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    {
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     "output_type": "stream",
     "text": [
      "Month=1, Predicted=377.559665, Expected=372.680000\n",
      "Month=2, Predicted=371.928882, Expected=371.140000\n",
      "Month=3, Predicted=370.277422, Expected=377.490000\n",
      "Month=4, Predicted=376.357375, Expected=375.440000\n",
      "Month=5, Predicted=374.429655, Expected=384.500000\n",
      "Month=6, Predicted=383.113063, Expected=387.990000\n",
      "Month=7, Predicted=386.625736, Expected=368.870000\n",
      "Month=8, Predicted=368.129425, Expected=372.930000\n",
      "Month=9, Predicted=371.711640, Expected=371.330000\n",
      "Month=10, Predicted=370.246921, Expected=367.950000\n",
      "Month=11, Predicted=366.893795, Expected=378.460000\n",
      "Month=12, Predicted=376.992759, Expected=378.680000\n",
      "Month=13, Predicted=377.367105, Expected=379.610000\n",
      "Month=14, Predicted=378.232695, Expected=394.790000\n",
      "Month=15, Predicted=392.949456, Expected=391.540000\n",
      "Month=16, Predicted=390.061858, Expected=391.400000\n",
      "Month=17, Predicted=389.853643, Expected=403.050000\n",
      "Month=18, Predicted=401.167283, Expected=387.500000\n",
      "Month=19, Predicted=386.326390, Expected=382.900000\n",
      "Month=20, Predicted=381.600027, Expected=409.380000\n",
      "Month=21, Predicted=407.215899, Expected=418.540000\n",
      "Month=22, Predicted=416.546021, Expected=375.270000\n",
      "Month=23, Predicted=374.781438, Expected=384.890000\n",
      "Month=24, Predicted=383.374401, Expected=378.460000\n",
      "Month=25, Predicted=377.407838, Expected=388.700000\n",
      "Month=26, Predicted=387.149283, Expected=357.530000\n",
      "Month=27, Predicted=357.149885, Expected=430.030000\n",
      "Month=28, Predicted=426.793993, Expected=432.770000\n",
      "Month=29, Predicted=430.739357, Expected=434.010000\n",
      "Month=30, Predicted=431.861754, Expected=449.190000\n",
      "Month=31, Predicted=446.697704, Expected=449.990000\n",
      "Month=32, Predicted=447.804693, Expected=450.380000\n",
      "Month=33, Predicted=448.253435, Expected=454.440000\n",
      "Month=34, Predicted=452.283470, Expected=459.070000\n",
      "Month=35, Predicted=456.913034, Expected=430.870000\n",
      "Month=36, Predicted=429.754538, Expected=433.340000\n",
      "Month=37, Predicted=431.711951, Expected=433.850000\n",
      "Month=38, Predicted=432.341179, Expected=431.910000\n",
      "Month=39, Predicted=430.463449, Expected=435.400000\n",
      "Month=40, Predicted=433.824827, Expected=435.660000\n",
      "Month=41, Predicted=434.150902, Expected=430.350000\n",
      "Month=42, Predicted=429.016177, Expected=427.950000\n",
      "Month=43, Predicted=426.611857, Expected=433.890000\n",
      "Month=44, Predicted=432.337539, Expected=421.940000\n",
      "Month=45, Predicted=420.869724, Expected=424.720000\n",
      "Month=46, Predicted=423.340991, Expected=419.410000\n",
      "Month=47, Predicted=418.268258, Expected=455.840000\n",
      "Month=48, Predicted=453.435341, Expected=455.740000\n",
      "Month=49, Predicted=453.975361, Expected=442.430000\n",
      "Month=50, Predicted=441.036563, Expected=433.920000\n",
      "Month=51, Predicted=432.628232, Expected=436.810000\n",
      "Month=52, Predicted=435.327062, Expected=442.220000\n",
      "Month=53, Predicted=440.632817, Expected=461.290000\n",
      "Month=54, Predicted=459.188882, Expected=463.170000\n",
      "Month=55, Predicted=461.344836, Expected=455.500000\n",
      "Month=56, Predicted=453.950036, Expected=453.970000\n",
      "Month=57, Predicted=452.384680, Expected=464.270000\n",
      "Month=58, Predicted=462.379950, Expected=444.010000\n",
      "Month=59, Predicted=442.963506, Expected=433.830000\n",
      "Month=60, Predicted=432.754968, Expected=436.870000\n",
      "Month=61, Predicted=435.575497, Expected=452.950000\n",
      "Month=62, Predicted=451.194637, Expected=415.680000\n",
      "Month=63, Predicted=415.359578, Expected=418.390000\n",
      "Month=64, Predicted=417.293920, Expected=418.940000\n",
      "Month=65, Predicted=417.931796, Expected=394.730000\n",
