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tests.py
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131 lines (97 loc) · 20.9 KB
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import unittest
import param_calc
class TestParameterCalculator(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.aps = sorted([mains*10 + subs*3 for mains in range(4) for subs in range(10)])
cls.ps = [param_calc.calcSkillPoint2Percent(ap)/100.0 for ap in cls.aps]
cls.abilities = {
'MainInkSave - ConsumeRt_Main_Low': [0.6, 0.8, 1.0],
'MainInkSave - ConsumeRt_Main': [0.55, 0.775, 1.0],
'MainInkSave - ConsumeRt_Main_High': [0.5, 0.7, 1.0],
'SubInkSaver - ConsumeRt_Sub_Low': [0.8, 0.9, 1.0],
'SubInkSaver - ConsumeRt_Sub': [0.7, 0.85, 1.0],
'SubInkSaver - ConsumeRt_Sub_High': [0.65, 0.825, 1.0],
'SquidMoveUp - MoveVel_Stealth_BigWeapon': [2.4, 2.064, 1.728],
'SquidMoveUp - MoveVel_Stealth': [2.4, 2.16, 1.92],
'SquidMoveUp - MoveVel_Stealth_ShortWeapon': [2.4, 2.208, 2.016],
'RunSpeedUp - MoveVel_Human_BigWeapon': [1.44, 1.16, 0.88],
'RunSpeedUp - MoveVel_Human': [1.44, 1.2, 0.96],
'RunSpeedUp - MoveVel_Human_ShortWeapon': [1.44, 1.24, 1.04],
'RunSpeedUp - MoveVelRt_Human_Shot': [1.25, 1.125, 1.0],
'RunSpeedUp - MoveVelRt_Human_ShotG': [1.3, 1.15, 1.0],
'SpecialIncreaseUp - SpecialRt_Charge': [1.3, 1.15, 1.0],
'RespawnTimeSave - Dying_AroudFrm': [30.0, 60.0, 90.0],
'RespawnTimeSave - Dying_ChaseFrm': [90.0, 180.0, 270.0],
'RespawnSpecialGaugeSave - SpecialRt_Restart': [1.0, 0.8, 0.5],
'RespawnSpecialGaugeSave - SpecialRt_Restart_SuperLanding': [1.25, 1.05, 0.75],
'OpInkEffectReduction - OpInk_JumpGnd_Msn': [1.05, 0.825, 0.6],
'OpInkEffectReduction - OpInk_JumpGnd': [1.05, 0.925, 0.8],
'OpInkEffectReduction - OpInk_VelGnd_ShotK': [1.0, 0.75, 0.5],
'OpInkEffectReduction - OpInk_VelGnd_Shot': [0.4, 0.26, 0.12],
'OpInkEffectReduction - OpInk_VelGnd': [0.72, 0.48, 0.24],
'OpInkEffectReduction - OpInk_Damage_Lmt': [0.2, 0.3, 0.4],
'OpInkEffectReduction - OpInk_Damage': [0.0015, 0.00225, 0.003],
'MarkingTimeReduction - MarkingTime_ShortRt_Thermal': [1.0, 1.0, 1.0],
'MarkingTimeReduction - Silhouette_DistFar': [290.0, 230.0, 170.0],
'MarkingTimeReduction - Silhouette_DistNear': [250.0, 190.0, 130.0],
'MarkingTimeReduction - MarkingTime_ShortRt_Trap': [0.1, 0.55, 1.0],
'MarkingTimeReduction - MarkingTime_ShortRt': [0.1, 0.55, 1.0],
'JumpTimeSave - DokanWarp_TameFrm': [20.0, 35.0, 80.0],
'JumpTimeSave - DokanWarp_MoveFrm': [96.6, 132.3, 138.0],
'InkRecoveryUp - RecoverFullFrm_Ink': [117.0, 148.5, 180.0],
'InkRecoveryUp - RecoverNrmlFrm_Ink': [220.0, 410.0, 600.0],
'BombDistanceUp - BombThrow_VelZ': [16.8, 14.0, 11.2],
'BombDistanceUp - BombThrow_VelZ_BombPiyo': [18.4, 16.0, 13.6],
'BombDistanceUp - BombThrow_VelZ_PointSensor': [18.4, 16.0, 13.6],
'BombDamageReduction - BurstDamageRt_SubH': [0.5, 0.75, 1.0],
