z_dsp delay width debug;add z_dsp.m and diff_plot_py.m
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%compare FIL with python script
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function diff_plot_py(fs,iir_out, Script_out,title1,title2,a,amp,edge)
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%输入数据长度不等时取其公共部分
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N = min(length(iir_out),length(Script_out));
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iir_out = iir_out(1:N);
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Script_out = Script_out(1:N);
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diff = (iir_out - Script_out)/amp;%求差，并归一化
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n = (0:1:N-1)/fs;
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%找出关心的数据点
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n_edge = find(n>=edge-1e-12);%edge代表下降沿
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n50 = find(n>=edge+20e-9-1e-12);%下降沿后20ns
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n20_40 = find((n>=edge+20e-9-1e-12) & (n<=edge+40e-9+1e-12));%下降沿后20ns到40ns
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n1000 = find(n>=edge+1000e-9-1e-12);%下降沿后1us
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n1000_1100 = find((n>=edge+1000e-9-1e-12) & (n<=edge+1100e-9+1e-12));%下降沿后1us到1.1us
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ne = find((abs(diff)>=1e-4) & (abs(diff)<1));%误差小于万分之一的点
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ne(1) = 1;
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% window_length = 100e-9*fs;
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% diff_mean_window = movmean(diff,window_length);
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% diff_std_window = movstd(diff,window_length);
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% n_mean_window = find((abs(diff_mean_window)>=1e-4) );%100ns窗，误差均值小于万分之一点
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% n_std_window = find((abs(diff_std_window)>=1e-4) ); %100ns窗，误差方差小于万分之一点
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% n_common = max(n_mean_window(end),n_std_window(end));
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%原始数据作图
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tiledlayout(2,1)
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ax1 = nexttile;
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plot(n,iir_out,n,Script_out)
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legend(title1,title2)
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xlabel('t/s')
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xlim(a)
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grid on
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hold on
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%差值做图
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ax2 = nexttile;
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plot(n,diff)
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xlabel('t/s')
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title('diff')
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grid on
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hold on
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xlim(a)
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linkaxes([ax1,ax2],'x');
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plot_p = @(x)[
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plot(n(x),diff(x),'r*');
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text(n(x), diff(x)+diff(x)*0.1, ['(',num2str(n(x)),',',num2str(diff(x)),')'],'color','k');
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];
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%标注出关心的点
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%plot_p(n_edge(1));%下降沿
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%plot_p(n50(1)); %下降沿20ns
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%plot_p(n1000(1)); %下降沿1us
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ne(1) = 1;
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%plot_p(ne(end)); %误差小于万分之一
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% [diff_max,R_mpos] = max(abs(diff));%误差最大值
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% plot_p(R_mpos);
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if a(2) <= 5e-6
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plot_p(n_edge(1));%下降沿
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% plot_p(R_mpos);
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elseif a(2) > 5e-6
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plot_p(n50(1)); %下降沿20ns
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plot_p(n1000(1)); %下降沿1us
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plot_p(ne(end)); %误差小于万分之一
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fprintf("Falling edge of 20ns~40ns mean :%.4e\t std :%.4e\t",mean(diff(n20_40)),std(diff(n20_40)));
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fprintf("Falling edge of 1us~1.1us mean :%.4e\t std :%.4e\n",mean(diff(n1000_1100)),std(diff(n1000_1100)));
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% fprintf("The error after falling edge of 1us is:%.4e\t",diff(n1000(1)));
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% fprintf("The time of erroe less than 1e-4 is :%.4e us\n",(n(ne(end))-n(n_edge(1))));
