In [1]:

    

%matplotlib inline
from __future__ import print_function, division
from nilmtk import DataSet
import numpy as np
import matplotlib
matplotlib.rcParams['figure.figsize'] = (16.0, 8.0)
import matplotlib.pyplot as plt
import sys


    

In [2]:

    

dataset = DataSet('/data/mine/vadeec/merged/ukdale.h5')
dataset.set_window('2013-01-01', '2013-01-02')
elec = dataset.buildings[1].elec
appliance = elec['washer dryer']
activations = appliance.activation_series()


    

In [3]:

    

activations[0]
activation = activations[0].dropna().values


    

In [4]:

    

plt.plot(activation);


    

In [8]:

    

from neuralnilm.rectangulariser import rectangularise


    

In [13]:

    

segments = rectangularise(activation, n_segments=10)
segments


    

    
Out[13]:





array([ 0.03769634,  0.15287958,  0.0565445 ,  0.01361257,  0.05445026,
        0.00942408,  0.0565445 ,  0.00628272,  0.50471204,  0.1078534 ])

In [15]:

    

changepoints = [0]
for segment in segments:
    changepoint = changepoints[-1] + int(round((segment * len(activation))))
    changepoints.append(changepoint)


    

In [16]:

    

changepoints


    

    
Out[16]:





[0, 36, 182, 236, 249, 301, 310, 364, 370, 852, 955]

In [17]:

    

fig, ax1 = plt.subplots()
ax1.plot(activation, 'r')
ax1.set_ylabel('power (watts)')
ax1.scatter(changepoints, [1000] * len(changepoints))
ax1.set_ylim((0, 2500))
ax1.set_xlim((0, len(activation)))


    

    
Out[17]:





(0, 955)






    

In [6]:

    

np.where(activation > 0)[0]


    

    
Out[6]:





array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,
        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,
        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,
        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,
        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,
        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,
        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,
       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,
       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,
       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,
       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,
       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,
       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,
       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,
       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,
       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,
       261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,
       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,
       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,
       313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,
       326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,
       339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,
       352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364,
       365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377,
       378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390,
       391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403,
       404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416,
       417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429,
       430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442,
       443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455,
       456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468,
       469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481,
       482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494,
       495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507,
       508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520,
       521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533,
       534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546,
       547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559,
       560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572,
       573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585,
       586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598,
       599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611,
       612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624,
       625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637,
       638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650,
       651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663,
       664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676,
       677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689,
       690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702,
       703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715,
       716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728,
       729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741,
       742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754,
       755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767,
       768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780,
       781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793,
       794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806,
       807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819,
       820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832,
       833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845,
       846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858,
       859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871,
       872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884,
       885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897,
       898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910,
       911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923,
       924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936,
       937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949,
       950, 951, 952, 953, 954])

In [5]:

    

np


    

    
Out[5]:





<module 'numpy' from '/usr/local/lib/python2.7/dist-packages/numpy/__init__.pyc'>

In [ ]: