{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "kl_py_basic.ipynb", "provenance": [], "collapsed_sections": [ "S657YY_JGpQs", "4sOLjihFGpQ_" ], "toc_visible": true, "include_colab_link": true }, "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": "3.7.4" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "TBcUfpu7qRkI", "colab_type": "text" }, "source": [ "

\n", " \n", " \n", "

\n", "\n", "\n", "

\n", "\n", "\n", "\n", "# Python Basics\n", "\n", "\n", "\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oPX6BbyLGpPC" }, "source": [ "# Python Basics" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "n_-Nn7IjGpPF", "outputId": "5e0ad3d2-b77f-44ab-837c-e230177dd034", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "# Printing a string\n", "print(\"Hello, Python!\")\n", "print('hi')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Hello, Python!\n", "hi\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Hz3Ra1KWGpPN" }, "source": [ "### Variables" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "jbb0M13SGpPO", "outputId": "4943ffaf-e62b-4d16-dbe4-77b2ea51590a", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "# defining a variable : In Python there is no need to mention the data type\n", "\n", "var1 = 10 # An integer assignment\n", "var2 = 3.146 # A floating point\n", "var3 = \"Hello\" # A string\n", "\n", "print(var1,' ',var2,' ',var3)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "10 3.146 Hello\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "tq2DpWF_GpPU", "outputId": "2fa12d16-12e9-4582-8328-97968260f803", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "print(\"bhuk lagli\")" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "bhuk lagli\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "UAa-Hw7UGpPZ", "outputId": "8ce172a5-27ee-4d13-8377-e4b6d8b24330", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "pi = 3.142\n", "print (\"Value of Pi is\",pi)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Value of Pi is 3.142\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VU3eLaPjGpPd" }, "source": [ "### Assignment" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "mDLEF386GpPe", "outputId": "4368cad5-1d4d-4ace-8e40-5ac7854adffc", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "# Assigning same value to multiple variables\n", "\n", "var1 = var2 = var3 = 1\n", "print(var1,' ',var2,' ',var3)\n", "\n", "# Assigning Different values to variable in a single expression\n", "\n", "var1, var2, var3 = 1, 2.5, \"john\"\n", "print(var1,' ',var2,' ',var3)\n", "\n", "# Note: commas can be used for multi-assignments" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "1 1 1\n", "1 2.5 john\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RBOteJTTGpPk" }, "source": [ "### Slicing" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "XhFGhI2rGpPm", "outputId": "c2d8149e-1c99-4db9-e27b-63cb2d33b455", "colab": { "base_uri": "https://localhost:8080/", "height": 145 } }, "source": [ "# String operations\n", "\n", "str = 'Hello World!' # A string\n", "\n", "print(str) # Prints complete string\n", "print(str[0]) # Prints first character of the string\n", "print(str[2:5]) # Prints characters starting from 3rd to 5th\n", "print(str[2:]) # Prints string starting from 3rd character\n", "print(str[:2])\n", "print(str * 2) # Prints string two times\n", "print(str + \"TEST\") # Prints concatenated string" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Hello World!\n", "H\n", "llo\n", "llo World!\n", "He\n", "Hello World!Hello World!\n", "Hello World!TEST\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "eNxJGik6GpPq" }, "source": [ "### Data types" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "MxHujSd1GpPr", "outputId": "17cef7fc-7ad6-49e5-ad68-5efa52563a5e", "colab": { "base_uri": "https://localhost:8080/", "height": 108 } }, "source": [ "# Python Lists\n", "list = [ 'abcd', 786 , 2.23, 'john', 70.2 ] # A list\n", "tuple = ( 'abcd', 786 , 2.23, 'john', 70.2 ) # A tuple. Tuples are immutable, i.e. cannot be edit later\n", "\n", "print(list) # Prints complete list\n", "print(list[0:2]) # Prints first element of the list\n", "print(tuple)\n", "print(tuple[1:2])\n", "\n", "print(tuple[1:3]) # Prints elements starting from 2nd till 3rd " ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "['abcd', 786, 2.23, 'john', 70.2]\n", "['abcd', 786]\n", "('abcd', 786, 2.23, 'john', 70.2)\n", "(786,)\n", "(786, 2.23)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "RVv7Kq1YGpPw", "outputId": "4b174289-528f-4db8-c94b-43ddabc62ca8", "colab": { "base_uri": "https://localhost:8080/", "height": 163 } }, "source": [ "# Lists are ordered sets of objects, whereas dictionaries are unordered sets. But the main difference is that items in dictionaries are accessed via keys and not via their position.