python_intro_prep_2024_edited.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZuckermanLab/coding2024/blob/main/python_intro_prep_2024_edited.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**It's OK to make a mistake**"
      ],
      "metadata": {
        "id": "1hLvEBUrv306"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "fdHCFyTNQ2Mt",
        "outputId": "60939a22-cdbd-4baa-ca73-bef1ac67e222"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "Missing parentheses in call to 'print'. Did you mean print(...)? (<ipython-input-12-752c75ce21f3>, line 1)",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-12-752c75ce21f3>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'hi there'\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(...)?\n"
          ]
        }
      ],
      "source": [
        "print 'hi there'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Printing words, numbers, math calculations**"
      ],
      "metadata": {
        "id": "uzOXvf2cwJr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('hi there')\n",
        "print('')  # leave a space\n",
        "# by the way, a '#' symbol means the line is a comment - it won't get processed\n",
        "\n",
        "a = 2\n",
        "b = 4\n",
        "c = a*b\n",
        "\n",
        "print('a=', a, '  b=', b, '  c=', c)\n",
        "\n",
        "# fancier way\n",
        "print(f'a={a}  b={b}  c={c}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVPGWXMhxeHO",
        "outputId": "ebb9a776-fee8-48bc-e6c9-22b18654a083"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hi there\n",
            "\n",
            "a= 2   b= 4   c= 8\n",
            "a=2  b=4  c=8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you try - print something by typing below and running the cell\n"
      ],
      "metadata": {
        "id": "24SujmHW2gYB"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Lists and loops**"
      ],
      "metadata": {
        "id": "sDDF61hKwgvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dan_list = ['dog', 'cat', 'mouse']\n",
        "\n",
        "for animal in dan_list:\n",
        "  print(animal)\n",
        "\n",
        "print('')\n",
        "\n",
        "for x in dan_list:\n",
        "  print(x)\n",
        "\n",
        "print('')\n",
        "\n",
        "for x in dan_list:\n",
        "  print(animal)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSNUTrERNqym",
        "outputId": "77130a40-c784-444f-c8d5-2887e1752d30"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dog\n",
            "cat\n",
            "mouse\n",
            "\n",
            "dog\n",
            "cat\n",
            "mouse\n",
            "\n",
            "mouse\n",
            "mouse\n",
            "mouse\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you try - make a list of numbers and print them.  then do a math operation on them too"
      ],
      "metadata": {
        "id": "F0WUPdMj2z0X"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A little more with loops**"
      ],
      "metadata": {
        "id": "iDpx-W5LwqHq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_list = range(5)  # range is a built-in python function\n",
        "print(int_list)\n",
        "\n",
        "print('')\n",
        "\n",
        "for i in int_list:\n",
        "  print(i)\n",
        "\n",
        "print('')\n",
        "\n",
        "my_name = 'Dan'\n",
        "for i in int_list:\n",
        "  print(f'My name is {my_name}')\n",
        "\n",
        "print('')\n",
        "\n",
        "my_name = 'Dan'\n",
        "for i in int_list:\n",
        "  print(i, i**2, i**3)  # raise to a power with **\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "060ws2N9OfCT",
        "outputId": "3893c59a-cfb5-4351-bd6a-32c42196808a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "range(0, 5)\n",
            "\n",
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "\n",
            "My name is Dan\n",
            "My name is Dan\n",
            "My name is Dan\n",
            "My name is Dan\n",
            "My name is Dan\n",
            "\n",
            "0 0 0\n",
            "1 1 1\n",
            "2 4 8\n",
            "3 9 27\n",
            "4 16 64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Functions**"
      ],
      "metadata": {
        "id": "9PWg6KG4xhol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# you try - make a loop over a range of integers and print i + i**2 for each\n",
        "# or write a function to sum the numbers from 1 to 100"
      ],
      "metadata": {
        "id": "eioJiDaj3bEH"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# functions are just lists of commands\n",
        "# they can take multiple 'arguments' (in parentheses) as inputs\n",
        "# they can 'return' an answer, which might be a number, string, list, ...\n",
        "\n",
        "def text_repeater(text, reps):\n",
        "  for i in range(reps):\n",
        "    print(text)\n",
