{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Welcome to Computational Cognitive Neuroscience**\n", "\n", "This notebook will walk you through the most important commands, functions and concepts you will need for the programming part of the course. If you have troubles following the material presented here or the exercises, you will most likely struggle with the labs and programming exercises in the assignments. However, to improve your underestanding of python as a language or basic programming concepts, you can look for tutorials online e.g. https://www.youtube.com/watch?v=_uQrJ0TkZlc (this starts at the most basic concepts like if statements and defining variables). \n", "\n", "\n", "**1. Define variables**\n", "\n", "Variables are value stores you can reference by name. In order to name a new variable you can simply type: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Native type of variable x is:\n", "Native type of variable y is:\n", "4\n", "Native type of variable v is:\n" ] } ], "source": [ "x = 4 #variable with name x has value 4 in memory\n", "y = True #bool\n", "\n", "#check type of x nd y\n", "#print will print output in the space beneath the cell. Great to check results. \n", "print('Native type of variable x is:' + str(type(x)))\n", "print('Native type of variable y is:' + str(type(y)))\n", "\n", "#you can compute with both of them, but be careful which type the result is: \n", "v = x * y\n", "print(v)\n", "print('Native type of variable v is:' + str(type(v)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Attention*: Python does not (always) type check as the last computation above shows. This means that you can change the type of the variable without explicitly noticing it. Be careful that you always have the right type (int, float, boolean, string, etc.) when computing with multiple different types. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2. Numpy - The tool for effecient array computations**\n", "\n", "The basic module we will be using is numpy. It is a comprehensive library based on matrix operations including the most important mathematical tools. It has been defined to be similar to Matlab and therefore it is easy to swtich from Matlab to Python with numpy for most. A rule of thumb is: If you need a defined mathematical operation or concept like standard devation, histograms, or computing eigenvalues of a matrix, there will be a native numpy function for it already. Simply google for it to find the exact function name and argument list in the numpy documentary. E.g. try googling \"eigenvalues numpy\" to find the specifications of how to compute eigenvalues with numpy. The link is also provided below. \n", "\n", "General documentation with links to numpy tutorials: \n", "https://numpy.org/doc/stable/\n", "\n", "Example for a function to compute eigenvalues of a matrix: \n", "https://numpy.org/doc/stable/reference/generated/numpy.linalg.eig.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.1 Import modules such as numpy, matplotlib and seaborn**\n", "\n", "Try executing the cell below to import the numpy module. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np #load module to be able to call functions as np.functionname()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you get an Error as output looking like this **Error:\"No module named numpy\"** follow the steps described below to install the missing module. \n", "\n", "We recommend to use pip to install numpy. If you are using a conda environment, you can also use the conda install in your command line. Both ways are explained here: https://numpy.org/install/\n", "Open up a terminal and type in your comand line:\n", "\n", "*pip install numpy*\n", "\n", "Also go ahead and install the two modules we will be using for plotting: \n", "\n", "*pip install matplotlib*\n", "\n", "*pip install seaborn*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.2 Working with numpy**\n", "\n", "We will be using numpy arrays as data structure. Matrices do exists in numpy but are limited in their functionality. Therefore whenever the word \"matrix\" is used in this tutorial, we will refer to a native numpy.ndarray. \n", "\n", "The standard data type of numpy arrays is float. This can be troublesome if you try to use them for indexing. However you can change the standard type by specifying the dtype in function calls. E.g. specifying dtype = 'int' will return an array with int entries which can be used to index another numpy array. \n", "\n", "Matrices