{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estructuras de datos\n",
    "\n",
    "Disclaimer. El proposito de este pequeno tutorial no es volverlos expertos en data handling, si no mas bien entregar las herramientas necesarias para desarrollar el proyecto.\n",
    "\n",
    "###\n",
    "Listas y arrays\n",
    "\n",
    "Ambos comparten propiedades similares, sin embargo, el array exige que sus elementos compartan formato y esta orientado a que sea mas facil aplicar operaciones matematicas.\n",
    "\n",
    "Ademas los arrays se declaran, ya que no vienen built in. Crearemos un array y jugaremos un poco con el:\n",
    "\n",
    "## Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trataremos de hacer un cubo de 10x10x10 (por lo que tendra 1000 elementos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.],\n",
       "        [ 10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.],\n",
       "        [ 20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.],\n",
       "        [ 30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.],\n",
       "        [ 40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.],\n",
       "        [ 50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.],\n",
       "        [ 60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.],\n",
       "        [ 70.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.],\n",
       "        [ 80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,  89.],\n",
       "        [ 90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,  99.]],\n",
       "\n",
       "       [[100., 101., 102., 103., 104., 105., 106., 107., 108., 109.],\n",
       "        [110., 111., 112., 113., 114., 115., 116., 117., 118., 119.],\n",
       "        [120., 121., 122., 123., 124., 125., 126., 127., 128., 129.],\n",
       "        [130., 131., 132., 133., 134., 135., 136., 137., 138., 139.],\n",
       "        [140., 141., 142., 143., 144., 145., 146., 147., 148., 149.],\n",
       "        [150., 151., 152., 153., 154., 155., 156., 157., 158., 159.],\n",
       "        [160., 161., 162., 163., 164., 165., 166., 167., 168., 169.],\n",
       "        [170., 171., 172., 173., 174., 175., 176., 177., 178., 179.],\n",
       "        [180., 181., 182., 183., 184., 185., 186., 187., 188., 189.],\n",
       "        [190., 191., 192., 193., 194., 195., 196., 197., 198., 199.]],\n",
       "\n",
       "       [[200., 201., 202., 203., 204., 205., 206., 207., 208., 209.],\n",
       "        [210., 211., 212., 213., 214., 215., 216., 217., 218., 219.],\n",
       "        [220., 221., 222., 223., 224., 225., 226., 227., 228., 229.],\n",
       "        [230., 231., 232., 233., 234., 235., 236., 237., 238., 239.],\n",
       "        [240., 241., 242., 243., 244., 245., 246., 247., 248., 249.],\n",
       "        [250., 251., 252., 253., 254., 255., 256., 257., 258., 259.],\n",
       "        [260., 261., 262., 263., 264., 265., 266., 267., 268., 269.],\n",
       "        [270., 271., 272., 273., 274., 275., 276., 277., 278., 279.],\n",
       "        [280., 281., 282., 283., 284., 285., 286., 287., 288., 289.],\n",
       "        [290., 291., 292., 293., 294., 295., 296., 297., 298., 299.]],\n",
       "\n",
       "       [[300., 301., 302., 303., 304., 305., 306., 307., 308., 309.],\n",
       "        [310., 311., 312., 313., 314., 315., 316., 317., 318., 319.],\n",
       "        [320., 321., 322., 323., 324., 325., 326., 327., 328., 329.],\n",
       "        [330., 331., 332., 333., 334., 335., 336., 337., 338., 339.],\n",
       "        [340., 341., 342., 343., 344., 345., 346., 347., 348., 349.],\n",
