LSTM_First_Kind.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "f9d85bdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "os.chdir('G:/Time_Series_Data')\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.layers import Dense, Embedding,Input,LSTM,GlobalMaxPooling1D\n",
    "from keras import Model\n",
    "from plot_keras_history import show_history, plot_history\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6d692548",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Land-Ocean Temperature Index (C)\n",
      "\n",
      "------\n",
      "--------------------------------\n",
      "\n",
      "------\n",
      "\n",
      "\n",
      "------\n",
      "Year No_Smoothing  Lowess(5)\n",
      "\n",
      "------\n",
      "----------------------------\n",
      "\n",
      "------\n",
      "1880     -0.16     -0.09\n",
      "\n",
      "------\n",
      "1881     -0.08     -0.12\n",
      "\n",
      "------\n",
      "1882     -0.10     -0.16\n",
      "\n",
      "------\n",
      "1883     -0.17     -0.20\n",
      "\n",
      "------\n",
      "1884     -0.28     -0.23\n",
      "\n",
      "------\n",
      "1885     -0.33     -0.26\n",
      "\n",
      "------\n",
      "1886     -0.31     -0.27\n",
      "\n",
      "------\n",
      "1887     -0.36     -0.27\n",
      "\n",
      "------\n",
      "1888     -0.17     -0.26\n",
      "\n",
      "------\n",
      "1889     -0.10     -0.25\n",
      "\n",
      "------\n",
      "1890     -0.35     -0.25\n",
      "\n",
      "------\n",
      "1891     -0.22     -0.26\n",
      "\n",
      "------\n",
      "1892     -0.27     -0.26\n",
      "\n",
      "------\n",
      "1893     -0.31     -0.26\n",
      "\n",
      "------\n",
      "1894     -0.31     -0.24\n",
      "\n",
      "------\n",
      "1895     -0.23     -0.22\n",
      "\n",
      "------\n",
      "1896     -0.11     -0.21\n",
      "\n",
      "------\n",
      "1897     -0.10     -0.18\n",
      "\n",
      "------\n",
      "1898     -0.27     -0.17\n",
      "\n",
      "------\n",
      "1899     -0.18     -0.17\n",
      "\n",
      "------\n",
      "1900     -0.08     -0.20\n",
      "\n",
      "------\n",
      "1901     -0.16     -0.23\n",
      "\n",
      "------\n",
      "1902     -0.27     -0.26\n",
      "\n",
      "------\n",
      "1903     -0.36     -0.28\n",
      "\n",
      "------\n",
      "1904     -0.46     -0.30\n",
      "\n",
      "------\n",
      "1905     -0.26     -0.33\n",
      "\n",
      "------\n",
      "1906     -0.22     -0.35\n",
      "\n",
      "------\n",
      "1907     -0.38     -0.37\n",
      "\n",
      "------\n",
      "1908     -0.42     -0.39\n",
      "\n",
      "------\n",
      "1909     -0.48     -0.40\n",
      "\n",
      "------\n",
      "1910     -0.43     -0.41\n",
      "\n",
      "------\n",
      "1911     -0.44     -0.39\n",
      "\n",
      "------\n",
      "1912     -0.36     -0.35\n",
      "\n",
      "------\n",
      "1913     -0.35     -0.32\n",
      "\n",
      "------\n",
      "1914     -0.15     -0.31\n",
      "\n",
      "------\n",
      "1915     -0.14     -0.30\n",
      "\n",
      "------\n",
      "1916     -0.35     -0.29\n",
      "\n",
      "------\n",
      "1917     -0.46     -0.29\n",
      "\n",
      "------\n",
      "1918     -0.30     -0.30\n",
      "\n",
      "------\n",
      "1919     -0.27     -0.29\n",
      "\n",
      "------\n",
      "1920     -0.27     -0.28\n",
      "\n",
      "------\n",
      "1921     -0.18     -0.26\n",
      "\n",
      "------\n",
      "1922     -0.29     -0.25\n",
      "\n",
      "------\n",
      "1923     -0.27     -0.24\n",
      "\n",
      "------\n",
      "1924     -0.27     -0.23\n",
      "\n",
      "------\n",
      "1925     -0.22     -0.22\n",
      "\n",
      "------\n",
      "1926     -0.10     -0.22\n",
      "\n",
      "------\n",
      "1927     -0.22     -0.21\n",
      "\n",
      "------\n",
      "1928     -0.20     -0.20\n",
      "\n",
      "------\n",
      "1929     -0.36     -0.19\n",
      "\n",
      "------\n",
      "1930     -0.15     -0.19\n",
      "\n",
      "------\n",
      "1931     -0.09     -0.18\n",
      "\n",
      "------\n",
      "1932     -0.16     -0.17\n",
      "\n",
      "------\n",
      "1933     -0.28     -0.16\n",
      "\n",
      "------\n",
      "1934     -0.12     -0.15\n",
      "\n",
      "------\n",
      "1935     -0.19     -0.13\n",
      "\n",
      "------\n",
      "1936     -0.14     -0.10\n",
      "\n",
      "------\n",
      "1937     -0.02     -0.06\n",
      "\n",
      "------\n",
      "1938      0.00     -0.01\n",
      "\n",
      "------\n",
      "1939     -0.01      0.04\n",
      "\n",
      "------\n",
      "1940      0.13      0.07\n",
      "\n",
      "------\n",
      "1941      0.19      0.09\n",
      "\n",
      "------\n",
      "1942      0.07      0.11\n",
      "\n",
      "------\n",
      "1943      0.09      0.10\n",
      "\n",
      "------\n",
      "1944      0.21      0.08\n",
      "\n",
      "------\n",
      "1945      0.09      0.04\n",
      "\n",
      "------\n",
      "1946     -0.07      0.01\n",
      "\n",
      "------\n",
      "1947     -0.03     -0.04\n",
      "\n",
      "------\n",
      "1948     -0.11     -0.07\n",
      "\n",
      "------\n",
      "1949     -0.11     -0.08\n",
      "\n",
      "------\n",
      "1950     -0.17     -0.08\n",
      "\n",
      "------\n",
      "1951     -0.07     -0.07\n",
      "\n",
      "------\n",
      "1952      0.01     -0.07\n",
      "\n",
      "------\n",
      "1953      0.08     -0.07\n",
      "\n",
      "------\n",
      "1954     -0.13     -0.07\n",
      "\n",
      "------\n",
      "1955     -0.14     -0.06\n",
      "\n",
      "------\n",
      "1956     -0.19     -0.05\n",
      "\n",
      "------\n",
      "1957      0.05     -0.04\n",
      "\n",
      "------\n",
      "1958      0.06     -0.01\n",
      "\n",
      "------\n",
      "1959      0.03      0.01\n",
      "\n",
      "------\n",
      "1960     -0.03      0.03\n",
      "\n",