      "Month=66, Predicted=394.419884, Expected=387.550000\n",
      "Month=67, Predicted=386.998613, Expected=387.990000\n",
      "Month=68, Predicted=387.285376, Expected=363.980000\n",
      "Month=69, Predicted=363.960979, Expected=361.770000\n",
      "Month=70, Predicted=361.314614, Expected=360.000000\n",
      "Month=71, Predicted=359.519999, Expected=362.680000\n",
      "Month=72, Predicted=361.975297, Expected=376.860000\n",
      "Month=73, Predicted=375.670575, Expected=372.240000\n",
      "Month=74, Predicted=371.360418, Expected=357.240000\n",
      "Month=75, Predicted=356.671252, Expected=358.190000\n",
      "Month=76, Predicted=357.300469, Expected=352.570000\n",
      "Month=77, Predicted=351.845042, Expected=328.980000\n",
      "Month=78, Predicted=328.829384, Expected=321.000000\n",
      "Month=79, Predicted=320.634868, Expected=323.000000\n",
      "Month=80, Predicted=322.395460, Expected=323.710000\n",
      "Month=81, Predicted=323.026225, Expected=327.000000\n",
      "Month=82, Predicted=326.130288, Expected=322.390000\n",
      "Month=83, Predicted=321.647404, Expected=326.410000\n",
      "Month=84, Predicted=325.389706, Expected=335.490000\n",
      "Month=85, Predicted=334.208339, Expected=336.490000\n",
      "Month=86, Predicted=335.276238, Expected=332.890000\n",
      "Month=87, Predicted=331.760744, Expected=322.930000\n",
      "Month=88, Predicted=322.033731, Expected=334.710000\n",
      "Month=89, Predicted=333.253302, Expected=338.070000\n",
      "Month=90, Predicted=336.701613, Expected=336.490000\n",
      "Month=91, Predicted=335.180316, Expected=313.200000\n",
      "Month=92, Predicted=312.591258, Expected=337.140000\n",
      "Month=93, Predicted=335.364105, Expected=379.530000\n",
      "Month=94, Predicted=376.854422, Expected=373.830000\n",
      "Month=95, Predicted=371.998573, Expected=384.810000\n",
      "Month=96, Predicted=382.513788, Expected=374.090000\n",
      "Month=97, Predicted=372.424335, Expected=387.210000\n",
      "Month=98, Predicted=384.981925, Expected=410.000000\n",
      "Month=99, Predicted=407.364257, Expected=404.100000\n",
      "Month=100, Predicted=402.074866, Expected=365.210000\n",
      "Month=101, Predicted=364.366844, Expected=329.460000\n",
      "Month=102, Predicted=329.161973, Expected=314.850000\n",
      "Month=103, Predicted=314.473413, Expected=329.110000\n",
      "Month=104, Predicted=328.034730, Expected=316.080000\n",
      "Month=105, Predicted=315.599088, Expected=304.090000\n",
      "Month=106, Predicted=303.652094, Expected=295.610000\n",
      "Month=107, Predicted=295.196867, Expected=287.100000\n",
      "Month=108, Predicted=286.740734, Expected=284.970000\n",
      "Month=109, Predicted=284.451591, Expected=284.290000\n",
      "Month=110, Predicted=283.676819, Expected=278.760000\n",
      "Month=111, Predicted=278.219117, Expected=275.840000\n",
      "Month=112, Predicted=275.212114, Expected=268.770000\n",
      "Month=113, Predicted=268.245289, Expected=271.140000\n",
      "Month=114, Predicted=270.342229, Expected=265.170000\n",
      "Month=115, Predicted=264.538665, Expected=265.000000\n",
      "Month=116, Predicted=264.189228, Expected=272.870000\n",
      "Month=117, Predicted=271.756440, Expected=263.430000\n",
      "Month=118, Predicted=262.686532, Expected=254.990000\n",
      "Month=119, Predicted=254.281967, Expected=253.270000\n",
      "Month=120, Predicted=252.443413, Expected=250.990000\n",
      "Month=121, Predicted=250.159251, Expected=246.390000\n",
      "Month=122, Predicted=245.605468, Expected=249.020000\n",
      "Month=123, Predicted=248.020674, Expected=246.070000\n",
      "Month=124, Predicted=245.163227, Expected=244.350000\n",
      "Month=125, Predicted=243.388488, Expected=242.940000\n",
      "Month=126, Predicted=241.955776, Expected=244.000000\n",
      "Month=127, Predicted=242.915891, Expected=246.640000\n",
      "Month=128, Predicted=245.453032, Expected=240.950000\n",
      "Month=129, Predicted=239.950476, Expected=239.870000\n",
      "Month=130, Predicted=238.771304, Expected=239.920000\n",
      "Month=131, Predicted=238.784561, Expected=238.280000\n",
