import numpy as np

print(np.__version__)

np.show_config()

print(' ')

# 3

z = np.zeros(10)

print(z)

print(' ')

# 4

z = np.ones(10)

print(z)

print(' ')

# 5

z = np.full(10, 2.5)

print(z)

# 6 python3 -c "import numpy; numpy.info(numpy.add) - документація про функцію add

print(' ')

# 7

z = np.zeros(10)

z[4] = 1

print(z)

print(' ')

# 8

z = np.arange(10, 50)

print(z)

print(' ')

# 9

z = np.arange(50)

print(z)

z = z[::-1]

print(z)

print(' ')

# 10

z = np.arange(9).reshape(3, 3)

print(z)

print(' ')

# 11

nz = np.nonzero([1, 2, 0, 0, 4, 0])

print(nz)

print(' ')

# 12

z = np.eye(3)

print(z)

print(' ')

# 13

z = np.random.random((3, 3, 3))

print(z)

print(' ')

# 14

z = np.random.random((10, 10))

zmin, zmax = z.min(), z.max()

print(z)

print('min = ', zmin, ' max = ', zmax)

print(' ')

# 15

z = np.random.random(30)

m = z.mean()

print(z)

print(m)

print(' ')

# 16

z= np.ones((10,10))

z[1:-1, 1:-1] = 0

print(z)

print(' ')

# 17

0 * np.nan

np.nan == np.nan

np.inf > np.nan

np.nan - np.nan

0.3 == 3 * 0.1

print(' ')

# 18

z = np.diag(np.arange(1, 5), k=-1)

print(z)

print(' ')

# 19

z = np.zeros((8, 8), dtype=int)

z[1::2, ::2] = 1

z[::2, 1::2] = 1

print(z)

print(' ')

# 20

print(np.unravel_index(100,(6,7,8)))

print(' ')

# 21

z = np.tile(np.array([[0, 1], [1, 0]]), (4,4))

print(z)

print(' ')

# 22

z = np.dot(np.ones((5, 3)), np.ones((3,2)))

print(z)

print(' ')

# 23

z = np.arange(11)

z[(3 < z) & (z <= 8)] *= -1

print(z)

print(' ')

# 24

z = np.zeros((5, 5))

z += np.arange(5)

print(z)

print(' ')

# 25

def generate():

# for x in xrange(10):

for x in range(10):

yield x

z = np.fromiter(generate(),dtype = float,count = -1)

print(z)

print(' ')

# 26

z = np.linspace(0, 1, 12)[1:-1]

print(z)

print(' ')

# 27

z = np.random.random(10)

z.sort()

print(z)

print(' ')

# 28

A = np.random.randint(0, 2, 5)

B = np.random.randint(0, 2, 5)

equal = np.allclose(A,B)

print(equal)

print(' ')

# 29

z = np.zeros(10)

z.flags.writeable = False

# z[0] = 0

print(z)

print(' ')

# 30

z = np.random.random((10, 2))

x, y = z[:, 0], z[:, 1]

r = np.hypot(x, y)

t = np.arctan2(y, x)

print(r)

print(t)

print(' ')

# 31

z = np.random.random(10)

z[z.argmax()] = 0

print(z)

print(' ')

# 32

z = np.zeros((10, 10), [('x', float), ('y', float)])

z['x'], z['y'] = np.meshgrid(np.linspace(0, 1, 10), np.linspace(0, 1, 10))

print(z)

print(' ')

# 33

x = np.arange(8)

y = x + 0.5

c = 1.0 / np.subtract.outer(x, y)

print(np.linalg.det(c))

print(' ')

# 34

for dtype in [np.int8, np.int32, np.int64]:

print(np.iinfo(dtype).min)

print(np.iinfo(dtype).max)

for dtype in [np.float32, np.float64]:

print(np.finfo(dtype).min)

print(np.finfo(dtype).max)

print(np.finfo(dtype).eps)

print(' ')

# 35

np.set_printoptions(threshold=np.nan)

z = np.zeros((25, 25))

print(z)

print(' ')

# 36

z= np.arange(100)

v = np.random.uniform(0, 100)

index = (np.abs(z - v)).argmin()

print(z[index])

print(' ')