       "        [350., 351., 352., 353., 354., 355., 356., 357., 358., 359.],\n",
       "        [360., 361., 362., 363., 364., 365., 366., 367., 368., 369.],\n",
       "        [370., 371., 372., 373., 374., 375., 376., 377., 378., 379.],\n",
       "        [380., 381., 382., 383., 384., 385., 386., 387., 388., 389.],\n",
       "        [390., 391., 392., 393., 394., 395., 396., 397., 398., 399.]],\n",
       "\n",
       "       [[400., 401., 402., 403., 404., 405., 406., 407., 408., 409.],\n",
       "        [410., 411., 412., 413., 414., 415., 416., 417., 418., 419.],\n",
       "        [420., 421., 422., 423., 424., 425., 426., 427., 428., 429.],\n",
       "        [430., 431., 432., 433., 434., 435., 436., 437., 438., 439.],\n",
       "        [440., 441., 442., 443., 444., 445., 446., 447., 448., 449.],\n",
       "        [450., 451., 452., 453., 454., 455., 456., 457., 458., 459.],\n",
       "        [460., 461., 462., 463., 464., 465., 466., 467., 468., 469.],\n",
       "        [470., 471., 472., 473., 474., 475., 476., 477., 478., 479.],\n",
       "        [480., 481., 482., 483., 484., 485., 486., 487., 488., 489.],\n",
       "        [490., 491., 492., 493., 494., 495., 496., 497., 498., 499.]],\n",
       "\n",
       "       [[500., 501., 502., 503., 504., 505., 506., 507., 508., 509.],\n",
       "        [510., 511., 512., 513., 514., 515., 516., 517., 518., 519.],\n",
       "        [520., 521., 522., 523., 524., 525., 526., 527., 528., 529.],\n",
       "        [530., 531., 532., 533., 534., 535., 536., 537., 538., 539.],\n",
       "        [540., 541., 542., 543., 544., 545., 546., 547., 548., 549.],\n",
       "        [550., 551., 552., 553., 554., 555., 556., 557., 558., 559.],\n",
       "        [560., 561., 562., 563., 564., 565., 566., 567., 568., 569.],\n",
       "        [570., 571., 572., 573., 574., 575., 576., 577., 578., 579.],\n",
       "        [580., 581., 582., 583., 584., 585., 586., 587., 588., 589.],\n",
       "        [590., 591., 592., 593., 594., 595., 596., 597., 598., 599.]],\n",
       "\n",
       "       [[600., 601., 602., 603., 604., 605., 606., 607., 608., 609.],\n",
       "        [610., 611., 612., 613., 614., 615., 616., 617., 618., 619.],\n",
       "        [620., 621., 622., 623., 624., 625., 626., 627., 628., 629.],\n",
       "        [630., 631., 632., 633., 634., 635., 636., 637., 638., 639.],\n",
       "        [640., 641., 642., 643., 644., 645., 646., 647., 648., 649.],\n",
       "        [650., 651., 652., 653., 654., 655., 656., 657., 658., 659.],\n",
       "        [660., 661., 662., 663., 664., 665., 666., 667., 668., 669.],\n",
       "        [670., 671., 672., 673., 674., 675., 676., 677., 678., 679.],\n",
       "        [680., 681., 682., 683., 684., 685., 686., 687., 688., 689.],\n",
       "        [690., 691., 692., 693., 694., 695., 696., 697., 698., 699.]],\n",
       "\n",
       "       [[700., 701., 702., 703., 704., 705., 706., 707., 708., 709.],\n",
       "        [710., 711., 712., 713., 714., 715., 716., 717., 718., 719.],\n",
       "        [720., 721., 722., 723., 724., 725., 726., 727., 728., 729.],\n",
       "        [730., 731., 732., 733., 734., 735., 736., 737., 738., 739.],\n",
       "        [740., 741., 742., 743., 744., 745., 746., 747., 748., 749.],\n",
       "        [750., 751., 752., 753., 754., 755., 756., 757., 758., 759.],\n",
       "        [760., 761., 762., 763., 764., 765., 766., 767., 768., 769.],\n",
       "        [770., 771., 772., 773., 774., 775., 776., 777., 778., 779.],\n",
       "        [780., 781., 782., 783., 784., 785., 786., 787., 788., 789.],\n",
