Install NumPy
brew install python3
# Get access to the scientific Python formulas
brew tap Homebrew/python
# Install Numpy and Matplotlib
brew install numpy --with-python3
pip3.4 install --upgrade pip
pip3 install matplotlib
If the optional parameter retstep is set, the function will also return the value of the spacing between adjacent values. So, the function will return a tuple ('samples', 'step'):
3つの数字は 1.18367347＋ 18367347 = 1.36734694
>>> samples, spacing = np.linspace(1, 10, endpoint=True, retstep=True)
>>> print(spacing)
0.1836734693877551
>>> print(samples)
[ 1. 1.18367347 1.36734694 1.55102041 1.73469388
1.91836735 2.10204082 2.28571429 2.46938776 2.65306122
2.83673469 3.02040816 3.20408163 3.3877551 3.57142857
3.75510204 3.93877551 4.12244898 4.30612245 4.48979592
4.67346939 4.85714286 5.04081633 5.2244898 5.40816327
5.59183673 5.7755102 5.95918367 6.14285714 6.32653061
6.51020408 6.69387755 6.87755102 7.06122449 7.24489796
7.42857143 7.6122449 7.79591837 7.97959184 8.16326531
8.34693878 8.53061224 8.71428571 8.89795918 9.08163265
9.26530612 9.44897959 9.63265306 9.81632653 10. ]
The syntax of arange:
arange([start,] stop[, step,], dtype=None)
>>> x = np.arange(10.4)
>>> print(x)
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
>>> x = np.arange(0.5, 10.4, 0.8)
>>> print(x)
[ 0.5 1.3 2.1 2.9 3.7 4.5 5.3 6.1 6.9 7.7 8.5 9.3
10.1]
>>> import numpy as np
>>> cvalues = [25.3, 24.8, 26.9, 23.9]
>>> C = np.array(cvalues)
>>> print(C)
[ 25.3 24.8 26.9 23.9]
>>> print(C*9/5 + 32)
[ 77.54 76.64 80.42 75.02]
>>> print(np.linspace(1, 10))
[ 1. 1.18367347 1.36734694 1.55102041 1.73469388
1.91836735 2.10204082 2.28571429 2.46938776 2.65306122
2.83673469 3.02040816 3.20408163 3.3877551 3.57142857
3.75510204 3.93877551 4.12244898 4.30612245 4.48979592
4.67346939 4.85714286 5.04081633 5.2244898 5.40816327
5.59183673 5.7755102 5.95918367 6.14285714 6.32653061
6.51020408 6.69387755 6.87755102 7.06122449 7.24489796
7.42857143 7.6122449 7.79591837 7.97959184 8.16326531
8.34693878 8.53061224 8.71428571 8.89795918 9.08163265
9.26530612 9.44897959 9.63265306 9.81632653 10. ]
>>> print(np.linspace(1, 10, 7))
[ 1. 2.5 4. 5.5 7. 8.5 10. ]
>>> print(np.linspace(1, 10, 7, endpoint=False))
[ 1. 2.28571429 3.57142857 4.85714286 6.14285714 7.42857143
8.71428571]
>>> import numpy as np
>>> a = np.array([2,3,4])
>>> a
array([2, 3, 4])
>>> a.dtype
dtype('int64')
>>> b = np.array([1.2, 3.5])
>>> b
array([ 1.2, 3.5])
>>> b.dtype
dtype('float64')
>>> b = np.array([(1.5,2,3), (4,5,6)])
>>> b
array([[ 1.5, 2. , 3. ],
[ 4. , 5. , 6. ]])
>>> b.dtype
dtype('float64')
>>> b[0][1]
2.0
>>> c = np.array([[1,2],[3,4]], dtype=complex)
>>> c
array([[ 1.+0.j, 2.+0.j],
[ 3.+0.j, 4.+0.j]])
>>> np.zeros((3,4))
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
>>> np.ones((2,3,4), dtype=np.int16)
array([[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]]], dtype=int16)
>>> np.arange(10, 30, 5)
array([10, 15, 20, 25])
>>>
>>> np.arange(0, 2, 0.3)
array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
>>> from numpy import pi
>>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2
array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ])
>>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points
>>> f = np.sin(x)