117 lines
2.8 KiB
Python
117 lines
2.8 KiB
Python
# coding=utf-8
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import time
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from math import *
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class GraduallySum(object):
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"""
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渐变数
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"""
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def __init__(self, coefficient, target, initValue):
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self.target = target
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self.coefficient = coefficient
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self.value = initValue
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def ResetCoefficient(self, newCoefficient):
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self.coefficient = newCoefficient
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def Refresh(self):
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self.value += self.coefficient * (self.target - self.value)
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def ResetTarget(self, newTarget):
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self.target = newTarget
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class GraduallySumTime(GraduallySum):
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"""
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时间相关渐变数
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"""
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def __init__(self, coefficient, target, initSum):
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self.time = time.time()
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super(GraduallySumTime, self).__init__(coefficient, target, initSum)
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def Refresh(self):
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_time = time.time()
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coe = self.coefficient * (_time - self.time)
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# 放置系数过大
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coe = coe if coe < 0.99 else 0.99
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self.value += coe * (self.target - self.value)
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def Clamp(value, _min, _max):
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# type:(float,float,float) -> float
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return _max if value > _max else _min if value < _min else value
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def ComBin(i, place, result):
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# type:(int,int,int) -> bool
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"""
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提取参数i位于place的结果并于result比较
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例如输入：0b00101,0b10000,0b01110,则会返回false
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"""
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return i & place == result & place
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def BinCal(i, place, result):
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# type:(int,int,bool) -> int
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"""
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为参数i设置位于place的result结果
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例如输入：0b00100,0b00101,True
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输出将会是：0b00101
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"""
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return ((i | place) ^ place) | place * result
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def CosOfTwoVec(vec1, vec2):
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"""
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获取两个相同维度向量的夹角cos值
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"""
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if vec1.__len__() != vec2.__len__():
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return 0
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nv1 = Normalize(vec1)
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nv2 = Normalize(vec2)
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_ = 0
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for index in range(nv1.__len__()):
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_ += nv1[index] * nv2[index]
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return _
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def Normalize(vec):
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# type:(Union[List,Tuple]) -> Union[List,Tuple]
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"""
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将任意维度向量转换为单位向量
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"""
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r = 0
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for _ in vec:
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r += _ ** 2.0
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r = r ** 0.5
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cls = vec.__class__
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return cls([(_ / r) for _ in vec])
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def GetTwoPointVec(pos1, pos2):
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# type:(Union[List,Tuple],Union[List,Tuple]) -> Tuple
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"""
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计算任意两点的方向向量, 非单位向量, pos1 -> pos2
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"""
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li = []
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if pos1.__len__() != pos2.__len__():
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return tuple(li)
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for index in range(pos1.__len__()):
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li.append(pos2[index] - pos1[index])
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return tuple(li)
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def MatProduct(vec, mat):
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"""
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计算向量或者坐标与矩阵相乘, 需要i=j
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"""
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new = [0.0 for _ in range(vec.__len__())]
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lengthIndex = range(vec.__len__())
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for _ in lengthIndex:
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for __ in lengthIndex:
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new[_] += vec[__] * mat[_][__]
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return new
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