      "Month=132, Predicted=237.165330, Expected=238.340000\n",
      "Month=133, Predicted=237.166549, Expected=237.140000\n",
      "Month=134, Predicted=235.982522, Expected=236.890000\n",
      "Month=135, Predicted=235.693955, Expected=239.990000\n",
      "Month=136, Predicted=238.671913, Expected=233.580000\n",
      "Month=137, Predicted=232.497635, Expected=234.690000\n",
      "Month=138, Predicted=233.437790, Expected=235.890000\n",
      "Month=139, Predicted=234.615752, Expected=235.270000\n",
      "Month=140, Predicted=234.011131, Expected=230.260000\n",
      "Month=141, Predicted=229.131933, Expected=231.280000\n",
      "Month=142, Predicted=230.019743, Expected=227.310000\n",
      "Month=143, Predicted=226.185064, Expected=232.240000\n",
      "Month=144, Predicted=230.867016, Expected=232.880000\n",
      "Month=145, Predicted=231.563340, Expected=233.780000\n",
      "Month=146, Predicted=232.419580, Expected=233.340000\n",
      "Month=147, Predicted=232.002649, Expected=229.730000\n",
      "Month=148, Predicted=228.490791, Expected=230.600000\n",
      "Month=149, Predicted=229.265860, Expected=231.010000\n",
      "Month=150, Predicted=229.676375, Expected=230.820000\n",
      "Month=151, Predicted=229.486573, Expected=236.500000\n",
      "Month=152, Predicted=234.976910, Expected=240.900000\n",
      "Month=153, Predicted=239.332437, Expected=238.520000\n",
      "Month=154, Predicted=237.094615, Expected=238.750000\n",
      "Month=155, Predicted=237.279429, Expected=244.140000\n",
      "Month=156, Predicted=242.519086, Expected=241.310000\n",
      "Month=157, Predicted=239.878036, Expected=241.290000\n",
      "Month=158, Predicted=239.810590, Expected=236.010000\n",
      "Month=159, Predicted=234.716646, Expected=231.390000\n",
      "Month=160, Predicted=230.155032, Expected=227.250000\n",
      "Month=161, Predicted=226.075716, Expected=229.970000\n",
      "Month=162, Predicted=228.634948, Expected=228.220000\n",
      "Month=163, Predicted=226.979284, Expected=230.750000\n",
      "Month=164, Predicted=229.377927, Expected=229.390000\n",
      "Month=165, Predicted=228.098945, Expected=230.610000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month=166, Predicted=229.241336, Expected=231.550000\n",
      "Month=167, Predicted=230.170079, Expected=225.800000\n",
      "Month=168, Predicted=224.608553, Expected=226.810000\n",
      "Month=169, Predicted=225.480497, Expected=223.730000\n",
      "Month=170, Predicted=222.518497, Expected=211.160000\n",
      "Month=171, Predicted=210.274631, Expected=229.210000\n",
      "Month=172, Predicted=227.529392, Expected=231.150000\n",
      "Month=173, Predicted=229.722277, Expected=232.850000\n",
      "Month=174, Predicted=231.348036, Expected=235.560000\n",
      "Month=175, Predicted=234.013207, Expected=227.340000\n",
      "Month=176, Predicted=226.103367, Expected=221.990000\n",
      "Month=177, Predicted=220.782936, Expected=257.310000\n",
      "Month=178, Predicted=254.917114, Expected=259.200000\n",
      "Month=179, Predicted=257.361161, Expected=262.460000\n",
      "Month=180, Predicted=260.508195, Expected=266.170000\n",
      "Month=181, Predicted=264.224497, Expected=264.670000\n",
      "Month=182, Predicted=262.879360, Expected=266.940000\n",
      "Month=183, Predicted=265.097316, Expected=271.990000\n",
      "Month=184, Predicted=270.078070, Expected=265.150000\n",
      "Month=185, Predicted=263.579940, Expected=267.630000\n",
      "Month=186, Predicted=265.896794, Expected=262.020000\n",
      "Month=187, Predicted=260.558427, Expected=279.940000\n",
      "Month=188, Predicted=277.833848, Expected=279.650000\n",
      "Month=189, Predicted=277.900899, Expected=282.770000\n",
      "Month=190, Predicted=280.913403, Expected=285.510000\n",
      "Month=191, Predicted=283.674847, Expected=280.950000\n",
      "Month=192, Predicted=279.340501, Expected=282.620000\n",
      "Month=193, Predicted=280.915491, Expected=281.530000\n",
      "Month=194, Predicted=279.932709, Expected=285.190000\n",
      "Month=195, Predicted=283.479862, Expected=288.490000\n",
      "Month=196, Predicted=286.767932, Expected=290.190000\n",
