cdw/data_work.ipynb

## data_work.ipynb
{
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      "cell_type": "code",
      "source": "ls",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "data_work.ipynb              inflammation-07.csv.png\r\ninflammation-01.csv          inflammation-07.csv_min.png\r\ninflammation-01.csv.png      inflammation-08.csv\r\ninflammation-01.csv_min.png  inflammation-08.csv.png\r\ninflammation-02.csv          inflammation-08.csv_min.png\r\ninflammation-02.csv.png      inflammation-09.csv\r\ninflammation-02.csv_min.png  inflammation-09.csv.png\r\ninflammation-03.csv          inflammation-09.csv_min.png\r\ninflammation-03.csv.png      inflammation-10.csv\r\ninflammation-03.csv_min.png  inflammation-10.csv.png\r\ninflammation-04.csv          inflammation-10.csv_min.png\r\ninflammation-04.csv.png      inflammation-11.csv\r\ninflammation-04.csv_min.png  inflammation-11.csv.png\r\ninflammation-05.csv          inflammation-11.csv_min.png\r\ninflammation-05.csv.png      inflammation-12.csv\r\ninflammation-05.csv_min.png  inflammation-12.csv.png\r\ninflammation-06.csv          inflammation-12.csv_min.png\r\ninflammation-06.csv.png      small-01.csv\r\ninflammation-06.csv_min.png  small-02.csv\r\ninflammation-07.csv          small-03.csv\r\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import numpy",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "data = numpy.loadtxt('inflammation-01.csv', delimiter=',')",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "print(data)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[[ 0.  0.  1. ...,  3.  0.  0.]\n [ 0.  1.  2. ...,  1.  0.  1.]\n [ 0.  1.  1. ...,  2.  1.  1.]\n ..., \n [ 0.  1.  1. ...,  1.  1.  1.]\n [ 0.  0.  0. ...,  0.  2.  0.]\n [ 0.  0.  1. ...,  1.  1.  0.]]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data.shape",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(60, 40)"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data.dtype",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "dtype('float64')"
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[2,1]",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "1.0"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[2,:]",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([  0.,   1.,   1.,   3.,   3.,   2.,   6.,   2.,   5.,   9.,   5.,\n         7.,   4.,   5.,   4.,  15.,   5.,  11.,   9.,  10.,  19.,  14.,\n        12.,  17.,   7.,  12.,  11.,   7.,   4.,   2.,  10.,   5.,   4.,\n         2.,   2.,   3.,   2.,   2.,   1.,   1.])"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[:,1]",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 0.,  1.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  0.,\n        0.,  1.,  1.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  1.,  0.,\n        0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,  0.,  1.,  1.,  1.,\n        1.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,  1.,  0.,  0.,  1.,  1.,\n        0.,  0.,  1.,  0.,  1.,  1.,  0.,  0.])"
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[2,:5]",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 0.,  1.,  1.,  3.,  3.])"
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[2,3:5]",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 3.,  3.])"
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "a = ['a', 'b', 'c', 'd', 'e']",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a[:]",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "['a', 'b', 'c', 'd', 'e']"
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a[2]",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "'c'"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a[:3]",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "['a', 'b', 'c']"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a[4:5]",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "['e']"
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[ 0.,  0.,  1., ...,  3.,  0.,  0.],\n       [ 0.,  1.,  2., ...,  1.,  0.,  1.],\n       [ 0.,  1.,  1., ...,  2.,  1.,  1.],\n       ..., \n       [ 0.,  1.,  1., ...,  1.,  1.,  1.],\n       [ 0.,  0.,  0., ...,  0.,  2.,  0.],\n       [ 0.,  0.,  1., ...,  1.,  1.,  0.]])"