      "------\n",
      "1961      0.06      0.01\n",
      "\n",
      "------\n",
      "1962      0.03     -0.01\n",
      "\n",
      "------\n",
      "1963      0.05     -0.03\n",
      "\n",
      "------\n",
      "1964     -0.20     -0.04\n",
      "\n",
      "------\n",
      "1965     -0.11     -0.05\n",
      "\n",
      "------\n",
      "1966     -0.06     -0.06\n",
      "\n",
      "------\n",
      "1967     -0.02     -0.05\n",
      "\n",
      "------\n",
      "1968     -0.08     -0.03\n",
      "\n",
      "------\n",
      "1969      0.05     -0.02\n",
      "\n",
      "------\n",
      "1970      0.02     -0.01\n",
      "\n",
      "------\n",
      "1971     -0.08      0.00\n",
      "\n",
      "------\n",
      "1972      0.01      0.00\n",
      "\n",
      "------\n",
      "1973      0.16     -0.00\n",
      "\n",
      "------\n",
      "1974     -0.07      0.01\n",
      "\n",
      "------\n",
      "1975     -0.01      0.02\n",
      "\n",
      "------\n",
      "1976     -0.10      0.04\n",
      "\n",
      "------\n",
      "1977      0.18      0.07\n",
      "\n",
      "------\n",
      "1978      0.07      0.12\n",
      "\n",
      "------\n",
      "1979      0.16      0.16\n",
      "\n",
      "------\n",
      "1980      0.26      0.20\n",
      "\n",
      "------\n",
      "1981      0.32      0.21\n",
      "\n",
      "------\n",
      "1982      0.14      0.22\n",
      "\n",
      "------\n",
      "1983      0.31      0.21\n",
      "\n",
      "------\n",
      "1984      0.16      0.21\n",
      "\n",
      "------\n",
      "1985      0.12      0.22\n",
      "\n",
      "------\n",
      "1986      0.18      0.24\n",
      "\n",
      "------\n",
      "1987      0.32      0.27\n",
      "\n",
      "------\n",
      "1988      0.39      0.31\n",
      "\n",
      "------\n",
      "1989      0.27      0.33\n",
      "\n",
      "------\n",
      "1990      0.45      0.33\n",
      "\n",
      "------\n",
      "1991      0.40      0.33\n",
      "\n",
      "------\n",
      "1992      0.22      0.33\n",
      "\n",
      "------\n",
      "1993      0.23      0.33\n",
      "\n",
      "------\n",
      "1994      0.31      0.34\n",
      "\n",
      "------\n",
      "1995      0.45      0.37\n",
      "\n",
      "------\n",
      "1996      0.33      0.40\n",
      "\n",
      "------\n",
      "1997      0.46      0.42\n",
      "\n",
      "------\n",
      "1998      0.61      0.44\n",
      "\n",
      "------\n",
      "1999      0.38      0.47\n",
      "\n",
      "------\n",
      "2000      0.39      0.50\n",
      "\n",
      "------\n",
      "2001      0.53      0.52\n",
      "\n",
      "------\n",
      "2002      0.62      0.54\n",
      "\n",
      "------\n",
      "2003      0.61      0.58\n",
      "\n",
      "------\n",
      "2004      0.53      0.60\n",
      "\n",
      "------\n",
      "2005      0.67      0.61\n",
      "\n",
      "------\n",
      "2006      0.63      0.62\n",
      "\n",
      "------\n",
      "2007      0.66      0.63\n",
      "\n",
      "------\n",
      "2008      0.54      0.64\n",
      "\n",
      "------\n",
      "2009      0.65      0.64\n",
      "\n",
      "------\n",
      "2010      0.72      0.64\n",
      "\n",
      "------\n",
      "2011      0.61      0.66\n",
      "\n",
      "------\n",
      "2012      0.64      0.69\n",
      "\n",
      "------\n",
      "2013      0.67      0.73\n",
      "\n",
      "------\n",
      "2014      0.74      0.78\n",
      "\n",
      "------\n",
      "2015      0.90      0.83\n",
      "\n",
      "------\n",
      "2016      1.01      0.87\n",
      "\n",
      "------\n",
      "2017      0.92      0.91\n",
      "\n",
      "------\n",
      "2018      0.85      0.92\n",
      "\n",
      "------\n",
      "2019      0.97      0.92\n",
      "\n",
      "------\n",
      "2020      1.01      0.93\n",
      "\n",
      "------\n",
      "2021      0.84      0.93\n",
      "\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "temp_data_set = {}\n",
    "temp_dev=[]\n",
    "year=[]\n",
    "with open(os.path.join('graph.txt'),encoding=\"utf-8\") as f:\n",
    "  # is just a space-separated text file in the format:\n",
    "  # word vec[0] vec[1] vec[2] ...\n",
    "  for i,line in enumerate(f):\n",
    "        print(line)\n",
    "        print('------')\n",
    "        if len(line)==25:\n",
    "            values = line.split()\n",
    "            year.append(values[0])\n",
    "            temp_dev.append(float(values[2]))\n",
    "            \n",
    "            \n",
    "Data_set=pd.DataFrame({'Year':year,'Temperature Increase (C)':temp_dev})\n",
    "Data_set['Year']=pd.to_datetime(Data_set.Year,format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "bb9a6d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data_set_emission\n",
    "Data_set2=pd.read_csv('annual-co2-emissions-per-country.csv')\n",
    "Data_set_emission_world=Data_set2[Data_set2['Entity']=='World'].loc[:,['Year','Annual CO2 emissions']]\n",
    "\n",
    "Data_set_emission_world\n",
    "Data_set_emission_world.Year=pd.to_datetime(Data_set_emission_world.Year,format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "0bf97119",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data_all=pd.merge(Data_set,Data_set_emission_world,on='Year')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a32b61",
   "metadata": {},
   "source": [
    "# Plotting temperature increase and co2 emmision by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "73b4402c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.0, '0'),\n",
       " Text(0, 10000000000.0, '1'),\n",
       " Text(0, 20000000000.0, '2'),\n",
       " Text(0, 30000000000.0, '3'),\n",
