# 3.计算总分
n = 0
"""
安全驾驶态度差:
妨碍道路畅通且不规则遵守：1-9题
超速驾驶：10-14题
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
"""
短式黑暗三联征:
"""
reverse_order = np.array([11, 16, 20, 24, 26]) + 31 - 1
data = p.reverce_score(data, reverse_order, 5)
three_feature_factor1 = (np.array(range(1, 10))) + 31 - 1
three_feature_factor2 = (np.array(range(10, 19))) + 31 - 1
three_feature_factor3 = (np.array(range(19, 28))) + 31 - 1
three_feature_factor_all = np.concatenate(
(three_feature_factor1, three_feature_factor2, three_feature_factor3),
axis=0)
# 记分求和 短式黑暗三联征
n = n + 4
data = p.sum_score(data, three_feature_factor1, three_feature_factor2,
three_feature_factor3, three_feature_factor_all)
# 责任性量表**************************************************************************
reverse_order = np.array([1, 2, 4, 5, 7, 8, 10, 12]) + 58 - 1
data = p.reverce_score(data, reverse_order, 5)
responsibility_factor1 = np.array(range(28, 40)) + 31 - 1
data = p.sum_score(data, responsibility_factor1)
n = n + 1
# # 心理特权感量表**************************************************************************
reverse_order = np.array([5]) + 70 - 1
data = p.reverce_score(data, reverse_order, 7)
psychological_factory1 = np.array(range(1, 10)) + 70 - 1
data = p.sum_score(data, psychological_factory1)
n = n + 1
antisocial_ = np.array(range(1, 8)) + 6 - 1
antisocial_8 = np.array(range(8, 23)) + 6 - 1 # >=3 则为1
data = v.sum_score_div(data, antisocial_, antisocial_8)
n = n + 1
"""
精神病态
"""
reverse_order = np.array([5, 11, 14, 17, 21, 22, 25]) + 28 - 1
data = p.reverce_score(data, reverse_order, 4)
Mental_illness1 = np.array([
1, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 20, 22, 24
]) + 28 - 1
Mental_illness2 = np.array([2, 3, 5, 8, 16, 18, 21, 23, 25, 26
]) + 28 - 1
data = p.sum_score(data, Mental_illness1, Mental_illness2)
n = n + 2
"""
冲动性/预谋性攻击
"""
reverse_order = np.array([4, 7]) + 54 - 1
data = p.reverce_score(data, reverse_order, 5)
attack1 = np.array([3, 4, 6, 7, 8, 16, 17, 18]) + 54 - 1
attack2 = np.array([1, 2, 5, 9, 10, 11, 12, 13, 14, 15, 19, 20
]) + 54 - 1
data = p.sum_score(data, attack1, attack2)
n = n + 2
"""
父母教养方式
"""
if __name__ == '__main__':
# 1.读取总的数据表格
file_name = "drug_file"
config = u.ReadConfig()
table, data = read_position_excel(config, file_name) # 得到全部的列数据和表头
# 2.清洗数据(忽略)
# 3.计算总分
n = 0
"""
短式黑暗三联征:马基雅维利主义人格[1,10)
"""
three_feature_factor = (np.array(range(1, 10))) + 3
# 记分求和 短式黑暗三联征:马基雅维利主义人格
title = np.array(["短式黑暗三联征:马基雅维利主义"])
n = n + len(title)
data = p.sum_score(data, three_feature_factor)
table = np.hstack((table, title))
"""
奖励/惩罚敏感性问卷
"""
sensitive = np.array(range(1, 49, 2)) + 13 - 1 #惩罚敏感
sensitive2 = np.array(range(2, 49, 2)) + 13 - 1 #奖励敏感
title2 = np.array(["个体对惩罚信息敏感", "个体对奖励信息敏感"])
data = p.sum_score(data, sensitive, sensitive2)
table = np.hstack((table, title2))
n = n + len(title2)
"""
领悟社会支持
"""
reverse_order = np.array([3, 4, 8, 11, 6, 7, 9, 12, 1, 2, 5, 10]) + 61 - 1
data = p.reverce_score(data, reverse_order, 7)