From 9ca1bb463052d202ee18d05c090b1dcaf50c3c98 Mon Sep 17 00:00:00 2001
From: 2509165048 <2509165048@student.edu.cn>
Date: Thu, 16 Apr 2026 16:04:08 +0800
Subject: [PATCH] 3-1-3

---
 4-16  2509165048.py | 139 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 139 insertions(+)
 create mode 100644 4-16  2509165048.py

diff --git a/4-16  2509165048.py b/4-16  2509165048.py
new file mode 100644
index 0000000..7a10188
--- /dev/null
+++ b/4-16  2509165048.py	
@@ -0,0 +1,139 @@
+import numpy as np
+image = np.array([
+    [255, 200, 150, 100],
+    [180, 140, 110, 80],
+    [120, 90,  60,  40],
+    [50,  30,  20,  10]
+])
+color_image = np.array([
+    [[255, 0, 0],   [0, 255, 0],   [0, 0, 255]],    
+    [[255, 255, 0], [0, 255, 255], [255, 0, 255]],  
+    [[128, 128, 128],[100, 100, 100],[50, 50, 50]]   
+])
+vec = np.array([1, 2, 3, 4, 5])
+print(vec)  
+mat = np.array([
+    [1, 2, 3],
+    [4, 5, 6]
+])
+print(mat)
+# 全0数组 - 常用于初始化
+zeros = np.zeros((3, 4))  # 3行4列的全0矩阵
+print(zeros)
+# [[0. 0. 0. 0.]
+#  [0. 0. 0. 0.]
+#  [0. 0. 0. 0.]]
+
+# 全1数组
+ones = np.ones((2, 3))
+# [[1. 1. 1.]
+#  [1. 1. 1.]]
+
+# 指定范围内的数组
+arr = np.arange(0, 10, 2)  # 0到10，步长2
+print(arr)  # [0 2 4 6 8]
+
+# 等差数组
+lin = np.linspace(0, 1, 5)  # 0到1之间均匀取5个数
+print(lin)  # [0.   0.25 0.5  0.75 1.  ]
+a = np.array([[1, 2, 3], [4, 5, 6]])
+
+print(a.ndim)   # 维度数量 = 2（是二维数组/矩阵）
+print(a.shape)   # 形状 = (2, 3) 2行3列
+print(a.size)    # 总元素数 = 6
+print(a.dtype)   # 数据类型 = int64
+
+# 对于图像数据，常用 uint8（无符号8位整数，0-255）
+image = np.array([[255, 128], [64, 0]], dtype=np.uint8)
+print(image.dtype)  # uint8
+a = np.array([10, 20, 30, 40, 50])
+
+print(a[0])   # 第一个元素 = 10
+print(a[-1])  # 最后一个元素 = 50
+print(a[1:3]) # 第2到第3个元素 = [20, 30]
+print(a[:3])  # 前3个元素 = [10, 20, 30]
+print(a[2:])  # 第3个到最后 = [30, 40, 50]
+mat = np.array([
+    [1,  2,  3,  4],
+    [5,  6,  7,  8],
+    [9, 10, 11, 12]
+])
+
+# 获取特定元素
+print(mat[0, 0])  # 第1行第1列 = 1
+print(mat[1, 2])  # 第2行第3列 = 7
+print(mat[2, -1]) # 第3行最后一列 = 12
+
+# 切片：[行, 列]
+print(mat[0, :])   # 第1行所有列 = [1, 2, 3, 4]
+print(mat[:, 1])   # 所有行第2列 = [2, 6, 10]
+print(mat[0:2, 0:2])  # 取前2行前2列
+# [[1 2]
+#  [5 6]]
+
+# 负索引
+print(mat[-1, :])  # 最后一行 = [9, 10, 11, 12]
+a = np.array([1, 2, 3])
+b = np.array([4, 5, 6])
+
+# 加减乘除（对应元素运算）
+print(a + b)   # [5 7 9]
+print(a - b)   # [-3 -3 -3]
+print(a * b)   # [4 10 18]  注意：这是元素乘法，不是矩阵乘法
+print(a / b)   # [0.25 0.4  0.5]
+
+# 标量运算（广播）
+print(a * 2)   # [2 4 6]
+print(a + 10)  # [11 12 13]
+print(a ** 2)  # [1 4 9]  平方
+A = np.array([[1, 2], [3, 4]])
+B = np.array([[5, 6], [7, 8]])
+
+# 使用 @ 运算符 或 np.dot()
+C = A @ B
+print(C)
+# [[19 22]
+#  [41 46]]
+
+# 或者
+C = np.dot(A, B)
+print(C)
+a = np.array([1, 2, 3, 4, 5])
+
+print(np.sum(a))      # 求和 = 15
+print(np.mean(a))     # 平均值 = 3.0
+print(np.max(a))      # 最大值 = 5
+print(np.min(a))      # 最小值 = 1
+print(np.std(a))      # 标准差
+print(np.argmax(a))   # 最大值的索引 = 4
+print(np.argmin(a))   # 最小值的索引 = 0
+
+# 对于二维数组，可以指定轴
+mat = np.array([[1, 2, 3], [4, 5, 6]])
+print(np.sum(mat, axis=0))  # 按列求和 = [5 7 9]
+print(np.sum(mat, axis=1))  # 按行求和 = [6 15]
+a = np.array([1, 2, 3, 4, 5, 6])
+
+# 展平（flatten）: 多维变一维
+print(a.flatten())  # [1 2 3 4 5 6]
+
+# 改变形状（reshape）
+b = a.reshape(2, 3)  # 变成2行3列
+print(b)
+# [[1 2 3]
+#  [4 5 6]]
+
+# 转置（transpose）: 行变列
+print(b.T)
+# [[1 4]
+#  [2 5]
+#  [3 6]]
+
+# 翻转
+c = np.array([[1, 2, 3], [4, 5, 6]])
+print(np.fliplr(c))  # 左右翻转
+# [[3 2 1]
+#  [6 5 4]]
+print(np.flipud(c))  # 上下翻转
+# [[4 5 6]
+#  [1 2 3]]
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