les_rnn_cnn_Abhishek.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7702ab6",
   "metadata": {},
   "source": [
    "# INFO 6106 - Neural Modeling: Final Project\n",
    "# Lake Snow Effect using RNN and CNN models\n",
    "**Submitted by:**\n",
    "- Aditi Krishna\n",
    "- Abhishek Shankar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f02a0970",
   "metadata": {},
   "source": [
    "This Notebook consists of the first method which we applied to try fitting the LES dataset along with the 1D and 2D images  which were presented to us. In this method we created new features and used the 1D images to calculate intensities on the the days and different times. The 2D images were used as part of the **Convolutional Neural Network (CNN)** whereas the numerical data was used in a **Recurrent Neural Network (RNN)**, both networks were **concatenated to predict occurence of snowfall**.\n",
    "\n",
    "Our Intuitions for building the models in different methodologies is elaborated in this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538cc463",
   "metadata": {},
   "source": [
    "## Importing libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4697e3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.11.0\n"
     ]
    }
   ],
   "source": [
    "#Time the entire notebook\n",
    "import timeit\n",
    "start_time = timeit.default_timer()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import models, layers, optimizers, losses, metrics\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,MaxPooling2D,\\\n",
    "     Dropout,Flatten,Dense,Activation,\\\n",
    "     BatchNormalization,GlobalAveragePooling2D\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "import os\n",
    "from keras import backend as K\n",
    "import matplotlib as plt\n",
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn import preprocessing\n",
    "from PIL import Image, ImageOps\n",
    "from keras.regularizers import l2\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, LSTM, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "#Ensuring the directories are correct\n",
    "DATA_dir = os.getcwd() +\"\\\\DATA\\\\\"\n",
    "photo_dir = os.getcwd() +\"\\\\DATA\\\\testing_img\\\\\"\n",
    "new_photo_dir = os.getcwd() +\"\\\\DATA\\\\new_testing_img\\\\\"\n",
    "\n",
    "#Version Check\n",
    "print('TensorFlow version:', tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93710fc",
   "metadata": {},
   "source": [
    "## Functions \n",
    "\n",
    "<font color='red'>The Explanation for each function is given below</font>\n",
    "\n",
    "1. save_pic - Saving Pictures to the working directory given a numpy array and file namme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05638576",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_pic(arr, filename):\n",
    "    # Plot the image\n",
    "    plt.imshow(arr, cmap='gray')\n",
    "    plt.axis('off')\n",
    "    new_photo_dir = os.getcwd() +\"\\\\DATA\\\\new_testing_img\\\\\"\n",
    "    # Save the image as PNG\n",
    "    plt.savefig(new_photo_dir+filename, bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8c3d23",
   "metadata": {},
   "source": [
    "2. pad_images - Padding Images along the required axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5a8b297",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_images(arr):\n",
    "    if arr.shape[1]==78:\n",
    "        arr1 = np.zeros((105,))\n",
    "        arr2 = arr\n",
    "        arr1_reshaped = arr1.reshape((105, 1, 1))\n",
    "        result = np.concatenate((arr2, arr1_reshaped), axis=1)\n",
    "    else:\n",
    "        # print(\"Padding Not Required\")\n",
    "        result = arr\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d542adae",
   "metadata": {},
   "source": [
    "3. clean_arrays - Ensuring arrays are the right shape and not of length one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11acafcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_arrays(validate):\n",
    "    validate['Elements'] = validate.iloc[:,0].apply(lambda x : np.array(x).shape[0] if len(np.array(x)) > 1 else None)\n",
    "    validate['Rows'] = validate.iloc[:,0].apply(lambda x : np.array(x).shape[1] if len(np.array(x)) > 1 else None)\n",
    "    validate = validate[[x for x in validate.columns if x not in ['Elements','Rows']]]\n",
    "    return validate   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba22d2c6",
   "metadata": {},
   "source": [
    "4. avg_intensity - Calculate intensity of clouds over the lake using the 1 Dimension data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "622fbc13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg_intensity(b):\n",
    "    b['Avg_Intensity'] = b['Lake_data_1D'].apply(lambda x : np.mean(eval(x.replace('nan','0'))))\n",
    "    b = b[[x for x in b.columns if x not in ['File_name_for_1D_lake','File_name_for_2D_lake', 'Lake_data_1D', 'Lake_data_1D_array']]]    \n",
    "    b['Avg_Intensity_Rounded'] = round(b['Avg_Intensity'],2)\n",
    "    return b    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a5fda3",
   "metadata": {},
   "source": [
    "5. preprocessing - Ensure the LES dataset is suitable for the Neural Network\n",
    "\n",
    "- 14:00 to 21:00 CST - Light is most visible at that point of time\n",
    "- Feature Engineering\n",
    "- Check the 2D numpy array of the Lake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f841c1a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(dataset):\n",
    "    a = dataset\n",
    "    #Calculate the average intensity of the image\n",
    "    a = avg_intensity(a)    \n",
    "    \n",
    "    a.iloc[:,1] = pd.to_datetime(a.iloc[:,1],format= '%H:%M' ).dt.time\n",
    "    cond1 = ((a.iloc[:,1] >= pd.to_datetime(\"14:00\",format= '%H:%M').time())  & (a.iloc[:,1] <= pd.to_datetime(\"21:00\",format= '%H:%M').time()))\n",
    "    a = a[cond1]\n",
    "    a = a[[x for x in a.columns if (('utc' not in x.lower()))]]\n",
    "    \n",
    "    #Convert columns to numeric\n",
    "    for cols in a.iloc[:,2:].columns:\n",
    "        if cols=='Lake_data_2D':\n",
    "            pass\n",
    "        else:\n",
    "            a[cols] = pd.to_numeric(a[cols], errors = 'coerce')\n",
    "    \n",
    "    a = a.dropna(axis=1, how='all')\n",
    "    \n",
    "    a = a[[x for x in a.columns if x not in ['File_name_for_1D_lake','File_name_for_2D_lake', 'Lake_data_1D']]]\n",
    "    \n",
    "    df = a.copy()\n",
    "    df['Temp (F)'] = df.groupby(['Date_CST'])['Temp (F)'].transform('mean') \n",
    "    df[\"Snowfall\"] = 0    \n",
    "    \n",
    "    # Point 1    \n",
    "    df['Temp_Humidity_F'] = df['Temp (F)'] *    df['RH (%)']/100\n",
    "    df['Temp (C)'] = (df['Temp (F)'] - 32) * 5/9    \n",
    "    df['Temp_Humidity_C'] = df['Temp (C)'] *    df['RH (%)']/100\n",
    "    df['Temp_Humidity_C_Mean'] = df['Temp_Humidity_C'].groupby(df['Date_CST']).transform('mean')\n",
    "    # High values indicates greater snowfall \n",
    "    # Point 2\n",
    "    df['Temp_diff'] = (df['Dewpt (F)'] - df['Temp (F)'])\n",
    "    # if almost same then condenation happens\n",
    "    #Point 3\n",
    "    df['Pressure_diff'] = df['Atm Press (hPa)'] - df['Sea Lev Press (hPa)']\n",
    "    #Negative temp indciates snowfall\n",
    "    #Point 4\n",
    "    df['Cloud Ceiling (ft)'] = df['Low Cloud Ht (ft)'].fillna(0) + df['Med Cloud Ht (ft)'].fillna(0) + df['High Cloud Ht (ft)'].fillna(0)\n",
    "    #CC is low and atmp_pressure is high - Stable atmosphere and no snowwfall at all \n",
    "    #CC is high and Humidity is high Sowfall possible\n",
    "    \n",
    "    #Point 5\n",
    "    df['Visibility (m)'] = df['Visibility (mi)'] * 1609.344\n",
    "    #snow can occur with visibility as low as 1/4 mile (or 402 meters)\n",
    "    #Lets take the days the mean of the days data for each datapoint\n",
    "    df['Mean_Cloud_Height'] = df['Cloud Ceiling (ft)'].groupby(df['Date_CST']).transform('mean')\n",
    "    df['Mean_RH'] = df['RH (%)'].groupby(df['Date_CST']).transform('mean')\n",
    "\n",
    "    #Final Condition\n",
    "    cond1 = (( df['Temp_Humidity_C'] > df['Temp_Humidity_C_Mean']) | \\\n",
    "             ((df['Cloud Ceiling (ft)'] > df['Mean_Cloud_Height']) & (df['RH (%)'] > df['Mean_RH'])) | \\\n",
    "             (df['Pressure_diff'] < 0) & \\\n",
    "              ((df[\"Temp (F)\"] <= 32) & \\\n",
    "             (df[\"Precip (in)\"] > 0) & (df['Avg_Intensity'] >= 0.25)))  \n",
    "    df['Snowfall'] = np.where(cond1,1,0)\n",
    "    \n",
    "    \n",
    "    d1 = df[['Lake_data_2D','Snowfall','Date_CST', 'Time_CST']]\n",
    "    d2 = df[[x for x in df.columns if x not in ['Lake_data_2D','Snowfall']]]   \n",
    "\n",
    "    d1.iloc[:,0] = d1.iloc[:,0].apply(lambda x : eval(x.replace(\"array(\",\"\").replace(\")\",\"\").replace('nan',\"0\")))\n",
    "    d1['Check'] = d1.iloc[:,0].apply(lambda x : 0 if len(x) > 1 else 1)\n",
    "\n",
    "    \n",
    "    d1 = d1[d1['Check']==0]    \n",
    "    \n",
    "    rows = round(d1.iloc[:,0].apply(lambda x : np.array(x).shape[0]).mean())\n",
    "    elements = round(d1.iloc[:,0].apply(lambda x : np.array(x).shape[1]).mean())\n",
    "    try:\n",
    "        #d1.iloc[:,0] = d1.iloc[:,0].apply(lambda x : np.array(x).reshape((rows,elements,1)) if len(x) > 1 else None)    \n",
    "        print(\"################ DONE \"+ str(vals) + \" ############\")\n",
    "    except:\n",