       "        [790., 791., 792., 793., 794., 795., 796., 797., 798., 799.]],\n",
       "\n",
       "       [[800., 801., 802., 803., 804., 805., 806., 807., 808., 809.],\n",
       "        [810., 811., 812., 813., 814., 815., 816., 817., 818., 819.],\n",
       "        [820., 821., 822., 823., 824., 825., 826., 827., 828., 829.],\n",
       "        [830., 831., 832., 833., 834., 835., 836., 837., 838., 839.],\n",
       "        [840., 841., 842., 843., 844., 845., 846., 847., 848., 849.],\n",
       "        [850., 851., 852., 853., 854., 855., 856., 857., 858., 859.],\n",
       "        [860., 861., 862., 863., 864., 865., 866., 867., 868., 869.],\n",
       "        [870., 871., 872., 873., 874., 875., 876., 877., 878., 879.],\n",
       "        [880., 881., 882., 883., 884., 885., 886., 887., 888., 889.],\n",
       "        [890., 891., 892., 893., 894., 895., 896., 897., 898., 899.]],\n",
       "\n",
       "       [[900., 901., 902., 903., 904., 905., 906., 907., 908., 909.],\n",
       "        [910., 911., 912., 913., 914., 915., 916., 917., 918., 919.],\n",
       "        [920., 921., 922., 923., 924., 925., 926., 927., 928., 929.],\n",
       "        [930., 931., 932., 933., 934., 935., 936., 937., 938., 939.],\n",
       "        [940., 941., 942., 943., 944., 945., 946., 947., 948., 949.],\n",
       "        [950., 951., 952., 953., 954., 955., 956., 957., 958., 959.],\n",
       "        [960., 961., 962., 963., 964., 965., 966., 967., 968., 969.],\n",
       "        [970., 971., 972., 973., 974., 975., 976., 977., 978., 979.],\n",
       "        [980., 981., 982., 983., 984., 985., 986., 987., 988., 989.],\n",
       "        [990., 991., 992., 993., 994., 995., 996., 997., 998., 999.]]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([])  # asi se declara un array\n",
    "numeros = np.arange(1000)  # los 1000 elementos\n",
    "a = np.append(a, numeros)\n",
    "a = a.reshape((10, 10, 10))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tambien se puede hacer todo directamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9],\n",
       "        [ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19],\n",
       "        [ 20,  21,  22,  23,  24,  25,  26,  27,  28,  29],\n",
       "        [ 30,  31,  32,  33,  34,  35,  36,  37,  38,  39],\n",
       "        [ 40,  41,  42,  43,  44,  45,  46,  47,  48,  49],\n",
       "        [ 50,  51,  52,  53,  54,  55,  56,  57,  58,  59],\n",
       "        [ 60,  61,  62,  63,  64,  65,  66,  67,  68,  69],\n",
       "        [ 70,  71,  72,  73,  74,  75,  76,  77,  78,  79],\n",
       "        [ 80,  81,  82,  83,  84,  85,  86,  87,  88,  89],\n",
       "        [ 90,  91,  92,  93,  94,  95,  96,  97,  98,  99]],\n",
       "\n",
       "       [[100, 101, 102, 103, 104, 105, 106, 107, 108, 109],\n",
       "        [110, 111, 112, 113, 114, 115, 116, 117, 118, 119],\n",
       "        [120, 121, 122, 123, 124, 125, 126, 127, 128, 129],\n",
       "        [130, 131, 132, 133, 134, 135, 136, 137, 138, 139],\n",
       "        [140, 141, 142, 143, 144, 145, 146, 147, 148, 149],\n",
       "        [150, 151, 152, 153, 154, 155, 156, 157, 158, 159],\n",
       "        [160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "        [170, 171, 172, 173, 174, 175, 176, 177, 178, 179],\n",
       "        [180, 181, 182, 183, 184, 185, 186, 187, 188, 189],\n",
       "        [190, 191, 192, 193, 194, 195, 196, 197, 198, 199]],\n",
       "\n",
       "       [[200, 201, 202, 203, 204, 205, 206, 207, 208, 209],\n",
       "        [210, 211, 212, 213, 214, 215, 216, 217, 218, 219],\n",