       " Text(0, 40000000000.0, '4')]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F8ncoKpK65ORkn8etVmujxxpm9xITE2UymXTppZdq1qxZqqys1AsvvKDZs2e3KIbi4nLV1YVu3jcjI1VFRY2TU9RjfPxjjHxjfPxjjHxjfPxryRi1TWrTZLk1xWI+smiwU8XF5SGKNDLC8TtkNpuanYiKivJr165dXbedzsaJVWZmZqPHEhLqp28rKiok1Zdhe/ToIUnasWNHKMIEAAABSktpoymjB1FuDaOomKnLyclRUlKSampqZLPZPI4lJycrOzu70Wv69u2rTZs26cCBA67H2rWrz1wTExNDGzAAAGiaW8drjy7t2KM1jKJipi4xMVHDhg2TJJWWlkqS7Ha7JGno0KFyOp0aO3ashgwZoq+//lqSXAsMFxcXuzpna2pqJElnnHFGWOMHAABqtMDwHfM+1d4DlcrKsLKgcBhERVInSVOmTJHValV+fr6k+mVO0tLSNHXqVG3dulXr1q1TcXGx3n77bUnSNddco+zsbNntdr3//vuqqqrSjz/+qMzMTI0dOzaSpwIAQFzy7nitqXXoiWWbVFpZG+HI4kPUJHW9evXSokWLtHHjRo0ZM0abN2/W0qVL1adPH+Xk5Gjw4MHq1KmTRowYIal+dm/x4sX63e9+p7lz5+qaa67R2WefrVdeeUXt27eP8NkAABB/SsptHo0RUn1iV1Jha+YVCKaouKauQV5enpYtW9bo8cTERL300kuNHu/QoYMeeuihcIQGAAD8SE9NatTxmmRJULqVa93DIWpm6gAAQGyj4zWyomqmDgAAxDCnNCCrPR2vEUJSBwAAgscppaVYjiwwLBK6MKL8CgAAYAAkdQAAAAbQ4vLr/v37VVRUJEnq0qWLunTpEvSgAAAA0DIBJXU///yznn/+eb377rsqLi72OHbcccfpkksu0Y033qiOHTuGJEgAAAD45rf8+vrrr+vXv/61XnrpJZWUlKhDhw7KzMxUly5dlJ6ergMHDuj555/XxRdfrA8//DAcMQMAgGhhkkqrarWrqEKl1XbJFOmA4pfPmbrFixfr9ddf1x133KGzzz5bPXr0kNnsmQfW1dVp165d+vTTT/W3v/1NNptNw4cPD2nQAAAgChzZ67Vha7CGdekGZLWn6zUCmp2p27Fjh4qLi/Xmm2/q2muvVc+ePRsldJJkNpvVq1cvXXfddVq+fLm++uorHTx4MKRBAwCAyGOv1+jSbFLXtm1b3XbbbTKZAp9HbdOmjaZPny6nk/QcAACjY6/X6NJs+TUzM1PPPPOMDh06JEnKysrStdde6/GcHTt2uGbqGlgsFnXq1Ck00QIAgKjBXq/RxWejRF5ent544w2ddtppuuaaaxod7969u5544gnKrQAAxCH2eo0uPhslvvjiC82dO1dnnXVWk8cTExM1depUPfvss5oxY0ZIAgQAAFHEVH8tXUm5TempSRrQk71eo4XPpO6HH37Qbbfd5vMNevbsqR9//DGYMQEAgChUV+dsttuVvV4jz2f5teF6On9KSkqCEQsAAIhi+w9U0O0axXwmdT///LMKCwt9vkFJSYlr2zAAAGBcB0ur6HaNYj6TutzcXN16663NNkKUl5dr+vTp6tatW0iCAwAA0aNjWoqrKaIB3a7Rw+c1ddddd52uv/56XXTRRTr//PN10kknqV27diotLdWWLVv073//W+Xl5Zo5c2aYwgUAAJHStbNVU0YPanRNHc0R0cHk9LNS8COPPKIXXnihyUWInU6nBg0apCVLljS520QsKS4uV11d6H4jMzJSVVRUFrL3j3WMj3+MkW+Mj3+MkW+Mjw9HOl4rbQ61S7GozlFHt2sTwvE7ZDab1KlTuyaP+Zypk6Q777xTmZmZmjdvnsrKPAP99a9/rVmzZsV8QgcAAJrhb39XErqo4Tepk6Trr79eV1xxhdasWaPdu3crLS1Np59+unr37h3q+AAAQAQ1t7/rnElDji5jgqjQbFK3ceNGZWVlubb8atu2rX7961/7fcOCggJJUt++fYMUIgAAiBRf+7uS1EWXZuum/fv317333utK0gLxxRdf6G9/+5v69OkTlOAAAEBkNezv6o6O1+jU7ExdSkqKJk+erHHjxql3794644wzlJWVpQ4dOigpKUlOp1PV1dU6ePCgCgoKtHr1atXU1Gjx4sVNNlUAAIDY07C/Kx2v0c/nNXX9+/fXq6++qrvvvltPPvmkz2Rt5MiRuvvuu9W+ffugBwkAACLEKQ3Iqt/ftbLWobaWBBK6KOW3UaJr165auHChvvnmG7377rvavHmzDhw4ILPZrMzMTOXl5WnkyJFcQwcAgFE5pbQUi/pmdaxfsoOELioF1P0qSXl5ecrLywtlLAAAIBocWZeupNym9NQkpaW0IZGLAQEndQAAIA74WpcOUY1VgwEAgEtz69KVVtZGODL4Q1IHAABcfK1Lh+h2TEldRUWFJKm2lqwdAAAjYV262BVwUldVVaU///nPOuWUUzRhwgRJ0s8//6zHH39cVVVVIQsQAACET8O6dA2Jnce6dIhqATdKzJw5U2vXrlVOTo6sVqskqXv37urTp49mzpypOXPmhCxIAAAQJm7r0pVU2JRuTWRduhgRcFJXV1en1atXy2w266GHHnI9XlhYqFWrVoUkOAAAEAFH1qVz7e1KQhcTAk7qDh48qNWrV+ukk05SdXW1vv32W61atUqLFi1SampqKGMEAACAHwEndVdddZX+93//V05nfbr+6quvSpKcTqeuvvrq0EQHAADCgwWHY17ASd2FF16oJUuWaN68edqyZYsqKyvVu3dvXXXVVRo9enQoYwQAAKHka8FhEruY0aIdJQYNGqSFCxeGKhYAABABzS04PGfSkKPX1SHqBZzUrV69WpKUmZmpnJwcPf/881q3bp1OOukkTZw4USkpKSELEgAAhI6vBYdJ6mJHwOvU3XLLLVq/fr26d++umTNn6i9/+Ys++eQTPfvss5oxY0YoYwQAACHEgsPGEHBSd+aZZ+r222/XqlWr9Morr0iSRo8era+++kqHDx8OWYAAACC0WHDYGAIuv5aXl+ull17SX//6V5lMJp144om655575HQ6tW/fvlDGCAAAgs2r23VATxYcjnUBJ3UjRozQQw89JKfTqW7duumJJ57Qli1bNHv2bO3atSuUMQIAgGDy0e3KgsOxK+Ckbvz48Tr//PO1b98+nXrqqUpKSpLFYtGjjz4ayvgAAECQ0e1qTAFfUydJPXv21JlnnqmkpCRJ0nHHHaeEhAQVFxcHJZjNmzdr7Nixuvrqq3Xttddq8+bNAb+2rq5O11xzjaZPnx6UWAAAMCpf3a6IXQHP1JWUlOjxxx/Xnj17ZLfbXTtLVFRUqKSkpNX7v+7cuVPjx4/XlVdeqWnTpmn69OkaN26cli1bpr59+/p9/bPPPquvvvpKWVlZrYoDAACja+h2dU/s6HaNfQEnddOnT9fHH3/c5LEuXbq0OpD58+errKxMeXl5kqSBAwdq+fLlmjdvnubOnevztfn5+Zo/f36rYwAAIB40dLt6X1NHc0RsCzip+/777zVjxgylpqZq+fLluvzyy+V0OvXuu+/qjjvuaFUQDofDNdPXsIhxw39Xr14tu92uNm2aDrWqqkpz5sxR7969tW3btlbFAQCAYdHtangBJ3VDhgzRuHHjVF5eLpPJpL179+oPf/iD9u3bp+XLl2vatGnHHMS2bdtUWVkpSbJYPC/QrKys1JYtW5Sbm9vka2fPnq1bbrlFCxYsOOafDwCAodHtGhcCbpQwm80aPXq0hgwZopNPPln//ve/dcYZZ+jpp5/Wa6+91qogCgsLPX6Ot6KioiZft2rVKiUnJ+ucc85p1c8HAMDImut2La2sjXBkCKaAZ+p+//vfa9y4cTKbzUpJSdEjjzyiCRMm6PDhw8rJyWlVEGVlZa7bJpPJ5/EGRUVFWrx4sZ577rlW/ewGnTq1C8r7+JKRkRrynxHLGB//GCPfGB//GCPfjDo+hduLmux2rax1qG9Wxxa9l1HHKFgiOT4BJ3U9evTQBx98oKqqKrVrV58ArVy5Utu3b1d2dnargkhOTvZ53Gq1Nnps5syZuuuuu5SYGJxOneLictXVhW7uOSMjVUVFjZNT1GN8/GOMfGN8/GOMfDPy+LRNatNkt2tbS0KLztnIYxQM4Rgfs9nU7ERUwEmdJB04cEArVqyQ2WzW+PHjtW/fPiUkJLQ6seratavrdsNSKe4yMzM97u/du1cff/yxNmzY4HqsvLxckrRixQodPnxYTz/9dKtiAgDAKOh2jQ8BJ3WffPKJ/vCHP8hms2nQoEEaP368+vTpo0ceeUT//e9/dd555x1zEDk5OUpKSlJNTY1sNs+FD5OTkxvNBDqdTtntdpWUlDR6L5vN5krwAACAJKc0IItuV6MLuFFiwYIFmjZtmp5//nn16dPH9XheXp4eeeSRVgWRmJioYcOGSZJKS0slSXa7XZI0dOhQOZ1OjR07VkOGDNHXX3+t7t27a+vWrR5/Bg8eLEm67LLL9NJLL7UqHgAADMcppaVYlNXZWt/xSkJnOAEndT179tS1116rs88+W6mp9RcBHjp0SIsWLdKePXtaHciUKVNktVqVn58vqX6Zk7S0NE2dOlVbt27VunXrVFxcrLfffrvVPwsAAMBoAi6/2mw2XX755crNzdWmTZt08803a+PGjaqoqNApp5zS6kB69eqlRYsWadasWRozZoxMJpOWLl2qPn36yGazafDgwSooKNCIESNa/bMAAIgLXgsOp6W0YYbOwEzOpjoTmlBWVqY//vGPWrNmjcfjxx9/vBYsWKATTjghJAGGC92vkcX4+McY+cb4+McY+Wa48fGx4PCxJnaGG6Mgi5nu19TUVD333HP65ptv9N1336mqqkq9e/fWOeec0+wWXgAAIDKaW3B4zqQhR3eRgKEEnI3ddNNN2r9/v1544QXl5eWFMiYAANBKJeW2JhccLqmwkdQZVMBJ3Y4dO1RVVaWEhIRQxgMAAIIgPTWpyQWH063BWbQf0Sfg7tc5c+bojDPOUFpaWqNjDR2rAAAgOjQsOJxkqZ+M8VhwGIYU8EzdG2+8odLSUp1//vnKzs5Wly5dXPu0bty4UStXrgxZkAAAoIVYcDjuBJzULV++XCaTSU6nU59//rnHsYbkDgAARJEjCw67rqEjoTO0gJO6/v3764ILLpDZ7Fmxra2t1WeffRb0wAAAABC4gJO6p556Sscff3yTx6699tqgBQQAAI4Riw3HtYCTuoyMDP39739XVlaWLrjgAklSRUWF3nvvPV1++eUhCxAAAAQgBIsNI7YE3P1633336eGHH9Yjjzziesxqtaq4uFiLFy8OSXAAACAwzS02XFpZG+HIEC4BJ3Xr16/XjTfe6JHUSVJiYqIWLVoU9MAAAIgbJqm0qla7iipUWm2XzEfvl9scAR07UFrT7GLDiA8Bl19PPPFE3XbbbbJY6jtoHA6H3n77bc2bN08Oh8PPqwEAQJO8yqZdO7XV6GHZeuaNfKVaLRoxpLde+WCb32NXXZjDYsNxLuCkrkuXLjr77LPVu3dv2e12FRQUqLq6Wk6nU7/5zW9CGSMAAIblXTY9Z1B3PfNGvmpqHRp1Wl9X0ubv2Ifrf9JVF2a7HvNYbJhr6uJCwEnd7bffrpqaGr3++uuy2+2ux4cNG6aZM2eGIjYAAAyv0R6tJh29737bz7EDJdVasXan7plwhux2B4sNx6GAk7rExETdf//9+sMf/qDvvvtOtbW16tevn3r27BnK+AAAMLTm9mhtuN+SY2UVtWqXlKC09OT6B0jo4krAjRINMjIydN5552nYsGGuhI69XwEAODbee7Su2bhHv798oJIsCfpowy5ddWF2QMfY2xUmp9MZUB6/e/duPffcc9q7d6/sdrsaXlZXV6cffvhBX375ZUgDDbXi4nLV1YXunzQZGakqKioL2fvHOsbHP8bIN8bHP8bIt7COj/ciwW3bqLSi9ugerVaL637H1CTV1TkDOxbiciu/Q76FY3zMZpM6dWrX5LGAy6+TJk3SDz/80OQx9n4FACBAPhYJdu3RWue1Z6sU2DHKrXEt4KSuqKhIl156qbp16+aRxNntdq1atSokwQEAYDTNLRI8Z9IQj0QNaKmAk7rf/OY3mjx5sjp27Njo2LBhw4IaFAAARtWo21VHFwkmqUNrBJzUHTx4UFdeeaUGDx7s8XhdXZ2+/vprffDBB0EPDgAAo2mu25VFgtFaASd17733niRp+fLljY5xTR0AAIFp6Hb1vqaONeXQWgEndd26ddOpp56qNm08X+JwOLRhw4agBwYAgCE5pQFZ7TVn0pCwda0iPgSc1C1cuFC9evVq8lhBQUGw4gEAwPicXh2sJHQIAp+LD3///feu280ldJJUXV0dtIAAAADQcj