<reponame>adam-dziedzic/time-series-ml
import numpy as np

fifty_words = np.array([[[-0.89094, -0.86099, -0.82438, -0.78214, -0.73573,
                          -0.68691, -0.63754, -0.58937, -0.54342, -0.50044,
                          -0.46082, -0.42469, -0.3924, -0.36389, -0.33906,
                          -0.31795, -0.30056, -0.28692, -0.27727, -0.27105,
                          -0.26777, -0.26678, -0.26742, -0.26927, -0.27202,
                          -0.27529, -0.27898, -0.2832, -0.28814, -0.29431,
                          -0.30185, -0.31086, -0.32135, -0.33326, -0.3465,
                          -0.36103, -0.37659, -0.39297, -0.40995, -0.42731,
                          -0.44484, -0.46226, -0.47921, -0.49534, -0.51031,
                          -0.52382, -0.53567, -0.54568, -0.55375, -0.55984,
                          -0.56397, -0.56617, -0.56653, -0.56511, -0.56201,
                          -0.55736, -0.55133, -0.54419, -0.53617, -0.52758,
                          -0.51876, -0.51004, -0.50174, -0.49418, -0.48731,
                          -0.48128, -0.47619, -0.4721, -0.46921, -0.4674,
                          -0.46649, -0.46659, -0.46785, -0.47045, -0.4749,
                          -0.48081, -0.48822, -0.49726, -0.50805, -0.52086,
                          -0.53596, -0.55266, -0.57081, -0.59021, -0.61063,
                          -0.63238, -0.65484, -0.67722, -0.69907, -0.72004,
                          -0.73987, -0.75965, -0.77816, -0.79527, -0.8109,
                          -0.82509, -0.83844, -0.85159, -0.86373, -0.87474,
                          -0.88447, -0.8929, -0.90125, -0.90911, -0.91606,
                          -0.92197, -0.92677, -0.93071, -0.93528, -0.93931,
                          -0.9426, -0.94494, -0.94622, -0.94735, -0.94879,
                          -0.94965, -0.94971, -0.94878, -0.94679, -0.94571,
                          -0.94428, -0.94176, -0.93756, -0.9311, -0.92258,
                          -0.91342, -0.90142, -0.88573, -0.86549, -0.83998,
                          -0.81054, -0.77682, -0.73745, -0.692, -0.64033,
                          -0.58278, -0.52295, -0.45966, -0.39357, -0.32536,
                          -0.2557, -0.18627, -0.11888, -0.052529, 0.012911,
                          0.077684, 0.14195, 0.2039, 0.26391, 0.3225, 0.37976,
                          0.43566, 0.48977, 0.53999, 0.58745, 0.63232, 0.6749,
                          0.71548, 0.75342, 0.78841, 0.82157, 0.85356, 0.88519,
                          0.91734, 0.94972, 0.98403, 1.0219, 1.0648, 1.1143,
                          1.1713, 1.2359, 1.3092, 1.3911, 1.4809, 1.5776,
                          1.6787,
                          1.7819, 1.8846, 1.9842, 2.0777, 2.1627, 2.2362,
                          2.2969,
                          2.3436, 2.3759, 2.3942, 2.3994, 2.3927, 2.3763,
                          2.3521,
                          2.3222, 2.2886, 2.2524, 2.2149, 2.1772, 2.1394,
                          2.1018,
                          2.0641, 2.0259, 1.9878, 1.9494, 1.9106, 1.8711,
                          1.8305,
                          1.7886, 1.7455, 1.7008, 1.6537, 1.6036, 1.5495, 1.492,
                          1.431, 1.3664, 1.2981, 1.2262, 1.1508, 1.0736,
                          0.99504,
                          0.91575, 0.83626, 0.75657, 0.67816, 0.60206, 0.52843,
                          0.45712, 0.38776, 0.31881, 0.2515, 0.18511, 0.11894,
                          0.052471, -0.01492, -0.082568, -0.14848, -0.21193,
                          -0.27229, -0.32905, -0.38303, -0.43222, -0.47628,
                          -0.51587, -0.55188, -0.58534, -0.61763, -0.64653,
                          -0.67227, -0.69521, -0.71576, -0.73517, -0.75286,
                          -0.76798, -0.7812, -0.79326, -0.80474, -0.81719,
                          -0.82831, -0.83768, -0.84538, -0.85165, -0.85731,
                          -0.86309, -0.86791, -0.87271, -0.87846, -0.88592,
                          -0.89619, -0.90783, -0.91942, -0.93018, -0.93939]]])