of appropriate size can be added, subtracted and multiplied in the linear algebra sense using +, - and @ (or np.matmul). * or np.multiply() will result in elemtwise multiplication. For two 1D arrays np.dot() returns the inner product. np.outer() returns the outer product of the input arrays. You need to be careful with this operation when you arrays which have dimensions > 2 as input. Any array with dimensions > 2 will be flattened before computing the outer product. np.transpose(array), array.transpose() and array.T will transpose the array. \n", "\n", "*Examples:*" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 3]\n", " [2 4]]\n", "(1, 3)\n", "(3, 1)\n", "[[1 2 3]\n", " [1 2 3]]\n" ] } ], "source": [ "#define a matrix\n", "x = np.array([[1,3],[2,4]])\n", "print(x)\n", "\n", "#in numpy it is easier to define row vectors to have shape 1x3 and column vectors will have shape 3x1 and are still matrixes. \n", "#array.shape will return the shape of the array \n", "#it is equivalent to calling the function np.shape(array)\n", "\n", "#row vector\n", "a = np.array([[1,2,3]])\n", "print(a.shape)\n", "\n", "#column vector\n", "b = np.array([[1],[2],[3]])\n", "print(b.shape)\n", "\n", "#concatenate vecotors (use built in function) and pay attention that shape is cosistent for concatenation\n", "c = np.concatenate([a,b.T])\n", "print(c)\n", "\n", "#special matrix functions: \n", "#will give you a predefined matrix of the arguments size -> have a look at the documentation of the functions to figure out what the do\n", "nulls = np.zeros([5])\n", "\n", "ones = np.ones([3,6,5])\n", "\n", "identity = np.identity(5) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Exercises:* \n", "1. Create a variable a with value 1\n", "2. Create an array of size 10 x 10 with all entries being 0. \n", "3. Create an array of size 10x20x30. Check the size of A. How does the native function len() differ from the numpy array.shape attribute or np.shape(array) function? How can you efficiently compute the number of all elements in an numpy array? \n", "4. Create a 10x5 matrix A of ones and a 5x1 vector x of ones. Multiply A and x. Why does A * x return an Error? How can you fix this? What does np.outer(A,x) compute? \n", "5. Create a vector x = [1,2,3,4,5]. Try multiplying x @ x.T and x.T@ x What does x*x compute? How can you compute the outer product of x with itself? \n", "*Tip*: Look up what the function np.arange() does. \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "###Write your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2.3 Indexing**\n", "\n", "Indexing is used to access a subset of the elements of an array. For example, given a set of time series strored in a 2D array, one might wish to extract the set of time samples for a single variable, or the set of variables at a single time sample. Like many other programming languages, Python indexing starts counting at 0.\n", "\n", "There are 3 major ways how to index arrays: \n", "1. Subscript indexing: To access a single value you can call it by their exact position along each dimension. All dimensions need to be accounted for in the call. E.g. A[2,3] returns the value of the 4th element of the 3rd row. \n", "\n", "2. Linear indexing/ slicing: To access multiple values or return a part of an array, you can specifiy which slice of the array you would like to return. E.g. A[2:8] of a 1-dimensional array will return all elements starting at the 3rd element until the 7th element (last index is excluded). To call all element of a specific dimension use [:], e.g. A[:,8] will give you the complete 9th row of a 2-dimensional array. You can also use negative index to start counting at the end A[-2] will return the 2nd to last value of array A. \n", "\n", "3. Tuples of indeces for each dimension: This is specifically practical for filtering the array for a specific condition. E.g. you want to only get the values of array A which are bigger than 1. Use np.where(A > 1) to return a tuple of the index for all values bigger 1. You can also directly use the condition as index writing A[A > 0]. \n", "\n", "*Examples:* " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.49615688 -0.62814647 0.7014297 -0.31083986 -1.13705658 -0.32650086]\n", " [-0.3207024 0.99898805 0.14233469 -1.49561231 -0.58188058 -0.25646618]\n", " [ 0.40732661 1.33068984 1.66280675 0.72117666 -0.52661199 -1.8522562 ]\n", " [ 0.51671204 1.01471511 1.04406253 0.94871187 -0.21655268 -0.38952712]\n", " [ 0.0672306 -0.16335798 -1.56767959 