5n6hYvXqxJkyYpISGh2ec4HA794x//0EMPPRT04AAAABAYn0nd8uXL9eabbwb0RiR1AAAAkeP3mrpAdhGj+xUAAD+8twZLacO1dAgqn0ndoEGD9Lvf/a5Rx6s7h8OhN954I+iBAQBgGD62BiOxQ7D4TOomTZqkc8891++bdO7cOWgBAQBgNGwNhnDw2f0aSEInSUOHDg1GLAAAGJKvrcGAYPGZ1AEAgNZr2BrMHVuDIdhI6gAACLGGrcEaEjuPrcGAIAl4RwkAAHCM2BoMYdCipK6qqkqffPKJHA6Hhg8frp07d8pqteq4444LVXwAABgDW4MhxAIuv+bn52vYsGG69dZbtXjxYklS79699fTTT+ubb74JWYAAAADwL+Ck7uGHH9aoUaN0//33q0ePHq7Hf/nLX+qBBx4ISXAAAMQsk1RaVatdRRUqrbZLrNOPEAu4/Jqdna0777xTklRQUCBJqq2t1fLly7V9+/bQRAcAQCxisWFEQMBJ3c8//6zJkycrNzdX+fn5mjlzptasWaN9+/YpJycnlDECABBTWGwYkRBwUnfPPfdo4sSJ+uCDDyRJX3/9tSTJarXq7rvvDk10AADEIF+LDZPUIVQCTuq6du2qt956SytXrtR3332nqqoq9e7dWyNHjlR6enoIQwQAILY0LDbsntix2DBCLeCk7r777tP+/fv1yCOPaPjw4aGMCQCAmNaw2LD3NXWsTYdQCjip++CDD1RZWamKigpm5gAA8cNUf41cSblN6alJSktp03Ri5vW8AT1ZbBjhFXBSd++992rlypXq1q1bo2M7duxQnz59ghoYAAARF2gXq4/nsdgwwiXgpG779u2qra3VpZdeqtzcXHXp0kUmU/2iO5999pmWLl0asiABAIiEQLtY6XZFNAg4qXvyySdlMpnkdDq1ZcsWV0LndDpdt1tr8+bNmjNnjux2u8xms2bMmKHc3Nxmn797927Nnj1b69atk9Vq1XXXXaebbropaPEAAOKQWxnVXuf0aHbonJ6sC07LUuGhKslkUlrbNiqtqFXhoSq6XRFxASd1xx9/vE4//XSZzZ6bUNTW1mrjxo2tDmTnzp0aP368rrzySk2bNk3Tp0/XuHHjtGzZMvXt27fR80tKSjRu3Djt3btXklRWVqbHHntMTqdTEydObHU8AIA45FVGverCHFcXa+f0ZA0f0lv//GCbamod6tqprUYPy9Yzb+Rr1Hl96XZFxAWc1D3zzDPKzs5u8tjOnTtbHcj8+fNVVlamvLw8SdLAgQO1fPlyzZs3T3Pnzm30/MWLF2vMmDE666yztGrVKj311FOSpIULF+rmm29mtg4A0GLeZdQP1/+kqy7M1isfbNMFp2W5EjpJOmdQdz3zRr5qah36aMMujbkw23WcbldEQou2CWtOeXl5q4JwOBxatWqVJCklJcXjv6tXr5bdblebNp6hDho0SOeee64kKTc3V99++60+/fRTlZSUqKamRsnJya2KCQAQJ46UWwu3F6my2u4x23agpFor1u7UPRPOUHml14LCJrnuHyip1jtrd2rUuX3Vp1uaMjukkNAh7AJO6q6//vomH6+rq9MPP/ygL7/88piD2LZtmyorKyVJFovntQeVlZXasmVLo2vrGhK6Bv3799enn36qLl26kNABAALjo9zaoKyiVu2SEtQuuW2TJVb3xO6tTwqONkeQ0CHMAk7q1q1b1+yx1pY6CwsLXbe9r9mTpKKiIr/v0fAcFkYGAATKV7m1URlV8lhQeM3GPfr95QNdJVhKroi0gJO63NxcnX322Y3KoKtWrdKpp57aqiDKyspct5tKEN2PN8XhcGjt2rVq166dJkyYcEwxdOrU7phe1xIZGakh/xmxjPHxjzHyjfHxjzHyVLi9qMly68ybfymnnOqYmqKuna0ym+v/burUsZ36dk/XwbIqdUxNUZeObTWgVyfXfffnGhW/Q75FcnwCTuqeffZZderUqdHjN9xwg+69995WBeGvXGq1Wn0eX7lypYqKivToo48qIyPjmGIoLi5XXV3o/mmVkZGqoiLfyWk8Y3z8Y4x8Y3z8Y4waa5vUpslya4rFfGQpEqeKiz2vG080SZlpyZKcOnSowuO+93ONht8h38IxPmazqdmJqICTuscee6zJx//73/8qPz//2CI7omvXrq7bTmfjxCozM7PZ15aVlenRRx/V2LFj9dvf/rZVcQAA4gt7tMJIAk7qli9f7lp82NvgwYNbFUROTo6SkpJUU1Mjm83mcSw5Odln5+2DDz6o/v37a8aMGa7HCgoKmlzbDgAAD05pQFb9Hq2VtQ61tSSQ0CFmBZzUde/eXaeeeqoSEhJcj1ksFnXv3l1XXXVVq4JITEzUsGHDtGLFCpWWlkqS7Ha7JGno0KFyOp0aO3asCgoK9OSTT7qu4fvXv/6ljRs3au7cufrxxx/ldDqVn5+vsrIykjoAQGCcUlqKRX2zOtaXzkjoEKMCTuoWLlyonj17hiyQKVOm6OOPP1Z+fr5Gjhypbdu2KS0tTVOnTtXWrVtd3bdvv/22Tj31VBUUFOi+++5TZWWlLrvsMo/3eumll0IWJwAAQDRqvH5IM2699dYmH//www99LncSqF69emnRokXauHGjxowZo82bN2vp0qXq06ePcnJyNHjwYHXq1EkjRoxQTU2NJk+e7Frbzp3JZNKJJ57Y6ngAAABiic+Zur1792rfvn2SpNLSUm3YsKHRNXU1NTX6f//v/+nDDz9sdTB5eXlatmxZo8cTExMbzb698847rf55AAAARuEzqbNYLHriiSe0YcMGSdLYsWObfF56enrQAwMAAEDgfJZfjzvuOL344osaNWqUnE5noz+S1KFDB91zzz1hCRYAgKAwSaVVtdpVVKHSartk7PWCESf8NkqYzWbNmjVLSUlJuv/++8MREwAAoeO132vD2nSdOoZ+ZyEglAJqlEhISGg2oSsqKtKhQ4eCGhQAAKHivd9rTa1DTyzbpP0HKiIcGdA6AS9p4nQ69e6772r37t2y2+2u8mtpaak2b96sJUuWhCxIAACCpaTc5rEtmFSf2B0sqzqy3RcQmwJO6h555BH9/e9/b/JY27ZtgxUPAAAhlZ6a1Gi/1yRLgjqmpoiVhxHLAk7q3nzzTV1wwQVKS0vTpk2bNGjQIDmdTn377beaMmVKKGMEACBomtvvtWtnq4qLyyMdHnDMAk7qfvnLX+rxxx+XJM2fP1+DBg3SWWedpYULF6qsrCxkAQIAEFRu+72WVNiUbk1UWluLzGZaYBHbAk7qzGazHn30UW3YsEGPPPKIxo0bp9zcXG3atEkWi0VXXnllKOMEACB4juz3mpZicd0HYl3A24RdeeWVevHFF/Xtt9+qsrJS9913nz7++GMdOHBAJhP/ugEAAIikFpVf33rrLf38888aMGCABgwYoFdffVWff/65hg4dGsIQAQAA4E/ASd3//d//qbCwUDfccIPrsYbkDgAAAJEVcFJ33333qaqqSueee65ycnJCGRMAAABaKOCk7tprr9WPP/7YZEJXWlqqtLS0oAYGAEDQmOp3kigptyk9NUlpKW1ojoDhBJzUnXPOOdq/f79mzZqlgQMHqkuXLjKb6/ss3nzzTc2aNStkQQIAcMya2et1QFZ7EjsYSsBJ3fXXX++zy5WkDkBUYoYm7jW31+ucSUOOLmkCGECL1qnr0qVLo8TObreruLg46IEBQKsxQwM1v9drSYWNpA6GEnBSN3fuXF100UVNHluzZk3QAgKAYGGGBlLze72mWxMjGBUQfAEvPtyQ0G3evFnr16+XJO3bt0/V1dU655xzQhMdALSC9wxN5/RkjTq3rwoPVam02i6xbnpcaNjrNcmSIEmuGdu0tiT2MJaAZ+p2796tSZMmafv27crLy9Mrr7yizp07a9asWbrhhhvUq1evEIYJAC3nPkPTOT1Zw4f01j8/2EYpNt40s9crnzuMJuCZurvuukt9+vTRxIkT1aVLF0lSYmKizjzzTN1zzz0hCxAAjpX7DM0Fp2W5EjrpaCm2tLI2wlEiLI7s9ZrV2VpfeiehgwEFPFPXr18/V/I2e/Zs1+OrV69Wfn5+8CMDgGPh1e06oGf9DE3hoSoulgdgaAEndT/++KPmzJmjk046STt37tRzzz2njz76SJs2bVJWVlYoYwSAwPjqdjWZuFgegKEFXH6dNm2a/u///k933HGH1qxZo7/+9a/auHGjEhISdPvtt4cyRgAISHPdrqWVtVwsD8DwAp6py87O1sqVK7VkyRJ9//33qqqqUu/evXXFFVeob9++oYwRAALibz2yRhfLWy0qrWBhYgDGEHBSJ0nt2rXTxIkTQxULALSK3/XIjlwsn5ZiqS/V/sTCxACMI+DyqyR98sknmjp1qi655BJdcskl+uMf/8jCwwCiRktKrL5KtQAQiwKeqfv73/+uOXPmyOk8+k/YH374Qe+++66mTZum8ePHhyI+APDtSLdr4fYitU1q4+p29bceGVtHGRx7/iIOBZzUPfPMM+rZs6cuuugiHXfccTKZTDp8+LDWrl2rBQsWkNQBCD8f3a6uxKyZv8jZOsrA2PMXcSrgpK5du3Z65ZVXlJ6e7vH4pEmTNHLkSI/HysvL1a5du6AECADNac3erg2lWu+/+NlpIPax5y/iVcBJ3cSJE/X888/r6quvlsl0dMPE999/XyNHjtS+ffskSXV1dVqyZInuvPPO4EcLAG5aVUJl6yjDorSOeBVwUnfvvffKZDJp4cKFTR7/29/+5nGfpA5AqLW6hOreDXvkPmIfpXXEqxZ1vzqdzoD+AEA4sKAwmsLvBeJVwDN148aN07XXXquEhASfz2sovwJAyLmVUCtrHWprSaCECkrriFsBJ3UzZsxo9tiBAwfUuXNn1/1p06a1LioACNSREmrfrI4qKirjL27Uo7SOOBRwUmez2fThhx9qz549qq2tdZVZ6+rq9Nlnn+mf//xnyIIEAACAbwEndVOmTNHq1asbPe50Oj26YQEg5JpaWDYU78nsTmzhM0ScC/ib8PPPP1e3bt2UmZnpkcTV1dVp69atIQkOABppZmHZTh1bsTYmi9XGPj5DIPCk7rTTTtNzzz3XZKPEt99+G9SgAKA5zS0s27d7uhKPsWjAYrWxj88QaEFSN336dD300EO6+OKLPR6vq6vTW2+9pZNPPjnowQGAt+YWlj1YVqXMtOSgvqdhFqv12h/XiGVJw3+GQAACTuqWL1+uJUuWNLtcyUMPPRS0oACgOc0tLNsxNUXHmqkYerHaOClLGvozBAIU8OLDr7zyCosNA4i45haW7drZGvT3NMJitc2VJUsrayMcWXAZ+TMEAhXwTF3Pnj01f/58HX/88Y0aJd58881QxAYAjTWzsKzZ3IoufAMvVmvosqRXt+uAnsb8DIFABZzUTZgwQRs3blS3bt08HjeZTH53mQCAoArFwrIGXazWsGVJH2Vlo32GQKBMzgDrp/379292PTqTyaTvvvsuqIGFW3FxuerqQvcNkJGRWr/aPZrE+PjHGPnG+DTDoNfUlVbVatpTaxslq63pduV3yD/GyLdwjI/ZbFKnTk0v4dSiFTuj9fq5zZs3a86cObLb7TKbzZoxY4Zyc3MjHRaAWBbLC9k2U5asrHWoXVIb1dU5teu/FbF3XtLRTt5DVcYtKwPHKOCkbtSoUbr00ksblVodDodee+21oAcWqJ07d2r8+PG68sorNW3aNE2fPl3jxo3TsmXL1Ldv34jFBSCGxfLslo/Ye3fsoE837Y3N85I8zm3UeX2NWVYGWiHg7teHH35YZ555pgYPHuzx58wzz9Rjjz0Wyhh9mj9/vsrKypSXlydJGjhwoMrLyzVv3ryIxQQgtsVyx6iv2PcfqIjZ85I8z+2jDbs05sJsul0BNwHP1NXV1enFF1/U8uXLlZGRoRdeeEE7duzQxx9/rPHjx0dk/1eHw6FVq1ZJklJSUjz+u3r1atntdrVpE4Q9IQHElVjuGPUVe6LNEZ3n5VYu7tg+WXWOuqNl77ZtVFpRf8xe53TFf6CkWu+s3alR5/ZVn25pyuyQQrcr4l7AGc+jjz6ql19+WR06dFB6erokqU+fPvrqq680Z84czZgxI1QxNmvbtm2qrKyUJFksnl9IlZWV2rJlC9