import numpy as np

from statsmodels.tools.tools import Bunch


epanechnikov_hsheather_q75 = Bunch()
epanechnikov_hsheather_q75.table = np.array([
    [.6440143,  .0122001,   52.79,  0.000,    .6199777,   .6680508],
    [62.39648,   13.5509,    4.60,  0.000,    35.69854,   89.09443]
])
epanechnikov_hsheather_q75.psrsquared = 0.6966
epanechnikov_hsheather_q75.rank = 2
epanechnikov_hsheather_q75.sparsity = 223.784434936344
epanechnikov_hsheather_q75.bwidth = .1090401129546568
# epanechnikov_hsheather_q75.kbwidth = 59.62067927472172  # Stata 12 results
epanechnikov_hsheather_q75.kbwidth = 59.30  # TODO: why do we need lower tol?
epanechnikov_hsheather_q75.df_m = 1
epanechnikov_hsheather_q75.df_r = 233
epanechnikov_hsheather_q75.f_r = .0044685860313942
epanechnikov_hsheather_q75.N = 235
epanechnikov_hsheather_q75.q_v = 745.2352905273438
epanechnikov_hsheather_q75.q = .75
epanechnikov_hsheather_q75.sum_rdev = 43036.06956481934
epanechnikov_hsheather_q75.sum_adev = 13058.50008841318
epanechnikov_hsheather_q75.convcode = 0


biweight_bofinger = Bunch()
biweight_bofinger.table = np.array([
    [.5601805,  .0136491,  41.04,  0.000,  .533289,  .5870719],
    [81.48233,  15.1604,  5.37,  0.000,  51.61335,  111.3513]
 ])
biweight_bofinger.psrsquared = 0.6206
biweight_bofinger.rank = 2
biweight_bofinger.sparsity = 216.8218989750115
biweight_bofinger.bwidth = .2173486679767846
biweight_bofinger.kbwidth = 91.50878448104551
biweight_bofinger.df_m = 1
biweight_bofinger.df_r = 233
biweight_bofinger.f_r = .0046120802590851
biweight_bofinger.N = 235
biweight_bofinger.q_v = 582.541259765625
biweight_bofinger.q = .5
biweight_bofinger.sum_rdev = 46278.05667114258
biweight_bofinger.sum_adev = 17559.93220318131
biweight_bofinger.convcode = 0


biweight_hsheather = Bunch()
biweight_hsheather.table = np.array([
    [.5601805,  .0128449,  43.61,  0.000,  .5348735,  .5854875],
    [81.48233,  14.26713,  5.71,  0.000,  53.37326,  109.5914]
 ])
biweight_hsheather.psrsquared = 0.6206
biweight_hsheather.rank = 2
biweight_hsheather.sparsity = 204.0465407204423
biweight_hsheather.bwidth = .1574393314202373
biweight_hsheather.kbwidth = 64.53302151153288
biweight_hsheather.df_m = 1
biweight_hsheather.df_r = 233
biweight_hsheather.f_r = .0049008427022052
biweight_hsheather.N = 235
biweight_hsheather.q_v = 582.541259765625
biweight_hsheather.q = .5
biweight_hsheather.sum_rdev = 46278.05667114258
biweight_hsheather.sum_adev = 17559.93220318131
biweight_hsheather.convcode = 0

biweight_chamberlain = Bunch()
biweight_chamberlain.table = np.array([
    [.5601805,  .0114969,  48.72,  0.000,  .5375294,  .5828315],
    [81.48233,  12.76983,  6.38,  0.000,  56.32325,  106.6414]
])
biweight_chamberlain.psrsquared = 0.6206
biweight_chamberlain.rank = 2
biweight_chamberlain.sparsity = 182.6322495257494
biweight_chamberlain.bwidth = .063926976464458
biweight_chamberlain.kbwidth = 25.61257055690209
biweight_chamberlain.df_m = 1
biweight_chamberlain.df_r = 233
biweight_chamberlain.f_r = .005475484218131
biweight_chamberlain.N = 235
biweight_chamberlain.q_v = 582.541259765625
biweight_chamberlain.q = .5
biweight_chamberlain.sum_rdev = 46278.05667114258
biweight_chamberlain.sum_adev = 17559.93220318131
biweight_chamberlain.convcode = 0

