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Flying Circus Python course notebook
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"[](http://code.jboy.space/flying-circus)\n", | |
"\n", | |
"## John D. Boy\n", | |
"\n", | |
"### Postdoctoral Researcher, Sociology Department\n", | |
"\n", | |
"#### www.jboy.space \n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"slideshow": { | |
"slide_type": "slide" | |
} | |
}, | |
"source": [ | |
"### Schedule\n", | |
"\n", | |
"| Approx. start time | Duration | Topic | |\n", | |
"|--------------------|----------|---------|---------------------------------------|\n", | |
"| 10:10 | 30 mins | Processing tabular data using NumPy | [↩](#01) |\n", | |
"| 10:40 | 20 mins | Loops | [↩](#02) |\n", | |
"| 11:00 | 30 mins | Lists and other data structures | [↩](#03) |\n", | |
"| 11:30 | 20 mins | Working with files | [↩](#04) |\n", | |
"| 11:50 | 30 mins | Conditionals | [↩](#05) |\n", | |
"| 12:20 | 30 mins | Functions | [↩](#06) |\n", | |
"\n", | |
"If we have any time remaining, we'll use it to cover **[error handling](#bonus)** and **debugging techniques**.\n", | |
"\n", | |
"No rest for the wicked 👹 Sorry!" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Flying_Circus.ipynb\n", | |
"flying-circus_site\n", | |
"inflammation-01.csv\n", | |
"inflammation-02.csv\n", | |
"inflammation-03.csv\n", | |
"inflammation-04.csv\n", | |
"inflammation-05.csv\n", | |
"inflammation-06.csv\n", | |
"inflammation-07.csv\n", | |
"inflammation-08.csv\n", | |
"inflammation-09.csv\n", | |
"inflammation-10.csv\n", | |
"inflammation-11.csv\n", | |
"inflammation-12.csv\n", | |
"small-01.csv\n", | |
"small-02.csv\n", | |
"small-03.csv\n" | |
] | |
} | |
], | |
"source": [ | |
"!ls" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Populating the interactive namespace from numpy and matplotlib\n" | |
] | |
} | |
], | |
"source": [ | |
"%pylab inline" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"01\"></a>\n", | |
"## 1. Working with a data array using the NumPy library" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# This is a comment. You might want to add comments occasionally." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Import a library into a program using `import libraryname`. If you want to refer to the library with something other than its full name, use `as`.\n", | |
"* Use the `numpy` library to work with arrays in Python.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"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.]])" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"np.loadtxt('inflammation-01.csv', delimiter=',')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"data = np.loadtxt('inflammation-01.csv', delimiter=',')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `variable = value` to assign a value to a variable in order to record it in memory.\n", | |
"* Variables are created on demand whenever a value is assigned to them.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Variable Type Data/Info\n", | |
"-------------------------------\n", | |
"data ndarray 60x40: 2400 elems, type `float64`, 19200 bytes\n" | |
] | |
} | |
], | |
"source": [ | |
"%whos" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"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" | |
] | |
} | |
], | |
"source": [ | |
"print(data)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `print(something)` to display the value of something. Within Jupyter Notebooks, you can also output values inline without calling `print()`.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"numpy.ndarray" | |
] | |
}, | |
"execution_count": 9, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"type(data)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"dtype('float64')" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data.dtype" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(60, 40)" | |
] | |
}, | |
"execution_count": 11, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data.shape" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* The expression `array.shape` gives the shape of an array.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"First value in data: 0.0\n" | |
] | |
} | |
], | |
"source": [ | |
"print('First value in data:', data[0, 0])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Middle value in data: 13.0\n" | |
] | |
} | |
], | |
"source": [ | |
"print('Middle value in data:', data[30, 20])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"[[ 0. 0. 1. 3. 1. 2. 4. 7. 8. 3.]\n", | |
" [ 0. 1. 2. 1. 2. 1. 3. 2. 2. 6.]\n", | |
" [ 0. 1. 1. 3. 3. 2. 6. 2. 5. 9.]\n", | |
" [ 0. 0. 2. 0. 4. 2. 2. 1. 6. 7.]]\n" | |
] | |
} | |
], | |
"source": [ | |
"print(data[0:4, 0:10])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `array[x, y]` to select a single element from an array.\n", | |
"* Array indices start at 0, not 1.\n", | |
"* Use `low:high` to specify a slice that includes the indices from `low` to `high-1`.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Maximum inflammation for patient 0: 18.0\n" | |
] | |
} | |
], | |
"source": [ | |
"print('Maximum inflammation for patient 0:', np.max(data[0, :]))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Median inflammation overall: 5.0\n" | |
] | |
} | |
], | |
"source": [ | |
"print('Median inflammation overall:', np.median(data))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([ 18., 18., 19., 17., 17., 18., 17., 20., 17., 18., 18.,\n", | |
" 18., 17., 16., 17., 18., 19., 19., 17., 19., 19., 16.,\n", | |
" 17., 15., 17., 17., 18., 17., 20., 17., 16., 19., 15.,\n", | |
" 15., 19., 17., 16., 17., 19., 16., 18., 19., 16., 19.,\n", | |
" 18., 16., 19., 15., 16., 18., 14., 20., 17., 15., 17.,\n", | |
" 16., 17., 19., 18., 18.])" | |
] | |
}, | |
"execution_count": 17, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data.max(axis=1)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([ 0. , 0.45 , 1.11666667, 1.75 ,\n", | |
" 2.43333333, 3.15 , 3.8 , 3.88333333,\n", | |
" 5.23333333, 5.51666667, 5.95 , 5.9 ,\n", | |
" 8.35 , 7.73333333, 8.36666667, 9.5 ,\n", | |
