319 lines
15 KiB
Python
319 lines
15 KiB
Python
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"""
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████╗ ████║██╔══██╗╚══██╔══╝██║ ██║██╔════╝████╗ ████║██╔══██╗╚══██╔══╝██║██╔════╝██╔══██╗██║
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██╔████╔██║███████║ ██║ ███████║█████╗ ██╔████╔██║███████║ ██║ ██║██║ ███████║██║
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██║╚██╔╝██║██╔══██║ ██║ ██╔══██║██╔══╝ ██║╚██╔╝██║██╔══██║ ██║ ██║██║ ██╔══██║██║
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██║ ╚═╝ ██║██║ ██║ ██║ ██║ ██║███████╗██║ ╚═╝ ██║██║ ██║ ██║ ██║╚██████╗██║ ██║███████╗
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╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝╚═╝ ╚═╝╚══════╝
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███╗ ███╗ ██████╗ ██████╗ ███████╗██╗ ██╗███╗ ██╗ ██████╗
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████╗ ████║██╔═══██╗██╔══██╗██╔════╝██║ ██║████╗ ██║██╔════╝
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██╔████╔██║██║ ██║██║ ██║█████╗ ██║ ██║██╔██╗ ██║██║ ███╗
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██║╚██╔╝██║██║ ██║██║ ██║██╔══╝ ██║ ██║██║╚██╗██║██║ ██║
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██║ ╚═╝ ██║╚██████╔╝██████╔╝███████╗███████╗██║██║ ╚████║╚██████╔╝
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╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝╚═╝╚═╝ ╚═══╝ ╚═════╝
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团队成员: 王岩琪 袁欣萍 郭禹含 龚智勋
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"""
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import pandas as pd
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import numpy as np
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def main():
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"""
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数据清洗
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"""
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with pd.ExcelFile("/home/bobmaster/Downloads/数学建模/附件1：化学成分及力学性能.xlsx") as origin_data:
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pd_chemicals_raw = pd.read_excel(origin_data, "化学成分", usecols=[0, 2, 3, 4, 5, 6, 7])
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pd_physics_raw = pd.read_excel(origin_data, "力学性能")
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pd_chemical = pd_chemicals_raw.iloc[1:, :]
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pd_physics = pd_physics_raw.dropna(how="any")
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# pd_chemical = pd_chemical.reindex(index = pd_chemical.index[::-1])
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pd_physics_ronglianhao = pd_physics.iloc[:, 0].astype("int64")
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pd_physics_qufu = pd_physics.iloc[:, 2]
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pd_physics_kangla = pd_physics.iloc[:, 3]
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pd_physics_yanshen = pd_physics.iloc[:, 4]
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# 提取相同熔炼号的力学性能数据
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comp_table = pd_physics.iloc[:, 0].duplicated(keep="last") # 比较表
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# phy_num = pd_physics.count() # 力学表数据量 11213
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phy_num = 11213
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# phy_ronglianhao = []
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phy_dict = {}
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phy_qufu = []
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phy_kangla = []
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phy_yanshen = []
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temp = 0
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for i in range(phy_num):
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phy_qufu.append(pd_physics_qufu[i])
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phy_kangla.append(pd_physics_kangla[i])
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phy_yanshen.append(pd_physics_yanshen[i])
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if (comp_table[i] == False):
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# phy_ronglianhao[temp] = pd_physics_ronglianhao[i]
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phy_dict[pd_physics_ronglianhao[i]] = [phy_qufu, phy_kangla, phy_yanshen]
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temp += 1
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phy_qufu = []
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phy_kangla = []
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phy_yanshen = []
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# 数据规约 - 力学性能数据均值和标准差
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phy_dict_qufu_mean = {}
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phy_dict_qufu_std = {}
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phy_dict_kangla_mean = {}
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phy_dict_kangla_std = {}
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phy_dict_yanshen_mean = {}
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phy_dict_yanshen_std = {}
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phy_dict_qufu_mean_list = []
