# Exact references for qdm_calibrate(): Python_reference/ref_binary_qdm.py
# (rational arithmetic; the smallest distance between a probability of an
# outcome and a grid value is about 9e-5, and between a probability of
# meeting a criterion and its target about 3e-3). The curves are the
# probabilities that the criteria are met at gamma = 0.01, ..., 0.99.
ref_cal <- list(
  D1 = list(
    gamma_go = 0.7, gamma_nogo = 0.41,
    prob_go = 0.04539569696408934,
    prob_nogo = 0.1820194948817842,
    curve_go = c(0.9095952581639182, 0.8351111911970428,
                 0.7555154491502801, 0.7326075499361495,
                 0.7235162610462224, 0.692488340917831,
                 0.6399247874548518, 0.5820187088857995,
                 0.5779353878310263, 0.5779353878310263,
                 0.5779353878310263, 0.5576463131234777,
                 0.51053955065456, 0.44881548888315265,
                 0.41763529273058597, 0.41763529273058597,
                 0.41763529273058597, 0.41652260019391374,
                 0.41652260019391374, 0.41652260019391374,
                 0.4074313113039867, 0.4074313113039867,
                 0.3767067387791402, 0.32414318531616115,
                 0.2662371067471088, 0.2662371067471088,
                 0.2662371067471088, 0.2662371067471088,
                 0.2662371067471088, 0.2662371067471088,
                 0.26609807242881733, 0.26609807242881733,
                 0.26609807242881733, 0.26609807242881733,
                 0.26362333239562147, 0.26362333239562147,
                 0.24992820696802612, 0.21645702449573354,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14854199690661835, 0.14854199690661835,
                 0.14823291564643162, 0.14451375200964325,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.07148966780888895,
                 0.07148966780888895, 0.04539569696408934,
                 0.03368078309878694, 0.029696746610759207,
                 0.029232732854534975, 0.029232732854534975,
                 0.029232732854534975, 0.029232732854534975,
                 0.029232732854534975, 0.029232732854534975,
                 0.029232732854534975, 0.029232732854534975,
                 0.029232732854534975, 0.029232732854534975,
                 0.013607283081933553, 0.010478962970312823,
                 0.010478962970312823, 0.010478962970312823,
                 0.009983074485407709, 0.009983074485407709,
                 0.009983074485407709, 0.009983074485407709,
                 0.00821548966950257, 0.0050006947855751,
                 0.00317499392345005, 0.0027876570426695756,
                 0.0027876570426695756, 0.0027876570426695756,
                 0.000842309435619432, 0.000618857999075094,
                 0.00019999396088181583),
    curve_nogo = c(0.9360312437209708, 0.8943008066738906,
                   0.824908404748071, 0.7894363741868631,
                   0.7894363741868631, 0.7754867625215374,
                   0.7272729569375629, 0.6434575664356205,
                   0.6428586680471624, 0.6428586680471624,
                   0.6428586680471624, 0.6351691028828969,
                   0.6351691028828969, 0.6004806757505677,
                   0.4743758221734216, 0.4743758221734216,
                   0.4743758221734216, 0.4743758221734216,
                   0.4743758221734216, 0.4742001669821098,
                   0.4742001669821098, 0.4742001669821098,
                   0.47107162118493706, 0.47107162118493706,
                   0.4522823483296004, 0.31250133729144036,
                   0.31250133729144036, 0.31250133729144036,
                   0.31250133729144036, 0.31250133729144036,
                   0.31250133729144036, 0.31250133729144036,
                   0.31250133729144036, 0.31250133729144036,
                   0.31250133729144036, 0.31250133729144036,
                   0.31250133729144036, 0.3124664371909982,
                   0.3115538301228717, 0.27568449524892386,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.1820194948817842,
                   0.1820194948817842, 0.10652210989787712,
                   0.09533801891255769, 0.09315300280024655,
                   0.09315300280024655, 0.092972328889183,
                   0.092972328889183, 0.092972328889183,
                   0.092972328889183, 0.0929681165691327,
                   0.0929681165691327, 0.0929681165691327,
                   0.0929681165691327, 0.0929681165691327,
                   0.0929681165691327, 0.0929681165691327,
                   0.056372802045602396, 0.04499540247596093,
                   0.04179868226405913, 0.04179868226405913,
                   0.04179868226405913, 0.041368453217656635,
                   0.041368453217656635, 0.041368453217656635,
                   0.041368453217656635, 0.041346697310887505,
                   0.041346697310887505, 0.041346697310887505,
                   0.019776015886914054, 0.019776015886914054,
                   0.016580561319679932, 0.016580561319679932,
                   0.01595627100187675, 0.015956037626780276,
                   0.015956037626780276, 0.015904416509333545,