       "        [220, 221, 222, 223, 224, 225, 226, 227, 228, 229],\n",
       "        [230, 231, 232, 233, 234, 235, 236, 237, 238, 239],\n",
       "        [240, 241, 242, 243, 244, 245, 246, 247, 248, 249],\n",
       "        [250, 251, 252, 253, 254, 255, 256, 257, 258, 259],\n",
       "        [260, 261, 262, 263, 264, 265, 266, 267, 268, 269],\n",
       "        [270, 271, 272, 273, 274, 275, 276, 277, 278, 279],\n",
       "        [280, 281, 282, 283, 284, 285, 286, 287, 288, 289],\n",
       "        [290, 291, 292, 293, 294, 295, 296, 297, 298, 299]],\n",
       "\n",
       "       [[300, 301, 302, 303, 304, 305, 306, 307, 308, 309],\n",
       "        [310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "        [320, 321, 322, 323, 324, 325, 326, 327, 328, 329],\n",
       "        [330, 331, 332, 333, 334, 335, 336, 337, 338, 339],\n",
       "        [340, 341, 342, 343, 344, 345, 346, 347, 348, 349],\n",
       "        [350, 351, 352, 353, 354, 355, 356, 357, 358, 359],\n",
       "        [360, 361, 362, 363, 364, 365, 366, 367, 368, 369],\n",
       "        [370, 371, 372, 373, 374, 375, 376, 377, 378, 379],\n",
       "        [380, 381, 382, 383, 384, 385, 386, 387, 388, 389],\n",
       "        [390, 391, 392, 393, 394, 395, 396, 397, 398, 399]],\n",
       "\n",
       "       [[400, 401, 402, 403, 404, 405, 406, 407, 408, 409],\n",
       "        [410, 411, 412, 413, 414, 415, 416, 417, 418, 419],\n",
       "        [420, 421, 422, 423, 424, 425, 426, 427, 428, 429],\n",
       "        [430, 431, 432, 433, 434, 435, 436, 437, 438, 439],\n",
       "        [440, 441, 442, 443, 444, 445, 446, 447, 448, 449],\n",
       "        [450, 451, 452, 453, 454, 455, 456, 457, 458, 459],\n",
       "        [460, 461, 462, 463, 464, 465, 466, 467, 468, 469],\n",
       "        [470, 471, 472, 473, 474, 475, 476, 477, 478, 479],\n",
       "        [480, 481, 482, 483, 484, 485, 486, 487, 488, 489],\n",
       "        [490, 491, 492, 493, 494, 495, 496, 497, 498, 499]],\n",
       "\n",
       "       [[500, 501, 502, 503, 504, 505, 506, 507, 508, 509],\n",
       "        [510, 511, 512, 513, 514, 515, 516, 517, 518, 519],\n",
       "        [520, 521, 522, 523, 524, 525, 526, 527, 528, 529],\n",
       "        [530, 531, 532, 533, 534, 535, 536, 537, 538, 539],\n",
       "        [540, 541, 542, 543, 544, 545, 546, 547, 548, 549],\n",
       "        [550, 551, 552, 553, 554, 555, 556, 557, 558, 559],\n",
       "        [560, 561, 562, 563, 564, 565, 566, 567, 568, 569],\n",
       "        [570, 571, 572, 573, 574, 575, 576, 577, 578, 579],\n",
       "        [580, 581, 582, 583, 584, 585, 586, 587, 588, 589],\n",
       "        [590, 591, 592, 593, 594, 595, 596, 597, 598, 599]],\n",
       "\n",
       "       [[600, 601, 602, 603, 604, 605, 606, 607, 608, 609],\n",
       "        [610, 611, 612, 613, 614, 615, 616, 617, 618, 619],\n",
       "        [620, 621, 622, 623, 624, 625, 626, 627, 628, 629],\n",
       "        [630, 631, 632, 633, 634, 635, 636, 637, 638, 639],\n",
       "        [640, 641, 642, 643, 644, 645, 646, 647, 648, 649],\n",
       "        [650, 651, 652, 653, 654, 655, 656, 657, 658, 659],\n",
       "        [660, 661, 662, 663, 664, 665, 666, 667, 668, 669],\n",
       "        [670, 671, 672, 673, 674, 675, 676, 677, 678, 679],\n",
       "        [680, 681, 682, 683, 684, 685, 686, 687, 688, 689],\n",
       "        [690, 691, 692, 693, 694, 695, 696, 697, 698, 699]],\n",
       "\n",
       "       [[700, 701, 702, 703, 704, 705, 706, 707, 708, 709],\n",
       "        [710, 711, 712, 713, 714, 715, 716, 717, 718, 719],\n",
