🎉 publish the paper and code

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
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+# Jupyter Notebook
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+
+# IPython
+profile_default/
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
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+
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
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+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
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+
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+
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+dmypy.json
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+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
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+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk

README.md

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+## 中南大学2022年-6月数学建模校赛
+选题A： 设计满足一定力学性能的材料化学成分配方
+详细要求见附件📎
+
+## 碎碎念
+被同学拉着，莫名奇妙的就“上车”了。在大伙共同的努力下，我们团队4位成员成功的在3天内完成了一篇还看得过去的论文。我也成功的晚睡了几回，嘿嘿。
+
+比赛时间只有3天，但我们对很多方面的知识还是新手，比如论文用`word`排版遇到了麻烦，在不同设备之间存在兼容性问题。此外，还存在问题考虑不周到，面对多重影响因子不知道用什么数学方法去解决等问题，我们专业知识还是欠缺太多。总结起来就是，一定要学会用`Latex`对论文进行排版，多学习一些统计学数学的方法。
+
+我虽然对这类比赛不是特别感兴趣，但既然做了还是有所收获的。比赛期间回顾了`Python`的基本语法，学习了`numpy`、`pandas`、`matplotlib`、`scikit-learn`等库的基本用法，在看到短短几行代码就能得出并校验线性回归方程和生成各种图表曲线时我的内心是振奋的，我从来没有参加过科研，但它给了我感觉。
+
+BobMaster要继续加油呀！🌱
+
+## License
+Copyright (c) 王岩琪 袁欣萍 郭禹含 龚智勋. All rights reserved.

heatmap.py

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+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+plt.rc('axes', unicode_minus=False)
+sns.set_theme(style="white")
+sns.set(font="WenQuanYi Zen Hei")
+
+
+def create_heatmap(dataset):
+    # 计算相关矩阵
+    corr = dataset.corr()
+
+    # 为上三角矩阵生成蒙版
+    mask = np.triu(np.ones_like(corr, dtype=bool))
+
+    # 设置图大小
+    f, ax = plt.subplots(figsize=(11, 9))
+
+    # 生成颜色图表
+    cmap = sns.diverging_palette(230, 20, as_cmap=True)
+
+    # 使用蒙版和正确的纵横比绘图
+    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
+                square=True, linewidths=.5, cbar_kws={"shrink": .5})
+
+    plt.show()

hist2d.py

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+import matplotlib.pyplot as plt
+import matplotlib.font_manager as fm
+
+
+myfont = fm.FontProperties(fname='/usr/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/wqy-zenhei.ttc')
+
+
+def create_hist2d(dist1, dist2, title, xlabel, ylabel, fig=(5, 5)):
+    fig, ax = plt.subplots(figsize=fig, tight_layout=True)
+    ax.set_title(title, fontproperties=myfont)
+    ax.set_xlabel(xlabel, fontproperties=myfont)
+    ax.set_ylabel(ylabel, fontproperties=myfont)
+    hist2d = ax.hist2d(dist1, dist2, bins=20)
+    plt.show()

linear_regression.py

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+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import statsmodels.api as sm
+
+
+class OlsModel:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.results = self.create_model()
+
+    def create_model(self):
+        x = sm.add_constant(self.x)
+        model = sm.OLS(self.y, x)
+        results = model.fit()
+        return results
+
+
+class MlrModel:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.results = self.create_model()
+
+    def create_model(self):
+        X_train, X_test, y_train, y_test = train_test_split(self.x, self.y, test_size=0.25, random_state=42)
+        model = LinearRegression().fit(X_train, y_train)
+        return model
+
+
+def 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):
+    print("屈服均值: " + str(qufu_mean_ols_model.results.predict(x)) + "\n"
+          "抗拉均值: " + str(kangla_mean_ols_model.results.predict(x)) + "\n"
+          "延伸率均值: " + str(yanshen_mean_ols_model.results.predict(x)) + "\n"
+          "屈服标准差: " + str(qufu_std_ols_model.results.predict(x)) + "\n"
+          "抗拉标准差: " + str(kangla_std_ols_model.results.predict(x)) + "\n"
+          "延伸率标准差: " + str(yanshen_std_ols_model.results.predict(x)) + "\n"
+          )
+
+
+def mlr_calcutate_all(x, qufu_mean_mlr_model, qufu_std_mlr_model,
+                  kangla_mean_mlr_model, kangla_std_mlr_model,
+                  yanshen_mean_mlr_model, yanshen_std_mlr_model):
+    print("屈服均值: " + str(qufu_mean_mlr_model.results.predict(x)) + "\n"
+          "抗拉均值: " + str(kangla_mean_mlr_model.results.predict(x)) + "\n"
+          "延伸率均值: " + str(yanshen_mean_mlr_model.results.predict(x)) + "\n"
+          "屈服标准差: " + str(qufu_std_mlr_model.results.predict(x)) + "\n"
+          "抗拉标准差: " + str(kangla_std_mlr_model.results.predict(x)) + "\n"
+          "延伸率标准差: " + str(yanshen_std_mlr_model.results.predict(x)) + "\n"
+          )
+
+
+
+