                   0.008258294030383372, 0.005946695387662566,
                   0.005946695387662566, 0.005330185061081506,
                   0.005328981541067139, 0.0032437514376666052,
                   0.0015560507274942057, 0.0014802327415182229,
                   0.0004087239987645357),
    oc = data.frame(
      Go = c(0.04539569696408934, 0.39951932424943226),
      NoGo = c(0.7337628932528911, 0.1820194948817842),
      Gray = c(0.22084140978301947, 0.41846118086878353),
      Miss = c(0.0, 0.0)
    )
  ),
  D3 = list(
    gamma_go = 0.39, gamma_nogo = 0.56,
    prob_go = 0.06644763953101097,
    prob_nogo = 0.05730992128,
    curve_go = c(0.9460485735951875, 0.8188773542124149,
                 0.8188773542124149, 0.8188773542124149,
                 0.8188773542124149, 0.6069253219077941,
                 0.6069253219077941, 0.6069253219077941,
                 0.6069253219077941, 0.6069253219077941,
                 0.6069253219077941, 0.6069253219077941,
                 0.3684792855650957, 0.3684792855650957,
                 0.3684792855650957, 0.3684792855650957,
                 0.3684792855650957, 0.3684792855650957,
                 0.3684792855650957, 0.3684792855650957,
                 0.3684792855650957, 0.3684792855650957,
                 0.3684792855650957, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.1777224564909369, 0.1777224564909369,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.06644763953101097, 0.06644763953101097,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.01875843226247128,
                 0.01875843226247128, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0038555549910526286, 0.0038555549910526286,
                 0.0005438044862929281, 0.0005438044862929281,
                 0.0005438044862929281, 0.0005438044862929281,
                 0.0005438044862929281, 0.0005438044862929281,
                 0.0005438044862929281, 0.0005438044862929281,
                 0.0005438044862929281, 4.704191057897302e-05,
                 4.704191057897302e-05, 4.704191057897302e-05,
                 4.704191057897302e-05, 4.704191057897302e-05,
                 1.8816764231589208e-06, 1.8816764231589208e-06,
                 1.8816764231589208e-06),
    curve_nogo = c(0.980408958976, 0.980408958976,
                   0.91655667712, 0.91655667712,
                   0.91655667712, 0.91655667712,
                   0.91655667712, 0.77466271744,
                   0.77466271744, 0.77466271744,
                   0.77466271744, 0.77466271744,
                   0.77466271744, 0.77466271744,
                   0.56182177792, 0.56182177792,
                   0.56182177792, 0.56182177792,
                   0.56182177792, 0.56182177792,
                   0.56182177792, 0.56182177792,
                   0.56182177792, 0.56182177792,
                   0.56182177792, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.334791442432, 0.334791442432,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.158212292608,
                   0.158212292608, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.05730992128,
                   0.05730992128, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.01526726656, 0.01526726656,
                   0.00281018368, 0.00281018368,
                   0.00281018368, 0.00281018368,
                   0.00281018368, 0.00281018368,
                   0.00281018368, 0.00281018368,
                   0.00281018368, 0.000318767104,
                   0.000318767104, 0.000318767104,
                   0.000318767104, 1.6777216e-05,
                   1.6777216e-05),
    oc = data.frame(
      Go = c(0.06644763953101097, 0.665208557568),
      NoGo = c(0.6315207144349043, 0.05730992128),
      Gray = c(0.30203164603408467, 0.277481521152),
      Miss = c(0.0, 0.0)
    )
  ),
  D5 = list(
    gamma_go = 0.8, gamma_nogo = 0.46,
    prob_go = 0.09182105764481728,
    prob_nogo = 0.12048530877083947,
    curve_go = c(0.9936791760551595, 0.9887909515032317,
                 0.9798444380610416, 0.9714401437935783,
                 0.944861550588807, 0.944861550588807,
                 0.944861550588807, 0.9345753549355929,
                 0.9317126563916205, 0.8728283514045517,
                 0.8728283514045517, 0.8728283514045517,
                 0.8728283514045517, 0.8640951064374595,
                 0.8640951064374595, 0.832578838007728,
                 0.8323350941117238, 0.7527131282954047,
                 0.7527131282954047, 0.7527131282954047,
                 0.7527131282954047, 0.7527131282954047,
                 0.7478215515424739, 0.7478215515424739,
                 0.7478215515424739, 0.7213713341484944,
                 0.7213713231533782, 0.5900143119153444,
                 0.5900143119153444, 0.5899777503309437,
                 0.5899777503309437, 0.5899777503309437,
                 0.5899777503309437, 0.5899777503309437,
                 0.5899777503309437, 0.5899752764297812,
                 0.5883503171230866, 0.5883503171230866,
                 0.5882376000013704, 0.5718094922936588,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.40998567708953937,
