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      "2015-01-02 17832.990234 1926.439941\n",
      "2015-01-05 17501.650391 1915.75\n",
      "2015-01-06 17371.640625 1882.449951\n",
      "2015-01-07 17584.519531 1883.829956\n",
      "2015-01-08 17907.869141 1904.650024\n",
      "2015-01-09 17737.369141 1924.699951\n",
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      "2015-01-13 17613.679688 1917.140015\n",
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      "2015-02-02 17361.039062 1952.680054\n",
      "2015-02-03 17666.400391 1951.959961\n",
      "2015-02-04 17673.019531 1962.790039\n",
      "2015-02-05 17884.880859 1952.839966\n",
      "2015-02-06 17824.289062 1955.52002\n",
      "2015-02-09 17729.210938 1947.0\n",
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      "2015-03-03 18203.369141 2001.380005\n",
      "2015-03-04 18096.900391 1998.290039\n",
      "2015-03-05 18135.720703 1998.380005\n",
      "2015-03-06 17856.779297 2012.939941\n",
      "2015-03-09 17995.720703 1992.819946\n",
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      "2015-03-12 17895.220703 1970.589966\n",
      "2015-03-13 17749.310547 1985.790039\n",
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      "2015-03-17 17849.080078 2029.910034\n",
      "2015-03-18 18076.189453 2028.449951\n",
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      "2015-03-26 17678.230469 2022.560059\n",
      "2015-03-27 17712.660156 2019.800049\n",
      "2015-03-30 17976.310547 2030.040039\n",
      "2015-03-31 17776.119141 2041.030029\n",
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      "2015-04-21 17949.589844 2144.790039\n",
      "2015-04-22 18038.269531 2143.889893\n",
      "2015-04-23 18058.689453 2173.409912\n",
      "2015-04-24 18080.140625 2159.800049\n",
      "2015-04-27 18037.970703 2157.540039\n",
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      "2015-04-29 18035.529297 2142.629883\n",
      "2015-04-30 17840.519531 2127.169922\n",
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      "2015-05-18 18298.880859 2113.719971\n",
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      "2015-05-20 18285.400391 2139.540039\n",
      "2015-05-21 18285.740234 2122.810059\n",
      "2015-05-22 18232.019531 2146.100098\n",
      "2015-05-26 18041.539062 2143.5\n",
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      "2015-05-28 18126.119141 2110.889893\n",
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      "2015-06-05 17849.460938 2068.100098\n",
      "2015-06-08 17766.550781 2065.189941\n",
      "2015-06-09 17764.039062 2064.030029\n",
      "2015-06-10 18000.400391 2051.320068\n",
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      "2015-06-16 17904.480469 2028.719971\n",
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      "2015-06-18 18115.839844 2041.880005\n",
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      "2015-06-24 17966.070312 2085.530029\n",
      "2015-06-25 17890.359375 2085.060059\n",
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      "2015-07-02 17730.109375 2107.330078\n",
      "2015-07-06 17683.580078 2053.929932\n",
      "2015-07-07 17776.910156 2040.290039\n",
      "2015-07-08 17515.419922 2016.209961\n",
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      "2015-07-10 17760.410156 2031.170044\n",
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      "2015-07-14 18053.580078 2059.22998\n",
      "2015-07-15 18050.169922 2072.909912\n",
      "2015-07-16 18120.25 2087.889893\n",
      "2015-07-17 18086.449219 2076.790039\n",
      "2015-07-20 18100.410156 2073.310059\n",
      "2015-07-21 17919.289062 2083.620117\n",
      "2015-07-22 17851.039062 2064.72998\n",
      "2015-07-23 17731.919922 2065.070068\n",
