latools/latools.py
def minimal_export(self, target_analytes=None, path=None):
        """
        Exports a analysis parameters, standard info and a minimal dataset,
        which can be imported by another user.

        Parameters
        ----------
        target_analytes : str or iterable
            Which analytes to include in the export. If specified, the export
            will contain these analytes, and all other analytes used during
            data processing (e.g. during filtering). If not specified, 
            all analytes are exported.
        path : str
            Where to save the minimal export. 
            If it ends with .zip, a zip file is created.
            If it's a folder, all data are exported to a folder.
        """
        if target_analytes is None:
            target_analytes = self.analytes
        if isinstance(target_analytes, str):
            target_analytes = [target_analytes]

        self.minimal_analytes.update(target_analytes)
        zip_archive = False

        # set up data path
        if path is None:
            path = self.export_dir + '/minimal_export.zip'
        if path.endswith('.zip'):
            path = path.replace('.zip', '')
            zip_archive = True
        if not os.path.isdir(path):
            os.mkdir(path)

        # export data
        self._minimal_export_traces(path + '/data', analytes=self.minimal_analytes)

         # define analysis_log header
        log_header = ['# Minimal Reproduction Dataset Exported from LATOOLS on %s' %
                      (time.strftime('%Y:%m:%d %H:%M:%S')),
                      'data_folder :: ./data/']
                      
        if hasattr(self, 'srmdat'):
            log_header.append('srm_table :: ./srm.table')

            # export srm table
            els = np.unique([re.sub('[0-9]', '', a) for a in self.minimal_analytes])
            srmdat = []
            for e in els:
                srmdat.append(self.srmdat.loc[self.srmdat.element == e, :])
            srmdat = pd.concat(srmdat)

            with open(path + '/srm.table', 'w') as f:
                f.write(srmdat.to_csv())

        # save custom functions (of defined)
        if hasattr(self, 'custom_stat_functions'):
            with open(path + '/custom_stat_fns.py', 'w') as f:
                f.write(self.custom_stat_functions)
            log_header.append('custom_stat_functions :: ./custom_stat_fns.py')

        log_header.append('# Analysis Log Start: \n')

        # format sample_stats correctly
        lss = [(i, l) for i, l in enumerate(self.log) if 'sample_stats' in l]
        rep = re.compile("(.*'stats': )(\[.*?\])(.*)")
        for i, l in lss:
            self.log[i] = rep.sub(r'\1' + str(self.stats_calced) + r'\3', l)

        # save log
        self.save_log(path, 'analysis.lalog', header=log_header)

        if zip_archive:
            utils.zipdir(directory=path, delete=True)
        
        return
def minimal_export(self, target_analytes=None, path=None):
        if target_analytes is None:
            target_analytes = self.analytes
        if isinstance(target_analytes, str):
            target_analytes = [target_analytes]

        self.minimal_analytes.update(target_analytes)
        zip_archive = False

        # set up data path
        if path is None:
            path = self.export_dir + '/minimal_export.zip'
        if path.endswith('.zip'):
            path = path.replace('.zip', '')
            zip_archive = True
        if not os.path.isdir(path):
            os.mkdir(path)

        # export data
        self._minimal_export_traces(path + '/data', analytes=self.minimal_analytes)

         # define analysis_log header
        log_header = ['# Minimal Reproduction Dataset Exported from LATOOLS on %s' %
                      (time.strftime('%Y:%m:%d %H:%M:%S')),
                      'data_folder :: ./data/']
                      
        if hasattr(self, 'srmdat'):
            log_header.append('srm_table :: ./srm.table')

            # export srm table
            els = np.unique([re.sub('[0-9]', '', a) for a in self.minimal_analytes])
            srmdat = []
            for e in els:
                srmdat.append(self.srmdat.loc[self.srmdat.element == e, :])
            srmdat = pd.concat(srmdat)

            with open(path + '/srm.table', 'w') as f:
                f.write(srmdat.to_csv())

        # save custom functions (of defined)
        if hasattr(self, 'custom_stat_functions'):
            with open(path + '/custom_stat_fns.py', 'w') as f:
                f.write(self.custom_stat_functions)
            log_header.append('custom_stat_functions :: ./custom_stat_fns.py')

        log_header.append('# Analysis Log Start: \n')

        # format sample_stats correctly
        lss = [(i, l) for i, l in enumerate(self.log) if 'sample_stats' in l]
        rep = re.compile("(.*'stats': )(\[.*?\])(.*)")
        for i, l in lss:
            self.log[i] = rep.sub(r'\1' + str(self.stats_calced) + r'\3', l)

        # save log
        self.save_log(path, 'analysis.lalog', header=log_header)

        if zip_archive:
            utils.zipdir(directory=path, delete=True)
        
        return
def minimal_export ( self , target_analytes = None , path = None ) : if target_analytes is None : target_analytes = self . analytes if isinstance ( target_analytes , str ) : target_analytes = [ target_analytes ] self . minimal_analytes . update ( target_analytes ) zip_archive = False # set up data path if path is None : path = self . export_dir + '/minimal_export.zip' if path . endswith ( '.zip' ) : path = path . replace ( '.zip' , '' ) zip_archive = True if not os . path . isdir ( path ) : os . mkdir ( path ) # export data self . _minimal_export_traces ( path + '/data' , analytes = self . minimal_analytes ) # define analysis_log header log_header = [ '# Minimal Reproduction Dataset Exported from LATOOLS on %s' % ( time . strftime ( '%Y:%m:%d %H:%M:%S' ) ) , 'data_folder :: ./data/' ] if hasattr ( self , 'srmdat' ) : log_header . append ( 'srm_table :: ./srm.table' ) # export srm table els = np . unique ( [ re . sub ( '[0-9]' , '' , a ) for a in self . minimal_analytes ] ) srmdat = [ ] for e in els : srmdat . append ( self . srmdat . loc [ self . srmdat . element == e , : ] ) srmdat = pd . concat ( srmdat ) with open ( path + '/srm.table' , 'w' ) as f : f . write ( srmdat . to_csv ( ) ) # save custom functions (of defined) if hasattr ( self , 'custom_stat_functions' ) : with open ( path + '/custom_stat_fns.py' , 'w' ) as f : f . write ( self . custom_stat_functions ) log_header . append ( 'custom_stat_functions :: ./custom_stat_fns.py' ) log_header . append ( '# Analysis Log Start: \n' ) # format sample_stats correctly lss = [ ( i , l ) for i , l in enumerate ( self . log ) if 'sample_stats' in l ] rep = re . compile ( "(.*'stats': )(\[.*?\])(.*)" ) for i , l in lss : self . log [ i ] = rep . sub ( r'\1' + str ( self . stats_calced ) + r'\3' , l ) # save log self . save_log ( path , 'analysis.lalog' , header = log_header ) if zip_archive : utils . zipdir ( directory = path , delete = True ) return
Exports a analysis parameters, standard info and a minimal dataset,
        which can be imported by another user.

        Parameters
        ----------
        target_analytes : str or iterable
            Which analytes to include in the export. If specified, the export
            will contain these analytes, and all other analytes used during
            data processing (e.g. during filtering). If not specified, 
            all analytes are exported.
        path : str
            Where to save the minimal export. 
            If it ends with .zip, a zip file is created.
            If it's a folder, all data are exported to a folder.
Exports a analysis parameters standard info and a minimal dataset which can be imported by another user .