error: 'ISOLATE___atomic_compare_exchange_4' was not declared in this scope

时间: 2025-03-17 07:05:55 浏览: 58
<think>嗯,用户遇到了编译错误,说是未声明的函数ISOLATE___atomic_compare_exchange_4。首先,我需要弄清楚这个错误的具体原因。根据用户提供的引用信息,GCC是通过__atomic_compare_exchange_n来实现compare_exchange_weak和compare_exchange_strong的。可能用户在使用原子操作时,编译器找不到对应的内置函数。 首先,可能用户没有包含必要的头文件,比如<atomic>。C++的原子操作需要包含这个头文件。另外,检查编译器是否支持C++11或更高版本,因为原子操作是C++11引入的。用户可能在使用旧版本的编译器或者没有启用C++11标准。 然后,错误中的ISOLATE前缀有点奇怪,可能是宏定义或其他预处理指令导致的。用户可能在代码中定义了某些宏,或者项目中有自定义的宏,导致函数名被替换。需要检查代码中的宏定义,特别是与原子操作相关的部分,看是否有宏将__atomic_compare_exchange_n重命名为ISOLATE___atomic_compare_exchange_4。这里的4可能是指操作的是4字节的整型,比如int32_t。 另外,用户可能在代码中直接调用了__atomic_compare_exchange_n函数,而没有使用标准的C++原子库。C++标准库中的atomic类已经封装了这些底层操作,建议使用std::atomic的compare_exchange_weak或compare_exchange_strong方法,而不是直接调用GCC的内置函数。这样代码可移植性更好,也避免依赖特定编译器的实现。 接下来,用户提供的引用[1]和[2]中提到了GCC的实现和例子,但用户可能混淆了直接使用编译器内置函数和标准库的使用方式。正确的做法是使用std::atomic<T>的成员函数,而不是直接调用__atomic_compare_exchange_n,除非有特殊需求需要绕过标准库。 解决方案方面,首先确保包含<atomic>头文件,使用std::atomic变量,并检查编译器选项是否启用了C++11或更高标准。例如,编译时加上-std=c++11或更高版本。如果用户代码中有自定义宏,需要检查是否错误地修改了函数名,导致编译器找不到正确的内置函数。 此外,用户提到的atomic_compare_exchange_4可能是特定平台或编译器版本的内部符号,标准代码中不应该直接出现这样的函数名。因此,用户需要检查代码是否有拼写错误,或者是否错误地手动调用了编译器内部函数,而应该改用标准的compare_exchange_weak/strong方法。 总结来说,解决步骤应该是:1.包含正确的头文件;2.确保使用std::atomic及其成员函数;3.检查编译器版本和标准设置;4.检查代码中的宏定义是否导致函数名被替换;5.避免直接使用编译器特定的内置函数,改用标准库方法。</think>### C++ `atomic_compare_exchange_4` 编译错误解决方案 #### 错误原因分析 1. **未正确使用C++标准库接口** `atomic_compare_exchange_weak` 和 `atomic_compare_exchange_strong` 是 `std::atomic` 的成员函数,需通过 `std::atomic<T>` 对象调用,而非直接调用底层编译器内置函数(如GCC的 `__atomic_compare_exchange_n`)[^1]。 2. **编译器内置函数命名规则** 错误中的 `ISOLATE___atomic_compare_exchange_4` 可能是由宏或编译器内部符号生成规则导致的,其中 `4` 表示操作的数据类型大小为4字节(如 `int32_t`)。此类符号通常不应直接出现在用户代码中。 3. **编译器或标准库版本兼容性** 需确认编译器支持C++11及以上标准,并正确包含头文件 `<atomic>`。 --- #### 分步解决方案 **步骤1:使用标准库接口** 直接通过 `std::atomic<T>` 对象调用 `compare_exchange_weak` 或 `compare_exchange_strong`。 示例代码: ```cpp #include <atomic> #include <iostream> std::atomic<int> x(0); void update_value(int new_val) { int expected = x.load(); while (!x.compare_exchange_weak(expected, new_val)) { // 失败后 expected 会被更新为当前值 } } ``` **步骤2:检查编译选项** 确保编译器启用C++11或更高标准: - GCC/Clang: 添加 `-std=c++11` 或 `-std=c++17` - MSVC: 设置 `/Zc:__cplusplus` 并启用 `/std:c++17` **步骤3:排查宏定义干扰** 检查代码中是否包含以下内容: ```cpp #define ISOLATE___atomic_compare_exchange_4 __atomic_compare_exchange_n ``` 若存在此类宏,需删除或修改以避免符号冲突。 **步骤4:验证数据类型匹配** 确保操作的 `std::atomic<T>` 类型与内置函数支持的类型一致(如 `int`, `long` 等),避免隐式类型转换。 --- #### 错误示例修正对比 **错误写法**(直接调用内置函数): ```cpp // 错误:未通过 std::atomic 接口操作 int x = 0; bool success = __atomic_compare_exchange_n(&x, &expected, desired, false, __ATOMIC_SEQ_CST, __ATOMIC_RELAXED); ``` **正确写法**: ```cpp // 正确:使用 std::atomic 封装 std::atomic<int> x(0); int expected = x.load(); bool success = x.compare_exchange_strong(expected, desired); ``` --- #### 相关扩展问题 1. **如何选择 `compare_exchange_weak` 和 `compare_exchange_strong`?** - `weak` 可能在某些平台(如ARM)因伪失败(spurious failure)导致循环次数增加,但性能更高[^1] - `strong` 保证仅在值不匹配时返回失败,适用于严格条件判断 2. **如何调试原子操作的竞争条件?** 可使用线程消毒工具(如TSAN)或调试器观察 `expected` 值的更新过程[^2] 3. **跨平台的原子操作兼容性问题?** 建议始终通过 `std::atomic` 接口操作,避免直接依赖编译器内置函数 --- : GCC的原子操作实现基于内置函数 `__atomic_compare_exchange_n`,但用户应优先使用 `std::atomic` 的标准接口。 : 原子操作的循环重试机制需结合 `expected` 值的动态更新,确保线程安全。
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开发者提供了一个脚本generate_ptm_coordinates.py,请问我要怎么运行? import numpy as np import pandas as pd from ExonPTMapper import mapping from ExonPTMapper import config as mapper_config from ptm_pose import pose_config from Bio import SeqIO import codecs import gzip import os import datetime import pyliftover from tqdm import tqdm def check_constitutive(ptm, nonconstitutive_list): """ For a given list of ptms, check if any of the ptms are found in the list of nonconstitutive ptms, meaning that they have previously been found to be missing from isoforms in Ensembl Parameters ---------- ptm : list List of PTMs to check (each ptm should be in the form of "UniProtID_ResiduePosition" (e.g. "P12345-1_Y100")) nonconstitutive_list : list List of PTMs that have been found to be nonconstitutive (based on data from ptm_info object generated by ExonPTMapper) """ if ptm in nonconstitutive_list: return False else: return True def extract_ptm_position_of_primary_isoform(row): """ For a given row in the ptm_coordinates dataframe, extract the position of the PTM in the primary isoform (canonical if available, else first isoform in list) """ pass def get_unique_ptm_info(ptm_coordinates): """ For a given row in the ptm_coordinates dataframe, isolate PTM entries corresponding to the canonical isoform ID, if available. If not, use the first isoform ID in the list. Further, indicate whether the entry corresponds to a canonical or alternative isoform. Parameters ---------- ptm_coordinates : pd.DataFrame DataFrame containing PTM coordinates, generated by ExonPTMapper package Returns ------- pd.DataFrame DataFrame containing PTM coordinates with additional columns indicating the UniProtKB accession number, the isoform ID, the type of isoform (canonical or alternative), the residue of the PTM, the position of the PTM in the isoform, and any alternative entries that also contain the PTM """ accession_list = [] #list of UniProtKB/Swiss-Prot accessions residue_list = [] position_list = [] isoform_list = [] isoform_type = [] for i,row in ptm_coordinates.iterrows(): ptm_entries = row['Source of PTM'].split(';') residue = ptm_entries[0].split('_')[1][0] #iterate through each PTM entry and extract any that are associated with a canonical isoform. This will usually only be one, but in rare cases a PTM may be associated with multiple genes found_in_canonical = False positions = [] accessions = [] isoform_entries = [] for ptm in ptm_entries: if ptm.split('_')[0] in mapper_config.canonical_isoIDs.values(): #check if the uniprot isoform ID