\n", "tel = {'jack': 4098, 'sape': 4139}\n", "tel['guido'] = 4127\n", "print(tel)\n", "print(tel['jack'])\n", "del tel['sape']\n", "tel['irv'] = 4127\n", "print(tel)\n", "print(tel.keys())\n", "print(sorted(tel.keys()))\n", "print(sorted(tel.values()))\n", "print('guido' in tel)\n", "print('jack' not in tel)\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "{'jack': 4098, 'sape': 4139, 'guido': 4127}\n", "4098\n", "{'jack': 4098, 'guido': 4127, 'irv': 4127}\n", "dict_keys(['jack', 'guido', 'irv'])\n", "['guido', 'irv', 'jack']\n", "[4098, 4127, 4127]\n", "True\n", "False\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EgzsftA5GpP9" }, "source": [ "### Conditioning and looping" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "V27dmoMeGpQA", "outputId": "d2dc98c7-0c4b-4a65-ab01-b4e9d23cf2a8", "colab": { "base_uri": "https://localhost:8080/", "height": 199 } }, "source": [ "# Square of odd numbers\n", "\n", "for i in range(0,10):\n", " \n", " if i%2 == 0:\n", " print(\"Square of \",i,\" is :\",i*i)\n", " \n", " else:\n", " print(i,\"is an odd number\")\n", "\n", " " ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Square of 0 is : 0\n", "1 is an odd number\n", "Square of 2 is : 4\n", "3 is an odd number\n", "Square of 4 is : 16\n", "5 is an odd number\n", "Square of 6 is : 36\n", "7 is an odd number\n", "Square of 8 is : 64\n", "9 is an odd number\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "E5HgAI6gGpQH" }, "source": [ "### Built-in Functions" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "OpdLQV2QGpQI", "outputId": "0b803711-28a1-45ca-a99e-5f9b974e690f", "colab": { "base_uri": "https://localhost:8080/", "height": 108 } }, "source": [ "print(\"Sum of array: \",sum([1,2,3,4]))\n", "print(\"Length of array: \",len([1,2,3,4]))\n", "print(\"Absolute value: \",abs(-1234))\n", "print(\"Round value: \",round(1.2234))\n", "\n", "import math as mt # importing a package\n", "print(\"Log value: \",mt.log(10))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Sum of array: 10\n", "Length of array: 4\n", "Absolute value: 1234\n", "Round value: 1\n", "Log value: 2.302585092994046\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "IfVRvHtaGpQM" }, "source": [ "### Functions" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "5Kdpe1u1GpQO", "outputId": "aa2dcb69-aca5-4e02-835a-2003166f81a4", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "def area(length,width):\n", " return length*width\n", "are = area(10,20)\n", "print(\"Area of rectangle:\",are)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Area of rectangle: 200\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "collapsed": true, "id": "aUIFjX1aGpQS" }, "source": [ "### Broadcasting\n", "* Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes\n", "\n", "### NumPy \n", "* Numpy is the fundamental package for scientific computing with Python. It contains among other things:\n", "* a powerful N-dimensional array object\n", "* sophisticated (broadcasting) functions\n", "* tools for integrating C/C++ and Fortran code\n", "* useful linear algebra, Fourier transform, and random number capabilities" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "ayeR_DtpGpQU", "outputId": "e60aa909-6ba2-433a-b3bc-783d8676fd7a", "colab": { "base_uri": "https://localhost:8080/", "height": 163 } }, "source": [ "import numpy as np # Importing libraries\n", "\n", "a = np.array([0, 1, 2])\n", "b = np.array([5, 5, 5])\n", "\n", "print(\"Matrix A\\n\", a)\n", "print(\"Matrix B\\n\", b)\n", "\n", "print(\"Regular matrix addition A+B\\n\", a + b)\n", "\n", "print(\"Addition using Broadcasting A+5\\n\", a + 5)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix A\n", " [0 1 2]\n", "Matrix B\n", " [5 5 5]\n", "Regular matrix addition A+B\n", " [5 6 7]\n", "Addition using Broadcasting A+5\n", " [5 6 7]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "x1nBrvhBGpQZ" }, "source": [ "### Broadcasting Rules\n", "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when\n", "\n", "1. they are equal, or\n", "2. one of them is 1\n" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "RixAtpt4GpQa", "outputId": "3c183a13-8a15-4e96-d2e1-1053657e9fc2", "colab": { "base_uri": "https://localhost:8080/", "height": 345 } }, "source": [ "# Lets go for a 2D