      "2015-07-24 17568.529297 2045.959961\n",
      "2015-07-27 17440.589844 2038.810059\n",
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      "2015-07-29 17751.390625 2037.619995\n",
      "2015-07-30 17745.980469 2019.030029\n",
      "2015-07-31 17689.859375 2030.160034\n",
      "2015-08-03 17598.199219 2008.48999\n",
      "2015-08-04 17550.689453 2027.98999\n",
      "2015-08-05 17540.470703 2029.76001\n",
      "2015-08-06 17419.75 2013.290039\n",
      "2015-08-07 17373.380859 2010.22998\n",
      "2015-08-10 17615.169922 2003.170044\n",
      "2015-08-11 17402.839844 1986.650024\n",
      "2015-08-12 17402.509766 1975.469971\n",
      "2015-08-13 17408.25 1983.459961\n",
      "2015-08-17 17545.179688 1968.52002\n",
      "2015-08-18 17511.339844 1956.26001\n",
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      "2015-08-20 16990.689453 1914.550049\n",
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      "2015-08-28 16643.009766 1937.670044\n",
      "2015-08-31 16528.029297 1941.48999\n",
      "2015-09-01 16058.349609 1914.22998\n",
      "2015-09-02 16351.379883 1915.219971\n",
      "2015-09-03 16374.759766 1915.530029\n",
      "2015-09-04 16102.379883 1886.040039\n",
      "2015-09-08 16492.679688 1878.680054\n",
      "2015-09-09 16253.570312 1934.199951\n",
      "2015-09-10 16330.400391 1962.109985\n",
      "2015-09-11 16433.089844 1941.369995\n",
      "2015-09-14 16370.959961 1931.459961\n",
      "2015-09-15 16599.849609 1937.560059\n",
      "2015-09-16 16739.949219 1975.449951\n",
      "2015-09-17 16674.740234 1976.48999\n",
      "2015-09-18 16384.580078 1995.949951\n",
      "2015-09-21 16510.189453 1964.680054\n",
      "2015-09-22 16330.469727 1982.060059\n",
      "2015-09-23 16279.889648 1944.640015\n",
      "2015-09-24 16201.320312 1947.099976\n",
      "2015-09-25 16314.669922 1942.849976\n",
      "2015-09-30 16284.700195 1962.810059\n",
      "2015-10-01 16272.009766 1979.319946\n",
      "2015-10-02 16472.369141 1969.680054\n",
      "2015-10-05 16776.429688 1978.25\n",
      "2015-10-06 16790.189453 1990.650024\n",
      "2015-10-07 16912.289062 2005.839966\n",
      "2015-10-08 17050.75 2019.530029\n",
      "2015-10-12 17131.859375 2021.630005\n",
      "2015-10-13 17081.890625 2019.050049\n",
      "2015-10-14 16924.75 2009.550049\n",
      "2015-10-15 17141.75 2033.27002\n",
      "2015-10-16 17215.970703 2030.26001\n",
      "2015-10-19 17230.539062 2030.27002\n",
      "2015-10-20 17217.109375 2039.359985\n",
      "2015-10-21 17168.609375 2042.97998\n",
      "2015-10-22 17489.160156 2023.0\n",
      "2015-10-23 17646.699219 2040.400024\n",
      "2015-10-26 17623.050781 2048.080078\n",
      "2015-10-27 17581.429688 2044.650024\n",
      "2015-10-28 17779.519531 2042.51001\n",
      "2015-10-29 17755.800781 2034.160034\n",
      "2015-10-30 17663.539062 2029.469971\n",
      "2015-11-02 17828.759766 2035.23999\n",
      "2015-11-03 17918.150391 2048.399902\n",
      "2015-11-04 17867.580078 2052.77002\n",
      "2015-11-05 17863.429688 2049.409912\n",
      "2015-11-06 17910.330078 2041.069946\n",
      "2015-11-09 17730.480469 2025.699951\n",
      "2015-11-10 17758.210938 1996.589966\n",
      "2015-11-11 17702.220703 1997.27002\n",
      "2015-11-12 17448.070312 1993.359985\n",
      "2015-11-13 17245.240234 1973.290039\n",
      "2015-11-16 17483.009766 1943.02002\n",
      "2015-11-17 17489.5 1963.579956\n",
      "2015-11-18 17737.160156 1962.880005\n",
      "2015-11-19 17732.75 1988.910034\n",
      "2015-11-20 17823.810547 1989.859985\n",
      "2015-11-23 17792.679688 2003.699951\n",
      "2015-11-24 17812.189453 2016.290039\n",
      "2015-11-25 17813.390625 2009.420044\n",
      "2015-11-27 17813.390625 2028.98999\n",
