Lab_427_Zhaozhong_Xu.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import shape\n",
        "# Import necessary libraries\n",
        "\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "# Load the dataset (.csv) using pandas package \n",
        "# Upload the file to Google Drive first and then read data from it in Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "CMS_calori_dataset = pd.read_csv('/content/drive/My Drive/hgcal.csv')\n",
        "\n",
        "# .head directive on the panda dataframe displays the first n-rows\n",
        "\n",
        "CMS_calori_dataset.head(n = 10)\n",
        "# Convert the panda dataframe into numpy 2D array\n",
        "CMS_calori_dataset_np = CMS_calori_dataset.to_numpy()\n",
        "# The converted numpy array has the dimension of 420 (rows) x 8 (columns)\n",
        "print(CMS_calori_dataset_np.shape)\n",
        "# Extract only x, y, z, eta, phi and energy columns from the dataset and stack them along column direction\n",
        "# Name this new 2D array CMS_calori_dataset_np_sub. \n",
        "CMS_calori_dataset_np_sub=CMS_calori_dataset_np[:,1:7]\n",
        "# The array should have dimension 420 (rows) x 6 (columns)\n",
        "print(CMS_calori_dataset_np_sub.shape)\n",
        "\n",
        "# Create the scaling function\n",
        "def scale_data(arr):\n",
        "  scaled_data=np.empty_like(arr)\n",
        "  #Use i to iterate over all columns.\n",
        "  for i in range(arr.shape[1]):\n",
        "  #Standard scaling of all columns  \n",
        "    mean = np.mean(arr[:, i])\n",
        "    std = np.std(arr[:, i])\n",
        "    scaled_data[:, i] = (arr[:, i] - mean) / std\n",
        "\n",
        "  return scaled_data\n",
        "# Test the function with CMS_calori_dataset_np_sub\n",
        "CMS_calori_dataset_np_sub_scaled = scale_data(CMS_calori_dataset_np_sub)\n",
        "\n",
        "print(CMS_calori_dataset_np_sub_scaled)\n",
        "\n",
        "# Confirm the data is scaled for 'x' column\n",
        "plt.figure(figsize = (10, 5))\n",
        "plt.hist(CMS_calori_dataset_np_sub_scaled[:, 0], bins = 20, facecolor = 'grey', edgecolor = 'black', linewidth = 2)\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "# Add proper x-label and y-label \n",
        "x=plt.xlabel(\"Scaled'x'\",fontsize=16)\n",
        "y=plt.ylabel('y',fontsize=16)\n",
        "plt.show()\n",
        "# Confirm the data is scaled for 'energy' column\n",
        "plt.figure(figsize = (10, 5))\n",
        "\n",
        "plt.hist(CMS_calori_dataset_np_sub_scaled[:, 5], bins = 20, facecolor = 'grey', edgecolor = 'black', linewidth = 2)\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "# Add proper x-label and y-label\n",
        "x=plt.xlabel(\"Scaled'energy'\",fontsize=16)\n",
        "y=plt.ylabel('y',fontsize=16)\n",
        "plt.show() \n",
        "# Create the splitting function\n",
        "def split_data(arr, split_proportions, axis):\n",
        "  # Convert to numpy array\n",
        "  array=np.array(split_proportions)\n",
        "  # Get the length of the splitting axis\n",
        "  n = arr.shape[axis]\n",
        "  # Calculate the cumulative sum and multiply by the axis length to get the split points\n",
        "  array1 = np.cumsum(array) * n\n",
        "  # Drop the last element and convert to integer list\n",
        "  indices = list(array1[:-1].astype(int))\n",
        "  split_data = np.split(arr, indices, axis=axis)\n",
        "\n",
        "  # Returns a list of numpy sub-arrays according to split proportions\n",
        "  return split_data\n",
        "  # Test your split function against scaled CMS Calorimieter dataset from exercise 1\n",
        "sub_data_list_1 = split_data(arr = CMS_calori_dataset_np_sub_scaled, split_proportions = [0.6, 0.2, 0.2], axis = 0)\n",
        "# Confirm that dataset has been split into correct shapes\n",
        "# The correct dimensions should be (252, 6) (84, 6) (84, 6)\n",
        "print(sub_data_list_1[0].shape, sub_data_list_1[1].shape, sub_data_list_1[2].shape)\n",
        "# Test your split function against scaled CMS Calorimieter dataset from exercise 1\n",
        "sub_data_list_2 = split_data(arr = CMS_calori_dataset_np_sub_scaled, \n",
        "                                                split_proportions = [0.5, 0.5], axis = 1)\n",
        "# Confirm that dataset has been split into correct shapes\n",
        "# The correct dimensions should be (420, 3) (420, 3)\n",
        "print(sub_data_list_2[0].shape, sub_data_list_2[1].shape)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "J8Lcc3gpAdao",
        "outputId": "c2f78c85-1950-4833-c3e6-918a71b8777f"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "(420, 8)\n",
            "(420, 6)\n",
            "[[ 1.91214438 -0.51027049 -0.44193343 -0.47341363 -0.31488841 -0.38410307]\n",
            " [-1.64893594  1.11270897 -0.61977219 -0.64676625  1.28785592  0.99873768]\n",
            " [ 1.91214438 -0.51027028 -0.82200995 -0.82011889 -0.31488835 -0.26200369]\n",
            " ...\n",
            " [-0.45937553 -0.84184024  1.01382735  0.95550021 -1.57037143 -0.20325779]\n",
            " [-0.24351375 -0.95259315  1.01382735  0.95550021 -1.3566721  -0.41567962]\n",
            " [ 0.59642074 -0.41902261  1.01382735  0.95550021 -0.39502545  0.64852692]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(252, 6) (84, 6) (84, 6)\n",
            "(420, 3) (420, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9p6FjCA2Udwt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}