激情驾驶：15-18题
"""
attitude1 = np.array(range(1, 10))
attitude2 = np.array(range(10, 15))
attitude3 = np.array(range(15, 19))
attitude_all = np.array(range(1, 19))
title = np.array(
["安全驾驶态度:妨碍道路畅通且不规则遵守", "安全驾驶态度:超速驾驶", "安全驾驶态度:激情驾驶", "整体安全驾驶态度差"])
n = n + len(title)
data = p.sum_score(data, attitude1, attitude2, attitude3, attitude_all)
table = np.hstack((table, title))
"""
驾驶员自我效能感,1-9反向记分
"""
reverse_order = np.array(range(1, 10)) + 19 - 1
data = p.reverce_score(data, reverse_order, 7)
self = np.array(range(1, 13)) + 19 - 1
title2 = np.array(["驾驶员自我效能感差"])
data = p.sum_score(data, self)
table = np.hstack((table, title2))
n = n + len(title2)
"""
多维度交通心理控制源:
1-5题：其他驾驶员原因
6-9题：自身原因
10-12题：车辆和环境原因
13-16题：命运原因
得分越高表明在个体越容易把交通事故归结为某一因素
"""
reason1 = np.array(range(1, 6)) + 31 - 1
reason2 = np.array(range(6, 10)) + 31 - 1
father_factor_all = np.concatenate(
(father_factor1, father_factor2, father_factor3, father_factor4,
father_factor5, father_factor6, mother_factor1, mother_factor2,
mother_factor3, mother_factor4, mother_factor5),
axis=0)
# print(data["14637李芸"][177:239])
# 记分求和 父母教养方式
n = n + 12
data = th.sum_score(data, father_factor1, mother_factor1, father_factor2,
mother_factor2, father_factor3, mother_factor3,
father_factor4, mother_factor4, father_factor5,
mother_factor5, father_factor6, father_factor_all)
# 自我控制量表**************************************************************************
reverse_order = np.array([2, 3, 9, 12, 15, 16]) + 147 - 1
data = p.reverce_score(data, reverse_order, 6)
control_factor1 = np.array([1, 10, 5, 14]) + 147 - 1
control_factor2 = np.array([4, 13, 15, 16, 6, 11]) + 147 - 1
control_factor3 = np.array([2, 12, 3, 7, 8, 9]) + 147 - 1
control_factor_all = np.concatenate(
(control_factor1, control_factor2, control_factor3), axis=0)
data = th.sum_score(data, control_factor1, control_factor2,
control_factor3, control_factor_all)
n = n + 4
# 领悟社会支持**************************************************************************
reverse_order = np.array([3, 4, 8, 11, 6, 7, 9, 12, 1, 2, 5, 10]) + 164 - 1
data = p.reverce_score(data, reverse_order, 6)
society_factory1 = np.array([3, 4, 8, 11]) + 164 - 1
society_factory2 = np.array([6, 7, 9, 12]) + 164 - 1
society_factory3 = np.array([1, 2, 5, 10]) + 164 - 1
society_factory_all = np.concatenate(