                 0.40998567708953937, 0.4099855121627952,
                 0.2781609336050496, 0.24728670677785117,
                 0.24704296288184696, 0.24704296288184696,
                 0.24704296288184696, 0.24704296288184696,
                 0.24704296288184696, 0.24704296288184696,
                 0.24704296288184696, 0.24704296288184696,
                 0.24704296288184696, 0.24704296288184696,
                 0.24704296288184696, 0.2105606574743696,
                 0.16623746728750743, 0.1359048935625405,
                 0.1271716485954483, 0.1271716485954483,
                 0.12717053533992517, 0.12717053533992517,
                 0.12643930365191258, 0.12643930365191258,
                 0.12643930365191258, 0.12643930365191258,
                 0.12643930365191258, 0.09182105764481728,
                 0.06542464506440712, 0.06524930731951521,
                 0.05513844941119291, 0.05513844941119291,
                 0.05513399638910042, 0.05513399638910042,
                 0.0538340289437447, 0.0538340289437447,
                 0.028559856206421687, 0.02824424826561626,
                 0.020155561938958417, 0.020155561938958417,
                 0.018627244069717278, 0.018627244069717278,
                 0.006320823944840479, 0.006320823944840479,
                 0.005086480466454774, 0.0017525193506920857,
                 0.0010521614950701466),
    curve_nogo = c(0.9565126094004167, 0.9482174353272205,
                   0.8831998104072866, 0.8769094011065444,
                   0.8769094011065444, 0.7560145747512932,
                   0.7560145747512932, 0.7526433779024815,
                   0.7526433779024815, 0.7484946184087029,
                   0.7242027810800944, 0.5840754413879621,
                   0.5840754413879621, 0.584021011598685,
                   0.584021011598685, 0.5828214146935686,
                   0.5828214146935686, 0.581339714874362,
                   0.5678442496918017, 0.5085494277910066,
                   0.3985987646637045, 0.3985987646637045,
                   0.3985987646637045, 0.3985987646637045,
                   0.3985987646637045, 0.3985900170189992,
                   0.3985900170189992, 0.3982901177927201,
                   0.3982901177927201, 0.3927861229375173,
                   0.3883410234798973, 0.3401873376133784,
                   0.23645127718457593, 0.23645127718457593,
                   0.23645127718457593, 0.23645127718457593,
                   0.23645127718457593, 0.23645127718457593,
                   0.23645127718457593, 0.23645127718457593,
                   0.23645127718457593, 0.23645127718457593,
                   0.23645127718457593, 0.23645044407555638,
                   0.23530353065864862, 0.12048530877083947,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.12044087925583517,
                   0.12044087925583517, 0.0663256993043717,
                   0.05481927710246143, 0.052340518191378936,
                   0.052340518191378936, 0.05233496413124863,
                   0.05214455192408728, 0.05214455192408728,
                   0.05214455192408728, 0.05214455192408728,
                   0.05214455192408728, 0.05214455192408728,
                   0.05214451621941502, 0.05214451621941502,
                   0.022539400835089284, 0.02253643886742233,
                   0.019392544173173883, 0.019392544173173883,
                   0.019392544173173883, 0.019008658450400378,
                   0.019008658450400378, 0.019008658450400378,
                   0.019008658450400378, 0.019008658450400378,
                   0.008734145982730568, 0.008733907951582126,
                   0.00623369804737794, 0.00623369804737794,
                   0.005784392344912404, 0.005784392344912404,
                   0.005784392344912404, 0.005784392344912404,
                   0.002929653182028718, 0.001811780121552814,
                   0.001462458488858542, 0.001462458488858542,
                   0.001462458488858542, 0.0004994168687168717,
                   0.0003098918586551501, 0.00013041961720086529,
                   5.724662212067328e-05),
    oc = data.frame(
      Go = c(0.09182105764481728, 0.4914505722089934),
      NoGo = c(0.5900144878372048, 0.12048530877083947),
      Gray = c(0.3181644545179779, 0.38806411902016713),
      Miss = c(0.0, 0.0)
    )
  )
)

cal_args <- list(
  D1 = list(scenario_go = data.frame(pi_t = 0.4, pi_c = 0.4),
            scenario_nogo = data.frame(pi_t = 0.7, pi_c = 0.4),
            target_go = 0.05, target_nogo = 0.2),
  D3 = list(scenario_go = data.frame(pi_t = 1 / 3),
            scenario_nogo = data.frame(pi_t = 0.6),
            target_go = 0.1, target_nogo = 0.1),
  D5 = list(scenario_go = data.frame(pi_t = 0.4, pi_c = 0.4),
            scenario_nogo = data.frame(pi_t = 0.7, pi_c = 0.4),
            target_go = 0.1, target_nogo = 0.2)
)

run_cal <- function(name, ...) {
  a <- cal_args[[name]]
  qdm_calibrate(ref_design(name), a$scenario_go, a$scenario_nogo,
                a$target_go, a$target_nogo, ...)
}