is a canonical isoform. if so, add the position to the list of positions positions.append(ptm.split('_')[1][1:]) accessions.append(ptm.split('-')[0]) #uniprot accession number isoform_entries.append(ptm.split('_')[0]) #uniprot isoform ID found_in_canonical = True #indicate that there is a PTM found in the canonical isoform #check if position in canonical was found. If so, join the positions into a single string. If not, use the position associated with the first listed isoform if found_in_canonical: positions = ';'.join(positions) accessions = ';'.join(accessions) isoform_entries = ';'.join(isoform_entries) isoform_type.append('Canonical') else: positions = ptm_entries[0].split('_')[1][1:] accessions = ptm_entries[0].split('-')[0] isoform_entries = ptm_entries[0].split('_')[0] isoform_type.append('Alternative') accession_list.append(accessions) residue_list.append(residue) position_list.append(positions) isoform_list.append(isoform_entries) ptm_coordinates['UniProtKB Accession'] = accession_list ptm_coordinates['Isoform ID'] = isoform_list ptm_coordinates['Isoform Type'] = isoform_type ptm_coordinates['Residue'] = residue_list ptm_coordinates['PTM Position in Isoform'] = position_list return ptm_coordinates def convert_coordinates(ptm_coordinates, from_coord = 'hg38', to_coord = 'hg19'): """ Given the ptm_coordinates dataframe, convert the genomic location of the PTMs from one coordinate system to another (e.g. hg38 to hg19, hg19 to hg38, etc.) Parameters ---------- ptm_coordinates : pd.DataFrame DataFrame containing PTM coordinates from_coord : str Coordinate system to convert from (e.g. 'hg38', 'hg19', 'hg18') to_coord : str Coordinate system to convert to (e.g. 'hg38', 'hg19', 'hg18') """ # convert coordinates to hg19 and hg38 new_coords = [] liftover_object = pyliftover.LiftOver(f'{from_coord}',f'{to_coord}') for i, row in tqdm(ptm_coordinates.iterrows(), total = ptm_coordinates.shape[0], desc = f'Converting from {from_coord} to {to_coord} coordinates'): new_coords.append(mapping.convert_genomic_coordinates(row[f'Gene Location ({from_coord})'], row['Chromosome/scaffold name'], row['Strand'], from_type = f'{from_coord}', to_type = f'{to_coord}', liftover_object = liftover_object)) return new_coords residue_dict = {'R': 'arginine', 'H':'histidine', 'K':'lysine', 'D':'aspartic acid', 'E':'glutamic acid', 'S': 'serine', 'T':'threonine', 'N':'asparagine', 'Q':'glutamine', 'C':'cysteine', 'U':'selenocystein', 'G':'glycine', 'P':'proline', 'A':'alanine', 'V':'valine', 'I':'isoleucine', 'L':'leucine', 'M':'methionine', 'F':'phenylalanine', 'Y':'tyrosine', 'W':'tryptophan'} def convert_modification_shorthand(mod, res, mod_group_type = 'fine'): if mod_group_type == 'fine': if mod == 'p': mod_name = f'Phospho{residue_dict[res]}' elif mod == 'ub': mod_name = 'Ubiquitination' elif mod == 'sm': mod_name = 'Sumoylation' elif mod == 'ga': if res == 'N': mod_name = 'N-Glycosylation' else: #if you want a more general classification, this and T can be either just O-GalNAc or O-glycosylation mod_name = 'O-GalNAc ' + residue_dict[res].capitalize() elif mod == 'gl': mod_name = 'O-GlcNAc ' + residue_dict[res].capitalize() elif mod == 'm1' or mod == 'me': mod_name = 'Methylation' elif mod == 'm2': mod_name = 'Dimethylation' elif mod == 'm3': mod_name = 'Trimethylation' elif mod == 'ac': mod_name = 'Acetylation' else: raise ValueError("ERROR: don't recognize PTM type %s"%(type)) elif mod_group_type == 'coarse': if mod == 'p': mod_name = 'Phosphorylation' elif mod == 'ub': mod_name = 'Ubiquitination' elif mod == 'sm': mod_name = 'Sumoylation' elif mod == 'ga' or mod == 'gl': mod_name = 'Glycosylation' elif mod == 'm1' or mod == 'me' or mod == 'm2' or mod == 'm3': mod_name = 'Methylation' elif mod == 'ac': mod_name = 'Acetylation' return mod_name def process_PSP_df(file, compressed = False, organism = None, include_flank = False, include_domain = False, include_MS = False, include_LTP = False, mod_group_type = 'fine'): """ Process the PhosphositePlus file into a dataframe with the Uniprot ID, residue, position, and modification, and any extra columns that are requested Parameters ---------- file : str The file to read in compressed : bool If the file is compressed or not organism : str The organism to filter the data by include_flank : bool If True, include the flanking amino acids include_domain : bool If True, include the domain include_MS : bool If True, include the MS_LIT and MS_CST columns include_LTP : bool If True, include the LT column (low throughput) mod_group_type : str The type of modification grouping to use (fine or coarse) """ if compressed: df = pd.read_csv(file, sep='\t', skiprows=3, compression='gzip') else: df = pd.read_csv(file, sep='\t', skiprows=3) df['Residue'] = df['MOD_RSD'].apply(lambda x: x.split('-')[0][0]) df['Position'] = df['MOD_RSD'].apply(lambda x: int(x.split('-')[0][1:])) df['Modification'] = df['MOD_RSD'].str.split('-').str[1] df['Modification'] = df.apply(lambda x: convert_modification_shorthand(x['Modification'], x['Residue'], mod_group_type=mod_group_type), axis=1) if organism is not None: df = df[df['ORGANISM'] == organism] extra_cols = [] if organism is not None: df = df[df['ORGANISM'] == organism] else: extra_cols += ['ORGANISM'] if include_flank: extra_cols += ['SITE_+/-7_AA'] if include_domain: extra_cols += ['DOMAIN'] if include_MS: extra_cols += ['MS_LIT'] extra_cols += ['MS_CST'] if include_LTP: extra_cols += ['LT_LIT'] df = df[['ACC_ID', 'Residue', 'Position', 'Modification'] + extra_cols] return df def combine_PSP_dfs(phosphositeplus_dir, compressed = True, organism = None, include_flank = False, include_domain = False, include_MS = False, include_LTP = False, mod_group_type = 'fine'): extension = '.gz' if compressed else '' phospho = process_PSP_df(phosphositeplus_dir+'Phosphorylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS,include_LTP=include_LTP, mod_group_type=mod_group_type) ubiq = process_PSP_df(phosphositeplus_dir+'Ubiquitination_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) sumo = process_PSP_df(phosphositeplus_dir+'Sumoylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) galnac = process_PSP_df(phosphositeplus_dir+'O-GalNAc_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) glcnac = process_PSP_df(phosphositeplus_dir+'O-GlcNAc_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) meth = process_PSP_df(phosphositeplus_dir+'Methylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) acetyl = process_PSP_df(phosphositeplus_dir+'Acetylation_site_dataset'+extension, compressed = compressed, organism = organism, include_flank = include_flank, include_domain = include_domain, include_MS = include_MS, include_LTP=include_LTP, mod_group_type=mod_group_type) df = pd.concat([phospho, ubiq, sumo, galnac, glcnac, meth, acetyl]) return df def extract_num_studies(psp_dir, pscout_data = None): psp_df = combine_PSP_dfs(psp_dir, compressed = True, organism = 'human', include_MS = True, include_LTP = True, mod_group_type = 'coarse') #get canonical isoform IDs canonical_ids = mapper_config.translator[mapper_config.translator['UniProt Isoform Type'] == 'Canonical'][['UniProtKB/Swiss-Prot ID', 'UniProtKB isoform ID']].set_index('UniProtKB isoform ID').squeeze().to_dict() #couple rare cases where the canonical isoform is listed with its isoform ID, which conflicts with how I processed the data. convert these ideas to base uniprot ID psp_df_isoids = psp_df[psp_df['ACC_ID'].isin(canonical_ids.keys())].copy() psp_correctids = psp_df[~psp_df['ACC_ID'].isin(canonical_ids.keys())].copy() psp_df_isoids['ACC_ID'] = psp_df_isoids['ACC_ID'].map(canonical_ids) psp_df = pd.concat([psp_df_isoids, psp_correctids]) #groupby number of experiments by PTM site and modification, taking the max value (some rare cases where entries are basically identical but have different numbers of experiments) psp_df['PTM'] = psp_df['ACC_ID'] + '_' + psp_df['Residue'] + psp_df['Position'].astype(str) psp_df = psp_df.groupby(['PTM', 'Modification'], as_index = False)[['MS_LIT', 'MS_CST', 'LT_LIT']].max() psp_df = psp_df.rename(columns = {'Modification': 'Modification Class'}) if pscout_data is not None: pscout_experiments = pscout_data[pscout_data['Number of Experiments'] > 0] pscout_experiments = pscout_experiments.dropna(subset = 'Modification Class') psp_df = psp_df.merge(pscout_experiments[['PTM', 'Number of Experiments']], on = 'PTM', how = 'outer') psp_df = psp_df.fillna(0) psp_df['MS_LIT'] = psp_df['MS_LIT'] + psp_df['Number of Experiments'] psp_df = psp_df.drop(columns = 'Number of Experiments') return psp_df def append_num_studies(ptm_coordinates, psp_df): #remove ms columns if present ptm_coordinates = ptm_coordinates.drop(columns = [i for i in ['MS_LIT', 'MS_CST', 'LT_LIT'] if i in ptm_coordinates.columns]) #add PTM column to PTM coordinates, allowing for alternative isoform IDs ptm_coordinates['PTM'] = ptm_coordinates.apply(lambda x: x['Isoform ID'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])) if x['Isoform Type'] == 'Alternative' else x['UniProtKB Accession'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])), axis = 1) #combine psp MS info with coordinate data, check to make sure size doesn't change original_shape = ptm_coordinates.shape[0] ptm_coordinates = ptm_coordinates.merge(psp_df[['PTM', 'Modification Class', 'MS_LIT', 'MS_CST', 'LT_LIT']], on = ['PTM', 'Modification Class'], how = 'left') if original_shape != ptm_coordinates.shape[0]: raise ValueError('Size of dataframe changed after merging PhosphoSitePlus data. Please check for duplicates.') #go through entries without info and make sure it is not due to isoform ID issues missing_db_info = ptm_coordinates[ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT']].isna().all(axis = 1)] ptm_coordinates = ptm_coordinates[~ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT']].isna().all(axis = 1)] for i, row in missing_db_info.iterrows(): mod = row['Modification Class'] sources = [s for s in row['Source of PTM'].split(';') if row['Isoform ID'] not in s] for s in sources: tmp_data = psp_df[(psp_df['PTM'] == s) & (psp_df['Modification Class'] == mod)] if tmp_data.shape[0] > 0: tmp_data = tmp_data.squeeze() missing_db_info.loc[i, 'MS_LIT'] = tmp_data['MS_LIT'] missing_db_info.loc[i, 'MS_CST'] = tmp_data['MS_CST'] missing_db_info.loc[i, 'LT_LIT'] = tmp_data['LT_LIT'] break ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info]) return ptm_coordinates def construct_mod_conversion_dict(): """ Reformat modification conversion dataframe to a dictionary for easy conversion between modification site and modification class """ modification_conversion = pose_config.modification_conversion[['Modification', 'Modification Class']].set_index('Modification') modification_conversion = modification_conversion.replace('Dimethylation', 'Methylation') modification_conversion = modification_conversion.replace('Trimethylation', 'Methylation') modification_conversion = modification_conversion.squeeze().to_dict() return modification_conversion def extract_number_compendia(ps_data_file): """ Given a proteomescout data file, extract the number of different compendia that support a PTM site in a format that works with ptm_coordinates file. The following experiment IDs are used to check for database evidence: 1395: HPRD 1790: PhosphoSitePlus 1323: Phospho.ELM 1688,1803: UniProt 1344: O-GlycBase 1575: dbPTM Parameters: ------------ ps_data_file: str Path to proteomescout data file Returns: ------------ ps_data: pd.DataFrame DataFrame with PTM site, modification class, and number of compendia that support the PTM site """ #load proteomescout data ps_data = pd.read_csv(ps_data_file, sep = '\t') ps_data = ps_data[ps_data['species'] == 'homo