        "\n",
        "text_repeater(\"oh it's you\", 4)\n",
        "\n",
        "text_repeater(\"No, it's me\", 2)\n",
        "\n",
        "def another_function():\n",
        "  print('')\n",
        "  print('this')\n",
        "  print('that')\n",
        "  print(2*3)\n",
        "\n",
        "another_function()\n",
        "\n",
        "def square_it(x):\n",
        "  return x**2\n",
        "\n",
        "print('')\n",
        "z = 5\n",
        "print( z, square_it(z))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZDbWgpexg1r",
        "outputId": "44c454b6-c115-48cf-e677-47328ff2cb4f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "oh it's you\n",
            "oh it's you\n",
            "oh it's you\n",
            "oh it's you\n",
            "No, it's me\n",
            "No, it's me\n",
            "\n",
            "this\n",
            "that\n",
            "6\n",
            "\n",
            "5 25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you try - make a function and call it (execute it)\n",
        "# for example - write a function that prints some text 3 times, and then sums a list of numbers"
      ],
      "metadata": {
        "id": "EQ3IfEpL4G-y"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Math plots with random data using Numerical Python (numpy)**"
      ],
      "metadata": {
        "id": "tO4M3Lb8zfj9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First, bring in the libraries of built-in functions we'll need\n",
        "import numpy as np  # numpy (numerical python) - very useful for all things math\n",
        "import matplotlib.pyplot as plt  # plotting tools\n",
        "\n",
        "# numpy arrays\n",
        "N = 15\n",
        "print('\\nNumpy arrays will be used many times in this lesson')\n",
        "x_arr = np.arange(N)\n",
        "y_arr = np.random.rand(N) # random\n",
        "print(x_arr)\n",
        "\n",
        "# simple plots\n",
        "plt.scatter(x_arr, y_arr) # plots points, taking x from first array and y from second\n",
        "plt.title('Sample plot')\n",
        "plt.xlabel('index of array')\n",
        "plt.ylabel('value of array element')\n",
        "plt.show()  # show it now in output of cell\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "u08hG6EXzlR2",
        "outputId": "97d8a5ff-1b92-4b0f-f69d-eea4fe7db96e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Numpy arrays will be used many times in this lesson\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the same data, but change labels"
      ],
      "metadata": {
        "id": "V5nRQ5z94mdg"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# new cell\n",
        "\n",
        "# second set of arrays\n",
        "# check if there are append commands in numpy and start from empty array?\n",
        "x_arr = np.arange(0, 10, 0.1)\n",
        "y_arr = np.zeros( len(x_arr) )  # initialize\n",
        "\n",
        "print('x_arr:', x_arr) # print stuff to see what it looks like\n",
        "print('')  # print a space to make it easy to see different arays\n",
        "print('y_arr:', y_arr)\n",
        "\n",
        "# initialize other arrays for comparison\n",
        "y_arr_2 = np.zeros( len(x_arr) )  # initialize\n",
        "y_arr_3 = np.zeros( len(x_arr) )\n",
        "y_arr_4 = np.zeros( len(x_arr) )\n",
        "\n",
        "i = 0\n",
        "for x in x_arr:\n",
        "  #print(x, i)\n",
        "  rando = np.random.rand() # random number between zero and one\n",
        "  y_arr[i] = 3*x + 5*( rando - 0.5 )  # line of slope 3 with random 'noise' scaled by 5\n",
        "  y_arr_2[i] = 2*x  # a simple line of slope 2\n",
        "  y_arr_3[i] = 3*x  # slope 3\n",
        "  y_arr_4[i] = 4*x  # slope 4\n",
        "  i = i+1\n",
        "\n",
        "print('')  # print a space to make it easy to see different arays\n",
        "print('y_arr:', y_arr)\n",
        "print('')\n",
        "print('y_arr_2:', y_arr_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9m92-vz6B23",
        "outputId": "9696257a-bbe3-4a47-a3a2-a0e46eb5ae81"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_arr: [0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7\n",
            " 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5\n",
            " 3.6 3.7 3.8 3.9 4.  4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5.  5.1 5.2 5.3\n",
            " 5.4 5.5 5.6 5.7 5.8 5.9 6.  6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.  7.1\n",
            " 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.  8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9\n",
            " 9.  9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9]\n",
            "\n",
            "y_arr: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
            " 0. 0. 0. 0.]\n",
            "\n",
            "y_arr: [-1.52742512  0.83571215  2.05332166  1.3359984  -1.0217016  -0.99042903\n",
            "  2.86995388  1.79560238  0.93154284  0.64364537  4.73834911  5.49097175\n",
