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网站建设标准规范,做pvc卡片的交流网站,图片压缩wordpress,网站建设论文html格式1. 文件层面#xff1a;.ds文件的物理存储结构 1.1 .ds文件格式 .ds文件 (ADS Dataset File) ├── 文件头 (Header) │ ├── 版本信息 │ ├── 创建时间 │ └── 元数据 ├── 变量块索引 (VariableBlock Index) │ ├── 块名称列表 │ ├── 块偏移量…1. 文件层面.ds文件的物理存储结构1.1.ds文件格式.ds文件 (ADS Dataset File) ├── 文件头 (Header) │ ├── 版本信息 │ ├── 创建时间 │ └── 元数据 ├── 变量块索引 (VariableBlock Index) │ ├── 块名称列表 │ ├── 块偏移量 │ └── 块大小信息 └── 数据块 (Data Blocks) ├── VariableBlock_1 │ ├── 独立变量定义 │ ├── 依赖变量定义 │ └── 数据矩阵 ├── VariableBlock_2 └── ...1.2 典型的ADS仿真.ds文件内容(以例子说明)fromkeysight.adsimportdefromkeysight.ads.deimportdb_uuasdbfromkeysight.edatoolboximportadsimportkeysight.ads.datasetasdatasetimportosimportmatplotlib.pyplotaspltfromIPython.coreimportgetipythonfrompathlibimportPathimportnumpyasnp workspace_pathD:/ADS_Python_Tutorials/tutorial4_wrkcell_namepython_schematiclibrary_nametutorial4_libdefcreate_and_open_an_empty_workspace(workspace_path:str):# Ensure there isnt already a workspace openifde.workspace_is_open():de.close_workspace()# Cannot create a workspace if the directory already existsifos.path.exists(workspace_path):raiseRuntimeError(fWorkspace directory already exists:{workspace_path})# Create the workspaceworkspacede.create_workspace(workspace_path)# Open the workspaceworkspace.open()# Return the open workspace and close when it finishedreturnworkspacedefcreate_a_library_and_add_it_to_the_workspace(workspace:de.Workspace):# assert workspace.path is not None# Libraries can only be added to an open workspaceassertworkspace.is_open# Well create a library in the directory of the workspacelibrary_pathworkspace.path/library_name# Create the libraryde.create_new_library(library_name,library_path)# And add it to the workspace (update lib.defs)workspace.add_library(library_name,library_path,de.LibraryMode.SHARED)libworkspace.open_library(library_name,library_path,de.LibraryMode.SHARED)lib.setup_schematic_tech()returnlib wscreate_and_open_an_empty_workspace(workspace_path)# Create and add library to the empty workspace using the pointer to the workspacelibcreate_a_library_and_add_it_to_the_workspace(ws)defcreate_schematic(library:de.Library):designdb.create_schematic(f{library_name}:{cell_name}:schematic)num_inds5num_capsnum_inds-1foriinrange(num_inds):inddesign.add_instance(ads_rflib:L:symbol,(i*2,0))ind.parameters[L].valuefL{i1}nHind.update_item_annotation()design.add_wire([(i*21,0),(i*22,0)])foriinrange(num_caps):capdesign.add_instance(ads_rflib:C:symbol,(i*21.5,-1),angle-90)cap.parameters[C].valuefC{i1}pFcap.update_item_annotation()design.add_wire([(i*21.5,0),(i*21.5,-1)])design.add_instance(ads_rflib:GROUND:symbol,(i*21.5,-2),angle-90)design.add_instance(ads_simulation:TermG:symbol,(-1,-1),angle-90)design.add_instance(ads_simulation:TermG:symbol,(10,-1),angle-90)design.add_wire([(-1,-1),(-1,0),(0,0)])design.add_wire([(10,-1),(10,0)])spdesign.add_instance(ads_simulation:S_Param:symbol,(0,2))sp.parameters[Start].value0.01 