Add files via upload

Author: Visualize-ML

Book1_Ch34_Python_Codes/Bk1_Ch34_01.py

@@ -0,0 +1,77 @@
+
+# =============================================================================
+# 快捷键 ctrl + 4 产生这部分注释模板
+# =============================================================================
+
+
+#%% 导入库
+# =============================================================================
+# 导入库
+# =============================================================================
+
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+#%% 等差数列
+# =============================================================================
+# 等差数列
+# =============================================================================
+
+a0 = 1 # 首项
+n = 10 # 项数
+d = 2 # 公差
+
+a_array = np.arange(a0, a0 + n*d, d)
+print('打印等差数列'); print(a_array)
+
+fig = plt.figure(figsize = (8,8))
+plt.scatter(np.arange(n), a_array)
+plt.title('Arithmetic Progression')
+plt.xlabel('Index, $n$')
+plt.ylabel('Value, $a_n$')
+plt.show()
+#%% 二元函数
+# =============================================================================
+# 二元函数
+# =============================================================================
+
+x1_array = np.linspace(-3, 3, 301)
+x2_array = np.linspace(-3, 3, 301)
+xx1, xx2 = np.meshgrid(x1_array, x2_array)
+# np.meshgrid() 函数将 x1_array 和 x2_array 两个一维数组转化为一个二维的网格点矩阵
+
+ff = xx1 * np.exp(-xx1**2 - xx2**2)
+# 二元函数的曲面数据
+
+# 可视化
+fig = plt.figure(figsize = (8,8))
+ax = fig.add_subplot(projection='3d')
+# 绘制二元函数网格曲面
+ax.plot_wireframe(xx1, xx2, ff, rstride=10, cstride=10)
+plt.show()
+#%%
+# =============================================================================
+# 鸢尾花数据
+# =============================================================================
+
+# 加载鸢尾花数据集
+iris_df = sns.load_dataset('iris')
+# 显示数据集前5行
+print('打印鸢尾花数据前5行'); print(iris_df.head())
+
+#%%%
+fig, ax = plt.subplots(figsize = (8,8))
+ax = sns.scatterplot(data=iris_df, x="sepal_length", 
+                     y="sepal_width", hue = "species")
+# hue 用不同色调表达鸢尾花的类别
+
+ax.set_xlabel('Sepal length (cm)')
+ax.set_ylabel('Sepal width (cm)')
+ax.set_xticks(np.arange(4, 8 + 1, step=1))
+ax.set_yticks(np.arange(1, 5 + 1, step=1))
+ax.axis('scaled')
+ax.grid(linestyle='--', linewidth=0.25, color=[0.7,0.7,0.7])
+ax.set_xbound(lower = 4, upper = 8)
+ax.set_ybound(lower = 1, upper = 5)
+
+#%%

Book1_Ch35_Python_Codes/Bk1_Ch35_01.py

@@ -0,0 +1,19 @@
+import streamlit as st
+import seaborn as sns
+import plotly.express as px
+
+# 显示标题
+st.title('Welcome to the world of :red[Streamlit]')
+# 显示章节标题
+st.header('Pandas DataFrame')
+# 显示 markdown 文本
+st.markdown("Load :blue[Iris Data Set]")
+# 从Seaborn导入鸢尾花数据帧
+df = sns.load_dataset('iris')
+# 显示数据帧
+st.write(df)
+# 显示章节标题
+st.header('Visualize Using Heatmap')
+fig = px.imshow(df.iloc[:,:-1])
+# 显示热图
+st.write(fig)

Book1_Ch35_Python_Codes/Bk1_Ch35_02.py

@@ -0,0 +1,23 @@
+import plotly.graph_objects as go
+import numpy as np
+import matplotlib.pyplot as plt
+import streamlit as st
+
+# 产生数据
+x1_array = np.linspace(-3, 3, 301)
+x2_array = np.linspace(-3, 3, 301)
+xx1, xx2 = np.meshgrid(x1_array, x2_array)
+
+# 二元函数的曲面数据
+ff = xx1 * np.exp(-xx1**2 - xx2**2)
+
+# Matplotly图像
+fig = plt.figure(figsize = (8,8))
+ax = fig.add_subplot(projection='3d')
+ax.plot_wireframe(xx1, xx2, ff, rstride=10, cstride=10)
+st.pyplot(fig)
+
+# Plotly图像
+fig = go.Figure(data=[go.Surface(z=ff, x=xx1, y=xx2,
+                      colorscale = 'RdYlBu_r')])
+st.plotly_chart(fig)