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phy_dict_qufu_std_list = []
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phy_dict_kangla_mean_list = []
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phy_dict_kangla_std_list = []
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phy_dict_yanshen_mean_list = []
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phy_dict_yanshen_std_list = []
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for key in phy_dict:
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np_physics_array_qufu = np.array(phy_dict[key][0])
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np_physics_array_kangla = np.array(phy_dict[key][1])
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np_physics_array_yanshen = np.array(phy_dict[key][2])
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phy_dict_qufu_mean[key] = np_physics_array_qufu.mean()
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phy_dict_qufu_std[key] = np_physics_array_qufu.std()
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phy_dict_kangla_mean[key] = np_physics_array_kangla.mean()
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phy_dict_kangla_std[key] = np_physics_array_kangla.std()
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phy_dict_yanshen_mean[key] = np_physics_array_yanshen.mean()
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phy_dict_yanshen_std[key] = np_physics_array_yanshen.std()
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# 清洗化学成分
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# 重建索引保证在同一熔炼号的情况下与力学指标数据匹配
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pd_chem_ronglianhao = pd_chemical.iloc[:, 0].astype("int64")
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pd_chem_ronglianhao = pd_chem_ronglianhao.drop_duplicates().reset_index().iloc[:, 1]
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pd_chem_E1_data = pd_chemical.iloc[:, 1].reset_index().iloc[:, 1]
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pd_chem_E2_data = pd_chemical.iloc[:, 2].reset_index().iloc[:, 1]
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pd_chem_E3_data = pd_chemical.iloc[:, 3].reset_index().iloc[:, 1]
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pd_chem_E4_data = pd_chemical.iloc[:, 4].reset_index().iloc[:, 1]
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pd_chem_E5_data = pd_chemical.iloc[:, 5].reset_index().iloc[:, 1]
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pd_chem_E6_data = pd_chemical.iloc[:, 6].reset_index().iloc[:, 1]
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pd_chem_E1 = {}
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pd_chem_E2 = {}
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pd_chem_E3 = {}
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pd_chem_E4 = {}
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pd_chem_E5 = {}
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pd_chem_E6 = {}
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temp = 0
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# 数据规约 - 化学成分
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# 0-701 清洗后得到的范围
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for i in range(702):
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if (i % 2 != 0 and temp != 321):
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pd_chem_E1[pd_chem_ronglianhao[temp]] = (pd_chem_E1_data[i - 1] + pd_chem_E1_data[i]) / 2
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pd_chem_E2[pd_chem_ronglianhao[temp]] = (pd_chem_E2_data[i - 1] + pd_chem_E2_data[i]) / 2
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pd_chem_E3[pd_chem_ronglianhao[temp]] = (pd_chem_E3_data[i - 1] + pd_chem_E3_data[i]) / 2
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pd_chem_E4[pd_chem_ronglianhao[temp]] = (pd_chem_E4_data[i - 1] + pd_chem_E4_data[i]) / 2
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pd_chem_E5[pd_chem_ronglianhao[temp]] = (pd_chem_E5_data[i - 1] + pd_chem_E5_data[i]) / 2
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pd_chem_E6[pd_chem_ronglianhao[temp]] = (pd_chem_E6_data[i - 1] + pd_chem_E6_data[i]) / 2
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temp += 1
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# 整理出最终所需数据并保证化学成分与力学性能数据一致性
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E1_list = []
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E2_list = []
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E3_list = []
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E4_list = []
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E5_list = []
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E6_list = []
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for key in pd_chem_E1:
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if key in phy_dict:
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E1_list.append(pd_chem_E1[key])
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E2_list.append(pd_chem_E2[key])
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E3_list.append(pd_chem_E3[key])
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E4_list.append(pd_chem_E4[key])
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E5_list.append(pd_chem_E5[key])
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E6_list.append(pd_chem_E6[key])