adiac_example = np.array([[[1.598, 1.5994, 1.5705, 1.5505, 1.5074, 1.4343, 1.369,
                        1.3053, 1.2103, 1.1167, 1.024, 0.92598, 0.82811,
                        0.73922, 0.64305, 0.55643, 0.46295, 0.36958, 0.27843,
                        0.18583, 0.095532, 0.010646, -0.080856, -0.16555,
                        -0.24349, -0.33543, -0.42611, -0.49749, -0.56685,
                        -0.64986, -0.73225, -0.7778, -0.84298, -0.9173,
                        -0.98797, -1.055, -1.0968, -1.1471, -1.1984, -1.2432,
                        -1.2816, -1.3126, -1.3357, -1.3511, -1.3769, -1.3879,
                        -1.3769, -1.3571, -1.3296, -1.2937, -1.2577, -1.2304,
                        -1.1742, -1.1142, -1.072, -1.0068, -0.94893, -0.87017,
                        -0.79017, -0.72451, -0.64284, -0.59163, -0.52139,
                        -0.43063, -0.35085, -0.26986, -0.17364, -0.089113,
                        -0.0039203, 0.085453, 0.17518, 0.26706, 0.36297,
                        0.45238, 0.54911, 0.64982, 0.74624, 0.84566, 0.94633,
                        1.0457, 1.1463, 1.2424, 1.344, 1.4418, 1.5407, 1.6241,
                        1.6696, 1.6852, 1.6967, 1.6975, 1.6874, 1.6385, 1.5737,
                        1.4786, 1.3769, 1.2766, 1.177, 1.0777, 0.9792, 0.88053,
                        0.77793, 0.68164, 0.59043, 0.49275, 0.40434, 0.30859,
                        0.21352, 0.12268, 0.037603, -0.052602, -0.13751,
                        -0.2204, -0.31556, -0.39461, -0.44608, -0.53344,
                        -0.60286, -0.67341, -0.73761, -0.81903, -0.88526,
                        -0.95008, -1.0026, -1.063, -1.1252, -1.1658, -1.2043,
                        -1.2485, -1.2738, -1.2844, -1.2733, -1.2867, -1.2919,
                        -1.2886, -1.2777, -1.2581, -1.2314, -1.1973, -1.1563,
                        -1.1105, -1.0845, -1.0258, -0.96255, -0.89507, -0.84781,
                        -0.77254, -0.69457, -0.632, -0.55122, -0.48459,
                        -0.40868, -0.32442, -0.24745, -0.16365, -0.076913,
                        0.0088012, 0.097274, 0.1859, 0.284, 0.38121, 0.46303,
                        0.55693, 0.65097, 0.7382, 0.83474, 0.92994, 1.0249,
                        1.1209, 1.2172, 1.3125, 1.4029, 1.481, 1.521, 1.5642,
                        1.5709, 1.5929]]])


import numpy as np

fifty_words = np.array([[[-0.89094, -0.86099, -0.82438, -0.78214, -0.73573,
                          -0.68691, -0.63754, -0.58937, -0.54342, -0.50044,
                          -0.46082, -0.42469, -0.3924, -0.36389, -0.33906,
                          -0.31795, -0.30056, -0.28692, -0.27727, -0.27105,
                          -0.26777, -0.26678, -0.26742, -0.26927, -0.27202,
                          -0.27529, -0.27898, -0.2832, -0.28814, -0.29431,
                          -0.30185, -0.31086, -0.32135, -0.33326, -0.3465,
                          -0.36103, -0.37659, -0.39297, -0.40995, -0.42731,
                          -0.44484, -0.46226, -0.47921, -0.49534, -0.51031,
                          -0.52382, -0.53567, -0.54568, -0.55375, -0.55984,
                          -0.56397, -0.56617, -0.56653, -0.56511, -0.56201,
                          -0.55736, -0.55133, -0.54419, -0.53617, -0.52758,
                          -0.51876, -0.51004, -0.50174, -0.49418, -0.48731,
                          -0.48128, -0.47619, -0.4721, -0.46921, -0.4674,
                          -0.46649, -0.46659, -0.46785, -0.47045, -0.4749,
                          -0.48081, -0.48822, -0.49726, -0.50805, -0.52086,
                          -0.53596, -0.55266, -0.57081, -0.59021, -0.61063,
                          -0.63238, -0.65484, -0.67722, -0.69907, -0.72004,
                          -0.73987, -0.75965, -0.77816, -0.79527, -0.8109,
                          -0.82509, -0.83844, -0.85159, -0.86373, -0.87474,
                          -0.88447, -0.8929, -0.90125, -0.90911, -0.91606,
                          -0.92197, -0.92677, -0.93071, -0.93528, -0.93931,
                          -0.9426, -0.94494, -0.94622, -0.94735, -0.94879,
                          -0.94965, -0.94971, -0.94878, -0.94679, -0.94571,
                          -0.94428, -0.94176, -0.93756, -0.9311, -0.92258,
                          -0.91342, -0.90142, -0.88573, -0.86549, -0.83998,
                          -0.81054, -0.77682, -0.73745, -0.692, -0.64033,
                          -0.58278, -0.52295, -0.45966, -0.39357, -0.32536,
                          -0.2557, -0.18627, -0.11888, -0.052529, 0.012911,
                          0.077684, 0.14195, 0.2039, 0.26391, 0.3225, 0.37976,
                          0.43566, 0.48977, 0.53999, 0.58745, 0.63232, 0.6749,
                          0.71548, 0.75342, 0.78841, 0.82157, 0.85356, 0.88519,
                          0.91734, 0.94972, 0.98403, 1.0219, 1.0648, 1.1143,
                          1.1713, 1.2359, 1.3092, 1.3911, 1.4809, 1.5776,
                          1.6787,
                          1.7819, 1.8846, 1.9842, 2.0777, 2.1627, 2.2362,
                          2.2969,
                          2.3436, 2.3759, 2.3942, 2.3994, 2.3927, 2.3763,
                          2.3521,
                          2.3222, 2.2886, 2.2524, 2.2149, 2.1772, 2.1394,
                          2.1018,
                          2.0641, 2.0259, 1.9878, 1.9494, 1.9106, 1.8711,
                          1.8305,
                          1.7886, 1.7455, 1.7008, 1.6537, 1.6036, 1.5495, 1.492,
                          1.431, 1.3664, 1.2981, 1.2262, 1.1508, 1.0736,
                          0.99504,
                          0.91575, 0.83626, 0.75657, 0.67816, 0.60206, 0.52843,
                          0.45712, 0.38776, 0.31881, 0.2515, 0.18511, 0.11894,
                          0.052471, -0.01492, -0.082568, -0.14848, -0.21193,
                          -0.27229, -0.32905, -0.38303, -0.43222, -0.47628,
                          -0.51587, -0.55188, -0.58534, -0.61763, -0.64653,
                          -0.67227, -0.69521, -0.71576, -0.73517, -0.75286,
                          -0.76798, -0.7812, -0.79326, -0.80474, -0.81719,
                          -0.82831, -0.83768, -0.84538, -0.85165, -0.85731,
                          -0.86309, -0.86791, -0.87271, -0.87846, -0.88592,
                          -0.89619, -0.90783, -0.91942, -0.93018, -0.93939]]])