epanechnikov_bofinger = Bunch()
epanechnikov_bofinger.table = np.array([
    [.5601805,  .0209663,  26.72,  0.000,  .5188727,  .6014882],
    [81.48233,  23.28774,  3.50,  0.001,  35.60088,  127.3638]
    ])
epanechnikov_bofinger.psrsquared = 0.6206
epanechnikov_bofinger.rank = 2
epanechnikov_bofinger.sparsity = 333.0579553401614
epanechnikov_bofinger.bwidth = .2173486679767846
epanechnikov_bofinger.kbwidth = 91.50878448104551
epanechnikov_bofinger.df_m = 1
epanechnikov_bofinger.df_r = 233
epanechnikov_bofinger.f_r = .0030024804511235
epanechnikov_bofinger.N = 235
epanechnikov_bofinger.q_v = 582.541259765625
epanechnikov_bofinger.q = .5
epanechnikov_bofinger.sum_rdev = 46278.05667114258
epanechnikov_bofinger.sum_adev = 17559.93220318131
epanechnikov_bofinger.convcode = 0

epanechnikov_hsheather = Bunch()
epanechnikov_hsheather.table = np.array([
    [.5601805,  .0170484,  32.86,  0.000,  .5265918,  .5937692],
    [81.48233,  18.93605,  4.30,  0.000,  44.17457,  118.7901]
    ])
epanechnikov_hsheather.psrsquared = 0.6206
epanechnikov_hsheather.rank = 2
epanechnikov_hsheather.sparsity = 270.8207209067576
epanechnikov_hsheather.bwidth = .1574393314202373
epanechnikov_hsheather.kbwidth = 64.53302151153288
epanechnikov_hsheather.df_m = 1
epanechnikov_hsheather.df_r = 233
epanechnikov_hsheather.f_r = .0036924796472434
epanechnikov_hsheather.N = 235
epanechnikov_hsheather.q_v = 582.541259765625
epanechnikov_hsheather.q = .5
epanechnikov_hsheather.sum_rdev = 46278.05667114258
epanechnikov_hsheather.sum_adev = 17559.93220318131
epanechnikov_hsheather.convcode = 0

epanechnikov_chamberlain = Bunch()
epanechnikov_chamberlain.table = np.array([
    [.5601805,  .0130407,  42.96,  0.000,  .5344876,  .5858733],
    [81.48233,  14.48467,  5.63,  0.000,  52.94468,  110.02]
    ])
epanechnikov_chamberlain.psrsquared = 0.6206
epanechnikov_chamberlain.rank = 2
epanechnikov_chamberlain.sparsity = 207.1576340635951
epanechnikov_chamberlain.bwidth = .063926976464458
epanechnikov_chamberlain.kbwidth = 25.61257055690209
epanechnikov_chamberlain.df_m = 1
epanechnikov_chamberlain.df_r = 233
epanechnikov_chamberlain.f_r = .0048272418466269
epanechnikov_chamberlain.N = 235
epanechnikov_chamberlain.q_v = 582.541259765625
epanechnikov_chamberlain.q = .5
epanechnikov_chamberlain.sum_rdev = 46278.05667114258
epanechnikov_chamberlain.sum_adev = 17559.93220318131
epanechnikov_chamberlain.convcode = 0

epan2_bofinger = Bunch()
epan2_bofinger.table = np.array([
    [.5601805,  .0143484,  39.04,  0.000,  .5319113,  .5884496],
    [81.48233,  15.93709,  5.11,  0.000,  50.08313,  112.8815]
    ])
epan2_bofinger.psrsquared = 0.6206
epan2_bofinger.rank = 2
epan2_bofinger.sparsity = 227.9299402797656
epan2_bofinger.bwidth = .2173486679767846
epan2_bofinger.kbwidth = 91.50878448104551
epan2_bofinger.df_m = 1
epan2_bofinger.df_r = 233
epan2_bofinger.f_r = .0043873130435281
epan2_bofinger.N = 235
epan2_bofinger.q_v = 582.541259765625
epan2_bofinger.q = .5
epan2_bofinger.sum_rdev = 46278.05667114258
epan2_bofinger.sum_adev = 17559.93220318131
epan2_bofinger.convcode = 0