    "        print(\"Please check the following year \"+ str(vals))     \n",
    "\n",
    "    return d1,d2        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca67446",
   "metadata": {},
   "source": [
    "6. model_metrics - Plot the loss and accuracy of the model on matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1ad047f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_metrics(history):\n",
    "\n",
    "    try:\n",
    "        # Plot the training and validation loss\n",
    "        plt.plot(history.history['loss'])\n",
    "        plt.plot(history.history['val_loss'])\n",
    "        plt.title('Model Loss')\n",
    "        plt.ylabel('Loss')\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.legend(['Train', 'Validation'], loc='upper right')\n",
    "        plt.show()\n",
    "    except:\n",
    "        print(\"Error no Loss\")\n",
    "    try:    \n",
    "        # Plot the training and validation accuracy\n",
    "        plt.plot(history.history['accuracy'])\n",
    "        plt.plot(history.history['val_accuracy'])\n",
    "        plt.title('Model Accuracy')\n",
    "        plt.ylabel('Accuracy')\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.legend(['Train', 'Validation'], loc='lower right')\n",
    "        plt.show()    \n",
    "    except:\n",
    "        print(\"Error No Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d4c6237",
   "metadata": {},
   "source": [
    "7. plot_pic - Visualise our clouds given a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72272cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pic(arr):\n",
    "    plt.imshow(arr, cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e2db4b",
   "metadata": {},
   "source": [
    "8. save_dict - Save our data as a pickle\n",
    "9. load_dict - Load our data to the IDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a7a6b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_dict(data,name):\n",
    "    #Add the data and the name of the file - gets saved to the current working directory\n",
    "    pickle_out = open(name+'.pickle', 'wb')\n",
    "    pickle.dump(data, pickle_out)\n",
    "    pickle_out.close()\n",
    "\n",
    "def load_dict(name):\n",
    "    pickle_in = open(name+'.pickle', 'rb')\n",
    "    new_dict = pickle.load(pickle_in)\n",
    "    return new_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6b390e",
   "metadata": {},
   "source": [
    "10. Precision, recall, f1 - Function to validate mertics used by the keras library to calculate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdb9e608",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recall_m(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    recall = true_positives / (possible_positives + K.epsilon())\n",
    "    return recall\n",
    "\n",
    "def precision_m(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    return precision\n",
    "\n",
    "def f1_m(y_true, y_pred):\n",
    "    precision = precision_m(y_true, y_pred)\n",
    "    recall = recall_m(y_true, y_pred)\n",
    "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0214a1e",
   "metadata": {},
   "source": [
    "12. shift_data - Create a sliding window using the Pandas shift API over the required period of time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c5fa73c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_data(df,timestep):\n",
    "    i = timestep\n",
    "    print(\"Predicting for \",i,\" hours :\")\n",
    "    df2 = df.copy()\n",
    "    df2['LESShift'+str(i)] = df2['Snowfall'].shift(-i)\n",
    "    df2 = df2[0:-i]    \n",
    "    return df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc41ff3",
   "metadata": {},
   "source": [
    "13. arrays_2_png - Professors function to visualise the clouds given the various parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a8a3ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def arrays_2_png(lat, lon, val, fig_name):\n",
    "    status_code = -1\n",
    "    if len(lat) == len(lon) == len(val):\n",
    "        plt.figure(figsize=(10, 10))\n",
    "        plt.scatter(lon, lat, c=val, cmap=cm.gray, marker='s')\n",
    "        plt.colorbar(orientation='vertical')\n",
    "        status_code = 0\n",
    "    else:\n",
    "        status_code = 255\n",
    "\n",
    "    return status_code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e00fd25",
   "metadata": {},
   "source": [
    "14. plot_corr - Function plots a graphical correlation matrix for each pair of columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1848073f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(df,size=10):\n",
    "    corr = df.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    ax.matshow(corr)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns)\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d241047",
   "metadata": {},
   "source": [
    "## Reading the dataset\n",
    "\n",
    "The data from each Fall-Winter season is read fromt the indivdual files to ensure that the systems RAM is not overloaded. The data is stored in dictionaries which is later written to a pickle file. This helped us work with each year induvidually or all years togther"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4c5c59f0",
   "metadata": {},
   "source": [
    "#Ensure the dataset is in the working directory (DATA_dir)\n",
    "\n",
    "df_lat_lon = df_lat_lon = pd.read_csv(DATA_dir + 'lat_long_1D_labels_for_plotting.csv')\n",
    "df_lat_lon.head(5)\n",
    "\n",
    "#Ensure the dataset is in the working directory (DATA_dir)\n",
    "#Iterate over all the files in the directory to load the yearly data\n",
    "data = dict()\n",
    "for files in os.listdir(DATA_dir):\n",
    "    if files.endswith(\".csv\"):   \n",
    "        if files == '2006Fall_2017Spring_GOES_meteo_combined.csv':\n",
    "            pass\n",
    "        elif files == 'lat_long_1D_labels_for_plotting.csv':\n",
    "            pass\n",
    "        else:\n",
    "            d1 = pd.read_csv(DATA_dir + files)\n",
    "            data[files.split('Fall_')[0]] = d1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e7776c",
   "metadata": {},
   "source": [
    "## Feature Engineering & Preprocessing\n",
    "\n",
    "**We know that the dataset is very imbalanced hence one of the methods we tried was to engineer new columns using the existing dataset. The dataset was improved with the assumptio**\n",
    "\n",
    "### Further analysis of columns to form features\n",
    " \n",
    "**These features can be used to come up with a condition to optimise the label**\n",
    "\n",
    "    1. RH% - Relative Humidity %\n",
    "    \n",
    "     High relative humidity levels can lead to the formation of low-level clouds such as stratus and fog, while lower     relative humidity levels may result in the formation of high-level clouds such as cirrus or no clouds at all.\n",
    "    \n",
    "    2. Pressure columns\n",
    "    \n",
    "    A positive difference between the atmospheric pressure and the sea level pressure indicates that the atmospheric pressure is higher than the standard pressure at sea level, which means the air is denser and more stable.\n",
    "    \n",
    "    3. Using the cloud ceiling temperature\n",
    "    \n",
    "    The cloud ceiling feature can provide valuable information for predicting cloud formation and potential for snowfall. By combining the low, medium, and high cloud heights, you get a comprehensive view of the vertical distribution of clouds in the atmosphere. This can be used to identify areas where clouds are likely to persist and produce precipitation.\n",
    "    \n",
    "    4. Intensity of Cloud from image data\n",
    "\n",
    "    a. lets see if the avg of each picture = 1  means there is some cloud with moisture\n",
    "    b. if avg intensity of each picture is less than 1 there is no cloud with moisture\n",
    "\n",
    "    In the image data, pixels with close-to-zero intensities represent the absence of clouds, while pixels with intensities close to 1 indicate the presence of moisture-retaining clouds. \n",
    "    It is important to distinguish between these two types of pixels when processing and analyzing the data, as they can have significant impacts on the resulting insights and predictions.\n",
    "\n",
    "### Hence we can come up with the following conditions to help form the labels\n",
    "\n",
    "**Variable Conditions**\n",
    "\n",
    "1. Temp_Humidity_C - Hourly humidity must be greater than daily avg humidity \n",
    "2. Cloud heights and Relative Humidity - These features must be high\n",
    "3. Pressures - Negative ppressure diff between sea and atmospheric pressure\n",
    "\n",
    "**Constant Conditions**\n",
    "1. Temperature must be < 0&deg;C\n",
    "2. Precip must be > 0\n",
    "3. Intensity of images must be > 0\n",
    "\n",
    "## Converting the conditions required for cloud formation and/or snowfall into python\n",
    "\n",
    "`cond1 = (( df['Temp_Humidity_C'] > df['Temp_Humidity_C_Mean']) | \\\n",
    "         ((df['Cloud Ceiling (ft)'] > df['Mean_Cloud_Height']) & (df['RH (%)'] > df['Mean_RH'])) | \\\n",
    "         (df['Pressure_diff'] < 0) & \\\n",
    "          ((df[\"Temp (F)\"] <= 32) & \\\n",
    "         (df[\"Precip (in)\"] > 0) & (df['Avg_Intensity'] > 0)))`\n",
    "\n",
    "**To run the data preperation - Change the boolean to `True`**\n",
    "\n",