fWAWixWO4Y9RV7cnJi9J2XW0k11WrRiCG99coH21RT61DXTm01eli2nnkjXzW1Dl11YY5H/AdKqvXWJwVHmyNI6BDnAi6/7tu3T1988YXWrFmjAQMGuB632WwRW6eusLDQddtsbnwqRUVF4QwHgEHE8kK2vmLv2tkadeflXlK94LQsV0InSecM6u5K6CTpw/U/6SpKrkCzAp6ps9ls2rdvn7Kzs2UymVRcXKxVq1bp8ccfbzKhCoeysqNtw02Vf92P+9Nce3AwZWSkhvxnxDLGx794HaO6Oqf2H6jQwdIqdUxLUdfO1iYXGw7m+HTq2E59u6frYFmVOqY2/zOjhWuMDlfphB7pevy281RSXt0o9rMHdfM4ry4d2+rng5V+xzYksZZWqca9JGySZ3nY6/6BkmqtWLtTM2/+pZxyhuRzidf/x1qCMfItkuMTcFJ33nnn6be//a0sFoucTqcWL14sqX6Zk2uvvTZkAfqSnOx7826rNfBtg1inLrIYH//idowC7EQNxfgkmqTMtGRJThUXlwf1vYPK5xgdjT0jI1XFxeVHz8vk1Npv94W3G9YrVu+SalPlYff7ZRW1SrGYj1wDGNzPJW7/H2sBxsi3SK9T53OKbcuWLdqyZYu2bt2qq6++Wn/961/VrVs3ORwOOZ1OpaWl6ZZbbtH06dNDErg/Xbt2dd1uag29zMzMcIYDIARiuRM1XI51jCIxtt4/072k+tGGXR7l1TUb9+j3lw+k3AoEyOdM3aWXXqqzzjpL//M//yNJGj58uIYPH67q6mpVVVWpQ4cOYQmyOTk5OUpKSlJNTY1sNpvHseTkZGVnZ0coMgDBEsudqOFyrGMUtrF1625172CVjpZU75lwhux2hzqmJumUfp2P7t9qtbCfKxAgn0ld79699fTTTysx0bMzKjk52aP0WVVV5VpKJJwSExM1bNgwrVixQqWlpZIku90uSRo6dGijjlgAsSeWO1HD5VjHKCxj66fcKtWXVNslJSgt/ejfK66kss6Ye/ICoeCz/HrSSSc1Suia8u677wYtoJaaMmWKrFar8vPzJdUvc5KWlqapU6dGLCYAwRPLnajhcqxjFI6x9VVuDdXPBOKVyeljQ9cLL7xQv/jFL3y+gcPh0Nq1a/Xpp58GPbhAffPNN5o1a5ZMJpNMJpMefPBBnXDCCS16DxolIovx8S+ux6ihfOejBBfX4yMd+xh5v85qcS3226q9Yd32aH148QaPQ53Tk3XrVafKbndEVUk17n+HAsAY+RbpRgmf5dfdu3drz549Pt/c6XRGZDcJd3l5eVq2bFlEYwAQQk5KcH4d6xi5v84kff9TEPa89bNHa6NyK58nEBQ+k7qUlBS/zRB1dXUs8gsABtBcN6xrx4ZjeJ+GPVr/eWRRYY9yK8kcEFQ+k7oLLrhAf/nLX/y+yYIFC4IWEABEJbcOzlaVJaM4Hu9u2M7pybrgtCwVHqqSTKaAf4b7+7BHKxA+fsuvgRg5cmRQggGAqBTgAsixHo97N2zn9GQNH9K70QxbID/Du6uWPVqB8PDZ/frtt9/q8ccf1+HDh32+yfHHHx/UoAAgmkTbAsihise9G/aC07JcCV1LfwYdy0Bk+Jypmz17tiRpzZo1zMYBiFtRsQCyjwV8gxaPUxqQ1V5zJg1R4aGqRj8j1WpReY2j6ZKvVzl4QM/2LBoMhJnPpO6yyy4LVxwAELUivgByAAv4Bi2eI92wMpk8fkbn9GSNGNJbf174ZeNyrJovB9OxDISPz/IrACDy5cRILODrfc7DTu+pV5opx0ZbeRqIVz5n6gAgIqKt09StLBnWcqLbAr6+9ksNSTxe52x3NC75NpRjyyujoDwNgKQOQJSJtk7TBuFeADkaFvB1O+fSanuz5dim4mN/XiD8KL8CiCoxUcozSaVVtdpVVKHSarvka1Md7+eaj94vtzmaPVZcZmu0gG8ku0l9lWOjIT4AzNQBiDJR0WnqS0tmEr2e27VTW40elq1n3shXqtWiEUN6uxIj92P1zRDZ0bWAr49ybFTEB4CZOgDRpaHT1F00lfJaMpPo/dxzBnV3JW0XnJbl0XjgfkyS6pzyGIeGBXwzO6REbgHfI+XYrM5WdW6fHH3xAXGOpA5AVIl0p6k/Tc0kNjQMeJdRG631ZtLR++63m7gf7SXNaP+cgHhE+RVAdIlUp2mAvNes816/zb2M2lwDQcN9X8cOlFTrgy9/0oO3nKmK6tqoG4do/5yAeMRMHYDo41bmi7ZSnr/129zLqN6zbWs27tHvLx+oJEuCPtqwy2OtOfdjUn2Cd93FA9QpNTEqx0FSVH9OQDxipi7UGtaZ2l6ktkltIr/eFoDW8bd+m1sZtckGAqvF9dqOqUk6pV/nozNdbseY+QLQUiR1oRSt620BaB0f67dJjcuob31SoDmThtTPZtV5rXcnHb3tfYzvCQAtQPk1hGJivS0AreJdjm2qjEoDAYBwYKYuhKJ+vS0gmkTb1mCBaqphgDIqgAggqQsh7y45KbrW2wKiRqxfquC9hRhlVAARQPk1hFjHCQgMlyoAQOsxUxdKbmWZylqH2loSKMMATeBSBQBoPWbqQu1IWWZg3wzWcQKaEe1bgwFALCCpAxBxXKoAAK1H+TXcYrXDDwgltpwCgFYjqQunWO/wA0LJu4OU/ycAoEUov4YRHX4AACBUSOrCyFeHHxB3TFJpVa12FVWotNoumSIdEADENsqvYcRixMARXIoAAEHHTF0Y0eEH1ONSBAAIPmbqwokOP0ASiw0DQCiQ1IUbHX4AlyIAQAhQfgUQdlyKAADBx0wdgPBxW3y7R5d2XIoAAEFEUgcgPPx1vJLQAUCrUH4FEBZ0vAJAaDFTF0nsAwujc/sdt9c56XgFgBAiqYsUFl+F0Xn9jl91YQ4drwAQQpRfI4RSFIzO+3f8w/U/6aoLs+l4BYAQYaYuQuJq8VXvMnPbNiqt8Co7wxh8lFsPlFRrxdqdumfCGbLbHXS8AkCQ8bdphMTN4qteJbiundpq9LBsPfNGvkfZuVPHdpGOFK0VQLm1rKJW7ZISlJaeXP8ACR0ABA3l1wiJl8VXvUtw5wzq7kropKNl5/0HKiIZJoKAcisARBYzdZFi9H1gj5ThCg9VeZaZTWqy7HywrEqZaclhDhJB0cxnTbkVAMKLmbpIOrIPbFZna/11dEb5y+5IGW7aU2u1Y1+pa6amQVP3O6amhDNCBIufz7qh3Gq433EAiEIkdQg69zLcRxt2aYxbCW7Nxj36/eUDG5Xkuna2RjJkHCNfnzXlVgAIr6gpv27evFlz5syR3W6X2WzWjBkzlJub6/M1u3fv1uzZs7Vu3TpZrVZdd911uummm2QymcIUNZri3tl7oKRa76zdqVHn9lWfbmnK7JCiNKulUdnZbOYzi2puXa0d2yerzlHXqMO1yc+acisAhE1UzNTt3LlT48ePV25urpYuXaoePXpo3LhxKigoaPY1JSUlGjdunFatWqWysjIVFhbqscce03PPPRfGyNGUhs7eBgdKqvXWJwX1f8mnWKQ6g5adjcqtxPrk69/oqy3/1bSn1mrmwi+1fc9h3581ny0AhE1UJHXz589XWVmZ8vLyJEkDBw5UeXm55s2b1+xrFi9erDFjxuj111/XpEmTXI8vXLhQTid/k0RSvHT2xgv3EusFp2XplQ+20eEKAFEo4uVXh8OhVatWSZJSUlI8/rt69WrZ7Xa1adM4zEGDBuncc8+VJOXm5urbb7/Vp59+qpKSEtXU1Cg5OQY7KY2yF2xLOnsbOie3F6ltUpvYPWejaW4RYa/uZTpcASB6RDyp27ZtmyorKyVJFovnv+4rKyu1ZcuWJq+ta0joGvTv31+ffvqpunTpErMJnaH2gj3S2evaHaOZhM5Q52wUfhYRZkFhAIhOES+/FhYWum6bzY3DKSoqCuh9Gp43fPjw4AQWZvG4F2w8nnMs8LWI8EcbdlFuBYAoFfGZurKyMtftprpW3Y83x+FwaO3atWrXrp0mTJhwTHF06hT6baoyMlKbPVa4vajJRXkrax3qm9Ux1KG1Wl2dU/sPVOhgaZU6pqWoa2er347WWD/nSPD1O9Qa7p9fjc3RZIl15s2/lFNOdUpL0Rm5XVVSXq2OqYF91uESqvExEsbIN8bHP8bIt0iOT8STOn+lUqvV//plK1euVFFRkR599FFlZGQcUxzFxeWqqwtd3SgjI1VFRc0nqG2T2jS5F2xbS4LP10WFYyyjxvQ5R4C/36FjFuCerSkW85FyulMWs47sAOJUcXF58GM6BiEbHwNhjHxjfPxjjHwLx/iYzaZmJ6LCWn5dsGCBfvGLX3j8efXVV13Hm+pazczM9PmeZWVlevTRRzV27Fj99re/DXrM4RLLHaPHWkaN5XM2EvZsBQBjCOtMXU1NTaNyampqqpKSklRTUyObzeZxLDk5WdnZ2T7f88EHH1T//v01Y8YM12MFBQXq27dv8AIPh1jbC7a57sgjamodKqmwHW2UaIrbOVfWOtTWkhDd5xzrvLur27ZRaQV7tgKAUYQ1qZs8ebImT57c6PE//vGPWrFihUpLSyVJdrtdkjR06FBZLBbZbDZNmDBBBQUFevLJJ3XqqadKkv71r39p48aNmjt3rn788Uc5nU7l5+errKws9pI6KbCO0WgQQLkuyZKgdGui//c6cs59szrWT1lH6znHOq/PrGuntho9LFvPvJGvUef1paMVAAwg4t2vkjRlyhRZrVbl5+dLql/mJC0tTVOnTpUkbd26VevWrVNxcbHefvttSfWzcffdd59++uknXXbZZRo+fLhGjBih6dOnq3///hE7l3hAuS72eH9m5wzqrmfeyGfPVgAwkIg3SkhSr169tGjRIs2aNUtjxoyRyWTS0qVL1adPH0lSTk6OBg8erIKCAo0YMUI1NTWaPHmya307dyaTSSeeeGK4TyH4onghYve9XaUgl+ui+Lxjjq8SudsiwuzZCgDGEBVJnSTl5eVp2bJlTR5LTEzUSy+95PHYO++8E46wIiPKF+Vt2Ns16OW6KD/vmBJgidw9sXvrkwLNmTSEPVsBIEZFRfkVnqJ9Ud5Qda1G+3nHEn8l8jUb9+j3lw+k5AoABhI1M3U4yru8KQXYTRpqbuW8Hl3aBb1TN2rPO5Y07KUbSEer1RI73dYAAL9I6qJQU+XNgLtJQ8VfaTQIyUBUnncscfuMAuporYuRbmsAQEAov0ahoJc3TVJpVa12FVWo3OZw3S6ttksB7u4UjtIoixEfA7fPtrjM5vqM6GgFgPjDTF00CuZCxG6zN6lWi0YM6a1XPtjW4kaEsJRGY20B5khr1AyRTUcrAMQxZuqi1ZFFebM6W1vVjeg+w3bBaVmuhE5q2WxbQ2nUXUhKo0E673jgPXta55THZ9TQ0ZrZIYWxBIA4QFIXK9zKbH7Lpm7PPVBac3SGzW1tsgapVovKaxxNl2bNR9/HbDZFpjTakvOOF0fGxLsZgpIrAMQ3yq+xoCXrt/lZn8z9duf0ZI0Y0lt/Xvhlo9Ks+zZSDT/z9mtODW9plHXrGvPRDHGgpFoffPmTHrzlTFVU11K+BoA4w0xdDGhJk4Kv9ck+2rDLY62yYaf3dCVx3qVZ922kGn7mY0u+lqSwlUZZt64x9zFpambuuosHqFNqIuVrAIhDzNTFgKaaFBrKpiXlNnVsn6w6R12T20F5r0/WMTVJp/TrrJIKm+wOZ/Ol2SZKteFeM4516xpzHxOaIQAA7kjqYoD3+m2+yqZNbQfVaH0y1TcjlFbbmy3NNnc/nGvGsW5dY95jwvZeAIAGlF9jgPf6bb7Kpt7bQfm6WN79fb1Ls9GwjRTr1jXGmAAAmmNyOp38215ScXG56upCNxQZGakqKio79jdo2KLrSNn0wUXrJEmjh2Vr2YfbPJ7aOT1Zt1516tHtoHyV5Nzet2NqkurqnEcbIawWlVbUhqUxotnxaSq+cpvSU5OUltImPmamjoxBpc2htkltlNa2Tdg+l1jS6v/H4gBj5Bvj4x9j5Fs4xsdsNqlTp3ZNHqP8GiucR7d08lc2bVRu9fUXvtNrqyjp6O1o2EaqIb62lvjshPXRAcz2XgAAd5RfY5CvsqlRy3Hx2gkbr+cNAGg5Zupikdd2Wu4drUYtx8V8J2xDGbmhdNxQQm2qlOz2XO9uZinGzhsAEDYkdbHKV9nUYAmdFOOdsF4l1KYWdnaVkuV78Wgphs4bABBWlF8RE2K569O7hNrUws4NJVVfi0dLsXXeAIDwYqYOscGr5OzRnRut3bBHyqjee7T62oO3vNKzzOy+eLTZLLW1JBiyvA4AaD2SOsQO95KzSfr+pyjuhvWxR6vU/B68TT23oZu5b1bH+lb5aDg/AEDUofyKmBTtXaG+9mj1XtjZfTHppvZzpdwKAAgEM3WISVHZDdtM12qTe7RaLa5SsvsevOznCgA4VszUISY1dMO6i2hX6JFy67Sn1mrmwi+1fc9hj/ga9mjN7JBSn3QeWdg5q7NVndsn+34uCR0AIAAkdYhJ0dYN25qu1Wg7FwBAbKL8itjUVDdsJMqUzXS4unet+t2DN1rOBQAQ00jqELu8F2COQELnq8O1VXvwktABAFqI8itwjHx1uFJCBQCEGzN1MA7v/VVDvBixewcuXasAgEgjqYMxeO2vGo7FiL33o23oWp0zaQhdqwCAsKP8CkOIxGLEdK0CAKIJM3UwhIgsRkzXKgAgipDUwRC8S6FSmBYjpmsVABAlKL/CECiFAgDiHTN1MAavUmjH1CTV1Tm1678Vwe2EDXOHLQAAgSKpg3E0lELbWkLTCRuBDlsAAAJF+RWGE6pO2Eh02AIAECiSOhiOr07YaHxfAACCgaQOhtPQCesuGJ2woXpfAACCgaQOhhOqTlg6bAEA0YxGCRhPU4sCWy0qrWhl1yqLDQMAohhJHYzJfVFgk/T9T0HqWmWxYQBAlKL8CsOjaxUAEA9I6mB4dK0CAOIBSR0Mj65VAEA8iJqkbvPmzRo7dqyuvvpqXXvttdq8eXOLXl9XV6drrrlG06dPD1GEiFV0rQIA4kFUJHU7d+7U+PHjlZubq6VLl6pHjx4aN26cCgoKAn6PZ599Vl999VUIo0TMcutanXnTGXr0D2epW+e22vXfCpVW2yWTj9eapNKqWu0qCuC5AABEUFR0v86fP19lZWXKy8uTJA0cOFDLly/XvHnzNHfuXL+vz8/P1/z580McJWLasewLy16vAIAYEvGZOofDoVWrVkmSUlJSPP67evVq2e12n6+vqqrSnDlz1Lt379AGCkNoSScsXbMAgFgS8aRu27ZtqqyslCRZLJ7XOFVWVmrLli0+Xz979mzdcsstSk9PD1WIMJCmOmFTrRaV1zgalVjpmgUAxJKIJ3WFhYWu22Zz43CKioqafe2qVauUnJysc845JySxwXi8O2E7pydrxJDe+vPCLzVz4ZeaNv8zfb/rsGSiaxYAEFsintSVlZW5bptMja9Cdz/urqioSIsXL9btt98esthgPN6dsMNO76lXPtjWZImVrlkAQCyJeKNEcnKyz+NWq7XJx2fOnKm77rpLiYnBmTXp1KldUN7Hl4yM1JD/jFgWrvHp1LGd+nZP18GyKtXUOJosx1bV1qmypFon9EjX47edp5LyanVMTVHXzlaZzZFrgeV3yDfGxz/GyDfGxz/GyLdIjk9Yk7oFCxZowYIFHo+dcsoprttOZ+OWwszMzEaP7d27Vx9//LE2bNjgeqy8vFyStGLFCh0+fFhPP/10i2IrLi5XXV3oWhozMlJVVNT0rCPCPz6JJikzLVml1XYlWRJciV1DOXbmc1800fHqVHFxedhi9MbvkG+Mj3+MkW+Mj3+MkW/hGB+z2dTsRFRYy681NTUqKyvz+JOamqqkpCRJks3meQF6cnKysrOzG72P0+mU3W5XSUmJ609Dl6zNZnMleIA/LSnHAgAQzcI6Uzd58mRNnjy50eN//OMftWLFCpWWlkqSK0EbOnSoLBaLbDabJkyYoIKCAj355JM69dRTtXXrVo/3GDt2rNatW6fLLrtMDz/8cOhPBsbgtjBxSYVNdoez2Y7XtBSupQMARK+IN0pI0pQpU2S1WpWfny+pfpmTtLQ0TZ06VZK0detWrVu3TsXFxXr77bcjGSqM6MjCxFmdrercPpmOVwBATIqKpK5Xr15atGiRNm7cqDFjxmjz5s1aunSp+vTpI0nKycnR4MGD1alTJ40YMSLC0cLI6HgFAMQqk7Op7oQ4RKNEZEXV+Jjqd5MoqbAp3ZpYn9BFwf8lUTVGUYjx8Y8x8o3x8Y8x8i3SjRIRX9IEiDoN+8Q2XEMXBQkdAAD+REX5FQAAAK1DUgcAAGAAJHUAAAAGQFIHAABgACR1AAAABkBSBwAAYAAkdQAAAAZAUgcAAGAAJHUAAAAGQFIHAABgACR1AAAABkBSBwAAYAAkdQAAAAbQJtIBRAuz2WSInxHLGB//GCPfGB//GCPfGB//GCPfQj0+vt7f5HQ6nSH96QAAAAg5yq8AAAAGQFIHAABgACR1AAAABkBSBwAAYAAkdQAAAAZAUgcAAGAAJHUAAAAGQFIHAABgACR1AAAABsA2YS1w8OBBLV68WOvWrdOSJUtcj9tsNr344ov617/+pdTUVB06dEinn366br31VnXs2DHg50jS5s2bNWfOHNntdpnNZs2YMUO5ublhP9dj1dwYSdLSpUu1bNky2e12paam6k9/+pNOO+00j+cEcv6xPEatHZ+VK1dq/vz52rVrl/r376/p06frlFNO8XhOPI+Pu61bt+qKK67Q888/rzPOOMP1eCyPjxS8MSotLdWyZcu0du1a5eTk6IorrlDfvn0lxfYYtWZ84uF72t93SLC+g+N9jCL2Xe2EX3v27HE+8MADzpNPPtmZnZ3tHDFihMfxyZMnO7Ozs53//Oc/nU6n0/npp586s7OznVdeeaXT4XAE/JwdO3Y4TzvtNOfDDz/sdDqdzmnTpjlPPfVU5/bt28N1qsfM3xg98cQTzuzsbOeLL77otNlszosvvtiZm5vr/Oqrr1zPCeT8Y3WMgjE+K1eudGZnZ3v8GTRokHPXrl2u58Tz+Lirqalxjhw50pmdne384osvXI/H6vg4ncEdo88++8x5xhlnOMeOHevcv3+/x7FYHaNgjI/Rv6f9fYcE6zs43scokt/VlF8DUFxcrDFjxig1NbXRsR9//FHvvfeeJGnQoEGSpLPOOkvt2rXTN998ox07dgT0HEmaP3++ysrKlJeXJ0kaOHCgysvLNW/evBCfYev5GqOSkhI9++yzkqT+/fvLYrHoV7/6lWw2mx588EHX8wI5/1gdo9aOT11dnV5++WU9/fTT+sc//uH6F19lZaX+8Y9/uN4rXsfH26OPPqpt27Y1ejxWx0cK3hi98847uummm9SjRw89//zzyszM9Dgeq2PU2vEx+vd0IN8hwfoOjucxivR3NUldAE4++WRlZ2erV69ejY6Vlpa6br///vuu2wkJCZKkxMTEgJ7jcDi0atUqSVJKSorHf1evXi273R6kswkNX2O0ceNG1dbWSpKSk5MlSf369ZMk/ec//9HOnTsDOv9YHqPWjs/PP/+se++9VxdccIF+8Ytf6G9/+5vM5vr/fQsLCyUprsfH3Weffeb6C9hdLI+PFJwx2r17t+6++245HA7dd999SkxM9HifWB6j1o6P0b+n/X2HBOs7ON7HKNLf1SR1LWAymRo91q9fP9e/DJ9++mm988472r17tw4fPqxLLrlEWVlZAT1n27ZtqqyslCRZLBaPn1FZWaktW7aE+OyCo6kxqqmpcd0uLy+XJKWnp7se27FjR0Dnb4QxOtbx6dq1q+svIUnq0qWLOnToIEnq2bOnJMX1+DQ4dOiQnnnmGU2fPr3R+xhhfKTWjdFTTz2liooKnXTSSU1eu2OEMTrW8TH697S/75BgfQfH+xhF+ruapK6VUlJS9MADDyghIUF2u1233Xabbr75Zo0ePVoPP/xwwM9pyOAlubJ6d0VFReE5oRBouPhakmvGwH2GwGazBXT+Rh2jQMbHW21trUpKSiRJv/nNbyQZ93eoJePzwAMP6I477nD9q9edUcdHCmyMKisrtXLlSkn1vz8TJkzQJZdcovvvv9+V6Bh1jAIZn3j7nvb+DgnWd3C8j5G/95BC+3tEUhcEw4cP1+zZs13/Qty5c6fef/99j2zb33PKyspcz23qX5rux2NNv379NHToUEnS8uXLZbPZdOjQIdfxrl27BnT+Rh2jQMbH22effSaHw6GLL75Y/fv3l2Tc36FAx+fVV19VTk6OTj755Cbfx6jjIwU2Rj/88INrduCaa67Rs88+qw4dOmjJkiW6+eab5XQ6DTtGgf4OxdP3tPd3SLC+g+N9jPy9h/fzgj1GJHVB8Pnnn2v27Nn6y1/+ojFjxkiqvzB3ypQprus4/D2n4TqP5lit1tCeRIjNnTtXN954o+x2uyZOnKiFCxdKqp/F7NevX0Dnb+Qx8jc+3l566SUdd9xxuu+++1yPxfP47Nq1Sx988IEmTpzY7HsYeXwk/2O0f/9+13OTkpLUpk0b13fR119/rfXr1xt6jAL5fyyevqe9v0OC9R0c72Pk7z2O9X0CxTp1rXTw4EFNmjRJNTU1uvDCCzV8+HAVFRXpo48+0t69e/XVV18pOzvb73PcZ2OcTmejn+PdoRZrUlJSNG3aNNf9G264QZL0q1/9SlarNaDzr6ur8/ucWOVvfNy9//77WrdunRYvXuyxdpaRf4f8jc+iRYu0fv16nXnmmZI8z3/SpEl64IEHlJWV5XrMaOMj+R+jNm2Oft03zNj17t3b9diOHTt00kknue4bbYz8jU8g3+VG+X+sqe+QYH0HG+V7+ljHyN97HMv7tAQzda20YcMGVVZWqmPHjkpMTJTJZNLUqVNdx4uLiwN6Tk5OjpKSkiQ1voYqOTlZ2dnZ4TmhMNi9e7e+/PJLJScn63//938lKaDzj5cxamp8Ghw4cED333+/Zs+e7WqVP3z4cFz9DjU1Pk6nU5WVlSopKVFJSYkOHz7sen55eblqamriZnykpseoT58+ruMN1+y0a9fO9VhSUlLcjFFT4xMv39PNfYd07do1KN/B8T5G/t4j1L9HJHUt4HA4JMnjXyINHVRVVVWujLuhw0Wqv0A3kOckJiZq2LBhko4uk9LQ1jx06NBGHTLRqqkxcud0OjVz5kzV1dVpxowZrr9oAjl/I4zRsY5Pw7E777xTF198sQYMGKCCggJt2bJFDzzwgNq0aRPX4zN58mRt3brV9adhuQBJWrx4sS6//HJDjI907GPUp08f5eTkSJL27Nkj6WhXqMlk0uDBgw0xRsc6PvHwPe3rO8RisQTlOzjex8jfe4T6u5qkLkA1NTXau3evpPoMvKKiQpJ0yimn6KSTTlJ5eblWr14tSfr0008lSZdccon69+8f0HMkacqUKbJarcrPz5dU3/aclpbm8a/FaNbcGDUoLS3V1KlT9fXXX2vu3Lm66qqrPI4Hcv6xPEatHZ+nnnpKn332mV5++WUNHz5cw4cP16hRo7Rx40a1b99eUnyPTyBieXyk1o/R3XffLYvFojVr1qi0tFTfffedpPrGiW7dukmK7TFqzfjEw/e0v++QYH0Hx/sYRfK72uRsqqALD4899phWrFihffv2uR7LzMzUjTfeqHHjxungwYN6/PHH9cUXX6h9+/aqqKjQyJEjdcstt7iuYwnkOZL0zTffaNasWTKZTDKZTHrwwQd1wgknhP2cW8rXGF144YV688039dVXX2ngwIG65pprdNxxxzX5PoGcfyyOUWvH54svvtANN9zQ5OzDRRdd5LEKeTyOj7c9e/boV7/6laT6mTr3vV9jcXyk4I3RunXr9Pjjj6ukpERt2rTRJZdcoptuusljaYVYHKNgjI+Rv6cD/Q4J1ndwvI5RpL+rSeoAAAAMgPIrAACAAZDUAQAAGABJHQAAgAGQ1AEAABgASR0AAIABkNQBAAAYAEkdAACAAZDUAUAz3nzzTQ0ePFg5OTnKycnRY4895rGv7AcffKChQ4dqwoQJ2rVrVwQjBQCSOgBo1qWXXqrp06e77qenp7u2+ZGk888/X1lZWZo/f76ysrIiESIAuLCjBAD4UFdXp1GjRmnbtm3KyMjQhx9+qOTkZEnSK6+8oszMTA0dOjSyQQKAmKkDAJ/MZrNuu+02SVJRUZFefvllSVJZWZk2bdqkoUOHav/+/fqf//kfzZw5U9dcc42+/fZb1+u///573Xnnnbrjjjs0ZMgQTZs2TVVVVZKk9evX67TTTlNOTo7eeOMNvfTSS/rFL36hZ555JvwnCiDmkdQBgB8XXHCBTjnlFEnSc889p/Lycj377LOaOHGiHA6HJk6cKLPZrJkzZyovL0+///3vVVFRoUOHDum6665TdXW1Hn30UY0YMUJvvvmmFi9eLEk6/fTT1b9/f0nSxx9/rPXr16uiosJjU3oACBRJHQAE4E9/+pMkqaSkRH/+859lMpnUp08frVy5Utu2bVNubq4kqVevXiouLtYnn3yiQ4cOqbKyUuvXr5ckpaWlSZJ2797tel+zuf5r2Gaz6YknntDbb7+tadOmhfPUABhEm0gHAACx4PTTT9c555yjNWvW6M0339SGDRskSd99950kafXq1Tp48KAOHjyovLw8ORwO9enTR2+//bbMZrP+/ve/69///rckyeFwNHr/gQMHSpL69u0bpjMCYDQkdQAQoLy8PK1Zs0aSlJqaKkmu6+Oys7N11113NXpNaWmppk2bpokTJ+r888/Xf/7zHzXVn9alS5cQRg4gHlB+BYBW6N27tyTp008/lc1mkyQ5nU7t2LFDP//8s2666SalpKToiiuu8Pk+DWVYADhWfIsAQIDcy6YNt0eOHCmr1aq9e/dqypQpWrlype6//35ZrVZt3LhR5eXl+umnn7RgwQLXLF9ZWZkKCwslSbW1teE/EQCGRFIHAH6UlZXprbfe0nvvved67PHHH9fGjRvVoUMHPfvss8rJydFnn32mBQsWaNSoUerSpYsGDx6sE044QcnJyXI4HLr99tuVnp6un376SdXV1fr222+1fft2SdL777+v/fv3R+oUARgAiw8DAAAYADN1AAAABkBSBwAAYAAkdQAAAAZAUgcAAGAAJHUAAAAGQFIHAABgACR1AAAABkBSBwAAYAAkdQAAAAZAUgcAAGAA/x/SYTVgSedRpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "sns.scatterplot(x='Year',y='Temperature Increase (C)',data=Data_all)\n",
    "ax.set_xlabel('Year',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "ax.set_ylabel('Temperature Increase (C)',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "date_x=pd.DataFrame({'dates_axis':['1880','1900','1920','1940','1960','1980','2000','2020']})\n",
    "ax.set_xticks(pd.to_datetime(date_x['dates_axis']))\n",
    "ax.set_xticklabels(['1880','1900','1920','1940','1960','1980','2000','2020'],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "ax.set_yticks([-0.4,-0.2,0,0.2,0.4,0.6,0.8,1])\n",
    "ax.set_yticklabels([-0.4,-0.2,0,0.2,0.4,0.6,0.8,1],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "sns.scatterplot(x='Year',y='Annual CO2 emissions',data=Data_all)\n",
    "ax.set_xlabel('Year',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "ax.set_ylabel(r'CO2 Emission  ($10^{10}$ tone)',fontsize=16,fontweight='bold',family='Times New Roman')\n",
    "date_x=pd.DataFrame({'dates_axis':['1880','1900','1920','1940','1960','1980','2000','2020']})\n",
    "ax.set_xticks(pd.to_datetime(date_x['dates_axis']))\n",
    "ax.set_xticklabels(['1880','1900','1920','1940','1960','1980','2000','2020'],fontsize=18,fontweight='bold',family='Times New Roman')\n",
    "ax.set_yticks([0,1e10,2e10,3e10,4e10])\n",
    "ax.set_yticklabels([0,1,2,3,4],fontsize=18,fontweight='bold',family='Times New Roman')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "e32bcbd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating sequences of data to be used for RNN\n",
    "min_max=MinMaxScaler()\n",
    "min_max_em=MinMaxScaler()\n",
    "train=Data_all.iloc[:130,:]\n",
    "test=Data_all.iloc[130:,:]\n",
    "temp=train['Temperature Increase (C)'].values\n",
    "train_T_scaled=min_max.fit_transform(temp.reshape((-1,1)))\n",
    "emis_=train['Annual CO2 emissions'].values\n",
    "train__scaled=min_max_em.fit_transform(emis_.reshape((-1,1)))\n",
    "X=np.concatenate((train_T_scaled, train__scaled), axis=1, out=None, dtype=None, casting=\"same_kind\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "c380f66e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-295-97e7a6bfc094>:11: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history=model.fit_generator(generator,epochs=40)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100/100 [==============================] - 2s 9ms/step - loss: 0.0708\n",
      "Epoch 2/40\n",
      "100/100 [==============================] - 1s 10ms/step - loss: 0.0046\n",
      "Epoch 3/40\n",
      "100/100 [==============================] - 1s 10ms/step - loss: 0.0036\n",
      "Epoch 4/40\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.0031\n",
      "Epoch 5/40\n",
      "100/100 [==============================] - 1s 10ms/step - loss: 0.0025\n",
      "Epoch 6/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0025\n",
      "Epoch 7/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0025\n",
      "Epoch 8/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0022\n",
      "Epoch 9/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0024\n",
      "Epoch 10/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0021\n",
      "Epoch 11/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0019\n",
      "Epoch 12/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0018\n",
      "Epoch 13/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0018\n",
      "Epoch 14/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0014\n",
      "Epoch 15/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0016\n",
      "Epoch 16/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0016\n",
      "Epoch 17/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0016\n",
      "Epoch 18/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0015\n",
      "Epoch 19/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0013\n",
      "Epoch 20/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0014\n",
      "Epoch 21/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0013\n",
      "Epoch 22/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0013\n",
      "Epoch 23/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0012\n",
      "Epoch 24/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0012\n",
      "Epoch 25/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0011\n",
      "Epoch 26/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0011\n",
      "Epoch 27/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0010\n",
      "Epoch 28/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 9.6349e-04\n",
      "Epoch 29/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 9.2875e-04\n",
      "Epoch 30/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 9.0184e-04\n",
      "Epoch 31/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 8.1299e-04\n",
      "Epoch 32/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 8.9570e-04\n",
      "Epoch 33/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 8.8349e-04\n",
      "Epoch 34/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 0.0010\n",