lineplot.py

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+import seaborn as sns
+
+sns.set(font="WenQuanYi Zen Hei")
+
+
+def create_lineplot(dataset, x, y):
+    sns.lineplot(x=x, y=y, data=dataset)
+

main.py

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+"""
+███╗   ███╗ █████╗ ████████╗██╗  ██╗███████╗███╗   ███╗ █████╗ ████████╗██╗ ██████╗ █████╗ ██╗
+████╗ ████║██╔══██╗╚══██╔══╝██║  ██║██╔════╝████╗ ████║██╔══██╗╚══██╔══╝██║██╔════╝██╔══██╗██║
+██╔████╔██║███████║   ██║   ███████║█████╗  ██╔████╔██║███████║   ██║   ██║██║     ███████║██║
+██║╚██╔╝██║██╔══██║   ██║   ██╔══██║██╔══╝  ██║╚██╔╝██║██╔══██║   ██║   ██║██║     ██╔══██║██║
+██║ ╚═╝ ██║██║  ██║   ██║   ██║  ██║███████╗██║ ╚═╝ ██║██║  ██║   ██║   ██║╚██████╗██║  ██║███████╗
+╚═╝     ╚═╝╚═╝  ╚═╝   ╚═╝   ╚═╝  ╚═╝╚══════╝╚═╝     ╚═╝╚═╝  ╚═╝   ╚═╝   ╚═╝ ╚═════╝╚═╝  ╚═╝╚══════╝
+
+███╗   ███╗ ██████╗ ██████╗ ███████╗██╗     ██╗███╗   ██╗ ██████╗
+████╗ ████║██╔═══██╗██╔══██╗██╔════╝██║     ██║████╗  ██║██╔════╝
+██╔████╔██║██║   ██║██║  ██║█████╗  ██║     ██║██╔██╗ ██║██║  ███╗
+██║╚██╔╝██║██║   ██║██║  ██║██╔══╝  ██║     ██║██║╚██╗██║██║   ██║
+██║ ╚═╝ ██║╚██████╔╝██████╔╝███████╗███████╗██║██║ ╚████║╚██████╔╝
+╚═╝     ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝╚═╝╚═╝  ╚═══╝ ╚═════╝
+
+团队成员: 王岩琪 袁欣萍 郭禹含 龚智勋
+
+"""
+
+import pandas as pd
+import numpy as np
+
+
+def main():
+    """
+    数据清洗
+    """
+
+    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:
+        pd_chemicals_raw = pd.read_excel(origin_data, "化学成分", usecols=[0, 2, 3, 4, 5, 6, 7])
+        pd_physics_raw = pd.read_excel(origin_data, "力学性能")
+
+    pd_chemical = pd_chemicals_raw.iloc[1:, :]
+    pd_physics = pd_physics_raw.dropna(how="any")
+    # pd_chemical = pd_chemical.reindex(index = pd_chemical.index[::-1])
+    pd_physics_ronglianhao = pd_physics.iloc[:, 0].astype("int64")
+    pd_physics_qufu = pd_physics.iloc[:, 2]
+    pd_physics_kangla = pd_physics.iloc[:, 3]
+    pd_physics_yanshen = pd_physics.iloc[:, 4]
+
+    # 提取相同熔炼号的力学性能数据
+    comp_table = pd_physics.iloc[:, 0].duplicated(keep="last")  # 比较表
+    # phy_num = pd_physics.count()  # 力学表数据量 11213
+    phy_num = 11213
+    # phy_ronglianhao = []
+    phy_dict = {}
+    phy_qufu = []
+    phy_kangla = []
+    phy_yanshen = []
+    temp = 0
+    for i in range(phy_num):
+        phy_qufu.append(pd_physics_qufu[i])
+        phy_kangla.append(pd_physics_kangla[i])
+        phy_yanshen.append(pd_physics_yanshen[i])
+        if (comp_table[i] == False):
+            # phy_ronglianhao[temp] = pd_physics_ronglianhao[i]
+            phy_dict[pd_physics_ronglianhao[i]] = [phy_qufu, phy_kangla, phy_yanshen]
+            temp += 1
+            phy_qufu = []
+            phy_kangla = []
+            phy_yanshen = []
+
+    # 数据规约 - 力学性能数据均值和标准差
+    phy_dict_qufu_mean = {}
+    phy_dict_qufu_std = {}
+    phy_dict_kangla_mean = {}
+    phy_dict_kangla_std = {}
