GW_RF_arXiv_2209_14542_II.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/am610/arXiv_2209.14542/blob/main/GW_RF_arXiv_2209_14542_II.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3qmDqiaT3nY"
      },
      "source": [
        "##### @Ayan Mitra\n",
        "##### 2022"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vw6M9EsBwTux",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01f7c873-9198-45ee-eb3e-2f8c860bed95"
      },
      "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"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy\n",
        "import os\n",
        "import joblib\n",
        "import io\n",
        "import logging\n",
        "logging.getLogger('tensorflow').disabled = True\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.model_selection import KFold, train_test_split, cross_val_score, cross_val_predict, GridSearchCV \n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, classification_report\n",
        "from sklearn.decomposition import PCA, IncrementalPCA\n",
        "from matplotlib.backends.backend_pdf import PdfPages\n",
        "from matplotlib.colors import LogNorm,Normalize\n",
        "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator)\n",
        "import scipy.fftpack\n",
        "#import hampel\n",
        "import random\n",
        "import sklearn\n",
        "import pandas\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from collections import Counter\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "#from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from scipy.interpolate import interp1d\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "# evaluate multinomial logistic regression model\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import sys\n",
        "import seaborn as sns\n",
        "from pylab import *\n",
        "from numpy import array\n",
        "from numpy import hstack\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "import matplotlib.pyplot as mpl\n",
        "from scipy import interpolate\n",
        "from numpy.fft import rfft, rfftfreq\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import median_filter\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from matplotlib import pyplot\n",
        "from sklearn.decomposition import PCA\n",
        "from google.colab import files\n",
        "from scipy.fft import fft, ifft, fftfreq\n",
        "\n",
        "column = ['t','orig_w','filt_w','smt_w']\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = pd.read_csv('drive/MyDrive/ML_dataS1.csv')\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "2cWM0iVGlzwd",
        "outputId": "39b8179e-6236-470b-e9e8-fa467e7febe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>-18.988</td>\n",
              "      <td>-18.984</td>\n",
              "      <td>-18.98</td>\n",
              "      <td>-18.976</td>\n",
              "      <td>-18.972</td>\n",
              "      <td>-18.968</td>\n",
              "      <td>-18.964</td>\n",
              "      <td>...</td>\n",
              "      <td>88.792684</td>\n",
              "      <td>87.148815</td>\n",
              "      <td>85.467697</td>\n",
              "      <td>83.754663</td>\n",
              "      <td>82.015250</td>\n",
              "      <td>80.255167</td>\n",
              "      <td>78.480253</td>\n",
              "      <td>76.696450</td>\n",
              "      <td>74.909758</td>\n",
              "      <td>s75A5O07.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>505 rows × 20001 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32c5e026-6843-4bb0-a2a1-490539f42a28')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-32c5e026-6843-4bb0-a2a1-490539f42a28');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ra5kvfJwTlEM",
        "outputId": "a054b861-95f6-4550-a407-5eec47aee4ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-2.0\n",
            "6.0\n",
            "\n",
            " SNR = 99\n",
            "Fitting 2 folds for each of 2 candidates, totalling 4 fits\n",
            "GRID Search Complete\n",
            "\n",
            " \n",
            " Tuned parameters \n",
            "\n",
            " RandomForestClassifier(max_depth=10, max_features=None, min_samples_leaf=2,\n",
            "                       n_estimators=370, warm_start=True)\n",
            "\n",
            " Accuracy = 0.683\n",
            "Confusion Matrix =  [[ 7  4  0  1]\n",
            " [ 4  5  0  0]\n",
            " [ 0  1  6  2]\n",
            " [ 0  0  1 10]]\n",
            "\n",
            " Accuracy = 0.683\n",
            "Confusion Matrix =  [[ 7  4  0  1]\n",
            " [ 4  5  0  0]\n",
            " [ 0  1  6  2]\n",
            " [ 0  0  1 10]]\n",
            "----------------\n",
            "Test Data \n",
            "----------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          12       0.64      0.58      0.61        12\n",
            "          15       0.50      0.56      0.53         9\n",
            "          27       0.86      0.67      0.75         9\n",
            "          40       0.77      0.91      0.83        11\n",
            "\n",
            "    accuracy                           0.68        41\n",
            "   macro avg       0.69      0.68      0.68        41\n",
            "weighted avg       0.69      0.68      0.68        41\n",
            "\n",
            "----------------\n",
            "Train Data\n",
            "----------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          12       1.00      1.00      1.00        84\n",
            "          15       1.00      1.00      1.00        90\n",
            "          27       1.00      1.00      1.00        93\n",
            "          40       1.00      1.00      1.00        93\n",
            "\n",
            "    accuracy                           1.00       360\n",
            "   macro avg       1.00      1.00      1.00       360\n",
            "weighted avg       1.00      1.00      1.00       360\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
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              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
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              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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          "metadata": {}
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        {
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          "data": {
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              "download(\"download_7108bd93-6da9-44c3-8dd9-2fd3494265b8\", \"Confusion.pdf\", 13148)"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x504 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time Taken: 8.058547973632812e-05\n"
          ]
        }
      ],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"GW_highres_RF_FREQUENCY.py\n",
        "\n",
        "Original file is located at\n",
        "    https://colab.research.google.com/drive/1fT5xzQ-bGbRnbneligPPZXz7TlifJvsO\n",
        "\n",
        "##### @Ayan Mitra\n",
        "##### 2022\n",
        "\"\"\"\n",
        "%timeit\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy\n",
        "import os\n",
        "import joblib\n",
        "import io\n",
        "import time\n",
        "startTime_1 = time.time()\n",
        "import logging\n",
        "logging.getLogger('tensorflow').disabled = True\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import sklearn.metrics as skm\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.model_selection import KFold, train_test_split, cross_val_score, cross_val_predict, GridSearchCV \n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, classification_report\n",
        "from sklearn.decomposition import PCA, IncrementalPCA\n",
        "from matplotlib.backends.backend_pdf import PdfPages\n",
        "from matplotlib.colors import LogNorm,Normalize\n",
        "from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator)\n",
        "import scipy.fftpack\n",
        "#import hampel\n",