epan2_hsheather = Bunch()
epan2_hsheather.table = np.array([
    [.5601805,  .0131763,   42.51,  0.000,   .5342206,   .5861403],
    [81.48233,  14.63518,    5.57,  0.000,   52.64815,   110.3165]
    ])
epan2_hsheather.psrsquared = 0.6206
epan2_hsheather.rank = 2
epan2_hsheather.sparsity = 209.3102085912557
epan2_hsheather.bwidth = .1574393314202373
epan2_hsheather.kbwidth = 64.53302151153288
epan2_hsheather.df_m = 1
epan2_hsheather.df_r = 233
epan2_hsheather.f_r = .0047775978378236
epan2_hsheather.N = 235
epan2_hsheather.q_v = 582.541259765625
epan2_hsheather.q = .5
epan2_hsheather.sum_rdev = 46278.05667114258
epan2_hsheather.sum_adev = 17559.93220318131
epan2_hsheather.convcode = 0

epan2_chamberlain = Bunch()
epan2_chamberlain.table = np.array([
    [.5601805,  .0117925,  47.50,  0.000,  .5369469,  .583414],
    [81.48233,  13.0982,  6.22,  0.000,  55.67629,  107.2884]
    ])
epan2_chamberlain.psrsquared = 0.6206
epan2_chamberlain.rank = 2
epan2_chamberlain.sparsity = 187.3286437436797
epan2_chamberlain.bwidth = .063926976464458
epan2_chamberlain.kbwidth = 25.61257055690209
epan2_chamberlain.df_m = 1
epan2_chamberlain.df_r = 233
epan2_chamberlain.f_r = .0053382119253919
epan2_chamberlain.N = 235
epan2_chamberlain.q_v = 582.541259765625
epan2_chamberlain.q = .5
epan2_chamberlain.sum_rdev = 46278.05667114258
epan2_chamberlain.sum_adev = 17559.93220318131
epan2_chamberlain.convcode = 0


rectangle_bofinger = Bunch()
rectangle_bofinger.table = np.array([
    [.5601805,  .0158331,  35.38,  0.000,  .5289861,  .5913748],
    [81.48233,  17.5862,  4.63,  0.000,  46.83404,  116.1306]
    ])
rectangle_bofinger.psrsquared = 0.6206
rectangle_bofinger.rank = 2
rectangle_bofinger.sparsity = 251.515372550242
rectangle_bofinger.bwidth = .2173486679767846
rectangle_bofinger.kbwidth = 91.50878448104551
rectangle_bofinger.df_m = 1
rectangle_bofinger.df_r = 233
rectangle_bofinger.f_r = .0039759001203803
rectangle_bofinger.N = 235
rectangle_bofinger.q_v = 582.541259765625
rectangle_bofinger.q = .5
rectangle_bofinger.sum_rdev = 46278.05667114258
rectangle_bofinger.sum_adev = 17559.93220318131
rectangle_bofinger.convcode = 0

rectangle_hsheather = Bunch()
rectangle_hsheather.table = np.array([
    [.5601805,  .0137362,  40.78,  0.000,  .5331174,  .5872435],
    [81.48233,  15.25712,  5.34,  0.000,  51.42279,  111.5419]
    ])
rectangle_hsheather.psrsquared = 0.6206
rectangle_hsheather.rank = 2
rectangle_hsheather.sparsity = 218.2051806505069
rectangle_hsheather.bwidth = .1574393314202373
rectangle_hsheather.kbwidth = 64.53302151153288
rectangle_hsheather.df_m = 1
rectangle_hsheather.df_r = 233
rectangle_hsheather.f_r = .004582842611797
rectangle_hsheather.N = 235
rectangle_hsheather.q_v = 582.541259765625
rectangle_hsheather.q = .5
rectangle_hsheather.sum_rdev = 46278.05667114258
rectangle_hsheather.sum_adev = 17559.93220318131
rectangle_hsheather.convcode = 0