GHzsp.parameters[Stop].value0.5 GHzsp.parameters[Step].value0.001 GHzsp.update_item_annotation()design.save_design()returndesign# Create schematic with the lib object/pointerdesigncreate_schematic(lib)##### VAR Definition #####L_values[100,40,100,40,100]C_values[30,10,30,10]var_instdesign.add_instance((ads_datacmps,VAR,symbol),(3.5,1.875),nameVAR1,angle90)forvalinrange(len(L_values)):var_inst.vars[fL{val1}]L_values[val]delvar_inst.vars[X]var_instdesign.add_instance((ads_datacmps,VAR,symbol),(5,1.875),nameVAR2,angle90)forvalinrange(len(C_values)):var_inst.vars[fC{val1}]C_values[val]delvar_inst.vars[X]##### Measurement Equation Block #####eq_list[groupdelay(-1/360)*diff(unwrap(phase(S(2,1))))/diff(freq),s21magmag(S(2,1)),s21phasephase(S(2,1)),]defadd_measeqn(design,eq_name,eq_list):# add MeasEqn to the schmaticmeaseqndesign.add_instance((ads_simulation,MeasEqn,symbol),(6.5,1.875),nameeq_name,angle-90)# change first existing equationmeaseqn.parameters[Meas].value[eq_list[0]]# add new equations with rest of equation listforiinrange(len(eq_list)-1):measeqn.parameters[Meas].repeats.append(db.ParamItemString(Meas,SingleTextLine,eq_list[i1]))measeqn.update_item_annotation()add_measeqn(design,Meas1,eq_list)### Netlist Creation and Simulation ###netlistdesign.generate_netlist()simulatorads.CircuitSimulator()target_output_diros.path.join(workspace_path,data)simulator.run_netlist(netlist,output_dirtarget_output_dir)##### Data Processing Plot #####output_datadataset.open(Path(os.path.join(target_output_dir,f{cell_name}.ds)))# Inspect available data blocks in the datasetprint(Available Data Blocks: ,output_data.varblock_names)# Finding relevant data block containing our resultsfordatablockinoutput_data.find_varblocks_with_var_name(groupdelay):print(Group Delay expression is found in:,datablock.name)gddatablock.namefordatablockinoutput_data.find_varblocks_with_var_name(S[2,1]):print(S21 measurement is found in:,datablock.name)spdatablock.name# Convert SP1.SP datablock to the pandas dataframemydataoutput_data[sp].to_dataframe().reset_index()# Convert Group Delay datablock to the pandas dataframemygdoutput_data[gd].to_dataframe().reset_index()# Extract data and convert S21 S11 to dBfreqmydata[freq]/1e6S2120*np.log10(abs(mydata[S[2,1]]))S1120*np.log10(abs(mydata[S[1,1]]))# Plot results using inline plot from matplotlibipythongetipython.get_ipython()ipython.run_line_magic(matplotlib,inline)_,axplt.subplots()ax.set_title(Python Filter Response)plt.xlabel(Frequency (MHz))plt.ylabel(S21 and S11 (dB))plt.grid(True)plt.plot(freq,S21)plt.plot(freq,S11)# Plot Group Delay results using inline plot from matplotlibfreqmygd[freq]/1e6groupdelaymygd[groupdelay]/1e-9ipythongetipython.get_ipython()ipython.run_line_magic(matplotlib,inline)_,axplt.subplots()ax.set_title(Filter