      "Month=197, Predicted=288.489177, Expected=295.760000\n",
      "Month=198, Predicted=293.937016, Expected=294.210000\n",
      "Month=199, Predicted=292.561300, Expected=293.890000\n",
      "Month=200, Predicted=292.247206, Expected=289.700000\n",
      "Month=201, Predicted=288.221383, Expected=289.120000\n",
      "Month=202, Predicted=287.611338, Expected=277.390000\n",
      "Month=203, Predicted=276.268156, Expected=277.890000\n",
      "Month=204, Predicted=276.555900, Expected=277.320000\n",
      "Month=205, Predicted=276.033266, Expected=280.000000\n",
      "Month=206, Predicted=278.600815, Expected=275.610000\n",
      "Month=207, Predicted=274.389460, Expected=277.710000\n",
      "Month=208, Predicted=276.333805, Expected=280.580000\n",
      "Month=209, Predicted=279.152435, Expected=279.380000\n",
      "Month=210, Predicted=278.026160, Expected=285.700000\n",
      "Month=211, Predicted=284.119198, Expected=287.510000\n",
      "Month=212, Predicted=285.985760, Expected=293.080000\n",
      "Month=213, Predicted=291.403713, Expected=312.160000\n",
      "Month=214, Predicted=309.990786, Expected=294.230000\n",
      "Month=215, Predicted=292.971012, Expected=286.670000\n",
      "Month=216, Predicted=285.339586, Expected=269.750000\n",
      "Month=217, Predicted=268.894953, Expected=270.970000\n",
      "Month=218, Predicted=269.784975, Expected=266.180000\n",
      "Month=219, Predicted=265.179049, Expected=269.940000\n",
      "Month=220, Predicted=268.695559, Expected=271.740000\n",
      "Month=221, Predicted=270.486997, Expected=260.640000\n",
      "Month=222, Predicted=259.732553, Expected=256.950000\n",
      "Month=223, Predicted=255.942826, Expected=254.850000\n",
      "Month=224, Predicted=253.839655, Expected=259.020000\n",
      "Month=225, Predicted=257.806832, Expected=263.820000\n",
      "Month=226, Predicted=262.496047, Expected=257.540000\n",
      "Month=227, Predicted=256.460489, Expected=249.110000\n",
      "Month=228, Predicted=248.160498, Expected=251.590000\n",
      "Month=229, Predicted=250.409216, Expected=243.000000\n",
      "Month=230, Predicted=242.116692, Expected=242.440000\n",
      "Month=231, Predicted=241.383082, Expected=240.040000\n",
      "Month=232, Predicted=239.033857, Expected=244.520000\n",
      "Month=233, Predicted=243.289471, Expected=247.500000\n",
      "Month=234, Predicted=246.224483, Expected=244.660000\n",
      "Month=235, Predicted=243.488094, Expected=246.130000\n",
      "Month=236, Predicted=244.839976, Expected=246.330000\n",
      "Month=237, Predicted=245.049410, Expected=249.270000\n",
      "Month=238, Predicted=247.880249, Expected=250.250000\n",
      "Month=239, Predicted=248.869722, Expected=251.910000\n",
      "Month=240, Predicted=250.481405, Expected=236.990000\n",
      "Month=241, Predicted=236.058375, Expected=233.600000\n",
      "Month=242, Predicted=232.507408, Expected=233.470000\n",
      "Month=243, Predicted=232.336741, Expected=229.470000\n",
      "Month=244, Predicted=228.430720, Expected=229.690000\n",
      "Month=245, Predicted=228.545127, Expected=228.730000\n",
      "Month=246, Predicted=227.598366, Expected=229.180000\n",
      "Month=247, Predicted=227.986453, Expected=229.100000\n",
      "Month=248, Predicted=227.893650, Expected=223.890000\n",
      "Month=249, Predicted=222.822181, Expected=225.480000\n",
      "Month=250, Predicted=224.250541, Expected=224.970000\n",
      "Month=251, Predicted=223.771775, Expected=224.570000\n",
      "Month=252, Predicted=223.346883, Expected=225.860000\n",
      "Month=253, Predicted=224.572275, Expected=225.850000\n",
      "Month=254, Predicted=224.567903, Expected=223.560000\n",
      "Month=255, Predicted=222.332761, Expected=230.210000\n",
      "Month=256, Predicted=228.721711, Expected=233.550000\n",
      "Month=257, Predicted=232.065120, Expected=236.890000\n",
      "Month=258, Predicted=235.338688, Expected=237.340000\n",
      "Month=259, Predicted=235.834192, Expected=237.350000\n",
      "Month=260, Predicted=235.855805, Expected=236.790000\n",
      "Month=261, Predicted=235.325652, Expected=237.430000\n",
      "Month=262, Predicted=235.945833, Expected=240.820000\n",