            "  1.78093894  2.64800711  4.28535568  3.48111241  6.16400211  3.69472783\n",
            "  3.87850538  6.72895573  4.59907472  5.61223964  5.52695864  6.33320257\n",
            "  7.55207628  5.26539209  5.48163062 10.35295847  6.17729323  7.30092503\n",
            "  9.57435244  8.44006688 10.78271078  9.44732783 12.38486995  8.77207103\n",
            "  8.55905185 12.7303531   9.98581512 10.07821831 13.51391648 10.12518105\n",
            " 10.65928981 11.21822188 14.40112688 12.19008283 15.85679903 14.98938332\n",
            " 14.54936015 12.35037077 14.51733927 13.51742062 17.53122769 17.15146165\n",
            " 18.26971188 17.87799499 14.36690841 18.69959967 17.09780797 15.31238779\n",
            " 18.14248174 16.08231465 19.64672966 21.19733957 21.48236021 19.68387752\n",
            " 20.13718699 18.19099684 18.52647549 19.6256284  19.62467853 21.36016997\n",
            " 22.02681482 21.80593391 23.9288407  21.58229244 25.04567921 22.40671392\n",
            " 21.98688423 21.21930418 22.1206316  21.94604733 26.72767644 23.62588602\n",
            " 26.74398668 25.706956   25.71399816 27.25561156 27.02225989 27.66811114\n",
            " 26.43496193 28.97113851 25.99627452 29.48265525 26.38574673 28.35076099\n",
            " 28.1299648  30.59563895 28.7457913  28.51463178]\n",
            "\n",
            "y_arr_2: [ 0.   0.2  0.4  0.6  0.8  1.   1.2  1.4  1.6  1.8  2.   2.2  2.4  2.6\n",
            "  2.8  3.   3.2  3.4  3.6  3.8  4.   4.2  4.4  4.6  4.8  5.   5.2  5.4\n",
            "  5.6  5.8  6.   6.2  6.4  6.6  6.8  7.   7.2  7.4  7.6  7.8  8.   8.2\n",
            "  8.4  8.6  8.8  9.   9.2  9.4  9.6  9.8 10.  10.2 10.4 10.6 10.8 11.\n",
            " 11.2 11.4 11.6 11.8 12.  12.2 12.4 12.6 12.8 13.  13.2 13.4 13.6 13.8\n",
            " 14.  14.2 14.4 14.6 14.8 15.  15.2 15.4 15.6 15.8 16.  16.2 16.4 16.6\n",
            " 16.8 17.  17.2 17.4 17.6 17.8 18.  18.2 18.4 18.6 18.8 19.  19.2 19.4\n",
            " 19.6 19.8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the various arrays\n",
        "\n",
        "# plot new arrays\n",
        "plt.scatter(x_arr, y_arr_4, label='slope=4')\n",
        "plt.scatter(x_arr, y_arr_3, label='slope=3')\n",
        "plt.scatter(x_arr, y_arr_2, label='slope=2')\n",
        "plt.scatter(x_arr, y_arr, label='noisy data')\n",
        "plt.title('Data with noise and some lines with different slopes')\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()  # show it now in output of cell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "cgf7qQpMSPJp",
        "outputId": "24382473-cafe-4e12-b1fc-9083e1caf761"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fitting Data**"
      ],
      "metadata": {
        "id": "U43IhrCr4uA4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def error_of_fit_abs( x_array, y_array, slope): # function to compute error between data and straight line\n",
        "  error = 0 # initialize, then add to this\n",
        "  for i in range( len(x_array) ):\n",
        "    x = x_array[i]\n",
        "    y = y_array[i]\n",
        "    error += abs(y - slope*x)  # the compound '+=' tells python to add whatever's on the right to the left variable\n",
        "    #print(x, y, slope*x, error)\n",
        "  return error\n",
        "\n",
        "print(2, error_of_fit_abs(x_arr, y_arr, 2))\n",
        "print(3, error_of_fit_abs(x_arr, y_arr, 3))\n",
        "print(4, error_of_fit_abs(x_arr, y_arr, 4))\n",
        "print('')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nx0yUnv044WJ",
        "outputId": "1cb8defe-c037-4cba-eacd-4ead48cdfd3f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 484.37761749437885\n",
            "3 134.6813158808338\n",
            "4 529.4378536264779\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make a loop for finer-grained 'grid search'\n",
        "slopes_array = np.arange(1.0, 5.0, 0.1)  # for plotting\n",
        "n_slopes = len(slopes_array) # number of elements in array\n",
        "\n",
        "x_slopes = np.zeros( n_slopes )\n",
        "y_fits = np.zeros( n_slopes )\n",
        "\n",
        "# make arrays for plotting\n",
        "i = 0\n",
        "for slope in slopes_array:\n",
        "  x_slopes[i] = slope\n",
        "  y_fits[i] = error_of_fit_abs(x_arr, y_arr, slope)\n",
        "  i = i+1\n",
        "\n",
        "plt.scatter(x_slopes, y_fits, label='Error in fit')\n",
        "plt.title(\"'Grid search' in one dimension\")\n",
        "plt.xlabel('Slope')\n",
        "plt.ylabel('Error in Fit')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()  # show it now in output of cell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2eWTK-KQ7cNW",
        "outputId": "271cb812-e7c8-4cf6-d42b-1321da36128e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyGiFVXZSURP"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2D loop ... suggests 2D grid search"
      ],
      "metadata": {
        "id": "t6gCEwkMHpDy"
      }
    }
  ]
}