        "import random\n",
        "import sklearn\n",
        "import pandas\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.utils import np_utils\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.pipeline import Pipeline\n",
        "from collections import Counter\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "#from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from scipy.interpolate import interp1d\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "# evaluate multinomial logistic regression model\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import sys\n",
        "import seaborn as sns\n",
        "from pylab import *\n",
        "from numpy import array\n",
        "from numpy import hstack\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM\n",
        "from keras.layers import Dense\n",
        "import matplotlib.pyplot as mpl\n",
        "from scipy import interpolate\n",
        "from numpy.fft import rfft, rfftfreq\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import median_filter\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from matplotlib import pyplot\n",
        "from sklearn.decomposition import PCA\n",
        "from google.colab import files\n",
        "from scipy.fft import fft, ifft, fftfreq\n",
        "\n",
        "column = ['t','orig_w','filt_w','smt_w']\n",
        "\n",
        "\n",
        "######\n",
        "# Functions\n",
        "######\n",
        "def plot(i):\n",
        "  plt.plot(Time.iloc[i],Signal.iloc[i],'.',label='%s'%i)\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "def noise(snr,input):\n",
        "  target_snr_db = snr\n",
        "  watts = input**2\n",
        "  sig_avg_watts = np.mean(watts)\n",
        "  sig_avg_db = 10 * np.log10(sig_avg_watts)\n",
        "  noise_avg_db = sig_avg_db - target_snr_db\n",
        "  noise_avg_watts = 10 ** (noise_avg_db / 10) # variance\n",
        "  mean_noise = 0 #mu\n",
        "  noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(watts))\n",
        "  x_volts = input\n",
        "  y_volts = x_volts + noise_volts\n",
        "  return y_volts\n",
        "\n",
        "def fourier_inverse(time, signal,filter_low,filter_high):\n",
        "  N = len(time)\n",
        "  T = (time[-1]-time[0])/N # Length / N\n",
        "  yf = fft(signal)\n",
        "  xf = fftfreq(N, T)[:N//2]\n",
        "  yf[filter_low:filter_high] = 0\n",
        "  iftgw = scipy.fft.ifft(yf)\n",
        "  return(time,iftgw)\n",
        "\n",
        "def fourier(time, signal,filter_low,filter_high):\n",
        "  N = len(time)\n",
        "  T = (time[-1]-time[0])/N # Length / N\n",
        "  yf = fft(signal)\n",
        "  xf = fftfreq(N, T)[:N//2]\n",
        "  yf[filter_low:filter_high] = 0\n",
        "  iftgw = scipy.fft.ifft(yf)\n",
        "  #print(np.shape(signal),np.shape(time))\n",
        "  return(time,yf)\n",
        "  \n",
        "#Confusion Matrix Plotter\n",
        "def cm_analysis(y_true, y_pred, labels, ymap=None, figsize=(12,12)):\n",
        "    \"\"\"\n",
        "    Generate matrix plot of confusion matrix with pretty annotations.\n",
        "    The plot image is saved to disk.\n",
        "    args:\n",
        "      y_true:    true label of the data, with shape (nsamples,)\n",
        "      y_pred:    prediction of the data, with shape (nsamples,)\n",
        "      filename:  filename of figure file to save\n",
        "      labels:    string array, name the order of class labels in the confusion matrix.\n",
        "                 use `clf.classes_` if using scikit-learn models.\n",
        "                 with shape (nclass,).\n",
        "      ymap:      dict: any -> string, length == nclass.\n",
        "                 if not None, map the labels & ys to more understandable strings.\n",
        "                 Caution: original y_true, y_pred and labels must align.\n",
        "      figsize:   the size of the figure plotted.\n",
        "    \"\"\"\n",
        "    if ymap is not None:\n",
        "        y_pred = [ymap[yi] for yi in y_pred]\n",
        "        y_true = [ymap[yi] for yi in y_true]\n",
        "        labels = [ymap[yi] for yi in labels]\n",
        "    cm = confusion_matrix(y_true, y_pred, labels=labels)\n",
        "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
        "    cm_perc = cm / cm_sum.astype(float) * 100\n",
        "    annot = np.empty_like(cm).astype(str)\n",
        "    nrows, ncols = cm.shape\n",
        "    for i in range(nrows):\n",
        "        for j in range(ncols):\n",
        "            c = cm[i, j]\n",
        "            p = cm_perc[i, j]\n",
        "            if i == j:\n",
        "                s = cm_sum[i]\n",
        "                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
        "            elif c == 0:\n",
        "                annot[i, j] = ''\n",
        "            else:\n",
        "                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
        "    cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
        "    cm.index.name = 'Actual Class'\n",
        "    cm.columns.name = 'Predicted Class'\n",
        "    fig, ax = plt.subplots(figsize=figsize)\n",
        "\n",
        "    sns.heatmap(cm, annot=annot, fmt='', ax=ax,cmap=\"viridis\")\n",
        "    plt.ylabel('Actual Class',fontsize=15)\n",
        "    plt.xlabel('Predicted Class',fontsize=15)\n",
        "    #plt.savefig(\"Confusion.pdf\", bbox_inches='tight')\n",
        "    plt.savefig(\"drive/MyDrive/Confusion.pdf\", bbox_inches='tight')\n",
        "    files.download('drive/MyDrive/Confusion.pdf')\n",
        "    plt.show()\n",
        "    \n",
        "    \n",
        "def evaluate(model, test_features, test_labels):\n",
        "    y_pred = model.predict(test_features)\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "    print ('\\n Accuracy = %.3f'%accuracy)\n",
        "    print ('Confusion Matrix = ',confusion_matrix(y_test,y_pred))\n",
        "    M = np.array(confusion_matrix(y_test,y_pred))\n",
        "    return (accuracy,M)\n",
        "######\n",
        "######\n",
        "\n",
        "\n",
        "\n",
        "np.random.seed(42)\n",
        "a = pd.read_csv('drive/MyDrive/ML_dataS1.csv')\n",
        "a = a.iloc[:, 1:]\n",
        "a = a[~a.Label.str.startswith('s75')]\n",
        "Label= a.iloc[:, -1]\n",
        "# \n",
        "t=[];s=[];\n",
        "Time = a.iloc[:,:10000]\n",
        "Signal = a.iloc[:, 10000:-1]\n",
        "a = a.iloc[:,:-1]\n",
        "label0 = []\n",
        "label1 = []\n",
        "label2 = []\n",
        "# \n",
        "for i in Label:\n",
        "    label0.append(i[1:3])\n",
        "    label1.append(i[3:6])\n",
        "    label2.append(i[6:])\n",
        "xlow=4250; xhigh=6251; #-2, +6\n",
        "T = Time.iloc[:,xlow:xhigh]\n",
        "S = Signal.iloc[:,xlow:xhigh]\n",
        "\n",
        "\n",
        "\n",
        "# \n",
        "# \n",
        "TX = []\n",
        "TY = []\n",
        "print(round(np.min(T.iloc[0]),1))\n",
        "print(round(np.max(T.iloc[0]),1))\n",
        "xnew = pd.Series(np.linspace((round(np.min(T.iloc[0]),1)),(round(np.max(T.iloc[0]),1)),10000, endpoint=True))\n",
        "for i in range(np.shape(a)[0]): # 46, 256\n",
        "  tx = T.iloc[i]\n",
        "  ty = S.iloc[i]\n",
        "  f1 = interp1d(tx,ty, kind='cubic',fill_value=\"extrapolate\")\n",
        "  #print('Original')\n",
        "  ynew = pd.Series(f1(xnew))\n",
        "  XC,YC =  xnew,ynew\n",
        "  SNR = 99\n",
        "  ynew = pd.Series(noise(SNR,ynew)) # snr adding\n",
        "  #print('noisy')\n",
        "  YN=(ynew)\n",
        "  TX.append(xnew)\n",
        "  TY.append(ynew)\n",
        "#\n",
        "print('\\n SNR = %s'%SNR)\n",
        "TS = pd.DataFrame(TY) # Trying GW wave signal\n",
        "a = TS\n",
        "X = a\n",
        "X_scaled = (X - X.mean(axis=0)) / X.std(axis=0)\n",
        "X = X_scaled\n",
        "y = np.array(label0).ravel()\n",
        "y = (y[:]).astype(int)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, shuffle = True,random_state=42)\n",