matrix\n", "c = np.array([[0, 1, 2],[3, 4, 5],[6, 7, 8]])\n", "d = np.array([[1, 2, 3],[1, 2, 3],[1, 2, 3]])\n", "\n", "e = np.array([1, 2, 3])\n", "\n", "print(\"Matrix C\\n\", c)\n", "print(\"Matrix D\\n\", d)\n", "print(\"Matrix E\\n\", e)\n", "\n", "print(\"Regular matrix addition C+D\\n\", c + d)\n", "\n", "print(\"Addition using Broadcasting C+E\\n\", c + e)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix C\n", " [[0 1 2]\n", " [3 4 5]\n", " [6 7 8]]\n", "Matrix D\n", " [[1 2 3]\n", " [1 2 3]\n", " [1 2 3]]\n", "Matrix E\n", " [1 2 3]\n", "Regular matrix addition C+D\n", " [[ 1 3 5]\n", " [ 4 6 8]\n", " [ 7 9 11]]\n", "Addition using Broadcasting C+E\n", " [[ 1 3 5]\n", " [ 4 6 8]\n", " [ 7 9 11]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "LX6QUE6qGpQd", "outputId": "5154e385-0092-4292-86f4-2af93a7e88e3", "colab": { "base_uri": "https://localhost:8080/", "height": 90 } }, "source": [ "M = np.ones((3, 3))\n", "print(\"Matrix M:\\n\",M)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix M:\n", " [[1. 1. 1.]\n", " [1. 1. 1.]\n", " [1. 1. 1.]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "gzLCvE2GGpQh", "outputId": "42ab0263-d7df-4e1a-b5f0-91ade5bc0e65", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 126 } }, "source": [ "print(\"Dimension of M: \",M.shape)\n", "print(\"Dimension of a: \",a.shape)\n", "print(\"Addition using Broadcasting\")\n", "print(M + a)\n", "# Broadcasting array with matrix" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Dimension of M: (3, 3)\n", "Dimension of a: (3,)\n", "Addition using Broadcasting\n", "[[1. 2. 3.]\n", " [1. 2. 3.]\n", " [1. 2. 3.]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WzeVueDSGpQm" }, "source": [ "## All in one program" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "j6vhzaU1GpQo", "outputId": "e8fd0858-c90b-4eab-f89a-491041161848", "colab": { "base_uri": "https://localhost:8080/", "height": 235 } }, "source": [ "# Importing libraries\n", "import timeit\n", "\n", "# Usage of builtin functions\n", "start = timeit.default_timer() \n", "\n", "# Defining a list\n", "array_list = [10,11,15,19,21,32] \n", "array_np_list = []\n", "\n", "# Print the list\n", "print(\"Original List\",array_list,\"\\n\") \n", "\n", "# Defining a function\n", "def prime(num): \n", " if num > 1: \n", " \n", " # check for factors\n", " # Iterating a range of numbers\n", " for i in range(2,num): \n", " if (num % i) == 0:\n", " \n", " # Appending data to list\n", " array_np_list.append(num) \n", " print(num,\"is not a prime number (\",i,\"times\",num//i,\"is\",num,\")\")\n", " \n", " # Terminating a loop run\n", " break \n", " else:\n", " print(num,\"is a prime number\")\n", " \n", "# Iterating a list\n", "for item in array_list:\n", " \n", " # Calling a function\n", " prime(item) \n", "\n", "print(\"\\nNon-prime List\",array_np_list,\"\\n\")\n", "\n", "end = timeit.default_timer()\n", "\n", "# Computing running time\n", "print(\"Time Taken to run the program:\",end - start, \"seconds\") " ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Original List [10, 11, 15, 19, 21, 32] \n", "\n", "10 is not a prime number ( 2 times 5 is 10 )\n", "11 is a prime number\n", "15 is not a prime number ( 3 times 5 is 15 )\n", "19 is a prime number\n", "21 is not a prime number ( 3 times 7 is 21 )\n", "32 is not a prime number ( 2 times 16 is 32 )\n", "\n", "Non-prime List [10, 15, 21, 32] \n", "\n", "Time Taken to run the program: 0.0030679620000455543 seconds\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "S657YY_JGpQs" }, "source": [ "### Note:\n", "* Python is a procedural Language\n", "* Two versions of Python 2 vs 3\n", "* No braces. i.e. indentation\n", "* No need to explicitly mention data type" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fACrwBDgGpQu" }, "source": [ "## Unvectorized vs Vectorized Implementations" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "a_RXGDyJGpQv", "outputId": "4cf08c4b-6145-454a-a451-5754c6818aba", "colab": { "base_uri": "https://localhost:8080/", "height": 145 } }, "source": [ "# Importing libraries\n", "import numpy as np\n", "\n", "# Defining matrices\n", "mat_a = [[6, 7, 8],[5, 4, 5],[1, 1, 1]]\n", "mat_b = [[1, 2, 3],[1, 2, 3],[1, 2, 3]]\n", "\n", "# Getting a row from matrix\n", "def get_row(matrix, row):\n", " return matrix[row]\n", "\n", "# Getting a coloumn from matrix\n", "def get_column(matrix, column_number):\n", " column = []\n", " \n", " for i in range(len(matrix)):\n", " column.append(matrix[i][column_number])\n", " \n", " return column\n", "\n", "# Multiply