Group Delay Response)plt.xlabel(Frequency (MHz))plt.ylabel(Group Delay (nsec))plt.grid(True)plt.plot(freq,groupdelay)电路仿真数据结构分析:CellName.ds ├── SP1.SP (S参数块) │ ├── ivars: [freq] │ ├── dvars: [S[1,1], S[2,1], S[1,2], S[2,2]] │ └── data: 频率 × S参数矩阵 ├── Meas1.m (测量方程块) │ ├── ivars: [freq] │ ├── dvars: [groupdelay, s21mag, s21phase] │ └── data: 频率 × 计算结果矩阵 └── 其他仿真块...2. 类层次关系详解2.1 类继承和包含关系图Dataset (collections.abc.Mapping) ├── 包含多个 VariableBlock 对象 │ ├── VariableBlock_1 (SP1.SP) │ ├── VariableBlock_2 (Meas1.m) │ └── VariableBlock_N (...) │ 每个 VariableBlock 包含 ├── ivars: List[Variable] # 独立变量列表 ├── dvars: List[Variable] # 依赖变量列表 │ 每个 Variable 包含 ├── name: str # 变量名 ├── data_type: Type # Python数据类型 ├── dtype: np.dtype # NumPy数据类型 ├── is_indep: bool # 是否为独立变量 ├── attrs: VariableAttributes # 变量属性 └── variable_type: str # 变量类型标识3. 数据组织详解3.1.ds文件到Dataset对象的映射defdemonstrate_file_to_dataset_mapping():演示文件到Dataset对象的映射过程# 1. 物理文件层面ds_file_pathworkspace/data/python_schematic.ds# 2. Dataset对象创建withdataset.open(ds_file_path)asds:# Dataset对象封装了底层的C实现print(fDataset._impl:{type(ds._impl)})print(fDataset._path:{ds._path})# 3. 文件内容映射到Python对象print(文件内容映射:)forvb_nameinds.varblock_names:print(f 文件块 {vb_name} - VariableBlock对象)vbds[vb_name]print(f 包含{vb.ivars_count}个独立变量)print(f 包含{vb.dvars_count}个依赖变量)3.2Dataset中多个VariableBlock的组织classDatasetStructureAnalyzer:数据集结构分析器def__init__(self,ds:dataset.Dataset):self.dsdsdefanalyze_varblock_organization(self):分析VariableBlock的组织方式print( VariableBlock组织结构 )# Dataset实现了Mapping接口print(fDataset作为Mapping:{isinstance(self.ds,dict)})print(f支持的操作:)print(f - len(ds):{len(self.ds)})print(f - SP1.SP in ds:{SP1.SPinself.ds})print(f - list(ds.keys()):{list(self.ds.keys())})# 遍历所有VariableBlockprint(f\n遍历方式:)print(1. 通过varblock_names:)fornameinself.ds.varblock_names:vbself.ds[name]print(f{name}-{type(vb)})print(2. 通过items():)forname,vbinself.ds.items():print(f{name}-{type(vb)})print(3. 通过varblocks属性:)forname,vbinself.ds.varblocks.items():print(f{name}-{type(vb)})deffind_related_varblocks(self):查找相关的VariableBlock# 查找包含特定变量的块freq_blockslist(self.ds.find_varblocks_with_var_name(freq))s_param_blockslist(self.ds.find_varblocks_with_var_name(S[2,1]))print(f\n 相关VariableBlock查找 )print(f包含freq的块:{[vb.nameforvbinfreq_blocks]})print(f包含S[2,1]的块:{[vb.nameforvbins_param_blocks]})returnfreq_blocks,s_param_blocks3.3VariableBlock中变量的组织defanalyze_variable_organization(vb:dataset.VariableBlock):分析VariableBlock中变量的组织print(f VariableBlock {vb.name} 变量组织 )# 独立变量和依赖变量的分离print(f独立变量 (ivars):{vb.ivars_count}个)print(f依赖变量 (dvars):{vb.dvars_count}个)# 变量访问方式print(f\n变量访问方式:)# 1. 通过索引访问ifvb.ivars:first_ivarvb.ivars[0]print(f第一个独立变量:{first_ivar.name})# 2. 通过名称查找try:freq_varvb.var(freq)print(f频率变量:{freq_var.name})exceptKeyError:print(未找到频率变量)# 3. 