        "n_estimators = [int(x) for x in np.linspace(start = 100 ,\n",
        "                                            stop = 370, num = 2)]\n",
        "                                            # returns 10 numbers\n",
        "# # Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt','log2',None]\n",
        "# # Maximum number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(10, 250, num = 2)]\n",
        "# \n",
        "max_depth.append(None)\n",
        "# # Minimum number of samples required to split a node\n",
        "min_samples_split =[int(x) for x in np.linspace(2, 101, num = 2)]\n",
        "# \n",
        "# # Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [int(x) for x in np.linspace(2, 101, num = 2)]\n",
        "# \n",
        "# # Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "oob_score = [True, False]\n",
        "warm_start= [True, False]\n",
        "# \n",
        "# # Create the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap,\n",
        "               'oob_score': oob_score,\n",
        "               'warm_start': warm_start}\n",
        "# # Use the random grid to search for best hyperparameters\n",
        "# # First create the base model to tune\n",
        "rf = RandomForestClassifier()\n",
        "# # Random search of parameters, using 3 fold cross validation, \n",
        "# # search across 100 different combinations, and use all available cores\n",
        "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,\n",
        "                               n_iter = 2, cv = 2, verbose=10, random_state=42, n_jobs = -1)\n",
        "# # Fit the random search model\n",
        "rf_random.fit(X_train, y_train)\n",
        "# \n",
        "\n",
        "print('GRID Search Complete')\n",
        "#\n",
        "best_random = rf_random.best_estimator_\n",
        "print('\\n \\n Tuned parameters \\n\\n',best_random)\n",
        "evaluate(best_random, X_test, y_test)\n",
        "mat = evaluate(best_random, X_test, y_test)[1]\n",
        "mat.dump('Confusion.dat')\n",
        "#\n",
        "#filename = 'RF_GW_highres_model_FREQ.sav' # original is without -2 in name\n",
        "#joblib.dump(best_random,  './'+filename )\n",
        "\n",
        "y_pred       = best_random.predict(X_test)\n",
        "y_pred_train = best_random.predict(X_train)\n",
        "report = pd.DataFrame(list(precision_recall_fscore_support(y_test, y_pred)), #y_train, y_pred_train\n",
        "            index=['Precision', 'Recall', 'F1-score', 'Support']).T\n",
        "\n",
        "# Now add the 'Avg/Total' row\n",
        "report.loc['Avg/Total', :] = precision_recall_fscore_support(y_test, y_pred,\n",
        "    average='weighted')\n",
        "report.loc['Avg/Total', 'Support'] = report['Support'].sum()\n",
        "\n",
        "\n",
        "print('----------------')\n",
        "print('Test Data ')\n",
        "print('----------------')\n",
        "print(skm.classification_report( y_test , y_pred ))\n",
        "print('----------------')\n",
        "print('Train Data')\n",
        "print('----------------')\n",
        "print(skm.classification_report( y_train , y_pred_train ))\n",
        "cm_analysis(y_test, y_pred, best_random.classes_, ymap=None, figsize=(8,7))\n",
        "startTime_2 = time.time()\n",
        "executionTime_2 = (time.time() - startTime_2)\n",
        "print('Time Taken: ' + str(executionTime_2))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "R = []\n",
        "for i in ['mean_test_score', 'std_test_score', 'param_n_estimators']:\n",
        "        print(i,\" : \",rf_random.cv_results_[i])\n",
        "        R.append(rf_random.cv_results_[i])\n",
        "R = pd.DataFrame(R).T\n",
        "R.columns = (['Mean','STD','N'])\n",
        "print(R)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTM-ZllWGKw6",
        "outputId": "0477f978-67fa-496b-d7ba-03710c4b689d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean_test_score  :  [0.5        0.25555556]\n",
            "std_test_score  :  [0.00555556 0.        ]\n",
            "param_n_estimators  :  [370 100]\n",
            "       Mean       STD      N\n",
            "0  0.500000  0.005556  370.0\n",
            "1  0.255556  0.000000  100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = pd.read_csv('drive/MyDrive/Mean_std.csv')"
      ],
      "metadata": {
        "id": "WrIS2QlyARcR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = a[a['STD'].notna()]"
      ],
      "metadata": {
        "id": "FZjtAzegAWFV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = [f\"feature {i}\" for i in range(X.shape[1])]"
      ],
      "metadata": {
        "id": "iMorgcfcxgQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.best_estimator_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkeiUOI72qiK",
        "outputId": "64a31701-f734-40b1-fa08-1897dbb7e3d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, max_features=None, min_samples_leaf=2,\n",
              "                       n_estimators=370, warm_start=True)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rff = RandomForestClassifier(max_depth=10, max_features=None, min_samples_leaf=2,\n",
        "                       n_estimators=370, warm_start=True)\n",
        "rff.fit(X_train, y_train)\n",
        "importances = rff.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in rff.estimators_], axis=0)"
      ],
      "metadata": {
        "id": "Ygle3v5txzZK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gini_t = []\n",
        "for i in range(10000):\n",
        "  gini_t.append(TX[0][importances['Features'][i]])\n",
        "gini = pd.concat([pd.Series(gini_t),importances['Gini-Importance']],axis=1)\n",
        "gini.columns = ['Time','Gini']  "
      ],
      "metadata": {
        "id": "0kKZuMToHsay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gini.to_csv('drive/MyDrive/Gini.csv')"
      ],
      "metadata": {
        "id": "ZkJwZ_sNacx6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f,a2 = plt.subplots()\n",
        "plt.bar(gini.Time,gini['Gini'],alpha=0.1,color='r',width = 0.2)\n",
        "#plt.scatter(gini.Time,importances['Gini-Importance'])\n",
        "a2.grid(False)\n",
        "#plt.colorbar()\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12)\n",
        "a2.tick_params(axis='both', which='major', labelsize=20)\n",
        "plt.tight_layout()\n",
        "plt.xlabel('Time (ms)')\n",
        "plt.ylabel('Gini importance')\n",
        "plt.savefig(\"drive/MyDrive/Gini.pdf\", bbox_inches='tight')\n",
        "files.download('drive/MyDrive/Gini.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "b43o4uRPTkbi",
        "outputId": "6a271bcd-615e-4e01-87fe-3fce55ff49b0"
      },
      "execution_count": null,
      "outputs": [
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_cmap = plt.get_cmap(\"viridis\")\n",
        "rescale = lambda y: (y - np.min(y)) / (np.max(y) - np.min(y))\n",
        "\n",
        "plt.bar(gini.Time,gini['Gini'],alpha=0.3,color=my_cmap(rescale(gini['Gini'])),width = 0.2)\n",
        "#plt.scatter(gini.Time,importances['Gini-Importance'])\n",
        "plt.grid(False)\n",
        "#plt.colorbar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "nRk9jUNOBkyt",
        "outputId": "0412320a-63b9-4f96-9842-a6674565f120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "2#Confusion Matrix Plotter\n",
        "def cm_analysis(y_true, y_pred, labels, ymap=None, figsize=(12,12)):\n",
        "    \"\"\"\n",
        "    Generate matrix plot of confusion matrix with pretty annotations.\n",
        "    The plot image is saved to disk.\n",
        "    args:\n",
        "      y_true:    true label of the data, with shape (nsamples,)\n",
        "      y_pred:    prediction of the data, with shape (nsamples,)\n",
        "      filename:  filename of figure file to save\n",
        "      labels:    string array, name the order of class labels in the confusion matrix.\n",
        "                 use `clf.classes_` if using scikit-learn models.\n",
        "                 with shape (nclass,).\n",
        "      ymap:      dict: any -> string, length == nclass.\n",