a row with coloumn\n", "def unv_dot_product(vector_one, vector_two):\n", " total = 0\n", " \n", " if len(vector_one) != len(vector_two):\n", " return total\n", " \n", " for i in range(len(vector_one)):\n", " product = vector_one[i] * vector_two[i]\n", " total += product\n", " \n", " return total\n", "\n", "# Multiply two matrixes\n", "def matrix_multiplication(matrix_one, matrix_two):\n", " m_rows = len(matrix_one)\n", " p_columns = len(matrix_two[0])\n", " result = []\n", " \n", " for i in range(m_rows):\n", " row_result = []\n", " \n", " for j in range(p_columns):\n", " row = get_row(matrix_one, i)\n", " column = get_column(matrix_two, j)\n", " product = unv_dot_product(row, column)\n", " \n", " row_result.append(product) \n", " result.append(row_result)\n", " \n", " return result\n", "\n", "print(\"Matrix A: \", mat_a,\"\\n\")\n", "print(\"Matrix B: \", mat_b,\"\\n\")\n", "\n", "print(\"Unvectorized Matrix Multiplication\\n\",matrix_multiplication(mat_a,mat_b),\"\\n\")\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix A: [[6, 7, 8], [5, 4, 5], [1, 1, 1]] \n", "\n", "Matrix B: [[1, 2, 3], [1, 2, 3], [1, 2, 3]] \n", "\n", "Unvectorized Matrix Multiplication\n", " [[21, 42, 63], [14, 28, 42], [3, 6, 9]] \n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "feaMM6ktGpQ6", "outputId": "4db49977-9f68-4d44-fe7f-cb881d021264", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 199 } }, "source": [ "# Vectorized Implementation\n", "npm_a = np.array(mat_a)\n", "npm_b = np.array(mat_b)\n", "\n", "print(\"Vectorized Matrix Multiplication\\n\",npm_a.dot(npm_b),\"\\n\") \n", "print(\"Vectorized Matrix Multiplication\\n\",npm_a * npm_b,\"\\n\") \n", "# A.dot(B) is a numpy built-in function for dot product" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Vectorized Matrix Multiplication\n", " [[21 42 63]\n", " [14 28 42]\n", " [ 3 6 9]] \n", "\n", "Vectorized Matrix Multiplication\n", " [[ 6 14 24]\n", " [ 5 8 15]\n", " [ 1 2 3]] \n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4sOLjihFGpQ_" }, "source": [ "### Tip:\n", "* Vectorization reduces number of lines of code\n", "* Always prefer libraries and avoid coding from scratch" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Zng_sHekGpRA" }, "source": [ "## Essential Python Packages: Numpy, Pandas, Matplotlib" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "A73VVIp_GpRB", "colab": {} }, "source": [ "# Load library\n", "import numpy as np" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "-L0tvgLBGpRH", "outputId": "77897ca5-c591-4094-8eeb-065c34ed1bea", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "source": [ "# Create row vector\n", "vector = np.array([1, 2, 3, 4, 5, 6])\n", "print(\"Vector:\",vector)\n", "\n", "# Select second element\n", "print(\"Element 2 in Vector is\",vector[1])" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Vector: [1 2 3 4 5 6]\n", "Element 2 in Vector is 2\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "yne7Gtv4GpRM", "outputId": "2d09354d-6a45-4780-d5f2-4eb94818186a", "colab": { "base_uri": "https://localhost:8080/", "height": 163 } }, "source": [ "# Create matrix\n", "matrix = np.array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])\n", "\n", "print(\"Matrix\\n\",matrix)\n", "\n", "# Select second row\n", "print(\"Second row of Matrix\\n\",matrix[1,:])\n", "print(\"Third coloumn of Matrix\\n\",matrix[:,2])" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix\n", " [[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n", "Second row of Matrix\n", " [4 5 6]\n", "Third coloumn of Matrix\n", " [3 6 9]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "PFPt-P6TGpRQ", "outputId": "e7ce433c-df54-4611-a5c6-71cf8d56916c", "colab": { "base_uri": "https://localhost:8080/", "height": 254 } }, "source": [ "# Create Tensor\n", "tensor = np.array([ [[[1, 1], [1, 1]], [[2, 2], [2, 2]]],\n", " [[[3, 3], [3, 3]], [[4, 4], [4, 4]]] ])\n", "\n", "print(\"Tensor\\n\",tensor)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Tensor\n", " [[[[1 1]\n", " [1 1]]\n", "\n", " [[2 2]\n", " [2 2]]]\n", "\n", "\n", " [[[3 3]\n", " [3 3]]\n", "\n", " [[4 4]\n", " [4 4]]]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a-sUzRo2GpRZ" }, "source": [ "### Matrix properties" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "hphJCsCDGpRa", "outputId": "1619f4b4-8169-4424-ec75-a218f884f91c", "colab": { "base_uri": "https://localhost:8080/", "height": 163 } }, "source": [ "# Create matrix\n", "matrix = np.array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])\n", "\n", "print(\"Matrix