sapiens'] ps_data['modifications'] = ps_data['modifications'].str.split(';') ps_data['evidence'] = ps_data['evidence'].str.split(';') ps_data = ps_data.explode(['modifications','evidence']) #extract site numbers and modification types into unique columns, then convert modification to broad class to match ptm coordinates dataframe ps_data['site'] = ps_data['modifications'].str.split('-').str[0] ps_data['Modification'] = ps_data['modifications'].apply(lambda x: '-'.join(x.split('-')[1:])) modification_conversion = construct_mod_conversion_dict() ps_data['Modification Class'] = ps_data['Modification'].map(modification_conversion) ps_data = ps_data.drop(columns = ['Modification']) #split evidence into a list, then check to see if which entries correspond to database rather than literature study ps_data['evidence'] = ps_data['evidence'].apply(lambda x: [i.strip(' ') for i in x.split(',')]) ps_data['database evidence'] = ps_data['evidence'].apply(lambda x: set(x).intersection({'1395', '1790', '1323','1688','1803','1344','1575'})) ps_data['experimental evidence'] = ps_data['evidence'].apply(lambda x: list(set(x).difference({'1395', '1790', '1323','1688','1803','1344','1575'}))) #add specific compendia with site compendia_dict = {'1395':'HPRD','1790':'PhosphoSitePlus', '1323':'Phospho.ELM', '1688':'UniProt', '1803':'UniProt', '1344':'O-GlycBase','1575':'dbPTM'} ps_data['Compendia'] = ps_data['database evidence'].apply(lambda x: ['ProteomeScout'] + [compendia_dict[i] for i in x]) #separate all accessions into separate rows for merge ps_data['accessions'] = ps_data['accessions'].str.split(';') ps_data = ps_data.explode('accessions') #convert isoform IDs from having . to a - for consistency ps_data['accessions'] = ps_data['accessions'].str.replace('.','-').str.strip(' ') #create PTM column ps_data['PTM'] = ps_data['accessions'] + '_' + ps_data['site'].str.strip(' ') ps_data = ps_data.dropna(subset = ['PTM']) #some ptms may have multiple entries (due to multiple similar modification types), so combine them ps_data = ps_data.groupby(['PTM', 'Modification Class'], as_index = False)[['Compendia', 'experimental evidence']].agg(sum) ps_data['Number of Compendia'] = ps_data['Compendia'].apply(lambda x: len(set(x))) ps_data['Compendia'] = ps_data['Compendia'].apply(lambda x: ';'.join(set(x))) ps_data['Number of Experiments'] = ps_data['experimental evidence'].apply(lambda x: len(set(x))) ps_data['experimental evidence'] = ps_data['experimental evidence'].apply(lambda x: ';'.join(set(x))) return ps_data def append_num_compendia(ptm_coordinates, pscout_data): """ Given a PTM coordinates dataframe and processed proteomescout data generated by extract_num_compendia(), append the number of compendia that support a PTM site to the PTM coordinates dataframe. Check all potential isoform IDs for compendia information. Parameters: ------------ ptm_coordinates: pd.DataFrame DataFrame with PTM site information pscout_data: pd.DataFrame DataFrame with PTM site, modification class, and number of compendia that support the PTM site """ #remove existing columns if present ptm_coordinates = ptm_coordinates.drop(columns = [i for i in ['Compendia', 'Number of Compendia'] if i in ptm_coordinates.columns]) if 'PTM' not in ptm_coordinates.columns: ptm_coordinates['PTM'] = ptm_coordinates.apply(lambda x: x['Isoform ID'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])) if x['Isoform Type'] == 'Alternative' else x['UniProtKB Accession'] + '_'+ x['Residue'] + str(int(x['PTM Position in Isoform'])), axis = 1) #merge the data ptm_coordinates = ptm_coordinates.merge(pscout_data, on = ['Modification Class', 'PTM'], how = 'left') #go through any missing entries and see if they can be filled in with other isoform IDs missing_db_info = ptm_coordinates[ptm_coordinates['Number of Compendia'].isna()] ptm_coordinates = ptm_coordinates[~ptm_coordinates['Compendia'].isna()] for i, row in tqdm(missing_db_info.iterrows(), total = missing_db_info.shape[0], desc = 'Going through missing compendia data, making sure not due to isoform ID issues'): mod = row['Modification Class'] sources = [s for s in row['Source of PTM'].split(';') if row['Isoform ID'] not in s] for s in sources: tmp_data = pscout_data[(pscout_data['PTM'] == s) & (pscout_data['Modification Class'] == mod)] if tmp_data.shape[0] > 0: tmp_data = tmp_data.squeeze() missing_db_info.loc[i, 'Compendia'] = tmp_data['Compendia'] missing_db_info.loc[i, 'Number of Compendia'] = tmp_data['Number of Compendia'] break ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info]) #fill in remainder entries as PhosphoSitePlus ptm_coordinates['Compendia'] = ptm_coordinates['Compendia'].fillna('PhosphoSitePlus') ptm_coordinates['Number of Compendia'] = ptm_coordinates['Number of Compendia'].fillna(1) return ptm_coordinates def separate_by_modification(ptm_coordinates, mod_mapper): #separate out modification class (important for functional analysis) ptm_coordinates['Modification Class'] = ptm_coordinates['Modification Class'].str.split(';') ptm_coordinates = ptm_coordinates.explode('Modification Class') #reduce modification columns to only those relevant to modification class mod_mapper = mod_mapper.squeeze() mod_mapper = mod_mapper.str.split(';') mod_mapper = mod_mapper.to_dict() def filter_mods(x, mod_class): """ Go through modification list and only keep those that are relevant to the modification class (removes mismatch due to exploded dataframe) """ x_list = x.split(';') return ';'.join([i for i in x_list if i in mod_mapper[mod_class]]) ptm_coordinates['Modification'] = ptm_coordinates.apply(lambda x: filter_mods(x['Modification'], x['Modification Class']), axis = 1) return ptm_coordinates def generate_ptm_coordinates(pscout_data_file, phosphositeplus_filepath, mod_mapper, remap_PTMs = False, output_dir = None, ptm_info = None): mapper = mapping.PTM_mapper() if