1395.2^x mod n = 1

本文介绍了一个C语言程序,用于解决给定正整数n时找到满足2^xmodn=1的最小正整数x的问题。通过不断乘以2并取模n来避免溢出,确保程序正确运行。

2^x mod n = 1

Time Limit: 2000/1000 MS (Java/Others) Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 16592 Accepted Submission(s): 5165

Problem Description
Give a number n, find the minimum x(x>0) that satisfies 2^x mod n = 1.

Input
One positive integer on each line, the value of n.

Output
If the minimum x exists, print a line with 2^x mod n = 1.

Print 2^? mod n = 1 otherwise.

You should replace x and n with specific numbers.

Sample Input
2
5

Sample Output
2^? mod 2 = 1
2^4 mod 5 = 1

一直超时,没有每次都取余

PiWxxNmrtwSwyvAh6udJEAhxbIcfC_wk-FEfaRuVe_4bsk7ZS-lxTpH2qaNi4v7AFRmPwFlhPwKMd1reXskRM2en1s1YoT74O-A6lkNQi8B6Xml2dh-O-wr3hl”>http://baike.baidu.com/link?url=Tcz-PiWxxNmrtwSwyvAh6udJEAhxbIcfC_wk-FEfaRuVe_4bsk7ZS-lxTpH2qaNi4v7AFRmPwFlhPwKMd1reXskRM2en1s1YoT74O-A6lkNQi8B6Xml2dh-O-wr3hl

#include <stdio.h>
#include <stdlib.h>

int main()
{
    int n,i;
    while(scanf("%d",&n)!=EOF)
    {
        int s=1,m;
        if(n==1||n%2==0)
        {
            printf("2^? mod %d = 1\n",n);
            continue;
        }
        for(i=1;;i++)
        {
            s=s*2;
            s=s%n;//每次取余防止溢出
            if(s==1)
            {
                m=i;
               break;
            }
        }
        printf("2^%d mod %d = 1\n",m,n);
    }
    return 0;
}
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
07-08
基于遗传算法的新的异构分布式系统任务调度算法研究(Matlab代码实现)内容概要:本文档围绕基于遗传算法的异构分布式系统任务调度算法展开研究,重点介绍了一种结合遗传算法的新颖优化方法,并通过Matlab代码实现验证其在复杂调度问题中的有效性。文中还涵盖了多种智能优化算法在生产调度、经济调度、车间调度、无人机路径规划、微电网优化等领域的应用案例,展示了从理论建模到仿真实现的完整流程。此外,文档系统梳理了智能优化、机器学习、路径规划、电力系统管理等多个科研方向的技术体系与实际应用场景,强调“借力”工具与创新思维在科研中的重要性。; 适合人群:具备一定Matlab编程基础,从事智能优化、自动化、电力系统、控制工程等相关领域研究的研究生及科研人员,尤其适合正在开展调度优化、路径规划或算法改进类课题的研究者; 使用场景及目标:①学习遗传算法及其他智能优化算法(如粒子群、蜣螂优化、NSGA等)在任务调度中的设计与实现;②掌握Matlab/Simulink在科研仿真中的综合应用;③获取多领域(如微电网、无人机、车间调度)的算法复现与创新思路; 阅读建议:建议按目录顺序系统浏览,重点关注算法原理与代码实现的对应关系,结合提供的网盘资源下载完整代码进行调试与复现,同时注重从已有案例中提炼可迁移的科研方法与创新路径。
【微电网】【创新点】基于非支配排序的蜣螂优化算法NSDBO求解微电网多目标优化调度研究(Matlab代码实现)内容概要:本文提出了一种基于非支配排序的蜣螂优化算法(NSDBO),用于求解微电网多目标优化调度问题。该方法结合非支配排序机制,提升了传统蜣螂优化算法在处理多目标问题时的收敛性和分布性,有效解决了微电网调度中经济成本、碳排放、能源利用率等多个相互冲突目标的优化难题。研究构建了包含风、光、储能等多种分布式能源的微电网模型,并通过Matlab代码实现算法仿真,验证了NSDBO在寻找帕累托最优解集方面的优越性能,相较于其他多目标优化算法表现出更强的搜索能力和稳定性。; 适合人群:具备一定电力系统或优化算法基础,从事新能源、微电网、智能优化等相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于微电网能量管理系统的多目标优化调度设计;②作为新型智能优化算法的研究与改进基础,用于解决复杂的多目标工程优化问题;③帮助理解非支配排序机制在进化算法中的集成方法及其在实际系统中的仿真实现。; 阅读建议:建议读者结合Matlab代码深入理解算法实现细节,重点关注非支配排序、拥挤度计算和蜣螂行为模拟的结合方式,并可通过替换目标函数或系统参数进行扩展实验,以掌握算法的适应性与调参技巧。
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