python/pymethylprocess/MethylationDataTypes.py
def combine(self, array_generator=None, outer_join=False): # FIXME add sort based on samples
        """Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays.

        Parameters
        ----------
        array_generator
            Generator of additional methylation arrays for computational memory minimization.
        """
        if len(self.methylation_arrays)> 1:
            pheno_df=pd.concat([methylArr.pheno for methylArr in self.methylation_arrays], join='inner' if not outer_join else 'outer')#.sort()
            beta_df=pd.concat([methylArr.beta for methylArr in self.methylation_arrays], join='inner')#.sort()
        else:
            pheno_df=self.methylation_arrays[0].pheno
            beta_df=self.methylation_arrays[0].beta
        if array_generator != None:
            for methylArr in array_generator:
                pheno_df=pd.concat([pheno_df,methylArr.pheno], join='inner' if not outer_join else 'outer')
                beta_df=pd.concat([beta_df,methylArr.beta], join='inner')
        return MethylationArray(pheno_df,beta_df)
Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays.

        Parameters
        ----------
        array_generator
            Generator of additional methylation arrays for computational memory minimization.
        
Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays.
Parameters

array_generator
Generator of additional methylation arrays for computational memory minimization.
Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays . Parameters array_generator Generator of additional methylation arrays for computational memory minimization .
def combine(self, array_generator=None, outer_join=False): 
        if len(self.methylation_arrays)> 1:
            pheno_df=pd.concat([methylArr.pheno for methylArr in self.methylation_arrays], join='inner' if not outer_join else 'outer')
            beta_df=pd.concat([methylArr.beta for methylArr in self.methylation_arrays], join='inner')
        else:
            pheno_df=self.methylation_arrays[0].pheno
            beta_df=self.methylation_arrays[0].beta
        if array_generator != None:
            for methylArr in array_generator:
                pheno_df=pd.concat([pheno_df,methylArr.pheno], join='inner' if not outer_join else 'outer')
                beta_df=pd.concat([beta_df,methylArr.beta], join='inner')
        return MethylationArray(pheno_df,beta_df)
def combine ( self , array_generator = None , outer_join = False ) : if len ( self . methylation_arrays ) > 1 : pheno_df = pd . concat ( [ methylArr . pheno for methylArr in self . methylation_arrays ] , join = 'inner' if not outer_join else 'outer' ) beta_df = pd . concat ( [ methylArr . beta for methylArr in self . methylation_arrays ] , join = 'inner' ) else : pheno_df = self . methylation_arrays [ 0 ] . pheno beta_df = self . methylation_arrays [ 0 ] . beta if array_generator != None : for methylArr in array_generator : pheno_df = pd . concat ( [ pheno_df , methylArr . pheno ] , join = 'inner' if not outer_join else 'outer' ) beta_df = pd . concat ( [ beta_df , methylArr . beta ] , join = 'inner' ) return MethylationArray ( pheno_df , beta_df )
Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays.
Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays .
# FIXME add sort based on samples """Combine the list of methylation arrays into one array via concatenation of beta matrices and phenotype arrays.

        Parameters
        ----------
        array_generator
            Generator of additional methylation arrays for computational memory minimization.
        """ #.sort() #.sort()
self
array_generator
outer_join