Shape:\",matrix.shape)\n", "print(\"Number of elements:\",matrix.size)\n", "print(\"Number of dimentions:\",matrix.ndim)\n", "print(\"Average of matrix:\",np.mean(matrix))\n", "print(\"Maximum number:\",np.max(matrix))\n", "print(\"Coloumn with minimum numbers:\",np.min(matrix, axis=1))\n", "print(\"Diagnol of matrix:\",matrix.diagonal())\n", "print(\"Determinant of matrix:\",np.linalg.det(matrix))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix Shape: (3, 3)\n", "Number of elements: 9\n", "Number of dimentions: 2\n", "Average of matrix: 5.0\n", "Maximum number: 9\n", "Coloumn with minimum numbers: [1 4 7]\n", "Diagnol of matrix: [1 5 9]\n", "Determinant of matrix: 0.0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "40Ito4oBGpRd" }, "source": [ "### Matrix Operations" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "_C_-Ds6MGpRe", "outputId": "7480d6fd-b768-4d07-a26d-7199e52acc82", "colab": { "base_uri": "https://localhost:8080/", "height": 308 } }, "source": [ "print(\"Flattened Matrix\\n\",matrix.flatten())\n", "print(\"Reshaping Matrix\\n\",matrix.reshape(9,1))\n", "print(\"Transposed Matrix\\n\",matrix.T)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Flattened Matrix\n", " [1 2 3 4 5 6 7 8 9]\n", "Reshaping Matrix\n", " [[1]\n", " [2]\n", " [3]\n", " [4]\n", " [5]\n", " [6]\n", " [7]\n", " [8]\n", " [9]]\n", "Transposed Matrix\n", " [[1 4 7]\n", " [2 5 8]\n", " [3 6 9]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "cMnk19GQGpRh", "outputId": "3d973e42-d9e0-47b6-a127-f12e9505e253", "colab": { "base_uri": "https://localhost:8080/", "height": 235 } }, "source": [ "# Create matrix\n", "matrix_a = np.array([[1, 1, 1],\n", " [1, 1, 1],\n", " [1, 1, 2]])\n", "\n", "# Create matrix\n", "matrix_b = np.array([[1, 3, 1],\n", " [1, 3, 1],\n", " [1, 3, 8]])\n", "\n", "print(\"Matrix Addition\\n\",np.add(matrix_a, matrix_b))\n", "print(\"Scalar Multiplication\\n\",np.multiply(matrix_a, matrix_b))\n", "print(\"Matrix Addition\\n\",np.dot(matrix_a, matrix_b))\n" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Matrix Addition\n", " [[ 2 4 2]\n", " [ 2 4 2]\n", " [ 2 4 10]]\n", "Scalar Multiplication\n", " [[ 1 3 1]\n", " [ 1 3 1]\n", " [ 1 3 16]]\n", "Matrix Addition\n", " [[ 3 9 10]\n", " [ 3 9 10]\n", " [ 4 12 18]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fmuOaNziGpRn" }, "source": [ "### Pandas" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "vVHk-r-uGpRq", "colab": {} }, "source": [ "import pandas as pd" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "5Eu__eBmGpRu", "outputId": "aa18fd3b-ff87-4a39-c70e-27951550c312", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "source": [ "df=pd.read_csv(\"/content/sample_data/mnist_test.csv\")\n", "print(\"Data\\n\")\n", "df" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Data\n", "\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ " 7 0 0.1 0.2 0.3 0.4 ... 0.662 0.663 0.664 0.665 0.666 0.667\n", "0 2 0 0 0 0 0 ... 0 0 0 0 0 0\n", "1 1 0 0 0 0 0 ... 0 0 0 0 0 0\n", "2 0 0 0 0 0 0 ... 0 0 0 0 0 0\n", "3 4 0 0 0 0 0 ... 0 0 0 0 0 0\n", "4 1 0 0 0 0 0 ... 0 0 0 0 0 0\n", "5 4 0 0 0 0 0 ... 0 0 0 0 0 0\n", "6 9 0 0 0 0 0 ... 0 0 0 0 0 0\n", "7 5 0 0 0 0 0 ... 0 0 0 0 0 0\n", "8 9 0 0 0 0 0 ... 0 0 0 0 0 0\n", "9 0 0 0 0 0 0 ... 0 0 0 0 0 0\n", "10 6 0 0 0 0 0 ... 0 0 0 0 0 0\n", "11 9 0 0 0 0 0 ... 0 0 0 0 0 0\n", "12 0 0 0 0 0 0 ... 0 0 0 0 0 0\n", "13 1 0 0 0 0 0 ... 0 0 0 0 0 0\n", "14 5 0 0 0 0 0 ... 0 0 0 0 0 0\n", "15 9 0 0 0 0 0 ... 0 0 0 0 0 0\n", "16 7 0 0 0 0 0 ... 0 0 0 0 0 0\n", "17 3 0 0 0 0 0 ... 0 0 0 0 0 0\n", "18 4 0 0 0 0 0 ... 0 0 0 0 0 0\n", "19 9 0 0 0 0 0 ... 0 0 0 0 0 0\n", "20 6 0 0 0 0 0 ... 0 0 0 0 0 0\n", "21 6 0 0 0 0 0 ... 0 0 0 0 0 0\n", "22 5 0 0 0 0 0 ... 0 0 0 0 0 0\n", "23 4 0 0 0 0 0 ... 0 0 0 0 0 0\n", "24 0 0 0 0 0 0 ... 0 0 0 0 0 0\n", "25 7 0 0 0 0 0 ... 0 0 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"outputId": "579d7b3a-a57d-4807-d1c1-74d077d4cb0e", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 283 } }, "source": [ "# Line plot\n", "plt.plot([1,2,3,4],[3,4,5,6])\n", "plt.xlabel('some numbers')\n", "plt.ylabel('some numbers')\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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D0xeyJyuX28/szE2ndKBalUqRLk1EyjiFQjmzYdc+7pmazOyULfRpXZ+nRvai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\npIBCQURECigURESkgEJBREQK/H/5Nl4vPkpkwAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "E_6KQep7GpSP", "outputId": "1df61ef1-777e-4c26-e890-8c6811df74f6", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "### Adding