遍历所有变量print(f\n所有变量列表:)all_varsvb.ivarsvb.dvarsfori,varinenumerate(all_vars):var_type独立ifvar.is_indepelse依赖print(f [{i}]{var.name}({var_type}变量,{var.data_type}))4. 实际示例S参数仿真数据4.1 典型的S参数数据结构defanalyze_s_parameter_data(ds_path:str):分析S参数仿真数据的完整结构withdataset.open(ds_path)asds:print( S参数数据结构分析 )# 查找S参数变量块s_param_vbNoneforvbinds.find_varblocks_with_var_name(S[2,1]):s_param_vbvbbreakifnots_param_vb:print(未找到S参数数据)returnprint(fS参数变量块:{s_param_vb.name})# 分析独立变量 (通常是频率)print(f\n独立变量分析:)forivarins_param_vb.ivars:print(f 变量名:{ivar.name})print(f 数据类型:{ivar.data_type})print(f 变量类型:{ivar.variable_type})# 如果是频率变量可能有单位信息ifunitsinivar.attrs:print(f 单位:{ivar.attrs[units]})# 分析依赖变量 (S参数)print(f\n依赖变量分析:)s_parameters[]fordvarins_param_vb.dvars:print(f 变量名:{dvar.name})print(f 数据类型:{dvar.data_type})# 通常是complexprint(f 变量类型:{dvar.variable_type})# 通常是s-parametersifdvar.name.startswith(S[):s_parameters.append(dvar.name)print(f\n检测到的S参数:{s_parameters})# 数据提取示例dfs_param_vb.to_dataframe().reset_index()print(f\nDataFrame结构:)print(f 形状:{df.shape})print(f 列名:{df.columns.tolist()})print(f 数据类型:)forcolindf.columns:print(f{col}:{df[col].dtype})returnanalyze_s_parameter_values(df)defanalyze_s_parameter_values(df):分析S参数数值特性print(f\n S参数数值分析 )# 频率分析iffreqindf.columns:freqdf[freq]print(f频率范围:{freq.min():.2e}-{freq.max():.2e}Hz)print(f频率点数:{len(freq)})print(f频率步长:{(freq.max()-freq.min())/(len(freq)-1):.2e}Hz)# S参数分析s_param_cols[colforcolindf.columnsifcol.startswith(S[)]fors_paramins_param_cols:s_datadf[s_param]# 复数数据分析magnitudenp.abs(s_data)phasenp.angle(s_data,degTrue)magnitude_db20*np.log10(magnitude)print(f\n{s_param}分析:)print(f 数据类型:{s_data.dtype})print(f 幅度范围:{magnitude.min():.6f}-{magnitude.max():.6f})print(f 幅度(dB)范围:{magnitude_db.min():.2f}-{magnitude_db.max():.2f}dB)print(f 相位范围:{phase.min():.2f}-{phase.max():.2f}度)# 检查数据完整性ifs_data.isna().any():print(f ⚠️ 包含{s_data.isna().sum()}个NaN值)ifnp.isinf(s_data).any():print(f ⚠️ 包含{np.isinf(s_data).sum()}个无穷值)4.2 测量方程数据结构defanalyze_measurement_equation_data(ds_path:str):分析测量方程数据结构withdataset.open(ds_path)asds:print( 测量方程数据结构分析 )# 查找测量方程变量块meas_blocks[]forvb_nameinds.varblock_names:if.minvb_name.lower()ormeasinvb_name.lower():meas_blocks.append(ds[vb_name])formeas_vbinmeas_blocks:print(f\n测量方程块:{meas_vb.name})# 分析计算得出的变量print(f计算变量:)fordvarinmeas_vb.dvars:print(f{dvar.name}:{dvar.data_type})# 特殊变量分析ifgroupdelayindvar.name.lower():print(f - 群延迟变量)elifmagindvar.name.lower():print(f - 幅度变量)elifphaseindvar.name.lower():print(f - 相位变量)# 转换为DataFrame进行分析dfmeas_vb.to_dataframe().reset_index()print(f 数据形状:{df.shape})# 分析计算结果的数值特性forcolindf.columns:ifcol!freq:# 跳过频率列datadf[col]ifnp.issubdtype(data.dtype,np.number):print(f{col}: 范围 [{data.min():.3e},{data.max():.3e}])5. API使用完整的数据遍历和提取5.1 完整的数据遍历示例defcomplete_data_traversal(ds_path:str):完整的数据遍历示例withdataset.open(ds_path)asds:print( 完整数据遍历 )# 