    "For this notebook the datasets have been preprocessed. The data is available in the  working directory."
   ]
  },
  {
   "cell_type": "raw",
   "id": "1739f154",
   "metadata": {},
   "source": [
    "run_cell = False\n",
    "if run_cell:\n",
    "    i = 0\n",
    "    \n",
    "    #Loading empty dictionaries\n",
    "    \n",
    "    images = dict()\n",
    "    weather = dict()\n",
    "    overall = dict()\n",
    "    \n",
    "    #Iterate over all the yearly data\n",
    "    for vals in data.keys():\n",
    "        a = data[vals]\n",
    "        \n",
    "        #Preprocessing datasets to ensure our features are created\n",
    "        d1,d2 = preprocessing(a)\n",
    "        \n",
    "        check1 = d1.copy()\n",
    "        check2 = d2.copy()\n",
    "        \n",
    "        #This is to ensure the data has arrays which produce pictures\n",
    "        check_pic = False\n",
    "        if check_pic:\n",
    "            pic = eval(a['Lake_data_2D'].iloc[10].replace(\"array(\",\"\").replace(\")\",\"\").replace('nan',\"0\"))\n",
    "            pic = np.array(pic)\n",
    "            print(\"Before Preprocessing \",pic.shape)    \n",
    "            plot_pic(pic)\n",
    "            pic = check1['Lake_data_2D'].iloc[10]\n",
    "            try:\n",
    "                pic = np.array(pic).reshape(78, 105, 1)\n",
    "            except:\n",
    "                pic = np.array(pic).reshape(79, 105, 1)\n",
    "            print(\"After Preprocessing \",pic.shape)    \n",
    "            plot_pic(pic)\n",
    "         \n",
    "        #Remove rows containg NA in the Lake_2D_data column \n",
    "        check1[check1.iloc[:,0].isna()==False]\n",
    "        check1['Lake_data_2D'] = check1.apply(lambda r: np.an if type(r['Lake_data_2D'])==float else r['Lake_data_2D'], axis=1)\n",
    "        check1.dropna(inplace=True)\n",
    "        check1['Date_Time'] = check1['Date_CST'] + \" \" + check1['Time_CST']\n",
    "        check2['Date_Time'] = check2['Date_CST'] + \" \" + check2['Time_CST']\n",
    "        final = check1.merge(check2, on ='Date_Time',how='left')    \n",
    "        \n",
    "        for_images = final[[x for x in check1.columns if x not in ['Date_CST','Time_CST','Check']]]\n",
    "        nr = ['Temp (C)',\n",
    "               'Temp_Humidity_C', 'Temp_Humidity_C_Mean','Avg_Intensity','Date_Time','Temp_diff']\n",
    "        \n",
    "        #removing any unecessary columns\n",
    "        for_weather = final[[x for x in final.columns if ((x not in nr) & (\"_x\" not in x) & (\"_y\" not in x))]]\n",
    "        for_images = clean_arrays(for_images)\n",
    "        \n",
    "        #Appending the data to the dictionary\n",
    "        images[vals] = for_images\n",
    "        weather[vals] = for_weather\n",
    "        overall[vals] = final\n",
    "\n",
    "    #Saving the entire dataset    \n",
    "    save_dict(overall,\"Input_Dataset\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791f1565",
   "metadata": {},
   "source": [
    "## Loading the dataset\n",
    "\n",
    "The preprocessed dataset is loaded from the pickle file to be analysed. The file is actually a dictionary with keys as the years and the values as the weather data associated with that particular year.\n",
    "\n",
    "`pd.concat(overall.values(), ignore_index=True)` helps convert the `dict` to a `dataframe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd36b437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET LOADED\n"
     ]
    }
   ],
   "source": [
    "#Ensure the file Input_Dataset is in the directory (check os.getcwd())\n",
    "overall = load_dict(\"Input_Dataset\")\n",
    "data1 = pd.concat(overall.values(), ignore_index=True)\n",
    "print(\"DATASET LOADED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b97c04c3",
   "metadata": {},
   "source": [
    "**Checking on the balance of the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90e3df1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Snowfall'].value_counts().plot(kind='barh')    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358713c4",
   "metadata": {},
   "source": [
    "### VALIDATING ASSUMPTION ABOUT INTENSITY - ANALYSING THE DATASET WHERE INTENSITY > 0\n",
    "\n",
    "Visually we are checking if the assumption we made about Heavy cloud presence when intensity > 0. Plotting the ppictures we can see that theere are clouds which are present over the lake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7d04947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%\n",
    "#Ensure the file lat_long_1D_labels_for_plotting.csv is in the working directory (DATA_dir)\n",
    "df_lat_lon = df_lat_lon = pd.read_csv('lat_long_1D_labels_for_plotting.csv')\n",
    "\n",
    "lat_lst = df_lat_lon['latitude'].to_list()\n",
    "lat_lst\n",
    "\n",
    "lon_lst = df_lat_lon['longitude'].to_list()\n",
    "lon_lst\n",
    "\n",
    "'''\n",
    "Validating our assumption about the intensity\n",
    "Important to ensure that the length of\n",
    "- Latitude\n",
    "- Longitude \n",
    "- Sample \n",
    "are the same \n",
    "'''\n",
    "check = data1.copy()\n",
    "check = check[check['Avg_Intensity_Rounded']>0.3].sample(10)\n",
    "\n",
    "[plot_pic(np.array(x)) for x in list(check['Lake_data_2D'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a59c66b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lake_data_2D', 'Snowfall', 'Date_CST_x', 'Time_CST_x', 'Check',\n",
       "       'Date_Time', 'Date_CST_y', 'Time_CST_y', 'Temp (F)', 'RH (%)',\n",
       "       'Dewpt (F)', 'Wind Spd (mph)', 'Wind Direction (deg)',\n",
       "       'Peak Wind Gust(mph)', 'Low Cloud Ht (ft)', 'Med Cloud Ht (ft)',\n",
       "       'High Cloud Ht (ft)', 'Visibility (mi)', 'Atm Press (hPa)',\n",
       "       'Sea Lev Press (hPa)', 'Altimeter (hPa)', 'Precip (in)',\n",
       "       'Wind Chill (F)', 'Avg_Intensity', 'Avg_Intensity_Rounded',\n",
       "       'Temp_Humidity_F', 'Temp (C)', 'Temp_Humidity_C',\n",
       "       'Temp_Humidity_C_Mean', 'Temp_diff', 'Pressure_diff',\n",
       "       'Cloud Ceiling (ft)', 'Visibility (m)', 'Mean_Cloud_Height', 'Mean_RH',\n",
       "       'Heat Index (F)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edff183f",
   "metadata": {},
   "source": [
    "## Rewriting converted images and resizing to 64 x 64 \n",
    "The images are pretty large and take up a lot of memory and processing time for the network, so we decided to resize them into 64 x 64 pixels. Then, we convert the images into grayscale and save them for training. We noticed that when the images are individually analysed with the label the neural network failed to learn from the data. Optimising on the size of the data ensured that the intensity was in place but the sizes were optimised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ceb923e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 79)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15031\n",
      "(64, 64)\n"
     ]
    }
   ],
   "source": [
    "import skimage.transform as st\n",
    "\n",
    "#Rotating the image\n",
    "data1['Lake_data_2D'] = data1['Lake_data_2D'].apply(lambda x : np.rot90(x))\n",
    "\n",
    "#Converting nested lists to arrays\n",
    "data1['Lake_data_2D'] = data1['Lake_data_2D'].apply(lambda x : np.array(x))\n",
    "print((data1['Lake_data_2D'].iloc[0]).shape)\n",
    "\n",
    "#check the image\n",
    "plot_pic(data1['Lake_data_2D'].iloc[0])\n",
    "\n",
    "#Resizing the image\n",
    "data1['Lake_data_2D'] = data1['Lake_data_2D'].apply(lambda x : st.resize(x, (64, 64)))\n",
    "data1 = data1.reset_index()\n",
    "print(len(data1))\n",
    "print((data1['Lake_data_2D'].iloc[0]).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78bbdd4d",
   "metadata": {},
   "source": [
    "## Ensuring the arrays are of the right size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "955ff2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Lake_data_2D'] = data1['Lake_data_2D'].apply(lambda x : np.array(x).reshape(x.shape[0],x.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1f7f2ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Data Before Shift :  15031\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Data Before Shift : \",len(data1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2151a0c2",
   "metadata": {},
   "source": [
    "## Sliding window\n",
    "\n",
    "We take advantage of `Pandas shift API`- this feature helps us build our data\n",
    "`shift_data(data1,48)` helps predict the occurence of snowfall 2 days in advance, our model was not performing too well when it was prediciting **72 Hours** in advance. Hence we went to a 48 hour timeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0ac7b92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting for  48  hours :\n"
     ]
    }
   ],
   "source": [
    "data1 = shift_data(data1,48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d53f0a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Data After Shift :  14983\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Data After Shift : \",len(data1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d549c973",
   "metadata": {},
   "source": [
    "## Removing columns from the main dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37fda360",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing all time columns from the dataset\n",