0.46254271 0.77468511 0.25916385]\n", " [-1.16268279 0.57199056 0.70135228 -0.13372021 0.43906393 1.1528434 ]\n", " [ 2.22656829 -1.22469629 -0.56995014 -0.41647378 -1.46377291 0.22987738]\n", " [ 0.44993794 -0.79050928 0.19618163 -0.2225414 0.44862591 -1.07333407]\n", " [ 1.20382184 0.06116717 0.27988857 1.17014302 -0.58534145 0.39007102]\n", " [-0.72834758 0.32026646 -0.31354007 -1.24502811 -0.76826471 0.51632318]]\n", "0.1423346850393753\n", "[-1.13705658 -0.58188058 -0.52661199 -0.21655268 0.77468511 0.43906393\n", " -1.46377291 0.44862591 -0.58534145 -0.76826471]\n", "[ 0.51671204 0.0672306 -1.16268279 2.22656829 0.44993794]\n", "1.2038218447850384\n", "[1.49615688 0.7014297 0.99898805 0.14233469 0.40732661 1.33068984\n", " 1.66280675 0.72117666 0.51671204 1.01471511 1.04406253 0.94871187\n", " 0.0672306 0.46254271 0.77468511 0.25916385 0.57199056 0.70135228\n", " 0.43906393 1.1528434 2.22656829 0.22987738 0.44993794 0.19618163\n", " 0.44862591 1.20382184 0.06116717 0.27988857 1.17014302 0.39007102\n", " 0.32026646 0.51632318]\n", "[0 0 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 7 7 7 8 8 8 8 8 9 9] [0 2 1 2 0 1 2 3 0 1 2 3 0 3 4 5 1 2 4 5 0 5 0 2 4 0 1 2 3 5 1 5]\n", "[1.49615688 0.7014297 0.99898805 0.14233469 0.40732661 1.33068984\n", " 1.66280675 0.72117666 0.51671204 1.01471511 1.04406253 0.94871187\n", " 0.0672306 0.46254271 0.77468511 0.25916385 0.57199056 0.70135228\n", " 0.43906393 1.1528434 2.22656829 0.22987738 0.44993794 0.19618163\n", " 0.44862591 1.20382184 0.06116717 0.27988857 1.17014302 0.39007102\n", " 0.32026646 0.51632318]\n", "True\n" ] } ], "source": [ "A = np.random.randn(10,6)\n", "print(A)\n", "\n", "#get the 3rd element of the 2nd row\n", "print(A[1,2])\n", "\n", "#print the 5 column of A\n", "print(A[:,4])\n", "\n", "#print a subset of the 1st column. Here we print the values starting at the 4rt element and stop before the 2nd to last element\n", "print(A[3:-2,0])\n", "\n", "#print the 2nd to last element of the 1st column of A. Why was it not included in the subset printed before? \n", "print(A[-2,0])\n", "\n", "#print all values bigger 0\n", "print(A[A > 0])\n", "\n", "\n", "#return indexes of all values bigger than 0\n", "rows, cols = np.where(A > 0)\n", "print(rows, cols)\n", "\n", "#use these to print all values bigger 0\n", "print(A[rows, cols])\n", "\n", "#use index tuple of np.where()\n", "all_dims = np.where(A > 0)\n", "\n", "#both can be used for indexing and are equal in their results: \n", "print(np.all(A[rows, cols] == A[all_dims]))\n", "#np.all() returns True if all entries of the array are True, otherwise it returns False\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Exercises*\n", "\n", "Create a random array A (use np.randn(size)) with size 10x50x3x15. \n", "Use two different ways to print all negative entries of A. How does the result from np.where(A) depending on the number of variables you store the result in. \n", "\n", "Read the documentation on np.nonzero(). Can you use it to index all nonnegative entries of A. How does np.nonzero() and np.where() differ? " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "###Write your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3. Loops and Conditional statements**\n", "\n", "Loops are used to create an iterative process. Essentially the same operation is repeated until the loop terminating statement is fulfilled. \n", "There are two different loops: \n", "\n", "*while(condition)* loop: The computation in the loop is run as long as the condition is true. If the condition is always True this creates an endless loop. \n", "\n", "*for(x in y)* loop: Repeats the computation whithin the loop len(y) times. The values of x can be used within the loop. We will extensively use *for* loops in this course. \n", "\n", "In addition to this there are also *condition statements*: \n", "\n", "*if(condition): ...*\n", " \n", "\n", "*if(condition): ... else: ...* \n", " \n", " \n", " \n", "*if(condition1): ... elif(condition2): ... else: ...*\n", "\n", "\n", "There is a range of other *flow control* statements such as *break* and *match*. We wont need them in this course. But they are usefull to further control the looping behaviour , e.g. break ends a loop prematurely or are a simplification for multiple nested *if...else* statements. If you like to know more have a look at the documentation: https://docs.python.org/3/tutorial/controlflow.html\n", "\n", "*Example*\n", "\n", "Compute the squares for 1 to 5\n", "\n", "*Tip*: Remember python starts counting at 0!