第一层Dataset级别print(fDataset:{ds.path})print(f包含{len(ds)}个变量块)# 第二层VariableBlock级别forvb_name,vbinds.items():print(f\n VariableBlock:{vb_name})print(f 独立变量:{vb.ivars_count}, 依赖变量:{vb.dvars_count})# 第三层Variable级别print(f 独立变量详情:)forivarinvb.ivars:print(f -{ivar.name}({ivar.data_type}))# 第四层Variable属性ifivar.attrs:forattr_name,attr_valueinivar.attrs.items():print(f 属性{attr_name}:{attr_value})print(f 依赖变量详情:)fordvarinvb.dvars:print(f -{dvar.name}({dvar.data_type}))ifdvar.attrs:forattr_name,attr_valueindvar.attrs.items():print(f 属性{attr_name}:{attr_value})# 数据提取try:dfvb.to_dataframe()print(f 数据矩阵:{df.shape})print(f 内存使用:{df.memory_usage(deepTrue).sum()/1024:.1f}KB)exceptExceptionase:print(f 数据提取失败:{e})5.2 高效的数据提取策略classEfficientDataExtractor:高效的数据提取器def__init__(self,ds_path:str):self.ds_pathds_path self._dsNonedef__enter__(self):self._dsdataset.open(self.ds_path)returnselfdef__exit__(self,exc_type,exc_val,exc_tb):ifself._ds:self._ds.__exit__(exc_type,exc_val,exc_tb)defextract_frequency_data(self):提取频率数据freq_blockslist(self._ds.find_varblocks_with_var_name(freq))ifnotfreq_blocks:returnNone# 选择第一个包含频率的块vbfreq_blocks[0]freq_varvb.var(freq)# 只提取频率列dfvb.to_dataframe(dvar_names[])# 只要独立变量returndf[freq].valuesdefextract_s_parameters(self,s_param_namesNone):提取指定的S参数ifs_param_namesisNone:s_param_names[S[1,1],S[2,1],S[1,2],S[2,2]]# 查找S参数块s_param_vbNoneforvbinself._ds.find_varblocks_with_var_name(S[2,1]):s_param_vbvbbreakifnots_param_vb:returnNone# 只提取需要的S参数available_s_params[namefornameins_param_namesifany(dvar.namenamefordvarins_param_vb.dvars)]dfs_param_vb.to_dataframe(dvar_namesavailable_s_params)returndf.reset_index()defextract_measurement_results(self,meas_namesNone):提取测量方程结果results{}forvb_nameinself._ds.varblock_names:if.minvb_name.lower():vbself._ds[vb_name]ifmeas_names:# 只提取指定的测量结果available_meas[namefornameinmeas_namesifany(dvar.namenamefordvarinvb.dvars)]ifavailable_meas:dfvb.to_dataframe(dvar_namesavailable_meas)results[vb_name]df.reset_index()else:# 提取所有测量结果dfvb.to_dataframe()results[vb_name]df.reset_index()returnresults# 使用示例defefficient_extraction_example(ds_path:str):高效提取示例withEfficientDataExtractor(ds_path)asextractor:# 只提取需要的数据freqextractor.extract_frequency_data()s_paramsextractor.extract_s_parameters([S[2,1],S[1,1]])measurementsextractor.extract_measurement_results([groupdelay])print(f频率点数:{len(freq)iffreqisnotNoneelse0})print(fS参数数据形状:{s_params.shapeifs_paramsisnotNoneelseNone})print(f测量结果块数:{len(measurements)})returnfreq,s_params,measurements6. 数据流向图物理文件 (.ds) ↓ dataset.open() - Dataset 对象 ↓ 遍历 varblock_names - 获取 VariableBlock 对象 ↓ 检查 ivars/dvars - 确认目标变量位置 ↓ varblock.to_dataframe() - Pandas DataFrame ↓ NumPy/Python List - 数据分析/存储这个层次结构设计使得ADS的仿真数据可以方便地与Python的科学计算生态系统集成同时保持了数据的完整性和访问效率。7.一个完备的ADS批量仿真工具库为了简化 ADS 仿真程控的开发难度我提供了一个通用的自动化工具库。该工具库封装了从环境配置、参数更新、仿真运行到结果提取的全流程使得用户只需关注“如何将参数应用到电路”这一核心逻辑。文章链接:ADS 自动化仿真框架