" 9.58333333, 10.63333333, 11.56666667, 12.35 ,\n", | |
" 13.25 , 11.96666667, 11.03333333, 10.16666667,\n", | |
" 10. , 8.66666667, 9.15 , 7.25 ,\n", | |
" 7.33333333, 6.58333333, 6.06666667, 5.95 ,\n", | |
" 5.11666667, 3.6 , 3.3 , 3.56666667,\n", | |
" 2.48333333, 1.5 , 1.13333333, 0.56666667])" | |
] | |
}, | |
"execution_count": 18, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"data.mean(axis=0)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `numpy.mean(array)`, `numpy.max(array)`, and `numpy.min(array)` to calculate simple statistics.\n", | |
"* Use `numpy.mean(array, axis=0)` or `numpy.mean(array, axis=1)` to calculate statistics across the specified axis (column-wise and row-wise, respectively).\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.image.AxesImage at 0x73496a0>" | |
] | |
}, | |
"execution_count": 19, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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P2JxElLs2nme6f3TbPqQ8SXngkB35rI8iMu0zKwfMqz631Yiz5oSz5gFnxQM2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\nPDdYBfMKzw1WwbzCc4NVMK/w3OD/AJ2y6pQmEWHAAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x72e0c18>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pyplot.imshow(data)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use the `pyplot` library to create simple visualizations. This is \"magically\" imported by `%pylab inline`.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x74299e8>]" | |
] | |
}, | |
"execution_count": 20, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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ceSZ8/DH8139l5C1FROps2jQ4+2zYZBMYMAAaN878NRLdQv/qK0/mzz6rZC4i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\nAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x736ba90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"avg_inflammation = np.mean(data, axis=0)\n", | |
"pyplot.plot(avg_inflammation)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x76b3a90>]" | |
] | |
}, | |
"execution_count": 21, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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bG5OOHEnaOQBkad2fftVVYfX0blU6udDNAfQqlJ5OQ29BNwfQrxB6Og09xX5zAIOIdX96\nJc/Q2W8OYFBl708nuYhuDiA/Zfb02i/odHMAeSurp9e6oTe7+dKlLOYA8hNTT6/MPvSxMenoUWn7\n9rInAVAls2ZJjz4qXXNN+NdPr0RymZyUliyR9u2jmwMoxrB7ei0berObb94sjY4W9jQAoJtvln79\n6+H09Not6FNT0uLF0ic/mSQXACjSyZNJdrnlFmnlymKfq58FPeqGznVaAAxTa08P8Xov0Z6h080B\nlGUYPb02yYVuDqBsRff0WuxDb91vzmIOoCz33hve/vToGjr7zQGEIMT96VElF7o5gNAU1dMr3dDp\n5gBCVURPr2xDp5sDCFkoPT2Khk43BxCyUHp68MmFbg4gFnn29Mo1dLo5gNjcfHNy/fStWwfr6ZVq\n6K33BWUxBxCLe++VDh4sp6cHe4bOfUEBxCqP+5FWJrlwX1AAsRu0p1diQee+oACqYpD7kUbf0Fu7\nOYs5gNgN+36kQZ2h080BVE2/PT3q5LJnT7LfnG4OoGr66enRLujvvCNdfjndHEB19drTo2zo7nRz\nANU3jJ5e+rVcuE4LgDqYNUvati3p6UVd76XU5EI3B1A3W7ZId90lHTggnTVDI4mqodPNAdTVG290\nPomNZkF3T67PMjKSbFUEAJyunwW9lIZ+zz10cwDI29DP0LlOCwB0Fvy2xWPHpGXLpPFxFnMAyNvQ\nztCnppJuPncu3RwAOhn6GbqZLTKz18zsJ2b2jZk+d2wsOUNfu3aQZwQAZOl7QTezsyTdJ+mLkj4l\naZmZzW33uZOT0vr10iOPhHvRrYmJibJH6Apz5ieGGSXmzFssc/ZjkDP0+ZJ+6u5vuPtvJT0iaXG7\nT4yhm8fyH5k58xPDjBJz5i2WOfsxyIL+B5J+0fL7N9PHzsB1WgCgeEPZ5UI3B4Di9b3LxcyukrTG\n3Relv79Nkrv73dM+r9htNABQUUN767+ZfUDSIUmfk/SWpL2Slrn7q319QQDAQPp+67+7v2dmqyTt\nVJJuHmAxB4DyFP7GIgDAcBT2omgvbzoqk5m9bmYHzOwFM9tb9jxNZvaAmR02sxdbHpttZjvN7JCZ\nPWlmF5Q5YzpTuzlXm9mbZvZ8+mtRmTOmM11sZk+Z2UEze8nMbk0fD+qYtpnzH9LHgzmmZvZBM/tx\n+m/mJTNbnT4e2rHMmjOYY9nKzM5K59mR/r7n41nIGXr6pqOfKOnr/y1pn6SvuPtruT/ZgMzsZ5Ku\ndPd3y56llZktlHRC0kPufln62N2Sjrn7uvSb5Gx3vy3AOVdLOu7uY2XO1srM5kia4+77zex8Sc8p\ned/EVxXQMZ1hzr9VQMfUzM5z99+kr6X9SNKtkv5GAR3LGeb8KwV0LJvM7B8lXSnpd9x9tJ9/70Wd\noXf9pqMAmAK4t+p07v6MpOnfZBZLejD9+EFJS4Y6VBsZc0rJcQ2Gu7/t7vvTj09IelXSxQrsmGbM\n2Xx/RzDH1N1/k374QSWvxbkCO5ZS5pxSQMdSSn4yk/TXksZbHu75eBa1kHX9pqMAuKRdZrbPzG4u\ne5gOLnT3w1LyD1/ShSXPM5NVZrbfzMbL/tF7OjP7uKR5kv5T0kWhHtOWOX+cPhTMMU3zwAuS3pa0\ny933KcBjmTGnFNCxTP2rpH/SqW84Uh/HM7gz0xJc7e5XKPnueEuaEGIR6iva35L0CXefp+QfUjA/\n2qYZ4zFJX0vPgKcfwyCOaZs5gzqm7j7l7pcr+Slnvpl9SgEeyzZz/qkCO5Zmdq2kw+lPZjP95NDx\neBa1oP+XpI+1/P7i9LHguPtb6f8ekfQDJbkoVIfN7CLp/db6y5Lnacvdj7Tc1eQ7kv68zHmazOxs\nJYvkw+7+ePpwcMe03ZyhHlN3/19JE5IWKcBj2dQ6Z4DH8mpJo+nref8u6bNm9rCkt3s9nkUt6Psk\n/bGZXWJm50r6iqQdBT1X38zsvPRMSGb2IUlfkPRyuVOdxnT6d+wdkpanH98k6fHpf6Ekp82Z/p+v\n6csK55h+V9Ir7r6p5bEQj+kZc4Z0TM3s95qZwsxmSfq8ktYf1LHMmPO1kI6lJLn77e7+MXf/hJK1\n8il3/ztJT6jX4+nuhfxS8h37kKSfSrqtqOcZcMY/krRf0guSXgppTklblewQ+j9JP1eyG2O2pN3p\ncd0p6XcDnfMhSS+mx/aHSlpg2XNeLem9lv/ez6f/H/1ISMd0hjmDOaaSLk3n2p/OdEf6eGjHMmvO\nYI5lm5k/I2lHv8eTNxYBQEXwoigAVAQLOgBUBAs6AFQECzoAVAQLOgBUBAs6AFQECzoAVAQLOgBU\nxP8DpFAe9vF8qV0AAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7439be0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"max_inflammation = np.max(data, axis=0)\n", | |
"pyplot.plot(max_inflammation)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x7717fd0>]" | |
] | |
}, | |
"execution_count": 22, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x76c4fd0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"min_inflammation = np.min(data, axis=0)\n", | |
"pyplot.plot(min_inflammation)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x796d470>]" | |
] | |
}, | |
"execution_count": 23, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7920400>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"pyplot.plot(max_inflammation, drawstyle='steps-mid')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Avoid interpolation between data points in a plot by passing the `drawstyle` argument to `pyplot.plot`.