'BombDamageReduction - BurstDamageRt_SubL': [0.6, 0.8, 1.0],
'BombDamageReduction - BurstDamageRt_Special': [0.65, 0.825, 1.0],
'BombDamageReduction - BurstDamageRt_Main': [1.0, 1.0, 1.0],
}
def assertListAlmostEqual(self, a, b, places=None, msg=None, delta=1e-01):
len_a = len(a)
self.assertEqual(len_a, len(b))
for i in range(len_a):
self.assertAlmostEqual(a[i],b[i], places, msg, delta)
def test_calcSkillPoint2Percent(self):
expected = [0.0,0.09657,0.18828,0.27513,0.303,0.35712,0.38337,0.43425,0.45888,0.50652,0.52953,0.552,0.57393,0.59532,0.61617,0.63648,0.65625,0.67548,0.69417,0.71232,0.72993,0.747,0.76353,0.77952,0.79497,0.80988,0.82425,0.83808,0.85137,0.86412,0.87633,0.89913,0.90972,0.92928,0.93825,0.95457,0.96192,0.98073,0.99468,1.0,]
self.assertListAlmostEqual(self.ps, expected)
def test_lerpN_06(self):
expected = [0.0,0.1785927,0.2921162,0.3863303,0.4148022,0.4682086,0.4933338,0.5407876,0.5632276,0.6057562,0.6259171,0.6453837,0.6641823,0.6823369,0.6998687,0.7167969,0.7331391,0.7489111,0.7641274,0.7788013,0.7929448,0.8065692,0.8196848,0.832301,0.8444266,0.8560698,0.8672381,0.8779384,0.8881772,0.8979606,0.907294,0.9246317,0.9326451,0.947382,0.9541128,0.9663157,0.9717935,0.9857624,0.9960766,1.0,]
s=0.6
results = [param_calc.lerpN(s,p) for p in self.ps]
self.assertListAlmostEqual(results, expected)
def test_lerpN_04(self):
expected = [0.0,0.0455024,0.1099875,0.1815973,0.2063025,0.2563613,0.2815614,0.3319847,0.3571006,0.4069096,0.4315222,0.455892,0.4799863,0.5037747,0.5272289,0.5503229,0.5730319,0.595333,0.6172047,0.6386268,0.6595802,0.680047,0.7000105,0.7194546,0.7383646,0.7567261,0.774526,0.7917515,0.808391,0.8244332,0.8398675,0.8688737,0.8824274,0.907595,0.9191939,0.9403886,0.9499722,0.9746058,0.9929734,1.0,]
s=0.4
results = [param_calc.lerpN(s,p) for p in self.ps]
self.assertListAlmostEqual(results, expected)
def test_lerpN_075(self):
expected = [0.0,0.3790281,0.5000536,0.5853117,0.6092271,0.6522305,0.6717163,0.7073729,0.7237565,0.7540437,0.7680761,0.7814389,0.7941772,0.8063303,0.8179332,0.8290168,0.8396087,0.8497336,0.8594139,0.8686697,0.8775191,0.8859787,0.8940637,0.9017876,0.9091631,0.9162018,0.9229142,0.92931,0.9353981,0.9411869,0.9466838,0.9568296,0.9614909,0.9700177,0.9738928,0.9808881,0.9840157,0.9919567,0.9977886,1.0,]
s=0.75
results = [param_calc.lerpN(s,p) for p in self.ps]
self.assertListAlmostEqual(results, expected)
def test_lerpN_01377(self):
expected = [0.0,0.0012481,0.0084266,0.0249376,0.0328639,0.052586,0.0644142,0.0920007,0.1077269,0.1428987,0.162262,0.1827438,0.2042867,0.2268278,0.2502993,0.2746288,0.2997403,0.3255545,0.3519889,0.3789588,0.4063775,0.4341565,0.4622063,0.4904365,0.5187562,0.5470746,0.5753009,0.6033451,0.631118,0.6585314,0.6854987,0.7377579,0.7628861,0.8107492,0.8333358,0.8754719,0.8948921,0.9458627,0.9848579,1.0,]
s=0.1377
results = [param_calc.lerpN(s,p) for p in self.ps]
self.assertListAlmostEqual(results, expected)