       "        [720, 721, 722, 723, 724, 725, 726, 727, 728, 729],\n",
       "        [730, 731, 732, 733, 734, 735, 736, 737, 738, 739],\n",
       "        [740, 741, 742, 743, 744, 745, 746, 747, 748, 749],\n",
       "        [750, 751, 752, 753, 754, 755, 756, 757, 758, 759],\n",
       "        [760, 761, 762, 763, 764, 765, 766, 767, 768, 769],\n",
       "        [770, 771, 772, 773, 774, 775, 776, 777, 778, 779],\n",
       "        [780, 781, 782, 783, 784, 785, 786, 787, 788, 789],\n",
       "        [790, 791, 792, 793, 794, 795, 796, 797, 798, 799]],\n",
       "\n",
       "       [[800, 801, 802, 803, 804, 805, 806, 807, 808, 809],\n",
       "        [810, 811, 812, 813, 814, 815, 816, 817, 818, 819],\n",
       "        [820, 821, 822, 823, 824, 825, 826, 827, 828, 829],\n",
       "        [830, 831, 832, 833, 834, 835, 836, 837, 838, 839],\n",
       "        [840, 841, 842, 843, 844, 845, 846, 847, 848, 849],\n",
       "        [850, 851, 852, 853, 854, 855, 856, 857, 858, 859],\n",
       "        [860, 861, 862, 863, 864, 865, 866, 867, 868, 869],\n",
       "        [870, 871, 872, 873, 874, 875, 876, 877, 878, 879],\n",
       "        [880, 881, 882, 883, 884, 885, 886, 887, 888, 889],\n",
       "        [890, 891, 892, 893, 894, 895, 896, 897, 898, 899]],\n",
       "\n",
       "       [[900, 901, 902, 903, 904, 905, 906, 907, 908, 909],\n",
       "        [910, 911, 912, 913, 914, 915, 916, 917, 918, 919],\n",
       "        [920, 921, 922, 923, 924, 925, 926, 927, 928, 929],\n",
       "        [930, 931, 932, 933, 934, 935, 936, 937, 938, 939],\n",
       "        [940, 941, 942, 943, 944, 945, 946, 947, 948, 949],\n",
       "        [950, 951, 952, 953, 954, 955, 956, 957, 958, 959],\n",
       "        [960, 961, 962, 963, 964, 965, 966, 967, 968, 969],\n",
       "        [970, 971, 972, 973, 974, 975, 976, 977, 978, 979],\n",
       "        [980, 981, 982, 983, 984, 985, 986, 987, 988, 989],\n",
       "        [990, 991, 992, 993, 994, 995, 996, 997, 998, 999]]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([])\n",
    "a = np.arange(1000).reshape((10,10,10))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para seleccionar elementos, se va de mas grande a mas pequeno:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33,\n",
       "       35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67,\n",
       "       69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(100).reshape((10,10))\n",
    "a[(a % 2) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "351\n",
      "351\n"
     ]
    }
   ],
   "source": [
    "print(a[3][5][1])\n",
    "print(a[3,5,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "        [980, 981, 982, 983, 984, 985, 986, 987, 988, 989],\n",
       "        [970, 971, 972, 973, 974, 975, 976, 977, 978, 979],\n",
       "        [960, 961, 962, 963, 964, 965, 966, 967, 968, 969],\n",
       "        [950, 951, 952, 953, 954, 955, 956, 957, 958, 959],\n",
       "        [940, 941, 942, 943, 944, 945, 946, 947, 948, 949],\n",
       "        [930, 931, 932, 933, 934, 935, 936, 937, 938, 939],\n",
       "        [920, 921, 922, 923, 924, 925, 926, 927, 928, 929],\n",
       "        [910, 911, 912, 913, 914, 915, 916, 917, 918, 919],\n",
       "        [900, 901, 902, 903, 904, 905, 906, 907, 908, 909]],\n",
       "\n",
       "       [[890, 891, 892, 893, 894, 895, 896, 897, 898, 899],\n",
       "        [880, 881, 882, 883, 884, 885, 886, 887, 888, 889],\n",
       "        [870, 871, 872, 873, 874, 875, 876, 877, 878, 879],\n",
       "        [860, 861, 862, 863, 864, 865, 866, 867, 868, 869],\n",