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl\nS+Dll92uW1M5ERkHXA20B3oBL4tIDjXXS92oUfCnP8Euu/iOJDs1bw4XXwxDhviOJDZuAf5PVeur\n6raquo0lyBVTdUnyzjv7jiR+bCV5S8m2gLsXd3T0EcCDwBnAVFW9MNTgsrA9zfLlbgVl5UrbNV+e\na6+FefNg221d+61ffoHtt3f12rfc4ju68ATVTkpErgbGFE+exBWg25OZr4kE+yRgiaq2T9zWEHga\nd/jPN8CfSl1NKn5srOfrt99Chw6524IwU5Ytc/sv/vc/2H1339GkJ+wWcCLynqoeEuL4sZ6zZVmy\nBPbeG5Yu9R1J/CxfDi1buqu52ZifhNkC7mBVPR9YpqpDgIOAdlUN0Li+je3aZecLMChnnglNmsAR\nR8DEiW6jz+TJ8OCD8OOPvqOLPlW9o+Q7n6r+WoUPtOOBY0vd1g94U1V3A94G+gcTabQMHgyXXGIJ\nctgaNoS//33zU0ZNuT4WkadFpIeIdC/+8h1UlFmpReoaNHCt4H76yXck0ZHsOVK/J/5cIyLNgZ8B\neytJgZVaVO7AA91XSS1auDru225zvaRN+URkV9yG2j2BP9rBqWqlbx2qOkVEWpW6+RTg8MT3jwCT\ncYlz1pgzxx0HP2+e70hyw5VXuo1806ZB586+o4m0bYE1wDElblMgB4+bSo4lyekpLrnYYQffkURD\nsknyRBFpANwKfIqbpEEdUZ1TLElOXb9+7nJ43762qa8S44F8YDSuRKoXVdh/UIbGqroEQFUXi0jj\n9EOMlhtugOuucyspJnx160J+vjv2+803fUcTXaray3cMcWNJcnqKk+QDDvAdSTRUmiSLSDXgLVVd\nDjwrIi8DW5VVk2gqN3eu9UJO1U47uU1Vt91mPWwrUUdV3xJXcLgAGCwinwBBbbYtt4hx8ODBf3yf\nl5dHXgxO45g6FT76CB5/3HckuaVXL/jHP2DSJDj6aN/RJGfy5MlMnjw59OcRketU9RYRuYsy5puq\n9g49iJgqKIBDD/UdRXzZ5r3NVZokq2qRiIwFOib+vhZYG3Zg2cpWktPTv7/rfnHttbaaXIG1iQ+3\n80TkCmARUC+N8ZaISBNVXSIiTYFyK8NLJslxoOquUOTnQ506vqPJLTVrukNb+veHo45ynW2irvQH\nvyHhtemYk/jzYyr4UGq2VFAA55/vO4r4atvW2sCVlOyvpbdE5HQR226WjqIi+Pprt3HPpKZlS7ex\n7/bbfUeXl8otAAAgAElEQVQSaVfhutH0BvYDzgOq8rZR3I+12EvAXxLf9wReTD/EaJg0CRYtcqua\nJvPOOMN9UHn2Wd+RRIuqTkx8Oxs4DbgG6Jv4+ruvuOLAyi3SYyvJm0u2BdxKYGtgI/AbGWponm3t\nab75xl0G+s6OdUjLggXuZL6vvnKt4bJFgC3g9gcG4lq21UzcrMUt3Sp57JNAHrA9sARX2/wC8Ayw\nE7AA1wJueRmPjdV8LSpym8b693fJmvFj0iS4/HKYNcutLsdJBlrAzcUlxjOBouLbE2VUQYwfqzlb\nmd9/d/sKVq+G6tV9RxNPhYXuxNEFgbzCoiWV+ZrsiXvbpBaSKclKLYLRqhWcfjqMHu0u15otPEEZ\nb6zJUNVzyvlRt3SDippnnnGtGE8/3Xckua1bN7ffYPx4d9CI2cxSVX3JdxBx8c037rVkCXLqdtoJ\nFi+GdeugVi3f0fiXVJKcKLM4F9hZVW8SkZ2AZqo6NdTosowlycEZMAD239+9qbZs6TuayLE31kqs\nX+86Wvzzn9az3DcRtxH3tNPgvPNc5wvzh3wReRB4ixJ7gVTVWsCVwUot0lejhmu5umCBa9OY65Kt\nSb4Hd4BI8SrTKmBsKBFlMUuSg9O6tTuQ4LzzYONG39FETr6IPGgHEJTvoYfca6hb1q2Px1OXLnDQ\nQXD33b4jiZxeQAfgOODkxNdJXiOKMEuSg2F1yZsk2yf5AFXtJCLTAVR1mYjYQnwVzZ0LJ5/sO4rs\n0bcvvPEGjBgBN97oO5pI6QXsjqtHLi63sAMIEtasgaFD4cWs2X6YHYYNg65d4aKL3Kl8BoDOiZMu\nTRIsSQ6GJcmbJLuSvF5EqpNoRSMiO1DFWsfSRKS+iDwjInNEZJaIZH3raltJDlb16vDYYzB2LLz/\nvu9oIqWzqu6vqj1VtVfi6wLfQUXFnXfCIYe4ch0THbvvDqeeaidqlvK+iOzpO4i4sCQ5GJYkb5Js\nknwn8DzQWESGA1OAEWk+9xjgP6q6B7Avm/pCZqXVq9156FY/G6wdd4T77oNzz4XlW/RbyFn2xlqO\nZcvcYTQ33eQ7ElOW/Hx44AH4/nvfkUTGgcAMEZkrIp+LyEwR+dx3UFFlSXIw2rSB+fN9RxENSbWA\nAxCR3YGjcO3f3lLVlJNaEdkWmK6qbSu5X9a0p5k+3TU4nznTdyTZ6bLL4Jdf4Kmn4rsRK8AWcHOA\ntkAhbrNPccvGSlvApfm8kZ+v/fq518n99/uOxJSnb19Ytcptqoy6DLSAa1XW7dYCbkuqsM02ru95\n/fq+o4m3Tz6BCy+EGTN8RxKsVOZrsn2S7wT+paqBXNQWkX2B+3GN0vfFnSp0lar+Vup+WTOB//Uv\nmDDBfZng/fab63nbq5frubrVVr4jqroAk+RQ31greN5Iz9dFi6B9e/j8c3cFwkTTzz+7srQPPoj+\n7vqwk+SwRX3OVsWPP8Kee7ortiY9y5a5Vqu//hrfRaeypDJfky23+AS4QUTmi8g/EocVpKMG0AkY\nq6qdgDVAvzTHjDSrRw5XnTrw73/DSy9BkybucIgnnnCTPdeo6oKyvnzH5dvQofDXv1qCHHXbbw99\n+thmXFM1VmoRnIYN3Z6fn3/2HYl/yR4m8gjwiIhsB5wO3CwiLVU11c/53wHfqurHib9PAK4v646D\nBw/+4/u8vDzy8vJSfMrMGT7cXfZv08adg96mDbzzDlxgW6dCteee8O67sHQpvPyyS5ovvRTuugt6\n9vQd3ZYmT57M5MmTfYeRE776Cp57zn1YNdF31VVuFfnTT93pmqZqRKQF8CjQBLfJ/gFVvdNvVOGy\nJDlYxZv3GjXyHYlfSdckA4hIF+As4BRgjqqm3NBMRN4FLlLVr0QkH6irqteXuk/sLgU99JBrSfbY\nY+7Umvnz3Qtt4UIYM8YlzSZz3n8f/vxnlyRF/RQmu3QbnrPOgg4d3BHUJh7uucddGXrtNd+RlC+q\nc1ZEmgJNVXWGiNTDXQ0+RVW/LHW/yM7Zqho2zLV3HJFuSwEDwJ/+BN27w9ln+44kOKEdSy0itwCn\nAfOBfwE3qWq6vQR6A0+ISE2gANfbNdbeesu9Cf/3v1ZaERUHHwyNG7ueuN3tOI2c9MknMGWK+wBr\n4uOvf3WdSN55B444wnc08aKqi4HFie9XJTbz7gh8WeEDY6ygwP2+N8GwNnBOsjXJ84GDgXxcQtte\nRA5L54lV9TNV7ayqHVS1u6r+ms54vs2eDT16uEv8liBHS58+cPvtvqMwvvTv7+pbt97adySmKmrV\ncquD/fq5zgUmNSLSGndq30d+IwmXlVsEy5JkJ9kT94qAt4EWwAxc78YPgCNDiitWliyBE090qx6H\nH+47GlPaaafBddfBRx/BAVl/ZI0p6e233S/6Cy/0HYlJxVlnucNFnn/ergSlIlFqMQHXPWqV73gq\nU1TkymzWrKn6Y7/4wpLkILVp43KaW26p+mObNnUtb7NBsi3gZgKdgQ9VtUOiZ/IIVQ3111Yc6qVW\nrICjj4bjj4cSewxNxNxxh2sp9fTTviMpX1TrG5MVtfmq6j4U9emTXXV1uebVV93/w5kzoUayyzoZ\nEuU5KyI1gJeBV1V1TDn30fz8/D/+7ntz/Lx5cNBBqW1yr1cPbrgBqiV7fdxUaMUKGDUKNmyo+mPH\njHG9zmvWDD6uqii9OX7IkCGh9UmepqqdRWQGcICqrhWRWaq6V1WDrlJwEXvTLe2zz1yrseOPdy+K\nbOonmG1WroTWrV19auvWvqMpW5TfcJMRtfn67LOu08zHH9sbZ5ypuprk88+PXoegKM9ZEXkU+ElV\n+1Rwn0jN2ddec6uXkyb5jsSkY+ed3f/DXXbxHcnmwuyT/J2INABeACaJyItAzvZdVYVx46BbNxgy\nBO680xLkqNtmG/cGO6bM9RSTbTZsgIEDYeRIS5DjTsT9fxw8GH7/3Xc08SAihwDnAkeKyHQR+VRE\njvMdV2Wsrjg7ZFM9c7J9kk9LfDtYRN4B6gMRbswTntWr3Ylu06a5LhZ77OE7IpOs3r1h333dm60d\nW5rdHn4YmjWDY47xHYkJwkEHuX7JY8fCtdf6jib6VPU9IOJNL7dkSXJ2yKYkucprLKr6rqq+pKrr\nwggo6o45xm0umDrVEuS42WknOO44eOAB35GYMP32m7vCM3KkXeHJJsOHu018v8a6D5KpiCXJ2SGn\nk+RcVljoDgd5+GFrJxVXffq48pj1631HYsIydix07gwHHug7EhOkvfZyXYT+8Q/fkZiwWJKcHSxJ\nzlFvvOE6WViNY3ztvz/ssIPrdGGyz/LlrmXRsGG+IzFhGDzYtQhbvNh3JCZoqpYkZwtLknPU66/D\nscf6jsKk64gj4N13fUdhwnDrrXDSSbDnnr4jMWFo1cp1ubAPQdnn559di7+GDX1HYtLVtq276h6h\nxikpsyQ5SevXu4MJjj7adyQmXYcf7jZdmuzyww9w773WrzzbDRgATz3l3oRN9rBV5OzRsKHbD7Js\nme9I0mdJcpKmTnW9/5o08R2JSVfXru70PatLzi433QQ9e0LLlr4jMWHaYQe46ioYNMh3JCZIliRn\nD5HsKbmwJDlJVmqRPRo0cJeDPv7YdyQmKF9/Df/+t1tlNNnvmmvgrbdgxgzfkZigWJKcXSxJzjGW\nJGeXww+3uuRsMmgQXH01NGrkOxKTCdts4w6LGTjQdyQmKJYkZxdLknPIL7/AnDlw8MG+IzFBsSQ5\ne8yYAe+845Jkkzsuvhhmz7b9BdnCkuTsYklyDnnzTTjsMKhd23ckJihdu8L777vji028DRjgVhTr\n1fMdicmk2rVh6FDo3z87dtHnOkuSs4slyTnESi2yT6NG7gS+6dN9R2LS8e678OWXblXR5J5zzoEV\nK2DiRN+RmHSsW+e60+y0k+9ITFAsSc4Rqu4QkWOO8R2JCZqVXMSbKvTr57pa1KrlOxrjQ/Xq7vjx\nAQNg40bf0ZhULVwIO+4INWv6jsQEpWVLWLQo/l2kvCbJIlJNRD4VkZd8xlGROXPcL+J27XxHYoJm\nSXK8vfQSrFkDPXr4jsT4dOKJrmPNE0/4jsSkykotsk+tWtCsGXz7re9I0uN7JfkqYLbnGCpUXGoh\n4jsSE7TDDoMpU2wFKo42bnSrh8OH2zHxuU4ERo1yHU7WrvUdjUmFJcnZKRtKLry9vYhIC+AE4EFf\nMSTDSi2yV9Om7nCYmTN9R2Kq6rHHYLvt3CqiMYceCnvv7U5cNPFjSXJ2siQ5PaOBvkBk9yX//ju8\n9x4cdZTvSExYrOQifn7/HfLz3eqhXeExxUaMcPXJK1f6jsRUlSXJ2SkbkuQaPp5URE4ElqjqDBHJ\nA8p9qxs8ePAf3+fl5ZGXlxd2eH/43/9gn31cvZvJTocfDhMmuGNuM23y5MlMnjw5808cc/feC/vu\nC4cc4jsSEyXt28PRR8Ptt7sPUSY+LEnOTm3awHPP+Y4iPaIeGkyKyAjgPGADUAfYBnhOVc8vdT/N\ndHxr1sC0afDBB/DMM3DqqXDjjRkNwWTQokUu4frxR/+1rSKCqsZ2bTQT83XFCth1V9e7fJ99Qn0q\nE0OFhbD//m7DdePG4T+fzdn0qbqFqMJCV0JlssfUqXDppfDJJ74jcVKZr16S5M0CEDkcuFZV/6+M\nn2VsAk+fDhdd5H657rMPHHSQ+/q//4OttspICMaTXXaBF15wNY0+Rf0NV0S+AX4FioD1qtql1M9D\nn6/5+fDNN/DII6E+jYmx3r3dB9477gj/uaI+ZysThST555+hbVtYtszKp7LNTz+5RY1ly3xH4qQy\nX72UW0RNURH87W/w5z/DJZdYUpxriuuSfSfJMVAE5Kmql195P/4Id98dnVUJE00DB8Kee7pjylu3\n9h2NqUxxqYUlyNln++1dJ6Jly6BhQ9/RpMZ78yRVfbesVeRMevJJd8nnyistQc5FtnkvaYLH3xnD\nhsF551niYyrWpAlcfrnVJceF1SNnL5H4b97zniT7tno19O/vLs35rkk1fhx9tEuSK2oftXQpXHgh\nzI50V+/QKTBJRKaJyEWZfOLCQndYxMCBmXxWE1d//zu89hp88YXvSExlLEnObpYkx9zNN0PXrnDw\nwb4jMb40a+YOFbnrLlduU/pAgkmToEMH+PhjeDDSXb1Dd4iqdsL1N79cRA7N1BPn57srPZnYjGXi\nb9tt3ZHlAwb4jsRUpqDA1SSb7NS2bbyT5JyuSV6wAMaOhRkzfEdifNt1V/jwQzj/fDjySNcWbrvt\n3Jvs00/Do49C8+bQrRvceqs7qjzXqOoPiT+XisjzQBdgSsn7hNGy8fPP3aE+8+alPZTJIZde6q4Q\nvvdecO0CrW1j8AoK4E9/8h2FCUubNvHOsbx3t6hI2Dtve/SAdu1gyJDQnsLETFGRO+r4vvugUSM3\nwR94wG1AALeiPGaMq2MOWpR3yotIXaCaqq4Ska2BN4AhqvpGifuEMl9PPtl9OPHRy9rE28MPw7hx\n8N//hrMxLMpzNhlR6G6x886upaOtJmen11+Hf/zDXZH1LZYt4CoS5gR+7z04+2z48kvYeutQnsLE\n2H/+47op9Oy5+ZvrqFHuCsQ//xn8c0b5DVdEdgaex9Ul1wCeUNVRpe4T+HydMsVt1ps7F2rXDnRo\nkwM2bnSHjNx6K5xwQvDjR3nOJsN3krx+PdSrB6tWQc2a3sIwIZo3D447DubP9x2JJclJKyqCAw5w\nLYLOPTfw4U0WKyyELl3g+++D/6Vub7ibU3X7BS66yH1YMSYVL7zgatqnTw9+c7bN2fTMn++uEhUW\negvBhGzdOthmG9ckoYbnAt9U5mtObtybMMG9AZ9zju9ITNzsvLO7LPj2274jyX6vvALLl7uVZGNS\ndcopULcuPPWU70gyR0TGicgSEfncdywVsc4W2a9WLWjaFL791nckqcm5JHnDBhg0yNWdWvNyk4oe\nPeBf//IdRXbbuNG1ZhwxIjc3SZrgiLgyqRtvdKtaOWI8cKzvICpjSXJuiHMbuJxLkh9/3LWROuYY\n35GYuDrzTHjxxS1bxZngPPWUu0R38sm+IzHZ4PDDYbfd3CbcXKCqU4CIHAZcPkuSc0Ock+ScagG3\nbp3rZPHoo7aKbFLXvDnsu687rOCUU3xHk33WrXOrfo88YvPUBGfECLd5r2dPt1nMBGf1avjtt6o/\n7ssvrewxF7RpA7NmwU8/Vf2x9er5PQk5p5LkBx90qwldu/qOxMTd2We7kgtLkoN3332w++5w2GG+\nIzHZpGNHyMtzvZNvuMF3NNERRG/zXXZxH26r+qG2enVXCmOyW+fOrknC449X7XEbNsDee7suR6kI\noq95znS3WLPGHRjx0kuw336BDGly2E8/uTeGRYuCayFoO+Vh5Uo3T197zfWkNiZIX38NBx7oVjAb\nNUp/vCjPWRFpBUxU1fYV3CftObtsGbRqBb/+ald+TLB+/BH22AN+/jmY8ay7RQXGjnW/HC1BNkFo\n1AgOOgheftl3JNnljjvgqKMsQTbh2GUXd7pbjqxeSuIrVMV1xZYgm6DtsIPb+7N8ub8YciJJXrHC\nNZMfOtR3JCabFJdcmGAsXepOM7R5asJ0440wfjwsXOg7kvCIyJPA+0A7EVkoIr3Cei7bfGfCIuJe\nWz77aOdEkjx6NBx7LOy1l+9ITDY59VR45x345RffkWSHkSPhrLPseFoTrmbN4JJL3CbubKWq56hq\nc1WtraotVXV8WM9lSbIJk+/OGFm/ce/HH+Guu+Cjj3xHYrJN/fpw/PGuXdnll/uOJt4WLnTdLGbN\n8h2JyQXXXQft2sGcOa7m0aSuoMB1+zEmDL6TZC8rySLSQkTeFpFZIjJTRHqH9VxDh7pdlbY6ZcLQ\nq5e7dGvSk58Pl17qTmYyJmwNGkDfvjBwoO9I4q+gwN5fTXhyMkkGNgB9VHUv4CDgchHZPegnmTfP\n1Yxaux8TlqOOgiVLYOZM35HE16xZ7gjqvn19R2JyyRVXwLRpdpUxXVZuYcKUk0myqi5W1RmJ71cB\nc4Adg36eAQPg2mvdDkljwlC9ujucwFaTU3fDDXD99a58xZhMqVPHXcHo1w8i3Ak10jZsgO++cy3g\njAlD27Z+k2TvfZJFpDUwGdg7kTCX/FnKPRw//BDOOAO++grq1k03SmPK9/XXcMgh7s2iZs3Ux4ly\nz9VkpDJfP/zQteSaO9clLcZkUvFhBWPGuM3dVZWLc7akwkJ3QMuCBcHFZExJv//uFlDWrHGLUumI\nXZ9kEakHTACuKp0gp0PVbcwYOtQSZBO+XXZxJzm+8orvSOJF1a3iDR5sCbLxo0YNGD4c+veHoiLf\n0cSPlVqYsG21FTRu7BahfPDW3UJEauAS5MdU9cXy7pfKkZkTJ7q2XD17ph+nMcko3sB36qnJPyaI\nIzPj7PXXXT33+ef7jsTksu7d3eEizzzjWhCa5FmSbDKhuC7ZR1mPt3ILEXkU+ElV+1RwnypfCtqw\nAdq3d4eHnHhiulEak5xVq2Cnndxxt02apDZGLl26LSpyp1/eeKNLUozx6a234G9/g9mzq1YylUtz\ntiz9+0O9etYlxISrVy849FC48ML0xolNuYWIHAKcCxwpItNF5FMROS6IsceNc0vzJ5wQxGjGJKde\nPbeK/PjjviOJh6efhlq14LTTfEdijOtS07q1e/8wyZs/31aSTfh8drjw1d3iPVWtrqodVLWjqnZS\n1dfSHXfZMhg0CO64w86RN5nXqxc89JDtlK/MunVuBXnUKJunJjpGjoSbbnIbhExyrNzCZELOJclh\nGTTIXbrt0MF3JCYXde0Ka9e63qumfOPGubY+RxzhOxJjNtl/f9el5s47fUcSH5Ykm0zwmSR7bwFX\nkarUS33+OXTr5o4Z3X77kAMzphzDhrm6xieeqPoqaS7UN65eDbvu6jbX7rdfhgIzJklz57rax6++\ngoYNK79/LszZ8ixbBi1bwooVdkXIhGvJEteqcenS9MaJTU1y0FShd28YMsQSZOPXFVe4N9g+fazs\noixjxrgVd0uQTRTttpurkx81ynck0VdY6Fb4LEE2YWvc2JVBrViR+efOiiT53/+G5cvh4ot9R2Jy\nXYMG8Oab8P777oObJcqb/Pwz3H67q/s0Jqry8+HBB2HRIt+RRFtBgSubMiZsIu4DWWFh5p879kny\n6tXQty/cdVf6p7EYE4QGDeCNN+Djj+Gyy+yQgmI33+xOwWzXznckxpRvxx3hr391h1GZ8lk9sskk\nX3XJsU+SR450l2+7dvUdiTGb1K/vDsuYORMuucQS5e++cxv2Bg3yHYkxlbv+enjuOVejbMpmSbLJ\npDZtXMvBTIt1klxYCPfeC7fc4jsSY7a07bbw2mvugJEhQ3xH49eQIa4cqnlz35EYU7nttoNrr3Wt\nCk3ZLEk2mWQrySno1w+uvtpdHjMmiurVcwdn/POfMGOG72j8+PJLeOEFuO4635EYk7zeveG991zZ\nlNmSJckmkyxJrqIPPnCbo/qUe6i1MdHQvLmrx73gAli/3nc0mXfDDfD3vyfXUsuYqKhb160k9+/v\nO5Lo2bABvv0WWrXyHYnJFZYkV4GqS46HD3e/yIyJur/8xbWxybXSoGnT3AfaK6/0HYkxVXfhhfDN\nN65jjdnku++gSROoXdt3JCZXtG4NCxbAxo2Zfd5YJsnPPOOOtj3vPN+RGJMcEbj/fhg9GmbN8h1N\n5vTv71pq2YdZE0c1a7oDgvr3t3aOJc2fb6UWJrPq1IFGjTLfmjF2SfLata4W+bbboFrsoje5rGVL\n94Z7wQXucmW2mzQJFi6EXr18R2JM6s48061ePfus70iiw+qRjQ8+Si5il2bedRfssw/k5fmOxJiq\nu/hi2HpruOMO35GEq6jIrb4NG+ZW44yJq2rV4O67rTNLSZYkGx/ats18klwjs0+Xnp9+chugpkzx\nHYkxqalWzZ3m1aULnHsuNGvmO6JwFK+6nXGG3ziMCcLBB/uOIFoKCuCUU3xHYXKNrSRXYsgQOPts\n2G0335EYk7o2beCTT7I3QV6/HgYOdAf9WEmUMdnHVpKNDz6S5NisJD//PLz4Inz6qe9IjElfNrdO\nGj8edtoJunXzHYkxJgwFBe7StzGZlFMrySJynIh8KSJficj1Fd13xgx3tO/zz7vdjcaYzEtmzq5Z\nA0OHwqhRrqOHMcaPqrzHVsXy5a67lL0Xm0zLmSRZRKoBdwPHAnsBPURk97Luu2QJnHoqjB0L++0X\nXAyTJ08ObrAsG99i9zd+VCU7Z+++Gw48EDp3Dj4Ge934Gd9ij5+qvMdWVWGhS1Yq+xBsrxs/48c5\n9srGb9IEVq+GlStDDWEzvlaSuwDzVHWBqq4H/gWUuQ2ge3d3EMOZZwYbQDa/kKI8dtjjxzn2iEtq\nzt56q+toEQZ73fgZ32KPpaTfY6sq2Xpke934GT/OsVc2vgjsvLP7oJYpvpLkHYFvS/z9u8RtW2je\nHAYNykhMxpjyJTVnTz0Vdg9kvcoYk4ak32OryjbtGZ8yXXIR+Y17Dz9sO+SNiYv8fN8RGGOq4uST\nq3b/2bOhT59wYjGmMm3buoXTceMqvl/DhvDoo+k/n6iHszZF5EBgsKoel/h7P0BV9eZS97ODQE1O\nUdVIbndLZs7afDW5KIpz1t5jjSlbVeerryS5OjAXOAr4AZgK9FDVORkPxhhTKZuzxsSHzVdjguGl\n3EJVN4rIFcAbuLrocTZ5jYkum7PGxIfNV2OC4WUl2RhjjDHGmCiL5Ja4sJqglxj/GxH5TESmi8jU\nAMYbJyJLROTzErc1FJE3RGSuiLwuIvUDHDtfRL4TkU8TX8elEXsLEXlbRGaJyEwR6R1U/GWMfWWQ\n8YtIbRH5KPH/caaI5AcYe3ljB/lvXy0xxktBxe1LnOZsmPO1gvGDes3bfK36+DZnS4nTfE2MF8v3\n2DDnaznjBzZnbb4mqGqkvnCJ+9dAK6AmMAPYPeDnKAAaBjjeoUAH4PMSt90MXJf4/npgVIBj5wN9\nAoq9KdAh8X09XB3b7kHEX8HYQcZfN/FndeBDXH/QoP7tyxo7yNivAR4HXgryNZPpr7jN2TDnawXj\nB/K6sfma0vg2Zzf/b4jVfE2MF8v32DDnayXjBxV/zs/XKK4kh9YEvQQhwFV0VZ0CLCt18ynAI4nv\nHwFODXBscP8NaVPVxao6I/H9KmAO0IIA4i9n7OJenUHFvybxbW1cjb0S3L99WWNDALGLSAvgBODB\nEjcHErcHsZqzYc7XCsaHAF43Nl9TGh9szpYUq/kK8X2PDXO+VjB+YHPW5ms0yy1Ca4JeggKTRGSa\niFwU8NjFGqvqEnAvZKBxwONfISIzROTBoC7xiUhr3CfqD4EmQcZfYuyPEjcFEn/icsp0YDEwSVWn\nBRV7OWMHFftooC+bfikQVNweZMOcDXu+QsBz1uZr0uMHFX+2zNlsmK8Qs/fYMOdrqfEDm7M2X6OZ\nJGfCIaraCfcp43IROTQDzxnkDsl7gDaq2gH34ro93QFFpB4wAbgq8Ym0dLwpx1/G2IHFr6pFqtoR\n9+m8i4jsVUasKcVexth7BhG7iJwILEmsAFT0idl21W6S6Tkb9L99oHPW5mvS49uc9cPeY0sIc76W\nM34g8dt8jWaSvAhoWeLvLRK3BUZVf0j8uRR4Hnf5KWhLRKQJgIg0BX4MamBVXaqJohrgAaBzOuOJ\nSA3cBHtMVV9M3BxI/GWNHXT8iTFXAJOB4wj4377k2AHFfgjwfyJSADwFHCkijwGLw3rNhCwb5mxo\n8xWCfc3bfK3a+DZnt5AN8xVi8h4b5nwtb/yg52wuz9coJsnTgF1EpJWI1ALOBl4KanARqZv41IWI\nbA0cA3wRxNBs/onlJeAvie97Ai+WfkCqYyf+5xbrTvrxPwTMVtUxJW4LKv4txg4qfhFpVHwpRkTq\nAEfjarLSjr2csb8MInZVHaCqLVW1De71/baq/hmYmG7cnsRxzoY5X7cYP+A5a/M1+fFtzm4pjvMV\n4i670iEAAAEDSURBVPseG+Z8LXP8IOK3+bppsMh94T6tzAXmAf0CHntn3G7e6cDMIMYHngS+B9YC\nC4FeQEPgzcR/xxtAgwDHfhT4PPHf8QKuzibV2A8BNpb4N/k08e+/XbrxVzB2IPED+yTGnJEYb2Di\n9iBiL2/swP7tE+Mdzqadt2nH7esrTnM2zPlawfhBveZtvlZ9fJuzW/43xGa+JsaM5XtsmPO1kvHT\njt/mq/uyw0SMMcYYY4wpJYrlFsYYY4wxxnhlSbIxxhhjjDGlWJJsjDHGGGNMKZYkG2OMMcYYU4ol\nycYYY4wxxpRiSbIxxhhjjDGlWJJsjDHGGGNMKZYkG2OMMcYYU8r/A122qOZA2sl3AAAAAElFTkSu\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x798d5c0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig = pyplot.figure(figsize=(10.0, 3.0))\n", | |