        "                 if not None, map the labels & ys to more understandable strings.\n",
        "                 Caution: original y_true, y_pred and labels must align.\n",
        "      figsize:   the size of the figure plotted.\n",
        "    \"\"\"\n",
        "    if ymap is not None:\n",
        "        y_pred = [ymap[yi] for yi in y_pred]\n",
        "        y_true = [ymap[yi] for yi in y_true]\n",
        "        labels = [ymap[yi] for yi in labels]\n",
        "    cm = confusion_matrix(y_true, y_pred, labels=labels)\n",
        "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
        "    cm_perc = cm / cm_sum.astype(float) * 100\n",
        "    annot = np.empty_like(cm).astype(str)\n",
        "    nrows, ncols = cm.shape\n",
        "    for i in range(nrows):\n",
        "        for j in range(ncols):\n",
        "            c = cm[i, j]\n",
        "            p = cm_perc[i, j]\n",
        "            if i == j:\n",
        "                s = cm_sum[i]\n",
        "                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
        "            elif c == 0:\n",
        "                annot[i, j] = ''\n",
        "            else:\n",
        "                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
        "    cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
        "    cm.index.name = 'Actual Class'\n",
        "    cm.columns.name = 'Predicted Class'\n",
        "    fig, ax = plt.subplots(figsize=figsize)\n",
        "\n",
        "    sns.heatmap(cm, annot=annot, fmt='', ax=ax,cmap=\"viridis\")\n",
        "    plt.ylabel('Actual Class',fontsize=15)\n",
        "    plt.xlabel('Predicted Class',fontsize=15)\n",
        "    #plt.savefig(\"Confusion.pdf\", bbox_inches='tight')\n",
        "    plt.savefig(\"drive/MyDrive/Confusion.pdf\", bbox_inches='tight')\n",
        "    files.download('drive/MyDrive/Confusion.pdf')\n",
        "    plt.show()\n",
        " \n",
        "cm_analysis(y_test, y_pred, best_random.classes_, ymap=None, figsize=(8,7))\n"
      ],
      "metadata": {
        "id": "3hkBwI9RUDD8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "689f0346-d585-41a2-f63c-9475bf69dbe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_6e52d624-2c1a-49f2-be22-a77fe0071f4f\", \"Confusion.pdf\", 13148)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read in data from Sultan's data file\n",
        "# Facing problem loading from his google drive location\n",
        "# Copy-pasting the values\n",
        "\n",
        "Recall    = pd.DataFrame([[1,10,15,25,35, 51,70,100],[0.22,0.33,0.40,0.56,0.56, 0.56,0.56,0.56],[0.45,0.50,0.50,0.50,0.50,0.50,0.50,0.50],[0.25,0.50,0.50,0.50,0.50,0.50,0.50,0.62],[0.5,1,1,1.,1,1,1.,1.]]).T\n",
        "Precision = pd.DataFrame([[1,10,25,51,60,70,100],[0.67,0.75,1.0,0.83,0.83,0.83,1.00],[0.14,0.20,0.20,0.20,0.20,0.20,0.20],[0.67, 0.67,0.8,1.0,1.0,1.0,0.83],[0.12,0.33,0.33,0.33,0.33,0.33,0.40]]).T\n",
        "Accuracy  = pd.DataFrame([[1,10,25,51,70,100],[0.29,0.48,0.58,0.58,0.62,0.68]]).T\n",
        "Recall.columns = ['SNR','S12','S15','S27','S40']\n",
        "Precision.columns = ['SNR','S12','S15','S27','S40']\n",
        "Accuracy.columns = ['SNR','Accuracy']"
      ],
      "metadata": {
        "id": "1_ov16XIUJFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Recall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QIqmCvdAT6KD",
        "outputId": "e5d70866-bf18-45a2-81d2-c9b92a35c3c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SNR   S12   S15   S27  S40\n",
              "0    1.0  0.22  0.45  0.25  0.5\n",
              "1   10.0  0.33  0.50  0.50  1.0\n",
              "2   15.0  0.40  0.50  0.50  1.0\n",
              "3   25.0  0.56  0.50  0.50  1.0\n",
              "4   35.0  0.56  0.50  0.50  1.0\n",
              "5   51.0  0.56  0.50  0.50  1.0\n",
              "6   70.0  0.56  0.50  0.50  1.0\n",
              "7  100.0  0.56  0.50  0.62  1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5ccb1c0-0edf-4584-96ef-9b559f1837bb\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNR</th>\n",
              "      <th>S12</th>\n",
              "      <th>S15</th>\n",
              "      <th>S27</th>\n",
              "      <th>S40</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15.0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>51.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.62</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5ccb1c0-0edf-4584-96ef-9b559f1837bb')\"\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5ccb1c0-0edf-4584-96ef-9b559f1837bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5ccb1c0-0edf-4584-96ef-9b559f1837bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "KJeG9lm86SAa",
        "outputId": "59776c96-ada3-4462-a4eb-878e3d208f0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SNR  Accuracy\n",
              "0    1.0      0.29\n",
              "1   10.0      0.48\n",
              "2   25.0      0.58\n",
              "3   51.0      0.58\n",
              "4   70.0      0.62\n",
              "5  100.0      0.68"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7381c550-7843-4164-a927-8ea76798c2aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNR</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51.0</td>\n",
              "      <td>0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7381c550-7843-4164-a927-8ea76798c2aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7381c550-7843-4164-a927-8ea76798c2aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7381c550-7843-4164-a927-8ea76798c2aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def interp(x,y):\n",
        "  f_ = interp1d(x,y, kind='cubic',fill_value=\"extrapolate\") # nearest, extrapolate,cubic\n",
        "  xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "  return (f_)\n",
        "plt.rcParams['figure.figsize'] = [7,5]\n",
        "xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "\n",
        "f,a2 = plt.subplots()\n",
        "f__ = interp(Precision.SNR,Precision.S12)\n",
        "plt.plot(xnew,f__(xnew),label='s12')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S15)\n",
        "plt.plot(xnew,f__(xnew),label='s15')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S27)\n",
        "plt.plot(xnew,f__(xnew),label='s27')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S40)\n",
        "plt.plot(xnew,f__(xnew),label='s40')\n",
        "plt.legend()\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Precision',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Precision.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/Precision.pdf')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "f,a2 = plt.subplots()\n",
        "f__ = interp(Recall.SNR,Recall.S12)\n",
        "plt.plot(xnew,f__(xnew),label='s12')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S15)\n",
        "plt.plot(xnew,f__(xnew),label='s15')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S27)\n",
        "plt.plot(xnew,f__(xnew),label='s27')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S40)\n",
        "plt.plot(xnew,f__(xnew),label='s40')\n",
        "plt.legend()\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Recall',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Recall.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/Recall.pdf')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "0DRJs6EzE_Qj",
        "outputId": "830dd056-58b3-4034-8726-27471f6f8eed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Recall    = pd.DataFrame([[1,10,25, 51,70,100],[0.22,0.33,0.56, 0.56,0.56,0.56],[0.45,0.50,0.50,0.50,0.50,0.50],[0.25,0.50,0.50,0.50,0.50,0.62],[0.5,1,1,1,1.,1.]]).T\n",
        "Precision = pd.DataFrame([[1,10,25,51,70,100],[0.67,0.75,1.0,0.83,0.83,1.00],[0.14,0.20,0.20,0.20,0.20,0.20],[0.67, 0.67,0.8,1.0,1.0,0.83],[0.12,0.33,0.33,0.33,0.33,0.40]]).T\n",
        "Accuracy  = pd.DataFrame([[1,10,25,51,70,100],[0.29,0.48,0.58,0.58,0.62,0.68]]).T\n",
        "Recall.columns = ['SNR','S12','S15','S27','S40']\n",
        "Precision.columns = ['SNR','S12','S15','S27','S40']\n",