def test_slope(self):
expected = [0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.1376812,0.75,0.5,0.6,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.6,0.6,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,]
names = sorted([*self.abilities.keys()])
results = [param_calc.get_slope(self.abilities[name]) for name in names]
self.assertListAlmostEqual(results, expected)
def test_effect(self):
expected = [[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,],[1.0,0.96619,0.934095,0.903715,0.89395,0.875015,0.86581,0.84803,0.839385,0.822725,0.814675,0.8068,0.799135,0.791645,0.78433,0.777225,0.77033,0.763575,0.75703,0.750695,0.744535,0.73855,0.732775,0.727175,0.72175,0.716535,0.71153,0.706665,0.70201,0.697565,0.693295,0.685315,0.681605,0.674745,0.671595,0.66589,0.663335,0.656755,0.651855,0.65,],[1.0,0.9517,0.90585,0.86245,0.8485,0.82145,0.8083,0.7829,0.77055,0.74675,0.73525,0.724,0.71305,0.70235,0.6919,0.68175,0.6719,0.66225,0.6529,0.64385,0.63505,0.6265,0.61825,0.61025,0.6025,0.59505,0.5879,0.58095,0.5743,0.56795,0.56185,0.55045,0.54515,0.53535,0.53085,0.5227,0.51905,0.50965,0.50265,0.5,],[1.0,0.96136,0.92468,0.88996,0.8788,0.85716,0.84664,0.82632,0.81644,0.7974,0.7882,0.7792,0.77044,0.76188,0.75352,0.7454,0.73752,0.7298,0.72232,0.71508,0.70804,0.7012,0.6946,0.6882,0.682,0.67604,0.67032,0.66476,0.65944,0.65436,0.64948,0.64036,0.63612,0.62828,0.62468,0.61816,0.61524,0.60772,0.60212,0.6,],[11.2,11.74096,12.25448,12.74056,12.8968,13.19976,13.34704,13.63152,13.76984,14.0364,14.1652,14.2912,14.41384,14.53368,14.65072,14.7644,14.87472,14.9828,15.08752,15.18888,15.28744,15.3832,15.4756,15.5652,15.652,15.73544,15.81552,15.89336,15.96784,16.03896,16.10728,16.23496,16.29432,16.40408,16.45448,16.54576,16.58664,16.69192,16.77032,16.8,],[13.6,14.06368,14.50384,14.92048,15.0544,15.31408,15.44032,15.68416,15.80272,16.0312,16.1416,16.2496,16.35472,16.45744,16.55776,16.6552,16.74976,16.8424,16.93216,17.01904,17.10352,17.1856,17.2648,17.3416,17.416,17.48752,17.55616,17.62288,17.68672,17.74768,17.80624,17.91568,17.96656,18.06064,18.10384,18.18208,18.21712,18.30736,18.37456,18.4,],[13.6,14.06368,14.50384,14.92048,15.0544,15.31408,15.44032,15.68416,15.80272,16.0312,16.1416,16.2496,16.35472,16.45744,16.55776,16.6552,16.74976,16.8424,16.93216,17.01904,17.10352,17.1856,17.2648,17.3416,17.416,17.48752,17.55616,17.62288,17.68672,17.74768,17.80624,17.91568,17.96656,18.06064,18.10384,18.18208,18.21712,18.30736,18.37456,18.4,],[180.0,173.9142,168.1371,162.6687,160.911,157.5027,155.8458,152.6454,151.0893,148.0905,146.6415,145.224,143.8443,142.4961,141.1794,139.9005,138.6594,137.4435,136.2654,135.1251,134.0163,132.939,131.8995,130.8915,129.915,128.9763,128.0754,127.1997,126.3618,125.5617,124.7931,123.3567,122.6889,121.4541,120.8871,119.8602,119.4003,118.2159,117.3339,117.0,],[600.0,563.292,528.446,495.462,484.86,464.302,454