"\n", | |
"axes1 = fig.add_subplot(1, 3, 1)\n", | |
"axes2 = fig.add_subplot(1, 3, 2)\n", | |
"axes3 = fig.add_subplot(1, 3, 3)\n", | |
"\n", | |
"axes1.set_ylabel('average')\n", | |
"axes1.plot(np.mean(data, axis=0))\n", | |
"\n", | |
"axes2.set_ylabel('max')\n", | |
"axes2.plot(np.max(data, axis=0))\n", | |
"\n", | |
"axes3.set_ylabel('min')\n", | |
"axes3.plot(np.min(data, axis=0))\n", | |
"\n", | |
"fig.tight_layout()\n", | |
"pyplot.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* You can also combine several plots into one.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"02\"></a>\n", | |
"## 2. Loops" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"word = 'lead'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"l\n", | |
"e\n", | |
"a\n", | |
"d\n" | |
] | |
} | |
], | |
"source": [ | |
"for char in word:\n", | |
" print(char)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `for variable in sequence` to process the elements of a sequence one at a time.\n", | |
"* The body of a for loop must be indented.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 27, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"1\n", | |
"2\n", | |
"3\n", | |
"4\n", | |
"5\n", | |
"6\n", | |
"7\n", | |
"8\n", | |
"9\n", | |
"10\n" | |
] | |
} | |
], | |
"source": [ | |
"for item in range(1, 11):\n", | |
" print(item)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `range(lower, upper)` to create a series of integers.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"longword = 'floccinaucinihilipilification'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"29\n" | |
] | |
} | |
], | |
"source": [ | |
"length = 0\n", | |
"\n", | |
"for char in longword:\n", | |
" length += 1\n", | |
"print(length)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"29" | |
] | |
}, | |
"execution_count": 30, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"len(longword)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `len(thing)` to determine the length of a sequence.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 3. Lists and other data structures" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 31, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"odds = [1, 3, 5, 7]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 32, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds are [1, 3, 5, 7]\n" | |
] | |
} | |
], | |
"source": [ | |
"print('odds are', odds)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* `[value1, value2, value3, ...]` creates a list.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"first and last: 1 7\n" | |
] | |
} | |
], | |
"source": [ | |
"print('first and last:', odds[0], odds[-1])" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Lists are indexed and sliced in the same way as strings and arrays.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 34, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"names is originally: ['Marx', 'Webber', 'Durkheim']\n", | |
"final value of names: ['Marx', 'Weber', 'Durkheim']\n" | |
] | |
} | |
], | |
"source": [ | |
"names = ['Marx', 'Webber', 'Durkheim']\n", | |
"print('names is originally:', names)\n", | |
"names[1] = 'Weber'\n", | |
"print('final value of names:', names)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 35, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"ename": "TypeError", | |
"evalue": "'str' object does not support item assignment", | |
"output_type": "error", | |
"traceback": [ | |
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", | |
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", | |
"\u001b[1;32m<ipython-input-35-a51584277af1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Engels'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'e'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", | |
"\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" | |
] | |
} | |
], | |
"source": [ | |
"name = 'Engels'\n", | |
"name[0] = 'e'" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Lists are mutable (i.e., their values can be changed in place).\n", | |
"* Strings are immutable (i.e., the characters in them cannot be changed).\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds after adding a value: [1, 3, 5, 7, 11]\n" | |
] | |
} | |
], | |
"source": [ | |
"odds.append(11)\n", | |
"print('odds after adding a value:', odds)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 37, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds after removing the first element: [3, 5, 7, 11]\n" | |
] | |
} | |
], | |
"source": [ | |
"del odds[0]\n", | |
"print('odds after removing the first element:', odds)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 38, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds after reversing: [11, 7, 5, 3]\n" | |
] | |
} | |
], | |
"source": [ | |
"odds.reverse()\n", | |
"print('odds after reversing:', odds)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 41, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds after adding/appending: [11, 7, 5, 3, 11, 11, 7, 5, 3, 11, 11]\n" | |
] | |
} | |
], | |
"source": [ | |
"odds += [11]\n", | |
"print('odds after adding/appending:', odds)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 42, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"odds after multiplying: [11, 7, 5, 3, 11, 11, 7, 5, 3, 11, 11, 11, 7, 5, 3, 11, 11, 7, 5, 3, 11, 11]\n" | |
] | |
} | |
], | |
"source": [ | |
"odds = odds * 2\n", | |
"print('odds after multiplying:', odds)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Adding and multiplying lists adds or multiplies items; it does not change the values.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"04\"></a>\n", | |
"## 4. Working with files" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 43, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"from glob import glob" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 44, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['inflammation-01.csv',\n", | |