        "Accuracy.columns = ['SNR','Accuracy']\n",
        "def interp(x,y):\n",
        "  f_ = interp1d(x,y, kind='cubic',fill_value=\"extrapolate\") # nearest, extrapolate,cubic\n",
        "  xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "  return (f_)\n",
        "plt.rcParams['figure.figsize'] = [7,5]\n",
        "xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "\n",
        "f,a2 = plt.subplots()\n",
        "f__ = interp(Precision.SNR,Precision.S12)\n",
        "plt.plot(Precision.SNR,Precision.S12,'o',markersize=12,label='s12')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S15)\n",
        "plt.plot(Precision.SNR,Precision.S15,'v',markersize=12,label='s15')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S27)\n",
        "plt.plot(Precision.SNR,Precision.S27,'+',markersize=12,label='s27')\n",
        "\n",
        "f__ = interp(Precision.SNR,Precision.S40)\n",
        "plt.plot(Precision.SNR,Precision.S40,'d',markersize=12,label='s40')\n",
        "plt.legend()\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Precision',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Precision.pdf\", bbox_inches='tight')\n",
        "files.download('drive/MyDrive/Precision.pdf')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "f,a2 = plt.subplots()\n",
        "f__ = interp(Recall.SNR,Recall.S12)\n",
        "plt.plot(Recall.SNR,Recall.S12,'o',markersize=12,label='s12')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S15)\n",
        "plt.plot(Recall.SNR,Recall.S15,'v',markersize=12,label='s15')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S27)\n",
        "plt.plot(Recall.SNR,Recall.S27,'+',markersize=12,label='s27')\n",
        "\n",
        "f__ = interp(Recall.SNR,Recall.S40)\n",
        "plt.plot(Recall.SNR,Recall.S40,'d',markersize=12,label='s40')\n",
        "plt.legend()\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Recall',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Recall.pdf\", bbox_inches='tight')\n",
        "files.download('drive/MyDrive/Recall.pdf')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "dSvUjEk8YT21",
        "outputId": "aea523d8-a3db-4668-b103-344cb3cd6069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_98d71a9b-99f5-4538-a0cc-36ba4beb8936\", \"Precision.pdf\", 12058)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_8c895182-aefd-4632-9eaf-384e4ccadd6e\", \"Recall.pdf\", 11255)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Recall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "qF1kAcDoAeHb",
        "outputId": "4f76055a-772f-45dc-ca45-e1fcbb7fad4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SNR   S12   S15   S27  S40\n",
              "0    1.0  0.22  0.45  0.25  0.5\n",
              "1   10.0  0.33  0.50  0.50  1.0\n",
              "2   25.0  0.56  0.50  0.50  1.0\n",
              "3   51.0  0.56  0.50  0.50  1.0\n",
              "4   70.0  0.56  0.50  0.50  1.0\n",
              "5  100.0  0.56  0.50  0.62  1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17720b66-97b6-42d1-9c1e-d3da49a44a73\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNR</th>\n",
              "      <th>S12</th>\n",
              "      <th>S15</th>\n",
              "      <th>S27</th>\n",
              "      <th>S40</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.62</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17720b66-97b6-42d1-9c1e-d3da49a44a73')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17720b66-97b6-42d1-9c1e-d3da49a44a73 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17720b66-97b6-42d1-9c1e-d3da49a44a73');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Recall    = pd.DataFrame([[1,10,25, 51,70,100],[0.22,0.33,0.56, 0.56,0.56,0.56],[0.45,0.50,0.50,0.50,0.50,0.50],[0.25,0.50,0.50,0.50,0.50,0.62],[0.5,1,1,1,1.,1.]]).T\n",
        "Precision = pd.DataFrame([[1,10,25,51,70,100],[0.67,0.75,1.0,0.83,0.83,1.00],[0.14,0.20,0.20,0.20,0.20,0.20],[0.67, 0.67,0.8,1.0,1.0,0.83],[0.12,0.33,0.33,0.33,0.33,0.40]]).T\n",
        "Accuracy  = pd.DataFrame([[1,10,25,51,70,100],[0.29,0.48,0.58,0.58,0.62,0.68]]).T\n",
        "print(Precision)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwNA53Q40RfE",
        "outputId": "92c29ae2-b85f-423c-ceba-e08a4ed34efa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       0     1     2     3     4\n",
            "0    1.0  0.67  0.14  0.67  0.12\n",
            "1   10.0  0.75  0.20  0.67  0.33\n",
            "2   25.0  1.00  0.20  0.80  0.33\n",
            "3   51.0  0.83  0.20  1.00  0.33\n",
            "4   70.0  0.83  0.20  1.00  0.33\n",
            "5  100.0  1.00  0.20  0.83  0.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f,a2 = plt.subplots()\n",
        "xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "f__ = interp(Accuracy.SNR,Accuracy.Accuracy)\n",
        "plt.plot(xnew,f__(xnew),label='Accuracy',c='b')\n",
        "#plt.plot(Accuracy.SNR,Accuracy.Accuracy,'o',c='r')\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Accuracy',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Accuracy.pdf\", bbox_inches='tight')\n",
        "files.download('drive/MyDrive/Accuracy.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "tf9_De8rEcd8",
        "outputId": "bcee6b85-01db-416a-e5c5-5c8389f89f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_23905712-8010-48c0-ba68-5c1eed224db5\", \"Accuracy.pdf\", 11282)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f,a2 = plt.subplots()\n",
        "xnew = np.linspace(0.04,100,100, endpoint=True)\n",
        "#f__ = interp(Accuracy.SNR,Accuracy.Accuracy)\n",
        "plt.plot(Accuracy.SNR,Accuracy.Accuracy,'o',label='Accuracy',c='b')\n",
        "#plt.plot(Accuracy.SNR,Accuracy.Accuracy,'o',c='r')\n",
        "plt.xlabel('SNR',fontsize=25);plt.ylabel('Accuracy',fontsize=25)\n",
        "a2.minorticks_on()\n",
        "plt.ylim(0.27,0.715)\n",
        "a2.yaxis.set_ticks_position('both')\n",
        "a2.xaxis.set_ticks_position('both')\n",
        "a2.tick_params(axis='both', which='minor', labelsize=12,direction=\"in\")\n",
        "a2.tick_params(axis='both', which='major', labelsize=20,direction=\"in\")\n",
        "plt.tight_layout()\n",
        "#plt.savefig(\"drive/MyDrive/Accuracy.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/Accuracy.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "GUFyjarzXoa_",
        "outputId": "08b4d7e5-ef9b-4a82-cb5b-e6ae8379bf12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_d3ce44e9-3469-4d6b-9dc9-a6a53c06b74b\", \"Accuracy.pdf\", 9902)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(XC,YC,label='Clean')\n",
        "plt.plot(XC,YN)\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "sUXW6XUAyMUn",
        "outputId": "5e1831d3-f15e-47a2-8531-5e203f2a4a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f04c7f2ed50>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#y7 = YN # SNR = 7\n",
        "#y50= YN\n",
        "y90= YN"
      ],
      "metadata": {
        "id": "9CK_CS7t5kQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t1201 = np.array(XC)\n",
        "gw1201 = np.array(y7)\n",
        "# Number of sample points\n",
        "N = len(t1201)\n",
        "# sample spacing\n",
        "T = (t1201[-1]-t1201[0])/N\n",
        "y = gw1201\n",
        "yf = fft(y)\n",
        "xf = fftfreq(N, T)[:N//2]\n",
        "\n",
        "\n",
        "plt.plot(xf[:200], 2.0/N * np.abs(yf[0:N//2])[:200])\n",
        "plt.yscale('log')\n",
        "plt.xscale('log')\n",
        "#plt.xlim(100,4000)\n",
        "#plt.ylim(10**(-24),10**(-21))\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "dMV1iXLFcfX1",
        "outputId": "f8bccae4-80b9-41fc-cc3f-6a9a0089f677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t1201 = np.array(XC)\n",
        "gw1201 = np.array(y50)\n",
        "# Number of sample points\n",
        "N = len(t1201)\n",
        "# sample spacing\n",
        "T = (t1201[-1]-t1201[0])/N\n",
        "y = gw1201\n",
        "yf2 = fft(y)\n",
        "xf = fftfreq(N, T)[:N//2]\n",
        "\n",
        "\n",
        "plt.plot(xf[:200], 2.0/N * np.abs(yf2[0:N//2])[:200],label='SNR=7')\n",