      "Month=263, Predicted=239.249702, Expected=239.360000\n",
      "Month=264, Predicted=237.901756, Expected=240.450000\n",
      "Month=265, Predicted=238.936423, Expected=235.770000\n",
      "Month=266, Predicted=234.438595, Expected=234.540000\n",
      "Month=267, Predicted=233.171019, Expected=232.590000\n",
      "Month=268, Predicted=231.274727, Expected=234.040000\n",
      "Month=269, Predicted=232.642995, Expected=237.120000\n",
      "Month=270, Predicted=235.650777, Expected=236.730000\n",
      "Month=271, Predicted=235.319915, Expected=237.820000\n",
      "Month=272, Predicted=236.364769, Expected=237.460000\n",
      "Month=273, Predicted=236.040561, Expected=236.320000\n",
      "Month=274, Predicted=234.931121, Expected=242.030000\n",
      "Month=275, Predicted=240.445829, Expected=242.860000\n",
      "Month=276, Predicted=241.353795, Expected=241.570000\n",
      "Month=277, Predicted=240.114372, Expected=242.500000\n",
      "Month=278, Predicted=241.004933, Expected=244.540000\n",
      "Month=279, Predicted=243.008467, Expected=237.990000\n",
      "Month=280, Predicted=236.706952, Expected=230.000000\n",
      "Month=281, Predicted=228.859963, Expected=236.760000\n",
      "Month=282, Predicted=235.282651, Expected=239.750000\n",
      "Month=283, Predicted=238.298732, Expected=240.780000\n",
      "Month=284, Predicted=239.323976, Expected=235.940000\n",
      "Month=285, Predicted=234.654336, Expected=233.140000\n",
      "Month=286, Predicted=231.860282, Expected=236.810000\n",
      "Month=287, Predicted=235.374310, Expected=225.600000\n",
      "Month=288, Predicted=224.578596, Expected=225.640000\n",
      "Month=289, Predicted=224.405983, Expected=227.580000\n",
      "Month=290, Predicted=226.296318, Expected=221.270000\n",
      "Month=291, Predicted=220.192906, Expected=227.050000\n",
      "Month=292, Predicted=225.660804, Expected=231.510000\n",
      "Month=293, Predicted=230.076173, Expected=235.990000\n",
      "Month=294, Predicted=234.463343, Expected=233.980000\n",
      "Month=295, Predicted=232.586404, Expected=235.890000\n",
      "Month=296, Predicted=234.398104, Expected=225.370000\n",
      "Month=297, Predicted=224.255212, Expected=224.030000\n",
      "Month=298, Predicted=222.771250, Expected=224.020000\n",
      "Month=299, Predicted=222.754273, Expected=223.620000\n",
      "Month=300, Predicted=222.349240, Expected=229.450000\n",
      "Month=301, Predicted=227.975109, Expected=224.350000\n",
      "Month=302, Predicted=223.127200, Expected=221.050000\n",
      "Month=303, Predicted=219.824545, Expected=225.730000\n",
      "Month=304, Predicted=224.306399, Expected=237.550000\n",
      "Month=305, Predicted=235.827377, Expected=237.910000\n",
      "Month=306, Predicted=236.365625, Expected=236.780000\n",
      "Month=307, Predicted=235.266740, Expected=244.740000\n",
      "Month=308, Predicted=242.978136, Expected=246.580000\n",
      "Month=309, Predicted=244.916000, Expected=254.270000\n",
      "Month=310, Predicted=252.400521, Expected=256.850000\n",
      "Month=311, Predicted=255.061143, Expected=261.470000\n",
      "Month=312, Predicted=259.599055, Expected=254.360000\n",
      "Month=313, Predicted=252.832193, Expected=254.840000\n",
      "Month=314, Predicted=253.203706, Expected=254.390000\n",
      "Month=315, Predicted=252.823507, Expected=247.870000\n",
      "Month=316, Predicted=246.519014, Expected=245.500000\n",
      "Month=317, Predicted=244.130068, Expected=248.780000\n",
      "Month=318, Predicted=247.286757, Expected=243.690000\n",
      "Month=319, Predicted=242.417535, Expected=253.520000\n",
      "Month=320, Predicted=251.843316, Expected=247.810000\n",
      "Month=321, Predicted=246.497819, Expected=249.880000\n",
      "Month=322, Predicted=248.385726, Expected=246.880000\n",
      "Month=323, Predicted=245.540390, Expected=246.150000\n",
      "Month=324, Predicted=244.776183, Expected=267.530000\n",
      "Month=325, Predicted=265.479023, Expected=269.450000\n",
      "Month=326, Predicted=267.711522, Expected=261.900000\n",
      "Month=327, Predicted=260.414763, Expected=263.310000\n",
      "Month=328, Predicted=261.691583, Expected=263.050000\n",
