← Back
Editing: _upfirdn.cpython-311.pyc
� d�c� � �` � d dl ZddlmZmZmZ ddgZg d�Zd� Zd� Z G d � d � � Z dd �Z dS )� N� )� _output_len�_apply� mode_enum�upfirdnr ) �constant�wrap�edge�smooth� symmetric�reflect� antisymmetric� antireflect�linec � � t | � � t | � � |z z }t j || j � � }| |dt | � � �< |� d|� � j dd�ddd�f � � � }|S )a� Store coefficients in a transposed, flipped arrangement. For example, suppose upRate is 3, and the input number of coefficients is 10, represented as h[0], ..., h[9]. Then the internal buffer will look like this:: h[9], h[6], h[3], h[0], // flipped phase 0 coefs 0, h[7], h[4], h[1], // flipped phase 1 coefs (zero-padded) 0, h[8], h[5], h[2], // flipped phase 2 coefs (zero-padded) N���)�len�np�zeros�dtype�reshape�T�ravel)�h�up�h_padlen�h_fulls �7/usr/lib/python3/dist-packages/scipy/signal/_upfirdn.py�_pad_hr . s� � � �1�v�v�#�a�&�&��2� �&�H� �X�h��� (� (�F��F�7�C��F�F�7�O� �^�^�B�� #� #� %�a�a�a���2��g� .� 4� 4� 6� 6�F� �M� c �L � | � � � } t | � � }|S )N)�lowerr )�mode�enums r � _check_moder% B s � � �:�:�<�<�D� �T�?�?�D� �Kr c � � e Zd ZdZd� Zdd�ZdS ) �_UpFIRDnzHelper for resampling.c �2 � t j |� � }|j dk s |j dk rt d� � �t j |j |t j � � | _ t j || j � � }t |� � | _ t |� � | _ | j dk s | j dk rt d� � �t || j � � | _ t j | j � � | _ t |� � | _ d S )Nr r z"h must be 1-D with non-zero lengthz Both up and down must be >= 1)r �asarray�ndim�size� ValueError� result_typer �float32� _output_type�int�_up�_downr � _h_trans_flip�ascontiguousarrayr � _h_len_orig)�selfr �x_dtyper �downs r �__init__z_UpFIRDn.__init__K s� � � �J�q�M�M�� �6�Q�;�;�!�&�A�+�+� � A�B�B� B� �N�1�7�G�R�Z� H� H��� �J�q�$�+� ,� ,���r�7�7�����Y�Y�� � �8�a�<�<�4�:��>�>� � <�=�=� =� #�A�t�x� 0� 0��� � 1�$�2D� E� E��� �q�6�6����r r r r c � � t | j |j | | j | j � � }t j |j t j �� � }|||< t j || j d�� � }||j z }t |� � }t t j || j � � | j || j | j |||� � |S )z@Apply the prepared filter to the specified axis of N-D signal x.)r �C)r �order)r r5 �shaper1 r2 r r) �int64r r/ r* r% r r3 )r6 �x�axisr# �cval� output_len� output_shape�outs r � apply_filterz_UpFIRDn.apply_filterZ s� � � ��!1�1�7�4�=�!%��4�:�7� 7� � �z�!�'���:�:�:� � '� �T���h�|�4�+<�C�H�H�H���a�f�}���4� � ���r�z�!�T� .�/�/��!�3��x���T�4�� 7� 7� 7� � r N)r r r )�__name__� __module__� __qualname__�__doc__r9 rE � r r r' r' H s= � � � � � � � � "� "� "� � � � � � r r' r r c � � t j |� � }t | |j ||� � }|� ||||� � S )a� Upsample, FIR filter, and downsample. Parameters ---------- h : array_like 1-D FIR (finite-impulse response) filter coefficients. x : array_like Input signal array. up : int, optional Upsampling rate. Default is 1. down : int, optional Downsampling rate. Default is 1. axis : int, optional The axis of the input data array along which to apply the linear filter. The filter is applied to each subarray along this axis. Default is -1. mode : str, optional The signal extension mode to use. The set ``{"constant", "symmetric", "reflect", "edge", "wrap"}`` correspond to modes provided by `numpy.pad`. ``"smooth"`` implements a smooth extension by extending based on the slope of the last 2 points at each end of the array. ``"antireflect"`` and ``"antisymmetric"`` are anti-symmetric versions of ``"reflect"`` and ``"symmetric"``. The mode `"line"` extends the signal based on a linear trend defined by the first and last points along the ``axis``. .. versionadded:: 1.4.0 cval : float, optional The constant value to use when ``mode == "constant"``. .. versionadded:: 1.4.0 Returns ------- y : ndarray The output signal array. Dimensions will be the same as `x` except for along `axis`, which will change size according to the `h`, `up`, and `down` parameters. Notes ----- The algorithm is an implementation of the block diagram shown on page 129 of the Vaidyanathan text [1]_ (Figure 4.3-8d). The direct approach of upsampling by factor of P with zero insertion, FIR filtering of length ``N``, and downsampling by factor of Q is O(N*Q) per output sample. The polyphase implementation used here is O(N/P). .. versionadded:: 0.18 References ---------- .. [1] P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice Hall, 1993. Examples -------- Simple operations: >>> import numpy as np >>> from scipy.signal import upfirdn >>> upfirdn([1, 1, 1], [1, 1, 1]) # FIR filter array([ 1., 2., 3., 2., 1.]) >>> upfirdn([1], [1, 2, 3], 3) # upsampling with zeros insertion array([ 1., 0., 0., 2., 0., 0., 3.]) >>> upfirdn([1, 1, 1], [1, 2, 3], 3) # upsampling with sample-and-hold array([ 1., 1., 1., 2., 2., 2., 3., 3., 3.]) >>> upfirdn([.5, 1, .5], [1, 1, 1], 2) # linear interpolation array([ 0.5, 1. , 1. , 1. , 1. , 1. , 0.5]) >>> upfirdn([1], np.arange(10), 1, 3) # decimation by 3 array([ 0., 3., 6., 9.]) >>> upfirdn([.5, 1, .5], np.arange(10), 2, 3) # linear interp, rate 2/3 array([ 0. , 1. , 2.5, 4. , 5.5, 7. , 8.5]) Apply a single filter to multiple signals: >>> x = np.reshape(np.arange(8), (4, 2)) >>> x array([[0, 1], [2, 3], [4, 5], [6, 7]]) Apply along the last dimension of ``x``: >>> h = [1, 1] >>> upfirdn(h, x, 2) array([[ 0., 0., 1., 1.], [ 2., 2., 3., 3.], [ 4., 4., 5., 5.], [ 6., 6., 7., 7.]]) Apply along the 0th dimension of ``x``: >>> upfirdn(h, x, 2, axis=0) array([[ 0., 1.], [ 0., 1.], [ 2., 3.], [ 2., 3.], [ 4., 5.], [ 4., 5.], [ 6., 7.], [ 6., 7.]]) )r r) r' r rE )r r? r r8 r@ r# rA �ufds r r r k sA � �T � �1� � �A� �1�a�g�r�4� (� (�C� � � �A�t�T�4� 0� 0�0r )r r r r r ) �numpyr �_upfirdn_applyr r r �__all__�_upfirdn_modesr r% r' r rJ r r �<module>rQ s� ��D � � � � :� :� :� :� :� :� :� :� :� :� �m� $��� � �� � � �(� � � � � � � � � � �Fm1� m1� m1� m1� m1� m1r
Save File
Cancel