p:i(fSSKrSSKJr SSKJrJrJr SS/r/SQr Sr S r "S S 5r S S jr g)N)array_namespace) _output_len_apply mode_enumupfirdnr) constantwrapedgesmooth symmetricreflect antisymmetric antireflectlinec[U5[U5*U--n[R"X R5nXS[U5&UR SU5R SS2SSS24R 5nU$)aStore coefficients in a transposed, flipped arrangement. For example, suppose upRate is 3, and the input number of coefficients is 10, represented as h[0], ..., h[9]. Then the internal buffer will look like this:: h[9], h[6], h[3], h[0], // flipped phase 0 coefs 0, h[7], h[4], h[1], // flipped phase 1 coefs (zero-padded) 0, h[8], h[5], h[2], // flipped phase 2 coefs (zero-padded) N)lennpzerosdtypereshapeTravel)huph_padlenh_fulls X/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/scipy/signal/_upfirdn.py_pad_hr /sl1v#a&2&H XXh (F7CFO ^^B # % %a2g . 4 4 6F Mc<UR5n[U5nU$)N)lowerr)modeenums r _check_moder&Cs ::= 1)rasarrayndimsize ValueError result_typerfloat32 _output_typeint_up_downr _h_trans_flipascontiguousarrayr _h_len_orig)selfrx_dtyperdowns r__init___UpFIRDn.__init__Ls JJqM 66Q;!&&A+AB BNN177GRZZH JJq++ ,r7Y 88a<4::><= =#Axx011$2D2DEq6r!c [URURUURUR5n[ R "UR[ RS9nXVU'[ R"X`RSS9nX!R-n[U5n[[ R "XR5URUURURX#U5 U$)z@Apply the prepared filter to the specified axis of N-D signal x.)rC)rorder)rr7shaper3r4rr+int64rr1r,r&rr5)r8xaxisr$cval output_len output_shapeouts r apply_filter_UpFIRDn.apply_filter[s !1!11774=!%4::7 zz!'': 'Thh|+<+>> import numpy as np >>> from scipy.signal import upfirdn >>> upfirdn([1, 1, 1], [1, 1, 1]) # FIR filter array([ 1., 2., 3., 2., 1.]) >>> upfirdn([1], [1, 2, 3], 3) # upsampling with zeros insertion array([ 1., 0., 0., 2., 0., 0., 3.]) >>> upfirdn([1, 1, 1], [1, 2, 3], 3) # upsampling with sample-and-hold array([ 1., 1., 1., 2., 2., 2., 3., 3., 3.]) >>> upfirdn([.5, 1, .5], [1, 1, 1], 2) # linear interpolation array([ 0.5, 1. , 1. , 1. , 1. , 1. , 0.5]) >>> upfirdn([1], np.arange(10), 1, 3) # decimation by 3 array([ 0., 3., 6., 9.]) >>> upfirdn([.5, 1, .5], np.arange(10), 2, 3) # linear interp, rate 2/3 array([ 0. , 1. , 2.5, 4. , 5.5, 7. , 8.5]) Apply a single filter to multiple signals: >>> x = np.reshape(np.arange(8), (4, 2)) >>> x array([[0, 1], [2, 3], [4, 5], [6, 7]]) Apply along the last dimension of ``x``: >>> h = [1, 1] >>> upfirdn(h, x, 2) array([[ 0., 0., 1., 1.], [ 2., 2., 3., 3.], [ 4., 4., 5., 5.], [ 6., 6., 7., 7.]]) Apply along the 0th dimension of ``x``: >>> upfirdn(h, x, 2, axis=0) array([[ 0., 1.], [ 0., 1.], [ 2., 3.], [ 2., 3.], [ 4., 5.], [ 4., 5.], [ 6., 7.], [ 6., 7.]]) )rrr+r(rrH) rrBrr:rCr$rDxpufds rrrlsHT  B 1 A 1ggr (C ::c&&q; <rYs@D1:: m $ (  Fo=r!