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% fprintf("The mean and std stably less than 1e-4 is :%.4e s\n",(n(n_common)-n(n_edge(1))));
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end
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@ -0,0 +1,209 @@
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clc;clear;close all
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% hdlsetuptoolpath('ToolName','Xilinx Vivado','ToolPath','D:\SoftWare\Xilinx\Vivado\2019.2\bin\vivado.bat');
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addpath(genpath('D:\Work\EnvData'));
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addpath(genpath('D:\Work\EnvData\data-v2'));
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% addpath(genpath('D:\Work\TailCorr_20241008_NoGit'));
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cd("D:\Work\EnvData\acz");
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obj1 = py.importlib.import_module('acz');
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py.importlib.reload(obj1);
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cd("D:\Work\TailCorr_20241008_NoGit");
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obj2 = py.importlib.import_module('wave_calculation');
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py.importlib.reload(obj2);
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cd("D:\Work\TailCorr\script_m");
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fs_L = 0.75e9; %硬件频率
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fs_H = 12e9; %以高频近似理想信号
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TargetFrequency = 3e9;
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Ideal2Low = fs_H/(fs_L/2);
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Ideal2Target = fs_H/TargetFrequency;
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G = 1;
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DownSample = 2;
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simulink_time = 20e-6; %1.5*16e-6;1.5e-3
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intp_mode = 3; %0不内插，1内插2倍，2内插4倍,3内插8倍
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dac_mode_sel = 0; %选择DAC模式，0出八路，1邻近插值，2邻近插值
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%按点数产生理想方波
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amp_rect = 1.5e4;
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%单位是ns front是到达时间，flat是持续时间，lagging是后边还有多少个0，会影响脚本的修正时间
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[front(1), flat(1), lagging(1)] = deal(50,100,7400);% 50,100,7400;100ns方波
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[front(2), flat(2), lagging(2)] = deal(50,4000,11500);% 50,4000,11500；4us方波
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for i = 1:2
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front_H(i) = front(i)*fs_H/1e9; flat_H(i) = flat(i)*fs_H/1e9; lagging_H(i) = lagging(i)*fs_H/1e9;
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wave_pre{i} = amp_rect*cat(2,zeros(1,front_H(i)),ones(1,flat_H(i)),zeros(1,lagging_H(i)));%脚本的单位是点数
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end
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%%% flattop波
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A = 1.5e4;
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[edge(1), length_flattop(1)] = deal(2,30);%ns，在fsn_L取1时是参数里的length
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[edge(2), length_flattop(2)] = deal(4,30);
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[edge(3), length_flattop(3)] = deal(4,50);
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[edge(4), length_flattop(4)] = deal(6,50);
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for i = 1:4
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[edge_H(i), length_H(i)] = deal(edge(i)*fs_H/1e9,length_flattop(i)*fs_H/1e9);
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wave_pre{i+2} = flattop(A, edge_H(i), length_H(i), 1);
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end
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%%% acz波
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amplitude = 1.5e4;
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carrierFreq = 0.000000;
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carrierPhase = 0.000000;
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dragAlpha = 0.000000;
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thf = 0.864;
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thi = 0.05;
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lam2 = -0.18;
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lam3 = 0.04;
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length_acz(1) = 30;
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length_acz(2) = 50;
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for i = 1:2
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length_acz_H(i) = int32(length_acz(i)*fs_H/1e9);
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wave_pre{i+6} = real(double(py.acz.aczwave(amplitude, length_acz_H(i), carrierFreq,carrierPhase, dragAlpha,thf, thi, lam2, lam3)));
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end
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% signalAnalyzer(wave_pre{2},'SampleRate',fs_H);
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for i = 1:8
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wave_pre{i} = cat(2,wave_pre{i},zeros(1,floor(simulink_time*fs_H))); %校正前的高频信号