       "        [850, 851, 852, 853, 854, 855, 856, 857, 858, 859],\n",
       "        [840, 841, 842, 843, 844, 845, 846, 847, 848, 849],\n",
       "        [830, 831, 832, 833, 834, 835, 836, 837, 838, 839],\n",
       "        [820, 821, 822, 823, 824, 825, 826, 827, 828, 829],\n",
       "        [810, 811, 812, 813, 814, 815, 816, 817, 818, 819],\n",
       "        [800, 801, 802, 803, 804, 805, 806, 807, 808, 809]],\n",
       "\n",
       "       [[790, 791, 792, 793, 794, 795, 796, 797, 798, 799],\n",
       "        [780, 781, 782, 783, 784, 785, 786, 787, 788, 789],\n",
       "        [770, 771, 772, 773, 774, 775, 776, 777, 778, 779],\n",
       "        [760, 761, 762, 763, 764, 765, 766, 767, 768, 769],\n",
       "        [750, 751, 752, 753, 754, 755, 756, 757, 758, 759],\n",
       "        [740, 741, 742, 743, 744, 745, 746, 747, 748, 749],\n",
       "        [730, 731, 732, 733, 734, 735, 736, 737, 738, 739],\n",
       "        [720, 721, 722, 723, 724, 725, 726, 727, 728, 729],\n",
       "        [710, 711, 712, 713, 714, 715, 716, 717, 718, 719],\n",
       "        [700, 701, 702, 703, 704, 705, 706, 707, 708, 709]],\n",
       "\n",
       "       [[690, 691, 692, 693, 694, 695, 696, 697, 698, 699],\n",
       "        [680, 681, 682, 683, 684, 685, 686, 687, 688, 689],\n",
       "        [670, 671, 672, 673, 674, 675, 676, 677, 678, 679],\n",
       "        [660, 661, 662, 663, 664, 665, 666, 667, 668, 669],\n",
       "        [650, 651, 652, 653, 654, 655, 656, 657, 658, 659],\n",
       "        [640, 641, 642, 643, 644, 645, 646, 647, 648, 649],\n",
       "        [630, 631, 632, 633, 634, 635, 636, 637, 638, 639],\n",
       "        [620, 621, 622, 623, 624, 625, 626, 627, 628, 629],\n",
       "        [610, 611, 612, 613, 614, 615, 616, 617, 618, 619],\n",
       "        [600, 601, 602, 603, 604, 605, 606, 607, 608, 609]],\n",
       "\n",
       "       [[590, 591, 592, 593, 594, 595, 596, 597, 598, 599],\n",
       "        [580, 581, 582, 583, 584, 585, 586, 587, 588, 589],\n",
       "        [570, 571, 572, 573, 574, 575, 576, 577, 578, 579],\n",
       "        [560, 561, 562, 563, 564, 565, 566, 567, 568, 569],\n",
       "        [550, 551, 552, 553, 554, 555, 556, 557, 558, 559],\n",
       "        [540, 541, 542, 543, 544, 545, 546, 547, 548, 549],\n",
       "        [530, 531, 532, 533, 534, 535, 536, 537, 538, 539],\n",
       "        [520, 521, 522, 523, 524, 525, 526, 527, 528, 529],\n",
       "        [510, 511, 512, 513, 514, 515, 516, 517, 518, 519],\n",
       "        [500, 501, 502, 503, 504, 505, 506, 507, 508, 509]],\n",
       "\n",
       "       [[490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "        [480, 481, 482, 483, 484, 485, 486, 487, 488, 489],\n",
       "        [470, 471, 472, 473, 474, 475, 476, 477, 478, 479],\n",
       "        [460, 461, 462, 463, 464, 465, 466, 467, 468, 469],\n",
       "        [450, 451, 452, 453, 454, 455, 456, 457, 458, 459],\n",
       "        [440, 441, 442, 443, 444, 445, 446, 447, 448, 449],\n",
       "        [430, 431, 432, 433, 434, 435, 436, 437, 438, 439],\n",
       "        [420, 421, 422, 423, 424, 425, 426, 427, 428, 429],\n",
       "        [410, 411, 412, 413, 414, 415, 416, 417, 418, 419],\n",
       "        [400, 401, 402, 403, 404, 405, 406, 407, 408, 409]],\n",
       "\n",
       "       [[390, 391, 392, 393, 394, 395, 396, 397, 398, 399],\n",
       "        [380, 381, 382, 383, 384, 385, 386, 387, 388, 389],\n",
       "        [370, 371, 372, 373, 374, 375, 376, 377, 378, 379],\n",
       "        [360, 361, 362, 363, 364, 365, 366, 367, 368, 369],\n",
