Numpy

Post 02: Meshgrid

Meshgrid is a numpy function that creates 2d rectangular arrays from two 1d arrays.
It provides all possible combinations of 1d array.

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams["figure.figsize"] = (10,7)
x = np.arange(-3,4,1)
y = np.arange(-5,6,1)
print("X:")
print(x)
print("\nY:")
print(y)
X:
[-3 -2 -1  0  1  2  3]

Y:
[-5 -4 -3 -2 -1  0  1  2  3  4  5]

xm, ym = np.meshgrid(x,y)
plt.plot(xm, ym, 'ro')
plt.show()

plt.plot(xm, ym, 'r-')
plt.plot(xm.T, ym.T, 'b-')
plt.show()

Concatenate & Split

a = np.arange(12).reshape(3,4)
a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
b = a * 2
b
array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

concatenate horizontally

# hstack takes only one single argument, we put both arrays as a tuple or list
np.hstack((a, b))
array([[ 0,  1,  2,  3,  0,  2,  4,  6],
       [ 4,  5,  6,  7,  8, 10, 12, 14],
       [ 8,  9, 10, 11, 16, 18, 20, 22]])
np.column_stack([a,b])
array([[ 0,  1,  2,  3,  0,  2,  4,  6],
       [ 4,  5,  6,  7,  8, 10, 12, 14],
       [ 8,  9, 10, 11, 16, 18, 20, 22]])
np.concatenate([a,b], axis=1)
array([[ 0,  1,  2,  3,  0,  2,  4,  6],
       [ 4,  5,  6,  7,  8, 10, 12, 14],
       [ 8,  9, 10, 11, 16, 18, 20, 22]])

concatenate vertically

np.vstack([a,b])
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])
np.row_stack([a,b])
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])
np.concatenate([a,b], axis=0)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

Splitting array

arr = np.concatenate([a,b])
arr
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])
# split vertically into two pieces (horizontal cut)
np.vsplit(arr,2)
[array([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]]),
 array([[ 0,  2,  4,  6],
        [ 8, 10, 12, 14],
        [16, 18, 20, 22]])]
# split horizontally into two pieces (vertical cut)
np.hsplit(arr, 2)
[array([[ 0,  1],
        [ 4,  5],
        [ 8,  9],
        [ 0,  2],
        [ 8, 10],
        [16, 18]]),
 array([[ 2,  3],
        [ 6,  7],
        [10, 11],
        [ 4,  6],
        [12, 14],
        [20, 22]])]
# Read data from file , split it into smaller dataasets containing 5 rows each

data = np.genfromtxt(r'./Numpy_Datasets/data.txt')
L = np.vsplit(data, 5)
for i, ds in enumerate(L):
    print(f'Dataset {i+1}:')
    print(r'-'*30)
    print(ds)
    np.save(f"./TEMP/dataset_{i+1}.npz", ds)
Dataset 1:
------------------------------
[[ 1.      87.      57.54435]
 [ 2.       8.       7.31704]
 [ 3.      56.      56.82095]
 [ 4.      63.      64.15579]
 [ 5.       2.       5.74522]]
Dataset 2:
------------------------------
[[ 6.      45.      19.56758]
 [ 7.      43.      39.62271]
 [ 8.      47.      34.95107]
 [ 9.       2.       9.38692]
 [10.      79.      36.41022]]
Dataset 3:
------------------------------
[[11.      67.      49.83894]
 [12.      24.      23.47974]
 [13.      61.      72.55357]
 [14.      85.      39.24693]
 [15.      63.      53.6279 ]]
Dataset 4:
------------------------------
[[16.       2.      16.72441]
 [17.      29.      37.25533]
 [18.      45.      18.78498]
 [19.      33.      19.8089 ]
 [20.      28.      46.03384]]
Dataset 5:
------------------------------
[[21.      21.      23.7864 ]
 [22.      27.      44.42627]
 [23.      65.      34.94804]
 [24.      61.      53.49576]
 [25.      10.      25.98564]]