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wave_preL{i} = wave_pre{i}(1:Ideal2Low:end); %校正前的低频信号
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end
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% signalAnalyzer(HardwareMeanIntpDataAlign{1},'SampleRate',3e9);
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%%%python脚本校正结果
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%S21参数
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amp_real = [0.025 0.015 0.0002 0 0 0];
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amp_imag = [0 0 0 0 0 0];
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time_real = [-1/250, -1/650, -1/1600 0 0 0];
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time_imag = [0 0 0 0 0 0];
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% amp_real = [0.0539981,-0.0319766,0.084015161,0.0048,0,0];
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% amp_imag = [0,-0.041014189,-0.052936266,0,0,0];
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% time_real = [-0.0024820146,-0.0080529118,-0.006728925,-0.0001,0,0];
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% time_imag = [0,-0.008137675,-0.0033212836,0,0,0];
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%
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% amp_real = [0.025 0.015 0.0002 0.2 0 0];
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% amp_imag = [0 0 0 0 0 0];
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% time_real = [-1/250, -1/650, -1/1600 -1/20 0 0];
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% time_imag = [0 0 0 0 0 0];
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amp_routing = amp_real + 1j*amp_imag;
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time_routing = time_real + 1j*time_imag;
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tau = -1./time_routing;
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convolve_bound = int8(3);
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calibration_time = int32(20e3);
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cal_method = int8(1);
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sampling_rateL = int64(fs_L/2);
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sampling_rate = int64(fs_H);
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%校正后的高频信号
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for i = 1:8
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wave_cal = cell(py.wave_calculation.wave_cal(wave_pre{i}, amp_real, amp_imag, time_real, time_imag, convolve_bound, calibration_time, cal_method, sampling_rate));
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wave_revised{i} = double(wave_cal{1,1});
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wave_calL = cell(py.wave_calculation.wave_cal(wave_preL{i}, amp_real, amp_imag, time_real, time_imag, convolve_bound, calibration_time, cal_method, sampling_rateL));
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wave_revisedL{i} = double(wave_calL{1,1});
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end
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%校正后的低频信号
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alpha = double(wave_calL{1,2});
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beta = double(wave_calL{1,3});
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beta(5:6) = 0;
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alpha_wideth=32;
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beta_width=32;
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alphaFixRe = ceil((2^(alpha_wideth-1))*real(alpha));
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alphaFixIm = ceil((2^(alpha_wideth-1))*imag(alpha));
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betaFixRe = ceil((2^(beta_width-1))*real(beta));
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betaFixIm = ceil((2^(beta_width-1))*imag(beta));
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%%%仿真
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for i = 1:8
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options=simset('SrcWorkspace','current');
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sim('z_dsp',[0,simulink_time]);
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sim2m = @(x)reshape(logsout.get(x).Values.Data,[],1);
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dout0{i} = sim2m("dout0");
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dout1{i} = sim2m("dout1");
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dout2{i} = sim2m("dout2");
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dout3{i} = sim2m("dout3");
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N(i) = length(dout0{i});
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cs_wave{i} = zeros(4*N(i),1);
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cs_wave{i}(1:4:4*N) = dout0{i};
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cs_wave{i}(2:4:4*N) = dout1{i};
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cs_wave{i}(3:4:4*N) = dout2{i};
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cs_wave{i}(4:4:4*N) = dout3{i};
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HardwareMeanIntpData{i} = cs_wave{i};%硬件校正后内插
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DownsamplingBy12GData{i} = wave_revised{i}(1:Ideal2Target:end);
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[DownsamplingBy12GDataAlign{i},HardwareMeanIntpDataAlign{i},Delay(i)] = ...