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phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key])
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phy_dict_qufu_std_list.append(phy_dict_qufu_std[key])
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phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key])
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phy_dict_kangla_std_list.append(phy_dict_kangla_std[key])
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phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key])
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phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key])
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np_E1 = np.array(E1_list)
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np_E2 = np.array(E2_list)
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np_E3 = np.array(E3_list)
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np_E4 = np.array(E4_list)
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np_E5 = np.array(E5_list)
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np_E6 = np.array(E6_list)
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# 初始化二维直方图数据
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# dist1 材料
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dist1_E1 = np_E1
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dist1_E2 = np_E2
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dist1_E3 = np_E3
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dist1_E4 = np_E4
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dist1_E5 = np_E5
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dist1_E6 = np_E6
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# dist2 力学性能均值
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dist2_qufu = np.array(phy_dict_qufu_mean_list)
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dist2_kangla = np.array(phy_dict_kangla_mean_list)
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dist2_yanshen = np.array(phy_dict_yanshen_mean_list)
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# dist3 力学性能标准差
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dist3_qufu = np.array(phy_dict_qufu_std_list)
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dist3_kangla = np.array(phy_dict_kangla_std_list)
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dist3_yanshen = np.array(phy_dict_yanshen_std_list)
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# 绘制化学成分与力学特性关系的二维直方图
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from hist2d import create_hist2d
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create_hist2d(dist1_E1, dist2_qufu, title="化学成分E1与屈服特性的关系", xlabel="E1 %", ylabel="屈服特性均值")
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create_hist2d(dist1_E1, dist2_kangla, title="化学成分E1与抗拉特性的关系", xlabel="E1 %", ylabel="抗拉特性均值")
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create_hist2d(dist1_E1, dist2_yanshen, title="化学成分E1与延伸率特性的关系", xlabel="E1 %", ylabel="延伸率特性均值")
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create_hist2d(dist1_E2, dist2_qufu, title="化学成分E2与屈服特性的关系", xlabel="E2 %", ylabel="屈服特性均值")
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create_hist2d(dist1_E2, dist2_kangla, title="化学成分E2与抗拉特性的关系", xlabel="E2 %", ylabel="抗拉特性均值")
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create_hist2d(dist1_E2, dist2_yanshen, title="化学成分E2与延伸率特性的关系", xlabel="E2 %", ylabel="延伸率特性均值")
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create_hist2d(dist1_E3, dist2_qufu, title="化学成分E3与屈服特性的关系", xlabel="E3 %", ylabel="屈服特性均值")
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create_hist2d(dist1_E3, dist2_kangla, title="化学成分E3与抗拉特性的关系", xlabel="E3 %", ylabel="抗拉特性均值")
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create_hist2d(dist1_E3, dist2_yanshen, title="化学成分E3与延伸率特性的关系", xlabel="E3 %", ylabel="延伸率特性均值")
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create_hist2d(dist1_E4, dist2_qufu, title="化学成分E4与屈服特性的关系", xlabel="E4 %", ylabel="屈服特性均值")
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create_hist2d(dist1_E4, dist2_kangla, title="化学成分E4与抗拉特性的关系", xlabel="E4 %", ylabel="抗拉特性均值")
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create_hist2d(dist1_E4, dist2_yanshen, title="化学成分E4与延伸率特性的关系", xlabel="E4 %", ylabel="延伸率特性均值")
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create_hist2d(dist1_E5, dist2_qufu, title="化学成分E5与屈服特性的关系", xlabel="E5 %", ylabel="屈服特性均值", fig=(10, 10))
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create_hist2d(dist1_E5, dist2_kangla, title="化学成分E5与抗拉特性的关系", xlabel="E5 %", ylabel="抗拉特性均值", fig=(10, 10))
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create_hist2d(dist1_E5, dist2_yanshen, title="化学成分E5与延伸率特性的关系", xlabel="E5 %", ylabel="延伸率特性均值", fig=(10, 10))
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create_hist2d(dist1_E6, dist2_qufu, title="化学成分E6与屈服特性的关系", xlabel="E6 %", ylabel="屈服特性均值")
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create_hist2d(dist1_E6, dist2_kangla, title="化学成分E6与抗拉特性的关系", xlabel="E6 %", ylabel="抗拉特性均值")
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create_hist2d(dist1_E6, dist2_yanshen, title="化学成分E6与延伸率特性的关系", xlabel="E6 %", ylabel="延伸率特性均值")
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# 创建 OLS 回归模型
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from linear_regression import OlsModel
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from linear_regression import ols_calcutate_all
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x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()
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# 材料与屈服特性均值回归模型
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y = np.array(dist2_qufu)
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qufu_mean_ols_model = OlsModel(x, y)
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# 如需打印报告请删掉下一行的注释
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# print(qufu_mean_ols_model.results.summary())
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# 材料与抗拉特性均值回归模型
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y = np.array(dist2_kangla)
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kangla_mean_ols_model = OlsModel(x, y)
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# 材料与延伸率特性均值回归模型
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y = np.array(dist2_yanshen)
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yanshen_mean_ols_model = OlsModel(x, y)
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# 材料与屈服特性标准差回归模型
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y = np.array(dist3_qufu)
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qufu_std_ols_model = OlsModel(x, y)
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# 材料与抗拉特性标准差回归模型
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y = np.array(dist3_kangla)
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kangla_std_ols_model = OlsModel(x, y)
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# 材料与延伸率特性标准差回归模型
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y = np.array(dist3_yanshen)
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yanshen_std_ols_model = OlsModel(x, y)
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# 给定熔炼号计算均值和标准差
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# ronglianhao = 90624
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# x1 = pd_chem_E1[ronglianhao]
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# x2 = pd_chem_E2[ronglianhao]
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# x3 = pd_chem_E3[ronglianhao]
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# x4 = pd_chem_E4[ronglianhao]
|
|||
|
|
# x5 = pd_chem_E5[ronglianhao]
|
|||
|
|
# x6 = pd_chem_E6[ronglianhao]
|
|||
|
|
# x = np.array([1, x1, x2, x3, x4, x5, x6])
|
|||
|
|
# ols_calcutate_all(x, qufu_mean_ols_model, qufu_std_ols_model,
|
|||
|
|
# kangla_mean_ols_model, kangla_std_ols_model,
|
|||
|
|
# yanshen_mean_ols_model, yanshen_std_ols_model)
|
|||
|
|
|
|||
|
|
"""
|
|||
|
|
屈服均值: [281.04367017]
|
|||
|
|
抗拉均值: [302.12712467]
|
|||
|
|
延伸率均值: [11.72968023]
|
|||
|
|
屈服标准差: [4.04484533]
|
|||
|
|
抗拉标准差: [3.60625011]
|
|||
|
|
延伸率标准差: [0.68357895]
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
from linear_regression import MlrModel
|
|||
|
|
from linear_regression import mlr_calcutate_all
|
|||
|
|
# 创建 MLR 多元线性回归模型
|
|||
|
|
x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()
|
|||
|
|
|
|||
|
|
# 材料与屈服特性均值回归模型
|
|||
|
|
y = np.array(dist2_qufu)
|
|||
|
|
qufu_mean_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 回归系数
|
|||
|
|
# qufu_mean_mlr_model.results.coef_
|
|||
|
|
# 常数，回归方程截距
|
|||
|
|
# qufu_mean_mlr_model.results.intercept_
|
|||
|
|
|
|||
|
|
# 材料与抗拉特性均值回归模型
|
|||
|
|
y = np.array(dist2_kangla)
|
|||
|
|
kangla_mean_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 材料与延伸率特性均值回归模型
|
|||
|
|
y = np.array(dist2_yanshen)
|
|||
|
|
yanshen_mean_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 材料与屈服特性标准差回归模型
|
|||
|
|
y = np.array(dist3_qufu)
|
|||
|
|
qufu_std_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 材料与抗拉特性标准差回归模型
|
|||
|
|
y = np.array(dist3_kangla)
|
|||
|
|
kangla_std_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 材料与延伸率特性标准差回归模型
|
|||
|
|
y = np.array(dist3_yanshen)
|
|||
|
|
yanshen_std_mlr_model = MlrModel(x, y)
|
|||
|
|
|
|||
|
|
# 给定熔炼号计算均值和标准差
|
|||
|
|
# ronglianhao = 90624
|
|||
|
|
# x1 = pd_chem_E1[ronglianhao]
|
|||
|
|
# x2 = pd_chem_E2[ronglianhao]
|
|||
|
|
# x3 = pd_chem_E3[ronglianhao]
|
|||
|
|
# x4 = pd_chem_E4[ronglianhao]
|
|||
|
|
# x5 = pd_chem_E5[ronglianhao]
|
|||
|
|
# x6 = pd_chem_E6[ronglianhao]
|
|||
|
|
# x = np.array([1, x1, x2, x3, x4, x5, x6]).reshape(-1,6)
|
|||
|
|
# ols_calcutate_all(x, qufu_mean_ols_model, qufu_std_ols_model,
|
|||
|
|
# kangla_mean_ols_model, kangla_std_ols_model,
|
|||
|
|
# yanshen_mean_ols_model, yanshen_std_ols_model)
|
|||
|
|
|
|||
|
|
"""
|
|||
|
|
屈服均值: [281.04919773]
|
|||
|
|
抗拉均值: [302.13923671]
|
|||
|
|
延伸率均值: [11.75333675]
|
|||
|
|
屈服标准差: [4.06391763]
|
|||
|
|
抗拉标准差: [3.6079243]
|
|||
|
|
延伸率标准差: [0.68167218]
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
from heatmap import create_heatmap
|
|||
|
|
# 绘制热点图
|
|||
|
|
|
|||
|
|
dataset = pd.DataFrame(
|
|||
|
|
{'屈服': phy_dict_qufu_mean_list, '抗拉': phy_dict_kangla_mean_list, '延伸率': phy_dict_yanshen_mean_list,
|
|||
|
|
'E1': np_E1, 'E2': np_E2, 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})
|
|||
|
|
create_heatmap(dataset)
|
|||
|
|
|
|||
|
|
# from lineplot import create_lineplot
|
|||
|
|
# 绘制折线图
|
|||
|
|
# x = "E1" 横坐标，从 E1-E6 选取
|
|||
|
|
# y = "屈服" 纵坐标，从屈服、抗拉、延伸率 选取
|
|||
|
|
# create_lineplot(dataset, x, y)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__=='__main__':
|
|||
|
|
main()
|