      "2015-11-30 17719.919922 1991.969971\n",
      "2015-12-01 17888.349609 2023.930054\n",
      "2015-12-02 17729.679688 2009.290039\n",
      "2015-12-03 17477.669922 1994.069946\n",
      "2015-12-04 17847.630859 1974.400024\n",
      "2015-12-07 17730.509766 1963.670044\n",
      "2015-12-08 17568.0 1949.040039\n",
      "2015-12-09 17492.300781 1948.23999\n",
      "2015-12-10 17574.75 1952.069946\n",
      "2015-12-11 17265.210938 1948.619995\n",
      "2015-12-14 17368.5 1927.819946\n",
      "2015-12-15 17524.910156 1932.969971\n",
      "2015-12-16 17749.089844 1969.400024\n",
      "2015-12-17 17495.839844 1977.959961\n",
      "2015-12-18 17128.550781 1975.319946\n",
      "2015-12-21 17251.619141 1981.189941\n",
      "2015-12-22 17417.269531 1992.560059\n",
      "2015-12-23 17602.609375 1999.219971\n",
      "2015-12-24 17552.169922 1990.650024\n",
      "2015-12-28 17528.269531 1964.060059\n",
      "2015-12-29 17720.980469 1966.310059\n",
      "2015-12-30 17603.869141 1961.310059\n",
      "2016-01-04 17148.939453 1918.76001\n",
      "2016-01-05 17158.660156 1930.530029\n",
      "2016-01-06 16906.509766 1925.430054\n",
      "2016-01-07 16514.099609 1904.329956\n",
      "2016-01-08 16346.450195 1917.619995\n",
      "2016-01-11 16398.570312 1894.839966\n",
      "2016-01-12 16516.220703 1890.859985\n",
      "2016-01-13 16151.410156 1916.280029\n",
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      "2016-01-15 15988.080078 1878.869995\n",
      "2016-01-19 16016.019531 1889.640015\n",
      "2016-01-20 15766.740234 1845.449951\n",
      "2016-01-21 15882.679688 1840.530029\n",
      "2016-01-22 16093.509766 1879.430054\n",
      "2016-01-25 15885.219727 1893.430054\n",
      "2016-01-26 16167.230469 1871.689941\n",
      "2016-01-27 15944.459961 1897.869995\n",
      "2016-01-28 16069.639648 1906.939941\n",
      "2016-01-29 16466.300781 1912.060059\n",
      "2016-02-01 16449.179688 1924.819946\n",
      "2016-02-02 16153.540039 1906.599976\n",
      "2016-02-03 16336.660156 1890.670044\n",
      "2016-02-04 16416.580078 1916.26001\n",
      "2016-02-05 16204.969727 1917.790039\n",
      "2016-02-11 15660.179688 1861.540039\n",
      "2016-02-12 15973.839844 1835.280029\n",
      "2016-02-16 16196.410156 1888.300049\n",
      "2016-02-17 16453.830078 1883.939941\n",
      "2016-02-18 16413.429688 1908.839966\n",
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      "2016-02-22 16620.660156 1916.359985\n",
      "2016-02-23 16431.779297 1914.219971\n",
      "2016-02-24 16484.990234 1912.530029\n",
      "2016-02-25 16697.289062 1918.569946\n",
      "2016-02-26 16639.970703 1920.160034\n",
      "2016-02-29 16516.5 1916.660034\n",
      "2016-03-02 16899.320312 1947.420044\n",
      "2016-03-03 16943.900391 1958.170044\n",
      "2016-03-04 17006.769531 1955.630005\n",
      "2016-03-07 17073.949219 1957.869995\n",
      "2016-03-08 16964.099609 1946.119995\n",
      "2016-03-09 17000.359375 1952.949951\n",
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      "2016-03-14 17229.130859 1972.27002\n",
      "2016-03-15 17251.529297 1969.969971\n",
      "2016-03-16 17325.759766 1974.900024\n",
      "2016-03-17 17481.490234 1987.98999\n",
      "2016-03-18 17602.300781 1992.119995\n",
      "2016-03-21 17623.869141 1989.76001\n",
      "2016-03-22 17582.570312 1996.810059\n",
      "2016-03-23 17502.589844 1995.119995\n",
      "2016-03-24 17515.730469 1985.969971\n",
      "2016-03-28 17535.390625 1982.540039\n",
      "2016-03-29 17633.109375 1994.910034\n",
      "2016-03-30 17716.660156 2002.140015\n",
      "2016-03-31 17685.089844 1995.849976\n",
      "2016-04-01 17792.75 1973.569946\n",
      "2016-04-04 17737.0 1978.969971\n",
      "2016-04-05 17603.320312 1962.73999\n",