       "        [350, 351, 352, 353, 354, 355, 356, 357, 358, 359],\n",
       "        [340, 341, 342, 343, 344, 345, 346, 347, 348, 349],\n",
       "        [330, 331, 332, 333, 334, 335, 336, 337, 338, 339],\n",
       "        [320, 321, 322, 323, 324, 325, 326, 327, 328, 329],\n",
       "        [310, 311, 312, 313, 314, 315, 316, 317, 318, 319],\n",
       "        [300, 301, 302, 303, 304, 305, 306, 307, 308, 309]],\n",
       "\n",
       "       [[290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "        [280, 281, 282, 283, 284, 285, 286, 287, 288, 289],\n",
       "        [270, 271, 272, 273, 274, 275, 276, 277, 278, 279],\n",
       "        [260, 261, 262, 263, 264, 265, 266, 267, 268, 269],\n",
       "        [250, 251, 252, 253, 254, 255, 256, 257, 258, 259],\n",
       "        [240, 241, 242, 243, 244, 245, 246, 247, 248, 249],\n",
       "        [230, 231, 232, 233, 234, 235, 236, 237, 238, 239],\n",
       "        [220, 221, 222, 223, 224, 225, 226, 227, 228, 229],\n",
       "        [210, 211, 212, 213, 214, 215, 216, 217, 218, 219],\n",
       "        [200, 201, 202, 203, 204, 205, 206, 207, 208, 209]],\n",
       "\n",
       "       [[190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
       "        [180, 181, 182, 183, 184, 185, 186, 187, 188, 189],\n",
       "        [170, 171, 172, 173, 174, 175, 176, 177, 178, 179],\n",
       "        [160, 161, 162, 163, 164, 165, 166, 167, 168, 169],\n",
       "        [150, 151, 152, 153, 154, 155, 156, 157, 158, 159],\n",
       "        [140, 141, 142, 143, 144, 145, 146, 147, 148, 149],\n",
       "        [130, 131, 132, 133, 134, 135, 136, 137, 138, 139],\n",
       "        [120, 121, 122, 123, 124, 125, 126, 127, 128, 129],\n",
       "        [110, 111, 112, 113, 114, 115, 116, 117, 118, 119],\n",
       "        [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]],\n",
       "\n",
       "       [[ 90,  91,  92,  93,  94,  95,  96,  97,  98,  99],\n",
       "        [ 80,  81,  82,  83,  84,  85,  86,  87,  88,  89],\n",
       "        [ 70,  71,  72,  73,  74,  75,  76,  77,  78,  79],\n",
       "        [ 60,  61,  62,  63,  64,  65,  66,  67,  68,  69],\n",
       "        [ 50,  51,  52,  53,  54,  55,  56,  57,  58,  59],\n",
       "        [ 40,  41,  42,  43,  44,  45,  46,  47,  48,  49],\n",
       "        [ 30,  31,  32,  33,  34,  35,  36,  37,  38,  39],\n",
       "        [ 20,  21,  22,  23,  24,  25,  26,  27,  28,  29],\n",
       "        [ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19],\n",
       "        [  0,   1,   2,   3,   4,   5,   6,   7,   8,   9]]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1, ::-1, ::1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fits file\n",
    "Ahora lo haremos en el caso de un archivo fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_file = 'tess2019058134432-s0009-0000000355151781-0139-s_lc.fits'\n",
    "hdu_list = fits.open(image_file)\n",
    "lc = hdu_list[1].data  # guardado!\n",
    "hdu_list.close()  # No estrictamente necesario\n",
    "t = lc['TIME']\n",
    "f = lc['SAP_FLUX']\n",
    "f_err = lc['SAP_FLUX_ERR']\n",
    "a = np.array([t, f, f_err])\n",
    "\n",
    "np.savetxt('lc.dat', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1955\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(np.isnan(a[1]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 18187)\n",
      "1955\n",
      "10.74  %\n"
     ]
    }
   ],
   "source": [
    "print(a.shape)\n",
    "print(np.sum(np.isnan(a[1])))  # para ver cuantos NaNs\n",