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "4\n", "9\n", "16\n", "25\n", "1\n", "4\n", "9\n", "16\n", "25\n" ] } ], "source": [ "for i in range(1,5+1): \n", " print(i**2)\n", " \n", "#with an array: \n", "times = np.arange(1,5+1) #creates array with entries 1 to 5\n", "for i in times: \n", " print(i**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Exercises*\n", "\n", "1. Compute a 10x1 array of size where each entry is the square of its index. \n", "\n", "2. Compute a 5x4 array where each entry is the product of the its two indeces. \n", "\n", "3. Compute a 20x1 array where each entry is sum of the last two entries where A[0] = 0 and A[1]= 1. *Tip: Pay attention to the maximum index of the array*" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "###write your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4. Plotting interpretable plots**\n", "\n", "The ability to create interpretable and well-labeled plots is essential to this course. Creating highquality plots typically involves plotting multiple things on the same graph, colour coding or otherwise distinguishing the various quantities plotted, clearly labeling using axes and legends with legible fontsize, making multiple subpanels within an overall figure, and using different kinds of data representation including heatmaps and histograms. We wil now briefly cover each of these concepts in Python using matplotlib.pyplot and seaborn as modules. \n", "\n", "If you are already familiar with matplotlib as plotting tool, you wont need to work through this section. Seaborn is an addition to matplotlib which results in better legible and aestathically more pleasing plots, but does not change the handles or syntax of most plotting functions. However, have a look at seaborn.heatmaps() function to familiarize yourself with it. \n", "\n", "*Note:* To improve illegibility for all, we set the colour palette (selection of colours used for plotting) to be suitable for colourblind readers as well. \n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "[(0.00392156862745098, 0.45098039215686275, 0.6980392156862745),\n", " (0.8705882352941177, 0.5607843137254902, 0.0196078431372549),\n", " (0.00784313725490196, 0.6196078431372549, 0.45098039215686275),\n", " (0.8352941176470589, 0.3686274509803922, 0.0),\n", " (0.8, 0.47058823529411764, 0.7372549019607844),\n", " (0.792156862745098, 0.5686274509803921, 0.3803921568627451),\n", " (0.984313725490196, 0.6862745098039216, 0.8941176470588236),\n", " (0.5803921568627451, 0.5803921568627451, 0.5803921568627451),\n", " (0.9254901960784314, 0.8823529411764706, 0.2),\n", " (0.33725490196078434, 0.7058823529411765, 0.9137254901960784)]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt \n", "import seaborn as sns \n", "sns.set() #activate the seaborn aesthatics\n", "\n", "#change the colour palette to be colour blind friendly\n", "sns.set_palette(\"colorblind\")\n", "sns.color_palette(\"colorblind\") #shows you the color palette used now\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Lineplots*\n", "As a first example on how to structure and present your data, we will be creating line plots. For this you can just call plt.plot(data). But there are a few tricks which allow you to create mutliple lineplots within the same figure. \n", "\n", "As example data we will be using randomized values and pretend that they are the data of a time dependent process. For the sake of creating annotations, we assume we measured 4 variables of this process: membrane potential in mV, external current in pA, temperature in C and the external calcium concentration in mM over 100ms.\n", "\n", "The most simple plot is created like this: \n", "We will create a figure and axis of a single subplot. Then we use the axis to plot the data we have created. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#create Data\n", "Data = np.random.randn(100,4)\n", "\n", "#create a figure and axes\n", "fig, ax = plt.subplots(1,1)\n", "#plot as lineplot, each dimension is one line in the plot\n", "ax.plot(Data)\n", "#show figure\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Why is this a bad plot? \n", "\n", "*Write your thoughts here*\n", "\n", "To improve the plot, we will be adding a legend, change the figure size, split the data in two subplots and add axis description as well as plot titles. You can also change the fontsizes explicitly in each command. *Exercise: Identify which command or handle does each of those things.*" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "improved_fig, improved_ax = plt.subplots(1,2, figsize = (20,5))\n", "improved_ax[0].plot(Data[:,0], label= 'membrane potential (mV)' )\n", "improved_ax[0].plot(Data[:,1], label= 'external current (pA)' )\n", "improved_ax[1].plot(Data[:,2], label= 'temperature (C)' )\n", "improved_ax[1].plot(Data[:,3], label= 'c(Ca) (mM)' )\n", "improved_ax[0].set_xlabel('ms')\n", "improved_ax[1].set_xlabel('ms')\n", "improved_ax[0].set_title('First two variables', fontsize = 24)\n", "improved_ax[1].set_title('Last two variables')\n", "improved_ax[0].legend()\n", "improved_ax[1].legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot looks less confusing than the first one. \n", "\n", "*Exercise: Now try to plot each variable in their own subplot and change the y-axis of each subplot to display the unit of the displayed variable. Save your figure as .png and .pdf.*\n", "\n", "*Tip:* Look up what plt.rc('text', usetex = True), plt.rc('font', family = 'serif'), plt.rcParams['axes.labelsize'] = 14, plt.rcParams['xtick.labelsize']=14, plt.rcParams['axes.titlesize'] =16 do. Can they help you get rid of redundant code? You will have to use these **before** you create your figure. Play around with the values to see their effect and find good standard values for your figures. \n", "\n", "figure.save() can help you to save your plot in different formats. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "###Write your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a plethora of plots you can create with this schema. Try plotting a heatmap of the data. The cell below shows you how to use the sns.heatmap() function. Also, try out other plots of the matplotlib and seaborn packages, e.g. plot data as a surfaceplot, as a histogram or plot categorical data as barplots. Are you able to create a figure with 4 subplots where each subplot shows the same data as a different plot? " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#to create a heatmap: \n", "fig_hm, ax_hm = plt.subplots(1,1)\n", "hm = sns.heatmap(Data.T, ax = ax_hm, cbar = True, center = 0)\n", "ax_hm.set_xlabel('ms')\n", "ax_hm.set_ylabel('variables')\n", "ax_hm.set_title('Data as heatmap')\n", "plt.show()\n", "\n", "### Write you code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5. Writing functions**\n", "\n", "With every operation so far, we have used function calls. But you can also create your own functions in order to group computations together. Functions have inputs and outputs. Using functions will make your code more readable and easier to debug, especially if you have to run the same computation multiple times with different parameters, defining a function will save you a lot of time. \n", "Below you can see an example of self-defined function. \n", "\n", "For good practice, you define functions first and keep the code clearly structured. However, the function needs to be in the memory in order to be callable. This means, that you can define a function in a later cell, if you execute this cell first and then execute the cell which uses the function to compute something. For automatic execution it is better to keep a clear structure and have all definitions before any executions. \n", "\n", "A typical good code structre is: \n", "1. Module import , e.g. import numpy as np\n", "2. define function, e.g. def functionname(inputs): return None\n", "3. define parameter values and variable names\n", "4. code to run \n", "5. plot results of computations\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def myfun(input1, input2, input3): \n", " \n", " output = (input1 * input3)/input2\n", " \n", " return output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**6. Vectorisation**\n", "\n", "Numpy was specifically created to take advantage of matrix and vector operations. Therefore using linear algebra and matrix multiplication (\"vectorisation\") is more effecient than creating writing loops, iterating over each entry of the arrays. However, the improvement in efficiency typically come at the cost of readability, as the resulting linear algebra expressions can sometimes appear inscrutable. Making sure to write comments and choose appropriate variable names etc. can help here. \n", "\n", "There is also a tradeoff between the time spent vectorising your code and the time saved in running it. If your code is running in a reasonable time, it may not be worth your time to vectorise your code. However, in certain situations the improvement in run time