      "Month=329, Predicted=261.504277, Expected=258.500000\n",
      "Month=330, Predicted=257.105572, Expected=286.740000\n",
      "Month=331, Predicted=284.394718, Expected=292.370000\n",
      "Month=332, Predicted=290.359255, Expected=288.000000\n",
      "Month=333, Predicted=286.207497, Expected=283.950000\n",
      "Month=334, Predicted=282.272642, Expected=284.000000\n",
      "Month=335, Predicted=282.310394, Expected=296.050000\n",
      "Month=336, Predicted=294.024193, Expected=296.990000\n",
      "Month=337, Predicted=295.174626, Expected=293.470000\n",
      "Month=338, Predicted=291.799975, Expected=290.190000\n",
      "Month=339, Predicted=288.611339, Expected=275.500000\n",
      "Month=340, Predicted=274.370903, Expected=276.890000\n",
      "Month=341, Predicted=275.487724, Expected=273.230000\n",
      "Month=342, Predicted=271.999273, Expected=276.860000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month=343, Predicted=275.433902, Expected=273.370000\n",
      "Month=344, Predicted=272.119018, Expected=283.270000\n",
      "Month=345, Predicted=281.629822, Expected=276.700000\n",
      "Month=346, Predicted=275.444460, Expected=260.240000\n",
      "Month=347, Predicted=259.368940, Expected=255.590000\n",
      "Month=348, Predicted=254.594480, Expected=254.420000\n",
      "Month=349, Predicted=253.386586, Expected=237.110000\n",
      "Month=350, Predicted=236.566977, Expected=238.190000\n",
      "Month=351, Predicted=237.270385, Expected=239.870000\n",
      "Month=352, Predicted=238.899728, Expected=239.490000\n",
      "Month=353, Predicted=238.492815, Expected=236.830000\n",
      "Month=354, Predicted=235.860917, Expected=245.990000\n",
      "Month=355, Predicted=244.645713, Expected=245.340000\n",
      "Month=356, Predicted=244.143519, Expected=240.900000\n",
      "Month=357, Predicted=239.785727, Expected=237.490000\n",
      "Month=358, Predicted=236.393643, Expected=244.820000\n",
      "Month=359, Predicted=243.419507, Expected=234.680000\n",
      "Month=360, Predicted=233.708381, Expected=235.540000\n",
      "Month=361, Predicted=234.329012, Expected=256.660000\n",
      "Month=362, Predicted=254.803681, Expected=236.490000\n",
      "Month=363, Predicted=235.611013, Expected=223.210000\n",
      "Month=364, Predicted=222.354319, Expected=219.810000\n",
      "Month=365, Predicted=218.872469, Expected=220.870000\n",
      "Month=366, Predicted=219.819384, Expected=221.100000\n",
      "Month=367, Predicted=220.019775, Expected=223.990000\n",
      "Month=368, Predicted=222.780320, Expected=229.000000\n",
      "Month=369, Predicted=227.650203, Expected=223.200000\n",
      "Month=370, Predicted=222.089611, Expected=216.760000\n",
      "Month=371, Predicted=215.726071, Expected=227.110000\n",
      "Month=372, Predicted=225.634038, Expected=228.580000\n",
      "Month=373, Predicted=227.223861, Expected=237.830000\n",
      "Month=374, Predicted=236.169052, Expected=228.990000\n",
      "Month=375, Predicted=227.769618, Expected=218.450000\n",
      "Month=376, Predicted=217.394667, Expected=229.070000\n",
      "Month=377, Predicted=227.519600, Expected=235.030000\n",
      "Month=378, Predicted=233.478966, Expected=236.090000\n",
      "Month=379, Predicted=234.575670, Expected=263.650000\n",
      "Month=380, Predicted=261.286564, Expected=274.480000\n",
      "Month=381, Predicted=272.267185, Expected=254.530000\n",
      "Month=382, Predicted=253.129904, Expected=240.000000\n",
      "Month=383, Predicted=238.769511, Expected=235.000000\n",
      "Month=384, Predicted=233.758928, Expected=226.320000\n",
      "Month=385, Predicted=225.291819, Expected=225.510000\n",
      "Month=386, Predicted=224.349946, Expected=218.000000\n",
      "Month=387, Predicted=217.057672, Expected=225.510000\n",
      "Month=388, Predicted=224.170350, Expected=184.000000\n",
      "Month=389, Predicted=184.089267, Expected=199.460000\n",
      "Month=390, Predicted=198.279454, Expected=204.220000\n",
      "Month=391, Predicted=203.191462, Expected=120.000000\n",
      "Month=392, Predicted=121.598251, Expected=260.000000\n",
      "Month=393, Predicted=255.717614, Expected=288.990000\n",