rectangle_chamberlain = Bunch()
rectangle_chamberlain.table = np.array([
    [.5601805,  .0118406,  47.31,  0.000,  .5368522,  .5835087],
    [81.48233,  13.1516,  6.20,  0.000,  55.57108,  107.3936]
    ])
rectangle_chamberlain.psrsquared = 0.6206
rectangle_chamberlain.rank = 2
rectangle_chamberlain.sparsity = 188.0923150272497
rectangle_chamberlain.bwidth = .063926976464458
rectangle_chamberlain.kbwidth = 25.61257055690209
rectangle_chamberlain.df_m = 1
rectangle_chamberlain.df_r = 233
rectangle_chamberlain.f_r = .0053165383171297
rectangle_chamberlain.N = 235
rectangle_chamberlain.q_v = 582.541259765625
rectangle_chamberlain.q = .5
rectangle_chamberlain.sum_rdev = 46278.05667114258
rectangle_chamberlain.sum_adev = 17559.93220318131
rectangle_chamberlain.convcode = 0

triangle_bofinger = Bunch()
triangle_bofinger.table = np.array([
    [.5601805,  .0138712,  40.38,  0.000,  .5328515,  .5875094],
    [81.48233,  15.40706,  5.29,  0.000,  51.12738,  111.8373]
    ])
triangle_bofinger.psrsquared = 0.6206
triangle_bofinger.rank = 2
triangle_bofinger.sparsity = 220.3495620604223
triangle_bofinger.bwidth = .2173486679767846
triangle_bofinger.kbwidth = 91.50878448104551
triangle_bofinger.df_m = 1
triangle_bofinger.df_r = 233
triangle_bofinger.f_r = .0045382436463649
triangle_bofinger.N = 235
triangle_bofinger.q_v = 582.541259765625
triangle_bofinger.q = .5
triangle_bofinger.sum_rdev = 46278.05667114258
triangle_bofinger.sum_adev = 17559.93220318131
triangle_bofinger.convcode = 0

triangle_hsheather = Bunch()
triangle_hsheather.table = np.array([
    [.5601805,  .0128874,  43.47,  0.000,  .5347898,  .5855711],
    [81.48233,  14.31431,  5.69,  0.000,  53.2803,  109.6844]
    ])
triangle_hsheather.psrsquared = 0.6206
triangle_hsheather.rank = 2
triangle_hsheather.sparsity = 204.7212998199564
triangle_hsheather.bwidth = .1574393314202373
triangle_hsheather.kbwidth = 64.53302151153288
triangle_hsheather.df_m = 1
triangle_hsheather.df_r = 233
triangle_hsheather.f_r = .004884689579831
triangle_hsheather.N = 235
triangle_hsheather.q_v = 582.541259765625
triangle_hsheather.q = .5
triangle_hsheather.sum_rdev = 46278.05667114258
triangle_hsheather.sum_adev = 17559.93220318131
triangle_hsheather.convcode = 0

triangle_chamberlain = Bunch()
triangle_chamberlain.table = np.array([
    [.5601805,  .0115725,  48.41,  0.000,  .5373803,  .5829806],
    [81.48233,  12.85389,  6.34,  0.000,  56.15764,  106.807]
    ])
triangle_chamberlain.psrsquared = 0.6206
triangle_chamberlain.rank = 2
triangle_chamberlain.sparsity = 183.8344452913298
triangle_chamberlain.bwidth = .063926976464458
triangle_chamberlain.kbwidth = 25.61257055690209
triangle_chamberlain.df_m = 1
triangle_chamberlain.df_r = 233
triangle_chamberlain.f_r = .0054396769790083
triangle_chamberlain.N = 235
triangle_chamberlain.q_v = 582.541259765625
triangle_chamberlain.q = .5
triangle_chamberlain.sum_rdev = 46278.05667114258
triangle_chamberlain.sum_adev = 17559.93220318131
triangle_chamberlain.convcode = 0

gaussian_bofinger = Bunch()
gaussian_bofinger.table = np.array([
    [.5601805,  .0197311,  28.39,  0.000,  .5213062,  .5990547],
    [81.48233,  21.91582,  3.72,  0.000,  38.30383,  124.6608]
    ])
gaussian_bofinger.psrsquared = 0.6206
gaussian_bofinger.rank = 2
gaussian_bofinger.sparsity = 313.4370075776719
gaussian_bofinger.bwidth = .2173486679767846
gaussian_bofinger.kbwidth = 91.50878448104551
gaussian_bofinger.df_m = 1
gaussian_bofinger.df_r = 233
gaussian_bofinger.f_r = .0031904337261521
gaussian_bofinger.N = 235
gaussian_bofinger.q_v = 582.541259765625
gaussian_bofinger.q = .5