can be substantial and the time spent vectorising can save you a lot of time in the long run, e.g. when you have to repeat the vectorised computation multiple times with changing parameter values. \n", "\n", "To compute the run time of a function or some arbitratry operations you can use this code snippet: " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time: 1.8960010493174195e-05\n" ] } ], "source": [ "import timeit\n", "\n", "start = timeit.default_timer()\n", "\n", "#Your function calls and operation here\n", "\n", "stop = timeit.default_timer()\n", "\n", "print('Time: ', stop - start) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Attention: You will need to have installed timeit. If you have not install it over pip as mentioned above.*\n", "\n", "**7. Parallelisation**\n", "\n", "Another way to imporve run time on loops is to use parallelisation. A parallel for loop runs through different elements of a for loop simultaneously accross multiple cores of your processor (or GPU). This requires the joblib module. Again use pip or conda to install joblib, but you can easily get by without parallel computing. \n", "\n", "In order to replace a for loop with the joblib command, you will define a function you call with the parameters defined by the joblib call. The nice thing about jolib is that the definition of the parameter values to use it is really similar to the looping condition of a for loop. Joblib will return a tuple of the ordered results. This means that the result of the first parameter will be the first element in the returned tuple. Here is example code for computing the squares for 0 to 99 using joblib instead of a for loop and comparing the runtime of both versions: " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loop time: 0.00016749498900026083\n", "Parallel time: 0.009311362984590232\n", "Vectorised time: 7.727500633336604e-05\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 0.0s finished\n" ] } ], "source": [ "import joblib\n", "\n", "def my_sqr(val): \n", " \n", " sqr = val**2\n", " \n", " return sqr\n", "\n", "values_to_compute = np.arange(0,100) \n", "\n", "\n", "start = timeit.default_timer()\n", "loop_sqrs = np.zeros([values_to_compute.shape[0]])\n", "\n", "for i in range(values_to_compute.shape[0]): \n", " loop_sqrs[i] = my_sqr(i)\n", "\n", "stop = timeit.default_timer()\n", "print('Loop time: ', stop - start)\n", "\n", "start = timeit.default_timer()\n", "\n", "paralell_sqrs = joblib.Parallel(n_jobs=-1, backend=\"threading\",verbose=1)(joblib.delayed(my_sqr)(value)for value in values_to_compute)\n", "\n", "stop = timeit.default_timer()\n", "\n", "print('Parallel time: ', stop - start) \n", "\n", "\n", "##Extra vecotorised version using the built-in numpy function for arrays\n", "\n", "start = timeit.default_timer() \n", "vectorised_sqrs = np.power(values_to_compute, 2)\n", "stop = timeit.default_timer()\n", "print('Vectorised time: ', stop - start) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Can you explain why the parallel code here is slower than the loop? Would you expect this if you perform more complex computations? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Extra: Trick for automatic checks**\n", "\n", "Python has built-in autocheck functions. They are handy to make sure your code is running smoothly without executing it multiple times and printing different variables or to check if the input is in the correct format. \n", "\n", "The easiest to use is the *assert* statementt. It is used to check if a condition is fulfilled. If the assert evaluates to **False** the call is terminated and returns an AssertionError. You can find examples for both cases below. \n", "\n", "You can also decide to specifically handle Errors and Exception with calls *try*, *except* and *raise*. This will not be needed for this course! However, if you would still like to have a look on how this would work have a look at the documentation here: https://docs.python.org/3/tutorial/errors.html" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m#chack if y and z have the same shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m: " ] } ], "source": [ "x = np.zeros([3,2])\n", "\n", "y = np.random.rand(3,2)\n", "\n", "#check if x and y have the same shape\n", "assert x.shape == y.shape\n", "\n", "#no output means that the condition is fulfilled\n", "\n", "z = np.random.rand(4,8)\n", "#chack if y and z have the same shape\n", "assert y.shape == z.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }