import numpy as np

from statsmodels.tools.testing import ParamsTableTestBunch

est = dict(
    mlag_var=3,
    fpe_var=7.47593408072e-13,
    aic_var=-19.4085401106207,
    hqic_var=-19.20760258859687,
    sbic_var=-18.91206199634062,
    tparms_var=30,
    k_var=30,
    df_eq_var=10,
    k_aux=18,
    k_eq=18,
    k_eq_var=3,
    k_dv_var=3,
    neqs_var=3,
    k_dv=3,
    neqs=3,
    N_cns=12,
    ic_ml=9,
    rc_ml=0,
    oid_df=0,
    N=199,
    rank=6,
    F_3_var=9.018236399703298,
    df_r3_var=189,
    df_m3_var=9,
    ll_3_var=364.1156201942387,
    r2_3_var=.3004252574875596,
    rmse_3_var=.0398398997327694,
    k_3_var=10,
    obs_3_var=199,
    F_2_var=5.002566602091567,
    df_r2_var=189,
    df_m2_var=9,
    ll_2_var=728.0001662442413,
    r2_2_var=.1923874161594955,
    rmse_2_var=.0064000343524738,
    k_2_var=10,
    obs_2_var=199,
    F_1_var=8.356742395485949,
    df_r1_var=189,
    df_m1_var=9,
    ll_1_var=694.4411801251371,
    r2_1_var=.2846617748967589,
    rmse_1_var=.0075756675969815,
    k_1_var=10,
    obs_1_var=199,
    df_r=193,
    df_r_var=189,
    ll=1945.759734821802,
    ll_var=1961.149741006759,
    detsig_ml_var=5.52855987611e-13,
    detsig_var=6.45335912865e-13,
    T_var=199,
    N_gaps_var=0,
    tmin=0,
    tmax=198,
    cmd="svar",
    cmdline="svar gdp cons inv, aeq(A) beq(B) lags(1/3) var dfk small",
    predict="svar_p",
    dfk_var="dfk",
    vcetype="EIM",
    lags_var="1 2 3",
    depvar_var="gdp cons inv",
    eqnames_var="gdp cons inv",
    endog_var="gdp cons inv",
    timevar="qtrdate",
    tsfmt="%tq",
    small="small",
    title="Structural vector autoregression",
    cns_b="[b_1_2]_cons = 0:[b_1_3]_cons = 0:[b_2_1]_cons = 0:[b_2_3]_cons = 0:[b_3_1]_cons = 0:[b_3_2]_cons = 0",
    cns_a="[a_1_1]_cons = 1:[a_1_2]_cons = 0:[a_1_3]_cons = 0:[a_2_2]_cons = 1:[a_2_3]_cons = 0:[a_3_3]_cons = 1",
    properties="b V",
)

params_table = np.array([
    1, np.nan, np.nan, np.nan,
    np.nan, np.nan,              193,  1.9723316757957,
    0, -.50680224519119,  .04791445158754, -10.577231469827,
    6.466439125e-21, -.60130543578568, -.41229905459671,              193,
    1.9723316757957,                0, -5.5360565201616,  .24220266982262,
    -22.857124259679,  8.232580974e-57,  -6.013760517815, -5.0583525225081,
    193,  1.9723316757957,                0,                0,
    np.nan, np.nan, np.nan, np.nan,
    np.nan,              193,  1.9723316757957,                0,
    1, np.nan, np.nan, np.nan,
    np.nan, np.nan,              193,  1.9723316757957,
    0,  3.0411768648574,  .28669329203947,  10.607771263929,
    5.260805180e-21,  2.4757226037298,   3.606631125985,              193,
    1.9723316757957,                0,                0, np.nan,
    np.nan, np.nan, np.nan, np.nan,
    193,  1.9723316757957,                0,                0,
    np.nan, np.nan, np.nan, np.nan,
    np.nan,              193,  1.9723316757957,                0,
    1, np.nan, np.nan, np.nan,
    np.nan, np.nan,              193,  1.9723316757957,
    0,  .00757566759698,  .00037973390425,   19.94993734326,
    8.739086225e-49,  .00682670638925,  .00832462880472,              193,
    1.9723316757957,                0,                0, np.nan,
    np.nan, np.nan, np.nan, np.nan,
    193,  1.9723316757957,                0,                0,
    np.nan, np.nan, np.nan, np.nan,
    np.nan,              193,  1.9723316757957,                0,
    0, np.nan, np.nan, np.nan,
    np.nan, np.nan,              193,  1.9723316757957,
    0,  .00512051886486,  .00025666841839,   19.94993734326,
    8.739086225e-49,  .00461428361309,  .00562675411662,              193,
    1.9723316757957,                0,                0, np.nan,
    np.nan, np.nan, np.nan, np.nan,
    193,  1.9723316757957,                0,                0,
    np.nan, np.nan, np.nan, np.nan,
    np.nan,              193,  1.9723316757957,                0,
    0, np.nan, np.nan, np.nan,
    np.nan, np.nan,              193,  1.9723316757957,
    0,  .02070894812762,  .00103804577284,   19.94993734326,
    8.739086225e-49,  .01866157756892,  .02275631868632,              193,
    1.9723316757957,                0]).reshape(18, 9)