      "Epoch 35/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 8.0396e-04\n",
      "Epoch 36/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 8.2850e-04\n",
      "Epoch 37/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 7.6414e-04\n",
      "Epoch 38/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 7.6300e-04\n",
      "Epoch 39/40\n",
      "100/100 [==============================] - 1s 9ms/step - loss: 7.2665e-04\n",
      "Epoch 40/40\n",
      "100/100 [==============================] - 1s 10ms/step - loss: 8.4937e-04\n"
     ]
    }
   ],
   "source": [
    "#code for return sequence case\n",
    "n_input=30\n",
    "n_features=2\n",
    "generator=TimeseriesGenerator(X,train_T_scaled,length=n_input,batch_size=1)\n",
    "\n",
    "input_=Input(shape=(n_input,2))\n",
    "x=LSTM(30,activation='relu')(input_)\n",
    "output=Dense(1)(x)\n",
    "model=Model(input_,output)\n",
    "model.compile(loss=\"mean_squared_error\")\n",
    "history=model.fit_generator(generator,epochs=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "564d5c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "show_history(history)\n",
    "plot_history(history, path=\"standard.png\")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "729783a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_49\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_51 (InputLayer)       [(None, 30, 2)]           0         \n",
      "                                                                 \n",
      " lstm_50 (LSTM)              (None, 30)                3960      \n",
      "                                                                 \n",
      " dense_50 (Dense)            (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,991\n",
      "Trainable params: 3,991\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "1e17316b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28f77081280>"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data predictions\n",
    "y_prediction=np.zeros((len(train['Temperature Increase (C)'].values),))\n",
    "k=0\n",
    "for i in range(len(train['Temperature Increase (C)'].values)-n_input):\n",
    "    y_prediction[k]=model.predict(generator[i][0])\n",
    "    k+=1\n",
    "y_pre_unscaled=min_max.inverse_transform(y_prediction.reshape(-1,1))\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(train['Year'].iloc[n_input:],y_pre_unscaled[:-n_input],label='Prediction')\n",
    "plt.scatter(train['Year'],temp[:].reshape((-1,1)),label='Training Data')\n",
    "plt.ylim([-0.5,0.7])\n",
    "yearly_data=pd.DataFrame({'Year':[1880,1900,1920,1940,1960,1980,2000]})\n",
    "xticks_data=pd.to_datetime(yearly_data['Year'].values,format='%Y')\n",
    "\n",
    "plt.yticks([-0.4,-0.2,0,0.2,0.4,0.6],fontweight='bold',fontsize=18,family='Times New Roman')\n",
    "plt.xticks(fontsize=20,rotation=0,family='Times New Roman',fontweight='bold')\n",
    "plt.xlabel('Year',fontsize=28,family='Times New Roman')\n",
    "plt.ylabel('Temperature Increase (C)',fontsize=28,family='Times New Roman')\n",
    "\n",
    "plt.legend(loc='upper left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "4518ba4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.022604]]\n",
      "[[1.0478244]]\n",
      "[[1.0812966]]\n",
      "[[1.1185433]]\n",
      "[[1.1589594]]\n",
      "[[1.2019894]]\n",
      "[[1.2456636]]\n",
      "[[1.2892094]]\n",
      "[[1.3339326]]\n",
      "[[1.3792495]]\n",
      "[[1.420827]]\n",
      "Percentage of Error for the test dataset\n",
      "2.0549100863319105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-299-b548abe93401>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test['predictions']=min_max.inverse_transform(y_test_predict.reshape((-1,1)))\n"
     ]
    }
   ],
   "source": [
    "temp_test=test['Temperature Increase (C)'].values\n",
    "test_T_scaled=min_max.transform(temp_test.reshape((-1,1)))\n",
    "emis_test=test['Annual CO2 emissions'].values\n",
    "test_scaled=min_max_em.transform(emis_test.reshape((-1,1)))\n",
    "X_test=np.concatenate((test_T_scaled, test_scaled), axis=1, out=None, dtype=None, casting=\"same_kind\")\n",
    "n_features=2\n",
    "first_eval_batch=X[-n_input:]\n",
    "current_batch=first_eval_batch.reshape((1,n_input,n_features))\n",
    "y_test_predict=[]\n",
    "for i in range(X_test.shape[0]):\n",
    "    y_now=model.predict(current_batch)\n",
    "    y_test_predict.append(y_now[0,0])\n",
    "    new_row=np.array([[y_now[0,0],test_scaled[i,0]]])\n",
    "    first_eval_batch=np.concatenate((first_eval_batch[1:],new_row),axis=0)\n",
    "    current_batch=first_eval_batch.reshape((1,n_input,n_features))\n",
    "    print(y_now)\n",
    "y_test_predict=np.array(y_test_predict)\n",
    "test['predictions']=min_max.inverse_transform(y_test_predict.reshape((-1,1)))\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('Percentage of Error for the test dataset')\n",
    "print(mean_squared_error(test['Temperature Increase (C)'],test['predictions'].values)/np.mean(np.abs(temp))*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "63ff1ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28f09389190>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(color_codes=True)\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "sns.scatterplot(y='Temperature Increase (C)',x='Year',data=train.iloc[:,:],label='train')\n",
    "sns.scatterplot(y='predictions',x='Year',data=test.iloc[:,:],label='predicted for test data')\n",
    "sns.scatterplot(y='Temperature Increase (C)',x='Year',data=test.iloc[:,:],label=' test data')\n",
    "plt.yticks([-0.4,-0.2,0,0.2,0.4,0.6,0.8],fontweight='bold',fontsize=18,family='Times New Roman')\n",
    "plt.xticks(fontsize=20,rotation=0,family='Times New Roman',fontweight='bold')\n",
    "plt.xlabel('Year',fontsize=28,family='Times New Roman')\n",
    "plt.ylabel('Temperature Increase (C)',fontsize=28,family='Times New Roman')\n",
    "\n",
    "plt.legend(loc='upper left',fontsize=14)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d59b56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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