.308,435.004,425.618,407.53,398.79,390.24,381.918,373.786,365.844,358.13,350.644,343.31,336.204,329.326,322.638,316.14,309.87,303.79,297.9,292.238,286.804,281.522,276.468,271.642,267.006,258.342,254.314,246.866,243.446,237.252,234.478,227.334,222.014,220.0,],[138.0,137.9482835,137.6510345,136.9679054,136.6394357,135.8232897,135.332656,134.192425,133.5395484,132.0846624,131.2834435,130.4344066,129.5437933,128.6102298,127.6361678,126.6293472,125.5934552,124.5209035,123.4258582,122.3123643,121.1779496,120.0259209,118.8668103,117.6974205,116.5211749,115.3495123,114.186674,113.0198064,111.8690828,110.7386065,109.6231313,107.459738,106.4184998,104.4329173,103.4946371,101.752207,100.9536705,98.8447122,97.2245393,96.6,],[80.0,57.2553795,49.9954622,44.8828853,43.4463713,40.8670788,39.6957111,37.5596517,36.5738269,34.75812,33.9165196,33.1136645,32.3504021,31.6208544,30.9230157,30.2583449,29.6250738,29.0153569,28.4342405,27.8804272,27.3497523,26.8412758,26.3570556,25.8933204,25.4493572,25.0273279,24.6265433,24.2408496,23.8752921,23.5293309,23.1997801,22.5910191,22.3110732,21.7984206,21.5651376,21.1459455,20.9595666,20.4833544,20.1321871,20.0,],[1.0,0.95653,0.915265,0.876205,0.86365,0.839305,0.82747,0.80461,0.793495,0.772075,0.761725,0.7516,0.741745,0.732115,0.72271,0.713575,0.70471,0.696025,0.68761,0.679465,0.671545,0.66385,0.656425,0.649225,0.64225,0.635545,0.62911,0.622855,0.61687,0.611155,0.605665,0.595405,0.590635,0.581815,0.577765,0.57043,0.567145,0.558685,0.552385,0.55,],[1.0,0.9106832,0.8539305,0.8068504,0.7925989,0.7659053,0.7533189,0.7296291,0.7183772,0.6971307,0.6870545,0.6773082,0.6679216,0.65884,0.6500531,0.6415932,0.633451,0.6255363,0.6179241,0.6106074,0.6035396,0.5967154,0.5901695,0.5838574,0.5777749,0.5719573,0.5664003,0.5610231,0.5558999,0.5510274,0.5463644,0.5376955,0.533685,0.5263015,0.5229248,0.516831,0.5141107,0.5071299,0.5019543,0.5,],[1.0,0.96136,0.92468,0.88996,0.8788,0.85716,0.84664,0.82632,0.81644,0.7974,0.7882,0.7792,0.77044,0.76188,0.75352,0.7454,0.73752,0.7298,0.72232,0.71508,0.70804,0.7012,0.6946,0.6882,0.682,0.67604,0.67032,0.66476,0.65944,0.65436,0.64948,0.64036,0.63612,0.62828,0.62468,0.61816,0.61524,0.60772,0.60212,0.6,],[1.0,0.91306,0.83053,0.75241,0.7273,0.67861,0.65494,0.60922,0.58699,0.54415,0.52345,0.5032,0.48349,0.46423,0.44542,0.42715,0.40942,0.39205,0.37522,0.35893,0.34309,0.3277,0.31285,0.29845,0.2845,0.27109,0.25822,0.24571,0.23374,0.22231,0.21133,0.19081,0.18127,0.16363,0.15553,0.1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results = []
names = sorted([*self.abilities.keys()])
for name in names:
results.append([param_calc.get_effect(self.abilities[name], ap) for ap in self.aps])
len_expected = len(expected)
self.assertEqual(len_expected, len(results))
for i in range(len_expected):
self.assertListAlmostEqual(expected[i], results[i])
if __name__ == '__main__':
unittest.main()