NumPy NumPy参考

• 数组对象
• 常数
• 通用功能（ufunc）
• 例行程序
•    数组创建例程
•    数组操作例程
•    Binary operations
•    String operations
•    C-Types Foreign Function Interface (numpy.ctypeslib)
•    Datetime Support Functions
•    Data type routines
•    Optionally Scipy-accelerated routines (numpy.dual)
•    Mathematical functions with automatic domain (numpy.emath)
•    Floating point error handling
•    Discrete Fourier Transform (numpy.fft)
•    Financial functions
•    Functional programming
•    NumPy-specific help functions
•    Indexing routines
•    输入输出
•    线性代数（numpy.linalg）
•    逻辑功能
•    Mathematical functions
•          numpy.sin
•          numpy.cos
•          numpy.tan
•          numpy.arcsin
•          numpy.arccos
•          numpy.arctan
•          numpy.hypot
•          numpy.arctan2
•          numpy.degrees
•          numpy.radians
•          numpy.unwrap
•          numpy.deg2rad
•          numpy.rad2deg
•          numpy.sinh
•          numpy.cosh
•          numpy.tanh
•          numpy.arcsinh
•          numpy.arccosh
•          numpy.arctanh
•          numpy.around
•          numpy.round_
•          numpy.rint
•          numpy.fix
•          numpy.floor
•          numpy.ceil
•          numpy.trunc
•          numpy.prod
•          numpy.sum
•          numpy.nanprod
•          numpy.nansum
•          numpy.cumprod
•          numpy.cumsum
•          numpy.nancumprod
•          numpy.nancumsum
•          numpy.diff
•          numpy.ediff1d
•          numpy.gradient
•          numpy.cross
•          numpy.trapz
•          numpy.exp
•          numpy.expm1
•          numpy.exp2
•          numpy.log
•          numpy.log10
•          numpy.log2
•          numpy.log1p
•          numpy.logaddexp
•          numpy.logaddexp2
•          numpy.i0
•          numpy.sinc
•          numpy.signbit
•          numpy.copysign
•          numpy.frexp
•          numpy.ldexp
•          numpy.nextafter
•          numpy.spacing
•          numpy.lcm
•          numpy.gcd
•          numpy.add
•          numpy.reciprocal
•          numpy.positive
•          numpy.negative
•          numpy.multiply
•          numpy.divide
•          numpy.power
•          numpy.subtract
•          numpy.true_divide
•          numpy.floor_divide
•          numpy.float_power
•          numpy.fmod
•          numpy.mod
•          numpy.modf
•          numpy.remainder
•          numpy.divmod
•          numpy.angle
•          numpy.real
•          numpy.imag
•          numpy.conj
•          numpy.conjugate
•          numpy.convolve
•          numpy.clip
•          numpy.sqrt
•          numpy.cbrt
•          numpy.square
•          numpy.absolute
•          numpy.fabs
•          numpy.sign
•          numpy.heaviside
•          numpy.maximum
•          numpy.minimum
•          numpy.fmax
•          numpy.fmin
•          numpy.nan_to_num
•          numpy.real_if_close
•          numpy.interp
•    Matrix library (numpy.matlib)
•    Miscellaneous routines
•    Padding Arrays
•    Polynomials
•    Random sampling (numpy.random)
•    Set routines
•    Sorting, searching, and counting
•    Statistics
•    Test Support (numpy.testing)
•    Window functions
• Packaging (numpy.distutils)
• NumPy Distutils - Users Guide
• NumPy C-API
• NumPy内部
• NumPy和SWIG

numpy.sinc（x ）[来源] ¶

返回sinc函数。

sinc函数为。

参量

X ndarray

要为其计算值的数组（可能是多维数组）sinc(x)。

退货

出ndarray

sinc(x)，其形状与输入相同。

笔记

sinc(0) 是极限值1。

sinc名称是“正弦基数”或“窦基数”的缩写。

sinc函数可用于各种信号处理应用中，包括抗混叠，Lanczos重采样滤波器的构造以及插值。

对于离散时间信号的带限插值，理想插值内核与Sinc函数成正比。

参考文献

1个

Weisstein，Eric W.“ Sinc函数”。来自MathWorld – Wolfram Web资源。http://mathworld.wolfram.com/SincFunction.html

2

Wikipedia，“ Sinc函数”，https： //en.wikipedia.org/wiki/Sinc_function

例子

>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-4, 4, 41)
>>> np.sinc(x)
 array([-3.89804309e-17,  -4.92362781e-02,  -8.40918587e-02, # may vary
        -8.90384387e-02,  -5.84680802e-02,   3.89804309e-17,
        6.68206631e-02,   1.16434881e-01,   1.26137788e-01,
        8.50444803e-02,  -3.89804309e-17,  -1.03943254e-01,
        -1.89206682e-01,  -2.16236208e-01,  -1.55914881e-01,
        3.89804309e-17,   2.33872321e-01,   5.04551152e-01,
        7.56826729e-01,   9.35489284e-01,   1.00000000e+00,
        9.35489284e-01,   7.56826729e-01,   5.04551152e-01,
        2.33872321e-01,   3.89804309e-17,  -1.55914881e-01,
       -2.16236208e-01,  -1.89206682e-01,  -1.03943254e-01,
       -3.89804309e-17,   8.50444803e-02,   1.26137788e-01,
        1.16434881e-01,   6.68206631e-02,   3.89804309e-17,
        -5.84680802e-02,  -8.90384387e-02,  -8.40918587e-02,
        -4.92362781e-02,  -3.89804309e-17])

>>> plt.plot(x, np.sinc(x))
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Sinc Function")
Text(0.5, 1.0, 'Sinc Function')
>>> plt.ylabel("Amplitude")
Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("X")
Text(0.5, 0, 'X')
>>> plt.show()