Book1_Ch35_Python_Codes/Bk1_Ch35_03.py

@@ -0,0 +1,21 @@
+import streamlit as st
+button_return = st.button("Click me")
+st.write(button_return)
+st.checkbox("Check me")
+st.radio("Choose one:", 
+         ["A", "B", "C"])
+st.selectbox("Choose one:", 
+             ["A", "B", "C"])
+st.multiselect("Choose many:", 
+               ["A", "B", "C", "D"])
+st.slider("Select a value:", 
+          0.0, 10.0, 5.0)
+st.select_slider("Select a value:", 
+                 options=[1, 2, 3, 4, 5])
+st.text_input("Enter your name")
+st.number_input("Enter a number")
+st.text_area("Enter your message")
+st.date_input("Select a date")
+st.time_input("Select a time")
+st.file_uploader("Upload a file")
+st.color_picker("Pick a color")

Book1_Ch35_Python_Codes/Bk1_Ch35_04.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import numpy as np
+from sympy import symbols,lambdify
+import matplotlib.pyplot as plt
+
+# 侧边框
+with st.sidebar:
+    st.header('Choose coefficients')
+    st.latex(r'f(x) = ax^2 + bx + c')
+    a = st.slider("a",min_value = -5.0, 
+                  max_value = 5.0,
+                  step = 0.01, value = 1.0)
+    b = st.slider("b",min_value = -5.0, 
+                  max_value = 5.0,
+                  step = 0.01, value = -2.0)
+    c = st.slider("c",min_value = -5.0, 
+                  max_value = 5.0,
+                  step = 0.01, value = -3.0)
+# 抛物线
+x = symbols('x')
+f_x = a*x**2 + b*x + c
+x_array = np.linspace(-5,5,101)
+f_x_fcn = lambdify(x, f_x)
+y_array = f_x_fcn(x_array)
+
+# 主页面
+st.title('Qudratic function')
+st.latex(r'f(x) = ')
+st.latex(f_x)
+
+# 可视化
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(x_array, y_array)
+
+ax.set_xlim([-5, 5])
+ax.set_ylim([-5, 5])
+ax.set_aspect('equal', adjustable='box')
+ax.set_xlabel('x')
+ax.set_ylabel('f(x)')
+st.write(fig)

Book1_Ch35_Python_Codes/Bk1_Ch35_05.py

@@ -0,0 +1,8 @@
+import streamlit as st
+
+# 在两列中显示不同的内容
+col1, col2 = st.columns(2)
+col1.write("This is column 1")
+col1.latex(r'f(x) = ax^2 + bx + c')
+
+col2.write("This is column 2")

Book1_Ch35_Python_Codes/Bk1_Ch35_06.py

@@ -0,0 +1,11 @@
+import streamlit as st
+# 创建两个选项卡，每个选项卡显示不同的内容
+tab_A, tab_B = st.tabs(["Tab A", "Tab B"])
+
+with tab_A:
+   st.header("Tab A Title")
+   st.write('This is Tab A.')
+
+with tab_B:
+   st.header("Tab B Title")
+   st.write('This is Tab B.')

Book1_Ch35_Python_Codes/Bk1_Ch35_07.py

@@ -0,0 +1,18 @@
+import streamlit as st
+import seaborn as sns
+import plotly.express as px
+
+# 显示标题
+st.title('Iris Dataset')
+
+# 从Seaborn导入鸢尾花数据帧
+df = sns.load_dataset('iris')
+# 第一个可展开区域
+with st.expander("Open and view DataFrame"):
+    # 显示数据帧
+    st.write(df)
+# 第二个可展开区域
+with st.expander("Open and view Heatmap"):
+    fig = px.imshow(df.iloc[:,:-1])
+    # 显示热图
+    st.write(fig)