      "Month=394, Predicted=286.240776, Expected=340.000000\n",
      "Month=395, Predicted=335.815993, Expected=350.000000\n",
      "Month=396, Predicted=346.516935, Expected=360.500000\n",
      "Month=397, Predicted=356.953436, Expected=375.000000\n",
      "Month=398, Predicted=371.393743, Expected=378.000000\n",
      "Month=399, Predicted=374.740092, Expected=377.100000\n",
      "Month=400, Predicted=374.108316, Expected=378.000000\n",
      "Month=401, Predicted=375.161434, Expected=378.000000\n",
      "Month=402, Predicted=375.352802, Expected=370.000000\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [12, 402]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-0aacdae2d131>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;31m# report performance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0mrmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test RMSE: %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrmse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;31m# line plot of observed vs predicted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/bash/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    236\u001b[0m     \"\"\"\n\u001b[1;32m    237\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 238\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    239\u001b[0m     output_errors = np.average((y_true - y_pred) ** 2, axis=0,\n\u001b[1;32m    240\u001b[0m                                weights=sample_weight)\n",
      "\u001b[0;32m~/anaconda3/envs/bash/lib/python3.6/site-packages/sklearn/metrics/regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \"\"\"\n\u001b[0;32m---> 75\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/bash/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 204\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [12, 402]"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from pandas import Series\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras_tqdm import TQDMCallback, TQDMNotebookCallback\n",
    "import gdax\n",
    "from time import sleep\n",
    "\n",
    "\n",
    "from math import sqrt\n",
    "from matplotlib import pyplot\n",
    "import numpy\n",
    "\n",
    "def get_loads(symbol, start=None, end=None, granularity=86400):\n",
    "    \"\"\" This was fiddly so I'm not live-coding this one\"\"\"\n",
    "    public_client = gdax.PublicClient()\n",
    "    if end is None:\n",
    "        end = pd.to_datetime('now')\n",
    "    if start is None:\n",
    "        start = end-pd.Timedelta(seconds=granularity)\n",
    "        \n",
    "    while True:\n",
    "        response = public_client.get_product_historic_rates(\n",
    "                product_id=symbol,\n",
    "                granularity=granularity,\n",
    "                start=start.isoformat(),\n",
    "                end=end.isoformat()\n",
    "            )\n",
    "        \n",
    "        if not response:\n",
    "            raise StopIteration()\n",
    "        if not isinstance(response,list):\n",
    "            raise ValueError(response)\n",
    "            \n",
    "        for r in response:\n",
    "            yield r\n",
    "        sleep(3)\n",
    "        end = pd.to_datetime(r[0], unit='s')\n",
    "        start = end-pd.Timedelta(seconds=granularity*len(response))\n",
    "        print(f\"{start}-{end}\")\n",
    "        \n",
    "def get_data():\n",
    "    df = pd.DataFrame(\n",
    "    get_loads('BTC-USD'),\n",
    "        columns=['time','low','high','open','close','volume']\n",
    "    )\n",
    "    df['time'] = pd.to_datetime(df['time'], unit='s')\n",
    "    return df.set_index('time')['close']\n",
    "    \n",
    "\n",
    "# date-time parsing function for loading the dataset\n",
    "def parser(x):\n",
    "\treturn datetime.strptime('190'+x, '%Y-%m')\n",
    "\n",
    "# frame a sequence as a supervised learning problem\n",
    "def timeseries_to_supervised(data, lag=1):\n",
    "\tdf = DataFrame(data)\n",
    "\tcolumns = [df.shift(i) for i in range(1, lag+1)]\n",
    "\tcolumns.append(df)\n",
    "\tdf = concat(columns, axis=1)\n",
    "\tdf.fillna(0, inplace=True)\n",
    "\treturn df\n",
    "\n",
