gseapy/parser.py
def query(self, dataset='hsapiens_gene_ensembl', attributes=[], 
              filters={}, filename=None):
        """mapping ids using BioMart.  

        :param dataset: str, default: 'hsapiens_gene_ensembl'
        :param attributes: str, list, tuple
        :param filters: dict, {'filter name': list(filter value)}
        :param host: www.ensembl.org, asia.ensembl.org, useast.ensembl.org
        :return: a dataframe contains all attributes you selected.

        **Note**: it will take a couple of minutes to get the results.
        A xml template for querying biomart. (see https://gist.github.com/keithshep/7776579)
        
        exampleTaxonomy = "mmusculus_gene_ensembl"
        exampleGene = "ENSMUSG00000086981,ENSMUSG00000086982,ENSMUSG00000086983"
        urlTemplate = \
        '''http://ensembl.org/biomart/martservice?query=''' \
        '''<?xml version="1.0" encoding="UTF-8"?>''' \
        '''<!DOCTYPE Query>''' \
        '''<Query virtualSchemaName="default" formatter="CSV" header="0" uniqueRows="0" count="" datasetConfigVersion="0.6">''' \
        '''<Dataset name="%s" interface="default"><Filter name="ensembl_gene_id" value="%s"/>''' \
        '''<Attribute name="ensembl_gene_id"/><Attribute name="ensembl_transcript_id"/>''' \
        '''<Attribute name="transcript_start"/><Attribute name="transcript_end"/>''' \
        '''<Attribute name="exon_chrom_start"/><Attribute name="exon_chrom_end"/>''' \
        '''</Dataset>''' \
        '''</Query>''' 
        
        exampleURL = urlTemplate % (exampleTaxonomy, exampleGene)
        req = requests.get(exampleURL, stream=True)
                   
        """
        if not attributes: 
            attributes = ['ensembl_gene_id', 'external_gene_name', 'entrezgene', 'go_id'] 
        # i=0
        # while (self.host is None) and (i < 3):
        #     self.host = self.ghosts[i]
        #     i +=1 
        self.new_query()
        # 'mmusculus_gene_ensembl'
        self.add_dataset_to_xml(dataset)
        for at in attributes:
            self.add_attribute_to_xml(at)
        # add filters
        if filters:
            for k, v in filters.items(): 
                if isinstance(v, list): v = ",".join(v)
                self.add_filter_to_xml(k, v)

        xml_query = self.get_xml()
        results = super(Biomart, self).query(xml_query)
        df = pd.read_csv(StringIO(results), header=None, sep="\t",
                         names=attributes, index_col=None)
        # save file to cache path.
        if filename is None: 
            mkdirs(DEFAULT_CACHE_PATH)
            filename = os.path.join(DEFAULT_CACHE_PATH, "{}.background.genes.txt".format(dataset))
        df.to_csv(filename, sep="\t", index=False)
      
        return df
def query(self, dataset='hsapiens_gene_ensembl', attributes=[], 
              filters={}, filename=None):
        if not attributes: 
            attributes = ['ensembl_gene_id', 'external_gene_name', 'entrezgene', 'go_id'] 
        # i=0
        # while (self.host is None) and (i < 3):
        #     self.host = self.ghosts[i]
        #     i +=1 
        self.new_query()
        # 'mmusculus_gene_ensembl'
        self.add_dataset_to_xml(dataset)
        for at in attributes:
            self.add_attribute_to_xml(at)
        # add filters
        if filters:
            for k, v in filters.items(): 
                if isinstance(v, list): v = ",".join(v)
                self.add_filter_to_xml(k, v)

        xml_query = self.get_xml()
        results = super(Biomart, self).query(xml_query)
        df = pd.read_csv(StringIO(results), header=None, sep="\t",
                         names=attributes, index_col=None)
        # save file to cache path.
        if filename is None: 
            mkdirs(DEFAULT_CACHE_PATH)
            filename = os.path.join(DEFAULT_CACHE_PATH, "{}.background.genes.txt".format(dataset))
        df.to_csv(filename, sep="\t", index=False)
      
        return df
def query ( self , dataset = 'hsapiens_gene_ensembl' , attributes = [ ] , filters = { } , filename = None ) : if not attributes : attributes = [ 'ensembl_gene_id' , 'external_gene_name' , 'entrezgene' , 'go_id' ] # i=0 # while (self.host is None) and (i < 3): #     self.host = self.ghosts[i] #     i +=1  self . new_query ( ) # 'mmusculus_gene_ensembl' self . add_dataset_to_xml ( dataset ) for at in attributes : self . add_attribute_to_xml ( at ) # add filters if filters : for k , v in filters . items ( ) : if isinstance ( v , list ) : v = "," . join ( v ) self . add_filter_to_xml ( k , v ) xml_query = self . get_xml ( ) results = super ( Biomart , self ) . query ( xml_query ) df = pd . read_csv ( StringIO ( results ) , header = None , sep = "\t" , names = attributes , index_col = None ) # save file to cache path. if filename is None : mkdirs ( DEFAULT_CACHE_PATH ) filename = os . path . join ( DEFAULT_CACHE_PATH , "{}.background.genes.txt" . format ( dataset ) ) df . to_csv ( filename , sep = "\t" , index = False ) return df
mapping ids using BioMart.  

        :param dataset: str, default: 'hsapiens_gene_ensembl'
        :param attributes: str, list, tuple
        :param filters: dict, {'filter name': list(filter value)}
        :param host: www.ensembl.org, asia.ensembl.org, useast.ensembl.org
        :return: a dataframe contains all attributes you selected.

        **Note**: it will take a couple of minutes to get the results.
        A xml template for querying biomart. (see https://gist.github.com/keithshep/7776579)
        
        exampleTaxonomy = "mmusculus_gene_ensembl"
        exampleGene = "ENSMUSG00000086981,ENSMUSG00000086982,ENSMUSG00000086983"
        urlTemplate = \
        '''http://ensembl.org/biomart/martservice?query=''' \
        '''<?xml version="1.0" encoding="UTF-8"?>''' \
        '''<!DOCTYPE Query>''' \
        '''<Query virtualSchemaName="default" formatter="CSV" header="0" uniqueRows="0" count="" datasetConfigVersion="0.6">''' \
        '''<Dataset name="%s" interface="default"><Filter name="ensembl_gene_id" value="%s"/>''' \
        '''<Attribute name="ensembl_gene_id"/><Attribute name="ensembl_transcript_id"/>''' \
        '''<Attribute name="transcript_start"/><Attribute name="transcript_end"/>''' \
        '''<Attribute name="exon_chrom_start"/><Attribute name="exon_chrom_end"/>''' \
        '''</Dataset>''' \
        '''</Query>''' 
        
        exampleURL = urlTemplate % (exampleTaxonomy, exampleGene)
        req = requests.get(exampleURL, stream=True)
mapping ids using BioMart .