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Chapter 2 done
Starting on stack with numpy in Chapter 3
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‎.ipynb_checkpoints/Chapter2.BasicPython-checkpoint.ipynb

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‎.ipynb_checkpoints/Chapter3.Stack-checkpoint.ipynb

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{
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"metadata": {
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"name": ""
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"The Scientific Python Stack"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The scientific Python stack consists of Numpy, Scipy, and Matplotlib. Numpy brings to Python what Matlab is well known for : strong array manipulation and matrix operations, elementary mathematical functions, random numbers, ..."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"\n",
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"A = np.ones(10)\n",
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"B = np.arange(5, 10, 0.1) # like range(), but allows non-integer stride\n",
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"C = np.linspace(0, 2 * np.pi, 30) # between a and b in c steps\n",
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"D = np.random.normal(0, 1, 20) # also beta, binomial, gamma, poisson, uniform, lognormal, negative binomial, geometric, ...\n",
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"E = np.sin(C)\n",
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"\n",
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"print A, \"\\n\"\n",
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"print B, \"\\n\"\n",
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"print C, \"\\n\"\n",
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"print D, \"\\n\"\n",
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"print E"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] \n",
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"\n",
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"[ 5. 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6. 6.1 6.2 6.3 6.4\n",
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" 6.5 6.6 6.7 6.8 6.9 7. 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9\n",
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" 8. 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9. 9.1 9.2 9.3 9.4\n",
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" 9.5 9.6 9.7 9.8 9.9] \n",
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"\n",
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"[ 0. 0.21666156 0.43332312 0.64998469 0.86664625 1.08330781\n",
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" 1.29996937 1.51663094 1.7332925 1.94995406 2.16661562 2.38327719\n",
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" 2.59993875 2.81660031 3.03326187 3.24992343 3.466585 3.68324656\n",
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" 3.89990812 4.11656968 4.33323125 4.54989281 4.76655437 4.98321593\n",
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" 5.1998775 5.41653906 5.63320062 5.84986218 6.06652374 6.28318531] \n",
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"\n",
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"[-0.98189204 -0.58611512 1.58800176 -0.9281483 1.06510968 0.8507472\n",
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" -1.4315527 -0.31871079 0.20131501 -0.08079119 -0.73679295 0.23427112\n",
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" 0.18814395 0.46756174 0.39062454 0.13378789 -0.49214194 -0.72168981\n",
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" -0.25113278 0.67123937] \n",
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"\n",
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"[ 0.00000000e+00 2.14970440e-01 4.19889102e-01 6.05174215e-01\n",
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" 7.62162055e-01 8.83512044e-01 9.63549993e-01 9.98533414e-01\n",
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" 9.86826523e-01 9.28976720e-01 8.27688998e-01 6.87699459e-01\n",
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" 5.15553857e-01 3.19301530e-01 1.08119018e-01 -1.08119018e-01\n",
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" -3.19301530e-01 -5.15553857e-01 -6.87699459e-01 -8.27688998e-01\n",
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" -9.28976720e-01 -9.86826523e-01 -9.98533414e-01 -9.63549993e-01\n",
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" -8.83512044e-01 -7.62162055e-01 -6.05174215e-01 -4.19889102e-01\n",
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" -2.14970440e-01 -2.44929360e-16]\n"
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]
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}
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],
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"prompt_number": 16
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Before we go on, a note on *namespaces*. A namespace is a way to \"partition\" your functions into different spaces. This is particularly useful because you may have functions with the same name, but they may not do the same thing :"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print sin(0.5)\n",
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"print np.sin(0.5)\n",
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"\n",
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"sin(0.5) == np.sin(0.5)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"0.479425538604\n",
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"0.479425538604\n"
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]
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},
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 41,
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"text": [
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"True"
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]
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}
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],
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"prompt_number": 41
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"x = [] # x is an empty list ( not a numpy array here )\n",
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"for i in range(10) :\n",
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" x.append(float(i) / 10)\n",
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"\n",
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"# Equivalent numpy array generation\n",
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"y = np.arange(0, 1, 0.1)\n",
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"\n",
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"# These should still be equal\n",
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"print sin(y), \"\\n\"\n",
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"print np.sin(y)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[ 0. 0.09983342 0.19866933 0.29552021 0.38941834 0.47942554\n",
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" 0.56464247 0.64421769 0.71735609 0.78332691] \n",
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"\n",
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"[ 0. 0.09983342 0.19866933 0.29552021 0.38941834 0.47942554\n",
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" 0.56464247 0.64421769 0.71735609 0.78332691]\n"
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]
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}
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],
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"prompt_number": 49
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Numpy arrays can be easily manipulated\n",
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"blah = np.random.lognormal(0, 1, (5, 3))\n",
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"\n",
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"print np.dot(blah, blah.T), \"\\n\"\n",
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"print blah.shape, \"\\n\"\n",
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"print type(blah)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[[ 8.51713884 6.91034565 7.68810156 2.69320072 4.27556815]\n",
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" [ 6.91034565 8.90777709 10.88645377 3.13778003 3.36210778]\n",
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" [ 7.68810156 10.88645377 14.61116563 2.24539017 2.38654341]\n",
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" [ 2.69320072 3.13778003 2.24539017 3.20013026 3.11656473]\n",
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" [ 4.27556815 3.36210778 2.38654341 3.11656473 3.70435789]] \n",
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"\n",
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"(5, 3) \n",
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"\n",
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"<type 'numpy.ndarray'>\n"
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]
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}
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],
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"prompt_number": 34
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# The numpy array is its own type, but it's still a container. It expands on the capabilities of the standard Python list.\n",
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"# Access is easier though : for multidimensional arrays, you just use a comma per dimension\n",
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"print type(blah[0, 0])"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"<type 'numpy.float64'>\n"
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]
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}
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],
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"prompt_number": 35
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# The : operator still works great. Standard arithmetic is ELEMENT-wise. Dimensions are broadcast.\n",
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"print blah[:, 1] * blah[:, 2] - 2.5"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[ 1.17124417 -0.56444793 -0.2318266 -2.40826853 -2.12920678]\n"
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]
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}
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],
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"prompt_number": 38
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [],
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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],
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"metadata": {}
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}
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]
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}

‎Chapter0.ShamelessPlug.ipynb

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{
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"metadata": {
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"name": ""
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"Chapter 0. Shameless Plug"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We're a growing community, and we have a website ! Check us out at"
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]
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},
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"**`princetonpy.com`**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We have a discussion forum on which you're welcome to discuss Python-related stuffs and ask questions. It's a great place to do so, because everyone can then see the discussion, rather than having it by email."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Also : quick thanks to our sponsers : the **Department of Ecology and Evolutionary Biology**, the **Department of Geosciences**, and **Bryan Grenfell (EEB)** for some extra funding. We really appreciate it !"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [],
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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],
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"metadata": {}
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}
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]
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}

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