python-08-numpy

numpy_arrays_00.py

numpy_data_array.py

import numpy as np

a=np.identity(3,dtype=np.float64)
print(a)
# [[ 1.  0.  0.]
#  [ 0.  1.  0.]
#  [ 0.  0.  1.]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>

a=np.eye(3, 5, k=0,dtype=np.float64)
print(a)
# [[ 1.  0.  0.  0.  0.]
#  [ 0.  1.  0.  0.  0.]
#  [ 0.  0.  1.  0.  0.]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>

a=np.ones((3, 5),dtype=np.float64)
print(a)
# [[ 1.  1.  1.  1.  1.]
#  [ 1.  1.  1.  1.  1.]
#  [ 1.  1.  1.  1.  1.]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>

a=np.ones_like(np.array([[0,1,0,2,5],[1,2,3,4,4]]),dtype=np.float64)
print(a)
# [[1 1 1 1 1]
#  [1 1 1 1 1]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>


a=np.full((3, 5),22,dtype=np.float64)
print(a)
# [[ 22.  22.  22.  22.  22.]
#  [ 22.  22.  22.  22.  22.]
#  [ 22.  22.  22.  22.  22.]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>

numpy_data_circular.py

################################################################################################
#	name:	numpy_data_linear.py
#	desc:	Genarate test data having linear relationship
#	date:	2019-02-02
#	Author:	conquistadorjd
################################################################################################
import numpy as np
from matplotlib import pyplot as plt

print('*** Program Started ***')

n = 50
x = np.arange(-n/2,n/2,1,dtype=np.float64)

r = np.random.uniform(10,10,(n,))

y =np.sqrt(r*r - x*x)

print('x',x, type(x[0]))
print('y',y, type(y[0]))

plt.scatter(x,y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Linear Relation")
plt.scatter(x,-y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Linear Relation")
plt.grid(color='black', linestyle='--', linewidth=0.5,markevery=int)
# plt.xlim( -15, 15 )  # set the xlim to xmin, xmax
# plt.ylim( -15, 15 )    # set the ylim to ymin, ymax
plt.legend(loc=2)
plt.axis('scaled')

plt.show()

plt.savefig('scarrerplot-01.png')

print('*** Program ended ***')

numpy_data_linear.jpeg

numpy_data_linear.py

################################################################################################
#	name:	numpy_data_linear.py
#	desc:	Genarate test data having linear relationship
#	date:	2019-02-02
#	Author:	conquistadorjd
################################################################################################
import numpy as np
from matplotlib import pyplot as plt

print('*** Program Started ***')

n = 50
x = np.arange(-n/2,n/2,1,dtype=np.float64)

m = np.random.uniform(0.3,0.5,(n,))
b = np.random.uniform(5,10,(n,))

y = x*m +b 
print('x',x, type(x[0]))
print('y',y, type(y[0]))

plt.scatter(x,y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Linear Relation")
plt.grid(color='black', linestyle='--', linewidth=0.5,markevery=int)
plt.legend(loc=2)
plt.axis('scaled')

plt.show()

plt.savefig('numpy_data_linear.jpeg')

print('*** Program ended ***')

numpy_data_random.py

import numpy as np

######################### Random values in a given shape.
a=np.random.rand(2,3)
print(a)
# [[ 0.7524278   0.21176809  0.73990734]
#  [ 0.28341776  0.11559792  0.15859365]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.ndarray'>
print(type(a[0][0]))
# <type 'numpy.float64'>

####################### Return a sample from the standard normal distribution
a=np.random.randn(5)
print(a)
# [ 1.0000366   0.0906066  -0.05027158 -0.14745128  1.35046138]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>

#######################Return a sample from the standard normal distribution
a=np.random.randn(5,4)
print(a)
# [[ 1.48864593 -0.75508993  1.57585151 -0.02507804]
#  [-1.11795072  0.16357727  0.76753395  0.02291213]
#  [-1.39439533  0.66704929 -0.01020978  0.12887067]
#  [-0.19386682  0.70650588  0.71049381 -0.40089744]
#  [-0.6845585   0.35872981  0.18581329 -0.51889034]]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>
print(type(a[0][0]))
# <type 'numpy.float64'>

###############Return random integers from low (inclusive) to high (exclusive).
a=np.random.randint(2,14,size=5)
print(a)
# [12  9  7  3  9]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.int64'>

###############Return random floats in the half-open interval [0.0, 1.0).
a=np.random.random_sample(5)
print(a)
# [-0.20534297  0.4333096   0.94111548 -0.61324519  0.8843922 ]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>


###############Draw samples from a binomial distribution.
n,p=10,0.5 # number of trials, probability of each trial
a=np.random.binomial(n,p,100)
print(a)
# [5 2 5 6 5 3 6 8 5 7 3 4 8 4 9 5 5 6 3 5 7 6 6 2 6 5 6 6 5 3 6 5 6 6 4 6 2
#  7 5 6 7 6 3 3 3 8 8 3 2 5 7 6 4 2 5 7 6 4 5 6 5 5 5 7 4 2 8 3 5 3 6 5 4 4
#  3 3 5 7 7 4 4 6 4 5 6 7 5 5 6 6 4 7 4 4 3 2 6 6 7 3]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.int64'>

############### Draw samples from a uniform distribution.
a=np.random.uniform(5,15,(3,))
print(a)
# [ 13.81416285   5.82087405  13.24553233]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>

numpy_data_random_01.jpeg

numpy_data_random_01.py

################################################################################################
#	name:	numpy_data_random_01.py
#	desc:	Genarate test data having linear relationship
#	date:	2019-02-02
#	Author:	conquistadorjd
################################################################################################
import numpy as np
from matplotlib import pyplot as plt

print('*** Program Started ***')

n = 50
x = np.arange(-n/2,n/2,1,dtype=np.float64)
y = a=np.random.uniform(-15,15,(n,))

print('x',x)
print('y',y)

plt.scatter(x,y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Random numbers")
plt.grid(color='black', linestyle='--', linewidth=0.5,markevery=int)
plt.legend(loc=2)
plt.axis('scaled')
plt.show()

plt.savefig('numpy_data_random_01.jpeg')

print('*** Program ended ***')

numpy_data_sequence.py

import numpy as np

a = np.array([1, 2, 3])
print(a)
#[1 2 3]
print(type(a))
#<type 'numpy.ndarray'>
b = np.array([[1, 2], [3, 4]])
print(b)
#[[1 2]
# [3 4]]
#################################################################
c = np.arange(3)
print(c)
# [0 1 2]
print(type(c))
# <type 'numpy.ndarray'>
print(type(c[0]))
# <type 'numpy.int64'>

d = np.arange(3.0)
print(d)
# [ 0.  1.  2.]
print(type(d))
# <type 'numpy.ndarray'>
print(type(d[0]))
# <type 'numpy.float64'>

e = np.arange(5 ,10, 1,dtype=np.float64)
print(e)
# [ 5.  6.  7.  8.  9.]
print(type(e))
# <type 'numpy.ndarray'>
print(type(e[0]))
# <type 'numpy.float64'>

##################################################################
a=np.linspace(2, 13, num=5,dtype=np.float64)
print(a)
# [  2.     4.75   7.5   10.25  13.  ]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>

a=np.logspace(1, 2, num=5,dtype=np.float64)
print(a)
# [  10.           17.7827941    31.6227766    56.23413252  100.        ]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>


a=np.geomspace(1, 2, num=5,dtype=np.float64)
print(a)
# [ 1.          1.18920712  1.41421356  1.68179283  2.        ]
print(type(a))
# <type 'numpy.ndarray'>
print(type(a[0]))
# <type 'numpy.float64'>