" 'inflammation-02.csv',\n", | |
" 'inflammation-03.csv',\n", | |
" 'inflammation-04.csv',\n", | |
" 'inflammation-05.csv',\n", | |
" 'inflammation-06.csv',\n", | |
" 'inflammation-07.csv',\n", | |
" 'inflammation-08.csv',\n", | |
" 'inflammation-09.csv',\n", | |
" 'inflammation-10.csv',\n", | |
" 'inflammation-11.csv',\n", | |
" 'inflammation-12.csv']" | |
] | |
}, | |
"execution_count": 44, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"glob('inflammation*.csv')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `glob(pattern)` to create a list of files whose names match a pattern. Patterns support [wildcards](http://www.tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm).\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 45, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"files = glob('inflammation*.csv')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 46, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-01.csv\n", | |
"inflammation-02.csv\n", | |
"inflammation-03.csv\n", | |
"inflammation-04.csv\n", | |
"inflammation-05.csv\n", | |
"inflammation-06.csv\n", | |
"inflammation-07.csv\n", | |
"inflammation-08.csv\n", | |
"inflammation-09.csv\n", | |
"inflammation-10.csv\n", | |
"inflammation-11.csv\n", | |
"inflammation-12.csv\n" | |
] | |
} | |
], | |
"source": [ | |
"for f in files:\n", | |
" print(f)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 47, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-01.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl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\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x8427748>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-02.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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XgH2APbP5RhEZJCIzReTTEretLyLDRWSKiLy24opSvvn2W7uic+ONoSPJb9df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\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x74b6e48>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-03.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nSkAAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x76165c0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"for f in files[0:3]:\n", | |
" print(f)\n", | |
"\n", | |
" data = numpy.loadtxt(fname=f, delimiter=',')\n", | |
"\n", | |
" fig = pyplot.figure(figsize=(10.0, 3.0))\n", | |
"\n", | |
" axes1 = fig.add_subplot(1, 3, 1)\n", | |
" axes2 = fig.add_subplot(1, 3, 2)\n", | |
" axes3 = fig.add_subplot(1, 3, 3)\n", | |
"\n", | |
" axes1.set_ylabel('average')\n", | |
" axes1.plot(np.mean(data, axis=0))\n", | |
"\n", | |
" axes2.set_ylabel('max')\n", | |
" axes2.plot(np.max(data, axis=0))\n", | |
"\n", | |
" axes3.set_ylabel('min')\n", | |
" axes3.plot(np.min(data, axis=0))\n", | |
"\n", | |
" fig.tight_layout()\n", | |
" pyplot.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"05\"></a>\n", | |
"## 5. Conditionals" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 48, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Not so big.\n", | |
"done\n" | |
] | |
} | |
], | |
"source": [ | |
"num = 42\n", | |
"if num > 100:\n", | |
" print('Wow! Much big!')\n", | |
"else:\n", | |
" print('Not so big.')\n", | |
"print('done')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 49, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"-3 is negative\n" | |
] | |
} | |
], | |
"source": [ | |
"num = -3\n", | |
"\n", | |
"if num > 0:\n", | |
" print(num, 'is positive')\n", | |
"elif num == 0:\n", | |
" print(num, 'is zero')\n", | |
"else:\n", | |
" print(num, 'is negative')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 50, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"num = 27\n", | |
"if num > 42:\n", | |
" print('Fantastic number!')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Use `if condition` to start a conditional statement, `elif condition` to provide additional tests, and `else` to provide a default. If `else` is omitted and no condition is matched, the conditional statement does nothing.\n", | |
"* The bodies of the branches of conditional statements must be indented.\n", | |
"* Use `==` to test for equality.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 51, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"At least one part is false.\n" | |
] | |
} | |
], | |
"source": [ | |
"if (1 > 0) and (-1 > 0):\n", | |
" print('Both parts are true.')\n", | |
"else:\n", | |
" print('At least one part is false.')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 52, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"At least one test is true.\n" | |
] | |
} | |
], | |
"source": [ | |
"if (1 < 0) or (-1 < 0):\n", | |
" print('At least one test is true.')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* `X and Y` is only true if both `X` and `Y` are true.\n", | |
"* `X or Y` is true if either `X` or `Y`, or both, are true.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 53, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Suspicious looking maxima!\n" | |
] | |
} | |
], | |
"source": [ | |
"data = np.loadtxt('inflammation-01.csv', delimiter=',')\n", | |
"if np.max(data, axis=0)[0] == 0 and np.max(data, axis=0)[20] == 20:\n", | |
" print('Suspicious looking maxima!')\n", | |
"elif np.sum(np.min(data, axis=0)) == 0:\n", | |
" print('Minima add up to zero!')\n", | |
"else:\n", | |
" print('Seems OK!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 54, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Minima add up to zero!\n" | |
] | |
} | |
], | |
"source": [ | |
"data = np.loadtxt('inflammation-03.csv', delimiter=',')\n", | |
"if np.max(data, axis=0)[0] == 0 and np.max(data, axis=0)[20] == 20:\n", | |
" print('Suspicious looking maxima!')\n", | |
"elif np.sum(np.min(data, axis=0)) == 0:\n", | |
" print('Minima add up to zero!')\n", | |
"else:\n", | |
" print('Seems OK!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 55, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"bool" | |
] | |
}, | |