remap_PTMs: mapper.find_ptms_all(phosphositeplus_file = phosphositeplus_filepath) mapper.mapPTMs_all() #get coordinate info if mapper.ptm_coordinates is None: raise ValueError('No PTM coordinates found. Please set remap_PTMs to True to generate PTM coordinates. If you want to include PhosphoSitePlus data, please also include location of phosphositeplus data file in phosphositeplus_filepath argument.') #copy ptm_coordinates file to be edited for easier use with PTM-POSE ptm_coordinates = mapper.ptm_coordinates.copy() ptm_coordinates = get_unique_ptm_info(ptm_coordinates) # convert coordinates to hg19 and hg38, then from hg19 to hg18 ptm_coordinates['Gene Location (hg19)'] = convert_coordinates(ptm_coordinates, from_coord = 'hg38', to_coord = 'hg19') ptm_coordinates['Gene Location (hg18)'] = convert_coordinates(ptm_coordinates, from_coord = 'hg19', to_coord = 'hg18') #separate out PTMs that are found in UniProt accessions ptm_coordinates['PTM Position in Isoform'] = ptm_coordinates['PTM Position in Isoform'].str.split(';') ptm_coordinates['UniProtKB Accession'] = ptm_coordinates['UniProtKB Accession'].str.split(';') ptm_coordinates['Isoform ID'] = ptm_coordinates['Isoform ID'].str.split(';') ptm_coordinates = ptm_coordinates.explode(['UniProtKB Accession', 'Isoform ID', 'PTM Position in Isoform']).reset_index() #for further analysis ptm_coordinates['PTM'] = ptm_coordinates['Isoform ID'] + '_' + ptm_coordinates['Residue'] + ptm_coordinates['PTM Position in Isoform'] if ptm_info is None: ptm_info = mapper.ptm_info.copy() #add whether or not the PTM is constitutive if 'PTM Conservation Score' not in ptm_info.columns: raise Warning('No PTM conservation score found in ptm_info object. Will not add column indicating whether the PTM is considered to be constitutive or not.') else: #grab nonconstitutive list from ptm_info object nonconstitutive_list = set(ptm_info[ptm_info['PTM Conservation Score'] != 1].index.values) const_list = [] for i, row in tqdm(ptm_coordinates.iterrows(), total = ptm_coordinates.shape[0], desc = 'Checking for constitutive PTMs'): const_list.append(check_constitutive(row['PTM'], nonconstitutive_list)) ptm_coordinates['Constitutive'] = const_list #add flanking sequence information ptm_coordinates['Canonical Flanking Sequence'] = ptm_coordinates['PTM'].apply(lambda x: ptm_info.loc[x, 'Flanking Sequence'] if x == x else np.nan) #drop unnecessary columns and save #ptm_coordinates = ptm_coordinates.drop(columns = ['PTM Position in Canonical Isoform', 'PTM'], axis = 1) ptm_coordinates = ptm_coordinates[['Gene name', 'UniProtKB Accession', 'Isoform ID', 'Isoform Type', 'Residue', 'PTM Position in Isoform', 'Modification', 'Modification Class', 'Chromosome/scaffold name', 'Strand', 'Gene Location (hg38)', 'Gene Location (hg19)', 'Gene Location (hg18)', 'Constitutive', 'Canonical Flanking Sequence', 'Source of PTM']] ptm_coordinates = ptm_coordinates.reset_index() #there will be some non-unique genomic coordinates, so reset index to ensure unique index values #separate out modification class (important for functional analysis) ptm_coordinates = separate_by_modification(ptm_coordinates, mod_mapper) #add filtering criteria ## add number of compendia pscout_data = extract_number_compendia(pscout_data_file) ptm_coordinates = append_num_compendia(ptm_coordinates, pscout_data) ## number of MS experiments (from PhosphoSitePlus, fill in any remainder with ProteomeScout experiments) psp_df = generate_ptm_coordinates.extract_num_studies(phosphositeplus_filepath, pscout_data=pscout_data) ptm_coordinates = generate_ptm_coordinates.append_num_studies(ptm_coordinates, psp_df) #do some final custom editing for outlier examples missing_db_info = ptm_coordinates[ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT', 'Compendia']].isna().all(axis = 1)] ptm_coordinates = ptm_coordinates[~ptm_coordinates[['MS_LIT', 'MS_CST', 'LT_LIT', 'Compendia']].isna().all(axis = 1)] #custom fixes ms_lit = {'Q13765_K108':1, 'Q13765_K100':2} ms_cst = {'Q13765_K108':np.nan, 'Q13765_K100':np.nan} lt_lit = {'Q13765_K108':np.nan, 'Q13765_K100':np.nan} custom_compendia = {'Q13765_K108':'PhosphoSitePlus;ProteomeScout', 'Q13765_K100': 'PhosphoSitePlus;ProteomeScout'} custom_num_compendia = {'Q13765_K108':2, 'Q13765_K100':2} for i,row in missing_db_info.iterrows(): ptm = row['PTM'] missing_db_info.loc[i, 'MS_LIT'] = ms_lit[ptm] missing_db_info.loc[i, 'MS_CST'] = ms_cst[ptm] missing_db_info.loc[i, 'LT_LIT'] = lt_lit[ptm] missing_db_info.loc[i, 'Compendia'] = custom_compendia[ptm] missing_db_info.loc[i, 'Number of Compendia'] = custom_num_compendia[ptm] ptm_coordinates = pd.concat([ptm_coordinates, missing_db_info]) ptm_coordinates[['Number of Compendia', 'Number of Experiments', 'MS_LIT', 'MS_CST', 'LT_LIT']] = ptm_coordinates[['Number of Compendia', 'Number of Experiments', 'MS_LIT', 'MS_CST', 'LT_LIT']].fillna(0).astype(int) if output_dir is not None: ptm_coordinates.to_csv(output_dir + 'ptm_coordinates.csv', index = False) #write to text file indicating when the data was last updated with open(output_dir + 'last_updated.txt', 'w') as f: f.write(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")) return ptm_coordinates

