'''
numpy用于处理矩阵运算,比python的列表好并不少。基本类ndarray
需要额外安装
# 使用 python 3+:
pip3 install numpy

# 使用 python 2+:
pip install numpy

> https://morvanzhou.github.io
'''

import numpy as np #为了方便使用numpy 采用np简写

##### 属性

array = np.array([[1,2,3],[2,3,4]]) #列表转化为矩阵
print(array)
"""
array([[1, 2, 3],
 [2, 3, 4]])
"""

print('number of dim:',array.ndim) # 维度
# number of dim: 2

print('shape :',array.shape) # 行数和列数
# shape : (2, 3)

print('size:',array.size) # 元素个数
# size: 6

print(array.tolist()) # 矩阵转化为python列表
'''
[[1, 2, 3], [2, 3, 4]]
'''

##### 创建
a = np.array([2,23,4]) # list 1d
print(a)
# [2 23 4]

a = np.array([2,23,4],dtype=np.int)
print(a.dtype)
# int 64

a = np.array([2,23,4],dtype=np.int32)
print(a.dtype)
# int32

a = np.array([2,23,4],dtype=np.float32)
print(a.dtype)
# float32

a = np.array([2,23,4],dtype=np.float32)
print(a.dtype)
# float32

a = np.array([[2,23,4],[2,32,4]]) # 2d 矩阵 2行3列
print(a)
"""
[[ 2 23 4]
 [ 2 32 4]]
"""

a = np.zeros((3,4)) # 数据全为0,3行4列
"""
array([[ 0., 0., 0., 0.],
 [ 0., 0., 0., 0.],
 [ 0., 0., 0., 0.]])
"""

a = np.ones((3,4),dtype = np.int) # 数据为1,3行4列
"""
array([[1, 1, 1, 1],
 [1, 1, 1, 1],
 [1, 1, 1, 1]])
"""

a = np.empty((3,4)) # 数据为empty,3行4列
"""
array([[ 0.00000000e+000, 4.94065646e-324, 9.88131292e-324,
 1.48219694e-323],
 [ 1.97626258e-323, 2.47032823e-323, 2.96439388e-323,
 3.45845952e-323],
 [ 3.95252517e-323, 4.44659081e-323, 4.94065646e-323,
 5.43472210e-323]])
"""

a = np.arange(10,20,2) # 10-19 的数据,2步长
"""
array([10, 12, 14, 16, 18])
"""

a = np.arange(12).reshape((3,4)) # 3行4列,0到11
"""
array([[ 0, 1, 2, 3],
 [ 4, 5, 6, 7],
 [ 8, 9, 10, 11]])
"""

a = np.linspace(1,10,20) # 开始端1,结束端10,且分割成20个数据,生成线段
"""
array([ 1. , 1.47368421, 1.94736842, 2.42105263,
 2.89473684, 3.36842105, 3.84210526, 4.31578947,
 4.78947368, 5.26315789, 5.73684211, 6.21052632,
 6.68421053, 7.15789474, 7.63157895, 8.10526316,
 8.57894737, 9.05263158, 9.52631579, 10. ])
"""

a = np.linspace(1,10,20).reshape((5,4)) # 更改shape
"""
array([[ 1. , 1.47368421, 1.94736842, 2.42105263],
 [ 2.89473684, 3.36842105, 3.84210526, 4.31578947],
 [ 4.78947368, 5.26315789, 5.73684211, 6.21052632],
 [ 6.68421053, 7.15789474, 7.63157895, 8.10526316],
 [ 8.57894737, 9.05263158, 9.52631579, 10. ]])
"""

##### 运算

a=np.array([10,20,30,40]) # array([10, 20, 30, 40])
b=np.arange(4) # array([0, 1, 2, 3])

c=a-b # array([10, 19, 28, 37])
c=a*b # array([ 0, 20, 60, 120])
c=b**2 # array([0, 1, 4, 9])

c=10*np.sin(a) 
# array([-5.44021111, 9.12945251, -9.88031624, 7.4511316 ])

print(b<3) 
# array([ True, True, True, False], dtype=bool)

### 多维数组运算
a=np.array([[1,1],[0,1]])
b=np.arange(4).reshape((2,2))

print(a)
# array([[1, 1],
# [0, 1]])

print(b)
# array([[0, 1],
# [2, 3]])

c_dot = np.dot(a,b)
# array([[2, 4],
# [2, 3]])
c_dot_2 = a.dot(b)
# array([[2, 4],
# [2, 3]])

### 运算 & 降维
a=np.random.random((2,4))
print(a)
# array([[ 0.94692159, 0.20821798, 0.35339414, 0.2805278 ],
# [ 0.04836775, 0.04023552, 0.44091941, 0.21665268]])
np.sum(a) # 4.4043622002745959
np.min(a) # 0.23651223533671784
np.max(a) # 0.90438450240606416

print("a =",a)
# a = [[ 0.23651224 0.41900661 0.84869417 0.46456022]
# [ 0.60771087 0.9043845 0.36603285 0.55746074]]

print("sum =",np.sum(a,axis=1))
# sum = [ 1.96877324 2.43558896]

print("min =",np.min(a,axis=0))
# min = [ 0.23651224 0.41900661 0.36603285 0.46456022]

print("max =",np.max(a,axis=1))
# max = [ 0.84869417 0.9043845 ]

