`i,dZddlZddlmZdZddZddZdZdd Zej d d Z ej d dZ dS)a Some of the functions defined here were ported directly from CuSignal under terms of the MIT license, under the following notice: Copyright (c) 2019-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. N)windowsc||jd}t|rA|tj||j}nft|tjr|j|fkrtd|}n.tj ||d|j}||z}|r"|tj |z }|S)Nz(window must have the same length as dataF)shapecallablecupyfftfftfreqastypedtype isinstancendarray ValueErrorr get_windowlinalgnorm)x normalizewindownws x/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/signal/_radartools/_radartools.py_pulse_preprocessrs  GBK F   Etx''**11!'::;;AA  - - E|t## !KLLLAA"61e44;;AGDDA E$    ## # HFc||j\}}tj||}||}t|||}tj||} tj||} tj| | z|} |jdkr| j} | S)a Pulse Compression is used to increase the range resolution and SNR by performing matched filtering of the transmitted pulse (template) with the received signal (x) Parameters ---------- x : ndarray Received signal, assume 2D array with [num_pulses, sample_per_pulse] template : ndarray Transmitted signal, assume 1D array normalize : bool Normalize transmitted signal window : array_like, callable, string, float, or tuple, optional Specifies the window applied to the signal in the Fourier domain. nfft : int, size of FFT for pulse compression. Default is number of samples per pulse Returns ------- compressedIQ : ndarray Pulse compressed output Nc) rr result_typerr ifftconjkindreal) rtemplaterrnfft num_pulsessamples_per_pulser tfft_xfft_touts rpulse_compressionr*1s:%&G!J!  Q ) )E | (Iv66A HLLD ! !E HLLD ! !E (-- ,d 3 3C zSh Jrc|j\}}||}t|jd|}tj||jS)a Pulse doppler processing yields a range/doppler data matrix that represents moving target data that's separated from clutter. An estimation of the doppler shift can also be obtained from pulse doppler processing. FFT taken across slow-time (pulse) dimension. Parameters ---------- x : ndarray Received signal, assume 2D array with [num_pulses, sample_per_pulse] window : array_like, callable, string, float, or tuple, optional Specifies the window applied to the signal in the Fourier domain. nfft : int, size of FFT for pulse compression. Default is number of samples per pulse Returns ------- pd_dataMatrix : ndarray Pulse-doppler output (range/doppler matrix) NF)rrTrr )rrr#r$r%xTs r pulse_dopplerr.]sG0%&G!J! | 13v . .B 8<<D ! ! ##rc||d|z zdz zS)aA Computes the value of alpha corresponding to a given probability of false alarm and number of reference cells N. Parameters ---------- pfa : float Probability of false alarm. N : int Number of reference cells. Returns ------- alpha : float Alpha value. g)pfaNs r cfar_alphar4~s$ q!A% &&rMbP?c|j}t|dkrtdtj|tj}t|dkrt|d|zd|zzkrt dtj|dtj}d|z}t||}d} t|d|zz d|zz | dzdz | dzf} t| | |||t||tj|||fnt|dkrt|dkst|dkrtd |\} } |\} }|dd| zz d| zz dkrt d |dd| zz d|zz dkrt d tj|dtj}tj|dtj}d| zd|zd| zzdzz}|dd| zdzz|zz }t||}d } |dd| | zzz | dzdz | dz}|dd|| zzz | dzdz | dz}||f} t| | ||||d|d|tj|| | | |f |||z dkfS) a Computes the cell-averaged constant false alarm rate (CA CFAR) detector threshold and returns for a given array. Parameters ---------- array : ndarray Array containing data to be processed. guard_cells_x : int One-sided guard cell count in the first dimension. guard_cells_y : int One-sided guard cell count in the second dimension. reference_cells_x : int one-sided reference cell count in the first dimension. reference_cells_y : int one-sided reference cell count in the second dimension. pfa : float Probability of false alarm. Returns ------- threshold : ndarray CFAR threshold return : ndarray CFAR detections z.Only 1D and 2D arrays are currently supported.)r r0z$Array too small for given parametersr)axisr ) z2Guard and reference cells must be two dimensional.z5Array first dimension too small for given parameters.z6Array second dimension too small for given parameters.)r:) rlen TypeErrorrzerosfloat32rcumsumr4_ca_cfar_1d_kernel_ca_cfar_2d_kernel)array guard_cellsreference_cellsr2rmask intermediater3alphatpbbpg guard_cells_x guard_cells_yreference_cells_xreference_cells_ybpg_xbpg_ys rca_cfarrPsw2 KE 5zzA~~HIII :e4< 0 0 0D 5zzQ u::[1+>> > >CDD D{5q EEE  3""E Q001{?B1v!!f%'3e\4&)%jj!T\%5H5H&1?&D E E E E Uq {  q C$8$8A$=$=+,, ,'2$ }/>,, 8a-' '!.?*? ?1 D D+,, , 8a-' '!.?*? ?1 D D+,, ,{5q EEE {0 && j>0){ outer_area = intermediate[tro]-intermediate[tlo]- intermediate[bro]+intermediate[blo]; } else if (i == 0 && j > 0){ outer_area = intermediate[tro]-intermediate[bro]; } else if (i > 0 && j == 0){ outer_area = intermediate[tro]-intermediate[tlo]; } else if (i == 0 && j == 0){ outer_area = intermediate[tro]; } inner_area = intermediate[tri]-intermediate[tli]- intermediate[bri]+intermediate[bli]; T = outer_area-inner_area; T = alpha/N*T; mask[offset] = T; } } } rAa extern "C" __global__ void _ca_cfar_1d_kernel(float * array, float * intermediate, float * mask, int width, int N, float alpha, int guard_cells, int reference_cells) { int i_init = threadIdx.x+blockIdx.x*blockDim.x; int i, x; int br, bl, sr, sl; float big_area, small_area, T; for (i=i_init; i0){ big_area = intermediate[br]-intermediate[bl]; } else{ big_area = intermediate[br]; } small_area = intermediate[sr]-intermediate[sl]; T = big_area-small_area; T = alpha/N*T; mask[x] = T; } } r@)FNN)NN)r5) __doc__rcupyx.scipy.signalrrr*r.r4rP RawKernelrAr@r1rrrTs2 &&&&&&   &))))X$$$$B'''*F$F$F$F$R$T^:%tu::z$T^%89r