--- 0x420133f2: omac1_aes_128 at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:920 --- 0x420135d6: ccmp_decrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/ccmp.c:141 --- 0x42013684: ccmp_encrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/ccmp.c:193 --- 0x42012f3e: esp_aes_gmac at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/crypto_ops.c:24 --- 0x42013060: sha256_vector at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:89 3fc9e3c0: 0x0000000a 0x00000020 0x00000001 0x00000000 0x00000020 0x00000000 0x00000005 0x00000000 3fc9e3e0: 0x00000000 0x00000001 0x00000001 0x00000000 0x00000001 0x00000000 0x00000006 0x00000000 3fc9e400: 0x000002f0 0x00000020 0x000000a1 0x00000000 0x00001801 0x00000007 0x00000001 0x40380c00 --- 0x40380c00: esp_ptr_in_rtc_dram_fast at E:/espidf/v5.4.1/esp-idf/components/esp_hw_support/include/esp_memory_utils.h:121 --- (inlined by) dram_alloc_to_iram_addr at E:/espidf/v5.4.1/esp-idf/components/heap/heap_caps_base.c:41 --- (inlined by) heap_caps_aligned_alloc_base at E:/espidf/v5.4.1/esp-idf/components/heap/heap_caps_base.c:150 3fc9e420: 0x1f2f3f4f 0x00001800 0x77770006 0x77777777 0x6c6c756e 0x69666977 0x00000000 0x00000000 3fc9e440: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000008 0x38000000 0x38383838 0x00383838 3fc9e460: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e480: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x38383838 0x38383838 3fc9e4a0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e4c0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000005 0x3c0d8554 3fc9e4e0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x3c0c4000 0x420b35a8 --- 0x420b35a8: main_task at E:/espidf/v5.4.1/esp-idf/components/freertos/app_startup.c:209 3fc9e500: 0x00000000 0x00001388 0x00000001 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e520: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 3fc9e540: 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 0x00000150 0x3fc9dd80 0x00000002 0x3fc957c4 3fc9e560: 0x3fc957c4 0x3fc9e554 0x3fc957bc 0x00000018 0x3fca4640 0x3fca4640 0x3fc9e554 0x00000000 3fc9e580: 0x00000001 0x3fc9d550 0x6e69616d 0x00000000 0x00000000 0x00000000 0x3fc9e540 0x00000001 3fc9e5a0: 0x00000000 0x3fca2a90 0x42004e7c 0x00000000 0x3fc9be80 0x3fc9bee8 0x3fc9bf50 0x00000000 --- 0x42004e7c: pthread_cleanup_thread_specific_data_callback at E:/espidf/v5.4.1/esp-idf/components/pthread/pthread_local_storage.c:126 3fc9e5c0: 0x00000000 0x00000001 0x00000000 0x00000000 0x00000000 0x42004706 0x00000000 0x00000000 --- 0x42004706: esp_cleanup_r at E:/espidf/v5.4.1/esp-idf/components/newlib/newlib_init.c:43 ELF file SHA256: 04a1caf88 Rebooting... ESP-ROM:esp32c3-api1-20210207 Build:Feb 7 2021 rst:0xc (RTC_SW_CPU_RST),boot:0xd (SPI_FAST_FLASH_BOOT) Saved PC:0x403807f4 --- 0x403807f4: esp_restart_noos at E:/espidf/v5.4.1/esp-idf/components/esp_system/port/soc/esp32c3/system_internal.c:112 SPIWP:0xee mode:DIO, clock div:1 load:0x3fcd5820,len:0x1574 load:0x403cc710,len:0xc30 load:0x403ce710,len:0x2f64 entry 0x403cc71a I (24) boot: ESP-IDF v5.4.1 2nd stage bootloader I (24) boot: compile time Jun 27 2025 16:28:05 I (24) boot: chip revision: v0.4 I (25) boot: efuse block revision: v1.3 I (27) boot.esp32c3: SPI Speed : 80MHz I (31) boot.esp32c3: SPI Mode : DIO I (35) boot.esp32c3: SPI Flash Size : 2MB I (39) boot: Enabling RNG early entropy source... I (43) boot: Partition Table: I (46) boot: ## Label Usage Type ST Offset Length I (52) boot: 0 nvs WiFi data 01 02 00009000 00006000 I (59) boot: 1 phy_init RF data 01 01 0000f000 00001000 I (65) boot: 2 factory factory app 00 00 00010000 00100000 I (72) boot: End of partition table I (75) esp_image: segment 0: paddr=00010020 vaddr=3c0c0020 size=22a38h (141880) map I (105) esp_image: segment 1: paddr=00032a60 vaddr=3fc92800 size=02df8h ( 11768) load I (108) esp_image: segment 2: paddr=00035860 vaddr=40380000 size=0a7b8h ( 42936) load I (117) esp_image: segment 3: paddr=00040020 vaddr=42000020 size=b4088h (737416) map I (235) esp_image: segment 4: paddr=000f40b0 vaddr=4038a7b8 size=07f0ch ( 32524) load I (241) esp_image: segment 5: paddr=000fbfc4 vaddr=50000200 size=0001ch ( 28) load I (248) boot: Loaded app from partition at offset 0x10000 I (248) boot: Disabling RNG early entropy source... I (260) cpu_start: Unicore app I (268) cpu_start: Pro cpu start user code I (268) cpu_start: cpu freq: 160000000 Hz I (269) app_init: Application information: I (269) app_init: Project name: internal_communication I (274) app_init: App version: 1 I (277) app_init: Compile time: Jun 27 2025 16:27:06 I (282) app_init: ELF file SHA256: 04a1caf88... I (286) app_init: ESP-IDF: v5.4.1 I (290) efuse_init: Min chip rev: v0.3 I (294) efuse_init: Max chip rev: v1.99 I (298) efuse_init: Chip rev: v0.4 I (302) heap_init: Initializing. RAM available for dynamic allocation: I (308) heap_init: At 3FC9B840 len 000247C0 (145 KiB): RAM I (313) heap_init: At 3FCC0000 len 0001C710 (113 KiB): Retention RAM I (319) heap_init: At 3FCDC710 len 00002950 (10 KiB): Retention RAM I (325) heap_init: At 5000021C len 00001DCC (7 KiB): RTCRAM I (331) spi_flash: detected chip: generic I (334) spi_flash: flash io: dio W (337) spi_flash: Detected size(8192k) larger than the size in the binary image header(2048k). Using the size in the binary image header. I (350) sleep_gpio: Configure to isolate all GPIO pins in sleep state I (356) sleep_gpio: Enable automatic switching of GPIO sleep configuration I (362) main_task: Started on CPU0 I (362) main_task: Calling app_main() I (372) pp: pp rom version: 9387209 I (372) net80211: net80211 rom version: 9387209 I (382) wifi:wifi driver task: 3fca44c0, prio:23, stack:6656, core=0 I (382) wifi:wifi firmware version: 79fa3f41ba I (382) wifi:wifi certification version: v7.0 I (382) wifi:config NVS flash: enabled I (392) wifi:config nano formatting: disabled I (392) wifi:Init data frame dynamic rx buffer num: 32 I (402) wifi:Init static rx mgmt buffer num: 5 I (402) wifi:Init management short buffer num: 32 I (402) wifi:Init dynamic tx buffer num: 32 I (412) wifi:Init static tx FG buffer num: 2 I (412) wifi:Init static rx buffer size: 1600 I (422) wifi:Init static rx buffer num: 10 I (422) wifi:Init dynamic rx buffer num: 32 I (422) wifi_init: rx ba win: 6 I (432) wifi_init: accept mbox: 6 I (432) wifi_init: tcpip mbox: 32 I (432) wifi_init: udp mbox: 6 I (442) wifi_init: tcp mbox: 6 I (442) wifi_init: tcp tx win: 5760 I (442) wifi_init: tcp rx win: 5760 I (442) wifi_init: tcp mss: 1440 I (452) wifi_init: WiFi