# 37

z = np.zeros(10, [('position', [('x', float, 1),

('b', float, 1)])])

print(z)

print(' ')

# 38

import scipy.spatial

z = np.random.random((10, 2))

d = scipy.spatial.distance.cdist(z, z)

print(d)

print(' ')

# 39

z = np.arange(10, dtype=np.int32)

z = z.astype(np.float32, copy=False)

print(' ')

# 40

# z = np.genfromtxt("missing.dat", delimiter=",") - для зчитування файлу

print(' ')

# 41

z = np.arange(9).reshape(3, 3)

for index, value in np.ndenumerate(z):

print(index, value)

for index in np.ndindex(z.shape):

print(index, z[index])

print(' ')

# 42

x, y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))

d = np.hypot(x, y)

sigma, mu = 1.0, 0.0

g = np.exp(-((d - mu)**2 / (2.0 * sigma ** 2)))

print(g)

print(' ')

# 43

n = 10

p = 3

z = np.zeros((n,n))

np.put(z, np.random.choice(range(n*n), p, replace=False), 1)

print(' ')

# 44

x = np.random.rand(5, 10)

y = x - x.mean(axis=1, keepdims=True)

print(' ')

# 45

z = np.random.randint(0, 10, (3, 3))

n = 1 #нумерація з нуля

print(z)

print(z[z[:, n].argsort()])

print(' ')

# 46

z = np.random.randint(0, 3, (3, 10))

print((~z.any(axis=0)).any())

print(' ')

# 47

z = np.ones(10)

i = np.random.randint(0, len(z), 20)

z += np.bincount(i, minlength=len(z))

print(z)

print(' ')

# 48

w, h = 16, 16

i = np.random.randint(0, 2, (h, w, 3)).astype(np.ubyte)

f = i[..., 0] * 256 *256 + i[..., 1] * 256 + i[..., 2]

n = len(np.unique(f))

print(np.unique(i))

print(' ')

#49

A = np.random.randint (0, 10, (3, 4, 3, 4))

sum = A.reshape(A.shape[: 2] + (-1,)).sum(axis=-1)

print(sum)

print(' ')

#50

# np.diag(np.dot(A, B))

# np.sum(A * B.T, axis=1)

#

# np.eisum("ij,ji->", A, B)

print(' ')

#51

z = np.array([1, 2, 3, 4, 5])

nz = 3

z0 = np.zeros(len(z) + (len(z) - 1) * (nz))

z0[::nz + 1]=z

print(z0)

print(' ')

#52

A = np.arange(25).reshape(5, 5)

A[[0, 1]]=A[[1, 0]]

print(A)

print(' ')

#53

faces = np.random.randint(0, 10, (10, 3))

f = np.roll(faces.repeat(2, axis=1), -1, axis=1)

f = f.reshape(len(f) * 3, 2)

f = np.sort(f, axis=1)

g = f.view(dtype = [('p0', f.dtype), ('p1', f.dtype)])

g = np.unique(g)

print(g)

print(' ')

#54

C = np.bincount([1, 1, 2, 3, 4, 4, 6])

A = np.repeat(np.arange(len(C)), C)

print(A)

print(' ')

#55

def moving_average(a, n = 3):

ret = np.cumsum(a, dtype = float)

ret[n:] = ret[n:] - ret[: -n]

return ret [n-1 :] / n

print(moving_average(20), 3)

print(' ')

#56

from numpy.lib import stride_tricks

def rolling(a, window):

shape = (a.size - window + 1, window)

strides = (a.itemsize, a.itemsize)

return stride_tricks.as_strided(a, shape=shape, strides=strides)

z = rolling(np.arange(10), 3)

print(z)

print(' ')

#57

z = np.random.randint(0, 2, 10)

print(z)

np.logical_not(z, out=z)

print(z)

z = np.random.uniform(-1.0,1.0,100)

print(z)

np.negative(z, out=z)

print(z)

print(' ')