"execution_count": 55, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"type(True)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 56, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"True is true!\n" | |
] | |
} | |
], | |
"source": [ | |
"if True:\n", | |
" print('True is true!')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 57, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Non-zero string is true.\n", | |
"Non-empty list is true.\n", | |
"One is true.\n" | |
] | |
} | |
], | |
"source": [ | |
"if '':\n", | |
" print('Empty string is true.')\n", | |
"if 'word':\n", | |
" print('Non-zero string is true.')\n", | |
"if []:\n", | |
" print('Empty list is true.')\n", | |
"if [1, 2, 3]:\n", | |
" print('Non-empty list is true.')\n", | |
"if 0:\n", | |
" print('Zero is true.')\n", | |
"if 1:\n", | |
" print('One is true.')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 58, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Empty string is false.\n", | |
"Empty list is false.\n", | |
"Zero is false.\n" | |
] | |
} | |
], | |
"source": [ | |
"if not '':\n", | |
" print('Empty string is false.')\n", | |
"if not 'word':\n", | |
" print('Non-zero string is false.')\n", | |
"if not []:\n", | |
" print('Empty list is false.')\n", | |
"if not [1, 2, 3]:\n", | |
" print('Non-empty list is false.')\n", | |
"if not 0:\n", | |
" print('Zero is false.')\n", | |
"if not 1:\n", | |
" print('One is false.')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Zero, the empty string, and the empty list are considered false; all other numbers, strings, and lists are considered true.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 59, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"['flying-circus_site', 'Flying_Circus.ipynb', 'inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv', 'small-01.csv', 'small-02.csv', 'small-03.csv']\n" | |
] | |
} | |
], | |
"source": [ | |
"files = glob('*')\n", | |
"print(files)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 60, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Large files: ['inflammation-01.csv', 'inflammation-02.csv', 'inflammation-03.csv', 'inflammation-04.csv', 'inflammation-05.csv', 'inflammation-06.csv', 'inflammation-07.csv', 'inflammation-08.csv', 'inflammation-09.csv', 'inflammation-10.csv', 'inflammation-11.csv', 'inflammation-12.csv'] \n", | |
"Small files: ['small-01.csv', 'small-02.csv', 'small-03.csv'] \n", | |
"Other files: ['flying-circus_site', 'Flying_Circus.ipynb']\n" | |
] | |
} | |
], | |
"source": [ | |
"large_files = []\n", | |
"small_files = []\n", | |
"other_files = []\n", | |
"\n", | |
"for file in files:\n", | |
" if not file.endswith('.csv'):\n", | |
" other_files.append(file)\n", | |
" elif file.startswith('inflammation-'):\n", | |
" large_files.append(file)\n", | |
" elif file.startswith('small-'):\n", | |
" small_files.append(file)\n", | |
"\n", | |
"print('Large files:', large_files, \n", | |
" '\\nSmall files:', small_files,\n", | |
" '\\nOther files:', other_files)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 61, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"The number of vowels in this sentence is 13\n" | |
] | |
} | |
], | |
"source": [ | |
"vowels = 'aeiou'\n", | |
"sentence = 'A Political Revolution Is Coming.'\n", | |
"count = 0\n", | |
"for char in sentence.lower():\n", | |
" if char in vowels:\n", | |
" count += 1\n", | |
" \n", | |
"print('The number of vowels in this sentence is', count)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"06\"></a>\n", | |
"## 6. Functions" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 62, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"def count_vowels(sentence):\n", | |
" vowels = 'aeiou'\n", | |
" count = 0\n", | |
" for char in sentence.lower():\n", | |
" if char in vowels:\n", | |
" count += 1\n", | |
" return count" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 64, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"5" | |
] | |
}, | |
"execution_count": 64, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"count_vowels('Winter is coming.')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Define a function using `def function_name(parameters)`.\n", | |
"* The body of a function must be indented.\n", | |
"* Call a function using `function_name(values)`.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 65, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"def four():\n", | |
" return 4" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 66, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"5" | |
] | |
}, | |
"execution_count": 66, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"1 + four()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"* Calling a function is like replacing it with its return value.\n", | |
"\n", | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 67, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"def visualize_data(dataset):\n", | |
" data = numpy.loadtxt(dataset, delimiter=',')\n", | |
"\n", | |
" fig = pyplot.figure(figsize=(10.0, 3.0))\n", | |
"\n", | |
" axes1 = fig.add_subplot(1, 3, 1)\n", | |
" axes2 = fig.add_subplot(1, 3, 2)\n", | |
" axes3 = fig.add_subplot(1, 3, 3)\n", | |
"\n", | |
" axes1.set_ylabel('average')\n", | |
" axes1.plot(np.mean(data, axis=0))\n", | |
"\n", | |
" axes2.set_ylabel('max')\n", | |
" axes2.plot(np.max(data, axis=0))\n", | |
"\n", | |
" axes3.set_ylabel('min')\n", | |
" axes3.plot(np.min(data, axis=0))\n", | |
"\n", | |