+    phy_dict_yanshen_mean = {}
+    phy_dict_yanshen_std = {}
+    phy_dict_qufu_mean_list = []
+    phy_dict_qufu_std_list = []
+    phy_dict_kangla_mean_list = []
+    phy_dict_kangla_std_list = []
+    phy_dict_yanshen_mean_list = []
+    phy_dict_yanshen_std_list = []
+
+    for key in phy_dict:
+        np_physics_array_qufu = np.array(phy_dict[key][0])
+        np_physics_array_kangla = np.array(phy_dict[key][1])
+        np_physics_array_yanshen = np.array(phy_dict[key][2])
+        phy_dict_qufu_mean[key] = np_physics_array_qufu.mean()
+        phy_dict_qufu_std[key] = np_physics_array_qufu.std()
+        phy_dict_kangla_mean[key] = np_physics_array_kangla.mean()
+        phy_dict_kangla_std[key] = np_physics_array_kangla.std()
+        phy_dict_yanshen_mean[key] = np_physics_array_yanshen.mean()
+        phy_dict_yanshen_std[key] = np_physics_array_yanshen.std()
+
+    # 清洗化学成分
+    # 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
+    pd_chem_ronglianhao = pd_chemical.iloc[:, 0].astype("int64")
+    pd_chem_ronglianhao = pd_chem_ronglianhao.drop_duplicates().reset_index().iloc[:, 1]
+    pd_chem_E1_data = pd_chemical.iloc[:, 1].reset_index().iloc[:, 1]
+    pd_chem_E2_data = pd_chemical.iloc[:, 2].reset_index().iloc[:, 1]
+    pd_chem_E3_data = pd_chemical.iloc[:, 3].reset_index().iloc[:, 1]
+    pd_chem_E4_data = pd_chemical.iloc[:, 4].reset_index().iloc[:, 1]
+    pd_chem_E5_data = pd_chemical.iloc[:, 5].reset_index().iloc[:, 1]
+    pd_chem_E6_data = pd_chemical.iloc[:, 6].reset_index().iloc[:, 1]
+    pd_chem_E1 = {}
+    pd_chem_E2 = {}
+    pd_chem_E3 = {}
+    pd_chem_E4 = {}
+    pd_chem_E5 = {}
+    pd_chem_E6 = {}
+
+    temp = 0
+
+    # 数据规约 - 化学成分
+    # 0-701 清洗后得到的范围
+    for i in range(702):
+        if (i % 2 != 0 and temp != 321):
+            pd_chem_E1[pd_chem_ronglianhao[temp]] = (pd_chem_E1_data[i - 1] + pd_chem_E1_data[i]) / 2
+            pd_chem_E2[pd_chem_ronglianhao[temp]] = (pd_chem_E2_data[i - 1] + pd_chem_E2_data[i]) / 2
+            pd_chem_E3[pd_chem_ronglianhao[temp]] = (pd_chem_E3_data[i - 1] + pd_chem_E3_data[i]) / 2
+            pd_chem_E4[pd_chem_ronglianhao[temp]] = (pd_chem_E4_data[i - 1] + pd_chem_E4_data[i]) / 2
+            pd_chem_E5[pd_chem_ronglianhao[temp]] = (pd_chem_E5_data[i - 1] + pd_chem_E5_data[i]) / 2
+            pd_chem_E6[pd_chem_ronglianhao[temp]] = (pd_chem_E6_data[i - 1] + pd_chem_E6_data[i]) / 2
+            temp += 1
+
+    # 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
+    E1_list = []
+    E2_list = []
+    E3_list = []
+    E4_list = []
+    E5_list = []
+    E6_list = []
+    for key in pd_chem_E1:
+        if key in phy_dict:
+            E1_list.append(pd_chem_E1[key])
+            E2_list.append(pd_chem_E2[key])
+            E3_list.append(pd_chem_E3[key])
+            E4_list.append(pd_chem_E4[key])
+            E5_list.append(pd_chem_E5[key])