#58

def distance(p0, p1, p):

return np.sqrt((d ** 2).sum(axis=1))

p0= np.random.uniform(-10, 10, (10, 2))

p1= np.random.uniform(-10, 10, (10, 2))

p= np.random.uniform(-10, 10, (1, 2))

print(distance(p0, p1, p))

print(' ')

#59

z = np.random.randint(0, 10, (10, 10))

shape = (5, 5)

fill = 0

position = (1, 1)

r = np.ones(shape, dtype = z.dtype) * fill

p = np.array(list(position)).astype(int)

rs = np.array(list(r.shape)).astype(int)

zs = np.array(list(z.shape)).astype(int)

r_start = np.zeros((len(shape),)).astype(int)

r_stop = np.array(list(shape)).astype(int)

z_start = (p - rs // 2)

z_stop = (p + rs // 2) + rs % 2

r_start = (r_start - np.minimum(z_start, 0)).tolist()

z_start = (np.maximum(z_start, 0)).tolist()

r_stop = np.maximum(r_start, (r_stop - np.maximum(z_stop - zs, 0))).tolist()

z_stop =(np.minimum(z_stop, zs)).tolist()

R = [slice(start, stop) for start, stop in zip(r_start, r_stop)]

Z = [slice(start, stop) for start, stop in zip(z_start, z_stop)]

r[R]=z[Z]

print(z)

print(r)

print(' ')

#60

z = np.random.uniform(0, 1, (10, 10))

rank = np.linalg.matrix_rank(z)

print(rank)

print(' ')

#61

z = np.random.randint(0, 10, 50)

print(np.bincount(z).argmax())

print(' ')

#62

z = np.random.randint(0, 5, (10, 10))

n = 3

i = 1 + (z.shape[0] - n)

j = 1 + (z.shape[1] - n)

c = stride_tricks.as_strided(z, shape=(i, j, n, n), strides=z.strides + z.strides)

print(c)

print(' ')

#63

# class Symetric(np.ndarray):

# def __setitem__(self, (i,j), value):

# super(Symetric, self).__setitem__((i, j), value)

# super(Symetric, self).__setitem__((i, j), value)

#

#

# def symetric(z):

# return np.asarray(z + z.T - np.diag(z.diagonal())).view(Symetric)

#

#

# S = symetric(np.random.randint(0, 10, (5, 5)))

# S[2, 3] = 42

# print(S)

print(' ')

# 64

p, n = 10, 20

M = np.ones((p, n, n))

V = np.ones((p, n, 1))

S = np.tensordot(M, V, axes=[[0, 2],[0, 1]])

print(S)

print(' ')

# 65

Z = np.ones((16, 16))

k = 4

S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),

np.arange(0, Z.shape[1], k), axis=1)

print(' ')

# 66

def iterate(Z):

return Z

Z = np.random.randint(0, 2, (50, 50))

for i in range(100):

print(Z)

Z = iterate(Z)

print(' ')

# 67

z = np.arange(10000)

np.random.shuffle(Z)

n = 5

print(Z[np.argpartition(-Z, n)[:n]])

print(' ')

# 68

def cartesian(arrays):

return ix

print(cartesian(([1, 2, 3], [4, 5], [6, 7])))

print(' ')

# 69

A = np.random.randint(0, 5, (8, 3))

B = np.random.randint(0, 5, (2, 2))

C = (A[..., np.newaxis, np.newaxis] == B)

rows = (C.sum(axis=(1, 2, 3)) >= B.shape[1]).nonzero()[0]

print(rows)

print(' ')

# 70

# Z = np.randon.randint(0, 5, (10, 3))

# E = np.logical_and.reduce(Z[:, 1:] == Z[:, :-1], axis=1)

# U = Z[~E]

# print(Z)

# print(U)

print(' ')

# 71

I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)

print(np.unpackbits(I[:, np.newaxis], axis=1))

print(' ')

# 72

Z = np.random.randint(0, 2, (6, 3))

T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))

_, idx = np.unique(T, return_index=True)

uZ = Z[idx]

print(uZ)

print(' ')

# 73

# np.einsum('i->', A)

# np.einsum('i,j->i', A, B)

# np.einsum('i,i', A, B)

# np.einsum('i,j', A, B)