elements to line plots\n", "t = np.arange(0.0, 2.0, 0.01) # Generate equally space numbers between 0 and 2\n", "s = 1 + np.sin(2*np.pi*t) # Apply sin function to the random numbers\n", "plt.plot(t, s)\n", "\n", "plt.xlabel('time (s)')\n", "plt.ylabel('voltage (mV)')\n", "plt.title('About as simple as it gets, folks')\n", "plt.grid(True)\n", "plt.savefig(\"test.png\") # Save a plot. Check the directory\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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D4Se9vcqG6kaWFGdFrI//4pJsNtU00dHd47aUMUlHdw8bqxsjsnUBICIsLclm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\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "V1hVavwOGpSV" }, "source": [ "### Bar Plot" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "DL7gd5ezGpSW", "outputId": "8725ebe1-f030-41a6-c035-7665d6ccfcdf", "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "source": [ "y = [3, 10, 7, 5, 3, 4.5, 6, 8.1]\n", "x = range(len(y))\n", "width = 1/1.5\n", "plt.bar(x, y, width, color=\"blue\")\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "yXnGWRJOGpSZ" }, "source": [ "### Scatter Plot" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "bBsbRia0GpSa", "outputId": "8fd412e6-eb5b-4e48-90c6-00e789246126", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 269 } }, "source": [ "N = 50\n", "# Generate random numbers\n", "x = np.random.rand(N)\n", "y = np.random.rand(N)\n", "colors = np.random.rand(N)\n", "area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii\n", "\n", "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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bHFPXeKiphQ93bmQmV+hyFfP5b97FK+U1MjGHECxgA5JgLLGxqHNHw376B6dw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\nMDzvR6zNOt3vAlBKMdA9Smxkgkw6i+WyCFcGqG+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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fPXYBbEtGpSe" }, "source": [ "### Histogram" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "B2h8ekAXGpSf", "outputId": "f9a24a7b-296d-4005-ea36-50bac098b4ce", "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 408 } }, "source": [ "mu, sigma = 100, 15\n", "x = mu + sigma*np.random.randn(10000) # Generate random values with some distribution\n", "\n", "# the histogram of the data\n", "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)\n", "\n", "# add a 'best fit' line\n", "y = mlab.normpdf( bins, mu, sigma)\n", "l = plt.plot(bins, y, 'r--', linewidth=1)\n", "\n", "plt.xlabel('Smarts')\n", "plt.ylabel('Probability')\n", "plt.title(r'$\\mathrm{Histogram\\ of\\ IQ:}\\ \\mu=100,\\ \\sigma=15$')\n", "plt.axis([40, 160, 0, 0.03])\n", "plt.grid(True)\n", "\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", " alternative=\"'density'\", removal=\"3.1\")\n", "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n", " \n" ], "name": "stderr" }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KHMtg8ezGpSn" }, "source": [ "### Pie Chart" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "1j6Pi7RZGpSp", "outputId": "f2e45373-48d8-4a42-ca3a-4459bd508f79", "colab": { "base_uri": "https://localhost:8080/", "height": 255 } }, "source": [ "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n", "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n", "sizes = [15, 30, 45, 10]\n", "explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n", " shadow=True, startangle=90)\n", "ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.\n", "\n", "plt.show()" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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TCblnhgBiaoGmWsd9dzrGynE3d3Wy65rl9nDz+0+96du49Ll41Sw/WxJu2j3F\n5orHYyndUqWEvaQScs8UAGK8SyXkfsoB/oHb9TRuV6dSM39tZRtGzfLfACdHa5Y/rGla/o9+1yxv\nyAq1vvFlNTU6gVQ5YS+phNwzY4G2ETlCfeLHxw0YNcuddimO1ix/gjHgd1zN8kFv45IHK9r2bny3\nLzXLHhmJ3Htzhnq9J5b6JtJL6gXaM6OBQG6GcJgdSBqZCHyA23X7SWqW/w/4F0YNuBMppffjV1a0\nrHntH72pWdal5G+fx9uaZ1H9/4mlEnIvqYTcMyOBgM2CGgiKLxtGPfIS3K7CE0/6ayvD/trK16K3\n0eioWd7z6d7Gdx6eH2o6sLknF1k4Jty88Qy7+naTeCoh95JKyN2ILiqUBYRtmkrIA+SzGDXLX+zq\npL+2sgb4LbABox7crge8bU3LHnvRX1v5utQjoVgPvNMSbntKTY02i0rIvaQScvesgATIsAiVkAfO\nMOAN3K57T1KzPA94HKNmeSiA79PFaz0fPvtIJNBy6MT7BKSu3/U1a0hNjTaNSsi9pF6o3bMRTchW\n1UIeaAL4AbAKt6v4xJPRmuX3MNZZ9tJRs3x4R33jkgcf1Xet33X87R+ZpTcdGWXttAKdkjAqIfeS\nSsjdO5qEVR9ywpQBn+B2fb2rk/7ayr3A74F36ahZDgcj+odPbXIte3idaPeHPsgLeT/8j8yCxIWs\ndEEt+N9LKiF372gLWfUhJ1Q28Hfcrmdxuzq1tKI1y08BfwVyidYsZ21doTuW/WrXfDU1Ohm0mx1A\nqlEJuXtHk7BFE1YzAxmkrsOoWT7nxBPRmuV1GDXLe3TkkKBNH956TuiwmhqdFNrMDiDVqITcvaNv\nbNWHbJoJwPu4Xb/E7er0mvXXVtYDd4ayvB8cyqPGVpKtlkhNDqqF3EsqIXdPJeTkYAX+FP3pfDKn\nOtKe7ZP+EZbNtiG2wb4JbbJQLeReUl/Bu6cScnJZF+N4ITAsd2auS2hCDSYlB9VC7iXVQu7e0SQs\n1N/LVFLKNuDNGKdLAbKKsqYnLiKlG6qF3EsqwXTPhlEfS3uEpNi2fhBbjNvjO/Ggs8wpgAu0TK3F\nVmCbbEJcStdUC7mXVELunkY0IbeFpd/kWAY1IcTLMU4NBwpzZ+YWCouqhEki6v3SSyohd89PtA45\nEFIvMLNIKYPAghinZwBkTVDdFUnmgNkBpBqVkLt3LCGrFrKZluL2xFpy83yRIVoyhmV02iJKMVW/\nNhQYjNTXu+4dTcK+IK1mBjKYxequcJY5hwLjc0/NzRQW0WlRIsVUe8wOINWoFnL3/ET/Tr6gaiGb\nQUoZAV6LcXoGILMmZpUkMCRL6sdRAAAc+0lEQVSle0eq5lapQfBeUgm5e36ig3ot7Sohm2Q5bk99\njHPnCYvwZQzP6LQ6nGIq1TruA5WQu7GgJhQBAoDVoxKyKYQQL3V13FnmzAcm5ZTmDNGsWmaCw1JO\nTiXkPlAJuWdaAFtzm6qySDQppY6xr15XSgAckx2quyL5qAG9PlAJuWeaAVuDX1cJOfFW4vYcjHHu\nfDRaM0ZkTEtoREpPqBZyH6iE3DMewHbQJ1WVRYKdpLrCCRTnnJLj1Gya2g08+WwxO4BUpBJyz3gA\n216vbG0Py4SMHLeFJWc/6mPmfB+nzPPxu3eNZQF2NunMeszH5PtauPYlP8GI7PL+//t+O5Pva6H4\nAR+Lt4UBONKqM+fxVmbM8/HqlmP7gl7+nJ8DLfrAP6m+eSXG8RJAOKao7ooktdrsAFKRSsg90whk\nABzxy7pEXNBugWVzs9nwrRzW35bNou1hVu0L84t32vjROXa2fT+X/EzB39d23nB585EIz20Ksem/\nclj0VQf/9WaAiC55dmOIb51p4+NvZHPvqiAAr9eEKBupMSo3+V4KUso1uD2xvvrOAXz2EXbVXZF8\ndlXNrYpVFaOcRPK9C5PT0T7M/V49IQlZCEFOhgAgpEMoYtTeLdsZ4arpxnyeuTNtvFrTOSG/tiXM\ndafYsFsFE/I1Jg/R+Hh/BJsm8IegPQwWDcK65N7KID8/z56Ip9RrQogXuzruLHPmANOzS7JzNLum\nNjFNPh+bHUCqUgm5Z+qITp/e3qQnbH5+RJecNt/H8DtbuGSilUlDNPIywaoZiXqMU2O/t3OXxf4W\nnbEucfTfY3I19rdIbii18VpNmEuebOWXc+zMWx3kxlNtOGyi02MkiViLCU0DtOyp2ap1nJxUd0Uf\nqYTcM0eACGDZcDAxLWQAiyZY/60c9v04l48PRNhS379+XlemYOENDtZ8M4fTCy28vjXMVdNtfGNB\ngKte8PPR3nCcIu8/XcpPcXu2xzg9G/BnFGao/uPkpFrIfaQScg9EJ4fsAnKq6/Wm9rBM6MLbeZmC\ni4qsfLQ3QnOb0dUAsM+rM9rZuXU7Oldjr+dYy3lfi87o3H+/3R9WtPOr8+08WxVizjgLFVdk4V6e\nPMvXarEngziAmY4pDrsl09JpN2rFXNG68bVmx5GqVELuuRqM7eYTMrB3pFWnuc1IqoGQ5O0dYUqG\naVw0wcJLm42WbMWGEJcXd95VqrzYynObQrSHJTubdGobdM4efWxXo9qGCPu8OhcWWfGHJJoAISDQ\nuTvaTLG6K4oBLXtadspOlZa6ZNtvt7H7r8bciX2P7qPmpzVs+802tv1mG4HdXRfyNH3QxNZfbGXr\nL7bS9EETAHpIZ9ddu6j9VS0NSxuO3nb/P/YT2JX4pSSEENVVc6s6bSKg9Ixa7a3ndhL9ADvQoh8Y\n49QmDOTF6nySua/6ieigS7jmFBuXTbUxfZiF617y8+tlbZQVWri1zEjIC2pCrDkQ4fcXZXLKcAvX\nTLcxfZ4PqyZ48IuZWLRjLeRfLWvnTxcbA3nXl9q44rkAf/4wyO8vTI7BPV3KLdod3s0xTs8C2uyj\n7Cm79nHDkgbso+zogWNdUCOvHYnrrNgN/rAvzOHXDjPpd5MQQrDNvQ1nmZPWra04pjoYdtkwdvxp\nBwX/UUBgTwCpS7KKshLxdE70kRkXTRcqIfdcHaADbG+UdWePHtiLnTrCwrrbcjodn5iv8fE3Oh8v\nL7ZRflxr+VcX2PnVBV0n2BeuPjaPYni2xspbs+MQcfycpLvCDpyeNSFLWrIsQxIcVlyEGkO0bGhh\n2JeG0bC4ofs7RPk2+sg5JQdrjvGWzTklh5aqFiwOC3pQR0ZkdNgZDr9ymFFzRw1E+D0Ra89DpQdU\nl0XPHcZ4yVs+PRRJ2MDeIBWru2IqYM2enrrdFXXP1DHy2pHR9QOPOfTyIWp/XUvdM3Xooc6Dt+Gm\nMLYhxz5wbfk2wk1hck7JIVQfYscfdlBwSQHedV4yx2diy0/8BulSynZgScIvnEZUC7mHFtSEIuXF\ntl1AweYjemMwItszLCI5vuOnEV3Kndod3vUxTp8NBDNHZaZkd4V3vRer00pWURa+6mPdrCOuHoHV\nZUWGJQeeOED9m/UMv3x4jx5TWARjvzUWABmW7Lp7F+O+P466Z+sINYTIOy8PZ5lzQJ5PF5ZVza1S\nywv0g2oh904NkCuB/V65y+RY0tJJuisygLPtY+zSkm0ZluCw4sJf68e7zkvNT2rY99A+fNU+9j68\nF1ueDSEEmk0jb04egR2dB+Os+VZCjcdGXUNNIaz5/96ealjWQN65eQS2B7BkWRj7X2OpX5S4CXNC\niNcTdrE0pRJy7xwd2Ks6HNlqcizpKlZ3xWTAmjsjd2oig4mnkVePZNpfp1F8dzFjvj2GnJIcxt42\nllCzkWillLSsbcE+uvMXr5wZOfg2+oi0Roi0Row+5RnHxhIirRFaNrSQd14eelA/2iUig12vdTJA\nVELuJ9Vl0Tt7iL7Ul2wPb/3SVCtCJO0st5SjS7lPEyLWpIIzgYh9dOpWV8Sy7+F9hFvCICFzXObR\nAbnAzgCN7zYy+uujseZYGV4+nO13GHNlhl8+/OgAH8Dh1w4z7LJhCE2QMyOHhqUNeH7tYchFiRn7\nlFKu23jzxn0JuVgaUwm5d+ox1rXI2eORviN+uX94thjgeovBQxPiZdyeTk06Z5nTCpyTMTIjZM21\njjQhtLjLKckhp8Ro4U74RdcVlFkTshg94djLK/+CfPIvyO/ytoU3FB79XcvQmPCzAa3K7EQIsSCh\nF0xTKdtlIYTwnfDvm4UQDwzkNRfUhCSwEsgH2FKvq26L+IrVXTEJyMwtzZ2SyGCUXlEJOQ5SNiGb\naGPHL8t3hdUi3HGiS3kY+DDG6TOAcOaYTLV2RRKSUm6smlulpkvHQVomZCFEkRBimRDiUyHEUiHE\nuOjxSUKIVUKIKiHEHzta2UKIQiHECiHEeiHERiHE+Sd5+L0YO1HbVx/QDze3SbXuaxxoQryC29Op\nANdZ5rQAs21Dbe0Wp2WMCaEp3RBCPGx2DOkilRNyVjSBrhdCrAd+f9y5+4EKKeWpwNPAfdHjfwP+\nJqUsBY4fgLgBWCylPA2YCcSqg+1YaKgSKAD49FCkKl5PaJDrstwNmABk587MnawGUJOPlLINeNLs\nONJFKifkgJTytI4f4LfHnZsNPBP9/UmM3SU6jncsev7McbdfDdwihHADpVLKlm6uvZrogOhbteGN\n3dxW6YYuZSOwPMbp04BI5ljVXZGUJM9Xza3ymB1GukjlhBw3UsoVwAXAfuAJIcRN3dxlO9Fui01H\n9MYjrYlbtD4daUK8itvTaTFmZ5lTA+ZY860Bq8s6zoTQlG4ITTxkdgzpJF0T8krguujvXwXej/6+\nCrgy+nvHeYQQ44FDUspHgceA00/24AtqQmFgBTAUYP1BXbWS+ydWdcU4wJk7M3eCUP0VSUdG5Kaq\nuVWVZseRTtI1IX8PowviU+BG4AfR4z8Efhw9PhljN2mAC4ENQoh1wLUYfc3dWUO02+Ll6tCGsC6T\nZ7uNFKJL6QXeiXH6NEDPGpeVdpNB0oGwiAfNjiHdpOzEECllzgn/fgJ4Ivr7buDiLu62HzhHSimF\nENdhLHaOlLICqOhlCLuAJsBxoEX6Nx3W188caTmzl48x6Al4HbcneOJxZ5lTAOdbci1+a561KPGR\nKScjdRkQmnja7DjSTbq2kGM5A1gfbSH/F/CTvj7QgpqQjrHU4FCAZzeGVupSJnThgHQgYiwmBIwB\n8p2nOccJTQy212nyk8yvmlvlNTuMdDOoXuhSyvellDOllKdKKS+QUm7r50N+CIQB2+YjetOOJr06\nDmEOGlJKP7A4xumZgMwcn5pLbaYzqcugsIj/MzuOdDSoEnK8LagJtQBvAyMBXqkOx5pppnRtIW5P\np7UmO7orNIfmsw2xTTQhLuUkZEg+UTW36pDZcaQjlZD7712