IRAM OP enabled I (452) wifi_init: WiFi RX IRAM OP enabled I (462) phy_init: phy_version 1200,2b7123f9,Feb 18 2025,15:22:21 I (492) phy_init: Saving new calibration data due to checksum failure or outdated calibration data, mode(0) I (522) wifi:mode : sta (34:cd:b0:97:58:bc) + softAP (34:cd:b0:97:58:bd) I (522) wifi:enable tsf I (522) wifi:Total power save buffer number: 16 I (522) wifi:Init max length of beacon: 752/752 I (522) wifi:Init max length of beacon: 752/752 I (532) mesh: <nvs>read layer:0, err:0x1102 I (532) mesh: <nvs>read assoc:0, err:0x1102 I (532) wifi:Set ps type: 1, coexist: 0 I (542) wifi:Total power save buffer number: 16 I (792) mesh: [IO]disable self-organizing<reconnect> I (792) mesh: [CONFIG]connect to router:nullwifi, 00:00:00:00:00:00 I (792) mesh_main: <MESH_EVENT_MESH_STARTED>ID:77:77:77:77:77:77 I (802) mesh_main: mesh starts successfully, heap:161708, root fixed<0>(tree), ps:1 assert failed: xTaskCreatePinnedToCore freertos_tasks_c_additions.h:163 (( ( ( ( BaseType_t ) xCoreID ) >= 0 && ( ( BaseType_t ) xCoreID ) < 1 ) ? ( ( BaseType_t ) 1 ) : ( ( BaseType_t ) 0 ) ) == ( ( Core 0 register dump: MEPC : 0x40380828 RA : 0x4038692c SP : 0x3fc9e1e0 GP : 0x3fc93000 --- 0x40380828: panic_abort at E:/espidf/v5.4.1/esp-idf/components/esp_system/panic.c:468 --- 0x4038692c: __ubsan_include at E:/espidf/v5.4.1/esp-idf/components/esp_system/ubsan.c:311 TP : 0x3fc9e540 T0 : 0x37363534 T1 : 0x7271706f T2 : 0x33323130 S0/FP : 0x0000007e S1 : 0x3fc9e3c7 A0 : 0x3fc9e238 A1 : 0x3c0c41f2 A2 : 0x0000007e A3 : 0x00000028 A4 : 0x00000001 A5 : 0x3fc9b000 A6 : 0x7a797877 A7 : 0x76757473 S2 : 0x00000000 S3 : 0x3fc9e230 S4 : 0x3fc9e238 S5 : 0x00000000 S6 : 0x00000003 S7 : 0x00000000 S8 : 0x00000000 S9 : 0x00000000 S10 : 0x00000000 S11 : 0x00000000 T3 : 0x6e6d6c6b T4 : 0x6a696867 T5 : 0x66656463 T6 : 0x62613938 MSTATUS : 0x00001881 MTVEC : 0x40380001 MCAUSE : 0x00000002 MTVAL : 0x00000000 --- 0x40380001: _vector_table at E:/espidf/v5.4.1/esp-idf/components/riscv/vectors_intc.S:54 MHARTID : 0x00000000 Stack memory: 3fc9e1e0: 0x00000000 0x00060000 0x3c0d726c 0x4038db14 0x00000000 0x00060000 0xc0380000 0x3c0c4174 --- 0x4038db14: newlib_include_assert_impl at E:/espidf/v5.4.1/esp-idf/components/newlib/assert.c:92 3fc9e200: 0x00333631 0x00000000 0x00000000 0x00000000 0x00000000 0x3fc947f0 0x3c0d726c 0x3fc94e2c 3fc9e220: 0x3c0c4258 0x3fc94800 0x3fc9e200 0x3fc94804 0x3c0c4174 0x3fc94a58 0x65737361 0x66207472 3fc9e240: 0x656c6961 0x78203a64 0x6b736154 0x61657243 0x69506574 0x64656e6e 0x6f436f54 0x66206572 3fc9e260: 0x72656572 0x5f736f74 0x6b736174 0x5f635f73 0x69646461 0x6e6f6974 0x3a682e73 0x20333631 3fc9e280: 0x28202828 0x28202820 0x73614220 0x70795465 0x20745f65 0x43782029 0x4965726f 0x20292044 3fc9e2a0: 0x30203d3e 0x20262620 0x20282028 0x65736142 0x65707954 0x2920745f 0x6f437820 0x44496572 3fc9e2c0: 0x3c202920 0x29203120 0x28203f20 0x42202820 0x54657361 0x5f657079 0x20292074 0x20292031 3fc9e2e0: 0x2028203a 0x61422028 0x79546573 0x745f6570 0x30202920 0x29202920 0x203d3d20 0x00282028 3fc9e300: 0x00000000 0x00000001 0x3c0c4a14 0x4200b2e0 0x00001000 0x3c0c5000 0x00000001 0x403893ee --- 0x4200b2e0: uart_event_task at D:/esp32_C3/internal_communication_client_0.1/main/uart.c:16 --- 0x403893ee: xTaskCreatePinnedToCore at E:/espidf/v5.4.1/esp-idf/components/freertos/esp_additions/freertos_tasks_c_additions.h:231 3fc9e320: 0x00000000 0x00000000 0x04c4b400 0x00000000 0x00000000 0x00000000 0x00000000 0x000277ac 3fc9e340: 0x00000003 0x3c0c5000 0x00000001 0x4200b510 0x0001c200 0x00000003 0x00000000 0x00000001 --- 0x4200b510: uart_init at D:/esp32_C3/internal_communication_client_0.1/main/uart.c:80 3fc9e360: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000322 0x3c0c5560 0x3c0c553c 0x4200b2b0 --- 0x4200b2b0: app_main at D:/esp32_C3/internal_communication_client_0.1/main/mesh_main.c:349 3fc9e380: 0x3c0c5560 0x00000001 0xa5a5a5a5 0xa5a5a5a5 0x3fc92b38 0x0000002c 0x00000001 0x4201306e --- 0x4201306e: hmac_sha256_vector at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:342 3fc9e3a0: 0x420132de 0x420131c2 0x42013224 0x420133f2 0x420135d6 0x42013684 0x42012f3e 0x42013060 --- 0x420132de: pbkdf2_sha1 at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:752 --- 0x420131c2: aes_128_cbc_encrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:445 --- 0x42013224: aes_128_cbc_decrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:467 --- 0x420133f2: omac1_aes_128 at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:920 --- 0x420135d6: ccmp_decrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/ccmp.c:141 --- 0x42013684: ccmp_encrypt at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/ccmp.c:193 --- 0x42012f3e: esp_aes_gmac at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/src/crypto/crypto_ops.c:24 --- 0x42013060: sha256_vector at E:/espidf/v5.4.1/esp-idf/components/wpa_supplicant/esp_supplicant/src/crypto/crypto_mbedtls.c:89 3fc9e3c0: 0x0000000a 0x00000020 0x00000001 0x00000000 0x00000020 0x00000000 0x00000005 0x00000000 3fc9e3e0: 0x00000000 0x00000001 0x00000001 0x00000000 0x00000001 0x00000000 0x00000006 0x00000000 3fc9e400: 0x000002f0 0x00000020 0x000000a1 0x00000000 0x00001801 0x00000007 0x00000001 0x40380c00 --- 0x40380c00: esp_ptr_in_rtc_dram_fast at E:/espidf/v5.4.1/esp-idf/components/esp_hw_support/include/esp_memory_utils.h:121 --- (inlined by) dram_alloc_to_iram_addr at E:/espidf/v5.4.1/esp-idf/components/heap/heap_caps_base.c:41 --- (inlined by) heap_caps_aligned_alloc_base at E:/espidf/v5.4.1/esp-idf/components/heap/heap_caps_base.c:150 3fc9e420: 0x1f2f3f4f 0x00001800 0x77770006 0x77777777 0x6c6c756e 0x69666977 0x00000000 0x00000000 3fc9e440: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000008 0x38000000 0x38383838 0x00383838 3fc9e460: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e480: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x38383838 