params_table_colnames = "b se t pvalue ll ul df crit eform".split()

params_table_rownames = [
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons"]

b = np.array([
    1, -.50680224519119, -5.5360565201616,                0,
    1,  3.0411768648574,                0,                0,
    1,  .00757566759698,                0,                0,
    0,  .00512051886486,                0,                0,
    0,  .02070894812762])

b_colnames = [
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons"]

b_rownames = "y1".split()

cov = np.array([
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,  .00229579467093,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,   .0586621332692,                0,
    0, -.04165561908647,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    -.04165561908647,                0,                0,  .08219304370043,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,  1.441978380e-07,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,  6.587867700e-08,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,  1.077539027e-06
    ]).reshape(18, 18)

cov_colnames = [
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons"]

cov_rownames = [
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons"]

constraints = np.array([
    1,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                1,                0,
    0,                0,                1,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    1,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                1,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                1,
    0,                0,                0,                0,
    0,                0,                0,                1,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                1,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                1,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                1,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    1,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                1,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                1,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                1,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                0,
    0,                0,                0,                1,
    0,                0,                0,                0
    ]).reshape(12, 19)

constraints_colnames = [
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_cons", "_cons", "_cons",
    "_cons", "_cons", "_cons", "_cons", "_r"]

constraints_rownames = "r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12".split()

Sigma = np.array([
    .00005739073954,  .00002908575565,  .00022926345064,  .00002908575565,
    .00004096043971,   .0000364524456,  .00022926345064,   .0000364524456,
    .00158721761072]).reshape(3, 3)

Sigma_colnames = "gdp cons inv".split()

Sigma_rownames = "gdp cons inv".split()

G_var = np.array([
    1,  .00772119249309,  .00832845872247,  .00812414768988,
    .00772450051084,  .00839168407728,  .00810118500591,  .00793331513676,
    .00846090823295,    .009386666817,  .00772119249309,  .00013590038218,
    .00010436399127,  .00039355021854,  .00008395547668,  .00009296949447,
    .0001468047815,  .00007985818625,  .00008622263703,  .00012491817455,
    .00832845872247,  .00010436399127,   .0001177915254,   .0001572415776,
    .00008140583018,  .00008416323485,  .00014479739125,  .00007884622137,
    .0000839417926,  .00012879456896,  .00812414768988,  .00039355021854,
    .0001572415776,  .00222357091844,   .0001852293649,  .00023227850984,
    .00042852108282,  .00014155595459,  .00015686829612,  .00027431611677,
    .00772450051084,  .00008395547668,  .00008140583018,   .0001852293649,
    .00013589031191,  .00010428130248,  .00039335411738,  .00008365141811,
    .00009289191013,   .0001446903813,  .00839168407728,  .00009296949447,
    .00008416323485,  .00023227850984,  .00010428130248,    .000118309348,
    .00015273148978,  .00008252427592,  .00008497769731,  .00014704611828,
    .00810118500591,   .0001468047815,  .00014479739125,  .00042852108282,
    .00039335411738,  .00015273148978,  .00222677863348,  .00017054880362,
    .0002272506141,  .00033983014403,  .00793331513676,  .00007985818625,
    .00007884622137,  .00014155595459,  .00008365141811,  .00008252427592,
    .00017054880362,  .00013762976929,  .00010632331501,  .00038874930061,
    .00846090823295,  .00008622263703,   .0000839417926,  .00015686829612,
    .00009289191013,  .00008497769731,   .0002272506141,  .00010632331501,
    .000119472079,  .00016022586575,    .009386666817,  .00012491817455,
    .00012879456896,  .00027431611677,   .0001446903813,  .00014704611828,
    .00033983014403,  .00038874930061,  .00016022586575,  .00210416228523
    ]).reshape(10, 10)

G_var_colnames = [
    "_cons",
    "L.gdp", "L.cons", "L.inv",
    "L2.gdp", "L2.cons", "L2.inv",
    "L3.gdp", "L3.cons", "L3.inv"]

G_var_rownames = [
    "_cons",
    "L.gdp", "L.cons", "L.inv",
    "L2.gdp", "L2.cons", "L2.inv",
    "L3.gdp", "L3.cons", "L3.inv"]


bf_var = np.array([
    -.28614799058891,  .02569110476595, -.18003096181942,   .6738689560015,
    .29544106895159,  .18370240194258,  .03057777928182, -.01444291994803,
    .01263245201514,  .00128149319157, -.12715587337617, -.08663431448056,
    -.35906668730993,  .25639388994688,  .20570668527827,  .41845237867104,
    .02404284475263,  .00384555072972,  .04190581088286,  .00483719365525,
    -1.8625374877103,  .33142498594011, -.48831009148236,  4.4033743272466,
    .87819807698004, -.12378698529172,  .22371717935155, -.09655522236577,
    .03345298758638, -.02059735685585])

bf_var_colnames = [
    "L.gdp", "L2.gdp", "L3.gdp",
    "L.cons", "L2.cons", "L3.cons",
    "L.inv", "L2.inv", "L3.inv",
    "_cons",
    "L.gdp", "L2.gdp", "L3.gdp",
    "L.cons", "L2.cons", "L3.cons",
    "L.inv", "L2.inv", "L3.inv",
    "_cons",
    "L.gdp", "L2.gdp", "L3.gdp",
    "L.cons", "L2.cons", "L3.cons",
    "L.inv", "L2.inv", "L3.inv",
    "_cons"]

bf_var_rownames = "r1".split()

