15 精币
"""
用于生成坐标轨迹
"""
import math
import random
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
class GTrace(object):
def __init__(self):
self.__pos_x = []
self.__pos_y = []
self.__pos_z = []
def __set_pt_time(self):
"""
设置各节点的时间
分析不同时间间隔中X坐标数量的占比
统计结果: 1. 80%~90%的X坐标在15~20毫秒之间
2. 10%~15%在20~200及以上,其中 [-a, 0, x, ...] 这里x只有一个,取值在110~200之间
坐标集最后3~5个坐标取值再50~400之间,最后一个坐标数值最大
滑动总时间的取值规则: 图片宽度260,去掉hk的宽度剩下200;
如果距离小于100,则耗时1300~1900之间
如果距离大于100,则耗时1700~2100之间
"""
__end_pt_time = []
__move_pt_time = []
self.__pos_z = []
total_move_time = self.__need_time * random.uniform(0.8, 0.9)
start_point_time = random.uniform(110, 200)
__start_pt_time = [0, 0, int(start_point_time)]
sum_move_time = 0
_tmp_total_move_time = total_move_time
while True:
delta_time = random.uniform(15, 20)
if _tmp_total_move_time < delta_time:
break
sum_move_time += delta_time
_tmp_total_move_time -= delta_time
__move_pt_time.append(int(start_point_time+sum_move_time))
last_pt_time = __move_pt_time[-1]
__move_pt_time.append(last_pt_time+_tmp_total_move_time)
sum_end_time = start_point_time + total_move_time
other_point_time = self.__need_time - sum_end_time
end_first_ptime = other_point_time / 2
while True:
delta_time = random.uniform(110, 200)
if end_first_ptime - delta_time <= 0:
break
end_first_ptime -= delta_time
sum_end_time += delta_time
__end_pt_time.append(int(sum_end_time))
__end_pt_time.append(int(sum_end_time + (other_point_time/2 + end_first_ptime)))
self.__pos_z.extend(__start_pt_time)
self.__pos_z.extend(__move_pt_time)
self.__pos_z.extend(__end_pt_time)
def __set_distance(self, _dist):
"""
设置要生成的轨迹长度
"""
self.__distance = _dist
if _dist < 100:
self.__need_time = int(random.uniform(500, 1500))
else:
self.__need_time = int(random.uniform(1000, 2000))
def __get_pos_z(self):
return self.__pos_z
def __get_pos_y(self):
_pos_y = [random.uniform(-40, -18), 0]
point_count = len(self.__pos_z)
x = np.linspace(-10, 15, point_count - len(_pos_y))
arct_y = np.arctan(x)
for _, val in enumerate(arct_y):
_pos_y.append(val)
return _pos_y
def __get_pos_x(self, _distance):
"""
绘制标准的数学函数图像: 以 tanh 开始 以 arctan 结尾
根据此模型用等比时间差生成X坐标
"""
# first_val = random.uniform(-40, -18)
# _distance += first_val
_pos_x = [random.uniform(-40, -18), 0]
self.__set_distance(_distance)
self.__set_pt_time()
point_count = len(self.__pos_z)
x = np.linspace(-1, 19, point_count-len(_pos_x))
ss = np.arctan(x)
th = np.tanh(x)
for idx in range(0, len(th)):
if th[idx] < ss[idx]:
th[idx] = ss[idx]
th += 1
th *= (_distance / 2.5)
i = 0
start_idx = int(point_count/10)
end_idx = int(point_count/50)
delta_pt = abs(np.random.normal(scale=1.1, size=point_count-start_idx-end_idx))
for idx in range(start_idx, point_count):
if idx*1.3 > len(delta_pt):
break
th[idx] += delta_pt[i]
i+=1
_pos_x.extend(th)
return _pos_x[-1], _pos_x
def get_mouse_pos_path(self, distance):
"""
获取滑动hk鼠标的滑动轨迹坐标集合
"""
result = []
_distance, x = self.__get_pos_x(distance)
y = self.__get_pos_y()
z = self.__get_pos_z()
for idx in range(len(x)):
result.append([int(x[idx]), int(y[idx]), int(z[idx])])
return int(_distance), result
if __name__ == "__main__":
_color = ["blue", "green", "red", "cyan", "magenta"]
trace = GTrace()
# for idx in range(0, 10):
# distance = random.uniform(70, 150)
# print("长度为: %d , 坐标为: \n" % distance)
# distance, mouse_pos_path = trace.get_mouse_pos_path(distance)
# print("长度为: %d , 坐标为: \" % distance, mouse_pos_path)
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发给gpt 让他改写为js代码 然后用易语言调用js
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