        "plt.plot(xf[:200], 2.0/N * np.abs(yf[0:N//2])[:200],label='SNR=50')\n",
        "plt.yscale('log')\n",
        "plt.xscale('log')\n",
        "#plt.xlim(100,4000)\n",
        "#plt.ylim(10**(-24),10**(-21))\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.xlabel('Frequency',fontsize=15)\n",
        "plt.ylabel('PSD',fontsize=15)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "W225fUbA0ie0",
        "outputId": "86757c4d-b20b-4309-e3b8-527bcc8b8993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.close()"
      ],
      "metadata": {
        "id": "118TKZ-CTw3F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fig, ax = plt.subplots()\n",
        "def noise_snr(snr,input,c):\n",
        "  from scipy.stats import norm\n",
        "  import statistics\n",
        "  import seaborn as sns\n",
        "  target_snr_db = snr\n",
        "  watts = input**2\n",
        "  sig_avg_watts = np.mean(watts)\n",
        "  sig_avg_db = 10 * np.log10(sig_avg_watts)\n",
        "  noise_avg_db = sig_avg_db - target_snr_db\n",
        "  noise_avg_watts = 10 ** (noise_avg_db / 10) # variance\n",
        "  #print('SNR = %s, Variance %s'%(snr, np.sqrt(noise_avg_watts)))\n",
        "  mean_noise = 0 #mu\n",
        "  sdiv = np.sqrt(noise_avg_watts)\n",
        "  noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(watts))\n",
        "  sns.distplot(noise_volts,label=r'SNR$=%s, \\sigma=%.2f$'%(snr,sdiv),color=c)\n",
        "  plt.legend()\n",
        "  plt.axvline(0,linestyle='--',c='black')\n",
        "  ax.yaxis.set_ticks_position('both')\n",
        "  ax.xaxis.set_ticks_position('both')\n",
        "  ax.minorticks_on()\n",
        "  ax.tick_params(which = 'both',direction=\"in\")\n",
        "  ax.set_ylabel('Density',fontsize=15)\n",
        "  ax.set_xlabel(r'$N(\\mu=0,\\sigma)$',fontsize=15)\n",
        "  ax.tick_params(length=6)\n",
        "  print(mean_noise,np.sqrt(noise_avg_watts))\n",
        "  x_volts = input\n",
        "  y_volts = x_volts + noise_volts\n",
        "  return noise_volts\n",
        "\n",
        "#fig, ax = plt.subplots()\n",
        "def noise_snr2(snr,input,c):\n",
        "  from scipy.stats import norm\n",
        "  import statistics\n",
        "  import seaborn as sns\n",
        "  target_snr_db = snr\n",
        "  watts = input**2\n",
        "  sig_avg_watts = np.mean(watts)\n",
        "  sig_avg_db = 10 * np.log10(sig_avg_watts)\n",
        "  noise_avg_db = sig_avg_db - target_snr_db\n",
        "  noise_avg_watts = 10 ** (noise_avg_db / 10) # variance\n",
        "  #print('SNR = %s, Variance %s'%(snr, np.sqrt(noise_avg_watts)))\n",
        "  mean_noise = 0 #mu\n",
        "  sdiv = np.sqrt(noise_avg_watts)\n",
        "  noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(watts))\n",
        "  #sns.distplot(noise_volts,label=r'SNR$=%s, \\sigma=%.2f$'%(snr,sdiv))\n",
        "  plt.plot(xnew,noise_volts,label=r'SNR$=%s, \\sigma=%.2f$'%(snr,sdiv),c=c,alpha=0.5)\n",
        "  plt.legend()\n",
        "  plt.axhline(0,linestyle='--',c='black')\n",
        "  ax.yaxis.set_ticks_position('both')\n",
        "  ax.xaxis.set_ticks_position('both')\n",
        "  ax.minorticks_on()\n",
        "  ax.tick_params(which = 'both',direction=\"in\")\n",
        "  ax.set_ylabel('Gaussian Noise',fontsize=15)\n",
        "  ax.set_xlabel('Time',fontsize=15)\n",
        "  ax.tick_params(length=6)\n",
        "  print(mean_noise,np.sqrt(noise_avg_watts))\n",
        "  x_volts = input\n",
        "  y_volts = x_volts + noise_volts\n",
        "  return noise_volts  \n",
        "def noise_v(snr,input) :\n",
        "  from scipy.stats import norm\n",
        "  import statistics\n",
        "  import seaborn as sns\n",
        "  target_snr_db = snr\n",
        "  watts = input**2\n",
        "  sig_avg_watts = np.mean(watts)\n",
        "  sig_avg_db = 10 * np.log10(sig_avg_watts)\n",
        "  noise_avg_db = sig_avg_db - target_snr_db\n",
        "  noise_avg_watts = 10 ** (noise_avg_db / 10) # variance\n",
        "  #print('SNR = %s, Variance %s'%(snr, np.sqrt(noise_avg_watts)))\n",
        "  mean_noise = 0 #mu\n",
        "  sdiv = np.sqrt(noise_avg_watts)\n",
        "  noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(watts)) \n",
        "  return noise_volts"
      ],
      "metadata": {
        "id": "wm6NSLf_cWAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "snr1 = 10\n",
        "snr2 = 20\n",
        "v1 = noise_v(snr1,ynew)\n",
        "v2 = noise_v(snr2,ynew)\n",
        "\n",
        "sdiv1 = np.std(v1)\n",
        "sdiv2 = np.std(v2)"
      ],
      "metadata": {
        "id": "Rd23CJGzFaa3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(xnew,v1,color='grey',alpha=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "ifEFF5_x6bId",
        "outputId": "14b21fd7-7aef-4c59-fd16-1eebd09a4452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff2d9c85d50>]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt(\"drive/MyDrive/x.csv\", xnew)\n",
        "np.savetxt(\"drive/MyDrive/v1.csv\", v1)\n",
        "np.savetxt(\"drive/MyDrive/v2.csv\", v2)"
      ],
      "metadata": {
        "id": "SVqGfXWwHMar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from matplotlib.transforms import Affine2D\n",
        "#import mpl_toolkits.axisartist.floating_axes as floating_axes\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [10, 8]\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "fig1 = plt.figure(1)\n",
        "frame1=fig1.add_axes((.1,.65,.89,.36))\n",
        "\n",
        "\n",
        "plt.plot(xnew,v1,color='grey',alpha=0.5,label=r'SNR$=%s, \\sigma=%.2f$'%(snr1,sdiv1))\n",
        "plt.plot(xnew,v2,color='r',alpha=0.5,label=r'SNR$=%s, \\sigma=%.2f$'%(snr2,sdiv2))\n",
        "plt.xlabel('Time',fontsize=15)\n",
        "plt.ylabel('White Noise $N(\\sigma, \\mu)$',fontsize=15)\n",
        "#plt.ylabel('$\\mu$',fontsize=25)\n",
        "plt.legend(fontsize=20,frameon=False)\n",
        "plt.autoscale(enable=True, axis=u'both', tight=True)\n",
        "plt.axhline(0,linestyle='--',c='black')\n",
        "frame1.yaxis.set_ticks_position('both')\n",
        "frame1.xaxis.set_ticks_position('both')\n",
        "frame1.minorticks_on()\n",
        "frame1.tick_params(which = 'minor',direction=\"in\")\n",
        "frame1.tick_params(direction=\"in\",length=6)\n",
        "plt.tick_params(axis='y', which='major', labelsize=20)\n",
        "plt.tick_params(axis='x', which='major', labelsize=2)\n",
        "\n",
        "\n",
        "\n",
        "frame2=fig1.add_axes((1.1,0.65,.3,.36)) # left, bottom, width, height\n",
        "\n",
        "sns.distplot(v1,color='grey',label=r'SNR$=%s, \\sigma=%.2f$'%(snr1,sdiv1))\n",
        "sns.distplot(v2,color='r',label=r'SNR$=%s, \\sigma=%.2f$'%(snr2,sdiv2))\n",
        "\n",
        "frame2.yaxis.set_ticks_position('both')\n",
        "frame2.xaxis.set_ticks_position('both')\n",
        "frame2.tick_params(direction=\"in\",length=6)\n",
        "frame2.tick_params(which = 'minor',direction=\"in\")\n",
        "plt.tick_params(axis='y', which='major', labelsize=15)\n",
        "plt.tick_params(axis='x', which='major', labelsize=20)\n",
        "plt.axvline(0,linestyle='--',color='black')\n",
        "frame2.set_ylabel('Density',fontsize=15)\n",
        "frame2.set_xlabel(r'$N(\\mu =0, \\sigma)$',fontsize=15)\n",
        "frame2.minorticks_on()\n",
        "plt.grid(False)\n",
        "plt.legend(fontsize=10,loc='upper right',frameon=False)\n",
        "plt.tight_layout()\n",
        "plt.savefig('drive/MyDrive/AWGN2.pdf',bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/AWGN2.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "FDfGB6DKGCrr",
        "outputId": "9086dbdc-38e8-4afc-811d-6a5ef14306d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "import io\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import colors\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from scipy.interpolate import interp1d\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "9uaI_ZXULVVt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [7, 5]\n",
        "x = [-4,-3,-2,-1,0,1,2,3,4,5,6,7]\n",