B = np.array([
    .00757566759698,                0,                0,                0,
    .00512051886486,                0,                0,                0,
    .02070894812762]).reshape(3, 3)

B_colnames = "gdp cons inv".split()

B_rownames = "gdp cons inv".split()

A = np.array([
    1,                0,                0, -.50680224519119,
    1,                0, -5.5360565201616,  3.0411768648574,
    1]).reshape(3, 3)

A_colnames = "gdp cons inv".split()

A_rownames = "gdp cons inv".split()

beq = np.array([
    np.nan,                0,                0,                0,
    np.nan,                0,                0,                0,
    np.nan]).reshape(3, 3)

beq_colnames = "c1 c2 c3".split()

beq_rownames = "r1 r2 r3".split()

aeq = np.array([
    1,                0,                0, np.nan,
    1,                0, np.nan, np.nan,
    1]).reshape(3, 3)

aeq_colnames = "c1 c2 c3".split()

aeq_rownames = "r1 r2 r3".split()

V_var = np.array([
    .02944043907167, -.00139167936464,  -.0010606099932, -.01749947940302,
    .00202095994301, -.00138072504574, -.00385176007523,  .00038731129816,
    .00020334459451, -.00004143863419,  .01492048062093, -.00070530622659,
    -.00053751952583, -.00886877545113,  .00102422703656, -.00069975455317,
    -.00195208065406,   .0001962902355,  .00010305549704, -.00002100119285,
    .11760811420253, -.00555945464168, -.00423690492187, -.06990659232715,
    .00807329290157, -.00551569453385, -.01538693895515,  .00154722391453,
    .00081231717487, -.00016553827919, -.00139167936464,  .03044262457881,
    -.00102187006012,  .00135423549927, -.01978648635158,  .00141733933507,
    .0005786735915, -.00404788575193,   .0001576008945, -.00004691846312,
    -.00070530622659,  .01542839048605, -.00051788604076,  .00068632959155,
    -.01002783570742,  .00071831075721,   .0002932730754, -.00205147758736,
    .00007987248718, -.00002377838245, -.00555945464168,  .12161162608255,
    -.0040821473633,  .00540987459008, -.07904268481996,  .00566196061062,
    .00231167441725, -.01617041813247,  .00062958109943,  -.0001874289971,
    -.0010606099932, -.00102187006012,   .0305750957042,  .00161206604309,
    .00123567563375, -.01979131075828,  .00006003651469,  .00052765232747,
    -.00406616514879,  -.0000494168379, -.00053751952583, -.00051788604076,
    .01549552714982,  .00081699869003,  .00062624318551, -.01003028072757,
    .00003042664044,  .00026741538424, -.00206074162672,  -.0000250445644,
    -.00423690492187,  -.0040821473633,  .12214081925136,  .00643985121405,
    .00493625386149, -.07906195726942,  .00023983274364,    .002107855628,
    -.01624344032441, -.00019740945784, -.01749947940302,  .00135423549927,
    .00161206604309,   .0174886287578, -.00252569799616, -.00071586401207,
    .00214091625575, -.00039776436038, -.00032773904917, -.00001762895502,
    -.00886877545113,  .00068632959155,  .00081699869003,  .00886327631977,
    -.00128002941513, -.00036280148857,  .00108502116518,  -.0002015878709,
    -.00016609888596, -8.934393985e-06, -.06990659232715,  .00540987459008,
    .00643985121405,  .06986324637298,   -.010089611016, -.00285972013799,
    .00855249213135, -.00158898161155, -.00130924581082,  -.0000704238191,
    .00202095994301, -.01978648635158,  .00123567563375, -.00252569799616,
    .02190111225253, -.00177986396497, -.00110297152268,  .00248014965403,
    -.00035987053166, -.00002801274167,  .00102422703656, -.01002783570742,
    .00062624318551, -.00128002941513,  .01109953286177, -.00090203905358,
    -.00055898844408,  .00125694541307, -.00018238319342, -.00001419692037,
    .00807329290157, -.07904268481996,  .00493625386149,   -.010089611016,
    .08749015273475, -.00711016720733, -.00440612996585,  .00990765535257,
    -.00143760405483, -.00011190477537, -.00138072504574,  .00141733933507,
    -.01979131075828, -.00071586401207, -.00177986396497,  .02191808721673,
    .00005751246451, -.00100612185989,   .0023961694647, -.00002263401879,
    -.00069975455317,  .00071831075721, -.01003028072757, -.00036280148857,
    -.00090203905358,  .01110813581173,  .00002914744614, -.00050990481753,
    .00121438406457, -.00001147097154, -.00551569453385,  .00566196061062,
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