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alignsignals(DownsamplingBy12GData{i}(1:round(TargetFrequency*20e-6)),HardwareMeanIntpData{i}(1:round(TargetFrequency*20e-6)),"Method","xcorr");
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end
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% signalAnalyzer(DownsamplingBy12GDataAlign{1},HardwareMeanIntpDataAlign{1},'SampleRate',3e9);
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%% 绘图并保存
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close all;
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Amp = 1.5e4;
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FallingEdge = [
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150e-9,4050e-9,...%矩形波
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30e-9,30e-9,50e-9,50e-9,...%flattop
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30e-9,50e-9%acz
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];
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name = [
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"rect_100ns_校正后的波形_下降沿后10ns.fig","rect_100ns_校正后的波形_下降沿后1us.fig";
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"rect_4us_校正后的波形_下降沿后10ns.fig","rect_4us_校正后的波形_下降沿后1us.fig";
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"flattop_上升沿2ns_持续时间30ns_校正后的波形_下降沿后10ns.fig","flattop_上升沿2ns_持续时间30ns_校正后的波形_下降沿后1us.fig";
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"flattop_上升沿4ns_持续时间30ns_校正后的波形_下降沿后10ns.fig","flattop_上升沿4ns_持续时间30ns_校正后的波形_下降沿后1us.fig";
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"flattop_上升沿4ns_持续时间50ns_校正后的波形_下降沿后10ns.fig","flattop_上升沿4ns_持续时间50ns_校正后的波形_下降沿后1us.fig";
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"flattop_上升沿6ns_持续时间50ns_校正后的波形_下降沿后10ns.fig","flattop_上升沿6ns_持续时间50ns_校正后的波形_下降沿后1us.fig";
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"acz_持续时间30ns_校正后的波形_下降沿后10ns.fig","acz_持续时间30ns_校正后的波形_下降沿后1us.fig";
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"acz_持续时间50ns_校正后的波形_下降沿后10ns.fig","acz_持续时间50ns_校正后的波形_下降沿后1us.fig";
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];
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Delay_mode = mode(Delay);
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for i = 1:8
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start_time(i) = abs(Delay_mode)/(TargetFrequency/1e9)*1e-9;%由于相位修正后会有偏移的点数，所以需要考虑上这个偏移的时间，采样率为3GHz，3个点对应1ns
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edge_Align(i) = FallingEdge(i) + start_time(i);
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tmp(i) = edge_Align(i) + 10e-9;
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a{i} = [start_time(i)-5e-9 tmp(i)];%[1/fs_H 50e-9];[50e-9 1.5e-6],[500e-9+10e-9 tmp-20e-9]
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b{i} = [tmp(i) 10e-6];
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fig1 = figure('Units','normalized','Position',[0.000390625,0.517361111111111,0.49921875,0.422916666666667]);
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diff_plot_py(TargetFrequency,HardwareMeanIntpDataAlign{i}', DownsamplingBy12GDataAlign{i}(1:floor(TargetFrequency*20e-6)),'HardwareRevised','ScriptRevised',a{i},Amp,edge_Align(i));
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title(name(i,1),Interpreter="none");
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savefig(name(i,1));
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fig2 = figure('Units','normalized','Position',[0.000390625,0.034027777777778,0.49921875,0.422916666666667]);
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diff_plot_py(TargetFrequency,HardwareMeanIntpDataAlign{i}', DownsamplingBy12GDataAlign{i}(1:floor(TargetFrequency*20e-6)),'HardwareRevised','ScriptRevised',b{i},Amp,edge_Align(i));
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title(name(i,2),Interpreter="none");
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savefig(name(i,2));
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end
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%% 可视化S21参数
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t = 0:1/(1e2):10000;
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for i = 1:1:length(amp_routing)
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S21_time(:,i) = amp_routing(i)*exp(time_routing(i)*t);
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end
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figure
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plot(t*1e-9,real(sum(S21_time,2)));
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grid on
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title("s(t)");
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savefig("S21参数");
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% signalAnalyzer(real(sum(S21_time,2)),'SampleRate',1e11);%时间是1ns，还得加上采样率
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% rmpath(genpath('D:\Work\EnvData'));
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% rmpath(genpath('D:\Work\EnvData\data-v2'));
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% rmpath(genpath('D:\Work\TailCorr_20241008_NoGit'));
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%% 图像可视化
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cd("D:\Work\TailCorr\仿真结果\20241101_125M八倍内插至1G_第1组S21参数")
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for i = 1:8
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close all
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open(name(i,1));
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open(name(i,2));
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pause()
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end
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