`i>ddlZddlmZddlmZddlmZddlmZm Z m Z ddl m Z m Z dZejedd d Dd d Dz Zd ZddZddZddZdZdZddZddZdS)N) get_typename)_normalize_axis_index)lfilter) axis_slice axis_assign axis_reverse) collapse_2d apply_iir_sosaj #include #include template __device__ T _compute_symiirorder2_fwd_hc( const int k, const T cs, const T r, const T omega) { T base; if(k < 0) { return 0; } if(omega == 0.0) { base = cs * pow(r, ((T) k)) * (k + 1); } else if(omega == M_PI) { base = cs * pow(r, ((T) k)) * (k + 1) * (1 - 2 * (k % 2)); } else { base = (cs * pow(r, ((T) k)) * sin(omega * (k + 1)) / sin(omega)); } return base; } template __global__ void compute_symiirorder2_fwd_sc( const int n, const int off, const T* cs_ptr, const T* r_ptr, const T* omega_ptr, const double precision, bool* valid, T* out) { int idx = blockDim.x * blockIdx.x + threadIdx.x; if(idx + off >= n) { return; } const T cs = cs_ptr[0]; const T r = r_ptr[0]; const T omega = omega_ptr[0]; T val = _compute_symiirorder2_fwd_hc(idx + off + 1, cs, r, omega); T err = val * val; out[idx] = val; valid[idx] = err <= precision; } template __device__ T _compute_symiirorder2_bwd_hs( const int ki, const T cs, const T rsq, const T omega) { T c0; T gamma; T cssq = cs * cs; int k = abs(ki); T rsupk = pow(rsq, ((T) k) / ((T) 2.0)); if(omega == 0.0) { c0 = (1 + rsq) / ((1 - rsq) * (1 - rsq) * (1 - rsq)) * cssq; gamma = (1 - rsq) / (1 + rsq); return c0 * rsupk * (1 + gamma * k); } if(omega == M_PI) { c0 = (1 + rsq) / ((1 - rsq) * (1 - rsq) * (1 - rsq)) * cssq; gamma = (1 - rsq) / (1 + rsq) * (1 - 2 * (k % 2)); return c0 * rsupk * (1 + gamma * k); } c0 = (cssq * (1.0 + rsq) / (1.0 - rsq) / (1 - 2 * rsq * cos(2 * omega) + rsq * rsq)); gamma = (1.0 - rsq) / (1.0 + rsq) / tan(omega); return c0 * rsupk * (cos(omega * k) + gamma * sin(omega * k)); } template __global__ void compute_symiirorder2_bwd_sc( const int n, const int off, const int l_off, const int r_off, const T* cs_ptr, const T* rsq_ptr, const T* omega_ptr, const double precision, bool* valid, T* out) { int idx = blockDim.x * blockIdx.x + threadIdx.x; if(idx + off >= n) { return; } const T cs = cs_ptr[0]; const T rsq = rsq_ptr[0]; const T omega = omega_ptr[0]; T v1 = _compute_symiirorder2_bwd_hs(idx + l_off + off, cs, rsq, omega); T v2 = _compute_symiirorder2_bwd_hs(idx + r_off + off, cs, rsq, omega); T diff = v1 + v2; T err = diff * diff; out[idx] = diff; valid[idx] = err <= precision; } )z -std=c++11cg|]}d|d S)zcompute_symiirorder2_bwd_sc<>.0ts o/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/scipy/signal/_splines.py rqs3444:Q999444)floatdoublecg|]}d|d S)zcompute_symiirorder2_fwd_sc.xs"DDDs< **DDDrz, gMbP? r)rr,r;Sum to find symmetric boundary conditions did not converge.r+rCF)r7r apply_firstepr7r)rndimshaper r.abs ValueErrorrfloat64float32finfoiexparange conjugatecumsumrr8anyisnanzerosrr_ atleast_2dr c_rreshapemoveaxisflags c_contiguouscopy)inputc0z1 precisionr- input_shape input_ndimpospow_z1diffr3r2r7zi_shapeall_zicoefy1_outs r_symiirorder1_ndrosw uz 2 2D+KJ zA~~(55{ x||q5666C9s?? ;$*T\22 2 2II [DJt|44 4 4IItz%+66;;;I I +aR1,EK @ @ @C 3YF DN6** *D{R!!!#-eQ#C#C#CDH !I IxR A AB x 2K IKK KHA~~u{1~q) Z 4 4 4F Q * *F 71aAsA% &D ?4 D *UA.. % u > > >EB RB S Jr2.. .B Q * *F 72q!QQ& 'D ?4 D 2r###TfEKIIIFCA~~gl3''+,gl3''+,A~~kk+&&mCT**y% ((**C JrcVtj|g|j}tj|g|j}tj|dkrt d|dks|dkrtj|jj}||z}tjd|jdz|j}||z}|tj |z}tj ||z|dz}||k}t|||j} tj | rt dtj d| f} | |j} ttjd|j| |dd| \} } tj | | f} | |dz z | d z} tj d| f} | |j} t|| | dd ddd | \} } tj | ddd | fS) aR Implement a smoothing IIR filter with mirror-symmetric boundary conditions using a cascade of first-order sections. The second section uses a reversed sequence. This implements a system with the following transfer function and mirror-symmetric boundary conditions:: c0 H(z) = --------------------- (1-z1/z) (1 - z1 z) The resulting signal will have mirror symmetric boundary conditions as well. Parameters ---------- input : ndarray The input signal. c0, z1 : scalar Parameters in the transfer function. precision : Specifies the precision for calculating initial conditions of the recursive filter based on mirror-symmetric input. 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The second section uses a reversed sequence. This implements the following transfer function:: cs^2 H(z) = --------------------------------------- (1 - a2/z - a3/z^2) (1 - a2 z - a3 z^2 ) where:: a2 = 2 * r * cos(omega) a3 = - r ** 2 cs = 1 - 2 * r * cos(omega) + r ** 2 Parameters ---------- input : ndarray The input signal. r, omega : float Parameters in the transfer function. precision : float Specifies the precision for calculating initial conditions of the recursive filter based on mirror-symmetric input. Returns ------- output : ndarray The filtered signal. )r)r`rrrcs r symiirorder2rs> E1eY 7 77r)rr))r9r))r9)r.cupy._core._scalarrcupy._core.internalrcupyx.scipy.signal._signaltoolsrcupyx.scipy.signal._arraytoolsrrrcupyx.scipy.signal._iir_utilsr r SYMIIR2_KERNEL RawModulerr(r8rorwrrrrr rrrs ++++++555555333333++++++++++DDDDDDDDaF  442444##!####$$$GGGGTC"C"C"C"L ( ( (LLL*ZZZZz888888r