    "# create a differenced series\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = list()\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn Series(diff)\n",
    "\n",
    "# invert differenced value\n",
    "def inverse_difference(history, yhat, interval=1):\n",
    "\treturn yhat + history[-interval]\n",
    "\n",
    "# scale train and test data to [-1, 1]\n",
    "def scale(train, test):\n",
    "    # fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# inverse scaling for a forecasted value\n",
    "def invert_scale(scaler, X, value):\n",
    "\tnew_row = [x for x in X] + [value]\n",
    "\tarray = numpy.array(new_row)\n",
    "\tarray = array.reshape(1, len(array))\n",
    "\tinverted = scaler.inverse_transform(array)\n",
    "\treturn inverted[0, -1]\n",
    "\n",
    "# fit an LSTM network to training data\n",
    "def fit_lstm(train, batch_size, nb_epoch, neurons):\n",
    "\tX, y = train[:, 0:-1], train[:, -1]\n",
    "\tX = X.reshape(X.shape[0], 1, X.shape[1])\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))\n",
    "\tmodel.add(Dense(1))\n",
    "\tmodel.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\tfor i in range(nb_epoch):\n",
    "\t\tmodel.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False,\n",
    "                 callbacks=[TQDMNotebookCallback(leave_inner=False,leave_outer=False)])\n",
    "\t\tmodel.reset_states()\n",
    "\treturn model\n",
    "\n",
    "# make a one-step forecast\n",
    "def forecast_lstm(model, batch_size, X):\n",
    "\tX = X.reshape(1, 1, len(X))\n",
    "\tyhat = model.predict(X, batch_size=batch_size)\n",
    "\treturn yhat[0,0]\n",
    "\n",
    "# load dataset\n",
    "#series = read_csv(source_file, header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
    "series = get_data()\n",
    "\n",
    "split = series.shape[0]//3\n",
    "\n",
    "# transform data to be stationary\n",
    "raw_values = series.values\n",
    "diff_values = difference(raw_values, 1)\n",
    "\n",
    "# transform data to be supervised learning\n",
    "supervised = timeseries_to_supervised(diff_values, 1)\n",
    "supervised_values = supervised.values\n",
    "\n",
    "# split data into train and test-sets\n",
    "train, test = supervised_values[0:-split], supervised_values[-split:]\n",
    "\n",
    "# transform the scale of the data\n",
    "scaler, train_scaled, test_scaled = scale(train, test)\n",
    "\n",
    "# fit the model\n",
    "lstm_model = fit_lstm(train_scaled, 1, 3000, 4)\n",
    "# forecast the entire training dataset to build up state for forecasting\n",
    "train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)\n",
    "lstm_model.predict(train_reshaped, batch_size=1)\n",
    "\n",
    "# walk-forward validation on the test data\n",
    "predictions = list()\n",
    "for i in range(len(test_scaled)):\n",
    "\t# make one-step forecast\n",
    "\tX, y = test_scaled[i, 0:-1], test_scaled[i, -1]\n",
    "\tyhat = forecast_lstm(lstm_model, 1, X)\n",
    "\t# invert scaling\n",
    "\tyhat = invert_scale(scaler, X, yhat)\n",
    "\t# invert differencing\n",
    "\tyhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)\n",
    "\t# store forecast\n",
    "\tpredictions.append(yhat)\n",
    "\texpected = raw_values[len(train) + i + 1]\n",
    "\tprint('Month=%d, Predicted=%f, Expected=%f' % (i+1, yhat, expected))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-23T13:51:10.589140Z",
     "start_time": "2018-04-23T13:51:10.557666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 13.408\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# report performance\n",
    "rmse = sqrt(mean_squared_error(raw_values[-split:], predictions))\n",
    "print('Test RMSE: %.3f' % rmse)\n",
    "# line plot of observed vs predicted\n",
    "%matplotlib nbagg\n",
    "pyplot.plot(raw_values[-split:])\n",
    "pyplot.plot(predictions)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-23T13:51:42.310193Z",
     "start_time": "2018-04-23T13:51:42.196706Z"
    }
   },
   "outputs": [],
   "source": [
    "lstm_model.save('lstm_1_3000_4.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:bash]",
   "language": "python",
   "name": "conda-env-bash-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}