    "data1 = data1[[x for x in data1.columns if x not in ['Date_CST_x', 'Time_CST_x', 'Check',\n",
    "       'Date_Time', 'Date_CST_y', 'Time_CST_y','index','New_Lake_2D_data']]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e21c9a",
   "metadata": {},
   "source": [
    "## Segregating data\n",
    "\n",
    "The dataset is segregated from the main dataset\n",
    "\n",
    "`weather_data` - consists of all the data from the weather station\n",
    "`image_data`   - consists of all the data from the satellite\n",
    "\n",
    "Both dataframes are ready to be used in the Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5ababcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking required column from the main dataset for image data\n",
    "image_data = data1[['Lake_data_2D','LESShift48']]\n",
    "image_data.iloc[:,0] = image_data.iloc[:,0].apply(lambda x : np.array(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a01606ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking required column from the main dataset for weather data\n",
    "weather_data = data1[[x for x in data1.columns if x not in ['Lake_data_2D','Snowfall']]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "47566966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pic(image_data.iloc[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9fc5b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of our image  (64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of our image \",image_data.iloc[0,0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e731b7c",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "\n",
    "- **Considering features which cause snowfall like Temperature and Precipitation were some of the columns considered**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a98b5697",
   "metadata": {},
   "outputs": [],
   "source": [
    "#nr - not required\n",
    "nr = ['Wind Spd (mph)',\n",
    "       'Wind Direction (deg)', 'Peak Wind Gust(mph)', 'Low Cloud Ht (ft)',\n",
    "       'Med Cloud Ht (ft)', 'High Cloud Ht (ft)', 'Visibility (mi)', 'Altimeter (hPa)'\n",
    "'Temp (C)', 'Temp_Humidity_C', 'Temp_Humidity_C_Mean' 'Temp_diff', \n",
    "       'Cloud Ceiling (ft)', 'Visibility (m)','Mean_RH','Altimeter (hPa)','Wind Chill (F)']\n",
    "\n",
    "#Remove columns which are not required\n",
    "weather_data = weather_data[[x for x in weather_data.columns if x not in nr]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fd04de",
   "metadata": {},
   "source": [
    "- **Check columns which we have taken in our numerical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6046061c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Temp (F)', 'RH (%)', 'Dewpt (F)', 'Atm Press (hPa)',\n",
       "       'Sea Lev Press (hPa)', 'Precip (in)', 'Avg_Intensity',\n",
       "       'Avg_Intensity_Rounded', 'Temp_Humidity_F', 'Temp (C)',\n",
       "       'Temp_Humidity_C_Mean', 'Temp_diff', 'Pressure_diff',\n",
       "       'Mean_Cloud_Height', 'Heat Index (F)', 'LESShift48'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67f69d9",
   "metadata": {},
   "source": [
    "- **Distribution of the columns of the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c0116cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i4uJie9nn8yk9PV25ublyOp1h208gEJDH49GkSZMUHx8ftnERPvQodMNKr+01Zo5YS0vGtOixvbHyt8Rc031H0nul5rwNzuMo+nXWHrW+g3I5IQeYhIQEDRo0SJKUnZ2tPXv2aMWKFfr617+upqYmHTt2LOgsTENDg1wulyTJ5XJp9+7dQeO1fkrp/Jq//eRSQ0ODnE7nRc++SJLD4ZDD4WizPj4+vkMa21HjInzoUfv5myMTIvwtMRHbdySY+O+Rx1H062w9au+xXPXvgWlpaZHf71d2drbi4+O1ZcsWe9uhQ4dUX18vt9stSXK73dq/f78aGxvtGo/HI6fTqaysLLvm/DFaa1rHAAAACOkMTElJiSZPnqyBAwfqxIkTWrt2rbZt26aqqir17t1b06dPV3Fxsfr16yen06lHHnlEbrdb48aNkyTl5uYqKytLU6ZMUXl5ubxerxYsWKDCwkL77MmsWbP0zDPP6NFHH9VDDz2krVu36tVXX9XGjRvDf/QAAMBIIQWYxsZGTZ06VUePHlXv3r01YsQIVVVVadKkSZKk5cuXKzY2VgUFBfL7/crLy9Ozzz5r3z8uLk4bNmzQ7Nmz5Xa71aNHD02bNk2LFy+2azIzM7Vx40bNnTtXK1as0IABA/TCCy/wEWoAAGALKcC8+OKLl9yemJioiooKVVRUXLQmIyPjslfiT5gwQe+++24oUwMAAF0I34UEAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxukV6AgDQVVz/vY2RnkK7OeIsld8c6VkAF8cZGAAAYBwCDAAAMA4BBgAAGIcAAwAAjBNSgCkrK9NXvvIV9erVS8nJybr33nt16NChoJoJEyYoJiYm6DZr1qygmvr6euXn5yspKUnJycmaN2+ezp07F1Szbds2jR49Wg6HQ4MGDVJlZeWVHSEAAOh0Qgow27dvV2FhoXbt2iWPx6NAIKDc3FydOnUqqG7GjBk6evSofSsvL7e3NTc3Kz8/X01NTdq5c6deeuklVVZWauHChXbN4cOHlZ+fr9tvv111dXUqKirSww8/rKqqqqs8XAAA0BmE9DHqN998M2i5srJSycnJqq2t1fjx4+31SUlJcrlcFxyjurpaBw8e1ObNm5WSkqKRI0dqyZIlmj9/vkpLS5WQkKBVq1YpMzNTy5YtkyQNHTpUb731lpYvX668vLxQjxEAAHQyV/V7YI4fPy5J6tevX9D6NWvW6OWXX5bL5dLdd9+txx57TElJSZKkmpoaDR8+XCkpKXZ9Xl6eZs+erQMHDmjUqFGqqalRTk5O0Jh5eXkqKiq66Fz8fr/8fr+97PP5JEmBQECBQOBqDjNI61jhHBPhRY9C54izru3+Yq2gPxF9WnvD4yh6ddbnuvYezxUHmJaWFhUVFemWW27RsGHD7PUPPPCAMjIylJaWpn379mn+/Pk6dOiQXnvtNUmS1+sNCi+S7GWv13vJGp/PpzNnzqh79+5t5lNWVqZFixa1WV9dXW2Hp3DyeDxhHxPhRY/aL1K/sGzJmJbI7BjtxuMo+nW2Hp0+fbpddVccYAoLC/Xee+/prbfeClo/c+ZM++/Dhw9XamqqJk6cqI8++kg33HDDle7uskpKSlRcXGwv+3w+paenKzc3V06nM2z7CQQC8ng8mjRpkuLj48M2LsKHHoVuWOm1vb7MEWtpyZgWPbY3Vv6WmGu6b7RPa494HEWvzvpc1/oOyuVcUYCZM2eONmzYoB07dmjAgAGXrB07dqwk6cMPP9QNN9wgl8ul3bt3B9U0NDRIkn3djMvlstedX+N0Oi949kWSHA6HHA5Hm/Xx8fEd0tiOGhfhQ4/az98cmRDhb4mJ2L7RPjyOol9n61F7jyWkTyFZlqU5c+bo9ddf19atW5WZmXnZ+9TV1UmSUlNTJUlut1v79+9XY2OjXePxeOR0OpWVlWXXbNmyJWgcj8cjt9sdynQBAEAnFVKAKSws1Msvv6y1a9eqV69e8nq98nq9OnPmjCTpo48+0pIlS1RbW6uPP/5Yv/71rzV16lSNHz9eI0aMkCTl5uYqKytLU6ZM0e9//3tVVVVpwYIFKiwstM+gzJo1S3/84x/16KOP6v3339ezzz6rV199VXPnzg3z4QMAABOFFGBWrlyp48ePa8KECUpNTbVvr7zyiiQpISFBmzdvVm5uroYMGaLvfve7Kigo0BtvvGGPERcXpw0bNiguLk5ut1vf/OY3NXXqVC1evNiuyczM1MaNG+XxeHTTTTdp2bJleuGFF/gINQAAkBTiNTCWdemPPKanp2v79u2XHScjI0ObNm26ZM2ECRP07rvvhjI9AADQRfBdSAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjdIv0BIDO5vrvbYz0FACg0+MMDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgnpABTVlamr3zlK+rVq5eSk5N177336tChQ0E1Z8+eVWFhofr376+ePXuqoKBADQ0NQTX19fXKz89XUlKSkpOTNW/ePJ07dy6oZtu2bRo9erQcDocGDRqkysrKKztCAADQ6YQUYLZv367CwkLt2rVLHo9HgUBAubm5OnXqlF0zd+5cvfHGG1q/fr22b9+uI0eO6L777rO3Nzc3Kz8/X01NTdq5c6deeuklVVZWauHChXbN4cOHlZ+fr9tvv111dXUqKirSww8/rKqqqjAcMgAAMF23UIrffPPNoOXKykolJyertrZW48eP1/Hjx/Xiiy9q7dq1uuOOOyRJq1ev1tChQ7Vr1y6NGzdO1dXVOnjwoDZv3qyUlBSNHDlSS5Ys0fz581VaWqqEhAStWrVKmZmZWrZsmSRp6NCheuutt7R8+XLl5eWF6dABAICpQgowf+v48eOSpH79+kmSamtrFQgElJOTY9cMGTJEAwcOVE1NjcaNG6eamhoNHz5cKSkpdk1eXp5mz56tAwcOaNSoUaqpqQkao7WmqKjoonPx+/3y+/32ss/nkyQFAgEFAoGrOcwgrWOFc0yEV6R75IizIrJfkzhiraA/EX1ae8NzXfSK9HNdR2nv8VxxgGlpaVFRUZFuueUWDRs2TJLk9XqVkJCgPn36BNWmpKTI6/XaNeeHl9btrdsuVePz+XTmzBl17969zXzKysq0aNGiNuurq6uVlJR0ZQd5CR6PJ+xjIrwi1aPymyOyWyMtGdMS6SngMniui36drUenT59uV90VB5jCwkK99957euutt650iLAqKSlRcXGxvezz+ZSenq7c3Fw5nc6w7ScQCMjj8WjSpEmKj48P27gIn0j3aFgp12pdjiPW0pIxLXpsb6z8LTGRng4uoLVHPNdFr0g/13WU1ndQLueKAsycOXO0YcMG7dixQwMGDLDXu1wuNTU16dixY0FnYRoaGuRyueya3bt3B43X+iml82v+9pNLDQ0NcjqdFzz7IkkOh0MOh6PN+vj4+A5pbEeNi/CJVI/8zbwgt5e/JYafV5TjuS76dbYetfdYQvoUkmVZmjNnjl5//XVt3bpVmZmZQduzs7MVHx+vLVu22OsOHTqk+vp6ud1uSZLb7db+/fvV2Nho13g8HjmdTmVlZdk154/RWtM6BgAA6NpCOgNTWFiotWvX6le/+pV69eplX7PSu3dvde/eXb1799b06dNVXFysfv36yel06pFHHpHb7da4ceMkSbm5ucrKytKUKVNUXl4ur9erBQsWqLCw0D6DMmvWLD3zzDN69NFH9dBDD2nr1q169dVXtXHjxjAfPgAAMFFIZ2BWrlyp48ePa8KECUpNTbVvr7zyil2zfPly/eM//qMKCgo0fvx4uVwuvfbaa/b2uLg4bdiwQXFxcXK73frmN7+pqVOnavHixXZNZmamNm7cKI/Ho5tuuknLli3TCy+8wEeoAQCApBDPwFjW5T/ymJiYqIqKClVUVFy0JiMjQ5s2bbrkOBMmTNC7774byvQAAEAXwXchAQAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjhBxgduzYobvvvltpaWmKiYnRL3/5y6Dt3/rWtxQTExN0u/POO4NqPvvsMz344INyOp3q06ePpk+frpMnTwbV7Nu3T7fddpsSExOVnp6u8vLy0I8OAAB0SiEHmFOnTummm25SRUXFRWvuvPNOHT161L79/Oc/D9r+4IMP6sCBA/J4PNqwYYN27NihmTNn2tt9Pp9yc3OVkZGh2tpaPfnkkyotLdXzzz8f6nQBAEAn1C3UO0yePFmTJ0++ZI3D4ZDL5brgtj/84Q968803tWfPHo0ZM0aS9PTTT+uuu+7Sj370I6WlpWnNmjVqamrSz372MyUkJOjGG29UXV2dnnrqqaCgAwAAuqaQA0x7bNu2TcnJyerbt6/uuOMO/eAHP1D//v0lSTU1NerTp48dXiQpJydHsbGxevvtt/XVr35VNTU1Gj9+vBISEuyavLw8/ed//qc+//xz9e3bt80+/X6//H6/vezz+SRJgUBAgUAgbMfWOlY4x0R4RbpHjjgrIvs1iSPWCvoT0ae1NzzXRa9IP9d1lPYeT9gDzJ133qn77rtPmZmZ+uijj/T9739fkydPVk1NjeLi4uT1epWcnBw8iW7d1K9fP3m9XkmS1+tVZmZmUE1KSoq97UIBpqysTIsWLWqzvrq6WklJSeE6PJvH4wn7mAivSPWo/OaI7NZIS8a0RHoKuAye66JfZ+vR6dOn21UX9gBz//33238fPny4RowYoRtuuEHbtm3TxIkTw707W0lJiYqLi+1ln8+n9PR05ebmyul0hm0/gUBAHo9HkyZNUnx8fNjGRfhEukfDSquu+T5N44i1tGRMix7bGyt/S0ykp4MLaO0Rz3XRK9LPdR2l9R2Uy+mQt5DO98UvflHXXXedPvzwQ02cOFEul0uNjY1BNefOndNnn31mXzfjcrnU0NAQVNO6fLFraxwOhxwOR5v18fHxHdLYjhoX4ROpHvmbeUFuL39LDD+vKMdzXfTrbD1q77F0+O+B+fTTT/WXv/xFqampkiS3261jx46ptrbWrtm6dataWlo0duxYu2bHjh1B74N5PB4NHjz4gm8fAQCAriXkAHPy5EnV1dWprq5OknT48GHV1dWpvr5eJ0+e1Lx587Rr1y59/PHH2rJli+655x4NGjRIeXl5kqShQ4fqzjvv1IwZM7R792797ne/05w5c3T//fcrLS1NkvTAAw8oISFB06dP14EDB/TKK69oxYoVQW8RAQCArivkALN3716NGjVKo0aNkiQVFxdr1KhRWrhwoeLi4rRv3z790z/9k7785S9r+vTpys7O1m9/+9ugt3fWrFmjIUOGaOLEibrrrrt06623Bv2Ol969e6u6ulqHDx9Wdna2vvvd72rhwoV8hBoAAEi6gmtgJkyYIMu6+Ecfq6oufwFjv379tHbt2kvWjBgxQr/97W9DnR4AAOgC+C4kAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJxukZ4AACB6DSutkr85JtLTCMnHS/MjPQVcA5yBAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgn5ACzY8cO3X333UpLS1NMTIx++ctfBm23LEsLFy5UamqqunfvrpycHH3wwQdBNZ999pkefPBBOZ1O9enTR9OnT9fJkyeDavbt26fbbrtNiYmJSk9PV3l5eehHBwAAOqWQA8ypU6d00003qaKi4oLby8vL9ZOf/ESrVq3S22+/rR49eigvL09nz561ax588EEdOHBAHo9HGzZs0I4dOzRz5kx7u8/nU25urjIyMlRbW6snn3xSpaWlev7556/gEAEAQGfTLdQ7TJ48WZMnT77gNsuy9OMf/1gLFizQPffcI0n6r//6L6WkpOiXv/yl7r//fv3hD3/Qm2++qT179mjMmDGSpKefflp33XWXfvSjHyktLU1r1qxRU1OTfvaznykhIUE33nij6urq9NRTTwUFHQAA0DWF9RqYw4cPy+v1Kicnx17Xu3dvjR07VjU1NZKkmpoa9enTxw4vkpSTk6PY2Fi9/fbbds348eOVkJBg1+Tl5enQoUP6/PPPwzllAABgoJDPwFyK1+uVJKWkpAStT0lJsbd5vV4lJycHT6JbN/Xr1y+oJjMzs80Yrdv69u3bZt9+v19+v99