        "y = [0.71,0.76,0.86,0.81,0.81,0.76,0.76,0.76,0.76,0.81,0.71,0.76]\n",
        "y = [yy-0.17 for yy in y]\n",
        "l = ['-4,4','-3,5','-2,6','-1,7','0,8','1,9','2,10','3,11','4,12','5,13','6,14','7,15']\n",
        "xnew = np.linspace(-4,7,100)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "f1 = interp1d(x,y, kind='cubic',fill_value=\"extrapolate\")\n",
        "ynew = pd.Series(f1(xnew))\n",
        "#plt.plot(x,y,'o',c='black')\n",
        "#ax=sns.barplot(x=x, y=y)\n",
        "#plt.plot(xnew,ynew,c='b')\n",
        "plt.bar(x,y,align='center', edgecolor='r', color='None',alpha=0.9,width=1,lw=1)\n",
        "\n",
        "plt.xlabel('Input Time Range [ms]',fontsize=25)\n",
        "plt.ylabel('Best Accuracy Score',fontsize=25)\n",
        "#plt.text(-0.2,0.825,'Rolling Window length of 8 X steps',fontsize=12)\n",
        "plt.axvline(-2,linestyle='--',c='black')\n",
        "plt.xticks(x, l, rotation=90,fontsize=20); # vertical\n",
        "plt.yticks(fontsize=15)\n",
        "ax.yaxis.set_ticks_position('both')\n",
        "ax.minorticks_on()\n",
        "ax.xaxis.set_tick_params(which='minor', bottom=False,top=False)\n",
        "ax.tick_params(length=6,left=True)\n",
        "ax.tick_params(which = 'both',direction=\"in\")\n",
        "\n",
        "#ax.minorticks_on()\n",
        "ax.yaxis.set_ticks_position('both')\n",
        "#ax.xaxis.set_ticks_position('both')\n",
        "#ax.tick_params(axis='both', which='minor', labelsize=12)\n",
        "#ax.tick_params(axis='both', which='major', labelsize=20)\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.tick_params( bottom = False)\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Rolling_window.pdf\", bbox_inches='tight')\n",
        "files.download('drive/MyDrive/Rolling_window.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "NS9OvhJb-wa_",
        "outputId": "b153f4c2-fe2f-4611-ad0b-c65c35a03eef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_95e3a7ed-92cd-46b7-b2ba-e158526ad792\", \"Rolling_window.pdf\", 15140)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [5, 4]\n",
        "fig, ax = plt.subplots()\n",
        "noise_snr(10,ynew,'grey')\n",
        "noise_snr(20,ynew,'r')\n",
        "plt.grid(False)\n",
        "plt.tight_layout()\n",
        "#plt.savefig(\"drive/MyDrive/Noise-1.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/Noise-1.pdf')\n",
        "\n",
        "\n",
        "buf = io.BytesIO()\n",
        "plt.savefig(buf, format='png',bbox_inches='tight')\n",
        "buf.seek(0)\n",
        "im = Image.open(buf)  #We open the current image saved in the buffer\n",
        "img_01=im.rotate(-90, Image.NEAREST, expand = 1, fillcolor = (255,255,255))\n",
        "buf.close()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "_qB9hOoGHPno",
        "outputId": "0087ed54-9533-4613-a39b-61e52a7a5265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 140.5111782733049\n",
            "0 44.433536005760864\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "noise_snr(90,ynew)\n",
        "plt.savefig(\"drive/MyDrive/Noise-2.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/Noise-2.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "1J-RIf1SKyNT",
        "outputId": "f7a794b6-734f-4872-ad08-c6c64a6c20ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.014051117827330489\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G8SzXlneLaQS"
      },
      "outputs": [],
      "source": [
        "\n",
        "def plot(i):\n",
        "  plt.plot(Time.iloc[i],Signal.iloc[i],'.',label='%s'%i)\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "def noise(snr,input):\n",
        "  target_snr_db = snr\n",
        "  watts = input**2\n",
        "  sig_avg_watts = np.mean(watts)\n",
        "  sig_avg_db = 10 * np.log10(sig_avg_watts)\n",
        "  # Calculate noise according to [2] then convert to watts\n",
        "  noise_avg_db = sig_avg_db - target_snr_db\n",
        "  noise_avg_watts = 10 ** (noise_avg_db / 10) # variance\n",
        "  # Generate an sample of white noise\n",
        "  mean_noise = 0 #mu \n",
        "  noise_volts = np.random.normal(mean_noise, np.sqrt(noise_avg_watts), len(watts))\n",
        "  # Noise up the original signal\n",
        "  x_volts = input\n",
        "  y_volts = x_volts + noise_volts\n",
        "  return y_volts\n",
        "\n",
        "def fourier_inverse(time, signal,filter_low,filter_high):\n",
        "  N = len(time)\n",
        "  T = (time[-1]-time[0])/N # Length / N\n",
        "  yf = fft(signal)\n",
        "  xf = fftfreq(N, T)[:N//2]\n",
        "  yf[filter_low:filter_high] = 0\n",
        "  iftgw = scipy.fft.ifft(yf)\n",
        "  return(time,iftgw)\n",
        "\n",
        "def fourier(time, signal,filter_low,filter_high):\n",
        "  N = len(time)\n",
        "  T = (time[-1]-time[0])/N # Length / N\n",
        "  yf = fft(signal)\n",
        "  xf = fftfreq(N, T)[:N//2]\n",
        "  yf[filter_low:filter_high] = 0\n",
        "  iftgw = scipy.fft.ifft(yf)\n",
        "  #print(np.shape(signal),np.shape(time))\n",
        "  return(time,yf)    "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = pd.read_csv('drive/MyDrive/ML_dataS1.csv')\n",
        "A = a"
      ],
      "metadata": {
        "id": "5fW6QUpvjxwM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3jHKt0J00kTi"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "y_pred = best_random.predict(X_test)\n",
        "report = pd.DataFrame(list(precision_recall_fscore_support(y_test, y_pred)), #y_train, y_pred_train\n",
        "            index=['Precision', 'Recall', 'F1-score', 'Support']).T\n",
        "\n",
        "# Now add the 'Avg/Total' row\n",
        "report.loc['Avg/Total', :] = precision_recall_fscore_support(y_test, y_pred,\n",
        "    average='weighted')\n",
        "report.loc['Avg/Total', 'Support'] = report['Support'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEB9fnTz7UE-",
        "outputId": "0a098006-e8fa-46c6-f4a9-bc384995d3bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=24, max_features='log2', min_samples_leaf=5,\n",
              "                       min_samples_split=9, n_estimators=300, warm_start=True)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6YksCX0-_Ho",
        "outputId": "96044881-abcc-4e57-c5a8-9d558cef6713"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'n_estimators': [200, 211, 222, 233, 244, 255, 266, 277, 288, 300], 'max_features': ['auto', 'sqrt', 'log2', None], 'max_depth': [1, 13, 25, 37, 49, 61, 73, 85, 97, 110, None], 'min_samples_split': [1, 3, 5, 7, 9, 12, 14, 16, 18, 21], 'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8, 9, 11], 'bootstrap': [True, False], 'oob_score': [True, False], 'warm_start': [True, False]}\n",
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "Best Fit Parameter {'warm_start': True, 'oob_score': False, 'n_estimators': 244, 'min_samples_split': 21, 'min_samples_leaf': 8, 'max_features': 'auto', 'max_depth': 61, 'bootstrap': False}\n",
            "0.65\n",
            "[[3 4 1 0]\n",
            " [0 2 0 0]\n",
            " [0 1 3 1]\n",
            " [0 0 0 5]]\n",
            "CPU times: user 1.83 s, sys: 78.1 ms, total: 1.91 s\n",
            "Wall time: 52.4 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "np.random.seed(42)\n",
        "random_state = 1\n",
        "nc = 300\n",
        "pca_model = PCA(n_components=nc) #120\n",
        "X_train_pca = pca_model.fit_transform(X_train)\n",
        "X_test_pca = pca_model.transform(X_test)\n",
        "X_train = X_train_pca\n",
        "X_test = X_test_pca\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "#pca_model = PCA(n_components=nc) #120\n",
        "#X_train_pca = pca_model.fit_transform(X_train)\n",
        "#X_test_pca = pca_model.transform(X_test)\n",
        "# Number of trees in random forest\n",
        "n_estimators = [int(x) for x in np.linspace(start = 200 , \n",
        "                                            stop = 300, num = 10)] \n",
        "                                            # returns 10 numbers \n",
