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)
•          numpy.fft.fft
•          numpy.fft.ifft
•          numpy.fft.fft2
•          numpy.fft.ifft2
•          numpy.fft.fftn
•          numpy.fft.ifftn
•          numpy.fft.rfft
•          numpy.fft.irfft
•          numpy.fft.rfft2
•          numpy.fft.irfft2
•          numpy.fft.rfftn
•          numpy.fft.irfftn
•          numpy.fft.hfft
•          numpy.fft.ihfft
•          numpy.fft.fftfreq
•          numpy.fft.rfftfreq
•          numpy.fft.fftshift
•          numpy.fft.ifftshift
•    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
•    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.fft.ifft(a, n=None, axis=-1, norm=None)[source]¶

Compute the one-dimensional inverse discrete Fourier Transform.

This function computes the inverse of the one-dimensional n-point discrete Fourier transform computed by fft. In other words, ifft(fft(a)) == a to within numerical accuracy. For a general description of the algorithm and definitions, see numpy.fft.

The input should be ordered in the same way as is returned by fft, i.e.,

• a[0] should contain the zero frequency term,

• a[1:n//2] should contain the positive-frequency terms,

• a[n//2 + 1:] should contain the negative-frequency terms, in increasing order starting from the most negative frequency.

For an even number of input points, A[n//2] represents the sum of the values at the positive and negative Nyquist frequencies, as the two are aliased together. See numpy.fft for details.

Parameters

aarray_like

Input array, can be complex.

nint, optional

Length of the transformed axis of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zeros. If n is not given, the length of the input along the axis specified by axis is used. See notes about padding issues.

axisint, optional

Axis over which to compute the inverse DFT. If not given, the last axis is used.

norm{None, “ortho”}, optional

New in version 1.10.0.

Normalization mode (see numpy.fft). Default is None.

Returns

outcomplex ndarray

The truncated or zero-padded input, transformed along the axis indicated by axis, or the last one if axis is not specified.

Raises

IndexError

If axes is larger than the last axis of a.

See also

numpy.fft

An introduction, with definitions and general explanations.

fft

The one-dimensional (forward) FFT, of which ifft is the inverse

ifft2

The two-dimensional inverse FFT.

ifftn

The n-dimensional inverse FFT.

Notes

If the input parameter n is larger than the size of the input, the input is padded by appending zeros at the end. Even though this is the common approach, it might lead to surprising results. If a different padding is desired, it must be performed before calling ifft.

Examples

>>> np.fft.ifft([0, 4, 0, 0])
array([ 1.+0.j,  0.+1.j, -1.+0.j,  0.-1.j]) # may vary

Create and plot a band-limited signal with random phases:

>>> import matplotlib.pyplot as plt
>>> t = np.arange(400)
>>> n = np.zeros((400,), dtype=complex)
>>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,)))
>>> s = np.fft.ifft(n)
>>> plt.plot(t, s.real, 'b-', t, s.imag, 'r--')
[<matplotlib.lines.Line2D object at ...>, <matplotlib.lines.Line2D object at ...>]
>>> plt.legend(('real', 'imaginary'))
<matplotlib.legend.Legend object at ...>
>>> plt.show()