0x38383838 3fc9e4a0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e4c0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000005 0x3c0d8554 3fc9e4e0: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0x3c0c4000 0x420b35a8 --- 0x420b35a8: main_task at E:/espidf/v5.4.1/esp-idf/components/freertos/app_startup.c:209 3fc9e500: 0x00000000 0x00001388 0x00000001 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 3fc9e520: 0x00000000 0x00000000 0x00000000 0x00000000 0x00000000 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 3fc9e540: 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 0xa5a5a5a5 0x00000150 0x3fc9dd80 0x00000002 0x3fc957c4 3fc9e560: 0x3fc957c4 0x3fc9e554 0x3fc957bc 0x00000018 0x3fca4640 0x3fca4640 0x3fc9e554 0x00000000 3fc9e580: 0x00000001 0x3fc9d550 0x6e69616d 0x00000000 0x00000000 0x00000000 0x3fc9e540 0x00000001 3fc9e5a0: 0x00000000 0x3fca2a90 0x42004e7c 0x00000000 0x3fc9be80 0x3fc9bee8 0x3fc9bf50 0x00000000 --- 0x42004e7c: pthread_cleanup_thread_specific_data_callback at E:/espidf/v5.4.1/esp-idf/components/pthread/pthread_local_storage.c:126 3fc9e5c0: 0x00000000 0x00000001 0x00000000 0x00000000 0x00000000 0x42004706 0x00000000 0x00000000 --- 0x42004706: esp_cleanup_r at E:/espidf/v5.4.1/esp-idf/components/newlib/newlib_init.c:43 ELF file SHA256: 04a1caf88 Rebooting... ESP-ROM:esp32c3-api1-20210207 Build:Feb 7 2021 rst:0xc (RTC_SW_CPU_RST),boot:0xd (SPI_FAST_FLASH_BOOT) Saved PC:0x403807f4 --- 0x403807f4: esp_restart_noos at E:/espidf/v5.4.1/esp-idf/components/esp_system/port/soc/esp32c3/system_internal.c:112 SPIWP:0xee mode:DIO, clock div:1 load:0x3fcd5820,len:0x1574 load:0x403cc710,len:0xc30 load:0x403ce710,len:0x2f64 entry 0x403cc71a I (24) boot: ESP-IDF v5.4.1 2nd stage bootloader I (24) boot: compile time Jun 27 2025 16:28:05 I (24) boot: chip revision: v0.4 I (25) boot: efuse block revision: v1.3 I (27) boot.esp32c3: SPI Speed : 80MHz I (31) boot.esp32c3: SPI Mode : DIO I (35) boot.esp32c3: SPI Flash Size : 2MB I (39) boot: Enabling RNG early entropy source... I (43) boot: Partition Table: I (46) boot: ## Label Usage Type ST Offset Length I (52) boot: 0 nvs WiFi data 01 02 00009000 00006000 I (59) boot: 1 phy_init RF data 01 01 0000f000 00001000 I (65) boot: 2 factory factory app 00 00 00010000 00100000 I (72) boot: End of partition table I (75) esp_image: segment 0: paddr=00010020 vaddr=3c0c0020 size=22a38h (141880) map I (105) esp_image: segment 1: paddr=00032a60 vaddr=3fc92800 size=02df8h ( 11768) load I (108) esp_image: segment 2: paddr=00035860 vaddr=40380000 size=0a7b8h ( 42936) load I (117) esp_image: segment 3: paddr=00040020 vaddr=42000020 size=b4088h (737416) map I (235) esp_image: segment 4: paddr=000f40b0 vaddr=4038a7b8 size=07f0ch ( 32524) load I (241) esp_image: segment 5: paddr=000fbfc4 vaddr=50000200 size=0001ch ( 28) load I (248) boot: Loaded app from partition at offset 0x10000 I (248) boot: Disabling RNG early entropy source... I (260) cpu_start: Unicore app I (268) cpu_start: Pro cpu start user code I (268) cpu_start: cpu freq: 160000000 Hz I (269) app_init: Application information: I (269) app_init: Project name: internal_communication I (273) app_init: App version: 1 I (277) app_init: Compile time: Jun 27 2025 16:27:06 I (282) app_init: ELF file SHA256: 04a1caf88... I (286) app_init: ESP-IDF: v5.4.1 I (290) efuse_init: Min chip rev: v0.3 I (294) efuse_init: Max chip rev: v1.99 I (298) efuse_init: Chip rev: v0.4 I (302) heap_init: Initializing. RAM available for dynamic allocation: I (308) heap_init: At 3FC9B840 len 000247C0 (145 KiB): RAM I (313) heap_init: At 3FCC0000 len 0001C710 (113 KiB): Retention RAM I (319) heap_init: At 3FCDC710 len 00002950 (10 KiB): Retention RAM I (325) heap_init: At 5000021C len 00001DCC (7 KiB): RTCRAM I (331) spi_flash: detected chip: generic I (334) spi_flash: flash io: dio W (337) spi_flash: Detected size(8192k) larger than the size in the binary image header(2048k). Using the size in the binary image header. I (350) sleep_gpio: Configure to isolate all GPIO pins in sleep state I (356) sleep_gpio: Enable automatic switching of GPIO sleep configuration I (362) main_task: Started on CPU0 I (362) main_task: Calling app_main() I (372) pp: pp rom version: 9387209 I (372) net80211: net80211 rom version: 9387209 I (382) wifi:wifi driver task: 3fca44c0, prio:23, stack:6656, core=0 I (382) wifi:wifi firmware version: 79fa3f41ba I (382) wifi:wifi certification version: v7.0 I (382) wifi:config NVS flash: enabled I (392) wifi:config nano formatting: disabled I (392) wifi:Init data frame dynamic rx buffer num: 32 I (402) wifi:Init static rx mgmt buffer num: 5 I (402) wifi:Init management short buffer num: 32 I (402) wifi:Init dynamic tx buffer num: 32 I (412) wifi:Init static tx FG buffer num: 2 I (412) wifi:Init static rx buffer size: 1600 I (422) wifi:Init static rx buffer num: 10 I (422) wifi:Init dynamic rx buffer num: 32 I (422) wifi_init: rx ba win: 6 I (432) wifi_init: accept mbox: 6 I (432) wifi_init: tcpip mbox: 32 I (432) wifi_init: udp mbox: 6 I (442) wifi_init: tcp mbox: 6 I (442) wifi_init: tcp tx win: 5760 I (442) wifi_init: tcp rx win: 5760 I (442) wifi_init: tcp mss: 1440 I (452) wifi_init: WiFi IRAM OP enabled I (452) wifi_init: WiFi RX IRAM OP enabled I (462) phy_init: phy_version 1200,2b7123f9,Feb 18 2025,15:22:21 I (492) phy_init: Saving new calibration data due to checksum failure or outdated calibration data, mode(0) I (572) wifi:mode : sta (34:cd:b0:97:58:bc) + softAP (34:cd:b0:97:58:bd) I (572) wifi:enable tsf I (572) wifi:Total power save buffer number: 16 I (572) wifi:Init max length of beacon: 752/752 I (582) wifi:Init max length of beacon: 752/752 I (582) mesh: <nvs>read layer:0, err:0x1102 I (582) mesh: <nvs>read assoc:0, err:0x1102 I (592) wifi:Set ps type: 1, coexist: 0

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