adiac_example = np.array([[[1.598, 1.5994, 1.5705, 1.5505, 1.5074, 1.4343, 1.369,
                        1.3053, 1.2103, 1.1167, 1.024, 0.92598, 0.82811,
                        0.73922, 0.64305, 0.55643, 0.46295, 0.36958, 0.27843,
                        0.18583, 0.095532, 0.010646, -0.080856, -0.16555,
                        -0.24349, -0.33543, -0.42611, -0.49749, -0.56685,
                        -0.64986, -0.73225, -0.7778, -0.84298, -0.9173,
                        -0.98797, -1.055, -1.0968, -1.1471, -1.1984, -1.2432,
                        -1.2816, -1.3126, -1.3357, -1.3511, -1.3769, -1.3879,
                        -1.3769, -1.3571, -1.3296, -1.2937, -1.2577, -1.2304,
                        -1.1742, -1.1142, -1.072, -1.0068, -0.94893, -0.87017,
                        -0.79017, -0.72451, -0.64284, -0.59163, -0.52139,
                        -0.43063, -0.35085, -0.26986, -0.17364, -0.089113,
                        -0.0039203, 0.085453, 0.17518, 0.26706, 0.36297,
                        0.45238, 0.54911, 0.64982, 0.74624, 0.84566, 0.94633,
                        1.0457, 1.1463, 1.2424, 1.344, 1.4418, 1.5407, 1.6241,
                        1.6696, 1.6852, 1.6967, 1.6975, 1.6874, 1.6385, 1.5737,
                        1.4786, 1.3769, 1.2766, 1.177, 1.0777, 0.9792, 0.88053,
                        0.77793, 0.68164, 0.59043, 0.49275, 0.40434, 0.30859,
                        0.21352, 0.12268, 0.037603, -0.052602, -0.13751,
                        -0.2204, -0.31556, -0.39461, -0.44608, -0.53344,
                        -0.60286, -0.67341, -0.73761, -0.81903, -0.88526,
                        -0.95008, -1.0026, -1.063, -1.1252, -1.1658, -1.2043,
                        -1.2485, -1.2738, -1.2844, -1.2733, -1.2867, -1.2919,
                        -1.2886, -1.2777, -1.2581, -1.2314, -1.1973, -1.1563,
                        -1.1105, -1.0845, -1.0258, -0.96255, -0.89507, -0.84781,
                        -0.77254, -0.69457, -0.632, -0.55122, -0.48459,
                        -0.40868, -0.32442, -0.24745, -0.16365, -0.076913,
                        0.0088012, 0.097274, 0.1859, 0.284, 0.38121, 0.46303,
                        0.55693, 0.65097, 0.7382, 0.83474, 0.92994, 1.0249,
                        1.1209, 1.2172, 1.3125, 1.4029, 1.481, 1.521, 1.5642,
                        1.5709, 1.5929]]])