": true,
"collapsed": false
},
{
"metadata": {},
"cell_type": "code",
"input": "",
"outputs": [],
"language": "python",
"trusted": true,
"collapsed": false
}
],
"metadata": {}
}
],
"metadata": {
"name": "",
"signature": "sha256:ad216ccfa1cd2443ec59108e450b727a050d0f12884d10cd74f5d24994a5f7d0"
},
"nbformat": 3
}
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