### 运算 & 索引
A = np.arange(2,14).reshape((3,4)) 

# array([[ 2, 3, 4, 5]
# [ 6, 7, 8, 9]
# [10,11,12,13]])
 
print(np.argmin(A)) # 0
print(np.argmax(A)) # 11

print(np.mean(A)) # 7.5
print(np.average(A)) # 7.5

print(A.mean()) # 7.5
print(np.median()) # 7.5 中位数
print(np.cumsum(A)) 
# [2 5 9 14 20 27 35 44 54 65 77 90] 累加
print(np.diff(A)) # 累差,最有一维元素个数会下降一个,[3,4] => [3,3]
# [[1 1 1]
# [1 1 1]
# [1 1 1]]

print(np.nonzero(A)) # 获取不为0的元素索引 
# (array([0,0,0,0,1,1,1,1,2,2,2,2]),array([0,1,2,3,0,1,2,3,0,1,2,3]))

A = np.arange(14,2, -1).reshape((3,4)) 

# array([[14, 13, 12, 11],
# [10, 9, 8, 7],
# [ 6, 5, 4, 3]])

print(np.sort(A)) 

# array([[11,12,13,14]
# [ 7, 8, 9,10]
# [ 3, 4, 5, 6]])

print(np.transpose(A)) 
print(A.T)

# array([[14,10, 6]
# [13, 9, 5]
# [12, 8, 4]
# [11, 7, 3]])
# array([[14,10, 6]
# [13, 9, 5]
# [12, 8, 4]
# [11, 7, 3]])

print(A)
# array([[14,13,12,11]
# [10, 9, 8, 7]
# [ 6, 5, 4, 3]])

print(np.clip(A,5,9)) 
# array([[ 9, 9, 9, 9]
# [ 9, 9, 8, 7]
# [ 6, 5, 5, 5]])

#### 索引 & 切片

A = np.arange(3,15)

# array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
 
print(A[3]) # 6

A = np.arange(3,15).reshape((3,4))
"""
array([[ 3, 4, 5, 6]
 [ 7, 8, 9, 10]
 [11, 12, 13, 14]])
"""
 
print(A[2]) 
# [11 12 13 14]

print(A[1][1]) # 8
print(A[1, 1]) # 8

print(A[1, 1:3]) # [8 9] 比较特殊

for row in A:
 print(row)
""" 
[ 3, 4, 5, 6]
[ 7, 8, 9, 10]
[11, 12, 13, 14]
"""

for column in A.T:
 print(column)
""" 
[ 3, 7, 11]
[ 4, 8, 12]
[ 5, 9, 13]
[ 6, 10, 14]
"""

import numpy as np
A = np.arange(3,15).reshape((3,4))
 
print(A.flatten()) 
# array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

for item in A.flat:
 print(item)
 
# 3
# 4
# ......
# 14

#### 数组合并 
A = np.array([1,1,1])
B = np.array([2,2,2])
 
print(np.vstack((A,B))) # vertical stack
"""
[[1,1,1]
 [2,2,2]]
"""
C = np.vstack((A,B)) 
print(A.shape(),C.shape()) 

# (3,) (2,3)

D = np.hstack((A,B)) # horizontal stack

print(D)
# [1,1,1,2,2,2]

print(A.shape(),D.shape()) 
# (3,) (6,)

print(A[np.newaxis,:])
# [[1 1 1]]

print(A[np.newaxis,:].shape)
# (1,3)

print(A[:,np.newaxis])
"""
[[1]
[1]
[1]]
"""

print(A[:,np.newaxis].shape)
# (3,1)

A = np.array([1,1,1])[:,np.newaxis]
B = np.array([2,2,2])[:,np.newaxis]
 
C = np.vstack((A,B)) # vertical stack
D = np.hstack((A,B)) # horizontal stack

print(D)
"""
[[1 2]
[1 2]
[1 2]]
"""

print(A.shape,D.shape)
# (3,1) (3,2)

C = np.concatenate((A,B,B,A),axis=0)

print(C)
"""
array([[1],
 [1],
 [1],
 [2],
 [2],
 [2],
 [2],
 [2],
 [2],
 [1],
 [1],
 [1]])
"""

D = np.concatenate((A,B,B,A),axis=1)

print(D)
"""
array([[1, 2, 2, 1],
 [1, 2, 2, 1],
 [1, 2, 2, 1]])
"""

###### 分割
A = np.arange(12).reshape((3, 4))
print(A)
"""
array([[ 0, 1, 2, 3],
 [ 4, 5, 6, 7],
 [ 8, 9, 10, 11]])
"""
print(np.split(A, 2, axis=1))
"""
[array([[0, 1],
 [4, 5],
 [8, 9]]), array([[ 2, 3],
 [ 6, 7],
 [10, 11]])]
"""
print(np.split(A, 3, axis=0))

# [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]

print(np.split(A, 3, axis=1))

# ValueError: array split does not result in an equal division

print(np.array_split(A, 3, axis=1))
"""
[array([[0, 1],
 [4, 5],
 [8, 9]]), array([[ 2],
 [ 6],
 [10]]), array([[ 3],
 [ 7],
 [11]])]
"""

print(np.vsplit(A, 3)) #等于 print(np.split(A, 3, axis=0))

# [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]


print(np.hsplit(A, 2)) #等于 print(np.split(A, 2, axis=1))
"""
[array([[0, 1],
 [4, 5],
 [8, 9]]), array([[ 2, 3],
 [ 6, 7],
 [10, 11]])]
"""

##### 复制
a = np.arange(4)
# array([0, 1, 2, 3])
b = a
c = a
d = b
b is a # True
c is a # True
d is a # True
d[1:3] = [22, 33] # array([11, 22, 33, 3])
print(a) # array([11, 22, 33, 3])
print(b) # array([11, 22, 33, 3])
print(c) # array([11, 22, 33, 3])

b = a.copy() # deep copy
print(b) # array([11, 22, 33, 3])
a[3] = 44
print(a) # array([11, 22, 33, 44])
print(b) # array([11, 22, 33, 3])