e9vl8kqRAIKBAIHA1hxWkdaxwjonwinSPHHFWRPZrEkesFfQnoo/JPeoqz8+Rfq7rKO09nrAGmEgqKyvTokWL2qyvrq5WUlJS2Pfn8XjCPibCK1I9Kr85Irs10pIxLZGeAi7DxB5t2rQp0lO4pjrb69Hp06fbVRfWAONyuSRJDQ0NSk1Ntdc3NDRo5MiRdk1jY2PQ/c6dO6fPPvvMvr/L5VJDQ0NQTetya83fKikpUXFxsb3s8/mUnp6u3NxcOZ3Oqzuw8wQCAXk8Hk2aNEnx8fFhGxfhE+keDSutuub7NI0j1tKSMS16bG+s/C0xkZ4OLsDkHr1XmhfpKVwTkX6u6yit76BcTlgDTGZmplwul7Zs2WIHFp/Pp7fffluzZ8+WJLndbh07dky1tbXKzs6WJG3dulUtLS0aO3asXfMf//EfCgQCdlM8Ho8GDx58wbePJMnhcMjhcLRZHx8f3yGN7ahxET6R6pG/2awn+0jyt8Tw84pyJvaoqz03d7bXo/YeS8gX8Z48eVJ1dXWqq6uT9NcLd+vq6lRfX6+YmBgVFRXpBz/4gX79619r//79mjp1qtLS0nTvvfdKkoYOHao777xTM2bM0O7du/W73/1Oc+bM0f3336+0tDRJ0gMPPKCEhARNnz5dBw4c0CuvvKIVK1YEnWEBAABdV8hnYPbu3avbb7/dXm4NFdOmTVNlZaUeffRRnTp1SjNnztSxY8d066236s0331RiYqJ9nzVr1mjOnDmaOHGiYmNjVVBQoJ/85Cf29t69e6u6ulqFhYXKzs7Wddddp4ULF/IR6i7o+u9tDPk+jjhL5Tf/9a0c0/7nCABon5ADzIQJE2RZF78qPSYmRosXL9bixYsvWtOvXz+tXbv2kvsZMWKEfvvb34Y6PQAA0AXwXUgAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDjdIj0BAADC6frvbYz0FEL28dL8SE/BOGE/A1NaWqqYmJig25AhQ+ztZ8+eVWFhofr376+ePXuqoKBADQ0NQWPU19crPz9fSUlJSk5O1rx583Tu3LlwTxUAABiqQ87A3Hjjjdq8efP/76Tb/+9m7ty52rhxo9avX6/evXtrzpw5uu+++/S73/1OktTc3Kz8/Hy5XC7t3LlTR48e1dSpUxUfH68f/vCHHTFdAABgmA4JMN26dZPL5Wqz/vjx43rxxRe1du1a3XHHHZKk1atXa+jQodq1a5fGjRun6upqHTx4UJs3b1ZKSopGjhypJUuWaP78+SotLVVCQkJHTBkAABikQwLMBx98oLS0NCUmJsrtdqusrEwDBw5UbW2tAoGAcnJy7NohQ4Zo4MCBqqmp0bhx41RTU6Phw4crJSXFrsnLy9Ps2bN14MABjRo16oL79Pv98vv99rLP55MkBQIBBQKBsB1b61jhHBMX54izQr9PrBX0J6IPPYp+9OjaupLXlM76etTe4wl7gBk7dqwqKys1ePBgHT16VIsWLdJtt92m9957T16vVwkJCerTp0/QfVJSUuT1eiVJXq83KLy0bm/ddjFlZWVatGhRm/XV1dVKSkq6yqNqy+PxhH1MtFV+85Xfd8mYlvBNBB2CHkU/enRtbNq06Yrv29lej06fPt2uurAHmMmTJ9t/HzFihMaOHauMjAy9+uqr6t69e7h3ZyspKVFxcbG97PP5lJ6ertzcXDmdzrDtJxAIyOPxaNKkSYqPjw/buLiwYaVVId/HEWtpyZgWPbY3Vv6WmA6YFa4WPYp+9Ojaeq80L+T7dNbXo9Z3UC6nwz9G3adPH335y1/Whx9+qEmTJqmpqUnHjh0LOgvT0NBgXzPjcrm0e/fuoDFaP6V0oetqWjkcDjkcjjbr4+PjO6SxHTUugvmbr/yJ098Sc1X3R8ejR9GPHl0bV/N60tlej9p7LB3+i+xOnjypjz76SKmpqcrOzlZ8fLy2bNlibz906JDq6+vldrslSW63W/v371djY6Nd4/F45HQ6lZWV1dHTBQAABgj7GZh///d/1913362MjAwdOXJEjz/+uOLi4vSNb3xDvXv31vTp01VcXKx+/frJ6XTqkUcekdvt1rhx4yRJubm5ysrK0pQpU1ReXi6v16sFCxaosLDwgmdYAABA1xP2APPpp5/qG9/4hv7yl7/oC1/4gm699Vbt2rVLX/jCFyRJy5cvV2xsrAoKCuT3+5WXl6dnn33Wvn9cXJw2bNig2bNny+12q0ePHpo2bZoWL14c7qkCAABDhT3ArFu37pLbExMTVVFRoYqKiovWZGRkXNUV2QAAoHPjyxwBAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACM0y3SEwAAoKu7/nsbQ76PI85S+c3SsNIq+ZtjOmBWl/bx0vxrvs/zcQYGAAAYhzMwXcSVpHsAAKIVZ2AAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAONEdYCpqKjQ9ddfr8TERI0dO1a7d++O9JQAAEAUiNoA88orr6i4uFiPP/643nnnHd10003Ky8tTY2NjpKcGAAAiLGoDzFNPPaUZM2bo29/+trKysrRq1SolJSXpZz/7WaSnBgAAIiwqvwupqalJtbW1KikpsdfFxsYqJydHNTU1F7yP3++X3++3l48fPy5J+uyzzxQIBMI2t0AgoNOnT2vkf7wmf8u1//bPKxWVje4g3VosnT7dom6BWDUb1KOuhB5FP3oU/SLdo7/85S8dMu6JEyckSZZlXbIuKl/X/vznP6u5uVkpKSlB61NSUvT+++9f8D5lZWVatGhRm/WZmZkdMkdEtwciPQFcFj2KfvQo+kWyR9ct69jxT5w4od69e190e1QGmCtRUlKi4uJie7mlpUWfffaZ+vfvr5iY8CVTn8+n9PR0ffLJJ3I6nWEbF+FDj6IfPYp+9Cj6ddYeWZalEydOKC0t7ZJ1URlgrrvuOsXFxamhoSFofUNDg1wu1wXv43A45HA4gtb16dOno6Yop9PZqf7BdEb0KPrRo+hHj6JfZ+zRpc68tIrKi3gTEhKUnZ2tLVu22OtaWlq0ZcsWud3uCM4MAABEg6g8AyNJxcXFmjZtmsaMGaObb75ZP/7xj3Xq1Cl9+9vfjvTUAABAhEVtgPn617+u//3f/9XChQvl9Xo1cuRIvfnmm20u7L3WHA6HHn/88TZvVyF60KPoR4+iHz2Kfl29RzHW5T6nBAAAEGWi8hoYAACASyHAAAAA4xBgAACAcQgwAADAOASYdvr44481ffp0ZWZmqnv37rrhhhv0+OOPq6mpKahu3759uu2225SYmKj09HSVl5dHaMZdV0VFha6//nolJiZq7Nix2r17d6Sn1CWVlZXpK1/5inr16qXk5GTde++9OnToUFDN2bNnVVhYqP79+6tnz54qKCho8wssce0sXbpUMTExKioqstfRo8j705/+pG9+85vq37+/unfvruHDh2vv3r32dsuytHDhQqWmpqp79+7KycnRBx98EMEZXxsEmHZ6//331dLSoueee04HDhzQ8uXLtWrVKn3/+9+3a3w+n3Jzc5WRkaHa2lo9+eSTKi0t1fPPPx/BmXctr7zyioqLi/X444/rnXfe0U033aS8vDw1NjZGempdzvbt21VYWKhdu3bJ4/EoEAgoNzdXp06dsmvmzp2rN954Q+vXr9f27dt15MgR3XfffRGcdde1Z88ePffccxoxYkTQenoUWZ9//rluueUWxcfH6ze/+Y0OHjyoZcuWqW/fvnZNeXm5fvKTn2jVqlV6++231aNHD+Xl5ens2bMRnPk1YOGKlZeXW5mZmfbys88+a/Xt29fy+/32uvnz51uDBw+OxPS6pJtvvtkqLCy0l5ubm620tDSrrKwsgrOCZVlWY2OjJcnavn27ZVmWdezYMSs+Pt5av369XfOHP/zBkmTV1NREappd0okTJ6wvfelLlsfjsf7hH/7B+s53vmNZFj2KBvPnz7duvfXWi25vaWmxXC6X9eSTT9rrjh07ZjkcDuvnP//5tZhixHAG5iocP35c/fr1s5dramo0fvx4JSQk2Ovy8vJ06NAhff7555GYYpfS1NSk2tpa5eTk2OtiY2OVk5OjmpqaCM4M0l8fL5Lsx0xtba0CgUBQv4YMGaKBAwfSr2ussLBQ+fn5Qb2Q6FE0+PWvf60xY8boa1/7mpKTkzVq1Cj99Kc/tbcfPnxYXq83qEe9e/fW2LFjO32PCDBX6MMPP9TTTz+tf/mXf7HXeb3eNr8puHXZ6/Ve0/l1RX/+85/V3Nx8wR7w84+slpYWFRUV6ZZbbtGwYcMk/fUxkZCQ0OZLV+nXtbVu3Tq98847Kisra7ONHkXeH//4R61cuVJf+tKXVFVVpdmzZ+vf/u3f9NJLL0n6/9eWrvi81+UDzPe+9z3FxMRc8vb+++8H3edPf/qT7rzzTn3ta1/TjBkzIjRzwByFhYV67733tG7dukhPBef55JNP9J3vfEdr1qxRYmJipKeDC2hpadHo0aP1wx/+UKNGjdLMmTM1Y8YMrVq1KtJTi7io/S6ka+W73/2uvvWtb12y5otf/KL99yNHjuj222/X3//937e5ONflcrW5Or912eVyhWfCuKjrrrtOcXFxF+wBP//ImTNnjjZs2KAdO3ZowIAB9nqXy6WmpiYdO3Ys6H/49Ovaqa2tVWNjo0aPHm2va25u1o4dO/TMM8+oqqqKHkVYamqqsrKygtYNHTpU//3f/y3p/19bGhoalJqaatc0NDRo5MiR12yekdDlz8B84Qtf0JAhQy55a72m5U9/+pMmTJig7OxsrV69WrGxwT8+t9utHTt2KBAI2Os8Ho8GDx4cdMU4OkZCQoKys7O1ZcsWe11LS4u2bNkit9sdwZl1TZZlac6cOXr99de1detWZWZmBm3Pzs5WfHx8UL8OHTqk+vp6+nWNTJw4Ufv371ddXZ19GzNmjB588EH77/Qosm655ZY2v37gf/7nf5SRkSFJyszMlMvlCuqRz+fT22+/3fl7FOmriE3x6aefWoMGDbImTpxoffrpp9bRo0ftW6tjx45ZKSkp1pQpU6z33nvPWrdunZWUlGQ999xzEZx517Ju3TrL4XBYlZWV1sGDB62ZM2daffr0sbxeb6Sn1uXMnj3b6t27t7Vt27agx8vp06ftmlmzZlkDBw60tm7dau3du9dyu92W2+2O4Kxx/qeQLIseRdru3butbt26WU888YT1wQcfWGvWrLGSkpKsl19+2a5ZunSp1adPH+tXv/qVtW/fPuuee+6xMjMzrTNnzkRw5h2PANNOq1evtiRd8Ha+3//+99att95qORwO6+/+7u+spUuXRmjGXdfTTz9tDRw40EpISLBuvvlma9euXZGeUpd0scfL6tWr7ZozZ85Y//qv/2r17dvXSkpKsr761a8G/acA197fBhh6FHlvvPGGNWzYMMvhcFhDhgyxnn/++aDtLS0t1mOPPWalpKRYDofDmjhxonXo0KEIzfbaibEsy4rMuR8AAIAr0+WvgQEAAOYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOP8HxfT2sFlz6n8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xenq6Fi1aJEnq1auXtm/frsWLFys3NzfUbQQAAM1MSAHmp06fPi1J6tixY8D4ihUr9Mc//lEOh0OjR4/W7NmzFR8fL0kqLy9Xnz59lJSUZM3Pzc3V5MmTtX//fvXv31/l5eXKzs4OWGdubq6mTp0atBav1yuv12vd93g8kiSfzyefz2eN19++eAw/oDfBhdIbW4z/WpcTUWzR/oB/8aOf9oafrR+xv2lcS+7PlW7zVQeYuro6TZ06Vb/61a/Uu3dva/yBBx5QWlqaUlJStGfPHs2cOVMHDhzQe++9J0lyu90B4UWSdd/tdjc6x+Px6Pvvv1ebNm0a1FNcXKx58+Y1GC8rK7PC08VcLleIW9xy0JvgrqQ3JYOuQyERaMHAunCXELHqe7Nu3bowVxJ52N80riX25+zZs1c076oDTH5+vvbt26ft27cHjD/++OPW7T59+ig5OVnDhw/XoUOH1K1bt6t9ussqLCxUQUGBdd/j8Sg1NVU5OTmy2+3WuM/nk8vl0ogRIxQbG3vN6jERvQkulN70LmpZ52rZov1aMLBOsz+LlrcuKtzlRJSf9mZfEW+B12N/07iW3J/6d1Au56oCzJQpU7RmzRpt27ZNXbp0aXRuVlaWJOngwYPq1q2bHA6Hdu/eHTCnurpakqzzZhwOhzV28Ry73X7Joy+SZLPZZLPZGozHxsZe8psfbBz0pjFX0htvbcv8Je6ti2qx23459b3h56oh9jeNa4n9udLtDekqJL/frylTpuj999/X5s2blZ6eftnHVFZWSpKSk5MlSU6nU3v37tXx48etOS6XS3a7XRkZGdacTZs2BazH5XLJ6XSGUi4AAGimQgow+fn5+uMf/6iVK1eqffv2crvdcrvd+v777yVJhw4d0oIFC1RRUaEjR47oz3/+s8aNG6chQ4YoMzNTkpSTk6OMjAyNHTtW//M//6MNGzZo1qxZys/Pt46gTJo0Sf/7v/+rGTNm6KuvvtKrr76q1atXa9q0aU28+QAAwEQhBZhly5bp9OnTGjp0qJKTk62vVatWSZLi4uK0ceNG5eTkqGfPnnryySc1ZswYffjhh9Y6YmJitGbNGsXExMjpdOqhhx7SuHHjNH/+fGtOenq61q5dK5fLpb59+2rRokV67bXXuIQaAABICvEcGL+/8cskU1NTtXXr1suuJy0t7bJn4w8dOlSff/55KOUBAIAWgr+FBAAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADBOSAGmuLhYv/zlL9W+fXslJibq7rvv1oEDBwLmnDt3Tvn5+erUqZPatWunMWPGqLq6OmBOVVWV8vLyFB8fr8TERE2fPl0XLlwImLNlyxbddtttstls6t69u0pLS69uCwEAQLMTUoDZunWr8vPztXPnTrlcLvl8PuXk5KimpsaaM23aNH344Yd69913tXXrVh09elT33HOPtby2tlZ5eXk6f/68duzYobfeekulpaWaM2eONefw4cPKy8vTHXfcocrKSk2dOlWPPfaYNmzY0ASbDAAATNcqlMnr168PuF9aWqrExERVVFRoyJAhOn36tF5//XWtXLlSw4YNkyS9+eab6tWrl3bu3KnBgwerrKxMX3zxhTZu3KikpCT169dPCxYs0MyZM1VUVKS4uDgtX75c6enpWrRokSSpV69e2r59uxYvXqzc3Nwm2nQAAGCqkALMT50+fVqS1LFjR0lSRUWFfD6fsrOzrTk9e/ZU165dVV5ersGDB6u8vFx9+vRRUlKSNSc3N1eTJ0/W/v371b9/f5WXlweso37O1KlTg9bi9Xrl9Xqt+x6PR5Lk8/nk8/ms8frbF4/hB/QmuFB6Y4vxX+tyIoot2h/wL370097ws/Uj9jeNa8n9udJtvuoAU1dXp6lTp+pXv/qVevfuLUlyu92Ki4tThw4dAuYmJSXJ7XZbcy4OL/XL65c1Nsfj8ej7779XmzZtGtRTXFysefPmNRgvKytTfHx8g3GXy3WFW9ry0JvgrqQ3JYOuQyERaMHAunCXELHqe7Nu3bowVxJ52N80riX25+zZs1c076oDTH5+vvbt26ft27df7SqaVGFhoQoKCqz7Ho9HqampysnJkd1ut8Z9Pp9cLpdGjBih2NjYcJQasehNcKH0pndRyzpXyxbt14KBdZr9WbS8dVHhLiei/LQ3+4p4C7we+5vGteT+1L+DcjlXFWCmTJmiNWvWaNu2berSpYs17nA4dP78eZ06dSrgKEx1dbUcDoc1Z/fu3QHrq79K6eI5P71yqbq6Wna7/ZJHXyTJZrPJZrM1GI+Njb3kNz/YOOhNY66kN97alvlL3FsX1WK3/XLqe8PPVUPsbxrXEvtzpdsb0lVIfr9fU6ZM0fvvv6/NmzcrPT09YPmAAQMUGxurTZs2WWMHDhxQVVWVnE6nJMnpdGrv3r06fvy4NcflcslutysjI8Oac/E66ufUrwMAALRsIR2Byc/P18qVK/Xf//3fat++vXXOSkJCgtq0aaOEhARNmDBBBQUF6tixo+x2u5544gk5nU4NHjxYkpSTk6OMjAyNHTtWJSUlcrvdmjVrlvLz860jKJMmTdIrr7yiGTNm6NFHH9XmzZu1evVqrV27tok3HwAAmCikIzDLli3T6dOnNXToUCUnJ1tfq1atsuYsXrxY//RP/6QxY8ZoyJAhcjgceu+996zlMTExWrNmjWJiYuR0OvXQQw9p3Lhxmj9/vjUnPT1da9eulcvlUt++fbVo0SK99tprXEINAAAkhXgExu+//GWSrVu31tKlS7V06dKgc9LS0i57Nv7QoUP1+eefh1IeAABoIfhbSAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGCcVuEuAGjMTU+vDXcJkiRbjF8lg6TeRRvkrY0KdzkA0OJxBAYAABiHIzAAcJ1EyhHFUBxZmBfuEoBL4ggMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAME7IAWbbtm0aPXq0UlJSFBUVpQ8++CBg+cMPP6yoqKiAr5EjRwbMOXnypB588EHZ7XZ16NBBEyZM0JkzZwLm7NmzR7fffrtat26t1NRUlZSUhL51AACgWQo5wNTU1Khv375aunRp0DkjR47UsWPHrK+33347YPmDDz6o/fv3y+Vyac2aNdq2bZsef/xxa7nH41FOTo7S0tJUUVGh559/XkVFRfrDH/4QarkAAKAZahXqA0aNGqVRo0Y1Osdms8nhcFxy2Zdffqn169fr008/1cCBAyVJL7/8su688079/ve/V0pKilasWKHz58/rjTfeUFxcnG699VZVVlbqhRdeCAg6AACgZQo5wFyJLVu2KDExUTfccIOGDRumZ555Rp06dZIklZeXq0OHDlZ4kaTs7GxFR0dr165d+pd/+ReVl5dryJAhiouLs+bk5ubqd7/7nf7+97/rhhtuaPCcXq9XXq/Xuu/xeCRJPp9PPp/PGq+/ffEYfhCJvbHF+MNdgiTJFu0P+Bc/ojfBNYfeXKv9QSTubyJJS+7PlW5zkweYkSNH6p577lF6eroOHTqk3/72txo1apTKy8sVExMjt9utxMTEwCJatVLHjh3ldrslSW63W+np6QFzkpKSrGWXCjDFxcWaN29eg/GysjLFx8c3GHe5XFe9jc1dJPWmZFC4Kwi0YGBduEuIWPQmOJN7s27dumu6/kja30Siltifs2fPXtG8Jg8w9913n3W7T58+yszMVLdu3bRlyxYNHz68qZ/OUlhYqIKCAuu+x+NRamqqcnJyZLfbrXGfzyeXy6URI0YoNjb2mtVjokjsTe+iDeEuQdIP/4NeMLBOsz+LlrcuKtzlRBR6E1xz6M2+otxrst5I3N9Ekpbcn/p3UC7nmryFdLGbb75ZnTt31sGDBzV8+HA5HA4dP348YM6FCxd08uRJ67wZh8Oh6urqgDn194OdW2Oz2WSz2RqMx8bGXvKbH2wckdUbb21k7fS9dVERV1OkoDfBmdyba70viKT9TSRqif250u295p8D8+233+rEiRNKTk6WJDmdTp06dUoVFRXWnM2bN6uurk5ZWVnWnG3btgW8D+ZyudSjR49Lvn0EAABalpADzJkzZ1RZWanKykpJ0uHDh1VZWamqqiqdOXNG06dP186dO3XkyBFt2rRJd911l7p3767c3B8OQ/bq1UsjR47UxIkTtXv3bn3yySeaMmWK7rvvPqWkpEiSHnjgAcXFxWnChAnav3+/Vq1apSVLlgS8RQQAAFqukAPMZ599pv79+6t///6SpIKCAvXv319z5sxRTEyM9uzZo3/+53/WLbfcogkTJmjAgAH6+OOPA97eWbFihXr27Knhw4frzjvv1K9//euAz3hJSEhQWVmZDh8+rAEDBujJJ5/UnDlzuIQaAABIuopzYIYOHSq/P/glgRs2XP6ky44dO2rlypWNzsnMzNTHH38cankAAKAF4G8hAQAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABgn5ACzbds2jR49WikpKYqKitIHH3wQsNzv92vOnDlKTk5WmzZtlJ2dra+//jpgzsmTJ/Xggw/KbrerQ4cOmjBhgs6cORMwZ8+ePbr99tvVunVrpaamqqSkJPStAwAAzVLIAaampkZ9+/bV0qVLL7m8pKREL730kpYvX65du3apbdu2ys3N1blz56w5Dz74oPbv3y+Xy6U1a9Zo27Ztevzxx63lHo9HOTk5SktLU0VFhZ5//nkVFRXpD3/4w1VsIgAAaG5ahfqAUaNGadSoUZdc5vf79eKLL2rWrFm66667JEn/9V//paSkJH3wwQe677779OWXX2r9+vX69NNPNXDgQEnSyy+/rDvvvFO///3vlZKSohUrVuj8+fN64403FBcXp1tvvVWVlZV64YUXAoIOAABomZr0HJjDhw/L7XYrOzvbGktISFBWVpbKy8slSeXl5erQoYMVXiQpOztb0dHR2rVrlzVnyJAhiouLs+bk5ubqwIED+vvf/96UJQMAAAOFfASmMW63W5KUlJQUMJ6UlGQtc7vdSkxMDCyiVSt17NgxYE56enqDddQvu+GGGxo8t9frldfrte57PB5Jks/nk8/ns8brb188hh9EYm9sMf5wlyBJskX7A/7Fj+hNcM2hN9dqfxCJ+5tI0pL7c6Xb3KQBJpyKi4s1b968BuNlZWWKj49vMO5yua5HWUaKpN6UDAp3BYEWDKwLdwkRi94EZ3Jv1q1bd03XH0n7m0jUEvtz9uzZK5rXpAHG4XBIkqqrq5WcnGyNV1dXq1+/ftac48ePBzzuwoULOnnypPV4h8Oh6urqgDn19+vn/FRhYaEKCgqs+x6PR6mpqcrJyZHdbrfGfT6fXC6XRowYodjY2Kvc0uYpEnvTu2hDuEuQ9MP/oBcMrNPsz6LlrYsKdzkRhd4E1xx6s68o95qsNxL3N5GkJfen/h2Uy2nSAJOeni6Hw6FNmzZZgcXj8WjXrl2aPHmyJMnpdOrUqVOqqKjQgAEDJEmbN29WXV2dsrKyrDn/8R//IZ/PZ33jXC6XevToccm3jyTJZrPJZrM1GI+Njb3kNz/YOCKrN97ayNrpe+uiIq6mSEFvgjO5N9d6XxBJ+5tI1BL7c6XbG/JJvGfOnFFlZaUqKysl/XDibmVlpaqqqhQVFaWpU6fqmWee0Z///Gft3btX48aNU0pKiu6++25JUq9evTRy5EhNnDhRu3fv1ieffKIpU6bovvvuU0pKiiTpgQceUFxcnCZMmKD9+/dr1apVWrJkScARFgAA0HKFfATms88+0x133GHdrw8V48ePV2lpqWbMmKGamho9/vjjOnXqlH79619r/fr1at26tfWYFStWaMqUKRo+fLiio6M1ZswYvfTSS9byhIQElZWVKT8/XwMGDFDnzp01Z84cLqEGAACSriLADB06VH5/8DPqo6KiNH/+fM2fPz/onI4dO2rlypWNPk9mZqY+/vjjUMsDAAAtAH8LCQAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOM3mbyEBAJreTU+vvSbrtcX4VTLohz8X0tSfUnxkYV6Trg+RiSMwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYp1W4C8D1cdPTay87xxbjV8kgqXfRBnlro65DVQAAXB2OwAAAAOMQYAAAgHEIMAAAwDhNHmCKiooUFRUV8NWzZ09r+blz55Sfn69OnTqpXbt2GjNmjKqrqwPWUVVVpby8PMXHxysxMVHTp0/XhQsXmrpUAABgqGtyEu+tt96qjRs3/vgkrX58mmnTpmnt2rV69913lZCQoClTpuiee+7RJ598Ikmqra1VXl6eHA6HduzYoWPHjmncuHGKjY3Vc889dy3KBQAAhrkmAaZVq1ZyOBwNxk+fPq3XX39dK1eu1LBhwyRJb775pnr16qWdO3dq8ODBKisr0xdffKGNGzcqKSlJ/fr104IFCzRz5kwVFRUpLi7uWpQMAAAMck0CzNdff62UlBS1bt1aTqdTxcXF6tq1qyoqKuTz+ZSdnW3N7dmzp7p27ary8nINHjxY5eXl6tOnj5KSkqw5ubm5mjx5svbv36/+/ftf8jm9Xq+8Xq913+PxSJJ8Pp98Pp81Xn/74rGWwBbjv/ycaH/Av/gRvQmO3gRHb4K7lr1pDvv3lvq7SrrybW7yAJOVlaXS0lL16NFDx44d07x583T77bdr3759crvdiouLU4cOHQIek5SUJLfbLUlyu90B4aV+ef2yYIqLizVv3rwG42VlZYqPj28w7nK5Qt00o5UMuvK5CwbWXbtCDEdvgqM3wdGb4K5Fb9atW9fk6wyXlva7SpLOnj17RfOaPMCMGjXKup2ZmamsrCylpaVp9erVatOmTVM/naWwsFAFBQXWfY/Ho9TUVOXk5Mhut1vjPp9PLpdLI0aMUGxs7DWrJ9L0Ltpw2Tm2aL8WDKzT7M+i5a3jg+wuRm+CozfB0ZvgrmVv9hXlNun6wqGl/q6SfnwH5XKu+SfxdujQQbfccosOHjyoESNG6Pz58zp16lTAUZjq6mrrnBmHw6Hdu3cHrKP+KqVLnVdTz2azyWazNRiPjY295Dc/2HhzFcon63rrovgk3iDoTXD0Jjh6E9y16E1z2re3tN9V0pV//67558CcOXNGhw4dUnJysgYMGKDY2Fht2rTJWn7gwAFVVVXJ6XRKkpxOp/bu3avjx49bc1wul+x2uzIyMq51uQAAwABNfgTmqaee0ujRo5WWlqajR49q7ty5iomJ0f3336+EhARNmDBBBQUF6tixo+x2u5544gk5nU4NHjxYkpSTk6OMjAyNHTtWJSUlcrvdmjVrlvLz8y95hAUAALQ8TR5gvv32W91///06ceKEbrzxRv3617/Wzp07deONN0qSFi9erOjoaI0ZM0Zer1e5ubl69dVXrcfHxMRozZo1mjx5spxOp9q2bavx48dr/vz5TV0qAAAwVJMHmHfeeafR5a1bt9bSpUu1dOnSoHPS0tKa1VnkAACgafG3kAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgnCb/W0gAAITTTU+vDXcJITuyMC/cJRiHIzAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcVqFuwAT3fT02nCXAABAi8YRGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxuEqJAAAwuynV7faYvwqGST1Ltogb21UmKpq3JGFeWF9fo7AAAAA4xBgAACAcQgwAADAOBEdYJYuXaqbbrpJrVu3VlZWlnbv3h3ukgAAQASI2ACzatUqFRQUaO7cufrrX/+qvn37Kjc3V8ePHw93aQAAIMwiNsC88MILmjhxoh555BFlZGRo+fLlio+P1xtvvBHu0gAAQJhF5GXU58+fV0VFhQoLC62x6OhoZWdnq7y8/JKP8Xq98nq91v3Tp09Lkk6ePCmfz2eN+3w+nT17VidOnFBsbOxV1dfqQs1VPS7Starz6+zZOrXyRau2LjIv2wsXehMcvQmO3gRHbxpnQn9OnDhxTdb73XffSZL8fn+j8yIywPztb39TbW2tkpKSAsaTkpL01VdfXfIxxcXFmjdvXoPx9PT0a1Jjc/VAuAuIYPQmOHoTHL0Jjt40LtL703nRtV3/d999p4SEhKDLIzLAXI3CwkIVFBRY9+vq6nTy5El16tRJUVE/plePx6PU1FR98803stvt4Sg1YtGb4OhNcPQmOHoTHL1pXEvuj9/v13fffaeUlJRG50VkgOncubNiYmJUXV0dMF5dXS2Hw3HJx9hsNtlstoCxDh06BH0Ou93e4l4UV4reBEdvgqM3wdGb4OhN41pqfxo78lIvIk/ijYuL04ABA7Rp0yZrrK6uTps2bZLT6QxjZQAAIBJE5BEYSSooKND48eM1cOBADRo0SC+++KJqamr0yCOPhLs0AAAQZhEbYO6991793//9n+bMmSO3261+/fpp/fr1DU7sDZXNZtPcuXMbvN0EetMYehMcvQmO3gRHbxpHfy4vyn+565QAAAAiTESeAwMAANAYAgwAADAOAQYAABiHAAMAAIzTogLMs88+q3/8x39UfHx80A+5i4qKavD1zjvvXN9Cw+BKelNVVaW8vDzFx8crMTFR06dP14ULF65voRHgpptuavAaWbhwYbjLCpulS5fqpptuUuvWrZWVlaXdu3eHu6SwKyoqavAa6dmzZ7jLCott27Zp9OjRSklJUVRUlD744IOA5X6/X3PmzFFycrLatGmj7Oxsff311+Ep9jq7XG8efvjhBq+jkSNHhqfYCNSiAsz58+f1r//6r5o8eXKj8958800dO3bM+rr77ruvT4FhdLne1NbWKi8vT+fPn9eOHTv01ltvqbS0VHPmzLnOlUaG+fPnB7xGnnjiiXCXFBarVq1SQUGB5s6dq7/+9a/q27evcnNzdfz48XCXFna33nprwGtk+/bt4S4pLGpqatS3b18tXbr0kstLSkr00ksvafny5dq1a5fatm2r3NxcnTt37jpXev1drjeSNHLkyIDX0dtvv30dK4xw/hbozTff9CckJFxymST/+++/f13riSTBerNu3Tp/dHS03+12W2PLli3z2+12v9frvY4Vhl9aWpp/8eLF4S4jIgwaNMifn59v3a+trfWnpKT4i4uLw1hV+M2dO9fft2/fcJcRcX66f62rq/M7HA7/888/b42dOnXKb7PZ/G+//XYYKgyfS/3uGT9+vP+uu+4KSz0maFFHYK5Ufn6+OnfurEGDBumNN9647J/0bgnKy8vVp0+fgA8SzM3Nlcfj0f79+8NYWXgsXLhQnTp1Uv/+/fX888+3yLfSzp8/r4qKCmVnZ1tj0dHRys7OVnl5eRgriwxff/21UlJSdPPNN+vBBx9UVVVVuEuKOIcPH5bb7Q54DSUkJCgrK4vX0P+3ZcsWJSYmqkePHpo8ebJOnDgR7pIiRsR+Em+4zJ8/X8OGDVN8fLzKysr07//+7zpz5ox+85vfhLu0sHK73Q0+Bbn+vtvtDkdJYfOb3/xGt912mzp27KgdO3aosLBQx44d0wsvvBDu0q6rv/3tb6qtrb3k6+Krr74KU1WRISsrS6WlperRo4eOHTumefPm6fbbb9e+ffvUvn37cJcXMer3HZd6DbW0/cqljBw5Uvfcc4/S09N16NAh/fa3v9WoUaNUXl6umJiYcJcXdsYHmKefflq/+93vGp3z5ZdfXvEJdLNnz7Zu9+/fXzU1NXr++eeNDDBN3ZvmLJReFRQUWGOZmZmKi4vTv/3bv6m4uJiP/YYkadSoUdbtzMxMZWVlKS0tTatXr9aECRPCWBlMct9991m3+/Tpo8zMTHXr1k1btmzR8OHDw1hZZDA+wDz55JN6+OGHG51z8803X/X6s7KytGDBAnm9XuN+OTVlbxwOR4OrS6qrq61lpvs5vcrKytKFCxd05MgR9ejR4xpUF5k6d+6smJgY63VQr7q6ulm8JppShw4ddMstt+jgwYPhLiWi1L9OqqurlZycbI1XV1erX79+Yaoqct18883q3LmzDh48SIBRMwgwN954o2688cZrtv7KykrdcMMNxoUXqWl743Q69eyzz+r48eNKTEyUJLlcLtntdmVkZDTJc4TTz+lVZWWloqOjrb60FHFxcRowYIA2bdpkXalXV1enTZs2acqUKeEtLsKcOXNGhw4d0tixY8NdSkRJT0+Xw+HQpk2brMDi8Xi0a9euy14t2hJ9++23OnHiREDYa8mMDzChqKqq0smTJ1VVVaXa2lpVVlZKkrp376527drpww8/VHV1tQYPHqzWrVvL5XLpueee01NPPRXewq+Dy/UmJydHGRkZGjt2rEpKSuR2uzVr1izl5+cbGe6uVnl5uXbt2qU77rhD7du3V3l5uaZNm6aHHnpIN9xwQ7jLu+4KCgo0fvx4DRw4UIMGDdKLL76ompoaPfLII+EuLayeeuopjR49WmlpaTp69Kjmzp2rmJgY3X///eEu7bo7c+ZMwJGnw4cPq7KyUh07dlTXrl01depUPfPMM/rFL36h9PR0zZ49WykpKS3i4ysa603Hjh01b948jRkzRg6HQ4cOHdKMGTPUvXt35ebmhrHqCBLuy6Cup/Hjx/slNfj6y1/+4vf7/f6PPvrI369fP3+7du38bdu29fft29e/fPlyf21tbXgLvw4u1xu/3+8/cuSIf9SoUf42bdr4O3fu7H/yySf9Pp8vfEWHQUVFhT8rK8ufkJDgb926tb9Xr17+5557zn/u3LlwlxY2L7/8sr9r167+uLg4/6BBg/w7d+4Md0lhd++99/qTk5P9cXFx/n/4h3/w33vvvf6DBw+Gu6yw+Mtf/nLJfcv48eP9fv8Pl1LPnj3bn5SU5LfZbP7hw4f7Dxw4EN6ir5PGenP27Fl/Tk6O/8Ybb/THxsb609LS/BMnTgz4KIuWLsrv5xphAABgFj4HBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADj/D83KyADvngT0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cols in weather_data.columns:\n",
    "    weather_data[cols].hist()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1a13b5",
   "metadata": {},
   "source": [
    "## Forming our Training and Testing Labels\n",
    "\n",
    "<font color='red'> Dont forget - Normalising The Numerical Data</font>\n",
    "\n",
    "MinMaxScaler is suitable when the range of the data is important, and StandardScaler is suitable when the shape of the distribution is important. It is important to choose the appropriate normalization technique based on the characteristics of the data and the specific problem you are trying to solve.\n",
    "\n",
    "We found that using both MinMaxScaler and StandardScaler has different impacts while constructing a neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0cbb3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the Sklearn StandardScaler API for normalising the data\n",
    "scaler = StandardScaler()\n",
    "\n",
    "y_weather = weather_data.iloc[:,-1]\n",
    "X_weather = weather_data.iloc[:,0:-1]\n",
    "X_weather[np.isnan(X_weather)] = 0  \n",
    "X_weather = scaler.fit_transform(X_weather)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "653884cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_image = np.stack(image_data.iloc[:,0])\n",
    "y_image = image_data.iloc[:,-1] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb4f272",
   "metadata": {},
   "source": [
    "**Crop images to remove extra padding from the image**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8be10b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using the train_test_split API from SKlearn\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(X_image, y_image, test_size=0.3, random_state=42)\n",
    "\n",
    "#Reshaping the dataframe to the right shape so that it can be used in the neural network\n",
    "x_train1 = x_train1.reshape(x_train1.shape[0],x_train1.shape[1], x_train1.shape[2], 1)[:,8:56,8:-8,:]#np.expand_dims(x_train1)\n",
    "x_test1 = x_test1.reshape(x_test1.shape[0],x_test1.shape[1], x_test1.shape[2], 1)[:,8:56,8:-8,:]#np.ex\n",
    "\n",