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[3,6]",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "2.0"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[4, 8]",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "2.0"
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "data[3,9]",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "7.0"
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<module 'matplotlib.pyplot' from '/Users/dave/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "who",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": "a\t data\t numpy\t plt\t \n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "2+2\n3+3\n4+4",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "8"
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "print(2+2)\nprint(3+3)\nprint(4+4)",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": "4\n6\n8\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "who",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": "a\t data\t numpy\t plt\t \n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "whos",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Variable   Type       Data/Info\n-------------------------------\na          list       n=5\ndata       ndarray    60x40: 2400 elems, type `float64`, 19200 bytes\nnumpy      module     <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\nplt        module     <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.imshow(data)",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x115847710>"
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.show()",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x11374dac8>",
            "image/png": 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iO6nEN7aTSkrG85hXH1vBPUs4A0NH9HGNZu4zo6P72NynE2CZSY/bItNa0uia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\nTQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.imshow(data)",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x115ad4668>"
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x11581b828>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# find max\ndatamax = numpy.max(data, axis=0)\nplt.plot(datamax)\n\ndatamean = numpy.mean(data, axis=0)\nplt.plot(datamean)\n\ndatamin = numpy.min(data, axis=0)\nplt.plot(datamin)",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x115b62cf8>]"
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x115a93c18>",
            "image/png": 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VLVinMzz5I/h5adyjL2j4PJzZAwdW2l1JipJiG3ccj+UtjW10Cm38PiB5fOKo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\nUkplM/8PI0MOrA6nThsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "fig = plt.figure()\n\nax0 = fig.add_subplot(1,3,1)\nax1 = fig.add_subplot(1,3,2, sharey=ax0)\nax2 = fig.add_subplot(1,3,3, sharey=ax0)\n\nax0.plot(datamax)\nax1.plot(datamean)\nax2.plot(datamin)\n\nax0.set_title(\"max\")\nax1.set_title(\"mean\")\nax2.set_title(\"min\")\n\n\nplt.tight_layout()",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x115b90400>",
            "image/png": 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Ajx/o5dp1Xtq9mZXWWKlWT/Q8OlFCuVXRJKiuYJhr2hpsW6IiHX6fl7HpuWKq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\nZUuaoJRSStmSJiillFK29P8D3JD3fSHprgoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "fig, axes = plt.subplots(1,4)\naxes[0].plot(datamin)\naxes[1].plot(datamean)\naxes[2].plot(datamax)\naxes[3].plot(numpy.std(data,axis=0))\nplt.tight_layout()",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x115d010b8>",
            "image/png": 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nPBVjsItfetPjKUrVOgtrpa4d0xSUUjWl1A3AFHCTiFy76ed3K6VOKKVOTE5O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\nrkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "word = \"hey there\"\nfor letter in word:\n    print(letter)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a = [1,2,3,4,5]\nfor entry in a:\n    print(entry*2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for i in range(5):\n    print(a[i])",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "len(a)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for i in range(len(a)):\n    print(a[i]*2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for entry in a:\n    print(entry*2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "fig, axes = plt.subplots(1,3)\naxes[0].plot(datamin)\naxes[1].plot(datamean)\naxes[2].plot(datamax)\n\nfor ax in axes:\n    ax.set_ylim((0,20))\nax.set_xlim((0,30))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "[1,2,3]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "['a', 'b']",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "['a', 1]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "mylist = ['a', 1, ['a', 'b']]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "mylist[2][1]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "numpy.array([[1,2],[2,3]])",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "mylist",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "mylist.append(8789234798237894)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "mylist",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "2**4",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "powers_of_2 = []\nfor i in range(5):\n    powers_of_2.append(2**i)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "powers_of_2",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "powers_of_3 = [i**3 for i in range(5)]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "powers_of_3",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "ages = [3, 40, 32]\nage_after_birthday = [age+1 for age in ages]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "print(age_after_birthday) #should give [4,41,33]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "ls",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import glob",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "glob.glob('inflammation*')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for fn in glob.glob('inflam*.csv'):\n    data = numpy.loadtxt(fn, delimiter=',')\n    fig, ax = plt.subplots(1,1)\n    im = ax.imshow(data, cmap='jet')\n    ax.set_title(fn)\n    fig.colorbar(im)\n    fig.savefig(fn+\".png\")\n",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "fn",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a = 6\nif a>20:\n    print(\"big a\")\nelif a<4:\n    print(\"small a\")\nelif a==6:\n    print(\"a is 6\")\nelse:\n    print(\"medium a\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "1>2",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "1>=1",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "3==2",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "3 is 2",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "a = None",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a is None",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "not (1 < 2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "a = 1",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "if not a==2:\n    print(\"not 2\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for value in range(6):\n    if value%2==0:\n        print(str(value)+\" is even\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "0 is even\n2 is even\n4 is even",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "\"hello \"+\"there\"",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "a = [34,22,43,56]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for value in a:\n    if value%2==0:\n        print(str(value)+\" is even\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def print_evens(listthing):\n    for value in listthing:\n        if value%2==0:\n            print(str(value)+\" is even\")",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "who",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "print_evens(a)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def add(a,b):\n    return a+b",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "add(1,2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "print(add(1,2))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "aplusb = add(1,2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "power(2,4)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "def power_and_add(a,b):\n    \"\"\"You gonna get back a power and \n    an add like this (power, add)\n    \"\"\"\n    return a**b, a+b",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "%%prun \n\n2+2\napowerb, aaddb = power_and_add(2,4)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "power_and_add?",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "aaddb",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "figandaxes = plt.subplots(3,1)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "figandaxes",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "[a+2 for a in range(3)]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "def plot_min(fn):\n    data = numpy.loadtxt(fn, delimiter=',')\n    datamin = numpy.min(data, axis=0)\n    fig, ax = plt.subplots(1,1)\n    ax.plot(datamin)\n    ax.set_title(fn)\n    return ax\n",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "for fn in glob.glob('inflam*.csv'):\n    ax = plot_min(fn)\n    ax.figure.savefig(fn+\"_min.png\")\n",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "plt.show()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "whos",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "newvar = 234\nfor i in range(3):\n    newvar.append(i*2)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "numpy.ones(10)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "a = [1,12]",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "numpy.zeros_like(a).dtype",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "plt.savefig?",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.0",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Desktop/data/Untitled.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}