+            E6_list.append(pd_chem_E6[key])
+
+            phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key])
+            phy_dict_qufu_std_list.append(phy_dict_qufu_std[key])
+            phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key])
+            phy_dict_kangla_std_list.append(phy_dict_kangla_std[key])
+            phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key])
+            phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key])
+
+    np_E1 = np.array(E1_list)
+    np_E2 = np.array(E2_list)
+    np_E3 = np.array(E3_list)
+    np_E4 = np.array(E4_list)
+    np_E5 = np.array(E5_list)
+    np_E6 = np.array(E6_list)
+
+    # 初始化二维直方图数据
+    # dist1 材料
+    dist1_E1 = np_E1
+    dist1_E2 = np_E2
+    dist1_E3 = np_E3
+    dist1_E4 = np_E4
+    dist1_E5 = np_E5
+    dist1_E6 = np_E6
+
+    # dist2 力学性能均值
+    dist2_qufu = np.array(phy_dict_qufu_mean_list)
+    dist2_kangla = np.array(phy_dict_kangla_mean_list)
+    dist2_yanshen = np.array(phy_dict_yanshen_mean_list)
+
+    # dist3 力学性能标准差
+    dist3_qufu = np.array(phy_dict_qufu_std_list)
+    dist3_kangla = np.array(phy_dict_kangla_std_list)
+    dist3_yanshen = np.array(phy_dict_yanshen_std_list)
+
+    # 绘制化学成分与力学特性关系的二维直方图
+
+    from hist2d import create_hist2d
+
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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")
+    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))
+    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))
+    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))
+    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")
+    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")
+    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")
+
+    # 创建 OLS 回归模型
+    from linear_regression import OlsModel
+    from linear_regression import ols_calcutate_all
+
+    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_ols_model = OlsModel(x, y)
+
+    # 如需打印报告请删掉下一行的注释
+    # print(qufu_mean_ols_model.results.summary())
+
+    # 材料与抗拉特性均值回归模型
+    y = np.array(dist2_kangla)
+    kangla_mean_ols_model = OlsModel(x, y)
+
+    # 材料与延伸率特性均值回归模型
+    y = np.array(dist2_yanshen)
+    yanshen_mean_ols_model = OlsModel(x, y)
+
+    # 材料与屈服特性标准差回归模型
+    y = np.array(dist3_qufu)
+    qufu_std_ols_model = OlsModel(x, y)
+
+    # 材料与抗拉特性标准差回归模型
+    y = np.array(dist3_kangla)
+    kangla_std_ols_model = OlsModel(x, y)
+
+    # 材料与延伸率特性标准差回归模型
+    y = np.array(dist3_yanshen)
+    yanshen_std_ols_model = OlsModel(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])
+    # 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()

organized_data.csv

@@ -0,0 +1,271 @@
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requirements.txt

@@ -0,0 +1,8 @@
+# generated by pip freeze
+matplotlib==3.5.2
+numpy==1.22.4
+pandas==1.4.3
+scikit-learn==1.1.1
+scipy==1.8.1
+seaborn==0.11.2
+statsmodels==0.13.2