    "print(str(np.sum(np.isnan(a[1])) * 100 / a.shape[1])[:5], ' %')  # % del total\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.54321783e+03, 1.54321922e+03, 1.54322061e+03, ...,\n",
       "        1.56847364e+03, 1.56847503e+03, 1.56847642e+03],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        6.43702344e+04, 6.44503438e+04, 6.44289570e+04],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "        2.85973511e+01, 2.86086903e+01, 2.85986500e+01]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaNs\n",
    "Despues de hacer una mascara para sacar los NaNs, vemos que queda.\n",
    "\n",
    "Esto haganlo de la manera que prefieran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 16232)\n",
      "16232\n"
     ]
    }
   ],
   "source": [
    "a = np.array([x[~np.isnan(a[1])] for x in a])  # limpia nans\n",
    "print(a.shape)  #\n",
    "print(18187-1955)\n",
    "# chequear siempre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.54409562e+03, 1.54409701e+03, 1.54409840e+03, ...,\n",
       "        1.56847364e+03, 1.56847503e+03, 1.56847642e+03],\n",
       "       [6.45091016e+04, 6.44955820e+04, 6.45880742e+04, ...,\n",
       "        6.43702344e+04, 6.44503438e+04, 6.44289570e+04],\n",
       "       [2.92132397e+01, 2.92123356e+01, 2.92148304e+01, ...,\n",
       "        2.85973511e+01, 2.86086903e+01, 2.85986500e+01]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "LOAD_TXT = False\n",
    "\n",
    "if LOAD_TXT:\n",
    "    a = np.loadtxt(blabla)\n",
    "else:\n",
    "    a = np.arange(100).reshape((10, 10))\n",
    "'''    \n",
    "np.savetxt('lc_nonan.dat', a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalizar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8503664601430223"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1] = a[1]/ np.amax(a[1])\n",
    "np.amin(a[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Curve Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reddsin(x, a, b, c):\n",
    "    return a * np.sin(b * x) + c\n",
    "\n",
    "def reddcos(x, a, b, c):\n",
    "    return a * np.cos(b * x) + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "popt, pcov = curve_fit(reddcos, a[0], a[1], p0=[0.1, 0.8, 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.90984159e-04,  8.00529222e-01,  9.74276738e-01])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85036646, 0.94469508, 0.94507779, ..., 0.99933266, 0.99939066,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-159-6e081a21db83>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-159-6e081a21db83>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    dib = ax.plot(a[0], a[1], alpha=0.9, label='data', 'bo')\u001b[0m\n\u001b[0m                                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "def plot(a, *b):\n",
    "\n",
    "    fig = plt.figure(figsize=(12, 10))\n",
    "    ax = plt.gca()\n",
    "    dib = ax.plot(a[0], a[1], alpha=0.9, label='data')\n",
    "\n",
    "    ax.set_title('Lightcurve', fontsize=28)\n",
    "    ax.set_xlabel('Time [??]', fontsize=22)\n",
    "    ax.set_ylabel('Amplitude [??]', fontsize=22)\n",
    "    \n",
    "    if b:\n",
    "        popt = b[0]\n",
    "        dib_fit = ax.plot(a[0], reddsin(a[0], popt[0], popt[1], popt[2]), alpha=0.9, label='fit')\n",
    "\n",
    "    #ax.text('a = ' + str(popt[0] + '\\n b = ' + str(popt[1])))\n",
    "    ax.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(a, popt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "append(a, b)  agrega b al array a\n",
    "arange(n) crea un array de n integers, partiendo del 0\n",
    "shape para ver la forma del array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}