    "x_train2, x_test2, y_train2, y_test2 = train_test_split(X_weather, y_weather, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fefd0105",
   "metadata": {},
   "source": [
    "- `x_train1`- Training data for images\n",
    "- `x_test1` - Testing/Validating data for images\n",
    "\n",
    "- `x_train2`- Training data for weather\n",
    "- `x_test2` - Testing/Validating data for weather\n",
    "\n",
    "- `y_train1` & `y_train2` - Training labels\n",
    "- `y_test1`  & `y_test2`  - Testing labels\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04bb361a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Size Images:  (10488, 48, 48, 1)  & Weather  (10488, 15)\n",
      "Training Labels Size Images:   (10488,)  & Weather  (10488,)\n",
      "Testing Data Size Images:   (4495, 48, 48, 1)  & Weather  (4495, 15)\n",
      "Testing Labels Size Images:   (4495,)  & Weather  (4495,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Data Size Images: \",x_train1.shape, \" & Weather \",x_train2.shape)  \n",
    "print(\"Training Labels Size Images:  \",y_train1.shape, \" & Weather \",y_train2.shape)  \n",
    "print(\"Testing Data Size Images:  \",x_test1.shape, \" & Weather \",x_test2.shape)  \n",
    "print(\"Testing Labels Size Images:  \",y_test1.shape, \" & Weather \",y_test2.shape)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67787e3e",
   "metadata": {},
   "source": [
    "## Building our Neural Network using this dataset\n",
    "\n",
    "The following techniques were experimented with to come up with  a Neural network to predict the occurence of snowfall.\n",
    "\n",
    "- **Analysing Individual images using a CNN**\n",
    "- **Analysing the Numerical data using an RNN**\n",
    "- **Analysing Yearly data individually using a combination of CNN and RNN**\n",
    "- **Transfer Learning to improve models Year over Year**\n",
    "- **Analysing all 10 years as a single dataset**\n",
    "\n",
    "### The conclusions drawn from the above techniques were\n",
    "\n",
    "- Analysing Individual images helped us come up with the fact that images needed reshaping and the default shape did not yield **our team** good results\n",
    "- Analysing Numerical data aided in coming up with an optimum LSTM network \n",
    "- When using individual years data we found that the learning rates were different and that it played an important parameter in our network\n",
    "- Transfer learning was a fun methodology which involved analysing each years data individually and then optimising the model of the following year with the wieights of the previous years model. \n",
    "\n",
    "\n",
    "### From the above techniques we were able to come up with an optimised Neural Network structure\n",
    "\n",
    "- Finally we found that the dataset got decent results only when all 10 years were used in the model.\n",
    "\n",
    "**Given Below is an optimised architecture of the Neural Network used for the dataset used in this notebook**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547a7f7a",
   "metadata": {},
   "source": [
    "## Model Architecture\n",
    "\n",
    "**About the CNN**\n",
    "\n",
    "The CNN uses an input layer and 2 different layers of `six neurons` each - we tried using higher number of neurons which lead to overfitting. The kernel size consists of `(3,3)`. \n",
    "\n",
    "An additional dropout layer is used which helps in randomly setting input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting.\n",
    "\n",
    "`GlobalAveragePooling2D` is another method which is used to replace Flatten in the network. We used this as most of our networks were overfitting.\n",
    "\n",
    "`ReLU` is a simple and efficient activation function that is commonly used in deep learning, but it can lead to dead neurons. `SELU` is a self-normalizing activation function that can help address the vanishing gradient problem and prevent dead neurons, and it can lead to better convergence and generalization performance.\n",
    "\n",
    "**About the RNN**\n",
    "\n",
    "The RNN uses an input layer and 2 `LSTM` layers of 6 neurons each. The input data which in this case consists of our weather data is fed into this network. The activation function used is a `tanh`.\n",
    "\n",
    "A dropout layer is also added here. The presence of dropout layers is because of the pattern in losses observed when this layer was not used. The model didnot learn well till this layer was introduced\n",
    "\n",
    "Each layer of the RNN has a mention of `return_sequence` this helps the model learn better once the neurons start seeing the data\n",
    "\n",
    "**Combining Both Models**\n",
    "\n",
    "Both the Networks are combined with a `relu` activation function. the output activation function is a `sigmoid` this is because of the fact that the labels are either `0` or `1`. A `softmax` was tried but we noticed that it gave us a constant accuracy. \n",
    "\n",
    "**About the final model**\n",
    "- `Learning Rate` - 0.01\n",
    "- `optimiser` - Adam\n",
    "- `Loss Function` - binary_crossentropy\n",
    "- `Metrics` - Accuracy(training and validation), precision, F1, Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d87f2c16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 48, 48, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 48, 48, 6)    60          ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 24, 24, 6)    0           ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 24, 24, 6)    330         ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)           [(None, 15)]         0           []                               \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 12, 12, 6)   0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 64)           1024        ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 12, 12, 6)    330         ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " reshape (Reshape)              (None, 1, 64)        0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 6, 6, 6)     0           ['conv2d_2[0][0]']               \n",
      "                                                                                                  \n",
      " lstm (LSTM)                    (None, 1, 6)         1704        ['reshape[0][0]']                \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 6)           0           ['max_pooling2d_2[0][0]']        \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " lstm_1 (LSTM)                  (None, 6)            312         ['lstm[0][0]']                   \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 6)            0           ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 6)            0           ['lstm_1[0][0]']                 \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 12)           0           ['dropout[0][0]',                \n",
      "                                                                  'dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 6)            78          ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            7           ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 3,845\n",
      "Trainable params: 3,845\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    #Delete an older model if it exists\n",
    "    del model\n",
    "except:\n",
    "    pass\n",
    "\n",
    "#Shape of the image getting analysed and the CNN neurons\n",
    "#MaxPooling2D layer helps in extracting the most salient features from the input \n",
    "#GlobalAveragePooling2D is used instead of flatten to reduce the number of features\n",
    "input_cnn = Input(shape=(48,48,1))\n",
    "cnn_layer = Conv2D(filters=6, kernel_size=(3,3), activation='relu', padding='same')(input_cnn)\n",
    "cnn_layer = MaxPooling2D(pool_size=(2,2))(cnn_layer)\n",
    "cnn_layer = Conv2D(filters=6, kernel_size=(3,3), activation='relu', padding='same')(cnn_layer)\n",
    "cnn_layer = MaxPooling2D(pool_size=(2,2))(cnn_layer)\n",
    "cnn_layer = Conv2D(filters=6, kernel_size=(3,3), activation='relu', padding='same')(cnn_layer)\n",
    "cnn_layer = MaxPooling2D(pool_size=(2,2))(cnn_layer)\n",
    "cnn_layer = tf.keras.layers.GlobalAveragePooling2D()(cnn_layer)\n",
    "cnn_layer = Dropout(0.25)(cnn_layer)\n",
    "cnn_model = Model(inputs=input_cnn, outputs=cnn_layer)\n",
    "\n",
    "#Shape of the input data for the RNN and the RNN neurons\n",
    "input_rnn = Input(shape=(x_train2[0].shape[0],))\n",
    "rnn_layer = Dense(64, activation='tanh')(input_rnn)\n",
    "rnn_layer = tf.keras.layers.Reshape((1,64))(rnn_layer)\n",
    "rnn_layer = LSTM(6, return_sequences=True)(rnn_layer)\n",
    "rnn_layer = LSTM(6, return_sequences=False)(rnn_layer)\n",
    "rnn_layer = Dropout(0.5)(rnn_layer)\n",
    "rnn_model = Model(inputs=input_rnn, outputs=rnn_layer)\n",
    "\n",
    "#Combining all the above models\n",
    "combined_layer = tf.keras.layers.concatenate([cnn_model.output, rnn_model.output])\n",
    "combined_layer = Dense(6, activation='relu')(combined_layer)\n",
    "output_layer = Dense(1, activation='sigmoid')(combined_layer)\n",
    "model = Model(inputs=[cnn_model.input, rnn_model.input], outputs=output_layer)\n",
    "\n",
    "#Visualise the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb41909",
   "metadata": {},
   "source": [
    "## Fitting the model on the training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e43a6352",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "656/656 [==============================] - 36s 33ms/step - loss: 0.6843 - accuracy: 0.5665 - f1_m: 0.7093 - precision_m: 0.5686 - recall_m: 0.9820 - val_loss: 0.6794 - val_accuracy: 0.5695 - val_f1_m: 0.7172 - val_precision_m: 0.5694 - val_recall_m: 1.0000\n",
      "Epoch 2/15\n",
      "656/656 [==============================] - 21s 33ms/step - loss: 0.6793 - accuracy: 0.5789 - f1_m: 0.7122 - precision_m: 0.5770 - recall_m: 0.9617 - val_loss: 0.6752 - val_accuracy: 0.5920 - val_f1_m: 0.7173 - val_precision_m: 0.5880 - val_recall_m: 0.9507\n",
      "Epoch 3/15\n",
      "656/656 [==============================] - 20s 31ms/step - loss: 0.6752 - accuracy: 0.5908 - f1_m: 0.7055 - precision_m: 0.5921 - recall_m: 0.9003 - val_loss: 0.6720 - val_accuracy: 0.6002 - val_f1_m: 0.7019 - val_precision_m: 0.6031 - val_recall_m: 0.8694\n",
      "Epoch 4/15\n",
      "656/656 [==============================] - 21s 32ms/step - loss: 0.6733 - accuracy: 0.5995 - f1_m: 0.7087 - precision_m: 0.5985 - recall_m: 0.8976 - val_loss: 0.6698 - val_accuracy: 0.5998 - val_f1_m: 0.7078 - val_precision_m: 0.5999 - val_recall_m: 0.8930\n",
      "Epoch 5/15\n",
      "656/656 [==============================] - 21s 32ms/step - loss: 0.6728 - accuracy: 0.5961 - f1_m: 0.7068 - precision_m: 0.5958 - recall_m: 0.9013 - val_loss: 0.6683 - val_accuracy: 0.6044 - val_f1_m: 0.7089 - val_precision_m: 0.6037 - val_recall_m: 0.8881\n",
      "Epoch 6/15\n",
      "656/656 [==============================] - 19s 29ms/step - loss: 0.6707 - accuracy: 0.6003 - f1_m: 0.7114 - precision_m: 0.5991 - recall_m: 0.9043 - val_loss: 0.6693 - val_accuracy: 0.6011 - val_f1_m: 0.7194 - val_precision_m: 0.5944 - val_recall_m: 0.9401\n",
      "Epoch 7/15\n",
      "656/656 [==============================] - 19s 29ms/step - loss: 0.6705 - accuracy: 0.6023 - f1_m: 0.7135 - precision_m: 0.5989 - recall_m: 0.9099 - val_loss: 0.6686 - val_accuracy: 0.6004 - val_f1_m: 0.7063 - val_precision_m: 0.6010 - val_recall_m: 0.8853\n",
      "Epoch 8/15\n",
      "656/656 [==============================] - 17s 26ms/step - loss: 0.6704 - accuracy: 0.6028 - f1_m: 0.7124 - precision_m: 0.6002 - recall_m: 0.9041 - val_loss: 0.6681 - val_accuracy: 0.6056 - val_f1_m: 0.7143 - val_precision_m: 0.6009 - val_recall_m: 0.9088\n",
      "Epoch 9/15\n",
      "656/656 [==============================] - 17s 26ms/step - loss: 0.6693 - accuracy: 0.6065 - f1_m: 0.7149 - precision_m: 0.6013 - recall_m: 0.9081 - val_loss: 0.6705 - val_accuracy: 0.6002 - val_f1_m: 0.7040 - val_precision_m: 0.6027 - val_recall_m: 0.8758\n",
      "Epoch 10/15\n",
      "656/656 [==============================] - 17s 26ms/step - loss: 0.6685 - accuracy: 0.6040 - f1_m: 0.7126 - precision_m: 0.6008 - recall_m: 0.9035 - val_loss: 0.6698 - val_accuracy: 0.6009 - val_f1_m: 0.7067 - val_precision_m: 0.6016 - val_recall_m: 0.8854\n",
      "Epoch 11/15\n",
      "656/656 [==============================] - 18s 27ms/step - loss: 0.6696 - accuracy: 0.6043 - f1_m: 0.7130 - precision_m: 0.6015 - recall_m: 0.9033 - val_loss: 0.6683 - val_accuracy: 0.6065 - val_f1_m: 0.7179 - val_precision_m: 0.6001 - val_recall_m: 0.9219\n",
      "Epoch 12/15\n",
      "656/656 [==============================] - 20s 30ms/step - loss: 0.6680 - accuracy: 0.6055 - f1_m: 0.7146 - precision_m: 0.6026 - recall_m: 0.9052 - val_loss: 0.6687 - val_accuracy: 0.6071 - val_f1_m: 0.7202 - val_precision_m: 0.5992 - val_recall_m: 0.9312\n",
      "Epoch 13/15\n",
      "656/656 [==============================] - 20s 30ms/step - loss: 0.6691 - accuracy: 0.6039 - f1_m: 0.7134 - precision_m: 0.6007 - recall_m: 0.9085 - val_loss: 0.6695 - val_accuracy: 0.6002 - val_f1_m: 0.7108 - val_precision_m: 0.5984 - val_recall_m: 0.9039\n",
      "Epoch 14/15\n",
      "656/656 [==============================] - 20s 30ms/step - loss: 0.6677 - accuracy: 0.6026 - f1_m: 0.7106 - precision_m: 0.5999 - recall_m: 0.9005 - val_loss: 0.6680 - val_accuracy: 0.6053 - val_f1_m: 0.7132 - val_precision_m: 0.6016 - val_recall_m: 0.9047\n",
      "Epoch 15/15\n",
      "656/656 [==============================] - 16s 24ms/step - loss: 0.6661 - accuracy: 0.6076 - f1_m: 0.7131 - precision_m: 0.6038 - recall_m: 0.8992 - val_loss: 0.6678 - val_accuracy: 0.6049 - val_f1_m: 0.7144 - val_precision_m: 0.6005 - val_recall_m: 0.9106\n"
     ]
    }
   ],
   "source": [
    "#Parameters of the model\n",
    "learning_rate = 0.01\n",
    "opt = 'adam'\n",
    "\n",
    "#Metrics to showcase during the analysis of the model\n",
    "metrics=['accuracy',f1_m,precision_m, recall_m]\n",
    "model.compile(optimizer=opt, loss='binary_crossentropy', metrics=metrics)\n",
    "\n",
    "#Explicitly mentioning the learning rate\n",
    "model.optimizer.get_config()['learning_rate'] = learning_rate\n",
    "\n",
    "#Monitor loss to ensure that loss doesnt increase\n",
    "# es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
    "\n",
    "history = model.fit([x_train1, x_train2], y_train1, epochs=15,validation_split=0.30, batch_size=16, validation_data=([x_test1, x_test2], y_test1)\n",
    "                   ,callbacks=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acba0a46",
   "metadata": {},
   "source": [
    "## Reviewing the overall Metrics\n",
    "\n",
    "- Our model converged early just after 15 epochs  - we noticed that there was massive overfitting after a certain number of epochs\n",
    "\n",
    "- Analysing the Loss on validation and training data\n",
    "\n",
    "- Analysing the accuracy on validation and training data\n",
    "\n",
    "- Output of prediction on our test dataset and its respective losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "36cba71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy and loss of the model\n",
    "model_metrics(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ca7c7fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/141 [==============================] - 5s 15ms/step\n",
      "[[1.]\n",
      " [0.]\n",
      " [1.]\n",
      " ...\n",
      " [1.]\n",
      " [0.]\n",
      " [1.]]\n"
     ]
    }
   ],
   "source": [
    "#Printing the predicted labels\n",
    "print(np.round(model.predict([x_test1, x_test2])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "266c05cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/141 [==============================] - 3s 19ms/step\n",
      "Total Accuracy: 60\n"
     ]
    }
   ],
   "source": [
    "# Prediction using X_test\n",
    "pred = np.round(model.predict([x_test1, x_test2]))\n",
    "# Convert to array\n",
    "pred = np.array(pred)\n",
    "\n",
    "# Assigning class based on prediction\n",
    "pred[pred>0.5] = 1\n",
    "pred[pred<1] = 0\n",
    "\n",
    "# Class-wise accuracy\n",
    "classwise1 = ((np.array(pred)[:,0] == np.array(y_test1))*(y_test1==1)).sum()/(y_test1==1).sum()\n",
    "classwise0 = ((np.array(pred)[:,0] == np.array(y_test1))*(y_test1==0)).sum()/(y_test1==0).sum()\n",
    "\n",
    "print(f'Total Accuracy: {((np.array(pred)[:,0] == np.array(y_test1)).sum()/len(pred))*100:.0f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0730b90c",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "\n",
    "From the above metrics we can see that our Neural Network has had **some** improvement over our base model but there is **scope for improving** its overall accuracy. The **training loss is decreasing** significantly along with the **validating loss**, the model converges early and achieves an overall accuracy of **60 %**.\n",
    "\n",
    "Some of the key takeaways from this model which can help us build a better model are. \n",
    "\n",
    "1. With respect to the data\n",
    "\n",
    "- Data Balancing technique - The approach taken to try to balance the data may need improvement\n",
    "\n",
    "- Feature extraction to ensure the right features are taken and the wrong ones are implemented\n",
    "\n",
    "- Manipulating the dimensions of the dataset to ensure more features are present for the model to make better decisions\n",
    "\n",
    "\n",
    "\n",
    "2. With respect to the model\n",
    "\n",
    "\n",
    "- Layers - The number of layers can be increased to facilitate the large quantities of data\n",
    "\n",
    "- Neurons - When dealing with images neurons with power 2 can be considered.\n",
    "\n",
    "- regularization - This can help in preventing overfitting\n",
    "\n",
    "- Early Stopping - Passing this parameter to the model can help in stopping the model from overshooting while managing the validation loss. \n",
    "\n",
    "Some of the Above techniques have been used in building a much better performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35673778",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "References 1. : https://sciencing.com/conditions-necessary-snow-7457372.html\n",
    "\n",
    "References 2  : https://nsidc.org/learn/parts-cryosphere/snow/science-snow   \n",
    "\n",
    "References 3  : https://stackoverflow.com/questions/49295311/what-is-the-difference-between-flatten-and-globalaveragepooling2d-in-keras   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "421534ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## TOTAL TIME TAKEN :  437.5575969\n"
     ]
    }
   ],
   "source": [
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"############## TOTAL TIME TAKEN : \", elapsed) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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