      "2016-04-06 17716.050781 1971.319946\n",
      "2016-04-07 17541.960938 1973.890015\n",
      "2016-04-08 17576.960938 1972.050049\n",
      "2016-04-11 17556.410156 1970.369995\n",
      "2016-04-12 17721.25 1981.319946\n",
      "2016-04-14 17926.429688 2015.930054\n",
      "2016-04-15 17897.460938 2014.709961\n",
      "2016-04-18 18004.160156 2009.099976\n",
      "2016-04-19 18053.599609 2011.359985\n",
      "2016-04-20 18096.269531 2005.829956\n",
      "2016-04-21 17982.519531 2022.099976\n",
      "2016-04-22 18003.75 2015.48999\n",
      "2016-04-25 17977.240234 2014.550049\n",
      "2016-04-26 17990.320312 2019.630005\n",
      "2016-04-27 18041.550781 2015.400024\n",
      "2016-04-28 17830.759766 2000.930054\n",
      "2016-04-29 17773.640625 1994.150024\n",
      "2016-05-02 17891.160156 1978.150024\n",
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      "2016-05-04 17651.259766 1976.709961\n",
      "2016-05-09 17705.910156 1967.810059\n",
      "2016-05-10 17928.349609 1982.5\n",
      "2016-05-11 17711.119141 1980.099976\n",
      "2016-05-12 17720.5 1977.48999\n",
      "2016-05-13 17535.320312 1966.98999\n",
      "2016-05-16 17710.710938 1967.910034\n",
      "2016-05-17 17529.980469 1968.060059\n",
      "2016-05-18 17526.619141 1956.72998\n",
      "2016-05-19 17435.400391 1946.780029\n",
      "2016-05-20 17500.939453 1947.670044\n",
      "2016-05-23 17492.929688 1955.25\n",
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      "2016-05-25 17851.509766 1960.51001\n",
      "2016-05-26 17828.289062 1957.060059\n",
      "2016-05-27 17873.220703 1969.170044\n",
      "2016-05-31 17787.199219 1983.400024\n",
      "2016-06-01 17789.669922 1982.719971\n",
      "2016-06-02 17838.560547 1985.109985\n",
      "2016-06-03 17807.060547 1985.839966\n",
      "2016-06-07 17938.279297 2011.630005\n",
      "2016-06-08 18005.050781 2027.079956\n",
      "2016-06-09 17985.189453 2024.170044\n",
      "2016-06-10 17865.339844 2017.630005\n",
      "2016-06-13 17732.480469 1979.060059\n",
      "2016-06-14 17674.820312 1972.030029\n",
      "2016-06-15 17640.169922 1968.829956\n",
      "2016-06-16 17733.099609 1951.98999\n",
      "2016-06-17 17675.160156 1953.400024\n",
      "2016-06-20 17804.869141 1981.119995\n",
      "2016-06-21 17829.730469 1982.699951\n",
      "2016-06-22 17780.830078 1992.579956\n",
      "2016-06-23 18011.070312 1986.709961\n",
      "2016-06-24 17400.75 1925.23999\n",
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      "2016-06-28 17409.720703 1936.219971\n",
      "2016-06-29 17694.679688 1956.359985\n",
      "2016-06-30 17929.990234 1970.349976\n",
      "2016-07-01 17949.369141 1987.319946\n",
      "2016-07-05 17840.619141 1989.849976\n",
      "2016-07-06 17918.619141 1953.119995\n",
      "2016-07-07 17895.880859 1974.079956\n",
      "2016-07-08 18146.740234 1963.099976\n",
      "2016-07-11 18226.929688 1988.540039\n",
      "2016-07-12 18347.669922 1991.22998\n",
      "2016-07-13 18372.119141 2005.550049\n",
      "2016-07-14 18506.410156 2008.77002\n",
      "2016-07-15 18516.550781 2017.26001\n",
      "2016-07-18 18533.050781 2021.109985\n",
      "2016-07-19 18559.009766 2016.890015\n",
      "2016-07-20 18595.029297 2015.459961\n",
      "2016-07-21 18517.230469 2012.219971\n",
      "\n",
      "h(X) = \n",
      "737.058366257889  X **  0  + \n",
      "0.07175867222677387  X **  1\n",
      "get_sumerror :  884608.869684\n",
      "\n",
      "2016-08-02 DOW30 :  18313.77\n",
      "2016-08-02 estimated KOSPI :  2051.2301849244136\n",
      "2016-08-02 KOSPI :  2019.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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+/XwiGuWCgPYXAqdGo2mfR+l4qBhRFEVpZ7R0wbxM95+phFpvPwLk4x+hcc7RA5iWocRd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2a0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "def daterange(start_date, end_date):\n",
