OperatorsFnArray.py

In[1]: import numpy as np np.random.seed(0) def compute_reciprocals(values): output = np.empty(len(values)) for i in range(len(values)): output[i] = 1.0 / values[i] return output values = np.random.randint(1, 10, size=5) compute_reciprocals(values) Out[1]: array([ 0.16666667, 1. , 0.25 , 0.25 , 0.125 ]) In[2]: big_array = np.random.randint(1, 100, size=1000000) %timeit compute_reciprocals(big_array) 1 loop, best of 3: 2.91 s per loop In[3]: print(compute_reciprocals(values)) print(1.0 / values) [ 0.16666667 1. 0.25 0.25 0.125 ] [ 0.16666667 1. 0.25 0.25 0.125 ] In[4]: %timeit (1.0 / big_array) 100 loops, best of 3: 4.6 ms per loop In[5]: np.arange(5) / np.arange(1, 6) Out[5]: array([ 0. , 0.5 , 0.66666667, 0.75 , 0.8 ]) In[6]: x = np.arange(9).reshape((3, 3)) 2**x Out[6]: array([[ 1, 2, 4], [ 8, 16, 32], [ 64, 128, 256]]) In[7]: x = np.arange(4) print("x =", x) print("x + 5 =", x + 5) print("x - 5 =", x - 5) print("x * 2 =", x * 2) print("x / 2 =", x / 2) print("x // 2 =", x // 2) # floor division x = [0 1 2 3] x + 5 = [5 6 7 8] x - 5 = [-5 -4 -3 -2] x * 2 = [0 2 4 6] x /2 = [ 0. 0.5 1. 1.5] x // 2 = [0 0 1 1] In[8]: print("-x = ", -x) print("x ** 2 = ", x ** 2) print("x % 2 = ", x % 2) -x = [ 0 -1 -2 -3] x ** 2 = [0 1 4 9] x% 2 = [0 1 0 1] In[9]: -(0.5*x + 1) ** 2 Out[9]: array([-1. , -2.25, -4. , -6.25]) In[10]: np.add(x, 2) Out[10]: array([2, 3, 4, 5]) In[11]: x = np.array([-2, -1, 0, 1, 2]) abs(x) Out[11]: array([2, 1, 0, 1, 2]) In[12]: np.absolute(x) Out[12]: array([2, 1, 0, 1, 2]) In[13]: np.abs(x) Out[13]: array([2, 1, 0, 1, 2]) In[14]:x=np.array([3-4j,4-3j,2+0j,0+1j]) np.abs(x) Out[14]: array([ 5., 5., 2., 1.]) In[15]: theta = np.linspace(0, np.pi, 3) In[16]: print("theta = ", theta) print("sin(theta) = ", np.sin(theta)) print("cos(theta) = ", np.cos(theta)) print("tan(theta) = ", np.tan(theta)) theta = [ 0. 1.57079633 3.14159265] sin(theta) = [ 0.00000000e+00 1.00000000e+00 1.22464680e-16] cos(theta) = [ 1.00000000e+00 6.12323400e-17 -1.00000000e+00] tan(theta) = [ 0.00000000e+00 1.63312394e+16 -1.22464680e-16] In[17]: x = [-1, 0, 1] print("x = ", x) print("arcsin(x) = ", np.arcsin(x)) print("arccos(x) = ", np.arccos(x)) print("arctan(x) = ", np.arctan(x)) x = [-1, 0, 1] arcsin(x) = [-1.57079633 0. 1.57079633] arccos(x) = [ 3.14159265 1.57079633 0. ] arctan(x) = [-0.78539816 0. 0.78539816] In[18]: x = [1, 2, 3] print("x =", x) print("e^x =",np.exp(x)) print("2^x =",np.exp2(x)) print("3^x =",np.power(3,x)) x = [1, 2, 3] e^x = [ 2.71828183 7.3890561 20.08553692] 2^x = [ 2. 4. 8.] 3^x = [ 3 9 27] In[19]: x = [1, 2, 4, 10] print("x =", x) print("ln(x) =", np.log(x)) print("log2(x) =", np.log2(x)) print("log10(x) =", np.log10(x)) x =[1,2,4,10] ln(x)=[ 0. 0.69314718 1.38629436 2.30258509] ln2(x)=[ 0. 1. 2. 3.32192809] ln10(x)=[ 0. 0.30103 0.60205999 1.] In[20]: x = [0, 0.001, 0.01, 0.1] print("exp(x) - 1 =", np.expm1(x)) print("log(1 + x) =", np.log1p(x)) exp(x) - 1 = [ 0. 0.0010005 0.01005017 0.10517092] log(1 + x) = [ 0. 0.0009995 0.00995033 0.09531018] In[21]: from scipy import special In[22]: # Gamma functions (generalized factorials) and related functions x=[1,5,10] print("gamma(x) =", special.gamma(x)) print("ln|gamma(x)| =", special.gammaln(x)) print("beta(x, 2) =", special.beta(x, 2)) gamma(x) = [ 1.00000000e+00 2.40000000e+01 3.62880000e+05] ln|gamma(x)| = [ 0. 3.17805383 12.80182748] beta(x, 2) = [ 0.5 0.03333333 0.00909091] In[23]: # Error function (integral of Gaussian) # its complement, and its inverse x = np.array([0, 0.3, 0.7, 1.0]) print("erf(x) =", special.erf(x)) print("erfc(x) =", special.erfc(x)) print("erfinv(x) =", special.erfinv(x)) erf(x) = [ 0. erfc(x) = [ 1. erfinv(x) = [ 0. 0.32862676 0.67780119 0.84270079] 0.67137324 0.32219881 0.15729921] 0.27246271 0.73286908 inf] In[24]: x = np.arange(5) y = np.empty(5) np.multiply(x, 10, out=y) print(y) [ 0. 10. 20. 30. 40.] In[25]: y = np.zeros(10) np.power(2, x, out=y[::2]) print(y) [ 1. 0. 2. 0. 4. 0. 8. 0. 16. 0.] In[26]: x = np.arange(1, 6) np.add.reduce(x) Out[26]: 15 In[27]: np.multiply.reduce(x) Out[27]: 120 In[28]: np.add.accumulate(x) Out[28]: array([ 1, 3, 6, 10, 15]) In[29]: np.multiply.accumulate(x) Out[29]: array([ 1, 2, 6, 24, 120]) In[30]: x = np.arange(1, 6) np.multiply.outer(x, x) Out[30]: array([[ 1, 2, 3, 4, 5], [ 2, 4, 6, 8, 10], [ 3, 6, 9, 12, 15], [ 4, 8, 12, 16, 20], [ 5, 10, 15, 20, 25]])