        "\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt','log2',None]\n",
        "# Maximum number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(1, 110, num = 10)]\n",
        "\n",
        "max_depth.append(None)\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split =[int(x) for x in np.linspace(1, 21, num = 10)]\n",
        "\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [int(x) for x in np.linspace(1, 11, num = 10)]\n",
        "\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "oob_score = [True, False]\n",
        "warm_start= [True, False]\n",
        "\n",
        "# Create the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap,\n",
        "               'oob_score': oob_score,\n",
        "               'warm_start': warm_start}\n",
        "print(random_grid)\n",
        "\n",
        "\n",
        "# Use the random grid to search for best hyperparameters\n",
        "# First create the base model to tune\n",
        "rf = RandomForestClassifier()\n",
        "# Random search of parameters, using 3 fold cross validation, \n",
        "# search across 100 different combinations, and use all available cores\n",
        "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, \n",
        "                               n_iter = 10, cv = 5, verbose=10, random_state=42, n_jobs = -1)\n",
        "# Fit the random search model\n",
        "rf_random.fit(X_train, y_train)\n",
        "\n",
        "print('Best Fit Parameter',rf_random.best_params_)\n",
        "\n",
        "\n",
        "from sklearn import metrics\n",
        "random_state = 1\n",
        "def evaluate(model, test_features, test_labels):\n",
        "    y_pred = model.predict(test_features)\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "    print (accuracy)\n",
        "    print(confusion_matrix(y_test,y_pred))\n",
        "    \n",
        "\n",
        "best_random = rf_random.best_estimator_\n",
        "evaluate(best_random, X_test, y_test)\n",
        "\n",
        "filename = 'RF_GW_highres_PCA_model_FREQ.sav' # original is without -2 in name\n",
        "joblib.dump(best_random,  'drive/MyDrive/'+filename )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NKds2R5Pr99",
        "outputId": "95b70ecd-acec-4d2c-c559-842fd788b38b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy 0.65\n",
            "[[3 4 1 0]\n",
            " [0 2 0 0]\n",
            " [0 1 3 1]\n",
            " [0 0 0 5]]\n"
          ]
        }
      ],
      "source": [
        "filename = 'RF_GW_highres_PCA_model_FREQ.sav'\n",
        "loaded_model = joblib.load('drive/MyDrive/'+filename)\n",
        "\n",
        "\n",
        "def evaluate(model, test_features, test_labels):\n",
        "    y_pred = model.predict(test_features)\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "    print ('accuracy',accuracy)\n",
        "    print(confusion_matrix(y_test,y_pred))\n",
        "    \n",
        "\n",
        "#best_random = rf_random.best_estimator_\n",
        "#evaluate(loaded_model, X_test, y_test)\n",
        "evaluate(best_random, X_test, y_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apO-MziNR03z"
      },
      "source": [
        "# Read data and plot"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F = (30.86 * 8.8524/100000)\n",
        "gini = pd.read_csv('drive/MyDrive/Gini.csv')\n",
        "plt.rcParams['figure.figsize'] = [10, 8]\n",
        "my_cmap = plt.get_cmap(\"viridis\")\n",
        "rescale = lambda y: (y - np.min(y)) / (np.max(y) - np.min(y))\n",
        "\n",
        "fig1 = plt.figure(1)\n",
        "frame1=fig1.add_axes((.1,.65,.89,.36))\n",
        "for j in range(100):\n",
        "    frame1.plot(TX[j],F*TY[j],'.',color = (1, j / 100.0, 0, 0.5),alpha=0.009)\n",
        "frame1.plot(TX[20],F*TY[20],'.',c='black',markersize=1)\n",
        "\n",
        "#frame1.set_xlabel('Time (ms)',fontsize=20)\n",
        "frame1.set_ylabel(r'$hD$ $\\mathrm{[10^2\\ cm]}$',fontsize=18)\n",
        "\n",
        "plt.xlim(-2.5,6.5)\n",
        "#plt.ylim(0.9,1.1)\n",
        "frame1.tick_params(axis='both', direction=\"in\",which='minor', labelsize=15)\n",
        "frame1.tick_params(axis='both', direction=\"in\",which='major', labelsize=12)\n",
        "frame1.yaxis.set_ticks_position('both')\n",
        "frame1.xaxis.set_ticks_position('both')\n",
        "frame1.minorticks_on()\n",
        "#frame1.tick_params(which = 'both',direction=\"in\")\n",
        "frame1.tick_params(length=6)\n",
        "\n",
        "#plt.legend(prop={'size': 20},frameon=False)\n",
        "#plt.text(-2.05,-2000,\"SNR $=%s$\"%txt,fontsize=25,color='black')\n",
        "plt.yticks(fontsize=18);plt.xticks(fontsize=18)\n",
        "#frame1.axes.xaxis.set_visible(False)\n",
        "frame1.set_xticklabels([])\n",
        "\n",
        "\n",
        "frame2=fig1.add_axes((.1,.3,.89,.32)) # left, bottom, width, height\n",
        "\n",
        "\n",
        "plt.bar(gini.Time,gini['Gini']*1000,alpha=0.99,color=my_cmap(rescale(gini['Gini'])),width = 0.2)\n",
        "#value = np.random.normal(loc=2.5,scale=np.std(gini['Time']),size=1000)\n",
        "#sns.displot(value,kind=\"kde\")\n",
        "\n",
        "plt.xlabel('$z_{spec}$',fontsize=25)\n",
        "frame2.tick_params(axis='both', direction=\"in\",which='minor', labelsize=15)\n",
        "frame2.tick_params(axis='both', direction=\"in\",which='major', labelsize=13)\n",
        "\n",
        "frame2.set_xlabel('Time [ms]',fontsize=18)\n",
        "frame2.set_ylabel('Gini Importance',fontsize=18)\n",
        "\n",
        "frame2.yaxis.set_ticks_position('both')\n",
        "frame2.xaxis.set_ticks_position('both')\n",
        "frame2.tick_params(direction=\"in\",length=6)\n",
        "frame2.minorticks_on()\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/Gini2.jpg\", bbox_inches='tight',dpi=1000)\n",
        "files.download('drive/MyDrive/Gini2.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "lCX6u3W1UCcq",
        "outputId": "bcd1153b-4a5f-40e4-c3cb-b7388d4ad983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e62a5c7a-aaa8-44b1-87b0-d86c371006f3\", \"Gini2.jpg\", 3612626)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "value = np.random.normal(loc=2.5,scale=np.std(gini['Time']),size=1000)\n",
        "sns.set_palette('viridis')\n",
        "sns.displot(value,kind=\"kde\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "id": "KmDZYtD01E_1",
        "outputId": "b044415a-a948-4daf-af53-cf60ddb5614d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f58a92986d0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA"
      ],
      "metadata": {
        "id": "QpuNFrtU5TzH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = [8, 6]\n",
        "pca = PCA()\n",
        "pca.fit(X.T)\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(np.cumsum(pca.explained_variance_ratio_),c='b')\n",
        "ax.set_xlabel('Number of Principal Components',fontsize=20)\n",
        "ax.set_ylabel('Cumulative Explained Variance',fontsize=20)\n",
        "plt.axvline(300,linestyle='--',c='black',label='$N = 300$')\n",
        "plt.axhline(.90,linestyle='--',c='r',alpha=0.5)\n",
        "plt.axhline(1,linestyle='--',c='r',alpha=0.2)\n",
        "#plt.xlim(-0.9,100)\n",
        "#plt.ylim(0.9,1.1)\n",
        "ax.yaxis.set_ticks_position('both')\n",
        "ax.xaxis.set_ticks_position('both')\n",
        "\n",
        "ax.minorticks_on()\n",
        "ax.tick_params(which = 'both',direction=\"in\")\n",
        "ax.tick_params(length=6)\n",
        "plt.legend(prop={'size': 20},frameon=False)\n",
        "#plt.text(-1.35,-0.2,\"%s\"%txt,fontsize=25,color='red')\n",
        "plt.yticks(fontsize=14);plt.xticks(fontsize=14)\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"drive/MyDrive/PCA.pdf\", bbox_inches='tight')\n",
        "#files.download('drive/MyDrive/PCA.pdf')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "phP0kZhR7sae",
        "outputId": "25c486bc-393a-435c-8bca-0db274111860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1fT5xzQ-bGbRnbneligPPZXz7TlifJvsO",
      "authorship_tag": "ABX9TyPvTzjc2NSLQB26DS8kA8Jq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}