MFeC0D/ZEDuz36rtMjidlCCFiVVcU\nAsOdpznHCE1YYtxGMYHUZUiza3eYHUe6Ugm5nxbUhOoxui5GAry1TbWSeyK63c/CGKdLAZlVlKUm\ngyQZGZKPVs2t6lHdvRAiEp1Ju0kIsUEI8RMh1HjAyag/TnwsAWyAWFAT3tYY0NXXue4twu3ptF38\n0e6KTK3FVmBTq7slERmRAc2u/a4Xd+mYTXsKcAnwBaA39x90VEKOgwU1ob1AFTAMYOmOiGold+Mk\n3RXDgVG5M3MLhUWkbFlmOtKD+n1Vc6v6tJiWNFbz+ybwXWHIFEL8I7rQ1zohxEUAQgiHEOIFIcRm\nIcS/hBCVQogzhRAWIcQT0cW/qoQQP4rnc0sWKiHHz0LAAfDcxtAmX1Cq+f0xSClDxN7u5xSArAmq\nuyKZ6GHdY8my/LE/jyGl3AFYMD50v2MckqXA9UCFECIToxy1SUo5HfgNRqkqGJOERkspZ0Tv84/+\nxJKsVEKOn60Yk0XyQzr68l3hD0yOJ5ktxe1pjnHuAmETvoyhGSm7d1460tv0H1XNrerUxdQPc4Cn\nAKSUW4DdwNTo8eeixzcCn0ZvvwOYKIS4XwhxKZCWNdAqIcdJdDeR1wAnwN/XhT6p9+t15kaVnGJ1\nVzjLnEOB8bmn5g4VVpGR4LCUGMK+8CfV36nud4tUCDERiACHe3tfKWUTRm36e8C3MNacSTsqIcdX\nFcaLzRXWkU+sDy1Us/f+nZQyArwa4/R0QGZNUmtXJAsZkSE9oH+1v48jhBgGzAcekMZ74n2Mhb8Q\nQkzFWEiqBqNi6Zro8ekYFTcIIYYCmpTyZeDXdLMAWKpSCTmOoqvAVQBDALFid2T/xsP6JyaHlWyW\n4/bEGhg6X1iEL2NYRnFCI1JiCjeH76v5aU1NH+/esYnEJowFpJYAHTXM8wBNCFEFPA/cHC2FnAcM\nE0JsBv4IbMJYBGw08F50M4qngP/u85NKYmoUO/6qgY8wBiP237squPTBL2aWZNlEtslxJYWTdFfk\nA5NySnMyNJuWmeCwlC5E/JF9eli/va/3l1LGnNQT3Wnkli5OtQFfk1K2CSEmYSTy3VLKIGnaKj6e\naiHHWbQv+QVABzLr/bJtYW14iclhJYXoV9V/xThdAgjHZIeqrkgCUkoZ8Ufmbv351kQvK+sAPhBC\nbMB4rfxXNBkPCiohD4AFNaFGjK9hIwH+uSH06YEWNaUaWInbE2ug83w0fBnDM6YlNCKlS+Hm8Cs1\nP6lZlujrSilbpJRnHrcI2FuJjsFMKiEPnBXAHqKboT68JrgwostOuyoPJifprnACxTnTc3K1DM2R\n4LCUE0T8kUPozDU7jsFIJeQBEh3gewLIBSzrDur1qw9EVpoblelizc4rAXBMdajqCpPJsAy37W27\nZsuPt7SaHctgpBLyAFpQE9oBLAVGAdxXGVze0i5jTYhIa7qUa3B79sQ4fS7Qah9hV90VJvPv8N+9\n4392rDA7jsFKJeSB9yrGyLHDFyT85KehBYOxNlmLsTOIs8yZDczInpadrdm13ASHpRynva79w/o3\n639pdhyDmUrIAyy6iP2TwAiARdvCO5fviiR8sCQJxOqumAaI7OJs1To2UdgbPtyyvuVy7zrvoB7n\nMJtKyInxMcac/FEAf10V/GB74+DZ7kmXsgq3J9Z2WbOBQEZhhip3M4ke0kP+Wv81dc/VNZgdy2Cn\nEnICRDdEfRRoAfIBfr+87dXmNtmnpQxTzUm6K7KA0xxTHHZLpiUvwWEpgJSSwI7AH3fft3u52bEo\nKiEnzIKakBe4H8gBMpvaCN69sv25YGRQFL3H2lm6GNCyp2WrqdIm8W/zv1z/Vv0fzI5DMaiEnEAL\nakK7gMcxui60DYf0huc3hv6VzmN8upQ1uD2bY5w+B2i3j7KrcjcT+Lf7V9cvrL/Ju86bvi/AFKMS\ncuKtxNipehzAi5vDWyr3R9J27eSTdFfYgdOzirI0S5ZlSILDGvTa9rbV1L9Vf7l3nddvdizKMSoh\nJ1h0rYvngW1Ep1b/+YPgsr0efYepgQ2cWNUVUwFr9nTVXZFo7Yfa99Yvrr/MszrmNHbFJCohm2BB\nTSgIPASEAKcukX9Y0f5Sum37pEu5E7dnXYzTZwPBzNGZqroigUJNoSONyxq/0vR+U6yqF8VEKiGb\nZEFNqAF4AGPt5IyDPhm4d1Xw2fawbDM5tLg5SXdFBnCWfYxdWrItwxMc1qAV9oU9je82frV+Uf0a\ns2NRuqYSsokW1IRqMBbbHgOIj/dHDt3/cfCpNKq8iNVdMRnIyJ2Rq/bNS5BwS9jTuKzxW4dfO/y2\n2bEosamEbL6lGCvDFRHdZWT+muDTYV2GzA2rf3Qp92NMiOnKGUDYPtquuisSINQUqj/y+pEfBHYE\nnjc7FuXkVEI2WXSQ7wmgEhgP8M6OyJ7H1oaei+gyYmZs/aEJ8TJuT6dyKmeZ0wrMzhiZEbLmWgtN\nCG1QCR4O1h165dB/Bw8Hn1TlbclPJeQkEF2q8zFgPdGk/GZteEfFhtDzKZyUY3VXTAIyc0tzJycy\nmMGobX/bnkMvH/pRpCXyuFqjIjWohJwkjqu82ES0RvnVLeHax9aGnkm17gtdysNArNrqMiCSOSZT\nTQYZQP6d/tpDrxz6pt6uv6CScepQCTmJLKgJtQMPArXAWICFteEdD60OPp1KA32aEK/g9nRKAs4y\npwU4zzbU1mZxWsaYENqg4Nvi23jktSM3e9d4F6tuitSiEnKSWVATCgD3AluIdl+8vSOy+77K4D9T\nqCQuVndFEeDIPTV3khAigeEMDjIiw80rm99vWNTwNe8672DfnSYlqYSchKJJ+X5gA0ZSFit2R/bf\n/VGwwheUXnOjOzldyibgvRinywA9c5zqroi3iD/iOfza4Vc9H3u+4V3n3WB2PErfqIScpBbUhNqA\necAaoiVxq/ZFDv50SdvD+73Ju4O1JsSruD2dto53ljk1YI41zxqwuqzjTAgtbbXXte+ue7run217\n2n7iXeetMTsepe9UQk5i0YG+RzAGyCYAtgMt0v+9t9r+uXp/5CNzo4spVnfFOMCZOzN3glD9FXEh\npZTe9d61B184eE+kNfJL7zpvrD0LlRShEnKSW1ATCgF/B57GmNGXG9aRf1jRvuTZqtBLoSQa7NOl\n9GKsZNeVmYCeNT5LdVfEgd6u++vfql/S9F7Tb5A84F3n9Zkdk9J/VrMDULoX3XFkcXmxbQ/wXcAB\nHHp2Y2jT1obI4R+eY7/WlSkKzI0SBLyO29PpA8JZ5hTA+ZZci9+aZy1KfGTppb2ufVf9kvp3w03h\nO73rvINmK7DBQLWQU8iCmlA18DvgIMZgn/ZJnX7kB4vaHt3ZpJvedyiEiNVdMQYYknta7jihCfWa\n6yO9Xfc1vtf43sHnDz4Wbgr/TCXj9KPeHClmQU2oHvgzRiVDEWBvDMj2Hy5qe27F7vC7uknbj0gp\n/cCiGKdnAjJrfJZau6IPpJQEdgc27a/Y/3rL+pb5wF+867xqQ9I0JNJ5+6B0Vl5sE8D5wM2AF2gG\n+NJU66QbSm2XZ2eI3ETGI6V8SdzhvfrE49Huiv/VsrSsMd8Y8x2hCUsi40p1EX+ksfG9xtX+rf5P\ngL9713nTdSMDBdWHnLKiixKtKC+27QO+h7FP34HXt4a3f7An/MD3Z9kvKivUZmkJqmgQMdY+BgqB\nEc4yp1Ml456TutT9tf71De80VMmQfBFY4l3nTakp9ErvqRZyGigvtuUB3wROwehfDgDMGm0Z8Z+n\n2y4bkaMN6DRlKWW7EGIobk+nkX5nmfNzwLWFNxTOzhieobosuiGllO0H2jc1vte4NXQktAao8K7z\nHjA7LiUxVEJOE+XFNgtwLnADxjefOkAXwK2n207//CTrZ+1WkTUQ15ZSLhB3eC8/8Xi0u+IPml3L\nHfPNMd8WFmEbiOuni/ZD7VuaVjRtbN/f3gg8A3zgXedN1dX+lD5QXRZpYkFNKAK8X15s+xS4CqN/\n2SOh6bG1obVv1oa3fH9WxiUlQ7XT4t2LcZLqimHA6NyZuQ6VjGMLNgR3NH/QvD6wM+DBGKx9w7vO\n22hyWIoJVAs5TZUX26YCt2DsbF0HBAE+O9Ey7sZTMy7LzxLD4nEdKWVICDEct6f5xHPOMufFwNdG\nXjfyLPtI+4x4XC+dhDyhvZ6VnrWtNa1NwEfAAu8670Gz41LMoxJyGisvttmAizFazDpG/7K0aWjf\nPitj1nljLedl2UR2f64hpVws7vBe2tU5Z5nTLWxiyNjbxt4mrCKjP9dJF1KXevBwcLNntacmsD3g\nA9YBr3jXefeaHZtiPpWQB4HyYtsw4HqMvezqgRYAhw3r1061zTx/nPVcV6YY0seH/wZuz2MnHnSW\nOQuAu5xnOO355+df39fY04Ue1H2B3YG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