" return fig.tight_layout()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 68, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-01.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl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\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7885e10>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-02.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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XgH2APbP5RhEZJCIzReTTEretLyLDRWSKiLy24opSvvn2W7uic+ONoSPJb9df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\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x76407f0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-03.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nrqgMw6hH6lYk33NPPD+yUR34M8l33um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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7995860>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-04.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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lFtlp3dr+7Ssr3eUWiMjxuB7JvUVkkIgMCi9WstlMcnb23deWXKRDRMYAVwPt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\nQmEAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x73440b8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-05.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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Bc1S1D7APsG2uATq7ldG2rSfI2Tj8cPjoIzjySDjwQBg9OnRE6aCqd6z8yqeq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\nuPSoW9c6seyxhx048uOPoSOKV3m5rb7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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7ac1470>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-06.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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Agc65r4O4bjWPGevxCtrurV073Wy7446+0yTPDz/AHnvAtGl6+mjchd3dQkRO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\nIki6+vXhxRf1VK8o9U02mRk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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x76c8c88>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-07.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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ffQcjR7q7PCY8N97oWlt+8YXvSGJhkarO9h1EnPz0E9SqBdvaS4mMFSXJCby5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\nPZOxiz7XWZKcLJYk55ivv4b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TO6svEye6f2vnzr4jMabyzjoLNmxwzznGsXpk44OPkotY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\nAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7841710>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-08.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nrqgMw6hH6lYk33NPPD+yUR34M8l33um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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7506080>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-09.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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ghqrunHmuMfAktvnPV8BJFd1NSnt//fZbaN0aPvvMF87GafZsWz3/9ts2ap9m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\nuYi8/LKt1PSSUoXh7LOtQsl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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x758c908>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-10.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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kBoBzblloEW1+vFgW0TjjDN295pJLfEeSfGvW6K3cXr3g/PN9R1M+Qe7eJSJn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\nSUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7603470>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-11.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nrqgMw6hH6lYk33NPPD+yUR34M8l33um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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x5b86780>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-12.csv\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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iamDNGteipkMH30nioVMn27yXpiLgKFWdo6qvAwcBac2/i8hIEVkoItNKfa2J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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7937550>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"datafiles = glob('inflammation-*.csv')\n", | |
"\n", | |
"for file in datafiles:\n", | |
" print(file)\n", | |
" visualize_data(file)\n", | |
" pyplot.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 69, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"def identify_problems(dataset):\n", | |
" data = np.loadtxt(dataset, delimiter=',')\n", | |
" if np.max(data, axis=0)[0] == 0 and np.max(data, axis=0)[20] == 20:\n", | |
" return 'Suspicious looking maxima!'\n", | |
" elif np.sum(np.min(data, axis=0)) == 0:\n", | |
" return 'Minima add up to zero!'\n", | |
" else:\n", | |
" return 'Seems OK!'" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 70, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"inflammation-01.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-02.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-03.csv\n", | |
"Minima add up to zero!\n", | |
"inflammation-04.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-05.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-06.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-07.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-08.csv\n", | |
"Minima add up to zero!\n", | |
"inflammation-09.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-10.csv\n", | |
"Suspicious looking maxima!\n", | |
"inflammation-11.csv\n", | |
"Minima add up to zero!\n", | |
"inflammation-12.csv\n", | |
"Suspicious looking maxima!\n" | |
] | |
} | |
], | |
"source": [ | |
"for file in datafiles:\n", | |
" print(file)\n", | |
" print(identify_problems(file))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<a id=\"bonus\"></a>\n", | |
"## Bonus: Error handling" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 71, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"The data file does not seem to exist: [Errno 2] No such file or directory: 'inflammation-13.csv'\n" | |
] | |
} | |
], | |
"source": [ | |
"try:\n", | |
" data = np.loadtxt('inflammation-13.csv', delimiter=',')\n", | |
"except FileNotFoundError as err:\n", | |
" print('The data file does not seem to exist:', err)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---\n", | |
"\n", | |
"[Thanks to Software Carpentry for the great learning materials!](https://swcarpentry.github.io/python-novice-inflammation/)" | |
] | |
} | |
], | |
"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": "3.4.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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