    "    for n in range(int ((end_date - start_date).days)):\n",
    "        yield start_date + datetime.timedelta(n)\n",
    "\n",
    "def make_train_data():\n",
    "    start = datetime.datetime(2015,1,2)\n",
    "    end = datetime.datetime(2016,7,22)\n",
    "    f1 = web.DataReader(\"^DJI\",'yahoo',start,end)\n",
    "    f2 = web.DataReader(\"^KS11\",'yahoo',start,end)\n",
    "    dates = []\n",
    "    x_data = []\n",
    "    y_data = []\n",
    "    for single_date in daterange(start, end):\n",
    "        date_str = single_date.strftime('%Y-%m-%d')\n",
    "        try:\n",
    "                f1_close = f1.ix[date_str]['Close']\n",
    "        except:\n",
    "                continue\n",
    "        try:\n",
    "                f2_close = f2.ix[date_str]['Close']\n",
    "        except:\n",
    "                continue\n",
    "        x_data.append(f1_close)\n",
    "        y_data.append(f2_close)\n",
    "        dates.append(date_str)\n",
    "    return dates, x_data, y_data\n",
    "                \n",
    "def make_poly_x(pn, x_data):\n",
    "    x_datas = []\n",
    "    for n in range(pn+1):\n",
    "        x_datas.append([ x ** n for x in x_data ])\n",
    "    return x_datas\n",
    "        \n",
    "def print_hypothesis(pn, theta):\n",
    "    print('h(X) = ')\n",
    "    for i, element in enumerate(theta):\n",
    "        if i == len(theta) - 1:\n",
    "            print(element.item(0), \" X ** \", i)\n",
    "        else:\n",
    "            print(element.item(0), \" X ** \", i, \" + \")\n",
    "\n",
    "def get_hypothesis(pn, theta, x):\n",
    "    y = 0\n",
    "    for i, element in enumerate(theta):\n",
    "        y += ( element.item(0) * ( x ** i) )\n",
    "    return y\n",
    "    \n",
    "def get_sumerror(pn, theta, x_data, y_data):\n",
    "    s = 0\n",
    "    for i, x in enumerate(x_data):\n",
    "      s += ( (y_data[i] - get_hypothesis(pn, theta, x)) ** 2)\n",
    "    return s\n",
    "\n",
    "# 프로그램 시작\n",
    "PICKLE_FILE = 'data.pck'\n",
    "import os.path\n",
    "if os.path.isfile(PICKLE_FILE): \n",
    "    f = open('data.pck', 'rb')\n",
    "    dates, x_data, y_data = pickle.load(f)\n",
    "else:\n",
    "    dates, x_data, y_data = make_train_data()\n",
    "    f = open('data.pck', 'wb')\n",
    "    pickle.dump([dates, x_data, y_data], f) \n",
    "f.close()\n",
    "\n",
    "for i in range(len(dates)):\n",
    "    print(dates[i], x_data[i], y_data[i])\n",
    "    \n",
    "# 정규방정식(normal equation) 알고리즘을 통해 cost 최소가 되는 가설함수를 찾습니다. \n",
    "print()\n",
    "POLY = 1\n",
    "x_datas = make_poly_x(POLY, x_data)\n",
    "X = np.matrix(x_datas).transpose()\n",
    "Xt = np.matrix(x_datas)\n",
    "Y = np.matrix(y_data).transpose()\n",
    "theta = np.dot(np.dot(np.linalg.pinv(np.dot(Xt, X)), Xt), Y)\n",
    "print_hypothesis(POLY, theta)\n",
    "print('get_sumerror : ', get_sumerror(POLY, theta, x_data, y_data))\n",
    "\n",
    "# 다우존스지수가 18313.77일 때 코스피지수 값을 예측합니다.\n",
    "print()\n",
    "x_test = 18313.77\n",
    "y_test = get_hypothesis(POLY, theta, x_test)\n",
    "print('2016-08-02 DOW30 : ', x_test)\n",
    "print('2016-08-02 estimated KOSPI : ', y_test)\n",
    "print('2016-08-02 KOSPI : ', 2019.03)\n",
    "\n",
    "# 그래프를 그립니다.\n",
    "plt.plot(x_data, y_data, 'ro')\n",
    "line_x = [ x for x in range( int(min(x_data)), int(max(x_data)) ) ]\n",
    "line_y = [ get_hypothesis(POLY, theta, x) for x in range( int(min(x_data)), int(max(x_data)) ) ]\n",
    "plt.plot(line_x, line_y)\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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